6,773 Matching Annotations
  1. Mar 2025
    1. Author response:

      The following is the authors’ response to the original reviews.

      Common comments

      (1) Significance of zero mutation rate

      Reviewers asked why we included mutation rate even though setting mutation rate to zero doesn’t change results. We think that including non-zero mutation rate makes our results more generalisable, and thus is a strength rather than weakness. To better motivate this choice, we have added a sentence to the beginning of Results:

      (2) Writing the mu=0 case first

      Reviewers suggested that we should first focus on the mu=0 case, and then generalize the result. The suggestions are certainly good. However, given the large amount of work involved in a re-organization, we have decided to adhere to our current narrative. However, we now only include equations where mu=0 in the main text, and have moved the case of nonzero mutation rate to Supplementary Information.

      Making equations more accessible

      We have taken three steps to make equations more readable.

      ● Equations in the main text correspond to the case of zero-mutation rate.

      ● The original section on equation derivation is now in a box in the main text so that readers have the choice of skipping it but interested readers can still get a gist of where equations came from.

      We have provided a much more detailed interpretation of the equation:

      (3) Validity of the Gaussian approximation

      Reviewers raised concerns about the validity of Gaussian approximation on F suggest that𝑓( 𝜏this) approximation is reasonable. Still, we added a discussion frequency. The fact that our calculations closely match simulations about the validity of this approximation in Box 1.

      We also added to SI with various cases of initial S and F sizes. This figure not normal. However, if initial S and F are both on the order of hundreds,𝑓(𝜏) then shows that when either initial S or initial F is small, the distribution of    is the distribution of 𝑓(𝜏) is approximately Gaussian.

      Public Reviews:

      Summary:

      The authors demonstrate with a simple stochastic model that the initial composition of the community is important in achieving a target frequency during the artificial selection of a community.

      Strengths:

      To my knowledge, the intra-collective selection during artificial selection has not been seriously theoretically considered. However, in many cases, the species dynamics during the incubation of each selection cycle are important and relevant to the outcome of the artificial selection experiment. Stochasticity from birth and death (demographic stochasticity) plays a big role in these species' abundance dynamics. This work uses a simple framework to tackle this idea meticulously.

      This work may or may not be hysteresis (path dependency). If this is true, maybe it would be nice to have a discussion paragraph talking about how this may be the case. Then, this work would even attract the interest of people studying dynamic systems.

      We have added this clarification in the main text:

      “Note that here, selection outcome is path-dependent in the sense of being sensitive to initial conditions. This phenomenon is distinct from hysteresis where path-dependence results from whether a tuning parameter is increased or decreased.

      Weaknesses:

      (1) Connecting structure and function

      In typical artificial selection literature, most of them select the community based on collective function. Here in this paper, the authors are selecting a target composition. Although there is a schematic cartoon illustrating the relationship between collective function (y-axis) and the community composition in the main Figure 1, there is no explicit explanation or justification of what may be the origin of this relationship. I think giving the readers a naïve idea about how this structure-function relationship arises in the introduction section would help. This is because the conclusion of this paper is that the intra-collective selection makes it hard to artificially select a community that has an intermediate frequency of f (or s). If there is really evidence or theoretical derivation from this framework that indeed the highest function comes from the intermediate frequency of f, then the impact of this paper would increase because the conclusions of this stochastic model could allude to the reasons for the prevalent failures of artificial selection in literature.

      We have added this to introduction: “This is a common quest: whenever a collective function depends on both populations, collective function is maximised, by definition, at an intermediate frequency (e.g. too little of either population will hamper function [23]).”

      (2) Explain intra-collective and inter-collective selection better for readers.

      The abstract, the introduction, and the result section use these terms or intra-collective and inter-collective selection without much explanation. For the wide readership of eLife, a clear definition in the beginning would help the audience grasp the importance of this paper, because these concepts are at the core of this work.

      This is a great point. We have added in Abstract:

      “Such collective selection is dictated by two opposing forces: during collective maturation, intra-collective selection acts like a waterfall, relentlessly driving the S-frequency to lower values, while during collective reproduction, inter-collective selection resembles a rafter striving to reach the target frequency. Due to this model structure, maintaining a target frequency requires the continued action of inter-collective selection.”

      and in Introduction

      “A selection cycle consists of three stages (Fig. 1). During collective maturation, intra-collective selection favors fast-growing individuals within a collective. At the end of maturation, inter-collective selection acts on collectives and favors those achieving the target composition. Finally during collective reproduction, offspring collectives sample stochastically from the parents, a process dominated by genetic drift.”

      (3) Achievable target frequency strongly depending on the degree of demographic stochasticity.

      I would expect that the experimentalists would find these results interesting and would want to consider these results during their artificial selection experiments. The main Figure 4 indicates that the Newborn size N0 is a very important factor to consider during the artificial selection experiment. This would be equivalent to how much bottleneck is imposed on the artificial selection process in every iteration step (i.e., the ratio of serial dilution experiment). However, with a low population size, all target frequencies can be achieved, and therefore in these regimes, the initial frequency now does not matter much. It would be great for the authors to provide what the N0 parameter actually means during the artificial selection experiments. Maybe relative to some other parameter in the model. I know this could be very hard. But without this, the main result of this paper (initial frequency matters) cannot be taken advantage of by the experimentalists.

      We have added an analytical approximation for N0˘, the Newborn size below which all target frequencies can be achieved in SI.

      Also, we have added lines indicating N0˘ in Fig4a.

      (4) Consideration of environmental stochasticity.

      The success (gold area of Figure 2d) in this framework mainly depends on the size of the demographic stochasticity (birth-only model) during the intra-collective selection. However, during experiments, a lot of environmental stochasticity appears to be occurring during artificial selection. This may be out of the scope of this study. But it would definitely be exciting to see how much environmental stochasticity relative to the demographic stochasticity (variation in the Gaussian distribution of F and S) matters in succeeding in achieving the target composition from artificial selection.

      You are correct that our work considers only demographic stochasticity.

      Indeed, considering other types of stochasticity will be an exciting future research direction. We added in the main text:

      “Overall our model considers mutational stochasticity, as well as demographic stochasticity in terms of stochastic birth and stochastic sampling of a parent collective by offspring collectives. Other types of stochasticity, such as environmental stochasticity and measurement noise, are not considered and require future research.”

      (5) Assumption about mutation rates

      If setting the mutation rates to zero does not change the result of the simulations and the conclusion, what is the purpose of having the mutation rates \mu? Also, is the unidirectional (S -> F -> FF) mutation realistic? I didn't quite understand how the mutations could fit into the story of this paper.

      This is a great point. We have added this to the beginning of Results to better motivate our study:

      “We will start with a complete model where S mutates to F at a nonzero mutation rate µ. We made this choice because it is more challenging to attain or maintain the target frequency when the abundance of fast-growing F is further increased via mutations. This scenario is encountered in biotechnology: an engineered pathway will slow down growth, and breaking the pathway (and thus faster growth) is much easier than the other way around. When the mutation rate is set to zero, the same model can be used to capture collectives of two species with different growth rates.

      See answer on common question 1.

      (6) Minor points

      In Figure 3b, it is not clear to me how the frequency difference for the Intra-collective and the Inter-collective selection is computed.

      We added a description in caption 3b.

      In Figure 5b, the gold region (success) near the FF is not visible. Maybe increase the size of the figure or have an inset for zoom-in. Why is the region not as big as the bottom gold region?

      We increased the resolution of Fig 5b so that the gold region near FF is more visible.

      We have added Fig 5c and the following explanation to the main text:

      “From numerical simulations, we identified two accessible regions: a small region near FF and a band region spanning from S to F (gold in Fig. 5b i). Intuitively, the rate at which FF grows faster than S+F is greater than the rate at which F grows faster than S (see section VIII in Supplementary Information). Thus, the problem can initially be reduced to a two-population problem (i.e. FF versus F+S; Fig. 5c left), and then expanded to a three-population problem (Fig. 5c right).”

      Recommendations For The Authors

      Since the conclusion of the model greatly depends on the noise (variation) of F and S in the Gaussian distribution, it would be nice to have a plot where the y-axis is the variation in terms of frequency and the x-axis is the s_0 or f_0 (frequency). In the plot, I would love to see how the variation in the frequency depends on the initial frequency of S and F. Maybe this is just trivial.

      In the SI, we added Fig6a, as per your request. Previous Fig6 became Fig6b.

      Reviewer #2 (Public review):

      The authors provide an analytical framework to model the artificial selection of the composition of communities composed of strains growing at different rates. Their approach takes into account the competition between the targeted selection at the level of the meta-community and the selection that automatically favors fast-growing cells within each replicate community. Their main finding is a tipping point or path-dependence effect, whereby compositions dominated by slow-growing types can only be reached by community-level selection if the community does not start and never crosses into a range of compositions dominated by fast growers during the dynamics.

      These results seem to us both technically correct and interesting. We commend the authors on their efforts to make their work reproducible even when it comes to calculations via extensive appendices, though perhaps a table of contents and a short description of these appendices at the start of SI would help navigate them.

      Thank you for the suggestion. We have added a paragraph at the beginning of SI.

      The main limitation in the current form of the article is that it could clarify how its assumptions and findings differ from and improve upon the rest of the literature:

      -  Many studies discuss the interplay between community-level evolution and species- or strain-level evolution. But "evolution" can be a mix of various forces, including selection, drift/randomness, and mutation/innovation.

      - This work's specificity is that it focuses strictly on constant community-level selection versus constant strain-level selection, all other forces being negligible (neither stochasticity nor innovation/mutation matter at either level, as we try to clarify now).

      Note that intra-collective selection is not strictly “constant” in the sense that selection favoring F is the strongest at intermediate F frequency (Fig 3). However, we think that you mean that intra- and inter-collective selection are present in every cycle, and this is correct for our case, and for community selection in general.

      -  Regarding constant community-level selection, it is only briefly noted that "once a target frequency is achieved, inter-collective selection is always required to maintain that frequency due to the fitness difference between the two types" [pg. 3 {section sign}2]. In other words, action from the selector is required indefinitely to maintain the community in the desired state. This assumption is found in a fraction of the literature, but is still worth clarifying from the start as it can inform the practical applicability of the results.

      This is a good point. We have added to abstract:

      “Such collective selection is dictated by two opposing forces: during collective maturation, intra-collective selection acts like a waterfall, relentlessly driving the S-frequency to lower values, while during collective reproduction, inter-collective selection resembles a rafter striving to reach the target frequency. Due to this model structure, maintaining a target frequency requires the continued action of inter-collective selection.”

      - More importantly, strain-level evolution also boils down here to pure selection with a constant target, which is less usual in the relevant literature. Here, (1) drift from limited population sizes is very small, with no meaningful counterbalancing of selection, (2) pure exponential regime with constant fitness, no interactions, no density- or frequency-dependence, (3) there is no innovation in the sense that available types are unchanging through time (no evolution of traits such as growth rate or interactions) and (4) all the results presented seem unchanged when mutation rate mu = 0 (as noted in Appendix III), meaning that the conclusions are not "about" mutation in any meaningful way.

      With regard to point (1), Figure 4a (reproduced below) shows how Newborn size affects the region of achievable targets. Indeed at large Newborn size (e.g. 5000 and above), no target frequency is achievable (since drift is too small to generate sufficient inter-community variation and consequently all communities are dominated by fast-growing F). However at Newborn size of for example 1000, there are two regions of accessible target frequencies. At smaller Newborn size, all target frequencies become achievable due to drift becoming sufficiently strong.

      With regard to points (2) and (3), we have added to Introduction

      “To enable the derivation of an analytical expression, we have made the following simplifications.

      First, growth is always exponential, without complications such as resource limitation, ecological interactions between the two populations, or density-dependent growth. Thus, the exponential growth equation can be used. Second, we consider only two populations (genotypes or species): the fast-growing F population with size F and the slow-growing S population with size S. We do not consider a spectrum of mutants or species, since with more than two populations, an analytical solution becomes very difficult.”

      With regard to point (4), we view this as a strength rather than weakness. We have added the following to the beginning of Results and Discussions:

      “We will start with a complete model where S mutates to F at a nonzero mutation rate µ. We made this choice because it is more challenging to attain or maintain the target frequency when the abundance of fast-growing F is further increased via mutations.”

      “When the mutation rate is set to zero, the same model can be used to capture collectives of two species with different growth rates.”

      See Point 1 of Common comments.

      - Furthermore, the choice of mutation mechanism is peculiar, as it happens only from slow to fast grower: more commonly, one assumes random non-directional mutations, rather than purely directional ones from less fit to fitter (which is more of a "Lamarckian" idea). Given that mutation does not seem to matter here, this choice might create unnecessary opposition from some readers or could be considered as just one possibility among others.

      We have added the following justification:

      “This scenario is encountered in biotechnology: an engineered pathway will slow down growth, and breaking the pathway (and thus faster growth) is much easier than the other way around.”

      It would be helpful to have all these points stated clearly so that it becomes easy to see where this article stands in an abundant literature and contributes to our understanding of multi-level evolution, and why it may have different conclusions or focus than others tackling very similar questions.

      Finally, a microbial context is given to the study, but the assumptions and results are in no way truly tied to that context, so it should be clear that this is just for flavor.

      We have deleted “microbial” from the title, and revised our abstract:

      Recommendations For The Authors

      (1) More details concerning our main remark above:

      - The paragraph discussing refs [24, 33] is not very clear in how they most importantly differ from this study. Our impression is that the resource aspect is not very important for instance, and the main difference is that these other works assume that strains can change in their traits.

      We are fairly sure that resource depletion is important in Rainey group’s study, as the attractor only evolved after both strains grew fast enough to deplete resources by the end of maturation. Indeed, evolution occurred in interaction coefficients which dictate the competition between strains for resources.

      Regardless, you raised an excellent point. As discussed earlier, we have added the following:

      “To enable the derivation of an analytical expression, we have made the following simplifications.

      First, growth is always exponential, without complications such as resource limitation, ecological interactions between the two populations, or density-dependent growth. Thus, the exponential growth equation can be used. Second, we consider only two populations (genotypes or species): the fast-growing F population with size F and the slow-growing S population with size S. We do not consider a spectrum of mutants or species, since with more than two populations, an analytical solution becomes very difficult.”

      - We would advise the main text to focus on mu = 0, and only say in discussion that results can be generalized.

      Your suggestion is certainly good. However, given the large amount of work involved in a reorganisation, we have decided to adhere to our current narrative. However, as discussed earlier, we have added this at the beginning of Results to help orient readers:

      “We will start with a complete model where S mutates to F at a nonzero mutation rate µ. We made this choice because it is more challenging to attain or maintain the target frequency when the abundance of fast-growing F is further increased via mutations.”

      “When the mutation rate is set to zero, the same model can be used to capture collectives of two species with different growth rates.”

      (2) We think the material on pg. 5 "Intra-collective evolution is the fastest at intermediate F frequencies, creating the "waterfall" phenomenon", although interesting, could be presented in a different way. The mathematical details on how to find the probability distribution of the maximum of independent random variables (including Equation 1) will probably be skipped by most of the readers (for experienced theoreticians, it is standard content; for experimentalists, it is not the most relevant), as such I would recommend displacing them to SM and report only the important results.

      This is an excellent suggestion. We have put a sketch of our calculations in a box in the main text to help orient interested readers. As before, details are in SI.

      Similarly, Equations 2, 3, and 4 are hard to read given the large amount of parameters and the low amount of simplification. Although exploring the effect of the different parameters through Figures 3 and 4 is useful, I think the role of the equations should be reconsidered:

      i. Is it possible to rewrite them in terms of effective variables in a more concise way?

      See Point 3 of Common comments.

      ii. Is it possible to present extreme/particular cases in which they are easier to interpret?

      We have focused on the case where the mutation rate is zero. This makes the mathematical expressions much simpler (see above).

      (3) Is it possible to explain more in detail why the distribution of f_k+1 conditional to f_k^* is well approximated by a Gaussian? Also, have you explored to what extent the results would change if this were not true (in light of the few universal classes for the maximum of independent variables)?

      Despite the appeal to the CLT and the histograms in the Appendix suggesting that the distribution looks a bit like a Gaussian at a certain scale, fluctuations on that scale are not necessarily what is relevant for the results - a rapid (and maybe wrong) attempt at a characteristic function calculation suggests that in your case, one does not obtain convergence to Gaussians unless we renormalize by S(t=0) and F(t=0), so it seems there is a justification missing in the text as is for the validity of this approximation (or that it is simply assumed).

      See point 4 of Common comments.

      Reviewer #3 (Public Reviews):

      The authors address the process of community evolution under collective-level selection for a prescribed community composition. They mostly consider communities composed of two types that reproduce at different rates, and that can mutate one into the other. Due to such differences in 'fitness' and to the absence of density dependence, within-collective selection is expected to always favour the fastest grower, but the collective-level selection can oppose this tendency, to a certain extent at least. By approximating the stochastic within-generation dynamics and solving it analytically, the authors show that not only high frequencies of fast growers can be reproducibly achieved, aligned with their fitness advantage. Small target frequencies can also be maintained, provided that the initial proportion of fast growers is sufficiently small. In this regime, similar to the 'stochastic corrector' model, variation upon which selection acts is maintained by a combination of demographic stochasticity and of sampling at reproduction. These two regions of achievable target compositions are separated by a gap, encompassing intermediate frequencies that are only achievable when the bottleneck size is small enough or the number of communities is (disproportionately) larger.

      A similar conclusion, that stochastic fluctuations can maintain the system over evolutionary time far from the prevalence of the faster-growing type, is then confirmed by analyzing a three-species community, suggesting that the qualitative conclusions of this study are generalizable to more complex communities.

      I expect that these results will be of broad interest to the community of researchers who strive to improve community-level selection, but are often limited to numerical explorations, with prohibitive costs for a full characterization of the parameter space of such embedded populations. The realization that not all target collective functions can be as easily achieved and that they should be adapted to the initial conditions and the selection protocol is also a sobering message for designing concrete applications.

      A major strength of this work is that the qualitative behaviour of the system is captured by an analytically solvable approximation so that the extent of the 'forbidden region' can be directly and generically related to the parameters of the selection protocol.

      Thanks so much for these positive comments.

      I however found the description of the results too succinct and I think that more could be done to unpack the mathematical results in a way that is understandable to a broader audience. Moreover, the phenomenon the authors characterize is of purely ecological nature. Here, mutations of the growth rate are, in my understanding, neither necessary (non-trivial equilibria can be maintained also when \mu =0) nor sufficient (community-level selection is necessary to keep the system far from the absorbing state) for the phenomenon described. Calling this dynamics community evolution reflects a widespread ambiguity, and is not ascribable just to this work. I find that here the authors have the opportunity to make their message clearer by focusing on the case where the 'mutation' rate \mu vanishes (Equations 39 & 40 of the SI) - which is more easily interpretable, at least in some limits - while they may leave the more general equations 3 & 4 in the SI.

      See points 1-4 of Common comments.

      Combined with an analysis of the deterministic equations, that capture the possibility of maintaining high frequencies of fast growers, the authors could elucidate the dynamics that are induced by the presence of a second level of selection, and speculate on what would be the result of real open-ended evolution (not encompassed by the simple 'switch mutations' generally considered in evolutionary game theory), for instance discussing the invasibility (or not) of mutant types with slightly different growth rates.

      Indeed, evolution is not restricted to two types. However, our main goal here is to derive an analytical expression, and it was difficult for even two types. For three-type collectives, we had to resort to simulations. Investigating the case where fitness effects of mutations are continuously distributed is beyond the scope of this study.

      The single most important model hypothesis that I would have liked to be discussed further is that the two types do not interact. Species interactions are not only essential to achieve inheritance of composition in the course of evolution but are generally expected to play a key role even on ecological time scales. I hope the authors plan to look at this in future work.

      In our system, the S and F do interact in a competitive fashion: even though S and F are not competing for nutrients (which are always in excess), they are competing for space. This is because a fixed number of cells are transferred to the next cycle. Thus, the presence of F will for example reduce the chance of S being propagated. We have added this clarification to our main text:

      “Note that even though S and F do not compete for nutrients, they compete for space: because the total number of cells transferred to the next cycle is fixed, an overabundance of one population will reduce the likelihood of the other being propagated.”

      Recommendations For The Authors

      I felt the authors could put some additional effort into making their theoretical results meaningful for a population of readers who, though not as highly mathematically educated as they are, can nonetheless appreciate the implications of simple relations or scaling. Below, you find some suggestions:

      (1) In order to make it clear that there is a 'natural' high-frequency equilibrium that can be reached even in the absence of selection, the authors could examine first the dynamics of the deterministic system in the absence of mutations, and use its equilibria to elucidate the combined role of the 'fitness' difference \omega and of the generation duration \tau in setting its value. The fact that these parameters always occur in combination (when there are no mutations) is a general and notable feature of the stochastic model as well. Moreover, this model would justify why you only focus on decreasing the frequency in the new generation.

      Note that the ‘natural’ high-frequency equilibrium in the absence of collective selection is when fast grower F becomes fixed in the population. Following your suggestion, we have introduced two parameters 𝑅τ and 𝑊τ to reflect the coupling between ‘fitness’ and ‘generation duration’:

      (2) Since the phenomenon described in the paper is essentially ecological in nature (as the author states, it does not change significantly if the 'mutation rate' \mu is set to zero), I would put in the main text Equations 39 & 40 of the SI in order to improve intelligibility.

      See Point 2 at the beginning of this letter.

      These equations can be discussed in some detail, especially in the limit of small f^*_k, where I think it is worth discussing the different dependence of the mean and the variance of the frequency distribution on the system's parameters.

      This is a great suggestion. We have added the following:

      “In the limit of small , Equation (3) becomes f while Equation (4) becomes . Thus, both Newborn size (N<sub>0</sub>) and fold-change in F/S during maturation (W<sub>τ</sub>) are important determinants of selection progress.

      (3) I would have appreciated an explanation in words of what are the main conceptual steps involved in attaining Equation 2, the underlying hypotheses (notably on community size and distributions), and the expected limits of validity of the approximation.

      See points 3 and 4 at the beginning of this letter.

      (4) I think that some care needs to be put into explaining where extreme value statistics is used, and why is the median of the conditional distribution the most appropriate statistics to look at for characterizing the evolutionary trajectory (which seems to me mostly reliant on extreme values).

      Great point! We added an explanation of using median value in Box 1.

      and also added figure 7 to explaining it in SI.

      Showing in a figure the different distributions you are considering (for instance, plotting the conditional distribution for one generation in the trajectories displayed in Figure 2) would be useful to understand what information \bar f provides on a sequence of collective generations, where in principle there may be memory effects.

      Thanks for this suggestion. We have added to Fig 2d panel to illustrate the shape and position of F frequency distributions in each step in the first two selection cycles.

      (5) Similarly, I do not understand why selecting the 5% best communities should push the system's evolution towards the high-frequency solution, instead of just slowing down the improvement (unless you are considering the average composition of the top best communities - which should be justified). I think that such sensitivity to the selection intensity should be appropriately referenced and discussed in the main text, as it is a parameter that experimenters are naturally led to manipulate.

      In the main text, we have added this explanation:

      “In contrast with findings from an earlier study [23], choosing top 1 is more effective than the less stringent “choosing top 5%”. In the earlier study, variation in the collective trait is partly due to nonheritable factors such as random fluctuations in Newborn biomass. In that context, a less stringent selection criterion proved more effective, as it helped retain collectives with favorable genotypes that might have exhibited suboptimal collective traits due to unfavorable nonheritable factors. However, since this study excludes nonheritable variations in collective traits, selecting the top 1 collective is more effective than selecting the top 5% (see Fig. 11 in Supplementary Information).”

      (6) Equation 1 could be explained in simpler terms as the product between the probability that one collective reaches the transmitted value times the probability that all others do worse than that. The current formulation is unclear, perhaps just a matter of English formulation.

      We have revised our description to state:

      “Equation (1) can be described as the product between two terms related to probability: (i) describes the probability density that any one of the g Adult collectives achieves f given , and (ii) describes the probability that all other g – 1 collectives achieve frequencies above f and thus not selected.”

      (7) I think that the discussion of the dependence of the boundaries of the 'waterfall' region with the difference in growth rate \omega is important and missing, especially if one wants to consider open-ended evolution of the growth rate - which can occur at steps of different magnitude.

      We added a new chapter and figure in supplementary information on the threshold values when \omega varies. As expected, smaller \omega enlarges the success area.

      We have also added a new figure panel to show how maturation time affects selection efficacy.

      (8) Notations are a bit confusing and could be improved. First of all, in most equations in the main text and SI, what is initially introduced as \omega appears as s. This is confusing because the letter s is also used for the frequency of the slow type.

      The letter S is used to denote an attribute of cells (S cells), the type of cells (Equations 1-3 of the SI) and the number of these cells in the population, sometimes with different meanings in the same sentence. This is confusing, and I suggest referring to slow cells or fast cells instead (or at least to S-cells and F-cells), and keeping S and F as variables for the number of cells of the two types.

      All typos related to the notation have been fixed. We use S and F as types, and S and F (italic) and population numbers.

      (9) On page 3, when introducing the sampling of newborns as ruled by a binomial distribution, the information that you are just transmitting one collective is needed, while it is conveyed later.

      We have added this emphasis:

      “At the end of a cycle, a single Adult with the highest function (with F frequency f closest to the target frequency ) is chosen to reproduce g Newborn collectives each with N<sub>0</sub> cells (‘Selection’ and ’Reproduction’ in Fig. 1).”

      (10) I found that the abstract talks too early about the 'waterfall' phenomenon. As this is a concept introduced here, I suggest the authors first explain what it is, then use the term. It is a useful metaphor, but it should not obscure the more formal achievements of the paper.

      We feel that the “waterfall” analogy offers a gentle helping hand to orient those who have not thought much about the phenomenon. We view abstract as an opportunity to attract readership, and thus the more accessible the better.

      (11) In the SI there are numerous typos and English language issues. I suggest the authors read carefully through it, and add line numbers to the next version so that more detailed feedback is possible.

      Thank you for going through SI. We have gone through the SI, and fixed problems.

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      Reply to the reviewers

      Manuscript number: RC-2024-02605

      Corresponding author: Woo Jae, Kim

      1. ____Point-by-point description of the revisions

      Reviewer #1

      General Comment: This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

      __Answer:____ __We would like to express our gratitude to the reviewer for their insightful comments and positive feedback on our manuscript. During the revision process, we serendipitously discovered that the heart-specific expression of the foraging gene plays a crucial role in regulating LMD behavior. We have elaborated on the significance of this finding in the revised manuscript and have addressed the reviewer's comments accordingly.

      Comment 1. *(optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD? *

      Answer: We sincerely thank the reviewer for her/his insightful comments regarding the integration of neuronal subsets into a signaling pathway and their potential role in modulating LMD behavior. During the revision process, we conducted further experiments to address this question. While we were unable to identify a specific small subset of EB neurons expressing foraging, we utilized the recently developed EB-split GAL4 driver line (SS00096), which is restricted to the EB region of the brain, to confirm that foraging expression in the EB is indeed crucial for generating LMD behavior (Fig. 4L-M). This finding underscores the importance of foraging in specific neural circuits within the EB for interval timing.

      Additionally, we discovered that foraging expression in Hand-GAL4-labeled pericardial cells (PCs) of the heart is essential for LMD behavior. These PCs are also partially labeled by fru-GAL4 and 30y-GAL4 drivers, indicating that foraging functions in both neuronal and non-neuronal tissues to regulate interval timing. Importantly, we observed that group-reared males exhibit higher calcium activity in PCs compared to socially isolated males, suggesting that social context-dependent calcium dynamics in the heart play a critical role in modulating LMD behavior.

      These findings highlight a novel integration of neuronal and cardiac mechanisms, where foraging expression in both the EB and heart coordinates calcium dynamics to regulate interval timing. This dual-tissue involvement provides a more cohesive understanding of how foraging integrates social cues with internal physiological states to modulate complex behaviors like LMD. We believe this integration of neuronal and cardiac pathways offers a comprehensive framework for understanding the gene’s pleiotropic roles in behavior. We have included these new findings in the revised manuscript to better address the reviewer’s question and to strengthen the discussion of how foraging functions across tissues to regulate interval timing behaviors.

      Comment 2. Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

      Answer: We appreciate the reviewer’s thoughtful consideration of potential genetic confounds related to the chromosomal locations of the elavc155 and repo-GAL4 transgenes. To address this concern, we conducted additional experiments using the nSyb-GAL4 driver, which is located on the third chromosome, and observed that knockdown of foraging with this driver also disrupts LMD behavior (Fig. S1G). This result aligns with our findings using elavc155 (chromosome I) and repo-GAL4 (chromosome III), indicating that the chromosomal location of the GAL4 transgene does not significantly impact the experimental outcomes.

      Furthermore, our extensive tissue-specific GAL4 screening, which included drivers on different chromosomes, consistently demonstrated that foraging knockdown effects on LMD are robust and reproducible across various genetic configurations. These results suggest that the observed behavioral deficits are due to the loss of foraging function rather than positional effects of the GAL4 transgenes. We thank the Reviewer for raising this important point and have taken care to address it thoroughly in our revised manuscript.

      Comment 3. Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

      Answer____: We thank the reviewer for her/his observation regarding the discrepancies in lacZ signal intensity on the surface of the male brain, which we attributed to variations in the dissection procedure. While replacing the current dataset with a new one is feasible, we have instead shifted our focus to address this concern by leveraging more reliable and validated tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we extensively examined multiple foraging-GAL4 lines and found that foraging expression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      Comment ____4. Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

      Answer: We appreciate the reviewer’s suggestion to provide additional rescue experiment data using the c61-GAL4 driver, similar to the results obtained with the 30y-GAL4 driver. While we do not currently have a UAS-for line to perform direct rescue experiments with c61-GAL4, we have conducted extensive follow-up experiments using both 30y-GAL4 driver to further validate the role of foraging in LMD behavior. These experiments consistently demonstrated that foraging knockdown in cells targeted by these drivers disrupts LMD, reinforcing the specificity and robustness of foraging’s role in interval timing.

      Additionally, our revised manuscript includes new findings that highlight the critical role of foraging expression in fru-positive heart neurons for generating male-specific mating investment. These heart neurons exhibit dynamic calcium activity changes in response to social context, further supporting the idea that foraging modulates LMD through both neuronal and non-neuronal mechanisms. While we acknowledge that direct rescue data with c61-GAL4 would strengthen the study, we believe the combination of 30y-GAL4 and c61-GAL4 knockdown results, along with the newly identified role of heart neurons, provides compelling evidence for foraging’s role in LMD.

      In addition, we have confirmed that the 30y-GAL4 driver labels fru-positive heart cells, further supporting the critical role of foraging expression in these cells for generating male-specific mating investment. This finding aligns with our broader results, demonstrating that foraging function in fru-positive heart neurons is essential for modulating interval timing behaviors, particularly LMD. We hope these additional analyses address the reviewer’s concerns and enhance the study’s conclusions regarding the specificity and robustness of foraging function in interval timing behaviors. We have incorporated the following findings into the main text:

      “Therefore, we conclude that the knockdown and genetic rescue effects observed with the Pdfr3A-GAL4 driver (Fig. 3J and 3N) and the 30y-GAL4 driver (Fig. 4A, S2A, and S2L) are attributable to their expression in the heart. In summary, our findings demonstrate that fru-positive heart cells expressing foraging and Pdfr play a critical role in mediating LMD behavior.”


      Reviewer #2

      General Comment: The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

      Answer: We sincerely thank the reviewer for their positive feedback on our manuscript and their recognition of our findings regarding the role of the foraging gene in modulating plastic LMD behavior as a model for interval timing. In addition to its function in the ellipsoid body (EB) of the central complex, we have identified a novel and critical role for foraging in fru-positive heart neurons. These neurons are essential for regulating male-specific mating investment, as demonstrated by dynamic calcium activity changes in response to social context. This discovery expands our understanding of foraging’s pleiotropic roles, highlighting its function not only in neural circuits but also in non-neuronal tissues, particularly the heart, to modulate interval timing behaviors. We believe these findings provide a more comprehensive view of how *foraging* integrates genetic, neural, and physiological mechanisms to regulate complex behaviors. We hope this additional insight into the role of fru-positive heart neurons further strengthens the manuscript and aligns with the reviewer’s interest in the broader implications of foraging function.


      Major concerns: __ Comment 1.__ Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

      Answer: We thank the reviewer for her/his insightful questions regarding the dominance of the forS allele in the presence of the overactive forR allele and the implications of the forR/forS transheterozygote phenotype. As the Reviewer noted, the forR allele is associated with higher PKG activity, while the forS allele exhibits lower PKG activity. The disruption of SMD in the presence of a single forR allele can be explained by the excessive PKG activity, which may hyperactivate or desensitize neural circuits required for SMD. Conversely, the forS homozygote disrupts LMD, suggesting that a minimum threshold of PKG activity is necessary for LMD generation.

      The forR/forS transheterozygote, which disrupts both LMD and SMD, presents an intriguing case. Unlike forR/+ or forS/+ heterozygotes, which show intact behaviors due to intermediate PKG activity levels, the forR/forS combination results in conflicting PKG activity levels that likely destabilize shared pathways required for both behaviors. We propose two hypotheses to explain this phenomenon:

      1. Metabolic Disruption: The foraginggene mediates adult plasticity and gene-environment interactions, particularly under conditions of food deprivation (Kent 2009). It influences body fat, carbohydrate metabolism, and gene expression levels, leading to metabolic and behavioral gene-environment interactions (GEI). In forR/forStransheterozygotes, the metabolic changes induced by each allele may accumulate without proper regulatory mechanisms, disrupting the male’s internal metabolic state and impairing the ability to accurately measure interval timing.

      Neuronal Polymorphism: The foraginggene regulates neuronal excitability, synaptic transmission, and nerve connectivity (Renger 1999). The forRand forS alleles may induce distinct neuronal polymorphisms, such as altered synaptic terminal morphology, which could lead to conflicting circuit dynamics in transheterozygotes. This neuronal mismatch may explain why forR/forS flies exhibit disrupted behaviors, unlike heterozygotes with a wild-type allele.

      These findings align with prior studies showing that PKG activity must be tightly regulated within context-dependent ranges for optimal behavior. The foraging gene’s pleiotropic roles, including its influence on metabolic and neural pathways, highlight the importance of allelic balance in maintaining behavioral robustness. The forR/forS transheterozygote phenotype underscores the complexity of foraging’s role in interval timing, where extreme or mismatched PKG activity levels disrupt circuit-specific thresholds critical for distinct behaviors. We hope this explanation clarifies the dominance effects and the role of PKG activity in LMD and SMD, and we have incorporated these insights into the revised manuscript to strengthen our discussion of foraging’s pleiotropic functions.

      We provide a concise explanation of this hypothesis in the Discussion section, as outlined below:

      “The foraging gene plays a critical role in regulating interval timing behaviors, with its allelic variants, rover and sitter, exhibiting distinct effects on LMD and SMD. These differences are primarily driven by their opposing impacts on cGMP-dependent protein kinase (PKG) activity. The forR allele, associated with higher PKG activity, disrupts SMD while maintaining normal LMD (Fig. 1A), suggesting that elevated PKG levels may hyperactivate or desensitize neural circuits specific to SMD processes. Conversely, the forS allele, characterized by lower PKG activity, impairs LMD but not SMD (Fig. 1B), indicating that reduced PKG activity fails to meet the neuromodulatory thresholds required for LMD coordination. The forR/forS transheterozygotes, which disrupt both LMD and SMD (Fig. 1C), reveal a complex interaction between these alleles, likely due to conflicting PKG activity levels or metabolic and neuronal polymorphisms that destabilize shared pathways. This phenomenon underscores the foraging gene’s pleiotropic roles, where allelic balance fine-tunes PKG activity to maintain behavioral robustness, while extreme or mismatched levels disrupt circuit-specific thresholds critical for distinct memory processes [6,10] .

      The foraging gene’s influence on interval timing behaviors extends beyond neural circuits to include metabolic and synaptic regulation. The intact behaviors observed in forR/+ or forS/+ heterozygotes suggest that intermediate PKG activity levels balance circuit dynamics, allowing for normal LMD and SMD. However, the dual deficits in forR/forS transheterozygotes highlight the importance of allelic balance, as conflicting PKG levels may lead to systemic disruptions in both metabolic and neural pathways. This aligns with previous studies showing that foraging mediates adult plasticity and gene-environment interactions, particularly under stress conditions, and regulates synaptic terminal morphology and neuronal excitability [29,77]. The gene’s role in integrating genetic and environmental cues further emphasizes its central role in adaptive behaviors. Collectively, these findings illustrate the complex interplay between PKG activity, neural circuits, and metabolic regulation in shaping interval timing behaviors, highlighting the foraging gene as a key modulator of behavioral plasticity in Drosophila [3,6,77].”

      Comment 2. Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

      Answer: We sincerely thank the reviewer for her/his suggestion to improve the flow of the manuscript. In response to reviewer’s feedback, we have removed Figure 3G-H and the related text (lines 193-201) from the main text. While the data on SMD behavior provided additional insights into the role of foraging in gustatory modulation via sNPF-expressing peptidergic neurons, we agree that its inclusion at this point in the manuscript could distract from the primary focus on LMD behavior and interval timing.

      Comment 3. Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons

      Answer: We thank the reviewer for their suggestion to use more specific GAL4 drivers to identify the exact subset of EB ring neurons (EB-RNs) where foraging function is necessary for LMD. In response, we utilized the EB-split-GAL4 driver SS00096, which has been previously employed to map the neuroanatomical ultrastructure of the EB (Turner-Evans 2020). Knockdown of foraging using this refined EB driver disrupted LMD behavior, confirming that foraging function in the EB is indeed crucial for interval timing.

      Regarding the reviewer’s observation about the Taghert lab’s findings on Pdfr+ EB-RNs and the expression of Cry in these neurons, we acknowledge this discrepancy. However, during the revision process, we discovered that foraging and Pdfr are co-expressed not only in EB neurons but also in fru-positive heart neurons, which play a complementary role in modulating LMD behavior. This finding suggests that the apparent contradiction may arise from the dual-tissue involvement of foraging in both EB neurons and heart cells. While foraging function in the EB is critical, its role in heart neurons may provide an additional layer of regulation for interval timing behaviors, potentially compensating for or interacting with EB-related mechanisms.

      We have incorporated these insights into the revised manuscript, emphasizing the importance of both EB and heart neurons in mediating LMD behavior. This dual-tissue perspective offers a more comprehensive understanding of foraging’s role in interval timing and addresses the potential discrepancies highlighted by the reviewer. We hope this clarification resolves the reviewer’s concerns and strengthens the manuscript’s conclusions regarding the neural and non-neural mechanisms underlying foraging function.

      Comment 4. Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.Please clarify if the Pdfr+ for+ EB neurons are also fru+.The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

      Answer: We thank the reviewer for her/his thoughtful questions regarding the molecular interaction between foraging and Pdfr signaling, as well as their observations on the atypical lacZ staining pattern. Below, we address each point in detail:

      1. Molecular Interaction Between foragingand PdfrSignaling: Our tissue-specific driver screening indicates that Pdfr and foraging do not co-express in the same neurons within the brain. Instead, we found that Pdfr and foraging are co-expressed in fru-positive heart cells, suggesting that PDF-Pdfr signaling in these cells modulates calcium activity in pericardial cells (PCs) in a social context-dependent manner. This finding aligns with our previous work showing that PDF signaling is crucial for LMD behavior (Kim 2013). We propose that PDF-Pdfr signaling operates not only through the brain’s sLNv to LNd neuronal circuit but also through a brain-to-heart signaling axis, influencing behaviors and physiological processes across multiple tissues.

      Removal of Lines 260-269: As suggested, we have removed lines 260-269, which discussed for+ AL neurons, as our findings do not implicate these neurons in LMD regulation. This revision helps streamline the manuscript and maintain focus on the relevant neural and cardiac mechanisms.

      Clarification on Pdfr+for+EB Neurons and fru Expression: While our data do not directly address whether Pdfr+ for+ EB neurons are also fru+, we have confirmed that foraging and Pdfr co-express in fru-positive heart cells. This suggests that fru may play a role in integrating foraging and Pdfr signaling in non-neuronal tissues, particularly in the heart, to regulate LMD behavior.

      Replacement of lacZ Staining Images: During the revision process, we extensively examined multiple foraging-GAL4lines and found that foragingexpression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior. Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment. We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      We hope these revisions meet the Reviewer’s expectations and provide a clearer understanding of the interplay between foraging and Pdfr signaling in interval timing behaviors.

      Comment 5. Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

      Answer: We appreciate the reviewer's suggestion regarding lines 303-312. Upon careful consideration, we believe this paragraph provides important context about the roles of dsx-positive and fru-positive cells in foraging behavior. Specifically, it highlights that the foraging function is associated with fru-positive cells rather than dsx-positive cells, which is a key distinction in our study. This information is relevant to understanding the broader implications of our findings, as it underscores the functional specificity of these genes in regulating behavior. However, to address the reviewer's concern, we have revised the paragraph to ensure it is more concise and directly tied to the study's conclusions. We have also integrated additional data from the new manuscript to further strengthen the factual value of this section. We hope this adjustment strikes the right balance between maintaining necessary context and avoiding any dilution of the final conclusions. Thank you for this thoughtful feedback.

      __Minor concerns: __

      __Comment 6. __Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

      Answer: We appreciate the reviewer’s insightful comments regarding our conclusion linking LMD to interval timing behavior. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes (Thornquist 2020).

      Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in Drosophila as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating (Thornquist 2020). These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

      In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper (Lee 2023), as detailed below:

      "We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

      In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time (Buhusi & Meck, 2005; Merchant et al, 2012; Allman et al, 2013; Rammsayer & Troche, 2014; Golombek et al, 2014; Jazayeri & Shadlen, 2015)."

      Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLoS Genetics, 19(5), e1010753.

      We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently (Huang 2024,Zhang 2024,Sun 2024).

      Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genetics, jkae255.

      Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. Behavior Genetics, 54(5), 416-425.

      Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Reports, 36, 101999.

      Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in Drosophila.

      “The mating duration (MD) of male fruit flies, Drosophila melanogaster, serves as an excellent model for studying interval timing behaviors. In Drosophila, two notable interval timing behaviors related to mating duration have been identified: Longer-Mating-Duration (LMD), which is observed when males are in the presence of competitors and extends their mating duration [15–17] and Shorter-Mating-Duration (SMD), which is characterized by a reduction in mating time and is exhibited by sexually experienced males [18,19]. The MD of male fruit flies serves as an excellent model for studying interval timing, a process that can be modulated by internal states and environmental contexts. Previous studies by our group (Kim 2013,Kim 2012,Zhang 2024,Lee 2023,Huang 2024) and others (Thornquist 2020,Crickmore 2013,Zhang 2019,Zhang 2021) have established robust frameworks for investigating MD using advanced genetic tools, enabling the dissection of neural circuits and molecular mechanisms that govern interval timing.

      The foraging gene emerged as a strong candidate for regulating LMD due to its well-documented role in behavioral plasticity and decision-making processes (Kent 2009,Alwash 2021,Anreiter 2019). The foraging gene encodes a cGMP-dependent protein kinase (PKG), which has been implicated in modulating foraging behavior, aggression, and other context-dependent behaviors in Drosophila. Its involvement in these processes suggests a potential role in integrating environmental cues and internal states to regulate interval timing, such as LMD. Furthermore, the molecular mechanisms underlying interval timing have been explored in other contexts, such as the work of the Crickmore et al., which has demonstrated the critical role of CREB (cAMP response element-binding protein) in regulating behavioral timing and plasticity. CREB-dependent signaling pathways, along with other molecular players like PKG, provide a broader framework for understanding how interval timing is orchestrated at the neural and molecular levels (Thornquist 2020,Zhang 2016,Zhang 2021,Zhang 2019,Crickmore 2013,Zhang 2023). By investigating foraging in the context of LMD, we aim to uncover how specific genetic and neural mechanisms fine-tune interval timing in response to social and environmental cues, contributing to a deeper understanding of the principles governing behavioral adaptation.”

      When describing larval neurons, we provide specific references to ensure clarity and accuracy, as outlined below:

      “Moreover, the cultured giant neural characteristics of these phenotypes are distinctly different [29].”

      We thank the reviewer for catching this error. We have corrected the incorrect label "Glia" to "Neuron" in Figures 2E, 2G, and 2H.

      Reviewer #3

      General Comment: This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

      Answer: We sincerely thank the reviewer for her/his thoughtful and comprehensive synthesis of our work, as well as their recognition of its key contributions. We are grateful that the reviewer highlighted the central findings of our study, including the allele-specific roles of forR (rover) and forS (sitter) in regulating distinct interval timing behaviors—specifically, the deficiencies of rovers in SMD and sitters in LMD. We also appreciate the reviewer’s emphasis on the sexually dimorphic expression of the *foraging* gene, particularly its male-biased expression in the ellipsoid body (EB), and its critical role in Pdfr-positive neurons for mediating LMD.

      We agree with the reviewer that the interplay between genetic factors (e.g., allelic variation in foraging) and environmental cues (e.g., circadian rhythms via Pdfr pathways) underscores the complexity of interval timing regulation. The rescue of LMD behavior by restoring foraging expression in Pdfr cells further supports our hypothesis that foraging operates through specialized neural circuits to integrate temporal and environmental inputs. This finding aligns with broader studies on interval timing mechanisms, such as the work of the Crickmore lab on CREB-dependent pathways, which have demonstrated how molecular and neural mechanisms converge to regulate behavioral plasticity and timing.

      In the revised manuscript, we will expand on these points to strengthen the discussion of foraging’s pleiotropic roles in time-dependent mating strategies and its potential links to evolutionary fitness. Specifically, we will incorporate additional insights from the new manuscript, including further evidence of how foraging balances behavioral plasticity with metabolic and neural demands, and how its expression in specific neuronal populations, such as the EB, contributes to adaptive behaviors. These updates will provide a more comprehensive understanding of the gene’s role in interval timing and its broader implications for behavioral adaptation. Once again, we thank the Reviewer for their valuable feedback, which has helped us refine and enhance the presentation of our findings.

      __Major concerns: __

      Comment 1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.

      __Answer:____ __We sincerely thank the reviewer for her/his insightful comment regarding the sexually dimorphic expression of the foraging gene. We agree that the lacZ signal in the male brain, as presented, may not be fully representative, and we appreciate the reviewer’s observation regarding the discrepancies in signal intensity, which we attribute to variations in dissection procedures. While replacing the current dataset with a new one is feasible, we have chosen to address this concern by shifting our focus to a more reliable and validated approach using tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we conducted an extensive examination of multiple foraging-GAL4 lines and found that foraging expression in the brain is often limited and inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To overcome this limitation, we employed well-characterized tissue-specific GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      By focusing on the heart and leveraging more reliable genetic tools, we believe this new analysis addresses the Reviewer’s concerns and provides a more robust and consistent approach to studying foraging function. We hope these findings meet the reviewer’s expectations and offer a clearer understanding of foraging’s role in mating duration. We are grateful for the Reviewer’s constructive feedback, which has significantly strengthened our study.

      Comment 2____. Key control genotypes are missing.

      Answer: We thank the Reviewer for raising this important point regarding control genotypes. We would like to clarify that all necessary control experiments have indeed been conducted, and the results are included in the manuscript. Detailed descriptions of these controls, including the specific genotypes and experimental conditions, are provided in the Methods section. For example, control experiments were performed to account for genetic background effects, GAL4 driver activity, and RNAi efficiency, ensuring the reliability and specificity of our findings. In the revised manuscript, we have further emphasized these control experiments and their outcomes to ensure transparency and reproducibility. We have also included additional details in the Results section to highlight how these controls validate our key findings. For instance, control genotypes lacking the foraging-RNAi or GAL4 drivers were used to confirm that the observed phenotypes are specifically due to the manipulation of foraging expression.

      We appreciate the Reviewer’s attention to this critical aspect of our study and hope that the additional clarification and emphasis on control experiments in the revised manuscript address their concerns. If there are specific control genotypes or experiments the reviewer would like us to include or elaborate on further, we would be happy to do so. Thank you for this valuable feedback.

      Comment 3____.fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

      Answer: We thank the reviewer for her/his insightful comment regarding the expression of fru in the ellipsoid body (EB) and its relevance to our model in Figure 5G. We agree that fru is not expressed in the EB, and we acknowledge the need to reconcile this aspect of our model. While initial evidence suggested a potential role for the EB in regulating foraging-dependent LMD behavior, further investigation has revealed that neurons outside the EB are more likely to be involved in this process.

      During our revision, we identified fru-positive heart neurons that coexpress Pdfr and foraging, which appear to play a critical role in modulating LMD behavior. These findings suggest that the heart, rather than the EB, may be a key site for foraging function in the context of interval timing and mating duration. Specifically, we demonstrated that calcium activity in these fru+ heart cells is dynamically regulated by social context, further supporting their role in modulating male mating investment.

      In light of these new findings, we revised Figure 5G as new Figure 6H and the accompanying model to reflect the updated understanding that fru+ heart neurons, rather than EB neurons, are central to the regulation of LMD behavior. This adjustment aligns with our broader goal of accurately representing the neural and molecular mechanisms underlying foraging’s role in interval timing. We appreciate the Reviewer’s feedback, which has helped us refine our model and strengthen the manuscript. We hope these revisions address their concerns and provide a clearer and more accurate representation of our findings. Thank you for this valuable input.

      Minor concerns: Comment 4____.

      Line 32, what do you mean by "overall success of the collective"

      Line 124-126: I suggest not using "sitter neurons" or "rover neurons". Line 301, typo with "male-specific".

      Answer: We thank the Reviewer for their careful reading and constructive feedback. We have addressed each of their comments as follows:

      1. Line 32: We agree with the reviewer that the phrase "overall success of the collective" was unclear and have completely revised the Abstract to remove this expression. The updated Abstract now provides a clearer and more concise summary of our findings.

      Lines 124-126: We appreciate the reviewer’s suggestion to avoid using the terms "sitter neurons" or "rover neurons," as they could be misleading. We have revised this phrasing to "neurons of sitter/rover allele" to more accurately reflect the genetic context of our study.

      Line 301: We have corrected the typo with "male-specific" to ensure accuracy and clarity in the text.

      We hope these revisions address the Reviewer’s concerns and improve the overall quality of the manuscript. Thank you for your valuable input, which has helped us refine our work.

      __Strengths and limitations of the study:______ This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

      __Answer:____ __We thank the reviewer for her/his thoughtful assessment of the strengths and limitations of our study. We agree that our work represents a significant advancement in understanding the role of the foraging gene in regulating mating behaviors through interval timing, particularly in identifying the critical role of Pdfr-expressing neurons in the ellipsoid body (EB) for long mating duration (LMD). However, we acknowledge that the initial manuscript did not fully elucidate how these neurons specifically modulate timing mechanisms or interact with other neural circuits involved in memory and decision-making.

      In response to this feedback, we have conducted additional experiments and analyses, which are now included in the revised manuscript. Specifically, we identified fru-positive heart neurons that coexpress Pdfr and foraging, and we demonstrated their essential role in LMD using calcium imaging (CaLexA). These findings provide a more comprehensive mechanistic understanding of how foraging influences interval timing through cardiac activity, which is dynamically regulated by social context. This new evidence addresses the reviewer’s concern by offering a clearer picture of the neural and molecular pathways underlying LMD.

      Regarding SMD behavior, we agree that it was comparatively underexplored in the initial manuscript. However, we have extensively studied SMD in other contexts, as highlighted in several of our previously published papers. These studies have investigated the sensory mechanisms, memory processes, peptidergic signaling, and clock gene functions associated with SMD (Zhang 2024,Zhang 2024,Sun 2024,Wong 2019,Kim 2024,Lee 2023). While the current manuscript focuses primarily on LMD, we will include a discussion of these findings to provide a more balanced perspective on the mechanisms underlying both LMD and SMD.

      We believe these revisions address the Reviewer’s concerns and significantly strengthen the manuscript by providing a more detailed mechanistic understanding of foraging’s role in interval timing and mating behaviors. We are grateful for the Reviewer’s constructive feedback, which has helped us improve the depth and clarity of our study. Thank you for your valuable input.

      __Advance:______ This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

      Answer: We sincerely thank the reviewer for her/his insightful comments and for recognizing the novel contributions of our study. We are pleased that the reviewer highlighted how our work expands the understanding of the foraging gene, which was previously primarily associated with food-search behavior. By demonstrating its role in interval timing—a cognitive process critical to mating behaviors in Drosophila—we challenge the conventional assumption that foraging is solely related to foraging strategies. Instead, our findings reveal its broader function in time-based decision-making processes, particularly in the context of mating duration.

      This discovery not only advances our understanding of the pleiotropic roles of foraging but also opens new avenues for exploring how genetic and neural mechanisms integrate temporal and environmental cues to regulate complex behaviors. We are grateful for the reviewer’s support and acknowledgment of the significance of our findings. Thank you for this valuable feedback.

      __Audience:______ The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

      Answer: We sincerely thank the reviewer for her/his thoughtful comment and for recognizing the broad relevance of our study. We are pleased that the reviewer highlighted the significant value our work offers to be specialized research communities, particularly in behavioral genetics and evolutionary biology, as well as to researchers using the Drosophila model. By elucidating the role of the foraging gene in interval timing and its impact on mating behaviors, our findings provide a foundation for future research on other behaviors that rely on precise timing and decision-making. This study not only advances our understanding of the genetic and neural mechanisms underlying interval timing but also opens new avenues for exploring how similar processes may operate in other species or contexts. We hope our work will inspire further investigations into the interplay between genetic variation, neural circuits, and environmental cues in shaping adaptive behaviors. Thank you for your valuable feedback and for acknowledging the potential impact of our research.

  2. Feb 2025
    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public Review):

      Summary:

      This paper presents a compelling and comprehensive study of decision-making under uncertainty. It addresses a fundamental distinction between belief-based (cognitive neuroscience) formulations of choice behavior with reward-based (behavioral psychology) accounts. Specifically, it asks whether active inference provides a better account of planning and decision making, relative to reinforcement learning. To do this, the authors use a simple but elegant paradigm that includes choices about whether to seek both information and rewards. They then assess the evidence for active inference and reinforcement learning models of choice behavior, respectively. After demonstrating that active inference provides a better explanation of behavioral responses, the neuronal correlates of epistemic and instrumental value (under an optimized active inference model) are characterized using EEG. Significant neuronal correlates of both kinds of value were found in sensor and source space. The source space correlates are then discussed sensibly, in relation to the existing literature on the functional anatomy of perceptual and instrumental decision-making under uncertainty.

      We are deeply grateful for your careful review of our work and your suggestions. Your insights have helped us identify areas where we can strengthen the arguments and clarify the methodology. We hope to apply the idea of active inference to our future work, emphasizing the integrity of perception and action.

      Reviewer #1 (Recommendations For The Authors):

      Many thanks for attending to my previous suggestions. I think your presentation is now much clearer and nicely aligned with the active inference literature.

      There is one outstanding issue. I think you have overinterpreted the two components of epistemic value in Equation 8. The two components that you have called the value of reducing risk and the value of reducing ambiguity are not consistent with the normal interpretation. These two components are KL divergences that measure the expected information gain about parameters and states respectively.

      If you read the Schwartenbeck et al paper carefully, you will see that the first (expected information gain about parameters) is usually called novelty, while the second (expected information gain about states) is usually called salience.

      This means you can replace "the value of reducing ambiguity" with "novelty" and "the value of reducing risk" with "salience".

      For your interest, "risk" and "ambiguity" are alternative ways of decomposing expected free energy. In other words, you can decompose expected free energy into (negative) expected information gain and expected value (as you have done). Alternatively, you can rearrange the terms and express expected free energy as risk and ambiguity. Look at the top panel of Figure 4 in:

      https://www.sciencedirect.com/science/article/pii/S0022249620300857

      I hope that this helps.

      We deeply thank you for your recommendations about the interpretation of the epistemic value in Equation 8. We have now corrected them to Novelty and Salience:

      In addition, in order to avoid terminology conflicts with active inference and to describe these two different uncertainties, we replaced Ambiguity in the article with Novelty, referring to the uncertainty that can be reduced by sampling, and replaced Risk with Variability, referring to the uncertainty inherent in the environment (variance).

      Reviewer # 2 (Public Review):

      Summary:

      Zhang and colleagues use a combination of behavioral, neural, and computational analyses to test an active inference model of exploration in a novel reinforcement learning task..

      Strengths:

      The paper addresses an important question (validation of active inference models of exploration). The combination of behavior, neuroimaging, and modeling is potentially powerful for answering this question.

      I appreciate the addition of details about model fitting, comparison, and recovery, as well as the change in some of the methods.

      We are deeply grateful for your careful review of our work and your suggestions. And we are also very sorry that in our last responses, there were a few suggestions from you that we did not respond them appropriately in our manuscript. We hope to be able to respond to these suggestions well in this revision. Thank you for your contribution to ensuring the scientificity and reproducibility of the work.

      The authors do not cite what is probably the most relevant contextual bandit study, by Collins & Frank (2018, PNAS), which uses EEG.

      The authors cite Collins & Molinaro as a form of contextual bandit, but that's not the case (what they call "context" is just the choice set). They should look at the earlier work from Collins, starting with Collins & Frank (2012, EJN).

      We deeply thank you for your comments. Now we add the relevant citations in the manuscript (line 46):

      “These studies utilized different forms of multi-armed bandit tasks, e.g the restless multi-armed bandit tasks (Daw et al., 2006; Guha et al., 2010), risky/safe bandit tasks (Tomov et al., 2020; Fan et al., 2022; Payzan et al., 2013), contextual multi-armed bandit tasks (Collins & Frank, 2018; Schulz et al., 2015; Collins & Frank, 2012)”

      Daw, N. D., O'doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441(7095), 876-879.

      Guha, S., Munagala, K., & Shi, P. (2010). Approximation algorithms for restless bandit problems. Journal of the ACM (JACM), 58(1), 1-50.

      Tomov, M. S., Truong, V. Q., Hundia, R. A., & Gershman, S. J. (2020). Dissociable neural correlates of uncertainty underlie different exploration strategies. Nature communications, 11(1), 2371.

      Fan, H., Gershman, S. J., & Phelps, E. A. (2023). Trait somatic anxiety is associated with reduced directed exploration and underestimation of uncertainty. Nature Human Behaviour, 7(1), 102-113.

      Payzan-LeNestour, E., Dunne, S., Bossaerts, P., & O’Doherty, J. P. (2013). The neural representation of unexpected uncertainty during value-based decision making. Neuron, 79(1), 191-201.

      Collins, A. G., & Frank, M. J. (2018). Within-and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. Proceedings of the National Academy of Sciences, 115(10), 2502-2507.

      Schulz, E., Konstantinidis, E., & Speekenbrink, M. (2015, April). Exploration-exploitation in a contextual multi-armed bandit task. In International conference on cognitive modeling (pp. 118-123).

      Collins, A. G., & Frank, M. J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35(7), 1024-1035.

      Placing statistical information in a GitHub repository is not appropriate. This needs to be in the main text of the paper. I don't understand why the authors refer to space limitations; there are none for eLife, as far as I'm aware.

      We deeply thank you for your comments. We calculated the average t-value of the brain regions with significant results over the significant time, and added the t-value results to the main text and supplementary materials.

      In answer to my question about multiple comparisons, the authors have added the following: "Note that we did not attempt to correct for multiple comparisons; largely, because the correlations observed were sustained over considerable time periods, which would be almost impossible under the null hypothesis of no correlations." I'm sorry, but this does not make sense. Either the authors are doing multiple comparisons, in which case multiple comparison correction is relevant, or they are doing a single test on the extended timeseries, in which case they need to report that. There exist tools for this kind of analysis (e.g., Gershman et al., 2014, NeuroImage). I'm not suggesting that the authors should necessarily do this, only that their statistical approach should be coherent. As a reference point, the authors might look at the aforementioned Collins & Frank (2018) study.

      We deeply thank you for your comments. We have now replaced all our results with the results after false discovery rate correction and added relevant descriptions (line 357,358):

      “The significant results after false discovery rate (FDR) (Benjamini et al., 1995, Gershman et al., 2014) correction were shown in shaded regions. Additional regression results can be found in Supplementary Materials.”

      Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300.

      Gershman, S. J., Blei, D. M., Norman, K. A., & Sederberg, P. B. (2014). Decomposing spatiotemporal brain patterns into topographic latent sources. NeuroImage, 98, 91-102.

      After FDR correction, our results have changed slightly. We have updated our Results and Discussion section.

      It should be acknowledged that the changes in these results may represent a certain degree of error in our data (perhaps because the EEG data is too noisy or because of the average template we used, ‘fsaverage’). Therefore, we added relevant discussion in the Discussion section (line527-529):

      “It should be acknowledged that our EEG-based regression results are somewhat unstable, and the brain regions with significant regression are inconsistent before and after FDR correction. In future work, we should collect more precise neural data to reduce this instability.”

      I asked the authors to show more descriptive comparison between the model and the data. Their response was that this is not possible, which I find odd given that they are able to use the model to define a probability distribution on choices. All I'm asking about here is to show predictive checks which build confidence in the model fit. The additional simulations do not address this. The authors refer to figures 3 and 4, but these do not show any direct comparison between human data and the model beyond model comparison metrics.

      We deeply thank you for your comments. We now compare the participants’ behavioral data and the model’s predictions trial by trial (Figure 5). We can clearly see the participants’ behavioral strategies in different states and trials and the model’s prediction accuracy. We have added the discussion related to Figure 5 (line 309-318):

      “Figure 5 shows the comparison between the active inference model and the behavioral data, where we can see that the model can fit the participants behavioral strategies well. In the “Stay-Cue" choice, participants always tend to choose to ask the ranger and rarely choose not to ask. When the context was unknown, participants chose the “Safe" option or the “Risky" option very randomly, and they did not show any aversion to variability. When given “Context 1", where the “Risky" option gave participants a high average reward, participants almost exclusively chose the “Risky" option, which provided more information in the early trials and was found to provide more rewards in the later rounds. When given “Context 2", where the “Risky" option gave participants a low average reward, participants initially chose the “Risky" option and then tended to choose the “Safe" option. We can see that participants still occasionally chose the “Risky" option in the later trials of the experiment, which the model does not capture. This may be due to the influence of forgetting. Participants chose the “Risky" option again to establish an estimate of the reward distribution.”

      Reviewer # 2 (Recommendations For The Authors):

      In the supplement, there are missing references ("[?]").

      Thank you very much for pointing out this. We have now fixed this error.

      Reviewer # 3 (Public review):

      Summary:

      This paper aims to investigate how the human brain represents different forms of value and uncertainty that participate in active inference within a free-energy framework, in a two-stage decision task involving contextual information sampling, and choices between safe and risky rewards, which promotes shifting between exploration and exploitation. They examine neural correlates by recording EEG and comparing activity in the first vs second half of trials and between trials in which subjects did and did not sample contextual information, and perform a regression with free-energy-related regressors against data "mapped to source space."

      Strengths:

      This two-stage paradigm is cleverly designed to incorporate several important processes of learning, exploration/exploitation and information sampling that pertain to active inference. Although scalp/brain regions showing sensitivity to the active-inference related quantities do not necessary suggest what role they play, they are illuminating and useful as candidate regions for further investigation. The aims are ambitious, and the methodologies impressive. The paper lays out an extensive introduction to the free energy principle and active inference to make the findings accessible to a broad readership.

      Weaknesses:

      In its revised form the paper is complete in providing the important details. Though not a serious weakness, it is important to note that the high lower-cutoff of 1 Hz in the bandpass filter, included to reduce the impact of EEG noise, would remove from the EEG any sustained, iteratively updated representation that evolves with learning across trials, or choice-related processes that unfold slowly over the course of the 2-second task windows.

      We are deeply grateful for your careful review of our work and your suggestions. We are very sorry that we did not modify our filter frequency (it would be a lot of work to modify it). Thank you very much for pointing this out. We noticed the shortcoming of the high lower-cutoff of 1 Hz in the bandpass filter. We will carefully consider the filter frequency when preprocessing data in future work. Thank you very much!

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Juvenile Hormone (JH) plays a key role in insect development and physiology. Although the intracellular receptor for JH was identified long ago, a number of studies have shown that part of JH functions should be fulfilled through binding to an unknown membrane receptor, which was proposed to belong to the RTK family. In this study, the authors screened all RTKs from the H. armigera genome for their ability to mediate responses to JH III treatment both in cultured cells and in developing animals. They also present convincing evidence that CAD96CA and FGFR1 directly bind JH III, and that their role might be conserved in other insect species.

      Strengths:

      Altogether, the experimental approach is very complete and elegant, providing evidence for the role of CAD96CA and FGFR1 in JH signalling using different techniques and in different contexts. I believe that this work will open new perspectives to study the role of JH and better understand what is the contribution of signalling through membrane receptors for JH-dependent developmental processes.

      Weaknesses:

      I don't see major weaknesses in this study. However, I think that the manuscript would benefit from further information or discussion regarding the relationship between the two newly identified receptors. Experiments (especially in HEK-293T cells) suggest that CAD96CA and FGFR1 are sufficient on their own to transduce JH signalling. However, they are also necessary since loss-of-function conditions for each of them are sufficient to trigger strong effects (while the other is supposed to be still present).

      Thank you for the suggestion. We have added the discussion in the text: "CAD96CA and FGFR1 have similar functions in JH signaling, including transmitting JH signal for Kr-h1 expression, larval status maintaining, rapid intracellular calcium increase, phosphorylation of transcription factors MET1 and TAI, and high affinity to JH III. CAD96CA and FGFR1 are essential in the JH signal pathway, and loss-of-function for each is sufficient to trigger strong effects on pupation. The difference is that CAD96CA expression has no tissue specificity, and the Fgfr1 gene is highly expressed in the midgut; possibly, it plays a significant role in the midgut. Other possibility is that they play roles by forming heterodimer with each other or other RTKs, which needs to be addressed in future study. CAD96CA and FGFR1 transmit JH III signals in three different insect cell lines, suggesting their conserved roles in other insects.".

      In addition, despite showing different expression patterns, the two receptors seem to display similar developmental functions according to loss-of-function phenotypes. It is therefore unclear how to draw a model for membrane receptor-mediated JH signalling that includes both CAD96CA and FGFR1.

      Thank you for your question. We have modified the figure and the legends to make the conception clear.

      Reviewer #2 (Public Review):

      Summary:

      Juvenile hormone (JH) is a pleiotropic terpenoid hormone in insects that mainly regulates their development and reproduction. In particular, its developmental functions are described as the "status quo" action, as its presence in the hemolymph (the insect blood) prevents metamorphosis-initiating effects of ecdysone, another important hormone in insect development, and maintains the juvenile status of insects. While such canonical functions of JH are known to be mediated by its intracellular receptor complex composed of Met and Tai, there have been multiple reports suggesting the presence of cell membrane receptor(s) for JH, which mediate non-genomic effects of this terpenoid hormone. In particular, the presence of receptor tyrosine kinase(s) that phosphorylate Met/Tai in response to JH and thus indirectly affect the canonical JH signaling pathway has been strongly suggested. Given the importance of JH in insect physiology and the fact that the JH signaling pathway is a major target of insect growth regulators, elucidating the identification and functions of putative JH membrane receptors is of great significance from both basic and applied perspectives. In the present study, the authors identified candidate receptors for such cell membrane JH receptors, CAD96CA and FGFR1, in the cotton bollworm Helicoverpa armigera.

      Strengths:

      Their in vitro analyses are conducted thoroughly using multiple methods, which overall supports their claim that these receptors can bind to JH and mediate their non-genomic effects.

      Weaknesses:

      Results of their in vivo experiments, particularly those of their loss-of-function analyses using CRISPR mutants are still preliminary, and the results rather indicate that these membrane receptors do not have any physiologically significant roles in vivo. More specifically, previous studies in lepidopteran species have clearly and repeatedly shown that precocious metamorphosis is the hallmark phenotype for all JH signaling-deficient larvae. In contrast, the present study showed that Cad96ca and Fgfr1 G0 mutants only showed a slight acceleration in their pupation timing, which is not a typical phenotype one would expect from JH signaling deficiency. This is inconsistent with their working model provided in Figure 6, which indicates that these cell membrane JH receptors promote the canonical JH signaling by phosphorylating Met/Tai.

      If the authors argue that this slight acceleration of pupation is indeed a major JH signaling-deficient phenotype in Helicoverpa, they need to provide more data to support their claim by analyzing CRISPR mutants of other genes involved in JH signaling, such as Jhamt and Met. An alternative explanation is that there is functional redundancy between CAD96CA and FGFR1 in mediating phosphorylation of Met/Tai. This possibility can be tested by analyzing double knockouts of these two receptors.

      Thank you for your question and suggestion. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and in cell lines, and overexpression of them in mammal HEK-293T heterogeneously. Figure 6 is drafted by these solidate evidences.

      Cad96ca and Fgfr1 G0 mutants caused slight acceleration of pupation is one of the types of evidence of JH signaling-deficient. Othe evidences include a set of gene expression and the block of JH induced-rapid intracellular calcium increase.

      Kr-h1 is a typical indicator gene at the downstream of Jhamt and in JH signaling, so we used it as an indicator to examine JH signaling. Jhamt and Met or other genes might be affected in Cad96ca and Fgfr1 G0 mutants, which can be examined in future study.

      We have discussed the question that Cad96ca and Fgfr1 G0 mutants only showed a slight acceleration in their pupation timing: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      Currently, the validity of their calcium imaging analysis in Figure 5 is also questionable. When performing calcium imaging in cultured cells, it is critically important to treat all the cells at the end of each experiment with a hormone or other chemical reagents that universally induce calcium increase in each particular cell line. Without such positive control, the validity of calcium imaging data remains unknown, and readers cannot properly evaluate their results.

      Thank you for your question. For Figure 5, our goal was to demonstrate that JH can induce calcium mobilization through CAD96CA and FGFR1. Controls have been established between different experimental groups within the same cell, as well as between different cells. Increasing the positive experimental group would make the results more complex.

      Reviewer #3 (Public Review):

      Summary:

      In this study, Li et al. identified CAD96CA and FGF1 among 20 receptor tyrosine kinase receptors as mediators of JH signaling. By performing a screen in HaEpi cells with overactivated JH signaling, the authors pinpointed two main RTKs that contribute to the transduction of JH. Using the CRISPR/Cas9 system to generate mutants, the authors confirmed that these RTKs are required for normal JH activation, as precocious pupariation was observed in their absence. Additionally, the authors demonstrated that both CAD96CA and FGF1 exhibit a high affinity for JH, and their activation is necessary for the proper phosphorylation of Tai and Met, transcription factors that promote the transcriptional response. Finally, the authors provided evidence suggesting that the function of CAD96CA and FGF1 as JH receptors is conserved across insects.

      Strengths:

      The data provided by the authors are convincing and support the main conclusions of the study, providing ample evidence to demonstrate that phosphorylation of the transducers Met and Tai mainly depends on the activity of two RTKs. Additionally, the binding assays conducted by the authors support the function of CAD96CA and FGF1 as membrane receptors of JH. The study's results validate, at least in H. amigera, the predicted existence of membrane receptors for JH.

      Weaknesses:

      The study has several weaknesses that need to be addressed. Firstly, it is not clear what criteria were used by the authors to discard several other RTKs that were identified as repressors of JH signaling. For example, while NRK and Wsck may not fulfill all the requirements to become JH receptors, other evidence, such as depletion analysis and target gene expression, suggests they are involved in proper JH signaling activation.

      Thank you for your question. We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. WSCK was not involved in the phosphorylation of MET and TAI and was discarded during subsequent screening. NRK did not bind to JH III, did not meet the screening strategy, and was discarded.

      We increased the information in the Introduction: "We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and cell lines, and overexpression of them in mammal HEK-293T heterogeneously.".

      We increased discussion: "This study found six RTKs that respond to JH induction by participating in JH induced-gene expression and intracellular calcium increase, however; they exert different functions in JH signaling, and finally CAD96CA and FGFR1 are determined as JH cell membrane receptors by their roles in JH induced-phosphorylation of MET and TAI and binding to JH III. We screen the RTKs transmitting JH signal primarily by examining some of JH induced-gene expression. By examining other genes or by other strategies to screen the RTKs might find new RTKs functioning as JH cell membrane receptors; however, the key evaluation indicators, such as the binding affinity of the RTKs to JH and the function in transmitting JH signal to maintain larval status are essential.".

      Secondly, the expression of the six RTKs, which, when knocked down, were able to revert JH signaling activation, was mainly detected in the last larval stage of H. amigera. However, since JH signaling is active throughout larval development, it is unclear whether these RTKs are completely required for pathway activation or only needed for high activation levels at the last larval stage.

      Thank you for the question. We knocked down the genes at last larval stage to observe pupation, which is a relatively simple and easily to be observed target to examine the role of the gene in JH-maintained larval status. The results from CRISPR/Cas9 experiments showed: "Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      Additionally, the mechanism by which different RTKs exert their functions in a specific manner is not clear. According to the expression profile of the different RTKs, one might expect some redundant role of those receptors. In fact the no reversion of phosphorilation of tai and met upon depletion of Wsck in cells with overactivated JH signalling seems to support this idea.

      Nevertheless, and despite the overlapping expression of the different receptors, all RTKs seem to be required for proper pathway activation, even in the case of FGF1 which seems to be only expressed in the midgut. This is an intriguing point unresolved in the study.

      Thank you for your comments. Yes, from our study, different RTKs exert their functions in a specific manner. We have increased discussion: "This study found six RTKs that respond to JH induction by participating in JH induced-gene expression and intracellular calcium increase, however; they exert different functions in JH signaling, and finally CAD96CA and FGFR1 are determined as JH cell membrane receptors by their roles in JH induced-phosphorylation of MET and TAI and binding to JH III. We screen the RTKs transmitting JH signal primarily by examining some of JH induced-gene expression. By examining other genes or by other strategies to screen the RTKs might find new RTKs functioning as JH cell membrane receptors; however, the key evaluation indicators, such as the binding affinity of the RTKs to JH and the function in transmitting JH signal to maintain larval status are essential.".

      Finally, the study does not explain how RTKs with known ligands could also bind JH and contribute to JH signaling activation. in Drosophila, FGF1 is activated by pyramus and thisbe for mesoderm development, while CAD96CA is activated by collagen during wound healing. Now the authors claim that in addition to these ligands, the receptors also bind to JH. However, it is unclear whether these RTKs are activated by JH independently of their known ligands, suggesting a specific binding site for JH, or if they are only induced by JH activation when those ligands are present in a synergistic manner. Alternatively, another explanation could be that the RTK pathways by their known ligands activation may induce certain levels of JH transducer phosphorylation, which, in the presence of JH, contributes to the full pathway activation without JH-RTK binding being necessary.

      Thank you for your professional questions. It is an exciting and challenging to explore the molecular mechanism by which multiple ligands transmit signals through the same receptor. It requires a long-term research plan and in-depth studies. We added discussion in the text: "CAD96CA (also known as Stitcher, Ret-like receptor tyrosine kinase) activates upon epidermal wounding in Drosophila embryos (Tsarouhas et al., 2014) and promotes growth and suppresses autophagy in the Drosophila epithelial imaginal wing discs (O'Farrell et al., 2013). There is a CAD96CA in the genome of the H. armigera, which is without function study. Here, we reported that CAD96CA prevents pupation by transmitting JH signal as a JH cell membrane receptor. We also showed that CAD96CA of other insects has a universal function of transmitting JH signal to trigger Ca2+ mobilization, as demonstrated by the study in Sf9 cell lines of S. frugiperda and S2 cell lines of D. melanogaster.

      FGFRs control cell migration and differentiation in the developing embryo of D. melanogaster (Muha and Muller, 2013). The ligand of FGFR is FGF in D. melanogaste_r (Du et al., 2018_). FGF binds FGFR and triggers cell proliferation, differentiation, migration, and survival (Beenken and Mohammadi, 2009; Lemmon and Schlessinger, 2010). Three FGF ligands and two FGF receptors (FGFRs) are identified in Drosophila (Huang and Stern, 2005). The Drosophila FGF-FGFR interaction is specific. Different ligands have different functions. The activation of FGFRs by specific ligands can affect specific biological processes (Kadam et al., 2009). The FGFR in the membrane of Sf9 cells can bind to Vip3Aa (Jiang et al., 2018). One FGF and one FGFR are in the H. armigera genome, which has yet to be studied functionally. The study found that FGFR prevents insect pupation by transmitting JH signal as a JH cell membrane receptor. Exploring the molecular mechanism and output by which multiple ligands transmit signals through the same receptor is exciting and challenging.".

      Reviewer #1 (Recommendations For The Authors):

      As an experimental suggestion, I will only propose that authors test the double knock-down/knock-out or overexpression of CAD96CA and FGFR1 to give some hints into how redundant/independent the two receptors are.

      Thank you very much for your professional advice. We agree with your point of view that double knockout of CAD96CA and FGFR1 is very important to resolve the redundant/independent of the two receptors, which can make our research more complete. Unfortunately, due to experimental difficulty and time constraints, we did not provide supplementary experiments. In this study, we aim to screen the cell membrane receptors of JH. Therefore, we focused on which RTKs can function as receptors. This article is a preliminary study to identify the cell membrane receptors of JH. To further understand the relationship between the two membrane receptors, we will conduct in-depth research in future work.

      Apart from that, here are some minor points about the manuscript:

      Figure 2A: changing the scale on the y-axis would help to better see the different genotypes (similar to the way it is presented in Figure 5).

      Thanks for your reminding, we have changed the scale in Figure 2A.

      Figure 4J: image settings could be improved to better highlight the green fluorescence.

      Thank you for your advice, we have improved the imaged in Figure 4J.

      In general, the manuscript would benefit from some proofreading since a number of sentences are incorrect.

      Thanks for your reminding, we have carefully revised the manuscript.

      Reviewer #2 (Recommendations For The Authors):

      (1) Although the authors note that there are 21 RTK genes in Drosophila (line 55), I can only see 16 Drosophila RTKs in Figure 1 - Figure Supplement 1. Some important Drosophila RTKs such as breathless are missing. The authors need to redraw the phylogenetic tree.

      Thanks for your reminding, we have presented the new phylogenetic tree in Figure 1-figure supplement 1.

      (2) The accelerated pupation phenotype in Cad96ca and Fgfr1 G0 mutants needs to be better described. In particular, it is critical to examine which developmental stage(s) are shortened in these mutant larvae. Refer to a similar study on a JH biosynthetic enzyme in Bombyx (PMID: 22412378) regarding how to describe the developmental timing phenotype.

      Thank you for your advice. We have re-shown Figure 4E and added the explanation in the text: "In 61 survivors of Cas9 protein plus Cad96ca-gRNA injection, 30 mutants were sequenced, and a mutation efficiency was 49.2%. Similarly, in the 65 survivors of Cas9 protein plus Fgfr1-gRNA injection, 35 mutants were sequenced, and a mutation efficiency was 53.8% (Figure 4C). The DNA sequences, deduced amino acids and off–target were analyzed (Figure 4—figure supplement 1). Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      (3) The editing efficiency described in lines 211-213 is obscure. Does this indicate the percentage of animals with noisy sequencing spectra or the percentage of mutation rates analyzed by TA cloning?

      Thanks for your reminder. We have revised the description in the text: "In 61 survivors of Cas9 protein plus Cad96ca-gRNA injection, 30 mutants were sequenced, and a mutation efficiency was 49.2%. Similarly, in the 65 survivors of Cas9 protein plus Fgfr1-gRNA injection, 35 mutants were sequenced, and a mutation efficiency was 53.8% (Figure 4C). The DNA sequences, deduced amino acids and off–target were analyzed (Figure 4—figure supplement 1). Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.".

      (4) In Figures 4F and G, the authors examined expression levels of some JH/ecdysone responsive genes only at 0 hr-old 6th instar larvae. This single developmental stage is not enough for this analysis. In particular, the expression level of Fgfr1 only goes up in the mid-6th instar according to their own data (Figure 1-Figure Supplement 4), so it is critical to examine expression levels of these genes at least throughout the 6th larval instar.

      Thank you for your advice. Indeed, it is essential to detect the expression levels of JH/ecdysone response genes in the whole sixth instar larvae. Because we observed that the mutation has a shorter feeding stage at the sixth instar, we examined the expression level of the JH/ecdysone response gene at the early sixth instar. Due to the number of mutants obtained in the experiment was small and non-destructive sampling could not be performed in sixth instar period, there were no enough samples to test. In the future, we will generate Cad96ca Fgfr1 double mutations to carry out studies and detect the expression level of JH/ecdysone response genes in the whole sixth instar.

      (5) As mentioned above, some important Drosophila RTKs such as breathless are missing in their analyses. As breathless is a close paralog of heartless (Htl), I am sure that Drosophila breathless is also orthologous to Helicoverpa FGFR1. The authors therefore need to analyze breathless in Figure 5B in addition to Htl.

      Thank you for your advice. We added experiments and the results are shown in Figure 5B and Figure 5—figure supplement 1.

      (6) More discussion about the reason why dsNrk and dsWsck can provide resistance to JHIII in Figure 1 is required.

      Thank you for your advice. We added explanation in the discussion: "It is generally believed that the primary role of JH is to antagonize 20E during larval molting (Riddiford, 2008). The knockdown of Cad96ca, Nrk, Fgfr1, and Wsck showed phenotypes resistant to JH III induction and the decrease of Kr-h1 and increase of Br-z7 expression, but knockdown of Vegfr and Drl only decrease Kr-h1, without increase of Br-z7. Br-z7 is involved in 20E-induced metamorphosis in H. armigera (Cai et al., 2014), whereas, Kr-h1 is a JH early response gene that mediates JH action (Minakuchi et al., 2009) and represses Br expression (Riddiford et al., 2010). The high expression of Br-z7 is possible due to the down-regulation of Kr-h1 in Cad96ca, Nrk, Fgfr1 and Wsck knockdown larvae. The different expression profiles of Br-z7 in Vegfr and Drl knockdown larvae suggest other roles of Vegfr and Drl in JH signaling, which need further study."

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors should consider optimizing their experimental approach by depleting the six candidate RTKs in an early larval stage rather than using a sensitized background with JH application in the last larval stage.

      Thank you for your precious suggestion. We knocked down the genes at last larval stage to observe pupation, which is a relatively simple and easily to be observed target to examine the role of the gene in JH-maintained larval status. The results from CRISPR/Cas9 experiments showed: "Most wild-type larvae showed a phenotype of pupation on time. However, in the Cad96ca mutant, 86% of the larvae (an editing efficiency of 67% by TA clone analysis) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 24 h earlier. In the Fgfr1 mutant, 91% of the larvae (an editing efficiency of 61%) had a shortened feeding stage in the sixth instar and entered the metamorphic molting stage earlier, showing early pupation, with the pupation time being 23 h earlier (Figure 4D and E). The data suggested that CAD96CA and FGFR1 support larval growth and prevent pupation in vivo.". To know the roles of other RTKs in the whole larval development needs future work since a lot of experiments are needed.

      (2) Including a positive control for JH signaling, such as met or tai, would strengthen the assays and provide a benchmark for evaluating the downregulation of target genes and phenotype reversion upon JH application. This addition, especially in Figure 1, would enhance the interpretability of the results.

      Thank you for your suggestion. We agree with your point of view that adding the detection of Met or Tai as a positive control. Our laboratory has reported in previous studies that knockdown of Met leads to decreased expression of genes in the JH signaling pathway and precocious pupation (PMID: 24872508), so we did not repeat this related experiment in this study. In the future, when performg Cad96ca and Fgfr1 double mutant experiments, Met mutant can be generated as a control to provide more references for the interpretation of the results.

      (3) I recommend revising the manuscript to improve readability, particularly in the Results section, where descriptions of the binding part are particularly dense.

      Thank you for your advice. We have carefully revised the manuscript.

      (4) In line 122, please add the reference Wang et al., 2016.

      Thank you for your reminding, we have added the reference in line 125 of the new manuscript.

      (5) The authors should clarify why they chose to test the possible binding to JH of only Cad96CA, FGFR1, and NRK after conducting various assays while including OTK in the study as a negative control. This explanation should be included in the text.

      Thank you for the suggestion. We added the explanation, as described in the text: "We screened the RTKs sequentially, including examining the roles of 20 RTKs identified in the H. armigera genome in JH regulated-gene expression to obtain primary candidates, followed by screening of the candidates by their roles in maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and affinity to JH. The cadherin 96ca (CAD96CA) and fibroblast growth factor receptor 1 (FGFR1) were finally determined as JH cell membrane receptors by their roles in JH regulated-gene expression, maintaining larval status, JH induced-rapid increase of intracellular calcium levels, JH induced-phosphorylation of MET and TAI, and their JH-binding affinity. Their roles as JH cell membrane receptors were further determined by knockdown and knockout of them in vivo and cell lines, and overexpression of them in mammal HEK-293T heterogeneously.".

      "Since Cad96CA, FGFR1, and NRK were not only involved in JH-regulated Kr-h1 expression, JH III-induced delayed pupation, and calcium levels increase, but also involved in MET and TAI phosphorylation, we further analyzed their binding affinity to JH III. OTK did not respond to JH III, so we used it as a control protein on the cell membrane to exclude the possibility of nonspecific binding.".

      (6) The observed embryonic lethality of cad96ca and FGF1 mutants in Drosophila contrasts with the ability of the respective mutants in H. armigera to reach the pupal stage. The authors should discuss this significant difference.

      Thank you for the suggestion. We added the explanation in the discussion, as described in the text: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      (7) Building upon the previous point, it is noteworthy that the cad96ca and FGF1 mutants exhibit only a 24-hour early pupation phenotype, contrasting with the 48-hour early pupation induced by Kr-h1 depletion. This discrepancy suggests that while the function of these RTKs is necessary, it may not be sufficient to fully activate JH signaling. The expression profile of these receptors, primarily observed in the last larval stage, supports this hypothesis.

      Thank you for your suggestion. We added the explanation in the discussion, as described in the text: "Homozygous Cad96ca null Drosophila die at late pupal stages (Wang et al., 2009). However, we found that 86% of the larvae of the Cad96ca mutant successfully pupated in G0 generation, although earlier than the control. Similarly, null mutation of Fgfr1 or Fgfr2 in mouse is embryonic lethal (Arman et al., 1998; Deng et al., 1994; Yamaguchi et al., 1994). In D. melanogaster, homozygous Htl (Fgfr) mutant embryos die during late embryogenesis, too (Beati et al., 2020; Beiman et al., 1996; Gisselbrecht et al., 1996). However, in H. armigera, 91% of larvae successfully pupated in G0 generation after Fgfr1 knockout. The low death rate after Cad96ca and Fgfr1 knockout might be because of following reasons, including the editing efficiency (67% and 61% for Cad96ca mutant and Fgfr1 mutant, respectively), the chimera of the gene knockout at the G0 generation, and the redundant RTKs that play similar roles in JH signaling, similar to the redundant roles of MET and Germ-cell expressed bHLH-PAS (GCE) in JH signaling (Liu et al., 2009), which needs to obtain alive G1 homozygote mutants and double knockout of these two receptors in future study. We indeed observed that the eggs did not hatch successfully after mixed-mating of G0 Cad96ca mutant or Fgfr1 mutant, respectively, but the reason was not addressed further due to the embryonic death. By the similar reasons, most of the Cad96ca and Fgfr1 mutants showed a slight acceleration of pupation (about one day) without the typical precocious metamorphosis (at least one instar earlier) phenotype caused by JH signaling defects (Daimon et al., 2012; Fukuda, 1944; Riddiford et al., 2010) and JH pathway gene deletions (Abdou et al., 2011; Liu et al., 2009). On other side, JH can regulate gene transcription by diffusing into cells and binding to the intracellular receptor MET to conduct JH signal, which might affect the results of gene knockdown and knockout.".

      (8) The expression profile of the RTK hits described in Supplementary Figure 4A appears to be limited to the last larval stage until pupation. The authors should clarify whether these receptors are expressed earlier, and the meaning of the letters in the plot should be described in the figure legend.

      Thank you for the suggestion. We added the explanation in the Figure 1—figure supplement 4 legend, as described in the text: "The expression profiles of Vegfr1, Drl, Cad96ca, Nrk, Fgfr1, and Wsck during development. 5F: fifth instar feeding larvae; 5M: fifth instar molting larvae; 6th-6 h to 6th-120 h: sixth instar at 6 h to sixth instar 120 h larvae; P0 d to P8 d: pupal stage at 0-day to pupal stage at 8-day F: feeding stage; M: molting stage; MM: metamorphic molting stage; P: pupae.".

      We are very sorry, but due to time limitations, we will investigate the expression profile of RTK throughout the larval stage in future work.

      (9) In Figure 4, panels F and G, the levels of Kr-h1 are shown in cad96ca and FGF1 mutants in the last larval stage. The authors should indicate whether Kr-h1 levels are also low in earlier larval stages or only detected in the last larval stage, as this would imply that these RTKs are only required at this stage.

      Thank you for your suggestion. In this study, the Cad96ca and Fgfr1 mutants' feeding stage was shortened in the sixth instar, and they entered the metamorphic molting stage earlier. So, we detected the expression of Kr-h1 in the sixth instar. It is an excellent idea to detect the expression of Kr-h1 at various larvae stages to analyze the stages in which CAD96CA and FGFR1 play a role and to study the relationship between CAD96CA and FGFR1 in future.

      (10) While Figure 5 demonstrates JH-triggered calcium ion mobilization in Sf9 cells and S2 cells, the authors should also include data on JH signaling target genes, such as Kr-h1, for a more comprehensive analysis.

      Thank you for your advice. We added experiments, as described in the text: "To demonstrate the universality of CAD96CA and FGFR1 in JH signaling in different insect cells, we investigated JH-triggered calcium ion mobilization and Kr-h1 expression in Sf9 cells developed from S. frugiperda and S2 cells developed from D. melanogaster. Knockdown of Cad96ca and Fgfr1 (named Htl or Btl in D. melanogaster), respectively, significantly decreased JH III-induced intracellular Ca2+ release and extracellular Ca2+ influx, and Kr-h1 expression (Figure 5A, B, Figure 5—figure supplement 1A and B). The efficacy of RNAi of Cad96ca and Fgfr1 was confirmed in the cells (Figure 5—figure supplement 1C and D), suggesting that CAD96CA and FGFR1 had a general function to transmit JH signal in S. frugiperda and D. melanogaster.".

      (11) The authors should consider improving the quality of images and some plots, particularly enlarging panels showing larval and pupal phenotypes, such as Figure 1B and Supplementary Figure C. Additionally, adding a plot showing the statistical analysis of the phenotype in Supplementary Figure C would enhance clarity. Some plots are overly busy and difficult to read due to small size, such as Figure 1C, Figure 2A, and all the plots in Figure 3. Figure 4E also requires improvement for better readability.

      Thank you for your suggestion. We have adjusted Figure 1B, Figure 1C, Figure 1—figure supplement 1C, Figure 2A and Figure 4E. However, for Figure 3, we have not found a better way to arrange and adapt them, considering the overall arrangement of the results and the page space, so we keep them in their original state.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work is meant to help create a foundation for future studies of the Central Complex, which is a critical integrative center in the fly brain. The authors present a systematic description of cellular elements, cell type classifications, behavioral evaluations and genetic resources available to the Drosophila neuroscience community.

      Strengths:

      The work contributes new, useful and systematic technical information in compelling fashion to support future studies of the fly brain. It also continues to set a high and transparent standard by which large-scale resources can be defined and shared.

      Weaknesses:

      manuscript p. 1

      "The central complex (CX) of the adult Drosophila melanogaster brain consists of approximately 2,800 cells that have been divided into 257 cell types based on morphology and connectivity (Scheer et al., 2020; Hulse et al. 2021; Wolff et al., 2015)."

      The 257 accumulated cell types have informational names (e.g., PBG2‐9.s‐FBl2.b‐NO3A.b) in addition to their associations with specific Gal4 lines and specific EM Body IDs. All this is very useful. I have one suggestion to help a reader trying to get a "bird's eye view" of such a large amount of detailed and multi-layered information. Give each of the 257 CX cell types an arbitrary number: 1 to 257. In fact, Supplemental File 2 lists ~277 cell types each with a number in sequence, so perhaps in principle, it is there. This could expedite the search function when a reader is trying to cross-reference CX cell type information from the text, to the Figures and/or to the Supplemental Figures. Also, the use of (arbitrary) cell type numbers could expedite the explanation of which cell types are included in any compilation of information (e.g., which ones were tested for specific NT expression).

      In this report we adhered to the nomenclature introduced in Hulse et al. 2021. We agree that the nomenclature of cell types in the CX is imperfect. There are inherent limitations to what can be done with present data. Even between the hemibrain and FAFB/Flywire EM datasets, it was not possible to derive a one-to-one correspondence in many cases, largely because we do not yet have enough information to distinguish between natural variation within a cell type and distinct cell types (see Schlegel et al. 2024).  Moreover, many cell type distinctions depend on connectivity differences that are observable only in EM datasets but not in LM images. Several research groups are currently engaged in a comprehensive and collaborative effort to update the CX nomenclature that will extend over the next few months as additional connectomes become available. This work will require hundreds of hours of effort from anatomical and computational experts in multiple laboratories who have a strong interest in the CX. Since the correspondence between the established Hulse et al nomenclature we use and this new nomenclature will be made clear, it will be easy to transfer our data to that new nomenclature. For all these reasons, we believe we should not unilaterally introduce any new naming systems at this time.

      manuscript p 2

      "Figure 2 and Figure 2-figure supplements 1-4 show the expression of 52 new split-GAL4 lines with strong GAL4 expression that is largely limited to the cell type of interest. .... We also generated lines of lesser quality for other cell types that in total bring overall coverage to more than three quarters of CX cell types."

      This section describes the generation and identification of specific split Gal4 lines, and the presentation is generally excellent. It represents an outstanding compendium of information. My reading of the text suggests ~200 cell types have Gal4 lines that are of immediate use (having high specificity or v close-to-high). Use of an arbitrary number system (mentioned above) could augment that description for the reasons stated. For example, which of the 257 cell types are represented by split Gal4 lines that constitute the ~1/3 representing "high-quality lines "? A second comment relates to this study 's functional analysis of the contributions of CX cell types to sleep physiology. The recent literature contains renewed interest in the specific expression patterns of Gal4 lines that can promote sleep-like behaviors. In particular Gal4 line expression outside the brain (in the VNC and outside the CNS) have been raised as important elements that need be included for interpretation interpretation of sleep regulation. This present study offers useful information about a large number of expression patterns, as well as a basis with which to seek additional information., including mention of VNC expression in many cases However, perhaps I missed it, but I could not find a short description of the over-all strategy used to describe the expression patterns and feel that could be helpful. Were all Gal4 lines studied for expression in the VNC? and in the peripheral NS? It is probably published elsewhere, but even a short reprise would still be useful.

      We added a couple of sentences to clarify that the lines were imaged in the adult female brain and VNC and many were also imaged in males. These data, including the ability to download the original confocal stacks, are contained in an on-line web source cited in the text. We also make clear that we did not assay expression outside of the brain, optic lobes and VNC. Therefore, we cannot rule out expression in the peripheral nervous system (other than detected in the axons of sensory neurons in the CNS) or in muscle or other non-neuronal cell types.

      manuscript p 9

      Neurotransmitter expression in CX cell types

      "To determine what neurotransmitters are used by the CX cell types, we carried out fluorescent in situ hybridization using EASI-FISH (Eddison and Irkhe, 2022; Close et al., 2024) on brains that also expressed GFP driven from a cell-type-specific split GAL4 line. In this way, we could determine what neurotransmitters were expressed in over 100 different CX cell types based on ...."

      Reading this description, I was uncertain whether the >100 cell types mentioned were tested with all the NT markers by EASI-FISH? Also, assigning arbitrary numbers to the cell types (same suggestion as above) could help the reader more readily ascertain which were the ~100 cell types classified in this context.

      The specific probes used for each cell type are indicated in Figure 9 and in Supplemental File 1.

      manuscript p 10

      "Our full results are summarized below, together with our analysis of neuropeptide expression in the same cell types."

      I recommend specifying which Figures and Tables contain the "full results" indicated.

      We changed the wording to read:

      “Our full results are summarized, together with our analysis of neuropeptide expression in the same cell types, in Figures 5 -9 and in Supplemental File 1.”

      NP expression in CX cell types

      Similar to the comments regarding studies of NT expression: were all ~100 cell types tested with each of the 17 selected NPs? Arbitrary numerical identifies could be useful for the reader to determine which cell types/ lines were tested and which were not yet tested.

      We expanded the description in Methods to now read:

      “For neurotransmitters, the specific probes used for each cell type are indicated in Figure 9 and in Supplemental File 1. For neuropeptides, each of the 17 selected NP probes shown in Figure 5—figure supplement 1 was used on all cell types in Figure 9 except those marked by “—” in the neuropeptide column.”

      manuscript p. 11

      "The neuropeptide expression patterns we observed fell into two broad categories."

      This section presents information that is extensive and extremely useful. It supports consideration of peptidergic cell signaling at a circuits level and in a systematic fashion that will promote future progress in this field. I have two comments. First, regarding the categorization of two NP expression patterns, discernible by differences in cell number: this idea mirrors one present in prior literature. Recently the classification of the transcription factor DIMM summarizes this same two-way categorization (e.g., doi: 10.1371/journal.pone.0001896). That included the fact that a single NP can be utilized by cell of either category.

      We inserted a sentence to acknowledge this earlier work:

      “Such large neurosecretory cells often express the transcription factor DIMM (Park et al. 2008).”

      Second, regarding this comment:

      "In contrast, neuropeptides like those shown in Figure 6 appear to be expressed in dozens to hundreds of cells and appear poised to function by local volume transmission in multiple distinct circuits."

      Signaling by NPs in this second category (many small cells) suggests more local diffusion, a smaller geographic expanse compared to "volume" signaling by the sparser larger peptidergic cells. Given this, I suggest re-consideration in using the term "volume" in this instance, perhaps in favor of "local" or "paracrine". This is only a suggestion and in fact rests almost entirely on speculation/ interpretation, as the field lacks a strong empirical basis to say how far NPs diffuse and act. A recent study in the fly brain of peptide co-transmitters (doi: 10.1016/j.cub.2020.04.025) provides an instructive example in which differences between the spatial extents of long-range (peptide 1) versus short-range (peptide 2) NP signaling may be inferred in vivo.

      We have modified the text to now read:

      “those shown in Figure 6 are expressed in dozens to hundreds of cells and appear poised to function by transmission to nearby cells in multiple distinct circuits.”  

      Spab was mentioned (Figure 6 legend) but discarded as a candidate NP to include based on a personal communication, as was Nplp1. The manuscript did not include reasons to do so, nor include a reference to spab peptide. I suggest including explicit reasons to discard candidate NPs.

      While there is strong supportive evidence for many NPs in Drosophila, the fact that other transcripts express NPs is more circumstantial often relying simply on sequence analysis and without convincing evidence for a specific cognate receptor. We note that Spab is not listed as a neuropeptide in the current release of FlyBase. In these cases, we relied on the opinion of individuals with extensive experience in studying Drosophila NPs. The results obtained with the probes for Spab and Nplp1 are still available in Supplemental File 1.

      In Fig 9-supplement 1, neurotransmitter biosynthetic enzymes were measured by RNA-seq for given CX cell types to augment the cell type classification. The same methods could be used to support cell type classification regarding putative peptidergic character (in Figure 9 supplement 2) by measuring expression levels of critical, canonical neuropeptide biosynthetic enzymes. These include the proprotein convertase dPC2 (amon); the carboxypeptidase dCPD/E (silver); and the amidating enzymes dPHM; dPal1; dPal2. PHM is most related to DBM (dopamine beta monooxygenase), the rate limiting enzyme for DA production, and greater than 90% of Drosophila neuropeptides are amidated. If the authors are correct in surmising widespread use of NPs by CX cell types (and I expect they are), there could be diagnostic value to report expression levels of this enzyme set across many/most CX cell types.

      In our admittedly limited experience, most cells express these enzymes and the level we observed in confirmed NP expressing cell types was not reproducibly higher.  (The complete data for all genes for the cell types we assayed are available from our deposition in the NCBI Gene Expression Omnibus with accession number GSE271123.) Given our small sample size we chose not to comment on this in the paper.

      Comment #6

      Screen of effects on Sleep behavior

      This work is large in scope and as suggested likely presents excellent starting points for many follow-up studies. I again suggest assigning stable number identities to the elements described. In this case, not cell types, but split Gal4 lines. This would expedite the cross-referencing of results across the four Supplemental Files 3-6. For example, line SS00273 is entry line #27 in S Files 3 and 4, but line entry #18 in S Files 5 and 6.

      We believe the interested reader can make this correspondence by searching the supplemental files which are excel spreadsheets. We note that both driver lines and cell types have stable identifiers that are used across Figures and Tables: the line numbers (for example, SS00273) for driver lines and the Hulse et al cell type names for cell types.

      manuscript p 26

      Clock to CX

      "Not surprisingly, the connectome reveals that many of the intrinsic CX cell types with sleep phenotypes are connected by wired pathways (Figure 12 and Figure 12-figure supplement 1)."

      Do intrinsic CX cells with sleep phenotypes also connect by wired pathways to CX cells that do not have sleep phenotypes?

      Yes, but we do not have high confidence that negative sleep phenotypes in our assays indicate no role in sleep.

      "The connectome also suggested pathways from the circadian clock to the CX. Links between clock output DN1 neurons to the ExR1 have been described in Lamaze et al. (2018) and Guo et al. (2018), and Liang et al. (2019) described a connection from the clock to ExR2 (PPM3) dopaminergic neurons."

      The introduction to this section indicates a focus on connectome-defined synaptic contacts. Whereas the first two studies cited featured both physiological and anatomic evidence to support connectivity from clock cells to CX, the third did not describe any anatomical connections, and that connection may in fact be due to diffuse not synaptic signaling

      I could not easily discern the difference between Figs 12 and 12-S1? These appear to be highly-related circuit models, wherein the second features more elements. Perhaps spell out the basis for the differences between the two models to avoid ambiguity.

      We clarify the supplemental diagram differs from the one in the main text by the inclusion of additional connections:

      “The strongest of these connections are diagrammed in Figure 12, with Figure 12—figure supplement 1 also showing additional weaker connections.”

      "...the cellular targets of Dh31 released from ER5 are unknown, however previous work (Goda et al., 2017; Mertens et al., 2005; Shafer et al., 2008) has shown that Dh31 can activate the PDF receptor raising the possibility of autocrine signaling."

      Regarding pharmacological evidence for Dh31 activation of Pdfr: strong in vivo evidence was developed in doi: 10.1016/j.neuron.2008.02.018: a strong pdfr mutation greatly reduces response to synthetic dh31 in neurons that normally express Pdfr

      We added the Shafer et al., 2008 reference. 

      manuscript p 30

      "Unexpectedly, we found that all neuropeptide-expressing cell types also expressed a small neurotransmitter."

      Did this conclusion apply only to CX cell types? - or was it also true for large peptidergic neurons? Prior evidence suggests the latter may not express small transmitters (doi: 10.1016/j.cub.2009.11.065). The question pertains to the broader biology of peptidergic neurons, and is therefore outside the strict scope of the main focus area - the CX. However, the text did initially consider peptidergic neurons outside the CX, so the information may be pertinent to many readers.

      We did not look at other cell types in the current study and so cannot provide an answer.

      Reviewer #2 (Public review):

      Summary:

      In this paper, Wolff et al. describe an impressive collection of newly created split-GAL4 lines targeting specific cell types within the central complex (CX) of Drosophila. The CX is an important area in the brain that has been involved in the regulation of many behaviors including navigation and sleep/wake. The authors advocate that to fully understand how the CX functions, cell-specific driver lines need to be created. In that respect, this manuscript will be of very important value to all neuroscientists trying to elucidate complex behaviors using the fly model. In addition, and providing a further very important finding, the authors went on to assess neurotransmitter/neuropeptides and their receptors expression in different cells of the CX. These findings will also be of great interest to many and will help further studies aimed at understanding the CX circuitries. The authors then investigated how different CX cell types influence sleep and wake. While the description of the new lines and their neurochemical identity is excellent, the behavioral screen seems to be limited.

      Strengths:

      (1) The description of dozens of cell-specific split-GAL4 lines is extremely valuable to the fly community. The strength of the fly system relies on the ability to manipulate specific neurons to investigate their involvement in a specific behavior. Recently, the need to use extremely specific tools has been highlighted by the identification of sleep-promoting neurons located in the VNC of the fly as part of the expression pattern of the most widely used dorsal-Fan Shaped Body (dFB) GAL4 driver. These findings should serve as a warning to every neurobiologist, make sure that your tool is clean. In that respect, the novel lines described in this manuscript are fantastic tools that will help the fly community.

      (2) The description of neurotransmitter/neuropeptides expression pattern in the CX is of remarkable importance and will help design experiments aimed at understanding how the CX functions.

      Weaknesses:

      (1) I find the behavioral (sleep) screen of this manuscript to be limited. It appears to me that this part of the paper is not as developed as it could be. The authors have performed neuronal activation using thermogenetic and/or optogenetic approaches. For some cell types, only thermogenetic activation is shown. There is no silencing data and/or assessment of sleep homeostasis or arousal threshold. The authors find that many CX cell types modulate sleep and wake but it's difficult to understand how these findings fit one with the other. It seems that each CX cell type is worthy of its own independent study and paper. I am fully aware that a thorough investigation of every CX neuronal type in sleep and wake regulation is a herculean task. So, altogether I think that this manuscript will pave the way for further studies on the role of CX neurons in sleep regulation.

      (2) Linked to point 1, it is possible that the activation protocols used in this study are insufficient for some neuronal types. The authors have used 29{degree sign} for thermogenetic activation (instead of the most widely used 31{degree sign}) and a 2Hz optogenetic activation protocol. The authors should comment on the fact that they may have missed some phenotypes by using these mild activation protocols.

      Our primary goal was to test the feasibility of using these tools in assessing sleep and wake function of neurons within the CX. In the process we uncovered several new neurons within the DFB-EB network that control sleep and make connections with previously identified sleep regulating neurons. For all single cell type lines and lines with sparse patterns and no VNC expression we present both optogenetics and thermogenetic data. The lines for which we only have thermogenetic but no optogenetic data are those which have multiple cell types or VNC expression. We felt that optogenetic data for these non-specific or contaminated lines would not reliably indicate a role for individual cell types in sleep regulation.

      Many previous studies that have used 31 degrees have done so for shorter durations and often using different times of the day for manipulations. The lack of consistency between studies using this temperature may be due in part to the fact that 31 degrees alters behaviors of flies (including controls) and, for this reason, is usually not used for 24-hour activation durations.

      To keep the screen consistent and ensure we capture changes in both daytime and nighttime sleep we used 29 degrees. The behavior of control flies is not as disrupted or altered at this temperature, and 29 degrees for activation is routinely used in behavioral experiments.

      We similarly selected an optogenetic stimulation protocol that minimizes the response of flies to the red-light pulses. We chose this protocol because we found, in earlier experiments in a different project, that this level of stimulation was able to elicit activation phenotypes across a range of cell types (including several known clock neurons). However, we cannot rule out false negatives in both the TrpA and optogenetic experiments and agree that we might have missed some phenotypes.

      Finally, as the reviewer rightfully points out, a thorough, detailed investigation of each cell type is a herculean task. We screened in both genders with very sparse, and often cell-type-specific, driver lines while using two distinct modes of activation and different methods for assessing sleep. For these reasons, we believe the GAL4 lines we identified provide excellent starting points for the additional investigations that will be required to better understand the roles of specific cell types.

      (3) There are multiple spelling errors in the manuscript that need to be addressed.

      Reviewer #3 (Public review):

      Summary:

      The authors created and characterized genetic tools that allow for precise manipulation of individual or small subsets of central complex (CX) cell types in the Drosophila brain. They developed split-GAL4 driver lines and integrated this with a detailed survey of neurotransmitter and neuropeptide expression and receptor localization in the central brain. The manuscript also explores the functional relevance of CX cell types by evaluating their roles in sleep regulation and linking circadian clock signals to the CX. This work represents an ambitious and comprehensive effort to provide both molecular and functional insights into the CX, offering tools and data that will serve as a critical resource for researchers.

      Strengths:

      (1) The extensive collection of split-GAL4 lines targeting specific CX cell types fills a critical gap in the genetic toolkit for the Drosophila neuroscience community.

      (2) By combining anatomical, molecular, and functional analyses, the authors provide a holistic view of CX cell types that is both informative and immediately useful for researchers across diverse disciplines.

      (3) The identification of CX cell types involved in sleep regulation and their connection to circadian clock mechanisms highlights the functional importance of the CX and its integrative role in regulating behavior and physiological states.

      (4) The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

      Weaknesses:

      While the manuscript is an outstanding resource, it leaves room for more detailed mechanistic exploration in some areas. Nonetheless, this does not diminish the immediate value of the tools and data provided.

      Appraisal:

      The authors have succeeded in achieving their aims of creating well-characterized genetic tools and providing a detailed survey of neurochemical and functional properties in the CX. The results strongly support their conclusions and open numerous avenues for future research. The work effectively bridges the gap between genetic manipulation, molecular characterization, and functional assessment, enabling a deeper understanding of the CX's diverse roles.

      Impact and Utility

      This manuscript will have a significant and lasting impact on the field, providing tools and data that facilitate new discoveries in the study of the CX, sleep regulation, circadian biology, and beyond. The genetic tools developed here are likely to become a standard resource for Drosophila researchers, and the comprehensive dataset on neurotransmitter and neuropeptide expression will inspire investigations into the interplay between neuromodulation and classical neurotransmission.

      Additional Context

      The breadth and depth of the resources presented in this manuscript justify its publication without further modification. By delivering an integrated dataset that spans anatomy, molecular properties, and functional relevance, the authors have created a resource that will serve the neuroscience community for years to come.

      Recommendations for the authors:

      Reviewing Editor:

      The reviewers suggest that a nomenclature, perhaps a numbering system, be adopted for different cell types and Gal4 drivers in order to facilitate reading of the manuscript and cross-referencing.

      We agree that a comprehensive reanalysis of the CX nomenclature is in order, but it is premature for us to attempt that as part of this study. This is best done after additional connectomes are generated to help resolve the degree of variation in morphology and connectivity between the same cell in multiple animals.

      Reviewer #3 (Recommendations for the authors):

      The authors have characterized a large number of split-GAL4 drivers targeting individual or small subsets of CX cell types. This manuscript delivers a detailed anatomical, molecular, and functional mapping of the CX.

      By integrating data on neurotransmitters, neuropeptides, and their receptors, the authors provide a holistic view of CX cell types that will undoubtedly serve as a foundation for future studies.

      The use of these genetic tools to identify CX cell types affecting sleep, as well as those linking the circadian clock to the CX, represents a significant advance. These findings hint at the diverse and integrative roles of the CX in regulating both behavior and physiological states.

      The authors' decision to present this work as a single, comprehensive manuscript rather than fragmenting it into smaller publications each focusing on separate central complex components is commendable. This decision prioritizes accessibility and utility for the broader neuroscience community, which will enable researchers to approach CX-related questions with a ready-made toolkit.

      While the manuscript leaves room for further exploration and mechanistic studies, the breadth and depth of the resources presented are more than sufficient to justify publication in their current form.

      The data on neuropeptide and receptor expression patterns, especially the observation that all examined CX cell types co-express a small neurotransmitter, opens intriguing new avenues of inquiry into the interplay between classical neurotransmission and neuromodulation in this region.

      This manuscript has provided a much-needed resource for the Drosophila neuroscience community and beyond. This work will facilitate important discoveries in CX function, sleep regulation, circadian biology, and more.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      The authors have performed extensive work generating reporter mice and performing single-cell analysis combined with in situ hybridization to arrive at 14 clusters of enterochromaffin (EC) cells. Then, they focus on Piezo channel expression in distal EC cells and find that these channels might play a role in regulating colonic motility. Overall, this is an informative study that comprehensively classifies EC cells in different regions of the small and large intestine. From a functional point of view, however, the authors seem to ignore the fact that the expression of Piezo-2-IRES-Cre is broad, which would raise concerns regarding their physiological conclusions.

      The authors may wish to consider the following specific points: 

      It is surprising that the number of ileal EC cells is less than that of the distal colon, and it would be interesting to know whether the authors can comment about ileal EC cells. It is unclear why ileal ECs were not included in the study, even though they are mentioned in the diagram (Fig. 2c).

      We have discussed the rationale for excluding ileal ECs in the methods section under “Elimination of ileal GFP+ cells”. In our initial scRNA-seq experiment, our yield of epithelial cells and GFP positive cells was low, and a large proportion of these cells appeared to not have fully committed to the EC lineage. Also to note, we have previously seen fewer ECs in the distal ileum than upper small intestine and colon (PMID: 26803512). Given the low yield, and some uncertainty regarding the nature of the ileal EC population sorted by our methods, we considered that data from ileal ECs may not be an accurate representation of ileal EC cell diversity. Thus, we did not use ileal ECs in our second scRNA-seq experiment.

      Based on their analysis, there are 10 EC cell clusters in SI while there are only 4 clusters in the colon. The authors should comment on whether this is reflective of lesser diversity among colonic ECs or due to the smaller number of colonic ECs collected.

      The 4 clusters identified in the colon are consistent with previous a previous publication (Glass et al., Mol. Metab. 2017, PMID: 29031728), supporting the idea that these clusters are representative of the major clusters of colonic ECs. Nonetheless, we anticipate that with greater sample sizes (in any region) further resolution of subtypes could be resolved. 

      The authors previously described that distal colonic EC cells exhibit various morphologies (Kuramoto et al., 2021). Do Ascl1(+) EC cells particularly co-localize with EC cells with long basal processes? Also, to validate the RNA seq data, the authors might show co-localization between Piezo2/Ascl1/Tph1 in distal EC cells. It would be interesting to see whether Ascl1-CreER (which is available in Jax) specifically labels distal colonic EC cells as this could provide a good genetic tool to specifically manipulate distal colonic EC cells.

      We have shown co-localization between Piezo2/Ascl1/Tph1 in Supplementary Figure 6a. Unfortunately we did not study cell morphology in the Ascl1 smRNA-FISH experiments as these used thin cryosections, whereas morphological assessment of EC processes is best performed with thick (>60 µm) sections. It would be interesting if neuronal-like expression profiles correlate with neuronal-like morphology, which could be addressed in future studies with spatial transcriptomics. 

      The authors used Piezo2-IRES-Cre mice, whose expression is rather broad. They might examine the distribution of Chrm3-mCitrine in the intestine (IF/IHC would be straightforward). And if the expression is in other cell types (which is most likely the case), they should justify that the observed phenotype derives from Piezo2-expressing EC cells. Alternatively, they could use Piezo2-Cre;ePetFlp (or Vil-Flp);Chrm3 to specifically express DREADD receptors in distal colonic EC cells. Also, what does 5HT release look like in jejunal EC cells in Piezo-CHRM3 mice?

      Unfortunately we no longer have access to the animals to do these experiments.

      For the same reasons as above, DTR experiments may also be non-specific. For example, based on the IF staining (Fig. 6b,d), there seems to be a loss of Tph1+ cells in the proximal colon of Piezo2-DTR mice, so the effects of the Piezo2-DTR likely extend beyond the distal colon. 

      Figures 6b and d show distal colon, not proximal colon. Our Tph1<sup>+</sup> cell counts indicate there was no loss of Tph1 cells in the proximal colon following intraluminal administrations of DT. 

      It is unclear why the localized loss of Piezo2 in Piezo2-DTR mice alters small intestinal transit (Fig. 6g,h). The authors should discuss the functional differences observed between Piezo2-DTR (intraluminal app) and Vil1Piezo2 KO mice i.e., small intestinal transit, 5HT release, etc. Are these differences due to the residual Piezo2 expression in Piezo2 KO mice? In this context, the authors may want to discuss their findings in the context of recent papers, such as those from the Patapoutian and Ginty groups. 

      We have made the following amendment to speculate on the reason for delayed small intestinal transit in the DTR experiments:

      “There are a several possible explanations for this. Some Piezo2+ cells in the small intestine could have been depleted. Alternatively, 5-HT released from Piezo2+Tph1+ cells in the distal colon may provide feedback to the small intestine to accelerate motility, and thus depletion of these cells would result in slower intestinal transit.” 

      We have also added a comment speculating on why we did not see similar slowing of small intestinal transit in the Villlin-Cre Piezo2 KO:

      “No difference was observed in small intestine transit… in contrast to the DTR experiments, in which small intestinal transit was delayed. This could be due to the depletion of EC cells in the DTR experiments, whereas they are retained in the Villin-Cre Piezo2 KO mice. 5-HT secretion from ECs can be induced by other stimulants (even when Piezo2 is knocked out), and thus colonic 5-HT could be providing feedback to the small intestine to accelerate motility in the Villin-Cre Piezo2 KO mice. Residual Piezo2 expression in these mice could also be contributing to this effect.”

      We have added a comment on neural Piezo2 in the discussion:

      “However, in contrast to Piezo2 signalling in ECs which results in accelerated gut transit, Piezo2 signalling in DRG neurons appears to slow transit (refs: Wolfson et al., Cell 2023; PMID: 37541195; Servin-Venves et al., Cell 2023, PMID: 37541196).”

      Reviewer #2 (Public Review):

      Summary:

      The authors investigated the expression profile of enterochromaffin (EC) cells after creating a new tryptophan hydroxylase 1 (Tph1) GFP-reporter mouse using scRNAseq and confirmative RNAscope analysis. They distinguish 14 clusters of Tph1+ cells found along the gut axis. The manuscript focuses on two of these, (i) a multihormonal cell type shown to express markers of pathogen/toxin and nutrient detection in the proximal small intestine, and (ii) on a EC-cluster in the distal colon, which expresses Piezo2, rendering these cells mechanosensitive. In- and ex- vivo data explore the role of the mechanosensitive EC population for intestinal/colonic transit, using chemogenetic activation, diptheria-toxin receptor dependent cell ablation and conditional gut epithelial specific Piezo2 knock-out. Whilst some of these data are confirmative of previous reports - Piezo2 has been implicated in mechanosensitive serotonin release previously, as referred to by the authors - the data are solid and emphasize the importance of mechanosensitive serotonin release for colonic propulsion. The transcriptomic data will guide future research.

      Strengths:

      The transcriptomic data, whilst confirmative, is more granular than previous data sets. Employing new tools to establish a role of mechanosensitive EC cells for colonic and thus total intestinal transit. 

      Weaknesses: 

      (1) The proposed villus/crypt distribution of the 14 cell types is not verified adequately. The RNAscope and immunohistochemistry samples presented do not allow assessment of whether this interpretation is correct - spatial transcriptomics, now approaching single-cell resolution, would be likely to help verify this claim.

      Spatial transcriptomics would be excellent in validating the spatial distribution of the EC cell types in future studies. In our work, although the villus/crypt cluster annotations are assumptions (based on the differential expression of Neurog3, Tac1, and Sct, which is well supported by the literature), we have validated the spatial segregation of key markers. We quantified the crypt/villus location of Cartpt, Ucn3, and Trpm2 overlap with Tph1 (Figure 2d), Oc3, Cck, and Tph1 (Figure 3d), and TK/5-HT (Supplementary Fig 2d). This work supports our predictions on the spatial distribution of these clusters.

      (2) The physiological function and/or functionality of most of the transcriptomically enriched gene products has not been assessed. Whilst a role for Piezo2 expressing cells for colonic transit is convincingly demonstrated, the nature of the mechanical stimulus or the stimulus-secretion coupling downstream of Piezo2 activation is not clear.

      While we have not investigated the mechanical forces involved in activating Piezo2, we can at least say that physiological mechanical stimulation activates Piezo2, as we measured fecal pellet output in the DTR experiments. 

      Reviewer #2 (Recommendations For The Authors):

      (1) Please state (even more) clearly if/that the apparently GFP+/Tph1+ cells which clustered with the GFP- cells (Suppl. Fig1d/e) were excluded from the subsequent analysis. The detectable Chg-a/b expression in the GFP- cells in Suppl. Fig1f seems to suggest that these (if they have been included in the GFP- group here) are genuine ECs. How do these cells relate to the non-EC cells in Fig1d, which seem to lack Tph1 expression? And given the information in the methods, what %age of these cells derived from the ileum?

      To clarify, data shown in Suppl. Fig 1d/e/f was from our first single cell profiling experiment whereas our subsequent clustering analysis utilizes data from a second (independent) single cell profiling experiment (e.g. Fig1d). 

      In the first profiling experiment, 23% of GFP<sup>+</sup> cells clustered with GFP<sup>-</sup> cells, and for the purposes of Suppl. Figures 1d/e/f, we called these “non-ECs”. In the second profiling experiment (e.g. shown in Fig 1d) we performed a more detailed cluster analysis focusing on only GFP<sup>+</sup> cells. In this second experiment, 19% of GFP<sup>+</sup> cells were identified as “non-EC cells” based on the presence of markers for stem cells, transit amplifying cells (TACs), immature enterocytes, mature enterocytes, colonocytes, T lymphocytes and mucosal mast cells (see Fig 1d and Suppl. Fig 1g). Similar to the first profiling dataset, many of the GFP<sup>+</sup> “non-EC cells” in the second dataset express Tph1, Chga, and Chgb, generally at lower levels than the “EC cells” (Suppl. Fig1i). It is possible that the stem cell and transit amplifying cell clusters are cells that are differentiating into EC cells. However, given that they have not fully committed to the lineage yet, we do not consider it appropriate to classify them as “EC cells”. With regards to the other “non-EC” clusters, we do not think that the expression of EC cell marker genes (Tph1, Chga, and Chgb) is evidence enough to call them genuine “EC cells” given the concurrent expression of markers of other lineages (e.g. enterocyte and mast cell markers Suppl. Fig 1g). The expression of Tph1 in murine mast cells is known, however the expression in enterocytes is unexpected and could be a result of imperfect/incomplete differentiation. Since the ileum was not included in the second profiling experiment we do not think the GFP<sup>+</sup> “non-EC cells” are an artifact from the ileum. 

      We have made some adjustments in the first section of the results to clarify some thoughts on this matter:

      “It is possible that some GFP is expressed in cells that have not yet fully committed to the EC lineage, or that there is some expression in cells outside this lineage, for example, in mast cells. Given the small sample size, we did not further investigate these cells in this dataset. In Supplementary Figures 1 d and f we refer to the GFP<sup>+</sup> cells that clustered with the GFP<sup>-</sup> cells as “non-EC cells”.”

      “It is possible that the stem cell and transit amplifying cell clusters include cells that are in the process of differentiating into EC cells. However, given that they have not fully committed to the lineage, we do not consider it appropriate to classify them as “EC cells” for the purposes of analyzing EC cell types in this study.”

      (2) The authors state: "Notably, OSR2 and HOXB13 were restricted to the ileum and rectum respectively in humans (Fig. 1f)." - the statement regarding OSR2 seems too strong, given that only the ileal part of the human small intestine was examined and that there is a small signal in the proximal colon in Figure 1f.

      Thanks, we have made the following amendment:

      "Notably, OSR2 and HOXB13 were preferentially enriched in the ileum and rectum respectively in these human samples (Fig. 1f)."

      (3) Please clarify Suppl Fig2g/h labelling as villus and crypt enrichment ("...enrichment in villus clusters (g) or crypt clusters (h)."), when enrichment for some genes in cluster 4 is shown in both g and h. Why was duodenal cluster 6 excluded from this subset of data?

      We suspect (although have not proven) that cluster 4 is at a later stage in maturation/migration than cluster, as indicated by a somewhat ‘middle ground’ level of Sct expression, and generally being ‘in between’ the villus clusters and cluster 5 in expression levels of differentially expressed genes shown in Suppl Fig 2g/h. We have added the following comment to the figure legend to clarify this. We have not included cluster 6 as it is transcriptionally quite distinct from the other clusters:

      “Note that cluster 4 shares some features in common with crypt and villus clusters and may represent cells at an intermediate stage of development.”

      (4) "Using smRNA-FISH, we further mapped Olfr558 and Il12a transcripts to a separate subset of EC cells expressing Cpb2 (Fig. 4b,c), confirming the presence of two subpopulations of EC cells associated with different physiological roles in the proximal colon." - Claiming populations with different physiological functionality seems a strong statement given the relatively weak Cpb2 signals observed and that mRNA detection necessarily is a transcriptomic time limited snap-shot. Please reformulate.

      We have made the following revision:

      “Using smRNA-FISH, we further mapped Olfr558 and Il12a transcripts to a separate subset of EC cells expressing Cpb2 (Fig. 4b,c), supporting the idea that there are subpopulations of EC cells in the proximal colon with gene transcripts associated with different physiological roles.”

      (5) What are the white signals in the overlay in Fig5a, given that the Piezo1 probe (white) apparently did not give any staining by itself? Please consider a positive control for the Piezo1 probe.

      The white signals in the overlay are Piezo1 staining that we do observe at what we consider background levels (also visible in the single-channel image).

      (6) "Systematic administration of DT led to lethality in the Piezo2-DTR mice within 12 hours, but not in the Rosa26LSL-DTR or Piezo2-cre mice (data not shown), likely due to the essential function of Piezo2 in respiration" - presumably this should be corrected to "Systemic administration ...".

      Thanks, this has been corrected to "Systemic administration ...".

      (7) "Although gastric emptying (GE) was not affected in the Piezo2-DTR animals after DT treatment, small intestine transit (SIT) time, a measurement to assess the motility of small intestine, presented a small but statistically significant slowdown in the former group (Fig. 6g,h), suggesting that some Piezo2+ cells in the small intestine were depleted." - alternatively there could, of course, be a slowing of SIT in response to slower colonic transit independent of small intestinal epithelial Piezo2 or 5HT - to me this seems more likely given that even proximal colonic cells are spared in Fig6c and this should be discussed.

      Thanks, that is a good point. We have made an amendment, which is shown in response to reviewer 1.

      (8) In the context of the Villin-Cre experiments it should be discussed that other colonic EECs although express Piezo2, which might contribute to the observed phenotypes.

      In our study, 97.7% of Piezo2+ cells in the distal colon had detectable Tph1 expression, suggesting that there is not a significant degree of overlap with other EEC types.

      (9) MC4R is several times referred to as a nutrient-sensing moeity (e.g. in the discussion: "...and receptors associated with nutrient sensing (Casr and Mc4r), ...") - whilst the melanocortin system is important for nutrient homeostasis, MC4R is itself not a "nutrient sensor", a term usually reserved for the detection of macronutrients, such as amino acids, fatty acids, and monosaccharides; please reformulate. 

      We have amended this to “nutrient sensing and homeostasis”.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aim to assess the effect of salt stress on root:shoot ratio, identify the underlying genetic mechanisms, and evaluate their contribution to salt tolerance. To this end, the authors systematically quantified natural variations in salt-induced changes in root:shoot ratio. This innovative approach considers the coordination of root and shoot growth rather than exploring biomass and the development of each organ separately. Using this approach, the authors identified a gene cluster encoding eight paralog genes with a domain-of-unknown-function 247 (DUF247), with the majority of SNPs clustering into SR3G (At3g50160). In the manuscript, the authors utilized an integrative approach that includes genomic, genetic, evolutionary, histological, and physiological assays to functionally assess the contribution of their genes of interest to salt tolerance and root development.

      Comments on revisions:

      As the authors correctly noted, variations across samples, genotypes, or experiments make achieving statistical significance challenging. Should the authors choose to emphasize trends across experiments to draw biological conclusions, careful revisions of the text, including titles and figure legends, will be necessary to address some of the inconsistencies between figures (see examples below). However, I would caution that this approach may dilute the overall impact of the work on SR3G function and regulation. Therefore, I strongly recommend pursuing additional experimental evidence wherever possible to strengthen the conclusions.

      (1) Given the phenotypic differences shown in Figures S17A-B, 10A-C, and 6A, the statement that "SR3G does not play a role in plant development under non-stress conditions" (lines 680-681) requires revision to better reflect the observed data.

      Thank you to the reviewer for the comment. We appreciate the acknowledgment that variations among experiments are inherent to biological studies. Figures 6A and S17 represent the same experiment, which initially indicated a phenotype for the sr3g mutant under salt stress. To ensure that growth changes were specifically normalized for stress conditions, we calculated the Stress Tolerance Index (Fig. 6B). In Figure 10, we repeated the experiment including all five genotypes, which supported our original observation that the sr3g mutant exhibited a trend toward reduced lateral root number under 75 mM NaCl compared to Col-0, although this difference was not significant (Fig. 10B). Additionally, we confirmed that the wrky75 mutant showed a significant reduction in main root growth under salt stress compared to Col-0, consistent with findings reported in The Plant Cell by Lu et al. 2023. For both main root length and lateral root number, we demonstrated that the double mutants of wrky75/sr3g displayed growth comparable to wild-type Col-0. This result suggests that the sr3g mutation compensates for the salt sensitivity of the wrky75 mutant.

      We completely agree with the reviewer that there is a variation in our results regarding the sr3g phenotype under control conditions, as presented in Fig. 6A/Fig. S17 and Fig. 10A-C. In Fig. 6A/Fig. S17, we did not observe any consistent trends in main root or lateral root length for the sr3g mutant compared to Col-0 under control conditions. However, in Fig. 10A-C, we observed a significant reduction in main root length, lateral root number, and lateral root length for the sr3g mutant under control conditions. We believe this may align with SR3G’s role as a negative regulator of salt stress responses. While loss of this gene benefits plants in coping with salt stress, it might negatively impact overall plant growth under non-stress conditions. This interpretation is further supported by our findings on the root suberization pattern in sr3g mutants under control conditions (Fig. 8B), where increased suberization in root sections 1 to 3, compared to Col-0, could inhibit root growth. While SR3G's role in overall plant fitness is intriguing, it is beyond the scope of this study. We cannot rule out the possibility that SR3G contributes positively to plant growth, particularly root growth. That said, we observed no differences in shoot growth between Col-0 and the sr3g mutant under control conditions (Fig. 7). Additionally, we calculated the Stress Tolerance Index for all aspects of root growth shown in Fig. 10 and presented it in Fig. S25.

      To address the reviewer request on rephrasing the lines 680-681 from"SR3G does not play a role in plant development under non-stress conditions" (lines 680-681) statement, this statement is found in lines 652-653 and corresponds to Fig. 7, where we evaluated rosette growth in the WT and sr3g mutant under both control and salt stress conditions. We did not observe any significant differences or even trends between the two genotypes under control conditions, confirming the accuracy of the statement. To clarify further, we have added “SR3G does not play a role in rosette growth and development under non-stress conditions”.

      (2) I agree with the authors that detecting expression differences in lowly expressed genes can be challenging. However, as demonstrated in the reference provided (Lu et al., 2023), a significant reduction in WRKY75 expression is observed in T-DNA insertion mutant alleles of WRKY75. In contrast, Fig. 9B in the current manuscript shows no reduction in WRKY75 expression in the two mutant alleles selected by the authors, which suggests that these alleles cannot be classified as loss-of-function mutants (line 745). Additionally, the authors note that the wrky75 mutant exhibits reduced main root length under salt stress, consistent with the phenotype reported by Lu et al. (2023). However, other phenotypic discrepancies exist between the two studies. For example, 1) Lu et al. (2023) report that w¬rky75 root length is comparable to WT under control conditions, whereas the current manuscript shows that wrky75 root growth is significantly lower than WT; 2) under salt stress, Lu et al. (2023) show that wrky75 accumulates higher levels of Na+, whereas the current study finds Na+ levels in wrky75 indistinguishable from WT. To confirm the loss of WRKY75 function in these T-DNA insertion alleles the authors should provide additional evidence (e.g., Western blot analysis).

      We sincerely appreciate the reviewer acknowledging the challenge of detecting expression differences in lowly expressed genes, such as transcription factors. Transcription factors are typically expressed at lower levels compared to structural or enzymatic proteins, as they function as regulators where small quantities can have substantial effects on downstream gene expression.

      That said, we respectfully disagree with the reviewer’s interpretation that there is no reduction in WRKY75 expression in the two mutant lines tested in Fig. 9C. Among the two independent alleles examined, wrky75-3 showed a clear reduction in expression compared to WT Col-0 under both control and salt stress conditions. Using the Tukey test to compare all groups, we observed distinct changes in the assigned significance letters for each case:

      Col/root/control (cd) vs wrky75-3/root/control (cd): Although the same significance letter was assigned, we still observed a clear reduction in WRKY75 transcript abundance. More importantly, the variation in expression is notably lower compared to Col-0.

      Col/shoot/control (bcd) vs wrky75-3/shoot/control (a): This is significant reduction compared to Col

      Col/root/salt (cd) vs wrky75-3/root/salt (bcd): Once again, the reduction in WRKY75 transcript levels corresponds to changes in the assigned significance letters.

      Col/shoot/salt (bc) vs wrky75-3/shoot/salt (ab): Once again, the reduction in WRKY75 transcript levels corresponds to changes in the assigned significance letters.

      To address the reviewer’s comment regarding the significant reduction in WRKY75 expression observed in T-DNA insertion mutant alleles of WRKY75 in the reference by Lu et al., 2023, we would like to draw the reviewer’s attention to the following points:

      a) Different alleles: The authors in The Plant Cell used different alleles than those used in our study, with one of their alleles targeting regions upstream of the WRKY75 gene. While we identified one of their described alleles (WRKY75-1, SALK_101367) on the T-DNA express website, which targets upstream of WRKY75, the other allele (wrky75-25) appears to have been generated through a different mechanism (possibly an RNAi line) that is not defined in the Plant Cell paper and does not appear on the T-DNA express website. The authors mentioned they have received these seeds as gifts from other labs in the acknowledgement ”We thank Prof. Hongwei Guo (Southern University of Science and Technology, China) and Prof. Diqiu Yu (Yunnan University, China) for kindly providing the WRKY75<sub>pro</sub>:GUS, 35S<sub>pro</sub>:WRKY75-GFP, wrky75-1, and wrky75-25 seeds. We thank Man-cang Zhang (Electrophysiology platform, Henan University) for performing the NMT experiment”.

      However, in our study, we selected two different T-DNAs that target the coding regions. While this may explain slight differences in the observed responses, both studies independently link WRKY75 to salt stress, regardless of the alleles used. For your reference, we have included a screenshot of the different alleles used.

      Author response image 1.

      b) Different developmental stages: They measured WRKY75 expression in 5-day-old seedlings. In our experiment, we used seedlings grown on 1/2x MS for 4 days, followed by transfer to treatment plates with or without 75 mM NaCl for one week. As a result, we analyzed older plants (12 days old) for gene expression analysis. Despite the difference in developmental stage, we were still able to observe a reduction in gene expression.

      c) Different tissues: The authors of The Plant Cell used whole seedlings for gene expression analysis, whereas we separated the roots and shoots and measured gene expression in each tissue type individually. This approach is logical, as WRKY75 is a root cell-specific transcription factor with higher expression in the roots compared to the shoots, as demonstrated in our analysis (Fig. 9C).

      Based on the reasoning above, we did work with loss-of-function mutants of WRKY75, particularly wrky75-3. To more accurately reflect the nature of the mutation, we have changed the term "loss-of-function" to "knock-down" in line 717.

      The reviewer mentioned phenotypic discrepancies between the two studies. We agree that there are some differences, particularly in the magnitude of responses or expression levels. However, despite variations in the alleles used, developmental stages, and tissue types, both studies reached the same conclusion: WRKY75 is involved in the salt stress response and acts as a positive regulator. We have discussed the differences between our study and The Plant Cell in the section above, summarizing them into three main points: different alleles, different developmental stages, and different tissue types.

      To address the reviewer’s comment regarding "Lu et al. (2023) report that wrky75 root length is comparable to WT under control conditions, whereas the current manuscript shows that wrky75 root growth is significantly lower than WT": We evaluated root growth differently than The Plant Cell study. In The Plant Cell (Fig. 5, H-J), root elongation was measured in 10-day-old plants with a single time point measurement. They transferred five-day-old wild-type, wrky75-1, wrky75-25, and WRKY75-OE plants to 1/2× MS medium supplemented with 0 mM or 125 mM NaCl for further growth and photographed them 5 days after transfer. In contrast, our study used 4-day-old seedlings, which were transferred to 1/2 MS with or without 0, 75, or 125 mM salt for additional growth (9 days). Rather than measuring root growth only at the end, we scanned the roots every other day, up to five times, to assess root growth rates. Essentially, the precision of our method is higher as we captured growth changes throughout the developmental process, compared to the approach used in The Plant Cell. We do not underestimate the significance of the work conducted by other colleagues in the field, but we also recognize that each laboratory has its own approach and specific practices. This variation in experimental setup is intrinsic to biology, and we believe it is important to study biological phenomena in different ways. Especially as the common or contrasting conclusions reached by different studies, performed by different labs and using different experimental setups are shedding more light on reproducibility and gene contribution across different conditions, which is intrinsic to phenotypic plasticity, and GxE interactions.

      The Plant Cell used a very high salt concentration, starting at 125 mM, while we were more cautious in our approach, as such a high concentration can inhibit and obscure more subtle phenotypic changes.

      To address the reviewer’s comment on "Lu et al. (2023) show that wrky75 accumulates higher levels of Na+, whereas the current study finds Na+ levels in wrky75 indistinguishable from WT," we would like to highlight the differences in the methodologies used in both studies. The Plant Cell measured Na+ accumulation in the wrky75 mutant using xylem sap (Supplemental Figure S10), which appears to be a convenient and practical approach in their laboratory. In their experiment, wild-type and wrky75 mutant plants were grown in soil for 3 weeks, watered with either a mock solution or 100 mM NaCl solution for 1 day, and then xylem sap was collected for Na+ content analysis. In contrast, our study employed a different method to measure Na+ and K+ ion content, using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) for root and shoot Na+ and K+ measurements. Additionally, we collected samples after two weeks on treatment plates and focused on the Na+/K+ ratio, which we consider more relevant than net Na+ or K+ levels, as the ratio of these ions is a critical determinant of plant salt tolerance. With this in mind, we observed a considerable non-significant increase in the Na+/K+ ratio in the shoots of the wrky75-3 mutant (assigned Tukey’s letter c) compared to the Col-0 WT (assigned Tukey’s letters abc) under 125 mM salt, suggesting that this mutant is salt-sensitive. Importantly, the Na+/K+ ratio in the double wrky75/sr3g mutants was reduced to the WT level under the same salt conditions, further indicating that the salt sensitivity of wrky75 is mitigated by the sr3g mutation.

      Based on the reasons mentioned above, we believe that conducting additional experiments, such as Western blot analysis, is unnecessary and would not contribute new insights or alter the context of our findings.

      Reviewer #2 (Public review):

      Summary:

      Salt stress is a significant and growing concern for agriculture in some parts of the world. While the effects of sodium excess have been studied in Arabidopsis and (many) crop species, most studies have focused on Na uptake, toxicity and overall effects on yield, rather than on developmental responses to excess Na, per se. The work by Ishka and colleagues aims to fill this gap.

      Working from an existing dataset that exposed a diverse panel of A. thaliana accessions to control, moderate, and severe salt stress, the authors identify candidate loci associated with altering the root:shoot ratio under salt stress. Following a series of molecular assays, they characterize a DUF247 protein which they dub SR3G, which appears to be a negative regulator of root growth under salt stress.

      Overall, this is a well-executed study which demonstrates the functional role played by a single gene in plant response to salt stress in Arabidopsis.

      Review of revised manuscript:

      The authors have addressed my point-by-point comments to my satisfaction. In the cases where they have changed their manuscript language, clarified figures, or added analyses I have no further comment. In some cases, there is a fruitful back-and-forth discussion of methodology which I think will be of interest to readers.

      I have nothing to add during this round of review. I think that the paper and associated discussion will make a nice contribution to the field.

      We sincerely appreciate the reviewer’s recognition of the significance of our work to the field.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Lines 518-519: The statement that other DUF247s exhibit similar expression patterns to SR3G, suggesting their responsiveness to salt stress, is not fully supported by Fig. S14. Please clarify the specific similarities (and differences) in the expression patterns of the DUF247s shown in Fig. S14, as their expression appears to be spatially and temporally diverse. Additionally, the scale is missing in Fig. S14.

      We thank the reviewer. We fixed the text and added expression scales to Figure S14.

      Line 684, Fig. 6A should be 7A.

      Thanks. It is fixed.

      Line 686, Fig. 7A should be 7B.

      Thanks. It is fixed.

      Lines 721-723: The signal quantification in Fig. 8B does not support the claim that "in section one,..., sr3g-5 showed more suberization compared to Col-0." Given the variability and noise often associated with histological dyes such as Fluorol Yellow staining, conclusions should be cautiously grounded in robust signal quantification. Additionally, please specify the number of biological replicates used in both Fig. 8B and C.

      We thank the reviewer for their comments. We believe the statement in the text accurately reflects our results presented in Figure 8B, where we stated “non-significant, but substantially higher levels of root suberization in sr3g-5 compared to Col-0 in sections one to three of the root under control condition (Fig. 8B).” Therefore, we kept the statement and have included the number of biological replicates in the figure legend.

      Lines 731-732: Please provide a more detailed explanation of how the significant changes in suberin monomer levels align with the Fluorol Yellow staining results, and clarify how these findings support the proposed negative role of SR3G in root suberization.

      Fluorol Yellow is a lipophilic dye widely used to label suberin in plant tissues, specifically in roots in this study. Given the inherent variability in histological assays, we confirmed the increase in suberization using an alternative method, Gas Chromatography–Mass Spectrometry (GC-MS). Both approaches revealed elevated suberin levels in the sr3g mutant compared to Col-0. Since the overall suberin content was higher in the mutant under both control and salt stress conditions, we proposed that SR3G acts as a negative regulator of root suberization.

      Lines 686-688 and Figure S24: The authors calculated water mass as FW-DW. A more standard approach for calculating water content is (FW-DW)/FW x 100. Please update the text or adjust the calculation accordingly. Additionally, if the goal is to test differences between WT and the mutant within each condition, a t-test would be a more appropriate statistical method.

      We thank the reviewer. We added water content % to the figure S24. We kept the statistical test as it is as we wanted to be able to observe changes across conditions and genotypes.

      Lines 633-635 states that "No significant difference was observed between sr3g-4 and Col-0 (Fig. S18), except for the Stress Tolerance Index (STI) calculated using growth rates of lateral root length and number." However, based on the Figure S18 legend and statistical analysis (i.e., ns), it appears that the sr3g-4 mutant shows no alterations in root system architecture compared to Col-0. Please revise the text to accurately reflect the results of the statistical analysis.

      We thank the reviewer. We now fixed the text to reflect the result.

      Lines 698-707: The statistical analysis does not support the reported differences in the Na+/K+ ratio for the single and double mutants of sr3g-5 and wrky75-3 (Fig. 10D, where levels connected by the same letters indicate they are not significantly different). Furthermore, the conclusion that "the SR3G mutation indeed compensated for the increased Na+ accumulation observed in the wrky75 mutant under salt stress" is also based on non-significant differences (Fig. S25B). Please revise the text to accurately reflect the results of the statistical analysis. Additionally, since each mutant is compared to the WT, I recommend using Dunnett's test for statistical analysis.

      We thank the reviewer for their feedback. We have carefully revised the text to better support our findings. As previously mentioned, variations among samples are evident and are well-reflected across all our datasets. We have presented all data and focused on identifying trends within our samples to guide interpretation.

      We observed that the SR3G mutation effectively compensated for the increased Na+ accumulation observed in the wrky75 mutant under salt stress. A closer examination of the shoot Na+/K+ ratio under 125 mM salt shows that the wrky75 single mutant has a higher Na+/K+ ratio (indicated by the letter "c") compared to Col-0 (indicated by "abc") and the two double mutants (also indicated by "abc"). Therefore, we have retained the statistical analysis as originally conducted, and maintain our conclusions as is.

      Figure 6: data in panel C present the Na/K ratio, not Na+ content. Based on the statistical analysis of root Na+ levels presented in Fig. S17C, there is no significant difference between sr3g-5 and WT. Please update the title of Fig. 6. In addition, in panel A, the title of the Y-axis and figure legend should be "Lateral root growth rate" without the word length, and in panel C, the statistical analysis is missing.

      We thank the reviewer. We updated Fig. 6 title and fixed the Y-axis in panel A, and added statistical letters to panel C. Legend was updated to reflect the changes.

      Figure 7: Please clearly label the time points where significant differences between genotypes are observed for both early and late salt treatments. Was there a significant difference recorded between WT and sr3g-5 on day 0 under early salt stress? Such differences may arise from initial variations in plant size within this experiment, as indicated by Fig. 7B, where significant differences in rosette area are evident starting from day 0. Additionally, please indicate the statistical analysis in panel E.

      We thank the reviewer for this suggestion. We updated the figure with a statistical test added to the panel E. Although the difference between sr3g mutant and Col-0 is indeed significant in its growth rate at day 0, we would like to draw the attention of the reviewer that this growth rate was calculated over the 24 hours after adding salt stress. Therefore, this difference in growth rate is related to exposure to salt stress. Moreover, the growth rate between Col-0 and sr3g mutant does not differ in two other treatments (Control and Late Salt Stress) further supporting the conclusion that sr3g is affecting rosette size and growth rate only under early salt stress conditions.

      We have also added the Salt Tolerance Index calculation to Figure S24 as additional evidence, controlling for potential differences in size between Col-0 and sr3g mutant.

      Figure S17: statistical analysis is not indicated in panels A, B, and D.

      We thank the reviewer for spotting that. We updated the figure with a statistical test.

      Figures S21-23: The quality of these figures is insufficient, hindering the ability to effectively interpret the authors' results and main message. Furthermore, a Dunnett's test, rather than a t-test, is the appropriate statistical method for this analysis.

      We thank the reviewer for this observation. We have now added a high resolution figures for all supplemental figures, which should increase the resolution of the figures. As we are comparing all of the genotypes to Col-0 one-by-one - the results of individual t-tests are sufficient for this analysis.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      (1) The mechanism by which STAMBPL1 mediates GRHL3 transcription through its interaction with FOXO1 is not sufficiently discussed, especially in relation to how STAMBPL1 regulates FOXO1. Some reported effects are modest.

      We appreciate the reviewer’s comments. In response, we have added a discussion on the potential mechanisms by which STAMPBL1 regulates FOXO1 transcriptional activity in Discussion, highlighted in red on page 18, lines 342 to 352. The specific reply content is as follows: “The transcriptional activity of FOXO1 is primarily regulated by its nucleocytoplasmic shuttling process (Van Der Heide, Hoekman et al. 2004). The PI3K/AKT pathway promotes the phosphorylation of FOXO1, resulting in the formation of a complex with members of the 14-3-3 family (including 14-3-3σ, 14-3-3ε, and 14-3-3ζ), which facilitates its export from the nucleus and inhibits its transcriptional activity (Huang and Tindall 2007, Tzivion, Dobson et al. 2011). It’s reported that TDAG51 prevents the binding of 14-3-3ζ to FOXO1 in the nucleus by interacting with FOXO1, thereby enhancing its transcriptional activity through increased accumulation within the nucleus (Park, Jeon et al. 2023). Our results indicate that the overexpression of STAMBPL1 and STAMBPL1-E292A did not affect the protein levels of FOXO1 (Fig.7E and Fig.S5E), but STAMBPL1 co-localizes with FOXO1 in the nucleus (Fig.7M) and interacts with it (Fig.7N and Fig.S5I-J). This suggests that STAMBPL1 enhances the transcriptional activity of FOXO1 on GRHL3 by interacting with nuclear FOXO1.” The result was added to Supplementary Figure 5 as Fig.S5E.

      Reviewer #2 (Public review):

      (1) A potential limitation of the study is the reliance on specific cellular and animal models, which may constrain the extrapolation of these findings to the broader spectrum of human TNBC biology. Furthermore, while the study provides evidence for a novel regulatory axis involving STAMBPL1, FOXO1, and GRHL3, the multifaceted nature of angiogenesis may implicate additional regulatory factors not exhaustively addressed in this research.

      We appreciate the valuable suggestions provided by the reviewer. In Discussion, we have added an in-depth discussion of the limitations of the study, as well as an analysis of the regulatory factors related to tumor angiogenesis, which highlighted in red on pages 20 to 21, lines 396 to 412. The relevant content added is as follows: “In this study, we utilized two triple-negative breast cancer cell lines, HCC1806 and HCC1937, along with human primary umbilical vein endothelial cells (HUVECs) and a nude mouse breast orthotopic transplantation tumor model to investigate the regulatory mechanism by which STAMBPL1 activates the GRHL3/HIF1α/VEGFA signaling pathway through its interaction with FOXO1, thereby promoting angiogenesis in TNBC. The results of this study have certain limitations regarding their applicability to human TNBC biology. Furthermore, in addition to the HIF1α/VEGFA signaling pathway emphasized in this study, tumor cells can continuously release or upregulate various pro-angiogenic factors, such as Angiopoietin and FGF, which activate endothelial cells, pericytes (PCs), cancer-associated fibroblasts (CAFs), endothelial progenitor cells (EPCs), and immune cells (ICs). This leads to capillary dilation, basement membrane disruption, extracellular matrix remodeling, pericyte detachment, and endothelial cell differentiation, thereby sustaining a highly active state of angiogenesis (Liu, Chen et al. 2023). It is important to collect clinical TNBC tissue samples in the future to analyze the expression of the STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA signaling axis. Furthermore, patient-derived organoid and xenograft models are useful to elucidate the regulatory relationship of this axis in TNBC angiogenesis”

      Reviewer #3 (Public review):

      The main weaknesses of this work are that the relevance of this molecular axis to the pathogenesis of TNBC is not clear, and it is not clearly established whether this is a regulatory pathway that occurs in hypoxic conditions or independently of oxygen levels.

      (1) With respect to the first point, both FOXO1 and GRHL3 have been previously described as tumor suppressors, with reports of FOXO1 inhibiting tumor angiogenesis. Therefore, this works describes an apparently contradictory function of these proteins in TNBC. While it is not surprising that the same genes perform divergent functions in different tumor contexts, a stronger evidence in support of the oncogenic function of these two genes should be provided to make the data more convincing. As an example, the data in support of high STAMBPL1, FOXO and GRHL3 gene expression in TNBC TCGA specimens provided in Figure 8 is not very strong and it is not clear what the non-TNBC specimens are (whether other breast cancers or other tumors, perhaps those tumors whether these genes perform tumor suppressive functions). To strengthen the notion that STAMBPL1, FOXO and GRHL3 are overexpressed in TNCB, the authors could provide a comparison with normal tissue, as well as the analysis of other publicly available datasets (like the NCI Clinical Proteomic Tumor Analysis Consortium as an example). Finally, is it not clear what are the basal protein expression levels of STAMBPL1 in the cell lines used in this study, as based on the data presented in Figures 2D and F it appears that the protein is not expressed if not exogenously overexpressed. It would be helpful if the authors addressed this issue and provided further evidence of STAMBPL1 expression in TNBC cell lines.

      We appreciate the suggestions. In this study, we utilized the BCIP online tool to analyze the Metabric database, incorporating adjacent normal tissues as controls. Although the expression levels of STAMBPL1, FOXO1, and GRHL3 in breast cancer tissues are not uniformly higher than those in adjacent tissues, their expression levels in triple-negative breast cancer (TNBC) are significantly elevated compared to non-TNBC. The results of this re-analysis have been added in Supplementary Figure 6 as Fig.S6A-C.

      About the question of the basal protein expression levels of STAMBPL1 in the cell lines used in this study, our response is that Fig. 2A showed the endogenous level of STAMBPL1 in HCC1806 and HCC1937. For Fig. 2D and 2F, the overexpressed STAMBPL1 was fused with a 3xFlag tag, resulting in a higher molecular weight compared to the endogenous STAMBPL1. In the revised Figure 2, we have indicated the positions of the endogenous (Endo.) and exogenous (OE.) STAMBPL1 bands with arrows.

      (2) Linked to these considerations is the second major criticism, namely that it is not made clear if this new regulatory axis is proposed to act in normoxic or hypoxic conditions. The experiments presented in this paper are performed in both conditions but a clear explanation as to why cells are exposed to hypoxia is not given and would be necessary being that HIF-1a transcription and not protein stability is being analyzed. Also, different hypoxic conditions are sometimes used, resulting in different mRNA levels of HIF-1a and its downstream targets and quite significant fluctuations within the same cell line from one experimental setting to the next. The authors should provide an explanation as to why experimental conditions are changed and, more importantly, the experiments presented in Figure 2 should be performed also in normoxia.

      Thanks for the comments. Under normoxic conditions, HIF1α is recognized by pVHL due to hydroxylation and is rapidly degraded via the proteasomal pathway. In contrast, under hypoxic conditions, HIF1α protein is accumulated. To investigate the effect of STAMBPL1 knockdown on HIF1A gene transcription levels, we conducted experiments under hypoxic conditions to avoid interference from the rapid degradation of HIF1α at the protein level, as shown in Figures 2B-C. Furthermore, under normoxic conditions, the overexpression of STAMBPL1 had been demonstrated to significantly enhance the protein levels of HIF1α and upregulate the transcription of VEGFA through HIF1α. To avoid the potential impact of excessive accumulation of HIF1α protein under hypoxic conditions on its protein level detection and the transcription of downstream VEGFA, the related experiments shown in Figure 2D-G were performed under normoxic conditions. We have explained the corresponding experimental conditions in the “Result” and “Figure legends” according to the reviewer's comments, highlighted in red.

      (3) Another critical point is that necessary experimental controls are sometimes missing, and this is reducing the strength of some of the conclusions enunciated by the authors. As examples, experiments where overexpression of STAMBPL1 is coupled to silencing of FOXO1 to demonstrate dependency lack FOXO1 silencing the absence of STAMBPL1 overexpression. Because diminishing FOXO1 expression affects HIF-1a/VEGF transcription even in the absence of STAMBPL1 (shown in Figure 7C, D), it is not clear if the data presented in Figure 7G are significant. The difference between HIF-1a expression upon FOXO1 silencing should be compared in the presence or absence of STAMBPL1 overexpression to understand if FOXO1 impacts HIF-1a transcription dependently or independently of STAMBPL1.

      Thank you for this comment. For Fig.7G-H, our experimental objective was to determine whether the activation of HIF1A/VEGFA transcription by STAMBPL1 via FOXO1. Therefore, under STAMBPL1 overexpression, we knocked down FOXO1 to investigate whether FOXO1 silencing could reverse the upregulation of HIF1A/VEGFA transcription induced by STAMBPL1 overexpression.

      (4) In addition, some minor comments to improve the quality of this manuscript are provided.

      (4.1) As a general statement, the manuscript is extremely synthetic. While this is not necessarily a negative feature, sometimes results are discussed in the figure legends and not in the main text (as an example, western blots showing HIF-1a expression) and this makes it hard to read thought the data in an easy and enjoyable manner.

      Thank you for this suggestion. We have revised the figure legends to make them clearer and more concise, highlighted in red.

      (4.2) The effect of STAMBPL1 overexpression on HIF-1a transcription is minor (Figure 2) The authors should explain why they think this is the case and whether hypoxia may provide a molecular environment that is more permissive to this type of regulation.

      Thank you for the comment. Under normoxic conditions, we conducted WB to examine the protein expression of HIF1α after the overexpression of STAMBPL1 and the knockdown of HIF1α. To visually illustrate the impact of STAMBPL1 overexpression on HIF1A protein levels, as well as the effectiveness of HIF1α knockdown, we annotated the grayscale analysis results of the bands in Figures 2D and 2F. As the reviewer pointed out, under normoxic conditions, HIF1α is rapidly degraded, which may explain why the upregulation of HIF1α protein levels by STAMBPL1 overexpression is not very pronounced.

      (4.3) HIF-1a does not appear upregulated at the protein level protein by STAMBPL1 or GRLH3 overexpression, even though this is stated in the legends of Figures 2 and 6. The authors should show unsaturated western blots images and provide quantitative data of independent experiments to make this point.

      Thank you for this comment. We have added the unsaturated image of HIF1α into Fig.2D, and performed a grayscale analysis of the HIF1α bands in Fig.2D and Fig.6A to indicate the relative protein level of HIF1α.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors previously reported that STAMBPL1 stabilizes MKP1 in TNBC. However, in this study, they focus on HIF1a. Given that STAMBPL1 affects HIF1a expression, it would be valuable to examine the levels of ROS in TNBC cells with or without STAMBPL1, as ROS is known to influence HIF1a stability.

      Thank you for your comments. It’s known that STAMBPL1 functions as a deubiquitinating enzyme. However, our study reveals that the upregulation of HIF1α by STAMBPL1 is independent of its deubiquitinating activity. This conclusion is supported by the observation that overexpression of the deubiquitinase active site mutant, STAMBPL1-E292A, also upregulated HIF1α expression (Figure 1F). Moreover, STAMBPL1 overexpression enhanced HIF1α transcription (Figures 4E and S3E), while STAMBPL1 knockdown was able to inhibit the transcription of HIF1α (Figures 2B-C). These results indicate that STAMBPL1 mediates the transcription of HIF1α but does not affect the stability of HIF1α. For these reasons, we think that it is unnecessary to examine the ROS levels.

      (2) Figure 1A: The regulation of HIF1a mRNA by STAMBPL1, but not its protein levels, could be better addressed by using MG132 to rule out the impact of protein degradation.

      Thanks for this comment. Under normoxic conditions, the oxygen-sensitive prolyl hydroxylases PHD1-3 act on HIF1α, specifically inducing hydroxylation at the proline 402 and 564 residues. These hydroxylated residues are recognized by the pVHL/E3 ubiquitin ligase complex, leading to ubiquitination and subsequent degradation via the proteasome pathway. Conversely, under hypoxic conditions, PHD1-3 are inactivated, and non-hydroxylated HIF1α is not recognized by the pVHL/E3 ubiquitin ligase complex, thereby avoiding ubiquitination and proteasomal degradation (DOI: 10.1073/pnas.95.14.7987, DOI: 10.1515/BC.2004.016, and DOI: 10.1042/BJ20040620). The mechanism of HIF1α accumulation under hypoxia is analogous to the action of the proteasome inhibitor MG132. When we treated cells with hypoxia, the ubiquitination and proteasomal degradation pathway of HIF1α was blocked. At this time, STAMBPL1 knockdown could downregulate the expression of HIF1α (Fig.1A). Meanwhile, since the knockdown of STAMBPL1 significantly downregulated the mRNA level of HIF1α under hypoxia (Fig.2B-C), we concluded that STAMBPL1 affects the expression of HIF1α by mediating its transcription. In addition, MG132 will block all proteasomal substrate degradation and may affect HIF1α mRNA levels indirectly.

      (3) Figure 2D and 2F: The effect of STAMBPL1 in promoting HIF1a expression is quite mild, and the effect of HIF1a knockdown is also modest. Given the high levels of STAMBPL1 in TNBC cell lines (Figure 2A), it would be better to repeat these experiments in a STAMBPL1-knockdown setting for clearer insights.

      We appreciate this insightful suggestion. Considering that the regulation of HIF1α expression by STAMBPL1 occurs at the transcriptional level, and to prevent excessive accumulation of HIF1a during hypoxia that could confound the effect of STAMBPL1 overexpression on HIF1α regulation, we opted to overexpress STAMBPL1 under normoxic conditions and subsequently knock down HIF1α, as shown in Fig.2D and Fig.2F. This approach allowed us to observe that STAMBPL1 overexpression can upregulate HIF1a expression to some extent. Additionally, in response to the reviewer's suggestion to knock down STAMBPL1, we have conducted the corresponding experiments, with results presented in Fig.1A-E and Fig.2B-C.

      (4) Figure 4A: Why does the RNA-seq pattern differ significantly between the two siRNAs? Additionally, the authors should clarify why they focus primarily on transcription factors, as other mechanisms, such as mRNA stability and RNA modification, could also influence gene transcription.

      Thank you for this comment. Two siRNAs for STAMBPL1 were designed and synthesized by a biotechnology company. Although both siRNAs target STAMBPL1, they target different sequences. While both siRNAs effectively knocked down STAMBPL1 (Fig. 1A and Fig. 2A), the possibility of off-target effects cannot be completely ruled out. Therefore, we needed to use two siRNAs simultaneously for RNA-seq, ensuring that the gene expression changes observed are due to the knockdown of STAMBPL1 by focusing on genes downregulated by both two siRNAs. Additionally, among the 27 genes downregulated by both two siRNAs, only 18 genes were annotated. Of these 18 genes, except for GRHL3, which is a transcription factor reported to be involved in gene transcription regulation, the remaining 17 genes have no documented association with RNA transcription, stability, or modification. Therefore, we focused on the GRHL3 gene.

      (5) Figure 5G: To investigate whether STAMBPL1 and GRHL3 function epistatically in the pathway, a double knockdown of STAMBPL1 and GRHL3 should be examined. Additionally, a double knockdown of STAMBPL1 and FOXO1 should be assessed.

      Thank you for your comment. In Figure 5G, we aimed to assess the knockdown efficiency of GRHL3 using siRNAs. To determine whether STAMBPL1 upregulates the HIF1a/VEGFA axis via GRHL3, we overexpressed STAMBPL1 and subsequently knocked down GRHL3. Our findings indicated that STAMBPL1 overexpression indeed enhanced the HIF1a/VEGFA axis, which was rescued by the knockdown of GRHL3, as shown in Figures 4E-F and S3E-F. Similarly, upon overexpressing STAMBPL1 and knocking down FOXO1, we observed that STAMBPL1 overexpression increased the GRHL3/HIF1a/VEGFA axis, which could also be rescued by knocking down FOXO1, as shown in Figures 7F-H. These results suggest that STAMBPL1 upregulates the GRHL3/HIF1a/VEGFA axis through FOXO1. We do not think it is a right way to double knock down STAMBPL1 and FOXO1 or GRHL3.

      (6) Figure 7: It remains unclear how STAMBPL1 regulates FOXO1. The authors show that STAMBPL1 increases the transcriptional activation of FOXO1 at the GRHL3 promoter, but it is not clear if STAMBPL1 is required for FOXO1 binding to the GRHL3 promoter. To address this, STAMBPL1-knockdown should be included to examine its effect on FOXO1 binding to the GRHL3 promoter. Furthermore, it would be important to determine whether the STAMBPL1-FOXO1 interaction is essential for GRHL3 transcription. Since the interaction sites of STAMBPL1-FOXO1 have been mapped, a mutant disrupting the interaction would provide better insight into how STAMBPL1 promotes GRHL3 transcription by interacting with FOXO1.

      Thank you for this comment. It has been reported that FOXO1 promotes the transcription of the GRHL3 gene by interacting with its promoter (DOI: 10.1093/nar/gkw1276). We also verified through ChIP assay that FOXO1 can bind to the promoter of GRHL3 gene (Fig.7I) and mediate its transcription. Specifically, knocking down FOXO1 significantly down-regulated the mRNA level of GRHL3 (Fig.7B), and the GRHL3 promoter lacking FOXO1 binding site almost completely lost transcriptional activity (Fig.7J), indicating that FOXO1 is crucial for the transcriptional activity of the GRHL3 promoter. Overexpression of STAMBPL1 enhances the activating effect of FOXO1 on the transcriptional activity of the GRHL3 promoter (Fig.7K). However, the up-regulation of GRHL3 transcription by overexpression of STAMBPL1 is completely blocked by FOXO1 knockdown (Fig.7F), and the knockdown of FOXO1 essentially blocks the binding of STAMBPL1 to the GRHL3 promoter (Fig.7L), suggesting that STAMBPL1 affects the transcriptional expression of GRHL3 based on FOXO1. As we added in Discussion, the transcription factor activity of FOXO1 is mainly regulated by its nucleoplasm shuttling process, and the accumulation of FOXO1 in nucleus can enhance its transcription factor activity (DOI: 10.1042/BJ20040167; DOI: 10.15252/embj.2022111867). In our research, neither STAMBPL1 nor its mutant of deubiquitinating enzyme site affected the expression of FOXO1 (Fig.S5E), but STAMBPL1 and FOXO1 co-located in the nucleus (Fig.7M), and they interacted with each other (Fig.7N, Fig.S5I-J). Therefore, we speculate that STAMBPL1 interacts with FOXO1 in the nucleus, obstructs the binding of FOXO1 with the members of 14-3-3 family, inhibits the export of FOXO1, thereby enhancing its transcriptional activity. This interaction between STAMBPL1 and FOXO1 does not necessarily affect the binding of FOXO1 with DNA, including the GRHL3 promoter.

      (7) Figure 8 A-C: What is the correlation among the expressions of STAMBPL1, FOXO1, and GRHL3 in TNBC tumors compared to non-TNBC tumors?

      Thank you for your comment. In Figure 8A-C, we analyzed the expression levels of STAMBPL1, FOXO1, and GRHL3 in both TNBC and non-TNBC samples using the BCIP. The results indicate that the expression levels of these three genes are significantly higher in TNBC compared to non-TNBC samples. To investigate the correlation among the expressions of STAMBPL1, FOXO1, and GRHL3 in TNBC versus non-TNBC, we further utilized the Metabric data. Besides the positive correlation trend between STAMBPL1 and GRHL3 expression in TNBC clinical samples (Pearson R = 0.27), no significant correlation was observed in the expression levels of STAMBPL1, FOXO1, and GRHL3 in TNBC and non-TNBC clinical samples (as shown in Author response image 1 below). Since STAMBPL1 and FOXO1 are involved as protein molecules in the transcriptional regulation of GRHL3 gene, and the data obtained from the Metabric database are the transcriptional levels of these three genes, this might be the reason why the correlation between their expressions was not observed.

      Author response image 1.

      Reviewer #2 (Recommendations for the authors):

      The authors have thoroughly elucidated the role of STAMBPL1 in TNBC. However, it would be beneficial to discuss the potential clinical implications of these findings, such as how targeting STAMBPL1 or FOXO1 might impact current treatment strategies for TNBC. However, several issues need to be addressed.

      Major:

      (1) While the study provides an exhaustive analysis of the molecular mechanisms, a comparison with other subtypes of breast cancer could enhance our understanding of the specificity of the STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA axis in TNBC.

      Thank you for your comment. According to report, STAMBPL1 is significantly associated with the mesenchymal characteristics of breast cancer (DOI: 10.1038/s41416-020-0972-x). We utilized cBioPortal (http://www.cbioportal.org/) to analyze the expression of STAMBPL1 across various clinical subtypes of breast cancer. The results indicated that STAMBPL1 is highly expressed in invasive breast cancer, which has been added to Supplementary Figure 6 as Fig.S6D. Given that TNBC is an aggressive type of invasive breast cancer, we further examined the expression of STAMBPL1 in TNBC compared to non-TNBC using BCIP (http://omicsnet.org/bcancer/database). Our findings revealed that the expression level of STAMBPL1 in TNBC was elevated relative to its levels in non-TNBC (Fig.8A). Additionally, since tumor angiogenesis is a critical factor influencing the metastasis of cancer cells, our study focused specifically on the pro-angiogenic effects of STAMBPL1 in TNBC.

      (2) The authors might consider discussing any potential off-target effects of the siRNA and shRNA used in the study to bolster the conclusions drawn from the knockdown experiments.

      We appreciate the reviewer's suggestion. It is well-known that siRNA or shRNA have off-target effects. To address this concern, we employed two siRNAs for each gene knockdown in our study. Specifically, we knocked down genes such as STAMBPL1, FOXO1, GRHL3, and HIF1A in two TNBC cell lines, HCC1806 and HCC1937, using two siRNAs. Except for siRNA#1 targeting HIF1A, which did not show a significant knockdown effect in HCC1806 cells (Fig.2D and Fig.6A), the knockdown effects of other siRNAs on their respective genes were effective, and the resulting phenotypes were consistent. As shown in Fig.2F and Fig.S4H, siRNA#1 targeting HIF1A had a significant knockdown effect in HCC1937 cells. The lower knockdown efficiency of this siRNA in HCC1806 cell line might be attributed to cell-specific factors.

      (3) It would be advantageous if the authors could provide further details on the patient demographics and tumor characteristics in the TCGA database analysis to better comprehend the clinical relevance of their findings.

      Thanks for the reviewer's suggestions. We have now indicated the number of clinical samples in each group in the legend of Fig.8A-C. Since we utilized the BCIP online database to analyze and compare the expression levels of the three genes STAMBPL1, FOXO1, and GRHL3 in TNBC and non-TNBC, we are unable to obtain more specific information regarding the tumor characteristics of each sample. However, our analysis clearly shows that the expression levels of these three genes are significantly higher in TNBC compared to non-TNBC.

      (4) The authors should consider discussing any limitations regarding the generalizability of their findings, such as potential variations among different TNBC subtypes or the specificity of their observations to certain stages of the disease.

      We appreciate the reviewer's comment. Accordingly, we have added a discussion on the limitation of this study in Discussion, highlighted in red font on pages 20 to 21, lines 396 to 412. In addition, we utilized the bc-GenExMiner online database to conduct a comparative analysis of STAMBPL1 expression in different subtypes of non-TNBC and TNBC. The result indicates that STAMBPL1 is highly expressed in mesenchymal-like and basal-like TNBC, which has been added into Supplementary Figure 6 as Fig.S6E. Since these two subtypes of TNBC are highly invasive and metastatic, it suggests that targeting the signaling pathway of STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA may offer clinical benefits for patients with invasive TNBC.

      Minor:

      The paper is generally well-written, but it's crucial to maintain vigilance for subject-verb agreement, proper use of tense, and consistent terminology.

      Thank you for this suggestion. We have thoroughly revised the article for issues such as grammar, including tense, subject-verb agreement, and terminology.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      Weaknesses:

      (1) The authors themselves propose in their Introduction that the "ECM-associated changes are increasingly perceived as causative, rather than consequential"; however, they have not conducted mechanistic (gain of function/loss of function) studies either in vitro or in vivo from any of their identified targets to truly prove causality. This remains one of the limitations of this study. Thus, future studies should investigate this point in detail. For instance, it would have been intriguing to dissect if knocking out specific genes involved in one specific model or genes common to both would yield distinct phenotypic outcomes.

      We agree with the reviewer that our study does not provide mechanistic verification of the function of identified targets with suggested role in the development and/or resolution of fibrosis. The current study was primarily conducted in order to identify these possible targets with focus on the identification of differences in extracellular matrix deposited in two selected models of liver fibrosis with different modes of action. To conduct further studies using knock-out/in models for verification of causality of proposed targets was at this point well beyond our intention. However, we are fully aware of the potential of identified molecules and further studies to disect their roles in liver diseases are part of future plans.

      (2) The majority of the conclusions are derived primarily from the proteomic analyses. Although well conducted, it would strengthen the study to corroborate some of the major findings by other means such as IHC/IF with the corresponding quantifications and not only representative images.

      We have now provided additional IF images and their quantifications in accordance with the Reviewer’s suggestions to our major MS findings to strenghten the significance of the MS data (see detailed answer below).

      Reviewer #2:

      Weaknesses:

      (1) As it currently stands, the data, whilst extensive, is primarily focussed on the proteomic data which is fairly descriptive and I am not clear on the additional insight gained in their approach that is not already detailed from the extensive transcriptomic studies. The manuscript overall would benefit from some mechanistic functional insight to provide new additional modes of action relevant to fibrosis progression.  

      We agree with the reviewer that our study could initially appear descriptive. However, this characteristics is inherent to most omics studies, which tend to provide hypothesis-free testing of a large number of analytes in order to find a multitude of candidate biomarkers(1). Importantly, we believe our study provides insights that go beyond the scope of previously published transcriptomic analyses.

      Specifically, our work focuses on compartment-specific changes in the liver proteome, with an emphasis on the extracellular matrix (ECM) composition and alterations in protein solubility—features that cannot be captured by transcriptomic studies. The matrisome is more than a structural scaffold; it functions as a reservoir for secreted factors, including growth factors and cytokines, which modulate the local cellular microenvironment. Transition dynamics between the insoluble matrisome and soluble protein pools influence the signaling capabilities and bioavailability of these factors. Moreover, fibrous ECM assemblies directly impact tissue mechanics, providing cells embedded within the matrix with spatially distinct biochemical and biomechanical contexts. The current understanding of matrisome composition in the context of specific liver disease etiologies is limited. Dr. Friedman, in his 2022 review on hepatic fibrosis, highlights the unmet need to elucidate etiology-specific protein signatures of the cirrhotic liver matrisome, which could serve as disease staging or prognostic biomarkers(2). Our study addresses this gap by characterizing the distinct matrisome profiles associated with hepatotoxic- versus cholestasis-driven liver injury. We believe our findings lay the groundwork for identifying etiology-specific biomarkers and potential therapeutic targets for antifibrotic interventions, offering a novel layer of insight beyond what transcriptomic data alone can provide.

      (2) Whilst there is some human data presented it is a minimal analysis without quantification that would imply relevance to disease state. Although studying disease progression in animals is a fundamental aspect of understanding the full physiological response of fibrotic disease, without more human insight makes any analysis difficult to fulfil their suggestion that these targets identified will be of use to treat human disease.

      We thank the reviewer for this comment. Our study primarily focuses on utilizing animal models to explore the fundamental physiological processes underlying the development and resolution of fibrotic liver disease. To address the translational relevance of our findings, we concentrated on clusterin, one of the key target proteins identified during our analysis of the insoluble proteome. Specifically, we investigated its localization in human liver samples, focusing on its association with collagen deposits (Figure 6F). To this end, we analyzed human liver samples of diverse etiologies and varying degrees of fibrotic damage, including samples representing four distinct stages of HCV-induced fibrosis (Figure 6F, lower panel). While this analysis highlights the presence and localization of clusterin in fibrotic deposits, we acknowledge that our study does not include extensive quantification or mechanistic insight into clusterin's role in human liver fibrosis. We believe that the data presented in this manuscript provide a valuable foundation for future investigations into clusterin’s involvement in liver fibrosis across different etiologies. Recognizing the translational importance of this work, we have already initiated a prospective study involving human patients, which aims to conduct a more comprehensive analysis of clusterin's function and its potential as a therapeutic target.

      To further support our findings on clusterin's role in fibrosis development and resolution and to address the reviewer's concern, we quantified clusterin deposits in the available human samples representing four distinct stages of HCV-induced fibrotic disease. Using immunofluorescence (IF) images at a 20x field of view, we measured both clusterin and collagen deposits to illustrate changes in clusterin abundance during fibrosis progression (stages F1–F4) in relation to collagen deposition dynamics. The quantified data have been included for the reviewer's consideration (Figure 1). However, it is important to emphasize that this quantification was conducted on a single human sample per fibrotic stage, which limits the statistical robustness of the analysis. A more comprehensive evaluation involving additional patient samples would be necessary for a more definitive conclusion. For this reason, we propose to include these results solely in our rebuttal letter and to incorporate a more extensive analysis in our intended follow-up study, where larger cohorts will allow for a thorough investigation of clusterin's role in human liver fibrosis.

      Author response image 1.

      Dynamics of clusterin abundance with the development of HCV-induced fibrotic disease in comparison to the changes in collagen deposits. IF images of human liver sections from different stages of chronic HCV infection were immunolabeled for clusterin and collagen 1. Clusterin- and collagenpositive (<sup>+</sup>) areas (as %) from three to eight fields of view (20x objective) were evaluated for each fibrosis stage (F1-F4). 

      (3) Some of the terminology is incorrect while discussing these models of injury used and care should be taken. For example - both models are toxin-induced and I do not think these data have any support that the DDC model has a higher carcinogenic risk. An investigation into the tumour-induced risk would require significant additional models. These types of statements are incorrect and not supported by this study.

      We are grateful to the reviewer for drawing our attention to the incorrect use of the term "toxin-induced". In two instances, where the wording was incorrect, we have corrected the term to hepatotoxin-induced as it was originally intended. While we believe that our proteomic signature data and identified signaling pathways suggest a potential carcinogenic risk associated with the cholestatic, but not the hepatotoxic model, we have toned down the statements on this issue in the article to respect the reviewer's perspective. These changes, which are highlighted in the track changes mode of the article, aim to make the conclusions of the study more precise and thus improve the clarity of our conclusions.

      Reviewer #1 (Recommendations for the authors): 

      (1) In the Discussion, the authors could consider pointing out that one limitation of the study is a lack of mechanistic (gain of function/loss of function) studies either in vitro or in vivo from any of their identified targets to truly prove causality. 

      As noted earlier, we fully agree with both reviewers that a limitation of this study is its descriptive nature, which is an inherent characteristic of omics-based research. In our manuscript, we aimed to "determine compartment-specific proteomic landscapes of liver fibrosis and delineate etiology-specific ECM components," with the overarching goal of providing a foundation for future antifibrotic therapies.

      The insights gained from our study will indeed serve as a critical basis for subsequent research, where we will prioritize mechanistic investigations to elucidate the roles of the identified targets. While we acknowledge the importance of gain- or loss-of-function studies to establish causality, we believe this falls outside the primary scope of the current manuscript. Instead, we envision these mechanistic approaches as key elements of our future research efforts. For this reason, we feel it is not necessary to further expand on this limitation in the current discussion.

      (2) The majority of the conclusions are derived primarily from the proteomic analyses. Although well conducted, it would strengthen the study to corroborate some of the major findings by other means such as IHC/IF with the corresponding quantifications and not only representative images. For example, the IF stainings for ECM1 should also be quantified - ECM1. 

      To strengthen our MS findings on ECM1 expression and to address the reviewer's concern, we have now included quantification of ECM1 using IF staining at selected time points in Figure S7E and we refer to these data in the Results section (p. 12 of the current manuscript). The IF quantification data correspond well to the MS data showing increase in ECM1 expression with fibrosis development and decline with partial fibrosis resolution.

      (3) S1 - it would be important to show Sirius Red images over the time course, especially for CCl4 T4 where fibrosis resolution is occurring. Proteomics data also show this group clusters more closely with control mice and seeing a representative image would add further credibility to this point. 

      Requsted Sirius Red images are now part of the Figure S1B, documenting partial fibrosis resolution and overall parenchyma healing in T4 in both models.

      (4) How comparable are the periods of the two models? 2 weeks in one model may not be the same as 2 weeks in the other depending on the severity of the pathogenesis. 

      We appreciate the reviewer’s comment regarding the comparability of time points between the two models. Indeed, the temporal dynamics of fibrosis development differ between the models employed in our study, and we have carefully considered this aspect to ensure the validity of our comparative analysis. To address this, we started our comparisons at a stage corresponding to the onset of fibrosis in each model. Specifically, quantification of Sirius Red-positive areas, indicative of collagen deposition (Figure S1B), revealed that 2 weeks of DDC treatment produced a comparable extent of fibrosis to that observed after 3 weeks of CCl₄ treatment. This point was designated as the initial fibrosis time point (T1, Figure S1B), from which further treatment was applied to induce more advanced fibrosis. This approach allowed us to standardize the comparison of fibrosis progression between the two models.

      (5) Figure 4A-D - cell-type-specific signatures should be corroborated by actual IHC or IF stainings if possible. HNF4a (hepatocytes), CK19 (cholangiocytes), aSMA (activated fibrogenic HSCs), immune cells (B220, F4/80, Cd11b, CD11c etc).

      We thank the reviewer for this valuable suggestion. To strengthen our analysis, we have now complemented the box plots of cell type-specific signatures derived from the MS data (Figure 4A-D) with immunofluorescence (IF) staining, which has been included in the Supplemental Data (Figure S6). Specifically, we provide representative IF images from control and T1-T4 time points for each model, documenting the changes in abundance with treatment in:

      A) Hepatocytes (HNF4α), activated hepatic stellate cells (αSMA), and cholangiocytes (CK19).

      B) Immune cell populations, including B cells (B220) and macrophages/monocytes/Kupffer cells (F4/80), as these immune cell groups were not only identified in our MS analysis but also have established roles in the selected models(3, 4, 5). 

      The representative images shown in Figure S6 show the dynamics of the cellular populations in each of the models, which correspond well with the MS data (compare Figures 4A-D and S5). These additional data further validate our findings and enhance the robustness of our conclusions.

      References:

      (1) Thiele M, Villesen IF, Niu L, et al. Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases. J Hepatol 2024;81:345-359.

      (2) Friedman SL, Pinzani M. Hepatic fibrosis 2022: Unmet needs and a blueprint for the future. Hepatology 2022;75:473-488.

      (3) Best J, Verhulst S, Syn WK, et al. Macrophage Depletion Attenuates Extracellular Matrix Deposition and Ductular Reaction in a Mouse Model of Chronic Cholangiopathies. PLoS One 2016;11:e0162286.

      (4) Aoyama T, Inokuchi S, Brenner DA, et al. CX3CL1-CX3CR1 interaction prevents carbon tetrachlorideinduced liver inflammation and fibrosis in mice. Hepatology 2010;52:1390-400.

      (5) Yang W, Chen L, Zhang J, et al. In-Depth Proteomic Analysis Reveals Phenotypic Diversity of Macrophages in Liver Fibrosis. J Proteome Res 2024;23:5166-5176.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Major concerns:

      For studies investigating capsaicin binding to KEAP1, the authors used capsaicin concentrations that are toxic to cells (Figures S1D and 4F, G). In vivo studies were performed only in 3 rats per group. The T-test was used for the comparison of more than two groups. Given the well-known issues with the specificity of the NRF2 antibody, the authors should provide appropriate controls, especially for IF and IHC staining.

      We sincerely appreciate your valuable comments. We repeated the experiments about CCK8 (Figure S1d) and Pull-down (Figure 4g), and then updated the results. In September 2022, GES-1 cells were more sensitive to capsaicin (CAP) because Gibco serum from North America was used. Later, in 2024, we changed the serum from Australia(Gibco: 10099-141), and we found that such GES-1 cells raised better, so we re-ran the test, and the IC50 was seen to be 304.8 μM, so concentrations used in this paper has no obvious toxicity to cells. What’s more, we repeated the Pull-down experiment with more reasonable concentrations of 32 μM and 100 μM, and the results were still in line with expectations. In summary, we concluded that the effect of CAP on GES-1 cells is closely related to the cell state, and that treatments of CAP from 32 to 100 μM can hinder the interaction between NRF2 and the Kelch domain of KEPA1. What’s more, at the cellular level, the experimental concentration of CAP was not more than 32 μM, which is a relatively safe concentration for cells.

      Thank you very much for your comments. We also pay attention to using more repetitions to increase the reliability of the experimental results in animal experiments. Therefore, recently we supplemented the experiment of Nfe2l2Knockout mice in Figure 9 (6 mice per group). Additionally, thank you very much for your comments on the use of T-test analysis, we reviewed the statistics and changed them by one-way ANOVA.

      Finally, thanks to your concern about the specificity of NRF2 antibody, we used commercialized NRF2 antibody which have been KO/KD validated (Cat No. 16396-1-AP, Proteintech) and can be used for IF and IHC staining. Each of our fluorescence result was equipped with Western Blotting in its active form at the size of 105-110 KDa for statistical analysis, the trend was consistent with the experimental results of IF and IHC, which fully proves the correctness of the results presented (Figure 2c and Figure S8j).

      Reviewer #2 (Public Review):

      Weaknesses:

      One major weakness of the study is that plausibility is taken as proof for causality. The finding that capsaicin directly binds to Keap1 and releases Nrf2 from its fate of degradation (in vitro) is taken for granted as the sole explanation for the observed improved gastric health upon alcohol exposure (in vivo). There is no consideration or exclusion of any potential unrelated off-target effect of capsaicin, or proteins other than Nrf2 that are also controlled by Keap1. 

      Another point that hampers full appreciation of the capsaicin effect in cells is that capsaicin is not investigated alone, but mostly in combination with alcohol only.

      Thank you very much for this comment. In the introduction, we clarified as follows: “Currently, experiments conducted in rats have demonstrated that red pepper/capsaicin (CAP) had significant protective effects on ethanol-induced gastric mucosal damage, and the mechanism may be related to the promotion of vasodilation(6,7), increased mucus secretion(8) and the release of calcitonin gene-related peptide (CGRP)(9,10). However, it is noteworthy that whether the antioxidant activity of CAP works has not been fully investigated.” Therefore, we also recognize that CAP does not exert its effects through the KEAP1-NRF2 pathway alone. Your advice is very useful. We further explored the TRPV1 and DPP3 to detect the potential off-target effects of CAP respectively. Capsazepine (CAPZ), which is TRPV1 receptor antagonist did not affect the protection of CAP against GES-1 (Fig S4f and S4g), which may indicate that CAP activation of NRF2 does not have to depend on TRPV1. The binding of CAP with DPP3, containing an ETGE motif and can bind to KEPA1, was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM(>100 μM), which may indicate the potential off-target effect of CAP is low because CAP had a strong binding force with KEAP1 about 31.45 μM (Fig S4h and S4i).

      Thank you very much for the comment of another point. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341(Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. Especially for the experiments of PS-341 and DTT, we had a group that only adds CAP, and it can be seen that the addition of CAP alone did not significantly up-regulate NRF2, which is completely different from traditional NRF2 activators (especially artificially designed covalent binding peptides which have serious side effects).  

      Reviewer #3 (Public Review):

      Weaknesses:

      While the study provides valuable insights into the molecular mechanisms and in vivo effects of CAP, further clinical studies are needed to validate its efficacy and safety in human subjects. The study primarily focuses on the acute effects of CAP on ethanol-induced gastric mucosa damage. Long-term studies are necessary to assess the sustained therapeutic effects and potential side effects of CAP treatment.

      Furthermore, the study primarily focuses on the interaction between CAP and the KEAP1-NRF2 axis in the context of ethanol-induced gastric mucosa damage. It may be beneficial to explore the broader effects of CAP on other pathways or conditions related to oxidative stress. CAP has been known for its interaction with the Transient Receptor Potential Vanilloid type 1 (TRPV1) channel and subsequent NRF2 signaling pathway activation. Those receptors are also expressed within the gastric mucosa and could potentially cross-react with CAP leading to the observed outcome. Including experiments to investigate this route of activation could strengthen the present study.

      While the design of CAP nanoparticles is innovative, further research is needed to optimize the nanoparticle formulation for enhanced efficacy and targeted delivery to specific tissues.

      Addressing these weaknesses through additional research and clinical trials can strengthen the validity and applicability of CAP as a therapeutic agent for oxidative stress-related conditions.

      Thank you very much for these suggestions. We also believe that CAP is very valuable and promising for protecting EtOH induced gastric mucosal injury, and actively promote patent applications and if conditions permit, longer drug research for biosecurity is essential. Because of the inherently new discovery of the binding of CAP and KEAP1, and the important role of NRF2 in various oxidative stress-related diseases, we used Human umbilical cord mesenchymal stem cells (HUC-MSCs) and  H<sub>2</sub>O<sub>2</sub> to explore the potential broader effects of CAP related to oxidative stress in cells (Figure 1l and 1m). At the same time, we also explored TRPV1 related experiments, and we were surprised to find that inhibiting TRPV1 did not affect the effect of CAP (Supplementary Figure 4f and 4g). We hope that more people can read this article and do more interesting research together.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):

      Although this study has been conducted in rats, a direct proof that albumin-coated capsaicin nanoparticles act through activation of Nrf2 in protecting gastric mucosa against alcohol toxicity could be well conducted in commercially available Nrf2-deficient mice.

      Thank you very much for your suggestion and the comment is very constructive for us to improve this paper. We purchased Nrf2-deficient mice (Cat. NO. NM-KO-190433) and performed experiments, and the results showed that knockout mice with Nrf2 were more sensitive to EtOH and the effects of CAP were partially eliminated (Figure 9), which further validated the role of Nrf2-related signaling pathway in EtOH-induced gastric mucosal injury and the therapeutic effect of CAP.

      Reviewer #1 (Recommendations For The Authors):

      Minor concerns include proofreading the paper. Actinomycin is not an inhibitor of translation.

      Thank you for your comment. We have revised “Actinomycin” to “Cycloheximide”.

      Reviewer #2 (Recommendations For The Authors):

      - Please have a careful look at your conclusions: just because two effects happen at the same time and may be plausible explanations for each other, it does not mean that they are really in a causative relationship in your given test system (unless unambiguously proven by additional experiments).

      Your suggestions are very constructive for us to improve this paper.

      We further discussed the role of capsaicin with TRPV1, DPP3 and Nrf2deficient mice, hoping to make our conclusions more credible to some extent. 

      - You may want to frankly discuss other targets of capsaicin (e.g. the TrpV1 receptor) that possibly could also account for your observations, and that binding to Keap1 not only releases Nrf2 from proteasomal degradation.

      Thank you for your comment. As a result, we further explored the TRPV1 and DPP3 to detect the potential off-target effects of CAP respectively. Capsazepine (CAPZ), which is TRPV1 receptor antagonist does not affect the protection of CAP against GES-1 (Fig S4f and S4g). DPP3 with an ETGE motif was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM, which may indicate the potential off-target effect of CAP is low (Fig S4h and S4i). At the same time, the activation of NRF2 by non-classical pathways such as CAP regulation of DPP3 or other proteins also deserves more discussion and experimental verification.

      - For Figure 1G it does not become entirely clear what has been done (and thus deduction of conclusions is hampered).

      Thank you for your comment. Network targets analysis (Figure 1g) was performed to obtain the potential mechanism of effects of CAP on ROS. Biological effect profile of CAP was predicted based our previous networkbased algorithm:drug CIPHER. Enrichment analysis was conducted based on R package ClusterProfiler v4.9.1 and pathways or biological processes enriched with significant P value less than 0.05 (Benjamini-Hochberg adjustment) were remained for further studies. Then pathways or biological processes related to ROS and significantly enriched were filtered and classified into three modules, including ROS, inflammation and immune expression. Network targets of CAP against ROS were constructed based on above analyses, and finally we combined proteomics to determine the research idea of this paper

      -  Figure 1L: is there a reason/explanation why UC.MSC needs a comparably very high concentration of capsaicin.

      Thank you for your comment. Because the experimental results of 8 μM and 32 μM on this cell were more stable, and the activation effect of NRF2 downstream was more obvious.

      -  Figure 2C: it is surprising that naïve (unstressed /untreated cells) already show a rather high nuclear abundance of Nrf2 (shouldn´t Nrf2 be continuously tagged for degradation by Keap1).

      Thank you for your comment. This is a real experimental result, and we have found in many experiments that the untreated group can also show NRF2 when immunoblotting. We think that this phenomenon may be related to the cell state at that time.

      -  Figure 2E: the claim of synergy between CAP and the proteasome inhibitor is not justified with this single figure.

      Thank you for your comment. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341 (Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. However, this synergy does warrant more research.

      -  CHX is cycloheximide (in the main text it is referred to as actinomycin).

      Thank you very much for your comment. We have revised “Actinomycin” to “Cycloheximide”.

      -  Figures 2G-H: why switch to rather high concentrations? Is it due to the overexpression of Keap1?

      Thank you for your comment. At the time of this part of the experiment, we had obtained in vitro data on the interaction of CAP and the Kelch domain of KEAP1 (about 32 μM). To keep the results uniform and valid, we chose a relatively higher concentration.

      -  Figure 2I: in the pics of mitochondria the control mitochondria look way more punctuated (likely fissed) than the ones treated with EtOH or EtOH + CAP. Wouldn´t one expect that EtOH leads to mitochondrial fission and CAP can prevent it?

      Thank you for your comment. MitoTracker® Red CMXRos (M9940, Solarbio, China) is a cell-permeable X-rosamine derivative containing weakly sulfhydryl reactive chloromethyl functional groups that label mitochondria. This product is an oxidized red fluorescent stain (Ex=579 nm, Em=599 nm) that simply incubates the cell and can be passively transported across the cell membrane and directly aggregated on the active mitochondria. Therefore, red does not represent broken mitochondria, but active mitochondria. Quantitative analysis of the mean branch length of mitochondria was calculated using MiNA software (https://github.com/ScienceToolkit/MiNA) developed by ImageJ.

      -  Figure 3C: figure legend is somewhat poor.

      Thank you for your comment. We have revised: “KEAP1-NRF2 interaction was detected with Surface plasmon resonance (SPR) in vitro.”

      -  Figure 3E: given that CAP disrupts Nrf2/Keap1- PPI, why is there no Nrf2 stabilization seen in the fourth lane (input/lysate)?

      Thank you for your comment. The fourth lane may promote the degradation of NRF2 due to overexpression of KEAP1.

      -  Figure 3H: high basal Nrf2 levels in unstressed/untreated HEK WT cells, why?

      Thank you for your comment. This is a real experimental result, and we have found in many experiments that the untreated group can also show NRF2 when immunoblotting in 293T cells. We think that this phenomenon may be related to the cell state at that time.

      -  Figure 3G/I: this data suggests to me that the alcohol-mediated toxicity is Keap1-dependent (rather than the protection by CAP), doesn´t it?

      Thank you for your comment. We can see that KEAP1-KO cells had a high expression of NRF2, which was also in line with our expectations, and EtOH-induced GES-1 damage may be closely related to oxidative stress.

      -  Figure 4a: the inclusion of an additional Keap1 binding protein (one with an ETGE motif) would have been desirable (to get information on specificity/risks of off-target (unwanted) effects of CAP). 

      Thank you for your comment. DPP3 with an ETGE motif was detected by BLI, and we found that the K<sub>D</sub> between CAP and DPP3 was 1.653 mM, which may indicate the potential off-target effect of CAP is low (Fig S4h and S4i).

      -  Figure 4D: why is there no stabilization of Nrf2 by CAP in lane 2 ? How can the DTT-mediated boost on Nrf2 levels be explained?

      Thank you for your comment. Multiple experiments have shown that CAP significantly up-regulates NRF2 in the presence of additional stimuli such as EtOH (Figure 1i),  H<sub>2</sub>O<sub>2</sub> (Figure 1l), PS-341 (Figure 2e) and DTT (Figure 4d), which pattern is consistent with our understanding of allosteric regulation and as expected. However, this synergy does warrant more research.

      -  Figure 4f: 5% DMSO is a rather high solvent concentration, why so high (the solvent alone seems to have quite marked effects).

      Thank you for your comment. Because our maximum concentration was set relatively high, we have also recognized relevant problems and resupplemented the more critical Pull-down experiment (Figure 4g). The current DMSO of 0.2% had no effect on the experimental results.

      -  Figure 5: it should be described in the figure legend which mutant is used. Based on the previous data, I would expect an investigation of mutants carrying amino acid exchanges at the newly identified allosteric site.

      Thank you for your comment. The mutated version involved substitutions at residues Y334A, R380A, N382A, N414A, R415A, Y572A, and S602A (the orthostatic site), which are residues reported to engage NRF2 and classic Keap1 inhibitors. The exploration of newly discovered allosteric sites is worthy of further study.

      -  Figure 6/7: I am not expert enough to judge formulations and histology scores. However, the benefit of the encapsulated capsaicin does not become entirely clear to me, as CAP and IRHSA@CAP mostly do not significantly differ in their elicited response.

      Thank you for your comment. On the one hand, nanomedicine improves the safety of administration: it helps to reduce the intense spicy irritation of CAP itself when administered in the stomach; On the other hand, the dosage of drugs is reduced to a certain extent to achieve better therapeutic effect.

      -  Figure 7: rebamipide was introduced as positive control in the text with an activating effect on Nrf2, but there is no induction of hmox and nqo in Figure 7f, why?

      Thank you for your comment. The effect of addition of positive control drug (Rebamipide) on NRF2 activation is not the focus of this paper. We speculate that the transcription and translation of related genes may not be completely synchronized when Rebamipide was taken at the same time.

      -  Figure 8: the CAP effect on inflammation is visible, however, a clear causal connection between ROS/Nrf2/KEap1 is not given in the presented experiments.

      Thank you for your comment. The simple mechanics of this paper are illustrated in the Graphic diagram. The activation of NRF2 exerts both antiinflammatory and antioxidant functions, which has been reported in many articles, but the causal relationship is still open to exploration.

      Points related to presentation:  

      -  The data with the encapsulated CAP appear a little as a sidearm that does not bolster your main message (maybe take out and elaborate on this topic more extensively in another manuscript).

      -  Revise the introduction on the Nrf2 signaling pathway as it is written at the moment, someone outside the Nrf2 field might have trouble understanding it.

      -  The use of language requires proofreading and revision.

      Thank you for your comment. We rearranged and proofread it.

      Reviewer #3 (Recommendations For The Authors):

      Overall, the manuscript is well-written and the results are presented in a concise and comprehensible manner.

      Some recommendations on the experimental evidence and further suggestions:

      • The authors should state how they assessed the distribution of the data. Description of data with mean and standard deviation as well as comparisons between different groups with t-test assumes that the underlying data is normally distributed.

      Your suggestions are very constructive for us to improve the paper.  The differences in the mean values between the two groups were analyzed using the student’s t-test, while the differences among multiple groups were analyzed using a one-way ANOVA test in the GraphPad Prism software.

      Therefore, we checked and proofread the statistical analysis.

      • Additional experiments further characterising and validating the activation of CAP via direct KELCH1-binding could include parallel experiments with similar agonists like dimethyl fumarate. It would be interesting to know how CAP activation compares to DMF activation.

      Thank you very much for your comment. We believe that the activation of NRF2 by DMF has been widely reported and well-studied, so we did not purchase this drug for comparative study here. If it can be promoted clinically in the future, we may consider comparing with DMF.

      • Also, the knock-down of NRF2 would be a suggested experiment to do because it rules out that the benefit of CAP is independent of KEAP1-NRF2 binding and activation.

      Thank you very much for your suggestions. We purchased Nrf2-deficient mice and performed experiments, and the results showed that knockout mice with Nrf2 were more sensitive to ethanol and the effects of CAP were partially eliminated (Figure 9), which further validated the role of Nrf2-related signaling pathway in alcohol-induced gastric mucosal injury and the therapeutic effect of CAP.

      Some corrections on text and figures:

      • Figure 1b: incorrect spelling of DNA stain. Should be Hoechst33324.

      Thank you very much for your comment. We have revised.

      • Figure 1c: don't put the label inside the plot.

      Thank you very much for your comment. We have revised.

      • Figure 1d: choose less verbose axes titles (this also applies to other figures).

      Thank you very much for your comment. We have revised.

      • Figures 1e and 1f: please state the units.

      Thank you very much for your comment. The enzyme activity of SOD and the content of MDA were compared with that of the control group.

      • Heading 2.2: NRF2-ARE instead of NRF-ARE.

      Thank you very much for your comment. We have revised.

      • Line 118: missing expression after immune.

      Thank you very much for your comment. We have revised.

      • Figure 1g: names of proteins are not readable.

      Thank you very much for your comment. We have revised.

      • Line 120: You performed transcriptomic analyses to identify differentially expressed GENES not proteomic.

      Thank you very much for your comment. This part of the work we do is proteomics.

      • Line 122: Fold change should be stated in both directions, i.e. absolute FC like |FC| > 1. Or did you select only upregulated DEGs? Is it not log2 FC?

      Thank you very much for your comment. We have revised.

      • Figure 1h (and Supplementary Figure 1a): Missing heatmap legend for FC.

      What do the colors show? Sample (column) description missing.

      Thank you very much for your comment. We used red to indicate up-regulation, blue to indicate down-regulation, and the vertical coordinate on the right side were antioxidant genes such as GSS and SOD1, respectively, and the proportion between the treatment group and the model group (CAP + EtOH/EtOH) had been calculated and labeled.

      • Line 145: A Western blot is not a proteomic analysis.

      Thank you very much for your comment. We have revised: “Concurrently, the elevated expression levels of GSS and Trx proteins, which were also downstream targets of NRF2, further validated by western blotting (Figure 1j).”

      • Supplementary Figure 2e-j: expression fold change is not the right quantity. The signal of the actual protein was quantified. And what are you comparing to with the statistics? The stars on one bar are not clear.

      Thank you very much for your comment. The expression level of this part was normalized compared with that of the control group. The significance differentiation analysis is compared with the model group.

      • What was the concentration of  H<sub>2</sub>O<sub>2</sub> used?

      Thank you very much for your comment. 200 μM  H<sub>2</sub>O<sub>2</sub> was used.

      • Figure 2d: use a more precise y-axis label.

      Thank you very much for your comment. We do want to compare the amount of NRF2 entering the nucleus, so the relative expression is compared to the internal reference

      • Figure 2g: missing molecular weight markers.

      Thank you very much for your comment. Since the ubiquitination modification is a whole membrane, and only marking the size of HA and GAPDH is not beautiful enough here.

      • Line 221: lactate is the endproduct of the anaerobic glycolytic pathway.

      Thank you very much for your comment. We have revised.

      • Supplementary Figure 3d: should it be PKM2 (instead of PKM) and LDHA (instead of LDH). Should fit with the text in the manuscript.

      Thank you very much for your comment. We have revised.

      • Supplementary Figures 3 e-f: brackets in y-axis labels are too bold.

      Thank you very much for your comment. We have revised.

      • Figures 3a and b. Brackets should only be used if two conditions are being compared statistically. Remove the one line with ns as it could imply that you have compared the first with the last condition only.

      Thank you very much for your comment. We have revised.

      • Consistent labeling of kDa in figures (no capital K in KDa).

      Thank you very much for your comment. We have revised.

      • Figure 4a. Move kDa on top of 70.

      Thank you very much for your comment. We have revised.

      • Figure 3 g-h: Why 2% EtOH. Used 5% previously?

      Thank you very much for your comment. Because here we changed the 293T cell line, 5% EtOH concentration is too high on this cell.

      • Supplementary Figure b-e: correct typo in y-axis label: expression.

      Thank you very much for your comment. We have revised.

      • Figure 4a: correct x-axis label for temperature unit. Too bold. Not readable.

      Add a clear label and unit for y-axis.

      Thank you very much for your comment. We have revised.

      • Figure 4 b-c: should have a legend explaining colors.

      Thank you very much for your comment. Our Figure legend already contains the meaning of colors: “(b) Computational docking of CAP molecule to KEAP1 surface pockets. The Keap1 protein is represented in gray, while the CAP molecule is shown in yellow. The seven key amino acids predicted to be crucial for the interaction are highlighted in blue. (c) Partial overlap of CAPbinding pocket with KEAP1-NRF2 interface. The KEAP1-NRF2 interaction interface is represented in purple.”

      • Supplementary Figure 5a. Add axis units.

      Thank you very much for your comment. We have revised.

      • Figure 4e: Missing b ions value for number 19.

      Thank you very much for your comment. This part is not missing, but corresponds to 19 of y ions.

      • Figure 7f: adjust brackets - they are too bold.

      Thank you very much for your comment. We have revised.

      • Supplementary Figure 8b-i: labels not readable. c should be spleen.

      Thank you very much for your comment. We have revised.

      • Line 787: specify BH adjustment to Benjamini-Hochberg.

      Thank you very much for your comment. We have revised.

      • Check spelling of µl throughout the Methods section e.g. line 854 - shouldn't be "ul".

      Thank you very much for your comment. We have revised.

      • Line 974: correct spelling of species names: E. coli should be in italics.

      Thank you very much for your comment. We have revised all of these corrections on text and figures. For me, the writing of papers will be more rigorous and careful in the future.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aimed to quantify feral pig interactions in eastern Australia to inform disease transmission networks. They used GPS tracking data from 146 feral pigs across multiple locations to construct proximity-based social networks and analyse contact rates within and between pig social units.

      Strengths:

      (1) Addresses a critical knowledge gap in feral pig social dynamics in Australia.

      (2) Uses robust methodology combining GPS tracking and network analysis.

      (3) Provides valuable insights into sex-based and seasonal variations in contact rates.

      (4) Effectively contextualizes findings for disease transmission modeling and management.

      (5) Includes comprehensive ethical approval for animal research.

      (6) Utilizes data from multiple locations across eastern Australia, enhancing generalizability.

      Weaknesses:

      (1) Limited discussion of potential biases from varying sample sizes across populations

      This is a really good comment, and we will address this in the discussion as one of the limitations of the study

      (2) Some key figures are in supplementary materials rather than the main text.

      We will move some of our supplementary material to the main text as suggested.

      (3) Economic impact figures are from the US rather than Australia-specific data.

      We included the impact figures that are available for Australia (for FDM), and we will include the estimated impact of ASF in Australia in the introduction.

      (4) Rationale for spatial and temporal thresholds for defining contacts could be clearer.

      We will improve the explanation of why we chose the spatial and temporal thresholds based on literature, the size of animals and GPS errors.

      (5) Limited discussion of ethical considerations beyond basic animal ethics approval.

      This research was conducted under an ethics committee's approval for collaring the feral pigs. This research is part of an ongoing pest management activity, and all the ethics approvals have been highlighted in the main manuscript.

      The authors largely achieved their aims, with the results supporting their conclusions about the importance of sex and seasonality in feral pig contact networks. This work is likely to have a significant impact on feral pig management and disease control strategies in Australia, providing crucial data for refining disease transmission models.

      Reviewer #2 (Public review):

      Summary:

      The paper attempts to elucidate how feral (wild) pigs cause distortion of the environment in over 54 countries of the world, particularly Australia.

      The paper displays proof that over $120 billion worth of facilities were destroyed annually in the United States of America.

      The authors have tried to infer that the findings of their work were important and possess a convincing strength of evidence.

      Strengths:

      (1) Clearly stating feral (wild) pigs as a problem in the environment.

      (2) Stating how 54 countries were affected by the feral pigs.

      (3) Mentioning how $120 billion was lost in the US, annually, as a result of the activities of the feral pigs.

      (4) Amplifying the fact that 14 species of animals were being driven into extinction by the feral pigs.

      (5) Feral pigs possessing zoonotic abilities.

      (6) Feral pigs acting as reservoirs for endemic diseases like brucellosis and leptospirosis.

      (7) Understanding disease patterns by the social dynamics of feral pig interactions.

      (8) The use of 146 GPS-monitored feral pigs to establish their social interaction among themselves.

      Weaknesses:

      (1) Unclear explanation of the association of either the female or male feral pigs with each other, seasonally.

      This will be better explained in the methods.

      (2) The "abstract paragraph" was not justified.

      We have justified the abstract paragraph as requested by the reviewer.

      (3) Typographical errors in the abstract.

      Typographical errors have been corrected in the Abstract.

      Reviewer #3 (Public review):

      Summary:

      The authors sought to understand social interactions both within and between groups of feral pigs, with the intent of applying their findings to models of disease transmission. The authors analyzed GPS tracking data from across various populations to determine patterns of contact that could support the transmission of a range of zoonotic and livestock diseases. The analysis then focused on the effects of sex, group dynamics, and seasonal changes on contact rates that could be used to base targeted disease control strategies that would prioritize the removal of adult males for reducing intergroup disease transmission.

      Strengths:

      It utilized GPS tracking data from 146 feral pigs over several years, effectively capturing seasonal and spatial variation in the social behaviors of interest. Using proximity-based social network analysis, this work provides a highly resolved snapshot of contact rates and interactions both within and between groups, substantially improving research in wildlife disease transmission. Results were highly useful and provided practical guidance for disease management, showing that control targeted at adult males could reduce intergroup disease transmission, hence providing an approach for the control of zoonotic and livestock diseases.

      Weaknesses:

      Despite their reliability, populations can be skewed by small sample sizes and limited generalizability due to specific environmental and demographic characteristics. Further validation is needed to account for additional environmental factors influencing social dynamics and contact rates.

      This is a really good point, and we thank the reviewer for pointing out this issue. We will discuss the potential biases due to sample size in our discussion. We agree that environmental factors need to be incorporated and tested for their influence on social dynamics, and this will be added to the discussion as we have plans to expand this research and conduct, the analysis to determine if environmental factors are influencing social dynamics.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Consider moving some key figures from supplementary materials to the main text to strengthen the presentation of results.

      We included a new figure to strengthen the presentation of results (Figure 3a-b), which shows the node level measures by sex and for direct and indirect networks.

      (2) Expand discussion of limitations, particularly addressing potential biases from varying sample sizes across populations.

      We added more detail and clarity about this potential bias into the limitation section within the discussion: “Different populations in our study had varying numbers of collared individuals, with some populations having only two individuals at certain times. This variability in sample size across populations is a limitation when interpreting the results. Small populations are often the result of a few individuals being trapped and collared, and this does not necessarily reflect the actual number of individuals in those groups.” Moreover, while reviewing the effect of the potential bias, we found that a General Linear Mixed Effect Model (Table 1) was not optimal for analysing the effect of sex on the network measures, and therefore this analysis has been done again using a non-parametric test (Wilcoxon rank-sum test)  for direct and indirect networks based on a 5 metres threshold (Table 1).

      (3) If available, include Australia-specific economic impact data in the introduction.

      We included the impact figures that are available for Australia (for FDM) in the introduction.

      (4) Clarify the rationale for chosen spatial and temporal thresholds for defining contacts.

      This has been added in the methodology: “Direct contact was defined when two individuals interacted either at 2, 5, or 350-metre buffers within a five-minute interval [36]. A previous study used 350 metres as a spatial threshold [16], while others use the approximate average body length of an individual [36]”

      (5) Consider adding a brief discussion of ethical considerations beyond basic animal ethics approval, addressing aspects like animal welfare during collaring and potential environmental impacts.

      Feral pigs are an invasive species in Australia, and managing their population is crucial to protecting native ecosystems. The trapping and collaring of these animals have been conducted following the stringent animal welfare requirements necessary to obtain animal ethics approval in Australia. However, it is important to consider the broader ethical implications. Animal welfare during collaring is a critical aspect and involves minimising stress and physical harm to the animals. The collars used are lightweight and properly fitted only on adults due to welfare issues collaring juveniles.

      (6) Add a statement about data availability/accessibility.

      The GPS data cannot be shared; however, the R codes will be deposited in GitHub (https://github.com/Tatianaproboste/Feral-Pig-Interactions) and the link has been added in the final version.

      (7) Expand on the implications of seasonal variation in contact rates for disease management strategies in the discussion.

      We have added this information in the discussion: “For example, controlling an outbreak during summer would potentially require more resources than an outbreak in other seasons due to the higher number of contact between individuals during summer.”

      Reviewer #2 (Recommendations for the authors):

      The typographical errors in the abstract to be corrected are:

      (1) Line 22: Remove the "are" before "threaten".

      This has been corrected.

      (2) Line 24: Replace the "to" before "extinction" with "into".

      This has been corrected.

      (3) Line 28: Rephrase the sentence.

      ‘Yet social dynamics are known to vary enormously from place to place, so knowledge generated for example in USA and Europe might not easily transfer to locations such as Australia.’

      (3) Line 29: Insert a "comma" after "Here".

      This has been corrected.

      (4) Lines 33 -34: Explain, clearly, the contact rates; is it between females to females or females to males?

      We have improved this phrase and now it reads: “…. with females demonstrating higher group cohesion (female-female) and males acting as crucial connectors between independent groups.”

      (5) Line 36: Make yourselves clear about what you mean by "targeting adult male".

      We believe “targeting adult males” is correct in this context.

      Reviewer #3 (Recommendations for the authors):

      (1) Line 22 and 44, I think are threaten "are" should be removed for better clarity.

      This has been corrected.

      (2) Line 71, the source and not "force" of infection.

      The force of infection is correct here.

      (3) Line 72, population "of".

      This has been corrected.

      (4) Under statistical analysis, the software version should be included.

      R has changed to multiple versions since we started this analysis.

      (5) Terminological consistency: as far as possible try to be consistent with the terms used in the text, such as using "contact rate" instead of "interaction rate" in order not to puzzle the readers.

      We have changed most of the “interactions” to “contact” instead as suggested.

      (6) Correct Typos: Identify typos and grammatical inconsistencies of any kind, especially in those complex sentences that may be hard to follow.

      The typos have been checked.

      (7) Under the methodology, briefly describe why specific thresholds were chosen and any limitations.

      We added the following into the method: “Direct contact was defined when two individuals interacted either at 2, 5, or 350-metre buffers within a five-minute interval [36]. A previous study used 350 metres as a spatial threshold [16], while others use the approximate average body length of an individual [36]”

      (8) The discussion should be strengthened by drawing clear links between the findings and actionable management strategies.

      We have strengthened the discussion by adding more specific actionable management strategies. For example, controlling an outbreak during summer would potentially require more resources than an outbreak in other seasons due to the higher number of contacts between individuals during summer.

      (9) Did you consider additional environmental factors, such as rainfall, food availability, or habitat features, to better understand how these influence seasonal variations in pig interactions and contact rates?

      This is something that we have in mind and will explore in future research. This has been partially explored but is based on how environmental factors and seasons affect the home range (Wilson et al 2023).

      (10) Figure Legends: Add more detailed descriptions in figure legends, especially for those figures showing network metrics or contact rates.

      More information has been added to the figure legends.

      (11) The paper includes too many figures, and thus, it is recommended to simplify or merge some figures where appropriate. In particular, this is recommended for those figures that plot more network measures across thresholds. Adding clear, summarized captions with interpretation on threshold and measure significance would be a great help in interpreting complicated visualizations.

      The figure that shows the comparison between global network measures, including average local transitivity, edge density, global transitivity, mean distance and number of edges for direct and indirect networks has been moved to supplementary material (Figure S3). We also included direct and indirect model-level measures by sex as in Figure 3 and improved the captions of the figures presented in the main document.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their comments and provide answers /clarifications and new data; There were 3 important recurrent points we already address here: 

      (a) The reviewers were concerned that the observed motor defects (measured by startle induced negative geotaxis- “SING”) where a reasonable behavioral measure of DAN function.

      Previously, Riemensperger et al., 2013 (PMID: 24239353) already linked synaptic loss of the dopaminergic PAM neurons to SING impairments. Furthermore, in a separate paper that we recently posted on BioRxiv, we show that the SING defects in PD mutants are rescued when the flies are fed L-DOPA (Kaempf et al 2024; BioRxiv). In this same paper we also show a very strong correlation between SING defects and defects in dopaminergic synaptic innervation of PAM DAN onto Mushroom body neurons. Both experiments suggest that the motor defects are the result of defects in dopamine release. Altogether, these data suggest that the combination of the SING assay and a quantification of the synaptic region of PAM DAN onto Mushroom body neurons is a suitable measure for DAN function.

      (b) The reviewers asked if the OPN dysfunction in young animals is connected to dopaminergic neuron (DAN) dysfunction in later life; 

      We have conducted additional experiments and have included the results (new Figure 6): Our young PD mutants (we included Aux<sup>R927G</sup>, Synj<sup>R258Q</sup> and LRRK2<sup>G2019S</sup>) show olfactory defects, but normal DAN function (measured by assessing the TH-labeled synaptic area onto the Mushroom body neurons and by SING). Aged PD mutants show both olfactory defects and DAN dysfunction. When we express the wildtype PD gene in (a.o.) OPN of PD mutants using the GH146-Gal4 (that does not drive expression in DAN) we are able to rescue the DAN defects (synaptic area and SING) that occur later in life. This indeed suggests there is a cell non-autonomous positive effect on DAN dysfunction that occurs at later stages in the life of our PD mutants (new Figure 6a). 

      In a set of independent experiments, we also fed one of our mutants (LRRK2<sup>G2019S</sup>) nicotine, activating Nicotinic acetylcholine receptors (that are also activated by the release of acetylcholine from cholinergic neurons such as OPN). While nicotine does not rescue the olfactory preference defect, the OPN synapse morphology defect or the OPN-associated defects in Ca<sup>2+</sup>-imaging in LRRK2<sup>G2019S</sup> mutants (Figure 6b), it does rescue the DAN-associated defects, including SING, synapse loss and defects in Ca<sup>2+</sup>-imaging (Figure 6c).

      Finally, we generated human induced dopaminergic neurons derived from iPSC with a LRRK2<sup>G2019S</sup> mutation and incubated these neurons with nicotine. Again, this induced a rescue of a LRRK2-mutant-induced defect in neuronal activity measured by Ca<sup>2+</sup>-imaging. This is specific to nicotine since the rescue was absent when cells were also incubated with mecamylamine, a non-competitive antagonist of nicotinic acetylcholine receptors, trumping the effects of nicotine (Figure 6d-e").

      (c) The reviewers indicated that the GH146 Gal 4 driver is expressed in other cells than OPN and thus, they noted that the defects we observe may not only be the result of OPN dysfunction. 

      It is correct that GH146-dependent Gal expression includes OPNs (that are cholinergic) and one pair of inhibitory APL neurons (that are GABAergic) (Li et al., 2017 (PMID: 29149607), Lui et al., 2009 (PMID: 19043409)). We have adapted the text to explicitly state this. There are only 2 APL per fly brain and our single cell sequencing experiment does not have the resolution to allow us to test if these neurons had a significant number of DEG. However, as indicated above (in (b)), we are able to rescue DAN dysfunction by mimicking cholinergic output (application of nicotine). These data do not exclude that APL-neuron problems contribute to the defects we observe in our PD mutants, but they do suggest that cholinergic output is critical to maintain normal DAN function.

      Public Reviews:  

      Reviewer #1 (Public Review):  

      This is a fantastic, comprehensive, timely, and landmark pan-species work that demonstrates the convergence of multiple familial PD mutations onto a synaptic program. It is extremely well written and I have only a few comments that do not require additional data collection. 

      Thank you for this enthusiastic endorsement.

      Major Comments:  

      neurons and the olfactory system are acutely impacted by these PD mutations. However, I wonder if this is the case:  

      (1) In the functional experiments performing calcium imaging on projection neurons I could not find a count of cell bodies across conditions. Since the loss of OPNs could explain the reduced calcium signal, this is a critical control to perform. A differential abundance test on the single-cell data would also suffice here and be easy for the authors to perform with their existing data. 

      This is indeed an important number, and we had included this in the Supplemental figure 2a.

      Also, the number of DAN and Visual projection neurons were not significantly different between the genotypes (Supplemental Figure 2a in the manuscript). 

      (2) One of the authors' conclusions is that cholinergic

      a. Most Drosophila excitatory neurons are cholinergic

      and only a subpopulation appear to be dysregulated by these mutations. The authors point out that visual neurons also have many DEGs, couldn't the visual system also be dysregulated in these flies? Is there something special about these cholinergic neurons versus other cholinergic neurons in the fly brain? I wonder if they can leverage their nice dataset to say something about vulnerability. 

      Yes, the reviewer is right, and we have changed our wording to be more specific. The reviewer also noted correctly that neurons in the visual system rank high in terms of number of DEGs, but we did not conduct elaborate experiments to assess if these visual system neurons are functional. Of note, several of our mutants show (subtle) electroretinogram defects, that are a measure of visual system integrity, but further work is needed to determine the origin of these defects. 

      The question about the nature of the underlying vulnerability pathways is interesting. In preliminary work we have selected a number of DEGs common to vulnerable cells in several PD mutants, and conducted a screen where we manipulated the expression of these DEGs and looked for rescue of the olfactory preference defects in our PD mutants. The strongest genetic interaction was with genes encoding proteins involved in proteostasis (Atg8/LC3, Lamp1 and Hsc70-4) (Reviewer Figure 3). While interesting, these results require further work to understand the underlying molecular mechanisms. We present these preliminary data here but have not included them in the main manuscript. 

      b. As far as I can tell, the cross-species analysis of DEGs (Figure 3) is agnostic to neuronal cell type, although the conclusion seems to suggest only cholinergic neurons were contrasted. Is this correct? Could you please clarify this in the text as it's an important detail. If not, Have the authors tried comparing only cholinergic neuron DEGs across species? That would lend strength to their specificity argument. The results for the NBM are impressive. Could the authors add more detail to the main text here about other regions to the main text? 

      The reviewer is correct that we compiled the DEG of all affected cells, the majority of which are cholinergic neurons. 

      For the human data we focused on the NBM samples, because it contained the highest fraction of cholinergic neurons (as compared to the other 2 regions), but even so, it was not possible to analyze the cholinergic neurons alone because the fraction of cholinergic neurons in the human material was too low to be statistically analyzed independently. Note that both wildtype and PD samples contained a low number of cholinergic neurons (i.e. the DEG differences we detected were not the result of sequencing different types of cells - see also Supplemental Figure 3b and d). We have indicated this more clearly in the text.

      c. Uniquely within the human data, are cholinergic neurons more dysregulated than others? I understand this is not an early timepoint but would still be useful to discuss. 

      As indicated in the previous point, unfortunately the fraction of cholinergic neurons in the human material was low and we were not able to analyze these cells on their own. 

      Author response image 1.

      Upregulation of protein homeostasis rescues hyposmia across familial models of PD. Results of a behavioral screen for cell-specific rescue of olfactory preference defects of young PD fly models using up and downregulation of deregulated genes in affected cell types. Genes implicated in the indicated pathways are over expressed or knocked down using GH146-Gal4 (OPN>) and UAS-constructs (over expression or RNAi) . UAS-only (-) and OPN>UAS (+) were scored in parallel and are compared to each other. n.d. not determined; Bars represent mean ± s.e.m.; grey zone indicates the variance of controls; n≥5 independent experiments per genotype, with ~50 flies each; red bars: p<0.05 in ANOVA and Bonferroni-corrected comparison to UAS-only control.

      d. In the discussion, the authors say that olfactory neurons are uniquely poised to be dysregulated as they are large and have high activity. Is this really true compared to other circuits? I didn't find the references convincing and I am not sure this has been borne out in electron microscopy reconstructions for anatomy.  

      We agree and have toned down this statement.

      Reviewer #2 (Public Review):  

      Summary:  

      Pech et al selected 5 Parkinson's disease-causing genes, and generated multiple

      Drosophila lines by replacing the Drosophila lrrk, rab39, auxilin (aux), synaptojanin

      (synj), and Pink1 genes with wild-type and pathogenic mutant human or Drosophila cDNA sequences. First, the authors performed a panel of assays to characterize the phenotypes of the models mentioned above. Next, by using single-cell RNA-seq and comparing fly data with human postmortem tissue data, the authors identified multiple cell clusters being commonly dysregulated in these models, highlighting the olfactory projection neurons. Next, by using selective expression of Ca<sup>2+</sup>-sensor GCaMP3 in the OPN, the authors confirmed the synaptic impairment in these models, which was further strengthened by olfactory performance defects.  

      Strengths:  

      The authors overall investigated the functionality of PD-related mutations at endogenous levels and found a very interesting shared pathway through singlecell analysis, more importantly, they performed nice follow-up work using multiple assays.  

      Weaknesses:  

      While the authors state this is a new collection of five familial PD knock-in models, the Aux<sup>R927G</sup> model has been published and carefully characterized in Jacquemyn et al., 2023. ERG has been performed for Aux R927G in Jacquemyn et al., 2023, but the findings are different from what's shown in Figure 1b and Supplementary Figure 1d, which the authors should try to explain. 

      We should have explained this better: the ERG assay in Jacquemyn et al., and here, in Pech et al., are different. While the ERGs in our previous publication were recorded under normal endogenous conditions, the flies in our current study were exposed to constant light for 7 days. This is often done to accelerate the degeneration phenotype. We have now indicated this in the text (and also refer to the different experimental set up compared to Jacquemyn et al).

      Moreover, according to the authors, the hPINK1control was the expression of human PINK1 with UAS-hPINK1 and nsyb-Gal4 due to technical obstacles. Having PINK1 WT being an overexpression model, makes it difficult to explain PINK1 mutant phenotypes. It will be strengthened if the authors use UAS-hPINK1 and nsyb-Gal4 (or maybe ubiquitous Gal4) to rescue hPink1L347P and hPink1P399L phenotypes.

      The UAS-hPink1 was originally created by the Lu lab (Yang et al., 2003, PMID: 12670421) and has been amply used before in Pink1 loss-of-function backgrounds (e.g. in Yang et al., 2006, PMID: 16818890). In our work, the control we refer to was UAS-hPink1 expression (driven by nSyb-gal4) in a Pink1 knock-out background. For unknown reasons we were unable to replace the fly Pink1 with a human pink1 cDNA, we explained this in the methods section and added a remark in the new manuscript.

      In addition, although the authors picked these models targeting different biology/ pathways, however, Aux and Synj both act in related steps of Clathrin-mediated endocytosis, with LRRK2 being their accessory regulatory proteins. Therefore, is the data set more favorable in identifying synaptic-related defects? 

      We picked these particular mutants, as they were the first we created in the context of a much larger collection of “PD flies” (see also Kaempf et al 2024, BioRxiv). We have made adaptations to the text to tone down the statement on the broad selection of mutants. 

      GH146-GAL4+ PNs are derived from three neuroblast lineages, producing both cholinergic and GABAergic inhibitory PNs (Li et al, 2017). Therefore, OPN neurons have more than "cholinergic projection neurons". How do we know from singlecell data that cholinergic neurons were more vulnerable across 5 models? 

      The reviewer is correct that GH146 drives expression in other cells than OPN and we now clearly state this in the text. We do present additional arguments that substantiate our conclusion that cholinergic neurons are affected: (1) our single cell sequencing identifies the most DEGs in cholinergic neurons. (2) nicotine (a compound activating cholinergic receptors) rescues dopamine-related problems in old PD-mutant flies. (3) Likewise, nicotine also alleviates problems we observed in LRRK2 mutant human induced dopaminergic neurons and this is blocked by mecamylamine, a non-competitive antagonist of nicotinic acetylcholine receptors.

      In Figure 1b, the authors assumed that locomotion defects were caused by dopaminergic neuron dysfunction. However, to better support it, the author should perform rescue experiments using dopaminergic neuron-specific Gal4 drivers. Otherwise, the authors may consider staining DA neurons and performing cell counting. Furthermore, the authors stated in the discussion, that "We now place cholinergic failure firmly ahead of dopaminergic system failure in flies", which feels rushed and insufficient to draw such a conclusion, especially given no experimental evidence was provided, particularly related to DA neuron dysfunction, in this manuscript. 

      Previously, Riemensperger et al., 2013 (PMID: 24239353) already linked synaptic loss of the dopaminergic PAM neurons to locomotion impairments (measured by SING). Furthermore, in a separate paper we show that the motor defects (SING) observed in PD mutants are rescued when the flies are fed L-DOPA, but not D-DOPA (Kaempf et al 2024; BioRxiv). In this same paper, we also show a significant correlation between SING defects and defects in dopaminergic synaptic innervation of PAM DAN onto Mushroom body neurons. We have referred to both articles in the revised manuscript.

      The statement on cholinergic failure ahead of dopaminergic failure was made in the context of the sequence of events: young flies did not show DAN defects, but they did display olfactory defects. The statement was indeed not meant to imply causality. However, we have now conducted new experiments where we express wild type PD genes using GH146-Gal4 (that does not express in DAN) in the PD mutants and assess dopaminergic-relevant phenotypes later in life (see also new Figure 6 in the manuscript). This shows that GH146Gal4-specific rescue is sufficient to alleviate the DAN-dependent SING defects in old flies. Likewise, as indicated above, application of nicotine is also sufficient to rescue the DAN-associated defects (in PD mutant flies and human induced mutant dopaminergic neurons).  

      It is interesting to see that different familial PD mutations converge onto synapses. The authors have suggested that different mechanisms may be involved directly through regulating synaptic functions, or indirectly through mitochondria or transport. It will be improved if the authors extend their analysis on Figure 3, and better utilize their single-cell data to dissect the mechanisms. For example, for all the candidates listed in Figure 3C, are they all altered in the same direction across 5 models?  

      This is indeed the case: the criteria for "commonly deregulated" included that the DEGs are changed in the same direction across several mutants. We ranked genes according to their mean gene expression across the mutants as compared it to the wildtype control: i.e. only if the DEGs are all up- or all down-regulated they end up on the top or bottom of our list. We added a remark in the revised manuscript. In preliminary work we also selected a number of the DEGs and conducted a screen where we manipulated the expression of these genes looking for rescue of the olfactory preference defects in our PD mutants. The strongest genetic interaction was with genes encoding proteins involved in proteostasis (Atg8/LC3, Lamp1 and Hsc70-4; and we also show a genetic interaction between EndoA and Lrrk in this work and in Matta et al., 2012) (Author response image 1 above). While interesting, these results require further work to understand the underlying molecular mechanisms. We present these preliminary data here, but have not included them in the main manuscript. 

      While this approach is carefully performed, the authors should state in the discussions the strengths and the caveats of the current strategy. For example, what kind of knowledge have we gained by introducing these mutations at an endogenous locus? Are there any caveats of having scRNAseq at day 5 only but being compared with postmortem human disease tissue?  

      We have included a “strengths and caveats section” in the discussion addressing these points.

      Reviewer #3 (Public Review):  

      Summary:  

      This study investigates the cellular and molecular events leading to hyposmia, an early dysfunction in Parkinson's disease (PD), which develops up to 10 years prior to motor symptoms. The authors use five Drosophila knock-in models of familial PD genes (LRRK2, RAB39B, PINK1, DNAJC6 (Aux), and SYNJ1 (Synj)), three expressing human genes and two Drosophila genes with equivalent mutations.  

      The authors carry out single-cell RNA sequencing of young fly brains and singlenucleus RNA sequencing of human brain samples. The authors found that cholinergic olfactory projection neurons (OPN) were consistently affected across the fly models, showing synaptic dysfunction before the onset of motor deficits, known to be associated with dopaminergic neuron (DAN) dysfunction.  

      Single-cell RNA sequencing revealed significant transcriptional deregulation of synaptic genes in OPNs across all five fly PD models. This synaptic dysfunction was confirmed by impaired calcium signalling and morphological changes in synaptic OPN terminals. Furthermore, these young PD flies exhibited olfactory behavioural deficits that were rescued by selective expression of wild-type genes in OPNs.  

      Single-nucleus RNA sequencing of post-mortem brain samples from PD patients with LRRK2 risk mutations revealed similar synaptic gene deregulation in cholinergic neurons, particularly in the nucleus basalis of Meynert (NBM). Gene ontology analysis highlighted enrichment for processes related to presynaptic function, protein homeostasis, RNA regulation, and mitochondrial function.  

      This study provides compelling evidence for the early and primary involvement of cholinergic dysfunction in PD pathogenesis, preceding the canonical DAN degeneration. The convergence of familial PD mutations on synaptic dysfunction in cholinergic projection neurons suggests a common mechanism contributing to early non-motor symptoms like hyposmia. The authors also emphasise the potential of targeting cholinergic neurons for early diagnosis and intervention in PD.  

      Strengths:  

      This study presents a novel approach, combining multiple mutants to identify salient disease mechanisms. The quality of the data and analysis is of a high standard, providing compelling evidence for the role of OPN neurons in olfactory dysfunction in PD. The comprehensive single-cell RNA sequencing data from both flies and humans is a valuable resource for the research community. The identification of consistent impairments in cholinergic olfactory neurons, at early disease stages, is a powerful finding that highlights the convergent nature of PD progression. The comparison between fly models and human patients' brains provides strong evidence of the conservation of molecular mechanisms of disease, which can be built upon in further studies using flies to prove causal relationships between the defects described here and neurodegeneration.  

      The identification of specific neurons involved in olfactory dysfunction opens up potential avenues for diagnostic and therapeutic interventions.  

      Weaknesses:  

      The causal relationship between early olfactory dysfunction and later motor symptoms in PD remains unclear. It is also uncertain whether this early defect contributes to neurodegeneration or is simply a reflection of the sensitivity of olfactory neurons to cellular impairments. The study does not investigate whether the observed early olfactory impairment in flies leads to later DAN deficits. Additionally, the single-cell RNA sequencing analysis reveals several affected neuronal populations that are not further explored. The main weakness of the paper is the lack of conclusive evidence linking early olfactory dysfunction to later disease progression.

      We agree that this is an interesting avenue to pursue and as indicated above in Figure 6 and in the reworked manuscript, we have now included data that strengthens the connection between early OPN defects and the later DAN dependent problems. Additional future work will be needed to elucidate the mechanisms of this cell-non autonomous effect. 

      The rationale behind the selection of specific mutants and neuronal populations for further analysis could be better qualified. 

      We have added further explanation in the reworked text.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):  

      Minor Comments:  

      (1) Questions about the sequencing methods and analysis approaches. From reading the methods and main text, I was confused about aspects of the Drosophila single-cell profiling. Firstly, did the authors multiplex their fly samples? 

      No, we did not. Genotypes were separately prepared and sequenced, but they were all processed in parallel to avoid batch effects. 

      Secondly, it seems like there are two rounds of dataset integration performed, Harmony and Seurat's CCA-based method. This seems unorthodox. Could the authors comment on why they perform two integrations? 

      Thanks for pointing this out, this was a mistake in the methods section (copied from a much older version of the manuscript). In this manuscript, we only used harmony for dataset integration and removed the methods on Seurat-CCA. 

      Finally, for all dataset integrations please state in the main text how datasets were integrated (by age, genotype, etc). 

      Datasets were integrated by sample id, corresponding to individual libraries.

      (2) The authors focus on OPNs with a really nice set of experiments. I noticed however that Kenyon cells were also dysregulated. What about Olfactory sensory neurons? Could the authors provide comments on this? 

      Olfactory sensory neurons are located in the antennae of the fly brain and were not captured by our analysis. However, the GH146-Gal4-specific rescue experiments indicate these sensory neurons are likely not severely functionally impaired. Kenyon cells are an interesting affected cell type to look at in future experiments, as they are directly connected to DANs.

      (3) There are several citations of Jenett et al 2012 that seem wrong (related to single-cell datasets).

      We are sorry for this and have corrected this in the text.  

      Reviewer #2 (Recommendations For The Authors):  

      (1) In the key resources table, a line called CG5010k.o. (chchd2k.o.) was mentioned, but was not used in the paper. The authors should remove it. 

      Sorry, this was from a previous older version of the manuscript. We fixed this.

      (2) Why did the authors use human CDS for LRRK2, Rab39B, and PINK1, but fly CDS for Aux and Synj1? Is it based on the conservation of amino acid residues? Although the authors cited a review (Kalia & Lang, 2015) to justify the selection of the mutations, for the interest of a broad audience, it is recommended that the authors expand their introduction for the rationale of their selection, including the pathogenicity of each selected mutation, original human genetics evidence, conservation between fly and human. 

      (a) We used Drosophila cDNA for rescue experiments with aux and synj since knockin of the human homologues at the locus of these genes did not rescue its loss-offunction (lethality). 

      (b) We expanded the introduction to provide further explanation on the selection of our mutants we analyzed in this work. We picked these particular mutants, as they were the first we created in the context of a much larger collection of “PD flies” (see also Kaempf et al 2024, BioRxiv). We have made adaptations to the text to tone down the statement on the broad selection of mutants. 

      (3) Supplemental Figure 1a, is mRNA level normalized to an internal control? If not, it is not appropriate to compare the results directly from two primer sets, since each primer set may have different amplification efficiency. 

      We are sorry for the lack of information. Indeed, mRNA levels were determined using the Δ-Δ-CT method, where Ct values were first normalized to the housekeeping gene Rp49, and next expressed as a percent of endogenous Drosophila gene expression. We expanded the methods section and now also enlist the primers for Rp49 along with the other qPCR primers in Supplemental File 1.

      (4) For Figure 2, it may be helpful to have a supplemental table or figure showcasing the clusters with significant changes (based on cell number-adjusted DEGs) for each model, i.e., what are those black cell clusters in Figure 2? "Thus, cellular identity and cellular composition are preserved in young PD fly models." In Figure S2A, the authors only show cell composition percentages for 3 cell clusters, are the bars 95% standard error? 

      The error bars in Supplemental Figure 2a represent the 95 % CI. We have included a new supplemental table with the number of cells per cell cluster for each mutant (Supplemental File 3).

      What about the remaining 183 cell clusters? Are there any KI-model cell clusters that are statistically different than controls? What about the annotated cell types (e.g., the 81 with cell identities)? Please consider at least providing or pointing to a table to state how many have significant differences, or if there are truly none. 

      As mentioned above, we have included a new supplemental table with the number of cells per cell cluster for each mutant (Supplemental File 3).

      (5) What are the rows in the sunburst plot in Figure 3a? Please be more descriptive in the figure legend or label the figure. 

      We have expanded on this in the figure legend and now also include a summary of the SynGO analysis in Supplemental File 7. In Figure 3a, a summary sunburst plot is presented, reflecting the GO terms (inner rings, indicated in a) with their subdivided levels (the complete list is provided in Supplemental File 7). In Figure 3a’ and a” the DEG data acquired from the different datasets (human vs fly) are applied to the sunburst plot where rings are color-coded according to enrichment Q-value.

      (6) In Table S4, which clusters (in the table) have normalized residuals that are outside of the 95% confidence interval of the regression model displayed in Figure S2e? They use this analysis to adjust for cell number bias and point out the "most significant cell clusters" affected in each model. This may be helpful for readers who want to grab a full list of responsive clusters. 

      We have included this information in Supplemental File 5 (Tab “Cell types outside of CIs”) in the supplemental data of the manuscript.

      (7) The human samples used all have different LRRK2 variants: for the crossspecies comparisons, do Lrrk flies have greater similarity to the human PD cases compared to the other fly models?

      No, comparing the vulnerable gene signatures from each of the fly mutants to the DEGs from the human samples does not show any greater similarity between the LRRK mutants compared to the other mutants.

      Reviewer #3 (Recommendations For The Authors):  

      Clarifications required:  

      Some of the mutations used are not common PD-associated genes, the authors should explain the rationale behind using these particular mutants, and not using well-established fly models of PD (like for example GBA flies) or SNCA overexpression.

      We opted to use knock-ins of mutations that are causal to Parkinsonism. Given flies do not express an alpha-synuclein homologue we were not able to add this ‘as such’ to our collection. Future work can indeed also include expression models or risk factor models (like GBA). As also requested by another reviewer, we did add further rationale and explanation to the genes we chose to analyze in this work.

      Why starvation rather than lifespan for PD models? For the lifespan data shown there are no error bars, if the stats test is a log-rank or Cox proportional hazards (usually used in survival analysis, this should be stated), it would also be good to have the survival plots for all the survival during starvation, not just PINK1. 

      While starvation assays can provide valuable insights into acute metabolic and physiological stress responses, we acknowledge that lifespan is a critical parameter and would provide a more comprehensive understanding of the PD models in our study. Based on this consideration and the reviewer’s feedback we have removed the starvation data from the manuscript. Unfortunately, we did not perform lifespan experiments, which is why these data were not included in the manuscript. However, based on our observations (though not detailed analysis), all genotypes tested—except for the PINK1 mutants—appeared to have a normal lifespan. For PINK1 mutants, most flies died by 25 days of age. Therefore, we conducted our assays using 15-day-old PINK1 mutant flies.

      Do the fly models used have different lifespans, and how close to death was the SING assay performed? Different mutations show different effects, most phenotypes are really mild (hRab39BG192R has no phenotype), and PINK1 has the strongest, are these simply reflections of how strong the model is?  

      The ages of flies we analyzed are indicated in the legend. As mentioned before, all but PINK1 mutants- had a normal life span: i.e. we did not detect abnormal low number of flies or premature death at 50 days of age, except for the PINK1 mutants tested in this manuscript where most flies died by 25 days of age. Therefore, we conducted our assays using 15-day-old PINK1 mutant flies.

      Rab39G192R has no phenotype in the tests presented, suggesting no degeneration, why use RabG192R for scRNA seq? Seems an odd choice, the authors should explain. 

      Single-cell sequencing was initiated before the full phenotypic characterization of all mutants was completed. Although basic characterization of the Rab39<sup>G192R</sup> mutant PD flies revealed either no significant phenotypes or only mild effects in the assays performed (Figure 1), the sequencing data provided additional insights into potential cellular and molecular alterations. Furthermore, all PD-mutant knock-ins, including Rab39<sup>G192R</sup> mutant PD flies, show dysfunctional synaptic terminals of their OPN neurons as they had significantly weaker Ca<sup>2+</sup>-responses, even though their synaptic area was increased (Figure 4 g-h). Furthermore, all mutants also had olfactory behavior defects (Figure 5 a). 

      When the authors state that “For example, in the NBM, an area associated with PD (Arendt et al., 1983), 20% of the DEG that has an orthologous gene in the fly are also found among the most deregulated genes across PD fly models" a test should be performed to confirm this is a significant overlap (such as a hypergeometric test). 

      We have performed this test, of the 2486 significantly differential human genes, 1149 have a fly orthologue, and of these, 28.46 % overlap with the deregulated fly genes (5 % top and bottom gene as shown in Supplemental Table 7). Performing a hypergeometric test confirms that this overlap is significant, with a p-value of 9.06e<sup>76</sup>. We have included this in the text.

      The authors speak of deregulation when speaking of the overlap between human and fly DE genes, but do the over-expressed genes in flies overlap with overexpressed genes in humans, or is the direction of transcription deregulation not concordant? If it is mostly not concordant, can the authors please comment as to why they might think that is the case? 

      In our fly experiments, we identified DEG in affected cell types and then defined common DEG by looking at the average change across the fly mutants. Genes that show a consistent change (all or mostly up, or all or mostly down) in the different mutants will end at the top of our list while genes that are up in some mutants and downregulated in others will average out and not end up in our commonly deregulated gene list. For comparison to the human data, we only looked for the presence of the human homologue, but did not assess if the change occurred in the same direction. More work will be needed to define the most relevant changes, but in a mini-screen we did select a number of DEG present in fly and human datasets from different functional categories and tested if they genetically interact with our PD mutants. As shown in Reviewer Figure 3, we find that modulating proteostasis pathway-encoding genes rescue the olfactory preference defect across many PD mutants. 

      Can the authors explain why only the NMB region was used for comparison with the fly data?  

      We used the NMB because this region has the highest number of cholinergic neurons to compare the deregulation in those neurons to the deregulation in the cholinergic OPN of mutant PD flies.

      In Figure 4, can the genotypes please be stated in full and why is the hPINK1 fly giving no detectable signal? 

      Despite several attempts, we failed to knock-in wild type hPink1 in the fly pink1 locus. Therefore, the hPink1 control used throughout the manuscript was the nSybGal4>UAS-hPink1 in Pink1 knock-out background, except for Figure 4. Particularly, for experiments in this figure, we could not use UAS-hPink1 with nSyb-Gal4, since we needed OPN-specific expression of Gal4 to drive UAS-GCamP expression.

      Therefore, this was labeled as “not determined” (“n.d.”), as indicated in the figure and the legend. We explained this better in the methods section, added a remark in the new manuscript and expanded the legend of Figure 4.

      The paper states that" These findings imply that factors affecting the function of cholinergic neurons might, by the absence of insufficient innervation, lead to DAN problems and degeneration, warranting further exploration of the underlying molecular mechanisms", this should be less strong, the paper never looks at DAN, only at OPN neurons. Fly neurons are mostly cholinergic, and human neurons are mostly glutamatergic, so jumping from one system to the other might not be as straightforward, the authors should comment on this. 

      We now included a new exciting experiment where we assessed DAN function in aged PD mutants where the wildtype gene was expressed in OPN using GH146-Gal4. We find this manipulation rescued DAN defects (measured by SING) in older flies. We further corroborated our observation by “replacing” cholinergic innervation with nicotine feeding in PD mutants. Also, this rescues the SING defect as well as the defects in neuronal activity in PAM DAN (based on live synaptic calcium imaging). Finally, we also show that incubating LRRK2<sup>G2019S</sup> mutant human induced dopaminergic neurons with nicotine is sufficient to rescue functional defects in these neurons (measured using calcium imaging). We included this data in the new manuscript and show them also in Figure 6 above (new Figure 6 in the revised manuscript). 

      Experiments that would improve the manuscript:  

      Does rescue of OPN function also rescue later progressive symptoms (geotaxis response)?  

      It does, as indicated in the previous point and shown in Figure 6.

      Do the fly PD models used show DAN degeneration? This could be assessed by stains with anti-TH stains. 

      We quantified DAN cell bodies using anti-TH, but see very little or no loss. There is, however, loss of synaptic innervation of the PAM onto the mushroom bodies. We included the data in a new Figure 6 (see also Figure 6). Furthermore, we have quantified this across the genetic space of familial Parkinsonism in Kaempf et al., 2024, BioRxiv. Note that this phenotype is also rescued by expressing wildtype CDS in their OPN using GH146-Gal4.

      Minor issues: 

      The final sentence on page 5 is repetitive with the introduction. 

      Indeed, we removed the redundant sentence.

      First line of the new section on page 6, the authors probably mean cholinergic olfactory projection neurons, not just cholinergic neurons. 

      Yes, and corrected.

      At the top of page 7 the authors state: "Additionally, we also found enrichment of genes involved in RNA regulation and mitochondrial function that are also important for the functioning of synaptic terminals", where is the data showing this? The authors should point to the supplemental file showing this.  

      We now included a reference to Supplemental File 7 that includes a summary of those data. Additionally, we also included references to back this claim.

      Just before the discussion, Rab39BG193R should be Rab39BG192R.  

      Sorry for this, it is now corrected.

      Stating "fifth row" in Fig 5c and d is confusing, can the figure be labelled more clearly?  

      We modified the figure (including extra marks and colors) and expanded the legend and the main text to differentiate better between expression of the rescues in OPN versus T1 neurons revealing that only expression in OPN neurons rescues the olfactory defects while expression in T1 neurons does not.

      In the methods, the authors describe clustering done both in Scanpy and Seurant, why were both run? Which clustering was used for further analysis?

      We only used Scanpy with Harmony and removed the methods on Seurat-CCA. Thanks for pointing this out, this was a mistake in the methods section (copied from a previous version of the manuscript).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Vision is a highly active process. Humans move their eyes 3-4 times per second to sample information with high visual acuity from our environment, and where eye movements are directed is critical to our understanding of active vision. Here, the authors propose that the cost of making a saccade contributes critically to saccade selection (i.e., whether and where to move the eyes). The authors build on their own recent work that the effort (as measured by pupil size) that comes with planning and generating an eye movement varies with saccade direction. To do this, the authors first measured pupil size for different saccade directions for each participant. They then correlated the variations in pupil size obtained in the mapping task with the saccade decision in a free-choice task. The authors observed a striking correlation: pupil size in the mapping task predicted the decision of where to move the eyes in the free choice task. In this study, the authors provide a number of additional insightful analyses (e.g., based on saccade curvature, and saccade latency) and experiments that further support their claim that the decision to move the eyes is influenced by the effort to move the eyes in a particular direction. One experiment showed that the same influence of assumed saccade costs on saccade selection is observed during visual search in natural scenes. Moreover, increasing the cognitive load by adding an auditory counting task reduced the number of saccades, and in particular reduced the costly saccades. In sum, these experiments form a nice package that convincingly establishes the association between pupil size and saccade selection.

      We thank the reviewer for highlighting the novelty and cogency of our findings.

      In my opinion, the causal structure underlying the observed results is not so clear. While the relationship between pupil size and saccade selection is compelling, it is not clear that saccade-related effort (i.e., the cost of a saccade) really drives saccade selection. Given the correlational nature of this relationship, there are other alternatives that could explain the finding. For example, saccade latency and the variance in landing positions also vary across saccade directions. This can be interpreted for instance that there are variations in oculomotor noise across saccade directions, and maybe the oculomotor system seeks to minimize that noise in a free-choice task. In fact, given such a correlational result, many other alternative mechanisms are possible. While I think the authors' approach of systematically exploring what we can learn about saccade selection using pupil size is interesting, it would be important to know what exactly pupil size can add that was not previously known by simply analyzing saccade latency. For example, saccade latency anisotropies across saccade directions are well known, and the authors also show here that saccade costs are related to saccade latency. An important question would be to compare how pupil size and saccade latency uniquely contribute to saccade selection. That is, the authors could apply the exact same logic to their analysis by first determining how saccade latencies (or variations in saccade landing positions; see Greenwood et al., 2017 PNAS) vary across saccade directions and how this saccade latency map explains saccade selection in subsequent tasks. Is it more advantageous to use one or the other saccade metric, and how well does a saccade latency map correlate with a pupil size map?

      We thank the reviewer for the detailed comment. 1) The reviewer first points out the correlational nature of many of our results. Thereafter, 2), the reviewer asks whether saccade latencies and landing precision also predict saccade selection, and could be these potential predictors be considered alternative explanations to the idea of effort driving saccade selection? Moreover, what can pupil size add to what can be learned from saccade latency?

      In brief, although we report a combination of correlational and causal findings, we do not know of a more parsimonious explanation for our findings than “effort drives saccade selection”. Moreover, we demonstrate that oculomotor noise cannot be construed as an alternative explanation for our findings.

      (1) Correlational nature of many findings.

      We acknowledge that many of our findings are predominantly correlational in nature. In our first tasks, we correlated pupil size during saccade planning to saccade preferences in a subsequent task. Although the link between across tasks was correlational, the observed relationship clearly followed our previously specified directed hypothesis. Moreover, experiments 1 and 2 of the visual search data replicated and extended this relationship. We also directly manipulated cognitive demand in the second visual search experiment. In line with the hypothesis that effort affects saccade selection, participants executed less saccades overall when performing a (primary) auditory dual task, and even cut the costly saccades most – which actually constitutes causal evidence for our hypothesis. A minimal oculomotor noise account would not directly predict a reduction in saccade rate under higher cognitive demand. To summarize, we have a combination of correlational and causal findings, although mediators cannot be ruled out fully for the latter. That said, we do not know of a more fitting and parsimonious explanation for our findings than effort predicting saccade selection (see following points for saccade latencies). We now address causality in the discussion for transparency and point more explicitly to the second visual search experiment for causal evidence.

      “We report a combination of correlational and causal findings. Despite the correlational nature of some of our results, they consistently support the hypothesis that saccade costs predicts saccade selection [which we predicted previously, 33]. Causal evidence was provided by the dual-task experiment as saccade frequencies - and especially costly saccades were reduced under additional cognitive demand. Only a cost account predicts 1) a link between pupil size and saccade preferences, 2) a cardinal saccade bias, 3) reduced saccade frequency under additional cognitive demand, and 4) disproportional cutting of especially those directions associated with more pupil dilation. Together, our findings converge upon the conclusion that effort drives saccade selection.”

      (2) Do anisotropies in saccade latencies constitute an alternative explanation?

      First of all, we would like to to first stress that differences in saccade latencies are indeed thought to reflect oculomotor effort (Shadmehr et al., 2019; TINS). For example, saccades with larger amplitudes and saccades where distractors need to be ignored are associated with longer latencies. Therefore, even if saccade latencies would predict saccade selection, this would not contrast the idea that effort drives saccade selection. Instead, this would provide convergent evidence for our main novel conclusion: effort drives saccade selection. There are several reasons why pupil size can be used as a more general marker of effort (see responses to R2), but ultimately, our conclusions do not hinge on the employed measure of effort per se. As stressed above in 1), we see no equally parsimonious explanation besides the cost account. Moreover, we predicted this relationship in our previous publication before running the currently reported experiments and analyses (Koevoet et al., 2023). That said, we are open to discuss further alternative options and would be looking forward to test these accounts in future work against each other – we are welcoming the reviewers’ (but also the reader’s) suggestions.

      We now discuss this in the manuscript as follows:

      “We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost.

      Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

      Second, we followed the reviewer’s recommendation in testing whether other oculomotor metrics would predict saccade selection. To this end, we conducted a linear regression across directions. We calculated pupil size, saccade latencies, landing precision and peak velocities maps from the saccade planning task. We then used AICbased backward model selection to determine the ‘best’ model model to determine which factor would predict saccade selection best. The best model included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences ~ pupil size + saccade latency + landing precision). Pupil size (b \=-42.853, t \= 4.791, p < .001) and saccade latency (b \=-.377, t \= 2.106, p \= .043; see Author response image 1) predicted saccade preferences significantly. In contrast, landing precision did not reach significance (b \= 23.631, t \= 1.675, p \= .104). This analysis shows that although saccade latency also predicts saccade preferences, pupil size remains a robust predictor of saccade selection. These findings demonstrate that minimizing oculomotor noise cannot fully explain the pattern of results.

      Author response image 1.

      The relationship between saccade latency (from the saccade planning task) and saccade preferences averaged across participants. Individual points reflect directions and shading represents bootstrapped 95% confidence intervals.

      We have added this argument into the manuscript, and discuss the analysis in the discussion. Details of the analysis have been added to the Supporting Information for transparency and further detail.

      “A control analysis ruled out that the correlation between pupil size and saccade preferences was driven by other oculomotor metrics such as saccade latency and landing precision (see Supporting Information).”

      “To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

      In addition to eye-movement-related anisotropies across the visual field, there are of course many studies reporting visual field anisotropies (see Himmelberg, Winawer & Carrasco, 2023, Trends in Neuroscience for a review). It would be interesting to understand how the authors think about visual field anisotropies in the context of their own study. Do they think that their results are (in)dependent on such visual field variations (see Greenwood et al., 2017, PNAS; Ohl, Kroell, & Rolfs, 2024, JEP:Gen for a similar discussion)?

      We agree that established visual field anisotropies are fascinating to be discussed in context of our own results. At the reviewer’s suggestion, we now expanded this discussion.

      The observed anisotropies in terms of saccade costs are likely related to established anisotropies in perception and early visual cortex. However, the exact way that these anisotropies may be linked remains elusive (i.e. what is cause, what is effect, are links causal?), and more research is necessary to understand how these are related.

      “The observed differences in saccade costs across directions could be linked to established anisotropies in perception [80–86], attention [87–92], saccade charac- teristics [87, 88, 92, 93], and (early) visual cortex [94–98] [also see 99]. For example, downward saccades are more costly than upward saccades, which mimics a similar asymmetry in early visual areas wherein the upper visual field is relatively under- represented [94–98]; similarly stronger presaccadic benefits are found for down- compared with upward saccades [87, 88]. Moreover, upward saccades are more pre- cise than downward saccades [93]. Future work should elucidate where saccade cost or the aforementioned anisotropies originate from and how they are related - something that pupil size alone cannot address.”

      We also added that the finding that more precise saccades are coupled with worse performance in a crowding task might be attributed to the increased effort associated with more precise saccades (Greenwood et al., 2017).

      “Adaptive resource allocation from, and to the oculomotor system parsimoniously explains a number of empirical observations. For example, higher cognitive demand is accompanied by smooth pursuits deviating more from to-be tracked targets [137], reduced (micro)saccade frequencies [Figure 4; 63, 64, 138, 139], and slower peak saccade velocities [140–142]. Relatedly, more precise saccades are accompanied with worse performance in a crowding task [93].”

      Finally, the authors conclude that their results "suggests that the eye-movement system and other cognitive operations consume similar resources that are flexibly allocated among each other as cognitive demand changes. The authors should speculate what these similar resources could mean? What are the specific operations of the auditory task that overlap in terms of resources with the eye movement system?

      We agree that the nature of joint resources is an interesting question. Our previous discussion was likely too simplistic here (see also responses to R3). We here specifically refer to the cognitive resources that one can flexibly distribute between tasks.

      Our data do not directly speak to the question of what the shared resources between the auditory and oculomotor tasks are. Nevertheless, both tasks charge working memory as saccade targets are mandatorily encoded into working memory prior to saccade onset (Van der Stigchel & Hollingworth, 2018), and the counting task clearly engages working memory. This may indicate some domain-generality between visual and auditory working memory during natural viewing (see Nozari & Martin, 2024 for a recent review), but this remains speculative. Another possibility is that not the working memory encoding associated with saccades per se, but that the execution of overt motor actions itself also requires cognitive processing as suggested by Beatty (1982): “the organization of an overt motor act places additional demands on informationprocessing resources that are reflected in the task-evoked pupillary response”.

      We have added upon this in more detail in the results and discussion sections.

      “Besides the costs of increased neural activity when exerting more effort, effort should be considered costly for a second reason: Cognitive resources are limited. Therefore, any unnecessary resource expenditure reduces cognitive and behavioral flexibility [22, 31, 36, 116]. As a result, the brain needs to distribute resources between cognitive operations and the oculomotor system. We found evidence for the idea that such resource distribution is adaptive to the general level of cognitive demand and available resources: Increasing cognitive demand through an additional pri- mary auditory dual task led to a lower saccade frequency, and especially costly sac- cades were cut. In this case, it is important to consider that the auditory task was the primary task, which should cause participants to distribute resources from the ocu- lomotor system to the counting task. In other situations, more resources could be distributed to the oculomotor system instead, for example to discover new sources of reward [22, 136]. Adaptive resource allocation from, and to the oculomotor system parsimoniously explains a number of empirical observations. For example, higher cognitive demand is accompanied by smooth pursuits deviating more from to-be tracked targets [137], reduced (micro)saccade frequencies [Figure 4; 63, 64, 138, 139], and slower peak saccade velocities [140–142]. Relatedly, more precise saccades are accompanied with worse performance in a crowding task [93]. Furthermore, it has been proposed that saccade costs are weighed against other cognitive operations such as using working memory [33, 143–146]. How would the resources between the oculomotor system and cognitive tasks (like the auditory counting task) be related? One possibility is that both consume from limited working memory resources [147, 148]. Saccades are thought to encode target objects in a mandatory fashion into (vi- sual) working memory [79], and the counting task requires participants to keep track of the auditory stream and maintain count of the instructed digit in working mem- ory. However, the exact nature of which resources overlap between tasks remain open for future investigation [also see 149]. Together, we propose that cognitive re- sources are flexibly (dis)allocated to and from the oculomotor system based on the current demands to establish an optimal balance between performance and cost minimization.”

      Reviewer #2 (Public Review):

      The authors attempt to establish presaccadic pupil size as an index of 'saccade effort' and propose this index as one new predictor of saccade target selection. They only partially achieved their aim: When choosing between two saccade directions, the less costly direction, according to preceding pupil size, is preferred. However, the claim that with increased cognitive demand participants would especially cut costly directions is not supported by the data. I would have expected to see a negative correlation between saccade effort and saccade direction 'change' under increased load. Yet participants mostly cut upwards saccades, but not other directions that, according to pupil size, are equally or even more costly (e.g. oblique saccades).

      Strengths:

      The paper is well-written, easy to understand, and nicely illustrated.

      The sample size seems appropriate, and the data were collected and analyzed using solid and validated methodology.

      Overall, I find the topic of investigating factors that drive saccade choices highly interesting and relevant.

      We thank the reviewer for pointing out the strengths of our paper.

      Weaknesses:

      The authors obtain pupil size and saccade preference measures in two separate tasks. Relating these two measures is problematic because the computations that underly saccade preparation differ. In Experiment 1, the saccade is cued centrally, and has to be delayed until a "go-signal" is presented; In Experiment 2, an immediate saccade is executed to an exogenously cued peripheral target. The 'costs' in Experiment 1 (computing the saccade target location from a central cue; withholding the saccade) do not relate to Experiment 2. It is unfortunate, that measuring presaccadic pupil size directly in the comparatively more 'natural' Experiment 2 (where saccades did not have to be artificially withheld) does not seem to be possible. This questions the practical application of pupil size as an index of saccade effort

      This is an important point raised by the reviewer and we agree that a discussion on these points improves the manuscript. We reply in two parts: 1) Although the underlying computations during saccade preparation might differ, and are therefore unlikely to be fully similar (we agree), we can still predict saccade selection between (Saccade planning to Saccade preference) and within tasks (Visual search). 2) Pupil size is a sluggish physiological signal, but this is outweighed by the advantages of using pupil size as a general marker of effort, also in the context of visual selection compared with saccade latencies.

      (1) Are delayed saccades (cost task) and the much faster saccades (preference task) linked?

      As the reviewer notes the underlying ‘type’ of oculomotor program may differ between voluntarily delayed-saccades and those in the saccade preference task. There are, however, also considerable overlaps between the oculomotor programs as the directions and amplitudes are identical. Moreover, the different types of saccades have considerable overlap in their underlying neural circuitry. Nevertheless, the underlying oculomotor programs likely still differ in some regard. Even despite these differences, we were able to measure differences across directions in both tasks, and costs and preferences were negatively and highly correlated between tasks. The finding itself therefore indicates that the costs of saccades measured during the saccade planning task generalize to those in the saccade preference task. Note also that we predicted this finding and idea already in a previous publication before starting the present study (Koevoet et al., 2023).

      We now address this interesting point in the discussion as follows:

      “We observed that aOordable saccades were preferred over costly ones. This is especially remarkable given that the delayed saccades in the planning task likely differ in their oculomotor program from the immediate saccades in the preference task in some regard.”

      (2) Is pupil size a sensible measure of saccade effort?

      As the reviewer points out, the pupillary signal is indeed relatively sluggish and therefore relatively slow and more artifical tasks are preferred to quantify saccade costs. This does not preclude pupil size from being applied in more natural settings, as we demonstrate in the search experiments – but a lot of care has to be taken to control for many possible confounding factors and many trials will be needed.

      That said, as saccade latencies may also capture differences in oculomotor effort (Shadmehr et al., 2019) they are a possible alternative option to assess effort in some oculomotor tasks (see below on why saccade latencies do not provide evidence for an alternative to effort driving saccade selection, but converging evidence). Whilst we do maintain that pupil size is an established and versatile physiological marker of effort, saccade latencies provide converging evidence for our conclusion that effort drives saccade selection.

      As for the saccade preference task, we are not able to analyze the data in a similar manner as in the visual search task for two reasons. First, the number of saccades is much lower than in the natural search experiments. Second, in the saccade preference task, there were always two possible saccade targets. Therefore, even if we were able to isolate an effort signal, this signal could index a multitude of factors such as deciding between two possible saccade targets. Even simple binary decisions go hand in hand with reliable pupil dilations as they require effort (e.g. de Gee et al., 2014).

      There are three major reasons why pupil size is a more versatile marker of saccade costs than saccade latencies (although as mentioned, latencies may constitute another valuable tool to study oculomotor effort). First, pupil size is able to quantify the cost of attentional shifts more generally, including covert attention as well as other effector systems such as head and hand movements. This circumvents the issue of different latencies of different effector systems and also allows to study attentional processes that are not associated with overt motor movements. Second, saccade latencies are difficult to interpret in natural viewing data, as fixation duration and saccade latencies are inherently confounded by one another. This makes it very difficult to separate oculomotor processes and the extraction of perceptual information from a fixated target. Thus, pupil size is a versatile marker of attentional costs in a variety of settings, and can measure costs that saccade latencies cannot (i.e. covert attention). Lastly, pupil size is highly established as a marker of effort which has been demonstrated across wide range of cognitive tasks and therefore not bound to eye movements alone (Bumke, 1911; Koevoet et al., 2024; Laeng et al., 2012; Loewenfeld, 1958; Mathôt, 2018; Robison & Unsworth, 2019; Sirois & Brisson, 2014; Strauch et al., 2022; van der Wel & van Steenbergen, 2018).

      We now discuss this as follows:

      “We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

      The authors claim that the observed direction-specific 'saccade costs' obtained in Experiment 1 "were not mediated by differences in saccade properties, such as duration, amplitude, peak velocity, and landing precision (Figure 1e,f)". Saccade latency, however, was not taken into account here but is discussed for Experiment 2.

      The final model that was used to test for the observed anisotropies in pupil size across directions indeed did not include saccade latencies as a predictor. However, we did consider saccade latencies as a potential predictor originally. As we performed AICbased backward model selection, however, this predictor was removed due to the marginal predictive contribution of saccade latency beyond other predictors explaining pupil size.

      For completeness, we here report the outcome of a linear mixed-effects that does include saccade latency as a predictor. Here, saccade latencies did not predict pupil size (b \= 1.859e-03, t \= .138, p \= .889). The asymmetry effects remained qualitatively unchanged: preparing oblique compared with cardinal saccades resulted in a larger pupil size (b \= 7.635, t \= 3.969, p < .001), and preparing downward compared with upward saccades also led to a larger pupil size (b \= 3.344, t \= 3.334, p \= .003).

      The apparent similarity of saccade latencies and pupil size, however, is striking. Previous work shows shorter latencies for cardinal than oblique saccades, and shorter latencies for horizontal and upward saccades than downward saccades - directly reflecting the pupil sizes obtained in Experiment 1 as well as in the authors' previous study (Koevoet et al., 2023, PsychScience).

      As the reviewer notes, there are substantial asymmetries across the visual field in saccade latencies. These assymetries in saccade latency could also predict saccade preferences. We will reply to this in three points: 1) even if saccade latency is a predictor of saccade preferences, this would not constitute as an alternative explanation to the conclusion of effort driving saccade selection, 2) saccade latencies show an up-down asymmetry but oblique-cardinal effects in latency may not be generalizable across saccade tasks, 3) pupil size remains a robust predictor of saccade preferences even when saccade latencies are considered as a predictor of saccade preferences.

      (1) We want to first stress that saccade latencies are thought to reflect oculomotor effort (Shadmehr et al., 2019). For example, saccades with larger amplitudes and saccades where distractors need to be ignored are associated with longer latencies. Therefore, even if saccade latencies predict saccade selection, this would not contrast the idea that effort drives saccade selection. Instead, this would provide convergent evidence for our main conclusion – effort predicting saccade selection (rather than pupil size predicting saccade selection per se).

      “We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

      (2) We first tested anisotropies in saccade latency in the saccade planning task (Wilkinson notation: latency ~ obliqueness + updownness + leftrightness + saccade duration + saccade amplitude + saccade velocity + landing error + (1+obliqueness + updownness|participant)). We found upward latencies to be shorter than downward saccade latencies (b \= -.535, t \= 3.421, p \= .003). In addition, oblique saccades showed shorter latencies than cardinal saccades (b \= -1.083, t \= 3.096, p \= .002) – the opposite of what previous work has demonstrated.

      We then also tested these latency anisotropies in another dataset wherein participants (n \= 20) saccaded toward a single peripheral target as fast as possible (Koevoet et al., submitted; same amplitude and eccentricity as in the present manuscript). There we did not find a difference in saccade latency between cardinal and oblique targets, but we did observe shorter latencies for up- compared with downward saccades. We are therefore not sure in which situations oblique saccades do, or do not differ from cardinal saccades in terms of latency, and even in which direction the effect occurs.

      In contrast, we have now demonstrated a larger pupil size prior to oblique compared with cardinal saccades in two experiments. This indicates that pupil size may be a more reliable and generalizable marker of saccade costs than saccade latency. However, this remains to be investigated further.

      (3) To gain further insights into which oculomotor metrics would predict saccade selection, we conducted a linear regression across directions. We created pupil size, saccade latencies, landing precision and peak velocities maps from the saccade planning task. We then used AIC-based model selection to determine the ‘best’ model to determine which factor would predict saccade selection best. The selected model included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences ~ pupil size + saccade latency + landing precision). Pupil size (b \=-42.853, t \= 4.791, p < .001) and saccade latency (b \=-.377, t \= 2.106, p \= .043) predicted saccade preferences significantly. In contrast, landing precision did not reach significance (b \= 23.631, t \= 1.675, p \= .104). This analysis shows that although saccade latency predicts saccade preferences, pupil size remains a robust predictor of saccade selection.

      “To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

      The authors state that "from a costs-perspective, it should be eOicient to not only adjust the number of saccades (non-specific), but also by cutting especially expensive directions the most (specific)". However, saccade targets should be selected based on the maximum expected information gain. If cognitive load increases (due to an additional task) an effective strategy seems to be to perform less - but still meaningful - saccades. How would it help natural orienting to selectively cut saccades in certain (effortful) directions? Choosing saccade targets based on comfort, over information gain, would result in overall more saccades to be made - which is non-optimal, also from a cost perspective.

      We thank the reviewer for this comment. Although we do not fully agree, the logic is quite close to our rationale and it is worth adding a point of discussion here. A vital part of the current interpretation is the instruction given to participants. In our second natural visual search task, participants were performing a dual task, where the auditory task was the primary task, whilst the search task was secondary. Therefore, participants are likely to adjust their resources to optimize performance on the primary task – at the expense of the secondary task. Therefore, less resources are made available and used to searching in the dual than in the single task, because these resources are needed for the auditory task. Cutting expensive directions does not help search in terms of search performance, but it does reduce the cost of search, so that more resources are available for the prioritized auditory task. Also note that the search task was rather difficult – participants did it, but it was tough (see the original description of the dataset for more details), which provides another reason to go full in on the auditory task at expense of the visual task. This, however, opens up a nice point of discussion: If one would emphasize the importance of search (maybe with punishment or reward), we would indeed expect participants to perform whichever eye movements are getting them to their goal fastest – thus reducing the relative influence of costs on saccade behavior. This remains to be tested however - we are working on this and are looking forward to discussing such findings in the future.

      Together, we propose that there is a trade-off between distributing resources either towards cognitive tasks or the oculomotor system (also see Ballard et al., 1995; Van der Stigchel, 2020). How these resources are distributed depends highly on the current task demands (also see Sahakian et al., 2023). This allows for adaptive behavior in a wide range of contexts.

      We now added these considerations to the manuscript as follows (also see our previous replies):

      “Do cognitive operations and eye movements consume from a similar pool of resources [44]? If so, increasing cognitive demand for non-oculomotor processes should result in decreasing available resources for the oculomotor system. In line with this idea, previous work indeed shows altered eye-movement behavior un- der effort as induced by dual tasks, for example by making less saccades under increased cognitive demand [62–64]. We therefore investigated whether less sac- cades were made as soon as participants had to count the occurrence of a specific digit in the auditory number stream in comparison to ignoring the stream (in Exp. 2; Figure 4a). Participants were instructed to prioritize the auditory digit-counting task over finding the visual search target. Therefore, resources should be shifted from the oculomotor system to the primary auditory counting task. The additional cognitive demand of the dual task indeed led to a decreased saccade frequency (t(24) = 7.224, p < .001, Cohen’s d = 1.445; Figure 4h).”

      I would have expected to see a negative correlation between saccade effort and saccade direction 'change' under increased load. Yet participants mostly cut upwards saccades, but not other directions that, according to pupil size, are equally or even more costly (e.g. oblique saccades).

      The reviewer’s point is taken from the initial comment, which we will address here. First, we’d like to point out that is it not established that saccade costs in different directions are always the same. Instead, it is possible that saccade costs could be different in natural viewing compared with our delayed-saccade task. Therefore, we used pupil size during natural viewing for the search experiments. Second, the reviewer correctly notes that oblique saccades are hardly cut when under additional cognitive demand. However, participants already hardly execute oblique saccades when not confronted with the additional auditory task (Figure 4b, d), making it difficult to reduce those further (i.e. floor effect). Participants chose to cut vertical saccades, possibly because these are more costly than horizontal saccades.

      We incorporated these point in our manuscript as follows:

      “To test this, we analyzed data from two existing datasets [63] wherein participants (total n = 41) searched for small targets (’Z’ or ’H’) in natural scenes (Figure 4a; [64]). Again, we tested whether pupil size prior to saccades negatively linked with saccade preferences across directions. Because saccade costs and preferences across directions could differ for different situations (i.e. natural viewing vs. saccade preference task), but should always be negatively linked, we established both cost and preferences independently in each dataset.”

      “We calculated a saccade-adjustment map (Figure 4g) by subtracting the saccade preference map in the single task (Figure 4f) from the dual task map (Fig- ure 4d). Participants seemingly cut vertical saccades in particular, and made more saccades to the top right direction. This pattern may have emerged as vertical saccades are more costly than horizontal saccades (also see Figure 1d). Oblique saccades may not have been cut because there were very little oblique saccades in the single condition to begin with (Figure 4d), making it difficult to observe a further reduction of such saccades under additional cognitive demand (i.e. a floor effect).”

      Overall, I am not sure what practical relevance the relation between pupil size (measured in a separate experiment) and saccade decisions has for eye movement research/vision science. Pupil size does not seem to be a straightforward measure of saccade effort. Saccade latency, instead, can be easily extracted in any eye movement experiment (no need to conduct a separate, delayed saccade task to measure pupil dilation), and seems to be an equally good index.

      There are two points here.

      (1) What is the practical relevance of a link between effort and saccade selection for eyemovement research and vision science?

      We see plenty – think of changing eye movement patterns under effort (be it smooth pursuits, saccade rates, distributions of gaze positions to images etc.) which have substantial implications for human factors research, but also neuropsychology. With a cost account, one may predict (rather than just observe) how eye movement changes as soon as resources are reduced/ non-visual demand increases. With a cost account, we can explain such effects (e.g. lower saccade rates under effort, cardinal bias, perhaps also central bias) parsimoniously that cannot be explained by what is so far referred to as the three core drivers of eye movement behavior (saliency, selection history, goals, e.g., Awh et al., 2012). Conversely, one must wonder why eye-movement research/vision science simply accepts/dismisses these phenomena as such, without seeking overarching explanations.

      (2) What is the usefulness of using pupil size to measure effort?

      We hope that our replies to the comments above illustrate why pupil size is a sensible, robust and versatile marker of attentional costs. We briefly summarize our most important points here.

      - Pupil size is an established measure of effort irrespective of context, as demonstrated by hundreds of original works (e.g. working memory load, multiple object tracking, individual differences in cognitive ability). This allows pupil size to be a versatile marker of the effort, and therefore costs, of non-saccadic attentional shifts such as covert attention or those realized by other effector systems (i.e. head or hand movements).

      - Our new analysis indicates that pupil size remains a strong and robust predictor of saccade preference, even when considering saccade latency.

      - Pupil size allows to study saccade costs in natural viewing. In contrast, saccade latencies are difficult to assess in natural viewing as fixation durations and saccade latencies are intrinsically linked and very difficult to disentangle.

      - Note however, that we think that it is interesting and useful so study effects of effort/cost on eye movement behavior. Whichever index is used to do so, we see plenty potential in this line of research, this paper is a starting point to do so.

      Reviewer #3 (Public Review):

      This manuscript extends previous research by this group by relating variation in pupil size to the endpoints of saccades produced by human participants under various conditions including trial-based choices between pairs of spots and search for small items in natural scenes. Based on the premise that pupil size is a reliable proxy of "effort", the authors conclude that less costly saccade targets are preferred. Finding that this preference was influenced by the performance of a non-visual, attentiondemanding task, the authors conclude that a common source of effort animates gaze behavior and other cognitive tasks.

      Strengths:

      Strengths of the manuscript include the novelty of the approach, the clarity of the findings, and the community interest in the problem.

      We thank the reviewer for pointing out the strengths of our paper.

      Weaknesses:

      Enthusiasm for this manuscript is reduced by the following weaknesses:

      (1) A relationship between pupil size and saccade production seems clear based on the authors' previous and current work. What is at issue is the interpretation. The authors test one, preferred hypothesis, and the narrative of the manuscript treats the hypothesis that pupil size is a proxy of effort as beyond dispute or question. The stated elements of their argument seem to go like this:

      PROPOSITION 1: Pupil size varies systematically across task conditions, being larger when tasks are more demanding.

      PROPOSITION 2: Pupil size is related to the locus coeruleus.

      PROPOSITION 3: The locus coeruleus NE system modulates neural activity and interactions.

      CONCLUSION: Therefore, pupil size indexes the resource demand or "effort" associated with task conditions.

      How the conclusion follows from the propositions is not self-evident. Proposition 3, in particular, fails to establish the link that is supposed to lead to the conclusion.

      We inadvertently laid out this rationale as described above, and we thank the reviewer for pointing out this initial suboptimal structure of argumentation. The notion that the link between pupil size and effort is established in the literature because of its neural underpinnings is inaccurate. Instead, the tight link between effort and pupil size is established based on covariations of pupil diameter and cognition across a wide variety of tasks and domains. In line with this, we now introduce this tight link predominantly based on the relationships between pupil size and cognition instead of focusing on putative neural correlates of this relationship.

      As reviewed previously (Beatty, 1982; Bumke, 1911; Kahneman, 1973; Kahneman & Beatty, 1966; Koevoet et al., 2024; Laeng et al., 2012; Mathôt, 2018; Sirois & Brisson, 2014; Strauch et al., 2022; van der Wel & van Steenbergen, 2018), any increase in effort is consistently associated with an increase in pupil size. For instance, the pupil dilates when increasing load in working memory or multiple object tracking tasks, and such pupillary effects robustly explain individual differences in cognitive ability and fluctuations in performance across trials (Alnæs et al., 2014; Koevoet et al., 2024; Robison & Brewer, 2020; Robison & Unsworth, 2019; Unsworth & Miller, 2021). This extends to the planning of movements as pupil dilations are observed prior to the execution of (eye) movements (Koevoet et al., 2023; Richer & Beatty, 1985). The link between pupil size and effort has thus been firmly established for a long time, irrespective of the neural correlates of these effort-linked pupil size changes.

      We again thank the reviewer for spotting this logical mistake, and now revised the paragraph where we introduce pupil size as an established marker of effort as follows:

      “We recently demonstrated that the effort of saccade planning can be measured with pupil size, which allows for a physiological quantification of saccade costs as long as low-level visual factors are controlled for [33]. Pupil size is an established marker of effort [36–44]. For instance, loading more in working memory or tracking more objects results in stronger pupil dilation [44–52]. Pupil size not only reflects cognitive (or mental) effort but also the effort of planning and executing movements [37, 53, 54]. We leveraged this to demonstrate that saccade costs can be captured with pupil size, and are higher for oblique compared with cardinal directions [33]. Here, we addressed whether saccade costs predict where to saccade.”

      We now mention the neural correlates of pupil size only in the discussion. Where we took care to also mention roles for other neurotransmitter systems:

      “Throughout this paper, we have used cost in the limited context of saccades.

      However, cost-based decision-making may be a more general property of the brain [31, 36, 114–116]. Every action, be it physical or cognitive, is associated with an in- trinsic cost, and pupil size is likely a general marker of this [44]. Note, however, that pupil dilation does not always reflect cost, as the pupil dilates in response to many sensory and cognitive factors which should be controlled for, or at least considered, when interpreting pupillometric data [e.g., see 39, 40, 42, 117]. Effort-linked pupil dilations are thought to be, at least in part, driven by activity in the brainstem locus coeruleus (LC) [40, 118–120] [but other neurotransmitters also affect pupil size, e.g. 121, 122]. Activity in LC with its widespread connections throughout the brain [120, 123–127] is considered to be crucial for the communication within and between neu- ral populations and modulates global neural gain [128–132]. Neural firing is costly [22, 133], and therefore LC activity and pupil size are (neuro)physiologically plausible markers of cost [40]. Tentative evidence even suggests that continued exertion of effort (accompanied by altered pupil dilation) is linked to the accumulation of glutamate in the lateral prefrontal cortex [134], which may be a metabolic marker of cost [also see 116, 134, 135]. “

      (2) The authors test one, preferred hypothesis and do not consider plausible alternatives. Is "cost" the only conceivable hypothesis? The hypothesis is framed in very narrow terms. For example, the cholinergic and dopamine systems that have been featured in other researchers' consideration of pupil size modulation are missing here. Thus, because the authors do not rule out plausible alternative hypotheses, the logical structure of this manuscript can be criticized as committing the fallacy of aOirming the consequent.

      As we have noted in the response to the reviewer’s first point, we did not motivate our use of pupil size as an index of effort clearly enough. For the current purpose, the neural correlates of pupil size are less relevant than the cognitive correlates (see previous point). We reiterate that the neuromodulatory underpinnings of the observed pupil size effects (which indeed possibly include effects of the cholinergic, dopaminergic and serotonergic systems), while interesting for the discussion on the neural origin of effects, are not crucial to our conclusion. We hope the new rationale (without focusing too much on the (irrelevant) exact neural underpinnings) convinces the reviewer and reader.

      Our changes to the manuscript are shown in our reply to the previous comment.

      The reviewer notes that other plausible alternative hypotheses could explain the currently reported results. However, we did not find a more parsimonuous explanation for our data than ‘Effort Drives Saccade Selection’. Effort explains why participants prefer saccading toward specific directions in (1) highly controlled and (2) more natural settings. Note that we also predicted this effect previously (Koevoet et al., 2023). Moreover, this account explains (3) why participants make less saccades under additional cognitive demand, and (4) why especially costly saccades are reduced under additional cognitive demand. We are very open to the reviewer presenting other possible interpretations of our data so these can be discussed to be put to test in future work.

      (3) The authors cite particular publications in support of the claim that saccade selection is influenced by an assessment of effort. Given the extensive work by others on this general topic, the skeptic could regard the theoretical perspective of this manuscript as too impoverished. Their work may be enhanced by consideration of other work on this general topic, e.g, (i) Shenhav A, Botvinick MM, Cohen JD. (2013) The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013 Jul 24;79(2):217-40. (ii) Müller T, Husain M, Apps MAJ. (2022) Preferences for seeking effort or reward information bias the willingness to work. Sci Rep. 2022 Nov 14;12(1):19486. (iii) Bustamante LA, Oshinowo T, Lee JR, Tong E, Burton AR, Shenhav A, Cohen JD, Daw ND. (2023) Effort Foraging Task reveals a positive correlation between individual differences in the cost of cognitive and physical effort in humans. Proc Natl Acad Sci U S A. 2023 Dec 12;120(50):e2221510120.

      We thank the reviewer for pointing us toward this literature. These papers are indeed relevant for our manuscript, and we have now incorporated them. Specifically, we now discuss how the costs of effort are weighed in relation to possible rewards during decision-making. We have also incorporated work that has investigated how the biomechanical costs of arm movements contribute to action selection.

      “Our findings are in line with established effort-based models that assume costs to be weighed against rewards during decision-making [102–107]. In such studies, reward and cognitive/physical effort are often parametrically manipulated to as- sess how much effort participants are willing to exert to acquire a given (monetary) reward [e.g. 108, 109]. Whereas this line of work manipulated the extrinsic costs and/or rewards of decision options (e.g. perceptual consequences of saccades [110, 111] or consequences associated with decision options), we here focus on the intrin- sic costs of the movement itself (in terms of cognitive and physical effort). Relatedly, the intrinsic costs of arm movements are also considered during decision-making: biomechanically aOordable movements are generally preferred over more costly ones [26–28]. We here extend these findings in two important ways. First, until now, the intrinsic costs of saccades and other movements have been inferred from gaze behavior itself or by using computational modelling [23, 25–28, 34, 35, 112]. In con- trast, we directly measured cost physiologically using pupil size. Secondly, we show that physiologically measured saccade costs predict where saccades are directed in a controlled binary preference task, and even during natural viewing. Our findings could unite state-of-the-art computational models [e.g. 23, 25, 34, 35, 113] with physiological data, to directly test the role of saccade costs and ultimately further our understanding of saccade selection.”

      (4) What is the source of cost in saccade production? What is the currency of that cost? The authors state (page 13), "... oblique saccades require more complex oculomotor programs than horizontal eye movements because more neuronal populations in the superior colliculus (SC) and frontal eye fields (FEF) [76-79], and more muscles are necessary to plan and execute the saccade [76, 80, 81]." This statement raises questions and concerns. First, the basis of the claim that more neurons in FEF and SC are needed for oblique versus cardinal saccades is not established in any of the publications cited. Second, the authors may be referring to the fact that oblique saccades require coordination between pontine and midbrain circuits. This must be clarified. Second, the cost is unlikely to originate in extraocular muscle fatigue because the muscle fibers are so different from skeletal muscles, being fundamentally less fatigable. Third, if net muscle contraction is the cost, then why are upward saccades, which require the eyelid, not more expensive than downward? Thus, just how some saccades are more effortful than others is not clear.

      Unfortunately, our current data do not allow for the specification of what the source is of differences in saccade production, nor what the currency is. We want to explicitly state that while pupil size is a sensitive measure of saccade costs, pupil size cannot directly inform what underlying mechanisms are causing differences in saccade costs across conditions (e.g. directions). Nevertheless, we do speculate about these issues because they are important to consider. We thank the reviewer for pointing out the shortcomings in our initial speculations.

      Broadly, we agree with the reviewer that a neural source of differences in costs between different types of saccades is more likely than a purely muscular account (also see Koevoet et al., 2023). Furthermore, we think that the observed differences in saccade costs for oblique vs. cardinal and up vs. down could be due to different underlying mechanisms. While we caution against overinterpreting single directions, tentative evidence for this may also be drawn by the different time course of effects for up/down versus cardinal/oblique, Figure 1c.

      Below we speculate about why some specific saccade directions may be more costly than others:

      Why would oblique saccades be more costly than cardinal saccades? We thank the reviewer for pointing out that oblique saccades additionally require coordination between pontine and midbrain circuits (Curthoys et al., 1984; King & Fuchs, 1979; Sparks, 2002). This point warrants more revised discussion compared to our initial version. We have incorporated this as follows:

      “The complexity of an oculomotor program is arguably shaped by its neural underpinnings. For example, oblique but not cardinal saccades require communication between pontine and midbrain circuits [73–75]. Such differences in neural complexity may underlie the additional costs of oblique compared with cardinal saccades. Besides saccade direction, other properties of the ensuing saccade such as its speed, distance, curvature, and accuracy may contribute to a saccade’s total cost [22, 33, 53, 76, 77] but this remains to be investigated directly.”

      Why would downward saccades be more costly than upward saccades? As the reviewer points out: from a net muscular contraction account of cost, one would expect the opposite pattern due to the movement of the eyelid. Instead, we speculate that our findings may be associated with the well-established anisotropy in early visual cortex along the vertical meridian. Specifically, the upper vertical meridian is represented at substantially less detail than the lower vertical meridian (Himmelberg et al., 2023; Silva et al., 2018). Prior to a saccade, attention is deployed towards the intended saccadic endpoint (Deubel & Schneider, 1996; Kowler et al., 1995). Attention tunes neurons to preferentially process the attended location over non-attended locations. Due to the fact that the lower visual field is represented at higher detail than the upper visual field, attention may tune neuronal responses differently when preparing up- compared with downward saccades (Hanning et al., 2024; Himmelberg et al., 2023). Thus, it may be more costly to prepare down- compared with upward saccades. This proposition, however, does not account for the lower costs associated horizontal compared with up- and downward saccades as the horizontal meridian is represented at a higher acuity than the vertical merdian. This makes it unlikely that this explains the pattern of results completely. Again, at this point we can only speculate why costs differ, yet we demonstrate that these differences in cost are decisive for oculomotor behavior. We now explicitly state the speculative nature of these ideas that would all need to be tested directly.

      We have updated our discussion of this issue as follows:

      “The observed differences in saccade costs across directions could be linked to established anisotropies in perception [80–86], attention [87–92], saccade charac- teristics [87, 88, 92, 93], and (early) visual cortex [94–98] [also see 99]. For example, downward saccades are more costly than upward saccades, which mimics a similar asymmetry in early visual areas wherein the upper visual field is relatively under- represented [94–98]; similarly stronger presaccadic benefits are found for down- compared with upward saccades [87, 88]. Moreover, upward saccades are more pre- cise than downward saccades [93]. Future work should elucidate where saccade cost or the aforementioned anisotropies originate from and how they are related - something that pupil size alone cannot address.”

      (5) The authors do not consider observations about variation in pupil size that seem to be incompatible with the preferred hypothesis. For example, at least two studies have described systematically larger pupil dilation associated with faster relative to accurate performance in manual and saccade tasks (e.g., Naber M, Murphy P. Pupillometric investigation into the speed-accuracy trade-off in a visuo-motor aiming task. Psychophysiology. 2020 Mar;57(3):e13499; Reppert TR, Heitz RP, Schall JD. Neural mechanisms for executive control of speed-accuracy trade-off. Cell Rep. 2023 Nov 28;42(11):113422). Is the fast relative to the accurate option necessarily more costly?

      We thank the reviewer for this interesting point that we will answer in two ways. First, we discuss the main point: the link between pupil size, effort, and cost. Second, we discuss the findings described specifically in these two papers and how we interpret these from a pupillometric account.

      First, one may generally ask whether 1) any effort results in pupil dilation, 2) whether any effort is costly, and 3) whether this means that pupil dilation always reflects effort and cost respectively. Indeed, it has been argued repeatedly, prominently, and independently (e.g., Bumke, 1911; Mathôt, 2018) that any change in effort (no matter the specific origin) is associated with an evoked pupil dilation. Effort, in turn, is consistently and widely experienced as aversive, both across tasks and cultures (David et al., 2024). Effort minimization may therefore be seen as an universal law of human cognition and behavior with effort as a to-be minimized cost (Shadmehr et al., 2019; Hull 1943, Tsai 1932). However, this does not imply that any pupil dilation necessarily reflects effort or that, as a consequence thereof, any pupil dilation is always signaling cost. For instance, the pupil dark response, the pupil far response and changes in baseline pupil size are not associated with effort. Baseline and task-evoked pupil dilation responses have to be interpreted differently (see below), moreover, the pupil also changes (and dilates) due to other factors (see Strauch et al., 2022; Mathôt, 2018, Bumke 1911, Loewenfeld, 1999 for reviews).

      Second, as for Naber & Murphy (2020) & Reppert at al. (2023) specifically: Both Reppert et al. (2023) and Naber & Murphy (2020) indeed demonstrate a larger baseline pupil size when participants made faster, less accurate responses. However, baseline pupil size is not an index of effort per-se, but task-evoked pupil dilation responses are (as studied in the present manuscript) (Strauch et al., 2022). For work on differences between baseline pupil diameter and task-evoked pupil responses, and their respective links with exploration and exploitation please see Jepma & Nieuwenhuis (2011). Indeed, the link between effort and larger pupil size holds for task evoked responses, but not baseline pupil size per se (also see Koevoet et al., 2023).

      Still, Naber (third author of the current paper) & Murphy (2020) also demonstrated larger task-evoked pupil dilation responses when participants were instructed to make faster, less accurate responses compared with making accurate and relatively slow responses. However, this difference in task-evoked response gains significance only after the onset of the movement itself, and peaks substantially later than response offset. Whilst pupil dilation may be sluggish, it isn’t extremely sluggish either. As feedback to the performance of the participant was displayed 1.25s after performing the movement and clicking (taking about 630ms), we deem it possible that this effect may in part result from appraising the feedback to the participant rather than the speed of the response itself (in fact, Naber and Murphy also discuss this option). In addition to not measuring saccades but mouse movements, it is therefore possible that the observed evoked pupil effects in Naber & Murphy (2020) are not purely linked to motor preparation and execution per se. Therefore, future work that aims to investigate the costs of movements should isolate the effects of feedback and other potential factors that may drive changes in pupil size. This will help clarify whether fast or more accurate movements could be linked to the underlying costs of the movements.

      Relatedly, we do not find evidence that pupil size during saccade planning predicts the onset latency of the ensuing saccade (please refer to our second response to Reviewer 2 for a detailed discussion).

      Together, we therefore do not see the results from Reppert et al. (2023) and Naber & Murphy (2020) to be at odds with our interpretation of evoked pupil size reflecting effort and cost in the context of planning saccades.

      We think that these are considerations important to the reader, which is why we now added them to the discussion as follows:

      “Throughout this paper, we have used cost in the limited context of saccades.

      However, cost-based decision-making may be a more general property of the brain [31, 36, 114–116]. Every action, be it physical or cognitive, is associated with an in- trinsic cost, and pupil size is likely a general marker of this [44]. Note, however, that pupil dilation does not always reflect cost, as the pupil dilates in response to many sensory and cognitive factors which should be controlled for, or at least considered, when interpreting pupillometric data [e.g., see 39, 40, 42, 117].”

      (6) The authors draw conclusions based on trends across participants, but they should be more transparent about variation that contradicts these trends. In Figures 3 and 4 we see many participants producing behavior unlike most others. Who are they? Why do they look so different? Is it just noise, or do different participants adopt different policies?

      We disagree with the transparency point of the reviewer. Note that we deviated from the norm here by being more transparent than common: we added individual data points and relationships rather than showing pooled effects across participants with error bars alone (see Figures 2c, 3b,c, 4c,e,f).

      Moreover, our effects are consistent and stable across participants and are highly significant. To illustrate, for the classification analysis based on cost (Figure 2E) 16/20 participants showed an effect. As for the natural viewing experiments (total > 250,000 fixations), we also find that a majority of participants show the observed effects: Experiment 1: 15/16 participants; Experiment 2: 16/25 participants; Experiment 2 – adjustment: 22/25 participants.

      We fully agree that it’s interesting to understand where interindividual variation may originate from. We currently have too little data to allow robust analyses across individuals and zooming in on individual differences in cost maps, preference maps, or potential personalized strategies of saccade selection. That said, future work could study this further. We would recommend to hereby reduce the number of directions to gain more pupil size data per direction and therefore cleaner signals that may be more informative on the individual level. With such stronger signals, studying (differences in) links on an individual level may be feasible and would be interesting to consider – and will be a future direction in our own work too. Nonetheless, we again stress that the reported effects are robust and consistent across participants, and that interindividual differences are therefore not extensive. Moreover, our results from four experiments consistently support our conclusion that effort drives saccade selection.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      - Based on the public review, I would recommend that the authors carefully review and correct the manuscript with regard to the causal conclusions. The study is largely correlational (i.e. the pupil was only observed, not manipulated) and therefore does not allow causal conclusions to be drawn about the relationship between pupil size and saccade selection. These causal conclusions become even more confusing when pupil size is equated with effort and saccade cost. As a consequence, an actual correlation between pupil size and saccade selection has led to the title that effort drives saccade selection. It would also be helpful for the reader to summarize in an additional section of the discussion what they consider to be a causal or correlational link based on their results.

      We agree with the reviewer, and we have indeed included more explicitly which findings are correlational and which causal in detail now. As outlined before we do not see a more parimanious explanation for our findings than our title, but we fully agree that the paper benefits from making the correlational/causal nature of evidence for this idea explicitly transparent.

      “We report a combination of correlational and causal findings. Despite the correlational nature of some of our results, they consistently support the hypothesis that saccade costs predicts saccade selection [which we predicted previously, 33]. Causal evidence was provided by the dual-task experiment as saccade frequencies - and especially costly saccades were reduced under additional cognitive demand. Only a cost account predicts 1) a link between pupil size and saccade preferences, 2) a cardinal saccade bias, 3) reduced saccade frequency under additional cognitive demand, and 4) disproportional cutting of especially those directions associated with more pupil dilation. Together, our findings converge upon the conclusion that effort drives saccade selection.”

      - Can the authors please elaborate in more detail on how they transformed the predictors of their linear mixed model for the visualization in Figure 1f? It is difficult to see how the coeOicients in the table and the figure match.

      We used the ‘effectsize’ package to provide effect sizes of for each predictor of the linear mixed-effects model (https://cran.r-project.org/web/packages/effectsize/index.html). We report absolute effect sizes to make it visually easier to compare different predictors. These details have now been included in the Methods section to be more transparent about how these effect sizes were computed.

      “Absolute effect sizes (i.e. r) and their corresponding 95% confidence intervals for the linear mixed-effects models were calculated using t and df values with the ’effectsize’ package (v0.8.8) in R.”

      - Could the authors please explain in more detail why they think that a trial-by-trial analysis in the free choice task adds something new to their conclusions? In fact, a trialby-trial analysis somehow suggests that the pupil size data would enter the analysis at a single trial level. If I understand correctly, the pupil size data come from their initial mapping task. So there is only one mean pupil size for a given participant and direction that goes into their analysis to predict free choice in a single trial. If this is the case, I don't see the point of doing this additional analysis given the results shown in Figure 2c.

      The reviewer understands correctly that pupil size data is taken from the initial mapping task. We then used these mean values to predict which saccade target would be selected on a trial-by-trial basis. While showing the same conceptual result as the correlation analysis, we opted to include this analysis to show the robustness of the results across individuals. Therefore we have chosen to keep the analysis in the manuscript but now write more clearly that this shows the same conceptual finding as the correlation analysis.

      “As another test of the robustness of the effect, we analyzed whether saccade costs predicted saccade selection on a trial-by-trial basis. To this end, we first determined the more aOordable option for each trial using the established saccade cost map (Figure 1d). We predicted that participants would select the more aOordable option. Complementing the above analyses, the more aOordable option was chosen above chance level across participants (M = 56.64%, 95%-CI = [52.75%-60.52%], one-sample t-test against 50%: t(19) = 3.26, p = .004, Cohen’s d = .729; Figure 2e). Together, these analyses established that saccade costs robustly predict saccade preferences.”

      Reviewer #2 (Recommendations For The Authors):

      The authors report that "Whenever the difference in pupil size between the two options was larger, saccades curved away more from the non-selected option (β = .004, SE = .001, t = 4.448, p < .001; Figure 3b), and their latencies slowed (β = .050, SE = .013, t = 4.323, p < .001; Figure 3c)". I suspect this effect might not be driven by the difference but by a correlation between pupil size and latency.

      The authors correlate differences in pupil size (Exp1) with saccade latencies (Exp2), I recommend correlating pupil size with the latency directly, in either task. This would show if it is actually the difference between choices or simply the pupil size of the respective individual option that is linked to latency/effort. Same for curvature.

      The reviewer raises a good point. Please see the previous analyses concerning the possible correlations between pupil size and saccade latency, and how they jointly predict saccade selection.

      Our data show that saccade curvature and latencies are linked with the difference in pupil size between the selected and non-selected options. Are these effects driven by a difference in pupil size or by the pupil size associated with the chosen option?

      To assess this, we conducted two linear mixed-effects models. We predicted saccade curvature and latency using pupil size (from the planning task) of the selected and nonselected options while controlling for the chosen direction (Wilkinson notation: saccade curvature/latency ~ selected pupil size + non-selected pupil size + obliqueness + vertical + horizontal + (1+ selected pupil size + non-selected pupil size|participant). We found that saccades curved away more from costlier the non-selected targets (β \=1.534, t \= 8.151, p < .001), and saccades curved away from the non-selected target less when the selected target was cheaper (β \=-2.571, t \= -6.602, p < .001). As the costs of the selected and non-selected show opposite effects on saccade curvature, this indicates that the difference between the two options drives oculomotor conflict.

      As for saccade latencies, we found saccade onsets to slow when the cost of the selected target was higher (b \= .068, t \= 2.844, p \= .004). In contrast, saccade latencies were not significantly affected by the cost of the non-selected target (β \= -.018, t \= 1.457, p \= .145), although numerically the effect was in the opposite direction. This shows that latencies were primarily driven by the cost of the selected target but a difference account cannot be fully ruled out.

      Together, these analyses demonstrate that the difference in costs between two alternatives reliably affects oculomotor conflict as indicated by the curvature analysis. However, saccade latencies are predominantly affected by the cost of the selected target – even when controlling for the obliqueness, updownness and leftrightness of the ensuing saccade. We have added these analyses here for completeness, but because the findings seem inconclusive for saccade latency we have chosen to not include these analyses in the current paper. We are open to including these analyses in the supplementary materials if the reviewer and/or editor would like us to, but have chosen not to do so due to conciseness and to keep the paper focused.

      I was wondering why the authors haven't analyzed the pupil size in Experiment 2. If the pupil size can be assessed during a free viewing task (Experiment 3), shouldn't it be possible to also evaluate it in the saccade choice task?

      We did not analyze the pupil size data from the saccade preference task for two reasons. First, the number of saccades is much lower than in the natural search experiments (~14.000 vs. ~250.000). Second, in the saccade preference task, there were always two possible saccade targets. Therefore, even if we were able to isolate an effort signal, this signal could index a multitude of factors such as deciding between two possible saccade targets (de Gee et al., 2014), and has the possibility of two oculomotor programs being realized instead of only a single one (Van der Stigchel, 2010).

      Discussion: "due to stronger presaccadic benefits for upward compared with downward saccades [93,94]". I think this should be the other way around.

      We thank the reviewer for pointing this out. We have corrected our mistake in the revised manuscript.

      Saccade latencies differ around the visual field; to account for that, results / pupil size should be (additionally) evaluated relative to saccade onset (rather than cue offset). It is interesting that latencies were not accounted for here (Exp1), since they are considered for Exp2 (where they correlate with a pupil size difference). I suspect that latencies not only correlate with the difference in pupil size, but directly with pupil size itself.

      We agree with the reviewer that locking the pupil size signal to saccade onset instead of cue offset may be informative. We included an analysis in the supporting information that investigates this (see Figure S1). The results of the analysis were conceptually identical.

      The reviewer writes that latencies were not accounted for in Experiment 1. Although saccade latency was not included in the final model reported in the paper, it was considered during AIC-based backward model selection. As saccade latency did not predict meaningful variance in pupil size, it was ultimately not included in the analysis as a predictor. For completeness, we here report the outcome of a linear mixed-effects that does include saccade latency as a predictor. Here, saccade latencies did not predict pupil size (β \= 1.859e-03, t \= .138, p \= .889). The assymetry effects remained qualitatively unchanged: preparing oblique compared with cardinal saccades resulted in a larger pupil size (β \= 7.635, t \= 3.969, p < .001), and preparing downward compared with upward saccades also led to a larger pupil size (β \= 3.344, t \= 3.334, p \= .003).

      In addition, we have included a new analysis in the supporting information that directly addresses this issue. We will reiterate the main results here:

      “To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

      We have also added this point in our discussion:

      “We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

      References

      Alnæs, D., Sneve, M. H., Espeseth, T., Endestad, T., van de Pavert, S. H. P., & Laeng, B. (2014). Pupil size signals mental eFort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. Journal of Vision, 14(4), 1. https://doi.org/10.1167/14.4.1

      Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437–443. https://doi.org/10.1016/j.tics.2012.06.010

      Ballard, D. H., Hayhoe, M. M., & Pelz, J. B. (1995). Memory Representations in Natural Tasks. Journal of Cognitive Neuroscience, 7(1), 66–80. https://doi.org/10.1162/jocn.1995.7.1.66

      Beatty, J. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91(2), 276–292. https://doi.org/10.1037/0033-2909.91.2.276

      Bumke, O. (1911). Die Pupillenstörungen bei Geistes-und Nervenkrankheiten (2nd ed.). Fischer.

      Curthoys, I. S., Markham, C. H., & Furuya, N. (1984). Direct projection of pause neurons to nystagmusrelated excitatory burst neurons in the cat pontine reticular formation. Experimental Neurology, 83(2), 414–422. https://doi.org/10.1016/S0014-4886(84)90109-2

      David, L., Vassena, E., & Bijleveld, E. (2024). The unpleasantness of thinking: A meta-analytic review of the association between mental eFort and negative aFect. Psychological Bulletin, 150(9), 1070–1093. https://doi.org/10.1037/bul0000443

      de Gee, J. W., Knapen, T., & Donner, T. H. (2014). Decision-related pupil dilation reflects upcoming choice and individual bias. Proceedings of the National Academy of Sciences, 111(5), E618–E625. https://doi.org/10.1073/pnas.1317557111

      Deubel, H., & Schneider, W. X. (1996). Saccade target selection and object recognition: Evidence for a common attentional mechanism. Vision Research, 36(12), 1827–1837. https://doi.org/10.1016/0042-6989(95)00294-4

      Greenwood, J. A., Szinte, M., Sayim, B., & Cavanagh, P. (2017). Variations in crowding, saccadic precision, and spatial localization reveal the shared topology of spatial vision. Proceedings of the National Academy of Sciences, 114(17), E3573–E3582. https://doi.org/10.1073/pnas.1615504114

      Hanning, N. M., Himmelberg, M. M., & Carrasco, M. (2024). Presaccadic Attention Depends on Eye Movement Direction and Is Related to V1 Cortical Magnification. Journal of Neuroscience, 44(12). https://doi.org/10.1523/JNEUROSCI.1023-23.2023

      Himmelberg, M. M., Winawer, J., & Carrasco, M. (2023). Polar angle asymmetries in visual perception and neural architecture. Trends in Neurosciences, 46(6), 445–458. https://doi.org/10.1016/j.tins.2023.03.006

      Jepma, M., & Nieuwenhuis, S. (2011). Pupil Diameter Predicts Changes in the Exploration–Exploitation Trade-oF: Evidence for the Adaptive Gain Theory. Journal of Cognitive Neuroscience, 23(7), 1587– 1596. https://doi.org/10.1162/jocn.2010.21548

      Kahneman, D. (1973). Attention and Effort. Prentice-Hall.

      Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science (New York, N.Y.), 154(3756), 1583–1585. https://doi.org/10.1126/science.154.3756.1583

      King, W. M., & Fuchs, A. F. (1979). Reticular control of vertical saccadic eye movements by mesencephalic burst neurons. Journal of Neurophysiology, 42(3), 861–876. https://doi.org/10.1152/jn.1979.42.3.861

      Koevoet, D., Strauch, C., Naber, M., & Van der Stigchel, S. (2023). The Costs of Paying Overt and Covert Attention Assessed With Pupillometry. Psychological Science, 34(8), 887–898. https://doi.org/10.1177/09567976231179378

      Koevoet, D., Strauch, C., Van der Stigchel, S., Mathôt, S., & Naber, M. (2024). Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization. WIREs Cognitive Science, e1668. https://doi.org/10.1002/wcs.1668

      Kowler, E., Anderson, E., Dosher, B., & Blaser, E. (1995). The role of attention in the programming of saccades. Vision Research, 35(13), 1897–1916. https://doi.org/10.1016/0042-6989(94)00279-U

      Laeng, B., Sirois, S., & Gredebäck, G. (2012). Pupillometry: A Window to the Preconscious? Perspectives on Psychological Science, 7(1), 18–27. https://doi.org/10.1177/1745691611427305

      Loewenfeld, I. E. (1958). Mechanisms of reflex dilatation of the pupil. Documenta Ophthalmologica, 12(1), 185–448. https://doi.org/10.1007/BF00913471

      Mathôt, S. (2018). Pupillometry: Psychology, Physiology, and Function. Journal of Cognition, 1(1), 16. https://doi.org/10.5334/joc.18

      Naber, M., & Murphy, P. (2020). Pupillometric investigation into the speed-accuracy trade-oF in a visuomotor aiming task. Psychophysiology, 57(3), e13499. https://doi.org/10.1111/psyp.13499

      Nozari, N., & Martin, R. C. (2024). Is working memory domain-general or domain-specific? Trends in Cognitive Sciences, 0(0). https://doi.org/10.1016/j.tics.2024.06.006

      Reppert, T. R., Heitz, R. P., & Schall, J. D. (2023). Neural mechanisms for executive control of speedaccuracy trade-oF. Cell Reports, 42(11). https://doi.org/10.1016/j.celrep.2023.113422

      Richer, F., & Beatty, J. (1985). Pupillary Dilations in Movement Preparation and Execution. Psychophysiology, 22(2), 204–207. https://doi.org/10.1111/j.1469-8986.1985.tb01587.x

      Robison, M. K., & Brewer, G. A. (2020). Individual diFerences in working memory capacity and the regulation of arousal. Attention, Perception, & Psychophysics, 82(7), 3273–3290. https://doi.org/10.3758/s13414-020-02077-0

      Robison, M. K., & Unsworth, N. (2019). Pupillometry tracks fluctuations in working memory performance. Attention, Perception, & Psychophysics, 81(2), 407–419. https://doi.org/10.3758/s13414-0181618-4

      Sahakian, A., Gayet, S., PaFen, C. L. E., & Van der Stigchel, S. (2023). Mountains of memory in a sea of uncertainty: Sampling the external world despite useful information in visual working memory. Cognition, 234, 105381. https://doi.org/10.1016/j.cognition.2023.105381

      Shadmehr, R., Reppert, T. R., Summerside, E. M., Yoon, T., & Ahmed, A. A. (2019). Movement Vigor as a Reflection of Subjective Economic Utility. Trends in Neurosciences, 42(5), 323–336. https://doi.org/10.1016/j.tins.2019.02.003

      Silva, M. F., Brascamp, J. W., Ferreira, S., Castelo-Branco, M., Dumoulin, S. O., & Harvey, B. M. (2018). Radial asymmetries in population receptive field size and cortical magnification factor in early visual cortex. NeuroImage, 167, 41–52. https://doi.org/10.1016/j.neuroimage.2017.11.021

      Sirois, S., & Brisson, J. (2014). Pupillometry. WIREs Cognitive Science, 5(6), 679–692. https://doi.org/10.1002/wcs.1323

      Sparks, D. L. (2002). The brainstem control of saccadic eye movements. Nature Reviews Neuroscience, 3(12), Article 12. https://doi.org/10.1038/nrn986

      Strauch, C., Wang, C.-A., Einhäuser, W., Van der Stigchel, S., & Naber, M. (2022). Pupillometry as an integrated readout of distinct attentional networks. Trends in Neurosciences, 45(8), 635–647. https://doi.org/10.1016/j.tins.2022.05.003

      Unsworth, N., & Miller, A. L. (2021). Individual DiFerences in the Intensity and Consistency of Attention. Current Directions in Psychological Science, 30(5), 391–400. https://doi.org/10.1177/09637214211030266

      Van der Stigchel, S. (2010). Recent advances in the study of saccade trajectory deviations. Vision Research, 50(17), 1619–1627. https://doi.org/10.1016/j.visres.2010.05.028

      Van der Stigchel, S. (2020). An embodied account of visual working memory. Visual Cognition, 28(5–8), 414–419. https://doi.org/10.1080/13506285.2020.1742827

      Van der Stigchel, S., & Hollingworth, A. (2018). Visuospatial Working Memory as a Fundamental Component of the Eye Movement System. Current Directions in Psychological Science, 27(2), 136–143. https://doi.org/10.1177/0963721417741710

      van der Wel, P., & van Steenbergen, H. (2018). Pupil dilation as an index of eFort in cognitive control tasks: A review. Psychonomic Bulletin & Review, 25(6), 2005–2015. https://doi.org/10.3758/s13423-018-1432-y

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this valuable study, the authors found that the macrolide drug rapamycin, which is an important pharmacological tool in the clinic and the research lab, is less specific than previously thought. They provide solid functional evidence that rapamycin activates TRPM8 and develop an NMR method to measure the specific binding of a ligand to a membrane protein.

      Strengths:

      The authors use a variety of complementary experimental techniques in several different systems, and their results support the conclusions drawn.

      Weaknesses:

      Controls are not shown in all cases, and a lack of unity across the figures makes the flow of the paper disjointed. The proposed location of the rapamycin binding pocket within the membrane means that molecular docking approaches designed for soluble proteins alone do not provide solid evidence for a rapamycin binding pocket location in TRPM8, but the authors are appropriately careful in stating that the model is consistent with their functional experiments.

      Impact:

      This work provides still more evidence for the polymodality of TRP channels, reminding both TRP channel researchers and those who use rapamycin in other contexts that the adjective "specific" is only meaningful in the context of what else has been explicitly tested.

      Reviewer #2 (Public Review):

      Summary:

      Tóth and Bazeli et al. find rapamycin activates heterologously-expressed TRPM8 and dissociated sensory neurons in a TRPM8-dependent way with Ca2+-imaging. With electrophysiology and STTD-NMR, they confirmed the activation is through direct interaction with TRPM8. Using mutants and computational modeling, the authored localized the binding site to the groove between S4 and S5, different than the binding pocket of cooling agents such as menthol. The hydroxyl group on carbon 40 within the cyclohexane ring in rapamycin is indispensable for activation, while other rapalogs with its replacement, such as everolimus, still bind but cannot activate TRPM8. Overall, the findings provide new insights into TRPM8 functions and may indicate previously unknown physiological effects or therapeutic mechanisms of rapamycin.

      Strengths:

      The authors spent extensive effort on demonstrating that the interaction between TRPM8 and rapamycin is direct. The evidence is solid. In probing the binding site and the structural-function relationship, the authors combined computational simulation and functional experiments. It is very impressive to see that "within" a rapamycin molecule, the portion shared with everolimus is for "binding", while the hydroxyl group in the cyclohexane ring is for activation. Such detailed dissection represents a successful trial in the computational biology-facilitated, functional experiment-validated study of TRP channel structuralactivity relationship. The research draws the attention of scientists, including those outside the TRP channel field, to previously neglected effects of rapamycin, and therefore the manuscript deserves broad readership.

      Weaknesses:

      The significance of the research could be improved by showing or discussing whether a similar binding pocket is present in other TRP channels, and hence rapalogs might bind to or activate these TRP channels. Additionally, while the finding on TRPM8 is novel, it is worthwhile to perform more comprehensive pharmacological characterization, including single-channel recording and a few more mutant studies to offer further insight into the mechanism of rapamycin binding to S4~S5 pocket driving channel opening. It is also necessary to know if rapalogs have independent or synergistic effects on top of other activators, including cooling agents and lower temperature, and their dependence on regulators such as PIP2.

      Additional discussion that might be helpful:

      The authors did confirm that rapamycin does not activate TRPV1, TRPA1 and TRPM3. But other TRP channels, particularly other structurally similar TRPM channels, should be discussed or tested. Alignment of the amino acid sequences or structures at the predicted binding pocket might predict some possible outcomes. In particular, rapamycin is known to activate TRPML1 in a PI(3,5)P2-dependent manner, which should be highlighted in comparison among TRP channels (PMID: 35131932, 31112550).

      Reviewer #3 (Public Review):

      Summary:

      Rapamycin is a macrolide of immunologic therapeutic importance, proposed as a ligand of mTOR. It is also employed as in essays to probe protein-protein interactions.

      The authors serendipitously found that the drug rapamycin and some related compounds, potently activate the cationic channel TRPM8, which is the main mediator of cold sensation in mammals. The authors show that rapamycin might bind to a novel binding site that is different from the binding site for menthol, the prototypical activator of TRPM8. These solid results are important to a wide audience since rapamycin is a widely used drug and is also employed in essays to probe protein-protein interactions, which could be affected by potential specific interactions of rapamycin with other membrane proteins, as illustrated herein.

      Strengths:

      The authors employ several experimental approaches to convincingly show that rapamycin activates directly the TRPM8 cation channel and not an accessory protein or the surrounding membrane. In general, the electrophysiological, mutational and fluorescence imaging experiments are adequately carried out and cautiously interpreted, presenting a clear picture of the direct interaction with TRPM8. In particular, the authors convincingly show that the interactions of rapamycin with TRPM8 are distinct from interactions of menthol with the same ion channel.

      Weaknesses:

      The main weakness of the manuscript is the NMR method employed to show that rapamycin binds to TRPM8. The authors developed and deployed a novel signal processing approach based on subtraction of several independent NMR spectra to show that rapamycin binds to the TRPM8 protein and not to the surrounding membrane or other proteins. While interesting and potentially useful, the method is not well developed (several positive controls are missing) and is not presented in a clear manner, such that the quality of data can be assessed and the reliability and pertinence of the subtraction procedure evaluated.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major points

      (1) Given the novelty of the STTD NMR approach, please provide more details and data for the reader.

      • I would like to see all of the collected spectra so that readers can see and judge the effect sizes for themselves, perhaps as an additional supplementary figure.

      We agree with the reviewer that the data transparency of the NMR measurements should be improved. We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of experiments. We carried out additional experimental replicas on new samples and addressed the variability of cell samples by rescaling the STD effects based on reference <sup>1</sup>H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the obtained STTD spectra from the three parallel NMR sessions (Figure S6).

      • I appreciate the labels for STDD-1, STDD-2, and STTD on the lower two spectra of Figure 2C. Is the top spectrum from STD-1 or is it prior to saturation? In Figure 2C, what do the x1 and x2 notations on the right-hand side of the spectra indicate?

      We showed the top spectrum as an overview and a demonstration of the spectral complexity of the samples. <sup>1</sup>H experiments were run before the STD measurements to assess the sample quality and stability. The demonstrated spectrum on sample 1 (TRPM8 with rapamycin in HEK cells) was recorded with more transients than the corresponding STDs, thus it is only visually comparable with the difference spectra after scaling (2x). Figure 2 was changed and all the spectra were replaced as mentioned before. All the recorded <sup>1</sup>H-experiments without saturation including the one removed are now available in the supplementary information (Figure S5).

      • The STTD NMR results with WT TRPM8 are consistent with rapamycin binding directly to the channel. Testing whether rapamycin binding observed with STTD NMR is disrupted by one of the most compelling mutations (D796A, D802A, G805A, or Q861A) would be a further test of this direct interaction.

      We thank the reviewer for the suggestion and agree that testing the most compelling mutants would be a promising next step. These mutations were generated in plasmid vectors and only transiently transfected into HEK cells. For NMR analysis we would need a high amount of cells stably overexpressing the mutant channels which were not available for experimentation.

      • Given that this is not a methods paper, it is probably outside the scope to further validate the STTD NMR measurements by performing parallel ITC, SPR, MST, or radiolabeled ligand experiments. Nevertheless, I would be excited to see such a comparison since STTD NMR appears to have promise as an experimental technique for assessing ligand binding to membrane proteins that does not require large amounts of purified protein or radioactive isotopes.

      We agree with the reviewer that additional independent biophysical measurements on the interactions are necessary to further validate the STTD methodology. This paper is a preliminary demonstration of the STTD concept and our group is currently working on the challenges of on-cell NMR (e.g., sample and spectral complexity) and the standardization of the proposed workflow.     

      (2) Please clarify the methods used to model of rapamycin binding. Docking can be imprecise in TRP channels, even with a sophisticated docking scheme (Hughes et al., 2019, doi: https://doi.org/10.7554/eLife.49572.001).  

      Thank you for mentioning this point and providing the reference. We have further clarified our methods and included the reference in our discussion, indicating the limitations of our approach.

      • As a positive control, does the docking strategy accurately predict binding of known compounds (menthol, icilin, etc.) to TRPM8 consistent with cryo-EM structures?  

      Yes, the binding site for menthol, based on a similar docking strategy as for rapamycin, is also presented, and matches with predictions from other publications. This is now clarified in the revised manuscript.

      • Why was homology modeling to the human sequence used with the mouse structure but not the avian structure?  

      At this onset of the project, only the avian structure was available, and it was used in the primary docking. Later, to get more precise docking relevant for human TRPM8 pharmacology, we did revert to the then available structure of the mouse ortholog.  

      • How many rapamycin structural clusters were built, and how many structures were there in each cluster? How many were used? "most populated" is unspecific.  

      Thank you for your comment. We have added the following highlighted information to the methods section to address your comment:

      “Representative conformations of rapamycin were identified by clustering of the 1000-membered pools, having the macrocycle backbone atoms compared with 1.0 Å RMSD cut-off. Middle structures of the ten most populated clusters, accounting for more than 90% of the total conformational ensemble generated by simulated annealing, were used for further docking studies. To refine initial docking results and to identify plausible binding sites, the above selected rapamycin structures were docked again, following the same protocol as above, except for the grid spacing which was set to 0.375 Å in the second pass. The resultant rapamycin-TRPM8 complexes were, again, clustered and ranked according to the corresponding binding free energies. Selected binding poses were subjected to further refinement. The three most populated and plausible binding poses were further refined by a third pass of docking, where amino acid side chains of TRPM8, identified in the previous pass to be in close contact with rapamycin (< 4 Å), were kept flexible. Grid volumes were reduced to these putative binding sites including all flexible amino acid side chains (21.0-26.2 Å x 26.2-31.5 Å x 24.8-29.2 Å).”

      However, it is important to clarify that the clusters are not built and their number is not specified by the user. The number of clusters found depends on how similar the structures are in the structural ensemble analyzed by clustering. A high number of clusters indicates a diverse, whereas a low number suggests a uniform structural ensemble. Furthermore, it is arbitrarily controlled by the similarity cutoff specified by the user. If the cutoff is selected well, then the number of structures is different in each cluster. There are some highly populated clusters and a few which only have one structure. The selection of how many cluster representatives are used is usually based on the decision of whether or not the sum of the population of selected clusters sufficiently covers the mapped conformational space.

      • Additionally, the rapamycin poses were generated using a continuum solvent model that is unlikely to replicate the conditions existing in the lipid bilayer or in a lipid-exposed binding pocket as is predicted here. It is therefore possible that the rapamycin poses chosen for docking do not represent the physiological rapamycin binding pose, hampering the ability of the docking algorithm to find an appropriate docking pocket.  

      • Furthermore, accurately docking that may bind to membrane-exposed pockets is a challenging problem, particularly because many scoring algorithms, including those employed by Autodock, do not distinguish between solvent-exposed and membrane-exposed faces of the protein. This affects the predicted binding energies.  

      We appreciate the reviewer's insightful comments. We add a note in discussion part, mentioning these important limitations.  

      • In Figure 4, it appears that the proposed rapamycin binding pocket is located at the interface between two subunits, but only one is shown. Is there any contact with residues in the neighboring subunit? Based on Figure S4, I assume not, but am unsure.

      Based on the estimated distances, we do not think that there are any relevant interactions with residues from neighboring subunits. This is now indicated in the results section.

      • Consider uploading the rapamycin-docked model to a public repository such as Zenodo for readers to examine and manipulate themselves  

      As suggested, the model will be uploaded in a public repository. A link to the file on Zenodo is now included.

      (3) Please discuss the spatial location of the proposed rapamycin binding pocket relative to the vanilloid binding pocket in TRPV1.

      • The mutagenesis indicates that D745, D802, G805, and Q861 are most important for rapamycin sensitivity in TRPM8. Interestingly, the proposed rapamycin binding pocket appears to overlap spatially with the vanilloid binding pocket in TRPV1. Consistent with this, Q861 aligns with E570 in TRPV1, which is a critical residue for resiniferatoxin sensitivity. Indeed, similar to Q861's modeled proximity to the cyclohexyl ring, the hydroxyl group of the vanillyl moity of capsaicin (4DY in 7LR0, for example) is in proximity to E750 in TRPV1. Additionally, searching PubChem by structural similarity suggests that vanillyl head group of the TRP channel modulators capsaicin and eugenol are similar structurally to the trans-2Methoxycyclohexan-1-ol ring. Without overlaying the two structures myself, it is difficult to say more than that, but I encourage the authors to comment on any similarities and differences they observe.

      • If the proposed rapamycin pocket is indeed similar to the location of the vanilloid binding site, the authors may wish to discuss other TRPM channel structures that show ligands and lipids bound to this pocket because this provides evidence that this pocket influences TRPM channel function. For example, how does the proposed rapamycin binding pocket compare to TRPM8 bound to agonist AITC (PDBID 8e4l), TRPM5 bound to inhibitor NDNA (7mbv), and TRPM2 bound to phosphatidylcholine (6co7)?

      • Other TRP channel structures with ligands or lipids modeled in this region include TRPV1 bound to resiniferatoxin, capsaicin, or phosphatidylinositol (7l2j, 7l24, 7l2s, 7l2t, 7l2u, 7lp9, 7lpc, 7lqy, 7mz6, 7mz9, 7mza); TRPV3 bound to phosphatidylcholine (7mij, 7mik, 7mim, 7min, 7ugg); TRPV5 bound to econazole (6b5v) or ZINC9155 (6pbf); TRPV6 bound to piperazine (7d2k, 7k4b, 7k4c, 7k4d, 7k4e, 7k4f) or cholesterol hemisuccinate (7s8c); TRPC6 bound to BTDM (7dxf) or phosphatidylcholine (6uza); and TRP1 bound to PIP2 (6pw5).

      We thank the reviewer for these valuable insights. We have included some additional discussion highlighting the similarities between the proposed rapamycin binding site and some of the other ligandchannel interactions in the TRP superfamily, in particular the well-known vanilloid binding site in TRPV1. However, to keep the discussion focused, we have not fully discussed all the indicated interactions, to best serve the clarity and scope of the manuscript.  

      (4) I would like to see negative control calcium imaging and electrophysiology data with untransfected HEK cells to confirm that the observed activation is mediated by TRPM8 to parallel the TRPM8 KO sensory neuron experiments.  

      This important information is now included in the revised manuscript (Figure S2).

      (5) The DM-nitrophen Ca uncaging experiments are an interesting method to test Ca sensitivity of rapamycin, but the results make these experiments more complex to interpret. Ca has been shown to be an obligate cofactor for icilin sensitivity in TRPM8 under conditions where both the internal and external Ca concentrations are tightly controlled (Kuhn et al., 2009, doi: https://doi.org/10.1074/jbc.M806651200), which is necessary because TRPM8 allows Ca permeation through the pore when open. The large icilin-evoked currents in Figure 5A and 5B indicate that the effective intracellular calcium concentration is not zero prior to calcium uncaging, which may be high enough to mask any Ca-dependence of rapamycin that occurs at low Ca concentrations. Given this ambiguity, the inside-out patch clamp configuration would provide more control over the internal and external Ca concentration than is achieved in the Ca uncaging experiments. Because the authors have already demonstrated their ability to perform such experiments (Figure 2 panel B), it would be nice to see tests of Ca dependence using inside-out patch clamp.

      As was already shown in Figure 2, Rapamycin activates TRPM8 in inside-out patches, and these experiments were performed using calcium-free cytosolic and extracellular solutions. Note that earlier studies have already shown that icilin activates outward TRPM8 currents in the full absence of calcium: see e.g. Janssens et al. eLife, 2016. Chuang et al. 2004. In the case of Icilin, increased calcium further potentiates the current, which is more prominent for the inward current.

      In the Ca uncaging experiments, considering the Kd of DM-nitrophen of 5 nM, we expect that the intracellular calcium concentration before the UV flash would be approximately 15 nM. Taken together, both the inside-out experiments and the flash uncaging experiments confirm that rapamycin responses are not directly regulated by intracellular calcium, contrary to icilin.

      (6) Sequence conservation within TRPM channels could be used in combination with the binding pocket model and mutagenesis to predict rapamycin selectivity for TRPM8 over other TRPMs. For example, some important residues, specifically G805 and Q861, are not conserved in TRPM3, which agrees with the lack of rapamycin sensitivity observed in TRPM3 (Figure S1). Further sequence comparison would provide testable hypotheses for future exploration of rapamycin sensitivity in other TRPMs that could validate the proposed binding pocket.

      Thank you for the suggestion. We now indicate in the discussion that only some of the key residues are conserved and make suggestions for future studies.  

      (7) Please unify the color scheme across the figures to improve clarity.

      • The authors frequently use the colors blue, red, and green to represent menthol and rapamycin in the figures, but they are inconsistent in which one represents menthol and which represents rapamycin. It would be clearer for the audience if, for example, rapamycin is always represented with red and menthol is always represented with blue.  

      Thank you for pointing this out. We have made the coloring schemes more uniform.

      • In Figure 1, panel E, the coloring for Menthol and Pregnenolone Sulfate changes between the TRPM8+/+ and TRPM8-/- panels.  

      Thank you for pointing this out. We have updated the coloring schemes to ensure consistency between the TRPM8+/+ and TRPM8-/- panels.

      • Figure 3 B and E, perhaps color the plot background as a 3-color gradient (blue to white to red) rather than yellow and aqua. Center the white at the WT ratio, keeping the dashed line, with diverging gradients to, for example, blue for mutations that selectively affect menthol sensitivity and red for rapamycin.

      Thank you for the suggestion – we have changed the figure accordingly.  

      • Figure 4 panels A and B use the same color (green) to show two different things (menthol molecule and mutated residues that affect rapamycin sensitivity). It would be clearer for readers to change these colors to agree with a unified color scheme such that, for example, the menthol molecule is colored blue and the rapamycin-neighboring residues are colored red.

      Thank you for the suggestion. We have updated the figure to use a unified color scheme, with the menthol molecule now colored green and the rapamycin-neighboring residues colored cyan, to enhance clarity for readers.

      • I recommend adding a figure or panel that shows side chains for all mutations, colored by menthol/rapamycin selectivity, as indicated by the functional data in Figure 3B and 3E. This will highlight spatial patterns of the selective residues that are discussed in the text.

      Thank you for your suggestion, we added all the side residues in Figure S10.

      Minor points

      (1) It would be nice to have one more concentration data point in the middle of the dose response curve shown in Figure 1 panel B. The response is not saturating at the top or foot of the curve in Figure 1 panel D, precluding a confident fit to a two-state Boltzmann function.

      Instead of adding a single data point to this figure, we performed independent measurements on a plate reader system, comparing concentration responses at room temperature and 37 degrees. These data are now included as Figure S1.   

      (2) The cartoon in Figure 2 panel B should be made more accurate. For example, only the transmembrane helices should be depicted embedded in the membrane, not the whole protein including the intracellular domain. Because the experiment was performed with cells, change the orientation of TRPM8 in the cartoon to show the intracellular domain of the protein facing away from the extracellular side of the membrane where the rapamycin is applied.

      Thank you for this comment. We have corrected the cartoon accordingly

      (3) Perhaps put the yellow circles under or around the carbon atoms to which the identified hydrogen atoms belong in Figure 2 panel E and Figure 4 panel C. I found it difficult to visualize and compare the STTD NMR results with the predicted binding pocket.

      Thank you for the feedback. We have added yellow circles around the carbon atoms corresponding to the identified hydrogen atoms in Figure S9.  

      (4) Regarding the sentence on p. 12 beginning "In agreement with this notion..."

      • Include icilin, Cooling Agent-10, and WS-3 as other cooling agents whose sensitivity has been modulated by mutation of Y745

      • Cryosim-3 responses were not tested in either of the two papers cited; please add citation to Yin et al., 2022, doi: https://doi.org/10.1126/science.add1268 .

      • Other relevant papers include:

      – Malkia et al., 2009, doi: https://doi.org/10.1186/1744-8069-5-62 which includes molecular docking showing the hydroxyl group of menthol interacting with Y745

      – Beccari et al., 2017, doi: https://doi.org/10.1038/s41598-017-11194-0 Figure 5 shows disruption of icilin and Cooling Agent-10 sensitivity by Y745A

      – Palchevskyi et al., 2023, doi: https://doi.org/10.1038/s42003-023-05425-6 Figure 3 shows disruption of icilin, cooling agent-10, WS-3, and menthol sensitivity by Y745A o Plaza-Cayon et al., 2022, https://doi.org/10.1002%2Fmed.21920 Review of TRPM8 mutations

      • typo: Y754H should be Y745H

      Thank you for these suggestions. We have added the above references to the text and corrected the typo.

      (5) The authors use the competitive action of everolimus on rapamycin activation as evidence that the different macrolides are binding to the same binding pocket. In addition, prior work showed that Y745H and N799A mutations (which render TRPM8 insensitive to menthol and icilin, respectively) do not affect TRPM8 sensitivity to the structurally-related compound tacrolimus (Arcas et al., 2019). This is consistent with the docking and mutagenesis results presented here.

      Thank you for this valuable suggestion. We discuss these data in the revised version.

      (6) Rapamycin sensitivity has also been observed in TRPML1 (Zhang et al. 2019, doi: https://doi.org/10.1371/journal.pbio.3000252).

      We added a short reference to this interesting finding in the discussion.

      (7) The whole-cell currents are very large in several of the electrophysiology experiments (for example Figure 3 panel D and Figure S1), which could lead to artifacts of voltage errors as well as ion accumulation/depletion. However, because this paper is not relying on reversal potential measurements or trying to quantify V1/2, these errors are unlikely to affect the qualitative conclusions drawn.

      This is a fair point, but indeed unlikely to affect our main conclusions. Note that we compensated between 70 and 90% of the series resistance, so we don’t expect voltage errors exceeding ~10 mV.

      (8) Ligand sensitivity is frequently species-dependent in TRP channels, so it is interesting that multiple species were used here and that both human and mouse isoforms exhibit rapamycin sensitivity. It should be emphasized that human TRPM8 was used in the calcium imaging and electrophysiology experiments, as well as some docking models, while the mouse isoform was used in the sensory neuron experiments and a mutated avian isoform was used for some docking models.

      This information is available in the Methods and we believe it is clear for the readers.

      (9) Perhaps discuss the unclear mechanism of G805A action in icilin (but not menthol, cold, or praziquantel) sensitivity because it is not in direct contact with the ligand. For example, Yin et al., 2019 propose flexibility allowing Ca binding site and larger binding site for icilin.

      Yin et al. (2019) suggests that the G805A mutation impacts icilin sensitivity by influencing the flexibility of the binding site and possibly affecting calcium binding. In our study, we found that G805A significantly reduces rapamycin sensitivity, likely due to its direct role in the rapamycin binding pocket rather than affecting calcium binding. This is now briefly mentioned in the results section.

      (10) The Figure S1 legend indicates that n=5 for all panels, so please show normalized population IV curves rather than individual examples. Additionally, it would be interesting to see what happens when each agonist is co-applied with rapamycin. Does rapamycin potentiate or inhibit agonist activation in these channels and/or TRPM8?

      We believe that normalized population IVs are not ideal for representing whole-cell currents, considering the substantial variation in current densities. We therefore prefer to show example traces in Figure S3 of the revised version but include mean values of current densities for all tested cells in the text.

      While the effects of co-application of rapamycin with activating ligands could be of interest, we consider this somewhat outside the scope of the present manuscript. The combination of HEK293 cell experiments, along with results obtained in WT and TRPM8-deficient mice does, in our opinion, sufficiently describe the selectivity of rapamycin towards TRPM8 compared to other sensory TRP channels.

      (11) Figure S1 panel A does not contain units for Rapamycin or AITC concentrations.

      Thank you for pointing this out. The units were added to the figure.  

      (12) It would be nice if the authors characterized the different mutations as predicted to contribute to site 1 (D796, H845, Q861, based on Figure S4), site 2 (D796, M801, F847, and R851), and/or site 3 (F847, V849, and R851).

      The indicated mutants were all tested, as shown in Figure 3.

      (13) The numbering scheme in Figure S4 does not appear to match the residue numbers in the rest of the paper for certain residues (HIS-844 rather than H845, PHE-846 rather than F847, VAL-848 rather than V849, ARG-850 rather than R851, and GLN-860 rather than Q861), and labels are often overlapping and difficult to see. I also find the transparent spheres very difficult to distinguish from the transparent background, which makes it difficult to appreciate the STTD NMR data overlay.

      We apologize for the confusing numbering scheme. The lower numbers refer to the initial docking that was done using the avian TRPM8 ortholog. We have made a newer, clearer version of Figure S4 and inserted as Figure S9.  

      (14) Please superpose the Ligplots in Figure S5 panels E and F as described in the LigPlus manual (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/manual/manual.html) to facilitate easier comparison.

      Thank you for the suggestion. We followed the suggestion to superpose the Ligplots as described but found that the result was visually cluttered and difficult to interpret. To avoid confusion, we instead decided to remove panels E and F from Figure S5, as we believe that the visualization in panels A-D is clear and informative.

      (15) Some n values are missing in figure legends.

      We checked all legends, and added n numbers were missing.

      (16) There is an inconsistent specification of error bars as SEM in the figure legends, though it is specified in methods.

      A question for my own edification: Here, you have looked at ligand interactions with the protein by saturating the protein resonances and observing transfer to the ligand. Would it be possible to instead saturate lipid or solute resonances and observe transfer to a ligand? I am curious whether this would be one way to measure equilibrium partitioning of ligand into a membrane and/or determine the effective concentration of a ligand in the membrane. Additionally, could one determine whether the compound is fully partitioned into the center of the membrane or just sitting on the surface?

      The reviewer highlights an interesting aspect. The widely used WaterLOGSY NMR experiment (doi: 10.1023/a:1013302231549) saturates water molecules then the magnetization is transferred to the ligand of interest. Characteristic changes in ligand resonances are observed in the case of a binding event with proteins. On the other hand, the selective saturation of lipids is -while theoretically possible –technically challenging mainly because of the inherent low signal-dispersion of lipids and peak overlapping with ligand resonances. Additionally, lipid systems are more dynamic compared to proteins and ligand-lipid interactions could be weaker and less specific, significantly affecting the sensitivity of STD experiments.

      Reviewer #2 (Recommendations For The Authors):

      Major:

      • Is it feasible to test rapamycin on TRPM8 with single-channel recording? This will allow us to better probe the mechanism of rapamycin activation and compare it with menthol, with parameters of singlechannel conductance and maximal open probability.

      In our experience, it is very difficult to obtain single-channel recordings from TRPM8. The channel expresses at high densities, typically leading to patches contain multiple channels, making a proper analysis of mean open and closed times very difficult. Therefore, we have decided not to include such measurements in the manuscript.

      • The authors classified rapamycin as a type I agonist, the type that stabilizes the open conformation, same as menthol but more prominent. Does that indicate that rapamycin work synergistically (rather than independently) with menthol, because co-application of them can allow them to add to each other in stabilizing the open conformation? I wonder if the authors agree that this could be tested with experiments as in Figure S3, by showing a much more prolonged deactivation with co-application of menthol and rapamycin than applying each alone.

      Thank you for the insightful suggestion. We conducted co-application experiments, and our results show that the deactivation time is indeed significantly prolonged when both compounds are applied together compared to each alone. In fact, very little deactivation is seen when both compounds are co-applied, which made it virtually impossible to perform reliable fits to the deactivation time course for the Menthol+Rapamycin condition. Instead, we have now included summary results showing the percentage of deactivation after 100 ms. We included these findings in FigureS8.  

      • It could be tested whether rapamycin activation of TRPM8 requires or overrides the requirement of PIP2 with inside-out patch by briefly exposing the patch to poly-lysine to sequester PIP2.

      This is certainly a good suggestion for further follow-up studies. However, we considered that examination of the (potential) interaction between ligands and PIP2 was outside the scope of the current manuscript.

      • Figure 1C suggests that the authors test rapamycin when there is a relatively high baseline TRPM8 activation (prior to rapamycin) activation. This raises the possibility that rapamycin is more a potentiator than an activator. I wonder if the following two experiments could address it: (1) perfuse rapamycin while holding at different membrane potentials, wash-off rapamycin in the solution and quickly (in a few seconds) test the activated current magnitude (before rapamycin dissociation), to compare whether a more depolarized membrane potential (high baseline open probability) allows rapamycin to potentiate more. (2) Perform the experiment at a higher temperature (low baseline open probability) and test whether rapamycin EC50 shifts to the right.

      Thank you for the thoughtful suggestion. Overall, we are not really in favor of making a distinction between a potentiator and an activator since it is not really feasible to create a situation where TRPM8 activity is zero. As suggested, we performed the dose response experiment at a higher temperature (37 °C) and observed that rapamycin’s EC<sub>50</sub> shifts to the right FigureS2. This is similar to what has been observed for menthol on TRPM8 and for many other ligands on other temperature-sensitive TRP channels.

      Minor:

      (1) The author should report hill coefficient together with EC50 when showing dose-responses.

      We have added Hill coefficients for all the fits.

      (2) In Figure 1 (E, F), it might be clearer to use Venn-diagram to show whether there is overlapping among rapamycin-, menthol-, and cinnamaldehyde-responsive neurons. According to the authors' explanation, we can predict that rapamycin-insensitive, menthol-sensitive neurons should predominantly be cinnamaldehyde-responsive.

      Thank you for your suggestion. In these experiments, we applied several agonists and the combination of them would result in a visually crowded Venn diagram difficult to interpret. However, we agree, with the reviewer’s suggestion, and discuss the percentage of the cinnamaldehyde+ neurons in the rapa- menthol+ population in Trpm8<sup>-/-</sup> neurons.

      (3) In Figure 3(C), since F847 does not respond to either menthol or rapamycin, it should be excluded from (B). Otherwise it is misleading.

      Thank you for pointing this out. To clarify, we have included a calcium imaging trace for the F847 mutant, demonstrating a clear response to rapamycin in FigureS9. This additional data highlights that F847 does respond to rapamycin, albeit with a more modest response amplitude. This is now also clarified in the results section.  

      (4) The word "potency" in pharmacology usually refers to a smaller EC50 number in dose-dependent experiments. In "Effect of rapamycin analogs on TRPM8" session, the authors use "potency" to refer to response to a single-dose experiment of different compounds. The experiment does not measure potency.

      Thank you for pointing out this mistake. We have corrected the text and replaced “potency” with “efficacy”.

      (5)  "2-methoxyl-" is misspelled in the text body.

      We have corrected the typo.

      (6) It will be nice to include "vehicle" in Figure 6B, or alternatively normalize all individual traces to vehicle. In Figure 6C and D, everolimus has almost no effect with compared to vehicle, and should not be shown as if it had ~8% in Figure 6B.

      We have added the vehicle values to Figure 6B from the same experiments.

      Reviewer #3 (Recommendations For The Authors):

      (1) The NMR method presented here as novel and employed to identify a proposed molecule bound to a membrane protein (TRPM8 in this case) is not well explained and presented. Since several spectra need to be subtracted, the authors should present the raw data and the results of the subtractions step by step. Also, it seems that the height of the peaks in each spectra will be highly variable and thus a reliable criterion employed to scale spectra before subtraction. None of these problems are discussed of described.

      The reviewer is right, that the data transparency should be improved and due to the high molecular complexity of the samples the size of the STD effects should be carefully scaled. We carried out additional experimental replicas on new samples and addressed the inherent sample/peak height variability by rescaling the STD effects based on reference <sup>1</sup>H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the computed STTD spectra from three parallel NMR sessions (Figure S6). We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of NMR experiments. We added the following paragraph to the main text: “To address the effect of the inherent variability of cellular samples on peak heights, STD effects were normalized based on the comparison of independent <sup>1</sup>H experiments (Figure S5). Three STTD replicates were computed, unambiguously confirming direct binding to TRPM8 in two datasets (Figure S6 A,B)”.

      Importantly since this signal subtraction method is proposed as a new development, control experiments employing well-established pairs of ligand and membrane protein receptor should be performed to demonstrate the reliability of the method.

      We agree with the reviewer, that the STTD experiment as a new development needs further validation, however, this paper is a preliminary demonstration of a new strategy building on the well-established STD and STDD NMR methodologies. Our group is actively engaged in studying additional biological samples to enhance our understanding of the applicability of STTD NMR. These efforts also aim to address challenges such as sample and spectral complexity by refining and standardizing the proposed workflow.

      (2) The tail currents shown in supplementary figure 3 are clearly not monoexponential. The fit to a single exponential can be seen to be inadequate and thus the comparison of kinetics of control, rapamycin and menthol is incorrect. At least two exponentials should be fitted and their values compared.

      We agree that the decay in the (combined) presence of agonists deviates from a simple monoexponential behavior. While we agree that fitting with two (or more) exponentials would provide a better fit, this also comes with greater variations/uncertainties in the fit parameters. This is particularly the case when inactivation is very slow and incomplete, or when the difference between slow and fast exponential time constants is <5, as seen with rapamycin and rapamycin +menthol. Therefore, we decided to provide monoexponential time constants as a proxy to describe the clear slowing down of activation and deactivation time courses in the presence of Type I agonists.   

      Also related to this aspect, recordings of TRPM8 currents can not be leak subtracted with a p/n protocol, thus a large fraction of the initial tail current must be the capacitive transient. There is no indication in the methods of how was this dealt with for the fitting of tail currents.

      As explained in the methods, capacitive transients and series resistance were maximally compensated. Therefore, we do not agree that a large fraction of the initial tail current must be capacitive. This can also be clearly seen in experiment such as Figure 1C, where the inward tail current is fully abolished in the presence of a TRPM8 antagonist. Likewise, very small and rapidly inactivating tail currents can be seen during voltage steps under control conditions (e.g. Figure S7  and S8 in the revised version).  

      (3) The docking procedure employed, as the authors show, is not appropriate for membrane proteins since it does not include a lipid membrane. It is not clear in the methods section if the MD minimization described applies only to the rapamycin molecule or to rapamycin bound to TRPM8.  

      It is also not clear if the important residue Q861 (and other residues that are identified as interacting with rapamycin) were identified from dockings or proposed based on other evidence.

      (4) Identifying amino acid residues that diminish the response to a ligand, does not uniquely imply that they form a binding site or even interact with said ligand. It is entirely possible that they can be involved in the allosteric networks involved in the activating conformational change. This caveat should be clearly posited by the authors when discussing their results.

      In our study, we identified several residues that significantly reduce the response to rapamycin when mutated, while retaining robust responses to menthol, which indicates that these mutations do not affect crucial conformational changes leading to channel gating. While our cumulative data suggest that these residues may be involved in direct interaction with rapamycin, we recognize the alternative possibility that they allosterically affect rapamycin-induced channel gating. This is now clearly stated in the first paragraph of the discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1 (Public Review):

      • While the title is fair with respect to the data shown, in the summary and the rest of the paper, the comparison between anesthetized and awake conditions is systematically stated, while more caution should be used.

      First, isoflurane is one of the (many) anesthetics commonly used in pre-clinical research, and its effect on the brain vasculature cannot be generalized to all the anesthetics. Indeed, other anesthesia approaches do not produce evident vasodilation; see ketamine + medetomidine mixtures. Second, the imaged awake state is head-fixed and body-constrained in mice. A condition that can generate substantial stress in the animals. In this study, there is no evaluation of the stress level of the mice. In addition, the awake imaging sessions were performed a few minutes after the mouse woke up from isoflurane induction, which is necessary to inject the MB bolus. It is known that the vasodilator effects of isoflurane last a long time after its withdrawal. This aspect would have influenced the results, eventually underestimating the difference with respect to the awake state.

      These limitations should be clearly described in the Discussion.

      Looking at Figure 2e, it takes more than 5' to reach the 5 Millions MB count useful for good imaging. However, the MB count per pixel drops to a few % at that time. This information tells me that (i) repeated measurements are feasible but with limited brain coverage since a single 'wake up' is needed to acquire a single brain section and (ii) this approach cannot fit the requirements of functional ULM that requires to merge the responses to multiple stimuli to get a complete functional image. Of course, a chronic i.v. catheter would fix the issue, but this configuration is not trivial to test in the experimental setup proposed by the authors, hindering the extension of the approach to fULM.

      Thank you for highlighting these limitations, as they address aspects that were not fully considered during the experimental design and manuscript writing. In response, we have added the following paragraphs to the discussion section, addressing these limitations of our study:

      (Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47). Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.

      Our proposed method enabled repeatable longitudinal brain imaging over a three-week period, addressing a key limitation of conventional ULM imaging and offering potential for various preclinical applications. However, there are still some limitations in this study. 

      One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.

      Another limitation of this study is the potential residual vasodilatory effect of isoflurane anesthesia on awake imaging sessions. The awake imaging sessions were conducted shortly after the mice had emerged from isoflurane anesthesia, required for the MB bolus injections. The lasting vasodilatory effects of isoflurane may have influenced vascular responses, potentially contributing to an underestimation of differences in vascular dynamics between anesthetized and awake state. Future applications of awake ULM in functional imaging using an indwelling jugular vein catheter presents a promising alternative to enable more accurate functional imaging in awake animals, addressing current limitations associated with anesthesia-induced vascular effects.”

      • Statistics are often poor or not properly described. 

      The legend and the text referring to Figure 2 do not report any indication of the number of animals analyzed. I assume it is only one, which makes the findings strongly dependent on the imaging quality of THAT mouse in THAT experiment. Three mice have been displayed in Figure 3, as reported in the text, but it is not clear whether it is a mouse for each shown brain section. Figure 5 reports quantitative data on blood vessels in awake VS isoflurane states but: no indication about the number of tested mice is provided, nor the number of measured blood vessels per type and if statistics have been done on mice or with a multivariate method.

      Also, a T-test is inappropriate when the goal is to compare different brain regions and blood vessel types.

      Similar issues partially apply to Figure 6, too.

      Thank you for bringing this to our attention. 

      We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

      (Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

      (Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

      Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

      We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

      We further clarified this point in the end of the Figure 2 caption:

      (Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

      (Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

      We added the following statement before introducing Figure 2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

      Reviewer 1 (Recommendations For the Authors):

      • If the study's main goal is to compare awake vs anesthetized ULM, the authors should test at least another anesthetic with no evident vasodilator effect.

      Thank you for this valuable suggestion. We would like to clarify that the primary aim of our study is not to comprehensively compare the effects of anesthesia versus the awake state, as a rigorous comparison would indeed require a more controlled experimental design, including additional anesthetics, a larger cohort of mice, and broader controls to ensure sufficient statistical power. We also add the following statement in the Discussion to clarify this point:

      (Line 314) “Therefore, in future studies, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

      We acknowledge that the initial organization of Figures 3–5 placed excessive emphasis on comparisons between the awake and anesthetized states, but without yielding consistently significant findings. Meanwhile, our longitudinal observations in original Figure 6 were underrepresented, despite their potential importance.

      In the revised version, we shifted our focus toward the main goal of awake longitudinal imaging. By consolidating the previous Figures 4 and 5 into the new Figure 4, we emphasize conclusions that are both more consistent and broadly applicable, avoiding areas that may lack sufficient rigor or consensus. Additionally, we expanded the quantitative analysis related to longitudinal imaging, highlighting its role as the ultimate objective of this study. The awake vs. anesthetized ULM comparison was intended to demonstrate the value of awake imaging and introduce the importance of awake longitudinal imaging. In the revised text, we have reframed this comparison to emphasize the specific response to isoflurane rather than a general response to anesthesia. For example, in Figures 3 and 4, we have replaced the original term "Anesthetized" with "Isoflurane". We have also added a discussion noting that isoflurane may induces more vasodilation than other anesthetic agents.

      (Line 310) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging(47).”

      • The claims made about the proposed experimental protocol to be suitable for the "long-term" (line 255) are not supported by the data and should be modified according to the presented evidence.

      Thank you for your valuable feedback. We agree that our current three-week experimental results do not yet fulfill the requirements for extended longitudinal imaging that may span several months. We have revised the relevant text accordingly. For instance, the phrase “Our proposed method enabled long-term, repeatable longitudinal brain imaging” has been modified to “Our proposed method enabled repeatable longitudinal brain imaging over a threeweek period.” (Similar changes also in Line 67, Line 318, and Line 337) Additionally, we have added the following paragraph in the discussion section to indicate that extending the monitoring period to several months is a meaningful direction for future exploration:

      (Line 337) “In our longitudinal study, consistent imaging results were obtained over a three-week period, demonstrating the feasibility of awake ULM imaging for this duration. However, for certain research applications, a monitoring period of several months would be valuable. Extending the duration of longitudinal awake ULM imaging to enable such long-term studies is a potential direction for future development.”

      Recommendations for improving the writing and presentation:

      • Reporting the number of mice and blood vessels and statistics for each quantitative figure.

      Thank you for highlighting this issue. We acknowledge that the quantitative figures in the previous version lacked clarity in specifying the number of mice, vessels, and associated statistics. In the revised version, we have ensured that each quantitative figure or its caption clearly indicate the specific mice, vessels, and statistical methods used. To further minimize any potential confusion, we have also added Supplementary Figure 1 to clearly label and reference each individual mouse included in the study.

      Minor corrections to the text and figures.

      • Line 22: "vascularity reduction from anesthesia" is not clear, nor it is a codified property of brain vasculature. Explain or rephrase.

      Thank you for your comment. We apologize for any confusion caused by the phrase “vascularity reduction from anesthesia” in the abstract. We agree that this phrasing was unclear without context. To improve clarity, we have revised this statement in the abstract to make it more straightforward and easier to understand. 

      (Line 24) “Vasodilation induced by isoflurane was observed by ULM. Upon recovery to the awake state, reductions in vessel density and flow velocity were observed across different brain regions.” 

      Additionally, we have added a section in the Methods titled Quantitative Analysis of ULM Images to provide a clear definition of vascularity. This section outlines how vascularity is quantified in our study, ensuring that our terminology is well-defined. 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • Line 76: putting the mice in a tube is also intended "To further reduce animal anxiety and minimize tissue motion" I agree with tissue motion, not with animal anxiety, which, indeed, I expect to be higher than if it could, for example, run on a ball or a treadmill.

      Thank you for pointing this out. We acknowledge the limitations of our setup regarding reducing animal anxiety. We have replaced the original phrase “to further reduce animal anxiety and minimize tissue motion” with “to further minimize tissue motion.” (Line 78) Additionally, we have added the following paragraph in Discussion section to address the limitations of our setup in reducing anxiety.

      (Line 321) “One of the limitations is the lack of objective measures to assess the effectiveness of head-fix habituation in reducing anxiety. This may introduce variability in stress levels among mice. Recent studies suggest that tracking physiological parameters such as heart rate, respiratory rate, and corticosterone levels during habituation can confirm that mice reach a low stress state prior to imaging(48). This approach would be highly beneficial for future awake imaging studies. Furthermore, alternative head-fixation setups, such as air-floated balls or treadmills, which allow the free movement of limbs, have been shown to reduce anxiety and facilitate natural behaviors during imaging(30). Adopting these approaches in future studies could enhance the reliability of awake imaging data by minimizing stress-related confounds.”

      • Line 79: PMP has been used by Sieu et al., Nat Methods, 2015; it should be acknowledged.

      Thank you for highlighting this. We have now included the reference to Sieu et al. Nat Methods, 2015 to appropriately acknowledge their use of PMP. (Line 81)

      • Figure: is there a reason why the plots start at 500 sec? What happened before that time?

      Thank you for your question regarding the starting time in the plots. Figures 1 and 2 are case studies using a single mouse to demonstrate the feasibility of our method. The “zero” timepoint was defined as the moment when anesthesia was stopped, and the microbubble injection began. However, the mouse does not fully recover immediately after anesthesia is stopped. As shown in Figure 1e, there is a period of approximately 500 seconds during which the pupil gradually dilates, indicating recovery. Only after this period does the mouse reach a relatively stable physiological state suitable for ULM imaging, which is why the plots in Figure 2 begin at T = 500 seconds.

      We recognize that this was not sufficiently explained in the main text and figure captions. In the revised manuscript, we have clarified this timing rationale in both the results section and the figure captions. We added the following sentence to the result section to introduce Fig.2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      We also added the following statement to note that this recover time varies across individual mice:

      (Line 154, Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.”

      Reviewer 2 (Public Review):

      • The only major comment (calling for further work) I would like to make is the relative weakness of the manuscript regarding longitudinal imaging (mostly Figure 6), compared to the exhaustive review of the effect of isoflurane on the vasculature (3 rats, 3 imaging planes, quantification on a large number of vessels, in 9 different brain regions). The 6 cortical vessels evaluated in Figure 6 feel really disappointing. As longitudinal imaging is supposed to be the salient element of this manuscript (first word appearing in the title), it should be as good and trustworthy as the first part of the paper. Figure 6c. is of major importance, and should be supported by a more extensive vessel analysis, including various brain areas, and validated on several animals to validate the robustness of longitudinal positioning with several instances of the surgical procedure. Figure 6d estimates the reliability of flow measurements on 3 vessels only. Therefore I recommend showing something similar to what is done in Figures 4 and 5: 3 animals, and more extensive quantification in different brain regions.

      We thank the reviewer for pointing out this issue. We acknowledge that the first version of the manuscript lacked in-depth quantitative analysis in the section on the longitudinal study, which should have been a focal point. It also did not provide a sufficient number of animals to demonstrate the reproducibility of the technique. In this revised version, we have included results from more animals and conducted a more comprehensive quantitative analysis, with the corresponding text updated accordingly. Specifically, we combined the previous Figures 4 and 5 into the current Figure 4 (corresponding revised text from Line 169 to Line 207). The revised Figures 5 and 6

      compare the results of the longitudinal study, presenting data from three mice (corresponding revised text from

      Line 224 to Line 258). Detailed information about the mice used has been added to Supplementary Figure 1, and Supplementary Figure 4 further provides a detailed display of the results for the three mice in longitudinal study. We hope that these adjustments will provide a more thorough validation of the longitudinal imaging.

      Reviewer 2 (Recommendations For The Authors):

      Minor comments:

      • The statistical analyses are not always explained: could they be stated briefly in the legends of each figure, or gathered in a statistical methods section with details for each figure? Be sure to use the appropriate test (e.g. student t-test is used in Fig 5 k whereas normality of distribution is not guaranteed.)

      Thank you for pointing this out. We acknowledge that the statistical analyses were not clearly explained in the original version. In the revised manuscript, we have ensured that the statistical methods are clearly described. 

      (Fig.4 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using t-test at each measurement point along the segments.”

      (Fig.6 caption) “b,c, Comparisons of vessel diameter (b) and flow velocity (c) for the selected arterial and venous segments. Statistical analysis was conducted using the two one-sided test (TOST) procedure, which evaluates the null hypothesis that the difference between the two weeks is larger than three times the standard deviation of one week.”

      Additionally, we corrected an error in the previous comparison of the violin plots on flow velocities, where a t-test was incorrectly applied; this has now been removed.

      • The authors use early in the manuscript the term vascularity, e.g. in "vascularity reduction", it is not exactly clear what they mean by vascularity, and would require a proper definition at that moment. If I am correct, a quantification of that "vascularity reduction" (page 5 line 132), is then done in Figures 5 d e f and j.

      Thank you for highlighting this issue. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • There is very little motion in the images presented, except for the awake "Bregma -4.2 mm" (Figure 3, directional maps), especially in the area including colliculi and mesencephalon, while the cortical vessels do not move. Can you comment on that?

      Thank you for highlighting this important aspect of motion in awake animal imaging. Motion correction is indeed a critical factor in such studies. In the original version of our discussion, we briefly addressed this issue (from Line 342 to Line 346), but we agree that a more detailed discussion is needed.

      To minimize motion artifacts, we conducted habituation to acclimate the animals to the head-fixation setup, which helps reduce anxiety during imaging. With thorough head-fixed habituation, the imaging quality is generally well-preserved. We also applied correlation-based motion correction techniques based on ULM images, which can partially correct for overall brain motion, as stated in the previous version. However, this ULM-images-based correction is limited to addressing only rigid motion.

      In the revised discussion, we have expanded on the limitations of our current motion correction approach and referenced recent work about more advanced motion correction methods:

      (Line 346) “While rigid motion correction is often effective in anesthetized animals, awake animal imaging presents greater challenges due to the more prominent non-rigid motion, particularly in deeper brain regions. This is evidenced in Supplementary Fig. 1 (Mouse 7), where cortical vessels remain relatively stable, but regions around the colliculi and mesencephalon exhibit more noticeable motion artifacts, indicating that displacement is more pronounced in deeper areas. To address these deeper, non-rigid motions, recent studies suggest estimating nonrigid transformations from unfiltered tissue signals before applying corrections to ULM vascular images(16,50). Such advanced motion correction strategies may be more effective for awake ULM imaging, which experiences higher motion variability. The development of more robust and effective motion correction techniques will be crucial to reduce motion artifacts in future awake ULM applications.”

      • Figure 1f maybe flip the color bar to have an upward up and downward down.

      Thank you for your suggestion. This display method indeed makes the images more intuitive. In the revised manuscript, all directional flow color bars have been flipped to ensure that upward flow is displayed as ‘up’ and downward flow as ‘down.’

      • Figure 2b the figure is a bit confusing in what is displayed between dashed lines, solid lines, dots... maybe it would be easier to read with

      - bigger dots and dashed lines in color for each of the 4 series

      - and so in the legend, thin solid lines in the corresponding color for the fit, but no solid line in the legend (to distinguish data/fit)

      - no lines for FWHM as they are not very visible, and the FWHM values are not mentioned for these examples.

      Thank you for your detailed suggestions. We agree that the original Fig. 2b appeared messy and confusing. Based on this feedback and other comments, we decided to replace the FWHM-based vessel diameter measurement with a more stable binarization-based approach. In the revised version, we selected a specific segment of each vessel and measured the diameter by calculating the distance from the vessel’s centerline to both side after binarization. Each point on the centerline of this segment provides a diameter measurement, which can be further used to calculate the mean and standard error. This updated method is more stable and reproducible, providing reliable measurements even for vessels that are not fully saturated. It also facilitates comparison across more vessels, helping to further demonstrate the generalizability of our saturation standard. We believe these adjustments make the revised Fig. 2b clearer and more readable.

      • Page 7, lines 144-147. This passage is not really clear when linking going up or down and going from the stem to the branches that it is specific to Figure 4a (and therefore to this particular location).

      Thank you for your insightful comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript. We have removed all content related to vessel classification based on branchin and branch-out criteria. This includes the original Classification of Cerebral Vessels section in the Methods, the relevant descriptions in the Results section under “ULM reveals detailed cerebral vascular changes from anesthetized to awake for the full depth of the brain”, limitation of this classification method in Discussion section, as well as related content in the original Figures 4 and 5.

      In the revised analysis, for the comparison between arteries and veins, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upwardflowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

      • Page 11 line 222 "higher vascular density" seems unprecise.

      Thank you for pointing this out. We have revised the sentence to more precisely convey our observations regarding changes in vascular diameter and vascularity within the ROI. We present these findings as evidence of the vasodilation effect under isoflurane, in alignment with existing research. The revised statement is as follows:

      (Line 275) “Statistical analysis from Fig. 4 shows that certain vessels exhibit a larger diameter under isoflurane anesthesia, and the vascularity, calculated as the percentage of vascular area within selected brain region ROIs, is also higher in the anesthetized state. These findings suggest a vasodilation effect induced by isoflurane, consistent with existing research(20,40,41,43,44).

      • Discussion: page 12, lines 257-267: it is not exactly clear how 3D imaging will help for the differentiation of veins/arteries. However, some methods have already been proposed to discriminate between arteries and veins using pulsatility (Bourquin et al., 2022) or 3D positioning when vessels are overlapped (Renaudin et al., 2023). The latter can also help estimate the out-of-plane positioning during longitudinal imaging.

      Bourquin, C., Poree, J., Lesage, F., Provost, J., 2022. In Vivo Pulsatility Measurement of Cerebral Microcirculation in Rodents Using Dynamic Ultrasound Localization Microscopy. IEEE Trans. Med. Imaging 41, 782-792. https://doi.org/10.1109/TMI.2021.3123912

      Renaudin, N., Pezet, S., Ialy-Radio, N., Demene, C., Tanter, M., 2023. Backscattering amplitude in ultrasound localization microscopy. Sci. Rep. 13, 11477. https://doi.org/10.1038/s41598-023-38531-w

      Thank you for pointing this out. We have revised the relevant paragraph in the discussion to clarify the potential advantages of advances in ULM imaging methods, such as those based on pulsatility (as described by Bourquin et al., 2022) or backscattering amplitude (as demonstrated by Renaudin et al., 2023). These established methods could be helpful for longitudinal imaging. Below is the revised text in the discussion section:

      (Line 370) “Advances in ULM imaging methods can benefit longitudinal awake imaging. For instance, dynamic ULM can differentiate between arteries and veins by leveraging pulsatility features(51). 3D ULM, with volumetric imaging array(52,53), enables the reconstruction of whole-brain vascular network, providing a more comprehensive understanding of vessel branching patterns. Meanwhile, 3D ULM also helps to mitigate the challenge of aligning the identical coronal plane for longitudinal imaging, a process that requires precise manual alignment in 2D ULM to ensure consistency. Additionally, this alignment issue can also be alleviated in 2D imaging using backscattering amplitude method, which may assist in estimating out-of-plane positioning during longitudinal imaging(54).”

      Reviewer 3 (Public Review):

      • It is unclear whether multiple animals were used in the statistical analysis.

      Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      • Generalizations are sometimes drawn from what seems to be the analysis of a single vessel.

      Thank you for pointing this out. To enhance the generalizability of our conclusions, we have expanded our analysis beyond single vessels in several parts of the study. For instance, in Figure 2, we analyzed three vessels at different depths within the same brain region of a single mouse, and we have included additional results in the Supplementary Figure 2 to further support these findings. Additionally, we have revised the language in the manuscript to ensure that conclusions are appropriately qualified and avoid overgeneralization.

      In Figures 4 and 6, we extended the analysis from single vessels to larger region-of-interest (ROI) analyses across entire brain regions. Unlike single-vessel measurements, which are susceptible to bias based on specific measurement locations, ROI-based analyses are less influenced by the operator and provide more objective, generalizable insights.

      • The description of the statistical analysis is mostly qualitative.

      We recognize that some aspects of the original statistical analysis (Figures 4 and 5 in the previous version) lacked rigor and description is more qualitative. The revised version of statistical analysis (Figure 4 and Figure 6) presents our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this kind of description offers more quantitative analysis.

      For more examples, please refer to the Results section where Figure 4 (Line 169 to Line 207) and Figure 6 (Line 224 to Line 258) are described. These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

      • Some terms used are insufficiently defined.

      • Additional limitations should be included in the discussion.

      • Some technical details are lacking. 

      Thank you for highlighting these issues. In response, we have made several improvements in the revised manuscript to address these issues. We have clarified terms such as “vascularity” (Line 547) and “saturation point” (Line 112) to ensure precision and prevent ambiguity. We have expanded the discussion (Line 310 to Line 377) to include limitations such as motion correction challenges and advances in ULM imaging methods, including dynamic ULM and backscattering amplitude techniques. We have added further details on interleaved sampling (Line 494 to Line 497), ULM tracking (Line 517 to Line 529), and quantitative analysis (Line 535 to Line 551) in the Methods section to provide a clearer understanding of our approach. 

      Please refer to our other responses for more specific adjustments.

      • Without information about whether the results obtained come from multiple animals, it is difficult to conclude that the authors generally achieved their aim. They do achieve it in a single animal. The results that are shown are interesting and could have an impact on the ULM community and beyond. In particular, the experimental setup they used along with the high reproducibility they report could become very important for the use of ULM in larger animal cohorts.

      We thank the reviewer for recognizing the impact of our work. We also acknowledge that there were some issues—specifically, we did not provide sufficient proof of reproducibility. In the revised version, we have included additional animal experiment results to ensure that the conclusions were not drawn from a single animal but are generally representative of our aim. (See supplementary figure 1 for detailed use of the animals) 

      Reviewer 3 (Recommendations For The Authors):

      • The manuscript would be more convincing by removing some of the superlatives used in the text. For instance, shouldn't "super-resolution ultrasound localization microscopy" simply be "ultrasound localization microscopy"? Expressions such as "first study", "essential", and "invaluable", etc could be replaced by more factual terms. The word "significant" is also used sometimes with statistics to back it up and sometimes without.

      Thank you for highlighting this issue. We have removed the superlatives throughout the manuscript to make the language more precise. For instance, we have simplified “super-resolution ultrasound localization microscopy” to “ultrasound localization microscopy” throughout the main text and removed expressions such as “first study” and “invaluable”. We also reviewed all uses of “essential” and “significant,” replacing “essential” with more modest alternatives where it does not indicate a strict requirement. Similarly, where “significant” does not refer to statistical significance, we have used other terms to avoid any ambiguity.

      • The section "Microbubble count serves as a quantitative metric for awake ULM image reconstruction" had several issues that I think should be addressed. Mainly, the authors make the case that after detecting 5 million microbubbles, there is no clear gain in detecting more. The argument is not very convincing as we know many vessels will not have had a microbubble circulate in them within that timeframe, which will be especially true in smaller vessels. While the analysis in Figure 2 shows nicely that the diameter estimate for vessels in the 20-30 um range is stable at 5 million microbubbles, it is not necessarily the case for smaller vessels. A better approach here might be to select, e.g., a total of 5 million detected microbubbles for practical reasons and then to determine which vessel parameters estimation (e.g., diameter, flow velocity) remain stable. In addition:

      a. Terms such as 'complete ULM reconstruction', 'no obvious change', 'ULM image saturation' are not well defined within the manuscript.

      Thank you for pointing out these issues and for offering a more rigorous approach. We completely agree with your suggestion. While our analysis demonstrated stable diameter estimates for vessels with diameter around 20 µm at 5 million microbubbles, this does not necessarily ensure stability for smaller vessels. Therefore, the choice of 5 million microbubbles was primarily for practical reasons. In the revised version, we have provided a more objective description and clarification of this limitation. We also recognize that terms such as “complete ULM reconstruction,” “no obvious change,” and “ULM image saturation” were not well defined and may have caused confusion, reducing the rigor of this manuscript. Based on your feedback, we have clearly defined “ULM image saturation” within the context of our study, removed absolute and ambiguous terms like “complete ULM reconstruction” and “no obvious change”. We revised the entire section accordingly:

      (Line 109) “To facilitate equitable comparison of brain perfusion at different states, a practical saturation point enabling stable quantification of most vessels needs to be established. Our observations indicated that when the cumulative MB count reached 5 million, ULM images achieved a relatively stable state. Accordingly, in this study, the saturation point was defined as a cumulative MB count of 5 million. There are also possible alternatives for ULM image normalization. For example, different ULM images can be normalized to have the same saturation rate. However, the proposed method of using the same number of cumulative MB count for normalization enables the analysis of blood flow distribution across different brain regions from a probabilistic perspective. The following analysis substantiates this criterion.

      Fig. 2a compares ULM directional vessel density maps and flow speed maps generated with 1, 3, 5, and 6 million MBs, using the same animal as shown in Fig. 1. To quantitatively confirm saturation, multiple vessel segments were selected for further analysis. Fig. 2b presents the measured vessel diameter for a specific segment at various MB counts. After binarizing the ULM map, the vessel diameter was measured by calculating the distance from the vessel centerline to the edge. Each point along the centerline of the segment provided a diameter measurement, enabling calculation of the mean and standard error. At low MB counts, vessels appeared incompletely filled, leading to inaccurate estimation of vessel diameter due to incomplete profiles. For example, at 1–2 million MBs, the binarized ULM map displayed a width of only one or two pixels along the segment. As a result, the measurements always yielded the same diameter values (two pixels, ~10um) with a consistently low standard error of the mean across the entire segment. With increased MB counts, the measured vessel diameter gradually rose, ultimately reaching saturation. The plots in Fig. 2b show that vessel diameter stabilized at 5 million MB count. Additionally, Fig. 2c illustrates the changes in flow velocity measured at different cumulative MB counts. The violin plots display the distribution of flow speed estimates for all valid centerline pixels within the selected segment. At low MB counts (1–3 million), flow velocity estimates fluctuated, but they stabilized as the MB count increased (4–6 million MBs). At 5 million MBs, flow velocity estimates were nearly identical to those at 6 million MBs, corroborating previous findings that vessel velocity measurements stabilize as MB count grows(39). To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

      b. The choice of 10 consecutive tracking frames is arbitrary and should be described as such unless a quantitative optimization study was conducted. Was there a gap-filling parameter? What was the maximum linking distance and what is its impact on velocity estimation?

      Thank you for your comment. We acknowledge that the choice of 10 consecutive tracking frames was based on our common practice rather than a specific quantitative optimization. Additionally, with the uTrack algorithm, we set both the gap-filling parameter and maximum linking distance to 10 pixels. Setting these parameters too high could potentially overestimate velocity. These details have now been added to the Methods section for clarity:

      (Line 517) “The choice of 10 consecutive frames (10 ms) was based on established practice but can be adjusted as needed. For the uTrack algorithm, two additional key parameters were specified: the maximum linking distance and the gap-filling distance, both set to 10 pixels (~50 microns). This configuration means that only bubble centroids within 10 pixels of each other across consecutive frames are considered part of the same bubble trajectory. Additionally, when the start and end points of two tracks fall within this threshold, the gap-filling parameter merges them into a single, continuous track. It is important to select these parameters carefully, as overly large values could lead to an overestimation of flow velocity. By setting the maximum linking distance to 10 pixels, we effectively limited the measurable velocity to 50 mm/s, under the assumption that no bubble would exceed a 50-micron displacement within the 1 ms interval between frames. After determining bubble tracks with the specified parameters for uTrack algorithm, accumulating the MB tracks resulted in the flow intensity map. Considering the velocity distribution across the mouse brain, this 50 mm/s limit ensures that the vast majority of blood flow is captured accurately.”

      c. 'The plots (Figure 2b) clearly indicate that the vessel diameter stabilized beyond 5 million MB count.' This is true for one vessel. To generalize that claim, the analysis should be performed quantitatively on a larger sample of vessels in various areas of the brain, across multiple animals.

      Thank you for pointing out this limitation. We agree that conclusions drawn from a single vessel cannot be generalized across all regions. Following your suggestion, we have added Supplementary Figure 2, where we analyzed multiple vessels from different brain regions across three mice. This expanded analysis further confirms that a 5 million MB count is sufficient to stabilize vessel diameter measurements across various samples.

      (Line 133) “To assess the generalizability of the 5 million MB saturation condition, vessel segments from three different mice across various brain regions were examined. The results, shown in Supplementary Fig. 2, confirm that this saturation criterion applies broadly. Although the 5 million MB threshold may not ensure absolute saturation for all vessels, it is generally effective for vessels larger than 15 μm. This MB count threshold was therefore adopted as a practical criterion.” 

      • "Statistical analysis validates the increase in blood flow induced by anesthesia" is a very interesting section but even though a quantitative analysis was conducted in Figure 5, the language used remains mostly qualitative. I think this section should include quantitative conclusions from the statistical analysis to increase the impact of this work.

      Thank you for your valuable feedback. We recognize that some aspects of the original quantitative analysis (Figures 4 and 5 in the previous version) lacked rigor, such as the classification of arteries, veins, and capillaries, and that the data presented in each row of Figure 5 represented only one mouse per coronal section, limiting the generalizability of statistical conclusions.

      In response to the reviewers’ feedback, the revised version incorporates a new approach by merging the previous Figure 4 and Figure 5 into a single, consolidated figure (now Figure 4). This updated figure aims to present our findings from multiple dimensions, ranging from individual vessels to individual cortical ROI of arteries and veins, and ultimately to broader brain regions. We have focused on quantitative analyses that consistently show similar trends across all animals. For instance, as illustrated in the revised Figure 4f, the average cortical arterial flow speed decreases by approximately 20% from anesthesia to wakefulness, while venous flow speed decreases by an average of 40%, with the reduction in venous flow speed being significantly greater than that of arterial flow. We believe that this approach offers more insightful analysis and enhances the overall impact of the study.

      For more examples, please refer to the revised Results section where Figure 4 are described (from Line 169 to Line 212). These sections have been extensively rewritten to emphasize quantitative interpretation of the data. Each part of the analysis now focuses more heavily on quantitative analyses that consistently show similar trends across all animals.

      • In the methods, it is claimed that 6 healthy female C57 mice were used in the study, but it is hard to tell whether more than one animal is shown in the figures. It is also unclear whether the statistics were performed within or across animals. Since one of the major strengths of the manuscript is that it shows the feasibility of performing reproducible measurements using ULM, most figures should be repeated for each individual animal and provided in supplementary data and statistics should be performed across animals.

      Thank you for bringing this to our attention. We acknowledge that the original version did not clearly indicate the use of individual animals. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. Additionally, we included statistics across animals in the revised Figures 4 and 6, and detailed data for each individual mouse are now provided in Supplementary Figures 3 and 4.

      • The effect of aliasing should be discussed given that 1) a high-frequency probe is used along with a correspondingly relatively low frame rate (1000 fps) and 2) Doppler filtering is used to separate upward from downward-moving microbubbles. There will be microbubbles that circulate faster than the Nyquist limit, which will thus appear as moving in the opposite direction in the Doppler spectrum. It would be important to double-check that the effect is not too important and to report this as a limitation in the discussion.

      Thank you for highlighting this important point. Aliasing is indeed a relevant issue to consider, especially for higher flow velocities in large vessels. We have added a discussion on this limitation in the revised manuscript:

      (Line 359) “Based on the maximum linking distance and gap closing parameters outlined in the Methods section, blood flow with velocities below 50 mm/s can be detected. However, the use of a directional filter to estimate flow direction may introduce aliasing. MBs moving at higher velocities may be subject to incorrect flow direction estimation due to aliasing effects. Given that the compounded frame rate is 1000 Hz, with an ultrasound center frequency of 20 MHz and a sound speed of 1540 m/s, the relationship between Doppler frequency and the axial blood flow velocity(12) indicates that aliasing will not occur for axial flow velocities below 19.25 mm/s. In all flow velocity maps presented in this study, the range is limited to a maximum of 15 mm/s, remaining below the critical threshold for aliasing. Additionally, all vessels analyzed in the violin plots for arteriovenous flow comparisons fall within this range. While cortical arterioles and venules generally exhibit moderate flow speeds, aliasing remains a factor to consider when combining directional filtering with velocity analysis.”

      • The method used to classify vessels may be incorrect and may not be needed. I would recommend the authors not use it and describe the vessels as vessels that branch in or out, etc. Applying an arbitrary threshold of 2 to detect capillaries is also not very convincing. I understand that the authors might decide to maintain this nomenclature, in which case I would recommend clearly explaining it at the beginning of the manuscript along with some of the caveats that are already reported in the discussion.

      Thank you for your comments on our vessel classification method. We recognize the limitations of the previous approach and, in order to enhance the rigor of the study, we have opted not to continue using this method in the revised manuscript.

      In the revised analysis regarding artery and vein, we focus solely on penetrating vessels in the cortex. For these vessels, it is generally accepted that downward-flowing vessels are arterioles, while upward-flowing vessels are venules. Accordingly, in the revised Figures 4 and 6, we analyze arterioles and venules exclusively in the cortex, without relying on the previous classification method that could be considered controversial.

      Additionally, we agree that classifying vessels with values below 2 as capillaries was not a robust approach. Thus, we have removed all related analyses from the revised manuscript.

      Minor comments:

      • Line 16: "resolves capillary-scale ..."; it is not clear that the resolution that is achieved in this work is at the capillary scale.

      Thank you for your valuable feedback. We understand that “capillary-scale” may overstate the achieved resolution in our work. To clarify, we have revised the sentence as follows:

      (Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.” 

      This adjustment more accurately reflects the resolution capabilities of ULM as used in our study.

      • Line 22: 'vascularity' is not well defined in the manuscript. Consider defining or using another term.

      Thank you for pointing out the need for clarification on vascularity. We acknowledge that our initial use of the term “vascularity” may have been unclear and potentially confusing. In the revised manuscript, we have included a clear definition of “vascularity” in the Methods section under Quantitative Analysis of ULM Images (Line 534). 

      The following sentence shows the definition of vascularity:

      (Line 547) “Vascularity was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.”

      We have also added an instant definition when it was firstly used in Results part:

      (Line 161) “When comparing vessel density maps, ULM images that are acquired in the awake state demonstrate a global reduction of vascularity, which refers to percentage of pixels that occupied by blood vessels.”

      • Line 30: I'm not convinced the first two sentences are useful.

      Thank you for pointing out this issue. The opening sentence of the article lacked focus and was too broad. We have rewritten the sentence as follows:

      (Line 34) “Sensitive imaging of correlates of activity in the awake brain is fundamental for advancing our understanding of neural function and neurological diseases.”

      • Line 37: 'micron-scale capillaries': this expression is unclear. Capillaries are typically micron-scaled, so it gives the impression that ULM can image ULM at the one-micron scale, which is not the case.

      Thank you for your helpful comment. We agree that “micron-scale capillaries” could be misleading, as it might imply a resolution at the single-micron level. To clarify, we have revised the sentence as follows:

      (Line 40) “ULM is uniquely capable of imaging microvasculature situated in deep tissue (e.g., at a depth of several centimeters).”

      This revised wording more accurately describes ULM’s capability without implying single-micron level resolution.

      • Line 74: I don't think motion-free imaging is possible in the context of awake animals. Consider 'limiting motion' instead.

      Thank you for pointing out the potential issue with the term “motion-free”. We agree that achieving entirely motion-free imaging is challenging, especially in the context of awake animals. In response to your suggestion, we have revised the sentence to better reflect this limitation:

      (Line 76) “To achieve consistent ULM brain imaging while allowing limited movement in awake animals, a headfixed imaging platform with a chronic cranial window was used in this study.”

      This revised wording more accurately conveys our approach to minimizing motion without implying that motion is completely eliminated.

      • Line 134:'clearly reveals decreased vessel diameter' How was that demonstrated?

      • Line 153: 'significant' according to which statistical test?

      • Line 167: 'slight increase', by how much, is it significant?

      • Line 183: 'smaller vessels' the center of the distribution is not at 10mm/s, and velocity is not necessarily correlated with diameter.

      • Line 184: 'more large vessels', see above. What is a large vessel, and how was this measured?

      • Line 205: 'significantly lower', according to which statistical test?

      We acknowledge that the original version did not properly use the terms of statistical analysis. In the revised manuscript, we have deleted the related points, and rewritten the statistical analysis part to ensure the terms are used correctly. Please refer to the revised part of “ULM reveals an increase in blood flow induced by isoflurane anesthesia” (From Line 169 to Line 209). In the revised Figures 4 and 6, we have also ensured that each quantitative analysis figure or its caption is clearly explained.

      •    Line 398: the interleaved sampling scheme should be described in more detail.

      Thank you for pointing out this issue. The previous version did not clearly explain the details of interleaved sampling. We have now added the following paragraph to the Ultrasound imaging sequence section in Methods:

      (Line 494) “Interleaved sampling is employed to capture high-frequency echoes more effectively. With the system’s sampling rate limited to 62.5 MHz, the upper limit of the center frequency of the transducer passband is 15.625 MHz. To mitigate aliasing, two transmissions are sent per angle, staggered in time. This approach effectively doubles the sampling rate, ensuring more accurate image reconstruction.”

      • Figure 1: Which mouse is it? Are these results consistent across all animals?

      • Figure 2: Which mouse is it? Are these results consistent across all animals?

      • Figure 3: Which mouse is it? Are these results consistent across all animals?

      • Figure 4: Which mouse is it? Are these results consistent across all animals?

      • Figure 5: Is it a single mouse or multiple mice? Are these results consistent across all animals?

      We acknowledge that the original version did not clearly indicate the numbers of animals in the statistical analysis. In the revised manuscript, we have added Supplementary Figure 1 to specify the mice used, and we have labeled each mouse accordingly in the figures or captions. In the revised Figures 4 and 6, we have ensured that each quantitative analysis figure or its caption clearly indicate the specific mice.

      For original Figures 1 and 2, these are presented as case studies to illustrate the methodology. Since the anesthesia time required for tail vein injection for each animal varies slightly, it is challenging to have the consistent time taken for each mouse to recover from anesthesia across all mice. For instance, in Figure 1, the mouse took nearly 500 seconds to recover from anesthesia, but this duration is not consistent across all animals, which is a limitation of the bolus injection technique. We have noted this point in the discussion (discussion on the limitation of bolus injection), and we have also clarified in the results section and figure captions that these figures represent a case study of a single mouse rather than a standardized recovery time for all animals.

      We further clarified this point in the end of the Figure 2 caption:

      (Fig.2 caption) “This figure presents a case study based on the same mouse shown in Fig 1. The x-axis for d-f begins at 500 seconds because, at this point, the mouse’s pupil size stabilized, indicating it had recovered to an awake state. Consequently, ULM images were accumulated starting from this time. It is important to note that not every mouse requires 500 seconds to fully awaken; the time to reach a stable awake state varies across individual mice.” We added the following statement before introducing Figure 1e:

      (Line 93) “Due to differences in tail vein injection timing and anesthesia depth, the time required for each mouse to fully awaken varied. Although it was not feasible to get pupil size stabilized just after 500 seconds for each animal, ULM reconstruction only used the data that acquired after the animal reached full pupillary dilation, to ensure that ULM accurately captures the cerebrovascular characteristics in the awake state.”

      We added the following statement before introducing Figure 2d:

      (Line 139) “To further verify that the proposed MB bolus injection method can help to achieve ULM image saturation shortly after mice awaken from anesthesia, an analysis on the change in MB concentration over time was conducted once pupil size had stabilized (T = 500s).”

      For Figures 3, 4, and 5 (in the revised version, Figures 4 and 5 have been combined into a single Figure 4), the data represents results from three individual mice, with each coronal plane corresponding to a different mouse. In the revised version, we have added labels to indicate the specific mouse in each image to improve clarity. We also recognize that some analyses in the original submission (original Figure 5) may have lacked sufficient statistical power due to the small sample size. Therefore, in the revised version, we have focused only on findings that were consistently observed across the three mice to ensure robust conclusions.

      Minor corrections and typos from all reviewers:

      We would like to sincerely thank the reviewers for their careful reading of our manuscript. We appreciate the time and effort taken to point out the minor typographical errors. We have carefully addressed and corrected all the identified typos, as listed below:

      From Reviewer #1:

      • Line 316: "insensate": correct, please.

      (Line 409) “After confirming that the mouse was anesthetized, the head of the animal was fixed in the stereotaxic frame.”

      From Reviewer #3:

      • Line 15: Super-resolution ultrasound localization microscopy -- consider removing super-resolution as it gives the impression that it is different from standard ULM.

      (Line 18) “Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution.”

      • Line 39: typo: activities should be activity.

      (Line 41) “ULM can also be combined with the principles of functional ultrasound (fUS) to image whole-brain neural activity at a microscopic scale.”

      • Line 47: typo: over under.

      (Line 50) “Therefore, in neuroscience research, brain imaging in the awake state is often preferred over imaging under anesthesia.”

      Once again, we are grateful for the reviewers’ thorough review and valuable input, which have helped us improve the clarity and precision of the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This paper investigates the neural mechanisms underlying the change in perception when viewing ambiguous figures. Each possible percept is related to an attractor-like brain state and a perceptual switch corresponds to a transition between these states. The hypothesis is that these switches are promoted by bursts of noradrenaline that change the gain of neural circuits. The authors present several lines of evidence consistent with this view: pupil diameter changes during the time point of the perceptual change; a gain change in neural network models promotes a state transition; and large-scale fMRI dynamics in a different experiment suggests a lower barrier between brain states at the change point. However, some assumptions of the computational model seem not well justified and the theoretical analysis is incomplete. The paper would also benefit from a more in-depth analysis of the experimental data.

      Strengths:

      The main strength of the paper is that it attempts to combine experimental measurements - from psychophysics, pupil measurements, and fMRI dynamics - and computational modeling to provide an emerging picture of how a perceptual switch emerges. This integrative approach is highly useful because the model has the potential to make the underlying mechanisms explicit and to make concrete predictions.

      Weaknesses:

      A general weakness is that the link between the three parts of the paper is not very strong. Pupil and fMRI measurements come from different experiments and additional analysis showing that the two experiments are comparable should be included. Crucially, the assumptions underlying the RNN modeling are unclear and the conclusions drawn from the simulation may depend on those assumptions.

      With this comment in mind we have made substantial effort to better integrate the three different aspects of our paper. On the pupillometry side, we now show that the dynamic uncertainty associated with perceptual categorisation shares a similar waveform with the observed fluctuations in pupil diameter around the switch point (Fig 2B). To better link the modelling to the behaviour we have also made the gain of the activation function of each sigmoidal unit change dynamically as a function of the uncertainty (i.e. the entropy) of the network’s classification generating phasic changes in gain that mimic the observed phasic changes in pupil dilation explicitly linking the dynamics of gain in the RNN to the observed dynamics of pupil diameter (our non-invasive proxy for neuromodulatory tone). Finally we note that the predictions of the RNN (flattened egocentric landscape and peaks in low-dimensional brain state velocity at the time point of the perceptual switch) were tested directly in the whole-brain BOLD data, which links the modelling and BOLD analysis. Finally we note that whilst we agree that an experiment in which pupilometry and BOLD data were collected simultaneously would be ideal, these data were not available to us at the time of this study.

      Main points:

      Perceptual tasks in pupil and fMRI experiments: how comparable are these two tasks? It seems that the timing is very different, with long stimulus presentations and breaks in the fMRI task and a rapid sequence in the pupil task. Detailed information about the task timing in the pupil task is missing. What evidence is there that the same mechanisms underlie perceptual switches at these different timescales? Quantification of the distributions of switching times/switching points in both tasks is missing. Do the subjects in the fMRI task show the same overall behavior as in the pupil task? More information is needed to clarify these points.

      We recognize the need for a more detailed and comparative analysis of the perceptual tasks used in our pupil and fMRI experiments, particularly regarding differences in timing, task structure, and instructions. The fMRI task incorporates jittered inter-trial intervals (ITIs) of 2, 4, 6, and 8 seconds, designed to enable effective deconvolution of the BOLD response (Stottinger et al., 2018). In contrast, the pupil task presents a more rapid sequence of stimuli without ITIs. These timing differences are reflected in the mean perceptual switch points: the 8th image in the fMRI task and the 9th image in the pupil task. This small yet consistent difference suggests subtle influences of task design on behavior.

      Despite these structural and instructional differences, our analyses indicate that overall behavioral patterns remain consistent across the two modalities. The distributions of switching times align closely, and no significant behavioral deviations were observed that might suggest a fundamental difference in the underlying mechanisms driving perceptual switches. These findings suggest that the additional time and structural differences in the fMRI task do not significantly alter the behavioral outcomes compared to the pupil task.

      To address these issues, we have added paragraphs in the Results, Methods, and Limitations sections of the manuscript. In the Results section, we provide a detailed comparison of switching point distributions across the two tasks, emphasizing behavioral consistencies and any observed variations. In the Methods section, we include an expanded description of task timing, instructions, and the presence or absence of catch trials to ensure clarity regarding the experimental setups. Finally, in the Limitations section, we acknowledge the structural differences between the tasks, particularly the lack of catch trials and rapid stimulus presentation in the pupil task, and discuss how these differences may influence perceptual dynamics.

      These additions aim to clarify how task-specific factors, such as timing, instructions, and catch trials, influence perceptual dynamics while highlighting the consistency in behavioral outcomes across both experimental setups. We believe these revisions address the concerns raised and enhance the manuscript’s transparency and rigor.

      Computational model:

      (1) Modeling noradrenaline effects in the RNN: The pupil data suggests phasic bursts of NA would promote perceptual switches. But as I understand, in the RNN neuromodulation is modeled as different levels of gain throughout the trial. Making the neural gain time-dependent would allow investigation of whether a phasic gain change can explain the experimentally observed distribution of switching times.

      We thank the reviewer for this very helpful suggestion. We updated the RNN so that, post-training, gain changes dynamically as a function of the network's classification uncertainty (i.e. the entropy of the network's output). Specifically, the gain dynamics of each unit in the neural network are governed by a linear ODE with a forcing function given by the entropy of the network’s classification (i.e. the uncertainty of the classification). This explicitly tests the hypothesis that uncertainty driven increases in gain near the perceptual switch (when the input is maximally ambiguous) speeds perceptual switches, and allows us to distinguish between tonic and phasic increases in gain (in the absence of uncertainty forcing gain decays exponentially to a tonic value of 1). Importantly, in line with our hypothesis, we found that switch times decreased as we increased the impact of uncertainty on gain (i.e. switch times decreased as the magnitude of uncertainty forcing increased). Finally, we wish to note that although making gain dynamical is relatively simple conceptually, actually implementing it and then analysing the dynamics turned out to be highly non-trivial. To our knowledge our model is the first RNN of reasonable size to implement dynamical gain requiring us to push the RNN modelling beyond the current state of the art (see Fig 2 - 4).

      (2) Modeling perceptual switches: in the results, it is described that the networks were trained to output a categorical response, but the firing rates in Fig 2B do not seem categorical but rather seem to follow the input stimulus. The output signals of the network are not shown. If I understand correctly, a trivial network that would just represent the two input signals without any internal computation and relay them to the output would do the task correctly (because "the network's choice at each time point was the maximum of the two-dimensional output", p. 22). This seems like cheating: the very operation that the model should perform is to signal the change, in a categorical manner, not to represent the gradually changing input signals.

      The output of the network was indeed trained to be categorical via a cross entropy loss function with the output defined by the max of the projection of the excitatory hidden units onto the output weights which is boilerplate RNN modelling practice. As requested we now show the output in Fig 2B. On the broader question of whether a trivially small network could solve the task we are in total agreement that with the right set of hand-crafted weights a two neuron sigmoidal network with winner-take-all readout could solve the task. We disagree, however, that using an RNN is cheating in any way. Many tasks in neuroscience can be trivially solved with a very small number of recurrent units (e.g. basically all 2AF tasks). The question we were interested in is how the brain might solve the task, and more specifically how neuromodulator control of gain changes the dynamics of our admittedly very simple task. We could have done this by hand crafting a small network to solve the task but we wanted to use the RNN modelling as a means of both hypothesis testing and hypothesis generation. We now expand on and justify this modelling choice in the second paragraph of the discussion:

      “We chose to use an RNN, instead of a simpler (more transparent) model as we wanted to use the RNN as a means of both hypothesis generation and hypothesis testing. Specifically, unlike more standard neuronal models which are handcrafted to reproduce a specific effect, when building an RNN the modeller only specifies the network inputs, labels, and the parameter constraints (e.g. Dale’s law) in advance. The dynamics of the RNN are entirely determined by optimisation. Post-training manipulations of the RNN are not built in, or in any way guaranteed to work, making them more analogous to experimental manipulations of an approximately task-optimal brain-like system. Confirmatory results are arguably, therefore, a first steps towards an in vitro experimental test.”

      (3) The mechanism of how increased gain leads to faster switches remains unclear to me. My first intuition was that increasing the gain of excitatory populations (the situation shown in Fig. 2E) in discrete attractor models would lead to deeper attractor wells and this would make it more difficult to switch. That is, a higher gain should lead to slower decisions in this case. However, here the switching time remains constant for a gain between 1 and 1.5. Lowering the gain, on the other hand, leads to slower switching. It is, of course, possible that the RNN behaves differently than classical point attractor models or that my intuition is incorrect (though I believe it is consistent with previous literature, e.g. Niyogi & Wong-Lin 2013 (doi:10.1371/journal.pcbi.1003099) who show higher firing rates - more stable attractors - for increased excitatory gain).

      We thank the reviewer for the astute observation, which we entirely agree with. The energy landscape analysis is a method still under active development within our group and we are still learning how to best explain it and its relationship to more traditional ways of quantifying potential-like energy functions of dynamical systems which we think the reviewer has in mind. We have now included a second type of energy landscape analysis which gives a complementary perspective on the RNN dynamics and is more straightforwardly comparable to typical potential functions. We describe the new analysis in the section “Large-scale neural predictions of recurrent neural network model” as follows:

      “Crucially, there are two complementary viewpoints from which we can construct an energy landscape; the first allocentric (i.e., third-person view) perspective quantifies the energy associated with each position in state space, whereas the second egocentric (i.e., first person view) perspective quantifies the energy associated relative changes independent of the direction of movement or the location in state space. The allocentric perspective is straightforwardly comparable to the potential function of a dynamical system but can only be applied to low dimensional data in settings where a position-like quantity is meaningfully defined. The egocentric perspective is analogous to taking the point of view of a single particle in a physical setting and quantifying the energy associated with movement relative to the particles initial location. An egocentric framework is thus more applicable, when signal magnitude is relative rather than absolute. See materials and methods, and (see Fig S4 for an intuitive explanation of the allocentric and egocentric energy landscape analysis on a toy dynamical system).”

      From the allocentric perspective it is entirely true that increasing gain increases the depth of the landscape, equivalent to increasing the depth of the attractor. However, because the input to the network changes dynamically the location of the approximate fixed-point attractor changes and the network state “chases” this attractor over the course of the trial. Importantly, the location of the energy minima changes more rapidly as gain increases, effectively forcing the network to rapidly change course at the point of the perceptual switch (see Fig 4). To quantify this effect we constructed a new measure - neural work - which describes the amount of “force” exerted on the low-dimensional neural trajectory by the vector field quantified by the allocentric landscape. Specifically we treat the allocentric landscape as analogous to a potential function and then leverage the fact that force is equal to the negative gradient of potential energy to calculate the work (force x displacement) done on the low dimensional trajectory at each time point. This showed that as gain increases the amount of work done on the neuronal trajectory at turning points increases analogous to the application of an external force transiently increasing the kinetic energy of an object. From the perspective of the egocentric landscape this results in a flattening of the landscape as there is a lower energy (i.e. higher probability) assigned to large deviations in the neuronal trajectory around the perceptual switch.

      Because of the novelty of the analyses we went to great lengths to carefully explain the methods in the updated manuscript. In addition we wrote a short tutorial style MATLAB script implementing both the allocentric and egocentric landscape analysis on a toy dynamical system with a known potential function (a supercritical pitchfork bifurcation).

      (4) From the RNN model it is not clear how changes in excitatory and inhibitory gain lead to slower/faster switching. In order to better understand the role of inhibitory and excitatory gain on switching, I would suggest studying a simple discrete attractor model (a rate model, for example as in Wong and Wang 2006 or Roxin and Ledberg, Plos Comp. Bio 2008) which will allow to study these effects in terms of a very few model parameters. The Roxin paper also shows how to map rate models onto simplified one-dimensional systems such as the one in Fig S3. Setting up the model using this framework would allow for making much stronger, principled statements about how gain changes affect the energy landscape, and under which conditions increased inhibitory gain leads to faster switching.

      One possibility is that increasing the excitatory gain in the RNN leads to saturated firing rates. If this is the reason for the different effects of excitatory and inhibitory gain changes, it should be properly explained. Moreover, the biological relevance of this effect should be discussed (assuming that saturation is indeed the explanation).

      We thank the reviewer for this excellent suggestion. After some consideration we decided that studying a reduced model would likely not do justice to the dynamical mechanisms of RNN especially after making gain dynamical rather than stationary. Still we very much share the reviewer’s concern that we need a stronger link between the (now dynamical) gain alterations and energy landscape dynamics. To this end we now describe and interrogate the dynamics of the RNN at a circuit level through selectivity and lesion based analyses, at a population level through analysis of the dynamical regime traversed by the network, and finally, through an extended energy landscape framework which has far stronger links to traditional potential based descriptions of low-dimensional dynamical systems (also see to comment 3. above).

      At a circuit level the speeding of perceptual switches is mediated by inhibition of the initially dominant population we describe in paragraphs 7 and 8 of the section “Computational evidence for neuromodulatory-mediated perceptual switches in a recurrent neural network” as follows:

      “Having confirmed our hypothesis that increasing gain as a function of the network uncertainty increased the speed of perceptual switches, we next sought to understand the mechanisms governing this effect starting with the circuit level and working our way up to the population level (c.f. Sheringtonian and Hopfieldian modes of analysis(66)). Because of the constraint that the input and output weights are strictly positive, we could use their (normalised) value as a measure of stimulus selectivity. Inspection of the firing rates sorted by input weights revealed that the networks had learned to complete the task by segregating both excitatory and inhibitory units into two stimulus-selective clusters (Fig 2C). As the inhibitory units could not contribute to the networks read out, we hypothesised that they likely played an indirect role in perceptual switching by inhibiting the population of excitatory neurons selective for the currently dominant stimulus allowing the competing population to take over and a perceptual switch to occur.

      To test this hypothesis, we sorted the inhibitory units by the selectivity of the excitatory units they inhibit (i.e. by the normalised value of the readout weights). Inspecting the histogram of this selectivity metric revealed a bimodal distribution with peaks at each extreme strongly inhibiting a stimulus selective excitatory population at the exclusion of the other (Fig S2). Based on the fact that leading up to the perceptual switch point both the input and firing rate of the dominant population are higher than the competing population, we hypothesized that gain likely speeds perceptual switches by actively inhibiting the currently dominant population rather than exciting/disinhibiting the competing population. We predicted, therefore, that lesioning the inhibitory units selective for the stimulus that is initially dominant would dramatically slow perceptual switches, whilst lesioning the inhibitory units selective for the stimulus the input is morphing into would have a comparatively minor slowing effect on switch times since the population is not receiving sufficient input to take over until approximately half way through the trial irrespective of the inhibition it receives. As selectivity is not entirely one-to-one, we expect both lesions to slow perceptual switches but differ in magnitude. In line with our prediction, lesioning the inhibitory units strongly selective for the initially dominant population greatly slowed perceptual switches (Fig 3F upper), whereas lesioning the population selective for the stimulus the input morphs into removed the speeding effect of gain but had a comparatively small slowing effect on perceptual switches (Fig 3F lower).”

      At the population level we characterised the dynamics of the 2D parameter space (defined by gain and the difference between the input dimensions) traversed by the network over the course of a trial as input and gain dynamically change. We describe this paragraphs 9-14 of the section “Computational evidence for neuromodulatory-mediated perceptual switches in a recurrent neural network” which we reprint below for the reviewers convenience :

      “Based on the selectivity of the network firing rates we hypothesised that the dynamics were shaped by a fixed-point attractor whose location and existence were determined by gain and  and thus changed dynamically over the course of a single trial(67-70). Because of the large size of the network, we could not solve for the fixed points or study their stability analytically. Instead we opted for a numerical approach and characterised the dynamical regime (i.e. the location and existence of approximate fixed-point attractors) across all combinations of gain and  visited by the network. Specifically, for each combination of elements in the parameter space  we ran 100 simulations with initial conditions (firing rates) drawn from a uniform distribution between [0,1], and let the dynamics run for 10 seconds of simulation time (10 times the length of the task - longer simulation times did not qualitatively change the results) without noise. As we were interested in the existence of fixed-point attractors rather than their precise location, at each time point we computed the difference in firing rate between successive time points across the network. For each simulation we computed both the proportion of trials that converged to a value below  10^-2 giving us proxy for the presence of fixed points, and the time to convergence, giving us a measure of the “strength” of the attractor.

      Across gain values when input had unambiguous values, the network rapidly converged across all initialisations (Fig 3A & 3C-H). When input became ambiguous, however, the dynamics acquired a decaying oscillation and did not converge within the time frame of the simulation. As gain increased, the range of  values characterised by oscillatory dynamics broadened. Crucially, for sufficiently high values of gain, ambiguous  values transitioned the network into a regime characterised by high amplitude inhibition-driven oscillations (Fig 3D & 3G). Each trial can, therefore, be characterised by a trajectory through this 2-dimensional parameter space, with dynamics shaped by the dynamical regimes of each location visited (Fig 3A-B).

      When uncertainty has a small impact on gain the network has a trajectory through an initial regime characterised by the rapid convergence to a fixed point where the population representing the initial stimulus dominated whilst the other was silent (Fig 3C), an uncertain regime characterised by oscillations with all neurons partially activated (Fig 3D), and after passing through the oscillatory regime, the network once again enters a new fixed-point regime where the population representing the initial stimulus is now silent and the other is dominant (Fig 3E).

      For high gain trails, the network again started and finished in states characterised by a rapid convergence to a fixed point representing the dominant input dimension (Fig 3F-H), but differed in how it transitioned between these states. Uncertain inputs now generated high amplitude oscillations with the network flip-flopping between active and silent states (Fig 3G). We hypothesised that, within the task, this has the effect of silencing the initially dominant population, and boosting the competing population. To test this we initialised each network with parameter values well inside the oscillatory regime (u = [ .5, .5]  , gain = 1.5) with initial conditions determined by the selectivity of each unit. Excitatory units selective for input dimension 1, as well as the associated inhibitory units projecting to this population, were fully activated, whilst the excitatory units selective for  input dimension 2 and the associated inhibitory units were silenced. As we predicted, when initialised in this state the network dynamics displayed an out of phase oscillation where the initially dominant population was rapidly silenced and the competing population was boosted after a brief delay (219 (ms), +/-114 Fig S3).”

      From this we concluded that at a population level, heightened gain leading up to the perceptual switch speeds the switch by transiently pushing the dynamics into an unstable dynamical regime replacing the fixed-point attractor representing the input with an oscillatory regime that actively inhibits the currently dominant population and boosts the competing population before transitioning back into a regime with a stable (approximate) fixed-point attractor representing the new stimulus (Fig 3F-H & Fig S3).

      As we describe in the our response to comment 3 above our extended energy-landscape analysis framework now includes an explicit link between the potential of the dynamical system and allocentric landscape, whilst also explaining how a transient deepening of the allocentric landscape (which can be essentially thought of analogous to a traditional potential function) relates to the flattening of the egocentric landscape.

      Finally, whilst we appreciate the interest in further characterising the effect of inhibitory gain compared with excitatory gain the topic is is largely orthogonal the aims of our paper so we have removed the discussion of inhibitory vs excitatory gain. Still, we understand that we need to do our due diligence and check that our results do not break down when we manipulate either inhibitory or excitatory gain in isolation. To this end we checked that dynamical gain still speeded perceptual switches when the effect was isolated to inhibitory or excitatory cells in isolation. We show the behavioural plots below for the reviewer’s interest.

      Author response image 1.

      Switch time as a function of uncertainty forcing

      Alternative mechanisms:

      It is mentioned in the introduction that changes in attention could drive perceptual switches. A priori, attention signals originating in the frontal cortex may be plausible mechanisms for perceptual switches, as an alternative to LC-controlled gain modulation. Does the observed fMRI dynamics allow us to distinguish these two hypotheses? In any case, I would suggest including alternative scenarios that may be compatible with the observed findings in the discussion.

      We agree with the reviewer, in that attention is itself a confound and a process that is challenging to disentangle from the perceptual switching process in the current task. Importantly, we were not arguing for exclusivity in our manuscript, but merely testing the veracity of the hypothesis that the ascending arousal system may play a causal role in mediating and/or speeding perceptual switches. Future work with experiments that more specifically aim to dissociate these different features will be required to tease apart these different possibilities.

      Reviewer #2 (Public Review):

      Strengths

      - the study combines different methods (pupillometry, RNNs, fMRI).

      - the study combines different viewpoints and fields of the scientific literature, including neuroscience, psychology, physics, dynamical systems.

      - This combination of methods and viewpoints is rarely done, it is thus very useful.

      - Overall well-written.

      Weaknesses

      - The study relies on a report paradigm: participants report when they identify a switch in the item category. The sequence corresponds to the drawing of an object being gradually morphed into another object. Perceptual switches are therefore behaviorally relevant, and it is not clear whether the effect reported correspond to the perceptual switch per se, or the detection of an event that should change behavior (participant press a button indicating the perceived category, and thus switch buttons when they identify a perceptual change). The text mentions that motor actions are controlled for, but this fact only indicates that a motor action is performed on each trial (not only on the switch trial); there is still a motor change confounded with the switch. As a result, it is not clear whether the effect reported in pupil size, brain dynamics, and brain states is related to a perceptual change, or a decision process (to report this change).

      We agree with the reviewer that the coupling of the motor change with the perceptual switch is confounded to some degree, but since motor preparation occurs on every trial we suspect that it is more accurate to describe it as confounded with task-relevance more than motor preparation per se.  While it is possible that pupil diameter, network topology and energy landscape features are all related to motor change rather than the perceptual switch, we note that the weight of evidence is against this interpretation, given the simple mechanistic explanation created by the coupling of perceptual uncertainty to network gain.

      - The study presents events that co-occur (perceptual switch, change in pupil size, energy landscape of brain dynamics) but we cannot identify the causes and consequences. Yet, the paper makes several claims about causality (e.g. in the abstract "neuromodulatory tone ... causally mediates perceptual switches", in the results "the system flattening the energy landscape ... facilitated an updating of the content of perception").

      We have made an effort to soften the causal language, where appropriate. In addition, we note that we have changed the title to “Gain neuromodulation mediates task-relevant perceptual switches: evidence from pupillometry, fMRI, and RNN Modelling” to reflect the fact that our claims do not extent to cases of perceptual switches where the stimulus is only passively observed.

      - Some effects may reflect the expectation of a perceptual switch, rather than the perceptual switch per se. Given the structure of the task, participants know that there will be a perceptual switch occurring once during a sequence of morphed drawings. This change is expected to occur roughly in the middle of the sequence, making early switches more surprising, and later switches less surprising. Differences in pupil response to early, medium, and late switches could reflect this expectation. The authors interpret this effect very differently ("the speed of a perceptual switch should be dependent on LC activity").

      The task includes catch trials designed to reduce the expectation of a perceptual switch. In these trials, a perceptual switch occurs either earlier or later than usual. While these trials are valuable for mitigating predictability, we did not focus extensively on them, as they were thoroughly discussed in the original paper. Additionally, due to the limited number of catch trials, it is difficult—if not impossible—to calculate a reliable mean surprise per image set.

      It is also worth noting that the pupil study does not include catch trials, which could contribute to differences in how perceptual switches are processed and interpreted between the fMRI and pupil experiments.

      - The RNN is far more complex than needed for the task. It has two input units that indicate the level of evidence for the two categories being morphed, and it is trained to output the dominant category. A (non-recurrent) network with only these two units and an output unit whose activity is a sigmoid transform of the difference in the inputs can solve the task perfectly. The RNN activity is almost 1-dimensional probably for this reason. In addition, the difficult part of the computation done by the human brain in this task is already solved in the input that is provided to the network (the brain is not provided with the evidence level for each category, and in fact, it does not know in advance what the second category will be).

      We agree that a simpler model could perform the task. We opted to use an RNN rather than hand craft a simpler model as we wanted to use the model as both a method of hypothesis testing and hypothesis generation. We now expand on and justify this modelling choice in the second paragraph of the discussion (also see our response to Reviewer 1 comment 4):

      “We chose to use an RNN, instead of a simpler (more transparent) model as we wanted to use the RNN as a means of both hypothesis generation and hypothesis testing. Specifically, unlike more standard neuronal models which are handcrafted to reproduce a specific effect, when building an RNN the modeller only specifies the network inputs, labels, and the parameter constraints (e.g. Dale’s law) in advance. The dynamics of the RNN are entirely determined by optimisation. Post-training manipulations of the RNN are not built in, or in any way guaranteed to work, making them more analogous to experimental manipulations of an approximately task-optimal brain-like system. Confirmatory results are arguably, therefore, a first steps towards an in vitro experimental test.”

      In other words, a simpler model would not have been appropriate to the aims. In addition we note that low dimensional dynamics are extremely common in the RNN literature and are in no way unique to our model. 

      - Basic fMRI results are missing and would be useful, before using elaborate analyses. For instance, what are the regions that are more active when a switch is detected?

      We explicitly chose to not run a standard voxelwise statistical parametric approach on these data, as the results were reported extensively in the original study (Stottinger et al., 2018).

      - The use of methods from physics may obscure some simple facts and simpler explanations. For instance, does the flatter energy landscape in the higher gain condition reflect a smaller number of states visited in the state space of the RNN because the activity of each unit gets in the saturation range? If correct, then it may be a more straightforward way of explaining the results.

      We appreciate the reviewer's concern as this would indeed be a problem. However, this is not the case for our network. At the time point of the perceptual switch where the egocentric landscape dynamics are at their flattest the RNN firing rates are approximately 50% activated nowhere near the saturation point. In addition, a flatter landscape in the egocentric and allocentric landscape analyses only occurs - mathematically speaking - when there are more states visited not less.

      In addition, we note that we are very sympathetic to the complexity of our physics based analyses and have gone to great lengths to describe them in an accessible manner in both the main text and methods. We have also included tutorial style code demonstrating how the analysis can be used on a toy dynamical system in the supplementary material.

      - Some results are not as expected as the authors claim, at least in the current form of the paper. For instance, they show that, when trained to identify which of two inputs u1 and u2 is the largest (with u2=1-u1, starting with u1=1 and gradually decreasing u1), a higher gain results in the RNN reporting a switch in dominance before the true switch (e.g. when u1=0.6 and u2=0.4), and vice et versa with a lower gain. In other words, it seems to correspond to a change in criterion or bias in the RNN's decision. The authors should discuss more specifically how this result is related to previous studies and models on gain modulation. An alternative finding could have been that the network output is a more (or less) deterministic function of its inputs, but this aspect is not reported.

      We appreciate this comment but it is simply not applicable to our network. There is no criterion in the RNN. We could certainly add one but this would be a significant departure from how decisions are typically modelled in RNNs. The (deterministic) readout is the max of the projection of the (instantaneous) excitatory firing rate onto the readout weights. A shift in criterion would imply that the dynamics are unaffected and the effect can be explained by a shift in the readout weights; this cannot be the case because the readout weights are stationary the change occurs at the level of the activation function.

      We are aware that there is a large literature in decision making and psychophysics that uses the term gain in a slightly different way. Here we are strictly referring to the gain of the activation function. Although we agree that it would be interesting and important to discuss the differing uses of the term gain, this is beyond the scope of the present paper.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their thoughtful comments.

      Based on their suggestions we will:

      (1) Use more accurate language to describe the hypothalamus regions under investigation in this study. While we aimed to primarily investigate the medial preoptic area (MPOA), our dissections and sequencing data in fact capture several regions of the anterior hypothalamus including the anteroventral periventricular (AVPV), paraventricular (PVN), supraoptic (SON), suprachiasmatic nuclei (SCN), and more. We will revise the language in our manuscript to reflect that our study in fact investigates the cellular evolution of the anterior hypothalamus across behaviorally divergent deer mice.

      (2) Revise our language to clarify that while our study provides a rich dataset for generating hypotheses about which cell types may contribute to behavioral differences, it does not provide any evidence of causal relationships. We hope to investigate this further in future work.

      (3) Clarify specific methodological choices for which reviewers had questions, especially about the hypothalamic regions for which we did histology to validate cell abundance differences and methodological choices related to mapping our cell clusters to Mus cell types.

      Our responses to each reviewer’s specific comments are below.

      Reviewer #1:

      The major limitation of the study is the absence of causal experiments linking the observed changes in MPOA cell types to species-specific social behaviors. While the study provides valuable correlational data, it lacks functional experiments that would demonstrate a direct relationship between the neuronal differences and behavior. For instance, manipulating these cell types or gene expressions in vivo and observing their effects on behavior would have strengthened the conclusions, although I certainly appreciate the difficulty in this, especially in non-musculus mice. Without such experiments, the study remains speculative about how these neuronal differences contribute to the evolution of social behaviors.

      Yes, we agree the study lacks functional experiments. We hope that the dataset is of value for generating hypotheses about how hypothalamic neuronal cell types may govern species-specific social behaviors, and for these hypotheses to be functionally tested by us and others in future work.

      Reviewer #2:

      Some methodology could be further explained, like the decision of a 15% cutoff value for cell type assignment per cluster, or the necessity of a multi-step analysis pipeline for gene enrichment studies.

      A 15% cutoff value for cell type assignment was chosen to include all known homology correspondences between our dataset and the Mus atlas. For example, i14:Avp/Cck cells from the Mus atlas represent Avp cells from the suprachiasmatic nuclei (SCN). Though only 17.3% of cluster 15 maps to i14:Avp/Cck, we know these two clusters correspond based on the expression of Avp and additional SCN marker genes in cluster 15 (Supp Fig 6). We will further explain this cutoff in the revised manuscript.

      Our gene enrichment study includes a multi-step analysis pipeline because we wanted to control for confounders that may be introduced because of gene expression level. Genes that are more highly expressed are more accurately quantified and thus more likely to be identified as differentially expressed. Therefore, we wanted to test for gene enrichments in our set of DE genes against a background of genes with similar expression levels. We will clarify this motivation in the revised manuscript.

      The authors should exercise strong caution in making inferences about these differences being the basis of parental behavior. It is possible, given connections to relevant research, but without direct intervention, direct claims should be avoided. There should be clear distinctions of what to conclude and what to propose as possibilities for future research.

      Yes, we agree that we are unable to make direct claims about neuronal differences being the basis of parental behavior. We will revise our language to be clearer about which relationships we are hypothesizing and what we propose as possibilities for future research.

      Histology is not performed on all regions included in the sequencing analysis.

      We apologize that our language describing the hypothalamic regions included in the sequencing analysis and those included in the histology is unclear. We aimed to dissect the medial preoptic region for the sequencing analysis, but additionally captured parts of the anterior hypothalamus including the paraventricular (PVN), supraoptic (SON), and suprachiasmatic nuclei (SCN), and more.  Our histology was performed across the entire hypothalamus and includes all regions included in the sequencing data. We will revise the manuscript to more accurately describe the hypothalamic regions for which we investigated.

      Reviewer #3:

      My primary concern is that the dataset is limited: 52,121 neuronal nuclei across 24 samples, which does not provide many cells per cluster to analyze comparatively across sex and species, particularly given the heterogeneity of the region dissected. The Supplementary table reports lower UMIs/genes per cell than is typically seen as well. Perhaps additional information could be obtained from the data by not restricting the analyses to cells that can be assigned to Mus types. A direct comparison of the two Peromyscus species could be valuable as would a more complete Peromyscus POA atlas.

      Our dataset reports ~1,500 genes and ~1,000 UMIs per nuclei which is indeed lower than is typically reported in other single nuclei datasets. Some of this discrepancy is due to a lower quality genome and annotated transcriptome available for Peromyscus compared to Mus musculus, which results in a lower mapping rate than is typically reported in Mus studies. However, our dataset was sufficient to identify known peptidergic cell types (Supp Fig 6) and to map homology to Mus cell types for 34 (64%) of our 53 clusters. Additionally, although some of our clusters contain small numbers of cells, our differential abundance analysis accounts for the variance in cell numbers observed across samples and should be robust against any increase in variance due to small numbers. In fact, even differential abundance of very small cell clusters such as oxytocin neurons (cell type 40) was validated by histology.

      We would like to clarify that all analyses were performed on all cell clusters, regardless of whether or not they could be assigned homology to a Mus cell type. All the cell types that we identified as differentially abundant or contained significant sex differences happened to be cell types for which homology to a Mus cell type could be defined. This may arise for a relatively uninteresting reason: cell types that have more distinct transcriptional signatures will be more accurately clustered, leading to more accurate identification of homology as well as more accurate measurements of differential abundance / expression. We will revise language to make this more clear in our manuscript.

      In Supplement 7, it appears that most neurons can be assigned as excitatory or inhibitory, but then so many of these cells remain in the unassigned "gray blob" seen in panel 1E. Clustering of excitatory and inhibitory neurons separately, as in prior cited work in Mus POA (refs 31 and 57) may boost statistical power to detect sex and species differences in cell types. Perhaps the cells that cannot be assigned to Mus contain too few reads to be useful, in which case they should be filtered out in the QC. The technical challenges of a comparative single-cell approach are considerable, so it benefits the scientific community to provide transparency about them.

      We are not certain about why we are unable to cluster and assign homology to many of our cells (i.e. cells in the unassigned “gray blob”). However, we note that even in the Mus atlas, many cells did not belong to obvious clusters by UMAP visualization and that several clusters lacked notable marker genes and were designated simply as “Gaba” and “Glut” clusters. Therefore, it is unsurprising that our own dataset also contains cells that lack the transcriptional signatures needed to be clustered and/or mapped to Mus cell types. We do know, however, that the median number of reads/nuclei is uniform across cell clusters and does not explain why some clusters could not be assigned to Mus. We will add this information to our revised manuscript.

      We do not think that a two-stage clustering (i.e. clustering first by excitatory vs. inhibitory neurons) is expected to gain power to resolve cell types in this case. Excitatory vs. inhibitory neurons are clearly separable on our UMAP (Supp Fig 7) so that information is already being used by our clustering procedure. However, we will explore this further in our revised manuscript to see if doing so will boost statistical power.

      The Calb1 dimorphism as observed by immunostaining, appears much more extensive in P. maniculatus compared to P. polionotus (Figures 3 E and F). This finding is not reflected in the counts of the i20:Gal/Moxd1 cluster. The use of Calb1 staining as a proxy for the Gal/Moxd1 cluster would be strengthened if the number of POA Calb1+ neurons that are found in each cluster was apparent. There may be additional Calb+ neurons in the cells that are not annotated to a Mus cluster. This clarification would add support to the overall conclusion that there is reduced sexual dimorphism in P. polionotus.

      From the Mus MPOA atlas (which includes both single-cell sequencing data and imaging-based spatial information), it is known that the i20:Gal/Moxd1 cluster comprises sexually dimorphic cells that make up both the BNST and the SDN-POA. These sexually dimorphic cells are well-studied and known to be marked by Calb1, which we used in immunostaining as a proxy for i20:Gal/Moxd1.

      However, we would like to clarify that in our study, the immunostaining of Calb1+ neurons and the sequencing counts of the i20:Gal/Moxd1 cluster are not completely reflective of each other because our sequencing dataset only captured the ventral portion of the BNST. Therefore our i20:Gal/Moxd1 counts contain a combination of some Calb1+ BNST cells and likely all Calb1+ SDN-POA cells and is difficult to interpret on its own. Our histology, however, covers the entire hypothalamus and is more reliable for identifying sex and species differences in each region. We will clarify this in the revised manuscript.

      The relationship between the sex steroid receptor expression and the sex bias in gene expression would be improved if the sex bias in sex steroid receptor expression was included in Supplementary Figure 10.

      We will include this in the revised manuscript.

      There is no explanation for the finding that there is a female bias in gene expression across all cell types in P. polionotus.

      We also find this observation interesting but don’t have a good explanation for why at this point. We plan to follow this up in future work.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This manuscript by Guo and Uusisaari describes a series of experiments that employ a novel approach to address long-standing questions on the inferior olive in general and the role of the nucleoolivary projection specifically. For the first time, they optimized the ventral approach to the inferior olive to facilitate imaging in this area that is notoriously difficult to reach. Using this approach, they are able to compare activity in two olivary regions, the PO and DAO, during different types of stimulation. They demonstrate the difference between the two regions, linked to Aldoc-identities of downstream Purkinje cells, and that there is co-activation resulting in larger events when they are clustered. Periocular stimulation also drives larger events, related to co-activation. Using optogenetic stimulation they activate the nucleoolivary (N-O) tract and observe a wide range of responses, from excitation to inhibition. Zooming in on inhibition they test the assumption that N-O activation can be responsible for suppression of sensoryevoked events. Instead, they suggest that the N-O input can function to suppress background activity while preserving the sensory-driven responses.

      Strengths:

      This is an important study, tackling the long-standing issue of the impossibility to do imaging in the inferior olive and using that novel method to address the most relevant questions. The experiments are technically very challenging, the results are presented clearly and the analysis is quite rigorous. There is quite a lot of room for interpretation, see weaknesses, but the authors make an effort to cover many options.

      Weaknesses:

      The heavy anesthesia that is required during the experiment could severely impact the findings. Because of the anesthesia, the firing rate of IO neurons is found to be 0.1 Hz, significantly lower than the 1 Hz found in non-anesthetized mice. This is mentioned and discussed, but what the consequences could be cannot be understated and should be addressed more. Although the methods and results are described in sufficient detail, there are a few points that, when addressed, would improve the manuscript.

      We sincerely thank the reviewer for their encouraging comments and recognition of our study’s significance. We fully acknowledge the confounding effects of the deep anesthesia used in our experiments, which was necessary to ensure the animals’ welfare while establishing this technically demanding methodology. We elaborate on these effects below and will further clarify them in the revised manuscript.

      Ultimately, the full resolution of this issue will require recordings in awake animals, as we consider our approach an advancement from acute slice preparations but not yet a complete representation of in vivo IO function. However, key findings from our study—such as amplitude modulation with co-activation and the potential role of IO refractoriness in complex spike generation—could be further explored in existing cerebellar cortical recordings from awake, behaving animals. We hope our work will motivate re-examination of such datasets to assess whether these mechanisms contribute to overall cerebellar function.

      Reviewer #1 (Recommendations for the authors):

      On page 10 the authors indicate that 2084 events were included for DAO and 1176 for PO. Is that the total number of events? What was the average and the range per neuron and the average recording duration?

      Thank you for pointing out lack of clarity. The sentence should say "in total, 2084 and 1176 detected events from DAO and PO were included in the study". We will add the averages and ranges of events detected per neuron in different categories, as well as the durations of the recordings (ranging from 120s to 270s) to the tables.

      On page 10 it is also stated that: "events in PO reached larger values than those in DAO even though the average values did not differ". Please clarify that statement. Which parameter + p-value in the table indicates this difference?

      Apologies for omission. Currently the observation is only visible in the longer tail to the right in the PO data in Figure 2B2. We will add the range of values (3.0-75.2 vs 3.1-39.6 for PO and DAO amplitudes, respectively) in text and the tables in the revision.

      Abbreviating airpuff to AP is confusing, I would suggest not abbreviating it.

      Understood. We will change AP to airpuff in the text. In figure labels, at least in some panels, the abbreviation will be necessary due to space constraints.

      What type of pulse was used to drive ChrimsonR? Could it be that the pulse caused a rebound-like phenomenon with the pulse duration that drove the excitation?

      As described on line 229 and in the Methods, we used 5-second trains of 5-ms LED light pulses. Importantly, these stimulation parameters were informed by our extensive in vitro examination of various stimulation patterns (Lefler et al., 2014), which consistently produced stable postsynaptic responses without inducing depolarization or rebound effects. Additionally, Loyola et al. (2024) reported no evidence of rebound activity in IO cells following optogenetic activation of N-O axons in the absence of direct neuronal depolarization. We will incorporate these considerations into the discussion, while also acknowledging that unequivocal confirmation of “direct” rebound excitation would require intracellular recordings, such as patch clamp experiments.

      The authors indicate that the excitatory activity was indistinguishable in shape from other calcium activity, but can anything be said about the timing (the scale bar in Figure 4A2 has no value, is it the same 2s pulse)?

      Apologies for oversight in labeling the scale bar in Figure 4A2 (it is 2s). While we deliberately refrain from making strong claims regarding the origin of the NO-evoked spikes, their timing can be examined in more detail in Figure 4 - Supplement 1, panels C and D. We will make sure this is clearly stated in the revised text.

      Did the authors check for accidental sparse transfection with ChrimsonR of olivary neurons in the post-mortem analysis?

      Good point! However, we have never seen this AAV9-based viral construct to drive trans-synaptic expression in the IO, nor is this version of AAV known to have the capacity for transsynaptic expression in general.

      No sign of retrograde labeling (via the CF collaterals in the cerebellar nuclei) was seen either. Notably, the hSyn promoter used to drive ChrimsonR expression is extremely ineffective in the IO. Thus, we doubt that such accidental labeling could underlie the excitatory events seen upon N-O stimulation. We will add these mentions with relevant references to the discussion of the revised manuscript.

      On page 18 the authors state that: "The lower SS rate was attributed to intrinsic factors of PNs, while the reduced frequency of CSs was speculated to result from increased inhibition of the IO via the nucleo-olivary (N-O) pathway targeting the same microzone." I think I understand what you mean to say, but this is a bit confusing.

      Agreed. We will rephrase this sentence to clarify that a lower SS rate in a given microzone may lead to increased activation of inhibitory N-O axons that target the region of IO that sends CF to the same microzone.

      Is airpuff stimulation not more likely to activate PO dan DAO because of the related modalities (more face vs. more trunk/limbs?), and thereby also more likely to drive event co-activation (as it is stated in the abstract).

      We agree that the specific innervation patterns of different IO regions likely explain the discrepancy between previous reports of airpuff-evoked complex spikes in cerebellar cortical regions targeted by DAO and the absence of airpuff responses in the particular region of DAO accessible via our surgical approach. As in the present dataset virtually no airpuff-evoked events were seen in DAO regions, we are unable to directly compare airpuff-evoked event co-activation between PO and DAO. The higher co-activation for PO was observed for "spontaneous" activity.

      The Discussion addresses the question of why N-O pathway activation does not remove the airpuff response.

      Given the potentially profound effect, I would propose to expand the discussion on the role of aneasthesia, including longer refractory periods but also potential disruption of normal network interactions (even though individually the stimulations work). Briefly indicating what is known about alpha-chloralose would help interpret the results as well.

      We fully agree that the anesthetic state introduces confounding factors that must be considered when interpreting our results. We will expand the discussion to address how anesthesia, particularly alphachloralose as well as tissue cooling, may contribute to prolonged refractory periods and potential disruptions in normal network interactions. However, we recognize that certain aspects cannot be fully resolved without recordings in awake animals. For this reason, we characterize our preparation as an "upgraded" in vitro approach rather than a fully representative in vivo model.

      Please clearly indicate that the age range of P35-45 is for the moment of virus injection and specify the age range for the imaging experiment.

      Apologies for the oversight. We will indicate these age ranges in the results (as they are currently only specified in Methods). The P35-45 range refers to moment of virus injection.

      The methods indicate that a low-pass filter of 1Hz was used. I am sure this helps with smoothing, but does it not remove a lot of potentially interesting information. How would a higher low-pass filter affect the analysis and results?

      We acknowledge that applying a 1 Hz low-pass filter inevitably removes high-frequency components, including potential IO oscillations and fine details such as spike "doublets." However, given the temporal resolution constraints of our recording approach, we prioritized capturing robust, interpretable events over attempting to extract finer features that might be obscured by both the indicator kinetics and imaging speed.

      While a higher cut-off frequency could, in principle, allow more precise measurement of rise times and peak timings, it would also amplify high-frequency noise, complicating automated event detection and reducing confidence in distinguishing genuine neural signals from artifacts. Given these trade-offs, we opted for a conservative filtering approach to ensure stable event detection. Future work, particularly with faster imaging rates and improved sensors (GCaMP8s) will be used to explore the finer temporal structure of IO activity. We will deliberate on these matters more extensively in the revised discussion.

      Reviewer #2 (Public review):

      The authors developed a strategy to image inferior olive somata via viral GCaMP6s expression, an implanted GRIN lens, and a one-photon head-mounted microscope, providing the first in vivo somatic recordings from these neurons. The main new findings relate to the activation of the nucleoolivary pathway, specifically that: this manipulation does not produce a spiking rebound in the IO; it exerts a larger effect on spontaneous IO spiking than stimulus (airpuff)-evoked spiking. In addition, several findings previously demonstrated in vivo in Purkinje cell complex spikes or inferior olivary axons are confirmed here in olivary somata: differences in event sizes from single cells versus co-activated cells; reduced coactivation when activating the NO pathway; more coactivation within a single zebrin compartment.

      The study presents some interesting findings, and for the most part, the analyses are appropriate. My two principal critiques are that the study does not acknowledge major technical limitations and their impact on the claims; and the study does not accurately represent prior work with respect to the current findings.

      We thank the reviewer for recognising the value of the findings in our "reduced" in vivo preparation, and apologize for omissions in the work that led to critique. We will elaborate on these matters below and prepare a revised manuscript.

      The authors use GCaMP6s, which has a tau1/2 of >1 s for a normal spike, and probably closer to 2 s (10.1038/nature12354) for the unique and long type of olivary spikes that give rise to axonal bursts (10.1016/j.neuron.2009.03.023). Indeed, the authors demonstrate as much (Fig. 2B1). This affects at least several claims:

      a. The authors report spontaneous spike rates of 0.1 Hz. They attribute this to anesthesia, yet other studies under anesthesia recording Purkinje complex spikes via either imaging or electrophysiology report spike rates as high as 1.5 Hz (10.1523/JNEUROSCI.2525-10.2011). This discrepancy is not acknowledged and a plausible explanation is not given. Citations are not provided that demonstrate such low anesthetized spike rates, nor are citations provided for the claim that spike rates drop increasingly with increasing levels of anesthesia when compared to awake resting conditions.

      We fully acknowledge that anesthesia is a major confounding factor in our study. Given the unusually invasive nature of our surgical preparation, we prioritized deep anesthesia to ensure the animals’ welfare. This, along with potential cooling effects from tissue removal and GRIN lens contact, likely contributed to the observed suppression of IO activity.

      We recognize that reported complex spike rates under anesthesia vary considerably across studies, and we will expand our discussion to provide a more comprehensive comparison with prior literature. Notably, different anesthetic protocols, levels of anesthesia, and recording methodologies can lead to widely different estimates of firing rates. While we cannot resolve this issue without recordings in awake animals, we will clarify that our observed rates likely reflect both the effects of anesthesia and specific methodological constraints. We will also incorporate additional references to studies examining cerebellar activity under different anesthetic conditions.

      More likely, this discrepancy reflects spikes that are missed due to a combination of the indicator kinetics and low imaging sensitivity (see (2)), neither of which are presented as possible plausible alternative explanations.

      We acknowledge that the combination of slow indicator kinetics and limited optical power in our miniature microscope setup constrains the temporal resolution of our recordings. However, we are confident that we can reliably detect events occurring at intervals of 1 second or longer. This confidence is based on data from another preparation using the same viral vector and optical system, where we observed spike rates an order of magnitude higher.

      That said, we do not make claims regarding the presence or absence of somatic events occurring at very short intervals (e.g., 100-ms "doublets," as described by Titley et al., 2019), as these would likely fall below our temporal resolution. We will clarify this limitation in the revised manuscript to ensure that the constraints of our approach are fully acknowledged.

      While GCaMP6s is not as sensitive as more recent variants (Zhang et al., 2023, PMID 36922596), our previous work (Dorgans et al., 2022) demonstrated that its dynamic range and sensitivity are sufficient to detect both spikes and subthreshold activity in vitro. Although the experimental conditions differ in the current miniscope experiments, we took measures to optimize signal quality, including excluding recordings with a low signal-to-noise ratio (see Methods). This need for high signal fidelity also informed our decision to limit the sampling rate to 20 fps. In future work, we plan to adopt newer GCaMP variants that were not available at the start of this project, which should further improve sensitivity and temporal resolution.

      Many claims are made throughout about co-activation ("clustering"), but with the GCaMP6s rise time to peak (0.5 s), there is little technical possibility to resolve co-activation. This limitation is not acknowledged as a caveat and the implications for the claims are not engaged with in the text.

      As noted in the manuscript (L492-), "interpreting fluorescence signals relative to underlying voltage changes is challenging, particularly in IO neurons with unusual calcium dynamics." We acknowledge that the slow rise time of GCaMP6s ( 0.5 s) limits our ability to precisely resolve the timing of co-activation at very short intervals. However, given the relatively slow timescales of IO event clustering and the inherent synchrony in olivary network dynamics, we believe that the observed co-activation patterns remain meaningful, even if finer temporal details cannot be fully resolved.

      To ensure clarity, we will expand this section to explicitly acknowledge the temporal resolution limitations of our approach and discuss their implications for interpreting co-activation. While the precise timing of individual spikes within a cluster may not be resolvable, the observed increase in event magnitude with coarse co-activation suggests that clustering effects remain functionally relevant even when exact spike synchrony is not detectable at millisecond resolution.

      This finding is consistent with the idea that co-activation enhances calcium influx, leading to larger amplitude events — a relationship that does not require perfect temporal resolution to be observed. The fact that this effect persists across a broad range of clustering windows (as shown in Figure 2 Supplement 2) further supports its robustness. While we cannot make strong claims about precise spike timing within these clusters nor about the mechanism underlying enhanced calcium signal, our results demonstrate that co-activation may influence IO activity in a quantifiable way. We will clarify these points in the revised manuscript to ensure that our findings are appropriately framed given the temporal constraints of our imaging approach.

      The study reports an ultralong "refractory period" (L422-etc) in the IO, but this again must be tempered by the possibility that spikes are simply being missed due to very slow indicator kinetics and limited sensitivity. Indeed, the headline numeric estimate of 1.5 s (L445) is suspiciously close to the underlying indicator kinetic limitation of 1-2 s.

      Our findings suggest a potential refractory period limiting the frequency of events in the inferior olive under our recording conditions. This interpretation is supported by the observed inter-event interval distribution, the inability of N-O stimulation to suppress airpuff-evoked events, and lower bounds reported in earlier literature on complex spike intervals recorded in awake animals under various behavioral contexts. Taking into account the likely cooling of tissue, a refractory period of 1.5s is not unreasonable. Of course, we recognize that the slow decay kinetics of GCaMP6s may cause overlapping fluorescence signals, potentially obscuring closely spaced events. This is in line with data presented in the Chen et al 2013 manuscript describing GCaMp6s (PMID: 36922596; Figure 3b showing events detected with intervals less than 500 ms).

      The consideration of refractoriness only arose late in the project while we were investigating the explanations for lack of inhibition of airpuff-evoked spikes. Future experiments, particularly in awake animals, will be instrumental in validating this interpretation. To ensure that the refractory period is understood as one possible mechanism rather than a definitive explanation, we will rephrase the discussion to clarify that while our data are compatible with a refractory period, they do not establish it conclusively.

      The study uses endoscopic one-photon miniaturized microscope imaging. Realistically, this is expected to permit an axial point spread function (z-PSF) on the order of 40um, which must substantially reduce resolution and sensitivity. This means that if there *is* local coactivation, the data in this study will very likely have individual ROIs that integrate signals from multiple neighboring cells. The study reports relationships between event magnitude and clustering, etc; but a fluorescence signal that contains photons contributed by multiple neighboring neurons will be larger than a single neuron, regardless of the underlying physiology - the text does not acknowledge this possibility or limitation.

      We acknowledge that the use of one-photon endoscopic imaging imposes limitations on axial resolution, potentially leading to signal contributions from neighboring neurons. To mitigate this, we applied CNMFe processing, which allows for the deconvolution of overlapping signals and the differentiation of multiple neuronal sources within shared pixels. However, as the reviewer points out, if two neurons are perfectly overlapping in space, they may be treated as a single unit.

      To clarify this limitation, we will expand the discussion to explicitly acknowledge the impact of one-photon imaging on signal separation and to emphasize that, while CNMFe helps resolve some overlaps, perfect separation is not always possible. As already noted in the manuscript (L495-), "the absence of optical sectioning in the whole-field imaging method can lead to confounding artifacts in densely labeled structures such as the IO’s tortuous neuropil." We will further elaborate on how this factor was considered in our analysis and interpretation.

      Second, the text makes several claims for the first multicellular in vivo olivary recordings. (L11; L324, etc).

      I am aware of at least two studies that have recorded populations of single olivary axons using two-photon Ca2+ imaging up to 6 years ago (10.1016/j.neuron.2019.03.010; 10.7554/eLife.61593). This technique is not acknowledged or discussed, and one of these studies is not cited. No argument is presented for why axonal imaging should not "count" as multicellular in vivo olivary recording: axonal Ca2+ reflects somatic spiking.

      We appreciate the reviewer’s point and acknowledge the important prior work using two-photon imaging to record olivary axonal activity in the cerebellar cortex. However, while axonal calcium signals do reflect somatic spiking, these recordings inherently lack information about the local network interactions within the inferior olive itself.

      A key motivation for our study was to observe neuronal activity within the IO at the level of its gap-junctioncoupled local circuits, rather than at the level of its divergent axonal outputs. The fan-like spread of climbing fibers across rostrocaudal microzones in the cerebellar cortex makes them relatively easy to record in vivo, but it also means that individual imaging fields contain axons from neurons that may be distributed across different IO microdomains. As a result, while previous work has provided valuable insight into olivary output patterns, it has not allowed for the examination of coordinated somatic activity within localized IO neuron clusters.

      With apologies, we recognize that this distinction was not sufficiently emphasized in our introduction. We will clarify this key point and ensure that the important climbing fiber imaging studies are properly cited and contextualized in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      The authors state: "we found no reports that examined coactivation levels between Z+ and Z- microzones in cerebellar complex spike recordings" (L359). Multiple papers (that are not cited) using AldolaceC-tdTomato mice with two photon Purkinje dendritic calcium imaging showed synchronization (at similar levels) within but not across z+/z- bands. (2015 10.1523/JNEUROSCI.2170-14.2015, 2023 https://doi.org/10.7554/eLife.86340).

      We apologize for the misleading phrasing. We will rephrase this statement to: "While complex spike coactivation within individual zebrin zones has been extensively studied (references), we found no reports directly comparing the levels of intra-zone co-activation between Z+ and Z microzones."

      Additionally, we will ensure that the relevant studies demonstrating synchronization within zebrin zones, as well as (lack of) interactions between neighboring zones, are properly cited and discussed in the revised manuscript.

      The figures could use more proofreading, and several decisions should be reconsidered:

      Normalizing the amplitude to maximum is not a good strategy, as it can overemphasize noise or extremely small-magnitude signals, and should instead follow standard convention and present in fixed units (3A2, 4B2, and even 2C).

      As noted earlier, we have excluded recordings and cells with high noise or a low signal-to-noise ratio for event amplitudes, ensuring that such data do not influence the color-coded panels. Importantly, all quantitative analyses and traces presented in the manuscript are normalized to baseline noise level, not to maximal amplitude, ensuring that noise or low-magnitude signals do not skew the analysis.

      The decision to use max-amplitude normalization in color-coded panels was made specifically to aid visualization of temporal structure across recordings. This approach allows for clearer comparisons without the distraction of inter-cell variability in absolute signal strength. However, we recognize the potential for confusion and will revise the Results text to explicitly clarify that the color-coded visualizations use a different scaling method than the quantitative analyses.

      x axes with no units: Figures 2B2, 2E1, 3B2, 3C2, 5B2, 5C2, 5D2.

      No colorbar units: 5A3 (and should be shown in real not normalized units).

      No y axis units: 5D1.

      No x axis label or units: 5E1.

      5E3 says "stim/baseline" for the y-axis units and then the first-panel title says "absolute frequencies" meaning it’s *not* normalized and needs a separate (accurate) y-axis with units.

      Illegibly tiny fonts: 2E1, 3E1, etc.

      We will correct all these in the revised manuscript. Thank you for careful reading.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      This study provides a thorough analysis of Nup107's role in Drosophila metamorphosis, demonstrating that its depletion leads to developmental arrest at the third larval instar stage due to disruptions in ecdysone biosynthesis and EcR signaling. Importantly, the authors establish a novel connection between Nup107 and Torso receptor expression, linking it to the hormonal cascade regulating pupariation.

      However, some contradictory results weaken the conclusions of the study. The authors claim that Nup107 is involved in the translocation of EcR from the cytoplasm to the nucleus. However, the evidence provided in the paper suggests it more likely regulates EcR expression positively, as EcR is undetectable in Nup107-depleted animals, even below background levels.

      We appreciate the concern raised in this public review. However, we must clarify that we do not claim that Nup107 regulates the translocation of EcR from the cytoplasm. It is important to note that we posited this hypothesis if Nup107 will regulate EcR nuclear translocation (9<sup>th</sup> line of 2<sup>nd</sup> paragraph on page 6). We have spelled this out more clearly as the 3<sup>rd</sup> sub-section title of the Results section, and in the discussion (8<sup>th</sup> line of 2<sup>nd</sup> paragraph on page 11). Overall, we have expressed surprise that Nup107 is not directly involved in the nuclear translocation of EcR.

      Ecdysone hormone acts through the EcR to induce the transcription of EcR also and creates a positive autoregulatory loop that enhances the EcR level through ecdysone signaling (1). Since Nup107 depletion leads to a reduction in ecdysone levels, it disrupts the transcription autoregulatory EcR expression loop. This can contribute to the reduced EcR levels seen in Nup107-depleted animals.

      Additionally, the link between Nup107 and Torso is not fully substantiated. While overexpression of Torso appears to rescue the lack of 20E production in the prothoracic gland, the distinct phenotypes of Torso and Nup107 depletion-developmental delay in the former versus complete larval arrest in the latter complicate understanding of Nup107's precise role.

      We understand that there are differences in the developmental delay when Tosro and Nup107 depletion is analyzed. However, the two molecules being compared here are very different, and the extent of Torso depletion is not evident in other studies (2). Even if the extent of depletion of Torso and Nup107 is similar, we believe that Nup107, being a more widely expressed protein, induces stronger defects owing to its importance in cellular physiology. We think that RNAi-mediated depletion of Nup107 causes a defect in 20E biosynthesis through the Halloween genes, inducing a developmental arrest.

      To clarify these discrepancies, further investigation into whether Nup107 interacts with other critical signaling pathways related to the regulation of ecdysone biosynthesis, such as EGFR or TGF-β, would be beneficial and could strengthen the findings.

      In summary, although the study presents some intriguing observations, several conclusions are not well-supported by the experimental data.

      We agree with the reviewer’s suggestion. As noted in the literature, five RTKs-torso, InR, EGFR, Alk, and Pvr-stimulate the PI3K/Akt pathway, which plays a crucial role in the PG functioning and controlling pupariation and body size (3). We have checked the torso and EGFR signaling. We rescued Nup107 defects with the torso overexpression, however, constitutively active EGFR (BL-59843) did not rescue the phenotype (data was not shown). Nonetheless, we plan to examine the EGFR pathway activation by measuring the pERK levels in Nup107-depleted PGs.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Kawadkar et al investigates the role of Nup107 in developmental progression via the regulation of ecdysone signaling. The authors identify an interesting phenotype of Nup107 whole-body RNAi depletion in Drosophila development - developmental arrest at the late larval stage. Nup107-depleted larvae exhibit mis-localization of the Ecdysone receptor (EcR) from the nucleus to the cytoplasm and reduced expression of EcR target genes in salivary glands, indicative of compromised ecdysone signaling. This mis-localization of EcR in salivary glands was phenocopied when Nup107 was depleted only in the prothoracic gland (PG), suggesting that it is not nuclear transport of EcR but the presence of ecdysone (normally secreted from PG) that is affected. Consistently, whole-body levels of ecdysone were shown to be reduced in Nup107 KD, particularly at the late third instar stage when a spike in ecdysone normally occurs. Importantly, the authors could rescue the developmental arrest and EcR mislocalization phenotypes of Nup107 KD by adding exogenous ecdysone, supporting the notion that Nup107 depletion disrupts biosynthesis of ecdysone, which arrests normal development. Additionally, they found that rescue of the Nup107 KD phenotype can also be achieved by over-expression of the receptor tyrosine kinase torso, which is thought to be the upstream regulator of ecdysone synthesis in the PG. Transcript levels of the torso are also shown to be downregulated in the Nup107KD, as are transcript levels of multiple ecdysone biosynthesis genes. Together, these experiments reveal a new role of Nup107 or nuclear pore levels in hormone-driven developmental progression, likely via regulation of levels of torso and torso-stimulated ecdysone biosynthesis.

      Strengths:

      The developmental phenotypes of an NPC component presented in the manuscript are striking and novel, and the data appears to be of high quality. The rescue experiments are particularly significant, providing strong evidence that Nup107 functions upstream of torso and ecdysone levels in the regulation of developmental timing and progression.

      Weaknesses:

      The underlying mechanism is however not clear, and any insight into how Nup107 may regulate these pathways would greatly strengthen the manuscript. Some suggestions to address this are detailed below.

      Major questions:

      (1) Determining how specific this phenotype is to Nup107 vs. to reduced NPC levels overall would give some mechanistic insight. Does knocking down other components of the Nup107 subcomplex (the Y-complex) lead to similar phenotypes? Given the published gene regulatory function of Nup107, do other gene regulatory Nups such as Nup98 or Nup153 produce these phenotypes?

      We thank this public review to raise this concern. Working with a Nup-complex like the Nup107 complex, this concern is anticipated but difficult to address as many Nups function beyond their complex identity. Our observations with all other members of the Nup107-complex, including dELYS, suggest that except Nup107, none of the other Nup107-complex members could induce larval developmental arrest.

      In this study, we primarily focused on the Nup107 complex (outer ring complex) of the NPC. We have not examined other nucleoporins outside of this complex, such as Nup98 and Nup153. However, previous studies have reported that Nup98 and Nup153 interact with chromatin, with these investigations conducted in Drosophila S2 cells (4, 5, 6). In the future, we may check whether Nup98 and Nup153 depletion can produce the arrest phenotype.

      (2) In a related issue, does this level of Nup107 KD produce lower NPC levels? It is expected to, but actual quantification of nuclear pores in Nup107-depleted tissues should be added. These and the above experiments would help address a key mechanistic question - is this phenotype the result of lower numbers of nuclear pores or specifically of Nup107?

      We agree with the concern raised here, and we plan to assess nucleoporin intensity using mAb414 antibody (exclusively FG-repeat Nup recognizing antibody) in the Nup107 depletion background. Our past observations suggest that Nup107-depletion does not affect the overall nuclear pore complex assembly in Drosophila salivary glands (Data is not shown).

      (3) Additional experiments on how Nup107 regulates the torso would provide further insight. Does Nup107 regulate transcription of the torso or perhaps its mRNA export? Looking at nascent levels of the torso transcript and the localization of its mRNA can help answer this question. Or alternatively, does Nup107 physically bind the torso?

      While the concern regarding torso transcript level is genuine, we have already reported in the manuscript that Nup107 levels directly regulate torso expression. When Nup107 is depleted torso levels go down, which in turn controls ecdysone production and subsequent EcR signaling (Figure 6B of the manuscript). However, the exact nature of Nup107 regulation on torso expression is still unclear. Since the Nup107 is known to interact with chromatin (7), it may affect torso transcription. The possibility of a physiologically relevant interaction between Nup107 and the torso in a cellular context is unlikely due to their distinct sub-cellular localizations. If we investigate this further, it will require a significant amount of time for having reagents and experimentation, and currently stands beyond the scope of this manuscript.

      (4) The depletion level of Nup107 RNAi specifically in the salivary gland vs. the prothoracic gland should be compared by RT-qPCR or western blotting.

      Although we know that the Nup107 protein signal is reduced in SG upon knockdown (Figure 3B), we have not compared the Nup107 transcript level in these two tissues (SG and PG). As suggested here, we will knock down Nup107 using SG and PG-specific drivers and quantify the Nup107 depletion level by RT-qPCR.

      (5) The UAS-torso rescue experiment should also include the control of an additional UAS construct - so Nup107; UAS-control vs Nup107; UAS-torso should be compared in the context of rescue to make sure the Gal4 driver is functioning at similar levels in the rescue experiment.

      This is a very valid point, and we took this into account while planning the experiment. To maintain the GAL4 function, we used the Nup107<sup>KK</sup>;UAS-GFP as control alongside the Nup107<sup>KK</sup>;UAS-torso. This approach ensures that GAL4 dilution does not affect observations made in the experiments. It can be noticed in Figure S7 that the presence of GFP signal in prothoracic glands and their reduced size indicates genes downstream to both UAS sequences are transcribed, and GAL4 dilution does not play a role here.

      Minor:

      (6) Figures and figure legends can stand to be more explicit and detailed, respectively.

      We will revisit all figures and their corresponding legends to ensure appropriate and explicit details are provided.

      Reviewer #3 (Public review):

      Summary:

      In this study by Kawadkar et al, the authors investigate the developmental role of Nup107, a nucleoporin, in regulating the larval-to-pupal transition in Drosophila through RNAi knockdown and CRISPR-Cas9-mediated gene editing. They demonstrate that Nup107, an essential component of the nuclear pore complex (NPC), is crucial for regulating ecdysone signaling during developmental transitions. The authors show that the depletion of Nup107 disrupts these processes, offering valuable insights into its role in development.

      Specifically, they find that:

      (1) Nup107 depletion impairs pupariation during the larval-to-pupal transition.

      (2) RNAi knockdown of Nup107 results in defects in EcR nuclear translocation, a key regulator of ecdysone signaling.

      (3) Exogenous 20-hydroxyecdysone (20E) rescues pupariation blocks, but rescued pupae fail to close.

      (4) Nup107 RNAi-induced defects can be rescued by activation of the MAP kinase pathway.

      Strengths:

      The manuscript provides strong evidence that Nup107, a component of the nuclear pore complex (NPC), plays a crucial role in regulating the larval-to-pupal transition in Drosophila, particularly in ecdysone signaling.

      The authors employ a combination of RNAi knockdown, CRISPR-Cas9 gene editing, and rescue experiments, offering a comprehensive approach to studying Nup107's developmental function.

      The study effectively connects Nup107 to ecdysone signaling, a key regulator of developmental transitions, offering novel insights into the molecular mechanisms controlling metamorphosis.

      The use of exogenous 20-hydroxyecdysone (20E) and activation of the MAP kinase pathway provides a strong mechanistic perspective, suggesting that Nup107 may influence EcR signaling and ecdysone biosynthesis.

      Weaknesses:

      The authors do not sufficiently address the potential off-target effects of RNAi, which could impact the validity of their findings. Alternative approaches, such as heterozygous or clonal studies, could help confirm the specificity of the observed phenotypes.

      This is a very valid point raised, and we are aware of the consequences of the off-target effects of RNAi. To assert the effects of authentic RNAi and reduce the off-target effects, we have used two RNAi lines (Nup107<sup>GD</sup> and Nup107<sup>KK</sup>) against Nup107. Both RNAi induced comparable levels of Nup107 reduction, and using these lines, ubiquitous and PG specific knockdown produced similar phenotypes. Although the Nup107<sup>GD</sup> line exhibited a relatively stronger knockdown compared to the Nup107<sup>KK</sup> line, we preferentially used the Nup107<sup>KK</sup> line because the Nup107<sup>GD</sup> line is based on the P-element insertion, and the exact landing site is unknown. Furthermore, there is an off-target predicted for the Nup107<sup>GD</sup> line, where a 19bp sequence aligns with the bifocal (bif) sequence. The bif-encoded protein is involved in axon guidance and regulation of axon extension. However, the Nup107<sup>KK</sup> line does not have a predicted off-target molecule, and we know its precise landing site on the second chromosome. Thus, the Nup107<sup>KK</sup> line was ultimately used in experimentation for its clearer and more reliable genetic background.

      We are also investigating Nup107 knockdown in the prothoracic gland, which exhibits polyteny. Additionally, the number of cells in the prothoracic gland is quite limited, approximately 50-60 cells (8). Given this, there is a possibility that a clonal study may not yield the phenotype. However, we will consider moving forward with this approach also.

      NPC Complex Specificity: While the authors focus on Nup107, it remains unclear whether the observed defects are specific to this nucleoporin or if other NPC components also contribute to similar defects. Demonstrating similar results with other NPC components would strengthen their claims.

      We thank this public review to raise this concern. Working with a Nup-complex like the Nup107 complex, this concern is anticipated but difficult to address as many Nups function beyond their complex identity. Our observations with all other members of the Nup107-complex, including dELYS, suggest that except Nup107, none of the other Nup107-complex members could induce larval developmental arrest. Since the study is primarily focused on the Nup107 complex (outer ring complex) of the NPC, we have not examined other nucleoporins outside of this complex.

      Although the authors show that Nup107 depletion disrupts EcR signaling, the precise molecular mechanism by which Nup107 influences this process is not fully explored. Further investigation into how Nup107 regulates EcR nuclear translocation or ecdysone biosynthesis would improve the clarity of the findings.

      We appreciate the concern raised. Through our observation, we have proposed the upstream effect of Nup107 on the PTTH-torso-20E-EcR axis regulating developmental transitions. We know that Nup107 regulates torso levels, but we do not know if Nup107 directly interacts with torso. We would like to address whether Nup107 exerts control on PTTH levels also.

      We must emphasize that Nup107 does not directly regulate the translocation of EcR. On the contrary, we have demonstrated that EcR translocation is 20E dependent and Nup107 independent. Through our observations, we have argued that Nup107 regulates the expression of Halloween genes required for ecdysone biosynthesis. We are interested in identifying if Nup107 associates directly or through some protein to chromatin to bring about the changes in gene expression required for normal development.

      There are some typographical errors and overly strong phrases, such as "unequivocally demonstrate," which could be softened. Additionally, the presentation of redundant data in different tissues could be streamlined to enhance clarity and flow.

      We thank the reviewer for this observation. We will remove all typographical errors and make reasonable statements based on our conclusions.

      References:

      (1) Varghese, Jishy, and Stephen M Cohen. “microRNA miR-14 acts to modulate a positive autoregulatory loop controlling steroid hormone signaling in Drosophila.” Genes & development vol. 21,18 (2007): 2277-82. doi:10.1101/gad.439807

      (2) Rewitz, Kim F et al. “The insect neuropeptide PTTH activates receptor tyrosine kinase torso to initiate metamorphosis.” Science (New York, N.Y.) vol. 326,5958 (2009): 1403-5. doi:10.1126/science.1176450

      (3) Pan, Xueyang, and Michael B O'Connor. “Coordination among multiple receptor tyrosine kinase signals controls Drosophila developmental timing and body size.” Cell reports vol. 36,9 (2021): 109644. doi:10.1016/j.celrep.2021.109644

      (4) Pascual-Garcia, Pau et al. “Metazoan Nuclear Pores Provide a Scaffold for Poised Genes and Mediate Induced Enhancer-Promoter Contacts.” Molecular cell vol. 66,1 (2017): 63-76.e6. doi:10.1016/j.molcel.2017.02.020

      (5) Pascual-Garcia, Pau et al. “Nup98-dependent transcriptional memory is established independently of transcription.” eLife vol. 11 e63404. 15 Mar. 2022, doi:10.7554/eLife.63404

      (6) Kadota, Shinichi et al. “Nucleoporin 153 links nuclear pore complex to chromatin architecture by mediating CTCF and cohesin binding.” Nature communications vol. 11,1 2606. 25 May. 2020, doi:10.1038/s41467-020-16394-3

      (7) Gozalo, Alejandro et al. “Core Components of the Nuclear Pore Bind Distinct States of Chromatin and Contribute to Polycomb Repression.” Molecular cell vol. 77,1 (2020): 67-81.e7. doi:10.1016/j.molcel.2019.10.017

      (8) Shimell, MaryJane, and Michael B O'Connor. “Endoreplication in the Drosophila melanogaster prothoracic gland is dispensable for the critical weight checkpoint.” microPublication biology vol. 2023 10.17912/micropub.biology.000741. 21 Feb. 2023, doi:10.17912/micropub.biology.000741

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Fuchsberger et al. demonstrate a set of experiments that ultimately identifies the de novo synthesis of GluA1-, but not GluA2-containing Ca2+ permeable AMPA receptors as a key driver of dopamine-dependent LTP (DA-LTP) during conventional post-before-pre spike-timing dependent (t-LTD) induction. The authors further identify adenylate cyclase 1/8, cAMP, and PKA as the crucial mitigators of these actions. While some comments have been identified below, the experiments presented are thorough and address the aims of the manuscript, figures are presented clearly (with minor comments), and experimental sample sizes and statistical analyses are suitable. Suitable controls have been utilized to confirm the role of Ca2+ permeable AMPAR. This work provides a valuable step forward built on convincing data toward understanding the underlying mechanisms of spike-timing-dependent plasticity and dopamine.

      Strengths:

      Appropriate controls were used.

      The flow of data presented is logical and easy to follow.

      The quality of the data, except for a few minor issues, is solid.

      Weaknesses:

      The drug treatment duration of anisomycin is longer than the standard 30-45 minute duration (as is the 500uM vs 40uM concentration) typically used in the field. Given the toxicity of these kinds of drugs long term it's unclear why the authors used such a long and intense drug treatment.

      In an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S 1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      Furthermore, in the electrophysiology experiments, we used 500 μM anisomycin in the patch pipette solution. Under these conditions, we recorded a stable EPSP baseline for 60 minutes, indicating that the treatment did not cause toxic effects to the cell (Figure S1F). This high concentration would ensure an effective block of local translation at dendritic sites. Nevertheless, we also carried out this experiment with cycloheximide at a lower standard concentration (10 µM) and observed a similar result with both protein synthesis inhibitors (Figure 1F).

      With some of the normalizations (such as those in S1) there are dramatic differences in the baseline "untreated" puromycin intensities - raising some questions about the overall health of slices used in the experiments.

      We agree with the Reviewer that there is a large variability in the normalised puromycin signal which might be due to variability in the health of slices. However, we assume that the same variability would be present in the treated slices, which showed, despite the variability, a significant inhibition of protein synthesis. To avoid any bias by excluding slices with low puromycin signal in the control condition, we present the full dataset.

      The large set of electrophysiology experiments carried out in our study (all recorded cells were evaluated for healthy resting membrane potential, action potential firing, and synaptic responses) confirmed that, generally, the vast majority of our slices were indeed healthy. 

      Reviewer #2 (Public Review):

      Summary:

      The aim was to identify the mechanisms that underlie a form of long-term potentiation (LTP) that requires the activation of dopamine (DA).

      Strengths:

      The authors have provided multiple lines of evidence that support their conclusions; namely that this pathway involves the activation of a cAMP / PKA pathway that leads to the insertion of calcium-permeable AMPA receptors.

      Weaknesses:

      Some of the experiments could have been conducted in a more convincing manner.

      We carried out additional control experiments and analyses to address the specific points that were raised.

      Reviewer #3 (Public Review):

      The manuscript of Fuchsberger et al. investigates the cellular mechanisms underlying dopamine-dependent long-term potentiation (DA-LTP) in mouse hippocampal CA1 neurons. The authors conducted a series of experiments to measure the effect of dopamine on the protein synthesis rate in hippocampal neurons and its role in enabling DA-LTP. The key results indicate that protein synthesis is increased in response to dopamine and neuronal activity in the pyramidal neurons of the CA1 hippocampal area, mediated via the activation of adenylate cyclases subtypes 1 and 8 (AC1/8) and the cAMP-dependent protein kinase (PKA) pathway. Additionally, the authors show that postsynaptic DA-induced increases in protein synthesis are required to express DA-LTP, while not required for conventional t-LTP.

      The increased expression of the newly synthesized GluA1 receptor subunit in response to DA supports the formation of homomeric calcium-permeable AMPA receptors (CP-AMPARs). This evidence aligns well with data showing that DA-LTP expression requires the GluA1 AMPA subunit and CP-AMPARs, as DA-LTP is absent in the hippocampus of a GluA1 genetic knock-out mouse model. Overall, the study is solid, and the evidence provided is compelling. The authors clearly and concisely explain the research objectives, methodologies, and findings. The study is scientifically robust, and the writing is engaging. The authors' conclusions and interpretation of the results are insightful and align well with the literature. The discussion effectively places the findings in a meaningful context, highlighting a possible mechanism for dopamine's role in the modulation of protein-synthesis-dependent hippocampal synaptic plasticity and its implications for the field. Although the study expands on previous works from the same laboratory, the findings are novel and provide valuable insights into the dynamics governing hippocampal synaptic plasticity.

      The claim that GluA1 homomeric CP-AMPA receptors mediate the expression of DA-LTP is fascinating, and although the electrophysiology data on GluA1 knock-out mice are convincing, more evidence is needed to support this hypothesis. Western blotting provides useful information on the expression level of GluA1, which is not necessarily associated with cell surface expression of GluA1 and therefore CP-AMPARs. Validating this hypothesis by localizing the protein using immunofluorescence and confocal microscopy detection could strengthen the claim. The authors should briefly discuss the limitations of the study.

      Although it would be possible to quantify the surface expression of GluA1 using immunofluorescence, it would not be possible to distinguish  between GluA1 homomers and GluA1-containing heteromers. It would therefore not be informative as to whether these are indeed CP-AMPARs. This is an interesting problem, which we have briefly discussed in the Discussion section.

      Additional comments to address:

      (1) In Figure 2A, the representative image with PMY alone shows a very weak PMY signal. Consequently, the image with TTX alone seems to potentiate the PMY signal, suggesting a counterintuitive increase in protein synthesis.

      We agree with the Reviewer that the original image was not representative and have replaced it with a more representative image.

      (2) In Figures 3A-B, the Western blotting representative images have poor quality, especially regarding GluA1 and α-actin in Figure 3A. The quantification graph (Figure 3B) raises some concerns about a potential outlier in both the DA alone and DA+CHX groups. The authors should consider running a statistical test to detect outlier data. Full blot images, including ladder lines, should be added to the supplementary data.

      We have replaced the western blot image in Figure 3A and have also presented full blot images including ladder lines in supplementary Figure S3.

      Using the ROUT method (Q=1%) we identified one outlier in the DA+CHX group of the western blot quantification. The quantification for this blot was then removed from the dataset and the experiment was repeated to ensure a sufficient number of repeats.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) How the authors perform these experiments with puromycin, these are puromycilation experiments - not SuNSET. The SuNSET protocol (surface sensing of translation) specifically refers to the detection of newly synthesized proteins externally at the plasma membrane. I'd advise to update the terminology used.

      We thank the Reviewer for pointing this out. We have updated this to ‘puromycin-based labelling assay’.

      (2) The legend presented in Figure 2F suggests WT is green and ACKO is orange, however, in Figure 2G the WT LTP trace is orange, consider changing this to green for consistency.

      We thank the Reviewer for this suggestion and agree that a matching colour scheme makes the Figure clearer. This has been updated.

      (3) In the results section, it is recommended to include units for the values presented at the first instance and only again when the units change thereafter.

      The units of the electrophysiology data were [%], this is included in the Results section. Results of western blots and IHC images were presented as [a.u.]. While we included this in the Figures, we have not specifically added this to the text of individual results. 

      (4) Two hours pre-treatment with anisomycin vs 30 minutes pretreatment with cycloheximide seems hard to directly compare - as the pharmokinetics of translational inhibition should be similar for both drugs. What was the rationale for the extremely long anisomycin pretreatment? What controls were taken to assess slice health either prior to or following fixation? This is relevant to the below point (5).

      In an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      IHC slices were visually assessed for health. The large set of electrophysiology experiments carried out in our study (all recorded cells were evaluated for healthy resting membrane potential, action potential firing, and synaptic responses) also confirmed that, generally, the vast majority of our slices were indeed healthy. 

      (5) In Supplementary Figure 1, there is a dramatic difference in the a.u. intensities across CHX (B) and AM (D), please explain the reason for this. It is understood these are normalised values to nuclear staining, please clarify if this is a nuclear area.

      We agree with the Reviewer that there is a large variability in normalised puromycin signal which may be due to variability in the health of the slices. However, we assume that the same variability would be present in the treated slices, which showed, despite the variability, a significant effect of protein synthesis inhibition. To prevent introducing bias by excluding slices with low puromycin signal in the control condition, we present the full dataset.

      The CA1 region of the hippocampus contains of a dense layer of neuronal somata (pyramidal cell layer). We normalized against the nuclear area as it provides a reliable estimate of the number of neurons present in the image. This approach minimizes bias by accounting for variation in the number of neurons within the visual field, ensuring consistency and accuracy in our analysis.

      (6) Please clarify the decision to average both the last 5 minutes of baseline recordings and the last 5 minutes of the recording for the normalisation of EPSP slopes.

      The baseline usually stabilises after a few minutes of recording, thus the last 5 minutes were used for baseline measurement, which are the most relevant datapoints to compare synaptic weight change to. After induction of STDP, potentiation or depression of synaptic weights develops gradually. Based on previous results, evaluating the EPSP slopes at 30-40 minutes after the induction protocol gives a reliable estimate of the amount of plasticity.

      Reviewer #2 (Recommendations For The Authors):

      The concentration of anisomycin used (0.5 mM) is very high.

      As described above, in an initial set of control experiments (Figure S 1C-D) we wanted to ensure that protein synthesis was definitely blocked and therefore used a relatively high concentration of anisomycin and a relatively long pre-incubation period. We agree with the Reviewer that this is higher than the standard concentration used for this drug and we cannot exclude the possibility that this treatment could compromise cell health in addition to the protein synthesis block. Therefore, we carried out an additional experiment with an alternative protein synthesis inhibitor cycloheximide at a lower standard concentration (10 µM) which confirmed a significant reduction in the puromycin signal (Figure S1A-B). Together these results support the conclusion that puromycin signal is specific to protein synthesis in our labelling assay.

      Furthermore, in the electrophysiology experiments, we also used 500 µM anisomycin in the patch pipette solution. Under these conditions, we recorded a stable EPSP baseline for 60 minutes, indicating that the treatment did not cause toxic effects to the cell (Figure S1F). This high concentration would ensure an effective block of local translation at dendritic sites. Nevertheless, we also carried out this experiment with cycloheximide at a lower standard concentration (10 µM) and observed a similar result with both protein synthesis inhibitors (Figure 1F).

      The authors conclude that the effect of DA is mediated via D1/5 receptors, which based on previous work seems likely. But they cannot conclude this from their current study which used a combination of a D1/D5 and a D2 antagonist.

      We thank the Reviewer for pointing this out. We agree and have updated this in the Discussion section to ‘dopamine receptors’, without specifying subtypes.

      There is no mention that I can see that the KO experiments were conducted in a blinded manner (which I believe should be standard practice). Did they verify the KOs using Westerns?

      Only a subset of the experiments was conducted in a blinded manner. However, the results were collected by two independent experimenters, who both observed significant effects in KO mice compared to WTs (TF and ZB).

      We received the DKO mice from a former collaborator, who verified expression levels of the KO mice (Wang et al., 2003). We verified DKO upon arrival in our facility using genotyping.

      Maybe I'm misunderstanding but it appears to me that in Figure 1F there is LTP prior to the addition of DA. (The first point after pairing is already elevated). I think the control of pairing without DA should be added.

      We thank the Reviewer for pointing this out. Based on previous results (Brzosko et al., 2015) we would expect potentiation to develop over time once DA is added after pairing, however, it indeed appears in the Figure here as if there was an immediate increase in synaptic weights after pairing. It should be noted, however, that when comparing the first 5 minutes after pairing to the baseline, this increase was not significant (t(9)=1.810, p =0.1037). Nevertheless, we rechecked our data and noticed that this initial potentiation was biased by one cell with an increasing baseline, which had both the test and control pathway strongly elevated. We had mistakenly included this cell in the dataset, despite the unstable conditions (as stated in the Methods section, the unpaired control pathway served as a stability control). We apologise for the error and this has now been corrected (Figure 1F). In addition, we present the control pathway in Figure S1G and I.

      We have also now included the control for post-before-pre pairing (Δt = -20 ms) without dopamine in a supplemental figure (Figure S1E and F).

      The Westerns (Figure 3A) are fairly messy. Also, it is better to quantify with total protein. Surface biotinylation of GluA1 and GluA2 would be more informative.

      We carried out more repeats of Western blots and have exchanged blots in Figure 3A.

      We observed that DA increases protein synthesis, we therefore cannot exclude the possibility that application of DA could also affect total protein levels. Thus quantifying with total protein may not be the best choice here. Quantification with actin is standard practice.

      While we agree with the Reviewer that surface biotinylation of GluA1 and GluA2 could in principle be more informative, we do not think it would work well in our experimental setup using acute slice preparation, as it strictly requires intact cells. Slicing generates damaged cells, which would take up the surface biotin reagents. This would cause unspecific biotinylation of the damaged cells, leading to a strong background signal in the assay.

      In Figure 4 panels D and E the baselines are increasing substantially prior to induction. I appreciate that long stable baselines with timing-dependent plasticity may not be possible but it's hard to conclude what happened tens of minutes later when the baseline only appears stable for a minute or two. Panels A and B show that relatively stable baselines are achievable.

      We agree with the Reviewer that the baselines are increasing, however, when looking at the baseline for 5 minutes prior to induction (5 last datapoints of the baseline), which is what we used for quantification, the baselines appeared stable. Unfortunately, longer baselines are not suitable for timing-dependent plasticity. In addition, all experiments were carried out with a control pathway which showed stable conditions throughout the recording.

      In general, the discussion could be better integrated with the current literature. Their experiments are in line with a substantial body of literature that has identified two forms of LTP, based on these signalling cascades, using more conventional induction patterns.

      We thank the Reviewer for this suggestion and have added more discussion of the two forms of LTP in the Discussion section.

      It would be helpful to include the drug concentrations when first described in the results.

      Drug concentration have now been included in the Results section.

      It is now more common to include absolute t values (not just <0.05 etc).

      While we indicate significance in Figures using asterisks when p values are below the indicated significance levels, we report absolute values of p and t values in the Results section.

      Similarly full blots should be added to an appendix / made available.

      We have now included full blot images in Supplementary Figure S3.

      A 30% tolerance for series resistance seems generous to me. (10-20% would be more typical).

      We thank the Reviewer for their suggestion, and will keep this in mind for future studies. However, the error introduced by the higher tolerance level is likely to be small and would not influence any of the qualitative conclusions of the manuscript.

      Whereas series resistance is of course extremely important in voltage-clamp experiments, changes in series resistance would be less of a concern in current-clamp recordings of synaptic events. We use the amplifier as a voltage follower, and there are two problems with changes in the electrode, or access, resistance. First, there is the voltage drop across the electrode resistance. Clearly this error is zero if no current is injected and is also negligible for the currents we use in our experiments to maintain the membrane voltage at -70 mV. For example, the voltage drop would be 0.2 mV for 20 pA current through a typical 10 MOhm electrode resistance, and a change in resistance of 30% would give less than 0.1 mV voltage change even if the resistance were not compensated. The second problem is distortion of the EPSP shape due to the low-pass filtering properties of the electrode set up by the pipette capacitance and series resistance (RC). This can be a significant problem for fast events, such as action potentials, but less of a problem for the relatively slow EPSPs recorded in pyramidal cells. Nevertheless, we take on board the advice provided by the Reviewer and will use the conventional tolerance of 20% in future experiments.

      Reviewer #3 (Recommendations For The Authors):

      In the references, the entry for Burnashev N et al. has a different font size. Please ensure that all references are formatted consistently.

      We thank the Reviewer for spotting this and have updated the font size of this reference.

    1. Author response:

      eLife Assessment

      Birdsong production depends on precise neural sequences in a vocal motor nucleus HVC. In this useful biophysical model, Daou and colleagues identify specific biophysical parameters that result in sparse neural sequences observed in vivo. While the model is presently incomplete because it is overfit to produce sequences and therefore not robust to real biological variation, the model has the potential to address some outstanding issues in HVC function.

      We are grateful for the extensive supportive comments from the reviewers, including broad, strong appreciation of the novel aspects of our manuscript. We believe these will be only strengthened in the next submission.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The paper presents a model for sequence generation in the zebra finch HVC, which adheres to cellular properties measured experimentally. However, the model is fine-tuned and exhibits limited robustness to noise inherent in the inhibitory interneurons within the HVC, as well as to fluctuations in connectivity between neurons. Although the proposed microcircuits are introduced as units for sub-syllabic segments (SSS), the backbone of the network remains a feedforward chain of HVC_RA neurons, similar to previous models.

      Strengths:

      The model incorporates all three of the major types of HVC neurons. The ion channels used and their kinetics are based on experimental measurements. The connection patterns of the neurons are also constrained by the experiments.

      Weaknesses:

      The model is described as consisting of micro-circuits corresponding to SSS. This presentation gives the impression that the model's structure is distinct from previous models, which connected HVC_RA neurons in feedforward chain networks (Jin et al 2007, Li & Greenside, 2006; Long et al 2010; Egger et al 2020). However, the authors implement single HVC_RA neurons into chain networks within each micro-circuit and then connect the end of the chain to the start of the chain in the subsequent micro-circuit. Thus, the HVC_RA neuron in their model forms a single-neuron chain. This structure is essentially a simplified version of earlier models.

      In the model of the paper, the chain network drives the HVC_I and HVC_X neurons. The role of the micro-circuits is more significant in organizing the connections: specifically, from HVC_RA neurons to HVC_I neurons, and from HVC_I neurons to both HVC_X and HVC_RA neurons.

      We thank Reviewer 1 for their thoughtful comments.

      While the reviewer is correct about the fact that the propagation of sequential activity in this model is primarily carried by HVC<sub>RA</sub> neurons in a feed-forward manner, we need to emphasize that this is true only if there is no intrinsic or synaptic perturbation to the HVC network. For example, we showed in Figures 10 and 12 how altering the intrinsic properties of HVC<sub>X</sub> neurons or for interneurons disrupts sequence propagation. In other words, while HVC<sub>RA</sub> neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics. Moreover, all existing models that describe premotor sequence generation in the HVC either assume a distributed model (Elmaleh et al., 2021) that dictates that local HVC circuitry is not sufficient to advance the sequence but rather depends upon momentto-moment feedback through Uva (Hamaguchi et al., 2016), or assume models that rely on intrinsic connections within HVC to propagate sequential activity. In the latter case, some models assume that HVC is composed of multiple discrete subnetworks that encode individual song elements (Glaze & Troyer, 2013; Long & Fee, 2008; Wang et al., 2008), but lacks the local connectivity to link the subnetworks, while other models assume that HVC may have sufficient information in its intrinsic connections to form a single continuous network sequence (Long et al. 2010). The HVC model we present extends the concept of a feedforward network by incorporating additional neuronal classes that influence the propagation of activity (interneurons and HVC<sub>X</sub> neurons). We have shown that any disturbance of the intrinsic or synaptic conductances of these latter neurons will disrupt activity in the circuit even when HVC<sub>RA</sub> neurons properties are maintained.

      In regard to the similarities between our model and earlier models, several aspects of our model distinguish it from prior work. In short, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties. We tuned the intrinsic and the synaptic properties bases on the traces collected by Daou et al. (2013) and Mooney and Prather (2005) as shown in Figure 3. The three classes of model neurons incorporated to our network as well as the synaptic currents that connect them are based on HodgkinHuxley formalisms that contain ion channels and synaptic currents which had been pharmacologically identified. This is an advancement over prior models that primarily focused on the role of synaptic interactions or external inputs. The model is based on a feedforward chain of microcircuits that encode for the different sub-syllabic segments and that interact with each other through structured feedback inhibition, defining an ordered sequence of cell firing. Moreover, while several models highlight the critical role of inhibitory interneurons in shaping the timing and propagation of bursts of activity in HVC<sub>RA</sub> neurons, our work offers an intricate and comprehensive model that help understand this critical role played by inhibition in shaping song dynamics and ensuring sequence propagation.

      How useful is this concept of micro-circuits? HVC neurons fire continuously even during the silent gaps. There are no SSS during these silent gaps.

      Regarding the concern about the usefulness of the 'microcircuit' concept in our study, we appreciate the comment and we are glad to clarify its relevance in our network. While we acknowledge that HVC<sub>RA</sub> neurons interconnect microcircuits, our model's dynamics are still best described within the framework of microcircuitry particularly due to the firing behavior of HVC<sub>X</sub> neurons and interneurons. Here, we are referring to microcircuits in a more functional sense, rather than rigid, isolated spatial divisions (Cannon et al. 2015). A microcircuit in our model reflects the local rules that govern the interaction between all HVC neuron classes within the broader network, and that are essential for proper activity propagation. For example, HVC<sub>INT</sub> neurons belonging to any microcircuit burst densely and at times other than the moments when the corresponding encoded SSS is being “sung”. What makes a particular interneuron belong to this microcircuit or the other is merely the fact that it cannot inhibit HVC<sub>RA</sub> neurons that are housed in the microcircuit it belongs to. In particular, if HVC<sub>INT</sub> inhibits HVC<sub>RA</sub> in the same microcircuit, some of the HVC<sub>RA</sub> bursts in the microcircuit might be silenced by the dense and strong HVC<sub>INT</sub> inhibition breaking the chain of activity again. Similarly, HVC<sub>X</sub> neurons were selected to be housed within microcircuits due to the following reason: if an HVC<sub>X</sub> neuron belonging to microcircuit i sends excitatory input to an HVC<sub>INT</sub> neuron in microcircuit j, and that interneuron happens to select an HVC<sub>RA</sub> neuron from microcircuit i, then the propagation of sequential activity will halt, and we’ll be in a scenario similar to what was described earlier for HVC<sub>INT</sub> neurons inhibiting HVC<sub>RA</sub> neurons in the same microcircuit.

      We agree that there are no sub-syllabic segments described during the silent gaps and we thank the reviewer to pointing this out. Although silent gaps are integral to the overall process of song production, we have not elaborated on them in this model due to the lack of a clear, biophysically grounded representation for the gaps themselves at the level of HVC. Our primary focus has been on modeling the active, syllable-producing phases of the song, where the HVC network’s sequential dynamics are critical for song. However, one can think the encoding of silent gaps via similar mechanisms that encode SSSs, where each gap is encoded by similar microcircuits comprised of the three classes of HVC neurons (let’s called them GAP rather than SSS) that are active only during the silent gaps. In this case, the propagation of sequential activity is carried throughout the GAPs from the last SSS of the previous syllable to the first SSS of the subsequent syllable. We’ll make sure to emphasize this mechanism more in the revised version of the manuscript.

      A significant issue of the current model is that the HVC_RA to HVC_RA connections require fine-tuning, with the network functioning only within a narrow range of g_AMPA (Figure 2B). Similarly, the connections from HVC_I neurons to HVC_RA neurons also require fine-tuning. This sensitivity arises because the somatic properties of HVC_RA neurons are insufficient to produce the stereotypical bursts of spikes observed in recordings from singing birds, as demonstrated in previous studies (Jin et al 2007; Long et al 2010). In these previous works, to address this limitation, a dendritic spike mechanism was introduced to generate an intrinsic bursting capability, which is absent in the somatic compartment of HVC_RA neurons. This dendritic mechanism significantly enhances the robustness of the chain network, eliminating the need to fine-tune any synaptic conductances, including those from HVC_I neurons (Long et al 2010).

      Why is it important that the model should NOT be sensitive to the connection strengths?

      We thank the reviewer for the comment. While mathematical models designed for highly complex nonlinear biological processes tangentially touch the biological realism, the current network as is right now is the first biologically realistic-enough network model designed for HVC that explains sequence propagation. We do not include dendritic processes in our network although that increases the realistic dynamics for various reasons. 1) The ion channels we integrated into the somatic compartment are known pharmacologically (Daou et al. 2013), but we don’t know about the dendritic compartment’s intrinsic properties of HVC neurons and the cocktail of ion channels that are expressed there. 2) We are able to generate realistic bursting in HVC<sub>RA</sub> neurons despite the single compartment, and the main emphasis in this network is on the interactions between excitation and inhibition, the effects of ion channels in modulating sequence propagation, etc. 3) The network model already incorporates thousands of ODEs that govern the dynamics of each of the HVC neurons, so we did not want to add more complexity to the network especially that we don’t know the biophysical properties of the dendritic compartments.

      Therefore, our present focus is on somatic dynamics and the interaction between HVC<sub>RA</sub> and HVC<sub>INT</sub> neurons, but we acknowledge the importance of these processes in enhancing network resiliency. Although we agree that adding dendritic processes improves robustness, we still think that somatic processes alone can offer insightful information on the sequential dynamics of the HVC network. While the network should be robust across a wide range of parameters, it is also essential that certain parameters are designed to filter out weaker signals, ensuring that only reliable, precise patterns of activity propagate. Hence, we specifically chose to make the HVC<sub>RA</sub>-to-HVC<sub>RA</sub> excitatory connections more sensitive (narrow range of values) such that only strong, precise and meaningful stimuli can propagate through the network representing the high stereotypy and precision seen in song production.

      First, the firing of HVC_I neurons is highly noisy and unreliable. HVC_I neurons fire spontaneous, random spikes under baseline conditions. During singing, their spike timing is imprecise and can vary significantly from trial to trial, with spikes appearing or disappearing across different trials. As a result, their inputs to HVC_RA neurons are inherently noisy. If the model relies on precisely tuned inputs from HVC_I neurons, the natural fluctuations in HVC_I firing would render the model non-functional. The authors should incorporate noisy HVC_I neurons into their model to evaluate whether this noise would render the model non-functional.

      We acknowledge that under baseline and singing settings, interneurons fire in an extremely noisy and inaccurate manner, although they exhibit time locked episodes in their activity (Hahnloser et al 2002, Kozhinikov and Fee 2007). In order to mimic the biological variability of these neurons, our model does, in fact, include a stochastic current to reflect the intrinsic noise and random variations in interneuron firing shown in vivo (and we highlight this in the Methods). If necessary and to make sure the network is resilient to this randomness in interneuron firing, we will investigate different approaches to enhance the noise representation even further and check its effect on sequence propagation.

      Second, Kosche et al. (2015) demonstrated that reducing inhibition by suppressing HVC_I neuron activity makes HVC_RA firing less sparse but does not compromise the temporal precision of the bursts. In this experiment, the local application of gabazine should have severely disrupted HVC_I activity. However, it did not affect the timing precision of HVC_RA neuron firing, emphasizing the robustness of the HVC timing circuit. This robustness is inconsistent with the predictions of the current model, which depends on finely tuned inputs and should, therefore, be vulnerable to such disruptions.

      We thank the reviewer for the comment. The differences between the Kosche et al. (2015) findings and the predictions of our model arise from differences in the aspect of HVC function we are modeling. Our model is more sensitive to inhibition, which is a designed mechanism for achieving precise song patterning. This is a modeling simplification we adopted to capture specific characteristics of HVC function. Hence, Kosche et al. (2015) findings do not invalidate the approach of our model, but highlights that HVC likely operates with several, redundant mechanisms that overall ensure temporal precision.Nevertheless, we will investigate further the effects of the degree of inhibition on song patterning.

      Third, the reliance on fine-tuning of HVC_RA connections becomes problematic if the model is scaled up to include groups of HVC_RA neurons forming a chain network, rather than the single HVC_RA neurons used in the current work. With groups of HVC_RA neurons, the summation of presynaptic inputs to each HVC_RA neuron would need to be precisely maintained for the model to function. However, experimental evidence shows that the HVC circuit remains functional despite perturbations, such as a few degrees of cooling, micro-lesions, or turnover of HVC_RA neurons. Such robustness cannot be accounted for by a model that depends on finely tuned connections, as seen in the current implementation.

      Our model of individual HVC<sub>RA</sub> neurons and as stated previously is reductive model that focuses on understanding the mechanisms that govern sequential neural activity. We agree that scaling the model to include many of HVC<sub>RA</sub> neurons poses challenges, specifically concerning the summation of presynaptic inputs. However, our model can still be adapted to a larger network without requiring the level of fine-tuning currently needed. In fact, the current fine-tuning of synaptic connections in the model is a reflection of fundamental network mechanisms rather than a limitation when scaling to a larger network. Besides, one important feature of this neural network is redundancy. Even if some neurons or synaptic connections are impaired, other neurons or pathways can compensate for these changes, allowing the activity propagation to remain intact.

      The authors examined how altering the channel properties of neurons affects the activity in their model. While this approach is valid, many of the observed effects may stem from the delicate balancing required in their model for proper function.

      In the current model, HVC_X neurons burst as a result of rebound activity driven by the I_H current. Rebound bursts mediated by the I_H current typically require a highly hyperpolarized membrane potential. However, this mechanism would fail if the reversal potential of inhibition is higher than the required level of hyperpolarization. Furthermore, Mooney (2000) demonstrated that depolarizing the membrane potential of HVC_X neurons did not prevent bursts of these neurons during forward playback of the bird's own song, suggesting that these bursts (at least under anesthesia, which may be a different state altogether) are not necessarily caused by rebound activity. This discrepancy should be addressed or considered in the model.

      In our HVC network model, one goal with HVC<sub>X</sub> neurons is to generate bursts in their underlying neuron population. Since HVC<sub>X</sub> neurons in our model receive only inhibitory inputs from interneurons, we rely on inhibition followed by rebound bursts orchestrated by the IH and the I<sub>CaT</sub> currents to achieve this goal. The interplay between the T-type Ca<sup>++</sup> current and the H current in our model is fundamental to generate their corresponding bursts, as they are sufficient for producing the desired behavior in the network. Due to this interplay, we do not need significant inhibition to generate rebound bursts, because the T-type Ca<sup>++</sup> current’s conductance can be stronger leading to robust rebound bursting even when the degree of inhibition is not very strong. We will highlight this with more clarity in the revised version.

      Some figures contain direct copies of figures from published papers. It is perhaps a better practice to replace them with schematics if possible.

      We will replace the relevant figures with schematic representations where possible.

      Reviewer #2 (Public review):

      Summary:

      In this paper, the authors use numerical simulations to try to understand better a major experimental discovery in songbird neuroscience from 2002 by Richard Hahnloser and collaborators. The 2002 paper found that a certain class of projection neurons in the premotor nucleus HVC of adult male zebra finch songbirds, the neurons that project to another premotor nucleus RA, fired sparsely (once per song motif) and precisely (to about 1 ms accuracy) during singing.

      The experimental discovery is important to understand since it initially suggested that the sparsely firing RA-projecting neurons acted as a simple clock that was localized to HVC and that controlled all details of the temporal hierarchy of singing: notes, syllables, gaps, and motifs. Later experiments suggested that the initial interpretation might be incomplete: that the temporal structure of adult male zebra finch songs instead emerged in a more complicated and distributed way, still not well understood, from the interaction of HVC with multiple other nuclei, including auditory and brainstem areas. So at least two major questions remain unanswered more than two decades after the 2002 experiment: What is the neurobiological mechanism that produces the sparse precise bursting: is it a local circuit in HVC or is it some combination of external input to HVC and local circuitry?

      And how is the sparse precise bursting in HVC related to a songbird's vocalizations?

      The authors only investigate part of the first question, whether the mechanism for sparse precise bursts is local to HVC. They do so indirectly, by using conductance-based Hodgkin-Huxley-like equations to simulate the spiking dynamics of a simplified network that includes three known major classes of HVC neurons and such that all neurons within a class are assumed to be identical. A strength of the calculations is that the authors include known biophysically deduced details of the different conductances of the three major classes of HVC neurons, and they take into account what is known, based on sparse paired recordings in slices, about how the three classes connect to one another. One weakness of the paper is that the authors make arbitrary and not well-motivated assumptions about the network geometry, and they do not use the flexibility of their simulations to study how their results depend on their network assumptions. A second weakness is that they ignore many known experimental details such as projections into HVC from other nuclei, dendritic computations (the somas and dendrites are treated by the authors as point-like isopotential objects), the role of neuromodulators, and known heterogeneity of the interneurons. These weaknesses make it difficult for readers to know the relevance of the simulations for experiments and for advancing theoretical understanding.

      Strengths:

      The authors use conductance-based Hodgkin-Huxley-like equations to simulate spiking activity in a network of neurons intended to model more accurately songbird nucleus HVC of adult male zebra finches. Spiking models are much closer to experiments than models based on firing rates or on 2-state neurons.

      The authors include information deduced from modeling experimental current-clamp data such as the types and properties of conductances. They also take into account how neurons in one class connect to neurons in other classes via excitatory or inhibitory synapses, based on sparse paired recordings in slices by other researchers.

      The authors obtain some new results of modest interest such as how changes in the maximum conductances of four key channels (e.g., A-type K<sup>+</sup> currents or Ca-dependent K<sup>+</sup> currents) influence the structure and propagation of bursts, while simultaneously being able to mimic accurately current-clamp voltage measurements.

      Weaknesses:

      One weakness of this paper is the lack of a clearly stated, interesting, and relevant scientific question to try to answer. In the introduction, the authors do not discuss adequately which questions recent experimental and theoretical work have failed to explain adequately, concerning HVC neural dynamics and its role in producing vocalizations. The authors do not discuss adequately why they chose the approach of their paper and how their results address some of these questions.

      For example, the authors need to explain in more detail how their calculations relate to the works of Daou et al, J. Neurophys. 2013 (which already fitted spiking models to neuronal data and identified certain conductances), to Jin et al J. Comput. Neurosci. 2007 (which already discussed how to get bursts using some experimental details), and to the rather similar paper by E. Armstrong and H. Abarbanel, J. Neurophys 2016, which already postulated and studied sequences of microcircuits in HVC. This last paper is not even cited by the authors.

      We thank the reviewer for this valuable comment, and we agree that we did not clarify enough throughout the paper the utility of our model or how it advanced our understanding of the HVC dynamics and circuitry. To that end, we will revise several places of the manuscript and make sure to cite and highlight the relevance and relatedness of the mentioned papers.

      In short, and as mentioned to Reviewer 1, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015; Jin et al., 2007), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties.

      No existing hypothesis had been challenged with our model, rather; our model is a distillation of the various models that’s been proposed for the HVC network. We go over this in detail in the Discussion. We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

      The authors' main achievement is to show that simulations of a certain simplified and idealized network of spiking neurons, which includes some experimental details but ignores many others, match some experimental results like current-clamp-derived voltage time series for the three classes of HVC neurons (although this was already reported in earlier work by Daou and collaborators in 2013), and simultaneously the robust propagation of bursts with properties similar to those observed in experiments. The authors also present results about how certain neuronal details and burst propagation change when certain key maximum conductances are varied.

      However, these are weak conclusions for two reasons. First, the authors did not do enough calculations to allow the reader to understand how many parameters were needed to obtain these fits and whether simpler circuits, say with fewer parameters and simpler network topology, could do just as well. Second, many previous researchers have demonstrated robust burst propagation in a variety of feed-forward models. So what is new and important about the authors' results compared to the previous computational papers?

      A major novelty of our work is the incorporation of experimental data with detailed network models. While earlier works have established robust burst propagation, our model uses realistic ion channel kinetics and feedback inhibition not only to reproduce experimental neural activity patterns but also to suggest prospective mechanisms for song sequence production in the most biophysical way possible. This aspect that distinguishes our work from other feed-forward models. We go over this in detail in the Discussion. However, the reviewer is right regarding the details of the calculations conducted for the fits, we will make sure to highlight this in the Methods and throughout the manuscript with more details.

      We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

      Also missing is a discussion, or at least an acknowledgment, of the fact that not all of the fine experimental details of undershoots, latencies, spike structure, spike accommodation, etc may be relevant for understanding vocalization. While it is nice to know that some models can match these experimental details and produce realistic bursts, that does not mean that all of these details are relevant for the function of producing precise vocalizations. Scientific insights in biology often require exploring which of the many observed details can be ignored and especially identifying the few that are essential for answering some questions. As one example, if HVC-X neurons are completely removed from the authors' model, does one still get robust and reasonable burst propagation of HVC-RA neurons? While part of the nucleus HVC acts as a premotor circuit that drives the nucleus RA, part of HVC is also related to learning. It is not clear that HVC-X neurons, which carry out some unknown calculation and transmit information to area X in a learning pathway, are relevant for burst production and propagation of HVC<sub>RA</sub> neurons, and so relevant for vocalization. Simulations provide a convenient and direct way to explore questions of this kind.

      One key question to answer is whether the bursting of HVC-RA projection neurons is based on a mechanism local to HVC or is some combination of external driving (say from auditory nuclei) and local circuitry. The authors do not contribute to answering this question because they ignore external driving and assume that the mechanism is some kind of intrinsic feed-forward circuit, which they put in by hand in a rather arbitrary and poorly justified way, by assuming the existence of small microcircuits consisting of a few HVC-RA, HVC-X, and HVC-I neurons that somehow correspond to "sub-syllabic segments". To my knowledge, experiments do not suggest the existence of such microcircuits nor does theory suggest the need for such microcircuits.

      Recent results showed a tight correlation between the intrinsic properties of neurons and features of song (Daou and Margoliash 2020, Medina and Margoliash 2024), where adult birds that exhibit similar songs tend to have similar intrinsic properties. While this is relevant, we acknowledge that not all details may be necessary for every aspect of vocalization, and future models could simplify concentrate on core dynamics and exclude certain features while still providing insights into the primary mechanisms.

      The question of whether HVC<sub>X</sub> neurons are relevant for burst propagation given that our model includes these neurons as part of the network for completeness, the reviewer is correct, the propagation of sequential activity in this model is primarily carried by HVC<sub>RA</sub> neurons in a feed-forward manner, but only if there is no perturbation to the HVC network. For example, we have shown how altering the intrinsic properties of HVC<sub>X</sub> neurons or for interneurons disrupts sequence propagation. In other words, while HVC neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics.

      We agree with the reviewer however that a potential drawback of our model is that its sole focus is on local excitatory connectivity within the HVC (Kornfeld et al., 2017; Long et al., 2010), while HVC neurons receive afferent excitatory connections (Akutagawa & Konishi, 2010; Nottebohm et al., 1982) that plays significant roles in their local dynamics. For example, the excitatory inputs that HVC neurons receive from Uvaeformis may be crucial in initiating (Andalman et al., 2011; Danish et al., 2017; Galvis et al., 2018) or sustaining (Hamaguchi et al., 2016) the sequential activity. While we acknowledge this limitation, our main contribution in this work is the biophysical insights onto how the patterning activity in HVC is largely shaped by the intrinsic properties of the individual neurons as well as the synaptic properties where excitation and inhibition play a major role in enabling neurons to generate their characteristic bursts during singing. This is true and holds irrespective of whether an external drive is injected onto the microcircuits or not. We will however elaborate on and investigate this more during the next submission.

      Another weakness of this paper is an unsatisfactory discussion of how the model was obtained, validated, and simulated. The authors should state as clearly as possible, in one location such as an appendix, what is the total number of independent parameters for the entire network and how parameter values were deduced from data or assigned by hand. With enough parameters and variables, many details can be fit arbitrarily accurately so researchers have to be careful to avoid overfitting. If parameter values were obtained by fitting to data, the authors should state clearly what the fitting algorithm was (some iterative nonlinear method, whose results can depend on the initial choice of parameters), what the error function used for fitting (sum of least squares?) was, and what data were used for the fitting.

      The authors should also state clearly the dynamical state of the network, the vector of quantities that evolve over time. (What is the dimension of that vector, which is also the number of ordinary differential equations that have to be integrated?) The authors do not mention what initial state was used to start the numerical integrations, whether transient dynamics were observed and what were their properties, or how the results depended on the choice of the initial state. The authors do not discuss how they determined that their model was programmed correctly (it is difficult to avoid typing errors when writing several pages or more of a code in any language) or how they determined the accuracy of the numerical integration method beyond fitting to experimental data, say by varying the time step size over some range or by comparing two different integration algorithms.

      We thank the reviewer again. The fitting process in our model occurred only at the first stage where the synaptic parameters were fit to the Mooney and Prather as well as the Kosche results. There was no data shared and we merely looked at the figures in those papers and checked the amplitude of the elicited currents, the magnitudes of DC-evoked excitations etc, and we replicated that in our model. While this is suboptimal, it was better for us to start with it rather than simply using equations for synaptic currents from the literature for other types of neurons (that are not even HVC’s or in the songbird) and integrate them into our network model. However, we will certainly highlight the details of this fitting process in the new submission. We will also highlight more technical details in the Methods regarding the exact number of ODEs, the initial conditions to run them, etc.

      Also disappointing is that the authors do not make any predictions to test, except rather weak ones such as that varying a maximum conductance sufficiently (which might be possible by using dynamic clamps) might cause burst propagation to stop or change its properties. Based on their results, the authors do not make suggestions for further experiments or calculations, but they should.

      We agree that making experimental testable predictions is crucial for the advancement of the model. Our predictions include testing whether eradication of a class of neurons such as HVC<sub>X</sub> neurons disrupts activity propagation which can be done through targeted neuron elimination. This also can be done through preventing rebound bursting in HVC<sub>X</sub> by pharmacologically blocking the I<sub>h</sub> channels. Others include down regulation of certain ion channels (pharmacologically done through ion blockers) and testing which current is fundamental for song production (and there a plenty of test based our results, like the SK current, the T-type Ca<sup>++</sup> current, the A-type K<sup>+</sup> current, etc). We will incorporate these into the revised manuscript to better demonstrate the model's applicability and to guide future research directions.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Structural colors (SC) are based on nanostructures reflecting and scattering light and producing optical wave interference. All kinds of living organisms exhibit SC. However, understanding the molecular mechanisms and genes involved may be complicated due to the complexity of these organisms. Hence, bacteria that exhibit SC in colonies, such as Flavobacterium IR1, can be good models.

      Based on previous genomic mining and co-occurrence with SC in flavobacterial strains, this article focuses on the role of a specific gene, moeA, in SC of Flavobacterium IR1 strain colonies on an agar plate. moeA is involved in the synthesis of the molybdenum cofactor, which is necessary for the activity of key metabolic enzymes in diverse pathways.

      The authors clearly showed that the absence of moeA shifts SC properties in a way that depends on the nutritional conditions. They further bring evidence that this effect was related to several properties of the colony, all impacted by the moeA mutant: cell-cell organization, cell motility and colony spreading, and metabolism of complex carbohydrates. Hence, by linking SC to a single gene in appearance, this work points to cellular organization (as a result of cell-cell arrangement and motility) and metabolism of polysaccharides as key factors for SC in a gliding bacterium. This may prove useful for designing molecular strategies to control SC in bacterial-based biomaterials.

      Strengths:

      The topic is very interesting from a fundamental viewpoint and has great potential in the field of biomaterials.

      Thank you for your comments.

      The article is easy to read. It builds on previous studies with already established tools to characterize SC at the level of the flavobacterial colony. Experiments are well described and well executed. In addition, the SIBR-Cas method for chromosome engineering in Flavobacteria is the most recent and is a leap forward for future studies in this model, even beyond SC.

      We appreciate these comments.

      Weaknesses:

      The paper appears a bit too descriptive and could be better organized. Some of the results, in particular the proteomic comparison, are not well exploited (not explored experimentally). In my opinion, the problem originates from the difficulty in explaining the link between the absence of moeA and the alterations observed at the level of colony spreading and polysaccharide utilization, and the variation in proteomic content.

      We will look at the organisation of the manuscript carefully in the coming, detailed revision, as suggested. In terms of the proteomics, there are clearly a large number of proteins affected by the moeA deletion. In terms of experimental exploration, we chose spreading, structural colour formation and starch degradation to test phenotypically, as the most relevant. For example, in L615-617, we discuss the downregulation of GldL (which is known to be involved Flavobacterial gliding motility [Shrivastava et al., 2013]) in the _moe_A KO as a possible explanation for the reduced colony spreading of moeA mutant. Changes in polysaccharide (starch) utilization were seen on solid medium, as well as in the proteomic profile where we observed the upregulation of carbohydrate metabolism proteins linked to PUL (polysaccharide utilisation locus) operons (Terrapon et al., 2015), such as PAM95095-90 (Figure 8), and other carbohydrate metabolism-related proteins, including a pectate lyase (Table S7) which is involved in starch degradation (Aspeborg et al., 2012). And as noted in L555-566 and Figure 9, starch metabolism was tested experimentally.

      First, the effect of moeA deletion on molybdenum cofactor synthesis should be addressed.

      MoeA is the last enzyme in the MoCo synthesis pathway, thus if only MoeA is absent the cell would accumulate MPT-AMP (molybdopterin-adenosine monophosphatase) (Iobbi-Nivol & Leimkühler, 2013), and the expressed molybdoenzymes would not be functional. In L582-585, we commented how the lack of molybdenum cofactor may affect the synthesis of molybdoenzymes. However, if you meant to analyse the presence of the small molecules, the cofactors, involved in these pathways, that was an assay we were not able to perform. Moreover, in L585-587, we addressed how the deletion of _moe_A affected the proteins encoded by the rest of genes in the operon.

      Second, as I was reading the entire manuscript, I kept asking myself if moeA (and by extension molybdenum cofactor) was really involved in SC or it was an indirect effect. For example, what if the absence of moeA alters the cell envelope because the synthesis of its building blocks is perturbed, then subsequently perturbates all related processes, including gliding motility and protein secretion? It would help to know if the effects on colony spreading and polysaccharide metabolism can be uncoupled. I don't think the authors discussed that clearly.

      The message of the paper is that the moeA gene, as predicted from a previous genomics analysis, is important in SC. This is based on the representation of the _moe_A gene in genomes of bacteria that display SC. This analysis does not predict the mechanism. When knocked out, a significant change in structural colour occurred, supporting this hypothesis. Whether this effect is direct or indirect is difficult to assess, as this referee rightly suggests. In order to follow up this central result, we performed proteomics (both intra- and extracellular). As we observed, the deletion of a single gene generated many changes in the proteomic profile, thus in the biological processes. Based on the known functions of molybdenum cofactor, we could only hypothesize that pterin metabolism is important for SC, not exactly how.

      We intend to discuss the links between gliding/spreading and polysaccharide metabolism more clearly, with reference to the literature, as quite a bit is known here including possible links to SC.

      Reviewer #2 (Public review):

      Summary:

      The authors constructed an in-frame deletion of moeA gene, which is involved in molybdopterin cofactor (MoCo) biosynthesis, and investigated its role in structural colors in Flavobacterium IR1. The deletion of moeA shifted colony color from green to blue, reduced colony spreading, and increased starch degradation, which was attributed to the upregulation of various proteins in polysaccharide utilization loci. This study lays the ground for developing new colorants by modifying genes involved in structural colors.

      Major strengths and weaknesses:

      The authors conducted well-designed experiments with appropriate controls and the results in the paper are presented in a logical manner, which supports their conclusions.

      We appreciate your comment.

      Using statistical tests to compare the differences between the wild type and moeA mutant, and adding a significance bar in Figure 4B, would strengthen their claims on differences in cell motility regarding differences in cell motility.

      Thank you. Figure 4B contains the significance bars that represent the standard deviation of the mean value of the three replicates, but we will modify it to make them more clear.

      Additionally, in the result section (Figure 6), the authors suggest that the shift in blue color is "caused by cells which are still highly ordered but narrower", which to my knowledge is not backed up by any experimental evidence.

      Thanks. We mentioned that the mutant cells are narrower than the wild type based on the estimated periodicity resulting from the goniometry analysis (L427-430). We will now say “likely to be narrower based on the estimated periodicity from the optical analysis” rather than just “narrower” in the revision.

      Overall, this is a well-written paper in which the authors effectively address their research questions through proper experimentation. This work will help us understand the genetic basis of structural colors in Flavobacterium and open new avenues to study the roles of additional genes and proteins in structural colors.

      Much appreciated.

      REFERENCES

      Aspeborg, Henrik, Pedro M. Coutinho, Yang Wang, Harry Brumer, and Bernard Henrissat. "Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5)." BMC evolutionary biology 12 (2012): 1-16.

      lobbi-Nivol, Chantal, and Silke Leimkühler. "Molybdenum enzymes, their maturation and molybdenum cofactor biosynthesis in Escherichia coli." Biochimica et Biophysica Acta (BBA)-Bioenergetics 1827, no. 8-9 (2013): 1086-1101.

      Shrivastava, Abhishek, Joseph J. Johnston, Jessica M. Van Baaren, and Mark J. McBride. "Flavobacterium johnsoniae GldK, GldL, GldM, and SprA are required for secretion of the cell surface gliding motility adhesins SprB and RemA." Journal of bacteriology 195, no. 14 (2013): 3201-3212.

      Terrapon, Nicolas, Vincent Lombard, Harry J. Gilbert, and Bernard Henrissat. "Automatic prediction of polysaccharide utilization loci in Bacteroidetes species." Bioinformatics 31, no. 5 (2015): 647-655.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      (1) Although there are many citations acknowledging relevant previous work, there often isn't a very granular attribution of individual previous findings to their sources. In the results section, it's sometimes ambiguous when the paper is recapping established background and when it is breaking new ground. For example, around equation 8 in the results (sv = r - rho*t), it would be good to refer to previous places where versions of this equation have been presented. Offhand, McNamara 1982 (Theoretical Population Biology) is one early instance and Fawcett et al. 2012 (Behavioural Processes) is a later one. Line 922 of the discussion seems to imply this formulation is novel here.

      We would like to clarify that original manuscript equation 8, , as we derive, is not new, as it is similarly expressed in prior foundational work by McNamara (1982), and we thank the reviewer for drawing our attention to the extension of this form by Fawcett, McNamara, Houston (2012).

      We now so properly acknowledge this foundational work and extension in the results section…

      “This global reward-rate equivalent immediate reward (see Figure 4) is the subjective value of a pursuit, svPursuit (or simply, sv, when the referenced pursuit can be inferred), as similarly expressed in prior foundational work (McNamara 1982), and subsequent extensions (see (Fawcett, McNamara, Houston (2012)).”

      …and in the Discussion section at the location referenced by the reviewer:

      “From it, we re-expressed the pursuit’s worth in terms of its global reward rate-equivalent immediate reward, i.e., its ‘subjective value’, reprising McNamara’s foundational formulation (McNamara 1982).”

      (2) The choice environments that are considered in detail in the paper are very simple. The simplicity facilitates concrete examples and visualizations, but it would be worth further consideration of whether and how the conclusions generalize to more complex environments. The paper considers "forgo" scenario in which the agent can choose between sequences of pursuits like A-B-A-B (engaging with option B at all opportunities, which are interleaved with a default pursuit A) and A-A-A-A (forgoing option B). It considers "choice" scenarios where the agent can choose between sequences like A-B-A-B and A-C-A-C (where B and C are larger-later and smaller-sooner rewards, either of which can be interleaved with the default pursuit). Several forms of additional complexity would be valuable to consider. [A] One would be a greater number of unique pursuits, not repeated identically in a predictable sequence, akin to a prey-selection paradigm. It seems to me this would cause t_out and r_out (the time and reward outside of the focal prospect) to be policy-dependent, making the 'apportionment cost' more challenging to ascertain. Another relevant form of complexity would be if there were [B] variance or uncertainty in reward magnitudes or temporal durations or if [C] the agent had the ability to discontinue a pursuit such as in patch-departure scenarios.

      A) We would like to note that the section “Deriving Optimal Policy from Forgo Decision-making worlds”, addresses the reviewer’s scenario of n-number of pursuits”, each occurring at their own frequency, as in prey selection, not repeating identically in a predictable sequence. Within our subsection “Parceling the world…”, we introduce the concept of dividing a world (such as that) into the considered pursuit type, and everything outside of it. ‘Outside’ would include any number of other pursuits currently part of any policy, as the reviewer intuits, thus making t<sup>out</sup> and r<sup>out</sup> policy dependent. Nonetheless, a process of excluding (forgoing) pursuits by comparing the ‘in’ to the ‘out’ reward rate (section “Reward-rate optimizing forgo policy…”) or its equivalent sv (section “The forgo decision can also be made from subjective value), would iteratively lead to the global reward rate maximizing policy. This manner of parceling into ‘in’ and ‘out’ thus simplifies visualization of what can be complex worlds. Simpler cases that resemble common experimental designs are given in the manuscript to enhance intuition.

      We thank the reviewer for this keen suggestion. We now include example figures (Supplemental 1 & 2) for multi-pursuit worlds which have the same (Supplemental 1) and different pursuit frequencies (Supplemental 2), which illustrate how this evaluation leads to reward-rate optimization. This addition demonstrates how an iterative policy would lead to reward rate maximization and emphasizes how parcellating a world into ‘in’ and ‘out’ of the pursuit type applies and is a useful device for understanding the worth of any given pursuit in more complex worlds. The policy achieving the greatest global reward rate can be realized through an iterative process where pursuits with lower reward rates than the reward rate obtained from everything other than the considered pursuit type are sequentially removed from the policy.

      B) We would also emphasize that the formulation here contends with variance or uncertainty in the reward magnitudes or temporal durations. The ‘in’ pursuit is the average reward and the average time of the considered pursuit type, as is the ‘out’ the average reward and average time outside of the considered pursuit type.

      C) In this work, we consider the worth of initiating one-or-another pursuit (from having completed a prior one), and not the issue of continuing within a pursuit (having already engaged it), as in patch/give-up. Handling worlds in which the agent may depart from within a pursuit, which is to say ‘give-up’ (as in patch foraging), is outside the scope of this work.

      (3) I had a hard time arriving at a solid conceptual understanding of the 'apportionment cost' around Figure 5. I understand the arithmetic, but it would help if it were possible to formulate a more succinct verbal description of what makes the apportionment cost a useful and meaningful quality to focus on.

      We thank the reviewer for pressing for a succinct and intuitive verbal description.

      We added the following succinct verbal description of apportionment cost… “Apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration.” This definition appears in new paragraphs (as below) describing apportionment cost in the results section “Time’s cost: opportunity & apportionment costs determine a pursuit’s subjective value”, and is accompanied by equations for apportionment cost, and a figure giving its geometric depiction (Figure 5). We also expanded original figure 5 and its legend (so as to illustrate the apportionment scaling factor and the apportionment cost), and its accompanying main text, to further illustrate and clarify apportionment cost, and its relationship to opportunity cost, and time’s cost.

      “What, then, is the amount of reward by which the opportunity cost-subtracted reward is scaled down to equal the sv of the pursuit? This amount is the apportionment cost of time. The apportionment cost of time (height of the brown vertical bar, Figure 5F) is the global reward rate after taking into account the opportunity cost (slope of the magenta-gold dashed line in Figure 5F) times the time of the considered pursuit. Equally, the difference between the inside and outside reward rates, times the time of the pursuit, is the apportionment cost when scaled by the pursuit’s weight, i.e., the fraction that the considered pursuit is to the total time to traverse the world (Equation 9, right hand side). From the perspective of decision-making policies, apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration (Equation 9 center, Figure 5F).

      Equation 9. Apportionment Cost.

      While this difference is the apportionment cost of time, the opportunity cost of time is the amount that would be expected from a policy of not taking the considered pursuit over a time equal to the considered pursuit’s duration. Together, they sum to Time’s Cost (Figure 5G). Expressing a pursuit’s worth in terms of the global reward rate obtained under a policy of accepting the pursuit type (Figure 5 left column), or from the perspective of the outside reward and time (Figure 5 right column), are equivalent. However, the latter expresses sv in terms that are independent of one another, conveys the constituents giving rise to global reward rate, and provides the added insight that time’s cost comprises an apportionment as well as an opportunity cost.”

      The above definition of apportionment cost adds to other stated relationships of apportionment cost found throughout the paper (original lines 434,435,447,450).

      I think Figure 6C relates to this, but I had difficulty relating the axis labels to the points, lines, and patterned regions in the plot.

      We thank the reviewer for pointing out that this figure can be made to be more easily understood.

      We have done so by breaking its key features over a greater number of plots so that no single panel is overloaded. We have also changed text in the legend to clarify how apportionment and opportunity costs add to constitute time’s cost, and also correspondingly in the main text.

      I also was a bit confused by how the mathematical formulation was presented. As I understood it, the apportionment cost essentially involves scaling the rest of the SV expression by t<sup>out</sup>/(t<sup>in</sup> + t<sup>out</sup>).

      The reviewer’s understanding is correct: the amount of reward of the pursuit that remains after subtracting the opportunity cost, when so scaled, is equivalent to the subjective value of that pursuit. The amount by which that scaling decreases the rest of the SV expression is equal to the apportionment cost of time.

      The way this scaling factor is written in Figure 5C, as 1/(1 + (1/t<sup>out</sup>) t<sup>in</sup>), seems less clear than it could be.

      To be sure, we present the formula in original Figure 5C in this manner to emphasize the opportunity cost subtraction as separable from the apportionment rescaling, expressing the opportunity cost subtraction and the apportionment scaling component of the equation as their own terms in parentheses.

      But we understand the reviewer to be referring to the manner by which we chose to express the scaling term. We presented it in this way in the original manuscript, (rather than its more elegant form recognized by the reviewer) to make direct connection to temporal discounting literature. In this literature, discounting commonly takes the same mathematical form as our apportionment cost scaling, but whereas the steepness of discounting in this literature is controlled by a free fit parameter, k, we show how for a reward rate maximizing agent, the equivalent k term isn’t a free fit parameter, but rather is the reciprocal of the time spent outside the considered pursuit type.

      We take the reviewer’s advice to heart, and now first express subjective value in the format that emphasizes opportunity cost subtraction followed by an apportionment downscaling, identifying the apportionment scaling term, t<sup>out</sup>/(t<sup>out</sup> + t<sup>in</sup>), ie the outside weight. Figure 5 now shows the geometric representation of apportionment scaling and apportionment cost. Only subsequently in the discounting function section then do we now in the revised manuscript rearrange this subjective value expression to resemble the standard discounting function form.

      Also, the apportionment cost is described in the text as being subtracted from sv rather than as a multiplicative scaling factor.

      What we describe in the original text is how apportionment cost is a component of time’s cost, and how sv is the reward less time’s cost. It would be correct to say that apportionment cost and opportunity cost are subtracted from the pursuit’s reward to yield the subjective value of the pursuit. This is what we show in the original Figure 5D graphically. Original Figure 5 and accompanying formulas at its bottom show the equivalence of expressing sv in terms of subtracting time’s cost as calculated from the global reward rate under a policy of accepting the considered pursuit, or, of subtracting opportunity cost and then scaling the opportunity cost subtracted reward by the apportionment scaling term, thereby accounting for the apportionment cost of time.

      The revision of original figure 5, its figure legend, and accompanying text now make clear the meaning of apportionment cost, how it can be considered a subtraction from the reward of a pursuit, or, equivalently, how it can be thought of as the result of scaling down of opportunity cost subtracted reward.

      It could be written as a subtraction, by subtracting a second copy of the rest of the SV expression scaled by t_in/(t_in + t_out). But that shows the apportionment cost to depend on the opportunity cost, which is odd because the original motivation on line 404 was to resolve the lack of independence between terms in the SV expression.

      On line 404 of the original manuscript, we point out that the simple equation―which is a reprisal of McNamara’s insight―is problematic in that its terms on the RHS are not independent: the global reward rate is dependent on the considered pursuit’s reward (see Fig5B). The alternative expression for subjective value that we derive expresses sv in terms that are all independent of one another. We may have unintentionally obscured that fact by having already defined rho<sup>in</sup> as r<sup>in</sup>/ t<sup>in</sup> and rho<sup>out</sup> as r<sup>out</sup>/t<sup>out</sup> on lines 306 and 307.

      Therefore, in the revision, Ap 8 is expressed so to keep clear that it uses terms that are all independent of one another, and only subsequently express this formula with the simplifying substitution, rho<sup>out</sup>.

      That all said, we understand the reviewer’s point to be that the parenthetical terms relating the opportunity cost and the apportionment rescaling both contain within them the parameter t<sup>out</sup>, and in this way these concepts we put forward to understand the alternative equation are non-independent. That is correct, but it isn’t at odds with our objective to express SV in terms that are independent with one another (which we do). Our motivation in introducing these concepts is to provide insight and intuition into the cost of time (especially now with a clear and simple definition of apportionment cost stated). We go to lengths to demonstrate their relationship to each other.

      (4) In the analysis of discounting functions (line 664 and beyond), the paper doesn't say much about the fact that many discounting studies take specific measures to distinguish true time preferences from opportunity costs and reward-rate maximization.

      We understand the reviewer’s comment to connote that temporal decision-making worlds in which delay time does not preclude reward from outside the current pursuit is a means to distinguish time preference from the impact of opportunity cost. One contribution of this work is to demonstrate that, from a reward-rate maximization framework, an accounting of opportunity cost is not sufficient to understand apparent time preferences as distinguishable from reward-rate maximization. The apportionment cost of time must also be considered to have a full appreciation of the cost of time. For instance, let us consider a temporal decision-making world in which there is no reward received outside the considered pursuit. In such a world, there is no opportunity cost of time, so apparent temporal discounting functions would appear as if purely hyperbolic as a consequence of the apportionment cost of time alone. Time preference, as revealed experimentally by the choices made between a SS and a LL reward, then, seem confounding, as preference can reverse from a SS to a LL option as the displacement of those options (maintaining their difference in time) increases (Green, Fristoe, and Myerson 1994; Kirby and Herrnstein 1995). While this shift, the so-called “Delay effect”, could potentially arise as a consequence of some inherent time preference bias of an agent, we demonstrate that a reward-rate maximal agent exhibits hyperbolic discounting, and therefore it would also exhibit the Delay effect, even though it has no time preference.

      In the revision we now make reference to the Delay Effect (in abstract, results new section “The Delay Effect” with new figure 14, and in the discussion), which is taken as evidence of time preference in human and animal literature, and note explicitly how a reward-rate maximizing agent would also exhibit this behavior as a consequence of apparent hyperbolic discounting.

      In many of the human studies, delay time doesn't preclude other activities.

      Our framework is generalizable to worlds in which being in pursuit does not preclude an agent from receiving reward during that time at the outside reward rate. Original Ap 13 solves for such a condition, and shows that in this context, the opportunity cost of time drops out of the SV equation, leaving only the consequences of the apportionment cost of time. We made reference to this case on lines 1032-1034 of the original manuscript: “In this way, such hyperbolic discounting models [models that do not make an accounting of opportunity cost] are only appropriate in worlds with no “outside” reward, or, where being in a pursuit does not exclude the agent from receiving rewards at the rate that occurs outside of it (Ap. 13).”

      The note and reference is fleeting in the original work. We take the reviewer’s suggestion and now add paragraphs in the discussion on the difference between humans and animals in apparent discounting, making specific note of human studies in which delay time doesn’t preclude receiving outside reward while engaged in a pursuit. Relatedly, hyperbolic discounting is oft considered to be less steep in humans than in animals. As the reviewer points out, these assessments are frequently made under conditions in which being in a pursuit does not preclude receiving reward from outside the pursuit. When humans are tested under conditions in which outside rewards are precluded, they exhibit far steeper discounting. We now include citation to that observation (Jimura et al. 2009). We handle such conditions in original AP 13, and show how, in such worlds, the opportunity cost of time drops out of the equation. The consequence of this is that the apparent discounting function would become less steep (the agent would appear as if more patient), consistent with reports.

      “Relating to the treatment of opportunity cost, we also note that many investigations into temporal discounting do not make an explicit distinction between situations in which 1) subjects continue to receive the usual rewards from the environment during the delay to a chosen pursuit, and 2) situations in which during a chosen pursuit’s delay no other rewards or opportunities will occur (Kable & Glimcher, 2007; Kirby & Maraković, 1996; McClure, Laibson, Loewenstein, & Cohen, 2004). Commonly, human subjects are asked to answer questions about their preferences between options for amounts they will not actually earn after delays they will not actually have to wait, during which it is unclear whether they are really investing time away from other options or not (Rosati et al., 2007). In contrast, in most animal experiments, subjects actually receive reward after different delays during which they do not receive new options or rewards. By our formulation, when a pursuit does not exclude the agent from receiving rewards at the rate that occurs outside, the opportunity cost of time drops out of the subjective value equation (Ap 12).

      Equation 10. The value of initiating a pursuit when pursuit does not exclude receiving rewards at the outside rate (Ap 12)

      Therefore, the reward-rate maximizing discounting function in these worlds is functionally equivalent to the situation in which the outside reward rate is zero, and will―lacking an opportunity cost―be less steep. This rationalizes why human discounting functions are often reported to be longer (gentler) than animal discounting functions: they are typically tested in conditions that negate opportunity cost, whereas animals are typically tested in conditions that enforce opportunity costs. Indeed, when humans are made to wait for actually received reward, their observed discounting functions are much steeper (Jimura et al. 2009). “

      In animal studies, rate maximization can serve as a baseline against which to measure additional effects of temporal discounting. This is an important caveat to claims about discounting anomalies being rational under rate maximization (e.g., line 1024).

      We agree that the purpose of this reward-rate maximizing framework is to serve as a point of comparison in which effects of temporal intervals and rewards that define the environment can be analyzed to better understand the manner in which animals and humans deviate from this ideal behavior. Our interest in this work is in part motivated by a desire to have a deeper understanding of what “true” time preference means. Using the reward-rate maximizing framework here provides a means to speak about time preferences (ie biases) in terms of deviation from optimality. From this perspective, a reward-rate maximal agent doesn’t exhibit time preference: its actions are guided solely by reward-rate optimizing valuation. Therefore, one contribution of this work is to show that purported signs of time preference (hyperbolic discounting, magnitude, sign, and (now) delay effect) can be explained without invoking time preference. What errors from optimality that remain following an proper accounting of reward-rate maximizing behavior should then, and only then, be considered from the lens of time preference (bias).

      (5) The paper doesn't feature any very concrete engagement with empirical data sets. This is ok for a theoretical paper, but some of the characterizations of empirical results that the model aims to match seem oversimplified. An example is the contention that real decision-makers are optimal in accept/reject decisions (line 816 and elsewhere). This isn't always true; sometimes there is evidence of overharvesting, for example.

      We would like to note that the scope of this paper is limited to examining the value of initiating a pursuit, rather than the value of continuing within a pursuit. The issue of continuing within a pursuit constitutes a third fundamental topology, which could be called give-up or patch-foraging, and is complex and warrants its own paper. In Give-up topologies, which are distinct from Forgo, and Choice topologies, the reviewer is correct in pointing out that the preponderance of evidence demonstrates that animals and humans are as if overpatient, adopting a policy of investing too much time within a pursuit, than is warranted_._ In Forgo instances, however, the evidence supports near optimality.

      (6) Related to the point above, it would be helpful to discuss more concretely how some of this paper's theoretical proposals could be empirically evaluated in the future. Regarding the magnitude and sign effects of discounting, there is not a very thorough overview of the several other explanations that have been proposed in the literature. It would be helpful to engage more deeply with previous proposals and consider how the present hypothesis might make unique predictions and could be evaluated against them.

      We appreciate the reviewer’s point that there are many existing explanations for these various ‘anomalous’ effects. We hold that the point of this work is to demonstrate that these effects are consistent with a reward-rate maximizing framework so do not require additional assumptions, like separate processes for small and large rewards, or the inclusion of a utility function.

      Nonetheless, there is a diversity of explanations for the sign and magnitude effect, and, (now with its explicit inclusion in the revision) the delay effect. Therefore, we now also include reference to additional work which proffers alternative explanations for the sign and magnitude effects, (as reviewed by (Kalenscher and Pennartz 2008; Frederick et al. 2002)), as well as a scalar timing account of non-stationary time preference (Gibbon, 1977).

      With respect to making predictions, this framework makes the following in regards to the magnitude, sign, and (now in the revision) delay effect: in Discussion, Magnitude effect subsection: “The Magnitude Effect should be observed, experimentally, to diminish when 1) increasing the outside time while holding the outside reward constant, (thus decreasing the outside reward rate), or when 2) decreasing the outside reward while holding the outside time constant (thus decreasing the outside reward rate). However, 3) the Magnitude Effect would exaggerate as the outside time increased while holding the outside reward rate constant.”, in Sign effect subsection: “…we then also predict that the size of the Sign effect would diminish as the outside reward rate decreases (and as the outside time increases), and in fact would invert should the outside reward rate turn negative (become net punishing), such that punishments would appear to discount more steeply than rewards.” Delay effect subsection: “...a sign of irrationality is that a preference reversal occurs at delays greater than what a reward-rate-maximizing agent would exhibit.”

      A similar point applies to the 'malapportionment hypothesis' although in this case there is a very helpful section on comparisons to prior models (line 1163). The idea being proposed here seems to have a lot in common conceptually with Blanchard et al. 2013, so it would be worth saying more about how data could be used to test or reconcile these proposals.

      We thank the reviewer for holding that the section of model comparisons to be very helpful. We believe the text previously dedicated to this issue to be sufficient in this regard. We have, however, adding substantively to the Malapportionment Hypothesis section (Discussion) and its accompanying figure, to make explicit a number of predictions from the Malapportionment hypothesis as it relates to Hyperbolic discounting, the Delay Effect, and the Sign and Magnitude Effects.

      Reviewer #1 Recommendations

      (1) As a general note about the figures, it would be helpful to specify, either graphically or in the caption, what fixed values of reward sizes and time intervals are being assumed for each illustration.

      Thank you for the suggestion. We attempted to keep graphs as uncluttered as possible, but agree that for original figures 4,5,16, and 17, which didn’t have numbered axes, that we should provide the amounts in the captions in the revised figures (4,5, and now 17,18). These figures did not have numerics as their shapes and display are to illustrate the form of the relationship between vectors, being general to the values they may take.

      We now include in the captions for these figures the parameter amounts used.

      (2) Should Equation 2 have t in the denominator instead of r?

      Indeed. We thank the reviewer for catching this typographical error.

      We have corrected it in the revision.

      (3) General recommendation:

      My view is that in order for the paper's eLife assessment to improve, it would be necessary to resolve points 1 through 4 listed under "weaknesses" in my public review, which pertain to clarity and acknowledgement of prior work. I think a lot hinges on whether the authors can respond to point #3 by making a more compelling case for the usefulness and generality of the 'apportionment cost' concept, since that idea is central to the paper's contribution.

      We believe these critical points (1-4) to improve the paper will now have been addressed to the reviewer’s satisfaction.

      Reviewer #2 (Public review):

      While the details of the paper are compelling, the authors' presentation of their results is often unclear or incomplete:

      (1) The mathematical details of the paper are correct but contain numerous notation errors and are presented as a solid block of subtle equation manipulations. This makes the details of the authors' approach (the main contribution of the paper to the field) highly difficult to understand.

      We thank the reviewers for having detected typographical errors regarding three equations. They have been corrected. The first typographical error in the original main text (Line 277) regards equation 2 and will be corrected so that equation 2 appears correctly as

      The second typo regards the definition of the considered pursuit’s reward rate which appear in the original main text (line 306), and has been corrected to appear as

      The third typographical error occurred in conversion from Google Sheets to Microsoft Word appearing in the original main text (line 703) and regards the subjective value expression when no reward is received in an intertrial interval (ITI). It has been corrected to appear as

      (2) One of the main contributions of the paper is the notion that time’s cost in decision-making contains an apportionment cost that reflects the allocation of decision time relative to the world. The authors use this cost to pose a hypothesis as to why subjects exhibit sub-optimal behavior in choice decisions. However, the equation for the apportionment cost is never clearly defined in the paper, which is a significant oversight that hampers the effectiveness of the authors' claims.

      We thank the reviewer for pressing on this critical point. Reviewers commonly identified a need to provide a concise and intuitive definition of apportionment cost, and to explicitly solve and provide for its mathematical expression.

      We added the following succinct verbal description of apportionment cost… “Apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration.” This definition appears in new paragraphs (as below) describing apportionment cost in the results section “Time’s cost: opportunity & apportionment costs determine a pursuit’s subjective value”, and is accompanied by equations for apportionment cost, and a figure giving its geometric depiction (Figure 5). We also expanded original figure 5 and its legend (so as to illustrate the apportionment scaling factor and the apportionment cost), and its accompanying main text, to further illustrate and clarify apportionment cost, and its relationship to opportunity cost, and time’s cost.

      “What, then, is the amount of reward by which the opportunity cost-subtracted reward is scaled down to equal the sv of the pursuit? This amount is the apportionment cost of time. The apportionment cost of time (height of the brown vertical bar, Figure 5F) is the global reward rate after taking into account the opportunity cost (slope of the magenta-gold dashed line in Figure 5F) times the time of the considered pursuit. Equally, the difference between the inside and outside reward rates, times the time of the pursuit, is the apportionment cost when scaled by the pursuit’s weight, i.e., the fraction that the considered pursuit is to the total time to traverse the world (Equation 9, right hand side). From the perspective of decision-making policies, apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration (Equation 9 center, Figure 5F).

      Equation 9. Apportionment Cost.

      While this difference is the apportionment cost of time, the opportunity cost of time is the amount that would be expected from a policy of not taking the considered pursuit over a time equal to the considered pursuit’s duration. Together, they sum to Time’s Cost (Figure 5G). Expressing a pursuit’s worth in terms of the global reward rate obtained under a policy of accepting the pursuit type (Figure 5 left column), or from the perspective of the outside reward and time (Figure 5 right column), are equivalent. However, the latter expresses sv in terms that are independent of one another, conveys the constituents giving rise to global reward rate, and provides the added insight that time’s cost comprises an apportionment as well as an opportunity cost.”

      (3) Many of the paper's figures are visually busy and not clearly detailed in the captions (for example, Figures 6-8). Because of the geometric nature of the authors' approach, the figures should be as clean and intuitive as possible, as in their current state, they undercut the utility of a geometric argument.

      We endeavored to make our figures as simple as possible. We have made in the revision changes to figures that we believe improve their clarity. These include: 1) breaking some figures into more panels when more than one concept was being introduced (such as in revised Figure 5 , 6, 7, and 8), 2) using the left hand y axis for the outside reward, and the right hand axis for the inside reward when plotting the “in” and “outside” reward, and indicating their respective numerics (which run in opposite directions), 3) adding a legend to the figures themselves where needed (revised figures 10, 11, 12, 14) 4) adding the values used to the figure captions, where needed, and 5) ensuring all symbols are indicated in legends.

      (4) The authors motivate their work by focusing on previously-observed behavior in decision experiments and tell the reader that their model is able to qualitatively replicate this data. This claim would be significantly strengthened by the inclusion of experimental data to directly compare to their model's behavior. Given the computational focus of the paper, I do not believe the authors need to conduct their own experiments to obtain this data; reproducing previously accepted data from the papers the authors' reference would be sufficient.

      Our objective was not to fit experimentally observed data, as is commonly the goal of implementation/computational models. Rather, as a theory, our objective is to rationalize the broad, curious, and well-established pattern of temporal decision-making behaviors under a deeper understanding of reward-rate maximization, and from that understanding, identify the nature of the error being committed by whatever learning algorithm and representational architecture is actually being used by humans and animals. In doing so, we make a number of important contributions. By identifying and analyzing reward-rate-maximizing equations, we 1) provide insight into what composes time’s cost and how the temporal structure of the world in which it is embedded (its ‘context’) impacts the value of a pursuit, 2) rationalize a diverse assortment of temporal decision-making behaviors (e.g., Hyperbolic discounting, the Magnitude Effect, the Sign Effect, and the Delay effect), explaining them with no assumed free-fit parameter, and then, by analyzing error in parameters enabling reward-rate maximization, 3) identify the likely source of error and propose the Malapportionment Hypothesis. The Malapportionment Hypothesis identifies the underweighting of a considered pursuit’s “outside”, and not error in pursuit’s reward rates, as the source of error committed by humans and animals. It explains why animals and humans can present as suboptimally ‘impatient’ in Choice, but as optimal in Forgo. At the same time, it concords with numerous and diverse observations in decision making regarding whether to initiate a pursuit. The nature of this error also, then, makes numerous predictions. These insights inform future computational and experimental work by providing strong constraints on the nature of the algorithm and representational architecture used to learn and represent the values of pursuits. Rigorous test of the Malapportionment Hypothesis will require wholly new experiments.

      In the revision, we also now emphasize and add predictions of the Malapportionment Hypothesis, updated its figure (Figure 21), its legend, and its paragraphs in the discussion.

      “We term this reckoning of the source of error committed by animals and humans the Malapportionment Hypothesis, which identifies the underweighting of the time spent outside versus inside a considered pursuit but not the misestimation of pursuit rates, as the source of error committed by animals and humans (Figure 21). This hypothesis therefore captures previously published behavioral observations (Figure 21A) showing that animals can make decisions to take or forgo reward options that optimize reward accumulation (Krebs et al., 1977; Stephens and Krebs, 1986; Blanchard and Hayden, 2014), but make suboptimal decisions when presented with simultaneous and mutually exclusive choices between rewards of different delays (Logue et al., 1985; Blanchard and Hayden, 2015; Carter and Redish, 2016; Kane et al., 2019). The Malapportionment Hypothesis further predicts that apparent discounting functions will present with greater curvature than what a reward-rate-maximizing agent would exhibit (Figure 21B). While experimentally observed temporal discounting would have greater curvature, the Malapportionment Hypothesis also predicts that the Magnitude (Figure 21C) and Sign effect (Figure 21D) would be less pronounced than what a reward-rate-maximizing agent would exhibit, with these effects becoming less pronounced the greater the underweighting. Finally, with regards to the Delay Effect (Figure 21E), the Malapportionment Hypothesis predicts that preference reversal would occur at delays greater than that exhibited by a reward-rate-maximizing agent, with the delay becoming more pronounced the greater the underweighting outside versus inside the considered pursuit by the agent.”

      (5) While the authors reference a good portion of the decision-making literature in their paper, they largely ignore the evidence-accumulation portion of the literature, which has been discussing time-based discounting functions for some years. Several papers that are both experimentally-(Cisek et al. 2009, Thurs et al. 2012, Holmes et al. 2016) and theoretically-(Drugowitsch et al. 2012, Tajima et al. 2019, Barendregt et al. 22) driven exist, and I would encourage the authors to discuss how their results relate to those in different areas of the field.

      In this manuscript, we consider the worth of initiating one or another pursuit having completed a prior one, and not the issue of continuing within a pursuit having already engaged in it. The worth of continuing a pursuit, as in patch-foraging/give-up tasks, constitutes a third fundamental time decision-making topology which is outside the scope of the current work. It engages a large and important literature, encompassing evidence accumulation, and requires a paper on the value of continuing a pursuit in temporal decision making, in its own right, that can use the concepts and framework developed here. The excellent works suggested by the reviewer will be most relevant to that future work concerning patch-foraging/give-up topologies.

      Reviewer #2 Recommendations:

      (1) In Equation 1, the term rho_d is referred to as the reward rate of the default pursuit, when it should be the reward of the default pursuit.

      Regarding Equation 1, it is formulated to calculate the average reward received and average time spent per unit time spent in the default pursuit. So, f<sub>i</sub> is the encounter rate of pursuit i for one unit of time spent in the default pursuit (lines 259-262). Added to the summation in the numerator, we have the average reward obtained in the default pursuit per unit time () and in the denominator we have the time spent in the default pursuit per unit time (1).

      We have added clarifying text to assist in meaning of the equation in Ap 1, and thank the reviewer for pointing out this need.

      (2) The notation for "in" and "out" of a considered pursuit type begins as being used to describe the contribution from a single pursuit (without inter-trial interval) towards global reward rate and the contribution of all other factors (other possible pursuits and inter-trial interval) towards global reward rate, respectively, but is then used to describe the pursuit's contribution and the inter-trial interval's contribution, respectively, to the global reward rate. This should be cleaned up to be consistent throughout, or at the very least, it should be addressed when this special case is considered the default.

      As understood by the reviewer, “in” and “out” of the considered pursuit type describes the general form by which a world can be cleaved into these two parts: the average time and reward received outside of the considered pursuit type for the average time and reward received within that pursuit type. A specific, simple, and common experimental instance would be a world composed of one or another pursuit and an intertrial interval.

      We now make clear how such a world composed of a considered pursuit and an inter trial interval would be but one special case. In example cases where t<sup>out</sup> represents the special case of an inter-trial interval, this is now stated clearly. For instance, we do so when discussing how a purely hyperbolic discounting function would apply in worlds in which no reward is received in t<sup>out</sup>, stating that this is often the case common to experimental designs where t<sup>out</sup> represents an intertrial interval with no reward. Importantly, by the new inclusion of illustrated worlds in the revision that have n-number pursuits that could occur from a default pursuit and 1) equal frequency (Supplemental 1), and 2) at differing frequencies (Supplemental 2), we make more clear the generalizability and utility of this t<sup>out</sup>/tin concept.

      (3) Figure 5 should make clear the decomposition of time's cost both graphically and functionally. As it stands, the figure does not define the apportionment cost.

      In the revision of original fig 5, we now further decompose the figure to effectively convey 1) what opportunity cost, and (especially) 2) the apportionment cost is, both graphically and mathematically, 3) how time’s cost is comprised by them, 4) how the apportionment scaling term scales the opportunity-cost-subtracted reward by time’s allocation to equal the subjective value, and 4) the equivalence between the expression of time’s cost using terms that are not independent of one another with the expression of time’s cost using terms that are independent of one another.

      (4) Figures 6-8 do not clearly define the dots and annuli used in panels B and C.

      We have further decomposed figures 6-8 so that the functional form of opportunity, apportionment, and time’s cost can be more clearly appreciated, and what their interrelationship is with respect to changing outside reward and outside time, and clearly identify symbols used in the corresponding legends.

      (5) The meaning of a negative subjective value should be specifically stated. Is it the amount a subject would pay to avoid taking the considered pursuit?

      As the reviewer intuits, negative subjective value can be considered the amount an agent ought be willing to pay to avoid taking the considered pursuit.

      We now include the following lines in “The forgo decision can also be made from subjective value” section in reference to negative subjective value…

      “A negative subjective value thus indicates that a policy of taking the considered pursuit would result in a global reward rate that is less than a policy of forgoing the considered pursuit. Equivalently, a negative subjective value can be considered the amount an agent ought be willing to pay to avoid having to take the considered pursuit.”

      (6) Why do you define the discounting function as the normalized subjective value? This choice should be justified, via literature citations or a well-described logical argument.

      The reward magnitude normalized subjective value-time function is commonly referred to as the temporal discounting function as it permits comparison of the discount rate isolated from a difference in reward magnitude and/or sign and is deeply rooted in historical precedent. As the reviewer points out, the term is overloaded, however, as investigations in which comparisons between the form of subjective value-time functions is not needed tend to refer to these functions as temporal discounting functions as well.

      We make clear in the revised text in the introduction our meaning and use of the term, the justification in doing so, and its historical roots.

      “Historically, temporal decision-making has been examined using a temporal discounting function to describe how delays in rewards influence their valuation. Temporal discounting functions describe the subjective value of an offered reward as a function of when the offered reward is realized. To isolate the form of discount rate from any difference in reward magnitude and sign, subjective value is commonly normalized by the reward magnitude when comparing subjective value-time functions (Strotz, 1956, Jimura, 2009). Therefore, we use the convention that temporal discounting functions are the magnitude-normalized subjective value-time function (Strotz, 1956).”

      Special addition. In investigating the historical roots of the discounting function prompted by the reviewer, we learned (Grüne-Yanoff 2015) that it was Mazur that simply added the “1+k” in the denominator of the hyperbolic discounting function. Our derivation for the reward-rate optimal agent makes clear why apparent temporal discounting functions ought have this general form.

      Therefore, we add the following to the “Hyperbolic Temporal Discounting Function section in the discussion…

      “It was Ainslie (Ainslie, 1975) who first understood that the empirically observed “preference reversals” between SS and LL pursuits could be explained if temporal discounting took on a hyperbolic form, which he initially conjectured to arise simply from the ratio of reward to delay (Grüne-Yanoff 2015). This was problematic, however, on two fronts: 1) as the time nears zero, the value curve goes to infinity, and 2) there is no accommodation of differences observed within and between subjects regarding the steepness of discounting. Mazur (Mazur, 1987) addressed these issues by introducing 1 + k into the denominator, providing for the now standard hyperbolic discounting function, . Introduction of “1” solved the first issue, though “it never became fully clear how to interpret this 1” (Grüne-Yanoff 2015; interviewing Ainslie). Introduction of the free-fit parameter, k, accommodated the variability observed across and within subjects by controlling the curvature of temporal discounting, and has become widely interpreted as a psychological trait, such as patience, or willingness to delay gratification (Frederick et al., 2002).”

      …continuing later in that section to explain why the reward-rate optimal agent would exhibit this general form…

      “Regarding form, our analysis reveals that the apparent discounting function of a reward-rate-maximizing agent is a hyperbolic function…

      …which resembles the standard hyperbolic discounting function, , in the denominator, where . Whereas Mazur introduced 1 + k to t in the denominator to 1) force the function to behave as t approaches zero, and 2) provide a means to accommodate differences observed within and between subjects, our derivation gives cause to the terms 1 and k, their relationship to one another, and to t in the denominator. First, from our derivation, “1” actually signifies taking t<sub>out</sub> amount of time expressed in units of t<sub>out</sub> (t<sub>out</sub>/t<sub>out</sub>=1) and adding it to t<sub>in</sub>  amount of time expressed in units of t<sub>out</sub> (ie, the total time to make a full pass through the world expressed in terms of how the agent apportions its time under a policy of accepting the considered pursuit).”

      Additional Correction. In revising the section, “Hyperbolic Temporal Discounting Functions” in the discussion, we also detected an error in our description of the meaning of suboptimal bias for SS. In the revision, the sentence now reads…

      More precisely, what is meant by this suboptimal bias for SS is that the switch in preference from LL to SS occurs at an outside reward rate that is lower—and/or an outside time that is greater —than what an optimal agent would exhibit.”

      (7) Figure 15B should have negative axes defined for the pursuit's now negative reward.

      Yes- excellent point.

      To remove ambiguity regarding the valence of inside and outside reward magnitudes, we have changed all such figures so that the left hand y-axis is used to signify the outside reward magnitude and sign, and so that the right hand y-axis is used to signify the inside reward magnitude and sign.

      With respect to the revision of original 15B, this change now makes clear that the inside reward label and numerics on the right hand side of the graph run from positive (top) to negative (bottom) values so that it can now be understood that the magnitude of the inside reward is negative in this figure (ie, a punishment). The left hand y-axis labeling the outside reward magnitude has numerics that run in the opposite direction, from negative (top) to positive (bottom). In this figure, the outside reward rate is positive whereas the inside reward rate is negative.

      (8) When comparing your discounting function to the TIMERR and Heuristic models, it would be useful to include a schematic plot illustrating the different obtainable behaviors from all models rather than just telling the reader the differences.

      We hold that the descriptions and references are sufficient to address these comparisons.

      (9) I would strongly suggest cleaning up all appendices for notation…

      The typographical errors that have been noted in these reviews have all been corrected. We believe the reviewer to be referring here to the manner that we had cross-referenced Equations in the appendices and main text which can lead to confusion between whether an equation number being referenced is in regard to its occurrence in the main text or its occurrence in the appendices.

      In the revision, we eliminate numbering of equations in the appendices except where an equation occurs in an appendix that is referenced within the main text. In the main text, important equations are numbered sequentially and note the appendix from which they derive. If an equation in an appendix is referenced in the main text, it is noted within the appendix it derives.

      …and replacing some of the small equation manipulations with written text describing the goal of each derivation.

      To increase clarity, we have taken the reviewer’s helpful suggestion, adding helper text in the appendices were needed, and have bolded the equations of importance within the Appendices (rather than removing equation manipulations making clear steps of derivation).

      (10) I would suggest moving the table in Appendix 11 to the main text where misestimation is referenced.

      So moved. This appendix now appears in the main text as table 1 “Definitions of misestimating global reward rate-enabling parameters”.

      Reviewer #3 (Public review):

      One broad issue with the paper is readability. Admittedly, this is a complicated analysis involving many equations that are important to grasp to follow the analyses that subsequently build on top of previous analyses.

      But, what's missing is intuitive interpretations behind some of the terms introduced, especially the apportionment cost without referencing the equations in the definition so the reader gets a sense of how the decision-maker thinks of this time cost in contrast with the opportunity cost of time.

      We thank the reviewer for encouraging us to formulate a succinct and intuitive statement as to the nature of apportionment cost. We thank the reviewer for pressing for a succinct and intuitive verbal description.

      We added the following succinct verbal description of apportionment cost… “Apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration.” This definition appears in a new paragraph (as below) describing apportionment cost in the results section “Time’s cost: opportunity & apportionment costs determine a pursuit’s subjective value”, and is accompanied by equations for apportionment cost, and a figure giving its geometric depiction (Figure 5). We also expanded original figure 5 and its legend (so as to illustrate the apportionment scaling factor and the apportionment cost), and its accompanying main text, to further illustrate and clarify apportionment cost, and its relationship to opportunity cost, and time’s cost.

      “What, then, is the amount of reward by which the opportunity cost-subtracted reward is scaled down to equal the sv of the pursuit? This amount is the apportionment cost of time. The apportionment cost of time (height of the brown vertical bar, Figure 5F) is the global reward rate after taking into account the opportunity cost (slope of the magenta-gold dashed line in Figure 5F) times the time of the considered pursuit. Equally, the difference between the inside and outside reward rates, times the time of the pursuit, is the apportionment cost when scaled by the pursuit’s weight, i.e., the fraction that the considered pursuit is to the total time to traverse the world (Equation 9, right hand side). From the perspective of decision-making policies, apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration (Equation 9 center, Figure 5F).

      Equation 9. Apportionment Cost.

      While this difference is the apportionment cost of time, the opportunity cost of time is the amount that would be expected from a policy of not taking the considered pursuit over a time equal to the considered pursuit’s duration. Together, they sum to Time’s Cost (Figure 5G). Expressing a pursuit’s worth in terms of the global reward rate obtained under a policy of accepting the pursuit type (Figure 5 left column), or from the perspective of the outside reward and time (Figure 5 right column), are equivalent. However, the latter expresses sv in terms that are independent of one another, conveys the constituents giving rise to global reward rate, and provides the added insight that time’s cost comprises an apportionment as well as an opportunity cost.”

      The above definition of apportionment cost adds to other stated relationships of apportionment cost found throughout the paper (original lines 434,435,447,450).

      Re-analysis of some existing empirical data through the lens of their presented objective functions, especially later when they describe sources of error in behavior.

      Our objective was not to fit experimentally observed data, as is commonly the goal of implementation/computational models. Rather, as a theory, our objective is to rationalize the broad, curious, and well-established pattern of temporal decision-making behaviors under a deeper understanding of reward-rate maximization, and from that understanding, identify the nature of the error being committed by whatever learning algorithm and representational architecture is actually being used by humans and animals. In doing so, we make a number of important contributions. By identifying and analyzing reward-rate-maximizing equations, we 1) provide insight into what composes time’s cost and how the temporal structure of the world in which it is embedded (its ‘context’) impacts the value of a pursuit, 2) rationalize a diverse assortment of temporal decision-making behaviors (e.g., Hyperbolic discounting, the Magnitude Effect, the Sign Effect, and the Delay effect), explaining them with no assumed free-fit parameter, and then, by analyzing error in parameters enabling reward-rate maximization, 3) identify the likely source of error and propose the Malapportionment Hypothesis. The Malapportionment Hypothesis identifies the underweighting of a considered pursuit’s “outside”, and not error in pursuit’s reward rates, as the source of error committed by humans and animals. It explains why animals and humans can present as suboptimally ‘impatient’ in Choice, but as optimal in Forgo. At the same time, it concords with numerous and diverse observations in decision making regarding whether to initiate a pursuit. The nature of this error also, then, makes numerous predictions. These insights inform future computational and experimental work by providing strong constraints on the nature of the algorithm and representational architecture used to learn and represent the values of pursuits. Rigorous test of the Malapportionment Hypothesis will require wholly new experiments.

      In the revision, we also now emphasize and add predictions of the Malapportionment Hypothesis, augmenting its figure (Figure 21), its legend, and its paragraphs in the discussion.

      “We term this reckoning of the source of error committed by animals and humans the Malapportionment Hypothesis, which identifies the underweighting of the time spent outside versus inside a considered pursuit but not the misestimation of pursuit rates, as the source of error committed by animals and humans (Figure 21). This hypothesis therefore captures previously published behavioral observations (Figure 21A) showing that animals can make decisions to take or forgo reward options that optimize reward accumulation (Krebs et al., 1977; Stephens and Krebs, 1986; Blanchard and Hayden, 2014), but make suboptimal decisions when presented with simultaneous and mutually exclusive choices between rewards of different delays (Logue et al., 1985; Blanchard and Hayden, 2015; Carter and Redish, 2016; Kane et al., 2019). The Malapportionment Hypothesis further predicts that apparent discounting functions will present with greater curvature than what a reward-rate-maximizing agent would exhibit (Figure 21B). While experimentally observed temporal discounting would have greater curvature, the Malapportionment Hypothesis also predicts that the Magnitude (Figure 21C) and Sign effect (Figure 21D) would be less pronounced than what a reward-rate-maximizing agent would exhibit, with these effects becoming less pronounced the greater the underweighting. Finally, with regards to the Delay Effect (Figure 21E), the Malapportionment Hypothesis predicts that preference reversal would occur at delays greater than that exhibited by a reward-rate-maximizing agent, with the delay becoming more pronounced the greater the underweighting outside versus inside the considered pursuit by the agent.”

      Reviewer #3 Recommendations:

      As mentioned above, the readability of this paper should be improved so that the readers can follow the derivations and your analyses better. To this end, careful numbering of equations, following consistent equation numbering formats, and differentiating between appendix referencing and equation numbering would have gone a long way in improving the readability of this paper. Some specific questions are noted below.

      To increase clarity, in the revision we eliminated numbering of equations in the appendices except where an equation occurs in an appendix that is referenced within the main text. In the main text, important equations are thus numbered sequentially as they appear and note the appendix from which they derive. If an equation in an appendix is referenced in the main text, it is noted within the appendix it derives.

      (1) In general, it is unclear what the default pursuit is. From the schematic on the left (forgo decision), it appears to be the time spent in between reward-giving pursuits. However, this schematic also allows for smaller rewards to be attained during the default pursuit as do subsequent equations that reference a default reward rate. Here is where an example would have really benefited the authors in getting their point across as to what the default pursuit is in practice in the forgo decisions and how the default reward rate could be modulated.

      (1) The description of the default pursuit has been modified in section “Forgo and Choice decision topologies” to now read… “After either the conclusion of the pursuit, if accepted, or immediately after rejection, the agent returns to a pursuit by default (the “default” pursuit). This default pursuit effectively can be a waiting period over which reward could be received, and reoccurs until the next pursuit opportunity becomes available.” (2) Additionally, helper text has been added to Ap1 regarding the meaning of time and reward spent in the default pursuit. Finally, (3) new figures concerning n-pursuits occurring at the same (Supplement 1) or different (Supplement 2) frequencies from a default pursuit is now added, providing examples as suggested by the reviewer.

      (2) I want to clarify my understanding of the topologies in Figure 1. In the forgo, do they roam in the "gold" pursuit indefinitely before they are faced with the purple pursuit? In general, comparing the 2 topologies, it seems like in the forgo decision, they can roam indefinitely in the gold topology or choose the purple but must return to the gold.

      The reviewer’s understanding of the topology is correct. The agent loops across one unit time in the default gold pursuit indefinitely, though the purple pursuit (or any pursuit that might exist in that world) occurs on exit from gold at its frequency per unit time. The default gold pursuit will then itself have an average duration in units of time spent in gold. As the reviewer states, the agent can re-enter into gold from having exited gold, and can enter gold from having exited purple, but cannot re-enter purple from having exited purple; rather, it must enter into the default pursuit.

      …Another point here is that this topology is highly simplified (only one considered pursuit). So it may be helpful to either add a schematic for the full topology with multiple pursuits or alternatively, provide the corresponding equations (at least in appendix 1 and 2) for the simplified topology so you can drive home the intuition behind derived expressions in these equations.

      We understand the reviewer to be noting that, while, the illustrated example is of the simple topology, the mathematical formulation handles the case of n-number pursuits, and that illustrating a world in which there are a greater number of pursuits, corresponding to original appendices 1&2, would assist readers in understanding the generality of these equations.

      An excellent suggestion. We have now n-pursuit world illustrations where each pursuit occurs at the same (Supplemental Figure 1) and at different frequencies (Supplemental Figure 2) to the manuscript, and have added text to assist in understanding the form of the equation and its relationship to unit time in the default pursuit in the main and in the appendices.

      (3) In Equation and Appendix 1, there are a few things that are unclear. Particularly, why is the expected time of the default option E(t_default )= 1/(∑_(i=1)^n f_i )? Similarly, why is the E(r_default )= ρ_d/(∑_(i=1)^n f_i )? Looking at the expression for E(r_default ), it implies that across all pursuits 1 through n, the default option is encountered only once. Ultimately, in Equation 1.4, (and Equation 1), the units of the two terms in the numerator don't seem to match. One is a reward rate (ρ_d) and the other is a reward value. This is the most important equation of the paper since the next several equations build upon this. Therefore, the lack of clarity here makes the reader less likely to follow along with the analysis in rigorous detail. Better explanations of the terms and better formatting will help alleviate some of these issues.

      The equation is formulated to calculate the average reward received and average time spent per unit time spent in the default pursuit. So, f<sub>i</sub> is the encounter rate of pursuit i for one unit of time spent in the default pursuit. Added to the summation in the numerator we have the average reward obtained in the default pursuit per unit time () and in the denominator we have the time spent in the default pursuit per unit time (1).

      Text explaining the above equation has been added to Ap 1.

      (4) In equation and appendix 2, I'm trying to relate the expressions for t_out and r_out to the definitions "average time spent outside the considered pursuit". If I understand the expression in Equation 2.4 on the right-hand side, the numerator is the total time spent in all of the pursuits in the environment and the denominator refers to the number of times the considered pursuit is encountered. It is unclear as to why this is the average time spent outside the considered pursuit. In my mind, the expression for average time spent outside the considered pursuit would look something like t_out=1+ ∑_(i≠in)〖p_i t_i 〗= 1+ ∑_(i≠in)〖f_i/(∑_(j=1)^n f_j ) * t_i 〗. It is unclear how these expressions are then equivalent.

      Regarding the following equation,

      f<sub>i</sub> is the probability that pursuit i will be encountered during a single unit of time spent in the default pursuit. The numerator of the expression is the average amount of time spent across all pursuits, excepting the considered pursuit, per unit time spent in the default pursuit. Note that the + 1 in the numerator is accounting for the unit of time spent in the default pursuit and is added outside of the sum. Since f<sub>in</sub> is the probability that the considered pursuit will be encountered per unit of time spent in the default pursuit, is the average amount of time spent in the default pursuit between encounters of the considered pursuit. By multiplying the average time spent across all outside pursuits per unit of time in the default pursuit by the average amount of time spent in the default pursuit between encounters of the considered pursuit, we get the average amount of time spent outside the considered pursuit per encounter of the considered pursuit. This is calculated as if the pursuit encounters are mutually exclusive within a single unit of time spent within the default pursuit, as this is the case as the length of our unit time (delta t) approaches zero.

      The above text explaining the equation has been added to Ap 2.

      (5) In Figure 3, one huge advantage of this separation into in-pursuit and out-of-pursuit patches is that the optimal reward rate maximizing rule becomes one that compares ρ_in and ρ_out. This contrasts with an optimal foraging rule which requires comparing to the global reward rate and therefore a circularity in solution. In practice, however, it is unclear how ρ_out will be estimated by the agent.

      How, in practice, a human or animal estimates the reward rates―be they the outside and/or global reward rate under a policy of accepting a pursuit―is the crux of the matter. This work identifies equations that would enable a reward-rate maximizing agent to calculate and execute optimal policies and emphasizes that the effective reward rates and weights of pursuits must be accurately appreciated for global reward rate optimization. In so doing, it makes a reckoning of behaviors commonly but erroneously treated as suboptimal. Then, by examining the consequences of misestimation of these enabling parameters, it identifies mis-weighting pursuits as the nature of the error committed by whatever algorithm and representational architecture is being used by humans and animals (the Malapportionment Hypothesis). This curious pattern identified and analyzed in this work thus provides a clue into the nature of the learning algorithm and means of representing the temporal structure of the environment that is used by humans and animals―the subject of future work.

      We note, however, that we do discuss existing models that grapple with how, in practice, how a human or animal may estimate the outside reward rate. Of particular importance is the TIMERR model, which estimates the outside reward rate from its past experience, and can make an accounting of many qualitative features widely observed. However, while appealing, it would mix prior ‘in’ and ‘outside’ experiences within that estimate, and so would fail to perform forgo tasks optimally. Something is still amiss, as this work demonstrates.

      (6) The apportionment time cost needs to be explained a little bit more intuitively. For instance, it is clear that the opportunity cost of time is the cost of not spending time in the rest of the environment relative to the current pursuit. But given the definition of apportionment cost here in lines 447- 448 "The apportionment cost relates to time's allocation in the world: the time spent within a pursuit type relative to the time spent outside that pursuit type, appearing in the denominator." The reference to the equation (setting aside the confusion regarding which equation) within the definition makes it a bit harder to form an intuitive interpretation of this cost. Please reference the equation being referred to in lines 447-448, and again, an example may help the authors communicate their point much better

      We thank the reviewer for pressing on this critical point.

      Action: We added the following succinct verbal description of apportionment cost… “Apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration.” This definition appears in a new paragraph (as below) describing apportionment cost in the results section “Time’s cost: opportunity & apportionment costs determine a pursuit’s subjective value”, and is accompanied by equations for apportionment cost, and a figure giving its geometric depiction (Figure 5).

      “What, then, is the amount of reward by which the opportunity cost-subtracted reward is scaled down to equal the sv of the pursuit? This amount is the apportionment cost of time. The apportionment cost of time (height of the brown vertical bar, Figure 5F) is the global reward rate after taking into account the opportunity cost (slope of the magenta-gold dashed line in Figure 5F) times the time of the considered pursuit. Equally, the difference between the inside and outside reward rates, times the time of the pursuit, is the apportionment cost when scaled by the pursuit’s weight, i.e., the fraction that the considered pursuit is to the total time to traverse the world (Equation 9, right hand side). From the perspective of decision-making policies, apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration (Equation 9 center, Figure 5F).

      Equation 9. Apportionment Cost.

      While this difference is the apportionment cost of time, the opportunity cost of time is the amount that would be expected from a policy of not taking the considered pursuit over a time equal to the considered pursuit’s duration. Together, they sum to Time’s Cost (Figure 5G). Expressing a pursuit’s worth in terms of the global reward rate obtained under a policy of accepting the pursuit type (Figure 5 left column), or from the perspective of the outside reward and time (Figure 5 right column), are equivalent. However, the latter expresses sv in terms that are independent of one another, conveys the constituents giving rise to global reward rate, and provides the added insight that time’s cost comprises an apportionment as well as an opportunity cost.”

      (7) The analyses in Figures 6 and 7 give a nice visual representation of how the time costs are distributed as a function of outside reward and time spent. However, without an expression for apportionment cost it is hard to intuitively understand these visualizations. This also relates to the previous point of requiring a more intuitive explanation of apportionment costs in relation to the opportunity cost of time. Based on my quick math, it seems that an expression for apportionment cost would be as follows: (r_in- ρ_out*t_in)*(t_in⁄t_out )/(t_in⁄t_out +1 ). The condition described in Figure 7 seems like the perfect place to compute the value of just apportionment cost when the opportunity cost is zero. It would be helpful to introduce the equation here.

      We designed original figure 7, as the reviewer appreciates, to emphasize that time has a cost even when there is no opportunity cost, being due entirely to the apportionment cost of time.

      We now provide the mathematical expression of apportionment cost and apportionment scaling in Figure 5, the point in the main text of its first occurrence.

      …and have expanded original figure 5, its legend (so as to illustrate the apportionment scaling factor and the apportionment cost), and its accompanying main text, to further illustrate and clarify apportionment cost, and its relationship to opportunity cost, and time’s cost.

      (8) The analysis regarding choice decisions is relatively straightforward, pending the concerns for the main equations listed above for the forgo decisions. Legends certainly would have helped me grasp Figures 10-12 better.

      We believe the reviewer is referring to missing labels for the Sooner Smaller pursuit, and the Larger Later Pursuit in these figures? We used the same conventions as in Figure 9, but we see now that adding these labels to these figures would be helpful, and add them in the revision.

      We have now added to the figures themselves figure legends indicating the Sooner Small Pursuit and the Larger Later Pursuit. We have also added to the main text to emphasize the points made in these figures regarding the impact of opportunity cost and apportionment cost.

      (9) The derivation of the temporal discounting function from subjective reward rate is much appreciated as it provides further evidence for potential equivalence between reward rate optimization and hyperbolic discounting, which is known to explain a slew of decision-making behaviors in the economics literature.

      We thank and greatly appreciate the reviewer for this recognition.

      In response to the reviewer’s comment, we have added text that further relates reward rate optimization to hyperbolic discounting…

      (1) We add discussion of how our normative derivation gives explanation to Mazur’s ad hoc addition of 1 + k to Ainslie’s reward/time hyperbolic discounting conception. See new first paragraph under “Hyperbolic Temporal Discounting Functions” for the historical origins of the standard hyperbolic equation (which are decidedly not normatively derived). And then see our discussion (new second paragraph in sections “The apparent discounting function of global….”) of how our normative derivation gives explanation to “1”, “k”, and their relationship to each other.

      (2) We add explicit treatment of the Delay Effect in a new “The Delay Effect” section of the results along with a figure, and in its corresponding Discussion section.

      Minor comments:

      (1) Typo in equation 2, should be t_i in the denominator within the summation, not r_i .

      We thank the reviewer for catching this typo, and have corrected it in the revision.

      (2) Before equation 6, typo when defining ρ_in= r_in/(t_in.). Should be t_in in the denominator, not r_out.

      We thank the reviewer for catching this typo, and have corrected it in the revision.

      (3) Please be consistent with equation numbers, placement of equation references, and the reason for placing appendix numbers. This will improve readability immensely.

      To increase clarity, in the revision we eliminated numbering of equations in the appendices except where an equation occurs in an appendix that is referenced within the main text. In the main text, important equations are thus numbered sequentially and note the appendix from which they derive. If an equation in an appendix is referenced in the main text, it is noted within the appendix it derives.

      (4) Line 505 - "dominants" should be dominates.

      Typo fixed as indicated

      (5) Figures 10-12: add legends to the figures.

      Now so included.

      (6) Lines 701-703: please rewrite the equation separately. It is highly unclear what rt is here.

      We thank the reviewer for bringing attention to this error. The error arose in converting from Google Sheets to Microsoft Word.

      The equation has now been corrected.

      Additional citations noted in reply and appearing in Main text

      Ainslie, George. 1975. “Specious Reward: A Behavioral Theory of Impulsiveness and Impulse Control.” Psychological Bulletin 59: 257–72.

      Frederick, Shane, George Loewenstein, Ted O. Donoghue, and T. E. D. O. Donoghue. 2002. “Time Discounting and Time Preference : A Critical Review.” Journal of Economic Literature 40: 351–401.

      Gibbon, John. 1977. “Scalar Expectancy Theory and Weber’s Law in Animal Timing.” Psychological Review 84: 279–325.

      Green, Leonard, Nathanael Fristoe, and Joel Myerson. 1994. “Temporal Discounting and Preference Reversals in Choice between Delayed Outcomes.” Psychonomic Bulletin & Review 1: 383–89.

      Grüne-Yanoff, Till. 2015. “Models of Temporal Discounting 1937-2000: An Interdisciplinary Exchange between Economics and Psychology.” Science in Context 28 (4): 675–713.

      Jimura, Koji, Joel Myerson, Joseph Hilgard, Todd S. Braver, and Leonard Green. 2009. “Are People Really More Patient than Other Animals? Evidence from Human Discounting of Real Liquid Rewards.” Psychonomic Bulletin & Review 16: 1071–75.

      Kalenscher, Tobias, and Cyriel M. A. Pennartz. 2008. “Is a Bird in the Hand Worth Two in the Future? The Neuroeconomics of Intertemporal Decision-Making.” Progress in Neurobiology 84 (3): 284–315.

      Kirby, Kris N., and R. J. Herrnstein. 1995. “Preference Reversals Due to Myopic Discounting of Delayed Reward.” Psychological Science 6 (2): 83–89.

      Mazur, James E. 1987. “An Adjusting Procedure for Studying Delayed Reinforcement.” In The Effect of Delay and of Intervening Events on Reinforcement Value., 55–73. Quantitative Analyses of Behavior, Vol. 5. Hillsdale, NJ, US: Lawrence Erlbaum Associates, Inc.

      McNamara, John. 1982. “Optimal Patch Use in a Stochastic Environment.” Theoretical Population Biology 21 (2): 269–88.

      Rosati, Alexandra G., Jeffrey R. Stevens, Brian Hare, and Marc D. Hauser. 2007. “The Evolutionary Origins of Human Patience: Temporal Preferences in Chimpanzees, Bonobos, and Human Adults.” Current Biology: CB 17: 1663–68.

      Strotz, R. H. 1956. “Myopia and Inconsistency in Dynamic Utility Maximization.” The Review of Economic Studies 23: 165–80.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors investigate the role of HSPA2 during mouse preimplantation development. Knocking down HSPA2 in zygotes, the authors describe lower chances of developing into blastocysts, which show a reduced number of inner cell mass cells. They find that HSPA2 mRNA and protein levels show some heterogeneity among blastomeres at the 4-cell stage and propose that HSPA2 could contribute to skewing their relative contribution to embryonic lineages. To test this, the authors try to reduce HSPA2 expression in one of the 2-cell stage blastomere and propose that it biases their contribution to towards extra-embryonic lineages. To explain this, the authors propose that HSPA2 would interact with CARM1, which controls chromatin accessibility around genes regulating differentiation into embryonic lineage.

      Strengths:

      (1) The study offers simple and straightforward experiments with large sample sizes.

      Thanks for your kind recognition.

      (2) Unlike most studies in the field, this research often relies on both mRNA and protein levels to analyses gene expression and differentiation.

      Thanks for your kind recognition.

      Weaknesses:

      (1) Image and statistical analyses are not well described.

      Thanks for your advisable comment. We redescribe the image and statistical analyses in our revised version (line 255-257).

      (2) The functionality of the overexpression construct is not validated.

      Thanks for your kind suggestion. We validate the functionality of the overexpression construct in our revised version (Figure S3).

      (3) Tracking of KD cells in embryos injected at the 2-cell stage with GFP is unclear.

      Thanks for your kind suggestion. We randomly co-injected green fluorescent protein (Gfp) mRNA as a linage tracer with either Hspa2-siRNA or NC-FAM into one of the 2 -cell, and then monitored embryo development to the blastocyst stage (line 342-344).

      (4) A key rationale of the study relies on measuring small differences in the levels of mRNA and proteins using semi-quantitative methods to compare blastomeres. As such, it is not possible to know whether those subtle differences are biologically meaningful. For example, the lowest HSPA2 level of the embryo with the highest level is much higher than the top cell from the embryo with the lowest level. What does this level mean then? Does this mean that some blastomeres grafted from strong embryos would systematically outcompete all other blastomeres from weaker embryos? That would be very surprising. I think the authors should be more careful and consider the lack of quantitative power of their approach before reaching firm conclusions. Although to be fair, the authors only follow a long trend of studies with the same intrinsic flaw of this approach.

      Thanks for your advisable comment. Indeed, despite the approach drew on previous research (Zhou Cell 2018), we were clearly aware that this approach can only reflect relative comparisons. This means that the relative difference among the blastomeres from the same embryo were detected and compared. We did not compare the absolute levels of mRNA between different embryos. We also offered simple and straightforward experiments with large sample sizes to confirm this conclusion.

      (5) Some of the analyses on immunostaining do not take into account that this technique only allows for semi-quantitative measurements and comparisons.

      a) Some of the microscopy images are shown with an incorrect look-up table.

      b) Some of the schematics are incorrect and misleading.

      Thanks for your advisable comment. We revised microscopy images and schematics in our revised version.

      Reviewer #2 (Public review):

      Summary:

      In this study, Gao et al. use RNA-seq to identify Hspa2 as one of the earliest transcripts heterogeneously distributed between blastomeres. Functional studies are performed using siRNA knockdown showing Hspa2 may bias cells toward the ICM lineage via interaction with the known methyltransferase CARM1.

      Strengths:

      This study tackles an important question regarding the origins of the first cell fate decision in the preimplantation embryo. It provides novelty in its identification of Hspa2 as a heterogeneous transcript in the early embryo and proposes a plausible mechanism showing interactions with Carm1. Multiple approaches are used to validate their functional studies (FISH, WB, development rates, proteomics). Given only 4 other transcripts/RNA have been identified at or before the 4-cell stage (LincGET, CARM1, PRDM14, HMGA1), this would be an important addition to our understanding of how TE vs ICM fate is established.

      Thanks for your kind recognition.

      The RNA-seq results leading the authors to focus on Hspa2 are not included in the manuscript. This dataset would serve as an important resource but is neither included nor discussed. Nor is it mentioned whether Hspa2 was identified in prior RNA-seq embryos studies (for example Deng Science 2014).

      Thanks for your advisable comment. To identify genes that show a significantly high variability across blastomeres in the same embryo, we regressed out the embryo effect by established a new method, which will be published and uploaded to the database in the future. Thus, the RNA-seq results leading the we focus on Hspa2 are not included in the manuscript.   

      In addition, the functional studies are centered on Hspa2 knockdown at the zygote (1-cell) stage, which would largely target maternal transcript. Given the proposed mechanism relies on Hspa2 heterogeneity post-ZGA (late 2-cell stage), the knockdown studies don't necessarily test this and thus don't provide direct support to the authors' conclusions. The relevance of the study would be improved if the authors could show that zygotic knockdown leads to symmetric Hspa2 levels at the late 2-cell and/or 4-cell stage. It may be possible that zygotic knockdown leads to lower global Hspa2 levels, but that asymmetry is still generated at the 4-cell stage.

      Thanks for your advisable comment. We showed that the Hspa2 levels at the late 2-cell and 4cell stage after zygotic knockdown in our revised version (Figure S1 G-H, line 450-452).

      Furthermore, the authors show that Hspa2 knockdown at the 1-cell stage lowers total Carm1 levels at the 4-cell stage. However, it is unclear how total abundance within the embryo alters lineage specification within blastomeres. The authors go on to propose a plausible mechanism involving Hspa2 and Carm1 interaction, but do not discuss how expression levels may be involved.

      Thanks for your advisable comment. Previous research suggests that heterogeneous activity of the methyltransferase CARM1 results in differential methylation of histone H3R26 to modulate establishment of lineage specification (Zernicka-Goetz Cell 2018). Thus, we didn't discuss the total abundance within the embryo alters lineage specification.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Major issue with analyses:

      Image analysis needs to be much better explained than simply saying that ImageJ was used. Where are cells measured (at their equatorial plane? What is the size of the ROI?)? Ideally, the ROI and/or raw measurements should be provided.

      Thanks for your advisable comment. We redescribe the Image analysis in our revised version (line 187-194). 

      What are the objective criteria determining whether a cell is counted as GFP positive, CDX2 positive, or OCT4 positive? This is very unclear and key to the interpretation of many experiments.

      Thanks for your advisable comment. We think that the cell containing fluorescence signals above background noise were counted positive.

      Statistical analyses mention ANOVA in the methods but the student's t-test in the figure legend. Which is which? Most data are heavily normalized, which would unlikely fit the description for Student's t-test analyses.

      Thanks for your advisable comment. We redescribe the statistical analyses in our materials and methods (line 253-260).

      Figure 5H describes a relative fluorescence intensity with control at 1. The legend describes a normalization to "DNA" (I guess the authors meant DAPI), which is unlikely to give 1. This suggests that additional normalization was done and is not described. Is that the case? Also, since the authors propose that HSPA2 would control Histone modification and chromatin packing, I do not think that using DAPI is an appropriate way of normalizing the fluorescence signal.

      Thanks for your advisable comment. We replaced DNA with DAPI in our revised version. Based on previous studies, we adopted DAPI as a normalized fluorescence signal (Zhou Cell 2018, Zernicka-Goetz Cell 2018).

      Figure 1E shows data normalized to the lowest level while Figure 1H is normalized to the highest level. A consistent representation would be welcome.

      Thanks for your advisable comment. We revised the Figure 1H in our revised version.

      Is Figure 1C showing a t-test between correlations?

      Yes, Figure 1C shows the t-test between correlation.

      (2) Major issue with the interpretation of semi-quantitative methods and measurements:

      qPCR, WB, immunostaining are all semi-quantitative methods that require some kind of normalization due to non-linear bias in the way the molecules are picked up. Such normalization makes it difficult to know whether a detectable difference is meaningful biologically speaking i.e. if a difference of 1 CT between blastomeres can be detected after qPCR, is it meaningful? If that were the case, then embryos with lower CT than others (Figure 1D) would not be able to develop into blastocyst, like siRNA injected embryos, or grafting a blastomere with a high CT onto an embryo with low CT would lead to the systematic differentiation of these strong blastomeres into ICM.

      Thanks for your advisable comment. The CT values represent the relative mRNA levels of Hspa2 between blastomeres, and the higher CT value represents the lower expression of Hspa2 at mRNA level. Figure 1D shows the Hspa2 mRNA levels between blastomeres. The blastomere with lowlevel expression of the Hspa2 mRNA is not bias an ICM fates.  

      The same goes for fluorescence analyses (Figure 1F). Can the authors also provide the measurements for DAPI as they did for HSPA2? I am sure that with enough measurements, DAPI is variable enough to give a statistical difference among blastomeres with questionable biological meaning.

      I think the reasoning used here (unfortunately following the reasoning that has been used in a series of studies by other groups) of ranking blastomeres after semi-quantitative measurement is fundamentally flawed.

      Thanks for your advisable comment. The DAPI was determined by the maximal area using a custom Python script. Based on previous studies, we adopted DAPI as a normalized fluorescence signal (Zhou Cell 2018). This approach is to normalize embryo-to-embryo variance from the technical reason.

      (3) Major issue with overexpression experiment:

      While the siRNA experiment is partially validated by qPCR and WB measurements of HSPA2 after KD, the overexpression experiment is not. Do the authors have any evidence that the construct they use is produced into protein and functional? Can the authors check by WB? Can the authors rescue the siRNA with their overexpression?

      Thanks for your advisable comment. We verified the overexpression experiment by WB in in our revised version (Figure S3, line 360-361). Considering that siRNA degrades mRNA and prevents the mRNA translation process, we did not co-inject the siRNA with their overexpression.

      The lack of effect of HSPA2 overexpression on blastocyst formation is difficult to reconcile with the interpretation from the authors that levels of HSPA2 bias lineages.

      Have the authors tried lower concentrations? Have the authors tried FISH on their half-injected 2cell embryos? Of course, if the antibody against HSPA2 would work with immunostaining, that would be ideal.

      Thanks for your advisable comment. We chose the concentrations for our study based on previous research (Zernicka-Goetz Cell 2016). To verified Hspa2 was successfully inject into one blastomere at the 2-cell stage, we observed green fluorescence after co-injected GFP mRNA with either siRNA or NC-FAM into one blastomere of the two-cell embryos. Thus, we didn't try FISH on half-injected 2-cell embryos. We tried to perform immunostaining experiments with various HSPA2 antibodies (Proteintech: 12797-1-AP, Abcam: ab108416) and no good results were achieved.

      Author response image 1.

      (4) Major issue with tracking of injected cells:

      It is unclear what counts as a GFP-positive cell. In Figure 3D, most cells appear to have the same level of GFP.

      Thanks for your advisable comment. The cell containing green fluorescence signals above background noise were counted GFP-positive in Figure 3D. Most cells seem to have the same level of GFP because they are daughter cells of the blastomeres injected with GFP.

      In the images of GFP-expressing cells used to track the control of KD cells shown in Figure 3A, it seems that the control embryos have mostly GFP cells in the ICM. Is that the case, or just a bad example?

      Thanks for your advisable comment. The green fluorescent signals in Figure 3A represented OCT4 protein, an ICM marker.

      Can the authors do FISH against HSPA2 and visualize their GFP cells to validate the heterogeneous expression in situ?

      Thanks for your advisable comment. We have verified the heterogeneous expression of HSPA2 in Figure1.

      (5) Issue with fluorescent images:

      Many images are shown with inappropriate look-up tables with saturated DAPI, OCT4, CDX2, and FISH. This raises the doubt that analyses were made on saturated images, which would be incorrect.

      The LUT of Figure 5H should be adjusted similarly between the control and siRNA.

      Thanks for your advisable comment. We revised some images which showed inappropriate lookup tables in our revised version. The LUT of Figure 5H had been adjusted between the control and siRNA. 

      (6) Issue with schematics:

      Schematics of blastomere isolation grown into blastocyst-like structures are misleading since the final blastocyst-like structure should not have a zona pellucida and should have fewer cells than regular blastocysts.

      Thanks for your advisable comment. We revised schematics of blastomere grown into morula in our revised version (Figure 1A and Figure S1A).

      The summary schematics in the final figure should not state HSPA2 -/- since experiments in the study did not use KO but KD.

      Thanks for your advisable comment. We revised the summary schematics in our revised version.

      The blastocysts are the same sizes as the cleavage stage or morula embryos which implies that cells lose volume to the lumen, which is not the case.

      Thanks for your advisable comment. We revised the schematics in our revised version.

      (7) Issue with data presentation:

      In the tables within the figures, the number of decimals given should be the same for the mean and SE (one decimal should be more than enough).

      Thanks for your advisable comment. We revised the figure 2H in our revised version.

      The comparison of cell number and distribution within embryos (e.g. Figure 2B) would be best represented by a correlation analysis of TE vs ICM cells.

      Thanks for your advisable comment. We add the figure of a correlation analysis of TE vs ICM cells in our revised version (Figure 3B).

      The docking simulations are described in the main text as "experiments".

      Thanks for your advisable comment. We redescribed the docking simulations in our revised version.

      (8) Issue with data interpretation:

      The reduced number of ICM cells is interpreted as a slowed-down cell cycle. This could also be explained by failed cytokinesis and the generation of binucleated or polyploid cells. Have the authors checked for that? For example, by looking at their DAPI staining. 

      Thanks for your advisable comment. Our RNA-seq results revealed that the differentially expressed genes (DEGs) at blastocyst stage with HSPA2 knocking down are closely related to negative regulation of cell cycle, G1/S transition of mitotic cell cycle, mitotic cell cycle phase transition and regulation of mitotic cell cycle phase transition. Additionally, the previous study demonstrated that knockdown of HSPA2 reduced cell proliferation and led to G1/S phase cell cycle arrest (Hu Ann Transl Med 2019). Additionally, the lower cell number in ICM may also associated with failed cytokinesis and the generation of binucleated or polyploid cells. Thus, we guessed that HSPA2 has a role in ICM lineage establishment, although half of the ICM cells were able to survive with HSPA2 deficiency (line 463-472).

      It is unclear to me why reduced ICM should lead to fewer blastocysts. Blastocysts should be able to form as long as their TE is fine. In Figure 2G, embryos seem to be cultured in close proximity, which is fine if they are healthy but not if some of the embryos start dying and releasing toxic compounds (e.g. ROS). Have the authors tried removing the dying KD embryos to see if the development of the remaining embryos would improve?

      Thanks for your advisable comment. We think HSPA2 may affect blastocyst development by affecting other signaling pathways. And, the GO enriched terms was closely related to blastocyst development (Figure 2E). There was no significant difference in morula formation rate between Hspa2-KD group and NC group, thus the assumption that the toxic compounds released by some of the embryos that lead to downregulation of blastocyst rate may not be correct. Indeed, the rate of blastocyst formation in Hspa2-KD embryos was reduced significantly lower when few embryos was cultured separately. In addition, we discussed the possibility that the lower cell number in ICM may also associated with failed cytokinesis and the generation of binucleated or polyploid cells.

      Author response image 2.

      Reviewer #2 (Recommendations for the authors):

      One of the significant findings in the paper is the discovery portion where Hspa2 is identified as a heterogeneous transcript. To improve the logic and impact of the manuscript, it may benefit from reorganizing some of the figures and text. For example:

      (1) The paragraph in the introduction (Lines 56-68) should be moved to the discussion as the Hspa2 reveal should be in section 3.1, not prior to the RNA-seq results presented in Figure 1.

      Thanks for your advisable comment. We think it is more logical that HSPA2 needs to be introduced in the introduction.

      (2) Add text at the beginning of Section 3.1 to describe the rationale and results for the RNAseq. It would help the readers if the authors clearly stated why they chose the 4-cell stage.

      Thanks for your advisable comment. We explain why we chose the 4-cell stage in our revised version (line 272-273).

      (3) As this is the first time Hspa2 is identified, consider moving Figure S1C to the main figure to show expression throughout development.

      Thanks for your advisable comment. We moved Figure S1C to the main figure in our revised version (line 286-291).

      (4) Figure 1C: the correlation between Hspa2 and ICM markers would be strengthened if additional transcripts were used (Oct4, Sox2, Sox21). The graph in 1C would also be more informative if represented as a scatter plot with correlation coefficients (Nanog log2TPM vs Hspa2 log2TPM), rather than bar graphs.

      Thanks for your advisable comment. We chose Nanog as the correlation between Hspa2 and Nanog, a ICM markers, was showing the strongest correlation in result. And, the figure 1C shows the stronger positive correlation between Nanog and Hspa2 in gene expression than random gene pairs (n=100, n means the number of random gene pairs). Thus, the figure 1C with bar graphs is easier to understand.

      (5) Figure 1D: how were individual blastomeres grouped into B1-4? Individually run and then pooled based on relative expression?

      Thanks for your advisable comment. Blastomeres are named B1 to B4 according to increasing Hspa2 concentration in figure 1E.

      (6) Figures 1F, 1I, 5H: the DAPI channel appears to be saturated, but is used to normalize fluorescence intensity and may incorrectly account for light scattering within the embryo. Please clarify by adding more details regarding image analysis. Were partial stacks through the nucleus used for analysis, or max projections? Graph axes should be "relative fluorescence intensity."

      Thanks for your advisable comment. We added the details of fluorescence images analysis. The graph axes had revised in our revised version.

      (7) Line 278: the results in Figure S1C would benefit from more text regarding expression patterns throughout development. The maternal transcript appears to have a sharp downregulation by the early 2-cell stage, and is then upregulated coinciding with ZGA.

      Thanks for your advisable comment. We added more describe of the Figure in main text (LINE 285-290).

      (8) For the analyses in Figure 2 I-J and 2K-L, were arrested embryos excluded from analysis? This is an important detail as including arrested embryos would significantly bias the RNA-seq results. 

      Thanks for your advisable comment. The arrested embryos were excluded in Figure 2 I-J and 2K-L.

      (9) Figures 2G-H would be aided by converting the table in 2H to a bar graph and adding development rates for all stages (2-, 4-, 8-, morula, and blast). This would also show when an arrest occurs.

      Thanks for your advisable comment. We converted the table in 2H to a bar graph.

      (10) Blast rates are represented with too many significant digits (Figures 2H, 4B). They should only be reported to the closest ones given the unit of measure (number of blasts divided by number of zygotes). For instance, a blast rate of 81.63 {plus minus} 2.000 reflects excessive precision that is not measured in the data, it should rather read 82 {plus minus} 2%. This is also true for % cells (Figures 3E, 4H).

      Thanks for your advisable comment. Values were rounded down to the one decimal place (rounded down).

      (11) The clarity and impact of Figure 3A and 3D would benefit from 2D slices through the ICM. 

      Thanks for your advisable comment. In order to get more comprehensive understanding of the 3D structure of blastocyst of Figure 3A and 3D, we did not choose 2D slices.

      (12) To improve clarity and logic, separate the 1-cell and 2-cell knockdown experiments in the text and figures:

      a) 1-cell knockdown with RNA-seq results (Fig 2A-F).

      b) 1-cell knockdown showing less ICM/pluripotency markers in (combine Figures 2G-M and Figures 3A-B; "new Fig 3").

      c) 2-cell knockdown tracing lineage (Figures 2D-E; "new Fig 4").

      The new Figures 3 and 4 should mirror one another (i.e. for each knockdown experiment, development rates and cell counts should be included). For the 2-cell knockdown (Figures 2 D-E), what were the developmental rates (8-cell, morula, blast)?

      Thanks for your advisable comment. However, in order to the overall logical of the article, we do not separate the 1-cell and 2-cell knockdown experiments in the text and figures. And, we added the developmental rates (8-cell, morula, blast) of 2-cell knockdown group in our revised version (Figure S2).

      For the overexpression experiment (Figure 4), why were injections performed at the zygote stage versus the 2-cell stage? Given the significant downregulation of maternal transcript demonstrated in Figure S1C, it seems plausible that the injected RNA was also downregulated.

      Thanks for your advisable comment. For the overexpression experiment, we first chose to inject Hspa2 mRNA at the zygote stage and found that the overexpression of Hspa2 does not induce blastomere cells to bias an ICM fate. The qRT-PCR results indicated that the expression level of Hspa2 in overexpression group was significantly increased compared with normal group at 4cell and blastocyst stage (Figure 4C, 4D).  In addition, there is no guarantee that an equal amount of Hspa2 mRNA be injected into each blastomere in 2-cell stage. Thus, we did not microinject Hspa2 mRNA into the 2-cell stage.

      The 3.5 subheading overstates the results as the Hspa2-Carm1 interaction is not linked to lineage segregation. For example, a more specific subtitle might be, "Hspa2 interacts with Carm1 and alters H3R26me2 levels."

      Thanks for your advisable comment. We revised the subtitle in our revised version (line 376).

      Figures 5B-C and 5D-E. The qRT-PCR and WB analysis of knockdown blasts shows a correlation between Hspa2 downregulation and Carm1 downregulation. However, if the proposed mechanism is Hspa2 binding to Carm1 to mediate downstream methylation, why would it be expected to alter transcript levels at the 4-cell or blast stage? Please add further details and discussion in the results and discussion sections.

      Thanks for your advisable comment. The reason we chose to work at the 4-cell stage is because previous studies on CARM1 have focused on the 4-cell stage (Zernicka-Goetz Cell 2018,2016). 

      In the discussion, the statement in Lines 430-431 is an overinterpretation: "the heterogeneity of HSPA2... acts as an upstream factor to drive [the] first cell-fate decision." The knockdown experiments don't alter heterogeneity per se, but total abundance. Furthermore, the results do not show that heterogeneity drives heterogeneity in H3R26me2 patterns, for example.

      Thanks for your advisable comment. We redescribe the relevant statement in the discussion.

      More needs to be said regarding the ICM cells that persisted in the 1-cell KD experiment (Fig 3B). Lines 449-450 point out this result, but do not propose any plausible explanations. For instance, ICM cells may still form due to the incomplete knockdown achieved or the possibility that redundant pathways exist.

      Thanks for your advisable comment. We redescribe the relevant statement in our revised version (line 468-473).

      The 5th paragraph of the discussion seems incomplete. The authors point out a possible link between Hspa2 and Hippo and Wnt signaling pathways, but need to expand their discussion on how this may act as an additional mechanism incorporating Hspa2 with lineage segregation.

      Thanks for your advisable comment. We redescribe the 5th paragraph of the discussion (line 483-494).

      Statistics: all comparisons with greater than 2 groups should be performed with a one-way ANOVA and multiple comparisons, rather than Student's t-test (Figures 1B, 1D, 1E, 1F).

      All figure legends lack statistical test details.

      Thanks for your advisable comment. All figure legends added statistical test details in statistical analysis.

      Minor comments:

      In all graphs, individual blastomere expression levels should be represented as boxwhisker/bar/scatter/violin plots since the comparison is groups rather than time points (i.e. symbols should not be connected with a line in Figures 1B, 1D, 1F-G, 1I, S1D, S1F).

      Thanks for your advisable comment. Each colored line represents a single cell, and the dots of the same color represent the blastomere of the same cell. Thus, we use a line representation individual blastomere.

      For all fluorescent images, having two representative images may be confusing for the reader. Figures may be improved by just including one representative image for each stage/treatment (Figures 1F, 1I, S1F, 3A, 3D, 4E, 4G).

      Thanks for your advisable comment. The figures just including one representative image for each stage in our revised version. In addition, two representative images from each group were shown for each treatment (Figures 3A, 3D, 4E, 4G).

      The manuscript would be improved with thorough grammar and typo editing.

      For example:

      (1) Lines 18, 73, the wording is confusing, consider: "knockdown of Hspa2 in one of the two-cell blastomeres biased its progeny towards the trophectoderm lineage.".

      (2) Line 23, overstatement. Consider: "we demonstrated that HSPA2 levels correlate with ICMassociated genes and that it interacts with the CARM1.".

      (3) Line 25 confusing wording, "via the execution of commitment and differentiation phases.".

      (4) Line 37, replace "that" with "of;" replace "cell-fate decisions" with "cell-fate decision".

      (5) Line 40: needs space before (CARM1).

      (6) Line 43: the wording is confusing, consider "can result in higher expression levels of".

      (7) Line 45: wording, consider "Recent [studies have] further suggested".

      (8) Line 70: plurality, consider "analyzed gene expression pattern".

      (9) Line 73 typo: "prevents its".

      (10) Line 76-77 wording, consider "Hspa2 expression patterns can bias cell fate in the mouse embryo".

      (11) Line 276: remove "in whole embryos," since MII eggs are not embryos.

      (12) Line 617 "There" should be "Three".

      (13) Axis label in Fig 3b "Totle" should be "Total".

      (14) Lines 417, 419 missing spaces.

      (15) Line 448 missing word, "interfering [with] the cell cycle".

      (16) Line 462 incorrect word, "[a]polar cells being specified as ICM".

      (17) Line 469 incorrect plural, "cell differentiation".

      Thanks for your advisable comment. We revised the whole manuscript carefully according to the reviewers' suggestions.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Reviewer #1:

      (1) To improve the clarity of the work, I suggest a final note to the authors to say more explicitly that objective accuracy has a finer resolution *due to the number of "special circles" per trial* in their task. This task detail got lost in my read of the manuscript, and confused me with respect to the resolution of each accuracy measure.

      We agree with the reviewer that this would be a useful clarification and have therefore added the following statement to the Methods section on p. 20:

      “It should be noted that the OIP has a slightly finer resolution due to the number of special circles per trial.”

      (2) Similarly for clarification, they could point out that their exclusion criteria removes subjects that have lower OIP than their AIP analysis allows (which is good for comparison between OIP and AIP). Thus, it removes the possibility that very poor performing subjects (OIP) are forced to have a higher than actual AIP due to the range).

      We agree this would be a useful statement to add and have included the following sentence in the Supplement on p. 8:

      “Such a restriction of the threshold parameter was intended to increase the comparability between AIP and OIP, and hence improved the calculation of the reminder bias.”


      The following is the authors’ response to the previous reviews.

      Reviewer #1:

      (1) Upon reading their response to the question I had regarding AIP and OIP, a few more questions came up regarding OIP, AIP, how they're calculations differ, and how the latter was computed in R. I hope these help readers to clarify how to interpret these key measures, and the hypotheses that rely upon them.

      Regarding fitting, and in relation to power, is16 queries adequate to estimate an AIP using the R's quickpsy? That is, assuming some noise in the choice process, how recoverable is a true indifference points from 16 trials? If there's a parameter recovery analysis (ie generating choice via the fitting parameters, which will have built-in stochasticity, and seeing how well you recover the parameter) of interest would be helpful. It may help to characterize why the present study might differ from prior studies (maybe a power issue here).

      The reviewer is absolutely correct that we should have provided more detail when describing our fitting procedure for the psychometric curves. We have now addressed this by adding the following statements to the Methods section and Supplement:

      Page 20 in the main manuscript: “Fitting was done using the quickpsy package in R and more detail is given in the Supplement.”

      Pages 8 and 9 in the Supplement: 

      “Psychometric curve fitting

      We used the quickpsy package in R to fit psychometric curves to each participant’s choice data to derive their actual indifference point (AIP), which was operationalised as the threshold parameter when predicting reminder choices from target values. We restricted the possible parameter ranges from 2 to 9 for the threshold parameter and from 1 to 500 for the slope parameter, based on the task’s properties and pilot data. Apart from those parameter ranges, we used only default settings of the quickpsy() function.

      Each participant has only 16 trials (2 for each target value) contribute to the curve fitting. To understand the robustness of the AIP based on such limited data, we conducted a parameter recovery analysis. We simulated 16 trials based on each psychometric function and re-ran the curve fitting based on those simulated choices. There was close correspondence between the actual and recovered threshold parameters (or AIPs) with a correlation of r = 0.97, p < 0.001 (see also Figure S1). In contrast, the slope parameter—which was not central to any of our analyses—exhibited greater variability during the initial fitting. This increased uncertainty likely contributed to its poor recovery in the simulation, as evidenced by a near-zero correlation (r = −0.01, p = 0.82).”

      (2) Along these lines, it would be helpful for the reader to actually see the individual psychometric curve, now how quickpsy was used (did you fit left and right asymptotes), etc, to understand how that fitting procedure works and how the assumptions of the fitting procedure compare to what can be gleaned through seeing the choice curves plotted.

      As stated above, we used default settings of the quickpsy() function and hence assumed symmetric asymptotes at 0 and 1. However, the reviewer mentions “left and right asymptotes”, so maybe this question is about restricting the possible parameter range for the threshold, which we restricted to values from 2 to 9, as described above.

      Regarding the individual curves, we have now include the following statement on page 9 in the Supplement: “Figures S2 to S31 show the individual psychometric curves that were estimated for each participant.” Please refer to the Supplement for the added figures.

      (3) A more full explanation of quickpsy, its parameters, and how choice curves look might also generate interesting further questions to think about with respect to biases and compulsivity. Two individuals might have similar indifference points, but an asymptote might reflect a bias to always have some percent chance of for example to take the reminders even at the lowest offer available for them.

      We agree that this is an interesting focus which we will keep in mind for future studies.

      (4) Regarding comparing OIP to AIP: 

      For OIP, as far as I can understand, the resolution of it is decreased compared to AIP.  Accuracies for OIP can only be 0/4,1/4,2/4,3/4, or 4/4. Yet, the resolution for AIP is the full range of offers (2 to 9) with respect to the parameter of interest (the indifference point). Could this bias the estimation of OIP (for instance, someone who scored 25% might actually be much closer to either 50 or 0, but we can't tell due to resolution?

      As mentioned in response to comment (1), we restricted the parameter range for the thresholds to 2 to 9 to increase comparability. The reviewer is right to point out that the OIP  still has lower resolution than the AIP, which is one of the downsides of having a shortened paradigm (cf. the longer version in Gilbert et al., 2019), which is optimised for online testing, especially if used in combination with additional questionnaires. We have no reason to believe though that this could have led to any bias, especially none that would contribute to the individual differences which are the main focus of our study.

      Gilbert, S. J., Bird, A., Carpenter, J. M., Fleming, S. M., Sachdeva, C., & Tsai, P.-C. (2020). Optimal use of reminders: Metacognition, effort, and cognitive offloading. Journal of Experimental Psychology: General, 149(3), 501–517. https://doi.org/10.1037/xge0000652

      (5) Additionally, it seems like the upper and lower bounds of OIP (0 and 10) differ from AIP (2 and 9). Could this also introduce bias (for example, if someone terrible performance, the mean would artificially be higher under AIP than OIP because the smallest indifference point is 2 under AIP, but could be 0 under OIP.

      See our response to comment (1), we fixed the range to 2 to 9 (which was the range of target values used in our study).

      (6) Finally seeing how CIT actually corresponds to accuracy overall (not a relative measure like AIP compared to OIP) I think would also be helpful as this is related to most points noted above.

      We included the suggested test as an exploratory analysis on pages 42-43 in the Supplement: “Third, we were interested in how the transdiagnostic phenotypes would correspond to performance. We therefore fitted a model which predicted internal accuracy (that is, unaided task performance on trials where no reminders could be used) from AD, CIT, and the other covariates (age, education and gender). We found that neither AD, β = -0.02, SE = 0.05, t = 0.44, p = 0.658, nor CIT, β = -0.03, SE = 0.05, t = -0.66, p = 0.510, predicted internal accuracy.

      The full results can be found in Table S13 as well as in Figure S32.”

    2. Reviewer #1 (Public review):

      Summary:

      Boldt et al test several possible relationships between trandiagnostically-defined compulsivity and cognitive offloading in a large online sample. To do so, they develop a new and useful cognitive task to jointly estimate biases in confidence and reminder-setting. In doing so, they find that over-confidence is related to less utilization of reminder-setting, which partially mediates the negative relationship between compulsivity and lower reminder-setting. The paper thus establishes that, contrary to the over-use of checking behaviors in patients with OCD, greater levels of transdiagnostically-defined compulsivity predicts less deployment of cognitive offloading. The authors offer speculative reasons as to why (perhaps it's perfectionism in less clinically-severe presentations that lowers the cost of expending memory resources), and sets an agenda to understand the divergence in cognitive between clinical and nonclinical samples. Because only a partial mediation had robust evidence, multiple effects may be at play, whereby compulsivity impacts cognitive offloading via overconfidence and also by other causal pathways.

      Strengths:

      The study develops an easy-to-implement task to jointly measure confidence and replicates several major findings on confidence and cognitive offloading. The study uses a useful measure of cognitive offloading - the tendency to set reminders to augment accuracy in the presence of experimentally manipulated costs. Moreover, the utilizes multiple measures of presumed biases -- overall tendency to set reminders, the empirically estimated indifference point at which people engage reminders, and a bias measure that compares optimal indifference points to engage reminders relative to the empirically observed indifference points. That the study observes convergenence along all these measures strengthens the inferences made relating compulsivity to the under-use of reminder-setting. Lastly, the study does find evidence for one of several a priori hypotheses and sets a compelling agenda to try to explain why such a finding diverges from an ostensible opposing finding in clinical OCD samples and the over-use of cognitive offloading.

      Weaknesses:

      Although I think this design and study are very helpful for the field, I felt that a feature of the design might reduce the tasks's sensitivity to measuring dispositional tendencies to engage cognitive offloading. In particular, the design introduces prediction errors, that could induce learning and interfere with natural tendencies to deploy reminder-setting behavior. These PEs comprise whether a given selected strategy will be or not be allowed to be engaged. We know individuals with compulsivity can learn even when instructed not to learn (e.g., Sharp, Dolan and Eldar, 2021, Psychological Medicine), and that more generally, they have trouble with structure knowledge (eg Seow et al; Fradkin et al), and thus might be sensitive to these PEs. Thus, a dispositional tendency to set reminders might be differentially impacted for those with compulsivity after an NPE, where they want to set a reminder, but aren't allowed to. After such an NPE, they may avoid moreso the tendency to set reminders. Those with compulsivity likely have superstitious beliefs about how checking behaviors lead to a resolution of catastrophes, that might in part originate from inferring structure in the presence of noise or from purely irrelevant sources of information for a given decision problem.<br /> It would be good to know if such learning effects exist, if they're modulated by PE (you can imagine PEs are higher if you are more incentivized - e.g., 9 points as opposed to only 3 points - to use reminders, and you are told you cannot use them), and if this learning effect confounds the relationship between compulsivity and reminder-setting.

      A more subtle point, I think this study can be more said to be an exploration than a deductive of test of a particular model -> hypothesis -> experiment. Typically, when we test a hypothesis, we contrast it with competing models. Here, the tests were two-sided because multiple models, with mutually exclusive predictions (over-use or under-use of reminders) were tested. Moreover, it's unclear exactly how to make sense of what is called the direct mechanism, which is supported by the partial (as opposed to complete) mediation.

      Comments on revisions:

      I have the following final comments for your manuscript revisions:

      To improve the clarity of the work, I suggest a final note to the authors to say more explicitly that objective accuracy has a finer resolution *due to the number of "special circles" per trial* in their task. This task detail got lost in my read of the manuscript, and confused me with respect to the resolution of each accuracy measure. Similarly for clarification, they could point out that their exclusion criteria removes subjects that have lower OIP than their AIP analysis allows (which is good for comparison between OIP and AIP). Thus, it removes the possibility that very poor performing subjects (OIP) are forced to have a higher than actual AIP due to the range).

    1. Author response:

      eLife Assessment

      This useful study integrates experimental methods from materials science with psychophysical methods to investigate how frictional stabilities influence tactile surface discrimination. The authors argue that force fluctuations arising from transitions between frictional sliding conditions facilitate the discrimination of surfaces with similar friction coefficients. However, the reliance on friction data obtained from an artificial finger, together with the ambiguous correlative analyses relating these measurements to human psychophysics, renders the findings incomplete.

      Our main goal with this paper was to show that the most common metric, i.e. average friction coefficient—widely used in tactile perception and device design—is fundamentally unsound, and to offer a secondary parameter that is compatible with the fact that human motion is unconstrained, leading to dynamic interfacial mechanics. In contrast with the summary assessment, we also note that the average friction coefficients in our study were not particularly similar, ranging from differences of 0.4 – 1, a typical range seen in most studies. We believe some of the comments originate from a misinterpretation of our statistically significant, but negative correlation between human results and friction coefficients – which leads to the spurious conclusion that nearly identical objects should be very easy to tell apart, thus supporting our central argument for the need of an alternative. We understand the Reviewers wanting to see that we can demonstrate that humans using instabilities in situ. This is seemingly reasonable, but we explain the significant challenges and fundamental unknowns to those experiments. However, we modified our title to reflect our focus on offering an alternative to the average coefficient of friction.

      We do not think it was feasible, at this stage, to demonstrate that humans use friction instabilities through direct manipulation and observation in human participants. In short, there are still several fundamental unknowns: (1) a decision-making model would need to be created, but it is unknown if tactile decision making follows other models, (2) it is further unknown what constitutes “tactile evidence”, though at our manuscript’s conclusion, we propose that friction instabilities are better suited for to be tactile evidence than the averaging of friction coefficients from a narrow range of human exploration (3) in the design of samples, from a friction mechanics and materials perspective, it is not at this point, possible to pre-program surfaces a priori to deliver friction instabilities and instead must be experimentally determined – especially when attempting to achieve this in controlled surfaces that do not create other overriding tactile cues, like macroscopic bumps or large differences in surface roughness. (4) Given that the basis for tactile percepts, like which object feels “rougher” or “smoother” is not sufficiently established and we have seen leads to confusion, it is necessary to use a 3-alternative forced choice task which avoids asking objects along a preset perceptual dimension – a challenge recognized by Reviewer 3. However, this would bring in issues of memory in the decision-making model. (5) The prior points are compounded by the fact that, we believe, tactile exploration must be performed in an unconstrained manner, i.e., without an apparatus generating motion onto a stationary finger. Work by Liu et al. (IEEE ToH, 2024) showed that recreating friction obtained during free exploration onto a stationary finger was uninterpretable by the participants, hinting at the importance of efference copies(1). We believe that each of the above-mentioned issues constitutes a significant advance in knowledge and would require discussion and dissemination with the community. Finally, one of our overarching goals is to create a consistent method to characterize surfaces, and given individual variability in human fingers and motion, a machine-based method that can rapidly, consistently, and sufficiently replicate tactile exploration is needed.

      Finally, we also justify our use of a mock finger to provide a method to characterize surfaces in tactile studies that other researchers could reasonably recreate, without creating a standard around individual humans, considering the variability in finger shape and motion during exploration. We do not believe this is an “either-or” argument, but rather that standardized methods to characterize surfaces and devices are greatly needed in the field. From these standardized methods, like surface roughness, some tabulated values of friction coefficient, or surface energy, etc., the current metrics to parameterize results are largely incapable of capturing the dynamic changes in forces expected during human tactile exploration.

      Our changes to the manuscript (Page 1 & SI Page 1, Title)

      “Alternatives to Friction Coefficient: Role of Frictional Instabilities for Fine Touch Perception”

      Reviewer 1 (Public review):

      Summary:

      In this paper, Derkaloustian et. al look at the important topic of what affects fine touch perception. The observations that there may be some level of correlation with instabilities are intriguing. They attempted to characterize different materials by counting the frequency (occurrence #, not of vibration) of instabilities at various speeds and forces of a PDMS slab pulled lengthwise over the material. They then had humans make the same vertical motion to discriminate between these samples. They correlated the % correct in discrimination with differences in frequency of steady sliding over the design space as well as other traditional parameters such as friction coefficient and roughness. The authors pose an interesting hypothesis and make an interesting observation about the occurrences of instability regimes in different materials while in contact with PDMS, which is interesting for the community to see in the publication. It should be noted that the finger is complex, however, and there are many factors that may be quite oversimplified with the use of the PDMS finger, and the consideration and discounting of other parameters are not fully discussed in the main text or SI. Most importantly, however, the conclusions as stated do not align with the primary summary of the data in Figure 2.

      Strengths:

      The strength of this paper is in its intriguing hypothesis and important observation that instabilities may contribute to what humans are detecting as differences in these apparently similar samples.

      We thank Reviewer 1 for their time on the manuscript, recognizing the approach we took, and offering constructive feedback. We believe that our conclusions, in fact, are supported by the primary summary of the data in Figure 2 but we believe that our use of R<sup>2</sup> could have led to misinterpretation. The trend with friction coefficient and percent correct was indeed statistically significant but was spurious because the slope was negative. In the revision, we add clarifying comments throughout, change from R<sup>2</sup> to r as to highlight the negative trend, and adjust the figures to better focus on friction coefficient.

      Finally, we added a new section to discuss the tradeoffs between using a real human finger versus a mock finger, and which situations may warrant the use of one or the other. In short, for our goal of characterizing surfaces to be used in tactile experiments, we believe a mock finger is more sustainable and practical than using real humans because human fingers are unique per participant, humans move their fingers at constantly changing pressures and velocities, and friction generated during free exploring human cannot be satisfactorily replicated by moving a sample onto a stationary finger. But, we do not disagree that for other types of experiments, characterizing a human participant directly may be more advantageous.

      Weaknesses:

      Comment 1 - The most important weakness is that the findings do not support the statements of findings made in the abstract. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. While the statistical test shows significance (and is interesting!), the R-squared value is 0.38, while the R-squared value for the "Friction Coefficient vs. Percent Correct" plot has an R-squared of 0.6 and a p-value of < 0.01 (including Figure 2B). This suggests that the results do not support the claim in the abstract: "We found that participant accuracy in tactile discrimination was most strongly correlated with formations of steady sliding, and response times were negatively correlated with stiction spikes. Conversely, traditional metrics like surface roughness or average friction coefficient did not predict tactile discriminability."

      We disagree that the trend with friction coefficient suggests the results do not support the claim because the correlation was found to be negative. However, we could have made the comparison more apparent and expanded on this point, given its novelty.

      While the R<sup>2</sup> value corresponding to the “Friction Coefficient vs. Percent Correct” plot is notably higher, our results show that the slope is negative, which would be statistically spurious. This is because a negative correlation between percent correct (accuracy in discriminating surfaces) and difference in friction coefficient means that the more similar two surfaces are (by friction coefficient), the easier it would be for people to tell them apart. That is, it incorrectly concludes that two identical surfaces would be much easier to tell apart than two surfaces with greatly different friction coefficients.

      This is counterintuitive to nearly all existing results, but we believe our samples were well-positioned to uncover this trend by minimizing variability, by controlling multiple physical parameters in the samples, and that the friction coefficient — typically calculated in the field as an average friction coefficient — ignores all the dynamic changes in forces present in elastic systems undergoing mesoscale friction, i.e., human touch, as seen in Fig. 1 in a mock finger and Fig. 3 in a real finger. By demonstrating this statistically spurious trend, we believe this strongly supports our premise that an alternative to friction coefficient is needed in the design of tactile psychophysics and haptic interfaces.

      We believe that this could have been misinterpreted, so we took several steps to improve clarity, given the importance of this finding: we separated the panel on friction coefficient to its own panel, we changed from R<sup>2</sup> to r throughout, and we added clarifying text. We also added a small section focusing on this spurious trend.

      Our changes to the manuscript (Page 10)

      “To compare the value of looking at frictional instabilities, we also performed GLMM fits on common approaches in the field, like a friction coefficient or material property typically used in tactile discrimination, shown in Fig. 2D-E. Interestingly, in Fig. 2D, we observed a spurious, negative correlation between friction coefficient (typically and often problematically simplified as across all tested conditions) and accuracy (r = -0.64, p < 0.01); that is, the more different the surfaces are by friction coefficient, the less people can tell them apart. This spurious correlation would be the opposite of intuition, and further calls into question the common practice of using friction coefficients in touch-related studies. The alternative, two-term model which includes adhesive contact area for friction coefficient(29) was even less predictive (see Fig. S6A of SI). We believe such a correlation could not have been uncovered previously as our samples are minimal in their physical variations. Yet, the dynamic changes in force even within a single sample are not considered, despite being a key feature of mesoscale friction during human touch.

      We investigate different material properties in Fig. 2E. Differences in average roughness R<sub>a</sub> (or other parameters, like root mean square roughness R<sub>rms</sub> (Fig. S6A of SI) did not show a statistically significant correlation to accuracy. Though roughness is a popular parameter, correlating any roughness parameter to human performance here could be moot: the limit of detecting roughness differences has previously been defined as 13 nm on structured surfaces33 and much higher for randomly rough surfaces,(46) all of which are magnitudes larger than the roughness differences between our surfaces. The differences in contact angle hysteresis – as an approximation of the adhesion contributions(47) – do not present any statistically significant effects on performance.”

      Comment 2, Part 1

      Along the same lines, other parameters that were considered such as the "Percent Correct vs. Difference in Sp" and "Percent Correct vs. Difference in SFW" were not plotted for consideration in the SI. It would be helpful to compare these results with the other three metrics in order to fully understand the relationships.

      We have added these plots to the SI. We note that we had checked these relationships and discussed them briefly, but did not include the plot. The plots show that the type of instability was not as helpful as its presence or absence.

      Our changes to the manuscript (Page 9)

      “Furthermore, a model accounting for slow frictional waves alone specifically shows a significant, negative effect on performance (p < 0.01, Fig. S5 of SI), suggesting that in these samples and task, the type of instability was not as important.”

      Added (SI Page 4)

      “and no correlation between accuracy and stiction spikes (Fig. S5).”

      Comment 2, Part 2

      Other parameters such as stiction magnitude and differences in friction coefficient over the test space could also be important and interesting.

      We agree these are interesting and have thought about them. We are aware that others, like Gueorguiev et al., have studied stiction magnitudes, and though there was a correlation, the physical differences in surface roughness (glass versus PMMA) investigated made it unclear if these could be generalized further(2). We are unsure how to proceed here with a satisfactory analysis of stiction magnitude, given that stiction spikes are not always generated. In fact, Fig. 1 shows that for many velocities and pressures, they do not form. However, we offer some speculation on why stiction spikes may be overrepresented in the literature because:

      (1) They are prone to being created if the finger was loaded for a long time onto a surface prior to movement, thus creating adhesion by contact aging which is unlike active human exploration. We avoid this by discarding the first pull in our measurements, and is a standard practice in mechanical characterization if contact aging needs to be avoided.

      (2) The ranges of velocities and pressures explored were small.

      (3) In an effort to generate strong tactile stimuli, highly adhesive or rough surfaces are used.

      (4) They are visually distinctive on a plot, but we are unaware of any mechanistic reason that mechanoreceptors would be extremely sensitive to this low frequency event over other signals.

      In ongoing work, however, we are always cognizant that if stiction spikes are a dominant factor, then a secondary analysis on their magnitude would be important.

      We interpret “difference in friction coefficient over the test space” to be, for a single surface, like C4, to find the highest average friction for a condition of single velocity and mass and subtract that from the lowest average friction for a condition of single velocity and mass. We calculated the difference in friction coefficient in the typical manner of the field, by averaging all data collected at all velocities and masses and assigning a single value for all of a surface, like C4. We had performed this, and have the data, but we are wary of overinterpreting secondary and tertiary metrics because they do not have any fundamental basis in traditional tribology, and this value, if used by humans, would suggest that they rapidly explore a large parameter space to find a “maximum” and “minimum” friction. Furthermore, the range in friction across the test space, after averaging, may in fact, be smaller than the range of friction in a single measurement. For example, in Fig. 1B, the friction coefficient can be calculated by dividing the data by the normal force ([applied mass + 6 g finger] × gravity). The friction coefficient in a single run varies widely, as expected.

      Fig. 2D shows a GLMM fit between percent correct responses across our pairs and the differences in friction coefficient for each pair, where we see a spurious negative correlation. As we had the data of all average friction coefficients for each condition for a given material, we also looked at the difference in maximum and minimum friction coefficients. For our tested pairs, these differences also lined up on a statistically significant, negative GLMM fit (r = -0.86, p < 0.005). However, the values for a given surface can vary drastically, with an interquartile range of 1.20 to 2.09 on a single surface. We fit participant accuracy to the differences in these IQRs across pairs. This also led to a negative GLMM fit (r = -0.65, p < 0.05). However, we are hesitant to add this to the manuscript for the reasons stated previously.

      Comment 3, Part 1

      Beyond this fundamental concern, there is a weakness in the representativeness of the PDMS finger, the vertical motion, and the speed of sliding to real human exploration.

      Overall, this is a continuous debate that we think offers two solutions. There is always a tradeoff between using a synthetic model of a finger versus a real human finger, and there is a place for both models. That is, while our mock finger will be more successful the closer it is to a human finger, it is not our goal to fully replace a human finger, rather our goal is to provide a method of characterizing surfaces that is indeed relevant on the length scale of human touch.

      The usefulness of the mock finger is in isolating the features of each surface that is independent of human variability, i.e., instabilities that form without changing loading conditions between sliding motions or even within one sliding motion. Of course, with this method, we still require confirmation of these features still forming during human exploration, which we show in Fig. 3.

      We believe that this method of characterizing surfaces at the mesoscale will ultimately lead to more successful human studies on tactile perception. Currently, and as shown in the paper, characterizing surfaces through traditional techniques, such as a commercial tribometer (friction coefficient, using a steel or hard metal ball), roughness (via atomic force microscopy or some other metrology), surface energy are less predictive. Thus, we believe this mock finger is stronger than the current state-of-the-art characterizing surfaces (we are also aware of a commercial mock finger company, but we were unable to purchase or obtain an evaluation model).

      One of the main – and severe – limitations of using a human finger is that all fingers are different, meaning any study focusing on a particular user may not apply to others or be recreated easily by other researchers. We cannot set a standard for replication around a real human finger as that participant may no longer be available, or willing to travel the world as a “standard”. Furthermore, the method in which changes their pressures and velocities is different. We note that this is a challenge unique to touch perception – how an object is touched changes the friction generated, and thus the tactile stimulus generated, whereas a standardized stimulus is more straightforward for light or sound.

      However, we do emphasize that we have strongly considered the balance between feasibility and ecological validity in the design of a mock finger. We have a mock finger, with the three components of stiffness of a human finger (more below). Furthermore, we have also successfully used this mock finger in correlations with human psychophysics in previous work, where findings from our mechanical experiments were predictive of human performance(3-6).

      Our changes to the manuscript Added (Page 2-3)

      “Mock finger as a characterization tool

      In this work, we use a mechanical setup with a PDMS mock finger to derive tactile predictors from controlled friction traces alternative to average friction coefficients. While there is a tradeoff in selecting a synthetic finger over a more accurate, real human finger in modeling touch, our aim to design a method of mesoscale surface characterization for more successful studies on tactile perception cannot be fulfilled using one human participant as a standard. We believe that with sufficient replication of surface and bulk properties as well as contact geometry, and controlled friction measurements collected at loading conditions observed during a tactile discrimination task, we can isolate unique frictional features of a set of surfaces that do not arise from human-to-human variability.

      The major component of a human finger, by volume, is soft tissue (~56%)(22), resulting in an effective modulus close to 100 kPa(23,24). In order to achieve this same softness, we crosslink PDMS in a 1×1×5 cm mold at a 30:1 elastomer:crosslinker ratio. However, two more features impart increased stiffness in a human finger. Most of this added rigidity is derived from the bone at the fingertip, the distal phalanx(23–25), which we mimic with an acrylic bone within our PDMS network. The stratum corneum, the stiffer, glassier outer layer of skin(26), is replicated with the surface of the mock finger glassified, or further crosslinked, after 8 hours of UV-Ozone treatment(27). This treatment also modifies the surface properties of the native PDMS to align with those of a human finger more closely. It minimizes the viscoelastic tack at the surface, resulting in a comparable non-sticky surface. At least one day after treatment, the finger surface returns to moderate hydrophilicity (~60º), as is typically observed for a real finger(28).

      The initial contact area formed before a friction trace is collected is a rectangle of 1×1 cm. While this shape is not entirely representative of a human finger with curves and ridges, human fingers flatten out enough to reduce the effects of curvature with even very light pressures(28–30). This implies that regardless of finger pressure, the contact area is largely load-independent, which is more accurately replicated with a rectangular mock finger. It is still a challenge to control pressure distribution with this planar interface, but non-uniform pressures are also expected during human exploration.

      Lastly, we consider fingerprints vs. flat fingers. A key finding of our previous work is that while fingerprints enhanced frictional dynamics at certain conditions, key features were still maintained with a flat finger.7 Furthermore, for some loading conditions, the more amplified signals could also result in more similar friction traces for different surfaces. We have continued to use flat fingers in our mechanical experiments, and have observed good agreement between these friction traces and human experiments(7,8,21,31).”

      (Page 3-4, Materials and Methods)

      “Mock Finger Preparation

      Friction forces across all six surfaces were measured using a custom apparatus with a polydimethylsiloxane (PDMS, Dow Sylgard 184) mock finger that mimics a human finger’s

      mechanical properties and contact mechanics while exploring a surface relatively closely(7,8). PDMS and crosslinker were combined in a 30:1 ratio to achieve a stiffness of 100 kPa comparable to a real finger, then degassed in a vacuum desiccator for 30 minutes. We are aware that the manufacturer recommended crosslinking ratio for Sylgard 184 is 10:1 due to potential uncrosslinked liquid residues(32), but further crosslinking concentrated at the surface prevents this. The prepared PDMS was then poured into a 1×1×5 cm mold also containing an acrylic 3D-printed “bone” to attach applied masses on top of the “fingertip” area contacting a surface during friction testing. After crosslinking in the mold at 60ºC for 1 hour, the finger was treated with UV-Ozone for 8 hours out of the mold to minimize viscoelastic tack.

      Mechanical Testing

      A custom device using our PDMS mock finger was used to collect macroscopic friction force traces replicating human exploration(7,8). After placing a sample surface on a stage, the finger was lowered at a slight angle such that an initial 1×1 cm rectangle of “fingertip” contact area could be established. We considered a broad range of applied masses (M \= 0, 25, 75, and 100 g) added onto the deadweight of the finger (6 g) observed during a tactile discrimination task. The other side of the sensor was connected to a motorized stage (V-508 PIMag Precision Linear Stage, Physikinstrumente) to control both displacement (4 mm across all conditions) and sliding velocity (v \= 5, 10, 25, and 45 mm s<sup>-1</sup>). Forces were measured at all 16 combinations of mass and velocity via a 250 g Futek force sensor (k \= 13.9 kN m<sup>-1</sup>) threaded to the bone, and recorded at an average sampling rate of 550 Hz with a Keithley 7510 DMM digitized multimeter. Force traces were collected in sets of 4 slides, discarding the first due to contact aging. Because some mass-velocity combinations were near the boundaries of instability phase transitions, not all force traces at these given conditions exhibited similar profiles.

      Thus, three sets were collected on fresh spots for each condition to observe enough occurrences of multiple instabilities, at a total of nine traces per combination for each surface.”

      Added References (Page 13)

      M. Murai, H.-K. Lau, B. P. Pereira and R. W. H. Pho, J. Hand Surg., 1997, 22, 935–941.

      A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, R. Soc. Open Sci., DOI:10.1098/rsos.170321.

      P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, J. R. Soc. Interface, DOI:10.1098/rsif.2015.0495.

      K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J. Biomech., 2014, 47, 3094– 3099.

      Y. Yuan and R. Verma, Colloids Surf. B Biointerfaces, 2006, 48, 6–12.

      Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392–4399.

      Comment 3, Part 2

      “The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS. The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS.

      We have approximated the softness of the finger with 100 kPa crosslinked PDMS, which is close to what has been reported for the bulk of a human fingertip(8,9). However, as mentioned in the Materials and Methods, there are two additional features of the mock finger that impart greater strength. The PDMS surrounds a rigid, acrylic bone comparable to the distal phalanx, which provides an additional layer of higher modulus(10). Additionally, the 8-hour UV-Ozone treatment decreases the viscoelastic tack of the pristine PDMS by glassifying, or further crosslinking the surface of the finger(11), therefore imparting greater stiffness at the surface similar to the contributions of the stratum corneum, along with a similar surface energy(12). This technique is widely used in wearables(13), soft robotics(14), and microfluidics(15) to induce both these material changes. Additionally, the finger is used at least a day after UV-Ozone treatment is completed in order for the surface to return to moderate hydrophilicity, similar to the outermost layer of human skin(16).

      Comment 3, Part 3

      In addition, the slanted position of the finger can cause non-uniform pressures across the finger. Both can contribute to making the PDMS finger have much more stick-slip than a real finger.

      To ensure that there is minimal contribution from the slanted position of the finger, an initial contact area of 1×1 cm is established before sliding and recording friction measurements. As the PDMS finger is a soft object, the portion in contact with a surface flattens and the contact area remains largely unchanged during sliding. Any additional stick-slip after this alignment step is caused by contact aging at the interface, but the first trace we collect is always discarded to only consider stick-slip events caused by surface chemistry. We recognize that it is difficult to completely control the pressure distribution due to the planar interface, but this is also expected when humans freely explore a surface.

      Comment 3, Part 4

      In fact, if you look at the regime maps, there is very little space that has steady sliding. This does not represent well human exploration of surfaces. We do not tend to use a force and velocity that will cause extensive stick-slip (frequent regions of 100% stick-slip) and, in fact, the speeds used in the study are on the slow side, which also contributes to more stick-slip. At higher speeds and lower forces, all of the materials had steady sliding regions.

      We are not aware of published studies that extensively show that humans avoid stickslip regimes. In fact, we are aware familiar with literature where stiction spike formation is suppressed – a recent paper by AliAbbasi, Basdogan et. al. investigates electroadhesion and friction with NaCl solution-infused interfaces, resulting in significantly steadier forces(17). We also directly showed evidence of instability formation that we observed during human exploration in Fig. 3B-C. These dynamic events are common, despite the lack of control of normal forces and sliding velocities. We also note that Reviewer 1, Comment 2, was suggesting that we further explore possible trends from parameterizing the stiction spike.

      We note that many studies have often not gone at the velocities and masses required for stiction spikes – even though these masses and velocities would be routinely seen in free exploration – this is usually due to constraints of equipment(18). Sliding events during human free exploration of surfaces can exceed 100 mm/s for rapid touches. However, for the surfaces investigated here, we observe that large regions of stick-slip can emerge at velocities as low as 5 mm/s depending on the applied load. The incidence of steady sliding appears more dependent on the applied mass, with almost no steady sliding observed at or above 75 g. Indeed, the force categorization along our transition zones is the main point of the paper.

      Comment 3, Part 5

      Further, on these very smooth surfaces, the friction and stiction are more complex and cannot dismiss considerations such as finger material property change with sweat pore occlusion and sweat capillary forces. Also, the vertical motion of both the PDMS finger and the instructed human subjects is not the motion that humans typically use to discriminate between surfaces.

      We did not describe the task sufficiently. Humans were only given the instruction to slide their finger along a single axis from top to bottom of a sample, not vertical as in azimuthal to gravity. We have updated our wording in the manuscript to reflect this.

      Our changes to the manuscript (Page 4)

      “Participants could touch for as long as they wanted, but were asked to only use their dominant index fingers along a single axis to better mimic the conditions for instability formation during mechanical testing with the mock finger.”

      (Page 11)

      “The participant was then asked to explore each sample simultaneously, and ran over each surface in strokes along a single axis until the participant could decide which of the two had “more friction”.”

      Comment 3, Part 6

      Finally, fingerprints may not affect the shape and size of the contact area, but they certainly do affect the dynamic response and detection of vibrations.

      We are aware of the nuance. Our previous work on the role of fingerprints on friction experienced by a PDMS mock finger showed enhanced signals with the incorporation of ridges on the finger and used a rate-and-state model of a heterogenous, elastic body to find corresponding trends (though there is no existing model of friction that can accurately model experiments on mesoscale friction)(7). The key conclusion was that a flat finger still preserved key dynamic features, and the presence of stronger or more vibrations could result in more similar forces for different surfaces depending on the sliding conditions.

      This is also in the context that we are seeking to provide a reasonable and experimentally accessible method to characterize surfaces, which will always be better as we get closer in replicating a true human finger. But our goal here was to replicate the finger sufficiently for use in human studies. We believe the more appropriate metric of success is if the mock finger is more successful than replacing traditional characterization experiments, like friction coefficient, roughness, surface energy, etc.

      Comment 4

      This all leads to the critical question, why are friction, normal force, and velocity not measured during the measured human exploration and in a systematic study using the real human finger? The authors posed an extremely interesting hypothesis that humans may alter their speed to feel the instability transition regions. This is something that could be measured with a real finger but is not likely to be correlated accurately enough to match regime boundaries with such a simplified artificial finger.

      We are excited that our manuscript offers a tractable manner to test the hypothesis that tactile decision-making models use friction instabilities as evidence. However, we lay out the challenges and barriers, and how the scope of this paper will lead us in that direction. We also clarify that our goals are to provide a method to characterize samples to better design tactile interfaces in haptics or in psychophysical experiments and raise awareness that the common methods of sample characterization in touch by an average friction coefficient or roughness is fundamentally unsound.

      In short, in our view, to further support our findings on instabilities would require answering:

      (1) Which one, or combination of, of the multiple swipes that people make responsible for a tactile decision? (The need for a decision-making model)

      (2) Establish what is, or may be, tactile evidence.

      (3) Establish tactile decision-making models are similar or different than existing decision-making models.

      (4) Test the hypothesis, in these models, that friction instabilities are evidence, and not some other unknown metric. This requires design samples that vary in the amount of evidence generated, but this evidence cannot be controlled directly. Rather, the samples indirectly vary evidence by how likely it is for a human to generate different types of friction instabilities during standard exploration.

      (5) Design a task that does not require the use of subjective tactile descriptors, like “which one feels rougher”, which we see cause confusion in participants, which will likely require accounting for memory effects.

      We elaborate these points below:

      To successfully perform this experiment, we note that freely exploring humans make multiple strokes on a surface. Therefore, we would need to construct a decision-making model. It has not yet been demonstrated whether tactile decision making follows visual decision making, but perhaps to start, we can assume it does. Then, in the design of our decision-making paradigm, we immediately run into the problem: What is tactile evidence?

      From Fig. 3C, we already can see that identifying evidence is challenging. Prior to this manuscript, people may have chosen the average force, or the highest force. Or we may choose the average friction force. Then, after deciding on the evidence, we need to find a method to manipulate the evidence, i.e., create samples or a machine that causes high friction, etc. We show that during the course of human touch, due to the dynamic nature of friction, the average can change a large amount and sample design becomes a central barrier to experiments. Others may suggest immobilizing the finger and applying a known force, but given how much friction changes with human exploration, there is no known method to make a machine recreate temporally and spatially varying friction forces during sliding onto a stationary finger. Finally, perhaps most importantly, in addition to mechanical challenges, a study by Liu, Colgate et al. showed that even if they recorded the friction (2D) of a finger exploring a surface and then replicated the same friction forces onto a finger, the participant could not determine which surface the replayed friction force was supposed to represent.1 This supports that the efference copy is important, that the forces in response to expected motion are important to determine friction. Finally, there is no known method to design instabilities a priori. They must be found through experiments. Especially since if we were to introduce, say a bump or a trough, then we bring in confounding variables to how participants tell surfaces apart.

      Furthermore, even if we had some consistent method to create tactile “evidence”, the paradigm also deserves some consideration. In our experience, the 3-AFC task we perform is important because the vocabulary for touch has not been established. That is, in 3-AFC, by asking to determine which one sample is unlike the others, we do not have to ask the participant questions like “which one is rougher” or “which one has less friction”. In contrast, 2-AFC, which is better for decision-making models because it does not include memory, requires the asking of a perceptual question like: “which one is rougher?”. In our ongoing work, taking two silane coatings, we found that participants could easily identify which surface is unlike the others above chance in a 3-AFC, but participants, even within their own trials, could not consistently identify one silane as perceptually “rougher” by 2-AFC. To us, this calls into question the validity of tactile descriptors, but is beyond the scope of this manuscript.

      This is not our only goal, but in the context of human exploration, in this manuscript here, we believed it was important to identify a mechanical parameter that was consistent with how humans explore surfaces, but was also a parameter that could characterize to some consistent property of a surface – irrespective of whether a human was touching it. We thought that designing human decision-making models and paradigms around the friction coefficient would not be successful.

      Given the scope of these challenges, we do not think it would be possible to establish these conceptual sequences in a single manuscript.

      Reviewer 2 (Public review):

      Summary:

      In this paper, the authors want to test the hypothesis that frictional instabilities rather than friction are the main drivers for discriminating flat surfaces of different sub-nanometric roughness profiles.

      They first produced flat surfaces with 6 different coatings giving them unique and various properties in terms of roughness (picometer scale), contact angles (from hydrophilic to hydrophobic), friction coefficient (as measured against a mock finger), and Hurst exponent.

      Then, they used those surfaces in two different experiments. In the first experiment, they used a mock finger (PDMS of 100kPA molded into a fingertip shape) and slid it over the surfaces at different normal forces and speeds. They categorized the sliding behavior as steady sliding, sticking spikes, and slow frictional waves by visual inspection, and show that the surfaces have different behaviors depending on normal force and speed. In a second experiment, participants (10) were asked to discriminate pairs of those surfaces. It is found that each of those pairs could be reliably discriminated by most participants.

      Finally, the participant's discrimination performance is correlated with differences in the physical attributes observed against the mock finger. The authors found a positive correlation between participants' performances and differences in the count of steady sliding against the mock finger and a negative correlation between participants' reaction time and differences in the count of stiction spikes against the mock finger. They interpret those correlations as evidence that participants use those differences to discriminate the surfaces.

      Strengths:

      The created surfaces are very interesting as they are flat at the nanometer scale, yet have different physical attributes and can be reliably discriminated.”

      We thank Reviewer 2 for their notes on our manuscript. The responses below address the reviewer’s comments and recommendations for revised work.

      Weaknesses:

      Comment 1

      In my opinion, the data presented in the paper do not support the conclusions. The conclusions are based on a correlation between results obtained on the mock finger and results obtained with human participants but there is no evidence that the human participants' fingertips will behave similarly to the mock finger during the experiment. Figure 3 gives a hint that the 3 sliding behaviors can be observed in a real finger, but does not prove that the human finger will behave as the mock finger, i.e., there is no evidence that the phase maps in Figure 1C are similar for human fingers and across different people that can have very different stiffness and moisture levels.

      The mechanical characterization conducted with the mock finger seeks to extract significant features of friction traces of a set of surfaces to use as predictors of tactile discriminability. The goal is to find a consistent method to characterize surfaces for use in tactile experiments that can be replicated by others and used prior to any human experiments. However, in the overall response and in a response to a similar comment by Reviewer 1, we also explain why we believe experiments on humans to establish this fact is not yet reasonable.

      Comment 2

      I believe that the authors collected the contact forces during the psychophysics experiments, so this shortcoming could be solved if the authors use the actual data, and show that the participant responses can be better predicted by the occurrence of frictional instabilities than by the usual metrics on a trial by trial basis, or at least on a subject by subject basis. I.e. Poor performers should show fewer signs of differences in the sliding behaviors than good performers.

      To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. This type of scenario is not compatible with the analysis suggested — and similar counterpoints can be made for other types of seemingly straightforward analysis.

      While we are interested and actively working on this, the study here is critical to establish types of evidence for a future decision-making model. We know humans change their friction constantly during real exploration, so it is unclear which of these constantly changing values we should input into the decision making model, and the future challenges we anticipate are explained in Comment 1.

      Comment 3

      The sample size (10) is very small.

      We recognize that, with all factors being equal, this sample size is on the smaller end. However, we emphasize the degree of control of samples is far above typical, with minimal variations in sample properties such as surface roughness, and every sample for every trial was pristine. Furthermore, the sample preparation (> 300 individual wafers were used) and cost became a factor. Although not typically appropriate, and thus not included in the manuscript, a post-hoc power analysis for our 100 trials of our pair that was closest to chance, P4, (53%, closest to chance at 33%) showed a power of 98.2%, suggesting that the study was appropriately powered.

      Reviewer 2 (Recommendations for the authors):

      Comment 1

      Differences in SS and Sp (Table 2) are NOT physical or mechanical differences but are obtained by counting differences in the number of occurrences of each sliding behavior. It is rather a weird choice.

      We disagree that differences in SS and Sp are not physical or mechanical, as these are well-established phenomena in the soft matter and tribology literature(19-21). These are known as “mechanical instabilities” and generated due to the effects of two physical phenomena: the elasticity of the finger (which is constant in our mechanical testing) and the friction forces present (which change per sample type). The motivation behind using these different shapes is that the instabilities, in some conditions, can be invariant to external factors like velocity. This would be quite advantageous for human exploration because, unlike friction coefficient, which changes with nearly any factor, including velocity and mass, the instabilities being invariant to velocity would mean that we are accurately characterizing a unique identifier of the surface even though velocity may be variable.

      This “weird choice” is the central innovation of this paper. This choice was necessary because we demonstrated that the common usage of friction coefficient is fundamentally flawed: we see that friction coefficient suggests that surface which are more different would feel more similar – indeed the most distinctive surfaces would be two surfaces that are identical, which is clearly spurious. One potential explanation for why we were able to see this is effect is because our surfaces have similar (< 0.6 nm variability) roughness, removing potential confounding factors, and this type of low roughness control has not been used in tactile studies to the best of our knowledge.

      Comment 2

      Figures 2B-C: why are the x-data different than Table 2?

      The x-data in Fig. 2B-C are the absolute differences in the number of occurrences measured for a given instability type or material property out of 144 pulls. Modeling the human participant results in our GLMMs required the independent variables to be in this form rather than percentages. We initially chose to list percent differences in Table 2 to highlight the ranges of differences instead of an absolute value, but have added both for clarity.

      Our changes to the manuscript (Page 7)

      “To determine if humans can detect these three different instabilities, we selected six pairs of surfaces to create a broad range of potential instabilities present across all three types. These are summarized in Table 2, where the first column for each instability is the difference in occurrence of that instability formed between each pair, and the second is the percent difference.”

      Comment 3

      "We constructed a set of coated surfaces with physical differences which were imperceptible by touch but created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding." Yet, in your experiment, participants could discriminate them, so this is incoherent.

      To clarify the point, macroscopic objects can differ in physical shape and in chemical composition. What we meant was that the physical differences, i.e., roughness, were below a limit (Skedung et al.) that participants, without a coating, would not be able to tell these apart(22). Therefore, the reason people could tell our surfaces apart was due to the chemical composition of the surface, and not any differences in roughness or physical effects like film stiffness (due to the molecular-scale thinness of the surface coatings, they are mechanically negligible). However, we concede that at the molecular scale, the traditional macroscopic distinction between physical and chemical is blurred.

      We have made minor revisions to the wording in the abstract. We clarify that the surface coatings had physical differences in roughness that were smaller than 0.6 nm, which based purely on roughness, would not be expected to be distinguishable to participants. Therefore, the reason participants can tell these surfaces apart is due to differences in friction generated by chemical composition, and we were able to minimize contributions from physical differences in the sample our study.

      Our changes to the manuscript (Page 1, Abstract)

      “We constructed a set of coated surfaces with minimal physical differences that by themselves, are not perceptible to people, but instead, due to modification in surface chemistry, the surfaces created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding.”

      Reviewer 3 (Public review):

      Strengths:  

      The paper describes a new perspective on friction perception, with the hypothesis that humans are sensitive to the instabilities of the surface rather than the coefficient of friction. The paper is very well written and with a comprehensive literature survey.

      One of the central tools used by the author to characterize the frictional behavior is the frictional instabilities maps. With these maps, it becomes clear that two different surfaces can have both similar and different behavior depending on the normal force and the speed of exploration. It puts forward that friction is a complicated phenomenon, especially for soft materials.

      The psychophysics study is centered around an odd-one-out protocol, which has the advantage of avoiding any external reference to what would mean friction or texture for example. The comparisons are made only based on the texture being similar or not.

      The results show a significant relationship between the distance between frictional maps and the success rate in discriminating two kinds of surface.”

      We thank Reviewer 3 for their notes and interesting discussion points on our manuscript. Below, we address the reviewer’s feedback and comments on related works.

      Weaknesses:

      Comment 1

      The main weakness of the paper comes from the fact that the frictional maps and the extensive psychophysics study are not made at the same time, nor with the same finger. The frictional maps are produced with an artificial finger made out of PDMS which is a poor substitute for the complex tribological properties of skin.

      A similar comment was made by Reviewers 1 and 2 and parts are replicated below. We are not claiming that our PDMS fingers are superior to real fingers, but rather, we cannot establish standards in the field by using real human fingers that vary between subjects and researchers. We believe the mock finger we designed is a reasonable mimic of the human finger by matching surface energy, heterogeneous mechanical structure, and the ability to test multiple physiologically relevant pressures and sliding velocities.

      We achieve a heterogeneous mechanical structure with the 3 primary components of stiffness of a human finger. The effective modulus of ~100 kPa, from soft tissue,8,9 is obtained with a 30:1 ratio of PDMS to crosslinker. The PDMS also surrounds a rigid, acrylic bone comparable to the distal phalanx, which provides an additional layer of higher modulus.10 Additionally, the 8-hour UV-Ozone treatment decreases the viscoelastic tack of the pristine PDMS by glassifying, or further crosslinking the surface of the finger,11 therefore imparting greater stiffness at the surface similar to the contributions of the stratum corneum, along with a similar surface energy.12 The finger is used at least a day after UV-Ozone treatment is completed in order for the surface to return to moderate hydrophilicity, similar to the outermost layer of human skin.16 We also discuss the shape of the contact formed. To ensure that there is minimal contribution from the slanted position of the finger, an initial contact area of 1×1 cm is established before sliding and recording friction measurements. As the PDMS finger is a soft object, the portion in contact with a surface flattens and the contact area remains largely unchanged during sliding. We recognize that it is difficult to completely control the pressure distribution due to the planar interface, but this variation is also expected when humans freely explore a surface. Finally, we consider flat vs. fingerprinted fingers. Our previous work on the role of fingerprints on friction experienced by a PDMS mock finger showed enhanced signals with the incorporation of ridges on the finger and used a rate-andstate model of a heterogenous, elastic body to find corresponding trends.7 The key conclusion was that a flat finger still preserved key dynamic features, and the presence of stronger or more vibrations could result in more similar forces for different surfaces depending on the sliding conditions. We note that we have subsequently used the controlled mechanical data collected with this flat mock finger in correlations with human psychophysics in previous work, where findings from our mechanical experiments were predictive of human performance.3–6 Ultimately, we see from our prior work and here that, despite the drawbacks of our mock finger, it outperforms other standard characterization technique in providing information about the mesoscale that correlates to tactile perception. We have added these details to the manuscript.

      We also note that an intermediate option, replicating real fingers, even in a mold, may also inadvertently limit trends from characterization to a specific finger. One of the main – and severe – limitations of using a human finger is that all fingers are different, meaning any study focusing on a particular user may not apply to others or be recreated easily by other researchers. We cannot set a standard for replication around a real human finger as that participant may no longer be available, or willing to travel the world as a “standard”. Furthermore, the method in which a single person changes their pressures and velocities as they touch a surface is highly variable. We also note that in the Summary Response, we noted that a study by Colgate et al. (IEEE ToH 2024) demonstrated that efference copies may be important, and thus constraining a human finger and replaying the forces recorded during free exploration will not lead to the participant identifying a surface with any consistency. Thus, it is important to allow humans to freely explore surfaces, but creates nearly limitless variability in friction forces.

      This is also against the backdrop that we are seeking to provide a method to characterize surfaces, which will be aided as we get closer in replicate a true human finger. Indeed, the more features we replicate, the more successful the mechanical data will be in correlating to tactile distinguishability. But reasonably, our success would be in replacing traditional characterization experiments, not in recreating the forces of an arbitrary human finger.

      Our changes to the manuscript Added (Page 2-3)

      “Mock finger as a characterization tool

      In this work, we use a mechanical setup with a PDMS mock finger to derive tactile predictors from controlled friction traces alternative to average friction coefficients. While there is a tradeoff in selecting a synthetic finger over a more accurate, real human finger in modeling touch, our aim to design a method of mesoscale surface characterization for more successful studies on tactile perception cannot be fulfilled using one human participant as a standard. We believe that with sufficient replication of surface and bulk properties as well as contact geometry, and controlled friction measurements collected at loading conditions observed during a tactile discrimination task, we can isolate unique frictional features of a set of surfaces that do not arise from human-to-human variability.

      The major component of a human finger, by volume, is soft tissue (~56%)(22), resulting in an effective modulus close to 100 kPa(23,24). In order to achieve this same softness, we crosslink PDMS in a 1×1×5 cm mold at a 30:1 elastomer:crosslinker ratio. However, two more features impart increased stiffness in a human finger. Most of this added rigidity is derived from the bone at the fingertip, the distal phalanx(23-25), which we mimic with an acrylic bone within our PDMS network. The stratum corneum, the stiffer, glassier outer layer of skin(26), is replicated with the surface of the mock finger glassified, or further crosslinked, after 8 hours of UV-Ozone treatment(27). This treatment also modifies the surface properties of the native PDMS to align with those of a human finger more closely. It minimizes the viscoelastic tack at the surface, resulting in a comparable non-sticky surface. At least one day after treatment, the finger surface returns to moderate hydrophilicity (~60º), as is typically observed for a real finger(28).

      The initial contact area formed before a friction trace is collected is a rectangle of 1×1 cm. While this shape is not entirely representative of a human finger with curves and ridges, human fingers flatten out enough to reduce the effects of curvature with even very light pressures(28-30). This implies that regardless of finger pressure, the contact area is largely load-independent, which is more accurately replicated with a rectangular mock finger. It is still a challenge to control pressure distribution with this planar interface, but non-uniform pressures are also expected during human exploration.

      Lastly, we consider fingerprints vs. flat fingers. A key finding of our previous work is that while fingerprints enhanced frictional dynamics at certain conditions, key features were still maintained with a flat finger(7). Furthermore, for some loading conditions, the more amplified signals could also result in more similar friction traces for different surfaces. We have continued to use flat fingers in our mechanical experiments, and have observed good agreement between these friction traces and human experiments(7,8,21,31).”

      (Page 3-4, Materials and Methods)

      “Mock Finger Preparation

      Friction forces across all six surfaces were measured using a custom apparatus with a polydimethylsiloxane (PDMS, Dow Sylgard 184) mock finger that mimics a human finger’s

      mechanical properties and contact mechanics while exploring a surface relatively closely(7,8). PDMS and crosslinker were combined in a 30:1 ratio to achieve a stiffness of 100 kPa comparable to a real finger, then degassed in a vacuum desiccator for 30 minutes. We are aware that the manufacturer recommended crosslinking ratio for Sylgard 184 is 10:1 due to potential uncrosslinked liquid residues(32), but further crosslinking concentrated at the surface prevents this. The prepared PDMS was then poured into a 1×1×5 cm mold also containing an acrylic 3D-printed “bone” to attach applied masses on top of the “fingertip” area contacting a surface during friction testing. After crosslinking in the mold at 60ºC for 1 hour, the finger was treated with UV-Ozone for 8 hours out of the mold to minimize viscoelastic tack.  

      Mechanical Testing

      A custom device using our PDMS mock finger was used to collect macroscopic friction force traces replicating human exploration(7,8). After placing a sample surface on a stage, the finger was lowered at a slight angle such that an initial 1×1 cm rectangle of “fingertip” contact area could be established. We considered a broad range of applied masses (M \= 0, 25, 75, and 100 g) added onto the deadweight of the finger (6 g) observed during a tactile discrimination task. The other side of the sensor was connected to a motorized stage (V-508 PIMag Precision Linear Stage, Physikinstrumente) to control both displacement (4 mm across all conditions) and sliding velocity (v \= 5, 10, 25, and 45 mm s<sup>-1</sup>). Forces were measured at all 16 combinations of mass and velocity via a 250 g Futek force sensor (k \= 13.9 kN m<sup>-1</sup>) threaded to the bone, and recorded at an average sampling rate of 550 Hz with a Keithley 7510 DMM digitized multimeter. Force traces were collected in sets of 4 slides, discarding the first due to contact aging. Because some mass-velocity combinations were near the boundaries of instability phase transitions, not all force traces at these given conditions exhibited similar profiles. Thus, three sets were collected on fresh spots for each condition to observe enough occurrences of multiple instabilities, at a total of nine traces per combination for each surface.”

      Added References (Page 13)

      M. Murai, H.-K. Lau, B. P. Pereira and R. W. H. Pho, J. Hand Surg., 1997, 22, 935–941.

      A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, R. Soc. Open Sci., DOI:10.1098/rsos.170321.

      P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, J. R. Soc. Interface, DOI:10.1098/rsif.2015.0495.

      K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J. Biomech., 2014, 47, 3094– 3099.

      Y. Yuan and R. Verma, Colloids Surf. B Biointerfaces, 2006, 48, 6–12.

      Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392–4399.

      Comment 2

      The evidence would have been much stronger if the measurement of the interaction was done during the psychophysical experiment. In addition, because of the protocol, the correlation is based on aggregates rather than on individual interactions.

      Our Response: We agree that this would have helped further establish our argument, but in the overall statement and in other reviewer responses, we describe the significant challenges to establishing this.

      To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. We also clarify that our goals are to provide a method to characterize samples to better design tactile interfaces in haptics or in psychophysical experiments.

      In short, in our view, to develop a decision-making model, the challenges are as follows:

      (1) Which one, or combination of, of the multiple swipes that people make responsible for a tactile decision?

      (2) Establish what is, or may be, tactile evidence.

      (3) Establish tactile decision-making models are similar or different than existing decision-making models.

      (4) Test the hypothesis, in these models, that friction instabilities are evidence, and not some other unknown metric.

      (5) Design a task that does not require the use of subjective tactile descriptors, like “which one feels rougher”, which we see cause confusion in participants, which will likely require accounting for memory effects.

      (6) Design samples that vary in the amount of evidence generated, but this evidence cannot be controlled directly. Rather, the samples indirectly vary evidence by how likely it is for a human to generate different types of friction instabilities during standard exploration.

      We elaborate these points below:

      To successfully perform this experiment, we note that freely exploring humans make multiple strokes on a surface. Therefore, we would need to construct a decision-making model. It has not yet been demonstrated whether tactile decision making follows visual decision making, but perhaps to start, we can assume it does. Then, in the design of our decision-making paradigm, we immediately run into the problem: What is tactile evidence?

      From Fig. 3C, we already can see that identifying evidence is challenging. Prior to this manuscript, people may have chosen the average force, or the highest force. Or we may choose the average friction force. Then, after deciding on the evidence, we need to find a method to manipulate the evidence, i.e., create samples or a machine that causes high friction, etc. We show that during the course of human touch, due to the dynamic nature of friction, the average can change a large amount and sample design becomes a central barrier to experiments. Others may suggest to immobilize the finger and applying a known force, but given how much friction changes with human exploration, there is no known method to make a machine recreate temporally and spatially varying friction forces during sliding onto a stationary finger. Finally, perhaps most importantly, in addition to mechanical challenges, a study by Liu, Colgate et al. showed that even if they recorded the friction (2D) of a finger exploring a surface and then replicated the same friction forces onto a finger, the participant could not determine which surface the replayed friction force was supposed to represent.1 This supports that the efference copy is important, that the forces in response to expected motion are important to determine friction. Finally, there is no known method to design instabilities a priori. They must be found through experiments, especially since if we were to introduce, say a bump or a trough, then we bring in confounding variables to how participants tell surfaces apart.

      Furthermore, even if we had some consistent method to create tactile “evidence”, the paradigm also deserves some consideration. In our experience, the 3-AFC task we perform is important because the vocabulary for touch has not been established. That is, in 3-AFC, by asking to determine which one sample is unlike the others, we do not have to ask the participant questions like “which one is rougher” or “which one has less friction”. In contrast, 2-AFC, which is better for decision-making models because it does not include memory, requires the asking of a perceptual question like: “which one is rougher?”. In our ongoing work, taking two silane coatings, we found that participants could easily identify which surface is unlike the others above chance in a 3-AFC, but participants, even within their own trials, could not consistently identify one silane as perceptually “rougher” by 2-AFC. To us, this calls into question the validity of tactile descriptors, but is beyond the scope of the current manuscript.

      This is not our only goal, but in the context of human exploration, in this manuscript here, we believed it was important to identify a mechanical parameter that was consistent with how humans explore surfaces, but was also a parameter that could characterize to some consistent property of a surface – irrespective of whether a human was touching it. We thought that designing human decision-making models and paradigms around the friction coefficient would not be successful.

      Given the scope of these challenges, we do not think it would be possible to establish this conceptual sequence in a single manuscript.

      Comment 3

      The authors compensate with a third experiment where they used a 2AFC protocol and an online force measurement. But the results of this third study, fail to convince the relation.

      With this experiment, our central goal was to demonstrate that the instabilities we have identified with the PDMS finger also occur with a human finger. Several instances of SS, Sp, and SFW were recorded with this setup as a participant touched surfaces in real time.

      Comment 4

      No map of the real finger interaction is shown, bringing doubt to the validity of the frictional map for something as variable as human fingers.

      Real fingers change constantly during exploration, and friction is state-dependent, meaning that the friction will depend on how the person was moving the moment prior. Therefore, a map is only valid for a single human movement – even if participants all were instructed to take a single swipe and start from zero motion, humans are unable to maintain constant velocities and pressures. Clearly, this is not sustainable for any analysis, and these drawbacks apply to any measured parameter, whether instabilities suggested here, or friction coefficients used throughout. We believe the difficulty of this approach emphasizes why a standard map of characterization of a surface by a mock finger, even with its drawbacks, is a viable path forward.

      Reviewer 3 (Recommendations for the authors):

      Comment 1

      It would be interesting to comment on a potential connection between the frictional instability maps and Schalamack waves

      Schallamach waves are a subset of slow frictional waves (SFW). Schallmach waves are very specifically defined. They are a are pockets of air that form between a soft sliding object and rigid surface, and propagate rear-to-front (retrograde waves) as a soft object is slid and buckles due to adhesive pinning. Wrinkles form at the detached portion of the soft material, until the interface reattaches and the process repeats.23 There is typically a high burden of proof to establish a Schallamach wave over a more general slow frictional wave. We note that it would be exceeding difficult to design samples that can reliably create subsets of SFW, but we are aware that this may be an interesting question at a future point in our work.

      Comment 2

      The force sensors look very compliant, and given the dynamic nature of the signal, it is important to characterize the frequency response of the system to make sure that the fluctuations are not amplified.

      Our Response: Thank you for noticing. We mistyped the sensor spring constant as 13.9 N m<sup>-1</sup> instead of kN m<sup>-1</sup>. However, below we show how the instabilities are derived from the mechanics at the interface due to the compliance of the finger. The “springs” of the force sensor and PDMS finger are connected in parallel. Since k<sub>sensor</sub> = 13.9 kN m<sup>-1</sup>, the spring constant of the system overall reflects the compliance of the finger, and highlights the oscillations arising solely from stick-slip. A sample calculation is shown below.

      Author response image 1.

      Fitting a line to the initial slope of the force trace for C6 gives the equation y = 25.679_x_ – 0.2149. The slope here represents force data over time data, and is divided by the velocity (25 mm/s) to determine 𝐹𝐹 the spring constant of the system . This value is lower than ksensor = 13.9 kN/m, indicating that the “springs” representing the force sensor and PDMS finger are connected in parallel: . The finger is the compliant component of the system, with k<sub>finger</sub> = 0.902 N/m, and of course, real human fingers are also compliant so this matches our goals with the design of the mock finger.

      Our changes to the manuscript (Page 4)

      (k \= 13.9 kN m<sup>-1</sup>)

      Comment 3

      The authors should discuss about the stochastic nature of friction:

      Wiertlewski, Hudin, Hayward, IEEE WHC 2011

      Greenspon, McLellan, Lieber, Bensmaia, JRSI 2020”

      We believe that, given the references, this comment on “stochastic” refers to the macroscopically-observable fluctuations (i.e., the mechanical “noise” which is not due to instrument noise) in friction arising from the discordant network of stick-slip phenomena occurring throughout the contact zone, and not the stochastic nature of nanoscale friction that occurs thermal fluctuations nor due to statistical distributions in bond breaking associated with soft contact.

      We first note that our small-scale fluctuations do not arise from a periodic surface texture that dominates in the frequency regime. However, even on our comparatively smooth surfaces, we do expect fluctuations due to nanoscale variation in contact, generation of stick-slip across at microscale length scales that occur either concurrently or discordantly across the contact zone, and the nonlinear dependence of friction to nearly any variation in state and composition(7).

      Perhaps the most relevant to the manuscript is that a major advantage of analysis by friction is that it sidesteps these ever-present microscale fluctuations, leading to more clearly defined classifiers or categories during analysis. Wiertlewski et. al. showed repeated measurements in their systems ultimately gave rise to consistent frequencies(24) (we think their system was in a steady sliding regime and the patterning gave rise to underlying macroscopic waves). These consistent frequencies, at least in soft systems and absent obvious macroscopic patterned features, would be expected to arise from the instability categories and we see them throughout.

      Comment 4

      It is stated that "we observed a spurious, negative correlation between friction coefficient and accuracy”.

      What makes you qualify that correlation as spurious?

      We mean this as in the statistical definition of “spurious”.

      This correlation would indicate that by the metric of friction coefficient, more different surfaces are perceived more similarly. Thus, two very different surfaces, like Teflon and sandpaper, by friction coefficient would be expected to feel very similar. Two nearly identical surfaces would be expected to feel very different – but of course, humans cannot consistently distinguish two identical surfaces. This finding is counterintuitive and refutes that friction coefficient is a reliable classifier of surfaces by touch. We do not think it is productive to determine a mechanism for a spurious correlation, but perhaps one reason we were able to observe this is because our study, to the best of our knowledge, is unique for having samples that are controlled in their physical differences in roughness and surface features.

      Our changes to the manuscript (Page 10)

      “To compare the value of looking at frictional instabilities, we also performed GLMM fits on common approaches in the field, like a friction coefficient or material property typically used in tactile discrimination, shown in Fig. 2D-E. Interestingly, in Fig. 2D, we observed a spurious, negative correlation between friction coefficient (typically and often problematically simplified as across all tested conditions) and accuracy (r = -0.64, p < 0.01); that is, the more different the surfaces are by friction coefficient, the less people can tell them apart. This spurious correlation would be the opposite of intuition, and further calls into question the common practice of using friction coefficients in touch-related studies. The alternative, two-term model which includes adhesive contact area for friction coefficient(29) was even less predictive (see Fig. S6A of SI). We believe such a correlation could not have been uncovered previously as our samples are minimal in their physical variations. Yet, the dynamic changes in force even within a single sample are not considered, despite being a key feature of mesoscale friction during human touch.

      We investigate different material properties in Fig. 2E. Differences in average roughness R<sub>a</sub> (or other parameters, like root mean square roughness R<sub>rms</sub> (Fig. S6A of SI) did not show a statistically significant correlation to accuracy. Though roughness is a popular parameter, correlating any roughness parameter to human performance here could be moot: the limit of detecting roughness differences has previously been defined as 13 nm on structured surfaces(33) and much higher for randomly rough surfaces(46), all of which are magnitudes larger than the roughness differences between our surfaces. The differences in contact angle hysteresis – as an approximation of the adhesion contributions(47) – do not present any statistically significant effects on performance.”

      Comment 5

      The authors should comment on the influence of friction on perceptual invariance. Despite inducing radially different frictional behavior for various conditions, these surfaces are stably perceived. Maybe this is a sign that humans extract a different metric?

      We agree – we are excited that frictional instabilities may offer a more stable perceptual cue because they are not prone to fluctuations (Recommendations for the authors, Comment 3) and instability formation, in many conditions, is invariant to applied pressures and velocities – thus forming large zones where a human may reasonable encounter a given instability.

      Raw friction is highly prone to variation during human exploration (in alignment with Recommendations for the authors, Comment 3), but ongoing work seeks to explain tactile constancy, or the ability to identify objects despite these large changes in force. Very recently published work by Fehlberg et. al. identified the role of modulating finger speed and normal force in amplifying the differences in friction coefficient between materials in order to identify them(25), and we postulate that their work may be streamlined and consistent with the idea of friction instabilities, though we have not had a chance to discuss this in-depth with the authors yet.

      We think that the instability maps show a viable path forward to how surfaces are stably perceived, and instabilities themselves show a potential mechanism: mathematically, instabilities for given conditions can be invariant to velocity or mass, creating zones where a certain instability is encountered. This reduces the immense variability of friction to a smaller, more stable classification of surfaces (e.g., a 30% SS surface or a 60% SS surface). A given surface will typically produce the same instability at a specific condition (we found some boundaries are extremely condition sensitive, but many conditions are not), whereas a single friction trace which is highly prone to variation is not a stable metric.

      Added References (Page 14)

      53 M. Fehlberg, E. Monfort, S. Saikumar, K. Drewing and R. Bennewitz, IEEE Trans. Haptics, 2024, 17, 957–963.

      References

      Z. Liu, J.-T. Kim, J. A. Rogers, R. L. Klatzky and J. E. Colgate, IEEE Trans. Haptics, 2024, 17, 441– 450.

      D. Gueorguiev, S. Bochereau, A. Mouraux, V. Hayward and J.-L. Thonnard, Sci Rep, 2016, 6, 25553.

      C. W. Carpenter, C. Dhong, N. B. Root, D. Rodriquez, E. E. Abdo, K. Skelil, M. A. Alkhadra, J. Ramírez, V. S. Ramachandran and D. J. Lipomi, Mater. Horiz., 2018, 5, 70–77.

      A. Nolin, A. Licht, K. Pierson, C.-Y. Lo, L. V. Kayser and C. Dhong, Soft Matter, 2021, 17, 5050– 5060.

      A. Nolin, K. Pierson, R. Hlibok, C.-Y. Lo, L. V. Kayser and C. Dhong, Soft Matter, 2022, 18, 3928– 3940.

      Z. Swain, M. Derkaloustian, K. A. Hepler, A. Nolin, V. S. Damani, P. Bhattacharyya, T. Shrestha, J. Medina, L. Kayser and C. Dhong, J. Mater. Chem. B, DOI:10.1039/D4TB01646G.

      C. Dhong, L. V. Kayser, R. Arroyo, A. Shin, M. Finn, A. T. Kleinschmidt and D. J. Lipomi, Soft Matter, 2018, 14, 7483–7491.

      A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, Royal Society Open Science, DOI:10.1098/rsos.170321.

      P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, Journal of The Royal Society Interface, DOI:10.1098/rsif.2015.0495.

      K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J Biomech, 2014, 47, 3094–3099.

      Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392– 4399.

      Y. Yuan and R. Verma, Colloids Surf B Biointerfaces, 2006, 48, 6–12.

      G. Yu, J. Hu, J. Tan, Y. Gao, Y. Lu and F. Xuan, Nanotechnology, 2018, 29, 115502.

      L. Zheng, S. Dong, J. Nie, S. Li, Z. Ren, X. Ma, X. Chen, H. Li and Z. L. Wang, ACS Appl. Mater. Interfaces, 2019, 11, 42504–42511.

      K. Ma, J. Rivera, G. J. Hirasaki and S. L. Biswal, Journal of Colloid and Interface Science, 2011, 363, 371–378.

      A. Mavon, H. Zahouani, D. Redoules, P. Agache, Y. Gall and Ph. Humbert, Colloids and Surfaces B: Biointerfaces, 1997, 8, 147–155.

      E. AliAbbasi, M. Muzammil, O. Sirin, P. Lefèvre, Ø. G. Martinsen and C. Basdogan, IEEE Trans. Haptics, 2024, 17, 841–849.

      G. Corniani, Z. S. Lee, M. J. Carré, R. Lewis, B. P. Delhaye and H. P. Saal, eLife, DOI:10.7554/eLife.93554.1.

      J. N. Israelachvili, Intermolecular and Surface Forces, Academic Press, 2011.

      S. Das, N. Cadirov, S. Chary, Y. Kaufman, J. Hogan, K. L. Turner and J. N. Israelachvili, J R Soc Interface, 2015, 12, 20141346.

      B. N. J. Persson, O. Albohr, C. Creton and V. Peveri, The Journal of Chemical Physics, 2004, 120, 8779–8793.

      L. Skedung, M. Arvidsson, J. Y. Chung, C. M. Stafford, B. Berglund and M. W. Rutland, Sci Rep, 2013, 3, 2617.

      K. Viswanathan, N. K. Sundaram and S. Chandrasekar, Soft Matter, 2016, 12, 5265–5275.

      M. Wiertlewski, C. Hudin and V. Hayward, in 2011 IEEE World Haptics Conference, 2011, pp. 25– 30.

      M. Fehlberg, E. Monfort, S. Saikumar, K. Drewing and R. Bennewitz, IEEE Transactions on Haptics, 2024, 17, 957–963.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Response to Reviewer 1

      Thank you for your recognition of our revised work.

      Response to Reviewer 2

      It would be useful to have a demonstration of where this model outperforms SaProt systematically, and a discussion about what the success of this model teaches us given there is a similar, previously successful model, SaProt.

      As two concurrent works, ProtSSN and SaProt employ different methods to incorporate the structure information of proteins. Generally speaking, for two deep learning models that are developed during a close period, it is challenging to conclude that one model is systematically superior to another. Nonetheless, on DTm and DDG (the two low-throughput datasets that we constructed), ProtSSN demonstrates better empirical performance than SaProt.  

      Moreover, ProtSSN is more efficient in both training and inference compared to SaProt. In terms of training cost, SaProt uses 40 million protein structures for pretraining (requiring 64 A100 GPUs for three months), whereas ProtSSN requires only about 30,000 crystal structures from the CATH database (trained on a single 3090 GPU for two days). Despite SaProt’s significantly higher training cost, its pretrained version does not exhibit superior performance on low-throughput datasets such as DTm, DDG, and Clinvar. Furthermore, the high training cost limits many users from retraining or fine-tuning the model for specific needs or datasets.

      Regarding the inference cost, ProtSSN requires only one embedding computation for a wild-type protein, regardless of the number of mutants (n). In contrast, SaProt computes a separate embedding and score for each mutant. For instance, when evaluating the scoring performance on ProteinGym, ProtSSN only needs 217 inferences, while SaProt needs more than 2M inferences. This inference speed is important in practice, such as high-throughput design and screening.

      Please remove the reference to previous methods as "few shot". This typically refers to their being trained on experimental data, not their using MSAs. A "few shot" model would be ProteinNPT.

      The definition of "few-shot" we used here is following ESM1v [1]. This concept originates from providing a certain number of examples as input to GPT-3 [2]. In the context of protein deep learning models, MSA serves as the wild-type protein examples.

      Also, Reviewer 1 uses the concept in the same way. 

      “Readers should note that methods labelled as "few-shot" in comparisons do not make use of experimental labels, but rather use sequences inferred as homologous; these sequences are also often available even if the protein has never been experimentally tested.”

      In the main text, we also included this definition as well as the reference of ESM-1v in lines 457-458.

      “We extend the evaluation on ProteinGym v0 to include a comparison of our zero-shot ProtSSN with few-shot learning methods that leverage MSA information of proteins (Meier et al., 2021).”

      (1) Meier J, Rao R, Verkuil R, et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems, 2021.

      (2) Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020.

      Furthermore, I don't think it is fair to state that your method is not comparable to these models -- one can run an MSA just as one can predict a structure. A fairer comparison would be to highlight particular assays for which getting an MSA could be challenging -- Transcription did this by showing that they outperform EVE when MSAs are shallow.

      We recognize that there are often differences in the definitions and classifications of various methodologies. Here, we follow the definitions provided by ProteinGym. As the most comprehensive and large scale open benchmark in the community, we believe this classification scheme should be widely accepted. All classifications are available on the official website of ProteinGym (https://proteingym.org/benchmarks), which categorizes methods into PLMs, Structure-based models, and Alignment-based models. For example, GEMME is classified as an alignment-based model, and MSA Transformer is considered a hybrid model combining alignment and PLM features.

      We believe that methodologies with different inputs and architectures can lead to inherent unfairness. Also, it is generally believed that models including evolutionary relationships tend to outperform end-to-end models due to the extra information and efforts involved during the training phase. Some empirical evidence and discussions are in the ablation studies of retrieval factors in Tranception [3]. Moreover, the choice of MSA search parameters can introduce uncertainty, which could have positive or negative impacts. 

      We showcase the impact of MSA depth on model performance with an additional analysis below. Author response image 1 visualizes the Spearman’s correlation between the scores of each model and the number of MSAs on 217 ProteinGym assays, where each point represents one of 217 assays. The summary correlation of each model with respect to all assays are reported in Author response table 1. These results demonstrate no clear correlation between MSA depth and model performance even for MSA-based models.

      Author response image 1.

      Scatter plots of the number of MSA sequences and spearman’s correlation.

      Author response table 1.

      Spearmar’s score of the number of MSA sequences and the model’s performance.

      (3) Notin P, Dias M, Frazer J, et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. International Conference on Machine Learning, 2022.

      The authors state that DTm and DDG are conceptually appealing because they come from low-throughput assays with lower experimental noise and are also mutations that are particularly chosen to represent the most interesting regions of the protein. I agree with the conceptual appeal but I don't think these claims have been demonstrated in practice. The cited comparison with Frazer as a particularly noisy source of data I think is particularly unconvincing: ClinVar labels are not only rigorously determined from multiple sources of evidence, Frazer et al demonstrates that these labels are actually more reliable than experiment in some cases. They also state that ProteinGym data doesn't come with environmental conditions, but these can be retrieved from the papers the assays came from. The paper would be strengthened by a demonstration of the conceptual benefit of these new datasets, say a comparison of mutations and signal for a protein that may be in one of these datasets vs ProteinGym.

      In the work by Frazer et al. [4], they mentioned that

      "However, these technologies do not easily scale to thousands of proteins, especially not to combinations of variants, and depend critically on the availability of assays that are relevant to or at least associated with human disease phenotypes." 

      It points out that the results of high-throughput experiments are usually based on the design of specific genes (such as BRCA1 and TP53.) and cannot be easily extended to thousands of other genes. At the same time, due to the complexity of the experiment, there may be problems with reproducibility or deviations from clinical relevance.

      This statement aligns with our perspective that high-throughput experiments inherently involve a significant amount of noise and error. It is important to clarify that the noise we discuss here arises from the limitations of high-throughput experiments themselves, instead of from the reliability of the data sources, such as systematic errors in experimental measurements. This latter issue is a complex problem common to all wetlab experiments and falls outside the scope of our study.

      Under this premise, low-throughput datasets like DTm and DDG can be considered to have less noise than high-throughput datasets, as they have undergone manual curation. As for your suggestion, while valuable, unfortunately, we were unable to identify datasets in DTM and DDG that align with those in ProteinGym after a careful search. Thus, we are unable to conduct this comparative experiment at this stage.

      (4) Frazer J, Notin P, Dias M, et al. Disease variant prediction with deep generative models of evolutionary data. Nature, 2021.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Chen and Phillips describe the dynamic appearance of cytoplasmic granules during embryogenesis analogous to SIMR germ granules, and distinct from CSR-1-containing granules, in the C. elegans germline. They show that the nuclear Argonaute NRDE-3, when mutated to abrogate small RNA binding, or in specific genetic mutants, partially colocalizes to these granules along with other RNAi factors, such as SIMR-1, ENRI-2, RDE-3, and RRF-1. Furthermore, NRDE-3 RIP-seq analysis in early vs. late embryos is used to conclude that NRDE-3 binds CSR-1-dependent 22G RNAs in early embryos and ERGO-1dependent 22G RNAs in late embryos. These data lead to their model that NRDE-3 undergoes small RNA substrate "switching" that occurs in these embryonic SIMR granules and functions to silence two distinct sets of target transcripts - maternal, CSR-1 targeted mRNAs in early embryos and duplicated genes and repeat elements in late embryos.

      Strengths:

      The identification and function of small RNA-related granules during embryogenesis is a poorly understood area and this study will provide the impetus for future studies on the identification and potential functional compartmentalization of small RNA pathways and machinery during embryogenesis.

      Weaknesses:

      (1) While the authors acknowledge the following issue, their finding that loss of SIMR granules has no apparent impact on NRDE-3 small RNA loading puts the functional relevance of these structures into question. As they note in their Discussion, it is entirely possible that these embryonic granules may be "incidental condensates." It would be very welcomed if the authors could include some evidence that these SIMR granules have some function; for example, does the loss of these SIMR granules have an effect on CSR-1 targets in early embryos and ERGO-1-dependent targets in late embryos?

      We appreciate reviewer 1’s concern that we do not provide enough evidence for the function of the SIMR granules. As suggested, we examined the NRDE-3 bound small RNAs more deeply, and we do observe a slight but significant increased CSR-class 22G-RNAs binding to NRDE-3 in late embryos of simr-1 and enri-2 mutants (see below, right). We hypothesize that this result could be due to a slower switch from CSR to ERGO 22G-RNAs in the absence of SIMR granules. We added these data to Figure 6G.

      (2) The analysis of small RNA class "switching" requires some clarification. The authors re-define ERGO1-dependent targets in this study to arrive at a very limited set of genes and their justification for doing this is not convincing. What happens if the published set of ERGO-1 targets is used? 

      As we mentioned in the manuscript, we initially attempted to use the previously defined ERGO targets. However, the major concern is fewer than half the genes classified as ERGO targets by Manage et al. and Fischer et al. overlap with one another (Figure 6—figure supplement 1D and below). We reason this might because the gene sets were defined as genes that lose small RNAs in various ERGO pathway mutants and because different criteria were used to define the lists as discussed in the manuscript (lines 471-476). As a result, some of the previously defined ERGO target genes may actually be indirect targets of the pathway. Here we focus on genes targeted by small RNAs enriched in an ERGO pathway Argonaute IP, which should be more specific.

      In this manuscript, we are interested specifically in the ERGO targets bound by NRDE-3, thus we utilized the IP-small RNA sequencing data from young adult animals (Seroussi et al, 2023), to define a new ERGO list. We are confident about this list because 1) Most of our new ERGO genes overlap with the overlap between ERGO-Manage and ERGO-Fischer list (see Figure 6—figure supplement 1D in our manuscript and below). 2) We observed the most significant decrease of small RNA levels and increase of mRNA levels in the nrde-3 mutants using our newly defined list (see Figure 6—figure supplement 1E-F in our manuscript).

      To further address reviewer 1’s concern about whether the data would look significantly different when using the ERGO-Manage and ERGO-Fischer lists, we made new scatter plots shown in Author response image 1 panels A-C below (ERGO-Manage – purple, ERGO-Fischer- yellow, and the overlap - yellow with purple ring). We found that the small switching pattern of NRDE-3 is consistent with our newly defined list, particularly if we look at the overlap of ERGO-Manage and ERGO-Fischer list (Author response image 1 panels D-F below, red).

      Author response image 1.

      Further, the NRDE-3 RIP-seq data is used to conclude that NRDE-3 predominantly binds CSR-1 class 22G RNAs in early embryos, while ERGO-1-dependent 22G RNAs are enriched in late embryos. a) The relative ratios of each class of small RNAs are given in terms of unique targets. What is the total abundance of sequenced reads of each class in the NRDE-3 IPs? 

      To address the reviewer’s question about the total abundance of sequenced reads of each class in the NRDE-3 IPs: Author response image 2 panel A-B below show the total RPM of CSR and ERGO class sRNAs in inputs and IPs at different stages. Focusing on late embryos, the total abundance of ERGO-dependent sRNAs is similar to CSR-class sRNAs in input, while much higher in IP, indicating an enrichment of ERGO-dependent 22G-RNAs in NRDE-3 consistent with our log2FC (IP vs input) in Figure 6B. This data supports our conclusion that NRDE-3 preferentially binds to ERGO targets in late embryos.

      Author response image 2.

      b) The "switching" model is problematic given that even in late embryos, the majority of 22G RNAs bound by NRDE-3 is the CSR-1 class (Figure 5D). 

      It is important to keep in mind the difference in the total number of CSR target genes (3834) and ERGO target genes (119).  The pie charts shown in Figure 6D are looking at the total proportion of the genes enriched in the NRDE-3 IP that are CSR or ERGO targets. For the NRDE-3 IP in late embryos, that would be 70/119 (58.8%) of ERGO targets are enriched, while 172/3834 (4.5%) of CSR targets are enriched. These data are also supported by the RPM graphs shown in Author response image 2 panels A-B above, which show that the majority of the small RNA bound by NRDE-3 in late embryos are ERGO targets. Nonetheless, NRDE-3 still binds to some CSR targets shown as Figure 6D and panel B, which may be because the amount of CSR-class 22G-RNAs is reduced gradually across embryonic development as the maternally-deposited NRDE-3 loaded with CSR-class 22G-RNAs is diluted by newly transcribed NRDE-3 loaded with ERGOdependent 22G-RNAs (lines 857-862). 

      c) A major difference between NRDE-3 small RNA binding in eri-1 and simr-1 mutants appears to be that NRDE-3 robustly binds CSR-1 22G RNAs in eri-1 but not in simr-1 in late embryos. This result should be better discussed.

      In the eri-1 mutant, we hypothesize that NRDE-3 robustly binds CSR-class 22G-RNAs because ERGOclass 22G-RNAs are not synthesized during mid-embryogenesis, so either NRDE-3 is unloaded (in granule at 100-cell stage in Figure 2A) or mis-loaded with CSR-class 22G-RNAs (in the nucleus at 100cell stage in Figure 2A). We don’t have a robust method to address the proportion of loaded vs. unloaded NRDE-3 so it is difficult to address the degree to which NRDE-3 is misloaded in the eri-1 mutant. In the simr-1 mutant, both classes of small RNAs are present and NRDE-3 is still preferentially loaded with ERGO-dependent 22G-RNAs, though we do see a subtle increase in association with CSR-class 22GRNAs. These data could suggest a less efficient loading of NRDE-3 with ERGO-dependent 22G-RNAs, but we would need more precise methods to address the loading dynamics in the simr-1 mutant.

      (3) Ultimately, if the switching is functionally important, then its impact should be observed in the expression of their targets. RNA-seq or RT-qPCR of select CSR-1 and ERGO-1 targets should be assessed in nrde-3 mutants during early vs late embryogenesis.

      The function of NRDE-3 at ERGO targets has been well studied (Guang et al, 2008) and is also assessed in our H3K9me3 ChIP-seq analysis in Figure 7E where, in mixed staged embryos, H3K9me3 level on ERGO targets (labeled as ‘NRDE-3 targets in young adults’) is reduced significantly in the nrde-3 mutant.

      To understand the function of NRDE-3 binding on CSR targets in early embryos, we attempted to do RTqPCR, smFISH, and anti-H3K9me3 CUT&Tag-seq on early embryos, and we either failed to obtain enough signal or failed to detect any significant difference (data not shown). We additionally tested the possibility that NRDE-3 functions with CSR-class 22G-RNAs in oocytes. We present new data showing that NRDE-3 represses RNA Pol II in oocytes to promote global transcriptional repression at the oocyteto-embryo transition, we now included these data in Figure 8. 

      Reviewer #2 (Public review):

      Summary:

      NRDE-3 is a nuclear WAGO-clade Argonaute that, in somatic cells, binds small RNAs amplified in response to the ERGO-class 26G RNAs that target repetitive sequences. This manuscript reports that, in the germline and early embryos, NRDE-3 interacts with a different set of small RNAs that target mRNAs. This class of small RNAs was previously shown to bind to a different WAGO-clade Argonaute called CSR1, which is cytoplasmic, unlike nuclear NRDE-3. The switch in NRDE-3 specificity parallels recent findings in Ascaris where the Ascaris NRDE homolog was shown to switch from sRNAs that target repetitive sequences to CSR-class sRNAs that target mRNAs.

      The manuscript also correlates the change in NRDE-3 specificity with the appearance in embryos of cytoplasmic condensates that accumulate SIMR-1, a scaffolding protein that the authors previously implicated in sRNA loading for a different nuclear Argonaute HRDE-1. By analogy, and through a set of corelative evidence, the authors argue that SIMR foci arise in embryogenesis to facilitate the change in NRDE-3 small RNA repertoire. The paper presents lots of data that beautifully documents the appearance and composition of the embryonic SIMR-1 foci, including evidence that a mutated NRDE-3 that cannot bind sRNAs accumulates in SIMR-1 foci in a SIMR-1-dependent fashion.

      Weaknesses:

      The genetic evidence, however, does not support a requirement for SIMR-1 foci: the authors detected no defect in NRDE-3 sRNA loading in simr-1 mutants. Although the authors acknowledge this negative result in the discussion, they still argue for a model (Figure 7) that is not supported by genetic data. My main suggestion is that the authors give equal consideration to other models - see below for specifics.

      We appreciate reviewer 2’s comments on the genetic evidence for the function of SIMR foci.  A similar concern was also brought up by reviewer 1. By re-examining our sequencing data, we found that there is a modest but significant increase in NRDE-3 association with CSR-class sRNAs in simr-1 and enri-2 mutants in late embryos. We believe that this data supports our model that SIMR-1 and ENRI-2 are required for an efficient switch of NRDE-3 bound small RNAs. Please refer our response to the reviewer 1 - point (1), and Figure 6G in the updated manuscript. 

      Reviewer #3 (Public review):

      Summary:

      Chen and Phillips present intriguing work that extends our view on the C. elegans small RNA network significantly. While the precise findings are rather C. elegans specific there are also messages for the broader field, most notably the switching of small RNA populations bound to an argonaute, and RNA granules behavior depending on developmental stage. The work also starts to shed more light on the still poorly understood role of the CSR-1 argonaute protein and supports its role in the decay of maternal transcripts. Overall, the work is of excellent quality, and the messages have a significant impact.

      Strengths:

      Compelling evidence for major shift in activities of an argonaute protein during development, and implications for how small RNAs affect early development. Very balanced and thoughtful discussion.

      Weaknesses:

      Claims on col-localization of specific 'granules' are not well supported by quantitative data

      We have now included zoomed images of individual granules to better show the colocalization in Figure 4 and Figure 4—figure supplement 1, and performed Pearson’s colocalization analysis between different sets of proteins in Figure 4B. 

      Reviewer #2 (Recommendations for the authors):

      - The manuscript is very dense and the gene names are not helpful. For example, the authors mention ERGO-1 without clarifying the type of protein, etc. I suggest the authors include a figure to go with the introduction that describes the different classes of primary and secondary sRNAs, associated Argonautes, and other accessory proteins. Also include a table listing relevant gene names, protein classes, main localizations, and proposed functions for easy reference by the readers.

      We agree that the genes names in different small RNA pathways are easily confused. We added a diagram and table in Figure 1—figure supplement 1 depicting the ERGO/NRDE and CSR pathways and added clarification about the ERGO/NRDE-3 pathway in the text from line 126-128.  

      - Line 424 - the wording here and elsewhere seems to imply that SIMR-1 and ENRI-2, although not essential, contribute to NRDE-3 sRNA loading. The sequencing data, however, do not support this - the authors should be clearer on this. If the authors believe there are subtle but significant differences, they should show them perhaps by adding a panel in Figure 5 that directly compares the NRDE-3 IPs in wildtype versus simr-1 mutants. Figure 5H however does not support such a requirement.

      As brought up by reviewer 1, we do not see difference in binding of ERGO-dependent sRNA in simr-1 mutant in late embryos. We do, however, see a modest, but significant, increase of CSR-sRNAs bound by NRDE-3 in simr-1 and enri-2 mutants, which we hypothesize could be due to a less efficient loading of ERGO-dependent 22G-RNAs by NRDE-3. The updated data are now in Figure 6G. We have also edited the text and model figure to soften these conclusions.

      - Condensates of PGL proteins appear at a similar time and place (somatic cells of early embryos) as the embryonic SIMR-1 foci. The PGL foci correspond to autophagy bodies that degrade PGL proteins. Is it possible that SIMR-1 foci also correspond to degradative structures? The possibility that SIMR-1 foci are targeted for autophagy and not functional would fit with the finding that simr-1 mutants do not affect NRDE-3 loading in embryos.

      We appreciate reviewer 2’s comments on possibility of SIMR granules acting as sites for degradation of SIMR-1 and NRDE-3. We think this is not the case for the following reasons: 1) if SIMR granules are sites of autophagic degradation, then we would expect that embryonic SIMR granules in somatic cells, like PGL granules, should only be observed in autophagy mutants; however we see them in wild-type embryos 2) we would not expect a functional Tudor domain to be required for granule localization; however in Figure 1—figure supplement 2B, we show that a point mutation in the Tudor domain of SIMR-1 abrogates SIMR granule formation, and 3) if NRDE-3(HK-AA) is recruited to SIMR granules for degradation while wild-type NRDE-3 is cytoplasmic, then NRDE-3(HK-AA) should shows a significantly reduced protein level comparing to wild-type NRDE-3. In the western blot in Figure 2—figure supplement 1B, NRDE-3 and NRDE-3(HK-AA) protein levels are similar, indicating that NRDE-3(HK-AA) is not degraded despite being unloaded. This is in contrast to what we have observed previously for HRDE-1, which is degraded in its unloaded state. If SIMR-1 played a role directly in promoting degradation of NRDE-3(HK-AA), we would similarly expect to see a change in NRDE-3 or NRDE-3(HK-AA) expression in a simr-1 mutant. We performed western blot and did not observe a significant change in protein expression for NRDE-3 (Figure 3—figure supplement 1A). 

      Although under wild-type conditions, SIMR granules do not appear to be sites of autophagic degradation, upon treatment with lgg-1 (an autophagy protein) RNAi, we found that SIMR-1, as well as many other germ granule and embryonic granule-localized proteins, increase in abundance in late embryos.  This data demonstrates that ZNFX-1, CSR-1, SIMR-1, MUT-2/RDE-3, RRF-1, and unloaded NRDE-3 are removed by autophagic degradation similar to what have been shown previously for PGL-1 proteins (Zhang et al, 2009, Cell). We added these data to Figure 5. It is important to emphasize, however, that the timing of degradation differs for each granule assayed (Lines 447-450), indicating that there must be multiple waves of autophagy to selectively degrade subsets of proteins when they are no longer needed by the embryo.

      - The observation that an NRDE-3 mutant that cannot load sRNAs localizes to SIMR-1 foci does not necessarily imply that wild-type unloaded NRDE-3 would also localize there. Unless the authors have additional data to support this idea, the authors should acknowledge that this hypothesis is speculative. In fact, why does cytoplasmic NRDE-3 not localize to granules in the rde-3;ego-1degron strain shown in Figure 6B?? Is it possible that the NRDE-3 mutant accumulates in SIMR-1 foci because it is unfolded and needs to be degraded?

      We believe that wild-type NRDE-3 also localize to SIMR foci when unloaded. This is supported by the localization of wild-type NRDE-3 in eri-1 and rde-3 mutants, where a subset of small RNAs are depleted. Wild-type NRDE-3 localizes to both somatic SIMR-1 granules and the nucleus, depending on embryo stage (Figure 2A, Figure 2—figure supplement 1C). The granule numbers in eri-1 and rde-3 mutants are less than the nrde-3(HK-AA) mutant, consistent with the imaging data that NRDE-3 only partially localize to somatic granule (Figure 2A – 100-cell stage).

      In the rde-3; ego-1 double mutant, the embryos have severe developmental defect: they cannot divide properly after 4-8 cell stage and exhibit morphology defects after that stage. In wild-type, SIMR foci does not appear until around 8-28-cell stage (shown in Figure 1C), so we believe that cytoplasmic NRDE-3 does not localize to foci in the double mutant is because of the timing.

      - The authors propose that NRDE-3 functions in nuclei to target mRNAs also targeted in the cytoplasm by CSR-1. If so, how do they propose that NRDE-3 might do this since little transcription occurs in oocytes/early embryos?? Are the authors suggesting that NRDE-3 targets germline genes for silencing specifically at the times that zygotic transcription comes back on, or already in maturing oocytes? Is the transcription of most CSR-1 targets silenced in early embryos??

      We appreciate the suggestions to check the function of NRDE-3 in oocytes. We tested this possibility and found it to be correct. NRDE-3 functions in oocytes for transcriptional repression by inhibiting RNA Pol II elongation. We added these data to Figure 8. We also attempted to do RT-qPCR, smFISH, and antiH3K9me3 Cut&Tag-seq on early embryos to further test the hypothesis that NRDE-3 acts with CSR-class 22G-RNAs in early embryos, but we either failed to obtain enough signal or failed to detect any significant difference (data not shown). Therefore, we think that the primary role for NRDE-3 bound to CSR-class 22G-RNAs may be for global transcriptional repression of oocytes prior to fertilization.

      - Line 684-686: "In summary, this work investigating the role of SIMR granules in embryos, together with our previous study of SIMR foci in the germline (Chen and Phillips 2024), has identified a new mechanism for small RNA loading of nuclear Argonaute proteins in C. elegans". This statement appears overstated/incorrect since there is no evidence that SIMR-1 foci are required for sRNA loading of NRDE3. The authors should emphasize other models, as suggested above.

      We have revised the text on line 869-871 to emphasize that SIMR granule regulate the localization of nuclear Argonaute proteins, rather than suggesting a direct role on controlling small RNA loading. We also edit the title, text, and legend for our model in Figure 9. 

      Reviewer #3 (Recommendations for the authors):

      Issues to be addressed:

      - The authors show a switch in 22G RNA binding by NRDE-3 during embryogenesis. While the data is convincing, it would be great if it could be tested if the preferred NRDE-3 replacement model is indeed correct. This could be done relatively easily by giving NRDE-3 a Dendra tag, allowing one to colour-switch the maternal WAGO-3 pool before the zygotic pool comes up. Such data would significantly enhance the manuscript, as this would allow the authors to follow the fate of maternal NRDE-3 more precisely, perhaps identifying a period of sharp decline of maternal NRDE-3.

      We think the NRDE-3 Dendra tag experiment suggested by the reviewer is a clever approach and we will consider generating this strain in the future. However, we feel that optimization of the color-switching tag between the maternal germline and the developing embryos is beyond the scope of this manuscript. To partially address the question about NRDE-3 fate during embryogenesis, we examined the single-cell sequencing data of C. elegans embryos from 1-cell to 16-cell stage (Tintori et al, 2016, Dev Cell; Visualization tool from John I Murray lab), as shown in Author response image 3 Panel A below, NRDE-3 transcript level increases as embryo develops, indicating that zygotic NRDE-3 is being actively expressed starting very early in development. We hypothesize that maternal NRDE-3 will either be diluted as the embryo develops or actively degraded during early embryogenesis. 

      Author response image 3.

      - Figure 3A: * should mark PGCs, but this seems incorrect. At the 8-cell stage there still is only one PGC (P4), not two, and at 100 cells there are only two, not three germ cells. Also, the identification of PGCs with a maker (PGL for instance) would be much more convincing.

      We apologize for the confusion in Figure 3A. We changed the figure legend to clarify that the * indicate nuclear NRDE-3 localization in somatic cells for 8- and 100-cell stage embryos rather than the germ cells.  

      - Overall, the authors should address colocalization more robustly. In the current manuscript, just one image is provided, and often rather zoomed-out. How robust are the claims on colocalization, or lack thereof? With the current data, this cannot be assessed. Pearson correlation, combined with line-scans through a multitude of granules in different embryos will be required to make strong claims on colocalization. This applies to all figures (main and supplement) where claims on different granules are derived from.

      We thank reviewer 3 for this important suggestion. To better address the colocalization, we included insets of individual granules in Figure 2D and Figure 4. We also performed colocalization analysis by calculating the Pearson’s R value between different groups of proteins in Figure 4B, to highlight that SIMR-1 colocalizes with ENRI-2, NRDE-3(HK-AA), RDE-3, and RRF-1, while CSR-1 colocalizes with EGO-1.

      For the proteins that lack colocalization in Figure 4—figure supplement 1, we also added insets of individual granules. Additionally, we included a new set of panels showing SIMR-1 localization compared to tubulin::GFP (Figure 4—figure supplement 1I) in response to a recent preprint (Jin et al, 2024, BioRxiv), which finds NRDE-3 (expressed under a mex-5 promoter) associating with pericentrosomal foci and the spindle in early embryos. We do not see SIMR-1 (or NRDE-3, data not shown) at centrosomes or spindles in wild-type conditions but made a similar observation for SIMR-1 in a mut-16 mutant (Figure 4E). All of the localization patterns were examined on at least 5 individual 100-cell staged embryos with same localization pattern.

      - Figure 7: Its title is: Function of cytoplasmic granules. This is a much stronger statement than provided in the nicely balanced discussion. The role of the granules remains unclear, and they may well be just a reflection of activity, not a driver. While this is nicely discussed in the text, figure 7 misses this nuance. For instance, the title suggests function, and also the legend uses phrases like 'recruited to granule X'. If granules are the results of activity, 'recruitment' is really not the right way to express the findings. The nuance that is so nicely worded in the discussion should come out fully in this figure and its legend as well.

      We have changed the title of Figure 7 (now Figure 9) to “Model for temporally- and developmentallyregulated NRDE-3 function” to deemphasize the role of the granules and to highlight the different functions of NRDE-3. Similarly, we have rephrased the text in the figure and legend and add a some details about our new results.

      Minor:

      Typo: line 663 Acaris

      We corrected the typo.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1:

      (1) Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript.

      Thank you for the comments. We revised some of the claims and conclusions to be more objective and result-supportive.

      (2) While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

      We do not fully agree with the reviewer's comments. We have demonstrated that changes in CPS levels during infection are associated with pathogenesis, which will guide future studies on the underlying mechanisms. A significant amount of effort is required for studying mechanisms, which is beyond the scope of this research. We concur with the reviewer that assertions should be made cautiously until further studies are conducted. We have revised these assertions to align with the data and to avoid extrapolating the results (pages 7, lines 126, 133-136; page 11, lines 216-218; page 13, line 264; and page 18, lines 378-383).

      (3) The difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

      S. suis is a part of the natural microbial community of swine tonsils but not mouse NALT. It is interesting to know if CPS levels are low in pig tonsils since CPS is hydrophilic and not conducive to bacterial adhesion. In the study, mice were i.n. infected with a high dose of the bacteria, which could increase opportunities for dissemination (acidic acid may not be a contributor since with or without it is similar). S. suis getting into other body compartments from pig tonsils might be triggered by other conditions, such as viral coinfection, nasal cavity inflammation, cold weather, and decreased immunity.

      Experiments with pig blood and phagocytes have shown that genes involved in the synthesis of CPS are upregulated in pig blood. In contrast, these genes are downregulated [1]. In addition, the absence of CPS correlated with increased hydrophobicity and phagocytosis, proposing that S. suis undergoes CPS phase variation and could play a role in the different steps of S. suis infection [2]. We showed direct evidence of encapsulation modulation associated with S. suis pathogenesis in mice. A pig infection model is required to confirm these findings.

      (4) The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take? 

      What causes CPS regulation in vivo is not known. CPS changes in different culture stages, indicating that stress, such as nutrition levels, is one of the signals triggering CPS regulation. The microenvironment in the body compartments is far more complex than in vitro, in which host cells, immune factors and others may affect CPS regulation, individually or collectively. The reviewer’ question is important but the suggested experiment is impracticable since bacterial numbers taken from organs are few, and culturing the bacteria in vitro would obliterate the in vivo status.  

      (5) Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

      We lower the tone and change the claim as “suggest a correlation between lower CPS in the NALT and a greater capacity for cellular association, whereas elevated CPS levels in the blood are linked to improved resistance against bactericidal activity. However, the mechanisms behind these associations remain unknown.” (page 7, lines 133-136).

      (6) The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

      It is a very interesting comment. The mice are SPF level. The microenvironment in SPF mouse NALT should be significantly different from conventional pig tonsils. Although NALT in mice resembles pig tonsils in function, many factors may contribute to the sensitivity to S. suis colonization in the pig nasal cavity, such as the microbiome and local steady-state immune system. More complex microbiota in tonsils could be one of the factors. Analyzing what makes S. suis inclined towards colonization in pig tonsils by SPF and conventional pigs are an ideal experiment to answer the question. 

      (7) Have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

      Thank you for your comments. The olfactory epithelium (OE) is located directly underneath the olfactory bulb in the olfactory mucosa area and lines approximately half of the nasal cavities of the nasal cavity. The remaining surface of the nasal cavity is lined by respiratory epithelium, which lacks neurons. The olfactory receptor neuron in OE is stained green in the images by β-tubulin III, a neuron-specific marker. The respiratory epithelium is colorless due to the absence of nerve cells. Similarly, the green color stained by β-tubulin III identifies the olfactory bulb. The accuracy of the anatomic compartments of the mouse nasal cavity has been checked and confirmed by referring to related literature [3, 4].

      References

      (1) Wu Z, Wu C, Shao J, Zhu Z, Wang W, Zhang W, Tang M, Pei N, Fan H, Li J, Yao H, Gu H, Xu X, Lu C. The Streptococcus suis transcriptional landscape reveals adaptation mechanisms in pig blood and cerebrospinal fluid. RNA. 2014 Jun;20(6):882-98.

      (2) Charland N, Harel J, Kobisch M, Lacasse S, Gottschalk M. Streptococcus suis serotype 2 mutants deficient in capsular expression. Microbiology (Reading). 1998 Feb;144 ( Pt 2):325-332.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      Reviewer 2:

      (1) However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

      Both reviewers were concerned about some of the interpretations of the results. We modified the interpretations in related lines throughout the manuscript (Please see the related responses to Reviewer 1).

      (2) In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

      We thank the reviewer for the comment. The experiment should be Bactericidal Assay instead of anti-phagocytosis killing. CPS is a main inhibitor of C3b deposition [1]. It interferes with complement-mediated and receptor-mediated phagocytosis; and direct killing. Data in Figure 2C is expressed as “% of bacterial survival in whole blood” for clarity (page 8, Fig. 2C and page 23, lines 489-490).

      (3) Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

      Anti-V5 may not stay long locally following intranasal administration. Efficient reduction of S. suis colonization in NALT supports that anti-V5 could recognize and neutralize the bacteria in NALT quickly, thereby reducing further dissemination in the body. Antibody-mediated phagocytosis may not play a major role because neutrophils are mainly present in the blood but not in the tissues.  

      (4) The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

      We understand the significance of your concerns and appreciate your careful review of Figure 7. The olfactory epithelium (OE) is situated directly beneath the olfactory bulb in the olfactory mucosa area and covers about half of the nasal cavity. This positioning allows information transduction between the olfactory and the olfactory epithelium. The remaining surface of the nasal cavity is lined with respiratory epithelium, which does not contain neurons and primarily serves as a protective barrier. In contrast, the olfactory epithelium consists of basal cells, sustentacular cells, and olfactory receptor neurons. The olfactory receptor neurons are specifically stained green in the images using β-tubulin III, a marker that is unique to neurons. The respiratory epithelium appears colorless due to the lack of nerve cells. Similarly, the green staining with β-tubulin III also highlights the olfactory bulb. The anatomical structures indicated in the images are consistent with those described in the literature [2, 3], confirming that the anatomy of the nasal cavity has been accurately identified.

      (5) Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

      Thank you for highlighting this issue. Cerebrospinal fluid (CSF) flows into the subarachnoid space surrounding the spinal cord and the brain. There are direct connections from this subarachnoid space to lymphatic vessels that wrap around the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. This connection allows for the drainage of CSF into the nasal submucosal lymphatics in mice [4, 5]. Bacteria may utilize this CSF outflow channel in the opposite direction, which explains the development of brain inflammation in the distal areas of brain tissue adjacent to the subarachnoid space. We have included additional relevant information in the revised manuscript (page 16, lines 323-325).

      (6) The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

      The high dose of inoculation used in the experiment may explain the rapid presence of S. suis in the CSF. Mice exhibit low sensitivity to S. suis infection, and the range for the effective intranasal infectious dose is quite narrow. Higher doses lead to the quick death of the mice, while lower doses do not initiate an infection at all. The dose used in this study is empirical and is intended to facilitate the observation of the progression of S. suis infection in mice.

      The NALT tissue and CSF samples are collected separately. After obtaining the NALT tissue, the nasal portion was carefully separated from the rest of the head along the line of the eyeballs. The brain tissue was then extracted from the remaining part of the head to collect the CSF, and it was lacerated to expose the subarachnoid space without being minced. This procedure aims to preserve the integrity of the brain tissue as much as possible. Further details about the CSF collection process can be found in the Materials and Methods section (page 24, lines 508-512).

      (7) To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Thank you. We respectfully disagree with the reviewer. We stained neurons using a neuron-specific marker to identify the anatomical structures of the olfactory bulb and olfactory epithelium (in green). We used an S. suis-specific antibody to highlight the bacteria present in these areas (in orange and red). The images, along with the bacteria found in the cerebrospinal fluid (CSF) and the brain inflammation observed early in the infection, strongly support our conclusion regarding brain invasion through the olfactory pathway. Please see the response to question 4 for further clarification.

      References

      (1) Seitz M, Beineke A, Singpiel A, Willenborg J, Dutow P, Goethe R, Valentin-Weigand P, Klos A, Baums CG. Role of capsule and suilysin in mucosal infection of complement-deficient mice with Streptococcus suis. Infect Immun. 2014 Jun;82(6):2460-71.

      (2) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Yoon JH, Jin H, Kim HJ, Hong SP, Yang MJ, Ahn JH, Kim YC, Seo J, Lee Y, McDonald DM, Davis MJ, Koh GY. Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage. Nature. 2024 Jan;625(7996):768-777.

      (5) Spera I, Cousin N, Ries M, Kedracka A, Castillo A, Aleandri S, Vladymyrov M, Mapunda JA, Engelhardt B, Luciani P, Detmar M, Proulx ST. Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves. EBioMedicine. 2023 May;91:104558.

      Response to Recommendations for the authors:

      Reviewer 1:

      Minor concerns for the manuscript:

      (1) In the introduction, please consider giving a little more background about the bacteria itself and how it causes pathogenesis.

      We appreciate your suggestion. We have included additional background on the virulent factors and the pathogenesis of the bacteria in the introduction to enhance understanding of the results (page 4, lines 63-69).

      (2) Figure 2C would be more correct to say percent survival as the CFUs before and after are what are being compared and not if the bacteria is being phagocytosed or not. Flow cytometry of the leukocytes and a fluorescent S. Suis would show phagocytosis. Unless that experiment is performed, the authors cannot claim that there is a resistance to phagocytosis.

      Thank you for your feedback. We agree with the reviewer that the experiment should be Bactericidal Assay rather than anti-phagocytosis killing. CPS interferes with complement-mediated phagocytosis and direct killing, and receptor-mediated phagocytosis. To enhance clarity, the data in Fig. 2C has been presented as “% of bacterial survival in whole blood” (page 8).  

      (3) There are two different legends present for Figure 1. Please resolve.

      We apologize for the oversight. The redundant figure legend has been removed (page 6).

      (4) There are places such as in lines 194-195, that there are assertions and interpretations about the data that are not directly drawn from the data. These hypotheses are valuable, but please move them to the discussion.

      Thank you for your suggestion. The hypothesis has been moved to the Discussion section (page 19, lines 402 - 405).

      (5) In Figure 4B, higher resolution images would strengthen the ability of non-microbiologists to see the differences in CPS levels in the cell wall.

      We achieved the highest resolution possible for clearer distinctions in CPS levels. To enhance the visualization of the different CPS levels in the images, we revised the description of the CPS changes in Figure 4B within the results section (page 11, lines 208-213).

      (6) In Figure 5 there is no D. Further, the schematics throughout would be easier to parse with the text if the challenge occurred at time 0. Consider revising them for clarity.

      Thank you for highlighting the error. We have removed "i.v + i.n (Fig. 5)" from Figure 5A and made adjustments to the schematic illustrations in Figures 5 and 6 as recommended by the reviewer (page 14).

      (7) What is the control for the serum? The findings for figures 5 and 6 would be much stronger if a non- S. Suis isotype control serum was also infused.

      We used a naive serum as a control to avoid interference from a non-S. suis isotype control that targets other surface molecules of S. suis serotypes.

      (8) Figure 6 legend does not include the anti-CPS treatment.

      Thank you. We have added anti-CPS serum in the legend (page 15, line 249).

      (9) Figure 7 legend does not include the time point for panel 7A.

      Thank you. The time point is shown on Fig.7A (page 17).

      (10) Figure 7 should show OB micrographs or entire brain including the OB.

      The neuron-specific marker, β-tubulin III, identifies the neuro cells in the olfactory bulb (OB) as shown in Fig. 7A. Unfortunately, we were unable to provide an image of the entire brain that includes the OB due to limitations in our section preparation. We apologize for the mislabeled structure in Fig. 7A, which may have caused confusion. We have corrected the labeling for consistency (see page 15, lines 257-260). Additionally, we included a drawing of the sagittal plane of the rodent's nose, depicting the compartments of the OB, olfactory epithelium (OE), nasal cavity (NC), and brain. This illustration, presented in Fig. 7B on page 17, aims to clarify the structural and functional connections between the nasopharynx and the CNS.

      (11) Some conclusions may be better drawn if figures were to be consolidated. As noted above, the data at times feels disjointed and the importance is more difficult for readers to follow because data are presented further apart. Particularly figures 5 and 6 which are similar with different time points and controls of antisera administrative routes; placing these figures together would be an example of increasing continuity throughout the paper.

      Thank you for the valuable suggestion. Figures 5 and 6, along with their related descriptions in the results section, have been combined for better cohesiveness (pages 14-15).

      Reviewer #2:

      To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Please refer to our responses to Reviewer 1's Question 7, Reviewer 2's Questions 4 and 7 in the public reviews, and Reviewer 1's Question 10 in the authors' recommendations.

    1. Step 1: Notice how you are feeling.Tuning in to your feelings is very important. When you are notaware of your feelings, it is easy for them to interfere in yourability to build strong, positive, relationships with families.Adele watches her niece’s son, Eduardo, each day—whichshe really enjoys. But her niece, Tasha, is often late to pickhim up and never calls. Adele is really frustrated and angry.She feels it’s very disrespectful and that she is being takenadvantage of. When her niece does eventually show up,Adele is very abrupt and annoyed in her tone. The two adultsbarely communicate. Eduardo glances from one to the otherand looks very tense. Tasha whisks him away and Eduardodoesn’t even say good-bye to his auntie whom he adores.Recognizing the impact on Eduardo, Adele decides to talk toTasha about her feelings and to see about making a plan to helpTasha arrive on time, and at least to call to let Adele know she isrunning late. When Adele takes the approach of partnering withTasha in solving the problem, versus blaming her, Tasha is opento discussing solutions.Step 2: Look at the interaction from thechild’s point of view.Tuning in to the child’s experience can reduce tension and leadto joint problem-solving. Take the example of a child throwing atantrum when their parent comes to pick them up. This situationcan naturally make a parent feel incompetent and embarrassed.But if you look at it from the child’s point of view, you canreframe the issue in a way that doesn’t make the parent feel badand that also helps them understand the complexity of the child’sbehavior: “It seems like Stephanie is trying to tell you, I’m havingso much fun with the dollhouse that I need a little time to adjustto the idea it’s time to leave for the day.” Or, “Stephanie has kepther emotions in all day and now that her safe person is here, shecan really let her feelings out. It is hard to share a day with so manychildren no matter how much fun it is.”In the cases where a child is more cooperative with you than theparent, again, help them see it from the child’s perspective:“Yes, Tony puts his coat on when I ask him to, but that’s because heknows I have to help the other kids too. Kids learn quickly that therules and expectations at home and here can be different. He tellsme all about how you make sure he is zipped up and how youalways check that he has his hat. He talks about you all the time.It is always hardest for parents and families. Children work thingsout with the people they are most connected to.”Step 3: Partner with families.Developing a plan together with families on how to handle achild-rearing issue helps you move forward as partners, insteadof competitors. For example, if you are trying to teach childrennot to hit when they are angry, but the parent hits the child todiscipline them at home, you can:f Use “I” statements. “I know we are both concerned aboutErica hitting other kids when she’s here. I really work with thekids on finding other ways to show angry feelings. I don’t hitthem because when adults hit children when they are angry,it teaches children to hit as well when they are mad.”f Ask for the parent’s perspective. Clarify the parent’s feelingsand beliefs on the issue. Ask questions to learn, not to passjudgment: “What are acceptable ways to you for Erica to expressher angry feelings? What do you do at home? What do you findworks? What doesn’t work? Would you be open to finding ways todiscipline her other than hitting?”f Most important: Look for a place to compromise. Ask theparent if they have ideas for next steps. What can the two ofyou agree on? What can you both work on? For example,“We both agree that Erica needs to find other ways to show heranger besides hitting. One strategy that seems to work here is tohave her stomp her feet as hard as she can to get her mad out.Are you comfortable with that? I also tell her that if she needs abreak, she can curl up on the couch with her teddy bear. Are thesestrategies you think you might want to try at home?” (If not, askthe parent(s) what they would be comfortable with.)Finally, don’t forget to check in.A relationship is a living thing that grows and changes overtime. It’s important to check in with families to see how thingsare going, how your agreed-upon plan is working, and whereyou might need to make some adjustments. Communication is

      When a child acts out during pickup, it may not necessarily be a reflection of poor parenting but rather a sign that the child feels safe expressing their emotions with their caregiver. Recognizing this can help educators support both the child and the parent during transitions.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      DiPeso et al. develop two tools to (i) classify micronucleated (MN) cells, which they call VCS MN, and (ii) segment micronuclei and nuclei with MMFinder. They then use these tools to identify transcriptional changes in MN cells.

      The strengths of this study are:

      (1) Developing highly specialized tools to speed up the analysis of specific cellular phenomena such as MN formation and rupture is likely valuable to the community and neglected by developers of more generalist methods.

      (2) A lot of work and ideas have gone into this manuscript. It is clearly a valuable contribution.

      (3) Combining automated analysis, single-cell labeling, and cell sorting is an exciting approach to enrich phenotypes of interest, which the authors demonstrate here.

      Weaknesses:

      (1) Images and ground truth labels are not shared for others to develop potentially better analysis methods.

      We regret this omission and thank the reviewer for pointing it out. Both the images and ground truth labels for VCS MN and MNFinder are now available on the lab’s github page and described in the README.txt files. VCS MN: https://github.com/hatch-lab/fast-mn. MNFinder: https://github.com/hatch-lab/mnfinder.

      (2) Evaluations of the methods are often not fully explained in the text.

      The text has been extensively updated to include a full description of the methods and choices made to develop the VCS MN and MNFinder image segmentation modules.

      (3) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is?

      VCS MN attempts to balance precision and recall with speed to reduce the fraction of MN changing state from intact to ruptured during a single cell cycle during a live-cell isolation experiment. In addition, we chose to prioritize inclusion of small MN adjacent to the nucleus in our positive calls. This meant that there were more false positives (lower PPV) than obtained by other methods but allowed us to include this highly biologically relevant class of MN in our MN+ population. Thus, for a comprehensive understanding of the consequences of MN formation and rupture, we recommend using the finder as is. However, for other visual cell sorting applications where a small number of highly pure MN positive and negative cells is preferred, such as clonal outgrowth or metastasis assays, we would recommend using the slower, but more precise, MNFinder to get a higher precision at a cost of temporal resolution. In addition, MNFinder, with its higher flexibility and object coverage, is recommended for all fixed cell analyses.

      Reviewer #2 (Public review):

      Summary:

      Micronuclei are aberrant nuclear structures frequently seen following the missegregation of chromosomes. The authors present two image analysis methods, one robust and another rapid, to identify micronuclei (MN) bearing cells. The authors induce chromosome missegregation using an MPS1 inhibitor to check their software outcomes. In missegregation-induced cells, the authors do not distinguish cells that have MN from those that have MN with additional segregation defects. The authors use RNAseq to assess the outcomes of their MN-identifying methods: they do not observe a transcriptomic signature specific to MN but find changes that correlate with aneuploidy status. Overall, this work offers new tools to identify MN-presenting cells, and it sets the stage with clear benchmarks for further software development.

      Strengths:

      Currently, there are no robust MN classifiers with a clear quantification of their efficiency across cell lines (mIoU score). The software presented here tries to address this gap. GitHub material (tools, protocols, etc) provided is a great asset to naive and experienced computational biologists. The method has been tested in more than one cell line. This method can help integrate cell biology and 'omics' studies.

      Weaknesses:

      Although the classifier outperforms available tools for MN segmentation by providing mIOU, it's not yet at a point where it can be reliably applied to functional genomics assays where we expect a range of phenotypic penetrance.

      We agree that the MNFinder module has limitations with regards to the degree of nuclear atypia and cell density that can be tolerated. Based on the recall and PPV values and their consistency across the majority conditions analyzed, we believe that MNFinder can provide reliable results for MN frequency, integrity, shape, and label characteristics in a functional genomics assay in many commonly used adherent cell lines. We also added a discussion of caveats for these analyses, including the facts that highly lobulated nuclei will have higher false positive rates and that high cell confluency may require additional markers to ensure highly accurate assignment of MN to nuclei.

      Spindle checkpoint loss (e.g., MPS1 inhibition) is expected to cause a variety of nuclear atypia: misshapen, multinucleated, and micronucleated cells. It may be difficult to obtain a pure MN population following MPS1 inhibitor treatment, as many cells are likely to present MN among multinucleated or misshapen nuclear compartments. Given this situation, the transcriptomic impact of MN is unlikely to be retrieved using this experimental design, but this does not negate the significance of the work. The discussion will have to consider the nature, origin, and proportion of MN/rupture-only states - for example, lagging chromatids and unaligned chromosomes can result in different states of micronuclei and also distinct cell fates.

      We appreciate the reviewer’s comments and now quantify the frequency of other nuclear atypias and MN chromosome content in RPE1 cells after 24 h Mps1 inhibition (Fig. S1). In summary, we find only small increases in nuclear atypia, including multinucleate cells, misshapen nuclei, and chromatin bridges, compared to the large increase in MN formation. This contrasts with what is observed when mitosis is delayed using nocodazole or CENPE inhibitors where nuclear atypia is much more frequent. Importantly, after Mps1 inhibition, RPE1 cells with MN were only slightly more likely to have a misshapen nucleus compared to cells without MN (Fig. S1C).

      Interestingly, this analysis showed that the VCS MN pipeline, which uses the Deep Retina segmenter to identify nuclei, has a strong bias against lobulated nuclei and frequently fails to find them (Fig. S2B). Therefore, the cell populations analyzed by RNAseq were largely depleted of highly misshapen nuclei and differences in nuclear atypia frequency between MN+ and MN- cells in the starting population were lost (Fig. S9A, compare to Fig. S1C). This strongly suggests that the transcript changes we observed reflect differences in MN frequency and aneuploidy rather than differences in nuclei morphology.

      We agree with the reviewer that MN rupture frequency and formation, and downstream effects on cell proliferation and DNA damage, are sensitive to the source of the missegregated chromatin. In the revised manuscript we make clear that we chose Mps1 inhibition because it is strongly biased towards whole chromosome MN (Fig. S1E), limiting signal from DNA damage products, including chromosome fragments and chromatin bridges. This provides a base line to disambiguate the consequences of micronucleation and DNA damage in more complex chromosome missegregation processes, such as DNA replication disruption and irradiation. 

      Reviewer #3 (Public review):

      Summary:

      The authors develop a method to visually analyze micronuclei using automated methods. The authors then use these methods to isolate MN post-photoactivation and analyze transcriptional changes in cells with and without micronuclei of RPE-1 cells. The authors observe in RPE-1 cells that MN-containing cells show similar transcriptomic changes as aneuploidy, and that MN rupture does not lead to vast changes in the transcriptome.

      Strengths:

      The authors develop a method that allows for automating measurements and analysis of micronuclei. This has been something that the field has been missing for a long time. Using such a method has the potential to advance micronuclei biology. The authors also develop a method to identify cells with micronuclei in real time and mark them using photoconversion and then isolate them via FACS. The authors use this method to study the transcriptome. This method is very powerful as it allows for the sorting of a heterogenous population and subsequent analysis with a much higher sample number than could be previously done.

      Weaknesses:

      The major weakness of this paper is that the results from the RNA-seq analysis are difficult to interpret as very few changes are found to begin with between cells with MN and cells without. The authors have to use a 1.5-fold cut-off to detect any changes in general. This is most likely due to the sequencing read depth used by the authors. Moreover, there are large variances between replicates in experiments looking at cells with ruptured versus intact micronuclei. This limits our ability to assess if the lack of changes is due to truly not having changes between these populations or experimental limitations. Moreover, the authors use RPE-1 cells which lack cGAS, which may contribute to the lack of changes observed. Thus, it is possible that these results are not consistent with what would occur in primary tissues or just in general in cells with a proficient cGAS/STING pathway.

      We agree with the reviewer’s assessment of the limitations of our RNA-Seq analysis. After additional analysis, we propose an alternative explanation for the lower expression changes we observe in the MN+ and Mps1 inhibitor RNA-Seq experiments. In summary, we find that VCS MN has a strong bias against highly lobulated nuclei that depletes this class of cells from both the bulk analysis and the micronucleated cell populations (Fig. S9A). Based on this result, we propose that our analysis reduces the contribution of nuclear atypia to these transcriptional changes and that nuclear morphology changes are likely a signaling trigger associated with aneuploidy.

      We believe that this finding strengthens our overall conclusion that MN formation and rupture do not cause transcriptional changes, as suppressing the signaling associated with nuclei atypia should increase sensitivity to changes from the MN. However, we cannot completely rule out that MN formation or rupture cause a broad low-level change in transcription that is obscured by other signals in the dataset.

      As to cGAS signaling, several follow up papers and even the initial studies from the Greenburg lab show that MN rupture does not activate cGAS and does not cause cGAS/STING-dependent signaling in the first cell cycle (see citations and discussion in text). Therefore, we expect the absence of cGAS in RPE1 cells will have no effect in the first cell cycle, but could alter the transcriptional profile after mitosis. Although analysis of RPE1  cGAS+ cells or primary cells in these experiments will be required to definitively address this point, we believe that our interpretation of our RNAseq results is sufficiently backed up by the literature to warrant our conclusion that MN formation and rupture do not induce a transcriptional response in the first cell cycle.

      Reviewer #1 (Recommendations for the authors):

      I do not recommend additional experimental or computational work. Instead, I just recommend adapting the claims of the manuscript to what has been done. I am just asking for further clarification and minor rewriting.

      (1) The manuscript is written like a molecular biology paper with sparse explanations of the authors' reasoning, especially in the development of their algorithms. I was often lost as to why they did things in one way or another.

      The revised manuscript has thorough explanations and additional data and graphics defining how and why the VCS MN and MNFinder modules were developed. We hope that this clears up many of the questions the reviewer had and appreciate their guidance on making it more readable for scientists from different backgrounds.

      (2) Evaluations of their method are often not fully explained, for example:

      "On average, 75% of nuclei per field were correctly segmented and cropped."

      "MN segments were then assigned to 'parent' nuclei by proximity, which correctly associated 97% of MN."

      Were there ground truth images and labels created? How many? For example, I don't know how the authors could even establish a ground-truth for associating MNs to nuclei if MNs happened to be almost equidistant between two nuclei in their images.

      I suggest a separate subsection early in the Results section where the underlying imaging data + labels are presented.

      We added new sections to the text and figures at the beginning of the VCS MN and MNFinder subsections (Fig. S2 and Fig. S5) with specific information about how ground truth images and labels were generated for both modules and how these were broken up for training, validation, and testing.

      We also added information and images to explain how ground truth MN/nucleus associations were derived. In summary, we took advantage of the fact that 2xDendra-NLS is present at low levels in the cytoplasm to identify cell boundaries. This combined with a subconfluent cell population allowed us to unambiguously group MN and nuclei for 98% of MN, we estimate. These identifications were used to generate ground truth labels and analyze how well proximity defines MN/nuclei groups (Fig.s S1 and S2).

      (3) Overall, I find the sections long and more subtitles would help me better navigate the manuscript.

      Where possible, we have added subtitles.

      (4) Everything following "To train the model, H2B channel images were passed to a Deep Retina neural net ..." is fully automated, it seems to me. Thus, there seems to be no human intervention to correct the output before it is used to train the neural network. Therefore, I do not understand why a neural network was trained at all if the pipeline for creating ground truth labels worked fully automatically. At least, the explanations are insufficient.

      We apologize for the initial lack of clarity in the text and included additional details in the revision. We used the Deep Retina segmenter to crop the raw images to areas around individual nuclei to accelerate ground truth labeling of MN. A trained user went through each nucleus crop and manually labeled pixels belonging to MN to generate the ground truth dataset for training, validation, and imaging in VCS MN (Fig. S2A).

      (5) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is? I understand that for bulk transcriptomics, enrichment may be sufficient but for many other questions, where the wrong cell type could contaminate the population, it is not.

      Remarks in the Results section on what the various accuracies mean for different applications would be good (so one does not need to wait for the Discussion section).

      One of the strengths of the visual cell sorting system is that any image analysis pipeline can be used with it. We used VCS MN for the transcriptomics experiment, but for other applications a user could run visual cell sorting in conjunction with MNFinder for increased purity while maintaining a reasonable recall or use a pre-existing MN segmentation program that gives 100% purity but captures only a specific subgroup of micronucleated cells (e.g. PIQUE). 

      To maintain readability, especially with the expansion of the results sections, we kept the discussion of how we envision using visual cell sorting for other MN-based applications in the discussion section.

      (6) I am confused about what "cell" is referring to in much of the manuscript. Is it the nucleus + MNs only? Is it the whole cell, which one would ordinarily think it is? If so, are there additional widefield images, where one can discern cell boundaries? I found the section "MNFinder accurately ..." very hard to read and digest for this reason and other ambiguous wording. I suggest the authors take a fresh look at their manuscript and see whether the text can be improved for clarity. I did not find it an easy read overall, especially the computational part.

      After re-examining how “cell” was used, we updated the text to limit its use to the MNFinder arm tasked with identifying MN-nucleus associations where the convex hull defined by these objects is used to determine the “cell” boundary. In all other cases we have replaced cell with “nucleus” because, as the reviewer points out, that is what is being analyzed and converted. We hope this is clearer.

      (7) Post-FACS PPVs are not that great (Figure 3c). It depends on the question one wants to answer whether ~70% PPV is good enough. Again, would be good to comment on.

      We added discussion of this result to the revision. In summary, a likely reason for the reduced PPV is that, although we maintain the cells in buffer with a Cdk1 inhibitor, we know that some proportion of the cells go through mitosis post-sorting. Since MN are frequently reincorporated into the nucleus after mitosis (Hatch et al, 2013; Zhang et al., 2015), we expect this to reduce the MN+ population. Thus, we expect that the PPV in the RNAseq population is higher than what we can measure by analyzing post-sorted cells that have been plated and analyzed later.

      (8) I am thoroughly confused as to why the authors claim that their system works in the "absence of genetic perturbations" and why they emphasize the fact that their cells are non-transformed: They still needed a fluorescent label and they induce MNs with a chemical Mps1 inhibitor. (The latter is not a genetic manipulation, of course, but they still need to enrich MNs somehow. That is, their method has not been tested on a cell population in which MNs occur naturally, presumably at a very low rate, unless I missed something.) A more careful description of the benefits of their method would be good.

      We apologize for the confusion on these points and hope this is clarified in the revision. We were comparing our system, which can be made using transient transfection, if desired, to current tools that disambiguate aneuploidy and MN formation by deleting parts of chromosomes or engineering double strand breaks with CRISPR to generate single chromosome-specific missegregation events. Most of these systems require transformed cancer cells to obtain high levels of recombination. In contrast, visual cell sorting can isolate micronucleated cells from any cell line that can exogenously express a protein, including primary cells and non-transformed cells like RPE1s.

      Other minor points:

      (1) The authors should not refer to "H2B channels" but to "H2B-emiRFP703 channels". It may seem obvious to the authors but for someone reading the manuscript for the very first time, it was not. I was not sure whether there were additional imaging modalities used for H2B/nucleus/chromatin detection before I went back and read that only fluorescence images of H2B-emiRFP703 were used. To put it another way, the authors are detecting fluorescence, not histones -- unless I misunderstood something.

      To address this point, we altered the text to read “H2B-emiRFP703” when discussing images of this construct. For MNFinder some images were of cells expressing H2B-GFP, which has also been clarified.

      (2) If the level of zoom on my screen is such that I can comfortably read the text, I cannot see much in the figure panels. The features that I should be able to see are the size of a title. The image panels should be magnified.

      In the revision, the images are appended to the end at full resolution to overcome this difficulty. Thank you for your forbearance.

      Reviewer #2 (Recommendations for the authors):

      The methods are adequately explained. The Results text narrating experiments and data analysis is clear. Interpretation of a few results could be clarified and strengthened as explained below.

      (1) RNAseq experiments are a good proof of principle. To strengthen their interpretation in Figures 4 and 6, I would recommend the authors cite published work on checkpoint/MPS1 loss-induced chromosome missegregation (PMID: 18545697, PMID: 33837239, PMC9559752) and consider in their discussion the 'origin' and 'proportion' of micronucleated cells and irregularly shaped nuclei expected in RPE1 lines. This will help interpret Figure 6 findings on aneuploidy signature accurately. Not being able to see an MN-specific signature could be due to the way the biological specimen is presented with a mixture of cells with 'MN only' or 'rupture' or 'MN along with misshapen nuclei'. These features may all link to aneuploidy rather than 'MN' specifically.

      We appreciate the reviewer’s suggestion and added a new analysis of nuclear atypia after Mps1 inhibition in RPE1 cells to Fig. S1. Overall, we found that Mps1 inhibition significantly, but modestly, increased the proportion of misshapen nuclei and chromatin bridges. Multinucleate cells were so rare that instead of giving them their own category we included them in “misshapen nuclei.” These results are consistent with images of Msp1i treated RPE1 cells from He et al. 2019 and Santaguida et al. 2017 and distinct from the stronger changes in nuclear morphology observed after delaying mitosis by nocodazole or CENPE inhibition.

      We also found that the Deep Retina segmenter used to identify nuclei in VCS MN had a significant bias against highly lobulated nuclei (Fig. S2B) that led to misshapen nuclei being largely excluded from the RNAseq analyses. As a result we found no enrichment of misshapen nuclei, chromatin bridges, or dead/mitotic nuclear morphologies in MN+ compared to MN- nuclei in our RNASeq experiments (Fig. S9A).

      (2) As the authors clarify in the response letter, one round of ML is unlikely to result in fully robust software; additional rounds of ML with other markers will make the work robust. It will be useful to indicate other ML image analysis tools that have improved through such reiterations. They could use reviews on challenges and opportunities using ML approaches to support their statement. Also in the introduction, I would recommend labelling as 'rapid' instead of 'rapid and precise' method.

      We updated the text to reference review articles that discuss the benefit of additional training for increasing ML accuracy and changed the text to “rapid.”

      (3) The lack of live-cell studies does not allow the authors to distinguish the origin of MN (lagging chromatids or unaligned chromosomes). As explained in 1, considering these aspects in discussion would strengthen their interpretation. Live-cell studies can help reduce the dependencies on proximity maps (Figure S2).

      The revised text includes new references and data (Fig. S1E) demonstrating that Mps1 inhibition strongly biases towards whole chromosome missegregation and that MN are most likely to contain a single centromere positive chromosome rather than chromatin fragments or multiple chromosomes.

      (4) Mean Intersection over Union (mIOU) is a good measure to compare outcomes against ground truth. However, the mIOU is relatively low (Figure 2D) for HeLa-based functional genomics applications. It will help to discuss mIOU for other classifiers (non-MN classifiers) so that they can be used as a benchmark (this is important since the authors state in their response that they are the first to benchmark an MN classifier). There are publications for mitochondria, cell cortex, spindle, nuclei, etc. where IOU has been discussed.

      We added references to classifiers for other small cellular structures. We also evaluated major sources of error in MNFinder found that false negatives are enriched in very small MN (3 to 9 pixels, or about 0.4 µm<sup>2</sup> – 3 µm<sup>2</sup>, Fig. S6B). A similar result was obtained for VCS MN (Fig. S3B). Because small changes in the number of pixels identified in small objects can have outsized effects on mIoU scores, we suspect that this is exerting downward pressure on the mIoU value. Based on the PPV and recall values we identified, we believe that MNFinder is robust enough to use for functional genomics and screening applications with reasonable sample sizes.

      (5) Figure 5 figure legend title is an overinterpretation. MN and rupture-initiated transcriptional changes could not be isolated with this technique where several other missegregation phenotypes are buried (see point 1 above).

      We decided to keep the figure title legend based on our analysis of known missegregation phenotypes in Fig. S1 and S9 showing that there is no difference in major classes of nuclear atypia between MN+ and MN- populations in this analysis. Although we cannot rule out that other correlated changes exist, we believe that the title represents the most parsimonious interpretation.

      Minor comments

      (1) The sentence in the introduction needs clarification and reference. "However, these interventions cause diverse "off-target" nuclear and cellular changes, including chromatin bridges, aneuploidy, and DNA damage." Off-target may not be the correct description since inhibiting MPS1 is expected to cause a variety of problems based on its role as a master kinase in multiple steps of the chromosome segregation process. Consider one of the references in point 1 for a detailed live-cell view of MPS1 inhibitor outcomes.

      We have changed “off-target” to “additional” for clarity.

      (2) In Figure 3 or S3, did the authors notice any association between the cell cycle phase and MN or rupture presence? Is this possible to consider based on FACS outcomes or nuclear shapes?

      Previous work by our lab and others have shown that MN rupture frequency increases during the cell cycle (Hatch et al., 2013; Joo et al., 2023). Whether this is stochastic or regulated by the cell cycle may depend on what chromosome is in the MN (Mammel et al., 2021) and likely the cell line. Unfortunately, the H2B-emiRFP703 fluorescence in our population is too variable to identify cell cycle stage from FACS or nuclear fluorescence analysis.

      (3) Figure 5 - Please explain "MA plot".

      An MA plot, or log fold-change (M) versus average (A) gene expression, is a way to visualize differently expressed genes between two conditions in an RNASeq experiment and is used as an alternative to volcano plots. We chose them for our paper because most of the expression changes we observed were small and of similar significance and the MA plot spreads out the data compared to a volcano plot and allowed a better visualization of trends across the population.

      (4) Page 7: "our results strongly suggest that protein expression changes in MN+ and rupture+ cells are driven mainly by increased aneuploidy rather than cellular sensing of MN formation and rupture.". This is an overstatement considering the mIOU limits of the software tool and the non-exclusive nature of MN in their samples.

      We agree that we cannot rule out that an unknown masking effect is inhibiting our ability to observe small broad changes in transcription after MN formation or rupture. However, we believe we have minimized the most likely sources of masking effects, including nuclear atypia and large scale aneuploidy differences, and thus our interpretation is the most likely one.

      Reviewer #3 (Recommendations for the authors):

      Overall, the authors need to explain their methods better, define some technical terms used, and more thoroughly explain the parameters and rationale used when implementing these two protocols for identifying micronuclei; primarily as this is geared toward a more general audience that does not necessarily work with machine learning algorithms.

      (1) A clearer description in the methods as to how accuracy was calculated. Were micronuclei counted by hand or another method to assess accuracy?

      We significantly expanded the section on how the machine learning models were trained and tested, including how sensitivity and specificity metrics were calculated, in both the results and the methods sections. The code used to compare ground truth labels to computed masks is also now included in the MNFinder module available on the lab github page. 

      (2) Define positive predictive value.

      The text now says “the positive predictive value (PPV, the proportion of true positives, i.e. specificity) and recall (the proportion of MN found by the classifier, i.e. sensitivity)…”.

      (3) Why is it a problem to use the VCS MN at higher magnifications where undersegmentation occurs? What do the authors mean by diminished performance (what metrics are they using for this?).

      We have included a representative image and calculated mIoU and recall for 40x magnification images analyzed by MNFinder after rescaling in Fig. 2A. In summary, VCS MN only correctly labeled a few pixels in the MN, which was sufficient to call the adjacent nucleus “MN+” but not sufficient for other applications, such as quantifying MN area. In addition, VCS MN did much worse at identifying all the MN in 40x images with a recall, or sensitivity, metric of 0.36. We are not sure why. Developing MNFinder provided a module that was well suited to quantify MN characteristics in fixed cell images, an important use case in MN biology.

      (4) The authors should compare MN that are analyzed and not analyzed using these methods and define parameters. Is there a size limitation? Closeness to the main nucleus?

      We added two new figures defining what contributes to module error for both VCS MN (Fig. S3) and MNFinder (Fig. S6). For VCS MN, false negatives are enriched in very large or very small MN and tend to be dimmer and farther from the nucleus than true positives. False positives are largely misclassification of small dim objects in the image as MN. For MNFinder, the most missed class of MN are very small ones (3-9 px in area) and the majority of false positives are misclassifications of elongated nuclear blebs as MN.

      (5) Are there parameters in how confluent an image must be to correctly define that the micronucleus belongs to the correct cell? The authors discussed that this was calculated based on predicted distance. However, many factors might affect proper calling on MN. And the authors should test this by staining for a cytosolic marker and calculating accuracy.

      We updated the text with more information about how the cytoplasm was defined using leaky 2x-Dendra2-NLS signal to analyze the accuracy of MN/nucleus associations (Fig. S2G-H). In addition, we quantified cell confluency and distance to the first and second nearest neighbor for each MN in our training and testing image datasets. We found that, as anticipated, cells were imaged at subconfluent concentrations with most fields having a confluency around 30% cell coverage (Fig. S2E) and that the average difference in distance between the closest nucleus to an MN and the next closest nucleus was 3.3 fold (Fig. S2F). We edited the discussion section to state that the ability of MN/nuclear proximity to predict associations at high cell confluencies would have to be experimentally validated.

      (6) The authors measure the ratio of Dendra2(Red) v. Dendra2 (Green) in Figure 3B to demonstrate that photoconversion is stable. This measurement, to me, is confusing, as in the end, the authors need to show that they have a robust conversion signal and are able to isolate these data. The authors should directly demonstrate that the Red signal remains by analyzing the percent of the Red signal compared to time point 0 for individual cells.

      We found a bulk analysis to be more powerful than trying to reidentify individual cells due to how much RPE1 cells move during the 4 and 8 hours between image acquisitions. In addition, we sort on the ratio between red and green fluorescence per cell, rather than the absolute fluorescence, to compensate for variation in 2xDendra-NLS protein expression between cells. Therefore, demonstrating that distinct ratios remained present throughout the time course is the most relevant to the downstream analysis.

      To address the reviewer’s concern, we replotted the data in Fig. 3B to highlight changes over time in the raw levels of red and green Dendra fluorescence (Fig. S7D). As expected, we see an overall decrease in red fluorescence intensity, and complementary increase in green fluorescence intensity, over 8 hours, likely due to protein turnover. We also observe an increase in the number of nuclei lacking red fluorescence. This is expected since the well was only partially converted and we expect significant numbers of unconverted cells to move into the field between the first image and the 8 hour image.

      (7) The authors isolate and subsequently use RNA-sequencing to identify changes between Mps1i and DMSO-treated cells. One concern is that even with the less stringent cut-off of 1.5 fold there is a very small change between DMSO and MPS1i treated cells, with only 63 genes changing, none of which were affected above a 2-fold change. The authors should carefully address this, including why their dataset sees changes in many more pathways than in the He et al. and Santaguida et al. studies. Is this due to just having a decreased cut-off?

      The reviewer correctly points out that we observed an overall reduction in the strength of gene expression changes between our dataset of DMSO versus Mps1i treated RPE1 cells compared to similar studies. We suggest a couple reasons for this. One is that the log<sub>2</sub> fold changes observed in the other studies are not huge and vary between 2.5 and -3.8 for He et al., 3.3 and -2.3 for Santaguida et al., and -0.8 and 1.6 for our study. This variability is within a reasonable range for different experimental conditions and library prep protocols. A second is that our protocol minimizes a potential source of transcriptional change – nuclear lobulation – that is present in the other datasets.

      For the pathway analysis we did not use a fold-change cut-off for any data set, instead opting to include all the genes found to be significantly different between control and Mps1i treated cells for all three studies. Our read-depth was higher than that of the two published experiments, which could contribute to an increased DEG number. However, we hypothesize that our identification of a broader number of altered pathways most likely arises from increased sensitivity due to the loss of covering signal from transcriptional changes associated with increased nuclear atypia. Additional visual cell sorting experiments sorting on misshapen nuclei instead of MN would allow us to determine the accuracy of this hypothesis.

      (8) Moreover, clustering (in Figure 5E) of the replicates is a bit worrisome as the variances are large and therefore it is unclear if, with such large variance and low screening depth, one can really make such a strong conclusion that there are no changes. The authors should prove that their conclusion that rupture does not lead to large transcriptional changes, is not due to the limitations of their experimental design.

      We agree with the reviewers that additional rounds of RNAseq would improve the accuracy of our transcriptomic analysis and could uncover additional DEGs. However, we believe the overall conclusion to be correct based on the results of our attempt to validate changes in gene expression by immunofluorescence. We analyzed two of the most highly upregulated genes in the ruptured MN dataset, ATF3 and EGR1. Although we saw a statistically significant increase in ATF3 intensity between cells without MN and those with ruptured MN, the fold change was so small compared to our positive control (100x less) that we believe it is it is more consistent with a small increase in the probability of aneuploidy rather than a specific signature of MN rupture.

      (9) The authors also need to address the fact that they are using RPE-1 cells more clearly and that the lack of effect in transcriptional changes may be simply due to the loss of cGAS-STING pathway (Mackenzie et al., 2017; Harding et al., 2017; etc.).

      As we discuss above in the public comments section, the literature is clear that MN do not activate cGAS in the first cell cycle after their formation, even upon rupture. Therefore, we do not expect any changes in our results when applied to cGAS-competent cells. However, this expectation needs to be experimentally validated, which we plan to address in upcoming work.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      This useful manuscript reports mechanisms behind the increase in fecundity in response to sub-lethal doses of pesticides in the crop pest, the brown plant hopper. The authors hypothesize that the pesticide works by inducing the JH titer, which through the JH signaling pathway induces egg development. Evidence for this is, however, inadequate.

      We greatly appreciate your valuable comments and constructive suggestions for our work. All in all, the manuscript has been carefully edited and improved following your suggestions. We also provide more evidence to support our statements by conducting new experiments. First, we found that also EB treatment of adult females can stimulate egg-laying. Second, EB treatment in female adults increases the number of mature eggs in the ovary and ovarioles. Third, EB treatment in females enhances the expression of the kr-h1 gene in the whole body of BPH. Finally, EB treatment in female adults increases the JHIII titer, but has no impact on the 20E titer.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Gao et al. have demonstrated that the pesticide emamectin benzoate (EB) treatment of brown planthopper (BPH) leads to increased egg-laying in the insect, which is a common agricultural pest. The authors hypothesize that EB upregulates JH titer resulting in increased fecundity.

      Strengths:

      The finding that a class of pesticide increases the fecundity of brown planthopper is interesting.

      We greatly appreciate your positive comments on our work.

      Weaknesses:

      (1) EB is an allosteric modulator of GluCl. That means EB physically interacts with GluCl initiating a structural change in the cannel protein. Yet the authors' central hypothesis here is about how EB can upregulate the mRNA of GluCl. I do not know whether there is any evidence that an allosteric modulator can function as a transcriptional activator for the same receptor protein. The basic premise of the paper sounds counterintuitive. This is a structural problem and should be addressed by the authors by giving sufficient evidence about such demonstrated mechanisms before.

      Thank you for your question. As the reviewer points out, EB physically interacts with its target protein GluCl and thus affects its downstream signaling pathway. In the manuscript, we reported that EB-treated brown planthoppers display increased expression of GluCl in the adult stage (Fig. 5A). Actually, there are many studies showing that insects treated with insecticides can increase the expression of target genes. For example, the relative expression level of the ryanodine receptor gene of the rice stem borer, Chilo suppressalis was increased 10-fold after treatment with chlorantraniliprole, an insecticide which targets the ryanodine receptor (Peng et al., 2017). Besides this, in Drosophila, starvation (and low insulin) elevates the transcription level of the sNPF and tachykinin receptors (Ko et al., 2015; Root et al., 2011). In brown planthoppers, reduction in mRNA and protein expression of a nicotinic acetylcholine receptor α8 subunit is associated with resistance to imidacloprid (Zhang et al., 2015). RNA interference knockdown of α8 gene decreased the sensitivity of N. lugens to imidacloprid (Zhang et al., 2015). Hence, expression of receptor genes can be regulated by diverse factors including insecticide treatment. In our case, we found that EB can upregulate its target gene GluCl. However, we did not claim that EB functions as transcriptional activator for GluCl, and we still do not know why EB treatment changes the expression of GluCl in the brown planthopper. Considering our experiments are lasting several days, it might be an indirect (or secondary) effect caused by other factors, which change the expression of GluCl gene upon EB action of the channel. One reason is maybe that the allosteric interaction with GluCl by EB makes it dysfunctional and the cellular response is to upregulate the channel/receptor to compensate. We have inserted text on lines 738 - 757 to explain these possibilities.

      (2) I am surprised to see a 4th instar larval application or treatment with EB results in the upregulation of JH in the adult stages. Complicating the results further is the observation that a 4th instar EB application results in an immediate decrease in JH titer. There is a high possibility that this late JH titer increase is an indirect effect.

      Thank you for your question. Treatment with low doses or sublethal doses of insecticides might have a strong and complex impact on insects (Gandara et al., 2024; Gong et al., 2022; Li et al., 2023; Martelli et al., 2022). We kept the 4th instar of brown planthoppers feeding on EB for four days. They will develop to 5th instar after four days treatment, which is the final nymphal stage of BPH. Since the brown planthopper is a hemimetabolous insect, we cannot rule out the possibility that an indirect effect of treatment with EB results in the upregulation of JH in the adult stages. In this new revised manuscript, we investigated the impact of EB treatment in the adult stage. We found that female adults treated with EB also laid more eggs than controls (Figure 1-figure supplement 1A). The following experiments were performed in adults to address how EB treated stimulates egg-laying in adult brown planthopper.

      (1) We found that EB treatment in adults increases the number of mature eggs in ovary (new Figure 2-figure supplement 1). We add this results in lines 234 – 238 and 281-285.

      (2) We measured the JH titer after the female adults had been treated with EB. We found that EB can also increase the JH titer but has no impact on the 20E titer in the female adult (Figure 3-S3A and B). We add this results in lines 351 – 356 and 281-285.

      (3) EB treatment in adults increases the gene expression of JHAMT and Kr-h1 (Figure 3-S3C and D). We add this results in lines 378 – 379, lines 387-390 and lines 457-462.

      (3) The writing quality of the paper needs improvement. Particularly with respect to describing processes and abbreviations. In several instances the authors have not adequately described the processes they have introduced, thus confusing readers.

      Thank you for your suggestion. We have thoroughly revised the paper to improve clarity.

      (4) In the section 'EB promotes ovarian development' the authors have shown that EB treatment results in increased detention of eggs which contradicts their own results which show that EB promotes egg laying. Again, this is a serious contradiction that nullifies their hypothesis.

      Thank you for pointing this out. We revised the figure 2B to show number of mature eggs in the ovary. The number of mature eggs in ovaries of females that fed on EB was higher than in control females. We also show that BPH fed with EB laid more eggs than controls. Thus, our results suggest that EB promotes ovary maturation (and egg production) and also increases egg laying (Figure 1 and Table S1). Thus, we found that EB treatment can increase both the production of eggs and increase egg laying. We add this results in lines 234 – 238.

      (5) Furthermore, the results suggest that oogenesis is not affected by EB application. The authors should devote a section to discussing how they are observing increased egg numbers in EB-treated insects while not impacting Oogenesis.

      Thank you for your suggestions, and apologies for the lack of clarity in our initial explanation. First, we found that EB treatment led to an increase in the number of eggs laid by female brown planthoppers (Figure 1). Through dissection experiments, we observed that EB-treated females had more mature eggs in their ovaries (Figure 2A and B), indicating that the increased egg-laying was due to a larger production of mature eggs in the ovaries after EB treatment. This is now explained on lines 229-238.

      Additionally, since there is no systematic description of oogenesis in the brown planthopper, we were the first to observe the oogenesis process in this species using immunohistochemistry and laser confocal microscopy. Based on the developmental characteristics, we defined the different stages of oogenesis (Figure 2C, Figure 2-figure supplement 2). We did not observe any significant effect of EB treatment on the various stages of oogenesis, indicating that EB treatment does not impair normal egg development (Figure 2D). Instead, the increase in vitellogenin accelerates the production of mature eggs. This is now explained on lines 243-262.

      During the maturation process, eggs require uptake of vitellogenin, and an increase in vitellogenin (Vg) content can accelerate egg maturation, producing more mature eggs. Our molecular data suggest that EB treatment leads to an upregulation of vg expression. Based on these findings, we conclude that the increase in egg-laying caused by EB treatment is due to the upregulation of vg (Figure 3I), which raises vitellogenin content, promoting the uptake of vitellogenin by maturing eggs and resulting in the production of more mature eggs. We have revised the text on lines 389-395 to clarify this point.

      (6) Met is the receptor of JH and to my understanding, remains mostly constant in terms of its mRNA or protein levels throughout various developmental periods in many different insects. Therefore, the presence of JH becomes the major driving factor for physiological events and not the presence of the receptor Met. Here the authors have demonstrated an increase in Met mRNA as a result of EB treatment. Their central hypothesis is that EB increases JH titer to result in enhanced fecundity. JH action will not result in the activation of Met. Although not contradictory to the hypothesis, the increase in mRNA content of Met is contrary to the findings of the JH field thus far.

      Thank you for your comment. Our results showed that EB treatment can mildly increase (about 2-fold) expression of the Met gene in brown planthoppers (Figure 3G). And our data indicated that Met and FAMeT expression levels were not influenced so much by EB compared with kr-h1 and vg (Figure 3H and I). We agree that JH action will not result in the increase of Met. However, we cannot rule out the possibility of other factors (indirect effects), induced by EB treatment that increase the mRNA expression level of Met. One recent paper reported that downregulation of transcription factor CncC will increase met expression in beetles (see Figure 6A in this reference) (Jiang et al., 2023). Many studies have reported that insecticide treatment will activate the CncC gene signaling pathway, which regulates detoxification gene expression (Amezian et al., 2023; Fu et al., 2024; Hu et al., 2021). Hence, it is possible that EB might influence the CncC gene pathway which then induces met expression. This EB effect on met upregulation may be similar to the upregulation of GluCl and some other secondary effects. We have discussed this on lines 725-738.

      (7) As pointed out before, it is hard to rationalize how a 4th instar exposure to EB can result in the upregulation of key genes involved in JH synthesis at the adult stage. The authors must consider providing a plausible explanation and discussion in this regard.

      Thank you for your comments. It must be mentioned that although we exposed the BPH to EB at 4th instar, we make the insect feed on the EB-treated rice plants for four days. After that, the insect will develop into 5<sup>th</sup> instar, the final nymphal stage of brown planthopper. Since brown planthoppers do not have a pupal stage, this might cause the EB presented to the insects last a longer time even in the adult stage. Besides this, we found that EB treatment will increase the weight of adult females (Figure 1-figure supplement 3E and F), which indicates that EB might increase food intake in BPHs that might produce more insulin peptide. Insulin might increase the JH synthesis at the adult stage. In our revised study we also investigate EB impairment in adult BPHs. We found that, similar to the nymphal stage, EB treatment in adult BPHs also increases the egg laying. Furthermore, the JH titer was increased after treatment of BPH with EB in adults. Besides this, GluCl and kr-h1 genes were also up-regulated after EB treatment in the adult stage. We have discussed this on lines 739-746.

      (8) I have strong reservations against such an irrational hypothesis that Met (the receptor for JH) and JH-Met target gene Kr-h1 regulate JH titer (Line 311, Fig 3 supplemental 2D). This would be the first report of such an event on the JH field and therefore must be analysed in depth. I strongly suggest the authors remove such claims from the manuscript without substantiating it.

      Thank you for your suggestions and comments. We have changed our claims in this revised MS. We found that EB treatment can enhance Kr-h1 expression. We have no evidence to support that JH can induce met expression. We have rewritten the manuscript to avoid confusion (see text on lines 725-735).

      (9) Kr-h1 is JH/Met target gene. The authors demonstrate that silencing of Kr-h1 results in inhibition of FAMeT, which is a gene involved in JH synthesis. A feedback loop in JH synthesis is unreported. It is the view of this reviewer that the authors must go ahead with a mechanistic detail of Kr-h1 mediated JH upregulation before this can be concluded. Mere qPCR experiments are not sufficient to substantiate a claim that is completely contrary to the current understanding of the JH signalling pathway.

      Thank you for your suggestions and comments. We agree that only qPCR experiments are not enough to provide this kind of claim. More evidences need to be provided to support this. We have revised the MS to avoid confusion (see text on lines 725-735).

      (10) The authors have performed knockdowns of JHAMT, Met, and Kr-h1 to demonstrate the effect of these factors on fecundity in BPH. Additionally, they have performed rescue experiments with EB application on these knockdown insects (Figure 3K-M). This, I believe, is a very flawed experiment. The authors demonstrate EB works through JHAMT in upregulating JH titer. In the absence of JHAMT, EB application is not expected to rescue the phenotype. But the authors have reported a complete rescue here. In the absence of Met, the receptor of JH, either EB or JH is not expected to rescue the phenotype. But a complete rescue has been reported. These two experimental results contradict their own hypothesis.

      Thank you for your comments. We thought that this rescue is possible since knockdown of the genes is incomplete when using dsRNA injection (and residual gene expression allows for EB action). It is not a total knockout and actually, these genes still have a low level of expression in the dsRNA-injected insects. Since EB can upregulate the expression of JHAMT, Met, and Kr-h1, it is reasonable that EB treatment can rescue the down-regulation effects of these three genes and make fecundity completely rescued. We have clarified this on lines 411-413).

      (11) A significant section of the paper deals with how EB upregulates JH titer. JH is a hormone synthesized in the Corpora Allata. Yet the authors have chosen to use the whole body for all of their experiment. Changes in the whole body for mRNA of those enzymes involved in JH synthesis may not reflect the situation in Corpora Allata. Although working with Corpora Allata is challenging, discarding the abdomen and thorax region and working with the head and neck region of the insect is easily doable. Results from such sampling are always more convincing when it comes to JH synthesis studies.

      Thank you for your suggestions. Because the head is very difficult to separate from the thorax region in brown planthoppers as you can see in Author response image 1. We are now trying to answer how EB regulates JH synthesis using Drosophila as a model.

      Author response image 1.

      The brown planthopper

      (12) The phenomenon reported was specific to BPH and not found in other insects. This limits the implications of the study.

      Thank you for your comments. The brown planthopper is a serious insect pest on rice in Asia. Our findings can guide the use of this insecticide in the field. Besides this, our findings indicated that EB, which targets GluCl can impair the JH titer. Our findings added new implications for how a neuronal system influences the JH signaling pathway. We will further investigate how EB influences JH in the future and will use Drosophila as a model to study the molecular mechanisms.

      (13) Overall, the molecular experiments are very poorly designed and can at best be termed superficial. There are several contradictions within the paper and no discussion or explanation has been provided for that.

      Thank you for your comments. We have revised the paper according to your suggestions and added further explanation of our results in the discussion parts and hope the conclusions are better supported in the new version. We have discussed this on lines 725-746 and 778-799.

      Reviewer #2 (Public Review):

      The brown plant hopper (BPH) is a notorious crop pest and pesticides are the most widespread means of controlling its population. This manuscript shows that in response to sublethal doses of the pesticide (EB), BPH females show enhanced fecundity. This is in keeping with field reports of population resurgence post-pesticide treatment. The authors work out the mechanism behind this increase in fecundity. They show that in response to EB exposure, the expression of its target receptor, GluCl, increases. This, they show, results in an increase in the expression of genes that regulate the synthesis of juvenile hormone (JH) and JH itself, which, in turn, results in enhanced egg-production and egg-laying. Interestingly, these effects of EB exposure are species-specific, as the authors report that other species of plant hoppers either don't show enhanced fecundity or show reduced fecundity. As the authors point out, it is unclear how an increase in GluCl levels could result in increased JH regulatory genes.

      We greatly appreciate your valuable comments and constructive suggestion to our work. We will try to figure out how EB interacts with its molecular target GluCl and then increases JH regulatory genes in the future work using Drosophila as models.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Overall, the molecular experiments are very poorly designed and can at best be termed superficial. There are several contradictions within the paper and no discussion or explanation has been provided for that.

      The authors should consider a thorough revision.

      Thank you for your comments. We have thoroughly revised the paper according to your suggestions and added further experiments and explanations of our results in the discussion parts.

      Reviewer #2 (Recommendations For The Authors):

      It would help the reader to have more schematics along with the figures. The final figure is helpful, but knowing the JH pathway, and where it acts would help with the interpretations as one reads the manuscript and the figures. The pathways represented in 4N or 5J are helpful but could be improved upon for better presentation.

      It would be nice to have some discussion on how the authors think EB exposure results in an increase in GluCl expression, and how that in turn affects the expression of so many genes.

      Thank you for your comments. We have thoroughly revised the paper according to your suggestions and added further experiments and explanations of how we think EB exposure results in an increase in JH titer and other genes in the discussion parts. We have added the test on lines 753-761.

      References

      Amezian, D., Fricaux, T., de Sousa, G., Maiwald, F., Huditz, H.-I., Nauen, R., Le Goff, G., 2023. Investigating the role of the ROS/CncC signaling pathway in the response to xenobiotics in Spodoptera frugiperda using Sf9 cells. Pesticide Biochemistry and Physiology 195, 105563.

      Fu, B., Liang, J., Hu, J., Du, T., Tan, Q., He, C., Wei, X., Gong, P., Yang, J., Liu, S., Huang, M., Gui, L., Liu, K., Zhou, X., Nauen, R., Bass, C., Yang, X., Zhang, Y., 2024. GPCR–MAPK signaling pathways underpin fitness trade-offs in whitefly. Proceedings of the National Academy of Sciences 121, e2402407121.

      Gandara, L., Jacoby, R., Laurent, F., Spatuzzi, M., Vlachopoulos, N., Borst, N.O., Ekmen, G., Potel, C.M., Garrido-Rodriguez, M., Böhmert, A.L., Misunou, N., Bartmanski, B.J., Li, X.C., Kutra, D., Hériché, J.-K., Tischer, C., Zimmermann-Kogadeeva, M., Ingham, V.A., Savitski, M.M., Masson, J.-B., Zimmermann, M., Crocker, J., 2024. Pervasive sublethal effects of agrochemicals on insects at environmentally relevant concentrations. Science 386, 446-453.

      Gong, Y., Cheng, S., Desneux, N., Gao, X., Xiu, X., Wang, F., Hou, M., 2022. Transgenerational hormesis effects of nitenpyram on fitness and insecticide tolerance/resistance of Nilaparvata lugens. Journal of Pest Science.

      Hu, B., Huang, H., Hu, S., Ren, M., Wei, Q., Tian, X., Esmail Abdalla Elzaki, M., Bass, C., Su, J., Reddy Palli, S., 2021. Changes in both trans- and cis-regulatory elements mediate insecticide resistance in a lepidopteron pest, Spodoptera exigua. PLOS Genetics 17, e1009403.

      Jiang, H., Meng, X., Zhang, N., Ge, H., Wei, J., Qian, K., Zheng, Y., Park, Y., Reddy Palli, S., Wang, J., 2023. The pleiotropic AMPK–CncC signaling pathway regulates the trade-off between detoxification and reproduction. Proceedings of the National Academy of Sciences 120, e2214038120.

      Ko, K.I., Root, C.M., Lindsay, S.A., Zaninovich, O.A., Shepherd, A.K., Wasserman, S.A., Kim, S.M., Wang, J.W., 2015. Starvation promotes concerted modulation of appetitive olfactory behavior via parallel neuromodulatory circuits. eLife 4, e08298.

      Li, Z., Wang, Y., Qin, Q., Chen, L., Dang, X., Ma, Z., Zhou, Z., 2023. Imidacloprid disrupts larval molting regulation and nutrient energy metabolism, causing developmental delay in honey bee Apis mellifera. eLife

      Martelli, F., Hernandes, N.H., Zuo, Z., Wang, J., Wong, C.-O., Karagas, N.E., Roessner, U., Rupasinghe, T., Robin, C., Venkatachalam, K., Perry, T., Batterham, P., Bellen, H.J., 2022. Low doses of the organic insecticide spinosad trigger lysosomal defects, elevated ROS, lipid dysregulation, and neurodegeneration in flies. eLife 11, e73812.

      Peng, Y.C., Sheng, C.W., Casida, J.E., Zhao, C.Q., Han, Z.J., 2017. Ryanodine receptor genes of the rice stem borer, Chilo suppressalis: Molecular cloning, alternative splicing and expression profiling. Pestic. Biochem. Physiol. 135, 69-77.

      Root, Cory M., Ko, Kang I., Jafari, A., Wang, Jing W., 2011. Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. Cell 145, 133-144.

      Zhang, Y., Wang, X., Yang, B., Hu, Y., Huang, L., Bass, C., Liu, Z., 2015. Reduction in mRNA and protein expression of a nicotinic acetylcholine receptor α8 subunit is associated with resistance to imidacloprid in the brown planthopper, Nilaparvata lugens. Journal of Neurochemistry 135, 686-694.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary: The authors of this study sought to define a role for IgM in responses to house dust mites in the lung.

      Strengths:

      Unexpected observation about IgM biology

      Combination of experiments to elucidate function

      Weaknesses:

      Would love more connection to human disease

      We thank the reviewer for these comments. At the time of this publication, we have not made a concrete link with human disease. While there is some anecdotal evidence of diseases such as Autoimmune glomerulonephritis, Hashimoto’s thyroiditis, Bronchial polyp, SLE, Celiac disease and other diseases in people with low IgM. Allergic disorders are also common in people with IgM deficiency, other studies have reported as high as 33-47%. The mechanisms for the high incidence of allergic diseases are unclear as generally, these patients have normal IgG and IgE levels. IgM deficiency may represent a heterogeneous spectrum of genetic defects, which might explain the heterogeneous nature of disease presentations. 

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Hadebe and colleagues describes a striking reduction in airway hyperresponsiveness in Igm-deficient mice in response to HDM, OVA and papain across the B6 and BALB-c backgrounds. The authors suggest that the deficit is not due to improper type 2 immune responses, nor an aberrant B cell response, despite a lack of class switching in these mice. Through RNA-Seq approaches, the authors identify few differences between the lungs of WT and Igm-deficient mice, but see that two genes involved in actin regulation are greatly reduced in IgM-deficient mice. The authors target these genes by CRISPR-Cas9 in in vitro assays of smooth muscle cells to show that these may regulate cell contraction. While the study is conceptually interesting, there are a number of limitations, which stop us from drawing meaningful conclusions.

      Strengths:

      Fig. 1. The authors clearly show that IgMKO mice have striking reduced AHR in the HDM model, despite the presence of a good cellular B cell response.

      Weaknesses:

      Fig. 2. The authors characterize the cd4 t cell response to HDM in IGMKO mice.<br /> They have restimulated medLN cells with antiCD3 for 5 days to look for IL-4 and IL-13, and find no discernible difference between WT and KO mice. The absence of PBS-treated WT and KO mice in this analysis means it is unclear if HDM-challenged mice are showing IL-4 or IL-13 levels above that seen at baseline in this assay.

      We thank the Reviewer for this comment. We would like to mention that a very minimal level of IL-4 and IL-13 in PBS mice was detected. We have indicated with a dotted line on the Figure to show levels in unstimulated or naïve cytokines. Please see Author response image 1 below from anti-CD3 stimulated cytokine ELISA data. The levels of these cytokines are very low and are not changed between WT and IgM<sup>-/-</sup> mice, this is also true for PMA/ionomycin-stimulated cells.

      Author response image 1.

      The choice of 5 days is strange, given that the response the authors want to see is in already primed cells. A 1-2 day assay would have been better.

      We agree with the reviewer that a shorter stimulation period would work. Over the years we have settled for 5-day re-stimulation for both anti-CD3 and HDM. We have tried other time points, but we consistently get better secretion of cytokines after 5 days.

      It is concerning that the authors state that HDM restimulation did not induce cytokine production from medLN cells, since countless studies have shown that restimulation of medLN would induce IL-13, IL-5 and IL-10 production from medLN. This indicates that the sensitization and challenge model used by the authors is not working as it should.

      We thank the reviewer for this observation. In our recent paper showing how antigen load affects B cell function, we used very low levels of HDM to sensitise and challenge mice (1 ug and 3 ug respectively). See below article, Hadebe et al., 2021 JACI. This is because Labs that have used these low HDM levels also suggested that antigen load impacts B cell function, especially in their role in germinal centres. We believe the reason we see low or undetectable levels of cytokines is because of this low antigen load sensitisation and challenge. In other manuscripts we have published or about to publish, we have shown that normal HDM sensitisation load (1 ug or 100 ug) and challenge (10 ug) do induce cytokine release upon restimulation with HDM. See the below article by Khumalo et al, 2020 JCI Insight (Figure 4A).

      Sabelo Hadebe, Jermaine Khumalo, Sandisiwe Mangali, Nontobeko Mthembu, Hlumani Ndlovu, Amkele Ngomti, Martyna Scibiorek, Frank Kirstein, Frank Brombacher. Deletion of IL-4Ra signalling on B cells limits hyperresponsiveness depending on antigen load. doi.org/10.1016/j.jaci.2020.12.635).

      Jermaine Khumalo, Frank Kirstein, Sabelo Hadebe, Frank Brombacher. IL-4Rα signalling in regulatory T cells is required for dampening allergic airway inflammation through inhibition of IL-33 by type 2 innate lymphoid cells. JCI Insight. 2020 Oct 15;5(20):e136206. doi: 10.1172/jci.insight.136206

      The IL-13 staining shown in panel c is also not definitive. One should be able to optimize their assays to achieve a better level of staining, to my mind.

      We agree with the reviewer that much higher IL-13-producing CD4 T cells should be observed. We don’t think this is a technical glitch or non-optimal set-up as we see much higher levels of IL-13-producing CD4 T cells when using higher doses of HDM to sensitise and challenge, say between 7 -20% in WT mice (see Author response image 2, lung stimulated with PMA/ionomycin+Monensin, please note this is for illustration purposes only and it not linked to the current manuscript, its merely to demonstrate a point from other experiments we have conducted in the lab).

      Author response image 2.

      In d-f, the authors perform a serum transfer, but they only do this once. The half life of IgM is quite short. The authors should perform multiple naïve serum transfers to see if this is enough to induce FULL AHR.

      We thank the reviewer for this comment. We apologise if this was not clear enough on the Figure legend and method, we did transfer serum 3x, a day before sensitisation, on the day of sensitisation and a day before the challenge to circumvent the short life of IgM. In our subsequent experiments, we have now used busulfan to deplete all bone marrow in IgM-deficient mice and replace it with WT bone marrow and this method restores AHR (Figure 3).

      This now appears in line 165 to 169 and reads

      “Adoptive transfer of naïve serum

      Naïve wild-type mice were euthanised and blood was collected via cardiac puncture before being spun down (5500rpm, 10min, RT) to collect serum. Serum (200mL) was injected intraperitoneally into IgM-deficient mice. Serum was injected intraperitoneally at day -1, 0, and a day before the challenge with HDM (day 10).”

      The presence of negative values of total IgE in panel F would indicate some errors in calculation of serum IgE concentrations.

      We thank the reviewer for this observation. For better clarity, we have now indicated these values as undetected in Figure , as they were below our detection limit.

      Overall, it is hard to be convinced that IgM-deficiency does not lead to a reduction in Th2 inflammation, since the assays appear suboptimal.

      We disagree with the reviewer in this instance, because we have shown in 3 different models and in 2 different strains and 2 doses of HDM (high and low) that no matter what you do, Th2 remains intact. Our reason for choosing low dose HDM was based on our previous work and that of others, which showed that depending on antigen load, B cells can either be redundant or have functional roles. Since our interest was to tease out the role of B cells and specifically IgM, it was important that we look at a scenario where B cells are known to have a function (low antigen load). We did find similar findings at high dose of HDM load, but effects on AHR were not as strong, but Th2 was not changed, in fact in some instances Th2 was higher in IgM-deficient mice.

      Fig. 3. Gene expression differences between WT and KO mice in PBS and HDM challenged settings are shown. PCA analysis does not show clear differences between all four groups, but genes are certainly up and downregulated, in particular when comparing PBS to HDM challenged mice. In both PBS and HDM challenged settings, three genes stand out as being upregulated in WT v KO mice. these are Baiap2l1, erdr1 and Chil1.

      Noted

      Fig. 4. The authors attempt to quantify BAIAP2L1 in mouse lungs. It is difficult to know if the antibody used really detects the correct protein. A BAIAP2L1-KO is not used as a control for staining, and I am not sure if competitive assays for BAIAP2L1 can be set up. The flow data is not convincing. The immunohistochemistry shows BAIAP2L1 (in red) in many, many cells, essentially throughout the section. There is also no discernible difference between WT and KO mice, which one might have expected based on the RNA-Seq data. So, from my perspective, it is hard to say if/where this protein is located, and whether there truly exists a difference in expression between wt and ko mice.

      We thank the reviewer for this comment. We are certain that the antibody does detect BAIAP2L1, we have used it in 3 assays, which we admit may show varying specificities since it’s a Polyclonal antibody. However, in our western blot, the antibody detects 1 band at 56.7kDa and no other bands, apart from what we think are isoforms. We agree that BAIAP2L1 is expressed by many cell types, including CD45+ cells and alpha smooth muscle negative cells and we show this in our supplementary Figure 9. Where we think there is a difference in expression between WT and IgM-deficient mice is in alpha-smooth muscle-positive cells. We have tested antibodies from different companies, and we find similar findings. We do not have access to BAIAP2L1 KO mice and to test specificity, we have also used single stain controls with or without secondary antibody and isotype control which show no binding in western blot and Immunofluorescence assays and Fluorescence minus one antibody in Flow cytometry, so that way we are convinced that the signal we are seeing is specific to BAIAP2L1.

      Fig. 5 and 6. The authors use a single cell contractility assay to measure whether BAIAP2L1 and ERDR1 impact on bronchial smooth muscle cell contractility. I am not familiar with the assay, but it looks like an interesting way of analysing contractility at the single cell level.

      The authors state that targeting these two genes with Cas9gRNA reduces smooth muscle cell contractility, and the data presented for contractility supports this observation. However, the efficiency of Cas9-mediated deletion is very unclear. The authors present a PCR in supp fig 9c as evidence of gene deletion, but it is entirely unclear with what efficiency the gene has been deleted. One should use sequencing to confirm deletion. Moreover, if the antibody was truly working, one should be able to use the antibody used in Fig 4 to detect BAIAP2L1 levels in these cells. The authors do not appear to have tried this.

      We thank the reviewer for these observations. We are in a process to optimise this using new polyclonal BAIAP2L1 antibodies from other companies, since the one we have tried doesn’t seem to work well on human cells via western blot. So hopefully in our new version, we will be able to demonstrate this by immunofluorescence or western blot.

      Other impressions:

      The paper is lacking a link between the deficiency of IgM and the effects on smooth muscle cell contraction.

      The levels of IL-13 and TNF in lavage of WT and IGMKO mice could be analysed.

      We have measured Th2 cytokine IL-13 in BAL fluid and found no differences between IgM-deficient mice and WT mice challenged with HDM (Author response image 1). We could not detected TNF-alpha in the BAL fluid, it was below detection limit.

      Author response image 3.

      IL-13 levels are not changed in IgM-deficient mice in the lung. Bronchoalveolar lavage fluid in WT or IgM-deficient mice sensitised and challenged with HDM. TNF-a levels were below the detection limit.

      Moreover, what is the impact of IgM itself on smooth muscle cells? In the Fig. 7 schematic, are the authors proposing a direct role for IgM on smooth muscle cells? Does IgM in cell culture media induce contraction of SMC? This could be tested and would be interesting, to my mind.

      We thank the Reviewer for these comments. We are still trying to test this, unfortunately, we have experienced delays in getting reagents such as human IgM to South Africa. We hope that we will be able to add this in our subsequent versions of the article. We agree it is an interesting experiment to do even if not for this manuscript but for our general understanding of this interaction at least in an in vitro system.

      Reviewer #3 (Public Review):

      Summary:

      This paper by Sabelo et al. describes a new pathway by which lack of IgM in the mouse lowers bronchial hyperresponsiveness (BHR) in response to metacholine in several mouse models of allergic airway inflammation in Balb/c mice and C57/Bl6 mice. Strikingly, loss of IgM does not lead to less eosinophilic airway inflammation, Th2 cytokine production or mucus metaplasia, but to a selective loss of BHR. This occurs irrespective of the dose of allergen used. This was important to address since several prior models of HDM allergy have shown that the contribution of B cells to airway inflammation and BHR is dose dependent.

      After a description of the phenotype, the authors try to elucidate the mechanisms. There is no loss of B cells in these mice. However, there is a lack of class switching to IgE and IgG1, with a concomitant increase in IgD. Restoring immunoglobulins with transfer of naïve serum in IgM deficient mice leads to restoration of allergen-specific IgE and IgG1 responses, which is not really explained in the paper how this might work. There is also no restoration of IgM responses, and concomitantly, the phenotype of reduced BHR still holds when serum is given, leading authors to conclude that the mechanism is IgE and IgG1 independent. Wild type B cell transfer also does not restore IgM responses, due to lack of engraftment of the B cells. Next authors do whole lung RNA sequencing and pinpoint reduced BAIAP2L1 mRNA as the culprit of the phenotype of IgM<sup>-/-</sup> mice. However, this cannot be validated fully on protein levels and immunohistology since differences between WT and IgM KO are not statistically significant, and B cell and IgM restoration are impossible. The histology and flow cytometry seems to suggest that expression is mainly found in alpha smooth muscle positive cells, which could still be smooth muscle cells or myofibroblasts. Next therefore, the authors move to CRISPR knock down of BAIAP2L1 in a human smooth muscle cell line, and show that loss leads to less contraction of these cells in vitro in a microscopic FLECS assay, in which smooth muscle cells bind to elastomeric contractible surfaces.

      Strengths:

      (1) There is a strong reduction in BHR in IgM-deficient mice, without alterations in B cell number, disconnected from effects on eosinophilia or Th2 cytokine production

      (2) BAIAP2L1 has never been linked to asthma in mice or humans

      Weaknesses:

      (1) While the observations of reduced BHR in IgM deficient mice are strong, there is insufficient mechanistic underpinning on how loss of IgM could lead to reduced expression of BAIAP2L1. Since it is impossible to restore IgM levels by either serum or B cell transfer and since protein levels of BAIAP2L1 are not significantly reduced, there is a lack of a causal relationship that this is the explanation for the lack of BHR in IgM-deficient mice. The reader is unclear if there is a fundamental (maybe developmental) difference in non-hematopoietic cells in these IgM-deficient mice (which might have accumulated another genetic mutation over the years). In this regard, it would be important to know if littermates were newly generated, or historically bred along with the KO line.

      We thank the reviewer for asking this question and getting us to think of this in a different way. This prompted us to use a different method to try and restore IgM function and since our animal facility no longer allows irradiation, we opted for busulfan. We present this data as new data in Figure 3. We had to go back and breed this strain and then generated bone marrow chimeras. What we have shown now with chimeras is that if we can deplete bone marrow from IgM-deficient mice and replace it with congenic WT bone marrow when we allow these mice to rest for 2 months before challenge with HDM (new Supplementary Figure 6 a-c) We also show that AHR (resistance and elastance) is partially restored in this way (Figure 3 a and b) as mice that receive congenic WT bone marrow after chemical irradiation can mount AHR and those that receive IgM-deficient bone marrow, can’t mount AHR upon challenge with HDM. If the mice had accumulated an unknown genetic mutation in non-hematopoietic cells, the transfer of WT bone marrow would not make a difference. So, we don’t believe the colony could have gained a mutation that we are unaware of. We have also shipped these mice to other groups and in their hands, this strains still only behaves as an IgM only knockout mice. See their publication below.

      Mark Noviski, James L Mueller, Anne Satterthwaite, Lee Ann Garrett-Sinha, Frank Brombacher, Julie Zikherman 2018. IgM and IgD B cell receptors differentially respond to endogenous antigens and control B cell fate. eLife 2018;7:e35074. DOI: https://doi.org/10.7554/eLife.35074 we have also added methods for bone marrow chimaeras and added results sections and new Figures related to this methods.

      Methods (line 171-182).

      “Busulfan Bone marrow chimeras

      WT (CD45.2) and IgM<sup>-/-</sup> (CD45.2) congenic mice were treated with 25 mg/kg busulfan (Sigma-Aldrich, Aston Manor, South Africa) per day for 3 consecutive days (75 mg/kg in total) dissolved in 10% DMSO and Phosphate buffered saline (0.2mL, intraperitoneally) to ablate bone marrow cells. Twenty-four hours after last administration of busulfan, mice were injected intravenously with fresh bone marrow (10x10<sup>6</sup> cells, 100mL) isolated from hind leg femurs of either WT (CD45.1) or IgM<sup>-/-</sup> mice(33). Animals were then allowed to complement their haematopoietic cells for 8 weeks. In some experiments the level of bone marrow ablation was assessed 4 days post-busulfan treatment in mice that did not receive donor cells. At the end of experiment level of complemented cells were also assessed in WT and IgM<sup>-/-</sup> mice that received WT (CD45.1) bone marrow.”

      Results (line 491-521)

      “Replacement of IgM-deficient mice with functional hematopoietic cells in busulfan mice chimeric mice restores airway hyperresponsiveness.

      We then generated bone marrow chimeras by chemical radiation using busulfan(33). We treated mice three times with busulfan for 3 consecutive days and after 24 hrs transferred naïve bone marrow from congenic CD45.1 WT mice or CD45.2 IgM<sup>-/-</sup> mice (Fig. 3a and Supplementary Fig. 5a). We showed that recipient mice that did not receive donor bone marrow after 4 days post-treatment have significantly reduced lineage markers (CD45+Sca-1+) or lineage negative (Lin-) cells in the bone marrow when compared to untreated or vehicle (10% DMSO) treated mice (Supplementary Figure 5b-c). We allowed mice to reconstitute bone marrow for 8 weeks before sensitisation and challenge with low dose HDM (Figure 3a). We showed that WT (CD45.2) recipient mice that received WT (CD45.1) donor bone marrow had higher airway resistance and elastance and this was comparable to IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor WT (CD45.1) bone marrow (Figure 3b). As expected, IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor IgM<sup>-/-</sup> (CD45.2) bone marrow had significantly lower AHR compared to WT (CD45.2) or IgM<sup>-/-</sup> (CD45.2) recipient mice that received WT (CD45.1) bone marrow (Figure 3b). We confirmed that the differences observed were not due to differences in bone marrow reconstitution as we saw similar frequencies of CD45.1 cells within the lymphocyte populations in the lungs and other tissues (Supplementary Fig. 5d). We observed no significant changes in the lung neutrophils, eosinophils, inflammatory macrophages, CD4 T cells or B cells in WT or IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor WT (CD45.1/CD45.2) or IgM<sup>-/-</sup> (CD45.2) bone marrow when sensitised and challenged with low dose HDM (Fig. 3c)

      Restoring IgM function through adoptive reconstitution with congenic CD45.1 bone marrow in non-chemically irradiated recipient mice or sorted B cells into IgM<sup>-/-</sup> mice (Supplementary Fig.  6a) did not replenish IgM B cells to levels observed in WT mice and as a result did not restore AHR, total IgE and IgM in these mice (Supplementary Fig.  6b-c).”

      The 2 new figures are

      Figure 3 which moved the rest of the Figures down and Supplementary Figure 5, which also moved the rest of the supplementary figures down.

      Discussion appears in line 757-766 of the untracked version of the article.

      To resolve other endogenous factors that could have potentially influenced reduced AHR in IgM-deficient mice, we resorted to busulfan chemical irradiation to deplete bone marrow cells in IgM-deficient mice and replace bone marrow with WT bone marrow. While it is well accepted that busulfan chemical irradiation partially depletes bone marrow cells, in our case it was not possible to pursue other irradiation methods due to changes in ethical regulations and that fact that mice are slow to recover after gamma rays irradiation. Busulfan chemical irradiation allowed us to show that we could mostly restore AHR in IgM-deficient recipient mice that received donor WT bone marrow when challenged with low dose HDM.

      (2) There is no mention of the potential role of complement in activation of AHR, which might be altered in IgM-deficient mice 

      We thank the reviewer for this comment. We have not directly looked at complement in this instance, however, from our previous work on C3-/- mice, there have been comparable AHR to WT mice under the HDM challenge.

      (3) What is the contribution of elevated IgD in the phenotype of the IgM-deficient mice. It has been described by this group that IgD levels are clearly elevated

      We thank the reviewer for this question. We believe that IgD is essentially what drives partial class switching to IgG, we certainly have shown that in the case of VSV virus and Trypanosoma congolense and Trypanosoma brucei brucei that elevated IgD drive delayed but effective IgG in the absence of IgM (Lutz et al, 2001, Nature). This is also confirmed by Noviski studies where they show that both IgM and IgD do share some endogenous antigens, so its likely that external antigens can activate IgD in a similar manner to prompt class switching.

      (4) How can transfer of naïve serum in class switching deficient IgM KO mice lead to restoration of allergen specific IgE and IgG1?

      We thank the Reviewer for these comments, we believe that naïve sera transferred to IgM deficient mice is able to bind to the surface of B cells via IgM receptors (FcμR / Fcα/μR), which are still present on B cells and this is sufficient to facilitate class switching. Our IgM<sup>-/-</sup> mouse lacks both membrane-bound and secreted IgM, and transferred serum contains at least secreted IgM which can bind to surfaces via its Fc portion. We measured HDM-specific IgE and we found very low levels, but these were not different between WT and IgM<sup>-/-</sup> adoptively transferred with WT serum. We also detected HDM-specific IgG1 in IgM<sup>-/-</sup> transferred with WT sera to the same level as WT, confirming a possible class switching, of course, we can’t rule out that transferred sera also contains some IgG1. We also can’t rule out that elevated IgD levels can partially be responsible for class switched IgG1 as discussed above.

      In the discussion line 804-812, we also added the following

      “We speculate that IgM can directly activate smooth muscle cells by binding a number of its surface receptors including FcμR, Fcα/μR and pIgR(52-54). IgM binds to FcμR strictly, but shares Fcα/μR and pIgR with IgA(5,52,54). Both Fcα/μR and pIgR can be expressed by non-structural cells at mucosal sites(54,55). We would not rule out that the mechanisms of muscle contraction might be through one of these IgM receptors, especially the ones expressed on smooth muscle cells(54,55). Certainly, our future studies will be directed towards characterizing the mechanism by which IgM potentially activates the smooth muscle.”

      We have discussed this section under Discussion section, line 731 to 757. In addition, since we have now performed bone marrow chimaeras we have further added the following in our discussion in line 757-766.

      To resolve other endogenous factors that could have potentially influenced reduced AHR in IgM-deficient mice, we resorted to busulfan chemical irradiation to deplete bone marrow cells in IgM-deficient mice and replace bone marrow with WT bone marrow. While it is well accepted that busulfan chemical irradiation partially depletes bone marrow cells, in our case it was not possible to pursue other irradiation methods due to changes in ethical regulations and that fact that mice are slow to recover after gamma rays irradiation. Busulfan chemical irradiation allowed us to show that we could mostly restore AHR in IgM-deficient recipient mice that received donor WT bone marrow when challenged with low dose HDM.

      We removed the following lines, after performing bone marrow chimaeras since this changed some aspects.

      Our efforts to adoptively transfer wild-type bone marrow or sorted B cells into IgM-deficient mice were also largely unsuccessful partly due to poor engraftment of wild-type B cells into secondary lymphoid tissues. Natural secreted IgM is mainly produced by B1 cells in the peritoneal cavity, and it is likely that any transfer of B cells via bone marrow transfer would not be sufficient to restore soluble levels of IgM(3,10).

      (5) Alpha smooth muscle antigen is also expressed by myofibroblasts. This is insufficiently worked out. The histology mentions "expression in cells in close contact with smooth muscle". This needs more detail since it is a very vague term. Is it in smooth muscle or in myofibroblasts.

      Response: We appreciate that alpha-smooth muscle actin-positive cells are a small fraction in the lung and even within CD45 negative cells, but their contribution to airway hyperresponsiveness is major. We also concede that by immunofluorescence BAIAP2L1 seems to be expressed by cells adjacent to alpha-smooth muscle actin (Fig. 5b), however, we know that cells close to smooth muscle (such as extracellular matrix and myofibroblasts) contribute to its hypertrophy in allergic asthma.

      James AL, Elliot JG, Jones RL, Carroll ML, Mauad T, Bai TR, et al. Airway Smooth Muscle Hypertrophy and Hyperplasia in Asthma. Am J Respir Crit Care Med [Internet]. 2012;185:1058–64. Available from: https://doi.org/10.1164/rccm.201110-1849OC

      (6) Have polymorphisms in BAIAP2L1 ever been linked to human asthma?

      No, we have looked in asthma GWAS studies, at least summary statics and we have not seen any SNPs can could be associated with human asthma.

      (7) IgM deficient patients are at increased risk for asthma. This paper suggests the opposite. So the translational potential is unclear

      We thank the reviewer for these comments. At the time of this publication, we have not made a concrete link with human disease. While there is some anecdotal evidence of diseases such as Autoimmune glomerulonephritis, Hashimoto’s thyroiditis, Bronchial polyp, SLE, Celiac disease and other diseases in people with low IgM. Allergic disorders are also common in people with IgM deficiency as the reviewer correctly points out, other studies have reported as high as 33-47%. The mechanisms for the high incidence of allergic diseases are unclear as generally, these patients have normal or higher IgG and IgE levels. IgM deficiency may represent a heterogeneous spectrum of genetic defects, which might explain the heterogeneous nature of disease presentations.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that

      (1) blood glucose levels predict albumin gylcation rates

      (2) larger species have lower blood glucose levels

      (3) lifespan positively correlates with blood glucose levels and

      (4) diet predicts albumin glycation rates.

      The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

      While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

      (1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

      (2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

      (3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

      (4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

      Reviewer #2 (Public review):

      Summary

      In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

      Strengths

      This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly wellsupported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

      Weaknesses

      My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a loglog model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

      Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

      We apologize for missing an answer to this part before. Indeed, glucose is always log transformed and this is explained in the text.

      The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

      Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

      It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

      Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Line 84: "glycation scavengers" such as polyamines - can you specify what these polyamines do exactly?

      A clarification of what we mean with "glycation scavengers" is added.

      (2) Line 87-89: specify that the work of Wein et al. and this sentence is about birds.

      This is now clarified.

      (3) Line 95: "88 species" add "OF BIRDS". Also, I think it would be nice if you specified here that you are relying on primary data.

      This is now clarified (line 96).

      (4) Line 90-119: I find this paragraph very long and complex, with too many details on the methodology. For instance, I agree with listing your hypothesis, e.g. that with POL, but then what variables you use to measure the pace of life can go in the materials and methods section (so all lines between 112-119).

      This is explained here as a previous reviewer considered this presentation was indeed needed in the introduction.

      (5) Line 122-124: The first sentence should state that you collected blood samples from various sources, and list some examples: zoos? collaborators? designated wild captures? Stating the sample size before saying what you did to get them is a bit weird. Besides, you skipped a very important detail about how these samples were collected, when, where, and using what protocols. We know very well, that glucose levels can increase quickly with handling stress. Was this considered during the captures? Moreover, you state that you had 484 individuals, but how many samples in total? One per individual or more?

      We kindly ask the reviewer to read the multiple supplementary materials provided, in which the questions of source of the samples, potential stress effects and sample sizes for each model are addressed. All individuals contributed with one sample. More details about the general sources employed are given now in lines 125-127.

      (6) Line 135-36: numbers below 10 should be spelled out.

      Ok. Now that is changed.

      (7) Line 136: the first time I saw that you had both wild and captive samples. This should be among the first things to be described in the methods, as mentioned above.

      As stated above, details on this are included in the supplementary materials, but further clarifications have now been included in the main text (question 5).

      (8) Line 137-138: not clear. So you had 46 samples and 9 species. But what does the 3-3-3 sample mean? or for each species you chose 9 samples (no, cause that would be 81 samples in total)?

      This has now been clarified (lines 139-140).

      (9) Line 139-141: what methodological constraints? Too high glucose levels? Too little plasma?

      There were cases in which the device (glucometer) produced an unspecific error. This did not correspond to too high nor too low glucose levels, as these are differently signalled errors. Neither the manual nor the client service provided useful information to discern the cause. This may perhaps be related to the composition of the plasma of certain species, interfering with the measurement. Some clarifications have been added (lines 143-146).

      (10) Line 143: should be ZIMS.

      Corrected.

      (11) Line 120-148: you generally talk about individuals here, but I feel it would be more precise to use 'samples'.

      The use is totally interchangeable, as we never measured more than one sample for a given individual within this study. Besides, in some cases, saying “sample” could result less informative.

      (12) Line 150: missing the final number of measurements for glucose and glycation.

      Please, read the ESM6 (Table ESM6.1), where this information is given.

      (13) Line 154-155: so you took multiple samples from the same individual? It's the first time the text indicates so. Or do you mean technical replicates were not performed on the same samples?

      As previously indicated, each individual included only one sample. Replicates were done only for some individuals to validate the technique, as it would be unfeasible to perform replicates of all of them. This part of the text is referring to the fact that not all samples were analysed at the same time, as it takes a considerable amount of time, and the mass spectrometry devices are shared by other teams and project. Clarifications in this sense are now added (lines 160-163).

      (14) Line 171-172: "After realizing that diet classifications from AVONET were not always suitable for our purpose" - too informal. Try rephrasing, like "After determining that AVONET diet classifications did not align with our research needs...", but you still need to specify what was wrong with it and what was changed, based on what argument?

      The new formulation suggested by the reviewer has now been applied (lines 181-183). The details are given in the ESM6, as indicated in the text. 

      (15) Line 174-176: You start a new paragraph, talking about missing values, but you do not specify what variable are you talking about. you talk about calculating means, but the last variable you mentioned was diet, so it's even more strange.

      We refer to life history traits. It has now been clarified in the text (line 185).

      (16) Line 177: what longevity records? Coming from where? How did you measure longevity? Maximum lifespan ever recorded? 80-90% longevity, life expectancy???

      We refer to maximum lifespan, as indicated in the introduction and in every other case throughout the manuscript. Clarifications have now been introduced (188-190).

      (17) Line 180-183: using ZIMS can be problematic, especially for maximum longevity. There are often individuals who had a wrong date of birth entered or individuals that were failed to be registered as dead. The extremes in this database are often way off. If you want to combine though, you can check the correlation of lifespans obtained from different sources for the overlapping species. If it's a strong correlation it can be ok, but intuitively this is problematic.

      The species for which we used ZIMS were those for which no other databases reported any values. We could try correlations for other species, but this issue is not necessarily restricted to ZIMS, as the primary origin of the data from other databases is often difficultly traceable. Also, ZIMS is potentially more updated that some of the other databases, mainly Amniotes database, from which we rely the most, as it includes the highest number of species in the most easily accessible format.

      (18) Line 181-186: in ZIMS you calculate the average of the competing records, otherwise you choose the max. Why use different preferences for the same data?

      This constitutes a misunderstanding, for which we include clarifications now (line 196). We were referring here to the fact that for maximum lifespan the maximum is always chosen, while for other variables an average is calculated. 

      (19) Line 198: Burn-in and thinning interval is quite low compared to your number of iterations. How were model convergences checked?

      Please, check ESM1.

      (20) Line 201-203: What's the argument using these priors? Why not use noninformative ones? Do you have some a priori expectations? If so, it should be explained.

      Models have now been rerun with no expectations on the variance partitions so the priors are less informative, given the lack of firm expectations, and results are similar. Smaller nu values are also tried.

      (21) Line 217: "carried" OUT.

      Corrected (now in line 229).

      (22) Line 233-234: "species average model" - what is this? it was not described in the methods.

      Please, read the ESM6.

      (23) Line 232-246: (a) all this would be better described by a table or plot. You can highlight some interesting patterns, but describing it all in the text is not very useful I think, (b) statistically comparing orders represented by a single species is a bit odd.

      (a) Figure 1 shows this graphically, but this part was found to be quite short without descriptions by previous reviewers. (b) We recognise this limitation, but this part is not presented as one of the main results of the article, and just constitutes an attempt to illustrate very general patterns, in order to guide future research, as in most groups glycation has never been measured, so this still constitutes the best illustration of such patterns in the literature.

      (24) Line 281: the first time I saw "mass-adjusted maximum lifespan" - what is this, and how was it calculated? It should be described in the methods. But in any case, neither ratios, nor residuals should be used, but preferably the two variables should be entered side by side in the model.

      Please, see ESM6 for the explanations and justifications for all of this.

      (25) Line 281: there was also no mention of quadratic terms so far. How were polynomial effects tested/introduced in the models? Orthogonal polynomials? or x+ x^2?

      Please, read ESM6.

      (26) Table 1. What is 'Centred Log10Body mass', should be added in the methods.

      Please, read ESM6.

      (27) Table 1: what's the argument behind separating terrestrial and aquatic carnivores?

      This was mostly based on the a priori separation made in AVONET, but it is also used in a similar way by Szarka and Lendvai 2024 (comparative study on glucose in birds), where differences in glucose levels between piscivorous and carnivorous are reported. We had some reasons to think that certain differences in dietary nutrient composition, as discussed later, can make this difference relevant.

      (28) Table 1: The variable "Maximum lifespan" is discussed and plotted as 'massadjusted maximum lifespan' and 'residual maximum lifespan'. First, this is confusing, the same name should be used throughout and it should be defined in the methods section. Second, it seems that non-linear effects were tested by using x + x^2. This is problematic statistically, orthogonal polynomials should be used instead (check polyfunction in R). Also, how did you decide to test for non-linear effects in the case of lifespan but not the other continuous predictors? Should be described in the methods again.

      Please, read ESM6. Data exploration was performed prior to carry out these models. Orthogonal polynomials were considered to difficult the interpretation of the estimates and therefore the patterns predicted by the models, so raw polynomials were used. Clarifications have now been included in line 297.

      (29) Figure 2. From the figure label, now I see that relative lifespan is in fact residual. This is problematic, see Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of evolutionary biology, 22(7), 1367-1375. Using body mass and lifespan side by side is preferred. This would also avoid forcing more emphasis on body mass over lifespan meaning that you subjectively introduce body mass as a key predictor, but lifespan and body size are highly correlated, so by this, you remove a large portion of variance that might in fact be better explained by lifespan.

      Please, read ESM6 for justifications on the use of residuals.

      Reviewer #2 (Recommendations for the authors):

      (1) If the semi-logarithmic relationship (glycation ~ log10(glucose)) is to be used to support the hypothesis about higher glycation resistance in species with high blood glucose (lines 318-321 and 386-388), it should be tested whether it is significantly better than the model assuming a simple linear relationship (i.e., glycation ~ glucose). Alternatively, if the coefficient is to be used to determine whether glycation rate slows down or accelerates with increasing glucose levels, log-log model (log10(glycation) ~ log10(glucose)) assuming power function relationship (glycation = a * glucose^b) should be used (as is for example in the literature about relationships between metabolic rates and body size). Probably the best approach would be to compare all three models (linear, semi-logarithmic, and log-log) and test if one performs significantly better. If none of them, then the linear model should be selected as the most parsimonious.

      Different options (linear, both semi-logarithmic combinations and log-log) have now been tested, with similar results. All of the models confirm the pattern of a significant positive relationship between glucose and glycation. Moreover, when standardizing the variables (both glucose and glycation, either log transformed or not), the estimate of the slope is almost equal for all the models. It is also lower than one, which in the case of both the linear and log-log confirms the stated prediction. The log-log model, showing a much lower DIC than the linear version, is now shown as the final model.

      (2) ESM6, line 46: Please note that Kruskal-Wallis rank sum test in ESM1 shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values (not only higher variation). With this in mind, what is the argument here about increased variation being the main driver of stress-induced change in glucose levels based on? It seems that both the median values and variation differ between baseline and stress-induced levels, and this should be acknowledged here.

      As discussed in the public answers, Kruskal Wallis does not allow to determine differences in mean, but just says that the groups are “different” (implicitly, in their ranksums, which does not mean necessarily in mean), while the Levene test performed signals heteroskedasticity. This makes this feature of the data analytically more grounded. Of course, when looking at the data, a higher mean can be perceived, but nothing can be said about its statistical significance. Still, some subtle changes have been introduced in corresponding section of the ESM6.

      (3) Have you recorded the sampling times? If yes, why not control them in the models? It is at least highly advisable to include the sampling times in the data (ESM5).

      As indicated in ESM6 lines 42-43, we do not have sampling times for most of the individuals (only zebra finches and swifts), so this cannot be accounted for in the models.

      (4) If sampling times will remain uncontrolled statistically, I recommend mentioning this fact and its potential consequences (i.e., rather conservative results) in the Methods section of the main text, not only in ESM6.

      A brief description of this has now been included in the main text (lines 129-132), referencing the more detailed discussion on the supplementary materials. Some subtle changes have also been included in the “Possible effects of stress” section of the ESM6.

      (5) ESM6, lines 52-53: The lower repeatability in Tomasek et al.' study compared to your study is irrelevant to the argument about the conservative nature of your results (the difference in repeatability between both studies is most probably due to the broader taxonomic coverage of the current study). The important result in this context is that repeatability is lower when sampling time is not considered within Tomasek et al's data set (ESM1). Therefore, I suggest rewording "showing a lower species repeatability than that from our data" to "showing lower species repeatability when sampling time is not considered" to avoid confusion. Please also note that you refer here to species repeatability but, in ESM1, you calculate individual repeatability. Nevertheless, both individual and species repeatabilities are lower when not controlling for sampling time because the main driver, in that case, is an increased residual variance.

      We recognize the current confusion in the way the explanation is exposed, and have significantly changed the redaction of the section. However, we would like to indicate that ESM1 shows both species and individual repeatability (for Tomasek et al. 2022 data, for ours only species as we do not have repeated individual values). Changes are now made to make it more evident.

      (6) I recommend providing brief guidelines for the interpretation of VIFs to the readers, as well as a brief discussion of the obtained values and their potential importance.

      Thank you for the recommendation. We included a brief description in lines 230-231. Also in the results section (lines 389-393).

      (7) Line: 264: Please note that the variance explained by phylogeny obtained from the models with other (fixed) predictors does not relate to the traits (glucose or glycation) per se but to model residuals.

      We appreciate the indication, and this has been rephrased accordingly (lines 280-286).

      (8) Change the term "confidence intervals" to "credible intervals" throughout the paper, since confidence interval is a frequentist term and its interpretations are different from Bayesian credible interval.

      Thank you for the remark, this has now been changed.

      (9) Besides lifespan, have you also considered quadratic terms for body mass? The plot in Figure 2A suggests there might be a non-linear relationship too.

      A quadratic component of body mass has not shown any significant effect on glucose in an alternative model. Also, a model with linear instead of log glucose (as performed in other studies) did not perform better by comparing the DICs, despite both showing a significant relationship between glucose and body mass. Therefore, this model remains the best option considered as presented in the manuscript.

      (10) ESM6, lines 115-116: It is usually recommended that only factors with at least 6 or 8 levels are included as random effects because a lower number of levels is insufficient for a good estimation of variance.

      In a Bayesian approach this does not apply, as random and fixed factors are estimated similarly. 

      (11) Typos and other minor issues:

      a) Line 66: Delete "related".

      b) Figure 2: "B" label is missing in the plot.

      c) Reference 9: Delete "Author".

      d) References 15 and 83 are duplicated. Keep only ref. 83, which has the correct citation details.

      e) ESM6, line 49: Change "GLLM" to "GLMM".

      Thank you for indicating this. Now it’s corrected.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Response to Reviewer 2’s comments:

      I am concerned that the results in Figure 8D may not be correct, or that the authors may be mis-interpreting them. From my reading of the paper they cite (Lammers & Flamholz 2023), the equilibrium sharpness limit for the network they consider in Figure 8 should be 0.25. But both solutions shown in Figure 8D fall below this limit, which means that they have sharpness levels that could have been achieved with no energy expenditure. If this is the case, then it would imply that while both systems do dissipate energy, they are not doing so productively; meaning that the same results could be achieved while holding Phi=0.

      I acknowledge that this could be due to a difference in how they measure sharpness, but wanted to raise it here in case it is, in fact, a genuine issue with the analysis.There should be an easy fix for this: just set the sharper "desired response" curve in 8b to be such that it demands non-equilibrium sharpness levels (0.25<S<0.5).

      Thank you for raising this point regarding the interpretation of our results in Figure 8D. We agree that if the equilibrium sharpness limit for this particular network is around 0.25 (as shown by Lammers & Flamholz 2023), then achieving a sharpness below this threshold could, in principle, be accomplished without any energy expenditure. However, in our current design approach, the loss function is solely designed to enforce agreement with a target mean mRNA level at different input concentrations; it does not explicitly constrain energy dissipation, noise, or other metrics. Consequently, the DGA has no built-in incentive to minimize or optimize energy consumption, which means the resulting solutions may dissipate energy without exceeding the equilibrium sharpness limit.

      In other words, the same input–output relationship could theoretically be achieved with \Phi =0 if an explicit constraint or regularization term penalizing energy usage had been included. As noted, adding such a term (e.g., penalizing \Phi^2) is conceptually straightforward but falls outside the scope of this study. Our primary goal is to demonstrate the flexibility of the DGA in designing a desired response, rather than to delve into energy–sharpness trade-offs or other biological considerations

      While we appreciate the suggestion to set a higher target sharpness that exceeds the equilibrium limit, we believe the current example effectively demonstrates the DGA’s ability to design circuits with desired input-output relationships, which is the primary focus of this study. Researchers interested in optimizing energy efficiency, burst size, burst frequency, noise, response time, mutual information, or other system properties can easily extend our approach by incorporating additional terms into the loss function to target these specific objectives.

      We hope this explanation addresses your concern and clarifies that the manuscript provides sufficient context for readers to interpret the results in Figure 8D correctly.


      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      We thank Reviewer #1 for their thoughtful feedback and appreciation of the manuscript's clarity. Our primary goal is to introduce the DGA  as a foundational tool for integrating stochastic simulations with gradient-based optimization. While we recognize the value of providing detailed comparisons with existing methods and a deeper analysis of the DGA’s limitations (such as rare event handling), these topics are beyond the scope of this initial work. Our focus is on presenting the core concept and demonstrating its potential, leaving more extensive evaluations for future research.

      Reviewer #2 (Public review):

      We thank Reviewer #2 for their detailed and constructive feedback. We appreciate the recognition of the DGA as a significant conceptual advancement for stochastic biochemical network analysis and design.

      Weaknesses:

      (1) Validation of DGA robustness in complex systems:

      Our primary goal is to introduce the DGA framework and demonstrate its feasibility. While validation on high-dimensional and non-steady-state systems is important, it is beyond the scope of this initial work. Future studies may improve scalability by employing techniques such as dynamically adjusting the smoothness of the DGA's approximations during simulation or using surrogate models that remain differentiable but more accurately capture discrete behaviors in critical regions, thus preserving gradient computation while improving accuracy.

      (2) Inference accuracy and optimization:

      We acknowledge that the non-convex loss landscape in the DGA can hinder parameter inference and convergence to global minima, as seen in Figure 5A. While techniques like multi-start optimization or second-order methods (e.g., L-BFGS) could improve performance, our focus here is on establishing the DGA framework. We plan to explore better optimization methods in future work to improve the accuracy of parameter inference in complex systems.

      (3) Use of simple models for demonstration:

      We selected well-understood systems to clearly illustrate the capabilities of the DGA. These examples were intended to demonstrate how the DGA can be applied, rather than to solve problems better addressed by analytical methods. Applying DGA to more complex, analytically intractable systems is an exciting avenue for future work, but introducing the method was our main objective in this study.

      Reviewer #3 (Public review):

      We thank the reviewer for their detailed and insightful feedback. We appreciate the recognition of the DGA as a significant advancement for enabling gradient-based optimization in stochastic systems.

      Weaknesses:

      (1) Application beyond steady-state analysis

      We acknowledge the limitation of focusing solely on steady-state properties. To extend the DGA for analyzing transient dynamics, time-dependent loss functions can be incorporated to capture system evolution over time. This could involve aligning simulated trajectories with experimental time-series data or using moment-matching across multiple time points. 

      (2) Numerical instability in gradient computation

      The reviewer correctly highlights that large sharpness parameters (a and b) in the sigmoid and Gaussian approximations can induce numerical instability due to vanishing or exploding gradients. To address this, adaptive tuning of a and b during optimization could balance smoothness and accuracy. Additionally, alternative smoothing functions (e.g., softmax-based reaction selection) and gradient regularization techniques (such as gradient clipping and trust-region methods) could improve stability and convergence.

      Reviewer #1 (recommendations):

      We thank the reviewer for their thoughtful and constructive feedback on our manuscript. Below, we address each of the comments and suggestions raised.

      Main points:

      (1) It would have been useful to have a brief discussion, based on a concrete example, of what can be achieved with the DGA and is totally beyond the reach of the Gillespie algorithm and the numerous existing stochastic simulation methods.

      Thank you for your comment. We would like to clarify that the primary aim of this work is to introduce the DGA and demonstrate its feasibility for tasks such as parameter estimation and network design. Unlike traditional stochastic simulation methods, the DGA’s differentiable nature enables gradient-based optimization, which is not possible with the classical Gillespie algorithm or its variants.

      (2) As often with machine learning techniques, there is a sense of black box, with a lack of mathematical details of the proposed method: as opposite to the exact Gillespie algorithm, whose foundations lie on solid mathematical results (exponentially-distributed waiting times of continuous-time Markov processes), the DGA involves uncontrolled approximations, that are only briefly mentioned in the paper. For instance, it is currently simply noted that "the approximations introduced by the DGA may be pronounced in more complex settings such as the calculation of rare events", without specifying how limiting these errors are. It would be useful to include a clearer and more comprehensive discussion of the limitations of the DGA: When does it work accurately? What are the approximations/errors and can they be controlled? When is it worth paying the price for those approximations/errors, and when is it better to stick to the Gillespie algorithm? Is this notably the case for problems involving rare events? Clearly, these are difficult questions, and the answers are problem specific. However, it would be important to draw the readers' attention on the issues, especially if the DGA is presented as a potentially significant tool in computational and synthetic biology.

      We acknowledge the importance of discussing the limitations of the DGA in more detail. While we have noted that the approximations introduced by the DGA may impact its accuracy in certain scenarios, such as rare-event problems, a deeper exploration of these trade-offs is outside the scope of this work. Instead, we provide sufficient context in the manuscript to guide readers on when the DGA is appropriate.

      (3) The DGA is here introduced and discussed in the context of non-spatial problems (simple gene regulatory networks). However, numerous problems in the life sciences and computational/synthetic biology, involve stochasticity and spatial degrees of freedom (e.g. for problems involving diffusion, migration, etc). It is notoriously challenging to use the Gillespie algorithm to efficiently simulate stochastic spatial systems, especially in the context of rare events (e.g., extinction or fixation problems). It would be useful to comment on whether, and possibly how, the DGA can be used to efficiently simulate stochastic spatial systems, and if it would be better suited than the Gillespie algorithm for this purpose.

      Thank you for pointing this out. Although our current work centers on non-spatial systems, we agree that many biological contexts incorporate both stochasticity and spatial degrees of freedom. Extending the DGA to efficiently simulate such systems would indeed require substantial modifications—for instance, coupling it with reaction-diffusion frameworks or spatial master equations. We believe this is an exciting direction for future research and mention it briefly in the discussion as a potential extension.

      Minor suggestions:

      (1) After Eq.(10): it would be useful to explain and motivate the choice of the ratio JSD/H.

      Done.

      (2) On page 6, just below the caption of Fig.4: it would be useful to clarify what is actually meant by "... convergence towards the steady-state distribution of the exact Gillespie simulation, which is obtained at a simulation time of 10^4".

      Done.

      (3) At the end of Section B on page 7: please clarify what is meant here by "soft directions".

      Done.

      Reviewer #2 (recommendations):

      We thank the reviewer for their thoughtful comments and constructive feedback. Below, we address each of the comments/suggestions.

      Main points:

      (1) Enumerate the conditions under which DGA assumptions hold (and when they do not). There is currently not enough information for the interested reader to know whether DGA would work for their system of interest. Without this information, it is difficult to assess what the true scope of DGA's impact will be. One simple idea would be to test DGA performance along two axes: (i) increasing number of model states and (ii) presence/absence of non-steady state dynamics. I acknowledge that these are very open-ended directions, but looking at even a single instance of each would greatly strengthen this work. Alternatively, if this is not feasible, then the authors should provide more discussion of the attendant difficulties in the main text.

      We agree that a detailed exploration of the conditions under which the DGA assumptions hold would be a valuable addition to the field. However, this paper primarily aims to introduce the DGA methodology and demonstrate its proof-of-concept applications. A comprehensive analysis along axes such as increasing model states or non-steady-state dynamics, while important, would require significant additional simulations and is beyond the scope of this work. In Appendix A, we have discussed the trade-off between accuracy and numerical stability. Additionally, we encourage future users to tune the hyperparameters a and b for their specific systems.

      (2) Demonstrate DGA performance in a more complex biochemical system. Clearly the authors were aware that analytic solutions exist for the 2-state system in Figure 7, but it this is actually also the case (I think) for mean mRNA production rate of the non-equilibrium system in Figure 8. To really demonstrate that DGA is practically viable, I encourage the authors to seek out an interesting application that is not analytically tractable.

      We appreciate the suggestion to validate DGA on a more complex biochemical system. However, the goal of this study is not to provide an exhaustive demonstration of all possible applications but to introduce the DGA and validate it in systems where ground-truth comparisons are available. While the non-equilibrium system in Figure 8 might be analytically tractable, its complexity already provides a meaningful demonstration of DGA’s ability to optimize parameters and design systems. Extending this work to analytically intractable systems is an exciting direction for future studies, and we hope this paper will inspire others to explore these applications.

      (3) Take steps to improve the robustness of parameter optimization and error bar calculations. (3a) When the loss landscape is degenerate, shallow, or otherwise "difficult," a common solution is to perform multiple (e.g. 25-100) inference runs starting from different random positions in parameter space. Doing this, and then taking the parameter set that minimizes the loss should, in theory, lead to a more robust recovery of the optimal parameter set.

      (3b) It seems clear that the Hessian approximation is underestimating the true error in your inference results. One alternative is to use a "brute force" approach like bootstrap resampling to get a better estimate for the statistical dispersion in parameter estimates. But I recognize that this is only viable if the inference is relatively fast. Simply recovering the true minimum will, of course, also help.

      (3a) We acknowledge the challenge posed by degenerate or shallow loss landscapes during parameter optimization. While performing multiple inference runs from different initializations is a common strategy, this approach is computationally intensive. Instead, we rely on standard optimization techniques (e.g., ADAM) to find a robust local minimum. 

      (3b) Thank you for your comment. We agree that Hessian-based error bars can underestimate uncertainty, particularly in degenerate or poorly conditioned loss landscapes. While methods like bootstrap and Monte Carlo can provide more robust estimates, they can be computationally prohibitive for larger-scale simulations. A simpler reason for not using them is the high resource demand from repeated simulations, which quickly becomes infeasible for complex or high-dimensional models. We note these trade-offs between robust estimation and practicality as an important area for further exploration.

      Moderate comments:

      (1) Figure 7: is it possible to also show the inferred kon values? Specifically, it would be of interest to see how kon varies with repressor concentration.

      Thank you for the suggestion. We have updated Figure 7 to include the inferred kon values, showing their variation with the mean mRNA copy number. However, we could not plot them against repressor concentration due to the lack of available data.

      (2) Figure 8B & D: the authors claim that the sharper system dissipates more energy, but doesn't 8D show the opposite of this? More importantly, it does not look like either network drives sharpness levels that exceed the upper equilibrium limit cited in [36]. So it is not clear that it is appropriate to look at energy dissipation here. In fact, it is likely that equilibrium networks could produce the curves in 8B, and might be worth checking.

      Thank you for pointing this out. We realized that the plotted values in Figure 8D were incorrect, as we had mistakenly plotted noise instead of energy dissipation. The plot has now been corrected. 

      (3) Figure 8: I really like this idea of using DGA to "design" networks with desired input-output properties, but I wonder if you could explore more a biologically compelling use-case. Specifically, what about some kind of switch-like logic where, as the activator concentration increases, you have first 0 genes on, then 1 promoter on, then 2 promoters on. This would achieve interesting regulatory logic, and having DGA try to produce step functions would ensure that you force the networks to be maximally sharp (i.e. about double what you're currently achieving).

      Thank you for this intriguing suggestion. While the proposed switch-like logic use case is indeed compelling, implementing such a system would require significant work. This goes beyond the scope of the current study, which focuses on demonstrating the feasibility of DGA for network design with simple input-output properties.

      Minor comments:

      (1) Figure 4B & C: the bar plots do not do a good job conveying the points made by the authors. Consider alternatives, such as scatter plots or box plots that could convey inference uncertainty.

      Done.

      (2) Figure 4B: consider using a log y-axis.

      The y-axis in Figure 4B is already plotted on a log scale.

      (3) Figure 4D is mentioned prior to 4C in the text. Consider reordering.

      Done. 

      (4) Figure 5B: it is difficult to assess from this plot whether or not the landscape is truly "flat," as the authors claim. Flat relative to what? Consider alternative ways to convey your point.

      Thank you for highlighting this ambiguity. By describing the loss landscape as “flat,” we intend to convey its relative insensitivity to parameter variations in certain regions, rather than implying a completely level surface. While we believe Figure 5B still provides a useful qualitative depiction of this behavior, we acknowledge that it does not quantitatively establish “flatness.” In future work, we plan to incorporate more rigorous measures—such as gradient magnitudes or Hessian eigenvalues—to more accurately characterize and communicate the geometry of the loss landscape.

      Reviewer #3 (recommendations):

      We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions, which have helped us improve the clarity and rigor of our manuscript. Below, we address each of the comments.

      (1) Precision is lacking in the introduction section. Do the authors mean the Direct SSA, sorted SSA, which is usually faster, and how about rejection sampling methods?

      Thank you for pointing this out. We have updated the introduction to explicitly mention the Direct SSA.

      (2) When mentioning PyTorch and Jax, would be good to also talk about Julia, as they have fast stochastic simulators.

      We have now mentioned Julia alongside PyTorch and Jax.

      (3) Mentioned references 22-27. Reference 26 is an odd choice; a better reference is from the same author the Automatic Differentiation of Programs with Discrete Randomness, G Arya, M Schauer, F Schäfer, C Rackauckas, Advances in Neural Information Processing Systems, NeurIPS 2022

      We have now cited the suggested reference.

      (4) Page 1, Section: 'To circumnavigate these difficulties, the DGA modifies....' Have you thought about how you would deal with the bias that will be introduced by doing this?

      Thank you for your insightful comment. We acknowledge the potential for bias due to the differentiable approximations in the DGA; however, our analysis has not revealed any systematic bias compared to the exact Gillespie algorithm. Instead, we observe irregular deviations from the exact results as the smoothness of the approximations increases.

      (5) Page 2, first sentence '... traditional Gillespie...' be more precise here - the direct algorithm.

      Thank you for your comment. We believe that the context of the paper, particularly the schematic in Figure 1, makes it clear that we are focusing on the Direct SSA. 

      (6) Page 2, second paragraph: ' In order to simulate such a system...' This doesn't fit here as this section is about tau-leaping. As this approach approximates discrete operations, it is unclear if it would work for large models, snap-shot data of larger scale and if it would be possible to extend it for time-lapse data

      Thank you for your comment. We respectfully disagree that this paragraph is misplaced. The purpose of this paragraph is to explain why the standard Gillespie algorithm does not use fixed time intervals for simulating stochastic processes. By highlighting the inefficiency of discretizing time into small intervals where reactions rarely occur, the paragraph provides necessary context for the Gillespie algorithm’s event-driven approach, which avoids this inefficiency.

      Regarding the applicability of the DGA to larger models, snapshot data, or time-lapse data, we acknowledge these are important directions and have noted them as potential extensions in the discussion section.

      (7) Page 2 Section B: 'In order to make use of modern deep-learning techniques...' It doesn't appear from the paper that any modern deep learning is used.

      Thank you for your comment. Although the DGA does not utilize deep learning architectures such as neural networks, it employs automatic differentiation techniques provided by frameworks like PyTorch and Jax. These tools allow efficient gradient computations, making the DGA compatible with modern optimization workflows.

      (8) Page 3, Fig 1(a). S matrix last row, B and C should swap places: B should be 1 and C is -1.

      Corrected the typo.

      (9) Fig1 needs a more detailed caption.

      Expanded the caption slightly for clarity.

      (10) Page 3 last paragraph: 'The hyperparameter b...' Consequences of this are relevant, for example can we now go below zero. Also, we lose more efficient algorithms here. It would be good to discuss this in more detail that this is an approx.. algorithm that is good for our case study, but for other to use it more tests are needed.

      Thank you for the comment. Appendix A discusses the trade-offs related to a and b, but we agree that more detailed analysis is needed. The hyperparameters are tailored to our case study and must be tuned for specific systems.

      (11) Page 4, Section C, first paragraph, 'The goal of making...' This is snapshot data. Would the framework also translate to time-lapse data? Also, it would be better to make it clearer earlier which type of data are the target of this study.

      Thank you for your suggestion. While the current study focuses on snapshot data and steady-state properties, we believe the DGA could be extended to handle time-lapse data by incorporating multiple recorded time points into its inference objective. Specifically, one could modify the loss function to penalize discrepancies across observed transitions between these time points, effectively capturing dynamic trajectories. We consider this an exciting area for future development, but it lies beyond our present scope.

      (12) Page 4 Section C, sentence '...experimentally measured moments'. Should later be mentioned as error, as moments are imperfect

      Thank you for your comment. We agree that experimentally measured moments are inherently noisy and may not perfectly represent the true system. However, within the context of the DGA, these moments serve as target quantities, and the discrepancy between simulated and measured moments is already accounted for in the loss function. 

      (13) Page 4 Section C, last sentence '...second-order...such as ADAM'. Another formulation would be better as second order can be confusing, especially in the context of parameter estimation

      We have revised the language to avoid confusion regarding “second-order” methods.

      (14) Fig 4(a) a density plot would fit better here

      Fig. 4(a) has been updated to a scatter density plot as suggested.

      (15) Fig 4(c) Would be interesting to see closer analysis of trade of between gradient and accuracy when changing a and b parameters

      Thank you for this suggestion. We acknowledge that an in-depth exploration of these trade-offs could provide deeper insights into the method’s performance. However, for now, we believe the current analysis suffices to highlight the utility of the DGA in the contexts examined.

      (16) Page 6 Section III, first sentence: This fits more to intro. Further the reference list is severely lacking here, with no comparison to other methods for actually fitting stochastic models.

      Thank you for the suggestion. We have added a few references there.

      (17) Page 6, Section A, sentence: '....experimental measured mean...' Why is it a good measure here (moment matching is not perfect), also do you have distribution data, would that not be better? How about accounting for measurement error?

      Thank you for the comment. While we do not have full distribution data, we acknowledge that incorporating experimental measurement error could enhance the framework. A weighted loss function could model uncertainty explicitly, but this is beyond the scope of the current study. 

      (18) Page 7, section B, first paragraph: 'Motivated by this, we defined the...'Why using Fisher-Information when profile-likelihood have proven to be better, especially for systems with few parameters like this.

      Thank you for the suggestion. While profile-likelihood is indeed a powerful tool for parameter uncertainty analysis, we chose Fisher Information due to its computational efficiency and compatibility with the differentiable nature of the DGA framework.

      (19)  Page 7, section C, sentence '...set kR/off=1..'. In this case, we cannot infer this parameter.

      Thank you for the comment. You are correct that setting kR/off = 1 effectively normalizes the rates, making this parameter unidentifiable. In steady-state analyses, not all parameters can be independently inferred because observable quantities depend on relative—rather than absolute—rate values (as evident when setting the time derivative to zero in the master equation). To infer all parameters, one would need additional information, such as time-series data or moments at finite time.

      (20)  Page 7 Section 2. Estimating parameters .... Sentence: '....as can be seen, there is very good agreement..' How many times the true value falls within the CI (because corr 0.68 is not great).

      Thank you for your comment. While a correlation coefficient of 0.68 indicates moderate agreement, the primary goal was to demonstrate the feasibility of parameter estimation using the DGA rather than achieving perfect accuracy. The coverage of the CI was not explicitly calculated, as the focus was on the overall trends and relative agreement.

      (21) Page 7 Section 2. Estimating parameters .... Sentence: 'Fig5(c) shows....' Is this when using exact simulator?

      Thank you for your question. Yes, the exact values in x-axis of Fig. 5(c) are obtained using the exact Gillespie simulation.

      (22) Page 7 Section 3 Estimating parameters for the... Sentence: 'Fig6(a) shows...' Why Cis are not shown?

      Thank you for your comment. CIs are not shown in Fig. 6(a) because this particular case is degenerate, making the calculation and meaningful representation of CIs challenging. 

      (23) Page 10, Sentence: 'As can be seen in Fig 7(b)...' Can you show uncertainty in measured value? It would be good to see something of a comparison against an exact method, at least on simulated synthetic data

      Thank you for the comment. Fig. 7(a) already includes error bars for the experimental data, which account for measurement uncertainty. However, in Fig. 7(b), we do not include error bars for the experimental values due to limitations in the available data.

      (24) Page 12, Section B Loss function '...n=600...' This is on a lower range. Have you tested with n=1000?

      Yes, we have tested with n=1000 and observed no significant difference in the results. This indicates that n=600 is sufficient for the purposes of this study. 

      (25) Fig 8(c) why there are no CI shown?

      Thank you for your comment. CIs were not included in Fig. 8(c) due to degeneracy, which makes meaningful confidence intervals difficult to compute.

      (26) Page 12 Conclusion, sentence: '..gradients via backpropagation...' Actually, by making the function continuous, both forward and reverse mode might be used. And in this case, forward-mode would actually be the fastest by quite a margin

      Thank you for your insightful comment. You are correct that by making the function continuous, both forward-mode and reverse-mode automatic differentiation can be used. We have now mentioned this point in the discussion.

      (27) Overall comment for the Conclusion section: It would be good to discuss how this framework compares to other model-fitting frameworks for models with stochastic dynamics. The authors mention dynamic data and more discussion on this would be very welcomed. Why use ADAM and not something established like BFGS for model fitting? It would be interesting to discuss how this can fit with other SSA algorithms (e.g. in practice sorting SSA is used when models get larger). Also, inference comparison against exact approaches would be very nice. As it is now, the authors truly only check the accuracy of the SSA on 1 model -it would be interesting to see for other models.

      Thank you for your detailed comments. While this study focuses on introducing the DGA and demonstrating its feasibility, we agree that comparisons with other model-fitting frameworks, testing on additional models, and integrating with other SSA variants like sorted SSA are important directions for future work. Similarly, extending the DGA to handle transient dynamics and exploring alternatives to ADAM, such as BFGS, are promising areas to investigate further.

    1. Much of the time, all students should be called on to use what they learn to solve knotty problems that defy a recipe-like answer, even though some will need to go about the task in a different way. Some students may need more scaffolding than others to make and support an argument, for example. Some may benefit from using more advanced resources as they construct their argument. Some may profit from a mini-lesson that recaps how to make and support a solid argument. Some may need to develop their arguments orally and have their work written by a peer or adult. Some may need to use materials in a language other than English, or write initially in a first language and then translate into English. But if we acknowledge that argumentation is a valuable skill, we must commit to helping all students master it by providing the appropriate scaffolding.

      A lot of times in todays time teachers only let their students finish something in the way they want and that’s it. I watch this happen with my older step kids and two of them struggle a bit with the essays. I think the teacher could let them do it another way in like creating a vision board or something they actually understand.

    1. Moreover, all depend on you to make assumptions about people too, drawing upon persona and assumptions of common knowledge, all of which may be untrue of marginalized groups. Design has a long way to go before its methods are truly equitable, focusing on the edge cases and margins of human experience and diversity, rather than on the dominant cases. It’s your responsibility as a designer to look for those methods and demand their use.

      This specific section near the end I think is a good contradiction and reminder to everything that we have been thinking about in the course. Very frequently we have been forcing ourselves to step away from our preferences and adopt an approach that detaches ourselves from the results and prioritizes the users. Even though this is the case I appreciate the focus on still understanding that we have a choice, and are not completely detached from the project we still are allowed to maintain judgment and maintain a level of assumption over the process which helps establish a bit of ownership over it.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study is useful as it provides further analysis of previously published data to address which specific genes are part of the masculinizing actions of E2 on female zebra finches, and where these key genes are expressed in the brain. However the data supporting the conclusion of masculinizing the song system are incomplete as the current manuscript is a re-analysis of differential gene expression modulated by E2 treatment between male/female zebra finches without manipulation of gene expression. The conclusions (and title) regarding song learning are also incompletely supported with no gene manipulation or song analysis. Importantly, the use of WGCNA for a question of sex-chromosome expression in species without dosage compensation is considered inadequate. As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.

      We are sorry the editor felt the manuscript so incomplete and inadequate. Though the tone of this assessment seems more severe than the below reviewer comments, we are also happy to see that the editor has considered our paper further for a revised publication, based on the reviewer’s comments. We address the editor’s comments as follows:

      While we agree that manipulation of some of the genes we discovered, whose expression levels are E2-sensitive in the song system, would take the study further in validating some proposed hypothesis in the discussion of the paper, we don’t think the outcome of gene manipulations would change the major conclusions from the results of the paper. In this study we performed estrogen hormone manipulations, with causal consequences on gene expression in song nuclei and associated song behavior. In a way this is analogous to gene manipulations, but manipulating directly the action of estrogen. The categories of genes impacted, and the differences among the sex chromosomes wouldn’t change.

      For the comment on WGCNA being inadequate for addressing questions on sex chromosome expression in species without dosage compensation, we think the evidence in our data does not bear that out. One main result of this paper is the separation of Z chromosome transcripts whose expression is most strongly regulated by chromosomal dosage (WGCNA module E) across regions from those subject to additional sources of regulation in song nuclei (other modules). It seems to us that rather than being confounded by the lack of dosage compensation, WGCNA allowed us to better resolve the effects of dosage on different genes within the sex chromosomes. We have added a new figure more directly examining sex chromosome transcript abundance within different modules. Briefly, we found that module E assigned Z chromosome genes exhibited almost exactly the male-biased expression ratio expected from no dosage compensation while the Z chromosome genes in song nuclei assigned to other modules were expressed below the dosage predicted value, consistent with module E containing those genes whose expression are most strongly regulated by dose across all brain regions sampled.

      At its core, WGCNA finds sets of correlated genes. The biological reality of the zebra finch transcriptome is that Z chromosome expression is largely anti-correlated with W chromosome due to dosage. However, this dosage effect is not felt equally by all genes and WGCNA provides an unbiased computational framework which can be used to separate dose from other potential sources of gene regulation. This is why roughly ⅓ of Z chromosome genes are not assigned to module E; for example the growth hormone receptor is assigned to module G based on its correlation with genes upregulated within HVC.

      “As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.”

      Concerning the comment on no analysis on song performance in the paper, all such analyses were conducted on our previous study on the same animals (Choe et al. 2021, Hormones & Behavior). The birds considered here were sacrificed at PHD30, prior to the onset of learned song behavior. However, females treated with E2 the same at the same time and allowed to mature into adulthood, went onto to develop rudimentary song. Further, induction of rudimentary song learning in females following E2 treatment has been well established since the early ‘80s. We have added the following text toward the end of the intro to make this more clear:

      “While the birds for this study were sacrificed prior to the developmental presentation of song behavior, we have previously shown that female finches treated in exactly the say way with E2 go on to produce rudimentary imitative songs as adults (Choe et al 2021), consistent with the known induction of vocal learning in females by E2 (REF).”

      Reviewer #1 (Recommendations For The Authors):

      Overall, this is a wonderfully designed and executed study that takes full advantage of new resources, such as the most complete zebra finch genome assembly yet, as well as the latest methods. I have very few suggestions as to the improvement of the manuscript. They are as follows:

      Results Section:

      In the paragraph "Identification of gene expression modules in song nuclei":

      "The E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy."

      Clarify if this comparison is to treated and/or untreated males.

      We thank the reviewer for their comment. The relative differences in the song nuclei sizes between the E2-treated females and the other groups is more complex that our original sentence implied. We have revised the main the text as follows

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      In the paragraph "Sex- and micro-chromosome gene expression across the telencephalon": "These animal and chromosome specific shifts in the transcriptomes could represent the systemic effects of allelic chromosomal structural variation..."

      The authors should clarify the meaning of a"llelic chromosomal structural variation" in this context, as it is an unusual phrase. Major chromosomal structural variation seems unlikely to produce these effects. Is it also possible that animal-specific modules with brain-wide higher could also result from laboratory contamination between all samples from one animal? This is not too likely but perhaps should be acknowledged or ruled out.

      We have removed the word allelic, which was unnecessary. We can’t envision how laboratory contamination could occur such that all of one animal’s samples would be affected to produce the observed result which is module and chromosome specific. An animal wide effect could emerge during sacrifice, but we can think of no reason that would affect these modules and not others. Rather, the most likely explanation is biological natural difference between animals. We have added this consideration of alternative explanations.

      In the section "Candidate gene drivers of HVC specialization in E2-treated females":

      When discussing GHR's role in cell growth and proliferation, the authors' argument could be expanded by including the documented role of GH signaling in anti-apoptotic protection of neurons from rounds of neural pruning during development as documented in the chicken, e.g. • Harvey S, Baudet M-L, Sanders EJ. 2009. Growth Hormone-induced Neuroprotection in the Neural Retina during Chick Embryogenesis. Annals of the New York Academy of Sciences, 1163: 414-416. https://doi.org/10.1111/j.1749-6632.2008.03641.x

      We thank the reviewer for sharing this publication with us.. We have added the following sentence to our discussion with the above citation. “Further, our results are consistent with growth hormone’s known role in avian anti-apoptotic protection, with elevated signaling associated with the survival of chicken neurons during rounds of pruning in the developing

      retina.”

      The authors' argument of the relevance of the passerine GH duplication would be strengthened by citing:

      • Rasband SA, Bolton PE, Fang Q, Johnson PLF, Braun MJ. 2023. Evolution of the Growth Hormone Gene Duplication in Passerine Birds, Genome Biol Evol, 15(3) https://doi.org/10.1093/gbe/evad033. Greatly expands on the Yuri et al. paper cited by characterizing of the molecular evolution of these genes across hundreds of avian species, supporting positive selection on multiple amino acid sites identified in both ancestral and duplicate (passerine) growth hormone.

      • Xie F, London SE, Southey BR et al. 2010. The zebra finch neuropeptidome: prediction, detection and expression. BMC Biol 8, 28. https://doi.org/10.1186/1741-7007-8-28 The authors report significantly different expression of the ancestral GH gene in the adult male zebra finch auditory forebrain after different song exposure experiences.

      We have amended the results section sentence and added all suggested citations. The sentence now reads: “The gene which encodes growth hormone receptor’s ligand, growth hormone, is interestingly duplicated and undergoing accelerated evolution in the genomes of songbirds (Rasband et al 2023); the GH ligand has been found to be upregulated in the zebra finch auditory forebrain following the presentation of familiar song (Xie et al 2010).”

      Figures:

      - Figure 1B. "Duration of sex typing" being a shorter bar compared to the others is not fully explained in the experimental design. Presumably at the end of this time period, the sex is non-invasively, phenotypically evident. I suggest an arrow pointing to the PHD/PHD range when sex is apparent in plumage/anatomy.

      - Figure 4. Caption appears to be truncated; "across all... genes"?

      Fixed

      - Figure 5. For 5E, 5F, 5G, 5H, consider enlarging the plots so overlapping gene symbols are readable. Alternately, smaller numbers or symbols could be used with a key in areas where overlapping symbols are hard to prevent.

      We agree that these are not the easiest to read; we originally offset the symbols in R to minimize overlaps, but it can only do so much for the more crammed panels. We have now added a supplemental .xlsx file with the underlying data from each of the 4 tests for readers that want to examine the data in more detail.

      Reviewer #2 (Recommendations For The Authors):

      Since WGCNA methods will inherently draw together sex-chromosome genes into the same module in systems without dosage compensation, I suggest the authors rerun the WGCNA using only female samples and only male samples. Then identify the composition of modules that differ between E2 and vehicle-treated females and compare these genes to males. Then from male WGCNA identify the composition of modules that differ between E2 and vehicle-treated males and compare to female modules.

      We thank the reviewer for their suggestions. However, we believe it is not as strong as the approach we used, which is grouping data from both sexes in the WGCNA analyses in a study that is looking for sex differences. The reviewer's proposed approach amounts to computing modules twice (once per sex), determining song system specialized modules and E2 responsive modules in both settings, then intersecting the two sets to find corresponding modules, all done to prevent the non-dose compensated sex chromosome genes from being drawn into the same module.

      While WGCNA does group the majority of sex chromosome genes into module E, it does not categorize them all this way (Fig 3). The module classification instead differentiates those sex chromosome genes whose expression are most explained by chromosome dosage / sex across regions (modE) from those whose expression is controlled by other sources of regulation; for an example of the latter, the growth hormone receptor (GHR) is one of several Z chromosome genes classified into modG as its expression better correlates with the genes specialized to HVC than it does with the majority of dosage-dependent Z chromosome genes found in modE. Further, to remove biological sex as a variable in a WGCNA analysis that is focused on sex differences seems counterintuitive.

      Instead, to quantitatively address the reviewer’s concern, we conducted additional analyses, that led to an added new figure, associated text, and tables, that better describes sex/chromosome dosage effects on the abundance (FPKM) and expression ratios of sex chromosome transcripts by module irrespective of brain region (Fig. 5). We find that the Z chromosome genes in modE were expressed at the expected chromosome dosage in the non-vocal surrounding regions (65.06% observed vs 66.6% expected) while in other modules, other Z chromosome genes were expressed at intermediate levels between equal expression and the expected chromosomal dosage. For example, the Z chromosome content of modules D and H exhibited near equal expression between sexes. Within the song system, Z chromosome gene content of modG was highly expressed in males beyond what is expected from chromosome dosage, consistent with modG’s male-specific upregulation in song nuclei relative to surrounds in the absence of E2. These results better demonstrate that in our WGCNA on the combined dataset we are able to separate those Z chromosome genes whose expression is predominantly dosage controlled from those subject to additional regulation such as song system specialization.

      Fig. S3 Legend: 'Black arrow' -> 'Red arrow'

      Change made.

      Fig. S5 - What part of the figure shows the 'human convergent signature'? Also, simply listing the number of genes mapped to a chromosome is misleading to readers unfamiliar with the zebra finch genome, you should either provide the number of genes on each chromosome or present as corrected by that number.

      Fig. S5 was the same type of analyses in Fig. 3 but with an older zebra finch genome assembly, where we had not included the panel a for enrichments with genes convergent in expression between songbird song regions and humans speech brain regions. However, we see that Fig. S5 was not adding any new important information to the paper, so we removed it.

      For the chromosome analyses in Fig. 3b, we provide both the total raw number of module assigned genes broken down by chromosome (The black bar plots on the right) as well as a statistical fold-enrichment value of modules per chromosome. Given the number of genes per chromosome and genes per module in our data, we computed the fold-enrichment for each intersection (observed intersection size / expected intersection size). To test for the significance of these enrichments, we bootstrapped FDR corrected p values for the enrichment of each chromosome-module pairing by randomizing the mapping of genes to modules to construct a null distribution of fold enrichments for each intersection. Our intent was not to describe the size of the chromosomes themselves, information readily available elsewhere, but to show the disproportionate chromosomal origins of the gene sets considered by this study. Performing this enrichment test using all annotated genes per chromosome would artificially increase enrichment values and make the analysis less conservative by confounding the results with the inherent enrichment for “brain function” in the assigned genes relative to all genes.

      At several places you say "we correlated expression of each sex chromosome transcript with sexual dimorphism within each region, such that expressed W genes would be positively correlated and depleted Z chromosome genes would be anticorrelated." What was the sexual dimorphism that was being correlated with? Is this the eigengene?

      We thank you for this comment. Our language was less clear than it could be. We tested for correlations of both the eigengene and the individual gene expression profiles with the biological sex of the animals. We have changed the text to:

      “To do this, we tested for a correlation between the expression of each sex chromosome transcript to the animals’ sex within each brain region. We found that female-enriched transcripts were positively correlated with sex and male-enriched transcripts were anticorrelated (Fig. 4f,g).”

      Fig. 4A: The 'true/false' boxes and animal A-L is confusing and unnecessary. I'd suggest just using M and F (or sex symbols) with a horizontal line below each set of 3 for respective E2 and Veh.

      Change made.

      Reviewer #3 (Recommendations For The Authors):

      General comments:

      After the initial characterization of the datasets and module identification, it is quite hard to follow the logic of the data presentation in the various other Results sections or to clearly understand how they relate to the main stated goal to identify factors related to sex differences in vocal learning. The most relevant findings relate to the presumed actions of hormone treatment and sex chromosome gene dosage in song nuclei, whereas analyses of other brain areas, other chromosomes, or speech-related genes serve more as controls and/or appear as distractions from the main theme. A suggestion to increase the clarity of the presentation and potential impact of the study is to change the order of the presentation, focusing first on the specific analyses and comparisons that most directly speak to the main goals of the study, and then secondarily and more briefly presenting the controls or less related comparisons.

      The reviewer’s suggestion for the results section organization is exactly what we had tried to do. We opened the first paragraph on identification of modules, then presented the song nuclei specific modules, followed by E2-changes to those modules; and the followed by other specific results for the remainder of the paper, including module enrichments to specific chromosomes. The reviewer mentioned our analyses of “other brain areas” (which we assume to mean the non-vocal surround regions), other chromosomes (which we assume means autosomes) and speech-related genes as controls were a distraction in the paper; but within our analysis, these other brain regions are essential controls needed to assess the song-system specificity of any observed sex differences observed from the very first paragraphs of the results; the autosomes were not controls for sex chromosome results, but primary results in of themselves; the overlap with speech-related genes was also not a control, but a novel discovery. We have revised these points in the paper to make them clearer, and revised some of the section titles and transitions between sections to help increase clarity of the main storyline of the paper.

      A related comment is that many of the inferences drawn from the WGCNA analysis were quite complex, thus independent verification of some predictions would be quite valuable. For example, consider the passage: "In non-vocal learning juvenile females, interestingly LMAN was specialized relative to the AN by the same gene modules as in males (B, F, and I) as well as an additional module G (Fig. 2b); RA was specialized by module A as in males, but not module L and by additional modules A and G. In contrast, neither juvenile female HVC nor Area X exhibited significant gene module expression specializations relative to their surrounds." Providing in situ hybridization verification of these regional gene expression predictions with a few representative genes seems quite feasible given the group's expertise and would considerably strengthen confidence in the module-based inferences.

      We performed in-situ independent validation of 36 candidate genes in our first study with this dataset (Choe et al 2021). We now mention this validation in the revised paper. The reviewer’s selection of one of our sentences though made us realize that our grammar used to explain the results was not as clear as it needs to be. We thus cleaned up the grammar of our module descriptions so that it should be communicated with less complexity, the main issue noted by the reviewer.

      Because this is a re-analysis of a previously published dataset, the authors should more explicitly describe somewhere in the Discussion how the present analysis advances the understanding of sex differences in songbird neuroanatomy and behavior beyond the previous analysis.

      We have added an additional sentence into the discussion more clearly separating the results of the current study from our previous work.

      Specific comments:

      Abstract:

      There is evidence (from Frank Johnson's lab) that RA does not completely atrophy in female zebra finches, but is still present with more preserved connectivity than previously thought, possibly related to non-singing function(s). A term like 'marked reduction' of female RA may more accurately reflect the current state of knowledge.

      We have changed the text to “partial atrophy”.

      The term "driver" is undefined and unclear at this point of the paper; a clear definition for "driver" is also lacking in the Intro.

      We now define “driver” or “genetic driver” as understood to mean “a genetic locus whose expression and/or inheritance strongly regulates the trait of interest”.

      When citing the literature on studies that identified "specific genes with specialized up- or down-regulated expression in song and speech circuits relative to the surrounding motor control circuits", the authors should also cite studies from other labs (e.g. Li et al., PNAS, 2007; Lovell et al, Plos One 2008; Lovell et al, BMC Genomics 2018; Nevue et al, Sci Rep. 2020), to be accurate and fair.

      Citations added

      For clarity, the authors should explicitly formulate the hypothesis they are proposing at the end of the Summary.

      We thank the reviewer for this comment. We have replaced the final sentence of the summary with: “We present a hypothesis where reduced dosage and expression of these Z chromosome genes changes the developmental trajectory of female HVC, partially preventable by estrogen treatment, contributing to the loss of song learning behavior.”

      Introduction:

      Vocal learning is arguably the ability to imitate 'vocal' sounds, this could be clarified here.

      We have amended the sentence to “Vocal learning is the ability to imitate heard sounds using a vocal organ…”

      Given they are currently considered sister taxa, can the author briefly explain what is the basis for assuming that songbirds and parrots independently evolved vocal learning?

      Although songbirds and parrots belong to a monophyletic clade, they are not sister taxa. There are two clades separating them that are vocal non-learners. We have cited the reference that demonstrated this (e.g. Jarvis et al 2014 Science).

      Why use Taeniopygia castanotis rather than the more broadly used Taeniopygia guttata?

      Zebra finches were recently reclassified and T.castanotis is now more accurate. The Indonesian Timor zebra finch retained T.guttata while the Australian finch, used here, was classified as T.castanotis.

      The authors state: "...vocal learning is strongly sexually dimorphic in zebra finches and many other vocal learning species" and cite Nottebohm and Arnold, Science, 1978. That landmark paper only shows dimorphism in song nuclei (not learning) in two songbird species. The authors should provide citations for other species and behavior, or modify the statement.

      We have added an additional citation (Odom et al.) to this sentence which covers the phylogeny more broadly.

      The authors refer to the nucleus RA as being located in the lateral intermediate arcopallium (LAI). Other labs have described this domain as the dorsal part of the intermediate arcopallium, thus AId or AID (Mello et al., JCN, 2019; Yuan and Bottjer, J Neurophys 2019; Yuan and Bottjer, eNeuro, 2020; Nevue et al., BCM Genomics, 2020). The authors should acknowledge this discrepancy in nomenclature so that data and conclusions can be more readily compared across studies.

      We thank the reviewer and agree that this is helpful. We have added a note at the first mention of LAI.

      The authors state that data from the gynandromorph bird described by Agate et al implicates "sex chromosome gene expression within the song system" as involved in the song system sexual dimorphism. That study, however, only rules out circulating gonadal steroids, and while suggesting a cell-autonomous mechanism like sex chromosome genes, it does not necessarily exclude other brain-autonomous factors like sex differences in local production of sex steroids.

      We say that this study “implicated” sex chromosome gene expression, which is accurate per the results and discussion of that study. We are unsure what “brain autonomous factors like sex differences in local production of sex steroids” means?. “Brain autonomous” and “local production” in the brain seem contradictory in this context?

      Results:

      The authors state that "the E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy". Can they clarify whether the VEH-treated females actually had smaller RAs than E2-treated females or VEH-treated males at this age? This is still quite early in development and it is unclear to what extent RA's marked sexual dimorphism in adults or later developmental ages has already taken place in untreated (or VEH-treated) birds. A related comment is that the authors state later on: "We interpret these findings to indicate that: LMAN and RA atrophy later in juvenile female development..." Does this mean these nuclei actually did not show the marked decreases predicted earlier in the text? Clarifying this point would be helpful.

      We thank the reviewer for pointing out this discrepancy, which reviewer #1 asked for clarification as well. RA size at this age is similar in males and females. However, HVC and Area X is smaller and absent respectively in females and E2 treatment partially prevents this atrophy. The text now reads:

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      The authors acknowledge that area X is absent in untreated and VEH-treated females. Could they please clarify how area X and the surrounding stratal tissue that excludes area X were identified for laser capture dissections in juvenile females?

      We have added the following statement to the main text portion discussing the dissections.

      “In the case of vehicle-treated females which lack Area X, a piece of striatum from the same location of where Area X is found in males was taken. “

      Some passages in Results discussing the authors' interpretation of the modules seem quite speculative and possibly belong instead in the Discussion. For example: "... that module A and G genes could be associated with the start of this atrophy; HVC and Area X are likely the first to atrophy or not develop; and lack of any gene module specialization in them at this age could mean that they would be more sensitive to estrogen prevention of vocal learning loss."

      As suggested, we have removed this text from the results; these ideas were already presented in the Discussion. We have merged the resulting small paragraph with the preceding paragraph.

      The authors state: "To assess the effects of chronic exogenous estrogen on the developing song system, we first performed a control analysis of modules in the E2-treated juvenile males." How can an assessment of estrogen effects be a "control" analysis? Does this refer to a contrast with females? Please clarify the language here.

      The reviewer is correct, that E2 treatment in males should not be considered a control experiment. We removed the word “control”.

      When discussing the GO-enriched terms for module G, it is unclear how the authors reached the conclusion about "proliferative", as the enriched terms do not refer to processes more directly indicative of proliferation like "cell division" or "cell cycle regulation". Rather, these terms seem more related to differentiation and growth, which do not necessarily imply proliferation. The authors also refer to "HVC proliferation" later on in the Discussion. However, there is conclusive evidence from several labs that proliferative events associated with postnatal neuronal addition and/or replacement in song nuclei occur in the subventricular zone, not in song nuclei like HVC itself, and that the growth of song nuclei largely reflects cell survival, as well as growth in size and complexity under the regulation of sex steroids.

      We agree that “proliferative” may have been a poor word choice here. We did not mean to indicate that cell division was occuring in HVC itself. Instead we meant to indicate that HVC is able to accommodate the new born neurons from the SVZ. We have replaced the word “proliferative” throughout. In the instance the reviewer mentions specifically we replaced it with,“...potentially act to integrate and differentiate late born neurons.”

      With regard to module E, referring to a telencephalon-wide sexually dimorphic gene expression program seems quite a stretch, given that only a few regions were sampled and compared between sexes. These related statements should be toned down.

      We have replaced “telencephalon-wide” with “more distributed across the finch telencephalon” and other similar language in each instance.

      The following passage is very speculative and should shortened and/or moved to the Discussion: "Based on the findings in these gene sets, we hypothesize that without excess estrogen in females, HVC expansion is prevented by not specializing the growth and neuronal migration promoting genes in module G to the HVC lineage by late development. This is potentially enacted by depleting necessary gene products from the Z sex chromosome, such as GHR, which are already present in only one copy."

      We have deleted this portion of the text, as the idea is already present in the discussion.

      Figure 5: To this reviewer, the comparisons of sex differences and of female response to E2 are the most relevant and informative ones, whereas the regional differences between song nuclei and surrounds refer to different cell populations and cell types where other processes may be occurring, independently of what occurs in song nuclei. It thus seems like the intersection analysis in panel 5i may be subtracting out important "core genes" in terms of E2 effects and/or sex differences in the most relevant cell populations, i.e. in this case within song nucleus HVC.

      Song learning and the vocal learning brain regions are specialized behaviors and associated nuclei which have a set of hundreds of specialized genes compared to the surrounds. Our previous findings shows that E2 drives the appearance of these specializations in female zebra finches. Thus, we considered this the most interesting question to focus on, which we have further highlighted. Nevertheless, in response to the reviewers suggestion, we have added a .xlsx supplemental file containing the results from each of the individual tests so readers may examine any single comparison, or set of comparisons, in more detail.

      Discussion:

      It is unclear what the term "critical period" refers to in: "during the critical period of atrophy for the female vocal circuit"; please clarify.

      We agree that our language was nebulous. We have replaced it with “as several male song control nuclei begin to expand and female nuclei partially atrophy”

      In: "HVC appeared unspecialized at the level of gene module expression in control females", does "unspecialized" refer to a lack of difference in gene expression when compared to surroundings? Please clarify. The same comment applies to other uses of "unspecialized" in this paragraph.

      Yes, unspecialized means lack of difference in gene expression in the song nucleus. To clarify this point, we have reworked that and the following sentence as follows:

      “HVC appeared unspecialized compared to the surrounding nidopallium at the level of gene module expression in control females, with no significantly differentially expressed MEGs . However, in E2-treated females, HVC exhibited a subset of the observed male HVC gene expression specializations. Similarly, the vehicle-treated female striatum located where Area X would be also lacked any specialized gene module expression, but the E2-treated female Area X exhibited a subset of the male Area X specializations, consistent with the known absence of Area X in vehicle-treated females and presence in E2-treated females.”

      The authors state: "...we surprisingly found that the most specialized genes were disproportionately from the Z chromosome", when discussing module G in HVC. Why is this so surprising? In a sense, this could be taken as consistent with the findings of Friedrich et al, 2022, where sex differences in the RA transcriptome were predominantly Z related on 20 dph. Arguably 20 dph is still quite close to 30 dph in the present study, when compared to 50 dph in Friedrich et al, when autosomes predominate.

      Our bioRxiv was originally posted in July 2021, prior to the publication of Friedrich et al, 2022; however we had previously added to our discussion that several of our results are consistent with the observations of Friedrich et al..

      We have a different interpretation of Z chromosome gene results in Friedrich et al.. While the percentage of specialized genes from the Z chromosome decreased, the absolute number of specialized Z chromosome genes actually increased over this interval. In Fig. 3a from Friedrich et al. it appears that ~28% of Z chromosome genes were sexually dimorphic in their expression in RA at PHD20 but that ~39% of Z chromosome genes were similarly dimorphic at PHD50. We interpret this result as the Z chromosome genes being among the earliest genes differentially expressed between the sexes, not that their differential expression or role ever subsequently decreased. We have reworked this portion of the discussion to make our point more clear:

      “This model of sex chromosome influenced song system development is consistent with recent observations comparing male and female zebra finch transcriptomes from RA at young juvenile (PHD20) and young adult (PHD50) ages in un-manipulated birds (Friedrich et al. 2022)57. While that study proposes that the role of the sex chromosome in maintaining transcriptomic sex differences diminishes across development, as the proportion of specialized genes that originate on the sex chromosomes diminishes, this effect was driven by large increases in differentially expressed autosomal genes rather than by any reduction in sex chromosome dimorphism; the percentage of differentially expressed Z chromosome genes increased from PHD20 (28%) to PHD50 (39%) (Friedrich et al). This leads us to conclude that sexually dimorphic Z chromosome expression at juvenile ages precedes the sexually dimorphic expression of the autosomes seen in adults. This is consistent with our hypothesis that sufficient expression of select Z chromosome gene products (GHR, etc..) is necessary for subsequent autosomal song system specializations (modG).”

      Further, when we write ”When examining the module G HVC specialization induced by E2-treatment in female HVC, we surprisingly found that the most specialized genes were disproportionately from the Z chromosome” we are referring to the upregulation of module G by E2 in female HVC, not the sex difference described in RA by Friedrich et al. which only utilized un-treated RA samples and thus is more likely related to our observations of module E.

      The term "sexual dimorphism" has been more traditionally used for sex differences that are very marked, like features that are highly regressed or absent in one sex, most often in females. Quantitative differences in gene expression, including dosage differences like those related to module E, are more appropriately described as sex differences rather than dimorphisms. That usage would be more consistent with most of the literature, and thus preferable.

      We did a google search for common definitions, and found more the opposite. Sexual dimorphism being used more often as differences of degree (with the zebra finch example as one of the top hits), and sex differences being used often as more absolute differences (like presence vs absence of the Y chromosome). Further, as in the reviewer’s first sentence, the definition of sexual dimorphism is a sex difference. That is, the two phrases can be interchangeable. Thus, we prefer to keep sexual dimorphism.

      Several references are incomplete or seem truncated, like 9 and 10.

      Fixed

      Table S2: Please examine and take into account the W gene curation presented in Table S3 of Friedrich et al., 2022.

      We have added additional supplementals (supplemetal_w_chrom_express.csv and supplemetal_z_chrom_express.csv) of the data provided in new Fig 5 incorporating the curation information from Table S3 from Friedrich et al.

      Data availability:

      Genes for all the main modules identified should be presented in a Supplemental Table, or through a link to a stable data repository.

      We have added an additional Supplemental Table supplemental_gene_module_assignment.csv with this information.

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      Reply to the reviewers

      Below is a point-by-point response to reviewers comments. We appreciate the reviewers' thoughtful consideration of the manuscript and __suggestions

      Reviewer #1

      Evidence, reproducibility and clarity

      In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed.

      In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis.

      As the reviewer points out a large part of this manuscript is the development of a novel methodology for analyzing the spatial ECM changes in a model of allergic airway inflammation. However, there are several novel responses described in the manuscript. Firstly, differing spatial organisation of immune cells across different mouse strains has not been shown before, particularly in a model of chronic allergic pathology that shares features of severe steroid-resistant asthma in people. Secondly, we show that specific macrophage-fibroblast interactions are occurring in the subepithelial region during DRA-induced allergic airway inflammation. Finally, we integrate all these established and novel findings with detailed spatial analysis of the cellular ECM environment, something which is sorely needed in the field.

      However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans.

      Whilst we appreciate that the dataset in this study is limited, imaging mass cytometry studies, especially when optimizing reagents, are costly, time consuming, and have limited throughput, not to mention the time required to develop new computational tools for data analysis. Investigating cell-matrix changes in mouse data is vitally important for understanding the mechanistic role of pathways and interactions during disease processes. Whilst we have not provided human datasets in this study, staining, data acquisition and analysis has been performed on FFPE samples, making our pipelines applicable to archival tissue banks. Regardless, we are currently preparing a publication showing the applicability of this technique to human samples. Many ECM components are well conserved between humans and mice and the cellular structure and architecture of the lung shares a lot of similarities. Many papers (PMID: 39437149, 38758780, 38581685, and 38142637) have used this imaging technology in the analysis of human cancer, which shows an even more complicated and dense cellular organisation.

      The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples.

      As mentioned above, we have taken steps to show that this technology is applicable to humans, though this is outside the scope of this already lengthy manuscript. Additionally, Steinbock, the main analysis pipeline, is well published in human datasets (PMID: 38758780, 39905080, 39759522, and 39761010) and the homology between ECM components is strong between mouse and human. The technology itself is completely species agnostic, so there is no reason to think that there would be issues when applying to humans, other than some differences in the marker expression of certain populations, which is well characterised in many cases.

      The reviewer’s comment regarding the use of additional techniques is valid. Firstly, these murine lung pathology samples are derived from the same mouse experiments used in our previous publication (PMID: 33587776), where we have analysed histology, immune mediators and cells using a variety of techniques including flow cytometry and ELISA. We will ensure this point is made clearer in the manuscript. In addition, for revision we plan to compliment IMC data presented with fluorescent immuno-staining to characterize cell populations in greater resolution and also using 3D precision cut lung slices to better characterize and visualize cell populations of interest in greater depth, directly addressing the reviewer’s concerns.

      The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided.

      As the DRA model has been characterized previously, we provided references in the text in order to save space. However, we agree with the reviewer and will provide this information up front in the introduction to make the manuscript more approachable for a non-specialist.

      In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed.

      Lungs were inflated prior to tissue collection. We agree that this is important information to include in the methods and we will update the manuscript to reflect this.

      Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included.

      We agree with the reviewer. The idea behind this figure was to have an approachable introduction to the manuscript. However, in line with the reviewer’s previous comments about focusing more on the biology we will move this to supplementary to keep the importance focused on the biological results. Mouse age and gender were included in the methods of the paper, aligning to the ARRIVE guidelines for reporting animal research. We will additionally clarify that these are adult mice

      Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis.

      This is a great idea and appreciate the reviewer’s suggestion. We will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localise across the lung. This addition will also highlight the caveat of IMC around image resolution of 1μm2 which limits the sensitivity of cell segmentation. We will discuss such limitations of the technique in general in the manuscript in response to this and later reviewer comments.

      The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology.

      We know that these exact animals are allergic as their immunological responses were characterized in a previous publication (PMID: 33587776) demonstrating eosinophil counts and cytokine responses measured by flow cytometry. However, in light of the reviewer’s comment, we will add histological images of the lung to this current manuscript. Such data, together with enhanced expression of RELMα and Ym2 from airway epithelial cells (Sup Fig 6) and the shift from ATI to ATII cells in both C57BL/6 and BALB/c mice after DRA treatment (Fig 5 g) will provide thorough evidence that the DRA model induces allergic airway inflammation and pathology in both mouse strains.

      The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts.

      UMAP reductions of IMC do not separate as clearly as those from single cell RNAseq or flow cytometry. This is because the staining intensity from IMC is much lower. Rather than being on a log scale, as for single cell or flow cytometry, the values are much closer to linear. Additionally, due to the limitations in IMC resolution and the fact that we did not have distinct membrane markers in our panel, cell mask generation is often non-optimal. This is particularly evident in regions where cells are in close proximity and where the limitations of, an effectively, two-dimensional 5-micron thick tissue section mean that there can be overlap between one cell and another. Whilst we acknowledge that some populations will be a mix of cell types we are limited by the number of markers we can use in IMC, as well as the limitations mentioned above. We have accounted for this by using methodologies to identify and focus on tissue regions (lisaClust) and correlate changes to differences in these regions rather than single cells per se.

      Examples of segmented cells should be shown to validate this approach.

      As per the reviewers comment above, we will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localised across the lung.

      It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way.

      We apologise that the reviewer found Figure 2e confusing. The aim of this figure was to provide a simple diagram to highlight how different classifications of cell types aligned. This was required because there were variations in the specificity of some clusters and to address specific questions it made more sense to analyse cells at a broader level. i.e. merging resting and activated ATI/II cells or grouping specific immune cell clusters into larger groups. We did consider a table, but we did not feel this was a “simpler” way to do it. As it is simply for reference, we will move Figure 2e to supplemental.

      The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area.

      We thank the reviewer for their comment here. We agree that this is a vital area that needs to be addressed as the immunomatrix becomes ever more important in understanding disease pathogenesis. We developed this novel method to begin to understand key spatial interactions between cells and ECM molecules, something missing from the majority of high-dimensional imaging datasets.

      However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

      We disagree with the reviewer on this point. Figure 4 shows that immune cell infiltration in the adventitial cuff is different between BALB/c and C57BL/6 mice. This is a new discovery and provides nuance to our previously published data (PMID: 33587776), which showed that in the bronchoalveolar lavage from these same mice there were no differences in immune cell populations at these chronic time points. Therefore, analysis of lavage cells or lung histology in isolation does not provide a full picture of allergic immune responses.

      Figure 5 shows neutrophils localised with alveolar macrophages in the alveolar parenchyma in this chronic DRA model completely distinct from the spatial advential cuff region occupied by other CD11b+ cells. In addition, we show that we can identify perturbations in the alveolar parenchyma by IMC and these correlate with known differences in allergy and asthma such as alterations in ATI/ATII balance, which has also not been shown in this model.

      Figure 6 demonstrates that we can identify a tissue region termed “subepithelial cells” which is the site of where remodelling events are known to occur in asthma and allergic pathology. This ECM-rich region is strongly associated with fibroblasts and immune cells which leads in to figure 7 showing that these cells are interacting.

      In addition to all of this the main focus of this manuscript is to link these analysis parameters to changes in the ECM environment and we have included this in each of these figures showing how these correlates with allergic changes and how they may be important in understanding these processes. In response to this reviewer’s point, we will highlight and make these novel findings clearer within the text of the manuscript.

      In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples.

      This is a keen observation by the reviewer. We were interested in this finding however as it was not the focus of the paper we did not investigate further. In our previous publication we show that there are increased neutrophil numbers in the BAL of these animals (PMID: 33587776) and as mentioned above, we show in figure 5 that neutrophils are found mainly in the alveolar parenchyma. This perhaps means that they are more sensitive to being washed out in the BAL and perhaps there are differences in their “stickiness” in BALB/c and C57BL/6 animals or during DRA-induced allergy. This is in contrast to eosinophils (likely within our CD11b+ cells) which are found in the adventitial cuff, a region is not likely to be captured by BAL wash, though we know that these cells are actively present in the BAL. Overall, though this is an interesting result it was not the focus of this already lengthy paper and is best investigated in another project.

      When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells.

      Again, the reviewer is entirely correct here. The cells we have identified are labelled as ATI as a best guess and correlate with ATII cells based on anatomical location – though this is likely shared by some of the populations mentioned by the reviewer. The majority of cells in this population are likely ATIs, as they are localized in the alveolar parenchyma and are cells that are not SPC+, though we cannot say for sure without more markers and we were already at the limit of the number of markers that we can run with one IMC panel. It is likely that there are contaminating lymphatic endothelial cells in this cluster. However, these will be a relatively minor population and do not change the main findings presented in the paper. To address this and other comments by the reviewer we plan to include a limitation section to the discussion that highlights exactly these points for future studies.

      The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors?

      We thank them for this suggestion. To answer this point, we will conduct immunofluorescent imaging to provide further characterization of these cells in greater depth, as we agree, this will be important to consider. To best visualize cells and their interactions in this adventitial region, we plan to use 3D precision cut lung slices from PBS versus DRA mice in combination with confocal imaging. This method will allow us to utilize antibodies and markers that do not work in the FFPE sections such as SiglecF (eosinophils), CD11c (DCs, macrophages), CD64 and CD169 (macrophages).

      The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

      We agree that the discussion could be used to reinforce the importance of the biological discoveries we have made (listed previously) in the discussion. However, we also believe that it is important to discuss the methodology as this is a novel way to explore ECM-cell interactions in the tissue as highlighted by the reviewer. There are many limitations to using IMC and similar techniques that should be highlighted for future studies so that we can develop better ways of quantifying the ECM environment during disease.

      Significance

      The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

      Whilst we appreciate the reviewers points here, we would like to highlight the time involved in generating such datasets, with a lot of careful optimization and experimental design aspects going into each study. Whilst we have also performed staining and analysis using our described method in human FFPE tissue, we are currently looking to further develop analysis tools to assess ECM-cell interactions. Additionally, data acquisition using IMC takes considerable time, and hence it is not feasible run and analysis the number of samples required to address some of the questions proposed by the reviewer.

      We believe our manuscript provides novel methodology to analyse ECM environments within spatial datasets, something that no other spatial datasets have explored to date. Furthermore, we provide numerous new biological findings in relation to how cells are organized within the tissue during allergic pathology and propose immune-fibroblast interactions that may be key for driving ECM remodelling in the lung. Integrating these analyses will be key for further understanding the role of the ECM in disease pathogenesis.

      The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

      As mentioned above, this analysis pipeline is easily applied to human samples or any other species as ECM molecule organization is largely conserved across species. Moreover, we have already explored this using human samples. However, adding human data to this manuscript is beyond the scope of this manuscript which was aiming to build one of the first methodologies for incorporating the ECM into this kind spatial analysis from the start in order to make biological discoveries. Regardless, we will add a discussion point on utilizing these pipelines to other species within the discussion of the manuscript.

      Flow cytometry has been published on this model and the exact samples used within this study as mentioned previously (PMID: 33587776), validating some of these findings – we will make this point more clearly in the manuscript. We do appreciate that it would be good to further expand on some findings presented in the manuscript. As such we will expand our immunostaining (as mentioned above) to give more detail on the infiltrating immune cell populations and their interactions with fibroblasts.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required): __

      Summary Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

      Major: ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions.

      We thank the reviewer for this excellent comment and pointing this out. We agree that this is very important and will add this data to the manuscript. All information was included by reference of the antibody clones. However, it is an important point to make and we will account for this during interpretation of the results.

      Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses.

      We are unsure what the reviewer is exactly referring to here. We have maintained a consistent nomenclature for these annotations throughout the manuscript. If the reviewer has an issue with the names we have provided for the regions; names were chosen these to be more informative than just naming them “region 1, 2, 3…”. Names in the manuscript were based on taking the lung tissue region and the prominent ECM molecules present. Whilst some level of detail will naturally be lost, we considered this the best way to keep data clear and consistent throughout the manuscript. For example, adventitial collagen describes the region predominantly around the adventitial cuff (fig 3c and d; shown in dark blue) that has high levels of Collagen I, III and VI. Yes, HA, laminin and fibronectin are also expressed, but at much lower levels. Regardless, all the information is present within the figures with readers to observe and make their own interpretations. We are happy to consider alternative names if the reviewer were to provide some guidance on what they thought was more appropriate.

      Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

      We provide heatmaps in the supplementary data which shows the exact marker expression pattern for all of the clusters we define (Sup Fig 1a). Additionally, we provide graphs showing the cellular contribution and spatial distribution of all the regions we defined with lisaClust (Fig 2h & I; Sup Fig 1d). Most activated cells are a feature of a specific clustered cell type only being present in either PBS or DRA treated animals. However, the features which have led to separation these cell types are available in the heatmaps as mentioned (Sup Fig 1a).

      We believe the reviewer may be confused about the purpose of DeepThresh. This algorithm is not for removing staining artifacts. Instead it uses expert annotation of a small training set to generate a method of accurately thresholding images for positive staining in relatively small ROIs which may have diverse structural features with different staining properties. We did not have space in the manuscript to go into this in more detail. However, we appreciate this may not be as clear as needed for readers, and hence, will provide supplementary data showing some example thresholding alongside the original staining in a new edit of the manuscript.

      CD11b+ and infiltrating cells are not an overlapping population, they were separately clustered by the algorithm, but we take the reviewers point that further characterisation could be done. As mentioned in comments from reviewer 1, there is a limitation in the number of markers we can use in IMC, especially with the number of ECM markers we included. Additionally, there are limitations in the appropriate antibodies (carrier-free) that work in FFPE mouse tissue with the antigen retrieval that we use for good, reliable staining of ECM components. As such, we will perform additional immunofluorescence staining in 3D precision cut lung slices to better characterize the CD11b+ population to address comments by both reviewers.

      Minor: Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone).

      This point was directly addressed above in “Major” points and appears to be a duplicate comment.

      ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed.

      The increase in C57BL/6 mice upon DRA treatment in panel A is not “significant” in the modern sense of the word. However, we would argue that stating it is “not significant” would also be a mistake. We prefer to use p values as an inferential measure of significance in combination with measures such as effect size and variance (PMID: 8465801). We find this more useful considering the vast number of mistakes made when interpreting p values (PMID: 18582619). The importance of not purely relying on p values for clinical research has been reviewed recently here (PMID: 39909800).

      Whilst we appreciate the reviewer’s requirement for significance, we do not want to make sweeping statements based off of a p value of 0.07, especially in only one experiment. Many papers have been published on the pitfalls of stringently adhering to p

      Spelling Error - "Immunte foci" in Figure 4h.

      We thank the reviewer for pointing this out and will correct this.

      Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions.

      We find it hard to comment on this without more detail of the cell-ECM interactions that the reviewer believes should be occurring. We analysed this in an unbiased way, so we have not forced interactions to appear based on our preconceptions. The regions being analysed in Fig 6g are the resting (PBS) and activated (DRA) airways that contain expected cell populations of airway epithelial cells and a low level of fibroblasts, likely from just under the airway epithelial cells. These cell populations align with AEC-associated matrix, laminin and hyaluronan, and adventitial collagen regions. Perhaps the reviewer is questioning why the airways are associated with adventitial collagens? The reason behind this, is due to adventitial cuff residing adjacent to a proportion of all airways, and hence any ECM associated with the adventitial cuff will likely be included in an airway region. However, as mentioned previously there are limitations to this analysis and we are very likely missing finer details due to issues such as resolution which we have discussed within the point-by-point on numerous occasions, and something we will directly address by adding a limitations section to the discussion of the revised manuscript.

      Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult.

      We agree with the reviewer on this point. The issue we had here was that Col-I and Col-III strongly overlap in these images, whilst one was green and one yellow the effect was to make them look the same in the final images. We will remake these images with clearer colours that better illustrate differences in Col-I and Col-III expression.

      Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

      Patches refers to an approach that is used to identify interconnected groups of similar cell types and is a method that is based off published data (PMID: 35363540). We will add further method details that explains this process to the revised manuscript.

      Detailed review: Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

      We thank the reviewer for this comment and also agree that the mouse models presented in the manuscript can provide insightful and mechanistic data for investigating human disease.

      Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

      As described in a previous comment this is not the function of DeepThresh. Manual annotation for training data was performed by consensus agreement of four independent researchers. In terms of performance against another tool, we are not aware of another tool which performs this function and hence cannot compare. However, we will add additional data showing the validation metrics for the pipeline to make future comparisons easier.

      Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

      Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

      This relates to the comment above made by reviewer 2. As mentioned, we agree with this key point and will provide this information from the respective antibody clones.

      However, we are unable to provide details on the molecular weight of heparan sulfate as this will vary depending on location/tissue/condition etc. The antibody recognises 10E4 epitope on HS which is found across a wide variety of tissues and species and will recognise many different sizes of HS and even porcine Heparan. Importantly it is relatively specific, not cross reacting with hyaluronan, keratan sulphate, chondroitin sulphate, or dermatan sulphate which is an issue for certain clones. Whilst the size of the HS is an interesting facet, consideration of changes in sulphation patterns would also be of interest, though these currently cannot be accurately assessed via purely immunostaining-based methodologies and would require more targeted biochemical techniques. In addition to this there are multiple nuances in 10E4 antibody binding (PMID: 15044385 and 11278655) which are interesting, but far beyond the scope of this study. Although captured in the antibody clone information, we will also ensure this is clear in the methods.

      In relation to Col4 isoforms specifically, often antibodies for the ECM are limited because of their repeating structures it is hard to generate specific antibodies. For collagen IV there many clones for Col4a1, but no specific clones for Col4a3/col4a5 etc, suitable for use in FFPE tissues and metal conjugation required for IMC. Therefore, we were very limited in what was available to detect them at all. We will bring this up in the discussion as this is an important point, not just for our data, but also for people attempting to replicate this kind of analysis.

      Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

      Our staining approach and analysis have only identified certain activated populations as pointed out by the reviewer. Most of the populations that we have identified as “activated” have been identified primarily only in mice administered DRA. The reason that we have not included “resting” and “activated” populations for all cell types is that these clusters were generated using a clustering algorithm based on the cellular markers used within the study. Each cluster was then simply labelled as best we could, using information from marker expression, published biological data, anatomical location, and sample identity (e.g. PBS or DRA).

      A caveat to using IMC and other similar imaging techniques is that we will miss certain “flavours” of cell populations because we simply do not have the markers, or scope to include markers, with which to identify these cells. This is partly a problem of appropriate antibody availability, but also for many populations there are no specific markers identified in the literature/databases. Single cell RNAseq has provided deep segmentation of some of these populations, but we (and others) have found that often these make poor antibody choices at the protein immunostaining level.

      We are unsure what the reviewer wants adding to plot 2i. This plot shows the cell cluster contribution to different lisaClust defined tissue regions. Hence the presence of alveolar fibroblasts in the resting and activate alveoli region. However, we will include more discussion on the limitations of markers and identification of specific cell populations in the discussion.

      Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal?

      We assume the reviewer is referring to the overlap of some grey circles though/over the red airway epithelial cells in the C57BL/6 DRA panel of figure 2h. This figure represents individual cells as circles with the centroid of the circle at the centroid of the cell. Cells are rarely perfect circles and, in this case, it has made it seem like the cell is coming through the airway epithelium, when likely it is a longer cell that sits directly under it. In addition to this, these are effectively 2-dimensional section (5um thick) that capture as small portion of the lung anatomy, hence occasionally this can result in unusual tissue structures that make no sense in the confines of a 2D section, but instead correlate with the larger 3D structure.

      How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)?

      Activated airway simply describes a region that is showing some evidence of activation markers such as RELMα and/or Ym2 etc. PBS itself, as with any other liquid administered into the lungs, will drive a very low level of inflammation, which is why it is used as a control in the animal model. Therefore, it is not surprising that we see a low number of these “activated” cells in PBS animals vice versa for their activated counterparts in DRA treated animals. This is similar for the other regions mentioned.

      How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

      We are somewhat confused by this question. We have termed the region “subepithelia” because it is mostly found under the airway epithelial cells. We found that this region expands during DRA treatment and covers areas close to the immune foci and inflammatory adventitia, hence they are next to each other.

      As described above, the names of these regions were chosen for simplicity and to communicate its general features. These, regions were identified by detection of nearby regions of cells with similar cellular compositions and the names we a “best fit”.

      Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region.

      We apologise that this was unclear for the reviewer. Rather than describing it as using the cell as a proxy locator to the ECM region we find it more accurate to think of it as we are characterizing the matrix environment of the cell i.e. what is the cell close to and what is it far away from. We will make this clearer in the results by changing the name to cellular matrix environment, rather than matrix environment.

      Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

      We struggled to ascertain what the reviewer was referring to here and what edits they were suggesting to the revised manuscript. However, this comment seems to assume that we have used cellular location as an input to the UMAP in figure 3b, which is untrue. This UMAP (and associated clustering) shows each cell as a dot which is organised based on its distance to the different matrix components. Effectively showing us how different cells cluster based on their cellular matrix environment, with no input of cellular based markers. We are unsure what the reviewer is referring to on line 486 – as they seem to be describing exactly what figure 3c already is (a spatial map of the UMAP clusters on representative images, which shows that a cells matrix environment does seem to show patterns that align with the general lung anatomy).

      Finally, the reviewer asks us to specify why our approach is superior, but we are unclear what the alternative approach is.

      This methodology is effectively a repurposing of the traditional UMAP and clustering methodology used in many single cell techniques, but instead of applying this to cellular markers we are applying it to a cells matrix environment as quantified by the matrix distances. If the reviewer could clarify this comment we would be happy to revisit it. As mentioned in the previous comment, we will more clearly describe cellular matrix environments in the revised manuscript and this may also help with the confusion.

      Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial.

      Respectfully, we completely disagree with the reviewer on this point. In the heatmap (Fig 3d) the Subepithelial & Vascular matrix environment correlates most strongly with the Vasculature and Subepithelial cells as shown by the stronger green-yellow colour in the corresponding cell of the heatmap.

      As mentioned previously in response to another comment by reviewer 2, there could be many reasons that we are not detecting collagen-IV in the AEC associate cell matrix environment. One likely explanation is that this is too fine for the resolution of IMC (1-micron2) or it could be that certain subchains are utilised here that are not recognized by the antibody we managed to optimize. Additionally, AEC-associated matrix environment is comprised of both mouse strains and includes higher representation from DRA treated animals. From our previous work (PMID: 33587776), we have shown that Col-IV expression around the AEC is reduced in DRA versus PBS -treated animals.

      No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM?

      This is a great point from the reviewer and their explanation is entirely possibly. As mentioned there are huge limitations in the resolution of IMC and so we are likely missing finer matrix structures. There is a huge recruitment of cells within this environment so it could be that we cannot clearly visualise fine ECM structure through this considering we are also looking at a 5-micron thick 2D tissue section. Additionally, cells maybe degrading the ECM in order to infiltrate into the tissue. This is definitely an interesting point to examine in further detail, but would need to be done with a different methodology. We will aim to look at an ECM molecules and its distribution within the inflammatory zone using 3D precision cut lung slices and also immune-staining of tissue sections to see whether we can better resolve this in a revised manuscript.

      Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

      Whilst the adventitial cuff does refer to the region immediately around a blood vessel in the lung, these structures are slightly more nuanced as blood vessels normally travel through the lung in close association with an airway. This is true all the way down to the close association with the capillaries and the alveolar spaces where gas exchange occurs. Indeed, previous publications (PMID: 30824323) have shown that these adventitial cuffs extend out from around the contiguous area around the blood vessel and associated airway and these can expand during inflammation (PMID: 24631179). This region is rich in Collagen-I and Collagen-III, as we have shown in this manuscript and previously (PMID: 33587776).

      Whilst we agree that there are likely microanatomical niches within this larger structure, our dataset lacks the resolution to study this in more detail. However, as mentioned above we can include matrix markers in our future IF staining to examine this region in more detail. The adventitial collagen environment described in this manuscript and beyond, are vital “meet and greet” spots for immune cell infiltrating into the lungs (PMID: 30824323) as well as being sites of iBALT formation (PMID: 24631179)

      We are unsure what the reviewer means by “…reduce the perimeter around blood vessels to the same borderline as seen in airways.” We have not defined a manual threshold for the border of the airways. These regions were all defined by SNN clustering and not manual segmentation. Whilst this methodology could be developed we do not believe that this dataset has the resolution to answer this question, as mentioned previously.

      Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b)

      We thank the reviewer for noticing this incorrect labelling and will update this.

      Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

      We define myeloid broadly as CD11b+ or alveolar macs. There were certain populations that were not stained, notably T cells. We were unable to have suitable or reliable staining in FFPE tissue with CD90, TCRa/b, CD3e antibodies via IMC. The same was true for Eosinophil markers (SiglecF, Ccr3, EPO, MBP). The additional experiments we will perform for a revised manuscript (using 3D precision cut lung slices and/or IF staining) should shed further light on these cells. Additionally, as we are not limited by the processing requirements of IMC, we can use a wider range of markers.

      Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

      We apologise that this was not clear to the reviewer. Labels are exclusive and represent the clusters that were identified in figure 2 and are at the finest level of detail that we felt we were able to biologically infer from the data. In terms of the reviewer’s first point about infiltrating cells, these are completely separate from the other cell types mentioned. As mentioned in the previous comment line 570, we were simply unable to find working antibodies for some of the common lung populations (a common problem for FFPE sections where antigens are often masked or lost due to fixation and processing) and so are limited to general annotations for these. For the reviewer’s second example of Neutrophils vs Ly6C+ cells, neutrophils were classified by expression of Ly6G, CD11b+, and Ym1 whereas there are many other cell types that express Ly6C, including, but not limited to, dendritic cells, monocytes, eosinophils, and even some T cells.

      We believe that the graph in combination with data in Fig 1c and supplementary Fig 1a, already shows what the reviewer is asking for.

      Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

      We assume the reviewer means Fig 5l and sup Fig 5i (as there is no figure sup Fig 4i). Whilst we did not include a graph to show that alveolar macrophages produce Ym1, we did include two references in the text and this has been widely shown in the literature for many years (PMID: 11141507 and 15148607). We are somewhat unclear on the reviewers second point. AEC (airway epithelial cells) can definitely also produce Ym1, though this can often be contentious because of issues with cross-reactivity with its highly homologous sister protein Ym2, which is also produced from airway epithelial cells under Type-2 settings. If the reviewer is referring to AEC (alveolar epithelial cells) then this is true. Activated alveoli are lisaClust regions with lots of alveolar macrophages which was the original statement we made and aligns with sup Fig 5i. Activated alveoli II have less alveolar macrophages and also have less Ym1, which would correlate though there are other cell types which can make Ym1 as well.

      Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

      We are again somewhat confused by this comment. Adventitial collagen only weakly correlates because it is not within the airway epithelial cells, instead it is adjacent in the subepithelial region which is shown in Fig 6j. We are unsure exactly what the reviewer is referring to in terms of “adventitial mapping” but are happy to comment on this if the reviewer can clarify what they mean.

      We agree with the reviewer that it is somewhat surprising to see so many fibroblasts in the resting and activated airway regions. There is a level of ambiguity here in what lisaClust decides to include in one region vs another. However, what it does show is that there are a large population of fibroblasts around the airway, possibly correlating with peribronchial fibroblasts. We did not observe immune cells in between the airway cells or immediately underneath it. We do not believe this is odd, as from our data it appears that these cells are more likely to be found in the adventitial (including peribronchial as mentioned previously) cuff. Cell are most certainly moving into the airways as shown from the BAL in our previous publication (PMID: 33587776). However, we are unlikely to capture this process in the snapshot of our histology across a relatively small section of the airways covered in our 2D sections.

      In regards to the reviewers comment about figure 6a we agree that some of the regions between the airways and blood vessels have been characterised as subepithelia. As mentioned previously we are happy to consider alternative names but have been unable to come up with an alternative that encompasses the cells and spatial region more accurately and clearly., Regardless, the main purpose of these names is to provide simple nomenclature to follow throughout the manuscript and make these types of analyses accessible to all readers. We believe that these are accurately labelled and have provided information about the constituent cell populations that are present within them, making the data and subsequent analysis transparent for others to view and explore. Our data suggests that the adventitial cuff may fulfil multiple roles during DRA-induced inflammation, some of which are more focused on immune cell recruitment and others which may correlate more with the fibroblast rich subepithelial region.

      The reviewer is entirely correct to point out that some blood vessels were not entirely annotated. We used vWF to manually separate blood vessels from the adjacent smooth muscle layers, which were not separated by the clustering originally. Notably it appears that veins seem to not separate as well as arteries suggesting another marker (e.g. CD31) may help with this, though we were limited in what we could include as mentioned previously. As this is only a small effect, which we do not have a way to correct, and blood vessels were not the focus of this manuscript, we have left the annotation as it is with raw data included.

      __Significance __

      Strength Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components. Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses. Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations. Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

      Limitations ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions. Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging. Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts. Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

      __Advance, gap filled __ Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

      __Audience __ The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

      __Own Expertise __ Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews

      Reviewer #1 (Public Review):

      Summary:

      The authors have created a system for designing and running experimental pipelines to control and coordinate different programs and devices during an experiment, called Heron. Heron is based around a graphical tool for creating a Knowledge Graph made up of nodes connected by edges, with each node representing a separate Python script, and each edge being a communication pathway connecting a specific output from one node to an iput on another. Each node also has parameters that can be set by the user during setup and runtime, and all of this behavior is concisely specified in the code that defines each node. This tool tries to marry the ease of use, clarity, and selfdocumentation of a purely graphical system like Bonsai with the flexibility and power of a purely code-based system like Robot Operating System (ROS).

      Strengths:

      The underlying idea behind Heron, of combining a graphical design and execution tool with nodes that are made as straightforward Python scripts seems like a great way to get the relative strengths of each approach. The graphical design side is clear, selfexplanatory, and self-documenting, as described in the paper. The underlying code for each node tends to also be relatively simple and straightforward, with a lot of the complex communication architecture successfully abstracted away from the user. This makes it easy to develop new nodes, without needing to understand the underlying communications between them. The authors also provide useful and well-documented templates for each type of node to further facilitate this process. Overall this seems like it could be a great tool for designing and running a wide variety of experiments, without requiring too much advanced technical knowledge from the users.

      The system was relatively easy to download and get running, following the directions and already has a significant amount of documentation available to explain how to use it and expand its capabilities. Heron has also been built from the ground up to easily incorporate nodes stored in separate Git repositories and to thus become a large community-driven platform, with different nodes written and shared by different groups. This gives Heron a wide scope for future utility and usefulness, as more groups use it, write new nodes, and share them with the community. With any system of this sort, the overall strength of the system is thus somewhat dependent on how widely it is used and contributed to, but the authors did a good job of making this easy and accessible for people who are interested. I could certainly see Heron growing into a versatile and popular system for designing and running many types of experiments.

      Weaknesses:

      (1) The number one thing that was missing from the paper was any kind of quantification of the performance of Heron in different circumstances. Several useful and illustrative examples were discussed in depth to show the strengths and flexibility of Heron, but there was no discussion or quantification of performance, timing, or latency for any of these examples. These seem like very important metrics to measure and discuss when creating a new experimental system.

      Heron is practically a thin layer of obfuscation of signal passing across processes. Given its design approach it is up to the code of each Node to deal with issues of timing, synching and latency and thus up to each user to make sure the Nodes they author fulfil their experimental requirements. Having said that, Heron provides a large number of tools to allow users to optimise the generated Knowledge Graphs for their use cases. To showcase these tools, we have expanded on the third experimental example in the paper with three extra sections, two of which relate to Heron’s performance and synching capabilities. One is focusing on Heron’s CPU load requirements (and existing Heron tools to keep those at acceptable limits) and another focusing on post experiment synchronisation of all the different data sets a multi Node experiment generates.   

      (2) After downloading and running Heron with some basic test Nodes, I noticed that many of the nodes were each using a full CPU core on their own. Given that this basic test experiment was just waiting for a keypress, triggering a random number generator, and displaying the result, I was quite surprised to see over 50% of my 8-core CPU fully utilized. I don’t think that Heron needs to be perfectly efficient to accomplish its intended purpose, but I do think that some level of efficiency is required. Some optimization of the codebase should be done so that basic tests like this can run with minimal CPU utilization. This would then inspire confidence that Heron could deal with a real experiment that was significantly more complex without running out of CPU power and thus slowing down.

      The original Heron allowed the OS to choose how to manage resources over the required process. We were aware that this could lead to significant use of CPU time, as well as occasionally significant drop of packets (which was dependent on the OS and its configuration). This drop happened mainly when the Node was running a secondary process (like in the Unity game process in the 3rd example). To mitigate these problems, we have now implemented a feature allowing the user to choose the CPU that each Node’s worker function runs on as well as any extra processes the worker process initialises. This is accessible from the Saving secondary window of the node. This stops the OS from swapping processes between CPUs and eliminates the dropping of packages due to the OS behaviour. It also significantly reduces the utilised CPU time. To showcase this, we initially run the simple example mentioned by the reviewer. The computer running only background services was using 8% of CPU (8 cores). With Heron GUI running but with no active Graph, the CPU usage went to 15%. With the Graph running and Heron’s processes running on OS attributed CPU cores, the total CPU was at 65% (so very close to the reviewer’s 50%). By choosing a different CPU core for each of the three worker processes the CPU went down to 47% and finally when all processes were forced to run on the same CPU core the CPU load dropped to 30%.  So, Heron in its current implementation running its GUI and 3 Nodes takes 22% of CPU load. This is still not ideal but is a consequence of the overhead of running multiple processes vs multiple threads. We believe that, given Heron’s latest optimisation, offering more control of system management to the user, the benefits of multi process applications outweigh this hit in system resources. 

      We have also increased the scope of the third example we provide in the paper and there we describe in detail how a full-scale experiment with 15 Nodes (which is the upper limit of number of Nodes usually required in most experiments) impacts CPU load. 

      Finally, we have added on Heron’s roadmap projects extra tasks focusing only on optimisation (profiling and using Numba for the time critical parts of the Heron code).

      (3) I was also surprised to see that, despite being meant specifically to run on and connect diverse types of computer operating systems and being written purely in Python, the Heron Editor and GUI must be run on Windows. This seems like an unfortunate and unnecessary restriction, and it would be great to see the codebase adjusted to make it fully crossplatform-compatible.

      This point was also mentioned by reviewer 2. This was a mistake on our part and has now been corrected in the paper. Heron (GUI and underlying communication functionality) can run on any machine that the underlying python libraries run, which is Windows, Linux (both for x86 and Arm architectures) and MacOS. We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). The Windows and Linux versions of Heron have undergone extensive debugging and all of the available Nodes (that are not OS specific) run on those two systems. We are in the process of debugging the Nodes’ functionality for RasPi. The MacOS version, although functional requires further work to make sure all of the basic Nodes are functional (which is not the case at the moment). We have also updated our manuscript (Multiple machines, operating systems and environments) to include the above information. 

      (4) Lastly, when I was running test experiments, sometimes one of the nodes, or part of the Heron editor itself would throw an exception or otherwise crash. Sometimes this left the Heron editor in a zombie state where some aspects of the GUI were responsive and others were not. It would be good to see a more graceful full shutdown of the program when part of it crashes or throws an exception, especially as this is likely to be common as people learn to use it. More problematically, in some of these cases, after closing or force quitting Heron, the TCP ports were not properly relinquished, and thus restarting Heron would run into an "address in use" error. Finding and killing the processes that were still using the ports is not something that is obvious, especially to a beginner, and it would be great to see Heron deal with this better. Ideally, code would be introduced to carefully avoid leaving ports occupied during a hard shutdown, and furthermore, when the address in use error comes up, it would be great to give the user some idea of what to do about it.

      A lot of effort has been put into Heron to achieve graceful shut down of processes, especially when these run on different machines that do not know when the GUI process has closed. The code that is being suggested to avoid leaving ports open has been implemented and this works properly when processes do not crash (Heron is terminated by the user) and almost always when there is a bug in a process that forces it to crash. In the version of Heron available during the reviewing process there were bugs that caused the above behaviour (Node code hanging and leaving zombie processes) on MacOS systems. These have now been fixed. There are very seldom instances though, especially during Node development, that crashing processes will hang and need to be terminated manually. We have taken on board the reviewer’s comments that users should be made more aware of these issues and have also described this situation in the Debugging part of Heron’s documentation. There we explain the logging and other tools Heron provides to help users debug their own Nodes and how to deal with hanging processes.

      Heron is still in alpha (usable but with bugs) and the best way to debug it and iron out all the bugs in all use cases is through usage from multiple users and error reporting (we would be grateful if the errors the reviewer mentions could be reported in Heron’s github Issues page). We are always addressing and closing any reported errors, since this is the only way for Heron to transition from alpha to beta and eventually to production code quality.

      Overall I think that, with these improvements, this could be the beginning of a powerful and versatile new system that would enable flexible experiment design with a relatively low technical barrier to entry. I could see this system being useful to many different labs and fields. 

      We thank the reviewer for positive and supportive words and for the constructive feedbacks. We believe we have now addressed all the raised concerns.  

      Reviewer #2 (Public Review):

      Summary:

      The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to

      (1) design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments,

      (2) execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

      The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

      Using three showcases of behavioral experiments on rats, the authors highlighted three benefits of their software design:

      (1) the knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,

      (2) the experiment can be executed in a distributed fashion across multiple machines that each has a different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,

      (3) he users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background.

      Strengths:

      (1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,

      (2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems.

      (3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.

      (4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

      Weaknesses:

      (1) While the software implementation is solid and has proven effective in designing the experiment showcased in the paper, the novelty of the design is not made clear in the manuscript. Conceptually, both the use of graphs and visual experimental flow design have been key features in many widely used softwares as suggested in the background section of the manuscript. In particular, contrary to the authors’ claim that only pre-defined elements can be used in Simulink or LabView, Simulink introduced MATLAB Function Block back in 2011, and Python code can be used in LabView since 2018. Such customization of nodes is akin to what the authors presented.

      In the Heron manuscript we have provided an extensive literature review of existing systems from which Heron has borrowed ideas. We never wished to say that graphs and visual code is what sets Heron apart since these are technologies predating Heron by many years and implemented by a large number of software. We do not believe also that we have mentioned that LabView or Simulink can utilise only predefined nodes. What we have said is that in such systems (like LabView, Simulink and Bonsai) the focus of the architecture is on prespecified low level elements while the ability for users to author their own is there but only as an afterthought. The difference with Heron is that in the latter the focus is on the users developing their own elements. One could think of LabView style software as node-based languages (with low level visual elements like loops and variables) that also allow extra scripting while Heron is a graphical wrapper around python where nodes are graphical representations of whole processes. To our knowledge there is no other software that allows the very fast generation of graphical elements representing whole processes whose communication can also be defined graphically. Apart from this distinction, Heron also allows a graphical approach to writing code for processes that span different machines which again to our knowledge is a novelty of our approach and one of its strongest points towards ease of experimental pipeline creation (without sacrificing expressivity). 

      (2) The authors claim that the knowledge graph can be considered as a self-documentation of an experiment. I found it to be true to some extent. Conceptually it’s a welcoming feature and the fact that the same visualization of the knowledge graph can be used to run and control experiments is highly desirable (but see point 1 about novelty). However, I found it largely inadequate for a person to understand an experiment from the knowledge graph as visualized in the GUI alone. While the information flow is clear, and it seems easier to navigate a codebase for an experiment using this method, the design of the GUI does not make it a one-stop place to understand the experiment. Take the Knowledge Graph in Supplementary Figure 2B as an example, it is associated with the first showcase in the result section highlighting this self-documentation capability. I can see what the basic flow is through the disjoint graph where 1) one needs to press a key to start a trial, and 2) camera frames are saved into an avi file presumably using FFMPEG. Unfortunately, it is not clear what the parameters are and what each block is trying to accomplish without the explanation from the authors in the main text. Neither is it clear about what the experiment protocol is without the help of Supplementary Figure 2A.

      In my opinion, text/figures are still key to documenting an experiment, including its goals and protocols, but the authors could take advantage of the fact that they are designing a GUI where this information, with properly designed API, could be easily displayed, perhaps through user interaction. For example, in Local Network -> Edit IPs/ports in the GUI configuration, there is a good tooltip displaying additional information for the "password" entry. The GUI for the knowledge graph nodes can very well utilize these tooltips to show additional information about the meaning of the parameters, what a node does, etc, if the API also enforces users to provide this information in the form of, e.g., Python docstrings in their node template. Similarly, this can be applied to edges to make it clear what messages/data are communicated between the nodes. This could greatly enhance the representation of the experiment from the Knowledge graph.

      In the first showcase example in the paper “Probabilistic reversal learning.

      Implementation as self-documentation” we go through the steps that one would follow in order to understand the functionality of an experiment through Heron’s Knowledge Graph. The Graph is not just the visual representation of the Nodes in the GUI but also their corresponding code bases. We mention that the way Heron’s API limits the way a Node’s code is constructed (through an Actor based paradigm) allows for experimenters to easily go to the code base of a specific Node and understand its 2 functions (initialisation and worker) without getting bogged down in the code base of the whole Graph (since these two functions never call code from any other Nodes). Newer versions of Heron facilitate this easy access to the appropriate code by also allowing users to attach to Heron their favourite IDE and open in it any Node’s two scripts (worker and com) when they double click on the Node in Heron’s GUI. On top of this, Heron now (in the versions developed as answers to the reviewers’ comments) allows Node creators to add extensive comments on a Node but also separate comments on the Node’s parameters and input and output ports. Those can be seen as tooltips when one hovers over the Node (a feature that can be turned off or on by the Info button on every Node).  

      As Heron stands at the moment we have not made the claim that the Heron GUI is the full picture in the self-documentation of a Graph. We take note though the reviewer’s desire to have the GUI be the only tool a user would need to use to understand an experimental implementation. The solution to this is the same as the one described by the reviewer of using the GUI to show the user the parts of the code relevant to a specific Node without the user having to go to a separate IDE or code editor. The reason this has not been implemented yet is the lack of a text editor widget in the underlying gui library (DearPyGUI). This is in their roadmap for their next large release and when this exists we will use it to implement exactly the idea the reviewer is suggesting, but also with the capability to not only read comments and code but also directly edit a Node’s code (see Heron’s roadmap). Heron’s API at the moment is ideal for providing such a text editor straight from the GUI.

      (3) The design of Heron was primarily with behavioral experiments in mind, in which highly accurate timing is not a strong requirement. Experiments in some other areas that this software is also hoping to expand to, for example, electrophysiology, may need very strong synchronization between apparatus, for example, the record timing and stimulus delivery should be synced. The communication mechanism implemented in Heron is asynchronous, as I understand it, and the code for each node is executed once upon receiving an event at one or more of its inputs. The paper, however, does not include a discussion, or example, about how Heron could be used to address issues that could arise in this type of communication. There is also a lack of information about, for example, how nodes handle inputs when their ability to execute their work function cannot keep up with the frequency of input events. Does the publication/subscription handle the queue intrinsically? Will it create problems in real-time experiments that make multiple nodes run out of sync? The reader could benefit from a discussion about this if they already exist, and if not, the software could benefit from implementing additional mechanisms such that it can meet the requirements from more types of experiments.

      In order to address the above lack of explanation (that also the first reviewer pointed out) we expanded the third experimental example in the paper with three more sections. One focuses solely on explaining how in this example (which acquires and saves large amounts of data from separate Nodes running on different machines) one would be able to time align the different data packets generated in different Nodes to each other. The techniques described there are directly implementable on experiments where the requirements of synching are more stringent than the behavioural experiment we showcase (like in ephys experiments). 

      Regarding what happens to packages when the worker function of a Node is too slow to handle its traffic, this is mentioned in the paper (Code architecture paragraph): “Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running.” This is also explained in more detail in Heron’s documentation. The reasoning for a no buffer system (as described in the documentation) is that for the use cases Heron is designed to handle we believe there is no situation where a Node would receive large amounts of data in bursts while very little data during the rest of the time (in which case a buffer would make sense). Nodes in most experiments will either be data intensive but with a constant or near constant data receiving speed (e.g. input from a camera or ephys system) or will have variable data load reception but always with small data loads (e.g. buttons). The second case is not an issue and the first case cannot be dealt with a buffer but with the appropriate code design, since buffering data coming in a Node too slow for its input will just postpone the inevitable crash. Heron’s architecture principle in this case is to allow these ‘mistakes’ (i.e. package dropping) to happen so that the pipeline continues to run and transfer the responsibility of making Nodes fast enough to the author of each Node. At the same time Heron provides tools (see the Debugging section of the documentation and the time alignment paragraph of the “Rats playing computer games”  example in the manuscript) that make it easy to detect package drops and either correct them or allow them but also allow time alignment between incoming and outgoing packets. In the very rare case where a buffer is required Heron’s do-it-yourself logic makes it easy for a Node developer to implement their own Node specific buffer.

      (4) The authors mentioned in "Heron GUI’s multiple uses" that the GUI can be used as an experimental control panel where the user can update the parameters of the different Nodes on the fly. This is a very useful feature, but it was not demonstrated in the three showcases. A demonstration could greatly help to support this claim.

      As the reviewer mentions, we have found Heron’s GUI double role also as an experimental on-line controller a very useful capability during our experiments. We have expanded the last experimental example to also showcase this by showing how on the “Rats playing computer games” experiment we used the parameters of two Nodes to change the arena’s behaviour while the experiment was running, depending on how the subject was behaving at the time (thus exploring a much larger set of parameter combinations, faster during exploratory periods of our shaping protocols construction). 

      (5) The API for node scripts can benefit from having a better structure as well as having additional utilities to help users navigate the requirements, and provide more guidance to users in creating new nodes. A more standard practice in the field is to create three abstract Python classes, Source, Sink, and Transform that dictate the requirements for initialisation, work_function, and on_end_of_life, and provide additional utility methods to help users connect between their code and the communication mechanism. They can be properly docstringed, along with templates. In this way, the com and worker scripts can be merged into a single unified API. A simple example that can cause confusion in the worker script is the "worker_object", which is passed into the initialise function. It is unclear what this object this variable should be, and what attributes are available without looking into the source code. As the software is also targeting those who are less experienced in programming, setting up more guidance in the API can be really helpful. In addition, the self-documentation aspect of the GUI can also benefit from a better structured API as discussed in point 2 above.

      The reviewer is right that using abstract classes to expose to users the required API would be a more standard practice. The reason we did not choose to do this was to keep Heron easily accessible to entry level Python programmers who do not have familiarity yet with object oriented programming ideas. So instead of providing abstract classes we expose only the implementation of three functions which are part of the worker classes but the classes themselves are not seen by the users of the API. The point about the users’ accessibility to more information regarding a few objects used in the API (the worker object for example) has been taken on board and we have now addressed this by type hinting all these objects both in the templates and more importantly in the automatically generated code that Heron now creates when a user chooses to create a Node graphically (a feature of Heron not present in the version available in the initial submission of this manuscript).  

      (6) The authors should provide more pre-defined elements. Even though the ability for users to run arbitrary code is the main feature, the initial adoption of a codebase by a community, in which many members are not so experienced with programming, is the ability for them to use off-the-shelf components as much as possible. I believe the software could benefit from a suite of commonly used Nodes.

      There are currently 12 Node repositories in the Heron-repositories project on Github with more than 30 Nodes, 20 of which are general use (not implementing a specific experiment’ logic). This list will continue to grow but we fully appreciate the truth of the reviewer’s comment that adoption will depend on the existence of a large number of commonly used Nodes (for example Numpy, and OpenCV Nodes) and are working towards this goal.

      (7) It is not clear to me if there is any capability or utilities for testing individual nodes without invoking a full system execution. This would be critical when designing new experiments and testing out each component.

      There is no capability to run the code of an individual Node outside Heron’s GUI. A user could potentially design and test parts of the Node before they get added into a Node but we have found this to be a highly inefficient way of developing new Nodes. In our hands the best approach for Node development was to quickly generate test inputs and/or outputs using the “User Defined Function 1I 1O” Node where one can quickly write a function and make it accessible from a Node. Those test outputs can then be pushed in the Node under development or its outputs can be pushed in the test function, to allow for incremental development without having to connect it to the Nodes it would be connected in an actual pipeline. For example, one can easily create a small function that if a user presses a key will generate the same output (if run from a “User Defined Function 1I 1O” Node) as an Arduino Node reading some buttons. This output can then be passed into an experiment logic Node under development that needs to do something with this input. In this way during a Node development Heron allows the generation of simulated hardware inputs and outputs without actually running the actual hardware. We have added this way of developing Nodes also in our manuscript (Creating a new Node).

      Reviewer #3 (Public Review):

      Summary:

      The authors present a Python tool, Heron, that provides a framework for defining and running experiments in a lab setting (e.g. in behavioural neuroscience). It consists of a graphical editor for defining the pipeline (interconnected nodes with parameters that can pass data between them), an API for defining the nodes of these pipelines, and a framework based on ZeroMQ, responsible for the overall control and data exchange between nodes. Since nodes run independently and only communicate via network messages, an experiment can make use of nodes running on several machines and in separate environments, including on different operating systems.

      Strengths:

      As the authors correctly identify, lab experiments often require a hodgepodge of separate hardware and software tools working together. A single, unified interface for defining these connections and running/supervising the experiment, together with flexibility in defining the individual subtasks (nodes) is therefore a very welcome approach. The GUI editor seems fairly intuitive, and Python as an accessible programming environment is a very sensible choice. By basing the communication on the widely used ZeroMQ framework, they have a solid base for the required non-trivial coordination and communication. Potential users reading the paper will have a good idea of how to use the software and whether it would be helpful for their own work. The presented experiments convincingly demonstrate the usefulness of the tool for realistic scientific applications.

      Weaknesses:

      (1) In my opinion, the authors somewhat oversell the reproducibility and "selfdocumentation" aspect of their solution. While it is certainly true that the graph representation gives a useful high-level overview of an experiment, it can also suffer from the same shortcomings as a "pure code" description of a model - if a user gives their nodes and parameters generic/unhelpful names, reading the graph will not help much. 

      This is a problem that to our understanding no software solution can possibly address. Yet having a visual representation of how different inputs and outputs connect to each other we argue would be a substantial benefit in contrast to the case of “pure code” especially when the developer of the experiment has used badly formatted variable names.

      (2) Making the link between the nodes and the actual code is also not straightforward, since the code for the nodes is spread out over several directories (or potentially even machines), and not directly accessible from within the GUI. 

      This is not accurate. The obligatory code of a Node always exists within a single folder and Heron’s API makes it rather cumbersome to spread scripts relating to a Node across separate folders. The Node folder structure can potentially be copied over different machines but this is why Heron is tightly integrated with git practices (and even politely asks the user with popup windows to create git repositories of any Nodes they create whilst using Heron’s automatic Node generator system). Heron’s documentation is also very clear on the folder structure of a Node which keeps the required code always in the same place across machines and more importantly across experiments and labs. Regarding the direct accessibility of the code from the GUI, we took on board the reviewers’ comments and have taken the first step towards correcting this. Now one can attach to Heron their favourite IDE and then they can double click on any Node to open its two main scripts (com and worker) in that IDE embedded in whatever code project they choose (also set in Heron’s settings windows). On top of this, Heron now allows the addition of notes both for a Node and for all its parameters, inputs and outputs which can be viewed by hovering the mouse over them on the Nodes’ GUIs. The final step towards GUI-code integration will be to have a Heron GUI code editor but this is something that has to wait for further development from Heron’s underlying GUI library DearPyGUI.

      (3) The authors state that "[Heron’s approach] confers obvious benefits to the exchange and reproducibility of experiments", but the paper does not discuss how one would actually exchange an experiment and its parameters, given that the graph (and its json representation) contains user-specific absolute filenames, machine IP addresses, etc, and the parameter values that were used are stored in general data frames, potentially separate from the results. Neither does it address how a user could keep track of which versions of files were used (including Heron itself).

      Heron’s Graphs, like any experimental implementation, must contain machine specific strings. These are accessible either from Heron’s GUI when a Graph json file is opened or from the json file itself. Heron in this regard does not do anything different to any other software, other than saving the graphs into human readable json files that users can easily manipulate directly.

      Heron provides a method for users to save every change of the Node parameters that might happen during an experiment so that it can be fully reproduced. The dataframes generated are done so in the folders specified by the user in each of the Nodes (and all those paths are saved in the json file of the Graph). We understand that Heron offers a certain degree of freedom to the user (Heron’s main reason to exist is exactly this versatility) to generate data files wherever they want but makes sure every file path gets recorded for subsequent reproduction. So, Heron behaves pretty much exactly like any other open source software. What we wanted to focus on as the benefits of Heron on exchange and reproducibility was the ability of experimenters to take a Graph from another lab (with its machine specific file paths and IP addresses) and by examining the graphical interface of it to be able to quickly tweak it to make it run on their own systems. That is achievable through the fact that a Heron experiment will be constructed by a small amount of Nodes (5 to 15 usually) whose file paths can be trivially changed in the GUI or directly in the json file while the LAN setup of the machines used can be easily reconstructed from the information saved in the secondary GUIs.

      Where Heron needs to improve (and this is a major point in Heron’s roadmap) is the need to better integrate the different saved experiments with the git versions of Heron and the Nodes that were used for that specific save. This, we appreciate is very important for full reproducibility of the experiment and it is a feature we will soon implement. More specifically users will save together with a graph the versions of all the used repositories and during load the code base utilised will come from the recorded versions and not from the current head of the different repositories. This is a feature that we are currently working on now and as our roadmap suggests will be implemented by the release of Heron 1.0. 

      (4) Another limitation that in my opinion is not sufficiently addressed is the communication between the nodes, and the effect of passing all communications via the host machine and SSH. What does this mean for the resulting throughput and latency - in particular in comparison to software such as Bonsai or Autopilot? The paper also states that "Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running."- it seems to be up to the user to debug and handle this manually?

      There are a few points raised here that require addressing. The first is Heron’s requirement to pass all communication through the main (GUI) machine. We understand (and also state in the manuscript) that this is a limitation that needs to be addressed. We plan to do this is by adding to Heron the feature of running headless (see our roadmap). This will allow us to run whole Heron pipelines in a second machine which will communicate with the main pipeline (run on the GUI machine) with special Nodes. That will allow experimenters to define whole pipelines on secondary machines where the data between their Nodes stay on the machine running the pipeline. This is an important feature for Heron and it will be one of the first features to be implemented next (after the integration of the saving system with git). 

      The second point is regarding Heron’s throughput latency. In our original manuscript we did not have any description of Heron’s capabilities in this respect and both other reviewers mentioned this as a limitation. As mentioned above, we have now addressed this by adding a section to our third experimental example that fully describes how much CPU is required to run a full experimental pipeline running on two machines and utilising also non python code executables (a Unity game). This gives an overview of how heavy pipelines can run on normal computers given adequate optimisation and utilising Heron’s feature of forcing some Nodes to run their Worker processes on a specific core. At the same time, Heron’s use of 0MQ protocol makes sure there are no other delays or speed limitations to message passing. So, message passing within the same machine is just an exchange of memory pointers while messages passing between different machines face the standard speed limitations of the Local Access Network’s ethernet card speeds. 

      Finally, regarding the message dropping feature of Heron, as mentioned above this is an architectural decision given the use cases of message passing we expect Heron to come in contact with. For a full explanation of the logic here please see our answer to the 3rd comment by Reviewer 2.

      (5) As a final comment, I have to admit that I was a bit confused by the use of the term "Knowledge Graph" in the title and elsewhere. In my opinion, the Heron software describes "pipelines" or "data workflows", not knowledge graphs - I’d understand a knowledge graph to be about entities and their relationships. As the authors state, it is usually meant to make it possible to "test propositions against the knowledge and also create novel propositions" - how would this apply here?

      We have described Heron as a Knowledge Graph instead of a pipeline, data workflow or computation graph in order to emphasise Heron’s distinct operation in contrast to what one would consider a standard pipeline and data workflow generated by other visual based software (like LabView and Bonsai). This difference exists on what a user should think of as the base element of a graph, i.e. the Node. In all other visual programming paradigms, the Node is defined as a low-level computation, usually a language keyword, language flow control or some simple function. The logic in this case is generated by composing together the visual elements (Nodes). In Heron the Node is to be thought of as a process which can be of arbitrary complexity and the logic of the graph is composed by the user both within each Node and by the way the Nodes are combined together. This is an important distinction in Heron’s basic operation logic and it is we argue the main way Heron allows flexibility in what can be achieved while retaining ease of graph composition (by users defining their own level of complexity and functionality encompassed within each Node). We have found that calling this approach a computation graph (which it is) or a pipeline or data workflow would not accentuate this difference. The term Knowledge Graph was the most appropriate as it captures the essence of variable information complexity (even in terms of length of shortest string required) defined by a Node.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      -  No buffering implies dropped messages when a node is busy. It seems like this could be very problematic for some use cases... 

      This is a design principle of Heron. We have now provided a detailed explanation of the reasoning behind it in our answer to Reviewer 2 (Paragraph 3) as well as in the manuscript. 

      -  How are ssh passwords stored, and is it secure in some way or just in plain text?  

      For now they are plain text in an unencrypted file that is not part of the repo (if one gets Heron from the repo). Eventually, we would like to go to private/public key pairs but this is not a priority due to the local nature of Heron’s use cases (all machines in an experiment are expected to connect in a LAN).  

      Minor notes / copyedits:

      -  Figure 2A: right and left seem to be reversed in the caption. 

      They were. This is now fixed. 

      -  Figure 2B: the text says that proof of life messages are sent to each worker process but in the figure, it looks like they are published by the workers? Also true in the online documentation.  

      The Figure caption was wrong. This is now fixed.

      -  psutil package is not included in the requirements for GitHub

      We have now included psutil in the requirements.

      -  GitHub readme says Python >=3.7 but Heron will not run as written without python >= 3.9 (which is alluded to in the paper)

      The new Heron updates require Python 3.11. We have now updated GitHub and the documentation to reflect this.

      -  The paper mentions that the Heron editor must be run on Windows, but this is not mentioned in the Github readme.  

      This was an error in the manuscript that we have now corrected.

      -  It’s unclear from the readme/manual how to remove a node from the editor once it’s been added.  

      We have now added an X button on each Node to complement the Del button on the keyboard (for MacOS users that do not have this button most of the times).

      -  The first example experiment is called the Probabilistic Reversal Learning experiment in text, but the uncertainty experiment in the supplemental and on GitHub.  

      We have now used the correct name (Probabilistic Reversal Learning) in both the supplemental material and on GitHub

      -  Since Python >=3.9 is required, consider using fstrings instead of str.format for clarity in the codebase  

      Thank you for the suggestion. Latest Heron development has been using f strings and we will do a refactoring in the near future.

      -  Grasshopper cameras can run on linux as well through the spinnaker SDK, not just Windows.  

      Fixed in the manuscript. 

      -  Figure 4: Square and star indicators are unclear.

      Increased the size of the indicators to make them clear.

      -  End of page 9: "an of the self" presumably a typo for "off the shelf"?  

      Corrected.

      -  Page 10 first paragraph. "second root" should be "second route"

      Corrected.

      -  When running Heron, the terminal constantly spams Blowfish encryption deprecation warnings, making it difficult to see the useful messages.  

      The solution to this problem is to either update paramiko or install Heron through pip. This possible issue is mentioned in the documentation.

      -  Node input /output hitboxes in the GUI are pretty small. If they could be bigger it would make it easier to connect nodes reliably without mis-clicks.

      We have redone the Node GUI, also increasing the size of the In/Out points.

      Reviewer #2 (Recommendations For The Authors):

      (1) There are quite a few typos in the manuscript, for example: "one can accessess the code", "an of the self", etc.  

      Thanks for the comment. We have now screened the manuscript for possible typos.

      (2) Heron’s GUI can only run on Windows! This seems to be the opposite of the key argument about the portability of the experimental setup.  

      As explained in the answers to Reviewer 1, Heron can run on most machines that the underlying python libraries run, i.e. Windows and Linux (both for x86 and Arm architectures). We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). We have now revised the manuscript and the GitHub repo to reflect this.

      (3) Currently, the output is displayed along the left edge of the node, but the yellow dot connector is on the right. It would make more sense to have the text displayed next to the connectors.  

      We have redesigned the Node GUI and have now placed the Out connectors on the right side of the Node.

      (4) The edges are often occluded by the nodes in the GUI. Sometimes it leads to some confusion, particularly when the number of nodes is large, e.g., Fig 4.

      This is something that is dependent on the capabilities of the DearPyGUI module. At the moment there is no way to control the way the edges are drawn.

      Reviewer #3 (Recommendations For The Authors):

      A few comments on the software and the documentation itself:

      - From a software engineering point of view, the implementation seems to be rather immature. While I get the general appeal of "no installation necessary", I do not think that installing dependencies by hand and cloning a GitHub repository is easier than installing a standard package.

      We have now added a pip install capability which also creates a Heron command line command to start Heron with. 

      -The generous use of global variables to store state (minor point, given that all nodes run in different processes), boilerplate code that each node needs to repeat, and the absence of any kind of automatic testing do not give the impression of a very mature software (case in point: I had to delete a line from editor.py to be able to start it on a non-Windows system).  

      As mentioned, the use of global variables in the worker scripts is fine partly due to the multi process nature of the development and we have found it is a friendly approach to Matlab users who are just starting with Python (a serious consideration for Heron). Also, the parts of the code that would require a singleton (the Editor for example) are treated as scripts with global variables while the parts that require the construction of objects are fully embedded in classes (the Node for example). A future refactoring might make also all the parts of the code not seen by the user fully object oriented but this is a decision with pros and cons needing to be weighted first. 

      Absence of testing is an important issue we recognise but Heron is a GUI app and nontrivial unit tests would require some keystroke/mouse movement emulator (like QTest of pytest-qt for QT based GUIs). This will be dealt with in the near future (using more general solutions like PyAutoGUI) but it is something that needs a serious amount of effort (quite a bit more that writing unit tests for non GUI based software) and more importantly it is nowhere as robust as standard unit tests (due to the variable nature of the GUI through development) making automatic test authoring an almost as laborious a process as the one it is supposed to automate.

      -  From looking at the examples, I did not quite see why it is necessary to write the ..._com.py scripts as Python files, since they only seem to consist of boilerplate code and variable definitions. Wouldn’t it be more convenient to represent this information in configuration files (e.g. yaml or toml)?  

      The com is not a configuration file, it is a script that launches the communication process of the Node. We could remove the variable definitions to a separate toml file (which then the com script would have to read). The pros and cons of such a set up should be considered in a future refactoring.

      Minor comments for the paper:

      -  p.7 (top left): "through its return statement" - the worker loop is an infinite loop that forwards data with a return statement?  

      This is now corrected. The worker loop is an infinite loop and does not return anything but at each iteration pushes data to the Nodes output.

      -  p.9 (bottom right): "of the self" → "off-the-shelf"  

      Corrected.

      -  p.10 (bottom left): "second root" → "second route"  

      Corrected.

      -  Supplementary Figure 3: Green start and square seem to be swapped (the green star on top is a camera image and the green star on the bottom is value visualization - inversely for the green square).  

      The star and square have been swapped around.

      -  Caption Supplementary Figure 4 (end): "rashes to receive" → "rushes to receive"  

      Corrected.

    1. As readers, we may seem a bit like TV viewers with remote controls.

      I think this is a great analogy because how often do we flip through channels or scroll through streaming sites until something catches our attention? The same goes for reading, if a paper or text is clustered, has no flow, or is boring, the reader is just going to skip over it.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      (1) This is a valuable manuscript that successfully integrates several data sets to determine genomic interactions with nuclear bodies.

      In this paper we both challenge and/or revise multiple long-standing “textbook” models of nuclear genome organization while also revealing new features of nuclear genome organization. Therefore, we argue that the contributions of this paper extend well beyond “valuable”. Specifically, these contributions include:

      a. We challenge a several decades focus on the correlation of gene positioning relative to the nuclear lamina. Instead, through comparison of cell lines, we show a strong correlation of di4erences in gene activity with di4erences in relative distance to nuclear speckles in contrast to a very weak correlation with di4erences in relative distance to the nuclear lamina. This inference of little correlation of gene expression with nuclear lamina association was supported by direct experimental manipulation of genome positioning relative to the nuclear lamina. Despite pronounced changes in relative distances to the nuclear lamina there was little change relative to nuclear speckles and little change in gene expression.

      b. We similarly challenge the long-standing proposed functional correlation between the radial positioning of genes and gene expression. Here, and in a now published companion paper (doi.org/10.1038/s42003-024-06838-7), we demonstrate how nuclear speckle positioning relative to nucleoli and the nuclear lamina varies among cell types, as does the inverse relationship between genome positioning relative to nuclear speckles and the nuclear lamina. Again, this is consistent with the primary correlation of gene activity being the positioning of genes relative to nuclear speckles and also explains previous observations showing a strong relationship between radial position and gene expression only in some cell types.

      c. We identified a new partially repressed, middle to late DNA replicating type of chromosome domain- “p-w-v fILADs”- by their weak interaction with the nuclear lamina, which, based on our LMNA/LBR KO experimental results, compete with LADs for nuclear lamina association. Moreover, we show that when fLADs convert to iLADs, most conversions are to this p-w-v fiLAD state, although ~ one third are to a normal, active, early replicating iLAD state. Thus, fLADs can convert between repressed, partially repressed, and active states, challenging the prevailing assumption of the division of the genome into two states – active, early replicating A compartment/iLAD regions versus inactive, late replicating, B compartment/LAD regions.

      d. We identified nuclear speckle associated domains as DNA replication initiation zones, with the domains showing strongest nuclear speckle attachment initiating DNA replication earliest in S-phase.

      e. We describe for the first time an overall polarization of nuclear genome organization in adherent cells with the most active, earliest replicating genomic regions located towards the equatorial plane and less expressed genomic regions towards the nuclear top or bottom surfaces. This includes polarization of some LAD regions to the nuclear lamina at the equatorial plane and other LAD regions to the top or bottom nuclear surfaces.

      We have now rewritten the text to make the significance of these new findings clearer.

      (2) Strength of evidence: The evidence supporting the central claims is varied in its strength ranging from solid to incomplete. Orthogonal evidence validating the novel methodologies with alternative approaches would better support the central claims.

      We argue that our work exploited methods, data, and analyses equal to or more rigorous than the current state-of-the-art. This indeed includes orthogonal evidence using alternative methods which both supported our novel methodologies as well as demonstrating their robustness relative to more conventional approaches. This explains how we were able to challenge/revise long-standing models and discover new features of nuclear genome organization. More specifically:

      a. Unlike most previous analyses, we have integrated both genomic and imaging approaches to examine the nuclear genome organization relative to not one, but several di4erent nuclear locales and we have done this across several cell types. To our knowledge, this is the first such integrated approach and has been key to our success in appreciating new features of nuclear genome organization.

      b. The 16-fraction DNA replication Repli-seq data we developed and applied to this project represents the highest temporal mapping of DNA replication timing to date.

      c. The TSA-seq approach that we used remains the most accurate sequence-based method for estimating microscopic distance of chromosome regions to di4erent nuclear locales. As implemented, this method is unusually robust and direct as it exploits the exponential micron-scale gradient established by the di4usion of the free-radicals generated by peroxidase labeling to measure relative distances of chromosome regions to labeled nuclear locales. We had previously demonstrated that TSA-seq was able to estimate the average distances of genomic regions to nuclear speckles with an accuracy of ~50 nm, as validated by light microscopy. The TSA-seq 2.0 protocol we developed and applied to this project maintained the original resolution of TSA-seq to estimate to an accuracy of ~50 nm the average distances of genomic regions from nuclear speckles, as validated by light microscopy, while achieving more than a 10-fold reduction in the required number of cells.

      We have rewritten the text to address the reviewer concerns that led them to their initial characterization of the TSA-seq as novel and not yet validated.

      First, we have added a discussion of how the use of nuclear speckle TSA-seq as a “cytological ruler” was based on an extensive initial characterization of TSA-seq as described in previous published literature. In that previous literature we showed how the conventional molecular proximity method, ChIP-seq, instead showed local accumulation of the same marker proteins over short DNA regions unrelated to speckle distances. Second, we reference our companion paper, now published, and describe how the extension of TSA-seq to measure relative distances to nucleoli was further validated and shown to be robust by comparison to NAD-seq and extensive multiplexed immuno-FISH data. We further discuss how in the same companion paper we show how nucleolar DamID instead was inconsistent with both the NAD-seq and multiplexed immuno-FISH data as well as the nucleolar TSA-seq.

      Third, we have added scatterplots showing exactly how highly the estimated microscopic distances to all three nuclear locales, measured in IMR90 fibroblasts, correlate with the TSA-seq measurements in HFF fibroblasts. This addresses the concern that we were not using the exact same fibroblast cell line for the TSA-seq versus microscopic measurements. The strong correlation already observed would only be expected to become even stronger with use of the exact same fibroblast cell lines for both measurements.

      Fourth, we have addressed the reviewer concern that the nuclear lamin TSA-seq was not properly validated because it did not match nuclear lamin Dam-ID. We have now added to the text a more complete explanation of how microscopic proximity assays such as TSA-seq measure something di4erent from molecular proximity assays such as DamID or NAD-seq. We have added further explanation of how TSA-seq complements molecular proximity assays such as DamID and NAD-seq, allowing us to extract further information than either measurement alone. We also briefly discuss why TSA-seq succeeds for certain nuclear locales using multiple independent markers whereas molecular proximity assays may fail against the same nuclear locales using the same markers. This includes brief discussion from our own experience attempting unsuccessfully to use DamID against nucleoli and nuclear speckles.

      Reviewer #1 (Public Review):

      (1) The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNAFISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq).

      We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches. We have now addressed this point in the revised manuscript text:

      a) We added text to describe how previously FISH was used to validate speckle TSA-seq by demonstrating a residual of ~50 nm between the TSA-seq predicted distance to speckles and the distance measured by light microscopy using FISH:

      "In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diffusion radius of tyramide-biotin free-radicals (Chen et al., 2018). Exploiting the measured exponential decay of the tyramide-biotin free-radical concentration, we showed how the mean distance of chromosomes to nuclear speckles could be estimated from the TSA-seq data to an accuracy of ~50 nm, as validated by FISH (Chen et al., 2018)."

      b) We note that we also previously have validated lamina (Chen et al, JCB 2018) and nucleolar (Kumar et al, 2024) TSA-seq and further validated speckle TSA-seq (Zhang et al, Genome Research 2021) by traditional immuno-FISH and/or immunostaining. The overall high correlation between lamina TSA-seq and the orthogonal lamina DamID method was also extensively discussed in the first TSA-seq paper (Chen et al, JCB 2018). Included in this discussion was description of how the di4erences between lamina TSA-seq and DamID were expected, given that DamID produces a signal more proportional to contact frequency, and independent of distance from the nuclear lamina, whereas TSA-seq produces a signal that is a function of microscopic distance from the lamina, as validated by traditional FISH.

      c) We added text to describe how the nucleolar TSA-seq previously was validated by two orthogonal methods- NAD-seq and multiplexed DNA immuno-FISH:

      "We successfully developed nucleolar TSA-seq, which we extensively validated using comparisons with two different orthogonal genome-wide approaches (Kumar et al., 2024)- NAD-seq, based on the biochemical isolation of nucleoli, and previously published direct microscopic measurements using highly multiplexed immuno-FISH (Su et al., 2020)."

      d) We have now added panels A&B to Fig. 7 and a new Supplementary Fig. 7 demonstrating further validation of TSA-seq based on showing the high correlation between the microscopically measured distances of many hundreds of genomic sites across the genome from di4erent nuclear locales and TSA-seq scores. As discussed in response #2 below, we have used comparison of distances measured in IMR90 fibroblasts with TSA-seq scores measured in HFF fibroblasts. We would argue therefore that these correlations are a lower estimate and therefore the correlation between microscopic distances and TSAseq scores would likely have been still higher if we had performed both assays in the exact same cell line.

      (2) Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines.

      The Kumar et al, Communications Biology, 2024 paper is now published and is cited properly in our revision. We apologize for this oversight and confusion our initial omission of this citation may have created. We had been writing this manuscript and the Kumar et al manuscript in parallel and had intended to co-submit. We planned to cross-reference the two at the time we co-submitted, adding the Kumar et al reference to the first version of this manuscript once we obtained a doi from bioRxiv. But we then submitted the Kumar et al manuscript several months earlier, but meanwhile forgot that we had not added the reference to our first manuscript version.

      (3) Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods.

      As we described in our response to Reviewer 1's comment #1, we had previously provided traditional FISH validation of lamina TSA-seq in our first TSA- seq paper as well as validation by comparison with lamina DamID (Chen et al, 2018).

      We also described how the nucleolar TSA-seq was extensively cross-validated in the Kumar et al, 2024 paper by both NAD-seq and the highly multiplexed immuno-FISH data from Su et al, 2020).

      We note additionally that in the Kumar et al, 2024 paper the nucleolar TSA-seq was additionally validated by correlating the predicted variations in centromeric association with nucleoli across the four cell lines predicted by nucleolar TSA-seq with the variations observed by traditional immunofluorescence microscopy.

      (4) Therefore, the interesting correlations described in this work are not based on robust technologies.

      This comment was made in reference to the Kumar et al paper not having been published, and, as noted in responses to points #2 and #3, the paper is now published.

      But we wanted to specifically note, however, that our experience is that TSA-seq has proven remarkably robust in comparison to molecular proximity assays. We've described in our responses to the previous points how TSA-seq has been cross-validated by both microscopy and by comparison with lamina DamID and nucleolar NAD-seq. We note also that in every application of TSA-seq to date, all antibodies that produced good immunostaining showed good TSA-seq results. Moreover, we obtained nearly identical results in every case in which we performed TSA-seq with different antibodies against the same target. Thus anti-SON and antiSC35 staining produced very similar speckle TSA-seq data (Chen et al, 2018), anti-lamin A and anti-lamin B staining produced very similar lamina TSA-seq data (Chen et al, 2018), antinucleolin and anti-POL1RE staining produced very similar DFC/FC nucleolar TSA-seq data (Kumar et al, 2024), and anti-MKI67IP and anti-DDX18 staining produced very similar GC nucleolar TSA-seq data (Kumar et al, 2024).

      This independence of results with TSA-seq to the particular antibody chosen to label a target differs from experience with methods such as ChIP, DamID, and Cut and Run/Tag in which results can differ or be skewed based on variable distance and therefore reactivity of target proteins from the DNA or due to other factors such as non-specific binding during pulldown (ChIP) or differential extraction by salt washes (Cut and Tag).

      Our experience in every case to date is that antibodies that produce similar immunofluorescence staining produce similar TSA-seq results. We attribute this robustness to the fact that TSA-seq is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical.

      We've now added the following text to our revised manuscript:

      "As previously demonstrated for both SON and lamin TSA-seq (Chen et al., 2018), nucleolar TSA-seq was also robust in the sense that multiple target proteins showing similar nucleolar staining showed similar TSA-seq results (Kumar et al., 2024); this robustness is intrinsic to TSA-seq being a microscopic rather than molecular proximity assay, and therefore not sensitive to the exact molecular binding partners and molecular distance of the target proteins to the DNA."

      (5) An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs.

      We disagree with the reviewer's overall assessment that that the use of the IMR90 data to further validate the TSA-seq is questionable because the TSA-seq data from HFF fibroblasts is not necessarily comparable with multiplexed immuno-FISH microscopic distances measured in IMR90 fibroblasts.

      In response we have now added panels to Fig. 7 and Supplementary Fig. 7, showing:

      a) There is very little di4erence in correlation between speckle TSA-seq and measured distances from speckles in IMR90 cells whether we use IMR90 or HFF cells SON TSA-seq data (R<sup>2</sup> = 0.81 versus 0.76) (new Fig. 7A).

      b) There is also a high correlation between lamina (R<sup>2</sup> = 0.62) and nucleolar (R<sup>2</sup> = 0.73) HFF TSA-seq and measured distances in IMR90 cells. Thus, we conclude that this high correlation shows that the multiplexed data from ~1000 genomic locations does validate the TSA-seq. These correlations should be considered lower bounds on what we would have measured using IMR90 TSA-seq data. Thus, the true correlation between distances of loci from nuclear locales and TSA-seq would be expected to be either comparable or even stronger than what we are seeing with the IMR90 versus HFF fibroblast comparisons.

      c) This correlation is cell-type specific (Fig. 7B, new SFig. 7). Thus, even for speckle TSAseq, highly conserved between cell types, the highest correlation of IMR90 distances with speckle TSA-seq is with IMR90 and HFF fibroblast data. For lamina and nucleolar TSA-seq, which show much lower conservation between cell types, the correlation of IMR90 distances is high for HFF data but much lower for data from the other cell types. This further justifies the use of IMR90 fibroblast distance measurements as a proxy for HFF fibroblast measurements.

      Thus, we have added the following text to the revised manuscript:

      "We reasoned that the nuclear genome organization in the two human fibroblast cell lines would be sufficiently similar to justify using IMR90 multiplexed FISH data [43] as a proxy for our analysis of HFF TSA-seq data. Indeed, the high inverse correlation (R= -0.86) of distances to speckles measured by MERFISH in IMR90 cells with HFF SON TSA-seq scores is nearly identical to the inverse correlation (R= -0.89) measured instead using IMR90 SON TSA-seq scores (Fig. 7A). Similarly, distances to the nuclear lamina and nucleoli show high inverse correlations with lamina and nucleolar TSA-seq, respectively (Fig. 7A). These correlations were cell type specific, particularly for the lamina and nucleolar distance correlations, as these correlations were reduced if we used TSA-seq data from other cell types (SFig. 7A). Therefore, the high correlation between IMR90 microscopic distances and HFF TSA-seq scores can be considered a lower bound on the likely true correlation, justifying the use of IMR90 as a proxy for HFF for testing our predictions."

      Reviewer #2 (Public Review):

      Weaknesses:

      (1) The experiments are largely descriptive, and it is difficult to draw many cause-andeffect relationships...The study would benefit from a clear and specific hypothesis.

      This study was hypothesis-generating rather than hypothesis-testing in its goal. Our research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies. Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project took a discovery-driven versus hypothesis-driven scientific approach. Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repliseq.

      Indeed, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure. We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology. We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships.

      However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing. This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina. Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells. Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

      We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

      (2) Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization).

      We appreciate this feedback and agree with the reviewer that this would be useful, especially for those not familiar with previous work in the field of 3D genome organization. In an earlier draft, we had included additional summary and interpretation statements in both the Introduction and Results sections. At the start of each Results section, we had also previously included brief discussion of what was known before and the context for the subsequent analysis contained in that section. However, we had thought we might be submitting to a journal with specific word limits and had significantly cut out that text.

      We have now restored this text and, in certain cases, added additional explanations and context.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Figures 1C and D. Please add the units at the values of each y-axis.

      We have done that.

      The representation of Figure 2C lacks clarity and is diJicult to understand. The x-axis labeling regarding the gene fraction number needs clarification.

      We've modified the text to the Fig. 2C legend: "Fraction of genes showing significant di=erence in relative positioning to nuclear speckles (gene fraction, x-axis) versus log2 (HFF FKPM / H1 FKPM) (y-axis);"

      "We next used live-cell imaging to corroborate that chromosome regions close to nuclear speckles, primarily Type I peaks, would show the earliest DNA replication timing." This sentence requires modification as Supplementary Figure 3F does not demonstrate that Type I peaks exhibit the earliest DNA replication timing; it only indicates that the first PCNA foci in S-phase are in proximity to nuclear speckles.

      We've modified the text to: "We next used live-cell imaging to show that chromosome regions close to nuclear speckles show the earliest DNA replication timing; this is consistent with the earliest firing DNA replication IZs, as determined by Repli-seq, aligning with Type 1 peaks that are closely associated with nuclear speckles."

      In Figure 5, the authors employed LaminB1-DamID to quantify LADs in LBR-KO and LMNA/LBR-DKO K562 cells. These are interesting results. However, for these experiments, it is crucial to assess LMNB1 signal at the nuclear periphery via immunofluorescence (IF) to confirm the absence of changes, ensuring that the DamID signal solely reflects contacts with the nuclear lamina. Furthermore, in this instance, their findings should be validated through DNA-FISH.

      Immunostaining of LMNB1 was performed and showed a normal staining pattern as a ring adjacent to the nuclear periphery. Images of this staining were included in the metadata tied to the sequencing data sets deposited on the 4D Nucleome Data portal. We thank the reviewer for bringing up this point, and have added a sentence mentioning this result in the Results Section:

      "Immunostaining against LMNB1 revealed the normal ring of staining around the nuclear periphery seen in wt cells (images deposited as metadata in the deposited sequencing data sets)."

      Because both TSA-seq and DamID have been extensively validated by FISH, as detailed in our previous responses to the public reviewer comments, we feel it is unnecessary to validate these findings by FISH.

      p-w-v-fiLADs should be labelled in Figure 5B.

      We've added labeling as suggested.

      "The consistent trend of slightly later DNA replication timing for regions (primarily p-w-v fiLADs) moving closer to the lamina" is not visible in the representation of the data of Figure 5G.

      We did not make a change as we believed this trend was apparent in the Figure.

      To reduce the descriptive nature of the data, it would be pertinent to conduct H3K9me3 and H3K27me3 ChIP-seq analyses in both the parental and DKO mutant cells. This would elucidate whether p-w-v-fiLADs and NADs anchoring to the nuclear lamina undergo changes in their histone modification profile.

      We believe further analysis of the reasons underlying these shifts in positioning, including such ChIP-seq or equivalent analysis, is of interest but beyond the scope of this publication. We see such measurements as the beginning of a new story but insuJicient alone to determine mechanism. Therefore we believe such experiments should be part of that future study.

      The description of Figure 7 lacks clarity. Additionally, it appears that TSA-seq for NADs and LADs may not be universally applicable across all cell types, particularly in flat cells, whereas DamID scores demonstrate less variation across cell lines, as also stated by the authors.

      TSA-seq is a complement to rather than a replacement for either DamID or NAD-seq. TSAseq reports on microscopic distances whereas both DamID and NAD-seq instead are more proportional to contact frequency with the nuclear lamina or nucleoli, respectively, and insensitive to distances of loci away from the lamina or nucleoli. Thus, TSA-seq provides additional information based on the intrinsic diJerences in what TSA-seq measures relative to molecular proximity methods such as DamID or NAD-seq. The entire point is that the convolution of the exponential point-spread-function of the TSA-seq with the shape of the nuclear periphery allows us to distinguish genomic regions in the equatorial plane versus the top and bottom of the nuclei. The TSA-seq is therefore highly "applicable" when properly interpreted in discerning new features of genome organization. As we stated in the revised manuscript, the lamina DamID and TSA-seq are complementary and provide more information together then either method along. The same is true for the NAD-seq and nucleolar TSA-seq comparison, as described in more detail in the Kumar, et al, 2024 paper.

      Introduction:

      The list of methodologies for mapping genomic contacts with nucleoli (NADs) should also include recent technologies, such as Nucleolar-DamID (Bersaglieri et al., PMID: 35304483), which has been validated through DNA-FISH.

      We did not include nucleolar DamID in the mention in the Introduction of methods for identifying diJerential lamina versus nucleolar interactions of heterochromatin- either from our own collaborative group or from the cited reference- because we did not have confidence in the accuracy of this method in identifying NADs. In the case of the published nucleolar DamID from our collaborative group, published in Wang et al, 2021, we later discovered that despite apparent agreement of the nucleolar DamID with a small number of published FISH localization the overall correlation of the nucleolar DamID with nucleolar localization was poor. As described in detail in the Kumar et al, 2024 publication, this poor correlation of the nucleolar DamID was established using three orthogonal methods- nucleolar TSA-seq, NAD-seq, and multiplexed immuno-FISH measurements from ~1000 genomic locations. Instead, we found that this nucleolar DamID showed high correlation with lamina DamID. We note that many strong NADs are also LADs, which we think is why validation with only several FISH probes is inadequate to demonstrate overall validation of the approach.

      We could not compare our nucleolar-DamID data in human cells with the alternative nucleolar-DamID results cited by the reviewer which were performed in mouse cells. We note that in this paper the nucleolar DamID FISH validation only included several putative NAD chromosome regions and, I believe, one LAD region. However, our initial comparison of the nucleolar DamID cited by the reviewer with unpublished TSA-seq data from mouse ESCs produced by the Belmont laboratory and with NAD-seq data from the Kaufman laboratory shows a similar lack of correlation of the nucleolar DamID signal with nucleolar TSA-seq and NAD-seq, as well as multiplexed immuno-FISH data from the Long Cai laboratory, as we saw in our analysis of own nucleolar DamID data in human cells.

      We have added explanation concerning the lack of correlation of our nucleolar DamID with orthogonal measurements of nucleolar proximity in the added text (below) to our revised manuscript:

      "Nucleolar DamID instead showed broad positive peaks over large chromatin domains, largely overlapping with LADs mapped by LMNB1 DamID (Wang et al., 2021). However, this nucleolar DamID signal, while strongly correlated with lamin DamID, showed poor correlation with either NAD-seq or nucleolar distances mapped by multiplexed immunoFISH (Kumar et al., 2024). We suspect the problem is that with molecular proximity assays the output signals are disproportionally dominated by the small fraction of target proteins juxtaposed in su=icient proximity to the DNA to produce a signal rather than the amount of protein concentrated in the target nuclear body. "

      Our mention of nucleolar TSA-seq was in the context of why we focused on nucleolar TSAseq and excluded our own nucleolar DamID. We chose not to discuss the second nucleolar DamID method cited above 1) because it was not appropriate to our discussion of our own experimental approach and 2) also because we cannot yet make a definitive statement of its accuracy for nucleolar mapping.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors start the manuscript by describing the 'radial genome organization' model and contrast it with the 'binary model' of genome organization. It would be helpful for the authors to contextualize their results a bit more with regard to these two diJerent models in the discussion.

      We have added several sentences in the first paragraph of the Discussion to accomplish this contextualization. The new paragraph reads:

      "Here we integrated imaging with both spatial (DamID, TSA-seq) and functional (Repli-seq, RNA-seq) genomic readouts across four human cell lines. Overall, our results significantly extend previous nuclear genome organization models, while also demonstrating a cell-type dependent complexity of nuclear genome organization. Briefly, in contrast to the previous radial model of genome organization, we reveal a primary correlation of gene expression with relative distances to nuclear speckles rather than radial position. Additionally, beyond a correlation of nuclear genome organization with radial position, in cells with flat nuclei we show a pronounced correlation of nuclear genome organization with distance from the equatorial plane. In contrast to previous binary models of genome organization, we describe how both iLAD / A compartment and LAD / B compartment contain within them smaller chromosome regions with distinct biochemical and/or functional properties that segregate di=erentially with respect to relative distances to nuclear locales and geometry."

      (2) Data should be provided demonstrating KO of LBR and LMNA - immunoblotting for both proteins would be one approach. In addition, it would be helpful to provide additional nuclear morphology measurements of the DKO cells (volume, surface area, volume of speckles/nucleoli, number of speckles/nucleoli).

      We've added additional description describing the generation and validation of the KO lines:

      "To create LMNA and LBR knockout (KO) lines and the LMNA/LBR double knockout (DKO) line, we started with a parental "wt" K562 cell line, clone #17, expressing an inducible form of Cas9 (Brinkman et al., 2018). The single KO and DKO were generated by CRISPR-mediated frameshift mutation according to the procedure described previously (Schep et al., 2021). The "wt" K562 clone #17 was used for comparison with the KO clones.

      The LBR KO clone, K562 LBR-KO #19, was generated, using the LBR2 oligonucleotide GCCGATGGTGAAGTGGTAAG to produce the gRNA, and validated previously, using TIDE (Brinkman et al., 2014) to check for frameshifts in all alleles as described elsewhere (Schep et al., 2021). The LMNA/LBR DKO, K562 LBR-LMNA DKO #14, was made similarly, starting with the LBR KO line and using the combination of two oligonucleotides to produce gRNAs:

      LMNA-KO1: ACTGAGAGCAGTGCTCAGTG, LMNA-KO2: TCTCAGTGAGAAGCGCACGC.

      Additionally, the LMNA KO line, K562 LMNA-KO #14, was made the same way but starting with the "wt" K562 cell line. Validation was as described above; additionally, for the new LMNA KO and LMNA/LBR DKO lines, immunostaining showed the absence of anti-LMNA antibody signal under confocal imaging conditions used to visualize the wt LMNA staining while the RNA-seq from these clones revealed an ~20-fold reduction in LMNA RNA reads relative to the wt K562 clone."

      As suggested, we also added morphological data for the DKO line in a modified SFig.5.

      (3) The rationale for using LMNB1 TSA-seq and LMNB1 DAMID is not immediately clear. The LMNB1 TSA-seq is more variable across cell types and replicates than the DAMID. Could the authors please compare the datasets a bit more to understand the diJerences? For example, the authors demonstrate that "40-70% of the genome shows statistically significant diJerences in Lamina TSA-seq over regions 100 kb or larger, with most of these regions showing little or no diJerences in speckle TSA-seq scores." If the LMNB1 DAMID data is used for this analysis or Figure 2D, is the same conclusion reached? Also, in Figure 6, the authors conclude that C1 and C3 LAD regions are enriched for constitutive LADs, while C2 and C4 LAD regions are fLADs. This is a bit surprising because the authors and others have previously shown that constitutive LADs have higher LMNB1 contact frequency than facultative LADs (Kind, et al Cell 2015, Figure 3C).

      Indeed, in the first TSA-seq paper (Chen et al, 2018) we did observe that cLADs had the highest LMNB TSA-seq scores; this was for K562 cells with round nuclei in which there is therefore no diJerence in lamina TSA-seq scores produced by nuclear shape over the entire nucleus.

      However, there are diJerences between TSA-seq and DamID in terms of what they measure and we refer the reviewer to the first TSA-seq paper (Chen et al, 2018) that explains in greater depth these diJerences. This first paper explains how DamID is indeed related to contact frequency but how the TSA-seq instead estimates mean distances from the target, in this case the nuclear lamina. This is because the diJusion of tyramide free radicals from the site of their constant HRP production produces an exponential decay gradient of tyramide free radical concentration at steady state.

      We have summarized these diJerences in in text we have added to introduce both DamID and TSA-seq in the second Results section:

      "DamID is a well-established molecular proximity assay; DamID applied to the nuclear lamina divides the genome into lamina-associated domains (LADs) versus nonassociated “inter-LADs” or “iLADs” (Guelen et al., 2008; van Steensel and Belmont, 2017). In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diJusion radius of tyramide-biotin free-radicals (Chen et al., 2018)... While LMNB1 DamID segments LADs most accurately, lamin TSA-seq provides distance information not provided by DamID- for example, variations in relative distances to the nuclear lamina of diJerent iLADs and iLAD regions. These diJerences between the LMNB1 DamID and LMNB TSA-seq signals are also crucial to a computational approach, SPIN, that segments the genome into multiple states based on their varying nuclear localization, including biochemically and functionally distinct lamina-associated versus near-lamina states (Consortium et al., 2024; Wang et al., 2021).

      Thus, lamin DamID and TSA-seq complement each other, providing more information together than either one separately."

      We note that these diJerences in lamina DamID and TSA-seq are crucial to being able to gain additional information by comparing variations in the lamina TSA-seq for LADs in Figs. 6&7. See our response to point (4) below, for further explanation.

      (4) In 7B/C, the authors show that the highest LMNB1 regions in HFF are equator of IMR90s. However, in Figure 7G, their cLAD score indicates that constitutive LADs are not at the equator. This is a bit surprising given the point above and raises the possibility that SON signals (as opposed to LMNB1 signals) might be more responsible for correlation to localization relative to the equator. Hence, it might be helpful if the authors repeat the analyses in Figures 7B/C in regions with diJering LMNB1 signals but similar SON signals (and vice versa).

      Again, this is based on the apparent assumption by the reviewer that DamID and TSA-seq work the same way and measure the same thing. But as explained above in the previous point, this is not true.

      In our first TSA-seq paper (Chen et al, 2018) we showed how we could use the exponential decay point-spread-function produced by TSA, measured directly by light microscopy, to convert sequencing reads from the TSA-seq into a predicted mean distance from nuclear speckles, approximated as point sources. These mean distances predicted from the SON TSA-seq data agreed with measured FISH distances to nuclear speckles to within ~50 nm for a set of DNA probes from diJerent chromosome regions. Moreover, varying TSA staining conditions changed the decay constants of this exponential decay, thus producing diJerences in the SON TSA-seq signals. By using these diJerent exponential decay functions to convert the TSA-seq scores from these independent data sets to estimated distances from nuclear speckles, we again observed a distance residual of ~50 nm; in this case though this distance residual of ~50 nm represented the mean residual observed genome-wide. This gives us great confidence that the TSA-seq is working as we have modeled it.

      As we mentioned in our response to point 3 above, we did see the highest LMNB TSA-seq signal for cLADs in K562 cells with round nuclei (Chen et al, 2018).

      But as we now show in our simulation performed in this paper for Fig. 7, the observed tyramide free radical exponential decay gradient convolved with the flat nuclear lamina shape produces a higher equatorial LMNB1 TSA-seq signal for LADs at the equatorial plane. We confirmed that LADs with this higher TSA-seq signal were enriched at the equatorial plane by mining the multiplexed IMR90 imaging data. Similar mining of the multiplexed FISH IMR90 data showed localization of cLADs away from the equatorial plane.

      We are not clear about the rationale for what the reviewer is suggesting about SON signals "being more responsible for correlation to localization to the equator". We have provided an explanation for the higher lamina TSA-seq scores for LADs near the equator based on the measured spreading of the tyramide free radicals convolved with the eJect of the nuclear shape. This makes a prediction that the observed variation in lamina TSA-seq scores for LADs with similar DamID scores is related to their positioning relative to the equatorial plane as we then validated through our mining of the IMR90 multiplexed FISH data.

      (5) FISH of individual LADs, v-fiLADs, and p-w-v-fiLADs relative to the lamina and speckle would be helpful to understand their relative positioning in control and LBR/LMNA double KO cells. This would significantly bolster the claim that "histone mark enrichments..more precisely revealed the diJerential spatial distribution of LAD regions...".

      Adequately testing these predictions made from the lamina/SON TSA-seq scatterplots by direct FISH measurements would require measurements from large numbers of diJerent chromosome regions through a highly multiplexed immuno-FISH approach. We are not equipped currently in any of our laboratories to do such measurements and we leave this therefore for future studies.

      Rather our statement is based on our use of TSA-seq analyzed through these 2D scatterplots and should be valid to the degree that our TSA-seq measurements do indeed correlate with microscopy derived distances.

      However, we do now include demonstration of a high correlation of speckle, lamina, and nucleolar TSA-seq with highly multiplexed immuno-FISH measurement of distances to these locales in a revised Fig. 7. The high correlation shown between the TSA-seq scores and measured distances does therefore add additional support to our claim that the reviewer is discussing, even without our own multiplexed FISH validation.

      (6) "In contrast, genes within genomic regions which in pair-wise comparisons of cell lines show a statistically significant diJerence in lamina TSA-seq show no obvious trend in their expression diJerences (Figure 2C).". This appears to be an overstatement based on the left panel of 2D.

      We do not follow the reviewer's point. In Fig. 2C we show little bias in the diJerences in gene expression between the two cell types for regions that showed diJerences in lamina TSA-seq. The reviewer is suggesting something otherwise based on their impression, not explicitly stated, of the left panel of Fig. 2D. But we see similar shades of blue extending vertically at low SON values and similar shades of red extending vertically at high SON values, suggesting a correlation of gene expression only with the SON TSA-seq score but not with the LMNB1 TSA-seq score displayed on the y-axis. This is also consistent with the very small and/or insignificant correlation coeJicients measured in our linear model relating diJerences in LMNB1 TSA-seq to diJerences in expression but the large correlation coeJicient observed for SON TSA-seq (Fig. 2E). Thus, we see Fig. 2C-E as self-consistent.

      (7) In the section on "Polarity of Nuclear Genome Organization" - "....Using the IMR90 multiplexed FISH data set [43]...." - The references are not numbered.

      We thank the reviewer for this correction.

      (8) I believe there is an error in the Figure 7B legend. The descriptions of Cluster 1 and 2 do not match those indicated in the figure.

      We again thank the reviewer for this correction.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      The authors present valuable findings on trends in hind limb morphology throughout the evolution of titanosaurian sauropod dinosaurs, the land animals that reached the most remarkable gigantic sizes. The solid results include the use of 3D geometric morphometrics to examine the femur, tibia, and fibula to provide new information on the evolution of this clade and understand the evolutionary trends between morphology and allometry. Further justification of the ontogenetic stages of the sampled individuals would help strengthen the manuscript's conclusions, and the inclusion of additional large-body mass taxa could provide expanded insights into the proposed trends.

      Most of the analyzed specimens, especially from the smaller taxa, come from adult or subadult specimens. None exhibit features that may indicate juvenile status. However, we lack information of the paleohistology that may be a stronger indicator on the ontogenetic status of the individual, and some of operative taxonomic units used in the study come from mean shape of all the sampled specimens.

      Current information on morphological differences between adult and subadult or juvenile specimens indicates that even early juvenile specimens may share same morphological features and overall morphology as the adult (e.g., see Curry-Rogers et al., 2016; Appendix S3). We included a comprehensive analysis of the impact of juvenile specimens as one of the aspects of the intraspecific variability that may alter our results in Appendix S3.

      Public Reviews:

      Reviewer #1:

      Weaknesses:

      Several sentences throughout the manuscript could benefit from citations. For example, the discussion of using hind limb centroid size as a proxy for body mass has no citations attributed. This should be cited or described as a new method for estimating body mass with data from extant taxa presented in support of this relationship. This particular instance is a very important point to include supporting documentation because the authors' conclusions about evolutionary trends in body size are predicated on this relationship.

      We address this issue in the text (Line 32 & 64). Centroid size seems a good indication as it’s the overall size of the entire hind limb, and the length of the femur and tibia is well correlated independently with the body size/mass. Also, as we use few landmarks and only those that are purely type I or II landmarks, with curves of semilandmarks bounded or limited by them, centroid size is not sensible to landmark number differences across the sample in our study (as the centroid size is dependent of the number of landmarks of the current study as well as the physical dimensions of the specimens).

      We have sampled and repeated all the analyses using other proxies like the femoral length and the body mass estimated from the Campione & Evans (2020) and Mazzeta et al. (2004) methods. The comprehensive description of the method is in Appendix S2, the alternative analyses can be accessed in the Appendix S3 and S4; and the code for the alternative analyses can be accessed in the modified Appendix S5. All offer similar results than the ones obtained in our analyses with the body size proxied with the hind limb landmark configuration centroid size.

      An additional area of concern is the lack of any discussion of taphonomic deformation in Section 3.3 Caveats of This Study, the results, or the methods. The authors provide a long and detailed discussion of taphonomic loss and how this study does a good job of addressing it; however, taphonomic deformation to specimens and its potential effects on the ensuing results were not addressed at all. Hedrick and Dodson (2013) highlight that, with fossils, a PCA typically includes the effects of taphonomic deformation in addition to differences in morphology, which results in morphometric graphs representing taphomorphospaces. For example, in this study, the extreme negative positioning of Dreadnoughtus on PC 2 (which the authors highlight as "remarkable") is almost certainly the result of taphonomic deformation to the distal end of the holotype femur, as noted by Ullmann and Lacovara (2016).

      We included a brief commentary in the Caveats of This Study (Line 467) and greatly expanded this issue in the Appendix S3. We followed the methodology proposed by Lefebvre et al. (2020) to discuss the effects of taphonomic deformation in the shape analyses.

      Our shape variables (PCs obtained from the shape PCA) should be viewed as taphomorphospaces as Hedrick and Dodson, as well as the reviewer, points in such cases.

      The analysis of the effects of taphonomy or errors induced by the landmark estimation method indicate that Dreadnoughtus schrani is one of the few sampled taxa that may have a noticeable impact on our analyses due lithostatic deformation. Other taxa like Mendozasaurus neguyelap or Ampelosaurus atacis may also induce some alterations to the PCs. In general, the trends of those PCs slightly altered by taphonomy, where D. scharni is the only sauropod that may alter an entire PC like PC2, did not exhibit phylogenetic signal and are a small proportion of the sample variance.

      The authors investigated 17 taxa and divided them into 9 clades, with only Titanosauria and Lithostrotia including more than two taxa (and four clades are only represented by one taxon). While some of these clades represent the average of multiple individuals, the small number of plotted taxa can only weakly support trends within Titanosauria. If similar general trends could be found when the taxa are parsed into fewer, more inclusive clades, it would support and strengthen their claims. Of course, the authors can only study what is preserved in the fossil record, and titanosaurian remains are often highly fragmentary; these deficiencies should therefore not be held against the authors. They clearly put effort and thought into their choices of taxa to include in this study, but there are limitations arising from this low sample size that inherently limit the confidence that can be placed on their conclusions, and this caveat should be more clearly discussed. Specifically, the authors note that their dataset contains many lithostrotians, but they do not discuss unevenness in body size sampling. As neither their size-category boundaries nor the taxa which fall into each of them are clearly stated, the reader must parse the discussion to glean which taxa are in each size category. It should be noted that the authors include both Jainosaurus and Dreadnoughtus as 'large' taxa even though the latter is estimated to have been roughly five times the body mass of the former, making Dreadnoughtus the only taxon included in this extreme size category. The effects that this may have on body size trends are not discussed. Additionally, few taxa between the body masses of Jainosaurus and Dreadnoughtus have been included even though the hind limbs of several such macronarians have been digitized in prior studies (such as Diamantinasaurus and Giraffititan; Klinkhamer et al. 2018). Also, several members of Colossosauria are more similar in general body size to Dreadnoughtus than Jainosaurus, but unfortunately, they do not preserve a known femur, tibia, and fibula, so the authors could not include them in this study. Exclusion of these taxa may bias inferences about body size evolution, and this is a sampling caveat that could have been discussed more clearly. Future studies including these and other taxa will be important for further evaluating the hypotheses about macronarian evolution advanced by Páramo et al. in this study.

      Sadly, we could not include some larger sized titanosaurians sauropods. As the reviewers points out, the lack of larger sauropods among the sampled taxa may hinder our results, as the “large-bodied” category is filled with some mid-sized taxa and the former Dreadnoughtus schrani which is five times larger than some of them. We tried to include Elaltitan lilloi, digitized for this study and included in preliminary analyses, but the fragmentary status increased greatly the error by the estimation method as there is only a proximal third or mid femur preserved from this taxon. Therefore we opted to exclude it from our database.

      Other taxa considered, as the reviewer suggest, was not readily available for the authors as the time of this study was conducted and including now may have increased the possible bias of our study. Giraffatitan brancai is an Late Jurassic brachiosaurid, which may again increase the number of early-branching titanosauriforms with large body masses while most of the smaller taxa sampled are recovered in deeply-branching macronarians (including Diamantinasaurus matildae if we would have also included it). Future analyses may include a wider sample of the mid to large-bodied titanosaurians, especially lithostrotians, as well as some colossosaurs like Patagotitan mayorum.

      Reviewer #1 (Recommendations For The Authors):

      These are all minor comments that would improve the manuscript.

      - There are a few typos throughout the manuscript such as: line 70 should be 2016 and line 242 should be forelimb.

      Corrected.

      - To me, the most interesting aspect of your study is the diversity and trends recovered in titanosaurian subclades and I would highlight this, not gigantism, in the title if you choose to revise the title.

      It has been addressed. The specificality of some of the tests and the implication to the acquisition of the spread limb posture and gigantism in early-branching taxa is important nonetheless, so we think that it may remain in the title.

      - The abstract should provide more details on the results such as none of the listed trends were statistically significant.

      Many of the trends exhibit phylogenetic signal, but not the allometric components. We have briefly addressed them.

      - Several sentences in the manuscript need citations such as: line 48 the reference to other megaherbivores, line 66 the discussion of poor understanding of the relationship of wide gauge posture and gigantism, and the use of centroid size as an estimate of body mass (see Public Review).

      We changed the line 66 to improve the focus on the current state of the art in the hypothesis of a relationship between arched limbs and in the increase of body size. We included a section relating centroid size as a proxy (due the good correlation between the femur and tibia length and the body mass) and the caveats of using it. We also expanded in the Appendix S2 the use of centroid size and the alternative models.

      - With titanosaur evolution, you mention that they are adapting to new niches and topography (line 64). What support is there for this versus they are adapting to be more successful in their current environment?

      Noted, we have changed the phrase to improved efficiency exploiting of inland environments, as thy can be either opening new inland niches or adapting better to current inland niches that were already exploited for less deeply branching sauropods. However, its testing is beyond the scope of the current work.

      - Line 384-385: the discussion of Rapetosaurus should mention that it is a juvenile and some studies have suggested that titanosaur limbs grow allometrically.

      We have included a small line. Whether Rapetosaurus krausei exhibit allometric growth or not may not change greatly the discussion, maybe only excluding it as morphologically convergent to Lirainosaurus and Muyelensaurus. But if that so, it will be further proof that small-sized titanosaurs exhibit the robust skeleton expected in the giant titanosaurs.

      - I would consider addressing the question of if we are certain enough in our understanding of titanosaurian phylogeny to rule out homology, especially when you discuss the uncertainty of the placement of specific taxa. Also, Diamantinasaurus is not the only titanosaur that has been proposed as a member of both basal and more derived subclades (e.g., Dreadnoughtus).

      We tried to assume a more conservative approach. We could not fully rule out that some of the features observed in the sampled deeply branching lithostrotians, especially saltasauroids, cannot be present in the entire somphospondylan lineage. However, none of the less deeply-branching or early-branching titanosaurs exhibit this kind of morphology. Recent studies propose the possibility that entire groups, included in this study like the Colossosauria, change its position in the phylogeny. However, despite the debated phylogenetic position of Diamantinasaurus or Dreadnoughtus, or even the inclusion of Colossosauria within the saltasauroids and the inclusion of the Ibero-Armorican lithostrotians as putative saltasaurids (Mocho et al. 2024). However, even considering these changes we did not notice any relevant differences in our conclusions about hind limb arched morphology nor about size. Distal hind limb overall robustness should indeed be addressed in the light of shifts in phylogenetic position and include some interesting sauropods like Diamantinasaurus or expand the large-sized Colossosauria or early-branching somphospondyls as it may have profound implications on the morphofunctional adaptations to specific feeding niches, e.g., see current hypotheses about rearing as mentioned in Bates et al. (2016), Ullmann et al. (2017) or Vidal et al. (2020). We had not enough information to conclude the presence of any plesiomorphic condition or analogous feature with our current sample and the debated titanosaurian phylogeny.

      - I understand this is not standard in the field, but your study provides the opportunity to conduct sensitivity testing of the effects of cartilage thickness and user articulation of the bones on PCA results. This would be an inciteful addition to the field of GMM.

      We are currently developing such a comprehensive analysis and several other implications on our past results. However, we feel that it is beyond the scope of the current study. We appreciate the suggestion nonetheless, as it would be a sensitivity test of the impact of several of our assumptions in the final results that is often not considered.

      - In Figure 1, if all the limbs were arranged the same way it would be easier to interpret. Consider flipping panels B and D to match A and C.

      Accepted.

      - In Figures 2-4, the views in C should be labeled in the figure or caption. Oceanotitan is also in the PCA plot but not included in the figure caption. Also, consider changing the names to represent the paraphyletic groupings you are using instead of formal clade names. For example, change 'Titanosauria' to 'Basal Titanosaurs' to reflect that it is not including all titanosaurs in the sample.

      Changes accepted for the shape PCA results. The informal (i.e., paraphyletic) terms such as “Basal Titanosaurs” were only used in the shape analyses as in the RMA, the Titanosauria (and other more inclusive groups) were used as natural groups. Each partial RMA model is based on a sample of all the taxa that are included within that particular clade (e.g., Titanosauria includes both Dreadnoughtus and Saltasaurus; Lithostrotia excludes the former).

      - I am concerned that centroid size does not scale evenly across the wide-ranging body mass of titanosaurs. I do not know if this affects your size trends or their significance, but as I mentioned above Dreadnoughtus is much bigger than most of the taxa included and that isn't as drastically apparent in centroid size (in Figure 5) as it is when taxa are plotted by body mass.

      Main problematic with centroid size of the hind limb is the shift in the body plan of deeply-branching titanosaurs as the Center of Masses is displaced toward the anterior portion of the body and it has been proposed due a large development of the forelimb region (e.g., Bates et al. 2016). However, it would only increase the effects of the phyletic body size reduction, as smaller taxa tend to have a 1:1 fore limb and hind limb ratio, e.g., from our past analyses as in Páramo et al. (2019), and the sacrum is not as beveled as in earlier somphospondyls, e.g., Vidal et al. (2020). The role of the low-browsing feeding habits of deeply-branching lithostrotians shall be explored elsewhere, as it may be the main driving force of this effect. Our point is, the proxy used may have some slight offset due some high-browsing giant early-branching titanosaurs which has a greater cranial region development which increase its body size and mass beyond our bare-minimum estimation based on the hind limb region. But, overall, this offset is assumed to be low. We repeated the analyses with the femoral length as proxy of body size and a mass estimation, including the quadratic equation based on both humeral and femoral lengths, and the results remain similar. Another problem that arises with the use of centroid size is the way it shall be calculated, but as we used an even number of landmarks and curve semilandmarks, and all of them bounded to anatomical features, it remains equal at least for our sample (but cannot be extrapolated to other geometric morphometric studies that do not use the same configurations)

      We appreciate the reviewer concerns nonetheless, as it was on of our own when designing this study, and we in the future will try to expand the analyses, or advise anyone expanding on this study, using total body size/volume estimations following Bates et al. (2016). Which also includes test of the effects of the different whole-body estimation models.

      Cites:

      Bates KT, Mannion PD, Falkingham PL, Brusatte SL, Hutchinson JR, Otero A, Sellers WI, Sullivan C, Stevens KA, Allen V. 2016. Temporal and phylogenetic evolution of the sauropod dinosaur body plan. Royal Society Open Science 3:150636. doi:10.1098/rsos.150636

      Mocho P, Escaso F, Marcos-Fernández F, Páramo A, Sanz JL, Vidal D, Ortega F. 2024. A Spanish saltasauroid titanosaur reveals Europe as a melting pot of endemic and immigrant sauropods in the Late Cretaceous. Commun Biol 7:1016. doi:10.1038/s42003-024-06653-0

      Páramo A, Ortega F, Sanz JL. 2019. A Niche Partitioning Scenario for the Titanosaurs of Lo Hueco (Upper Cretaceous, Spain). International Congress of Vertebrate Morphology (ICVM) - Abstract Volume, Journal of Morphology. Prague. p. S197.

      Ullmann PV, Bonnan MF, Lacovara KJ. 2017. Characterizing the Evolution of Wide-Gauge Features in Stylopodial Limb Elements of Titanosauriform Sauropods via Geometric Morphometrics. The Anatomical Record 300:1618–1635. doi:10.1002/ar.23607

      Vidal D, Mocho P, Aberasturi A, Sanz JL, Ortega F. 2020. High browsing skeletal adaptations in Spinophorosaurus reveal an evolutionary innovation in sauropod dinosaurs. Sci Rep 10:6638. doi:10.1038/s41598-020-63439-0

      Reviewer #2:

      The authors report a quantitative comparative study regarding hind limb evolution among titanosaurs. I find the conclusions and findings of the manuscript interesting and relevant. The strength of the paper would be increased if the authors were to improve their reporting of taxon sampling and their discussion of age estimation and the potential implications that uncertainty in these estimates would have for their conclusions regarding gigantism (vs. ontogenetic patterns).

      Considering the observations made by reviewer #1, we included a data about the impact of ontogenetic patterns and other intraspecific variability in the Appendix S3. We considered to increase the sample but it has not been possible at the time of this study was carried out.

      Reviewer #2 (Recommendations For The Authors):

      I have a few concerns/requests for the authors, that I hope can be easily resolved.

      Comments:

      - What drove taxon sampling?

      Random sampling of somphospondylan sauropods focused on the Lithostrotia clade for the thesis project of one of the authors, APB. Logistics were also one of the bias on our sample, and based on the available titanosaurian material we left out several macronarians that has been already sampled but would further induce a early-branching large sauropod, deeply-branching small sauropod that may alter our results.

      - Which phylogenies were used to create the supertree applied to the analyses? What references were used to time-calibrate the tips and deeper nodes? I couldn't find any reference to this. Additionally, more information regarding the R packages and analytical pipeline would be appreciated: e.g. were measurements used in the analyses log-transformed?

      A comprehensive description of the methodology is provided in Appendix S2.

      - Age estimate: can the author confirm the skeletal maturity of the sampled individuals? If this is not the case, how can the author be sure that the patterns towards gigantism are not reflecting different ontogenetic stages? I believe this should be part of both methods and discussion.

      As commented before, we excluded small, probable juvenile specimens from our sample. We have no paleohistological sample backing the claims of the ontogenetic status of some of the specimens that were included or excluded were calculating the mean shape for the operative taxonomic units. However, we followed a criteria to identify the relative ontogenetic status and it has been included in Appendix S3.

      - The authors used the centroid size for regressions in Figure 6. Although I believe that this is a good variable, would the author be willing to use body mass and log-transformed femur length in addition to what was done? These would be very useful considering that these variables are (relatively) independent from shape/morphology.

      Accepted, we tested our hypotheses with three alternative models based on femoral length, combined femoral and humeral lengths for body mass estimations. Methodology can be found in Appendix S2, results on Appendix S4, code for the alternative methods in Appendix S5.

      - Data access: will stl. Files of the limb elements be shared and freely available? In this case, where the files will be deposited?

      At the time of the current study, some of the sampled specimens cannot be available (material under study) but the mean shapes can be generated after the landmarks and semilandmark curves and the “atlas” mesh.

      - Additionally, outstanding references regarding limb evolution, GMM, role of ontogeny, and evolution of columnar gait are missing. The authors should reinforce the literature review with the following (alphabetical order):

      Bonnan, M. F. (2003). The evolution of manus shape in sauropod dinosaurs: implications for functional morphology, forelimb orientation, and phylogeny. Journal of Vertebrate Paleontology, 23(3), 595-613.

      Botha, J., Choiniere, J. N., & Benson, R. B. (2022). Rapid growth preceded gigantism in sauropodomorph evolution. Current Biology, 32(20), 4501-4507.

      Curry Rogers, K., Whitney, M., D'Emic, M., & Bagley, B. (2016). Precocity in a tiny titanosaur from the Cretaceous of Madagascar. Science, 352(6284), 450-453.

      Day, J. J., Upchurch, P., Norman, D. B., Gale, A. S., & Powell, H. P. (2002). Sauropod trackways, evolution, and behavior. Science, 296(5573), 1659-1659.

      Fabbri, M., Navalón, G., Benson, R. B., Pol, D., O'Connor, J., Bhullar, B. A. S., ... & Ibrahim, N. (2022). Subaqueous foraging among carnivorous dinosaurs. Nature, 603(7903), 852-857.

      Fabbri, M., Navalón, G., Mongiardino Koch, N., Hanson, M., Petermann, H., & Bhullar, B. A. (2021). A shift in ontogenetic timing produced the unique sauropod skull. Evolution, 75(4), 819-831.

      González Riga, B. J., Lamanna, M. C., Ortiz David, L. D., Calvo, J. O., & Coria, J. P. (2016). A gigantic new dinosaur from Argentina and the evolution of the sauropod hind foot. Scientific Reports, 6(1), 19165.

      Lefebvre, R., Allain, R., & Houssaye, A. (2023). What's inside a sauropod limb? First three‐dimensional investigation of the limb long bone microanatomy of a sauropod dinosaur, Nigersaurus taqueti (Neosauropoda, Rebbachisauridae), and implications for the weight‐bearing function. Palaeontology, 66(4), e12670.

      McPhee, B. W., Benson, R. B., Botha-Brink, J., Bordy, E. M., & Choiniere, J. N. (2018). A giant dinosaur from the earliest Jurassic of South Africa and the transition to quadrupedality in early sauropodomorphs. Current Biology, 28(19), 3143-3151.

      Martin Sander, P., Mateus, O., Laven, T., & Knötschke, N. (2006). Bone histology indicates insular dwarfism in a new Late Jurassic sauropod dinosaur. Nature, 441(7094), 739-741.

      Remes, K. (2008). Evolution of the pectoral girdle and forelimb in Sauropodomorpha (Dinosauria, Saurischia): osteology, myology and function (Doctoral dissertation, München, Univ., Diss., 2008).

      Sander, P. M., & Clauss, M. (2008). Sauropod gigantism. Science, 322(5899), 200-201.

      Yates, A. M., & Kitching, J. W. (2003). The earliest known sauropod dinosaur and the first steps towards sauropod locomotion. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1525), 1753-1758.

      We appreciate this suggestion and we already used some of the articles in our study but the selection of cites were based also in the available manuscript space enforced by the edition guidelines. We would have like to include several of these works but we had opted to include some of the works that summarize some of them, whereas excluding others.

    1. We can leave; we can conform; or we can express ourselves, argue and protest, and try to change the situation. I am arguing for the last. Becoming visible and using one's voice can be dangerous — even in purportedly free societies like the U.S. — but these strategies can also be successful in the long-run, contributing to local and systemic change.

      This gives the same energy as if you are not a part of the solution you are part of the problem. And to some extent, I think this is absolutely true. We need to be passionate about our children and be able to take care of our students and treat them well and with kindness. I would caution however that it is equaly important to not divide people in our language and educate and empathize with those who may disagree.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2.  We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text. 

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.  

      Figure 5 has been reworked for readability. More data have been added in SI. 

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful. 

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state. 

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    2. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2. We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text.

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.

      Figure 5 has been reworked for readability. More data have been added in SI.

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful.

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state.

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      The manuscript by Dr. Shinkai and colleagues is about the posttranslational modification of a highly important protein, MT3, also known as the growth inhibitory factor. Authors postulate that MT3, or generally all MT isoforms, are sulfane sulfur binding proteins. The presence of sulfane sulfur at each Cys residue has, according to the authors, a critical impact on redox protein properties and almost does not affect zinc binding. They show a model in which 20 Cys residues with sulfane sulfur atoms can still bind seven zinc ions in the same clusters as unmodified protein. They also show that recombinant MT3 (but also MT1 and MT2) protein can react with HPE-IAM, an efficient trapping reagent of persulfides/polysulfides. This reaction performed in a new approach (high temperature and high reagent concentration) resulted in the formation of bis-S-HPE-AM product, which was quantitatively analyzed using LC-MS/MS. This analysis indicated that all Cys residues of MT proteins are modified by sulfane sulfur atoms. The authors performed a series of experiments showing that such protein can bind zinc, which dissociates in the reaction with hydrogen peroxide or SNAP. They also show that oxidized MT3 is reduced by thioredoxin. It gives a story about a new redox-dependent switching mechanism of zinc/persulfide cluster involving the formation of cystine tetrasulfide bridge.

      The whole story is hard to follow due to the lack of many essential explanations or full discussion. What needs to be clarified is the conclusion (or its lack) about MT3 modification proven by mass spectrometry. Figure 1B shows the FT-ICR-MALDI-TOF/MS spectrum of recombinant MT3. It clearly shows the presence of unmodified MT3 protein without zinc ions. Ions dissociate in acidic conditions used for MALDI sample preparation. If the protein contained all Cys residues modified, its molecular weight would be significantly higher. Then, they show the MS spectrum (low quality) of oxidized protein (Fig. 1C), in which new signals (besides reduced apo-MT3) are observed. They conclude that new signals come from protein oxidation and modification with one or two sulfur atoms. If the conclusion on Cys residue oxidation is reasonable, how this protein contains sulfur is unclear. What is the origin of the sulfur if apo-MT does not contain it? Oxidized protein was obtained by acidification of the protein, leading to zinc dissociation and subsequent neutralization and air oxidation. Authors should perform a detailed isotope analysis of the isotopic envelope to prove that sulfur is bound to the protein. They say that the +32 mass increase is not due to the appearance of two oxygen donors. They do not provide evidence. This protein is not a sulfane sulfur binding protein, or its minority is modified. Moreover, it is unacceptable to write that during MT3 oxidation are "released nine molecules of H2". How is hydrogen molecule produced? Moreover, zinc is not "released", it dissociates from protein in a chemical process.

      Thank you for your comment. According to your suggestion, we have rewritten the corresponding sentences below, together with addition of new Fig.1D.

      First, the sentence “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc was removed during MS analysis.” was changed to “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc dissociates from protein in acidic conditions used for MALDI sample preparation.” in the introduction section. Second, we have added the following sentence “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.” in the discussion section. Third, we have added new Fig. 1D and the corresponding citation in the introduction. Fourth, the sentence “An increase in mass of 32 Da can also result from addition of two oxygen atoms, but we attributed it to one sulfur atom for reasons described later.” was changed to “Note that an increase in mass of 32 Da can also result from addition of two oxygen atoms.”.

      Another important point is a new approach to the HPE-IAM application. Zinc-binding MT3 was incubated with 5 mM reagent at 60°C for 36 h. Authors claim that high concentration was required because apoMT3 has stable conformation. Figure 2B shows that product concentration increases with higher temperature, but it is unclear why such a high temperature was used. Figure 1D shows that at 37°C, there is almost no reaction at 5 mM reagent. Changing parameters sounds reasonable only when the reaction is monitored by mass spectrometry. In conclusion, about 20 sulfane sulfur atoms present in MT3 would be clearly visible. Such evidence was not provided. Increased temperature and reagent concentration could cause modification of cysteinyl thiol/thiolates as well, not only persulfides/polysulfides. Therefore, it is highly possible that non-modified MT3 protein could react with HPE-IAM, giving false results. Besides mass spectrometry, which would clearly prove modifications of 20 Cys, authors should use very important control, which could be chemically synthesized beta- or alfa-domain of MT3 reconstituted with zinc (many protocols are present in the literature). Such models are commonly used to test any kind of chemistry of MTs. If a non-modified chemically obtained domain would undergo a reaction with HPE-IAM under such rigorous conditions, then my expectation would be right.

      Thank you for your comments. Although we have already confirmed that no false-positive results were observed using this method in Fig. 5 (previously Fig. 4), we have conducted additional experiments by preparing chemically synthesized α- and β-domains of GIF/MT-3, as well as recombinant α- and β-domains of GIF/MT-3. As shown in the new Fig. S2A, the chemically synthesized α- and β-domains of GIF/MT-3 detected almost no sulfane sulfur (less than 1 molecule per protein), whereas the recombinant α- and β-domains detected several molecules of sulfane sulfur (more than 5 molecules per protein) (Fig. S2A). Therefore, I would like to emphasize here that the cysteine residue itself cannot be the source of the bis-S-HPE-AM product (sulfane sulfur derivative).

      Accordingly, we have added the following sentence in the results section: “Because this assay was performed at relatively high temperatures (60°C), we also examined the sulfane sulfur levels of several mutant proteins using chemically synthesized α- and β-domains of GIF/MT-3 to eliminate false-positive results. As shown in Fig. S2A, sulfane sulfur (less than 1 molecule per protein) was undetectable in chemically synthesized α- and β-domains of GIF/MT-3, whereas several molecules of sulfane sulfur per protein were detected in recombinant α- and β-domains exhibited (Fig. S2B, left panel). These findings indicated that the sulfane sulfur detected in our assay was derived from biological processes executed during the production of GIF/MT-3 protein. We further analyzed mutant proteins with β-Cys-to-Ala and α-Cys-to-Ala substitutions and found that their sulfane sulfur levels were comparable with those of the α- and β-domains of GIF/MT-3, respectively (Fig. S2B, left panel). Additionally, Ser-to-Ala mutation did not affect the sulfane sulfur levels of GIF/MT-3. The zinc content of each mutant protein was also determined under these conditions (Fig. S2B, right panel).”

      - The remaining experiments provided in the manuscript can also be applied for non-modified protein (without sulfane sulfur modification) and do not provide worthwhile evidence. For instance, hydrogen peroxide or SNAP may interact with non-modified MTs. Zinc ions dissociate due to cysteine residue modification, and TCEP may reduce oxidized residue to rescue zinc binding. Again, mass spectrometry would provide nice evidence.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified proteins (without sulfane sulfur modification). However, the experiments shown in Fig. 4 and Fig. 6 were conducted to investigate the role of sulfane sulfur under oxidative stress conditions, rather than to examine sulfur modification in the protein itself. As mentioned previously, it is difficult to detect sulfur modifications directly in the protein using MALDI-TOF/MS (Fig. 1), as sulfur modifications appear to dissociate during the laser desorption/ionization process.

      - The same is thioredoxin (Fig. 7) and its reaction with oxidized MT3. Nonmodified and oxidized MT3 would react as well.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified MT-3 protein. However, to the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx system. In fact, this experiment is not intended to prove that MT-3 is sulfane sulfur-binding protein. Rather, it demonstrates the novel finding that apo-MT3 serves as an excellent substrate for Trx and that the sulfane sulfur (persulfide structure) remains intact throughout the reduction process.

      - If HPE-IAM reacts with Cys residues with unmodified MT3, which is more likely the case under used conditions, the protein product of such reaction will not bind zinc. It could be an explanation of the cyanolysis experiment (Fig. 6).

      Thank you for your comment. As you pointed out, HPE-IAM reacts with cysteine residues in unmodified MT-3, thereby preventing zinc from binding to the protein. However, we did not use HPE-IAM prior to measuring zinc binding. Instead, HPE-IAM was used solely for determining the sulfane sulfur content in the protein, and thus it cannot explain the results of the cyanolysis experiment.

      - Figure 4 shows the reactivity of (pol)sulfides with TCEP and HPE-IAM. What are redox potentials? Do they correlate with the obtained results?

      Thank you for your comment. However, we must apologize as we do not fully understand the rationale behind determining redox potentials in this experiment. We believe the data itself to be very clear and presenting convincing results.

      - Raman spectroscopy experiments would illustrate the presence of sulfane sulfur in MT3 only if all Cys were modified.

      Yes, that is correct. Since approximately 20 sulfane sulfur atoms are detected in the protein with 20 cysteine residues, we believe that nearly all cysteine residues are modified by sulfane sulfur. Therefore, Raman spectroscopy is considered applicable to our current study.

      - The modeling presented in this study is very interesting and confirms the flexibility of metallothioneins. MT domains are known to bind various metal ions of different diameters. They adopt in this way to larger size the ions. The same mechanism could be present from the protein site. The presence of 9 or 11 sulfur atoms in the beta or alfa domain would increase the size of the domains without changing the cluster structure.

      We truly appreciate your positive evaluation of this work.

      - Comment to authors. Apo-MT is not present in the cell. It exists as a partially metallated species. The term "apo-MT" was introduced to explain that MTs are not fully saturated by metals and function as a metal buffer system. Apo-MT comes from old ages when MT was considered to be present only in two forms: apo-form and fully saturated forms.

      Thank you for your insightful comments. We find it reasonable to understand that apo-MT exists as a partially metallated species within the cell.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors reveal that GIF/MT-3 regulates zinc homeostasis depending on the cellular redox status. The manuscript technically sounds, and their data concretely suggest that the recombinant MTs, not only GIF/MT-3 but also canonical MTs such as MT-1 and MT-2, contain sulfane sulfur atoms for the Zn-binding. The scenario proposed by the authors seems to be reasonable to explain the Zn homeostasis by the cellular redox balance.

      Strengths:

      The data presented in the manuscript solidly reveal that recombinant GIF/MT-3 contains sulfane sulfur.

      Weaknesses:

      It is still unclear whether native MTs, in particular, induced MTs in vivo contain sulfane sulfur or not.

      Thank you for pointing out the strengths and weaknesses of this manuscript. Based on your suggestions, we have determined the sulfane sulfur content in the native GIF/MT-3 protein, as explained in our response to "Recommendations for the Authors #2."

      Reviewer #3 (Public Review):

      Summary:

      The authors were trying to show that a novel neuronal metallothionein of poorly defined function, GIF/MT3, is actually heavily persulfidated in both the Zn-bound and apo (metal-free) forms of the molecule as purified from a heterologous or native host. Evidence in support of this conclusion is compelling, with both spectroscopic and mass spectrometry evidence strongly consistent with this general conclusion. The authors would appear to have achieved their aims.

      Strengths:

      The analytical data are compelling in support of the author's primary conclusions are strong. The authors also provide some modeling evidence that strongly supports the contention that MT3 (and other MTs) can readily accommodate sulfane sulfur on each of the 20 cysteines in the Zn-bound structure, with little perturbation of the structure. This is not the case with Cys trisulfides, which suggests that the persulfide-metallated state is clearly positioned at lower energy relative to the immediately adjacent thiolate- or trisulfidated metal coordination complexes.

      Weaknesses:

      The biological significance of the findings is not entirely clear. On the one hand, the analytical data are clearly solid (albeit using a protein derived from a bacterial over-expression experiment), and yes, it's true that sulfane S can protect Cys from overoxidation, but everything shown in the summary figure (Fig. 8D) can be done with Zn release from a thiol by ROS, and subsequent reduction by the Trx/TR system. In addition, it's long been known that Zn itself can protect Cys from oxidation. I view this as a minor weakness that will motivate follow-up studies. Fig. 1 was incomplete in its discussion and only suggests that a few S atoms may be covalently bound to MT3 as isolated. This is in contrast to the sulfate S "release" experiment, which I find quite compelling.

      Impact:

      The impact will be high since the finding is potentially disruptive to the metals in the biology field in general and the MT field for sure. The sulfane sulfur counting experiment (the HPE-IAM electrophile trapping experiment) may well be widely adopted by the field. Those of us in the metals field always knew that this was a possibility, and it will interesting to see the extent to which metal-binding thiolates broadly incorporate sulfate sulfur into their first coordination shells.

      Thank you for pointing out the strengths and weaknesses of this manuscript. As you noted, the explanations and discussions regarding Fig. 1 were missing. To address this, we have added the following sentences to the discission section: “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.”

      Reviewer #1 (Recommendations For The Authors):

      Overall, the topic of the study is interesting, but the provided evidence is insufficient to claim that MT3 is a sulfane sulfur-binding protein. Indeed, some recent studies showed that natural and recombinant MT proteins can be modified, but only one or a few cysteine residues were modified. Authors should follow my suggestion and apply mass spectrometry to all performed reactions and, first of all, to freshly obtained protein. I strongly suggest using chemically synthesized and reconstituted domains to test whether the home-developed approach is appropriate. Moreover, native MS and ICP-MS analysis of MT3 would support their claims.

      Thank you for your insightful comments. Following your suggestions, we have prepared chemically synthesized proteins of the α- and β-domains of GIF/MT-3 and conducted additional experiments, as explained in response comments to “Public Review #1”. Regarding the MS analysis, we have also added a discussion on the difficulty of detecting sulfur modifications in the protein.

      Reviewer #2 (Recommendations For The Authors):

      I have some minor points which should be considered by the authors.

      (1) Table 1: In the simulation by MOE, the authors speculated 7 atoms of metal bound to GIF/MT-3. Although a total of 7 atoms of Zn or Cd are actually bound to MTs as a divalent ion, the number of Cu and Hg bound to MTs as a monovalent ion is scientifically controversial. Several ideas have been proposed in the literature, however, "7 atoms of Cu or Hg" could be inappropriate as far as I know. The authors should simulate again using a more appropriate number of Cu or Hg in MTs.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      On the other hand, some researchers have reported that mercury binds to MT as a divalent ion, and the formation of Hg<sub>7</sub>MT is possible (not just other forms). Therefore, we decided to continue using the score value for mercury shown in Table 1.

      (2) If possible, native MT samples isolated from an experimental animal should be evaluated for the sulfane sulfur content. Canonical MTs, MT-1 and MT-2, are highly inducible by not only heavy metals but also oxidative stress. Under the oxidative stress condition such as the exposure of hydrogen peroxide, it is questionable whether the induced Zn-MTs contain sulfane sulfur or not.

      According to your suggestion, we evaluated the sulfane sulfur content in native GIF/MT-3 samples isolated from mouse brain cytosol (Fig. 10). The measured amount was 3.3 per protein. This suggests that sulfane sulfur in GIF/MT-3 could be consumed under oxidative conditions, as you anticipated. Another possible explanation for the discrepancy between the native form and recombinant protein is likely related to metal binding in the protein. It is generally understood that both zinc and copper bind to GIF/MT-3 in approximately equal proportions in vivo. When we prepared recombinant copper-binding GIF/MT-3 protein, the sulfane sulfur content in the protein was significantly different (approximately 4.0 per protein) compared to the Zn<sub>7</sub>GIF/MT-3 form. Further studies are needed to clarify the relationship between sulfane sulfur binding and the types of metals in the future.

      (3) The biological significance of sulfane sulfur in MTs is still unclear to me.

      Thank you for your comments. To address this question, we have added the following sentence to the discussion section: “The biological significance of sulfane sulfur in MTs lies in its ability to 1) contribute to metal binding affinity, 2) provide a sensing mechanism against oxidative stress, and 3) aid in the regeneration of the protein.”

      (4) According to the widely accepted nomenclature of MT, "MT3" should be amended to "MT-3".

      According to your suggestion, we have amended from MT3 to MT-3 throughout the manuscript.

      Reviewer #3 (Recommendations For The Authors):

      Most of my comments are editorial in nature, largely focused on what I perceive as overinterpretation or unnecessary speculation.

      The authors state in the abstract that the intersection of sulfane sulfur and Zn enzymes "has been overlooked." This is not actually true - please tone down to "under investigated" or something like this.

      Based on your suggestion, we have replaced the term “has been overlooked” with “has been under investigated” in the abstract.

      Line 228: The discussion of Fig. 6C involved too much speculation. I cannot see a quantitative experiment that supports this.

      Based on your suggestion, we have removed Fig. 6C (currently referred to as Fig. 7C). Additionally, we have revised the sentence from “implying that the sulfane sulfur is an essential zinc ligand in apo-GIF/MT3 and that an asymmetric SSH or SH ligand is insufficient for native zinc binding (Fig. 6C)” to “implying the contribution of sulfane sulfur to zinc binding in GIF/MT-3”.

      Line 247 "persulfide in apo-GIF/MT3 seems.." I think the authors mean that the Zn form of the protein is resistant to Trx or TCEP.

      Thank you for pointing this out. We realized that the term “persulfide in apo-GIF/MT3” might be confusing. Therefore, we have replaced it with “persulfide formation derived from apo-GIF/MT3” in the corresponding sentence.

      Molecular modeling: We need more details- were these structures energy-minimized in any way? Can the authors comment on the plethora of S-S dihedral angles in these structures, and whether they are consistent with expectations of covalent geometry? Please add text to explain or even a table that compiles these data.

      Thank you for your comment. Yes, energy minimization calculations for structural optimization were conducted during homology modeling in MOE. In fact, we have already stated in the Methods section that “Refinement of the model with the lowest generalized Born/volume integral (GBVI) score was achieved through energy minimization of outlier residues in Ramachandran plots generated within MOE.” In this model, covalent geometry, including the S-S dihedral angles, is also taken into consideration.

      What is a thermostability score? Perhaps a bit more discussion here and what relationship this has to an apparent (or macroscopic) metal affinity constant.

      The thermostability score is used to compare the thermal stability between the wild-type and mutant proteins. As shown in Equation (1) in the method section, it is calculated by subtracting the energy of the hypothetical unfolded state from the energy of the folded state. Since obtaining the structure of the unfolded state requires extensive computational effort, MOE employs an empirical formula based on two-dimensional structural features to estimate it. The ΔΔG values represent the difference between ΔGf(WT) and ΔGf(Mut). However, because it is difficult to directly determine ΔGf(Mut) and ΔGf(WT), MOE calculates ΔΔG using the thermodynamic cycle equivalence: ΔΔGs =ΔGsf (WT→Mut) - ΔGsu (WT→Mut), as expressed in Equation (1).

      On the other hand, the affinity score represents the interaction energy between the target ligand and the protein. In this study, we calculated the affinity score by selecting metal atoms as the ligands. The interaction energy (E int) is defined as:

      E int = E complex − E receptor − E ligand

      where each term is as follows:

      E complex : Potential energy of the complex.

      E receptor : Potential energy of the receptor alone.

      E ligand : Potential energy of the ligand alone.

      Each potential energy term includes contributions from bonded interactions such as bond lengths and bond angles. However, since there is no structural difference among E receptor, and E ligand, the bonded energy components cancel out. Consequently, E int is determined as:

      E int = ΔEele +ΔEvdW +ΔE sol

      Here, a negative E int indicates that the complex is more stable, while a positive E int implies that the receptor and ligand are more stable in their dissociated states.

      We have revised the sentence "The affinity score was also calculated using MOE software as the difference between the ΔΔGs values of the protein, free zinc, and metal–protein complex” to "The affinity score was also calculated using MOE software as the difference between the potential energy values of the protein, free zinc, and metal–protein complex” to correct the misdescription.

      Lines 278-280: The authors state that they observe a "marked enhancement of metal binding affinity, and rearrangement of zinc ions." I don't see support for this rather provocative conclusion. This is the expectation of course. I would love to see actual experimental data on this point, direct binding titrations with metals performed before and after the release of the sulfate sulfur atoms.

      Thank you for your comments. Although this statement is based on the 3D modeling simulation, we have also experimentally observed that the diminishment of sulfane sulfur in GIF/MT-3 resulted in a decrease in zinc binding levels, as shown in Fig. 7. However, conducting direct binding titration experiments was difficult for us due to the difficulty in preparing pure GIF/MT-3 protein with or without sulfane sulfur. Therefore, we have revised the sentence "marked enhancement of metal binding affinity, and rearrangement of zinc ions" to simply "enhancement of metal binding affinity" to avoid over-speculation.

      Table I- quantitatively lower stability for the Cu complex- the stoichiometry is clearly wrong in this simulation- please redo this simulation with the right stoichiometry or Cu to MT3- consult a Stillman paper.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      I like the model for reversible metal release mediated by the thioredoxin system (Fig. 8D)- but you can also do this with thiols- nothing really novel here. Has it been generally established that tetraulfides are better substrates for the Trx/TR system? The data shown in Fig. 7B seems to suggest this, but is this broadly true, from the literature?

      There are reports describing that persulfides and polysulfides are reduced by the thioredoxin system. However, it is not well-established that tetraulfides are better substrates for the Trx/TR system. To the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx/TR system. Further research is required to compare the catalytic efficiency between proteins containing disulfide and those with tetraulfide moieties.

      Line 380: Many groups have reported that many proteins are per- or polysulfidated in a whole host of cells using mass spectrometry workflows, and that terminal persulfides can be readily reduced by general or specific Trx/TR systems. This work could be better acknowledged in the context of the authors' demonstration of the reduction of the tetrasulfides, which itself would appear to be novel (and exciting!).

      We truly appreciate your positive evaluation of this work.

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      Reply to the reviewers

      Manuscript number: RC-2024-02824

      Corresponding author(s): Rita tewari

      1. General Statements [optional]

      We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (red text) to all specific points raised by the reviewers

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      • *

      We would like to thank all the reviewers for using their valuable time to review our manuscript and to provide constructive comments and suggestions. We have now revised the manuscript taking their comments into consideration; our responses to these comments are detailed below (in red).

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Minor comments: In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

      We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).

      However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a "normal" nuclear spindle and attached kinetochores was observed.

      The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.

      Discussion Section:

      Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?

      We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.

      Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?

      We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).

      In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

      ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Introduction Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

      We have modified the sentence in which we discuss current opinions about 'open' and 'closed' mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.

      Results

      Fig 7 is the final figure; but would be more useful upfront.

      We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.

      Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

      We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see below, new Fig. S2), but a similar problem was encountered.

      Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.

      These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

      We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See the above figure; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).

      The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

      The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

      We have modified the sentence on line 213 to accommodate this suggestion.

      Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

      We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A, which is displayed below. We have also modified the sentence on line 219 to reflect these additions.

      The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.

      We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.

      Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

      We agree with the reviewer's analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see below Fig A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Fig. B, below.

      I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

      We appreciate the reviewer's correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.

      Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

      We agree with the reviewer's suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.

      The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

      We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN. We discuss the overlap in the findings of the two studies in lines 590-605.

      While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

      We have toned down the wording of this conclusion in lines 665-677.

      In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

      We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.

      It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

      We have deleted the sentence that raised this suggestion.

      While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

      The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

      Following the reviewer's suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.

      The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

      In agreement with the reviewer's comments we have moved these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Major comments: My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

      In more detail:

      • The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.

      As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.

      • I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.

      As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.

      • Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.

      We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.

      In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.

      It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding ( - I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify

      1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,

      DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).

      2) the proportion of anucleated gametes in WT and the KO lines

      We have added these data in Fig. 3K and Fig. S10G

      3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.

      We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.

      Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos

      We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.

      We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. Please see the figure below. These data are added in Fig. 3K (for Sun1-KO) and S10G (for Allan-KO).

      • The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.

      Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.

      • Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

      We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).

      Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.

      . Some additional points where the data is not entirely sound yet or could be improved:

      • Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?

      High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3).

      • The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?

      There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation. We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).

      Can the authors trace their NE in ExM according to the NHS-Ester signal?

      We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B below).

      • Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?

      We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.

      • Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.

      We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now shown in Fig. 7D, E), and refer to the bite back data shown in current Fig. 7E.

      • Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?

      We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.

      • Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.

      We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.

      • In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?

      We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.

      • Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?

      Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.

      • The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

      We have extended this discussion now in lines 590-605.

      Suggestions how to improve the writing and data presentation.

      • General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).

      Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.

      Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.

      We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.

      Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).

      • Are the scale bars of all expansion microscopy images adjusted for the expansion factor?

      Yes, the scale bars are adjusted accordingly.

      • The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)

      The figure legends have been streamlined, with removal of the description of results.

      • Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?

      They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.

      • Line 267: Reference to the original nek4-KO paper missing

      This reference is now included.

      • Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.

      We have moved these data to supplementary information and modified the text.

      • Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.

      We have corrected the text to the start of exflagellation.

      • Line 588: Spelling mistake in SUN1-domain

      Corrected.

      • Line 726/731: i missing in anti-GFP

      Corrected.

      • Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.

      Moved to right place

      • Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"

      Changed.

      • Line 974, 976: please correct to WT-GFP and dsun1

      Corrected.

      • Line 1041, 1044: WT-GFP instead of WTGFP.

      Corrected to WT-GFP.

      • Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?

      We have added the time points to the images in these figures.

      • Fig 1D/Line 727: the scale of the scale bar on the inset is missing.

      We have added the scale bar.

      • Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.

      We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.

      • Fig 5B: What is NP

      Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).

      • Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.

      We have added the arrow.

      • Fig S2C: Is the scale bar really the same for the zygote and the ookinete?

      We have checked this and used the same for both zygote and ookinete.

      • Fig S3C, S7C: which stages was qRT-PCR done on?

      Gametocytes activated for 8 min.

      • Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?

      The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.

      • Fig S3: Panel letters E and G are missing

      We have updated the lettering in current Fig. S5

      • Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?

      We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.

      • Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins

      We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.

      • Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?

      We have now included the full image showing the inset (new Fig. S8).

      • Fig S5C and S8C: Change "DNA" to "Nucleus".

      We have changed "DNA" to "Nucleus". Now they are Fig. S8K and S11I.

      Reviewer #3 (Significance (Required)):

      Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

      We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      Summary:

      • In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.*
      • To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background.*
      • Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features.*
      • Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.*
      • Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo.*
      • However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.*

      Major comments:

      • It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice? The previous studies referred to in the manuscript (ref 21 and 23) are both using wt mice of younger age, 3.5 and 3 weeks respectively. It is known that age influences immune status, and some of the experiments in these previous studies are performed in even younger animals (3 to 8 days suckling mice) likely for this specific reason. The different mice strains in these studies may also influence their susceptibility to infection.

      • *While LGTV and LGTVT:prME fails to result in symptomatic infection in wt mice in our study, a certain level of localized infection is likely taking place and the outcome will depend on the immune status of the animals (age/immune deficiencies). What we tried to highlight in the manuscript was that the relative pathogenicity (TBEV/LGTV The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

      Thank you for pointing out the lack of clarity. All the tropism studies, figure 4 and 5, were done in adult WT mice infected i.c. to allow the virus to surpass the initial barrier of peripheral immune response and establish infection in the brain. We have now stressed this in the result section and in the relevant figure legends.

      Minor comments:

      • Figures need some re-working:*

      • Figure 1 :

      • 1D : only the difference between TBEV and LGTVT:prME is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.* Thank you for this suggestion. However, as there is no statistical difference between LGTV and ChLGTV in Fig 1D we have maintained the figure as originally made.

      • Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected. Thank you, this has been corrected in the figure.

      • Figure 3 : Route of infection could be added to figure labels for more clarity. Thank you, we have added this to the figure.

      • Figure 4A : Labelling the Mock panel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy. We agree that adding these labels improves the clarity and accessibility of the figure and have added this to 4A.

      • Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend. Thank you, we have added the explanation that microglia were stained by Iba1 and increased the size of the images in Figure 4. Additionally, co-staining of viral antigens and the neuronal marker UCHL-1 has been added as the new Figure 4E and Iba-1 staining moved to 4F.


      Significance

      Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. * The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain. * However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

      We agree that studying tropism in MAVS-/- mice might be misleading and that is why the whole tropism study was performed in adult WT mice, we have clarified in the text that these data are from WT mice. In addition to the significance of this study in highlighting the respective contribution of structural proteins and the immune response in shaping tropism, this study also provides a __well-characterized chimeric virus __with a safety profile comparable to LGTV while retaining key structural and antigenic features of TBEV, model that has already helped advance studies on flavivirus receptor interactions and structural dynamics.

      Reviewer #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, * pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralization using serum of TBEV vaccinated individuals.*

      General comment: * In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.*

      Thank you for these very relevant comments and suggestions. In line with these, we have now added a later timepoint (8 days) for LGTV:prME in IPS1-/- mice to better understand the kinetics of the chimeric virus at later time points (Figure 3). Additionally, we have added a neuronal marker in figure 4. The explanation of quantification of the fluorescence data is described in detail in the material and method, where the concept of this arbitrary unit (AU) used for quantification is described.

      Specific points: * • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....*

      We agree here and have specified this accordingly.

      • Line 70 - Low pathogenicity in which species? All? Humans? The sentence refers to mice as there has not been any human clinical case with LGTV. We have added that to the text.

      • Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient Corrected accordingly.

      • Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs? We have clarified in the text that this is referring to similarity to the pathogenicity of LGTV.

      • Line 95: Good to mention in the text the cell type in which the foci are seen We agree, this information has been added to the main text in addition to the figure legend.

      • Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before) We compare the structure of our chimeric virus with the previously published Kuutsalo-14 strain (ref 25). The use of 93/783 in this study is to ensure the same strain of TBEV is used as was used to generate LGTV:prME and to compare the chimeric virus to infectious clones of the parental viruses rescued and passaged in the same way as the chimeric virus itself to ensure differences observed is indeed due to the genetic factors.

      • Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation. Thank you for bringing this to our attention. The figure 3E is displaying data for MAVS-/- mice infected with 10^5 FFU, where the some animals meet end-point criteria already around day 7-9. To address this comment, we have added an additional timepoint at day 8 (seven animals) to explore the trend in viral loads in the brain. However, we refrain from analyzing later time points as this would require a high number of starting animals to ensure adequate numbers surviving to e.g. the suggested day 14, which is not in line with RRR.

      • Interestingly, there is not significant increase in viral loads of LGTV:prME infected animals between day 6 and 8. In line with this, IF imaging analysis of brains from later end-point animals (day 10-14) has shown limited staining of viral antigen in the brain (data not included in manuscript but could be provided to reviewers if requested). This suggests that inflammation is driving the pathology in these animals rather than uncontrolled viral replication. This has also been noted in the text. The tropism and imaging is done in WT mice infected i.c.. and the time/infectious dose has been adjusted to ensure similar clinical manifestation as presented in supplemental Figure 2A. These mice are then euthanized around day 5-6 and processed for brain imaging, line 189.

      • Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2). Viral serum titers have been analyzed previously in MAVS-/- mice in Kurhade et al 2016, and they are high at day 2 and go down to almost detection limit day 4, meaning earlier drop than in peripheral organ and was not included in these experiments. For neutralization, the included time points for the experiments in Figure 3E-H the time points are too short for robust detection of IgG antibody responses.

      • Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV? We appreciate the comment and we have clarified that WT mice are i.c. inoculated. Since we wanted to compare the three viruses, we needed to use an inoculation route that is working for all three viruses. While the tropism after peripheral infection of TBEV is a very interesting question, it remains outside the scope of this study as this cannot be compared with LGTV in WT mice.

      • Figure 4B - What could account for the large variation seen in the TBEV group? This is a very good question that is difficult to answer. Although these are inbred mice, we have previously seen that there are differences in infection rate between different mice using whole brain imaging (Chotiwan et al 2023).

      • Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above. Thank you for this suggestion we have added a neuronal marker. We have also clarified in the text that we confirm the infection pattern in rhinal cortex with confocal microscopy. Microglia activation has been added to the discussion.

      • Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses? Quantification of microglial activation is sensitive to imaging quality and area of imaging and requires large sample sets to ensure validity in the conclusions. Here we do not observe any clear differences nor claim that the microglia activation is different between the different viral strains.

      • Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F? Thank you for pointing this out the Figure 4D should be Figure 4E, this has been corrected.

      • Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A. Thank you we have now removed the mock, LGTV and LGTVT:prME from fig 5C to more clearly communicate the message of difference in infection between GM and WM for TBEV specifically.

      • Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5? The quantification of OPT in figure 5C is not at the level of single cell resolution but rather virus signal over mock. We agree the cells in the cerebellum has different sizes but we do not claim that the Purkinje layer is more infected compared to the granular cell layer, only that Purkinje cells are infected which is relevant in human TBE.

      NS5 antibody is raised against a peptide in the TBEV NS5 protein which is highly conserved. The aa identity between TBEV and LGTV is 93%, we have not seen a difference in the staining between the different viruses using this antibody.

      • Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal? We have added "as measured by stronger fluorescent signal" to better explain what we mean with higher infection.

      • Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus. We have changed the wording to infected cells.

      • Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here? We were unsure about the specific concern raised by the reviewer in this comment and, therefore, have not made any changes. If the reviewer could clarify their request, we would be happy to address it accordingly.

      • Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x Thank you for this suggestion we have added this point into the discussion.

      • Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis? We have now added some information about cerebellum symptoms in human TBE infection to the discussion.

      • Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination" Thank you for these suggestions we have now added these aspects in the conclusions.

      • Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing. Thank you for this suggestion. We have added a section describing the statistics in the methods.

      Significance

      Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

      • Expertise: arboviruses, imaging, neurovirulence, animal models*
      • Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.*

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): * SUMMARY: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism. *

      MAJOR COMMENTS: * The conclusions are supported by the data.*

      • However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition. We thank the reviewer for this suggestion. In line with this we have now added Ifnb1 and Rsad2 RNA levels in different peripheral organs and we see that early on in infection most mice infected with LGTVT:prME show higher upregulation of these genes. These data have been added as a new panel F and G in figure 3.

      • Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E. The rational for not using the C protein of TBEV is that we did not want to reduce the RNA to C interaction which, could affect the packaging or encapsidation. In line with this, previous research on chimeric flaviviruses has shown that exchanging the prM-E proteins are usually well tolerated while exchanging the C-protein may lead to attenuation or even failure to rescue the virus.

      • Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment. A549 cells were used in the spread and kinetic study because it is an IFN competent cell type which TBEV and LGTV grows well in. The structural studies were performed in A549 MAVS cells because the lack of MAVS results in higher virus titers. The ability of these cells to produce large amount of virus while grown without serum greatly facilitated the purification protocols for cry-EM and mass spectrometry analysis. This has been highlighted in the text of both the material and method and very briefly in the result.

      The pathogenicity with peripheral infection can only be done with MAVS-/- mice as they are more sensitive to LGTV and it is a lethal model. Adult WT mice are resistant to LGTV infection i.p.. As the immune response is important in shaping the tropism, a direct comparison of the viruses is best analyzed in a WT mouse model.

      MINOR COMMENTS:

        • Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.* We have removed recombinant.
      • Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I All experiments in figure 2 are performed in A549 MAVS-/- as highlighted in the material and methods.

      • Line 158 - to be change "Fig 2I" with "fig 2J" Corrected

      • Line 159 - as above: fig 2J to be change with figure 2k Corrected

      *Significance: *

      • The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses. *

      • My field of expertise: virus-host interaction, at molecular level.*

  3. griersplagueyear.wordpress.com griersplagueyear.wordpress.com
    1. “Subordinates,” Garrett said. “Okay, so under ‘Communication,’ here’s thefirst comment. ‘He’s not good at cascading information down to staff.’ Washe a whitewater rafter, Clark? I’m just curious.”“Yes,” Clark said, “I’m certain that’s what the interviewee was talkingabout. Actual literal cascades.”“This one’s my other favorite. ‘He’s successful in interfacing with clientswe already have, but as for new clients, it’s low-hanging fruit. He takes ahigh-altitude view, but he doesn’t drill down to that level of granularitywhere we might actionize new opportunities.’ ”Clark winced. “I remember that one. I think I may have had a minor strokein the office when he said that.”

      This memory of who they were and that what they did didn't give them meaning. Clark looking back also allows him to reflect on how much he has grown.

    1. Eff orts to study infant mortality have continued to trend toward studying the problem at the molecular level: the missing or defective gene, the environmental toxin. Such eff orts, while personally rewarding to investi-gators, risk irrelevancy and unethical indictment when existing solutions operate at the macroscopic level. Group empowerment socioeco nom ical ly, health education, and abolition of racism have no gene markers, but they do raise a diff erent issue. When infant mortality and disparity are examined in these contexts, there is no question that we know enough. Th e question is: as a resource- rich society facing signifi cant health disparities that can potentially be resolved, are we “good” enough?

      It is interesting to think that when an infant dies, the immediate response is to look at what went wrong with the baby internally, but not necessarily consider external factors. While genetics may play a key role in infant mortality rates, this is not always the case. This passage does a good job at bringing this issue to light, explaining that external factors do play a substantial role in infant mortality rates and should not be ignored. When evaluating factors that lead to an infant's passing, internal and external circumstances should be equally evaluated.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Cheng et al explore the utility of analyte ratios instead of relative abundance alone for biological interpretation of tissue in a MALDI MSI workflow. Utilizing the ratio of metabolites and lipids that have complimentary value in metabolic pathways, they show the ratio as a heat map which enhances the understanding of how multiple analytes relate to each other spatially. Normally, this is done by projecting each analyte as a unique color but using a ratio can help clarify visualization and add to biological interpretability. However, existing tools to perform this task are available in open-source repositories, and fundamental limitations inherent to MALDI MSI need to be made clear to the reader. The study lacks rigor and controls, i.e. without quantitative data from a variety of standards (internal isotopic or tissue mimetic models for example), the potential delta in ionization efficiencies of different species subtracts from the utility of pathway analysis using metabolite ratios.

      We thank the reviewer for comments on the availability of four other commercial and open-source tools for performing ratio imaging: ENVI® Geospatial Analysis Software, MATLAB image processing toolbox, Spectral Python (SPy) and QGIS. We now highlight these in the introduction (page 3 line 80-86). However, in contrast to these target ratio imaging methods, our approach uniquely enables the untargeted discovery of correlated (or anti-correlated) ratios of molecular features, whether the species are structurally known or unknown.

      ENVI® Geospatial Analysis Software and MATLAB image processing toolbox for hyperspectral imaging are both paid programs, limiting free access and software evaluation for the potential application of untargeted ratio-metric imaging. We are able to evaluate the application of MATLAB RatioImage since Weill Cornell Medicine has an institutional subscription for Mathwork-MATLAB. Notably, MATLAB RatioImage computes and displays an individual intensity modulated ratiometric image by choosing a numerator and denominator image. This software tool only images the ratios of selected metabolites from an input list of multiple species and does not allow for the possibility of untargeted ratiometric images of all metabolite pairs.

      While Spectral Python (SPy) and QGIS are both freely-available software packages, and both can perform individual metabolite ratio images, neither allows for untargeted ratiometric imaging of all pairs from a multiple metabolite input list. Table S1 (below) provides a comparison of the ratio imaging tool that we offer in comparison with other previously available tools.

      We appreciate the reviewer’s insightful comments on differential ionization efficiency among metabolites and the importance of using stable isotope internal standard to gain absolute quantification.

      A fundamental advantage of our ratiometric imaging tool is to provide better image contrast for tissue regions with differential ionization efficiency, with the potential to discover new “metabolic” regions that can be revealed by metabolite ratio. Note that comparison for ratio image abundance is limited to tissue groups in the equivalent region which is expected to have similar ionization efficiency for given metabolites. Furthermore, the power of our strategy is to provide untargeted (and targeted) ratio imaging as a hypothesis generation tool and this use does not require absolute quantification. If cost was not an issue, an extensive group of stable isotope standards could theoretically be used for absolute metabolite quantification of target metabolites with known identity.

      Using the tissue mimetic model, we generate calibration curve for stable isotope standards spiked in carboxymethylcellulose (CMC)-embedded brain homogenate cryosections and quantify the concentration of brain glucose, lactate and ascorbate concentrations. Similar ratio images among these metabolites are obtained from abundance data compared to quantified concentration data (Fig S3). While stable isotope standards are often used to obtain quantitative concentration of metabolite/lipid of interest, it is not applicable for untargeted metabolite ratios that include an assessment of structurally undefined species. Nevertheless, our data indicates that absolute quantification is not necessary for the targeted and untargeted ratio imaging described here (Page 6, line 196-205).

      Reviewer #2 (Public Review):

      Summary:

      In the article, "Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging" the authors describe their software package in R for visualizing metabolite ratio pairs. I think the novelty of this manuscript is overstated and there are several notable issues with the figures that prevent detailed assessment but the work would be of interest to the mass spectrometry community.

      Strengths:

      The authors describe a software that would be of use to those performing MALDI MSI. This software would certainly add to the understanding of metabolomics data and enhance the identification of critical metabolites.

      Weaknesses:

      The authors are missing several references and discussion points, particularly about SIMS MSI, where ratio imaging has been previously performed.

      There are several misleading sentences about the novelty of the approach and the limitations of metabolite imaging.

      Several sentences lack rigor and are not quantitative enough.

      The figures are difficult to interpret/ analyze in their current state and lack some critical components, including labels and scale bars.

      We thank reviewer for very helpful comments. The tone of the manuscript has been adjusted to highlight the real novelty of this method in the ease of computing and application to MS specific projects (abstract line 26-30 ). All figures have been updated to include labels and scale bars with improved resolution. References for ratio imaging use of SIMS MSI has been added in the introduction (Page 3, line 80-89).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Comments:

      In the Abstract it is stated that: "the research community lacks a discovery tool that images all metabolite abundance ratio pairs." However, the following tools exist that perform this fundamental task.

      A "pixel by pixel" data frame in .csv form has a very similar data structure to many instruments like satellite imaging or other hyperspectral tools. It is true this does not exist in the MALDI-specific context, but it would not be difficult to perform this task on the following programs. Highlight the novelty here is not ratios but the ease of computing them and the application in the specific project. Also, describe the available tools and what shortcomings others lack that this package provides. A supplemental table of MSI data analysis tools and the function of each would be a good addition.

      List of tools to perform band ratio computation with minimal modification:

      (1) ENVI IDL: geospatial imaging tool that allows ratio computation between spectral bands.

      (2) MATLAB image processing toolbox for hyperspectral imaging.

      (3) Spectral Python package (SPy).

      (4) QGIS with plugins can be used for hyperspectral image analysis with a ratio between bands.

      We revised the abstract and introduction to include novelty and comparison to other existing methods listed in Table S1.

      "untargeted R package workflow" - If there are functions used outside the SCiLS Lab API client then write it up and include a GitHub link for open access to fit the mission of eLife.

      As shown in Scheme I. We develop two types of codes for untargeted ratio imaging. The first type uses Scils lab API client to extend the function of targeted and targeted ratio imaging and all related spatial image analysis. This is suitable for Scils lab users. The second type does not require Scils lab API, it allows extracting pixel data from imzml file then proceed targeted and untargeted imaging and analysis. Both codes are now deposit in Github via public access (https://github.com/qic2005/Untargeted-massspectrometry-ratio-imaging.git).

      "across cells and tissue subregions" The value in reporting cell type and tissue type-specific differences in any metric is powerful, but not done in this paper. Only whole samples are compared such as "KO vs WT" and the annotations in Figure 3 are not leveraged for increased biological relevance. This paper treats each image as a homogenization experiment in a practical sense beyond just visually inspecting each image. Remove this claim or do the calculations on region/tissue/cell-type specific differences with the appropriate tools to show the data beyond simple heat map images.

      We have deleted the sentence containing across cells and tissue subregions from the abstract.

      "enhances spatial image resolution" Clarify. The resolution in MALDI is set by the raster size of the pixels which is an instrument parameter and cannot be changed post-acquisition. Image-specific methods to increase resolution exist, but dividing the value in one peak column by another does not change functional resolution in the context of the instruments here.

      We thank reviewer for pointing out this typo. We have changed it to enhance spatial image contrast in the abstract (line 34).

      "pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" - Appropriately calibrate the impact of this work and correct this statement to better reflect the capabilities of this approach. Do not oversell the exploration of pathway activity since the raw quantity reported as relative abundance does not provide biologically interpretable pathway information. This is due to unaccounted differences in ionization efficiencies between analytes in a pathway and lack of determination of rate. Without a calibration curve and more techniques on the analytical chemistry side of the project, it is possible a relative abundance of one analyte (like the product of a pathway) could be higher than the relative abundance of another analyte (a precursor), but due to structural differences, the actual quantity of the higher relative abundance species could be significantly different or even lower than its counterpart. Secondly, "functional activity" cannot be assessed in this manner without isotopic labeling or additional techniques. This does not subtract from the overall validity and impact of the work, but highlighting these shortcomings and slight alterations to the claim are important for a multidisciplinary audience.

      Although we show that abundance ratio results in similar image to concentration ratio for brain metabolites such as lactate, glucose and ascorbate, we agree with the reviewer that abundance ratio is different from the absolute concentration ratio in numerical value due to difference in ionization efficiency. We delete the sentence “pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" from the abstract. We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, line 239-247).

      "We further show that ratio imaging minimizes systematic variations in MSI data by sample handling and instrument drift, improves image resolution, enables anatomical mapping of metabotype heterogeneity, facilitates biomarker discovery, and reveals new spatially resolved tissue regions of interest (ROIs) that are metabolically distinct but otherwise unrecognized."

      Instrument drift is not accounted for by ratios as it impacts the process before ratio computation. "metabotype" - spelling?

      Instrument drift here refers to individual ion abundance changes during long data acquisition. Ratio may offer a better read-out than individual metabolite abundance alone. However, for acquired data after total ion normalization, ratio data would not have difference from non-ratio data. Therefore, we delete instrument drift from the sentence (Page 2, line 33, and Page 3, line 99)

      Metabotype is a term widely used for metabolomics field. It is categorized by similar metabolic profiles, which are based on combinations of specific metabolites. https://nutritionandmetabolism.biomedcentral.com/articles/10.1186/s12986-020-00499-z

      Results 3: Justify the claim that the ratio reduces artifacts. A ratio is the value from one m/z area over another and would seem that the quality of the ratio would be always lower than the individually higher quality pixel signal of the two analytes that compose a ratio.

      Ratio images are indeed the heatmaps of pixel-by-pixel ratio data, set by the scale of all ratio values. For very abundant ion pairs, their individual image may not be better than the ratio image, depending on the abundance changes among pixels within tissue sections. Similarly, the quality of ratio image may not be higher than the individual image if distribution of ratios does not change much among pixels in tissue sections. For example, metabolite or lipids in Figures 2 and 5 are abundant, but non-ratio images do not have better quality than ratio images. Furthermore, ratio image provides additional information on how the ratio of the two metabolite pair changes pixel-by pixel in all tissue sections, such additional information could be useful for data interpretation.

      Results 4: The metabolite pairs are biologically sensible but should be clearly stated that they do not account for differences in ionization efficiency between metabolites and cannot provide quantitative pathway analysis with a high degree of biological confidence.

      We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, 239-247, 254-255).

      Results 4: "cell-type specific metabolic activity at cellular (10 µm) spatial resolution" Prove the cell type differences with IHC coregistration or MALDI IHC if you want to make claims about them. Just visually determining a tissue type of a scan of a slide is inadequate to support this claim.

      We agree with reviewer’s comments. We meant to provide additional information on cellular level metabolic activity such as adenosine nucleotide phosphorylation status (ATP/AMP) ratio at 10µm resolution. Hippocampus neurons provide a good example for depicting this utility. We have rewritten the claim to highlight the role of ratio imaging in providing additional metabolic information (Page 8, line 288-290).

      Minor Comments:

      Table 2 "Aspartiate" spelling

      We have corrected it.

      Describe the process and mathematical background for ratio computation in the Methods section. As this paper introduces a package, describing its underlying functions has value.

      We have added R-script comments to illustrate the untargeted ratio calculation using the R-mathematical function of combination and division between any two metabolite pairs in a data matrix (Page 4, line 139-141)

      "we annotate missing values with 1/5 the minimum value quantified in all pixels in which it was detected" This is explicit (ie only values with exactly 1/5 the value are annotated" - make it clear this is a threshold.

      We apologize for misunderstanding. Missing values are either have no value or have solid zero in their abundance. We first calculate the minimum abundance of a particular m/z among all pixels with detectable abundance ( i.e. excluding non-missing values), then use 1/5 this minimum value as a threshold to annotate missing value (Page 4, 133-139).

      Figure 1: legend scils is branded SCiLS and EXCEL does not need caps lock (Excel).

      Figure 1 legend has been corrected.

      Conflicts of interest "None" - there are Bruker employees on a paper about MALDI method development in a field they dominate.

      We added Joshua Fischer as a Bruker employee.

      Figure 3: The legend does not describe the purple arrow in J.

      Purple arrow description is added to figure legend.

      Figure 5: Fix orientation inconsistencies in G, H, I, and J. Especially in J - they are opposite directions. This is arbitrary and determined in SCiLS lab with simple rotation.

      Orientation has been made consistent in G,H, I and J.

      Figure S8: Provide exact number of biological and technical replicates used to generate this figure.

      Figure S8, now Figure S9, was generated from 4 biological replicates of KO and 4 biological replicates of WT brain section in the ROI7 region. This information has been added to the figure legend.

      Figure S9: Make consistent orientation of all brains

      We have made brain orientations consistent.

      In addition to ionization efficiencies impacting the value of the numeric relative abundance where ratio computation originates from, it should be mentioned how different classes of metabolites are differentially impacted by the euthanasia and collection methods used for various tissue types. For example, it is well established the ATP/AMP ratio can change drastically from tissue collection.

      We have added this to page 8, line 315-319.

      Perform standards to adjust for ionization efficiency between different m/z features.

      Untargeted ratio imaging serves as an add-on MSI data analysis tool with primary use in comparing ratio among equivalent regions/pixels with similar ionization efficiencies. It is a hypothesis generation tool. Standards adjust for ionization efficiency would be a great idea for a more accurate assessment of ratio values. Due to the cost and availability of stable isotope standards for different m/z, we chose glucose, lactate and ascorbate to showcase that abundance ratio and concentration ratio result in similar images among example brain metabolite lactate, glucose and ascorbate (page 6, 196-205).

      Add more controls to support the claims.

      We have 4 biological replicates for each genotype of brain. We have added the number of controls in all figure legends.

      Significantly tone down the claims, it is unclear how knowledgeable the authors are about the current literature of SW regarding MALDI.

      The tone has been significantly tuned down throughout the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      "relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections" is misleading, since tandem MS can be performed in an imaging context and is often also compatible with the same instrument.

      We have deleted this sentence in the abstract.

      Intro:

      Paragraph 1: The authors mention MALDI and DESI, but I would argue that SIMS is more abundantly used than DESI within single-cell applications.

      We have added SIMS to the introduction Page 3, line 67.

      Paragraph 2: While it may not be all detected pairs, there are many examples of ratio imaging in the MALDI MSI and SIMS communities, particularly for bacterial signaling. These would be important examples to reference.

      We have added the application of SIMS ratio imaging to the introduction, page 3, line 74-75.

      Materials :

      Paragraph 1: More specificity on sample size is required. 3 or 4 per group is not specific. Which has four and which has three? Why are they different?

      We have corrected sample numbers for specific genotype in the text and figure legends. The number of sections per group is different due to the availability of fresh-frozen tissues (Page 4, line 115-117).

      Results:

      Paragraph 1: Am I correct in reading that an .imzml can't be used directly? Why not?

      Imaging Mass Spectrometry Markup Language (imzml) is a common data format for mass spectrometry imaging. It was developed to allow the flexible and efficient exchange of large MS imaging data between different instruments and data analysis software (Schramm et al, 2012). It contains two sets of data: the mass spectral data which is stored in a binary file (.ibd file) to ensure efficient storage and the XML metadata (.imzml file) which stores instrumental parameters, sample details. Therefore, it can’t be used directly. We have added this to result 1(Page 5, line 160-169).

      Paragraph 4: "Additionally, nonlipid small molecule metabolites suffer from smearing and/or diffusion during cryosection processing, including over the course of matrix deposition for MALDI-MSI." This is misleading. There are several examples of MALDI MSI of small metabolites that are nonlipids, where smearing or diffusion have not occurred. It would be beneficial to have a more accurate discussion of this instead. The authors should also provide some evidence of this, since they continue to focus on it for the full paragraph and don't provide references.

      We initially meant the poor image quality of small molecule metabolites is due to its interaction with aqueous phase of spraying solution, rapid degradation rate and matrix interference. We have deleted this sentence in the revised version.

      Section 5 Paragraph 2; "However, ratio imaging revealed a much greater aspartate to glutamate ratio in an unusual "moon arc" region across the amygdala and hypothalamus relative to the rest of the coronal brain." Much greater isn't scientifically accurate or descript. Use real numbers and be quantitative.

      We used pixel data from all 8 sections to obtain quantitative changes in the ratio-generated “moon arc” region compared to the rest of coronal brain (page 8, line 331-337). Ratio imaging revealed a average of 1.59-fold increase in aspartate to glutamate ratio in an unusual “moon arc” region across the amygdala and hypothalamus (mean abundance 0.563 in 6345 pixels) relative to the rest of the coronal brain (mean abundance 0.353 in 45742 pixels, Figure 5D). Similar but different arc-like structures are encompassed within the ventral thalamus and hypothalamus, wherein glutamate to glutamine ratio show a 1.63-fold increase in intensity compared to the rest of the brain (mean abundance of 0.695 in 7108 pixels vs 0.428 in 44979 pixels, Figure 5E).

      Section 8 Paragraph 2: "UMAPing" is not scientifically written.

      We have replaced UMAPing with UMAP.

      Figure 2 is difficult to interpret, given the small sizes of the images. Align the images, reduce the white space, clearly label the different tissues, add scale bars, increase size, etc. This applies to all figures, except for 3. This will make it possible to review.

      All figures have been resized by removing extra space between sections.

      Figure 3. There seems to be a change in tissue after section I, so a different diagram would be helpful. SCD has a high abundance in an area that seems to be off of the tissue. Can the authors explain this? Some of the images also appear to be low signal-to-noise. Example spectra in the SI would be helpful, so I can more accurately judge the quality of the data.

      We apologize for the discrepancy. All images are from the same sample. We initially cropped the individual image from multiple page PDF plot, then inserted it in Figure 3. Resizing and cropping inconsistency may lead to the small difference in image size. In the revised version, we plot all images in one page, which eliminates the inconsistency.

      Figure 3 example pixel data, ratio pixel data, mass spectra and ratio images can be downloaded below:

      https://wcm.box.com/s/2d5jch45ar8upjzytljnylt6doewcsqc

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public reviews

      Reviewer #1 (Public review):

      Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

      We thank the reviewer for the positive feedback and plan to improve the presentation of the work.

      Reviewer #2 (Public review):

      However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered?

      We will improve the description of the model assumptions and the interpretation of the data on the basis of these assumptions.

      Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

      We will improve the analysis of this result in light of the experimentally determined frequency of the tgs-1+ neoblast population.

      Recommendations for the authors

      Reviewing Editor Comments:

      After consultation, we have compiled a list of the key changes to be made to the manuscript, along with reviewer-specific recommendations to follow.

      (1) Include a section that explicitly describes the assumptions and limitations of the study, particularly with respect to the following assumptions:

      We thank the reviewers for the comment. We added a description of the model assumptions in the methods section “Assumptions underlying neoblast colony growth model”.

      a) All known types of specialized neoblasts cycle at the same rate (see points from Reviewer 1).

      We thank the reviewers for the comment. The current data used to estimate τ (Lei et al., Dev Cell, 2016) does not allow the direct estimation of individual cycling behaviors. Consequently, we assume that all specialized neoblasts cycle at the same average rate, a simplification supported by the model's accurate prediction of colony growth.

      b) The assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. The manuscript does not mention that there may be fundamental differences among these different FSTFs that could be uncovered by future work. A strong addition to the paper would be to test other epithelial genes (e.g. p53, chd4, egr5) to show reproducible behavior within a single lineage.

      We thank the reviewers for the comment. Colony size reduction following inhibition of Smed-p53 and failure to produce epidermal progenitors is strongly supported by previous analysis (Wagner et al., Cell Stem Cell, 2012). We refer to this observation in the paper in the section titled: “Inhibition of zfp-1 does not induce overexpression of other lineages in homeostasis”. We added the following sentence to the discussion (Line 460-462): Interestingly, suppression of Smed-p53, a TF expressed in neoblasts and required for epidermal cell production, has resulted in a similar reduction in colony size (Wagner et al., Cell Stem Cell, 2012).

      Of note, Chd4 expression is not limited to specialized neoblasts or to a specific lineage (Scinome et al., Development, 2010), and therefore its inhibition likely has a more complex outcome than an effect on a single lineage. Furthermore, egr-5 is not expressed in neoblasts (Tu et al, eLife, 2015), making this experimental condition more challenging to examine in the context of neoblast colonies at the time points assessed in this study.

      c) The fact that the data used to feed the model relies on radiated animals which are likely to have altered cell cycle rates compared to unirradiated animals (see comment by Reviewer 1). Of note, the model predicts a steady increase in colony size, but colony size does not change between 9dpi and 12dpi.

      We thank the reviewers for the comment. The colony size in control animals increased between 9 and 12 dpi (Fig 3B), as predicted by the model. In zfp-1 (RNAi) animals, the median colony size has also increased over this period, at a slower rate, which we attribute to the increase in q. We attribute the unchanged average colony size to an increase in the frequency of cells failing to proliferate, because of selection of a fate they cannot fully differentiate into.

      d) In light of both reviewers' comments about colony expansion vs. feedback, the authors should discuss how predicted changes to division frequencies might change as homeostasis is reached, or explain how their model accounts for the predicted rate differences under homeostatic conditions in which overall neoblast numbers do not change. Can the model estimate when this transition might occur?

      We thank the reviewers for the comment. Our colony assays are constrained by the animals survival following sub-total irradiation (16 to 20 days). In this timeframe, the neoblast population is overwhelmingly smaller in comparison to non-irradiated animals. Therefore, the animals do not reach homeostasis during the experiment, and the model does not allow to estimate the time the system would need to return to homeostasis.

      (2) In Figure 2D, the assumption is that these adjacent smedwi-1+ cells are sisters. Previous data analyzing this relied on EdU or H3P staining to show a shared division history. When these images were collected is therefore extremely critical to include (the methods suggest 7, 9, or 12 days). The authors should justify why they believe that these adjacent cells are derived from a single neoblast that has divided only once.

      We thank the reviewers for the comment. The images were collected at 7 dpi. We modified the figure legend and the associated methods to include this information. At this early time point, smedwi-1+ cell dyads are spatially separated from other neighboring cells, suggesting that they are the product of a single cell division. Importantly, our data is in complete agreement with previous estimates of symmetric renewal division rate (Raz et al., Cell Stem Cell, 2021; Lei et al, Developmental Cell, 2016).

      (3) Clarify the wording 'pre-selected' in the abstract as described by Reviewer 1.

      We thank the reviewers for the comment, and for clarity we replaced the wording “pre-select” with “select”. 

      (4) Experimental details that are important to the interpretation should be added. For example, how is belonging to a colony defined? This is important because some of the data (e.g. Figure S1A: similar numbers of smedwi-1+ cells are observed at 2dpi and 4dpi, but 4dpi is considered a colony whereas 2dpi is not). The timing of quantification should be included in each figure (it is missing in Figure S2, and Figure 3C and 3D). How the authors distinguish biological vs technical replicates is not mentioned.

      We thank the reviewers for the comment. Subtotal irradiation may result in formation of a spatially-isolated cluster of neoblasts that is not distributed throughout the animal (Wagner et al., Science, 2011). This localized cluster of neoblasts is defined as a neoblast colony (Wagner et al., Science, 2011; Wagner et al., Cell Stem Cell, 2012). The small number of high smedwi-1+ cells observed at 4 dpi in our experiments aligns with this definition (Fig S1A). By contrast, the low smedwi-1 expression detected across the animal 2 dpi does not fit this definition and likely reflects remnants of dying neoblasts resulting from irradiation. The following text was added to the figure legend: “isolated cells expressing low levels of smedwi-1+ were scattered in the planarian parenchyma, likely reflecting remnants of dying neoblasts”.

      (5) Figure 5F appears to use SMEDWI-1 antibody (based on capital letters and increased signal in the brain). Is this the case? The methods do not mention the use of a SMEDWI-1 antibody, and the text indicates that these are progenitors, but SMEDWI-1 protein is well known to not mark neoblasts. If the antibody was used, the authors should not claim that these are neoblasts.

      We thank the reviewers for the comment. The SMEDWI-1 antibody used in the experiments described in Figure 5F indeed labels neoblasts and their progeny (Guo et al., Developmental cell, 2006). The methods section “Immunofluorescence combined with FISH” details the labeling procedure, which combines FISH and IF using this antibody.

      All microscopy images are difficult to see. Perhaps this is because they are formatted as CMYK images. They should be converted to RGB format to make them appear less dull.

      We thank the reviewer for the comment. Improved version of the figures has now been uploaded.

      The terminology used in Figure 5 to describe upregulation should not be "overexpression".  We thank the reviewers for the comment.

      We changed the terminology to “upregulated”.

      Reviewer #1 (Recommendations for the authors):

      I think the authors should include a section that explicitly lays out the assumptions and limitations of the study. For example, I believe that determining tau requires assuming that all different types of specialized neoblasts cycle at the same rates. Also there is the assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. It seems to remain possible that a future study could find that a subset of FSTFs might indeed exert "either/or" decisions in fating, just not the particular genes under investigation here.

      We thank the reviewer for the comment. We added a description of the model assumptions in the methods section.

      In the abstract, the wording "pre-selected" is somewhat puzzling to me. I would interpret a preselection as a process that defines the next specified state prior to its manifestation. Instead, and as I understand the authors argue this as well, the study provides good evidence that the determination mechanism is random in that subsequent neoblast choices do not likely depend on prior states. So I would suggest changing that wording.

      We thank the reviewer for the comment. We replaced “pre-select” with “select”

      Is it possible to determine the uncertainty in measuring tau the cell cycle time and would this have an impact on subsequent modeling?

      We thank the reviewers for the comment. The current data that was used to estimate tau (Lei et al., Dev Cell, 2016) does not allow us to directly estimate the uncertainty in measuring τ.

      For lines 154-164 I would suggest doing a little more to explicitly write out the logic of determining the growth constants within the main text and not just in methods, for ease of reading.

      We thank the reviewer for the comment, and added explanations for how we determined the growth constant in the text. The text now reads (lines 160-166): “Considering an average cell cycle length of 29.7 hours, we calculated the value of q using the following approach: the probabilities of all cell division outcomes must sum to 1. Our experimental data showed that symmetric renewal (p) and asymmetric division (a) occur at equal rates (i.e., p = a). By fitting these parameters to the experimental data, we determined that the difference between the probabilities of symmetric renewal and symmetric differentiation (i.e., p - q) was = 0.345 (Fig 2E, S1D-E). Therefore, with these criteria, we estimated the probabilities of cell division outcomes in the colony as p = 0.45, a = 0.45, and q = 0.1 (Fig 2G; Methods).”

      Line 192 why does post-mitotic progeny number linearly relate to neoblast number? In clones, a change in q has an exponential effect. I feel like I am missing something.

      We thank the reviewer for the comment. In colonies, 50% of cell divisions result in the production of post-mitotic progeny (asymmetric division). Therefore, the number of produced progenitors in a given cell cycle is linearly correlated with the number of neoblasts. This statement is in line with previous analysis of planarian colony size (Wagner et al., Cell Stem Cell, 2012).

      Line103 it also seems possible, although less likely, that the specified state is not fixed within a given cell cycle and could be that cells that try to switch into zeta-neoblasts mid-cell cycle arrest in proliferation etc just for that time.

      We thank the reviewer for the comment and agree that this is a possibility. However, our observations suggest that incorporating this factor into the model is unnecessary for accurately predicting colony size.

      In terms of the feedback mechanism proposed to operate in homeostasis, I think in the case of zfp-1 it is quite likely that loss of epidermal differentiation results in wound responses (this phenomenon has been documented in egr-5 RNAi in Tu et al 2015 I believe). This could play out differently in the clone assay because the effects of sublethal irradiation on this process would predominate in both control versus zfp1(RNAi) conditions.

      We thank the reviewer for the comment. Our RNA-seq analysis following zfp-1 inhibition did not show overexpression of injury-induced genes at an early time point (6 days; Fig. 5B-C). However, an increase in cycling cells was detected much earlier via EdU labeling (3 days; Fig. 5D). In the case of egr-5 suppression, Tu et al. analyzed injury-induced gene expression at a later stage (21 days of RNAi), where they found significant epidermal defects (see Fig. 5C in Tu et al.). We agree that sublethal irradiation effects likely predominate in colony analysis for both control and zfp-1 (RNAi) animals. In homeostasis, additional factors likely influence cell proliferation and differentiation.

      It seems likely that some of the differences noted between homeostasis versus clone growth could ultimately arise from the different growth parameters under each setting. Could the rate parameters be estimated from prior data in homeostasis as well? It seems to me that with the framework the authors use, homeostasis must involve a net zero change to neoblast abundance (also shown by Wagner 2011 by the sigmoidal curve of neoblast abundance at the endpoint of clone expansion). Therefore, in these conditions p=q by definition. Experimental evidence from Lei 2016 (Figure S7M) suggests asymmetric divisions and symmetric renewing divisions are about equally abundant (5/12 41% sym renewing vs 7/12 69% asymmetric renewing). Therefore, under homeostasis, there would be an estimated p=q=0.3 and a=0.4. Compared to clone growth conditions then, in homeostasis, it seems that roughly the rate of symmetric renewal decreases and the rate of symmetric differentiation also increases. I wonder, could this kind of difference potentially account for the differences between homeostasis versus clone expansion settings? It is also worth noting that the clone expansion context has been used as a sensitized genetic background for identifying effects of gene inhibition on neoblast self-renewal, so perhaps the reason this works is that the rates of selfrenewal are relatively less in homeostasis so that clone expansion represents a case where there is greater demand for self-renewal.

      We thank the reviewer for the comment. We agree that under homeostatic conditions, where the population size remains stable, the average probability of symmetric renewal matches the average probability of symmetric differentiation or elimination. By contrast, during colony expansion, the probability of symmetric renewal exceeds that of symmetric differentiation or elimination. The differences in response to a lineage block between homeostasis and colony expansion can have multiple interpretations. However, data from homeostatic animals does not permit the analysis of individual neoblasts or their specific responses to a lineage block. Consequently, we cannot determine whether the proliferative response following the lineage block during homeostasis is a direct response to the lineage block or an indirect effect resulting from changes in other neoblasts. We discuss these possibilities further in lines 472 - 484.

      In terms of the memory effect, I recall some arguments presented in the Raz 2021 study that were consistent with a slight memory for neoblast specification being retained. I believe this was a minor point from detecting a slightly higher likelihood of identifying 2-cell clones that both took on prog1+ identity compared to the population average. If this is the case, it may be worth the authors commenting on reconciling those observations with their model.

      We thank the reviewer for their comment. Raz et al. (Cell Stem Cell, 2021) reported that in the asymmetric division of a zeta-neoblast, which generates a prog-2+ cell and a neoblast, there was a slightly higher observed frequency of zfp-1 expression in the neoblast compared to the expected rate (Expected: 32%, Observed: 44%). This small increase may reflect a mild memory effect, experimental variability, or both. However, statistical analysis using Fisher's exact test yielded a non-significant p-value (p = 0.1), suggesting that this difference could be attributed to experimental variability. Other data from Raz et al., such as lineage representation in early colonies, also did not show significant memory effects, indicating that any such effects, if present, are minimal and difficult to detect. Therefore, while we do not, and cannot, rule out the presence of minor memory effects, we expect that effects of this magnitude will have minimal impact on our model.

      Reviewer #2 (Recommendations for the authors):

      Figure 2C and 2D:

      Please provide the specific time points for the data presented.

      We thank the reviewer for the comment. The information was added to the figure legend.

      Colony growth and homeostasis:

      It would be beneficial to estimate a time point at which colony growth transitions to a model with a cell-cell feedback mechanism, similar to that observed in homeostasis. This would help in understanding the dynamics and timing of these processes.

      We thank the reviewers for the comment. Our colony assays were constrained by the animals survival following sub-total irradiation (16 to 20 days). Neoblast numbers are substantially reduced compared to unirradiated animals, preventing us from determining the time point at which homeostasis is achieved.

      Methods:

      μl should be μL  

      The text was changed accordingly.

      Line 526: H2O should be H2O

      The text was changed accordingly.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This well-written report uses functional neuroimaging in human observers to provide convincing evidence that activity in the early visual cortex is suppressed at locations that are frequently occupied by a task-irrelevant but salient item. This suppression appears to be general to any kind of stimulus, and also occurs in advance of any item actually appearing. The work in its present form will be valuable to those examining attention, perception, learning and prediction, but with a few additional analyses could more informatively rule out potential alternative hypotheses. Further discussion of the mechanistic implications could clarify further the broad extent of its significance. 

      We thank the editor and the reviewers for the positive evaluation of our manuscript and the thoughtful comments. Below we provide a detailed point-by-point reply to the reviewers’ comments.

      In addition to addressing the reviewers' comments, we have improved the figure legends by explicitly describing the type of error bars depicted in the figures, information which was previously only listed in the Materials and Methods section. Specifically, the statement: “Error bars denote within-subject SEM” was added to several figures, as applicable. We believe that briefly reiterating this information in the figure legends enhances clarity and enables readers to interpret the results more accurately and efficiently. We also updated our code and data sharing statement, as well as opened the repository for the public: “Analysis and experiment code, as well as data required to replicate the results reported in this manuscript are available here: https://doi.org/10.17605/OSF.IO/G4RXV. Raw MRI data is available upon request.”

      Public Reviews

      Reviewer #1 (Public review): 

      Summary: 

      The authors investigated if/how distractor suppression derived from statistical learning may be implemented in early visual cortex. While in a scanner, participants conducted a standard additional singleton task in which one location more frequently contained a salient distractor. The results showed that activity in EVC was suppressed for the location of the salient distractor as well as for neighbouring neutral locations. This suppression was not stimulus specific - meaning it occurred equally for distractors, targets and neutral items - and it was even present in trials in which the search display was omitted. Generally, the paper was clear, the experiment was well-designed, and the data are interesting. Nevertheless, I do have several concerns mostly regarding the interpretation of the results. 

      (1) My biggest concern with the study is regarding the interpretation of some of the results. Specifically, regarding the dynamics of the suppression. I appreciate that there are some limitations with what you might be able to say here given the method but I do feel as if you have committed to a single interpretation where others might still be at play. Below I've listed a few alternatives to consider. 

      We agree with the reviewer that there are important alternatives to consider. Adequately addressing these alternatives will substantially increase the inferences we can draw from our data. Therefore, we address each alternative interpretation in detail below.

      (a) Sustained Suppression. I was wondering if there is anything in your results that would speak for or against the suppression being task specific. That is, is it possible that people are just suppressing the HPDL throughout the entire experiment (i.e., also through ITI, breaks, etc., rather than just before and during the search). Since the suppression does not seem volitional, I wonder if participants might apply a blanket suppression to HPDL un l they learn otherwise. Since your localiser comes a er the task you might be able to see hints of sustained suppression in the HPDL during these trials.  

      It is indeed possible that participants suppressed the HPDL throughout the entire experiment, instead of proactively instantiating suppression on each trial. While possible, we believe that this account is less likely to explain the present results, given the utilized analysis approach, a voxel-wise GLM fit to the BOLD data per run (see Materials and Methods for details). Specifically, we derived parameter estimates from this GLM per location to estimate the relative suppression. Sustained suppression would modulate BOLD responses throughout the run, i.e. presumably also during the implicit baseline period used to estimate the contrast parameter estimates per location. Hence, sustained suppression should not result in a differential modulation between locations, as the BOLD response at the HPDL during the baseline period would be equally suppressed as during the trial. Inspired by the reviewer’s comment, we now clarify this critical point in the manuscript’s Discussion section:

      “Third, participants might have suppressed the HPDL consistently throughout the experiment. This sustained suppression account differs from the proactive suppression proposed here. While this alternative is plausible, we believe that it is less likely to account for the present results, given the analysis conducted. Specifically, we computed voxel-wise parameter estimates and contrasted the obtained betas between locations. Under a sustained suppression account, the HPDL would show suppression even during the implicit baseline period, which would obscure the observed BOLD suppression at and near the HPDL.” 

      (b) Enhancement followed by suppression. Another alternative that wasn't discussed would be an initial transient enhancement of the HPDL which might be brought on by the placeholders followed by more sustained suppression through the search task. Of course, on the whole this would look like suppression, but this still seems like it would hold different implications compared to simply "proactive suppression". This would be something like search and destroy however could be on the location level before the actual onset of the search display.  

      R1 correctly points out that BOLD data, given the poor temporal resolution, do not allow for the detection of potential transient enhancements at the HPDL followed by a later and more pronounced suppression (akin to “search and destroy”). We fully agree with this assessment. However, we also argue that a transient enhancement followed by sustained suppression before search display onset constitutes proactive suppression in line with our interpretation, because suppression would still arise proactively (i.e., before search, and hence distractor, onset). Whether transient enhancement precedes suppression cannot be elucidated by our data, but we believe that it constitutes an interesting avenue for future studies using me-resolved and spatially specific recording methods. We now clarify this important implementational variation in the updated manuscript.

      “Finally, due to the limited temporal resolution of BOLD data, the present data do not elucidate whether the present suppression is preceded by a brief attentional enhancement of the HPDL, as implied by some prior work (Huang et al., 2024). On this account the HPDL would see transient enhancement, followed by sustained suppression, akin to a ‘search and destroy’ mechanism. Critically, we believe that this variation would nonetheless constitute proactive distractor suppression as the suppression would still arise before search onset. Using temporally and spatially resolved methods to explore potential transient enhancements preceding suppression is a promising avenue for future research charting the neural mechanisms underlying distractor suppression.”

      (2) I was also considering whether your effects might be at least partially attributable to priming type effects. This would be on the spatial (not feature) level as it is clear that the distractors are switching colours. Basically, is it possible that on trial n participants see the HPDL with the distractor in it and then on trial n+1 they suppress that location. This would be something distinct from the statistical learning framework and from the repetition suppression discussion you have already included. To test for this, you could look at the trials that follow omission or trials. If there is no suppression or less suppression on these trials it would seem fair to conclude that the suppression is at least in part due to the previous trial. 

      We agree with the reviewer that it is plausible that participants particularly suppress locations which on previous trials contained a distractor. To address this possibility, we conducted a new analysis and adjusted the manuscript accordingly:

      “Second, participants may have suppressed locations that contained the distractor on the previous trial, reflecting a spatial priming effect. This account constitutes a complementary but different perspective than statistical learning, which integrates implicit prior knowledge across many trials. We ruled out that spatial priming explains the present results by contrasting BOLD suppression magnitudes on trials with the distractor at the HPDL and trials where the distractor was not at the HPDL on the previous trial. Results, depicted in Supplementary Figure 4 showed that distractor suppression was statistically significant across both trial types, including trials without a distractor at the HPDL on the preceding trial. This indicates that the observed BOLD suppression is unlikely to be driven by priming and is instead more consistent with statistical learning. Moreover, results did not yield a statistically significant difference between trial types based on the distractor location in the preceding trial. However, these results should not be taken to suggest that spatial priming cannot contribute to distractor suppression; for details see: Supplementary Figure 4.” (p. 13).

      We note that this analysis approach slightly differs from the reviewer’s suggestion, which considered omission trials. However, we decided to exclude trials immediately following an omission to ensure that both conditions were matched as closely as possible. In particular, omission trials represent extended rest periods, which could alter participants’ state and especially modulate the visually evoked BOLD responses (e.g., potentially increasing the dynamic range) compared to trials that did not follow omissions. Our analysis approach avoids this difference while still addressing the hypothesis put forward by the reviewer. We now provide the full explanation and results figure of this priming analysis in the figure text of Supplementary Figure 4: 

      Reviewer #2 (Public review): 

      The authors of this work set out to test ideas about how observers learn to ignore irrelevant visual information. Specifically, they used fMRI to scan participants who performed a visual search task. The task was designed in such a way that highly salient but irrelevant search items were more likely to appear at a given spatial location. With a region-of-interest approach, the authors found that activity in visual cortex that selectively responds to that location was generally suppressed, in response to all stimuli (search targets, salient distractors, or neutral items), as well as in the absence of an anticipated stimulus. 

      Strengths of the study include: A well-written and well-argued manuscript; clever application of a region of interest approach to fMRI design, which allows articulating clear tests of different hypotheses; careful application of follow-up analyses to rule out alternative, strategy-based accounts of the findings; tests of the robustness of the findings to detailed analysis parameters such as ROI size; and exclusion of the role of regional baseline differences in BOLD responses. 

      We thank the reviewer for the positive evaluation of our manuscript.

      The report might be enhanced by analyses (perhaps in a surface space) that distinguish amongst the multiple "early" retinotopic visual areas that are analysed in the aggregate here. 

      We agree with the reviewer that an exploratory analysis separating early visual cortex (EVC) into its retinotopic areas could be an interesting addition. Our reasoning to combine early visual areas into one mask in the original analyses was two-fold: First, we did not have an a priori reason to expected distinct neural suppression between these early ROIs. Therefore, we did not acquire retinotopy data to reliably separate early visual areas (e.g. V1, V2 and V3), instead opting to increase the number of search task trials. The lack of retinotopy data inherently limits the reliability of the resulting cortical segmentation. However, we now performed an analysis separating early visual cortex into V1 and V2 and report the details as Supplementary Text 1:

      “In an exploratory analysis we investigated whether subdivisions of EVC exhibit different representations of priority signals. In brief, we used FreeSurfer to reconstruct brain surfaces (recon-all) from each subject’s anatomical scan. From these reconstructions we derived V1_exvivo and V2_exvivo labels, which were transformed into volume space using ‘mri_label2vol’ and merged into a bilateral mask for each ROI. We then selected the voxels within each ROI that were most responsive to the four stimulus locations, based on independent localizer data. This voxel selection followed the procedure outlined in the Materials and Methods: Region of Interest (ROI) Definition. To accommodate the subdivision into two ROIs (V1 and V2) compared to the single EVC ROI in the main analysis, we halved the number of voxels selected per location. Finally, we applied the same ROI analysis to investigate distractor suppression during search and omission trials, following the procedure described in Materials and Methods: Statistical Analysis. 

      Results of this more fine-grained ROI analyses are depicted in Supplementary Figure 1. First, the results from V2 qualitatively mirrored our primary ROI analysis. BOLD responses in V2 differed significantly between stimulus types (main effect of stimulus type: F<sub>(2,54)</sub> = 31.11, p < 0.001, 𝜂 = 0.54). Targets elicited larger BOLD responses compared to distractors (t<sub>(27)</sub> = 3.05, p<sub>holm</sub> = 0.004, d = 0.06) and neutral stimuli (t<sub>(27)</sub> = 7.82, p<sub>holm</sub> < 0.001, d = 0.14). Distractors also evoked larger responses than neutral stimuli (t<sub>(27)</sub> = 4.78, p<sub>holm</sub> < 0.001, d = 0.09). These results likely reflect top-down modulation due to target relevance and bo om-up effects of distractor salience. Consistent with the primary ROI analysis, the manipula on of distractor predictability showed a distinct pattern of location specific BOLD suppression in V2 (main effect of location: F<sub>(1.1,52.8)</sub> = 5.01, p = 0.030, 𝜂 = 0.16). Neural populations with receptive fields at the HPDL showed significantly reduced BOLD responses compared to the diagonally opposite neutral location (NL-far; post hoc test HPDL vs NL-far: t<sub>(27)</sub> = 2.69, p<sub>holm</sub> = 0.022, d = 0.62). Again, this suppression was not confined to the HPDL but also extended to close by neutral locations (NL-near vs NL-far: t<sub>(27)</sub> = 2.79, p<sub>holm</sub> = 0.022, d = 0.65). BOLD responses did not differ between HPDL and NL-near locations (HPDL vs NL-near: t<sub>(27)</sub> = 0.11, p<sub>holm</sub> = 0.915, d = 0.03; BF<sub>10</sub> = 0.13). As in the EVC ROI analysis, this suppression pattern was consistent across distractor, target, and neutral stimuli presented at the HPDL and NL-near locations compared to NL-far. In sum, neural responses in V2 were significantly modulated by the distractor contingencies, evident as reduced BOLD responses in neural populations with receptive fields at the HPDL and neutral locations near the location of the frequent distractor (NL-near), relative to the neutral location diagonally across the HPDL (NL-far). 

      In V1, BOLD responses also differed significantly between stimulus types (main effect of stimulus type: F<sub>(1.3,35.6)</sub> = 6.69, p = 0.009, 𝜂 = 0.20). Targets elicited larger BOLD responses compared neutral stimuli (t<sub>(27)</sub> = 3.52, p<sub>holm</sub> = 0.003, d = 0.12) and distractors evoked larger responses than neutral stimuli (t<sub>(27)</sub> = 2.62, p<sub>holm</sub> = 0.023, d = 0.09). However, no difference between targets and distractors was observed (t<sub>(27)</sub> = 0.90, p<sub>holm</sub> = 0.375, d = 0.03; BF<sub>10</sub> = 0.17), suggesting reduced sensitivity to task-related effects in V1. Indeed, analyzing the effect of distractor predictability for BOLD responses in V1 showed a different result than in V2 and the combined EVC ROI. There was no significant main effect of location (F<sub>(2,54)</sub> = 2.20, p = 0.120, 𝜂 = 0.08; BF<sub>10</sub> = 0.77). BOLD responses at NL-near and NL-far were similar (BF<sub>10</sub> = 0.171), with the only reliable difference found between target stimuli at the HPDL and NL-far locations (W = 94, p<sub>holm</sub> = 0.012, r = 0.54).”  

      We include the new result figure as Supplementary Figure 5

      We now include reference to these results in the manuscript’s Discussion section:

      “Are representations of priority signals uniform across EVC? A priori we did not have any hypotheses regarding distinct neural suppression profiles across different early visual areas, hence our primary analyses focused stimulus responses neural populations in EVC, irrespective of subdivision. However, an exploratory analysis suggests that distractor suppression may show different patterns in V1 compared to V2 (Supplementary Figure 5 and Supplementary Text 1). In brief, results in V2 mirrored those reported for the combined EVC ROI (Figure 4). In contrast, results in V1 appeared to be only partially modulated by distractor contingencies, and if so, the modulation was less robust and not as spatially broad as in V2. This suggests the possibility of different effects of distractor predictability across subdivisions of early visual areas. However, these results should be interpreted with caution. First, our design did not optimize the delineation of early visual areas (e.g., no functional retinotopy), limiting the accuracy of V1 and V2 segmentation. Additionally, analyses were conducted in volumetric space, which further reduces spatial precision. Future studies could improve this by including retinotopy runs to accurately delineate V1, V2, and V3, and by performing analyses in surface space. Higher-resolution functional and anatomical MRI sequences would also help elucidate how distractor suppression is implemented across EVC with greater precision.”

      Furthermore, the study could benefit from an analysis that tests the correlation over observers between the magnitude of their behavioural effects and their neural responses. 

      R2 highlights that behavioral facilitation and neural suppression could be correlated across participants. The rationale is that if neural suppression in EVC is related to the facilitation of behavioral responses, we should expect a positive relationship between neural suppression at the HPDL and RTs across participants. In this analysis we focused on the contrast between HPDL and NL-far, as this contrast was statistically significant in both the RT (Figure 2) and the neural suppression analysis (Figure 4). First, we computed for each participant the behavioural benefit of distractor suppression as: RT<sub>facilitation</sub> = RT<sub>NL-far</sub> – RT<sub>HPDL</sub>. Thereby RT facilitation reflects the response speeding due to a distractor appearing at the high probability distractor location compared to the far neutral location. Next, we computed neural suppression as: BOLD<sub>suppression</sub> = BOLD<sub>NL-far</sub> – BOLD<sub>HPDL</sub> Thus, positive values reflect the suppression of BOLD responses at the HPDL comparted to the NL-far location. The BOLD suppression index was computed for each stimulus type separately, as in the main ROI analysis (i.e. for Targets, Neutrals and Distractors). Finally, we correlated RT<sub>facilitation</sub> with BOLD<sub>suppression</sub> across participants using Pearson correlation. Results showed a small, but not statistically significant correlation between RT facilitation and BOLD suppression for distractor (r<sub>(26)</sub> = 0.22, p = 0.257), target (r<sub>(26)</sub> = 0.10, p = 0.598) and neutral (r<sub>(26)</sub> = 0.13, p = 0.519) stimuli. Thus, while the direc on of the correlation was in line with the specula on by the reviewer in the “ Recommendations for the authors”, results were not statistically reliable and therefore inconclusive. As also noted in our preliminary reply to the reviewer comments, it was a priori unlikely that this analysis would yield a statistically significant correlation. An a priori power analysis suggested that, to reach a power of 0.8 at a standard alpha of 0.05, given the present sample size of n=28, the effect size would need to exceed r > 0.75, which seemed unlikely for the correlation of behavioural and neural difference scores. Given the inconclusive nature of the results, we prefer to not include this additional analysis in the manuscript, as we believe that it does not add to the main message of the paper but have it accessible to the interested reader in the public “peer review process”.

      The study provides an advance over previous studies, which iden fied enhancement or suppression in visual cortex as a function of search target/distractor predictability, but in less spatially-specific way. It also speaks to open questions about whether such suppression/enhancement is observed only in response to the arrival of visual information, or instead is preparatory, favouring the la er view. The theoretical advance is moderate, in that it is largely congruent with previous frameworks, rather than strongly excluding an opposing view or providing a major step change in our understanding of how distractor suppression unfolds. 

      We agree with the reviewer that our results are an advancement of prior work, particularly with respect to narrowing down the role of sensory areas and the proactive nature of distractor suppression. However, we argue that this represents a significant step forward for several reasons. First, to our knowledge, the literature on distractor suppression, and visual search in general, is by no means unanimous with respect to the conclusion that distractor suppression is instantiated proactively (Huang et al., 2021, 2022). Indeed, there are several studies suggesting the opposite account; reactive suppression (Chang et al., 2023) or contributions by both proactive and reactive mechanisms (Sauter et al., 2021; Wang et al., 2019). Moreover, studies in support of proactive distractor suppression did not investigate the involvement of (early) sensory areas during suppression. Conversely, to our knowledge most studies investigating the involvement of sensory cortex during distractor suppression did not address the question whether suppression arises proactive or reactively.

      Recommendations for the authors: 

      Reviewer #1 ( Recommendations for the authors): 

      Minor Points: 

      (1) There are several disconnects between the behaviour and the MR results - i.e. not stimulus specific yet there are no deficits for targets appearing the HPDL, also no behavioural suppression for the NLNear but neural suppression found. Nevertheless, the behaviour is used as a way to rule out potential attentional strategies when considering whether there is enhancement in the NL-Far condition. I realise you have a few other points here, but I think it's worth addressing what could be seen as a double standard.

      The reviewer points out an important concern, which we feel could have better been addressed in the manuscript. From our point of view a partial dissociation between neural modulations in EVC and eventual behavioural facilitation is not surprising, given the extensive neural processing beyond EVC required for behaviour. However, this assessment may differ, if one stresses an explicit volitional attentional strategy over an implicit statistical learning account. That said, we clearly do not want to create the impression of using a double standard. The lack of behavioural facilitation for targets at NLfar is not a critical part of our argument against explicit attentional strategies. Therefore, we rephrased the relevant paragraph in the Discussion section to now emphasize the importance of the control analysis excluding participants who reported the correct HPDL in the questionnaire (Figure 5), but nonetheless yielded qualitatively identical results to the main ROI analysis (Figure 4). In our opinion, this control analysis provides more compelling evidence against a volitional attentional strategy account without the risk of crea ng the impression of applying a double standard in the interpretation of behavioural data. Additionally, we now acknowledge the limitation of relying on behavioral data in ruling out volitional attentional strategies in the updated manuscript:

      “It is well established that attention enhances BOLD responses in visual cortex (Maunsell, 2015; Reynolds & Chelazzi, 2004; Williford & Maunsell, 2006). If participants learned the underlying distractor contingencies, they could deploy an explicit strategy by directing their attention away from the HPDL, for example by focusing attention on the diagonally opposite neutral location. This account provides an alternative explanation for the observed EVC modulations. However, while credible, the current findings are not consistent with such an interpretation. First, there was no behavioral facilitation for target stimuli presented at the far neutral location, contrary to what one might expect if participants employed an explicit strategy. However, given the partial dissociation between neural suppression in EVC and behavioral facilitation, additional neural data analyses are required to rule out volitional attention strategies. Thus, we performed a control analysis that excluded all participants that indicated the correct HPDL location in the questionnaire, thereby possibly expressing explicit awareness of the contingencies. This control analysis yielded qualitatively identical results to the full sample, showing significant distractor suppression in EVC. Therefore, it is unlikely that explicit attentional strategies, and the enhancement of locations far from the HPDL, drive the results observed here. Instead the current finding are consistent with an account emphasizing the automa c deployment of spatial priors (He et al., 2022) based on implicitly learned statistical regularities.”

      (2) Does the level of suppression change in any way through the experiment? I.e., does it get stronger in the second vs. first half of the experiment? 

      The reviewer askes an interesting question, whether BOLD suppression may change across the experiment. To address this question, we performed an additional analysis testing BOLD suppression in EVC during the first compared to second half of the MRI experiment. Here we defined BOLD suppression as: BOLD<sub>suppression</sub> = ((BOLD<sub>NL-far</sub> – BOLD<sub>HPDL</sub>) + (BOLD<sub>NL-far</sub> – BOLD<sub>NL-near</sub>)) / 2. Thus, in this formula on of BOLD suppression we summarize the two primary BOLD suppression effects observed in our main results (Figure 4). Additionally, as we previously did not observe any significant differences in BOLD suppression magnitudes between different stimulus types (i.e. suppression was similar for target, distractor and neutral stimuli), we collapsed across stimulus types in this analysis.

      Results, depicted below, showed that during both the initial (Run 1+2) and later part (Run 4+5) of the MRI experiment BOLD suppression was statistically significant (BOLD suppression Run 1+2: W = 331, p = 0.003, r = 0.63; BOLD suppression Run 4+5: W = 320, p = 0.007, r= 0.58) , confirming our main results of reliable distractor suppression even in this subset of trials. However, we did not observe any statistically significant differences between early and late runs of the experiment (t<sub>(27)</sub> = -0.21, p = 0.835, d = -0.04). In fact, a Bayesian paired t-test provided evidence for the absence of a difference in BOLD suppression between early compared to later runs (BF<sub>10</sub> = 0.205), suggesting that distractor suppression in EVC was stable throughout the experiment. A qualitatively similar, pattern was evident during omission trials, with significant distractor suppression during early runs (t<sub>(27)</sub> = 2.70, p = 0.012, d = 0.51), but not quite a statistically significant modulation for later runs (t<sub>(27)</sub> = 1.97, p = 0.059, d = 0.37). Again, there was no evidence for a difference in suppression magnitudes across the experiment (W = 198, p = 0.920, d = -0.025) and support for the absence of a difference in BOLD suppression between early and late runs (BF<sub>10</sub> = 0.278).

      Author response image 1.

      Analysis of BOLD suppression magnitudes in EVC across the MRI experiment phases. BOLD suppression was comparable between early (Run 1+2) and late (Run 4+5) phases of the MRI experiment, suggesting consistent suppression in EVC following statistical learning. Error-bars denote within-subject SEM. * p < 0.05, ** p < 0.01, = BF<sub>10</sub> < 1/3.

      In sum, results suggest that distractor suppression in EVC was stable across runs and did not change significantly throughout the experiment. This result was a priori likely, given that participants already underwent behavioral training before entering the MRI. This enabled them to establish modified spatial priority maps, containing the high probability distractor location contingencies, already before the first MRI run. While specula ve, it is possible that participants may still have consolidated the spatial priority maps during the initial runs, but that this additional consolation is not evident in the data, as later runs may see less engagement by participants due to increasing fa gue towards the end of the MRI experiment. Indeed, rapid learning and stable suppression throughout the remainder of the experiment is also reported by prior work (Lin et al., 2021). We believe that it is highly interesting for future studies to investigate the development of distractor suppression across learning, with initial exposure to the contingencies inside the MRI. However, as the present results are inconclusive, we prefer to not include this analysis in the main manuscript, as it may not provide significant additional insight into the neural mechanisms underlying distractor suppression. 

      (3) In the methods vs. results you have reported the probabili es slightly differently. In the methods you say the HPDL was 6x more likely to contain a distractor whereas in the results you say 4x. Based on the reported trial numbers I think it should be 4, but probably you want to double check that this is consistent and correct throughout. 

      We thank the reviewer for bringing this inconsistency to our attention. We have corrected this oversight in the adjusted manuscript: 

      “One of the four locations of interest was designated the high probability distractor location (HPDL), which contained distractor stimuli (unique color) four mes more o en than any of the remaining three locations of interest. In other words, if a distractor was present on a given trial (42 trials per run), the distractor appeared 57% (24 trials per run) at the HPDL and at one of the other three locations with equal probability (i.e., 14% or 6 trials per run per location).” 

      Reviewer #2 ( Recommendations for the authors): 

      The authors have performed their analyses in the volume rather than the surface, and have grouped together V1, V2, and V3 as "early visual cortex". As the authors' claims lean heavily on the idea that they are measuring "early" visual responses, the study would be improved by delinea ng the ROIS within these different retinotopic regions. Such an approach might be facilitated by analysing data on the reconstructed surface. 

      Please refer to our reply to this analysis suggested in the Public review.

      The authors rightly tread carefully on the causal link between their neural findings and the behavioural outcomes. The picture might be clarified somewhat further by testing for a positive relationship between behavioural effect sizes and neural effect sizes across participants. e.g. to what extent is the search advantage when distractors are presented at the "HPDL" linked to greater suppression of BOLD at the HDPL region of early visual cortex? 

      Please refer to our reply to this analysis suggested in the Public review.

      Some of the claims based on null hypotheses would be better supported by Bayesian tests e.g. page 6 "This pattern of results was the same regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NL-far ..." and "BOLD responses between HPDL and NL-near locations did not reliably differ ..." This is similar to the approach that the authors adopted later in the section "Ruling out attentional modulation".

      We agree with the reviewer that our ROI analyses would benefit from providing evidence for the absence of a modulation. Accordingly, we updated our results by adding equivalent Bayesian tests. Bayes Factors were computed using JASP 0.18.2 (JASP Team, 2024; RRID:SCR_015823) with default settings; i.e. for Bayesian paired t-tests with a Cauchy prior width of 0.707. Qualitative interpretations of BFs were based on Lee and Wagenmakers (2014). We now report the obtained BF in the Results section. 

      “BOLD responses between HPDL and NL-near locations did not reliably differ (HPDL vs NL-near: t<sub>(27)</sub> = 0.47, p<sub>holm</sub> = 0.643, d = 0.08; BF<sub>10</sub> = 0.19).”

      And:

      “Neural responses at HPDL and NL-near did not reliably differ (t<sub>(27)</sub> = 0.21, p<sub>holm</sub> = 0.835 d = 0.04; BF<sub>10</sub> = 0.21).”

      Moreover, we now denote any equivalent results (defined as BF<sub>10</sub><1/3) in Fig. 4 and Fig. 5, and included the descrip on of the associated symbol in the figure text (“ = BF<sub>10</sub> < 1/3”).

      Additionally, we now also report the BF for all paired t-tests reported in Supplementary Table 1.

      Finally, we addressed the statement: “This pattern of results was the same regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NLfar”. Our inten on was to emphasize that the pattern of results reported in the sentence preceding it was evident for distractor, target, or neutral stimulus, and not to suggest that the magnitude of the effect is the same. Hence, to more accurate reflect the results, we changed this sentence to:  “This pattern of results was present regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NL-far”

    1. In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health [m1]. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image [m2] Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience [m3] - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state [m4], where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset [m5].

      The chapter's discussion on 'trauma dumping' resonated with me. I've noticed an increase in unfiltered sharing of personal traumas on social media platforms. While it's essential to have spaces for open expression, I'm concerned about the potential emotional burden this places on unsuspecting readers and whether such platforms are suitable for processing deep-seated issues. How can we balance authentic sharing with the need to protect the mental well-being of the broader online community

    2. 13.1. Social Media Influence on Mental Health# In 2019 the company Facebook (now called Meta) presented an internal study that found that Instagram was bad for the mental health of teenage girls, and yet they still allowed teenage girls to use Instagram. So, what does social media do to the mental health of teenage girls, and to all its other users? The answer is of course complicated and varies. Some have argued that Facebook’s own data is not as conclusive as you think about teens and mental health [m1]. Many have anecdotal experiences with their own mental health and those they talk to. For example, cosmetic surgeons have seen how photo manipulation on social media has influenced people’s views of their appearance: People historically came to cosmetic surgeons with photos of celebrities whose features they hoped to emulate. Now, they’re coming with edited selfies. They want to bring to life the version of themselves that they curate through apps like FaceTune and Snapchat. Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image [m2] Comedian and director Bo Burnham has his own observations about how social media is influencing mental health: “If [social media] was just bad, I’d just tell all the kids to throw their phone in the ocean, and it’d be really easy. The problem is it - we are hyper-connected, and we’re lonely. We’re overstimulated, and we’re numb. We’re expressing our self, and we’re objectifying ourselves. So I think it just sort of widens and deepens the experiences of what kids are going through. But in regards to social anxiety, social anxiety - there’s a part of social anxiety I think that feels like you’re a little bit disassociated from yourself. And it’s sort of like you’re in a situation, but you’re also floating above yourself, watching yourself in that situation, judging it. And social media literally is that. You know, it forces kids to not just live their experience but be nostalgic for their experience while they’re living it, watch people watch them, watch people watch them watch them. My sort of impulse is like when the 13 year olds of today grow up to be social scientists, I’ll be very curious to hear what they have to say about it. But until then, it just feels like we just need to gather the data.” Director Bo Burnham On Growing Up With Anxiety — And An Audience [m3] - NPR Fresh Air (10:15-11:20) It can be difficult to measure the effects of social media on mental health since there are so many types of social media, and it permeates our cultures even of people who don’t use it directly. Some researchers have found that people using social media may enter a dissociation state [m4], where they lose track of time (like what happens when someone is reading a good book). Researchers at Facebook decided to try to measure how their recommendation algorithm was influencing people’s mental health. So they changed their recommendation algorithm to show some people more negative posts and some people more positive posts. They found that people who were given more negative posts tended to post more negatively themselves. Now, this experiment was done without informing users that they were part of an experiment, and when people found out that they might be part of a secret mood manipulation experiment, they were upset [m5]. 13.1.1. Digital Detox?# Some people view internet-based social media (and other online activities) as inherently toxic and therefore encourage a digital detox [m6], where people take some form of a break from social media platforms and digital devices. While taking a break from parts or all of social media can be good for someone’s mental health (e.g., doomscrolling is making them feel more anxious, or they are currently getting harassed online), viewing internet-based social media as inherently toxic and trying to return to an idyllic time from before the Internet is not a realistic or honest view of the matter. In her essay “The Great Offline,” [m7] Lauren Collee argues that this is just a repeat of earlier views of city living and the “wilderness.” As white Americans were colonizing the American continent, they began idealizing “wilderness” as being uninhabited land (ignoring the Indigenous people who already lived there, or kicking them out or killing them). In the 19th century, as wilderness tourism was taking off as an industry, natural landscapes were figured as an antidote to the social pressures of urban living, offering truth in place of artifice, interiority in place of exteriority, solitude in place of small talk. Similarly, advocates for digital detox build an idealized “offline” separate from the complications of modern life: Sherry Turkle, author of Alone Together, characterizes the offline world as a physical place, a kind of Edenic paradise. “Not too long ago,” she writes, “people walked with their heads up, looking at the water, the sky, the sand” — now, “they often walk with their heads down, typing.” […] Gone are the happy days when families would gather around a weekly televised program like our ancestors around the campfire! But Lauren Collee argues that by placing the blame on the use of technology itself and making not using technology (a digital detox) the solution, we lose our ability to deal with the nuances of how we use technology and how it is designed: I’m no stranger to apps that help me curb my screen time, and I’ll admit I’ve often felt better for using them. But on a more communal level, I suspect that cultures of digital detox — in suggesting that the online world is inherently corrupting and cannot be improved — discourage us from seeking alternative models for what the internet could look like. I don’t want to be trapped in cycles of connection and disconnection, deleting my social media profiles for weeks at a time, feeling calmer but isolated, re-downloading them, feeling worse but connected again. For as long as we keep dumping our hopes into the conceptual pit of “the offline world,” those hopes will cease to exist as forces that might generate change in the worlds we actually live in together. So in this chapter, we will not consider internet-based social media as inherently toxic or beneficial for mental health. We will be looking for more nuance and where things go well, where they do not, and why.

      This chapter talks about how social media affects mental health, especially for teenage girls. It shows that social media can help us connect but also make us feel lonely and anxious. Bo Burnham talks about how people are always worried about how they look online, which can cause a lot of anxiety. The part about “Snapchat Dysmorphia” is pretty shocking because it shows that filters are changing how people see themselves, making them feel unhappy with their real appearance. I found it interesting when Lauren Collee argued that quitting social media isn’t the solution because it ignores the bigger issue of how social media is designed. It made me think about whether social media can be made healthier or if it’s just made to keep us hooked by playing on our fears and insecurities.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

      They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

      Strengths:

      Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

      Main weaknesses of the experimental setup:

      (1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.<br />

      We acknowledge that our in vitro experiments may not ideally replicate the in vivo situation, a common limitation of such experiments, our primary aim was to explore the molecular relationship between PRDM16 and BMP signaling in gene regulation. Such molecular investigations are challenging to conduct using in vivo tissues. In vitro NSCs treated with BMP4 has been used a model to investigate NSC proliferation and quiescence, drawing on previous studies (e.g., Helena Mira, 2010; Marlen Knobloch, 2017). Crucially, to ensure the relevance of our in vitro findings to the in vivo context, we confirmed that cultured cells could indeed be induced into quiescence by BMP4, and this induction necessitated the presence of PRDM16. Furthermore, upon identifying target genes co-regulated by PRDM16 and SMADs, we validated PRDM16's regulatory role on a subset of these genes in the developing Choroid Plexus (ChP) (Fig. 7 and Suppl.Fig7-8). Only by combining evidence from both in vitro and in vivo experiments could we confidently conclude that PRDM16 serves as an essential co-factor for BMP signaling in restricting NSC proliferation.

      (2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

      We agree that Prdm16 KO cells carrying the Prdm16-expressing vector would be a good comparison with those with KO_vector. However, despite more than 10 attempts with various optimization conditions, we were unable to establish a viable cell line after infecting Prdm16 KO cells with the Prdm16-expressing vector. The overall survival rate for primary NSCs after viral infection is low, and we observed that KO cells were particularly sensitive to infection treatment when the viral vector was large (the Prdm16 ORF is more than 3kb).

      As an alternative oo assess vector effects, we instead included two other control cell lines, wt and KO cells infected with the 3xNLS_Flag-tag viral vector, and presented the results in supplementary Fig 2.  When we compared the responses of the four lines — wt, KO, wt infected with the Flag vector, KO infected with the Flag vector — to the addition and removal of BMP4, we confirmed that the viral infection itself has no significant impacts on the responses of these cells to these treatments regarding changes in cell proliferation and Ttr induction.

      Given that wt cells and the KO cells, with or without viral backbone infection behave quite similarly in terms of cell proliferation, we speculate that even if we were successful in obtaining a cell line with Prdm16-expressing vector in the KO cells, it may not exhibit substantial differences compared to wt cells infected with Prdm16-expressing vector.

      Other experimental weaknesses that make the evidence less convincing:

      (1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?<br />

      The reviwer’s point is that there was no significant increase in Ttr expression in Prdm16_KO cells after BMP4 treatment (Fig. 2E), but there remained residule Ttr mRNA signals in the Prdm16 mutant ChP (Fig. 1C). We think the difference lies in the measuable level of Ttr expression between that induced by BMP4 in NSC culture and that in the ChP. This is based on our immunostaining expreriment in which we tried to detect Ttr using a Ttr antibody. This antibody could not detect the Ttr protein in BMP4-treated Prdm16_expressing NSCs but clearly showed Ttr signal in the wt ChP. This means that although Ttr expression can be significantly increased by BMP4 in vitro to a level measurable by RT-qPCR, its absolute quantity even in the Prdm16_expressing condition is much lower compared to that in vivo. Our results in Fig 1C and Fig 2E, as well as Fig 7B, all consistently showed that Prdm16 depletion significantly reduced Ttr expression in in vitro and in vivo.

      (2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

      H3K4me3 has widely been used as an indicator of active transcription and is a mark for cell identity genes. And it has been demonstrated that H3K4me3 has a direct function in regulating transciption at the step of RNApolII pausing release. As stated in the text, there are advantages and disadvantages of using H3K4me3 compared to using RNA-seq. RNA-seq profiles all gene products, which are affected by transcription and RNA stability and turnover. In contrast, H3K4me3 levels at gene promoter reflects transcriptional activity. In our case, we aimed to identify differential gene expression between proliferation and quiescence states. The transition between these two states is fast and dynamic. RNA-seq may not be able to identify functionally relevant genes but more likely produces false positive and negative results. Therefore, we chose H3K4me3 profiling.

      We agree that transcription may change without histone methylation changes. This may cause an under-estimation of the number of changed genes between the conditions. 

      We validated 7 out of 31 genes (Wnt7b, Id3, Mybl2, Spc24, Spc25, Ndc80 and Nuf2). We chose these genes based on two critira: 1) their function is implicated in cell proliferation and cell-cycle regulation based on gene ontology analysis; 2) their gene products are detectable in the developing ChP based on the scRNA-seq data. Three of these genes (Wnt7b, Id3, Mybl2) are not related to the kinetochore. We now clarify this description in the revised text.

      (3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

      This indicates that in addition to co-repressing cell-cycle genes, BMP and PRDM16 have independent fucntions. For example, it was reported that BMP regulates neuronal and astrocyte differentiation (Katada, S. 2021), while our previous work demonstrated that Prdm16 controls temporal identity of NSCs (He, L. 2021).

      (4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

      The track in Fig 3G shows the absolute signal of H3K4me3 after mapping the sequencing reads to the genome and normaliz them to library size. Compare the signal in Prdm16_E with BMP4 and that in Prdm16_E without BMP4, the one with BMP4 has a lower peak. The same trend can be seen for the pair of Prdm16_KO cells with or without BMP4.  The heatmap in Fig. 3E shows the relative level of H3K4me3 in three conditions. The Prdm16_E cells with BMP4 has the lowest level, while the other two conditions (Prdm16_KO with BMP4 and Prdm16_E without BMP4) display a higher level. These two graphs show a consistent trend of H3K4me3 changes at the Wnt7b promoter across these conditions.

      (5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

      In our text, we indicated the genes containing PRDM16 binding peaks in the figures and described them as “Text in black in Fig. 6A and Supplementary Fig. 5A”. We will add the precise number “25 of these genes” in the main text to clarify it. To define a negative control set of genes, we will use BMP-only repressed 184-31 =153 genes (excluding PRDM16-BMP4 co-repressed), and of these 153 genes, we will determine how many have PRDM16 peaks in the E12.5 ChP data, say X. Then we will use binomial test to calculate p-value binom_test(25, 31, X/153, alternative=“greater).

      We are confused with the second part of the comment “And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.” If the reviewer meant why we didn’t sequence the material from sequential-ChIP or validate more taget genes, the reason is the limitation of the material. Sequential ChIP requires a large quantity of the antibodies, and yields little material barely sufficient for a few qPCR after the second round of IP. This yielded amount was far below the minimum required for library construction. The PRDM16 antibody was a gift, and the quantity we have was very limited. We made a lot of efforts to optimize all available commercial antibodies in ChIP and Cut&Tag, but none of them worked.

      (6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

      To address these questions, in the revised manuscript we will include an individal channel of Wnt2b and mark the boundaries. We will also provide full-view images and examples of spot segmentation in supplementary figures as space limitation in the main figures.

      (7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

      We agree that this result (the correlation between mKi67 and Axin2) alone only suggests that Wnt signaling is related to the proliferation defect in the Prdm16 mutant, and does not necessarily mean that Wnt is downstream of PRDM16 and BMP. Our concolusion is backed up by two additional lines of evidences:  the Cut&Tag data in which PRDM16 binds to regulatory regions of Wnt7b and Wnt3a; BMP and PRDM16 co-repress Wnt7b in vitro.

      An ideal result is that down-regulating Wnt signaling in Prdm16 mutant can rescue Prdm16 mutant phenotype. Such an experiment is technically challenging. Wnt plays diverse and essential roles in NSC regulation, and one would need to use a celltype-and stage-specific tool to down-regulate Wnt in the background of Prdm16 mutation. Moreover, Wnt genes are not the only targets regulated by PRDM16 in these cells, and downregulating Wnt may not be sufficient to rescue the phenotype. 

      Weaknesses of the presentation:

      Overall, the manuscript is not easy to read. This can cause confusion.

      We will revise the text to improve the clarity.

      Reviewer #2 (Public review):

      Summary:

      This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

      The key findings of the study are:

      (1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.

      (2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.

      (3) BMP signaling and PRDM16 cooperatively repress proliferation genes.

      (4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.

      (5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.

      (6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.

      (7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

      In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

      Strengths:

      (1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

      (2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

      (3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

      (4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

      (5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

      Weaknesses:

      (1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

      While mechansims controlling PRDM16 protein stability and nuclear localization in the developing brain are interesting, the scope of this paper is revealing the function of PRDM16 in the choroid plexus and its interaction with BMP signaling. We will be happy to pursuit this direction in our next study.

      (2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

      As stated above, we acknowledge that findings from cultured NSCs may not directly apply to ChP cells in vivo. We are cautious with our statements. The cell culture work was aimed to identify potential mechanisms by which PRDM16 and SMADs interact to regulate gene expression and target genes co-regulated by these factors. We expect that not all targets from cell culture are regulated by PRDM16 and SMADs in the ChP, so we validated expression changes of several target genes in the developing ChP and now included the new data in Fig. 7 and Supplementary Fig. 7. Out of the 31 genes identified from cultured cells, four cell cycle regulators including Wnt7b, Id3, Spc24/25/nuf2 and Mybl2, showed de-repression in Prdm16 mutant ChP. These genes can be relevant downstream genes in the ChP, and other target genes may be cortical NSC-specific or less dependent on Prdm16 in vivo.

      (3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

      We agree that the finding of the AP1 motif enriched at the PRDM16 and SMAD co-binding regions in Prdm16 KO cells can only indirectly suggest AP1 as a co-factor for SMAD relocation. That’s why we used ChIP-qPCR to examine the presence of C-fos at these sites. Although we only validated two targets, the result confirms that C-fos binds to the sites only in the Prdm16 KO cells but not Prdm16_expressing cells, suggesting AP1 is a co-factor.  We results cannot rule out the presence of other co-factors.

      Reviewer #3 (Public review):

      Summary:

      Bone morphogenetic protein (BMP) signaling instructs multiple processes during development including cell proliferation and differentiation. The authors set out to understand the role of PRDM16 in these various functions of BMP signaling. They find that PRDM16 and BMP co-operate to repress stem cell proliferation by regulating the genomic distribution of BMP pathway transcription factors. They additionally show that PRDM16 impacts choroid plexus epithelial cell specification. The authors provide evidence for a regulatory circuit (constituting of BMP, PRDM16, and Wnt) that influences stem cell proliferation/differentiation.

      Strengths:

      I find the topics studied by the authors in this study of general interest to the field, the experiments well-controlled and the analysis in the paper sound.

      Weaknesses:

      I have no major scientific concerns. I have some minor recommendations that will help improve the paper (regarding the discussion).

      We will revise the discussion according the suggestions.

    1. Exclusions of LGBTQ-related information signal to students thatsuch people are not respected members of the school community, and in thevacuum of official school silence bias from students can go unchallenged.

      I think that silence is inherently hurtful because it does not convey support and respect for the LGBTQ+ community. In the absence of formal discussion or acknowledgement, students may perceive LGBTQ+ identities as unacceptable or even something to be ignored. Similarly, neutrality can sometimes be seen as a form of neglect by failing to stand up to prejudice and discrimination. In educational settings, if we don't actively advocate for LGBTQ+ students and challenge harmful stereotypes, then this “neutrality” is actually a tacit endorsement of disrespect. Schools should therefore actively promote a culture of inclusion where every student feels respected and understood, rather than exacerbating their marginalization through silence.

    2. Me~bers o[ school communities may believe that sexuality is not anappropriate topic for young people. However, there are significant numbersof LGBTQ and ally students in schools, as well as significant numbers ofsexually aware heterosexual students.

      This is an interesting concept to go by, I think because we live in a society that subconciously supports heterosexual values, by not talking about LGBTQ topics, it is then perceived as not being adressed. In another reality, the presence of sexuality, hetero or homo, should not and would not matter, therefore not needing to be adressed in the curriculum, and I think that is what proponents of that idea really mean.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Response to Reviewers

      We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised.

      The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.

      Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.

      Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.

      In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?

      We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.

      However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.

      Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.

      Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.

      We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.

      "Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"

      Reviewer #1 (Significance (Required)):

      Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.

      MAJOR COMMENTS

      1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.

      Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.

      2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.

      We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.

      As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.

      An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.

      We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.

      3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.

      We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.

      4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?

      We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:

      "In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."

      We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.

      5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.

      We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).

      MINOR COMMENTS:

      a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?

      We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.

      b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.

      Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).

      c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.

      You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.

      d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:

      . Please increase font size whenever possible.

      . Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).

      . Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.

      . Please label the Venn's diagrams comparisons in Sup. Fig. 4b.

      . In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.

      We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.

      e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.

      We have altered this accordingly.

      f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.

      We have now discussed the conflicting evidence in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.

      An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.

      The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.

      AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.

      REVIEWER EXPERTISE: Basic science close to the field.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We thank Reviewers for highlighting the strengths of our work along with suggestions for future directions.

      We agree with the Reviewers that RPS26 depletion may impact not only RAN translation initiation and codon selection (as showed in the experiments in Figure 4G), but also other mechanisms, such as speed of PIC scanning, as we stated in the discussion. Although, we did provide the data showing that mRNA of exogenous FMR1-GFP does not change upon RPS26 depletion (Figure 3B&C), hence observed effect most likely stems from translation regulation. In addition, an experiment with ASO-ACG treatment (Figure 4G) suggests that near cognate start codon selection or speed of PIC scanning may be a part of the regulation of RAN translation sensitive to RPS26 depletion. In addition, our latest unpublished results (Niewiadomska D. et al., in revision), indicate that FMRpolyG in fusion with GFP is fairly stable, in particular, while derived from long repeats (>90xCGG), suggesting that the protein stability is not at play in RPS26-dependent regulation.

      We would like to stress that in order to avoid bias in result interpretation and to mimic the natural situation, the majority of experiments concerning levels of FMRpolyG were performed in cell models with stable expression of ACG-initiated FMRpolyG. Currently, we do not possess a cell model with stable expression of AUG-initiated FMRpolyG, and the experiments based on transient transfection system would not necessarily be comparable to the results obtained in stable expression system. However, we believe that the experiment presented in Figure 2B serves as a good control for overall translation level upon RPS26 depletion indicating that RPS26 insufficiency does not affect global translation and the observed regulation is specific to some mRNAs including the one encoding FMRpolyG frame. We also show that the level of ca. 80% of identified canonical proteins, including FMRP, did not change upon RPS26 silencing (SILAC-MS, Figure 4A). Indeed, we did not explore the ribosome composition upon RPS26 and TSR2 depletion, although, most likely the pool of functional ribosomes in the cell is sufficient enough to support the basal translation level (SUnSET assays, Figure 2B & 5C). However, we cannot exclude possibility that for some mRNAs, including one encoding for FMRpolyG, the observed effect can be partially caused by lowering the number of fully active ribosomes, especially in experiments with transient transfection experiments where transgene expression is hundreds times higher than for average native mRNA.

      Finally, we agree with the Reviewer that in vitro translation assay would provide the evidence of direct effect of RPS26 on FMRpolyG level, however, we did not manage to overcome technical difficulties in obtaining cellular lysate devoid of RPS26 from vendor companies.


      The following is the authors’ response to the original reviews.

      General Comments

      We thank Reviewers for the critical comments and experimental suggestions. We considered most of the advices in the revised version of the manuscript, which allowed for a more balanced interpretation of the results presented, and further supported major statement of the manuscript that insufficiency of the RPS26 and RPS25 plays a role in modulating the efficiency of noncanonical RAN translation from FMR1 mRNA, which results in the production of toxic polyglycine protein (FMRpolyG). Firstly, performing new experiments, we showed that silencing of the RPS26 and its chaperone protein TSR2, which regulates loading/exchange of RPS26 in maturing small ribosome subunit, did not elicit global translation inhibition. Secondly, we demonstrated that in contrary to RPS26 and RPS25 depletion, silencing the RPS6 protein, a core component of 40S subunit, did not affect FMRpolyG production, further supporting the specific effect of RPS26 and RPS25 on RAN translation regulation of mutant FMR1 mRNA. We also observed that depletion of RPS26, RPS25 and RPS6 had significant negative effect on cells proliferation which is in line with previously published results indicating that insufficiencies of ribosomal proteins negatively affect cell growth. Moreover, we showed that FMRpolyG production is significantly affected by RPS26 depletion while initiated at ACG, but not other near cognate start codons. Importantly, translation of FMRP initiated at canonical AUG codon of the same mRNA upstream the CGGexp was not affected by RPS26 silencing, similarly to vast majority of the human proteome. This implies that RAN translation of FMR1 mRNA mediated by RPS26 insufficiency is likely to be dependent on start codon selection/fidelity. In essence, we provide a series of evidences indicating that cellular amount of 40S ribosomal proteins RPS26 and RPS25 is important factor of CGGrelated RAN translation regulation. Finally, we also decided to tone down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion, affects RAN translation, rather than composition of 40S ribosomal subunit per se influences RAN translation. We have addressed all specific concerns below and made changes to the new version of manuscript.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Tutak et al use a combination of pulldowns, analyzed by mass spectrometry, reporter assays, and fluorescence experiments to decipher the mechanism of protein translation in fragile X-related diseases. The topic is interesting and important.

      Although a role for Rps26-deficient ribosomes in toxic protein translation is plausible based on already available data, the authors' data are not carefully controlled and thus do not support the conclusions of the paper.

      We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process. We believe your guidance has been instrumental in significantly enhancing the quality of our research. Below, we have addressed your comments pointby-point.

      Strengths:

      The topic is interesting and important.

      Weaknesses:

      In particular, there is very little data to support the notion that Rps26-deficient ribosomes are even produced under the circumstances. And no data that indicate that they are involved in the RAN translation. Essential controls (for ribosome numbers) are lacking, no information is presented on the viability of the cells (Rps26 is an essential protein), and the differences in protein levels could well arise from block in protein synthesis, and cell division coupled to differential stability of the proteins.

      We agree that data presented in the first version of the manuscript did not directly address the following processes: ribosome content, global translation rate and cell viability upon RPS26 depletion. Therefore we addressed some of the issues in the revised version of the manuscript. In particular, we showed that RPS26 and TSR2 knock down did not inhibit global translation (new Figure 2B & 4C), hence we concluded that the changes of FMRpolyG level did not arise from general translational shut down. On the other hand, RPS26, RPS25 and RPS6 depletion negatively affected cells proliferation (new Figure 2A,5D,6C), which is in line with a number of previously published researches (e.g. Cheng et al, 2019; Havkin-Solomon et al, 2023). However, the rate of proliferation abnormalities is limited. We agree that observed effects on RAN translation from mutant FMR1 mRNA may stem from the combination of altered protein synthesis, conditions of the cells but also cis-acting factors of mRNA sequence/structure. In new experiments we showed that single nucleotide substitution of ACG by other near cognate start codons change sensitivity of RAN translation to insufficiency of RPS26 (new Figure 4F). Also the inhibitory effect of antisense oligonucleotide binding to the region of 5’UTR containing ACG initiation codon (ASO_ACG) is different in cells differing in amount of RPS26 (new Figure 4G).

      We also agree that our data only partially supports the role of RPS26-defficient ribosomes in RAN translation. Therefore, we have toned down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion affects RAN translation. We also changed the title of the manuscript to: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25, negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions” (Previously it was: “Ribosomal composition affects the noncanonical translation and toxicity of polyglycine-containing proteins in fragile X-associated conditions”.

      Specific points:

      (1) Analysis of the mass spec data in Supplemental Table S3 indicates that for many of the proteins that are differentially enriched in one sample, a single peptide is identified. So the difference is between 1 peptide and 0. I don't understand how one can do a statistical analysis on that, or how it would give out anything of significance. I certainly do not think it is significant. This is exacerbated by the fact that the contaminants in the assay (keratins) are many, many-fold more abundant, and so are proteins that are known to be mitochondrial or nuclear, and therefore likely not actual targets (e.g. MCCC1, PC, NPM1; this includes many proteins "of significance" in Table S1, including Rrp1B, NAF1, Top1, TCEPB, DHX16, etc...).

      The data in Table S6/Figure 3A suffer from the same problem.

      I am not convinced that the mass spec data is reliable.

      We thank Reviewer for the comment concerning MS data; however, we believe that it may stem from misunderstanding of the data presented in Table S3 and S6. Both tables represent the output from MaxQuant analysis (so-called ProteinGroup) of MS .raw files, without any filtering. As stated in the Material&Methods, we applied default parameters suggested by MaxQuant developers to analyze MS data, these include identification of proteins based on at least 1 unique peptide, and thus some of the proteins with only 1 unique peptide are shown in Tables S1 and S3. Reviewer is also right that in this output table common contaminants, such as keratins are included. However, these identifications are denoted as “CON_”, and are further filtered out during statistical analysis in Perseus software. During the statistical analysis we first filtered out irrelevant protein groups identifications, such as contaminants, or only identified by site modifications.

      We have changed the names of Supplementary Table files, giving more detailed description. We hope this will help to avoid misunderstanding for broader public. Secondly, when comparing the data presented in Table S3 and volcano plot presented in Figure 1B, one can notice that indeed the majority of identified proteins are not statistically significant (grey points), thus not selected for further stratification. Lack of significance of these proteins may be partially due to poor MS identification, however, they are not included in the following parts of the manuscript. Further, we selected only eight proteins (out of over 150) for stratification by orthogonal techniques, thus we argue that this step validates the biological relevance of chosen candidate RAN-translation modifiers. One should also keep in mind that pull down samples analyzed by MS often yield lower intensity and identification rates, when comparing to whole cell analysis, as a result of lower protein input or stringent washes used during sample preparation.

      Regarding the data presented in Table S6 (SILAC data), we argue that these data are of very good quality. More than 2,000 proteins were identified in a 125min gradient, with over 80% of proteins that were identified with at least 2 unique peptides. Each of three biological replicates was analyzed three times (technical replicates), giving total of 9 high resolution MS runs. Together, we strongly believe that this data is of high confidence.

      (2) The mass-spec data however claims to identify Rps26 as a factor binding the toxic RNA specifically. The rest of the paper seeks to develop a story of how Rps26-deficient ribosomes play a role in the translation of this RNA. I do not consider that this makes sense.

      Indeed, we identified RPS26 as a protein that co-precipitated with FMR1 containing expanded CGG repeats (Supplementary Figure 1G) and found that depletion of RPS26 hindered RAN translation of FMRpolyG, suggesting that RPS26 positively affects RAN translation. However, we did not state that RPS26 directly interacts with toxic RNA. In order to confirm the specificity of RAN translation regulation by RPS26 insufficiency, we tested whether depletion of other 40S ribosomal protein, RPS6, affects FMRpolyG synthesis. Our experiments showed that there was no any significant effect on RAN translation efficiency post RPS6 silencing (new Figure 5C). Importantly, we showed that RPS26 depletion did not inhibit global translation (new Figure 2B). In addition, mutagenesis of near-cognate start codon (new Figure 4F) and ASO_ACG treatment (new Figure 4G) provided the evidences that modulation of FMRpolyG biosynthesis by RPS26 level may depend on start codon selection. In essence, our data suggest that RPS26 depletion specifically affects synthesis of FMRpolyG, but not FMRP derived from the same FMR1 mRNA with CGGexp. However, we do not claim that the observed effect is the consequence of a direct interaction between RPS26 and 5’UTR of FMR1 mRNA. Downregulation of FMRpolyG biosynthesis could be an outcome of the alteration of ribosomal assembly, decrease of efficiency and fidelity of PIC scanning/initiation or impeded elongation or a combination of all these processes. In the manuscript we presented the results of experiments which tested many of these possibilities.

      (3) Rps26 is an essential gene, I am sure the same is true for DHX15. What happens to cell viability? Protein synthesis? The yeast experiments were carefully carried out under experiments where Rps26 was reduced, not fully depleted to give small growth defects.

      We agree with the Reviewer that RPS26 and DHX15 are essential proteins, similarly to all RNA binding proteins, and caution should be taken during experimental design. To address this, we titrated different concentrations of siRPS26, and found that administration of 5 nM siRPS26, which just partially silenced RPS26, decreased FMRpolyG by around 50% (new Figure 1D). This impact was even greater with 15 nM siRPS26, as we observed around 80% decrease of FMRpolyG.

      Havkin-Solomon et al. (2023), showed that proliferation rate is decreased in cells with mutated C-terminus of RPS26, which is required for contacting mRNA. In accordance with this study, we showed that cells with knocked down RPS26 proliferate less efficiently (new Figure 2A), but depletion of RPS26 did not impact the global translation (new Figure 2B). In addition, our SILAC-MS data indicates that ~80% of proteins with determined expression level were not affected by RPS26 insufficiency, and ~20% of the proteins turned out to be sensitive to RPS26 decrease. Although, these data do not take into account the protein stability.

      (4) Knockdown efficiency for all tested genes must be shown to evaluate knockdown efficiency.

      The current version of the manuscript contains representative western blots with validation of knock-down efficiency (for example in Figure 3B, C, E, Figure 6A) and we included knock-down validations where applicable (Figures 1D, 2B, 4G and 5C).

      (5) The data in Figure 1E have just one mock control, but two cell types (control si and Rps26 depletion).

      Mock control corresponds to the cells treated with lipofectamine reagent and was included in the study to determine the “background” signal from cells treated with delivery agent and reagents used to measure the apoptosis process. These cells were neither expressing FMRpolyG, nor siRNAs. Luminescence signals were normalized to the values obtained from mock control. We added more details describing this assay in the Figure 1 legend.

      (6) The authors' data indicate that the effects are not specific to Rps26 but indeed also observed upon Rps25 knockdown. This suggests strongly that the effects are from reduced ribosome content or blocked protein synthesis. Additional controls should deplete a core RP to ascertain this conclusion.

      We agree that observed effects may stem from reduced ribosome content, however, we argue that this is the only possibility and explanation. Previously, it was shown that RPS25 regulates G4C2-related RAN translation, but knock out of RPS25 does not affect global translation (Yamada S, 2019, Nat. Neuroscience). Similarly, we showed that KD of RPS26 or TSR2 did not reduce significantly global translation rate (SUnSET assay; new Figure 2B and 5C, respectively).

      Moreover, in a new version of manuscript we included a control experiment, where we silenced core ribosomal protein (RPS6) and found that RPS6 depletion did not affect RAN translation from mutant FMR1 mRNA (new Figure 5C), thus strengthening our conclusion about specific RAN translation regulation by the level of RPS26 and RPS25.

      Finally, our observation aligns well with current knowledge about how deficiency of different ribosomal proteins alters translation of some classes of mRNAs (Luan Y, 2022, Nucleic Acids Res; Cheng Z, 2019, Mol Cell). It was shown that depletion of RPS26 affects translation rate of different mRNAs compared to depletion of other proteins of small ribosomal subunit.

      (7) Supplemental Figure S3 demonstrates that the depletion of S26 does not affect the selection of the start codon context. Any other claim must be deleted. All the 5'-UTR logos are essentially identical, indicating that "picking" happens by abundance (background).

      Supplementary Figure 3D represents results indicating that the mutation in -4 position (from G to A) did not affect the RAN translation regardless of RPS26 presence or depletion. However, this result does not imply that RPS26 does not affect the selection of start codon of sequence- or RNA structure-context. We verified this particular -4 position, as it was suggested previously as important RPS26-sensitive site in yeasts (Ferretti M, 2017, Nat Struct Mol Biol). We agree with Reviewer that all 5’UTR logos presented in our paper did not show statistical significance for neither tested position for human mRNAs. On the contrary, we observed that regulation sensitive to RPS26 level depends on the selection of start codon of RAN translation, in particular ACG initiation (new Figure 4F&G). RPS26 depletion affected ACG-initiated but not GTG- or CTG-initiated RAN translation.

      In the previous version of the manuscript, we wrote that we did not identify any specific motifs or enrichment within analyzed transcripts in comparison to the background. On the other hand, we found that the GC-content among analyzed transcripts is higher within 5’UTRs and in close proximity to ATG in coding sequences (Figure 4D), what suggests the importance of RNA stable structures in this region. In addition, we showed that mRNAs encoding proteins responding to RPS26 depletion have shorter than average 5’UTRs (new Figure 4E).

      (8) Mechanism is lacking entirely. There are many ways in which ribosomes could have mRNA-specific effects. The authors tried to find an effect from the Kozak sequence, unsuccessfully (however, they also did not do the experiment correctly, as they failed to recognize that the Kozak sequence differs between yeast, where it is A-rich, and mammalian cells, where it is GGCGCC). Collisions could be another mechanism.

      Indeed, collisions as well as other mechanisms such as skewed start codon fidelity may have an effect on efficiency of FMRpolyG biosynthesis. In the current version of the manuscript, we show that RPS26 amount-sensitive regulation seems to be start codonselection dependent (new Figure 4F&G).

      Reviewer #2 (Public Review):

      Summary:

      Translation of CGG repeats leads to the accumulation of poly G, which is associated with neurological disorders. This is a valuable paper in which the authors sought out proteins that modulate RAN translation. They determined which proteins in Hela cells bound to CGG repeats and affected levels of polyG encoded in the 5'UTR of the FMR1 mRNA. They then showed that siRNA depletion of ribosomal protein RPS26 results in less production of FMR1polyG than in control. There are data supporting the claim that RPS26 depletion modulates RAN translation in this RNA, although for some results, the Western results are not strong. The data to support increased aggregation by polyG expression upon S26 KD are incomplete.

      We thank the Reviewer for critical comments and suggestions. We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process.

      Below each specific point, we addressed the mentioned issues.

      Strengths:

      The authors have proteomics data that show the enrichment of a set of proteins on FMR1 RNA but not a related RNA.

      We thank Reviewer for appreciation of provided MS-screening results, which identified proteins enriched on FMR1 RNA with expanded CGG repeats.

      Weaknesses:

      - It is insinuated that RPS26 binds the RNA to enhance CGG-containing protein expression. However, RPS26 reduction was also shown previously to affect ribosome levels, and reduced ribosome levels can result in ribosomes translating very different RNA pools.

      In previous version of the manuscript we did not state that RPS26 binds directly to RNA with expanded CGG repeats and we did not show the experiment indicating direct interaction between studied RNA and RPS26. What we showed is that RPS26 was enriched on FMR1 RNA MS samples, however, we did not verify whether it is direct or indirect interaction. We also tried to test hypothesis that lack of RPS26 in PIC complex may affect efficiency of RAN translation initiation via specific, previously described in yeast Kozak context (Ferretti M, 2017, Nat Struct Mol Biol). As we described this hypothesis was negatively validated. However, we showed that other features of 5’UTR sequences (e.g. higher GC-content or shorter leader sequence) are potentially important for translation efficiency in cells with depleted RPS26.

      Indeed, RPS26 is involved in 40S maturation steps (Plassart L, 2021, eLife) and its insufficiency or mutations or blocking its inclusion to 40S ribosome may result in incomplete 40S maturation, which subsequently might negatively affect translation per se. However, we did not observe global translation inhibition after RPS26 depletion or depletion of TSR2, the chaperon involved in incorporation/exchange RPS26 to small ribosomal subunit (new Figure 2B and 5C). In addition, our SILAC-MS data indicates that majority of studied proteins (including FMRP, the main product of FMR1 gene) were not affected by RPS26 depletion which can be carefully extrapolated to global translation. In revised manuscript we also showed that relatively low silencing of RPS26 also decreased FMRpolyG production in model cells (new Figure 1D).

      We agree that reduced ribosome levels can result in different efficiency of translation of different RNA pools. We enhance this statement in revised manuscript. However, we also showed that the same mRNA containing different near cognate start codons (single/two nucleotide substitution) specific to RAN translation, or targeting this codon with antisense oligonucleotides resulted in altered sensitivity of FMR1 mRNA translation to RPS26 depletion (new Figure 4F).

      - A significant claim is that RPS26 KD alleviates the effects of FMRpolyG expression, but those data aren't presented well.

      We thank the Reviewer for this comment. In the new version of the manuscript, we have added new microscopic images and improved the explanation of Figure 1E. We have also completed the interpretation of Figure 1F in the main text, figure image as well as figure legend, and we hope that these changes will ameliorate understanding of our data.

      Recommendations For The Authors:

      - A significant claim is that RPS26 KD alleviates the effects of FMR polyG expression, but those data aren't presented well:

      Figure 1D (supporting data in S2) and 2D - the authors need to show representative images of a control that has aggregation and indicate aggregates being counted on an image. The legend states that there are no aggregates, but the quantification of aggregates/nucleus is ~1, suggesting there are at least 1 per cell. It is preferred to show at least a representative of what is quantified in the main figure instead of a bar graph.

      The representative images of control and siRPS26-treated cells are now shown in revised version of Figure 1E. Additionally, we completed the Figure legend concerning this part, as well as extended description of the experiment in Materials&Methods section.

      Figure 1E - it is unclear what luminescence signal is being measured. Is this a dye for an apoptotic marker? More information is needed in the legend.

      This information was added to the legend of modified Figure 1F (previously 1E) as suggested.

      - Some of the Western blots are not very convincing. Better evidence for the changes in bar graphs would improve how convincing the data are:

      Fig 2B. The western for FMR95G in the first model is not very convincing. The difference by eye for the second siRNA seems to give a larger effect than the first for 95G construct but they appear almost the same on the graph. More supporting information for the quantification is needed.

      We provided better explanation for WB quantification in M&M section in the manuscript. Alos, we provided additional blot demonstrating independent biological replicate of the mentioned experiment in supplementary materials (Supplementary Figure S2E).

      Figure 4A, the blots for RPS26 and FMR95G are not convincing. They are quite smeary compared to all of the others shown for these proteins in other figures. Could a different replicate be shown?

      We provided additional blot demonstrating the effect on transiently expressed FMRpolyG affected by depletion of TSR2 in COS7 cell line (Supplementary Figure S4A).

      Figure 5A and 5B blots are not ideal. Could a different replicate be shown? Or show multiple replicates in the supplemental figure?

      We provided additional blots from the same experiment, although data is not statistically significant, most likely due to low quality of normalization factor, which is Vinculin (Supplementary Figure S5A). Nevertheless, the level of FMRpolyG is decreased by ~70% after RPS25 silencing in SH-SY5Y cells.

      Figure 2C. Please use the same y axes for all four Westerns in B and C. One would like to compare 95 and 15 repeats, but it is difficult when the y axes are different.

      Thank you for this comment. The y axis was adjusted as suggested by the Reviewer.

      Figure 3D-The text suggests a significant difference between positive and negative responders that is not clear in the figure.

      In the main body of the manuscript we state that: “We did not observe any significant differences in the frequency of individual nucleotide positions in the 20-nucleotide vicinity of the start codon relative to the expected distribution in the BG”, which is in line with the graph showed in Figure 4D (previously 3D).

      Reviewer #3 (Public Review):

      Tutak et al provide interesting data showing that RPS26 and relevant proteins such as TSR2 and RPS25 affect RAN translation from CGG repeat RNA in fragile X-associated conditions. They identified RPS26 as a potential regulator of RAN translation by RNAtagging system and mass spectrometry-based screening for proteins binding to CGG repeat RNA and confirmed its regulatory effects on RAN translation by siRNA-based knockdown experiments in multiple cellular disease models and patient-derived fibroblasts. Quantitative mass spectrometry analysis found that the expressions of some ribosomal proteins are sensitive to RPS26 depletion while approximately 80% of proteins including FMRP were not influenced. Since the roles of ribosomal proteins in RAN translation regulation have not been fully examined, this study provides novel insights into this research field. However, some data presented in this manuscript are limited and preliminary, and their conclusions are not fully supported.

      (1) While the authors emphasized the importance of ribosomal composition for RAN translation regulation in the title and the article body, the association between RAN translation and ribosomal composition is apparently not evaluated in this work. They found that specific ribosomal proteins (RPS26 and RPS25) can have regulatory effects on RAN translation (Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B), and that the expression levels of some ribosomal proteins can be changed by RPS26 knockdown (Figure 3B, however, the change of the ribosome compositions involved in the actual translation has not been elucidated). Therefore, their conclusive statement, that is, "ribosome composition affects RAN translation" is not fully supported by the presented data and is misleading.

      We thank the Reviewer for critical comments and suggestions. We agree that the initial title and some statements in the text were misleading and the presented data did not fully support the aforementioned statement regarding ribosomal composition affecting FMRpolyG synthesis. Therefore, in the revised version of the manuscript we included a control experiment indicating that depletion of another core 40S ribosomal protein (RPS6) did not impact the FMRpolyG synthesis (new Figure 5C), which supports our hypothesis that RPS26 and RPS25 are specific CGG-related RAN translation modifiers. To precisely deliver a main message of our work, we changed the title that will indicate the specific effect of RPS26 and RPS25 insufficiency on RAN translation of FMRpolyG. Proposed title: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25 negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions”. We also changed all statements regarding “ribosomal composition” in main text of the new version of manuscript.

      (2) The study provides insufficient data on the mechanisms of how RPS26 regulates RAN translation. Although authors speculate that RPS26 may affect initiation codon fidelity and regulate RAN translation in a CGG repeat sequence-independent manner (Page 9 and Page 11), what they really have shown is just identification of this protein by the screening for proteins binding to CGG repeat RNA (Figure 1A, 1B), and effects of this protein on CGG repeat-RAN translation. It is essential to clarify whether the regulatory effect of RPS26 on RAN translation is dependent on CGG repeat sequence or near-cognate initiation codons like ACG and GUG in the 5' upstream sequence of the repeat. It would be better to validate the effects of RPS26 on translation from control constructs, such as one composed of the 5' upstream sequence of FMR1 with no CGG repeat, and one with an ATG substitution in the 5' upstream sequence of FMR1 instead of near-cognate initiation codons.

      We agree that the data presented in the manuscript implies that insufficiency of RPS26 plays a pivotal role in the regulation of CGG-related RAN translation and in the revised version of the manuscript we included a series of experiments indicating that ACG codon selection seems to be an important part of RPS26 level-dependent regulation of polyglycine production (new Figure 4F&G; see point 3 below for more details). Importantly, in the luciferase assay showed on Figure 4F we used the AUG-initiated firefly luciferase reporter as normalization control.

      Moreover, to verify if FMRpolyG response to RPS26 deficiency depends on the type of reporter used, we repeated many experiments using FMRpolyG fused with different tags. The luciferase-based assays were in line with experiments conducted on constructs with GFP tag (new Figure 1D), thus strengthening our previous data. Moreover, in the series of experiments, we show that FMRP synthesis which is initiated from ATG codon located in FMR1 exon 1, was not affected by RPS26 depletion (Figure 3E & 4C), even though its translation occurs on the same mRNA as FMRpolyG. This indicates a specific RPS26 regulation of polyglycine frame initiated from ACG near cognate codon.

      (3) The regulatory effects of RPS26 and other molecules on RAN translation have all been investigated as effects on the expression levels of FMRpolyG-GFP proteins in cellular models expressing CGG repeat sequences Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B). In these cellular experiments, there are multiple confounding factors affecting the expression levels of FMRpolyG-GFP proteins other than RAN translation, including template RNA expression, template RNA distribution, and FMRpolyG-GFP protein degradation. Although authors evaluated the effect on the expression levels of template CGG repeat RNA, it would be better to confirm the effect of these regulators on RAN translation by other experiments such as in vitro translation assay that can directly evaluate RAN translation.

      We agree that there are multiple factors affecting final levels of FMRpolyG-GFP proteins including aforementioned processes. We evaluated the level of FMR1 mRNA, which turned out not to be decreased upon RPS26 depletion (Figure 3B&C), therefore, we assumed that what we observed, was the regulation on translation level, especially that RPS26 is a ribosomal protein contacting mRNA in E-site. We believe that direct assays such as in vitro translation may be beneficial, however, depletion of RPS26 from cellular lysate provided by the vendor seems technically challenging, if not completely impossible. Instead, we focused on sequence/structure specific regulation of RAN translation with the emphasis on start-codon initiation selection. It resulted in generating the valuable results pointing out the RPS26 role in start codon fidelity (Figure 4F&G). These new results showed that translation from mRNAs differing just in single or two nucleotide substitution in near cognate start codon (ACG to GUG or ACG to CUG), although results in exactly the same protein, is differently sensitive to RPS26 silencing (new Figure 4F). Similar differences were observed for translation efficiency from the same mRNA targeted or not with antisense oligonucleotide complementary to the region of RAN translation initiation codon (new Figure 4G). These results also suggest that stability of FMRpolyG is not affected in cells with decreased level of RPS26.

      (4) While the authors state that RPS26 modulated the FMRpolyG-mediated toxicity, they presented limited data on apoptotic markers, not cellular viability (Figure 1E), not fully supporting this conclusion. Since previous work showed that FMRpolyG protein reduces cellular viability (Hoem G, 2019,Front Genet), additional evaluations for cellular viability would strengthen this conclusion.

      We thank the Reviewer for this suggestion. We addressed the apoptotic process in order to determine the effect of RPS26 depletion on RAN translation related toxicity (Figure 1F). In revised version of the manuscript, we also added the evaluation on how cells proliferation was affected by RPS26, RPS25, RPS6 and TSR2 depletion. Our data indicate that TSR2 silencing slightly impacted the cellular fitness (new Figure 5D), whereas insufficiencies of RPS26, RPS25 and RPS6 had a much stronger negative effect on proliferation (new Figure 2A, 5D, 6C), which is in line with previous data (Cheng Z 2019, Mol Cell; Luan Y, 2022, Nucleic Acids Res). The difference in proliferation rate after treatment with siRPS26 makes proper interpretation of cellular viability assessment very difficult.

      Recommendations For The Authors:

      (1) It would be nice to validate the effects of overexpression of RPS26 and other regulators on RAN translation, not limited to knockdown experiments, to support the conclusion.

      We did not performed such experiments because we believed that RPS26 overexpression may have no or marginal effect on translation or RAN translation. It is likely impossible to efficiently incorporate overexpressed RPS26 into 40S subunits, because the concentration of all ribosomal proteins in the cells is very high.

      (2) It would be better to explain how authors selected 8 proteins for siRNA-based validation (Figure 1C, 1D, S1D) from 32 proteins enriched in CGG repeat RNA in the first screening.

      We selected those candidates based on their functions connected to translation, structured RNA unwinding or mRNA processing. For example, we tested few RNA helicases because of their known function in RAN translation regulation described by other researchers. This explanation was added to the revised version of the manuscript.

      (3) Original image data showing nuclear FMRpolyG-GFP aggregates should be presented in Figure 1D.

      The representative images of control and siRPS26-treated cells are now shown in modified version of Figure 1E and described with more details in the legend.

      (4) Image data in Figure 2A and 2D have poor signal/noise ratio and the resolution should be improved. In addition, aggregates should be clearly indicated in Figure 2D in an appropriate manner.

      The stable S-FMR95xG cellular model is characterized by very low expression of RANtranslated FMR95xG, therefore, it is challenging to obtain microscopic images of better quality with higher GFP signal. In the L-99xCGG model expression of transgene is higher. Therefore, we provided new image in the new version of Figure 3D (former 2D). Moreover, we showed aggregates on the image obtained using confocal microscopy (new Supplementary Figure 2D).

      (5) The detailed information on patient-derived fibroblast (age and sex of the patient, the number of CGG repeats, etc.) in Figure 2F needed to be presented.

      This information was added to the figure legend (Figure 3F; previously 2F) and in the Material and Methods section as suggested.

      (6) It would be better to normalize RNA expression levels of FMR1 and FMR1-GFP by the housekeeping gene in Figure S2C, like other RT-qPCR experimental data such as Figure 2B.

      Normalization of FMR1-GFP to GAPDH is now shown in modified version of Figure S2C (right graph) as requested by the Reviewer.

      (7) It would be better to add information on molecular weight on all Western blotting data.

      (8) Marks corresponding to molecular weight ladder were added to all images.

      Full blots, including protein ladders were deposited in Zenodo repository, under doi: 10.5281/zenodo.13860370

      References

      Cheng Z, Mugler CF, Keskin A, Hodapp S, Chan LYL, Weis K, Mertins P, Regev A, Jovanovic M & Brar GA (2019) Small and Large Ribosomal Subunit Deficiencies Lead to Distinct Gene Expression Signatures that Reflect Cellular Growth Rate. Mol Cell 73: 36-47.e10

      Havkin-Solomon T, Fraticelli D, Bahat A, Hayat D, Reuven N, Shaul Y & Dikstein R (2023) Translation regulation of specific mRNAs by RPS26 C-terminal RNA-binding tail integrates energy metabolism and AMPK-mTOR signaling. Nucleic Acids Res 51: 4415–4428

      Hoem,G., Larsen,K.B., Øvervatn,A., Brech,A., Lamark,T., Sjøttem,E. and Johansen,T. (2019) The FMRpolyGlycine protein mediates aggregate formation and toxicity independent of the CGG mRNA hairpin in a cellular model for FXTAS. Front. Genet., 10, 1–18.

      Luan Y, Tang N, Yang J, Liu S, Cheng C, Wang Y, Chen C, Guo YN, Wang H, Zhao W, et al (2022) Deficiency of ribosomal proteins reshapes the transcriptional and translational landscape in human cells. Nucleic Acids Res 50: 6601–6617

      Plassart L, Shayan R, Montellese C, Rinaldi D, Larburu N, Pichereaux C, Froment C, Lebaron S, O’donohue MF, Kutay U, et al (2021) The final step of 40s ribosomal subunit maturation is controlled by a dual key lock. Elife 10

    1. Author response:

      The following is the authors’ response to the original reviews.

      It would be great if the authors could add clarification about the NMDS analyses and the associated results (Fig. 1, Table 1 and Tables S2-4). The overall aim of these analyses was to see how plot characteristics (e.g. canopy cover) and composition of one taxonomic group were related to the composition of another taxonomic group. The authors quantified species composition by two axes from NMDS. (1) This analysis may yield an interpretation problem: if we only find one of the axes, but not the other, was significantly related to one variable, it would be difficult to determine whether that specific variable is important to the species composition because the composition is co-determined by two axes. (2) It is unclear how the authors did the correlation analyses for Tables S2-4. If correlation coefficients were presented in these tables, then these coefficients should be the same or very similar if we switch the positions of y vs. x. That is, the correlation between host vs. parasite phylogenetic composition would be very close to the correlation between parasite vs. phylogenetic composition, but not as the author found that these two relationships were quite different, leading to the interpretation of bottom-up or top-down processes. It is also unclear which correlation coefficient was significant or not because only one P value was provided per row in these tables. (3) In addition to the issues of multiple axes (point 1), NMDS axes simply define the relative positions of the objects in multi-dimensional space, but not the actual dissimilarities. Other methods, such as generalized dissimilarity modeling, redundancy analysis and MANOVA, can be better alternatives.

      Thank you for the thorough and constructive review. We have taken the concerns and questions raised by the editors and reviewers into account and provided clarification about the NMDS analyses as well as additional analyses to confirm our results. First, we have now added a brief explanation in the manuscript regarding the interpretation of the two NMDS axes and how they relate to species composition. Specifically, we clarified that while NMDS defines the relative positions of objects in multi-dimensional space, the two axes together provide a more comprehensive representation of the community composition, which is not solely determined by either axis independently. Second, we acknowledge that alternative approaches could help further strengthen our conclusions. To address this, we incorporated Mantel tests and PERMANOVA (with ‘adonis2’) as additional validation methods. These analyses allowed us to summarize compositional patterns while testing our hypotheses within the framework of the plot characteristics and taxonomic relationships. We have added these analyses and their results in the manuscript to reinforce our findings.

      In methods: L478-481 “To strengthen the robustness of our findings based on NMDS, we further validated the results using Mantel test and PERMANOVA (with ‘adonis2’) for correlation between communities and relationships between communities and environmental variables.”

      L469-475 “NMDS was used to summarize the variation in species composition across plots. The two axes extracted from the NMDS represent gradients in community composition, where each axis reflects a subset of the compositional variation. These axes should not be interpreted in isolation, as the overall species composition is co-determined by their combined variation. For clarity, results were interpreted based on the relationships of variables with the compositional gradients captured by both axes together."

      In results: L172-177 “The PERMANOVA analysis also highlighted the important role of canopy cover for host and parasitoid community (Table S6-9). The Mantel test revealed a consistent pattern with the NMDS analysis, highlighting a pronounced relationship between the species composition of hosts and parasitoids (Table S10). However, the correlation between the phylogenetic composition of hosts and parasitoids was not significant.”

      In discussion: L257-261 “However, this significant pattern was observed only in the NMDS analysis and not in the Mantel test, suggesting that the non-random interactions between hosts and parasitoids could not be simply predicted by their community similarity and associations between the phylogenetic composition of hosts and parasitoids are more complex and require further investigation in the future.”

      -- One additional minor point: "site" would be better set as a fixed rather than random term in the linear mixed-effects models, because the site number (2) is too small to make a proper estimate of random component.

      Now we treated “site” as a fixed factor in our models, interacting with tree species richness/tree MPD and tree functional diversity to reflect the variation of spatial and tree composition between the two sites. We found the main results did not change, as both sites showed consistent patterns for effects of tree richness/MPD on network metrics, which is more pronounced in one site.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors analyzed how biotic and abiotic factors impact antagonistic host-parasitoid interaction systems in a large BEF experiment. They found the linkage between the tree community and host-parasitoid community from the perspective of the multi-dimensionality of biodiversity. Their results revealed that the structure of the tree community (habitat) and canopy cover influence host-parasitoid compositions and their interaction pattern. This interaction pattern is also determined by phylogenetic associations among species. This paper provides a nice framework for detecting the determinants of network topological structures.

      Strengths:

      This study was conducted using a five-year sampling in a well-designed BEF experiment. The effects of the multi-dimensional diversity of tree communities have been well explained in a forest ecosystem with an antagonistic host-parasitoid interaction.

      The network analysis has been well conducted. The combination of phylogenetic analysis and network analysis is uncommon among similar studies, especially for studies of trophic cascades. Still, this study has discussed the effect of phylogenetic features on interacting networks in depth.

      Weaknesses:

      (1) The authors should examine species and interaction completeness in this study to confirm that their sampling efforts are sufficient.

      (2) The authors only used Rao's Q to assess the functional diversity of tree communities. However, multiple metrics of functional diversity exist (e.g., functional evenness, functional dispersion, and functional divergence). It is better to check the results from other metrics and confirm whether these results further support the authors' results.

      (3) The authors did not elaborate on which extinction sequence was used in robustness analysis. The authors should consider interaction abundance in calculating robustness. In this case, the author may use another null model for binary networks to get random distributions.

      (4) The causal relationship between host and parasitoid communities is unclear. Normally, it is easy to understand that host community composition (low trophic level) could influence parasitoid community composition (high trophic level). I suggest using the 'correlation' between host and parasitoid communities unless there is strong evidence of causation.

      Thank you very much for your thoughtful and constructive review of our manuscript. We have carefully addressed your comments and made several revisions to improve the clarity and robustness of our work.1) We appreciate your suggestion regarding species and interaction completeness. To confirm that our sampling efforts were sufficient, we have now included a figure (Fig. S1) showing the species accumulation curve and the coverage of interactions in our study. This ensures that the data collected provide a comprehensive representation of the system. 2) Regarding the use of only Rao’s Q to assess functional diversity, we acknowledge that multiple metrics of functional diversity exist. However, due to the large number of predictors in our analysis, we decided to streamline our approach and focus on Rao’s Q as it provides a robust measure for our research objectives. We have discussed this decision in the revised manuscript and clarified that, while additional metrics could be informative, we believe Rao’s Q sufficiently captures the key aspects of functional diversity in our study. 3) We have elaborated on the robustness analysis and the null model used in our study. Specifically, we now clarified which extinction sequence (random extinction) was used in our manuscript, and explained interaction abundance was incorporated into the robustness calculations (networklevel function, weighted=TURE; see L506). 4) We have revised the text to clarify the relationship between host and parasitoid communities. As you correctly pointed out, while it is intuitive that host community composition influences parasitoid community composition, we have reframed our analysis to emphasize the correlation between the two communities rather than implying causation without strong evidence. We have revised the manuscript to reflect this distinction.

      Reviewer #2 (Public Review):

      Summary:

      In their manuscript, Multi-dimensionality of tree communities structure host-parasitoid networks and their phylogenetic composition, Wang et al. examine the effects of tree diversity and environmental variables on communities of reed-nesting insects and their parasitoids. Additionally, they look for the correlations in community composition and network properties of the two interacting insect guilds. They use a data set collected in a subtropical tree biodiversity experiment over five years of sampling. The authors find that the tree species, functional, and phylogenetic diversity as well as some of the environmental factors have varying impacts on both host and parasitoid communities. Additionally, the communities of the host and parasitoid showed correlations in their structures. Also, the network metrices of the host-parasitoid network showed patterns against environmental variables.

      Strengths:

      The main strength of the manuscript lies in the massive long-term data set collected on host-parasitoid interactions. The data provides interesting opportunities to advance our knowledge on the effects of environmental diversity (tree diversity) on the network and community structure of insect hosts and their parasitoids in a relatively poorly known system.

      Weaknesses:

      To me, there are no major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results (namely the top-down control in the system). Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning. Sometimes, the logic of the text could be improved to better support the studied hypotheses throughout the text. Also, the results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses should be described somewhere in the intro or presented with the results.

      Thank you very much for your valuable comments and suggestions on our manuscript! We appreciate your feedback and have made revisions accordingly. Specifically, we have rephrased the interpretation of the results and conclusions to better align with the analyses and avoid overstatements, particularly concerning the top-down control in the system. In addition, we have expanded the methods section by providing more details, especially regarding the statistical approaches, to address the points you raised. To enhance the clarity of the manuscript, we have also ensured that the logic of the text better supports the hypotheses throughout. Please see our point-by-point responses below for additional clarifications.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Line 120: "... and large ecosystems susceptible to global change (add citation here)": Citation(s)?

      Now we provided the missed citations.

      Line 141: Add sampling completeness information.

      Now we provide a new figure about sampling completeness (Fig. S1) in the supplementary materials, showing the adequate sampling effort for our study.

      Line 151: use more metrics in the evaluation of functional diversity

      We used tree functional diversity Rao’s Q, which is an integrated and wildly used metric to represent functional dissimilarity of trees. As our study focus on multiple diversity indices of trees, it would be better to do not pay more attention to one type of diversity. Thank you for your suggestion!

      Line 164: host vulnerability. Although generality and vulnerability are commonly used in network analysis, it is better to link these metrics with the trophic level, like the 'host vulnerability' you used. Thus, you can use 'parasitoid generality' instead of 'generality'.

      Thanks for your suggestion. Now the metrics were labeled with the trophic levels in the full text.

      Line 169: two'.'

      Corrected.

      Line 173: 'parasitoid robustness' Or 'robustness of parasitoids'?

      Now changed it to ‘robustness of parasitoid’.

      Lines 173, 468: For the robustness estimations, maybe use null model for binary networks to get random distributions?

      Thanks for the suggestion. Actually, we have used Patefield null models to compare the randomized robustness and observed, helping to assess whether the robustness of the observed network is significantly different compared to expected by chance. All robustness indices across plots were significantly different from a random distribution, See results section L197-201.

      Line 184: modulating interacting communities of hosts and parasitoids.

      Changed accordingly.

      Line 186: determined host-parasitoid interaction patterns

      Changed accordingly.

      Line 191: Biodiversity loss in this study refers to low trophic levels.

      Now we clarified this point.

      Line 190: understand

      Changed accordingly.

      Lines 215-216: Reorganize these sentences

      Line 227: indirectly influenced by...

      Changed accordingly.

      Line 238: Be more specific. Which type of further study?

      Rephased it more specific.

      Lines 297-299: rewrite this sentence to make it more transparent.

      Now we rewrote the sentence accordingly.

      Line 302: Certain

      Changed accordingly.

      Line 453: effective

      Changed accordingly.

      Finally, the authors should check the text carefully to avoid grammatical errors.

      Thanks, now we have checked the full text to avoid grammatical errors.

      Reviewer #2 (Recommendations For The Authors):

      I feel that the authors have very interesting data and have a solid set of analyses. I do not have major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results. Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning at this point.

      I feel that the largest caveat of the manuscript remains in the representation of the rationale of the study. I felt the introduction could be more concise and be better focused to back up the study questions and hypotheses. Many times, the sentences were too vague and unspecific, and thus, it was difficult to understand what was meant to be said. The authors could mention something more about how community composition of hosts and parasitoids are expected to change with the studied experimental design regarding the metrices you mention in the introduction (stronger hypotheses). The results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses must be described somewhere in the intro or results, if the journal/authors want to keep the methods last structure. Also, the results and discussion could be more focused around the hypotheses. Naturally, these things can be easily fixed.

      I also disagree with the interpretation of results finding top-down control in the system (it might well be there, but I do not think that the current methods and tests are suitable in finding it). First, the used methodology cannot distinguish parasitoids if the hosts are not there and the probability to detect parasitoid likely depends on the abundance of the host. Thus, the top-down regulation is difficult to prove (is it the parasitoids that have driven the host population down). Secondly, I would be hesitant to say anything about the top-down and bottom-up control in the systems as the data in the manuscript is pooled across five years while the top-down/bottom-up regulation in insect systems usually spans only one season/generation in time (much shorter than five years). Consequently, the analyses are comparing the communities of species that some of most likely do not co-exist (they were found in the same space but not during the same time). Luckily, the top-down/bottom-up effects could potentially be explored by using separately the time steps of the now pooled community data: e.g., does the population of the host decrease in t if the parasitoids are abundant in t-1? There are also other statistical tests to explore these patterns.

      In the manuscript "Phylogenetic composition" refers to Mean Pairwise Distance. I would use "phylogenetic diversity" instead throughout the text. Also, to my understanding, in trees both "phylogenetic composition" and "phylogenetic diversity" are used even though based on their descriptions, they are the same.

      Detailed comments:

      Punctuation needs to be checked and edited at some point (I think copy-pasting had left things in the wrong places). Please check that "-" instead of "-" is used in host-parasitoid.

      1-2 The title is not very matching with the content. "Multi-dimensionality" is not mentioned in the text. "phylogenetic composition" -> "phylogenetic diversity"

      We didn’t find the role of functional diversity of trees in host-parasitoid interactions, but we still have tree richness and phylogenetic diversity. I also disagree with that using phylogenetic diversity to replace phylogenetic composition, because diversity highlights higher or lower phylogenetic distance among communities, while the later highlights the phylogenetic dissimilarity across communities.

      53-57 This sentence is quite vague and because of it, difficult to follow. Consider rephrasing and avoiding unspecified terms such as "tree identity", "genetic diversity", and "overall community composition of higher trophic levels" (at least, I was not sure what taxa/level you meant with them).

      Rephased.

      L58-61 “Especially, we lack a comprehensive understanding of the ways that biotic factors, including plant richness, overall community phylogenetic and functional composition of consumers, and abiotic factors such as microclimate, determining host–parasitoid network structure and host–parasitoid community dynamics.”

      56 I would remove "interact" as no interactions were tested.

      Removed accordingly.

      59-60 This needs rephrasing. I feel "taxonomic and phylogenetic composition should be just "species composition". To better match, what was done: "taxonomic, phylogenetic, and network composition of both host and parasitoid communities" -> "species and phylogenetic diversity of both host and parasitoid communities and the composition their interaction networks"

      Changed accordingly.

      62 Remove "tree composition".

      Done.

      62 Replace "taxonomic" with "species". Throughout the text.

      Done.

      63-64 "Generally, top-down control was stronger than bottom-up control via phylogenetic association between hosts and parasitoids" I disagree, see my comments elsewhere.

      Now we rephased the sentence.

      L68-70 “Generally, phylogenetic associations between hosts and parasitoids reflect non-randomly structured interactions between phylogenetic trees of hosts and parasitoids.”

      68 "habitat structure and heterogeneity" This is too strong and general of a statement based on the results. You did not really measure habitat structure or heterogeneity.

      Now we rephased the statement to avoid strong and general description.

      L71-73 “Our study indicates that the composition of higher trophic levels and corresponding interaction networks are determined by plant diversity and canopy cover especially via trophic phylogenetic links in species-rich ecosystems.”

      69 Specify "phylogenetic links". Trophic links?

      Specified to “trophic phylogenetic links”.

      75-77 The sentence is a bit difficult to follow. Consider rephrasing.

      Now we rephased it.

      L79-82 “Changes in network structure of higher trophic levels usually coincide with variations in their diversity and community, which could be in turn affected by the changes in producers via trophic cascades”

      76 Be more specific about what you mean by "community of trophic levels".

      Specified to “community composition”.

      79 Remove "basal changes of", it only makes the sentence heavier.

      Done.

      81 What is "species codependence"?

      We sim to describe the species co-occurrence depending on their closely relationships. For clarity, now we changed to “species coexistence”

      82 What do you mean by "complex dynamics"?

      Rephased to “mechanisms on dynamics of networks”.

      83 onward: I would not focus so much on top-down/bottom-up as I feel that your current analyses cannot really say anything too strong about these causalities but are rather correlative.

      Thanks, we now removed the relevant contents from the discussion. However, we kept one sentence in the Introduction, because it should be highlighted to make reviewers aware of this (the other text on about this were removed).

      89 Remove "environmental".

      Done.

      90 Specify what you mean by "these forces".

      Done.

      98-99 I have difficulties following the logic here "potential specialization of their hosts may cascade up to impact the parasitoids' presence or absence". Consider rephrasing.

      Now we rephased it.

      L101-102 “…and their host fluctuations may cascade up to impact the parasitoids’ presence or absence.”

      100 Be more specific with "habitat-level changes".

      Specified to “community-level changes”

      100 I do not see why host-parasitoid systems would be ideal to study "species interactions". There are much simpler and easier systems available.

      Changed to “… one of ideal…”

      101-103 "influence of" on what?

      Now we rephased the sentence.

      L104-105 “Previous studies mainly focused on the influence of abiotic factors on host-parasitoid interactions”

      104 Be more specific in "the role of multiple components of plant diversity".

      Now we specified "the role of multiple components of plant diversity".

      L107-108 “…the role of multiple components of plant diversity (i.e. taxonomic, functional and phylogenetic diversity)…”

      106 "diversity associations" of what?

      “diversity associations between host and parasitoids”.

      108 Specify the "direct and indirect effects".

      Now we specified it to “…direct and indirect effects (i.e. one pathway and more pathways via other variables)…”

      110-113 A bit heavy sentence to follow. Consider rephrasing.

      Now we rephased the sentence to make it more readable.

      114 Give an example of "phylogenetic dependences".

      Done. Phylogenetic dependences (e.g. phylogenetic diversity)

      117 Move the "e.g. taxonomic, phylogenetic, functional" within brackets in 117 after "dimensions of biodiversity".

      Done.

      120 "(add citation here)" Yes please!

      Done.

      120-121 Specify "such relationships".

      Done. Specified to “multiple dimensions of biodiversity”

      128-130 This is difficult to follow. Please rephrase.

      Now we rephased the sentence.

      L135-137 “We aimed to discern the primary components of the diversity and composition of tree communities that affect higher trophic level interactions via quantifying the strength and complexity of associations between hosts and parasitoid.”

      131-132 Remove "phylogenetic and". It is redundant to phylogenetic diversity.

      Done.

      128 Tested robustness does not really capture "stability of associations".

      Yes, we agree. Now we rephased the sentence and exclude the “stability” description.

      133 Specify "phylogenetic processes".

      Now we specified “phylogenetic processes”.

      L140-141 “…especially via phylogenetic processes (e.g. lineages of trophic levels diverge and evolve over time)…”

      141 I would like to have more details on the data set somewhere in the results. How many individuals and species were found in each plot (on average)? Was there a lot of temporal variation (e.g. between the seasons)? On how many sites were the insect species found?

      Thanks for your suggestion. Now we provide more details on the data set in the results (L153-156), including mean values of individuals and species in each plot. However, the temporal variation should be studied for another relative independent topic, as our study focus on the general patter of interactions between hosts and parasitoids. Therefore, we would not put more information on temporal changes to make readers get lost in the text.

      153-156 “Among them, we found 56 host species (12 bees and 44 wasps, mean abundance and richness are 400.05 and 45.14, respectively, for each plot) and 50 parasitoid species (38 Hymenoptera and 12 Diptera, mean abundance and richness are 14.07 and 9.05, respectively, for each plot).”

      149 tree -> trees

      Done.

      149 Should there read also some else than "NMDS scores"?

      Thanks! Now we provided more details about “NMDS scores”.

      L161-162 “(NMDS axis scores; i.e. preserving the rank order of pairwise dissimilarities between samples)”

      149 You could mention the amount of variation explained by the first two axes of the NMDSs. Now it is difficult to estimate how much the models actually explain.

      Thanks for your comments! However, we could not directly provide the explanatory power of the two axes, because NMDS is based on rank-order distances rather than linear relationships like in PCA. However, the goodness of fit for the NMDS solution is typically evaluated using the stress value. We provide the stress value in the figure caption.

      150 "tree MPD" is mentioned for the first time. Spell it out.

      Done.

      150 Explain "eastness".

      Done.

      L163-164 “…eastness (sine-transformed radian values of aspect) )”

      151 How was "tree functional diversity" quantified?

      Please see methods. L437-L438.

      160 Specify that you talk about phylogenetic compositions of the host and parasitoid communities here.

      We would keep it refined here, keeping consistent with species composition here. Phylogenetic composition just represents the dissimilarities of phylogenetic linages within a community.

      161 Describe "parafit" test here when first mentioned.

      Done, see methods L485-487.

      182 Keep on referring to tables and figures in the discussion! Also, more clearly discuss your hypotheses. There are lots of discussions on top-down/bottom-up control. It could be good to form a hypothesis on them and predict what kind of patterns would suggest either one and what would you expect to find regarding them.

      Now we referred figures and tables in the discussion. As the contents on top-down and bottom-up control were not fit very well with our study (as also suggested by reviewers), so we rephased the discussion and also clearly discuss our hypotheses in the discussion. See L218, L226, and L237 etc.

      186 "partly determined host-parasitoid networks" Be more specific.

      Done.

      L206-207 “…partly determined host-parasitoid network indices, including vulnerability, linkage density, and interaction evenness.”

      195 Tell what you mean by "other biotic factors".

      Specified it: “…other biotic factors such as elevation and slope…”

      197-198 "It seems likely that these results are based on bee linkages to pollen resources" I would be hesitant to conclude this as the bees most likely forage way beyond the borders of the 30m by 30m study plots.

      Thanks for your concern about this problem. While it is true that bees can forage beyond 30 x 30m, the study focuses on their nesting behavior and activity within this defined area, rather than their entire foraging range. Existing literature shows bees often forage locally when resources are available (e.g. Ebeling et al., 2012 Oecologia; Guo et al., year, Basic and Applied Ecology). Therefore, we are confident that this pattern could be associated with the resources around the trap nests.

      223 "This could be further tested by collecting the food directly used by the wasps (caterpillars)" A bit unnecessary addition.

      Thanks for your suggestion. Yes, this definitely is a good point, but currently we don’t have enough data of caterpillars, but we will follow this in the future.

      232-238 I disagree with the authors on the interpretation of the causality of the results here. I think that the community of parasitoids simply indicates which host species are available, while the host community does not have an as strong effect on parasitoid community as parasitoids do not utilise the whole species pool of the hosts. (Presence of parasitoid tells that the host is around while the presence of the host does not necessarily tell about the presence of the parasitoid.) To me, this would rather indicate a bottom-up than top-down regulation. Similar patterns are also visible in species communities of hosts and parasites.

      Thank you for your suggestion. We agree with you that parasitoids are more depended on hosts, as host could not be always attacked by parasitoids. Now we rephased our explanation to follow this argument.

      L254-256 “Such pattern could be further confirmed by the significant association between host phylogenetic composition and parasitoid phylogenetic composition (Fig. 1c), which suggested that their interactions are phylogenetically structured to some extent.”

      247-266 The logic in this section is difficult to follow. Try rephrasing.

      Now we rephased the section for a clearer logic.

      L270-287 “Tree community species richness did not significantly influence the diversity of hosts targeted by parasitoids (parasitoid generality), but caused a significant increase in the diversity of parasitoids per host species (host vulnerability) (Fig. 3a; Table 2). This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity (Lopez-Carretero et al. 2014). Such positive relationship between host vulnerability and tree species richness suggested that host-parasitoid interactions could be driven through bottom-up effects via benefit from tree diversity. For example, parasitoid species increases more than host diversity with increasing tree species richness (Guo et al. 2021), resulting increasing of host vulnerability at community level. According to the enemies hypothesis (Root 1973), which posits a positive effects of plant richness on natural enemies, the higher trophic levels in our study (e.g. predators and parasitoids) would benefit from tree diversity and regulate herbivores thereby (Staab and Schuldt 2020). Indeed, previous studies at the same site found that bee parasitoid richness and abundance were positively related to tree species richness, but not their bee hosts (Fornoff et al. 2021, Guo et al. 2021). Because our dataset considered all hosts and reflects an overall pattern of host-parasitoid interactions, the effects of tree species richness on parasitoid generality might be more complex and difficult to predict, as we found that neither tree species richness nor tree MPD were related to parasitoid generality.”

      249 "This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity" This is a bit contradicting your vulnerability results as niche differentiation should increase specialization and diversity and specialization should decrease vulnerability (less host per parasitoid).

      Thanks! We understand that the concepts of “generality” and “vulnerability” can be a bit confusing. To clarify, “fewer hosts per parasitoid” actually corresponds to lower generality at the community level.

      332-337 How did you select the species growing on your plots? Or was only species number considered? What was the pool of tree species growing on the selected plots? Was the selection similar at both sites?

      Now we provided more information on the experiment design.

      L354-356 “The species pools of the two plots are nonoverlapping (16 species for each site). The composition of tree species within the study plots is based on a “broken-stick” design (see Bruelheide et al. 2014).”

      342 Remove "centrally per plot"?

      Done.

      346-347 Was the selection of different reed diameters similar in all the plots?

      Diameters and the relative distribution of diameters was similar in all trap nests.

      399 & 432 Are "phylogenetic diversity of the tree communities" and "phylogenetic composition of trees" the same? They are both described as mean pairwise distance.

      These two are actually different, as we use this to distinguish the phylogenetic diversity with communities and rank order of dissimilarities between tree communities. Here, the phylogenetic diversity of the tree communities is mean pairwise phylogenetic distance of species for tree communities. Tree phylogenetic composition is the rank order of pairwise dissimilarities between tree communities based on NMDS.

      400 Do you think that MPD makes any sense with the monocultures (value is always 0)? Does this have a potential to bias your analyses and result?

      We agree your point. However, we do not think that this is a major problem in the analyses. We followed the experimental design and considered low phylogenetic relatedness of tree species in a plot (Likewise in monocultures, the tree species richness is always 1).

      402-405 MNTD is not mentioned before or after this. Consider removing this section.

      We tested the potential effects of MNTD in our models. Now we mentioned it in our results.

      L194-195 “Tree mean nearest taxon distance (MNTD) was unrelated to any network indices.”

      405 "Phylogenetic metrics of trees" Which ones?

      Both tree MPD and MNTD. Now we have noted it in the manuscript. (L432)

      410 Further details on "Rao's Q" and how the functional diversity of the communities was calculated are needed.

      Now more details were provided.

      L435-438 “Specifically, seven leaf traits were used for calculation of tree functional diversity, which was calculated as the mean pairwise distance in trait values among tree species, weighted by tree wood volume, and expressed as Rao's Q”

      413 Specify "higher trophic levels".

      Now we specified the trophic levels.

      L440-441 “…higher trophic levels in our study area, such as herbivores and predators”

      417-424 What about the position of the plots within study sites? Is there potential for edge effects (e.g. bees finding easier the trap nest close to the edge of the experimental forest)? Were there any differences between the two sites? What is the elevation range of the plots?

      Thanks for concerning the details of our study. First, all the plots were randomly distributed within the study sites (see Fig. S2). Admittedly, there are several plots are located in the edges of the site. However, we did not consider the potential edge effects in our analysis. Of course, this will be a good point in our future studies. Moreover, the biggest difference between the two is the non-overlapping tree species pool, and the two study sites are apart from 5 km in the same town. Finally, there is not too distinct elevation gradient across the plots (112 m - 260 m).

      432-434 "The species and phylogenetic composition of trees, hosts, and parasitoids were quantified at each plot with nonmetric multidimensional scaling (NMDS) analysis based on Morisita-Horn distances" This section needs to be more specific and detailed. Did you do the NMDS separately for each plot as suggested in the text?

      We provided more details of the section.

      L462-465 “The minimum number of required dimensions in the NMDS based on the reduction in stress value was determined in the analysis (k = 2 in our case). We centred the results to acquire maximum variance on the first dimension, and used the principal components rotation in the analysis.”

      435 Specify how picante was used (function and arguments)!

      Now we specified the function.

      L465-467 “The phylogenetic composition was calculated by mean pairwise distance among the host or parasitoid communities per plot with the R package “picante” with ‘mpd’ function.”

      436 "standardized values" Of what? How was the standardisation done?

      Now we citied a supplementary table (Table S2) to specify it (see L469). For the standardization, we used ‘scale’ function in R, which standardizes data by centering and scaling data. Specifically, it subtracts the mean and divides by the standard deviation for each variable.

      443 Provide more details on parafit.

      Actually, we have provided the reason why we use the parafit test and the usage.

      L483-486 “We used a parafit test (9,999 permutations) with the R package “ape” to test whether the associations were non-random between hosts and parasitoids. This is widely used to assess host-parasite co-phylogeny by analyzing the congruence between host and parasite phylogenies using a distance-based matrix approach.”

      449-451 Rephrase the sentence.

      Rephased.

      L490-491 “We constructed quantitative host-parasitoid networks at community level with the R package “bipartite” for each plot of the two sites.”

      451 "six" Should this be five?

      Yes, should be five, thanks.

      470-481 What package and function were used for the LMMs?

      As we now used linear models, we do no longer use a R package for LMMs.

      470 "mix" -> mixed

      Changed to linear models.

      472 "six" Should this be five?

      Again, we changed it to five.

      479-481 How did you treat the variables from the two different sites when testing for the correlations to avoid two geographic clusters of data points?

      Now we considered the two study sites as fixed factor in our linear models. Moreover, tree-based variables were additionally included as interaction terms with the study sites.

      501 "mix" -> mixed

      Changed to linear models.

      The panel selection for figures 3 and 4 seems random. Justify it!

      Thank you. To avoid including too many figures in the main text, which could potentially confuse readers, we have selected the key results that are of primary interest. The remaining figures are provided in the appendix for reference.

      533 "Note that axes are on a log scale for tree species richness." Why the log-scale if the analyses were performed with linear fit? Also, the drawn regression lines do not match the model description (non-linear, while a linear model is described in the text). The models should probably be described in more detail.

      We used log-transformed to promote the normality of the data. The drawn regression lines are linear lines, which fit our models.

      539 "Values were adjusted for covariates of the final regression model." How?

      We used residual plot to directly visualizes the relationship between the predictor and the response variable with the fitted regression line, making it easier to assess the model's fit.

      Fig. S4 text does not match the figure.

      Thanks! We now deleted the unmatched text in the figure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      Summary: 

      In this work, Noorman and colleagues test the predictions of the "four-stage model" of consciousness by combining psychophysics and scalp EEG in humans. The study relies on an elegant experimental design to investigate the respective impact of attentional and perceptual blindness on visual processing. 

      The study is very well summarised, the text is clear and the methods seem sound. Overall, a very solid piece of work. I haven't identified any major weaknesses. Below I raise a few questions of interpretation that may possibly be the subject of a revision of the text. 

      We thank the reviewer for their positive assessment of our work and for their extremely helpful and constructive comments that helped to significantly improve the quality of our manuscript.

      (1) The perceptual performance on Fig1D appears to show huge variation across participants, with some participants at chance levels and others with performance > 90% in the attentional blink and/or masked conditions. This seems to reveal that the procedure to match performance across participants was not very successful. Could this impact the results? The authors highlight the fact that they did not resort to postselection or exclusion of participants, but at the same time do not discuss this equally important point. 

      Performance was indeed highly variable between observers, as is commonly found in attentional-blink (AB) and masking studies. For some observers, the AB pushes performance almost to chance level, whereas for others it has almost no effect. A similar effect can be seen in masking. We did our best to match accuracy over participants, while also matching accuracy within participants as well as possible, adjusting mask contrast manually during the experimental session. Naturally, those that are strongly affected by masking need not be the same participants as those that are strongly affected by the AB, given the fact that they rely on different mechanisms (which is also one of the main points of the manuscript). To answer the research question, what mattered most was that at the group-level, performance was well matched between the two key conditions. As all our statistical inferences, both for behavior and EEG decoding, rest on this group level. We do not think that variability at the individualsubject level detracts from this general approach.  

      In the Results, we added that our goal was to match performance across participants:

      “Importantly, mask contrast in the masked condition was adjusted using a staircasing procedure to match performance in the AB condition, ensuring comparable perceptual performance in the masked and the AB condition across participants (see Methods for more details).”

      In the Methods, we added:

      “Second, during the experimental session, after every 32 masked trials, mask contrast could be manually updated in accordance with our goal to match accuracy over participants, while also matching accuracy within participants as well as possible.”

      (2) In the analysis on collinearity and illusion-specific processing, the authors conclude that the absence of a significant effect of training set demonstrates collinearity-only processing. I don't think that this conclusion is warranted: as the illusory and nonillusory share the same shape, so more elaborate object processing could also be occurring. Please discuss. 

      We agree with this qualification of our interpretation, and included the reviewer’s account as an alternative explanation in the Discussion section:  

      “It should be noted that not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processes representing the triangular shapes as well.”

      (3) Discussion, lines 426-429: It is stated that the results align with the notion that processes of perceptual segmentation and organization represent the mechanism of conscious experience. My interpretation of the results is that they show the contrary: for the same visibility level in the attentional blind or masking conditions, these processes can be implicated or not, which suggests a role during unconscious processing instead. 

      We agree with the reviewer that the interpretation of this result depends on the definition of consciousness that one adheres to. If one takes report as the leading metric for consciousness (=conscious access), one can indeed conclude that perceptual segmentation/organization can also occur unconsciously. However, if the processing that results in the qualitative nature of an image (rather than whether it is reported) is taken as leading – such as the processing that results in the formation of an illusory percept – (=phenomenal) the conclusion can be quite different. This speaks to the still ongoing debate regarding the existence of phenomenal vs access consciousness, and the literature on no-report paradigms amongst others (see last paragraph of the discussion). Because the current data do not speak directly to this debate, we decided to remove  the sentence about “conscious experience”, and edited this part of the manuscript (also addressing a comment about preserved unconscious processing during masking by Reviewer 2) by limiting the interpretation of unconscious processing to those aspects that are uncontroversial:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling deep unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.”

      (4) The two paradigms developed here could be used jointly to highlight nonidiosyncratic NCCs, i.e. EEG markers of visibility or confidence that generalise regardless of the method used. Have the authors attempted to train the classifier on one method and apply it to another (e.g. AB to masking and vice versa)? What perceptual level is assumed to transfer? 

      To avoid issues with post-hoc selection of (visible vs. invisible) trials (discussed in the Introduction), we did not divide our trials into conscious and unconscious trials, and thus did not attempt to reveal NCCs, or NCCs generalizing across the two paradigms. Note also that this approach alone would not resolve the debate regarding the ‘true’ NCC as it hinges on the operational definition of consciousness one adheres to; also see our response to the previous point the reviewer raised. Our main analysis revealed that the illusory triangle could be decoded with above-chance accuracy during both masking and the AB over extended periods of time with similar topographies (Fig. 2B), so that significant cross-decoding would be expected over roughly the same extended period of time (except for the heightened 200-250 ms peak). However, as our focus was on differences between the two manipulations and because we did not use post-hoc sorting of trials, we did not add these analyses.

      (5) How can the results be integrated with the attentional literature showing that attentional filters can be applied early in the processing hierarchy? 

      Compared to certain manipulations of spatial attention, the AB phenomenon is generally considered to represent an instance of  “late” attentional filtering. In the Discussion section we included a paragraph on classic load theory, where early and late filtering depend on perceptual and attentional load. Just preceding this paragraph, we added this:  

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      Reviewer #2 (Public Review): 

      Summary: 

      This is a very elegant and important EEG study that unifies within a single set of behaviorally equated experimental conditions conscious access (and therefore also conscious access failures) during visual masking and attentional blink (AB) paradigms in humans. By a systematic and clever use of multivariate pattern classifiers across conditions, they could dissect, confirm, and extend a key distinction (initially framed within the GNWT framework) between 'subliminal' and 'pre-conscious' unconscious levels of processing. In particular, the authors could provide strong evidence to distinguish here within the same paradigm these two levels of unconscious processing that precede conscious access : (i) an early (< 80ms) bottom-up and local (in brain) stage of perceptual processing ('local contrast processing') that was preserved in both unconscious conditions, (ii) a later stage and more integrated processing (200-250ms) that was impaired by masking but preserved during AB. On the basis of preexisting studies and theoretical arguments, they suggest that this later stage could correspond to lateral and local recurrent feedback processes. Then, the late conscious access stage appeared as a P3b-like event. 

      Strengths: 

      The methodology and analyses are strong and valid. This work adds an important piece in the current scientific debate about levels of unconscious processing and specificities of conscious access in relation to feed-forward, lateral, and late brain-scale top-down recurrent processing. 

      Weaknesses: 

      - The authors could improve clarity of the rich set of decoding analyses across conditions. 

      - They could also enrich their Introduction and Discussion sections by taking into account the importance of conscious influences on some unconscious cognitive processes (revision of traditional concept of 'automaticity'), that may introduce some complexity in Results interpretation 

      - They should discuss the rich literature reporting high-level unconscious processing in masking paradigms (culminating in semantic processing of digits, words or even small group of words, and pictures) in the light of their proposal (deeper unconscious processing during AB than during masking). 

      We thank the reviewer for their positive assessment of our study and for their insightful comments and helpful suggestions that helped to significantly strengthen our paper. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we followed the reviewer’s suggestions and revised the Results/Discussion to include references to influences on unconscious processes and expanded our discussion of unconscious effects during masking vs. AB.  

      Reviewer #3 (Public Review): 

      Summary: 

      This work aims to investigate how perceptual and attentional processes affect conscious access in humans. By using multivariate decoding analysis of electroencephalography (EEG) data, the authors explored the neural temporal dynamics of visual processing across different levels of complexity (local contrast, collinearity, and illusory perception). This is achieved by comparing the decidability of an illusory percept in matched conditions of perceptual (i.e., degrading the strength of sensory input using visual masking) and attentional impairment (i.e., impairing topdown attention using attentional blink, AB). The decoding results reveal three distinct temporal responses associated with the three levels of visual processing. Interestingly, the early stage of local contrast processing remains unaffected by both masking and AB. However, the later stage of collinearity and illusory percept processing are impaired by the perceptual manipulation but remain unaffected by the attentional manipulation. These findings contribute to the understanding of the unique neural dynamics of perceptual and attentional functions and how they interact with the different stages of conscious access. 

      Strengths: 

      The study investigates perceptual and attentional impairments across multiple levels of visual processing in a single experiment. Local contrast, collinearity, and illusory perception were manipulated using different configurations of the same visual stimuli. This clever design allows for the investigation of different levels of visual processing under similar low-level conditions. 

      Moreover, behavioural performance was matched between perceptual and attentional manipulations. One of the main problems when comparing perceptual and attentional manipulations on conscious access is that they tend to impact performance at different levels, with perceptual manipulations like masking producing larger effects. The study utilizes a staircasing procedure to find the optimal contrast of the mask stimuli to produce a performance impairment to the illusory perception comparable to the attentional condition, both in terms of perceptual performance (i.e., indicating whether the target contained the Kanizsa illusion) and metacognition (i.e., confidence in the response). 

      The results show a clear dissociation between the three levels of visual processing in terms of temporal dynamics. Local contrast was represented at an early stage (~80 ms), while collinearity and illusory perception were associated with later stages (~200-250 ms). Furthermore, the results provide clear evidence in support of a dissociation between the effects of perceptual and attentional processes on conscious access: while the former affected both neuronal correlates of collinearity and illusory perception, the latter did not have any effect on the processing of the more complex visual features involved in the illusion perception. 

      Weaknesses: 

      The design of the study and the results presented are very similar to those in Fahrenfort et al. (2017), reducing its novelty. Similar to the current study, Fahrenfort et al. (2017) tested the idea that if both masking and AB impact perceptual integration, they should affect the neural markers of perceptual integration in a similar way. They found that behavioural performance (hit/false alarm rate) was affected by both masking and AB, even though only the latter was significant in the unmasked condition. An early classification peak was instead only affected by masking. However, a late classification peak showed a pattern similar to the behavioural results, with classification affected by both masking and AB. 

      The interpretation of the results mainly centres on the theoretical framework of the recurrent processing theory of consciousness (Lamme, 2020), which lead to the assumption that local contrast, collinearity, and the illusory perception reflect feedforward, local recurrent, and global recurrent connections, respectively. It should be mentioned, however, that this theoretical prediction is not directly tested in the study. Moreover, the evidence for the dissociation between illusion and collinearity in terms of lateral and feedback connections seems at least limited. For instance, Kok et al. (2016) found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers. Lee & Nguyen (2001), instead, found that V1 neurons respond to illusory contours of the Kanizsa figures, particularly in the superficial layers. They all mention feedback connections, but none seem to point to lateral connections. 

      Moreover, the evidence in favour of primarily lateral connections driving collinearity seems mixed as well. On one hand, Liang et al. (2017) showed that feedback and lateral connections closely interact to mediate image grouping and segmentation. On the other hand, Stettler et al. (2002) showed that, whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. Furthermore, the other studies mentioned in the manuscript did not investigate feedback connections but only lateral ones, making it difficult to draw any clear conclusions. 

      We thank the reviewer for their careful review and positive assessment of our study, as well as for their constructive criticism and helpful suggestions. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we addressed the reviewer’s comments and suggestions by better relating our study to Fahrenfort et al.’s (2017) paper and by highlighting the limitations inherent in linking our findings to distinct neural mechanisms (in particular, to lateral vs. feedback connections).

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors): 

      -  Methods: it states that "The distance between the three Pac-Man stimuli as well as between the three aligned two-legged white circles was 2.8 degrees of visual angle". It is unclear what this distance refers to. Is it the shortest distance between the edges of the objects? 

      It is indeed the shortest distance between the edges of the objects. This is now included in the Methods.

      -  Methods: It's unclear to me if the mask updating procedure during the experimental session was based on detection rate or on the perceptual performance index reported on Fig1D. Please clarify. 

      It was based on accuracy calculated over 32 trials. We have included this information in the Methods.

      -  Methods and Results: I did not understand why the described procedure used to ensure that confidence ratings are not contaminated by differences in perceptual performance was necessary. To me, it just seems to make the "no manipulations" and "both manipulations" less comparable to the other 2 conditions. 

      To calculate accurate estimates of metacognitive sensitivity for the two matched conditions, we wanted participants to make use of the full confidence scale (asking them to distribute their responses evenly over all ratings within a block). By mixing all conditions in the same block, we would have run the risk of participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition). We made this point explicit in the Results section and in the Methods section:

      “To ensure that the distribution of confidence ratings in the performancematched masked and AB condition was not influenced by participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition, respectively), the masked and AB condition were presented in the same experimental block, while the other block type included the no and both manipulations condition.”

      “To ensure that confidence ratings for these matched conditions (masked, long lag and unmasked, short lag) were not influenced by participants anchoring their confidence ratings to the very easy and very difficult unmatched conditions (no and both manipulations, respectively), one type of block only contained the matched conditions, while the other block type contained the two remaining, unmatched conditions (masked, short lag and unmasked, long lag).”

      - Methods: what priors were used for Bayesian analyses? 

      Bayesian statistics were calculated in JASP (JASP Team, 2024) with default prior scales (Cauchy distribution, scale 0.707). This is now added to the Methods.

      - Results, line 162: It states that classifiers were applied on "raw EEG activity" but the Methods specify preprocessing steps. "Preprocessed EEG activity" seems more appropriate. 

      We changed the term to “preprocessed EEG activity” in the Methods and to “(minimally) preprocessed EEG activity (see Methods)” in the  Results, respectively.

      - Results, line 173: The effect of masking on local contrast decoding is reported as "marginal". If the alpha is set at 0.05, it seems that this effect is significant and should not be reported as marginal. 

      We changed the wording from “marginal” to “small but significant.”  

      - Fig1: The fixation cross is not displayed. 

      Because adding the fixation cross would have made the figure of the trial design look crowded and less clear, we decided to exclude it from this schematic trial representation. We are now stating this also in the legend of figure 1.  

      - Fig 3A: In the upper left panel, isn't there a missing significant effect of the "local contrast training and testing" condition in the first window? If not, this condition seems oddly underpowered compared to the other two conditions. 

      Thanks for the catch! The highlighting in bold and the significance bar were indeed lacking for this condition in the upper left panel (blue line). We corrected the figure in our revision.

      - Supplementary text and Fig S6: It is unclear to me why the two control analyses (the black lines vs. the green and purple lines) are pooled together in the same figure. They seem to test for different, non-comparable contrasts (they share neither training nor testing sets), and I find it confusing to find them on the same figure. 

      We agree that this may be confusing, and deleted the results from one control analysis from the figure (black line, i.e., training on contrast, testing on illusion), as the reviewer correctly pointed out that it displayed a non-comparable analysis. Given that this control analysis did not reveal any significant decoding, we now report its results only in the Supplementary text.  

      - Fig S6: I think the title of the legend should say testing on the non-illusory triangle instead of testing on the illusory triangle to match the supplementary text. 

      This was a typo – thank you! Corrected.  

      Reviewer #2 (Recommendations For The Authors): 

      Issue #1: One key asymmetry between the three levels of T2 attributes (i.e.: local contrast; non-illusory triangle; illusory Kanisza triangle) is related to the top-down conscious posture driven by the task that was exclusively focusing on the last attribute (illusory Kanisza triangle). Therefore, any difference in EEG decoding performance across these three levels could also depend to this asymmetry. For instance, if participants were engaged to report local contrast or non-illusory triangle, one could wonder if decoding performance could differ from the one used here. This potential confound was addressed by the authors by using decoders trained in different datasets in which the main task was to report one the two other attributes. They could then test how classifiers trained on the task-related attribute behave on the main dataset. However, this part of the study is crucial but not 100% clear, and the links with the results of these control experiments are not fully explicit. Could the author better clarity this important point (see also Issue #1 and #3). 

      The reviewer raises an important point, alluding to potential differences between decoded features regarding task relevance. There are two separate sets of analyses where task relevance may have been a factor, our main analyses comparing illusion to contrast decoding, and our comparison of collinearity vs. illusion-specific processing.  

      In our main analysis, we are indeed reporting decoding of a task-relevant feature (illusion) and of a task-irrelevant feature (local contrast, i.e., rotation of the Pac-Man inducers). Note, however, that the Pac-Man inducers were always task-relevant, as they needed to be processed to perceive illusory triangles, so that local contrast decoding was based on task-relevant stimulus elements, even though participants did not respond to local contrast differences in the main experiment. However, we also ran control analyses testing the effect of task-relevance on local contrast decoding in our independent training data set and in another (independent) study, where local contrast was, in separate experimental blocks, task-relevant or task-irrelevant. The results are reported in the Supplementary Text and in Figure S5. In brief, task-relevance did not improve early (70–95 ms) decoding of local contrast. We are thus confident that the comparison of local contrast to illusion decoding in our main analysis was not substantially affected by differences in task relevance. In our previous manuscript version, we referred to these control analyses only in the collinearity-vs-illusion section of the Results. In our revision, we added the following in the Results section comparing illusion to contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      In addition to our main analysis, there is the concern that our comparison of collinearity vs. illusion-specific processing may have been affected by differences in task-relevance between the stimuli inducing the non-illusory triangle (the “two-legged white circles”, collinearity-only) and the stimuli inducing the Kanizsa illusion (the PacMan inducers, collinearity-plus-illusion). We would like to emphasize that in our main analysis classifiers were always used to decode T2 illusion presence vs. absence (collinearity-plus-illusion), and never to decode T2 collinearity-only. To distinguish collinearity-only from collinearity-plus-illusion processing, we only varied the training data (training classifiers on collinearity-only or collinearity-plus-illusion), using the independent training data set, where collinearity-only and collinearity-plus-illusion (and rotation) were task-relevant (in separate blocks). As discussed in the Supplementary Information, for this analysis approach to be valid, collinearity-only processing should be similar for the illusory and the non-illusory triangle, and this is what control analyses demonstrated (Fig. S7). In any case, general task-relevance was equated for the collinearity-only and the collinearity-plus-illusion classifiers.  

      Finally, in supplementary Figure 6 we also show that our main results reported in Figure 2 (discussed at the top of this response) were very similar when the classifiers were trained on the independent localizer dataset in which each stimulus feature could be task-relevant.  

      Together, for the reasons described above, we believe that differences in EEG decoding performance across these three stimulus levels did  are unlikely to depend also depend on a “task-relevance” asymmetry.

      Issue #2: Following on my previous point the authors should better mention the concept of conscious influences on unconscious processing that led to a full revision of the notion of automaticity in cognitive science [1 , 2 , 3 , 4]. For instance, the discovery that conscious endogenous temporal and spatial attention modulate unconscious subliminal processing paved the way to this revision. This concept raises the importance of Issue#1: equating performance on the main task across AB and masking is not enough to guarantee that differences of neural processing of the unattended attributes of T2 (i.e.: task-unrelated attributes) are not, in part, due to this asymmetry rather than to a systematic difference of unconscious processing strengtsh [5 , 6-8]. Obviously, the reported differences for real-triangle decoding between AB and masking cannot be totally explained by such a factor (because this is a task-unrelated attribute for both AB and masking conditions), but still this issue should be better introduced, addressed, clarified (Issue #1 and #3) and discussed. 

      We would like to refer to our response to the previous point: Control analyses for local contrast decoding showed that task relevance had no influence on our marker for feedforward processing. Most importantly, as outlined above, we did not perform real-triangle decoding – all our decoding analyses focused on comparing collinearity-only vs. collinearity-plus-illusion were run on the task-relevant T2 illusion (decoding its presence vs. absence). The key difference was solely the training set, where the collinearity-only classifier was trained on the (task-relevant) real triangle and the collinearity-plus-illusion classifier was trained on the (task-relevant) Kanizsa triangle. Thus, overall task relevance was controlled in these analyses.  

      In our revision, we are now also citing the studies proposed by the reviewer, when discussing the control analyses testing for an effect of task-relevance on local contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      Issue #3: In terms of clarity, I would suggest the authors to add a synthetic figure providing an overall view of all pairs of intra and cross-conditions decoding analyses and mentioning main task for training and testing sets for each analysis (see my previous and related points). Indeed, at one point, the reader can get lost and this would not only strengthen accessibility to the detailed picture of results, but also pinpoint the limits of the work (see previous point). 

      We understand the point the reviewer is raising and acknowledge that some of our analyses, in particular those using different training and testing sets, may be difficult to grasp. But given the variety of different analyses using different training and testing sets, different temporal windows, as well as different stimulus features, it was not possible to design an intuitive synthetic figure summarizing the key results. We hope that the added text in the Results and Discussion section will be sufficient to guide the reader through our set of analyses.  

      In our revision, we are now more clearly highlighting that, in addition to presenting the key results in our main text that were based on training classifiers on the T1 data, “we replicated all key findings when training the classifiers on an independent training set where individual stimuli were presented in isolation (Fig. 3A, results in the Supplementary Information and Fig. S6).” For this, we added a schematic showing the procedure of the independent training set to Figure 3, more clearly pointing the reader to the use of a separate training data set.  

      Issue #4: In the light of these findings the authors should discuss more thoroughly the question of unconscious high-level representations in masking versus AB: in particular, a longstanding issue relates to unconscious semantic processing of words, numbers or pictures. According to their findings, they tend to suggest that semantic processing should be more enabled in AB than in masking. However, a rich literature provided a substantial number of results (including results from the last authors Simon Van Gaal) that tend to support the notion of unconscious semantic processing in subliminal processing (see in particular: [9 , 10 , 11 , 12 , 13]). So, and as mentioned by the authors, while there is evidence for semantic processing during AB they should better discuss how they would explain unconscious semantic subliminal processing. While a possibility could be to question the unconscious attribute of several subliminal results, the same argument also holds for AB studies. Another possible track of discussion would be to differentiate AB and subliminal perception in terms of strength and durability of the corresponding unconscious representations, but not necessarily in terms of cognitive richness. Indeed, one may discuss that semantic processing of stimuli that do not need complex spatial integration (e.g.: words or digits as compared to illusory Kanisza tested here) can still be observed under subliminal conditions. 

      We thank the reviewer for pointing us to this shortcoming of our previous Discussion. Note that our data does not directly speak to the question of high-level unconscious representations in masking vs AB, because such conclusions would hinge on the operational definition of consciousness one adheres to (also see response to Reviewer 1). Nevertheless, we do follow the reviewer’s suggestions and added the following in the Discussion (also addressing a point about other forms of attention raised by Reviewer 1):

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      And, in a following paragraph in the Discussion:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling high-level unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.  

      Reviewer #3 (Recommendations For The Authors): 

      (1) The objective of Fahrenfort et al., 2017 seems very similar to that of the current study. What are the main differences between the two studies? Moreover, Fahrenfort et al., 2017 conducted similar decoding analyses to those performed in the current study.

      Which results were replicated in the current study, and which ones are novel? Highlighting these differences in the manuscript would be beneficial. 

      We now provide a more comprehensive coverage of the study by Fahrenfort et al., 2017. In the Introduction, we added a brief summary of the key findings, highlighting that this study’s findings could have reflected differences in task performance rather than differences between masking and AB:

      “For example, Fahrenfort and colleagues (2017) found that illusory surfaces could be decoded from electroencephalogram (EEG) data during the AB but not during masking. This was taken as evidence that local recurrent interactions, supporting perceptual integration, were preserved during inattention but fully abolished by masking. However, masking had a much stronger behavioral effect than the AB, effectively reducing task performance to chance level. Indeed, a control experiment using weaker masking, which resulted in behavioral performance well above chance similar to the main experiment’s AB condition, revealed some evidence for preserved local recurrent interactions also during masking. However, these conditions were tested in separate experiments with small samples, precluding a direct comparison of perceptual vs. attentional blindness at matched levels of behavioral performance. To test …”

      In the Results , we are now also highlighting this key advancement by directly referencing the previous study:

      “Thus, whereas in previous studies task performance was considerably higher during the AB than during masking (e.g., Fahrenfort et al., 2017), in the present study the masked and the AB condition were matched in both measures of conscious access.” When reporting the EEG decoding results in the Results section, we continuously cite the Fahrenfort et al. (2017) study to highlight similarities in the study’s findings. We also added a few sentences explicitly relating the key findings of the two studies:

      “This suggests that the AB allowed for greater local recurrent processing than masking, replicating the key finding by Fahrenfort and colleagues (2017). Importantly, the present result demonstrates that this effect reflects the difference between the perceptual vs. attentional manipulation rather than differences in behavior, as the masked and the AB condition were matched for perceptual performance and metacognition.”

      “This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”  

      We also more clearly highlighted where our study goes beyond Fahrenfort et al.’s (2017), e.g., in the Results:

      “The addition of this element of collinearity to our stimuli was a key difference to the study by Fahrenfort and colleagues (2017), allowing us to compare non-illusory triangle decoding to illusory triangle decoding in order to distinguish between collinearity and illusion-specific processing.”

      And in the Discussion:

      “Furthermore, the addition of line segments forming a non-illusory triangle to the stimulus employed in the present study allowed us to distinguish between collinearity and illusion-specific processing.”

      Also, in the Discussion, we added a paragraph “summarizing which results were replicated in the current study, and which ones are novel”, as suggested by the reviewer:

      “This pattern of results is consistent with a previous study that used EEG to decode Kanizsa-like illusory surfaces during masking and the AB (Fahrenfort et al., 2017). However, the present study also revealed some effects where Fahrenfort and colleagues (2017) failed to obtain statistical significance, likely reflecting the present study’s considerably larger sample size and greater statistical power. For example, in the present study the marker for feedforward processing was weakly but significantly impaired by masking, and the marker for local recurrency was significantly impaired not only by masking but also by the AB, although to a lesser extent. Most importantly, however, we replicated the key findings that local recurrent processing was more strongly impaired by masking than by the AB, and that global recurrent processing was similarly impaired by masking and the AB and closely linked to task performance, reflecting conscious access. Crucially, having matched the key conditions behaviorally, the present finding of greater local recurrency during the AB can now unequivocally be attributed to the attentional vs. perceptual manipulation of consciousness.”

      Finally, we changed the title to “Distinct neural mechanisms underlying perceptual and attentional impairments of conscious access despite equal task performance” to highlight one of the crucial differences between the Fahrenfort et al., study and this study, namely the fact that we equalized task performance between the two critical conditions (AB and masking).

      (2) It is not clear from the text the link between the current study and the literature on the role of lateral and feedback connections in consciousness (Lamme, 2020). A better explanation is needed. 

      To our knowledge, consciousness theories such as recurrent processing theory by Lamme make currently no distinction between the role of lateral and feedback connections for consciousness. The principled distinction lies between unconscious feedforward processing and phenomenally conscious or “preconscious” local recurrent processing, where local recurrency refers to both lateral (or horizontal) and feedback connections. We added a sentence in the Discussion:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness …”

      (3) When training on T1 and testing on T2, EEG data showed an early peak in local contrast classification at 75-95 ms over posterior electrodes. The authors stated that this modulation was only marginally affected by masking (and not at all by AB); however, the main effect of masking is significant. Why was this effect interpreted as nonrelevant? 

      Following this and Reviewer 1’s comment, we changed the wording from “marginal” to “weak but significant.” We considered this effect “weak” and of lesser relevance, because its Bayes factor indicated that the alternative hypothesis was only 1.31 times more likely than the null hypothesis of no effect, representing only “anecdotal” evidence, which is in sharp contrast to the robust effects of the consciousness manipulations on illusion decoding reported later. Furthermore, later ANOVAs comparing the effect of masking on contrast vs. illusion decoding revealed much stronger effects on illusion decoding than on contrast decoding (BFs>3.59×10<sup>4</sup>).

      (4) The decoding analysis on the illusory percept yielded two separate peaks of decoding, one from 200 to 250 ms and another from 275 to 475 ms. The early component was localized occipitally and interpreted as local sensory processing, while the late peak was described as a marker for global recurrent processing. This latter peak was localized in the parietal cortex and associated with the P300. Can the authors show the topography of the P300 evoked response obtained from the current study as a comparison? Moreover, source reconstruction analysis would probably provide a better understanding of the cortical localization of the two peaks. 

      Figure S4 now shows the P300 from electrode Pz, demonstrating a stronger positivity between 375 and 475 ms when the illusory triangle was present than when it was absent. We did not run a source reconstruction analysis.  

      (5) The authors mention that the behavioural results closely resembled the pattern of the second decoding peak results. However, they did not show any evidence for this relationship. For instance, is there a correlation between the two measures across or within participants? Does this relationship differ between the illusion report and the confidence rating? 

      This relationship became evident from simply eyeballing the results figures: Both in behavior and EEG decoding performance dropped from the both-manipulations condition to the AB and masked conditions, while these conditions did not differ significantly. Following a similar observation of a close similarity between behavior and the second/late illusion decoding peak in the study by Fahrenfort et al. (2017), we adopted their analysis approach and ran two additional ANOVAs, adding “measure” (behavior vs. EEG) as a factor. For this analysis, we dropped the both-manipulations condition due to scale restrictions (as noted in footnote 1: “We excluded the bothmanipulations condition from this analysis due to scale restrictions: in this condition, EEG decoding at the second peak was at chance, while behavioral performance was above chance, leaving more room for behavior to drop from the masked and AB condition.”). The analysis revealed that there were no interactions with condition:

      “The pattern of behavioral results, both for perceptual performance and metacognitive sensitivity, closely resembled the second decoding peak: sensitivity in all three metrics dropped from the no-manipulations condition to the masked and AB conditions, while sensitivity did not differ significantly between these performancematched conditions (Fig. 2C). Two additional rm ANOVAs with the factors measure (behavior, second EEG decoding peak) and condition (no-manipulations, masked, AB)<sup>1</sup> for perceptual performance and metacognitive sensitivity revealed no significant interaction (performance: F</iv><sub>2,58</sub>=0.27, P\=0.762, BF<sub>01</sub>=8.47; metacognition: F</iv><sub>2,58</sub=0.54, P\=0.586, BF<sub>2,58</sub>=6.04). This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”

      (6) The marker for illusion-specific processing emerged later (200-250 ms), with the nomanipulation decoding performing better after training on the illusion than the nonillusory triangle. This difference emerged only in the AB condition, and it was fully abolished by masking. The authors confirmed that the illusion-specific processing was not affected by the AB manipulations by running a rm ANOVA which did not result in a significant interaction between condition and training set. However, unlike the other non-significant results, a Bayes Factor is missing here. 

      We added Bayes factors to all (significant and non-significant) rm ANOVAs.

      (7) The same analysis yielded a second illusion decoding peak at 375-475 ms. This effect was impaired by both masking and AB, with no significant differences between the two conditions. The authors stated that this result was directly linked to behavioural performance. However, it is not clear to me what they mean (see point 5). 

      We added analyses comparing behavior and EEG decoding directly (see our response to point 5).

      (8) The introduction starts by stating that perceptual and attentional processes differently affect consciousness access. This differentiation has been studied thoroughly in the consciousness literature, with a focus on how attention differs from consciousness (e.g., Koch & Tsuchiya, TiCS, 2007; Pitts, Lutsyshyna & Hillyard, Phil. Trans. Roy. Soc. B Biol. Sci., 2018). The authors stated that "these findings confirm and enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness clearly distinguishing and specifying the neural profiles of each processing stage of the influential four-stage model of conscious experience". I found it surprising that this aspect was not discussed further. What was the state of the art before this study was conducted? What are the mentioned neural profiles? How did the current results enrich the literature on this topic? 

      We would like to point out that our study is not primarily concerned with the conceptual distinction between consciousness and attention, which has been the central focus of e.g., Koch and Tsuchiuya (2007). While this literature was concerned with ways to dissociate consciousness and attention, we tacitly assumed that attention and consciousness are now generally considered as different constructs. Our study is thus not dealing with dissociations between attention and consciousness, nor with the distinction between phenomenal consciousness and conscious access, but is concerned with different ways of impairing conscious access (defined as the ability to report about a stimulus), either via perceptual or via attentional manipulations. For the state of the art before the study was conducted, we would like to refer to the motivation of our study in the Introduction, e.g., previous studies’ difficulties in unequivocally linking greater local recurrency during attentional than perceptual blindness to the consciousness manipulation, given performance confounds (we expanded this Introduction section). We also expanded a paragraph in the discussion to remind the reader of the neural profiles of the 4-stage model and to highlight the novelty of our findings related to the distinction between lateral and feedback processes:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness (Block, 2005; Dehaene et al., 2006; Hatamimajoumerd et al., 2022; Lamme, 2010; Pitts et al., 2018; Sergent & Dehaene, 2004), clearly distinguishing the neural profiles of each processing stage of the influential four-stage model of conscious experience (Fig. 1A). Along with the distinct temporal and spatial EEG decoding patterns associated with lateral and feedback processing, our findings suggest a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-tofeedback connections, ultimately leading to global recurrency and conscious report.”  

      (9) When stating that this is the first study in which behavioural measures of conscious perception were matched between the attentional blink and masking, it would be beneficial to highlight the main differences between the current study and the one from Fahrenfort et al., 2017, with which the current study shares many similarities in the experimental design (see point 1). 

      We would like to refer the reviewer to our response to point 1), where we detail how we expanded the discussion of similarities and differences between our present study and Fahrenfort et al. (2017).

      (10) The discussion emphasizes how the current study "suggests a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-to-feedback connections, ultimately leading to global recurrency and conscious report". For transparency, it is though important to highlight that one limit of the current study is that it does not provide direct evidence for the specified types of connections (see point 6). 

      We added a qualification in the Discussion section:

      “Although the present EEG decoding measures cannot provide direct evidence for feedback vs. lateral processes, based on neurophysiological evidence, …”

      Furthermore, we added this qualification in the Discussion section:

      “It should be noted that the not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processing as well.”

      References

      Angelucci, A., Levitt, J. B., Walton, E. J. S., Hupe, J.-M., Bullier, J., & Lund, J. S. (2002). Circuits for local and global signal integration in primary visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 22(19), 8633–8646.

      Bair, W., Cavanaugh, J. R., & Movshon, J. A. (2003). Time course and time-distance relationships for surround suppression in macaque V1 neurons. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23(20), 7690–7701.

      Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9(2), 46–52.

      Chen, M., Yan, Y., Gong, X., Gilbert, C. D., Liang, H., & Li, W. (2014). Incremental integration of global contours through interplay between visual cortical areas. Neuron, 82(3), 682–694.

      Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10(5), 204–211.

      Hatamimajoumerd, E., Ratan Murty, N. A., Pitts, M., & Cohen, M. A. (2022). Decoding perceptual awareness across the brain with a no-report fMRI masking paradigm. Current Biology: CB. https://doi.org/10.1016/j.cub.2022.07.068

      JASP Team. (2024). JASP (Version 0.19.0)[Computer software]. https://jasp-stats.org/ Kentridge, R. W., Heywood, C. A., & Weiskrantz, L. (2004). Spatial attention speeds discrimination without awareness in blindsight. Neuropsychologia, 42(6), 831– 835.

      Kiefer, M., & Brendel, D. (2006). Attentional Modulation of Unconscious “Automatic” Processes: Evidence from Event-related Potentials in a Masked Priming Paradigm. Journal of Cognitive Neuroscience, 18(2), 184–198.

      Kouider, S., & Dehaene, S. (2007). Levels of processing during non-conscious perception: a critical review of visual masking. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 857–875.

      Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220.

      Luck, S. J., & Hillyard, S. A. (1994). Electrophysiological correlates of feature analysis during visual search. Psychophysiology, 31(3), 291–308.

      Naccache, L., Blandin, E., & Dehaene, S. (2002). Unconscious masked priming depends on temporal attention. Psychological Science, 13(5), 416–424.

      Pitts, M. A., Lutsyshyna, L. A., & Hillyard, S. A. (2018). The relationship between attention and consciousness: an expanded taxonomy and implications for ‘noreport’ paradigms. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373(1755), 20170348.

      Sergent, C., & Dehaene, S. (2004). Is consciousness a gradual phenomenon? Evidence for an all-or-none bifurcation during the attentional blink. Psychological Science, 15(11), 720–728.

      Van den Bussche, E., Van den Noortgate, W., & Reynvoet, B. (2009). Mechanisms of masked priming: a meta-analysis. Psychological Bulletin, 135(3), 452–477. van Gaal, S., & Lamme, V. A. F. (2012). Unconscious high-level information processing: implication for neurobiological theories of consciousness: Implication for neurobiological theories of consciousness. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 18(3), 287–301.

      Vogel, E. K., Luck, S. J., & Shapiro, K. L. (1998). Electrophysiological evidence for a postperceptual locus of suppression during the attentional blink. Journal of Experimental Psychology. Human Perception and Performance, 24(6), 1656– 1674.

    1. hile being out and public may work for some stuJentsand may help an entire school community prepare for a student's or facultymember's transition, the choice to remain private also neeJs to be respecteJwith transgender students as with sexual minority students.

      It's sad to think that this isn't already acknowledged and prepared for. Establishing options for students who are apart of a sexual or gender minority and who want to keep their identities private shouldn't be something that we have to fight for. Not only would such changes allow for a more welcoming and inclusive school system, but I would think that these changes would also lead to significantly better academic performance and improvements in the quality of life of said students, along with the students and teachers whom they interact with.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Kroll et al. conduct an in-depth behavioral analysis of F0 knockouts of 4 genes associated with late-onset Alzheimer's Disease (AD), together with 3 genes associated with early-onset AD. Kroll and colleagues developed a web application (ZOLTAR) to compare sleep-associated traits between genetic mutants with those obtained from a panel of small molecules to promote the identification of affected pathways and potential therapeutic interventions. The authors make a set of potentially important findings vis-à-vis the relationship between AD-associated genes and sleep. First, they find that loss-of-function in late-onset AD genes universally results in night-time sleep loss, consistent with the well supported hypothesis that sleep disruption contributes to Alzheimer's-related pathologies. psen-1, an early-onset associated AD gene, which the authors find is principally responsible for the generation of AB40 and AB42 in zebrafish, also shows a slight increase in activity at night and slight decreases in night-time sleep. Conversely, psen-2 mutations increase daytime sleep, while appa/appb mutations have no impact on sleep. Finally, using ZOLTAR, the authors identify serotonin receptor activity as potentially disrupted in sorl1 mutants, while betamethasone is identified as a potential therapeutic to promote reversal of psen2 knockout-associated phenotypes.

      This is a highly innovative and thorough study, yet a handful of key questions remain. First, are night-time sleep loss phenotypes observed in all knockouts for late-onset AD genes in the larval zebrafish a valid proxy for AD risk?

      We cannot say, but it is an interesting question. We selected the four late-onset Alzheimer’s risk genes (APOE, CD2AP, CLU, SORL1) based on human genetics data and brain expression in zebrafish larvae, not based on their likelihood to modify sleep behaviour, which we could have tried by searching for overlaps with GWAS of sleep phenotypes, for example. Consequently, we find it remarkable that all four of these genes caused a night-time sleep phenotype when mutated. We also find it reassuring that knockout of appa/appb and psen2 did not cause a night-time sleep phenotype, which largely excludes the possibility that the phenotype is a technical artefact (e.g. caused by the F0 knockout method) or a property of every gene expressed in the larval brain.

      Having said that, it could still be a coincidence, rather than a special property of genes associated with late-onset AD. In addition to testing additional late-onset Alzheimer’s risk genes, the ideal way to answer this question would be to test in parallel a random set of genes expressed in the brain at this stage of development. From this random set, one could estimate the proportion of genes that cause a night-time sleep phenotype when mutated. One could then use that information to test whether late-onset Alzheimer’s risk genes are indeed enriched for genes that cause a night-time sleep phenotype when mutated.

      For those mutants that cause night-time sleep disturbances, do these phenotypes share a common underlying pathway? e.g. Do 5-HT reuptake inhibitors promote sleep across all 4 late-onset genes in addition to psen1? Can 5-HT reuptake inhibitors reverse other AD-related pathologies in zebrafish? Can compounds be identified that have a common behavioral fingerprint across all or multiple AD risk genes? Do these modify sleep phenotypes?

      To attempt to answer these questions, we used ZOLTAR to generate predictions for all the knockout behavioural fingerprints presented in the study, in the same way as for sorl1 in Fig. 5 and Fig. 5–supplement 1. Here are the indications, targets, and KEGG pathways which are shared by the largest number of knockouts (Author response image 1):

      – One indication is shared by 4/7 knockouts: “opioid dependence” (significant for appa/appb, psen1, apoea/apoeb, cd2ap).

      – Four targets are shared by 4/7 knockouts: “strychnine-binding glycine receptor” (psen1, apoea/apoeb, clu, sorl1); “neuronal acetylcholine receptor beta-2” (psen1, apoea/apoeb, cd2ap, clu); thyroid peroxidase (psen1, apoea/apoeb, cd2ap, clu); carbonic anhydrase IV (appa/appb, psen1, psen2, cd2ap).

      – Three KEGG pathways are shared by 5/7 knockouts: “cholinergic synapse” (psen1, apoea/apoeb, cd2ap, clu, sorl1); tyrosine metabolism (psen2, apoea/apoeb, cd2ap, clu, sorl1); and “nitrogen metabolism” (appa/appb, psen1, psen2, apoea/apoeb, cd2ap).

      As reminder, we hypothesised that loss of Sorl1 affected serotonin signalling based on the following annotations being significant: indication “depression”, target “serotonin transporter”, and KEGG pathway “serotonergic synapse”. Indication “depression” is only significant for sorl1 knockouts; target “serotonin transporter” is also significant for appa/appb and psen2 knockouts; and KEGG pathway “serotonergic synapse” is also significant for psen2 knockouts. ZOLTAR therefore does not predict serotonin signalling to be a major theme common to all mutants with a night-time sleep loss phenotype.

      Particularly interesting is cholinergic signalling appearing in the most common targets and KEGG pathways. Acetylcholine signalling is a major theme in research on AD. For example, the first four drugs ever approved by the FDA to treat AD were acetylcholinesterase inhibitors, which increase acetylcholine signalling by preventing its breakdown by acetylcholinesterase. These drugs are generally considered only to treat symptoms and not modify disease course, but this view has been called into question (Munoz-Torrero, 2008; Relkin, 2007). If, as ZOLTAR suggests, mutations in several Alzheimer’s risk genes affect cholinergic signalling early in development, this would point to a potential causal role of cholinergic disruption in AD.

      Author response image 1.

      Common predictions from ZOLTAR for the seven Alzheimer’s risk genes tested. Predictions from ZOLTAR which are shared by multiple knockout behavioural fingerprints presented in the study. Only indications, targets, and KEGG pathways which are significant for at least three of the seven knockouts tested are shown, ranked from the annotations which are significant for the largest number of knockouts.

      Finally, the web- based platform presented could be expanded to facilitate comparison of other behavioral phenotypes, including stimulus-evoked behaviors.

      Yes, absolutely. The behavioural dataset we used (Rihel et al., 2010) did not measure other stimuli than day/night light transitions, but the “SauronX” platform and dataset (MyersTurnbull et al., 2022) seems particularly well suited for this. To provide some context, we and collaborators have occasionally used the dataset by Rihel et al. (2010) to generate hypotheses or find candidate drugs that reverse a behavioural phenotype measured in the sleep/wake assay (Ashlin et al., 2018; Hoffman et al., 2016). The present work was the occasion to enable a wider and more intuitive use of this dataset through the ZOLTAR app, which has already proven successful. Future versions of ZOLTAR may seek to incorporate larger drug datasets using more types of measurements.

      Finally, the authors propose but do not test the hypothesis that sorl1 might regulate localization/surface expression of 5-HT2 receptors. This could provide exciting / more convincing mechanistic support for the assertion that serotonin signaling is disrupted upon loss of AD-associated genes.

      While working on the Author Response, we made some changes to the analysis ran by ZOLTAR to calculate enrichments (see Methods and github.com/francoiskroll/ZOLTAR, notes on v2). With the new version, 5-HT receptor type 2 is not a significantly enriched target for the sorl1 knockout fingerprint but type 4 is. 5-HT receptor type 4 was also shown to interact with sorting nexin 27, a subunit of retromer, so is a promising candidate (Joubert et al., 2004). Antibodies against human 5-HT receptor type 2 and 4a exist; whether they would work in zebrafish remains to be tested. In our experience, the availability of antibodies suitable for immunohistochemistry in the zebrafish is a serious experimental roadblock.

      Note, all the results presented in the “Version of Records” are from ZOLTAR v2.

      Despite these important considerations, this study provides a valuable platform for highthroughput analysis of sleep phenotypes and correlation with small-molecule-induced sleep phenotypes.

      Strengths:

      - Provides a useful platform for comparison of sleep phenotypes across genotypes/drug manipulations.

      - Presents convincing evidence that night-time sleep is disrupted in mutants for multiple late onset AD-related genes.

      - Provides potential mechanistic insights for how AD-related genes might impact sleep and identifies a few drugs that modify their identified phenotypes

      Weaknesses:

      - Exploration of potential mechanisms for serotonin disruption in sorl1 mutants is limited.

      - The pipeline developed can only be used to examine sleep-related / spontaneous movement phenotypes and stimulus-evoked behaviors are not examined.

      - Comparisons between mutants/exploration of commonly affected pathways are limited.

      Thank you for these excellent suggestions, please see our answers above.

      Reviewer #2 (Public Review):

      Summary:

      This work delineates the larval zebrafish behavioral phenotypes caused by the F0 knockout of several important genes that increase the risk for Alzheimer's disease. Using behavioral pharmacology, comparing the behavioral fingerprint of previously assayed molecules to the newly generated knockout data, compounds were discovered that impacted larval movement in ways that suggest interaction with or recovery of disrupted mechanisms.

      Strengths:

      This is a well-written manuscript that uses newly developed analysis methods to present the findings in a clear, high-quality way. The addition of an extensive behavioral analysis pipeline is of value to the field of zebrafish neuroscience and will be particularly helpful for researchers who prefer the R programming language. Even the behavioral profiling of these AD risk genes, regardless of the pharmacology aspect, is an important contribution. The recovery of most behavioral parameters in the psen2 knockout with betamethasone, predicted by comparing fingerprints, is an exciting demonstration of the approach. The hypotheses generated by this work are important stepping stones to future studies uncovering the molecular basis of the proposed gene-drug interactions and discovering novel therapeutics to treat AD or co-occurring conditions such as sleep disturbance.

      Weaknesses:

      - The overarching concept of the work is that comparing behavioral fingerprints can align genes and molecules with similarly disrupted molecular pathways. While the recovery of the psen2 phenotypes by one molecule with the opposite phenotype is interesting, as are previous studies that show similar behaviorally-based recoveries, the underlying assumption that normalizing the larval movement normalizes the mechanism still lacks substantial support. There are many ways that a reduction in movement bouts could be returned to baseline that are unrelated to the root cause of the genetically driven phenotype. An ideal experiment would be to thoroughly characterize a mutant, such as by identifying a missing population of neurons, and use this approach to find a small molecule that rescues both behavior and the cellular phenotype. If the connection to serotonin in the sorl1 was more complete, for example, the overarching idea would be more compelling.

      Thank you for this cogent criticism.

      On the first point, we were careful not to claim that betamethasone normalises the molecular/cellular mechanism that causes the psen2 behavioural phenotype. Having said that, yes, to a certain extent that would be the hope of the approach. As you say, every compound which normalises the behavioural fingerprint will not normalise the underlying mechanism, but the opposite seems true: every compound that normalises the underlying mechanism should also normalise the behavioural fingerprint. We think this logic makes the “behaviour-first” approach innovative and interesting. The logic is to discover compounds that normalise the behavioural phenotype first, only subsequently test whether they also normalise the molecular mechanism, akin to testing first whether a drug resolves the symptoms before testing whether it actually modifies disease course. While in practice testing thousands of drugs in sufficient sample sizes and replicates on a mutant line is challenging, the dataset queried through ZOLTAR provides a potential shortcut by shortlisting in silico compounds that have the opposite effect on behaviour.

      You mention a “reduction in movement bouts” but note here that the number of behavioural parameters tested is key to our argument. To take the two extremes, say the only behavioural parameter we measured in psen2 knockout larvae was time active during the day, then, yes, any stimulant used at the right concentration could probably normalise the phenotype. In this situation, claiming that the stimulant is likely to also normalise the underlying mechanism, or even that it is a genuine “phenotypic rescue”, would not be convincing. Conversely, say we were measuring thousands of behavioural parameters under various stimuli, such as swimming speed, position in the well, bout usage, tail movements, and eye angles, it seems almost impossible for a compound to rescue most parameters without also normalising the underlying mechanism. The present approach is somewhere inbetween: ZOLTAR uses six behavioural parameters for prediction (e.g. Fig 6a), but all 17 parameters calculated by FramebyFrame can be used to assess rescue during a subsequent experiment (Fig. 6c). For both, splitting each parameter in day and night increases the resolution of the approach, which partly answers your criticism. For example, betamethasone rescued the day-time hypoactivity without causing night-time hyperactivity, so we are not making the “straw man argument” explained above of using any broad stimulant to rescue the hypoactivity phenotype.

      Furthermore, for diseases where the behavioural defect is the primary concern, such as autism or bipolar disorder, perhaps this behaviour-first approach is all that is needed, and whether or not the compound precisely rescues the underlying mechanism is somewhat secondary. The use of lithium to prevent manic episodes in bipolar disorder is a good example. It was initially tested because mania was thought to be caused by excess uric acid and lithium can dissolve uric acid (Mitchell and Hadzi-Pavlovic, 2000). The theory is now discredited, but lithium continues to be used without a precise understanding of its mode of action. In this example, behavioural rescue alone, assuming the secondary effects are tolerable, is sufficient to be beneficial to patients, and whether it modulates the correct causal pathway is secondary.

      On the second point, we agree that testing first ZOLTAR on a mutant for which we have a fairly good understanding of the mechanism causing the behavioural phenotype could have been a productive approach. Note, however, that examples already exist in the literature (Ashlin et al., 2018; Hoffman et al., 2016). The example from Hoffman et al. (2016) is especially convincing. Drugs generating behavioural fingerprints that positively correlate with the cntnap2a/cntnap2b double knockout fingerprint were enriched with NMDA and GABA receptor antagonists. In experiments analogous to our citalopram and fluvoxamine treatments (Fig. 5c,d and Fig. 5–supplement 1c,d), cntnap2a/cntnap2b knockout larvae were overly sensitive to the NMDA receptor antagonist MK-801 and the GABAA receptor antagonist pentylenetetrazol (PTZ). Among other drugs tested, zolpidem, a GABAA receptor agonist, caused opposite effects on wild-type and cntnap2a/cntnap2b knockout larvae. Knockout larvae were found to have fewer GABAergic neurons in the forebrain. While these studies did not use precisely the same analysis that ZOLTAR runs, they used the same rationale and behavioural dataset to make these predictions (Rihel et al., 2010), which shows that approaches like ZOLTAR can point to causal processes.

      On your last point, we hope our experiment testing fluvoxamine, another selective serotonin reuptake inhibitor (SSRI), makes the connection between Sorl1 and serotonin signalling more convincing.

      - The behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram is based on a small number of animals. The KO Euclidean distance measure is also more spread out than for the other datasets, and it looks like only five or so fish are driving the group difference. It also appears as though the numbers were also from two injection series. While there is nothing obviously wrong with the data, I would feel more comfortable if such a strong statement of a result from a relatively subtle phenotype were backed up by a higher N or a stable line. It is not impossible that the observed difference is an experimental fluke. If something obvious had emerged through the HCR, that would have also supported the conclusions. As it stands, if no more experiments are done to bolster the claim, the confidence in the strength of the link to serotonin should be reduced (possibly putting the entire section in the supplement and modifying the discussion). The discussion section about serotonin and AD is interesting, but I think that it is excessive without additional evidence.

      We mostly agree with this criticism. One could interpret the larger spread of the data for sorl1 KO larvae treated with 10 µM citalopram as evidence that the knockout larvae do indeed react differently to the drug at this dose, regardless of being driven by a subset of the animals. The result indeed does not survive removing the top 5 (p = 0.87) or top 3 (p = 0.18) sorl1 KO + 10 µM larvae, but this amounts to excluding 20 (3/14) or 35 (5/14) % of the datapoints as potential outliers, which is unreasonable. In fact, excluding the top 5 sorl1 KO + 10 µM is equivalent to calling any datapoint with z-score > 0.2 an outlier (z-scores of the top 5 datapoints are 0.2–1.8). Applying consistently the same criterion to the scrambled + 10 µM group would remove the top 6 datapoints (z-scores = 0.5–3.9). Comparing the resulting two distributions again gives the sorl1 KO + 10 µM distribution as significantly higher (p = 0.0015). We would also mention that Euclidean distance, as a summary metric for distance between behavioural fingerprints, has limitations. For example, the measure will be more sensitive to changes in some parameters but not others, depending on how much room there is for a given parameter to change. We included this metric to lend support to the observation one can draw from the fingerprint plot (Fig. 5c) that sorl1 mutants respond in an exaggerated way to citalopram across many parameters, while being agnostic to which parameter might matter most.

      Given that the HCR did not reveal anything striking, we agree with you that too much of our argument relied on this result being robust. As you and Reviewer #3 suggested, we repeated this experiment with a different SSRI, fluvoxamine (Fig. 5–supplement 1). We cannot readily explain why the result was opposite to what we found with citalopram, but in both cases sorl1 knockout larvae reacted differently than their control siblings, which adds an argument to our claim that ZOLTAR correctly predicted serotonin signalling as a disrupted pathway from the behavioural fingerprint. Accordingly, we mostly kept the Discussion on Sorl1 the same, although we concede that we may not have identified the molecular mechanism.

      - The authors suggest two hypotheses for the behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram. While the first is tested, and found to not be supported, the second is not tested at all ("Ruling out the first hypothesis, sorl1 knockouts may react excessively to a given spike in serotonin." and "Second, sorl1 knockouts may be overly sensitive to serotonin itself because post-synaptic neurons have higher levels of serotonin receptors."). Assuming that the finding is robust, there are probably other reasons why the mutants could have a different sensitivity to this molecule. However, if this particular one is going to be mentioned, it is surprising that it was not tested alongside the first hypothesis. This work could proceed without a complete explanation, but additional discussion of the possibilities would be helpful or why the second hypothesis was not tested.

      There are no strong scientific reasons why this hypothesis was not tested. The lead author (F Kroll) moved to a different lab and country so the project was finalised at that time. We do not plan on testing this hypothesis at this stage. However, we adapted the wording to make it clear this is one possible alternative hypothesis which could be tested in the future. The small differences found by HCR are actually more in line with the new results from the fluvoxamine experiment, so it may also be that both hypotheses (pre-synaptic neurons releasing less serotonin when reuptake is blocked; or post-synaptic neurons being less sensitive) contribute. The fluvoxamine experiment was performed in a different lab (ICM, Paris; all other experiments were done in UCL, London) in a different wild-type strain (TL in ICM, AB x Tup LF in UCL), which complicates how one interprets this discrepancy.

      - The authors claim that "all four genes produced a fairly consistent phenotype at night". While it is interesting that this result arose in the different lines, the second clutch for some genes did not replicate as well as others. I think the findings are compelling, regardless, but the sometimes missing replicability should be discussed. I wonder if the F0 strategy adds noise to the results and if clean null lines would yield stronger phenotypes. Please discuss this possibility, or others, in regard to the variability in some phenotypes.

      For the first part of this point, please see below our answer to Reviewer #3, point (2) c.

      Regarding the F0 strategy potentially adding variability, it is an interesting question which we tested in a larger dataset of behavioural recordings from F0 and stable knockouts for the same genes (unpublished). In summary, the F0 knockout method does not increase clutchto-clutch or larva-to-larva variability in the assay. F0 knockout experiments found many more significant parameters and larger effect sizes than stable knockout experiments, but this difference could largely be explained by the larger sample sizes of F0 knockout experiments. In fact, larger sample sizes within individual clutches appears to be a major advantage of the F0 knockout approach over in-cross of heterozygous knockout animals as it increases sensitivity of the assay without causing substantial variability. We plan to report in more detail on this analysis in a separate paper as we think it would dilute the focus of the present work.

      - In this work, the knockout of appa/appb is included. While APP is a well-known risk gene, there is no clear justification for making a knockout model. It is well known that the upregulation of app is the driver of Alzheimer's, not downregulation. The authors even indicate an expectation that it could be similar to the other knockouts ("Moreover, the behavioural phenotypes of appa/appb and psen1 knockout larvae had little overlap while they presumably both resulted in the loss of Aβ." and "Comparing with early-onset genes, psen1 knockouts had similar night-time phenotypes, but loss of psen2 or appa/appb had no effect on night-time sleep."). There is no reason to expect similarity between appa/appb and psen1/2. I understand that the app knockouts could unveil interesting early neurodevelopmental roles, but the manuscript needs to be clarified that any findings could be the opposite of expectation in AD.

      On “there is no reason to expect similarity […]”, we disagree. Knockout of appa/appb and knockout of psen1 will both result in loss of Aβ (appa/appb encode Aβ and psen1 cleaves Appa/Appb to release Aβ, cf. Fig. 3e). Consequently, a phenotype caused by the loss of Aβ, or possibly other Appa/Appb cleavage products, should logically be found in both appa/appb and psen1 knockouts.

      On “it is well known that the upregulation of APP is the driver of Alzheimer’s, not downregulation”; we of course agree. Among others, the examples of Down syndrome, APP duplication (Sleegers et al., 2006), or mouse models overexpressing human APP show definitely that overexpression of APP is sufficient to cause AD. Having said that, we would not be so quick in dismissing APP knockout as potentially relevant to understanding of AD.

      Loss of soluble Aβ due to aggregation could contribute to pathology (Espay et al., 2023). Without getting too much into this intricate debate, links between levels of Aβ and risk of disease are often counter-intuitive too. For example, out of 138 PSEN1 mutations screened in vitro, 104 reduced total Aβ production and 11 even seemingly abolished the production of both Aβ40 and Aβ42 (Sun et al., 2017). In short, loss of soluble Aβ occurs in both AD and in our appa/appb knockout larvae.

      We added a sentence in Results (section psen2 knockouts […]) to briefly justify our appa/appb knockout approach. To be clear, we do not want to imply, for example, that the absence of a night-time sleep phenotype for appa/appb is contradictory to the body of literature showing links between Aβ and sleep, including in zebrafish (Özcan et al., 2020). As you say, our experiment tested loss of App, including Aβ, while the literature typically reports on overexpression of APP, as in APP/PSEN1-overexpressing mice (Jagirdar et al., 2021).

      Reviewer #3 (Public Review):

      In this manuscript by Kroll and colleagues, the authors describe combining behavioral pharmacology with sleep profiling to predict disease and potential treatment pathways at play in AD. AD is used here as a case study, but the approaches detailed can be used for other genetic screens related to normal or pathological states for which sleep/arousal is relevant. The data are for the most part convincing, although generally the phenotypes are relatively small and there are no major new mechanistic insights. Nonetheless, the approaches are certainly of broad interest and the data are comprehensive and detailed. A notable weakness is the introduction, which overly generalizes numerous concepts and fails to provide the necessary background to set the stage for the data.

      Major points

      (1) The authors should spend more time explaining what they see as the meaning of the large number of behavioral parameters assayed and specifically what they tell readers about the biology of the animal. Many are hard to understand--e.g. a "slope" parameter.

      We agree that some parameters do not tell something intuitive about the biology of the animal. It would be easy to speculate. For example, the “activity slope” parameter may indicate how quickly the animal becomes tired over the course of the day. On the other hand, fractal dimension describes the “roughness/smoothness” of the larva’s activity trace (Fig. 2–supplement 1a); but it is not obvious how to translate this into information about the physiology of the animal. We do not see this as an issue though. While some parameters do provide intuitive information about the animal’s behaviour (e.g. sleep duration or sunset startle as a measure of startle response), the benefit of having a large number of behavioural parameters is to compare behavioural fingerprints and assess rescue of the behavioural phenotype by small molecules (Fig. 6c). For this purpose, the more parameters the better. The “MoSeq” approach from Wiltschko et al., 2020 is a good example from literature that inspired our own Fig. 6c. While some of the “behavioural syllables” may be intuitive (e.g. running or grooming), it is probably pointless to try to explain the ‘meaning’ of the “small left turn in place with head motion” syllable (Wiltschko et al., 2020). Nonetheless, this syllable was useful to assess whether a drug specifically treats the behavioural phenotype under study without causing too many side effects. Unfortunately, ZOLTAR has to reduce the FramebyFrame fingerprint (17 parameters) to just six parameters to compare it to the behavioural dataset from Rihel et al., 2010, but here, more parameters would almost certainly translate into better predictions too, regardless of their intuitiveness.

      It is true however that we did not give much information on how some of the less intuitive parameters, such as activity slope or fractal dimension, are calculated or what they describe about the dataset (e.g. roughness/smoothness for fractal dimension). We added a few sentences in the legend of Fig. 2–supplement 1.

      (2) Because in the end the authors did not screen that many lines, it would increase confidence in the phenotypes to provide more validation of KO specificity. Some suggestions include:

      a. The authors cite a psen1 and psen2 germline mutant lines. Can these be tested in the FramebyFrame R analysis? Do they phenocopy F0 KO larvae?

      We unfortunately do not have those lines. We investigated the availability of importing a psen2 knockout line from abroad, but the process of shipping live animals is becoming more and more cost and time prohibitive. However, we observed the same pigmentation phenotype for psen2 knockouts as reported by Jiang et al., 2018, which is at least a partial confirmation of phenocopying a loss of function stable mutant.  

      b. psen2_KO is one of the larger centerpieces of the paper. The authors should present more compelling evidence that animals are truly functionally null. Without this, how do we interpret their phenotypes?

      We disagree that there should be significant doubt about these mutants being truly functionally null, given the high mutation rate and presence of the expected pigmentation phenotype (Jiang et al., 2018, Fig. 3f and Fig. 3–supplement 3a). The psen2 F0 knockouts were virtually 100% mutated at three exons across the gene (mutation rates were locus 1: 100 ± 0%; locus 2: 99.99 ± 0.06%; locus 3: 99.85 ± 0.24%). Additionally, two of the three mutated exons had particularly high rates of frameshift mutations (locus 1: 97 ± 5%; locus 2: 88 ± 17% frameshift mutation rate). It is virtually impossible that a functional protein is translated given this burden of frameshift mutations. Phenotypically, in addition to the pigmentation defect, double psen1/psen2 F0 knockout larvae had curved tails, the same phenotype as caused by a high dose of the γ-secretase inhibitor DAPT (Yang et al., 2008). These double F0 knockouts were lethal, while knockout of psen1 or psen2 alone did not cause obvious morphological defects. Evidently, most larvae must have been psen2 null mutants in this experiment, otherwise functional Psen2 would have prevented early lethality.

      Translation of zebrafish psen2 can start at downstream start codons if the first exon has a frameshift mutation, generating a seemingly functional Psen2 missing the N-terminus (Jiang et al., 2020). Zebrafish homozygous for this early frameshift mutation had normal pigmentation, showing it is a reliable marker of Psen2 function even when it is mutated. This mechanism is not a concern here as the alternative start codons are still upstream of two of the three mutated exons (the alternative start codons discovered by Jiang et al., 2020 are in exon 2 and 3, but we targeted exon 3, exon 4, and exon 6).

      We understand that the zebrafish community may be cautious about F0 phenotyping compared to stably generated mutants. As mentioned to Reviewer #2, we are planning to assemble a paper that expressly compares behavioural phenotypes measured in F0 vs. stable mutants to allay some of these concerns. Our current manuscript, which combines CRISPR-Cas9 rapid F0 screening with in silico pharmacological predictions, inevitability represents a first step in characterizing the functions of these genes. 

      c. Related to the above, for cd2AP and sorl1 KO, some of the effect sizes seem to be driven by one clutch and not the other. In other words, great clutch-to-clutch variability. Should the authors increase the number of clutches assayed?

      Correct, there is substantial clutch-to-clutch variability in this behavioural assay. This is not specific to our experiments. Even within the same strain, wild-type larvae from different clutches (i.e. non-siblings) behave differently (Joo et al., 2021). This is why it is essential to compare behavioural phenotypes within individual clutches (i.e. from a single pair of parents, one male and one female), as we explain in Methods (section Behavioural video-tracking) and in the documentation of the FramebyFrame package. We often see two different experimental designs in literature: comparing non-sibling wild-type and mutant larvae, or pooling different clutches which include all genotypes (e.g. pooling multiple clutches from heterozygous in-crosses or pooling wild-type clutches before injecting them). The first experimental design causes false positive findings (Joo et al., 2021), as the clutchto-clutch variability we and others observe gets interpreted as a behavioural phenotype. The second experimental design should not cause false positives but likely decreases the sensitivity of the assay by increasing the spread within genotypes. In both cases, the clutch-to-clutch variability is hidden, either by interpreting it as a phenotype (first case) or by adding it to animal-to-animal variability (second case). Our experimental design is technically more challenging as it requires obtaining large clutches from unique pairs of parents. However, this approach is better as it clearly separates the different sources of variability (clutch-to-clutch or animal-to-animal). As for every experiment, yes, a larger number of replicates would be better, but we do not plan to assay additional clutches at this time. Our work heavily focuses on the sorl1 and psen2 knockout behavioural phenotypes. The key aspects of these phenotypes were effectively tested in four experiments (five to six clutches) as sorl1 knockout larvae were also tracked in the citalopram and fluvoxamine experiments (Fig. 5 and Fig. 5–supplement 1), and psen2 knockout larvae were also tracked in the small molecule rescue experiment (Fig. 6 and Fig. 6–supplement 1).

      The psen2 behavioural phenotype replicated well across the six clutches tested (pairwise cosine similarities: 0.62 ± 0.15; Author response image 2a). 5/6 clutches were less active and initiating more sleep bouts during the day, as we claimed in Fig. 3.

      In the citalopram experiment, the H<sub>2</sub>O-treated sorl1 knockout fingerprint replicated fairly well the baseline recordings in Fig. 4, despite the smaller sample size (cos = 0.30 and 0.78; Author response image 2b, see “KO Fig. 5”). 5/6 of the significant parameters presented in Fig. 4–supplement 4 moved in the same direction, and knockout larvae were also hypoactive during the day but hyperactive at night. Note that two clutches were tracked on the same 96-well plate in this experiment. We calculated each larva’s z-score using the average of its control siblings, then we averaged all the z-scores to generate the fingerprint. The H<sub>2</sub>O treated sorl1 knockout clutch from the fluvoxamine experiment did not replicate well the baseline recordings (cos = 0.08 and 0.11; Author response image 2b, see “KO Fig. 5–suppl. 1”). Knockout larvae were hypoactive during the day as expected, but behaviour at night was not as robustly affected. As mentioned above, knockouts were made in a different genetic background (TL, instead of AB x Tup LF used for all other experiments), which could explain the discrepancy.

      We also took the opportunity to check whether our SSRI treatments replicated well the data from Rihel et al., 2010. For both citalopram (n = 3 fingerprints in the database) and fluvoxamine (n = 4 fingerprints in the database), replication was excellent (cos ≥ 0.67 for all comparisons of a fingerprint from this study vs. a fingerprint from Rihel et al. 2010; Author response image 2c,d). Note that the scrambled + 10 µM citalopram and + 10 µM fluvoxamine fingerprints correlate extremely well (cos = 0.92; can be seen in Author response image 2c,d), which was predicted by the small molecule screen dataset.

      Author response image 2.

      Replication of psen2 and sorl1 F0 knockout fingerprints and SSRI treatments from Rihel et al., 2010. a, (left) Every psen2 F0 knockout behavioural fingerprint generated in this study. Each dot represents the mean deviation from the same-clutch scrambled-injected mean for that parameter (z-score, mean ± SEM). From the experiments in Fig. 6, presented is the psen2 F0 knockout + H<sub>2</sub>O fingerprints. The fingerprints in grey (“not shown”) are from a preliminary drug treatment experiment we did not include in the final study. These fingerprints are from psen2 F0 knockout larvae treated with 0.2% DMSO, normalised to scrambled-injected siblings also treated with 0.2% DMSO. (right) Pairwise cosine similarities (−1.0–1.0) for the fingerprints presented. b, Every sorl1 F0 knockout behavioural fingerprint, as in a). c, The scrambled-injected + citalopram (10 µM) fingerprints (grey) in comparison to the citalopram (10–15 µM) fingerprints from the Rihel et al., 2010 database (green). d, The scrambled-injected + fluvoxamine (10 µM) fingerprint (grey) in comparison to the fluvoxamine fingerprints from the Rihel et al., 2010 database (pink). In c) and d), the scrambled-injected fingerprints are from the experiments in Fig. 5 and Fig. 5–suppl. 1, but were converted here into the behavioural parameters used by Rihel et al., 2010 for comparison. Parameters: 1, average activity (sec active/min); 2, average waking activity (sec active/min, excluding inactive minutes); 3, total sleep (hr); 4, number of sleep bouts; 5, sleep bout length (min); 6, sleep latency (min until first sleep bout).

      (3) The authors make the point that most of the AD risk genes are expressed in fish during development. Is there public data to comment on whether the genes of interest are expressed in mature/old fish as well? Just because the genes are expressed early does not at all mean that early- life dysfunction is related to future AD (though this could be the case, of course). Genes with exclusive developmental expression would be strong candidates for such an early-life role, however. I presume the case is made because sleep studies are mainly done in juvenile fish, but I think it is really a prejy minor point and such a strong claim does not even need to be made.

      This is a fair criticism but we do not make this claim (“early-life dysfunction is related to future AD”) from expression alone. The reviewer is probably referring to the following quote:

      “[…] most of these were expressed in the brain of 5–6-dpf zebrafish larvae, suggesting they play a role in early brain development or function,” which does not mention future risk of AD. We do suggest that these genes have a function in development. After all, every gene that plays a role in brain development must be expressed during development, so this wording seemed reasonable. Nevertheless, we adapted the wording to address this point and Reviewer #2’s complaint below. As noted, the primary goal was to check that the genes we selected were indeed expressed in zebrafish larvae before performing knockout experiments. Our discussion does raise the hypothesis that mutations in Alzheimer’s risk genes impact brain development and sleep early in life, but this argument primarily relies on our observation that knockout of late-onset Alzheimer’s risk genes causes sleep phenotypes in 7-day old zebrafish larvae and from previous work showing brain structural differences in children at high genetic risk of AD (Dean et al., 2014; Quiroz et al., 2015), not solely on gene expression early in life.

      Please also see our answer to a similar point raised by Reviewer #2 below (cf. Author response image 7).

      (4) A common quandary with defining sleep behaviorally is how to rectify sleep and activity changes that influence one another. With psen2 KOs, the authors describe reduced activity and increased sleep during the day. But how do we know if the reduced activity drives increased behavioral quiescence that is incorrectly defined as sleep? In instances where sleep is increased but activity during periods during wake are normal or elevated, this is not an issue. But here, the animals might very well be unhealthy, and less active, so naturally they stop moving more for prolonged periods, but the main conclusion is not sleep per se. This is an area where more experiments should be added if the authors do not wish to change/temper the conclusions they draw. Are psen2 KOs responsive to startling stimuli like controls when awake? Do they respond normally when quiescent? Great care must be taken in all models using inactivity as a proxy for sleep, and it can harm the field when there is no acknowledgment that overall health/activity changes could be a confound. Particularly worrisome is the betamethasone data in Figure 6, where activity and sleep are once again coordinately modified by the drug.

      This is a fair criticism. We agree it is a concern, especially in the case of psen2 as we claim that day-time sleep is increased while zebrafish are diurnal. We do not rely heavily on the day-time inactivity being sleep (the ZOLTAR predictions or the small molecule rescue do not change whether the parameter is called sleep or inactivity), but our choice of labelling can fairly be challenged.

      To address “are psen2 KO responsive to startling stimuli like controls when awake/when quiescent”, we looked at the larvae’s behaviour immediately after lights abruptly switched on in the mornings. Almost every larva, regardless of genotype, responded strongly to every lights-off transition during the experiment. Instead, we chose the lights-on transition for this analysis because it is a weaker startling stimulus for the larvae than the lights-off transition (Fig. 3–supplement 3), potentially exposing differences between genotypes or behavioural states (quiescent or awake). We defined a larva as having reacted to the lights switching on if it made a swimming bout during the second (25 frames) a er the lights-on transition. Across two clutches and two lights-on transitions, an average of 65% (range 52–73%) of all larvae reacted to the stimulus. psen2 knockout larvae were similarly likely, if not more likely, to respond (in average 69% responded, range 60–76%) than controls (60% average, range 44– 75%). When the lights switched on, about half of the larvae (39–51%) would have been classified as asleep according to the one-minute inactivity definition (i.e. the larva did not move in the minute preceding the lights transition). This allowed us to also compare behavioural states, as suggested by the reviewer. For three of the four light transitions, larvae which were awake when lights switched on were more likely to react than asleep larvae, but this difference was not striking (overall, awake larvae were only 1.1× more likely to react; Author response image 3). Awake psen2 knockout larvae were 1.1× (range 1.04–1.11×) more likely to react than awake control larvae, so, yes, psen2 knockout larvae respond normally when awake. Asleep psen2 knockout larvae were 1.4× (range 0.63–2.19×) more likely to react than asleep control larvae, so psen2 knockouts are also more or equally likely to react than control larvae when asleep. In summary, the overall health of psen2 knockouts did not seem to be a significant confound in the experiment. As the reviewer suggested, if psen2 knockout larvae were seriously unhealthy, they would not be as responsive as control larvae to a startling stimulus.

      Author response image 3.

      psen2 F0 knockouts react normally to lights switching on, indicating they are largely healthy. At each lights-on transition (9 AM), each larva was categorised as awake if it had moved in the preceding one minute or asleep if it had been inactive for at least one minute. Darker tiles represent larvae which performed a swimming bout during the second following lights-on; lighter tiles represent larvae which did not move during that second. The total count of each waffle plot was normalised to 25 so plots can be compared to each other. The real count is indicated in the corner of each plot. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

      Next, we compared inactive period durations during the day between psen2 and control larvae. If psen2 knockout larvae indeed sleep more during the day compared to controls, we may predict inactive periods longer than one minute to increase disproportionately compared to the increase in shorter inactive periods. This broadly appeared to be the case, especially for one of the two clutches (Author response image 4). In clutch 1, inactive periods lasting 1–60 sec were equally frequent in both psen2 and control larvae (fold change 1.0× during both days), while inactive periods lasting 1–2 min were 1.5× (day 1) and 2.5× (day 2) more frequent in psen2 larvae compared to control larvae. In clutch 2, 1–60 sec inactive periods were also equally frequent in both psen2 and control larvae, while inactive periods lasting 1–2 min were 3.4× (day 1) and 1.5× (day 2) more frequent in psen2 larvae compared to control larvae. Therefore, psen2 knockouts disproportionately increased the frequency of inactive periods longer than one minute, suggesting they genuinely slept more during the day.

      Author response image 4.

      psen2 F0 knockouts increased preferentially the frequency of longer inactive bouts. For each day and clutch, we calculated the mean distribution of inactive bout lengths across larvae of same genotype (psen2 F0 knockout or scrambled-injected), then compared the frequency of inactive bouts of different lengths between the two genotypes. For example, in clutch 1 during day 2, 0.01% of the average scrambled-injected larva’s inactive bouts lasted 111–120 seconds (X axis 120 sec) while 0.05% of the average psen2 F0 knockout larva lasted this long, so the fold change was 5×. Inactive bouts lasting < 1 sec were excluded from the analysis. In clutch 2, day 1 plot, two datapoints fall outside the Y axis limit: 140 sec, Y = 32×; 170 sec, Y = 16×. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

      Ultimately, this criticism seems challenging to definitely address experimentally. A possible approach could be to use a closed-loop system which, after one minute of inactivity, triggers a stimulus that is sufficient to startle an awake larva but not an asleep larva. If psen2 knockout larvae indeed sleep more during the day, the stimulus should usually not be sufficient to startle them. Nevertheless, we believe the two analyses presented here are consistent with psen2 knockout larvae genuinely sleeping more during the day, so we decided to keep this label. We agree with the reviewer that the one-minute inactivity definition has limitations, especially for day-time inactivity.

      (5) The conclusions for the serotonin section are overstated. Behavioural pharmacology purports to predict a signaling pathway disrupted with sorl1 KO. But is it not just possible that the drug acts in parallel to the true disrupted pathway in these fish? There is no direct evidence for serotonin dysfunction - that conclusion is based on response to the drug. Moreover, it is just one drug - is the same phenotype present with another SSRI? Likewise, language should be toned down in the discussion, as this hypothesis is not "confirmed" by the results (consider "supported"). The lack of measured serotonin differences further raises concern that this is not the true pathway. This is another major point that deserves further experimental evidence, because without it, the entire approach (behavioral pharm screen) seems more shaky as a way to identify mechanisms. There are any number of testable hypotheses to pursue such as a) Using transient transgenesis to visualize 5HT neuron morphology (is development perturbed: cell number, neurite morphology, synapse formation); b) Using transgenic Ca reporters to assay 5HT neuron activity.

      Regarding the comment, “is it not just possible that the drug acts in parallel to the true disrupted pathway”, we think no, assuming we understand correctly the question. Key to our argument is the fact that sorl1 knockout larvae react differently to the drug(s) than control larvae. As an example, take night-time sleep bout length, which was not affected by knockout of sorl1 (Fig. 4–supplement 4). For the sake of the argument, say only dopamine signalling (the “true disrupted pathway”) was affected in sorl1 knockouts and that serotonin signalling was intact. Assuming that citalopram specifically alters serotonin signalling, then treatment should cause the same increase in sleep bout length in both knockouts and controls as serotonin signalling is intact in both. This is not what we see, however. Citalopram caused a greater increase in sleep bout length in sorl1 knockouts than in scrambled-injected larvae. In other words, the effect is non-additive, in the sense that citalopram did not add the same number of z-scores to sorl1 knockouts or controls. We think this shows that serotonin signalling is somehow different in sorl1 knockouts. Nonetheless, we concede that the experiment does not necessarily say much about the importance of the serotonin disruption caused by loss of Sorl1. It could be, for example, that the most salient consequence of loss of Sorl1 is cholinergic disruption (see reply to Reviewer #1 above) and that serotonin signalling is a minor theme.

      Furthermore, we agree with the reviewer and Reviewer #2 that the conclusions were overly confident. As suggested, we decided to repeat this experiment with another SSRI, fluvoxamine. Please find the results of this experiment in Fig. 5–supplement 1. The suggestions to further test the serotonin system in the sorl1 knockouts are excellent as well, however we do not plan to pursue them at this stage.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Comments:

      - Data are presented in a variety of different ways, occasionally making comparisons across figures difficult. Perhaps at a minimum, behavioral fingerprints as in Figure 3 - Supplementary Figure 1 should be presented for all mutants in the main figures.

      We like this suggestion! Thank you. We brought the behavioural fingerprints figure (previously Fig. 4–supplement 5) as main Fig. 4, and put the figure focused on the sorl1 knockout behavioural phenotype in supplementary, with the other gene-by-gene figures.

      - It is not clear why some data were selected for supplemental rather than main figures. In many cases, detailed phenotypic data is provided for one example mutant in the main figures, and then additional mutants are described in detail in the supplement. Again, to facilitate comparisons between mutants, fingerprints could be provided for all mutants in a main figure, with detailed analyses moved to the supplements.

      The logic was to dedicate one main figure to psen2 (Fig. 3) as an example of an early-onset Alzheimer’s risk gene, and one to sorl1 (previously Fig. 4) as an example of a late-onset Alzheimer’s risk gene. We focused on them in main figures as they are both tested again later (Fig. 5 and Fig. 6). Having said that, we agree that the fingerprints may be a better use of main figure space than the parameters plots. In addition to the above (fingerprints of lateonset Alzheimer’s risk genes in main figure), we rearranged the figures in the early-onset AD section to have the psen2 F0 knockout fingerprint in main.

      - The explication of the utility of behavioral fingerprinting on page 35 is somewhat confusing. The authors describe drugs used to treat depression as enriched among small molecules anti-correlating with the sorl1 fingerprint. However, in Figure 5 - Supplementary Figure 1, drugs used to treat depression are biased toward positive cosines, which are indicated as having a more similar fingerprint to sorl1. These drugs should be described as more present among compounds positively correlating with the sorl1 fingerprint.

      Sorry, the confusion is about “(anti-)correlating”. Precisely, we meant “correlating and/or anti-correlating”, not just anti-correlating. We changed to that wording. In short, the analysis is by design agnostic to whether compounds with a given annotation are found more on the positive cosines side (le side in Fig. 5–supplement 1a) or the negative cosines side (right side). This is because the dataset often includes both agonists and antagonists to a given pathway but these are difficult to annotate. For example, say 10 compounds in the dataset target the dopamine D4 receptor, but these are an unknown mix of agonists and antagonists. In this case, we want ZOLTAR to generate a low p-value when all 10 compounds are found at extreme ends of the list, regardless of which end(s) that is (e.g. top 8 and bottom 2 should give an extremely low p-value). Initially, we were splitting the list, for each annotation, into positive-cosine fingerprints and negative-cosine fingerprints and testing enrichment on both separately, but we think the current approach is better as it reflects better the cases we want to detect and considers all available examples for a given annotation in one test. In sum, yes, in this case drugs used to treat depression were mostly in the positive-cosine side, but the other drugs on the negative-cosine side also contributed to what the p-value is, so it reflects better the analysis to say “correlating and/or anticorrelating”. You can read more about our logic for the analysis in Methods (section Behavioural pharmacology from sorl1 F0 knockout’s fingerprint).

      - The authors conclude the above-described section by stating: "sorl1 knockout larvae behaved similarly to larvae treated with small molecules targeting serotonin signaling, suggesting that the loss of Sorl1 disrupted serotonin signaling." Directionality here may be important. Are all of the drugs targeting the serotonin transporter SSRIs or similar? If so, then a correct statement would be that loss of Sorl1 causes similar phenotypes to drugs enhancing serotonin signaling. Finally, based on the correlation between serotonin transporter inhibitor trazodone and the sorl1 crispant phenotype, it is potentially surprising that the SSRI citalopram caused the opposite phenotype from sorl1, that is, increased sleep during the day and night. It is potentially interesting that this result was enhanced in mutants, and suggests dysfunction of serotonin signaling, but the statement that "our behavioral pharmacology approach correctly predicted from behaviour alone that serotonin signaling was disrupted" is too strong a conclusion.

      We understand “disrupt” as potentially going either way, but this may not be the common usage. We changed to “altered”.

      The point regarding directionality is excellent, however. We tested the proportion of serotonin transporter agonists and antagonists (SSRIs) on each side of the ranked list of small molecule fingerprints. We used the STITCH database for this analysis as it has more drug–target interactions, but likely less curated, than the Therapeutic Target Database (Szklarczyk et al., 2016). As with the Therapeutic Target Database, most fingerprints of compounds interacting with the serotonin transporter SLC6A4 were found on the side of positive cosines (p ~ 0.005 using the custom permutation test), which replicates Fig. 5a with a different source for the drug–target annotations (Author response image 5). On the side of positive cosines (small molecules which generate behavioural fingerprints correlating with the sorl1 fingerprint), there were 2 agonists and 26 antagonists. On the side of negative cosines (small molecules which generate behavioural fingerprints anti-correlating with the sorl1 fingerprint), there were 3 agonists and 2 antagonists. Using a Chi-squared test, this suggests a significant (p = 0.002) over-representation of antagonists (SSRIs) on the positive side (expected count = 24, vs. 26 observed) and agonists on the negative side (expected count = 1, vs. 3 observed). If SLC6A4 antagonists, i.e. SSRIs, indeed tend to cause a similar behavioural phenotype than knockout of sorl1, this would point in the direction of our original interpretation of the citalopram experiment; which was that excessive serotonin signalling is what causes the sorl1 behavioural phenotype.

      Author response image 5.

      Using the STITCH database as source of annotations also predicts SLC6A4 as an enriched target for the sorl1 behavioural fingerprint. Same figures as Fig. 5a,b but using the STITCH database (Szklarczyk et al., 2016) as source for the drug targets. a, Compounds annotated by STITCH as interacting with the serotonin transporter SLC6A4 tend to generate behavioural phenotypes similar to the sorl1 F0 knockout fingerprint. 40,522 compound–target protein pairs (vertical bars; 1,592 unique compounds) are ranked from the fingerprint with the most positive cosine to the fingerprint with the most negative cosine in comparison with the mean sorl1 F0 knockout fingerprint. Fingerprints of drugs that interact with SLC6A4 are coloured in yellow. Simulated p-value = 0.005 for enrichment of drugs interacting with SLC6A4 at the top (positive cosine) and/or bottom (negative cosine) of the ranked list by a custom permutation test. b, Result of the permutation test for top and/or bottom enrichment of drugs interacting with SLC6A4 in the ranked list. The absolute cosines of the fingerprints of drugs interacting with SLC6A4 (n = 52, one fingerprint per compound) were summed, giving sum of cosines = 15.9. To simulate a null distribution, 52 fingerprints were randomly drawn 100,000 times, generating a distribution of 100,000 random sum of cosines. Here, only 499 random draws gave a larger sum of cosines, so the simulated p-value was p = 499/100,000 = 0.005 **.

      If this were true, we would expect, as the reviewer suggested, SSRI treatment (citalopram or fluvoxamine) on control larvae to give a similar behavioural phenotype as knockout of sorl1. However, this generally did not appear to be the case (sorl1 knockout fingerprint vs. SSRI-treated control fingerprint, cosine = 0.08 ± 0.35; Author response image 6).

      Author response image 6.

      sorl1 F0 knockouts in comparison to controls treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the citalopram experiment) in comparison with the scrambled-injected + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H<sub>2</sub>O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the fluvoxamine experiment) in comparison with the scrambled-injected + fluvoxamine (10 µM) fingerprint.

      The comparison with trazodone is an interesting observation, but it is only a weak serotonin reuptake inhibitor (Ki for SLC6A4 = 690 nM, vs. 8.9 nM for citalopram; Owens et al., 1997) and it has many other targets, both as agonist or antagonist, including serotonin, adrenergic, and histamine receptors (Mijur, 2011). In any case, the average trazodone fingerprint does not correlate particularly well to the sorl1 knockout fingerprint (cos = 0.3). Finally, the sorl1 knockout behavioural phenotype could be primarily caused by altered serotonin signalling in the hypothalamus, where we found both the biggest difference in tph1a/1b/2 HCR signal intensity (Fig. 5f) and the highest expression of sorl1 across scRNA-seq clusters (Fig. 1– supplement 2). In this case, it would be correct to expect sorl1 knockouts to react differently to SSRIs than controls, but it would be incorrect to expect SSRI treatment to cause the same behavioural phenotype, as it concurrently affects every other serotonergic neuron in the brain.

      Finally, we agree the quoted conclusion was too strong given the current evidence. We since tested another SSRI, fluvoxamine, on sorl1 knockouts.

      - Also in reference to Figure 5: in panel c, data are presented as deviation from vehicle treated. Because of this data presentation choice, it's no longer possible to determine whether, in this experiment, sorl1 crispants sleep less at night relative to their siblings. Does citalopram rescue / reverse sleep deficits in sorl1 mutants?

      On your first point, please see our response to Reviewer #3 (2)c and Author Response 2b above.

      On “does citalopram rescue/reverse sleep deficits in sorl1 mutants”: citalopram (and fluvoxamine) tends to reverse the key aspects of the sorl1 knockout behavioural phenotype by reducing night-time activity (% time active and total Δ pixels), increasing night-time sleep, and shortening sleep latency (Author response image 7). Extrapolating from the hypothesis presented in Discussion, this may be interpreted as a hint that sorl1 knockouts have reduced levels of 5-HT receptors, as increasing serotonin signalling using an SSRI tends to rescue the phenotype. However, we do not think that focusing on the significant behavioural parameters necessarily make sense here. Rather, one should take all parameters into account to conclude whether knockouts react differently to the drug than wild types (also see answer to Reviewer #3, (7) on this). For example, citalopram increased more the night-time sleep bout length of sorl1 knockouts than the one of controls (Fig. 5), but this parameter was not modified by knockout of sorl1 (Fig. 4). To explain the rationale more informally, citalopram is only used as a tool here to probe serotonin signalling in sorl1 knockouts, whether it worsens or rescues the behavioural phenotype is somewhat secondary, the key question is whether knockouts react differently than controls.

      Author response image 7.

      Comparing untreated sorl1 F0 knockouts vs. treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the citalopram experiment) in comparison with the sorl1 knockout + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H<sub>2</sub>O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the fluvoxamine experiment) in comparison with the sorl1 + fluvoxamine (10 µM) fingerprint.

      - Possible molecular pathways targeted by tinidazole, fenoprofen, and betamethasone are not described.

      Tinidazole is an antibiotic, fenoprofen is a non-steroidal anti-inflammatory drug (NSAIDs), betamethasone is a steroidal anti-inflammatory drug. Interestingly, long-term use of NSAIDs reduces the risk of AD (in ’t Veld Bas A. et al., 2001). Several mechanisms are possible (Weggen et al., 2007), including reduction of Aβ42 production by interacting with γ-secretase (Eriksen et al., 2003). However, we did not explore the mechanism of action of these drugs on psen2 knockouts so do not feel comfortable speculating. We do not know, for example, whether these findings apply to betamethasone.

      Minor Comments:

      - On page 25, panel "g" should be labeled as "f".

      Thank you!

      - On page 35, a reference should be provided for the statement "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes.".

      Thank you, this is now corrected. There were the same studies as mentioned in Introduction.

      - On page 43, the word "and" should be added - "in wild-type rats and mice, overexpressing mutated human APP and PSEN1, AND restricting sleep for 21 days...".

      Right, this sentence could be misread, we edited it. “overexpressing […]” only applied to the mice, not the rats (as they are wild-type); and both are sleep-deprived.

      - On page 45, a reference should be provided for the statement "SSRIs can generally be used continuously with no adverse effects" and this statement should potentially be softened.

      The reference is at the end of that sentence (Cirrito et al., 2011). You are correct though; we reformulated this statement to: “SSRIs can generally be used safely for many years”. SSRIs indeed have side effects.

      - On page 54, a 60-minute rolling average is described as 45k rows, but this seems to be a 30-minute rolling average.

      Thank you! We corrected. It should have been 90k rows, as in: 25 frames-per-second × 60 seconds × 60 minutes.

      Reviewer #2 (Recommendations For The Authors):

      "As we observed in the scRNA-seq data, most genes tested (appa, appb, psen1, psen2, apoea, cd2ap, sorl1) were broadly expressed throughout the 6-dpf brain (Fig. 1d and Fig. 1supplement 3 and 4)."

      - apoea and appb are actually not expressed highly in the scRNA-seq data, and the apoea in situ looks odd, as if it has no expression. The appb gene mysteriously does not look as though it has high expression in the Raj data, but it is clearly expressed based on the in situ. I had previously noticed the same discrepancy, and I attribute it to the transcriptome used to map the Raj data, as the new DanioCell data uses a new transcriptome and indicates high appb expression in the brain. Please point out the discrepancy and possible explanation, perhaps in the figure legend.

      All excellent points, thank you. We included them directly in Results text.

      "most of these were expressed in the brain of 5-6-dpf zebrafish larvae, suggesting they play a role in early brain development or function."

      - Evidence of expression does not suggest function, particularly not a function in brain development. As one example, almost half of the genome is expressed prior to the maternal-zygotic transition but does not have a function in those earliest stages of development. There are numerous other instances where expression does not equal function. Please change the sentence even as simply as "it is possible that they".

      We mostly agree and edited to “[…], so they could play a role […]”.

      Out of curiosity, we plotted, for each zebrafish developmental stage, the proportion of Alzheimer’s risk gene orthologues expressed in comparison to the proportion of all genes expressed (Author response image 8). We defined “all genes” as every gene that is expressed in at least one of the developmental stages (n = 24,856), not the complete transcriptome, to avoid including genes that are never expressed in the brain or whose expression is always below detection limit. We counted a gene as “expressed” if at least three cells had detectable transcripts. Using these definitions, 82 ± 7% of genes are expressed during development. For every developmental stage except 5 dpf (so 11/12), a larger proportion of Alzheimer’s risk genes than all genes are expressed (+5 ± 4%).

      Author response image 8.

      Proportion of Alzheimer’s risk genes orthologues expressed throughout zebrafish development. Proportion of Alzheimer’s risk genes orthologues (n = 42) and all genes (n = 24,856) expressed in the zebrafish brain at each developmental stage, from 12 hours post-fertilisation (hpf) to 15 days post-fertilisation (dpf). “All genes” corresponds to every gene expressed in the brain at any of the developmental stages, not the complete transcriptome. A gene is considered “expressed” (green) if at least three cells had detectable transcripts. Single-cell RNA-seq dataset from Raj et al., 2020.

      "This frame-by-frame analysis has several advantages over previous methods that analysed activity data at the one-minute resolution."

      - Which methods are these? There are no citations. There are certainly existing methods in the zebrafish field that can produce similar data to the method developed for this project. This new package is useful, as most existing software is not written in R, so it would help scientists who prefer this programming language. However, I would be careful not to oversell its novelty, since many methods do exist that produce similar results.

      We added the references. There were referenced above after “we combined previous sleep/wake analysis methods”, but should have been referenced again here.

      We are not convinced by this criticism. We would obviously not claim that the FramebyFrame package is as sophisticated and versatile as video-tracking tools like SLEAP or DeepLabCut, but we do think it answers a genuine need that was not addressed by other methods. Specifically, we know of many labs recording pixel count data across multiple days using the Zebrabox or DanioVision (we added support for DanioVision data after submission), but there were no packages to extract behavioural parameters from these data. Other methods involved standalone scripts with no documentation or version tracking. We would concede the FramebyFrame package is mostly targeted at these labs, but we already know of six labs routinely using it and were recently contacted by a researcher tracking Daphnia in the Zebrabox.

      "F0 knockouts of both cutches" - "clutches"

      Thank you!

      Reviewer #3 (Recommendations For The Authors):

      I would suggest totally revamping the Introduction section, and being sure to provide readers with the context and background they need for the data that comes thereafter. Key areas to touch on, in no particular order, include:

      • Far more detail on the behavioral pharm screen upon which this paper builds, as a brief overview of that approach and the data generated are needed.

      Thank you for the suggestion, we added a sentence hinting at this work in the last Introduction paragraph.

      • Limitations of current zebrafish sleep/arousal assays that motivated the authors to develop a new, temporally high-resolution system.

      We think this is better explained in Results, as is currently. For example, we need to point to Fig. 2–supplement 2a,b,c to explain that one-minute methods were missing sleep bouts and how FramebyFrame resolves this issue.

      • A paragraph about sleep and AD, that does a better job of citing work in humans, mammalian, and invertebrate models that motivate the interest in the connection pursued here.

      Sorry, we think this would place too much focus on sleep and AD. We want the main topic of the paper to be the behavioural pharmacology approach, not AD or sleep per se. As the Introduction states, we see Alzheimer’s risk genes as a case study for the behavioural pharmacology approach, rather than the reason why the approach was developed. Additionally, presenting sleep and AD in Introduction risks sounding like ZOLTAR is specifically designed for this context, while we conceived of it as much more generalisable and explicitly encourage its use to study genes associated to other diseases. Note that the paragraph you suggest is, we think, mostly present in Discussion (section Disrupted sleep and serotonin signalling […]).

      • I modestly suggest eliminating making such a strong case for a gene-first approach being the best way to understand disease. It is not a zero-sum game, and there is plenty to learn from proteomics, metabolomics, etc. I suspect nobody will argue with the authors saying they leveraged the strength of their system and focused on key AD genes of interest.

      From your point below, we understand the following quote is the source of the issue: “For finding causal processes, studying the genome, rather than the transcriptome or epigenome, is advantageous because the chronology from genomic variant to disease is unambiguous […]”. We did not want to suggest it is a zero-sum game, but we now understand how it can be read this way. We adapted slightly the wording. What we want to do is highlight the causality argument as the advantage of the genomics approach. We feel we do not read this argument often enough, while it remains a ‘magic power’ of genomics. One essentially does not have to worry about causality when studying a pathogenic germline variant, while it is a constant concern when studying the transcriptome or epigenome (i.e. did the change in this transcript’s level cause disease, or vice-versa?). To take an example in the context of AD, arguments based on genomics (e.g. Down syndrome or APP duplication) are often the definite arbiters when debating the amyloid hypothesis, exactly because their causality cannot be doubted.

      Minor comments

      (1) The opening of the introduction is perhaps overly broad, spending an entire paragraph on genome vs transcriptome, etc and making the claim that a gene-first approach is the best path. It isn't zero-sum, and the authors could just get right into AD and study genes of interest. Similar issues occur throughout the manuscript, with sentences/paragraphs that are not necessarily needed.

      Please see our answer to your previous point. On the introduction being overly broad, we perfectly agree it is broad, but related to your point about presenting sleep and AD in the Introduction, we wish to talk about finding causal processes from genomics findings using behavioural pharmacology. We purposefully present research on AD as one instance of this broader goal, not the primary topic of the paper.

      Another example are these sentences, which could be totally removed as the following paragraph starts off making the same point much more succinctly. "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes. Presumably, the same processes are disrupted in zebrafish sorl1 knockouts, and some caused the behavioural alterations we observed. Can we now follow the thread backwards and predict some of the biological processes in which Sorl1 is involved based on the behavioural profile of sorl1 knockouts?"

      Thanks for the suggestion, but we think these sentences are useful to place back this Results section in the context of the Introduction. Think of the paper as mainly about the behavioural pharmacology approach, not on Alzheimer’s risk genes. The function of the paragraph here is not simply to explain the method by which we decided to study sorl1; it is to reiterate the rationale behind the behavioural pharmacology approach so that the reader understands where this Results section fits in the overall structure.

      (2) Related to the above, the authors use lecanemab as an example to support their approach, but there has been a great deal of controversy regarding this drug. I don't think such extensive justification is needed. This study uses AD risk genes as a case study in a newly developed behavioral pharm pipeline. A great deal of the rest of the intro seems to just fill space and could be more focused on the study at hand. Interestingly, a er gene selection, the next step in their pipeline is sleep/wake analysis yet nothing is covered about AD and sleep in the intro. Some justification of that approach (why focus on sleep/wake as a starting point for behavioral pharm rather than learning and memory?) would be a better use of intro space.

      There has indeed been controversy about lecanemab, but even the harshest critiques of the amyloid hypothesis concede that it slows down cognitive decline (Espay et al., 2023). That is all that is needed to support our argument, which is that research on AD started primarily from genomics and thereby yielded a disease-modifying drug. The controversy seems mostly focused on whether this effect size is clinically significant, and we think we correctly represent this uncertainty (e.g. “antibodies against Aβ such as lecanemab show promise in slowing down disease progression” and “the beneficial effects from targeting Aβ aggregation currently remain modest”).

      Your next point is entirely fair. We mostly answered it above. To explain further, the primary reason why we measured sleep/wake behaviour is to match the behavioural dataset from Rihel et al., 2010 so we can use it to make predictions, not to study sleep in the context of AD per se. Sure, perhaps learning and memory would have been interesting, but we do not know of any study testing thousands of small molecules on zebrafish larvae during a memory task. We understand it can be slightly confusing though, as we then spend a paragraph of Discussion on sleep as a causal process in AD, but we obviously need to discuss this topic given the findings. However, to reiterate, we purposefully designed FramebyFrame and ZOLTAR to be useful beyond studying sleep/wake behaviour. For example, FramebyFrame would not calculate 17 behavioural parameters if the only goal was to measure sleep. We now mention the Rihel et al., 2010 study in the Introduction as you suggested above (“Far more detail on the behavioral pharm screen […]”), as that is the real reason why sleep/wake behaviour was measured in the first place.

      (3) Also related to the above, another more relevant point that could be talked about in the intro is the need for more refined approaches to analyze sleep in zebrafish, given the effort that went into the new analysis system described here. Again, I think the context for why the authors developed this system would be more meaningful than the current content.

      Thank you, we think we answered this point above (especially below Limitations of current zebrafish sleep/arousal assays […]).

      (4) GWAS can stand for Genome-wide associate studies (plural) so I do not think the extra "s" is needed (GWASs) .

      Indeed, that seems to be the common usage. Thank you.

      (5) AD candidate risk genes were determined from loci using "mainly statistic colocalization". Can the authors add a few more details about what was done and what the "mainly" caveat refers to?

      “Mainly” simply refers to the fact that other methods were used by Schwartzentruber et al. (2021) to annotate the GWAS loci with likely causal genes, but that most calls were ultimately made from statistic colocalisation. Readers can refer to this work to learn more about the methods used.

      (6) The authors write "The loss of psen1 only had mild effects on behaviour" but I think they mean "sleep behaviors" as there could be many other behaviors that are disrupted but were not assessed. The same issue a few sentences later with "Behaviour during the day was not affected" and at the end of the following paragraph.

      Yes, that would be more precise, thank you.

      (7) For the Sorl1 pharmacology data, it is very hard to understand what is being measured behaviorally. Are the authors measuring sleep +/- citalopram, or something else, and why the change to Euclidean distance rather than all the measures we were just introduced to earlier in the manuscript?

      We understand these plots (Fig. 5c,d) are less intuitive, but it is important that we show the difference in behaviour compared to H<sub>2</sub>O-treated larvae of same genotype. The claim is that citalopram has a larger effect on knockouts than on controls, so the reader needs to focus on the effect of the drug on each genotype, not on the effect of sorl1 knockout. We added the standard fingerprints (i.e. setting controls to z-score = 0) here in Author response figures.

      Euclidean distance takes as input all the measures we introduced. The point is precisely not to select a single measure. For example, say we were only plotting active bout number during the day, we would conclude that 10 µM citalopram has the same effect on knockouts and controls. Conversely, if we had taken sleep bout length at night, we would conclude 10 µM has a stronger effect on knockouts. What is the correct parameter to select? Using Euclidean distance resolves this by taking all parameters into account, rather than arbitrarily choosing one.

      And what exactly is a "given spike in serotonin"? and how is this hypothesis the conclusion based on the lack of evidence for the second hypothesis? As the authors say, there could be other ways sorl1 knockouts are more sensitive to citalopram, so the absence of evidence for one hypothesis certainly does not support the other hypothesis.

      We mean a given release of serotonin in the synaptic cleft. We have fixed this wording. 

      We tend to disagree on the second point. We can think of two ways that sorl1 knockouts are more sensitive to citalopram: 1) they produce more serotonin, so blocking reuptake causes a larger spike in knockouts; or 2) blocking reuptake causes the same increase in both knockouts and wild-types but knockouts react more strongly to serotonin. We cannot in fact think of another way to explain the citalopram results. Not finding overwhelming evidence for 1) surely supports 2) somewhat, even if we do not have direct evidence for it. As an analogy, if two diagnoses are possible for a patient, testing negative for the first one supports the other one, even before it is directly tested.

      (8) Again some language is used without enough care. Fish are referred to as "drowsier" under some drug conditions. How do the authors know the animal is drowsy? The phenotype is more specific - more sleep, less activity.

      Thank you, we switched to “Furthermore, fenoprofen worsened the day-time hypoactivity of psen2 knockout larvae […]”.

      (9) This sentence is misleading as it gives the impression that results in this manuscript suggest the conclusion: "Our observation that disruption of genes associated with AD diagnosis after 65 years reduces sleep in 7-day zebrafish larvae suggest that disrupted sleep may be a common mechanism through which these genes exert an effect on risk." That idea is widely held in the field, and numerous other previous manuscripts/reviews should be cited for clarity of where this hypothesis came from.

      This idea is not widely held in the field. You likely read this point as “disrupted sleep is a risk factor for AD”, which, yes, is widely discussed in the field, but is not precisely what we are saying. We hypothesise that mutations in some of the Alzheimer’s risk genes cause disrupted sleep, possibly from a very early age, which then causes AD decades later. Studies and reviews on sleep and AD rarely make this hypothesis, at least not explicitly. The closest we know of are a few recent human genetics studies, typically using Mendelian Randomisation, finding that higher genetic risk of AD correlates with some sleep phenotypes, such as sleep duration (Chen et al., 2022; Leng et al., 2021). The work of Muto et al. (2021) is particularly interesting as it found correlations between higher genetic risk of AD and some sleep phenotypes in men in their early twenties, which seems unlikely to be a consequence of early pathology (Muto et al., 2021). Note, however, that even these studies do not mention sleep possibly being disrupted early in development, which is what our findings in zebrafish larvae support. As we mention, we think a team should test whether sleep is different in infants at higher genetic risk of AD, essentially performing an analogous, but obviously much more difficult, experiment as we did in zebrafish larvae. We do not know of any study testing this or even raising this idea, so evidently it is not widely held. Having said that, the studies we mention here were not referenced in the Discussion paragraph. We have now corrected this.

      Ashlin TG, Blunsom NJ, Ghosh M, Cockcroft S, Rihel J. 2018. Pitpnc1a Regulates Zebrafish Sleep and Wake Behavior through Modulation of Insulin like Growth Factor Signaling. Cell Rep 24:1389–1396. doi:10.1016/j.celrep.2018.07.012

      Chen D, Wang X, Huang T, Jia J. 2022. Sleep and LateOnset Alzheimer’s Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects. Front Genet 13. doi:10.3389/fgene.2022.794202

      Cirrito JR, Disabato BM, Restivo JL, Verges DK, Goebel WD, Sathyan A, Hayreh D, D’Angelo G, Benzinger T, Yoon H, Kim J, Morris JC, Mintun MA, Sheline YI. 2011. Serotonin signaling is associated with lower amyloid-β levels and plaques in transgenic mice and humans. Proc Natl Acad Sci U S A 108:14968–14973. doi:10.1073/pnas.1107411108

      Dean DC, Jerskey BA, Chen K, Protas H, Thiyyagura P, RoonJva A, O’Muircheartaigh J, Dirks H, Waskiewicz N, Lehman K, Siniard AL, Turk MN, Hua X, Madsen SK, Thompson PM, Fleisher AS, Huentelman MJ, Deoni SCL, Reiman EM. 2014. Brain Differences in Infants at Differential Genetic Risk for Late-Onset Alzheimer Disease A Cross-sectional Imaging Study. JAMA Neurol 71:11–22. doi:10.1001/jamaneurol.2013.4544

      Eriksen JL, Sagi SA, Smith TE, Weggen S, Das P, McLendon DC, Ozols VV, Jessing KW, Zavitz KH, Koo EH, Golde TE. 2003. NSAIDs and enantiomers of flurbiprofen target γ-secretase and lower Aβ42 in vivo. J Clin Invest 112:440–449. doi:10.1172/JCI18162

      Espay AJ, Herrup K, Kepp KP, Daly T. 2023. The proteinopenia hypothesis: Loss of Aβ42 and the onset of Alzheimer’s Disease. Ageing Res Rev 92:102112. doi:10.1016/j.arr.2023.102112

      Hoffman EJ, Turner KJ, Fernandez JM, Cifuentes D, Ghosh M, Ijaz S, Jain RA, Kubo F, Bill BR, Baier H, Granato M, Barresi MJF, Wilson SW, Rihel J, State MW, Giraldez AJ. 2016. Estrogens Suppress a Behavioral Phenotype in Zebrafish Mutants of the AuJsm Risk Gene, CNTNAP2. Neuron 89:725–733. doi:10.1016/j.neuron.2015.12.039

      in ’t Veld Bas A, Ruitenberg A, Hofman A, Launer LJ, van Duijn CM, Stijnen T, Breteler MMB, Stricker BHC. 2001. Nonsteroidal Anti inflammatory Drugs and the Risk of Alzheimer’s Disease. N Engl J Med 345:1515–1521. doi:10.1056/NEJMoa010178

      Jagirdar R, Fu C-H, Park J, Corbek BF, Seibt FM, Beierlein M, Chin J. 2021. Restoring activity in the thalamic reticular nucleus improves sleep architecture and reduces Aβ accumulation in mice. Sci Transl Med 13:eabh4284. doi:10.1126/scitranslmed.abh4284

      Jiang H, Newman M, Lardelli M. 2018. The zebrafish orthologue of familial Alzheimer’s disease gene PRESENILIN 2 is required for normal adult melanotic skin pigmentation. PLOS ONE 13:e0206155. doi:10.1371/journal.pone.0206155

      Jiang H, Pederson SM, Newman M, Dong Y, Barthelson K, Lardelli M. 2020. Transcriptome analysis indicates dominant effects on ribosome and mitochondrial function of a premature termination codon mutation in the zebrafish gene psen2. PloS One 15:e0232559. doi:10.1371/journal.pone.0232559

      Joo W, Vivian MD, Graham BJ, Soucy ER, Thyme SB. 2021. A Customizable Low-Cost System for Massively Parallel Zebrafish Behavioral Phenotyping. Front Behav Neurosci 14.

      Joubert L, Hanson B, Barthet G, Sebben M, Claeysen S, Hong W, Marin P, Dumuis A, Bockaert J. 2004. New sorting nexin (SNX27) and NHERF specifically interact with the 5-HT4a receptor splice variant: roles in receptor targeting. J Cell Sci 117:5367–5379. doi:10.1242/jcs.01379

      Leng Y, Ackley SF, Glymour MM, Yaffe K, Brenowitz WD. 2021. Genetic Risk of Alzheimer’s Disease and Sleep Duration in Non-Demented Elders. Ann Neurol 89:177–181. doi:10.1002/ana.25910

      Mitchell PB, Hadzi-Pavlovic D. 2000. Lithium treatment for bipolar disorder. Bull World Health Organ 78:515–517.

      Mikur A. 2011. Trazodone: properties and utility in multiple disorders. Expert Rev Clin Pharmacol 4:181–196. doi:10.1586/ecp.10.138

      Munoz-Torrero D. 2008. Acetylcholinesterase Inhibitors as Disease-Modifying Therapies for Alzheimer’s Disease. Curr Med Chem 15:2433–2455. doi:10.2174/092986708785909067

      Muto V, Koshmanova E, Ghaemmaghami P, Jaspar M, Meyer C, Elansary M, Van Egroo M, Chylinski D, Berthomier C, Brandewinder M, Mouraux C, Schmidt C, Hammad G, Coppieters W, Ahariz N, Degueldre C, Luxen A, Salmon E, Phillips C, Archer SN, Yengo L, Byrne E, Collette F, Georges M, Dijk D-J, Maquet P, Visscher PM, Vandewalle G. 2021. Alzheimer’s disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep 44. doi:10.1093/sleep/zsaa137

      Myers-Turnbull D, Taylor JC, Helsell C, McCarroll MN, Ki CS, Tummino TA, Ravikumar S, Kinser R, Gendelev L, Alexander R, Keiser MJ, Kokel D. 2022. Simultaneous analysis of neuroactive compounds in zebrafish. doi:10.1101/2020.01.01.891432

      Owens MJ, Morgan WN, Plok SJ, Nemeroff CB. 1997. Neurotransmiker receptor and transporter binding profile of antidepressants and their metabolites. J Pharmacol Exp Ther 283:1305– 1322.

      Özcan GG, Lim S, Leighton PL, Allison WT, Rihel J. 2020. Sleep is bi-directionally modified by amyloid beta oligomers. eLife 9:e53995. doi:10.7554/eLife.53995

      Quiroz YT, Schultz AP, Chen K, Protas HD, Brickhouse M, Fleisher AS, Langbaum JB, Thiyyagura P, Fagan AM, Shah AR, Muniz M, Arboleda-Velasquez JF, Munoz C, Garcia G, Acosta-Baena N, Giraldo M, Tirado V, Ramírez DL, Tariot PN, Dickerson BC, Sperling RA, Lopera F, Reiman EM. 2015. Brain Imaging and Blood Biomarker Abnormalities in Children With Autosomal Dominant Alzheimer Disease: A Cross-Sectional Study. JAMA Neurol 72:912–919. doi:10.1001/jamaneurol.2015.1099

      Relkin NR. 2007. Beyond symptomatic therapy: a reexamination of acetylcholinesterase inhibitors in Alzheimer’s disease. Expert Rev Neurother 7:735–748. doi:10.1586/14737175.7.6.735

      Rihel J, Prober DA, Arvanites A, Lam K, Zimmerman S, Jang S, Haggarty SJ, Kokel D, Rubin LL, Peterson RT, Schier AF. 2010. Zebrafish Behavioral Profiling Links Drugs to Biological Targets and Rest/Wake Regulation. Science 327:348–351. doi:10.1126/science.1183090

      Sleegers K, Brouwers N, Gijselinck I, Theuns J, Goossens D, Wauters J, Del-Favero J, Cruts M, van Duijn CM, Van Broeckhoven C. 2006. APP duplication is sufficient to cause early onset Alzheimer’s dementia with cerebral amyloid angiopathy. Brain J Neurol 129:2977–2983. doi:10.1093/brain/awl203

      Sun L, Zhou R, Yang G, Shi Y. 2017. Analysis of 138 pathogenic mutations in presenilin-1 on the in vitro production of Aβ42 and Aβ40 peptides by γ-secretase. Proc Natl Acad Sci 114:E476– E485. doi:10.1073/pnas.1618657114

      Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. 2016. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384. doi:10.1093/nar/gkv1277

      Weggen S, Rogers M, Eriksen J. 2007. NSAIDs: small molecules for prevention of Alzheimer’s disease or precursors for future drug development? Trends Pharmacol Sci 28:536–543. doi:10.1016/j.Jps.2007.09.004

      Wiltschko AB, Tsukahara T, Zeine A, Anyoha R, Gillis WF, Markowitz JE, Peterson RE, Katon J, Johnson MJ, Daka SR. 2020. Revealing the structure of pharmacobehavioral space through motion sequencing. Nat Neurosci 23:1433–1443. doi:10.1038/s41593-020-00706-3

      Yang T, Arslanova D, Gu Y, Augelli-Szafran C, Xia W. 2008. Quantification of gamma-secretase modulation differentiates inhibitor compound selectivity between two substrates Notch and amyloid precursor protein. Mol Brain 1:15. doi:10.1186/1756-6606-1-15

    1. Tasks that are easily copied, completed by cheating, or solved with an answer key, probably should not be factored in as assessment. These lower demand tasks should be seen as practice leading up to proficiency in applying new learning.

      I think that this is tricky in K-12 Math in the current educational system. When factoring in the need to assess many students, mathematical procedural fluency is most easily assessed through written tests, which are easily copied or solved with an answer key. Adding additional tasks such as long written explanations disadvantages those students who may have strong mathematical skill but poor language skill. Although, mathematical communication is also important, it can be done purely symbolically, and this method of communication is usually most like how we use math outside of school. At the same time, we must assess procedural fluency. Without more time spent on Math and more Math integrated into other subjects, my experience is that students don't acquire adequate fluency without motivation to specifically practice those skills for assessment.

      Then again, perhaps tests which are protected against copying and cheating, such as through invigilation, are not the tasks referred to here...

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Weaknesses:<br /> (1) While the overall results are interesting, I am somewhat left confused about how to interpret the difference in the scores derived from different conditions. For example, the authors stated "Comparing the weights for in-group and out-group distractors, the effect of proximity was larger than that of aggression and grooming" in p.8. Does this mean that the proximity is indeed the type of behavior most affected in the out-group condition compared to the in-group condition? The out-group effects are difficult to examine with actual behavioral data, but some in-group effects such as those involving OT can be tested, which possibly provides good insights into interpreting the differences of the weights observed across the experimental conditions.

      Thank you for your thoughtful comments and for highlighting an important aspect of our findings. The statement in page 8 refers to the relative impact of different social behaviors—proximity, aggression, and grooming—on the derived weights for in-group and out-group distractors. Specifically, the data suggest that proximity exerts a stronger influence than aggression or grooming in differentiating the effects of out-group versus in-group distractors. Regarding the out-group condition, we acknowledge that it presents challenges for direct behavioral observation, as interactions involving out-group members are often more difficult to quantify in naturalistic settings. However, we agree with you about the suggestion to test certain in-group effects, particularly those influenced by oxytocin (OT), as they offer a more controlled framework to validate and interpret the observed differences in weights across experimental conditions. In line with this, we examined specific in-group behaviors under OT administration to disentangle their contributions to attentional dynamics (Fig. 4 and Fig. 5 e to h). By integrating controlled experimental manipulations, we think these results could provide deeper insights into how social relationships shape the observed patterns of attention.

      (2) I think it is important to provide how variable spontaneous social interactions were across sessions and how impactful the variability of the interactions is on the SEI and IEI, as it helps to understand how meaningful the differences of weights are across the conditions, but such data are missing. In line with this point, although the conclusions still hold as those data were obtained during the same experimental periods, shouldn't the weights in Fig. 3f and Figs. 4g and 4h (saline) be expected to be similar, if not the same?

      Thank you for your insightful comments. As highlighted, we utilized the entire experimental period as the dataset to evaluate the monkeys' social interactions. The experiments presented in Figures 3 and 4 were designed to examine how social relationships correlate with patterns of social attention under two distinct conditions: without manipulation (Fig. 3) and with nebulized exposure to oxytocin and saline (Fig. 4). Theoretically, the weights observed in the unmanipulated condition and the nebulized saline condition should be similar. However, our results indicate that distractor biases shifted significantly following nebulized saline exposure (Fig. 4) compared to the unmanipulated condition (Fig. 3) (MK: p = 9.3×10<sup>-3</sup>, ML: p = 9.77×10<sup>-4</sup>, MC: p = 9.77×10<sup>-4</sup>, MA: p = 0.09; n<sub>1</sub> = n<sub>2</sub> = 12 experimental days; Two-sided Wilcoxon signed-rank test). This suggests that the nebulization process itself, despite acclimating the monkeys to saline exposure for approximately two weeks prior to the experiments, still influenced their attentional behaviors.

      While the primary goal of nebulization was to assess the effects of oxytocin on social attention, our main conclusions remain robust, even considering the impact of nebulization on distractor biases. We acknowledge that variability in spontaneous social interactions across days or experimental sessions could be an important factor influencing the SEI and IEI. The dynamic nature of social interactions within the colony is likely affected by numerous variables. Future research will aim to integrate these factors into a more comprehensive and dynamic framework to better interpret their influence on social attention metrics.

      Reviewer #2 (Public review):

      Weaknesses:<br /> (1) The study's conclusions are based on observations of only four monkeys, which limits the generalizability of the findings. Larger sample sizes could strengthen the validity of the results.

      Thank you for your valuable comment. We acknowledge that the relatively small sample size could influence the generalizability of the findings.  However, despite this limitation, our work systematically examined multifaceted social relationships among monkeys and their attentional strategies within a well-controlled experimental setup. We reported results across sessions and conditions (e.g., in-group vs. out-group; saline vs. Oxytocin), which strengthens the reliability of the observed effects of social networks within this context. We agree that increasing the sample size would improve the generalizability of the results. Future studies with a larger cohort will be critical for confirming the robustness of our findings and expanding their broader applicability. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research with larger sample sizes to validate and extend our conclusions.

      (2) The limited set of stimulus images (in-group and out-group faces) may introduce unintended biases. This could be addressed by increasing the diversity of stimuli or incorporating a broader range of out-group members.

      Thank you for your thoughtful comment. We acknowledge that the use of a limited set of six monkey faces as stimuli for in-group and out-group conditions could potentially introduce biases. To address this concern, we conducted an additional analysis to minimize the potential impact of individual images on our findings using the current dataset. Specifically, we randomly excluded one in-group and one out-group image and reanalyzed distractor biases using the remaining two images (Supplementary Fig. 3a). For each subject, this approach generated three sets of two distractors per group, resulting in 81(3<sup>4</sup>) combinations across four monkey subjects, and a total of 81 × 81 subject-distractor pairings. We statistically compared distractor biases between in-group and out-group faces for each combination (Supplementary Fig. 3b). As shown in Supplementary Fig. 3c, 99.30% of the 6,561 combinations demonstrated significantly lower distractor biases towards in-group faces compared to out-group faces (two-sided Wilcoxon signed-rank test, p < 0.05). These results suggest that the observed differences in social attention between in-group and out-group monkeys are unlikely to be driven by specific images within the stimulus set. That said, we agree that increasing the diversity of stimulus images or incorporating a broader range of out-group members would improve the generalizability of the results. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research to incorporate a more diverse stimulus set to validate and extend our findings.

      “However, these conclusions may be constrained by the relatively small sample size and the homogeneity of stimulus set in the study. Future research focusing on larger, more diverse cohorts and incorporating a broader range of stimuli will enhance the generalizability and applicability of the findings.”

      Reviewer #1 (Recommendations for the authors):

      It is difficult to distinguish "Getting fighted" and "Fighting partner" in Fig. 1b (esp. when printed). I thought Actor showed "Fighting partner" several times in Session 2, but it seems to be "Getting fighted" judging from Figs. 1c and 1d. Is this correct? If so, I would suggest to change the color to improve visibility.

      Thank you for your valuable comment. We apologize for the confusion in the previous version. To improve clarity, we have both terms to “begin fighting” and “being fought”. As shown in Figure 1b, we now explicitly define the identities of the two monkeys as the actor (K) and the partner (L), with all behaviors described from the perspective of the actor. For example, when the actor (K) initiates the fight, it is marked as “begin fighting”, whereas when the partner (L) initiates the fight, the actor (K) is the recipient and labeled as “being fought”. Additionally, we have implemented your suggestion by changing the colors to enhance visibility, especially for the terms “begin fighting” and “being fought”.

      Reviewer #2 (Recommendations for the authors): 

      I have some minor concerns:

      (1) Figure1B, caption for x axis is missing, 4 means 4 days?

      Thank you so much for the comment. We have clarified the x-axis in Figure 1B, where the label "4" corresponds to 4 hours of video typing on each experimental day. The revised figure now includes the appropriate label for better clarity. We appreciate your careful attention to this detail.

      (2) I am slightly concerned about animal safety. How do the experimenters ensure the animals' safety and well-being in cases of aggressive interactions or attacks?

      Thank you for your comment. We share your concern regarding animal safety and take re the well-being of the monkeys in the study. All experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee at the Institute of Biophysics, Chinese Academy of Sciences (IBP-NHP-002(22)). The monkeys were housed together in the same colony room for over four years, in interconnected cages that allowed for direct physical interaction. Animal behaviors in cages were closely monitored via a live video system to ensure their safety. To prevent potential injuries, a sliding partition system was in place, enabling the isolation of individual animals when necessary, minimizing risks to their well-being.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      his study shows a new mechanism of GS regulation in the archaean Methanosarcina mazei and clarifies the direct activation of GS activity by 2-oxoglutarate, thus featuring another way in which 2-oxoglutarate acts as a central status reporter of C/N sensing.

      Mass photometry and single particle cryoEM structure analysis convincingly show the direct regulation of GS activity by 2-OG promoted formation of the dodecameric structure of GS. The previously recognized small proteins GlnK1 and Sp26 seem to play a subordinate role in GS regulation, which is in good agreement with previous data. Although these data are quite clear now, there remains one major open question: how does 2-OG further increase GS activity once the full dodecameric state is achieved (at 5 mM)? This point needs to be reconsidered.

      Weaknesses:

      It is not entirely clear, how very high 2-OG concentrations activate GS beyond dodecamer formation.

      The data presented in this work are in stark contrast to the previously reported structure of M. mazei GS by the Schumacher lab. This is very confusing for the scientific community and requires clarification. The discussion should consider possible reasons for the contradictory results.

      Importantly, it is puzzling how Schumacher could achieve an apo-structire of dodecameric GS? If 2-OG is necessary for dodecameric formation, this should be discussed. If GlnK1 doesn't form a complex with the dodecameric GS, how could such a complex be resolved there?

      In addition, the text is in principle clear but could be improved by professional editing. Most obviously there is insufficient comma placement.

      We thank Reviewer #1 for the professional evaluation and raising important points. We will address those comments in the updated manuscript and especially improve the discussion in respect to the two points of concern.

      (1) How can GlnA1 activity further be stimulated with further increasing 2-OG after the dodecamer is already fully assembled at 5 mM 2-OG.

      We assume a two-step requirement for 2-OG, the dodecameric assembly and the priming of the active sites. The assembly step is based on cooperative effects of 2-OG and does not require the presence of 2-OG in all 2-OG-binding pockets: 2-OG-binding to one binding pocket also causes a domino effect of conformational changes in the adjacent 2-OG-unbound subunit, as also described for Methanothermococcus thermolithotrophicus GS in Müller et al. 2023. Due to the introduction of these conformational changes, the dodecameric form becomes more favourable even without all 2-OG binding sites being occupied. With higher 2-OG concentrations present (> 5mM), the activity increased further until finally all 2-OG-binding pockets were occupied, resulting in the priming of all active sites (all subunits) and thereby reaching the maximal activity.

      (2) The contradictory results with previously published data on the structure of M. mazei by Schumacher et al. 2023.

      We certainly agree that it is confusing that Schumacher et al. 2023 obtained a dodecameric structure without the addition of 2-OG, which we claim to be essential for the dodecameric form. 2-OG is a cellular metabolite that is naturally present in E. coli, the heterologous expression host both groups used. Since our main question focused on analysing the 2-OG effect on GS, we have performed thorough dialysis of the purified protein to remove all 2-OG before performing MP experiments. In the absence of 2-OG we never observed significant enzyme activity and always detected a fast disassembly after incubation on ice. We thus assume that a dodecamer without 2-OG in Schumacher et al. 2023 is an inactive oligomer of a once 2-OG-bound form, stabilized e.g. by the presence of 5 mM MgCl2.

      The GlnA1-GlnK1-structure (crystallography) by Schumacher et al. 2023 is in stark contrast to our findings that GlnK1 and GlnA1 do not interact as shown by mass photometry with purified proteins. A possible reason for this discrepancy might be that at the high protein concentrations used in the crystallization assay, complexes are formed based on hydrophobic or ionic protein interactions, which would not form under physiological concentrations.

      Reviewer #2 (Public Review):

      Summary:

      Herdering et al. introduced research on an archaeal glutamine synthetase (GS) from Methanosarcina mazei, which exhibits sensitivity to the environmental presence of 2-oxoglutarate (2-OG). While previous studies have indicated 2-OG's ability to enhance GS activity, the precise underlying mechanism remains unclear. Initially, the authors utilized biophysical characterization, primarily employing a nanomolar-scale detection method called mass photometry, to explore the molecular assembly of Methanosarcina mazei GS (M. mazei GS) in the absence or presence of 2-OG. Similar to other GS enzymes, the target M. mazei GS forms a stable dodecamer, with two hexameric rings stacked in tail-to-tail interactions. Despite approximately 40% of M. mazei GS existing as monomeric or dimeric entities in the detectable solution, the majority spontaneously assemble into a dodecameric state. Upon mixing 2-OG with M. mazei GS, the population of the dodecameric form increases proportionally with the concentration of 2-OG, indicating that 2-OG either promotes or stabilizes the assembly process. The cryo-electron microscopy (cryo-EM) structure reveals that 2-OG is positioned near the interface of two hexameric rings. At a resolution of 2.39 Å, the cryo-EM map vividly illustrates 2-OG forming hydrogen bonds with two individual GS subunits as well as with solvent water molecules. Moreover, local side-chain reorientation and conformational changes of loops in response to 2-OG further delineate the 2-OG-stabilized assembly of M. mazei GS.

      Strengths & Weaknesses:

      The investigation studies the impact of 2-oxoglutarate (2-OG) on the assembly of Methanosarcina mazei glutamine synthetase (M mazei GS). Utilizing cutting-edge mass photometry, the authors scrutinized the population dynamics of GS assembly in response to varying concentrations of 2-OG. Notably, the findings demonstrate a promising and straightforward correlation, revealing that dodecamer formation can be stimulated by 2-OG concentrations of up to 10 mM, although GS assembly never reaches 100% dodecamerization in this study. Furthermore, catalytic activities showed a remarkable enhancement, escalating from 0.0 U/mg to 7.8 U/mg with increasing concentrations of 2-OG, peaking at 12.5 mM. However, an intriguing gap arises between the incomplete dodecameric formation observed at 10 mM 2-OG, as revealed by mass photometry, and the continued increase in activity from 5 mM to 10 mM 2-OG for M mazei GS. This prompts questions regarding the inability of M mazei GS to achieve complete dodecamer formation and the underlying factors that further enhance GS activity within this concentration range of 2-OG.

      Moreover, the cryo-electron microscopy (cryo-EM) analysis provides additional support for the biophysical and biochemical characterization, elucidating the precise localization of 2-OG at the interface of two GS subunits within two hexameric rings. The observed correlation between GS assembly facilitated by 2-OG and its catalytic activity is substantiated by structural reorientations at the GS-GS interface, confirming the previously reported phenomenon of "funnel activation" in GS. However, the authors did not present the cryo-EM structure of M. mazei GS in complex with ATP and glutamate in the presence of 2-OG, which could have shed light on the differences in glutamine biosynthesis between previously reported GS enzymes and the 2-OG-bound M. mazei GS.

      Furthermore, besides revealing the cryo-EM structure of 2-OG-bound GS, the study also observed the filamentous form of GS, suggesting that filament formation may be a universal stacking mechanism across archaeal and bacterial species. However, efforts to enhance resolution to investigate whether the stacked polymer is induced by 2-OG or other factors such as ions or metabolites were not undertaken by the authors, leaving room for further exploration into the mechanisms underlying filament formation in GS.

      We thank Reviewer #2 for the detailed assessment and valuable input. We will address those comments in the updated manuscript and clarify the message.

      (1) The discrepancy of the dodecamer formation (max. at 5 mM 2-OG) and the enzyme activity (max. at 12.5 mM 2-OG). We assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site. See also Reviewer #1 R.1). We assume this is the reason why the activity of dodecameric GlnA1 can be further enhanced by increased 2-OG concentration until all catalytic sites are primed.

      (2) The lack of the structure of a 2-OG and ATP-bound GlnA1. Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (3) The observed GlnA1-filaments are an interesting finding. We certainly agree with the referee on that point, that the stacked polymers are potentially induced by 2-OG or ions. However, it is out of the main focus of this manuscript to further explore those filaments. Nevertheless, this observation could serve as an interesting starting point for future experiments.

      Reviewer #3 (Public Review):

      Summary:

      The current manuscript investigates the effect of 2-oxoglutarate and the Glk1 protein as modulators of the enzymatic reactivity of glutamine synthetase. To do this, the authors rely on mass photometry, specific activity measurements, and single-particle cryo-EM data.

      From the results obtained, the authors convey that glutamine synthetase from Methanosarcina mazei exists in a non-active monomeric/dimeric form under low concentrations of 2-oxoglutarate, and its oligomerization into a dodecameric complex is triggered by higher concentration of 2-oxoglutarate, also resulting in the enhancement of the enzyme activity.

      Strengths:

      Glutamine synthetase is a crucial enzyme in all domains of life. The dodecameric fold of GS is recurrent amongst prokaryotic and archaea organisms, while the enzyme activity can be regulated in distinct ways. This is a very interesting work combining protein biochemistry with structural biology.

      The role of 2-OG is here highlighted as a crucial effector for enzyme oligomerization and full reactivity.

      Weaknesses:

      Various opportunities to enhance the current state-of-the-art were missed. In particular, omissions of the ligand-bound state of GnK1 leave unexplained the lack of its interaction with GS (in contradiction with previous results from the authors). A finer dissection of the effect and role of 2-oxoglurate are missing and important questions remain unanswered (e.g. are dimers relevant during early stages of the interaction or why previous GS dodecameric structures do not show 2-oxoglutarate).

      We thank Reviewer #3 for the expert evaluation and inspiring criticism.

      (1) Encouragement to examine ligand-bound states of GlnK1. We agree and plan to perform the suggested experiments exploring the conditions under which GlnA1 and GlnK1 might interact. We will perform the MP experiments in the presence of ATP. In GlnA1 activity test assays when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (2) The exact role of 2-OG could have been dissected much better. We agree on that point and will improve the clarity of the manuscript. See also Reviewer #1 R.1.

      (3) The lack of studies on dimers. This is actually an interesting point, which we did not consider during writing the manuscript. Now, re-analysing all our MP data in this respect, GlnA1 is likely a dimer as smallest species. Consequently, we will add more supplementary data which supports this observation and change the text accordingly.

      (4) Previous studies and structures did not show the 2-OG. We assume that for other structures, no additional 2-OG was added, and the groups did not specifically analyse for this metabolite either. All methanoarchaea perform methanogenesis and contain the oxidative part of the TCA cycle exclusively for the generation of glutamate (anabolism) but not a closed TCA cycle enabling them to use internal 2-OG concentration as internal signal for nitrogen availability. In the case of bacterial GS from organisms with a closed TCA cycle used for energy metabolism (oxidation of acetyl CoA) like e.g. E. coli, the formation of an active dodecameric GS form underlies another mechanism independent of 2-OG. In case of the recent M. mazei GS structures published by Schumacher et al. 2023, the dodecameric structure is probably a result from the heterologous expression and purification from E. coli. (See also Reviewer #1 R.2). One example of methanoarchaeal glutamine synthetases that do in fact contain the 2-OG in the structure, is Müller et al. 2023.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific issues:

      L 141: 2-OG levels increase due to slowing GOGAT reaction (due to Gln limitation as a consequence of N-starvation).... (2-OG also increases in bacteria that lack GDH...)

      As the GS-GOGAT cycle is the major route of ammonium assimilation, consumption of 2-OG by GDH is probably only relevant under high ammonium concentrations.

      In Methanoarchaea, GS is strictly regulated and expression strongly repressed under nitrogen sufficiency - thus glutamate for anabolism is mainly generated by GDH under N sufficiency consuming 2-OG delivered by the oxidative part of the TCA cycle (Methanogenesis is the energy metabolism in methanoarchaea, a closed TCA cycle is not present) thus 2-OG is increasing under nitrogen limitation, when no NH3 is available for GDH.

      L148: it is not clear what is meant by: "and due to the indirect GS activity assay"

      We apologize for not being clear here. The GS activity assay used is the classical assay by Sahpiro & Stadtman 1970 and is a coupled optical test assay (coupling the ATP consumption of the GS activity to the oxidation of NADH by lactate dehydrogenase). Based on the coupled test assay the measurements of low activities show a high deviation. We now added this information in the revised MS respectively.

      L: 177: arguing about 2-OG affinities: more precisely, the 0.75 mM 2-OG is the EC50 concentration of 2-OG for triggering dodecameric formation; it might not directly reflect the total 2-OG affinity, since the affinity may be modulated by (anti)cooperative effects, or by additional sites... as there may be different 2-OG binding sites involved... (same in line 201)

      Thank you for the valuable input. We changed KD to EC50 within the entire manuscript. Concerning possible additional 2-OG binding sites: we did not see any other 2-OG in the cryo-EM structure aside from the described one and we therefore assume that the one described in the manuscript is the main and only one. Considering the high amounts of 2-OG (12.5 mM) used in the structure, it is quite unlikely that additional 2-OG sites exist since they would have unphysiologically low affinities.

      In this respect, instead of the rather poor assay shown in Figure 1D, a more detailed determination of catalytic activation by different 2-OG concentrations should be done (similar to 1A)... This would allow a direct comparison between dodecamerization and enzymatic activation.

      We agree and performed the respective experiments, which are now presented in revised Fig. 1D

      Discussion: the role of 2-OG as a direct activator, comparison with other prokaryotic GS: in other cases, 2-OG affects GS indirectly by being sensed by PII proteins or other 2-OG sensing mechanisms (like 2OG-NtcA-mediated repression of IF factors in cyanobacteria)

      We agree and have added that information in the discussion as suggested.

      290. Unclear: As a second step of activation, the allosteric binding of 2-OG causes a series of conformational.... where is this site located? According to the catalytic effects (compare 1A and 1D) this site should have a lower affinity …

      Thank you very much for pointing this out. Binding of 2-OG only occurs in one specific allosteric binding-site. Binding however, has two effects on the GlnA1: dodecamer assembly and priming of the active site (with two specific EC50, which are now shown in Fig. 1A and D).

      See also public comment #1 (1).

      Reviewer #2 (Recommendations For The Authors):

      The primary concern for me is that mass photometry might lead to incorrect conclusions. The differences in the forms of GS seen in SEC and MP suggest that GS can indeed form a stable dodecamer when the concentration of GS is high enough, as shown in Figure S1B. I strongly suggest using an additional biophysical method to explore the connection between GS and 2-OG in terms of both assembly and activity, to truly understand 2-OG's role in the process of assembly and catalysis.

      We apologize if we did not present this clear enough, however the MP analysis of GlnA1 in the absence of 2-OG showed always (monomers/) dimers, dodecamers were only present in the presence of 2-OG. The SEC analysis in Fig. S1B has been performed in the presence of 12.5 mM 2-OG, we realized this information is missing in the figure legend - we now added this in the revised version. The 2-OG is in addition visible in the Cryo EM structure. Thus, we do not agree to perform additional biophysical methods.

      As for the other experimental findings, they appear satisfactory to me, and I have no reservations regarding the cryoEM data.

      (1) Mass photometry is a fancy technique that uses only a tiny amount of protein to study how they come together. However, the concentration of the protein used in the experiment might be lower than what's needed for them to stick together properly. So, the authors saw a lot of single proteins or pairs instead of bigger groups. They showed in Figure S1B that the M. mazei GS came out earlier than a 440-kDa reference protein, indicating it's actually a dodecamer. But when they looked at the dodecamer fraction using mass photometry, they found smaller bits, suggesting the GS was breaking apart because the concentration used was too low. To fix this, they could try using a technique called analytic ultracentrifuge (AUC) with different amounts of 2-OG to see if they can spot single proteins or pairs when they use a bit more GS. They could also try another technique called SEC-MALS to do similar tests. If they do this, they could replace Figure 1A with new data showing fully formed GS dodecamers when they use the right amount of 2-OG.

      Thank you for this input. In MP we looked at dodecamer formation after removing the 2-OG entirely and re-adding it in the respective concentration. We think that GlnA1 is much more unstable in its monomeric/dimeric fraction and that the complete and harsh removal of 2-OG results in some dysfunctional protein which does not recover the dodecameric conformation after dialysis and re-addition of 2-OG. Looking at the dodecamer-peak right after SEC however, we exclusively see dodecamers, which is now included as an additional supplementary figure (suppl. Fig. 1C). Consequently, we did not perform additional experiments.

      (2) Building on the last point, the estimated binding strength (Kd) between 2-OG and GS might be lower than it really is, because the GS often breaks apart from its dodecameric form in this experiment, even though 2-OG helps keep the pairs together, as seen with cryoEM. What if they used 5-10 times more GS in the mass photometry experiment? Would the estimated bond strength stay the same? Could they use AUC or other techniques like ITC to find out the real, not just estimated, strength of the bond?

      We agree that the term KD is not suitable. We have changed the term KD to EC50 as suggested by reviewer #1, which describes the effective concentration required for 50 % dodecamer assembly. Furthermore, we disagree that the dodecamer breaks apart when the concentrations are as low as in MP experiments. The actual reason for the breaking is rather the harsh dialysis to remove all 2-OG before MP experiments. Right after SEC, the we exclusively see dodecamer in MP (suppl. Fig. S1C). See also #2 (1).

      (3) The fact that the GS hardly works without 2-OG is interesting. I tried to understand the experiment setup, but it wasn't clear as the protocol mentioned in the author's 2021 FEBS paper referred to an old paper from 1970. The "coupled optical test assay" they talked about wasn't explained well. I found other papers that used phosphometry assays to see how much ATP was used up. I suggest the authors give a better, more detailed explanation of their experiments in the methods section. Also, it's unclear why the GS activity keeps going up from 5 to 12.5 mM 2-OG, even though they said it's saturated. They suggested there might be another change happening from 5 to 12.5 mM 2-OG. If that's the case, they should try to get a cryo-EM picture of the GS with lots of 2-OG, both with and without ATP/glutamate (or the Met-Sox-P-ADP inhibitor), to see what's happening at a structural level during this change caused by 2-OG.

      We agree with the reviewer that the GS assay was not explained in detail (since published and known for several years). However, we now added the more detailed description of the assay in the revised MS, which also measures the ATP used up by GS, but couples the generation of ADP to an optical test assay producing pyruvate from PEP with the generated ADP catalysed by pyruvate kinase present in the assay. This generated pyruvate is finally reduced to lactate by the present lactate dehydrogenase consuming NADH, the reduction of which is monitored at 340 nm.

      The still increasing activity of GS after dodecamer formation (max. at 5 mM 2-OG) and the continuously increasing enzyme activity (max. at 12.5 mM 2-OG): See also public reviews, we assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site.

      The suggested additional experiments with and without ATP/Glutamate: Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (4) Please remake Figure S2, the panels are too small to read the words. At least I have difficulty doing so.

      We assume the reviewer is pointing to Suppl. Fig S3, we now changed this figure accordingly.

      Line 153, the reference Schumacher et al. 23, should be 2023?

      Yes, thank you. We corrected that.

      Line 497. I believe it's UCSF ChimeraX, not Chimera.

      We apologize and corrected accordingly.

      Reviewer #3 (Recommendations For The Authors):

      Recent studies on the Methanothermococcus thermolithotrophicus glutamine synthetase, published by Müller et al., 2024, have identified the binding site for 2-oxoglutarate as well as the conformational changes that were induced in the protein by its presence. In the present study, the authors confirm these observations and additionally establish a link between the presence of 2-oxoglutarate and the dodecameric fold and full activation of GS.

      Curiously, here, the authors could not confirm their own findings that the dodecameric GS can directly interact with the PII-like GlnK1 protein and the small peptide sP26. However, the lack of mention of the GlnK-bound state in these studies is very alarming since it certainly is highly relevant here.

      We agree with the reviewer that we have not observed the interaction with GlnK1 and sP26 in the recent study. Consequently, we speculate that yet unknown cellular factor(s) might be required for an interaction of GlnA1 with GlnK1 and sP26, which were not present in the in vitro experiments using purified proteins, however they were present in the previous pull-down approaches (Ehlers et al. 2005, Gutt et al. 2021). Another reason might be that post-translational modifications occur in M. mazei, which might be important for the interaction, which are also not present in purified proteins expressed in E. coli.

      The manuscript interest could have been substantially increased if the authors had done finer biochemical and enzymatic analyses on the oligomerization process of GS, used GlnK1 bound to known effectors in their assays and would have done some more efforts to extrapolate their findings (even if a small niche) of related glutamine synthetases.

      We thank the reviewer for their valuable encouragement to explore ligand-bound-states of GlnK1. However, in this manuscript we mainly focused on 2-OG as activator of GlnA1 and decided to dedicate future experiments to the exploration of conditions that possibly favor GlnK1-binding.

      In principle, we have explored the ATP bound GlnK1 effects on GlnA1 activity in the activity assays (Fig. 2E) since ATP (3.6 mM) is present. GlnK1 however showed no effects on GlnA1 activity.

      In general, the manuscript is poorly written, with grammatically incorrect sentences that at times, which stands in the way of passing on the message of the manuscript.

      Particular points:

      (1) It is mentioned that 2-OG induces the active oligomeric (dodecamer, 12-mer) state of GlnA1 without detectable intermediates. However, only 62 % of the starting inactive enzyme yields active 12-mers. Note that this is contradicted in line 212.

      Thanks for pointing out this discrepancy. After removing all 2-OG as we did before MP-experiments, GlnA1 doesn’t reach full dodecamers anymore when 2-OG is re-added. This is not because the 2-OG amount is not enough to trigger full assembly, but because the protein is much more unstable in the absence of 2-OG, so we predict that some GlnA1 breaks during dialysis. See also answer reviewer #2 (1) and supplementary figure S1C.

      Is there any protein precipitation upon the addition of 2-OG? Is all protein being detected in the assay, meaning, is monomer/dimer + dodecamer yields close to 100% of the total enzyme in the assay?

      There is no protein precipitation upon the addition of 2-OG, indeed, GlnA1 is much more stable in the presence of 2-OG. In the mass photometry experiments, all particles are measured, precipitated protein would be visible as big entities in the MP.

      Please add to Figure 1 the amount of monomer/dimer during titration. Some debate why there is no full conversion should be tentatively provided.

      We agree with the reviewer and included the amount of monomer/dimer in the figure, as well as some discussion on why it is not fully converted again. GlnA1 is unstable without 2-OG and it was dialysed against buffer without 2-OG before MP measurements. This sample mistreatment resulted in no full re-assembly after re-adding 2-OG (although full dodecamers before dialysis (suppl. Fig. S1C).

      (2) Figure 1B reflects an exemplary result. Here, the addition of 0.1 mM 2-OG seems to promote monomer to dimer transition. Why was this not studied in further detail? It seems highly relevant to know from which species the dodecamer is assembled.

      We thank the reviewer for their comment. However, we would like to point out that, although not shown in the figure, GlnA1 is always mainly present as dimers as the smallest entity. As suggested earlier, we have added the amount of monomers/dimers to Figure 1A, which shows low monomer-counts at all 2-OG concentrations (Fig.1A). Although not depicted in the graph starting at 0.01 mM OG, we also see mainly dimers at 0 mM 2-OG.

      How does the y-axis compare to the number and percentage of counts assigned to the peaks? In line 713, it is written that the percentage of dodecamer considers the total number of counts, and this was plotted against the 2-OG concentration.

      We thank the reviewer for addressing this unclarity. Line 713 corresponds to Figure 1A, where we indeed plotted the percentage of dodecamer against the 2-OG-concentration. Thereby, the percentage of dodecamer corresponds to the percentage calculated from the Gaussian Fit of the MP-dodecamer-peak. In Figure 1 B, however, the y-axis displays the relative amount of counts per mass, multiple similar masses then add up to the percentage of the respective peak (Gaussian Fit above similar masses).

      (3) Lines 714 and 721 (and elsewhere): Why only partial data is used for statistical purposes?

      We in general only show one exemplary biological replicate, since the quality of the respective GlnA1 purification sometimes varied (maximum activity ranging from 5 - 10 U/mg). Therefore, we only compared activities within the same protein purification. For the EC50 calculations of all measurements, we refer to the supplement.

      (4) Lines 192-193: It is claimed that GlnK1 was previously shown to both regulate the activity of GlnA1 and form a complex with GlnA1. Please mention the ratio between GlnK1 and GlnA1 in this complex.

      We now included the requested information (GlnA1:GlnK1 1:1, (Ehlers et al. 2005); His6-GlnA1 (0.95 μM), His6-GlnK1 (0.65 μM); 2:1,4, Gutt et al. 2021).

      It is also known that PII proteins such as GlnK1 can bind ADP, ATP, and 2-OG. Interestingly, however, for various described PII proteins, 2-OG can only bind after the binding of ATP.

      So, the crucial question here is what is the binding state of GlnK1? 

      Were these assays performed in the absence of ATP? This is key to fully understand and connect the results to the previous observations. For example, if the GlnK1 used was bound to ADP but not to ATP, then the added 2-OG might indeed only be able to affect GlnA1 (leading to its activation/oligomerization). If this were true and according to the data reported, ADP would prevent GlnK1 from interacting with any oligomeric form of GlnA1. However, if GlnK1 bound to ATP is the form that interacts with GlnA1 (potentially validating previous results?) then, 2-OG would first bind to GlnK1 (assuming a higher affinity of 2-OG to GlnK1), eventually causing its release from GlnA1 followed by binding and activation of GlnA1.

      These experiments need to be done as they are essential to further understand the process. Given the ability of the authors to produce the protein and run such assays, it is unclear why they were not done here. As written in line 203, in this case, "under the conditions tested" is not a good enough statement, considering what is known in the field and how many more conclusions could easily be taken from such a setup.

      Thanks for the encouragement to investigate the ligand-bound states of GlnK1. We agree and plan to perform the suggested mass photometry experiments exploring the conditions under which GlnA1 and GlnK1 might interact in future work. In GlnA1 activity test assays, when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (5) Figure 2D legend claims that the graphic shows the percentage of dodecameric GlnA1 as a function of the concentration of 2-OG. This is not what the figure shows; Figure 2D shows the dodecamer/dimer (although legend claims monomer was used, in line 732) ratio as a function of 2-OG (stated in line 736!). If this is true, a ratio of 1 means 50 % of dodecamers and dimers co-exist. This appears to be the case when GlnK1 was added, while in the absence of GlnK1 higher ratios are shown for higher 2-OG concentration implying that about 3 times more dodecamers were formed than dimers. However, wouldn´t a 50 % ratio be physiologically significant?

      We apologize for the partially incorrect and also misleading figure legend and corrected it. Indeed, the ratio of dodecamers and dimers is shown. Furthermore, we did not use monomeric GlnA1 (the smallest entity is mainly a dimer, see Fig 1A), however, the molarity was calculated based on the monomer-mass. Concerning the significance of the difference between the maximum ratio of GlnA1 and GlnK1: The ratio does appear higher, but this is mostly because adding large quantities of GlnK1 broadens all peaks at low molecular weight. This happens because the GlnK1 signal starts overlapping with the signal from GlnA1, leading to inflated GlnA1 dimer counts. We therefore do not think that this is biologically significant, especially as the activities do not differ under these conditions.

      (6) Is it possible that the uncleaved GlnA1 tag is preventing interaction with GlnK1? This should be discussed.

      This is of course a very important point. We however realized that Schumacher et al. also used an N-terminal His-tag, so we assume that the N-terminal tag is not hampering the interaction.

      (7) Line 228: Please detail the reported discrepancies in rmsd between the current protein and the gram-negative enzymes.

      The differences in rmsd between our M.mazei GlnA1 structure and the structure of gram-negative enzymes is caused by a) sequence similarity: E.g. M.mazei GlnA1 compared to B.subtilis GlnA have a sequence percent identity of 58.47; b) ligands in the structure: The B.Subtilis structure contains L-Methionine-S-sulfoximine phosphate, a transition state inhibitor, while the M. mazei  structure contains 2OG; c) Methodology: The structural determination methods also contribute to these differences. B. subtilis GlnA was determined using X-ray crystallography, while the M. mazei GlnA1 structure was resolved using Cryo-EM, where the protein behaves differently in ice compared to a crystal.

      (8) Line 747: The figure title claims "dimeric interface" although the manuscript body only refers to "hexameric interface" or "inter-hexamer interface" (line 224). Moreover, the figure 4 legend uses terms such as vertical and horizontal dimers and this too should be uniformized within the manuscript.

      Thank you for your valuable feedback. We have updated both the figure title and the figure legend as well in the main text to ensure consistency in the description.

      (9) Line 752: The description of the color scheme used here is somehow unclear.

      Thanks for pointing this out. We changed the description to make it more comprehensive.

      (10) Please label H14/15 and H14´/H15´in Fig 4C zoom.

      We agree that this has not been very clear. We added helix labels.

      (11) In Figure 4D legend, make sure to note that the binding sites for the substrate are based on homologies with another enzyme poised with these molecules.

      The same should be clear in the text: sites are not known, they are assumed to be, based on homologies (paragraph starting at line 239).

      Concerning this comment we want to point out that we studied the exact same enzyme as the Schumacher group, except that we used 2-OG in our experiments, which they did not.

      (12) Figure 3 appears redundant in light of Figure 4. 

      (13) Line 235: When mentioning F24, please refer to Figure 5.

      Thank you, we changed that accordingly.

      (14) Please provide the distances for the bonds depicted in Figure 4B.

      Thanks for pointing this out, we added distance labels to Figure 4B. For reasons of clarity only to three H-bonds.

      (15) Line 241: D57 is likely serving to abstract a proton from ammonium, what is residue Glu307 potentially doing? The information seems missing in light of how the sentence is built.

      Thanks for pointing this out. According to previous studies both residues are likely involved in proton abstraction - first from ammonium, and then from the formed gamma-ammonium group. Additionally, they contribute in shielding the active site from bulk solvent to prevent hydrolysis of the formed phospho-glutamate.

      (16) Why do the authors assume that increased concentrations of 2-OG are a signal for N starvation only in M. mazei and not in all prokaryotic equivalent systems (line 288)?

      In line 288, we did not claim that this is a unique signal for M. mazei. It is also the central N-starvation signal in Cyanobacteria but not directly perceived by the cyanobacterial GS through binding directly to GS.

      The authors should look into the residues that bind 2-OG and check if they are conserved in other GS. The results of this sequence analysis should be discussed in line with the variable prokaryotic glutamine synthetase types of activity modulation that were exposed in the introduction and Figure 7.

      Please refer to supplementary figure S5, where we already aligned the mentioned glutamine synthetase sequences. Since this was also already discussed in Müller et al. 2024, we did not want to repeat their observations and refer to our supplementary figure in too much detail.

      (17) Figure 5 title: Replace TS by transition state structures of homology enzymes, or alike.

      Thank you for this suggestion. We did not change the title however, since it is not a homologue but the exact same glutamine synthetase from Methanosarcina mazei.

      (18) Line 249: D170 is not shown in Figure 5A or elsewhere in Figure 5.

      Thank you for pointing this out. We added D170 to figure 5A.

      (19) Representative density for the residues binding 2-OG should be provided, maybe in a supplemental figure.

      Thank you for the suggestion. We added the densities of 2-OG-binding residues to figure 4B

      (20) Line 260: Please add a reference when describing the phosphoryl transfer.

      We thank the reviewer for this important point and added that accordingly.

      (21) Line 296: The binding of 2-OG indeed appears to be cooperative, such that at concentrations above its binding affinity to the protein, only dodecamers are seen (under experimental conditions). However, claiming that the oligomerization is fast is not correct when the experimental setup includes 10 minutes of incubation before measurements are done. Please correct this within the entire manuscript.

      A (fast) continuous kinetic assay could have confirmed this point and revealed the oligomerization steps and the intermediaries in the process (maybe monomer/dimers, then dimers/hexamers, and then hexamers/dodecamers). Such assays would have been highly valuable to this study.

      We thank the reviewer for this suggestion, but disagree. It is indeed a rather fast regulation (as activity assays without pre-incubation only takes 1 min longer to reach full activity, see the newly included suppl. Fig S6). Considering other regulation mechanisms like e.g. transcription or translation regulation, an activation that takes only 60 s is actually quite quick.

      (22) Line 305 (and elsewhere in the manuscript): the authors state that 2-OG primes the active site for a transition state. This appears incorrect. The transition state is the highest energy state in an enzymatic reaction progressing from substrate to product. Meaning, the transition state is a state that has a more or less modified form of the original substrate bound to the active site. This is not the case.

      In line 366 an "active open state" appears much more adequate to use. 

      We agree and changed accordingly throughout the manuscript.

      (23) Line 330: Please delete "found". Eventually replace it with "confirmed": As the authors write, others have described this residue as a ligand to glutamine.

      Thanks, we changed that accordingly, although previous descriptions were just based on homologies without the experimental validation.

      (24) The discussion in at various points summarizing again the results. It should be trimmed and improved.

      (25) Line 381: replace "two fast" with "fast"?

      We thank the reviewer for this suggestion, but disagree on this point. We especially wanted to highlight that there are two central nitrogen-metabolites involved in the direct regulation of GlnA1, that means TWO fast direct processes mediated by 2-OG and glutamine.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

      The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

      To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

      Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells compared to DNMT1 KO alone.

      Strengths:

      The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

      Weaknesses:

      Suggestions for refinement:

      The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants more detailed description. How many genes experience misregulation or aberrant expression? What phenotypic changes occur in these cells? Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1.

      The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transpositionpositive colonies? Further exploration of this phenomenon would be intriguing.

      Reviewer #2 (Public review):

      In this study, Kavaklıoğlu et al. investigated and presented evidence for a role for domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation dependent manner, due to DNMT1 deletion in HAP1 cell line. The authors then identified L1TD1 associated RNAs using RIPSeq, which display a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found L1TD1 protein associated with L1-RNPs and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expression, and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish feasibility of this relationship existing in vivo in either development or disease, or both.

      Comments on revised version:

      In general, the authors did an acceptable job addressing the major concerns throughout the manuscript. This revision is much clearer and has improved in terms of logical progression.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors have addressed all my questions in the revised version of the manuscript.

      Reviewer #2 (Recommendations for the authors):

      Revised comments:

      A few points we'd like to see addressed are our comments about the model (Figure S7C), as this is important for the readership to understand this complex finding. Please try to apply some quantification, if possible (question 8). Please do your best to tone down the direct relationship of these findings to embryology (question 11). Based on both reviewer comments, we believe addressing reviewer #1s "Suggestions for refinement" (2 points), would help us change our view of solid to convincing.

      Responses to changes:

      Major

      (1) The study only used one knockout (KO) cell line generated by CRISPR/Cas9.

      Considering the possibility of an off-target effect, I suggest the authors attempt one or both of these suggestions.

      A)  Generate or acquire a similar DMNT1 deletion that uses distinct sgRNAs, so that the likelihood of off-targets is negligible. A few simple experiments such as qRT-PCR would be sufficient to suggest the same phenotype.

      B)  Confirm the DNMT1 depletion also by siRNA/ASO KD to phenocopy the KO effect.

      (2) In addition to the strategies to demonstrate reproducibility, a rescue experiment restoring DNMT1 to the KO or KD cells would be more convincing. (Partial rescue would suffice in this case, as exact endogenous expression levels may be hard to replicate).

      We have undertook several approaches to study the effect of DNMT1 loss or inactivation: As described above, we have generated a conditional KO mouse with ablation of DNMT1 in the epidermis. DNMT1-deficient keratinocytes isolated from these mice show a significant increase in L1TD1 expression. In addition, treatment of primary human keratinocytes and two squamous cell carcinoma cell lines with the DNMT inhibitor aza-deoxycytidine led to upregulation of L1TD1 expression. Thus, the derepression of L1TD1 upon loss of DNMT1 expression or activity is not a clonal effect.

      Also, the spectrum of RNAs identified in RIP experiments as L1TD1-associated transcripts in HAP1 DNMT1 KO cells showed a strong overlap with the RNAs isolated by a related yet different method in human embryonic stem cells. When it comes to the effect of L1TD1 on L1-1 retrotranspostion, a recent study has reported a similar effect of L1TD1 upon overexpression in HeLa cells [4].

      All of these points together help to convince us that our findings with HAP1 DNMT KO are in agreement with results obtained in various other cell systems and are therefore not due to off-target effects. With that in mind, we would pursue the suggestion of Reviewer 1 to analyze the effects of DNA hypomethylation upon DNMT1 ablation.

      Thank you for addressing this concern. The reference to Beck 2021 and the additional cells lines (R2: keratinocytes and R3: squamous cell carcinoma) provides sufficient evidence that this result is unlikely to be a result of clonal expansion or off targets.

      Question: Was the human ES Cell RIP Experiment shown here? What is the overlap?

      We refer to the recently published study by Jin et al. (PMID: 38165001). As stated in the Discussion, the majority of L1TD1-associated transcripts in HAP1 cells (69%) identified in our study were also reported as L1TD1 targets in hESCs suggesting a conserved binding affinity of this domesticated transposon protein across different cell types.  

      (3) As stated in the introduction, L1TD1 and ORF1p share "sequence resemblance" (Martin 2006). Is the L1TD1 antibody specific or do we see L1 ORF1p if Fig 1C were uncropped?

      (6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).

      This is a relevant question. We are convinced that the L1TD1 antibody does not crossreact with L1 ORF1p for the following reasons: Firstly, the antibody does not recognize L1 ORF1p (40 kDa) in the uncropped Western blot for Figure 1C (Figure R4A). Secondly, the L1TD1 antibody gives only background signals in DKO cells in the indirect immunofluorescence experiment shown in Figure 1E of the manuscript.

      Thirdly, the immunogene sequence of L1TD1 that determines the specificity of the antibody was checked in the antibody data sheet from Sigma Aldrich. The corresponding epitope is not present in the L1 ORF1p sequence.

      Finally, we have shown that the ORF1p antibody does not cross-react with L1TD1 (Figure R4B).

      Response: Thank you for sharing these images. These full images relieve concerns about specificity. The increase of ORF1P in R4B and Main figure 3C is interesting and pointed out in the manuscript. Not for the purposes of this review, but the observation of reduced transposition despite increased ORF1P could be an interesting follow up to this study (combined with the similar UPF1 result could indicate a complex of some kind).

      (4) In abstract (P2), the authors mentioned that L1TD1 works as an RNA chaperone, but in the result section (P13), they showed that L1TD1 associates with L1 ORF1p in an RNA independent manner. Those conclusions appear contradictory. Clarification or revision is required.

      Our findings that both proteins bind L1 RNA, and that L1TD1 interacts with ORF1p are compatible with a scenario where L1TD1/ORF1p heteromultimers bind to L1 RNA. The additional presence of L1TD1 might thereby enhance the RNA chaperone function of ORF1p. This model is visualized now in Suppl. Figure S7C.

      Response: Thank you for the model. To further clarify, do you mean that L1TD1 can bind L1 RNA, but this is not needed for the effect, however this "bonus" binding (that is enabled by heteromultimerization) appears to enhance the retrotransposition frequency? Do you think L1TD1 is binding L1 RNA in this context or simply "stabilizing" ORF1P (Trimer) RNP?

      Based on our data, L1TD1 associates with L1 RNA and interacts with L1 ORF1p. Both features might contribute to the enhanced retrotransposition frequency. Interestingly, the L1TD1 protein shares with its ancestor L1 ORF1p the non-canonical RNA recognition motif and the coiled-coil motif required for the trimerization but has two copies instead of one of the C-terminal domain (CTD), a structure with RNA binding and chaperone function. We speculate that the presence of an additional CTD within the L1TD1 protein might thereby enhance the RNA binding and chaperone function of L1TD1/ORF1p heteromultimers.

      (5) Figure 2C fold enrichment for L1TD1 and ARMC1 is a bit difficult to fully appreciate. A 100 to 200-fold enrichment does not seem physiological. This appears to be a "divide by zero" type of result, as the CT for these genes was likely near 40 or undetectable. Another qRT-PCR based approach (absolute quantification) would be a more revealing experiment. This is the validation of the RIP experiments and the presentation mode is specifically developed for quantification of RIP assays (Sigma Aldrich RIP-qRT-PCR: Data Analysis Calculation Shell). The unspecific binding of the transcript in the absence of L1TD1 in DNMT1/L1TD1 DKO cells is set to 1 and the value in KO cells represents the specific binding relative the unspecific binding. The calculation also corrects for potential differences in the abundance of the respective transcript in the two cell lines. This is not a physiological value but the quantification of specific binding of transcripts to L1TD1. GAPDH as negative control shows no enrichment, whereas specifically associated transcripts show strong enrichement. We have explained the details of RIPqRT-PCR evaluation in Materials and Methods (page 14) and the legend of Figure 2C in the revised manuscript.

      Response: Thank you for the clarification and additional information in the manuscript.

      (6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).

      See response to (3).

      Response: Thanks.

      (7) Figure S4A and S4B: There appear to be a few unusual aspects of these figures that should be pointed out and addressed. First, there doesn't seem to be any ORF1p in the Input (if there is, the exposure is too low). Second, there might be some L1TD1 in the DKO (lane 2) and lane 3. This could be non-specific, but the size is concerning. Overexposure would help see this.

      The ORF1p IP gives rise to strong ORF1p signals in the immunoprecipitated complexes even after short exposure. Under these conditions ORF1p is hardly detectable in the input. Regarding the faint band in DKO HAP1 cells, this might be due to a technical problem during Western blot loading. Therefore, the input samples were loaded again on a Western blot and analyzed for the presence of ORF1p, L1TD1 and beta-actin (as loading control) and shown as separate panel in Suppl. Figure S4A.

      The enhanced image is clearer. Thanks.

      S4A and S4B now appear to the S6A and S6B, is that correct? (This is due to the addition of new S1 and S2, but please verify image orders were not disturbed).

      Yes, the input is shown now as a separate panel in Suppl. Figure S6A.

      (8) Figure S4C: This is related to our previous concerns involving antibody cross-reactivity. Figure 3E partially addresses this, where it looks like the L1TD1 "speckles" outnumber the ORF1p puncta, but overlap with all of them. This might be consistent with the antibody crossreacting. The western blot (Figure 3C) suggests an upregulation of ORF1p by at least 23x in the DKO, but the IF image in 3E is hard to tell if this is the case (slightly more signal, but fewer foci). Can you return to the images and confirm the contrast are comparable? Can you massively overexpose the red channel in 3E to see if there is residual overlap? In Figure 3E the L1TD1 antibody gives no signal in DNMT1/L1TD1 DKO cells confirming that it does not recognize ORF1p. In agreement with the Western blot in Figure 3C the L1 ORF1p signal in Figure 3E is stronger in DKO cells. In DNMT1 KO cells the L1 ORF1p antibody does not recognize all L1TD1 speckles. This result is in agreement with the Western blot shown above in Figure R4B and indicates that the L1 ORF1p antibody does not recognize the L1TD1 protein. The contrast is comparable and after overexposure there are still L1TD1 specific speckles. This might be due to differences in abundance of the two proteins.

      Response: Suggestion: Would it be possible to use a program like ImageJ to supplement the western blot observation? Qualitatively, In figure 3E, it appears that there is more signal in the DKO, but this could also be due to there being multiple cells clustered together or a particularly nicely stained region. Could you randomly sample 20-30 cells across a few experiments to see if this holds up. I am interested in whether the puncta in the KO image(s) is a very highly concentrated region and in the DKO this is more disperse. Also, the representative DKO seems to be cropped slightly wrong. (Please use puncta as a guide to make the cropping more precise)

      As suggested by the reviewer we have quantified the signals of 60 KO cells and 56 DKO cells in three different IF experiments by ImageJ. We measured a 1.4-fold higher expression level of L1 ORF1p in DKO cells. However, the difference is not statistically significant. This is most probably due to the change in cell size and protein content during the cell cycle with increasing protein contents from G1 to G2. Western blot analysis provides signals of comparable protein amounts representing an average expression levels over ten thousands of cells. Nevertheless, the quantification results reflect in principle the IF pictures shown in Figure 3E but IF is probably not the best method to quantify protein amounts. We have also corrected Figure 3E.

      Author response image 1.

      (9) The choice of ARMC1 and YY2 is unclear. What are the criteria for the selection?

      ARMC1 was one of the top hits in a pilot RIP-seq experiment (IP versus input and IP versus IgG IP). In the actual RIP-seq experiment with DKO HAP1 cells instead of IgG IP as a negative control, we found ARMC1 as an enriched hit, although it was not among the top 5 hits. The results from the 2nd RIP-seq further confirmed the validity of ARMC1 as an L1TD1interacting transcript. YY2 was of potential biological relevance as an L1TD1 target due to the fact that it is a processed pseudogene originating from YY1 mRNA as a result of retrotransposition. This is mentioned on page 6 of the revised manuscript.

      Response: Appreciated!

      (10) (P16) L1 is the only protein-coding transposon that is active in humans. This is perhaps too generalized of a statement as written. Other examples are readily found in the literature.

      Please clarify.

      We will tone down this statement in the revised manuscript.

      Response: Appreciated! To further clarify, the term "active" when it comes to transposable elements, has not been solidified. It can span "retrotransposition competent" to "transcripts can be recovered". There are quite a few reports of GAG transcripts and protein from various ERV/LTR subfamilies in various cells and tissues (in mouse and human at least), however whether they contribute to new insertions is actively researched.

      (11) In both the abstract and last sentence in the discussion section (P17), embryogenesis is mentioned, but this is not addressed at all in the manuscript. Please refrain from implying normal biological functions based on the results of this study unless appropriate samples are used to support them.

      Much of the published data on L1TD1 function are related to embryonic stem cells [3- 7].

      Therefore, it is important to discuss our findings in the context of previous reports.

      Response: It is well established that embryonic stem cells are not a perfect or direct proxies for the inner cell mass of embryos, as multiple reports have demonstrated transcriptomic, epigenetic, chromatin accessibility differences. The exact origin of ES cells is also considered controversial. We maintain that the distinction between embryos/embryogenesis and the results presented in the manuscript are not yet interchangeable. An important exception would be complex models of embryogenesis such as embryoids, (or synthetic/artificial embryo models that have been carefully been termed as such so as to not suggest direct implications to embryos). https://www.nature.com/articles/ncb2965  

      https://link.springer.com/article/10.1007/s00018-018-2965-y  

      https://www.cell.com/developmental-cell/abstract/S1534-5807(24)00363-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1534580724003630%3Fshowall%3Dtrue

      We have deleted the corresponding paragraph in the Discussion.

      (12) Figure 3E: The format of Figures 1A and 3E are internally inconsistent. Please present similar data/images in a cohesive way throughout the manuscript. We show now consistent IF Figures in the revised manuscript.

      Response: Thanks

      Minor:

      In general:

      Still need checking for typos, mostly in Materials and Methods section; Please keep a consistent writing style throughout the whole manuscript. If you use L1 ORF1p, then please use L1 instead of LINE-1, or if you keep LINE-1 in your manuscript, then you should use LINE-1 ORF1p.

      A lab member from the US checked again the Materials and Methods section for typos. We keep the short version L1 ORF1p.

      (1) Intro:

      - Is L1Td1 in mice and Humans? How "conserved" is it and does this suggest function? Murine and human L1TD1 proteins share 44% identity on the amino acid level and it was suggested that the corresponding genes were under positive selection during evolution with functions in transposon control and maintenance of pluripotency [8].

      - Why HAP1? (Haploid?) The importance of this cell line is not clear.

      HAP1 is a nearly haploid human cancer cell line derived from the KBM-7 chronic myelogenous leukemia (CML) cell line [9, 10]. Due to its haploidy is perfectly suited and widely used for loss-of-function screens and gene editing. After gene editing cells can be used in the nearly haploid or in the diploid state. We usually perform all experiments with diploid HAP1 cell lines. Importantly, in contrast to other human tumor cell lines, this cell line tolerates ablation of DNMT1. We have included a corresponding explanation in the revised manuscript on page 5, first paragraph.

      - Global methylation status in DNMT1 KO? (Methylations near L1 insertions, for example?)

      The HAP1 DNMT1 KO cell line with a 20 bp deletion in exon 4 used in our study was validated in the study by Smits et al. [11]. The authors report a significant reduction in overall DNA methylation. However, we are not aware of a DNA methylome study on this cell line. We show now data on the methylation of L1 elements in HAP1 cells and upon DNMT1 deletion in the revised manuscript in Suppl. Figure S1B.

      Response: Looks great!

      (2) Figure 1:

      - Figure 1C. Why is LMNB used instead of Actin (Fig1D)?

      We show now beta-actin as loading control in the revised manuscript.

      - Figure 1G shows increased Caspase 3 in KO, while the matching sentence in the result section skips over this. It might be more accurate to mention this and suggest that the single KO has perhaps an intermediate phenotype (Figure 1F shows a slight but not significant trend).

      We fully agree with the reviewer and have changed the sentence on page 6, 2nd paragraph accordingly.

      - Would 96 hrs trend closer to significance? An interpretation is that L1TD1 loss could speed up this negative consequence.

      We thank the reviewer for the suggestion. We have performed a time course experiment with 6 biological replicas for each time point up to 96 hours and found significant changes in the viability upon loss of DNMT1 and again significant reduction in viability upon additional loss of L1TD1 (shown in Figure 1F). These data suggest that as expected loss of DNMT1 leads to significant reduction viability and that additional ablation of L1TD1 further enhances this effect.

      Response: Looks good!

      - What are the "stringent conditions" used to remove non-specific binders and artifacts (negative control subtraction?)

      Yes, we considered only hits from both analyses, L1TD1 IP in KO versus input and L1TD1 IP in KO versus L1TD1 IP in DKO. This is now explained in more detail in the revised manuscript on page 6, 3rd paragraph.

      (3) Figure 2:

      - Figure 2A is a bit too small to read when printed.

      We have changed this in the revised manuscript.

      - Since WT and DKO lack detectable L1TD1, would you expect any difference in RIP-Seq results between these two?

      Due to the lack of DNMT1 and the resulting DNA hypomethylation, DKO cells are more similar to KO cells than WT cells with respect to the expressed transcripts.

      - Legend says selected dots are in green (it appears blue to me). We have changed this in the revised manuscript.

      - Would you recover L1 ORF1p and its binding partners in the KO? (Is the antibody specific in the absence of L1TD1 or can it recognize L1?) I noticed an increase in ORF1p in the KO in Figure 3C.

      Thank you for the suggestion. Yes, L1 ORF1p shows slightly increased expression in the proteome analysis and we have marked the corresponding dot in the Volcano plot (Figure 3A).

      - Should the figure panel reference near the (Rosspopoff & Trono) reference instead be Sup S1C as well? Otherwise, I don't think S1C is mentioned at all.

      - What are the red vs. green dots in 2D? Can you highlight ERV and ALU with different colors?

      We added the reference to Suppl. Figure S1C (now S3C) in the revised manuscript. In Figure 2D L1 elements are highlighted in green, ERV elements in yellow, and other associated transposon transcripts in red.

      Response: Much better, thanks!

      - Which L1 subfamily from Figure 2D is represented in the qRT-PCR in 2E "LINE-1"? Do the primers match a specific L1 subfamily? If so, which? We used primers specific for the human L1.2 subfamily.

      - Pulling down SINE element transcripts makes some sense, as many insertions "borrow" L1 sequences for non-autonomous retro transposition, but can you speculate as to why ERVs are recovered? There should be essentially no overlap in sequence.

      In the L1TD1 evolution paper [8], a potential link between L1TD1 and ERV elements was discussed:

      "Alternatively, L1TD1 in sigmodonts could play a role in genome defense against another element active in these genomes. Indeed, the sigmodontine rodents have a highly active family of ERVs, the mysTR elements [46]. Expansion of this family preceded the death of L1s, but these elements are very active, with 3500 to 7000 speciesspecific insertions in the L1-extinct species examined [47]. This recent ERV amplification in Sigmodontinae contrasts with the megabats (where L1TD1 has been lost in many species); there are apparently no highly active DNA or RNA elements in megabats [48]. If L1TD1 can suppress retroelements other than L1s, this could explain why the gene is retained in sigmodontine rodents but not in megabats."

      Furthermore, Jin et al. report the binding of L1TD1 to repetitive sequences in transcripts [12]. It is possible that some of these sequences are also present in ERV RNAs.

      Response: Interesting, thanks for sharing

      - Is S2B a screenshot? (the red underline).

      No, it is a Powerpoint figure, and we have removed the red underline.

      (4) Figure 3:

      - Text refers to Figure 3B as a western blot. Figure 3B shows a volcano plot. This is likely 3C but would still be out of order (3A>3C>3B referencing). I think this error is repeated in the last result section.

      - Figure and legends fail to mention what gene was used for ddCT method (actin, gapdh, etc.).

      - In general, the supplemental legends feel underwritten and could benefit from additional explanations. (Main figures are appropriate but please double-check that all statistical tests have been mentioned correctly).

      Thank you for pointing this out. We have corrected these errors in the revised manuscript.

      (5) Discussion:

      - Aluy connection is interesting. Is there an "Alu retrotransposition reporter assay" to test whether L1TD1 enhances this as well?

      Thank you for the suggestion. There is indeed an Alu retrotransposition reporter assay reported be Dewannieux et al. [13]. The assay is based on a Neo selection marker. We have previously tested a Neo selection-based L1 retrotransposition reporter assay, but this system failed to properly work in HAP1 cells, therefore we switched to a blasticidin based L1 retrotransposition reporter assay. A corresponding blasticidin-based Alu retrotransposition reporter assay might be interesting for future studies (mentioned in the Discussion, page 11 paragraph 4 of the revised manuscript.

      (6) Material and Methods :

      - The number of typos in the materials and methods is too numerous to list. Instead, please refer to the next section that broadly describes the issues seen throughout the manuscript.

      Writing style

      (1) Keep a consistent style throughout the manuscript: for example, L1 or LINE-1 (also L1 ORF1p or LINE-1 ORF1p); per or "/"; knockout or knock-out; min or minute; 3 times or three times; media or medium. Additionally, as TE naming conventions are not uniform, it is important to maintain internal consistency so as to not accidentally establish an imprecise version.

      (2) There's a period between "et al" and the comma, and "et al." should be italic.

      (3) The authors should explain what the key jargon is when it is first used in the manuscript, such as "retrotransposon" and "retrotransposition".

      (4) The authors should show the full spelling of some acronyms when they use it for the first time, such as RNA Immunoprecipitation (RIP).

      (5) Use a space between numbers and alphabets, such as 5 μg. (6) 2.0 × 105 cells, that's not an "x".

      (7) Numbers in the reference section are lacking (hard to parse).

      (8) In general, there are a significant number of typos in this draft which at times becomes distracting. For example, (P3) Introduction: Yet, co-option of TEs thorough (not thorough, it should be through) evolution has created so-called domesticated genes beneficial to the gene network in a wide range of organisms. Please carefully revise the entire manuscript for these minor issues that collectively erode the quality of this submission. Thank you for pointing out these mistakes. We have corrected them in the revised manuscript. A native speaker from our research group has carefully checked the paper. In summary, we have added Supplementary Figure S7C and have changed Figures 1C, 1E, 1F, 2A, 2D, 3A, 4B, S3A-D, S4B and S6A based on these comments.

      Response: Thank you for taking these comments on board!

    1. Author response:

      Reviewer #1 (Public review):

      Wang et al., recorded concurrent EEG-fMRI in 107 participants during nocturnal NREM sleep to investigate brain activity and connectivity related to slow oscillations (SO), sleep spindles, and in particular their co-occurrence. The authors found SO-spindle coupling to be correlated with increased thalamic and hippocampal activity, and with increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the neocortex, especially the medial prefrontal cortex (mPFC). They concluded the brain-wide activation pattern to resemble episodic memory processing, but to be dissociated from task-related processing and suggest that the thalamus plays a crucial role in coordinating the hippocampal-cortical dialogue during sleep.

      The paper offers an impressively large and highly valuable dataset that provides the opportunity for gaining important new insights into the network substrate involved in SOs, spindles, and their coupling. However, the paper does unfortunately not exploit the full potential of this dataset with the analyses currently provided, and the interpretation of the results is often not backed up by the results presented. I have the following specific comments.

      Thank you for your thoughtful and constructive feedback. We greatly appreciate your recognition of the strengths of our dataset and findings Below, we address your specific comments and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We hope these revisions address your comments and further strengthen our manuscript. Thank you again for the constructive feedback.

      (1) The introduction is lacking sufficient review of the already existing literature on EEG-fMRI during sleep and the BOLD-correlates of slow oscillations and spindles in particular (Laufs et al., 2007; Schabus et al., 2007; Horovitz et al., 2008; Laufs, 2008; Czisch et al., 2009; Picchioni et al., 2010; Spoormaker et al., 2010; Caporro et al., 2011; Bergmann et al., 2012; Hale et al., 2016; Fogel et al., 2017; Moehlman et al., 2018; Ilhan-Bayrakci et al., 2022). The few studies mentioned are not discussed in terms of the methods used or insights gained.

      We acknowledge the need for a more comprehensive review of prior EEG-fMRI studies investigating BOLD correlates of slow oscillations and spindles. However, these articles are not all related to sleep SO or spindle. Articles (Hale et al., 2016; Horovitz et al., 2008; Laufs, 2008; Laufs, Walker, & Lund, 2007; Spoormaker et al., 2010) mainly focus on methodology for EEG-fMRI, sleep stages, or brain networks, which are not the focus of our study. Thank you again for your attention to the comprehensiveness of our literature review, and we will expand the introduction to include a more detailed discussion of the existing literature, ensuring that the contributions of previous EEG-fMRI sleep studies are adequately acknowledged.

      Introduction, Page 4 Lines 62-76

      “Investigating these sleep-related neural processes in humans is challenging because it requires tracking transient sleep rhythms while simultaneously assessing their widespread brain activation. Recent advances in simultaneous EEG-fMRI techniques provide a unique opportunity to explore these processes. EEG allows for precise event-based detection of neural signal, while fMRI provides insight into the broader spatial patterns of brain activation and functional connectivity (Horovitz et al., 2008; Huang et al., 2024; Laufs, 2008; Laufs, Walker, & Lund, 2007; Schabus et al., 2007; Spoormaker et al., 2010). Previous EEG-fMRI studies on sleep have focused on classifying sleep stages or examining the neural correlates of specific waves (Bergmann et al., 2012; Caporro et al., 2012; Czisch et al., 2009; Fogel et al., 2017; Hale et al., 2016; Ilhan-Bayrakcı et al., 2022; Moehlman et al., 2019; Picchioni et al., 2011). These studies have generally reported that slow oscillations are associated with widespread cortical and subcortical BOLD changes, whereas spindles elicit activation in the thalamus, as well as in several cortical and paralimbic regions. Although these findings provide valuable insights into the BOLD correlates of sleep rhythms, they often do not employ sophisticated temporal modeling (Huang et al., 2024), to capture the dynamic interactions between different oscillatory events, e.g., the coupling between SOs and spindles.”

      (2) The paper falls short in discussing the specific insights gained into the neurobiological substrate of the investigated slow oscillations, spindles, and their interactions. The validity of the inverse inference approach ("Open ended cognitive state decoding"), assuming certain cognitive functions to be related to these oscillations because of the brain regions/networks activated in temporal association with these events, is debatable at best. It is also unclear why eventually only episodic memory processing-like brain-wide activation is discussed further, despite the activity of 16 of 50 feature terms from the NeuroSynth v3 dataset were significant (episodic memory, declarative memory, working memory, task representation, language, learning, faces, visuospatial processing, category recognition, cognitive control, reading, cued attention, inhibition, and action).

      Thank you for pointing this out, particularly regarding the use of inverse inference approaches such as “open-ended cognitive state decoding.” Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7. We will refocus the main text on direct neurobiological insights gained from our EEG-fMRI analyses, particularly emphasizing the hippocampal-thalamocortical network dynamics underlying SO-spindle coupling, and we will acknowledge the exploratory nature of these findings and highlight their limitations.

      Discussion, Page 17-18 Lines 323-332

      “To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

      (3) Hippocampal activation during SO-spindles is stated as a main hypothesis of the paper - for good reasons - however, other regions (e.g., several cortical as well as thalamic) would be equally expected given the known origin of both oscillations and the existing sleep-EEG-fMRI literature. However, this focus on the hippocampus contrasts with the focus on investigating the key role of the thalamus instead in the Results section.

      We appreciate your insight regarding the relative emphasis on hippocampal and thalamic activation in our study. We recognize that the manuscript may currently present an inconsistency between our initial hypothesis and the main focus of the results. To address this concern, we will ensure that our Introduction and Discussion section explicitly discusses both regions, highlighting the complementary roles of the hippocampus (memory processing and reactivation) and the thalamus (spindle generation and cortico-hippocampal coordination) in SO-spindle dynamics.

      Introduction, Page 5 Lines 87-103

      “To address this gap, our study investigates brain-wide activation and functional connectivity patterns associated with SO-spindle coupling, and employs a cognitive state decoding approach (Margulies et al., 2016; Yarkoni et al., 2011)—albeit indirectly—to infer potential cognitive functions. In the current study, we used simultaneous EEG-fMRI recordings during nocturnal naps (detailed sleep staging results are provided in the Methods and Table S1) in 107 participants. Although directly detecting hippocampal ripples using scalp EEG or fMRI is challenging, we expected that hippocampal activation in fMRI would coincide with SO-spindle coupling detected by EEG, given that SOs, spindles, and ripples frequently co-occur during NREM sleep. We also anticipated a critical role of the thalamus, particularly thalamic spindles, in coordinating hippocampal-cortical communication.

      We found significant coupling between SOs and spindles during NREM sleep (N2/3), with spindle peaks occurring slightly before the SO peak. This coupling was associated with increased activation in both the thalamus and hippocampus, with functional connectivity patterns suggesting thalamic coordination of hippocampal-cortical communication. These findings highlight the key role of the thalamus in coordinating hippocampal-cortical interactions during human sleep and provide new insights into the neural mechanisms underlying sleep-dependent brain communication. A deeper understanding of these mechanisms may contribute to future neuromodulation approaches aimed at enhancing sleep-dependent cognitive function and treating sleep-related disorders.”

      Discussion, Page 16-17 Lines 292-307

      “When modeling the timing of these sleep rhythms in the fMRI, we observed hippocampal activation selectively during SO-spindle events. This suggests the possibility of triple coupling (SOs–spindles–ripples), even though our scalp EEG was not sufficiently sensitive to detect hippocampal ripples—key markers of memory replay (Buzsáki, 2015). Recent iEEG evidence indicates that ripples often co-occur with both spindles (Ngo, Fell, & Staresina, 2020) and SOs (Staresina et al., 2015; Staresina et al., 2023). Therefore, the hippocampal involvement during SO-spindle events in our study may reflect memory replay from the hippocampus, propagated via thalamic spindles to distributed cortical regions.

      The thalamus, known to generate spindles (Halassa et al., 2011), plays a key role in producing and coordinating sleep rhythms (Coulon, Budde, & Pape, 2012; Crunelli et al., 2018), while the hippocampus is found essential for memory consolidation (Buzsáki, 2015; Diba & Buzsá ki, 2007; Singh, Norman, & Schapiro, 2022). The increased hippocampal and thalamic activity, along with strengthened connectivity between these regions and the mPFC during SO-spindle events, underscores a hippocampal-thalamic-neocortical information flow. This aligns with recent findings suggesting the thalamus orchestrates neocortical oscillations during sleep (Schreiner et al., 2022). The thalamus and hippocampus thus appear central to memory consolidation during sleep, guiding information transfer to the neocortex, e.g., mPFC.”

      (4) The study included an impressive number of 107 subjects. It is surprising though that only 31 subjects had to be excluded under these difficult recording conditions, especially since no adaptation night was performed. Since only subjects were excluded who slept less than 10 min (or had excessive head movements) there are likely several datasets included with comparably short durations and only a small number of SOs and spindles and even less combined SO-spindle events. A comprehensive table should be provided (supplement) including for each subject (included and excluded) the duration of included NREM sleep, number of SOs, spindles, and SO+spindle events. Also, some descriptive statistics (mean/SD/range) would be helpful.

      We appreciate your recognition of our sample size and the challenges associated with simultaneous EEG-fMRI sleep recordings. We acknowledge the importance of transparently reporting individual subject data, particularly regarding sleep duration and the number of detected SOs, spindles, and SO-spindle events. To address this, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (5)Density of detected SOs; (6)Density of detected spindles; (7)Density of detected SO-spindle coupling events.

      However, most of the excluded participants were unable to fall asleep or had too short a sleep duration, so they basically had no NREM sleep period, so it was impossible to count the NREM sleep duration, SO, spindle, and coupling numbers.

      Supplementary Materials, Page 42-54, Table S1-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      (5) Was the 20-channel head coil dedicated for EEG-fMRI measurements? How were the electrode cables guided through/out of the head coil? Usually, the 64-channel head coil is used for EEG-fMRI measurements in a Siemens PRISMA 3T scanner, which has a cable duct at the back that allows to guide the cables straight out of the head coil (to minimize MR-related artifacts). The choice for the 20-channel head coil should be motivated. Photos of the recording setup would also be helpful.

      Thank you for your comment regarding our choice of the 20-channel head coil for EEG-fMRI measurements. We acknowledge that the 64-channel head coil is commonly used in Siemens PRISMA 3T scanners; however, the 20-channel coil was selected due to specific practical and technical considerations in our study. In particular, the 20-channel head coil was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil allowed us to maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.

      We have made this clearer in the revised manuscript.

      Methods, Page 20 Lines 385-392

      “All MRI data were acquired using a 20-channel head coil on a research-dedicated 3-Tesla Siemens Magnetom Prisma MRI scanner. Earplugs and cushions were provided for noise protection and head motion restriction. We chose the 20-channel head coil because it was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil helped maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.”

      (6) Was the EEG sampling synchronized to the MR scanner (gradient system) clock (the 10 MHz signal; not referring to the volume TTL triggers here)? This is a requirement for stable gradient artifact shape over time and thus accurate gradient noise removal.

      Thank you for raising this important point. We confirm that the EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This synchronization was achieved using the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift. As a result, the gradient artifact waveform remained stable across volumes, allowing for more effective artifact correction during preprocessing. We appreciate your attention to this critical aspect of EEG-fMRI data acquisition.

      We have made this clearer in the revised manuscript.

      Methods, Page 19-20 Lines 371-383

      “EEG was recorded simultaneously with fMRI data using an MR-compatible EEG amplifier system (BrainAmps MR-Plus, Brain Products, Germany), along with a specialized electrode cap. The recording was done using 64 channels in the international 10/20 system, with the reference channel positioned at FCz. In order to adhere to polysomnography (PSG) recording standards, six electrodes were removed from the EEG cap: one for electrocardiogram (ECG) recording, two for electrooculogram (EOG) recording, and three for electromyogram (EMG) recording. EEG data was recorded at a sample rate of 5000 Hz, the resistance of the reference and ground channels was kept below 10 kΩ, and the resistance of the other channels was kept below 20 kΩ. To synchronize the EEG and fMRI recordings, the BrainVision recording software (BrainProducts, Germany) was utilized to capture triggers from the MRI scanner. The EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This was achieved via the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift.”

      (7) The TR is quite long and the voxel size is quite large in comparison to state-of-the-art EPI sequences. What was the rationale behind choosing a sequence with relatively low temporal and spatial resolution?

      We acknowledge that our chosen TR and voxel size are relatively long and large compared to state-of-the-art EPI sequences. This decision was made to optimize the signal-to-noise ratio (SNR) and reduce susceptibility-related distortions, which are particularly critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. A longer TR allowed us to sample whole-brain activity with sufficient coverage, while a larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures such as the thalamus and hippocampus, which are key regions of interest in our study. We appreciate your concern and hope this clarification provides sufficient rationale for our sequence parameters.

      We have made this clearer in the revised manuscript.

      Methods, Page 20-21 Lines 398-408

      “Then, the “sleep” session began after the participants were instructed to try and fall asleep. For the functional scans, whole-brain images were acquired using k-space and steady-state T2*-weighted gradient echo-planar imaging (EPI) sequence that is sensitive to the BOLD contrast. This measures local magnetic changes caused by changes in blood oxygenation that accompany neural activity (sequence specification: 33 slices in interleaved ascending order, TR = 2000 ms, TE = 30 ms, voxel size = 3.5 × 3.5 × 4.2 mm<sup>3</sup>, FA = 90°, matrix = 64 × 64, gap = 0.7 mm). A relatively long TR and larger voxel size were chosen to optimize SNR and reduce susceptibility-related distortions, which are critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. The longer TR allowed whole-brain coverage with sufficient temporal resolution, while the larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures (e.g., the thalamus and hippocampus), which are key regions of interest in this study.”

      (8) The anatomically defined ROIs are quite large. It should be elaborated on how this might reduce sensitivity to sleep rhythm-specific activity within sub-regions, especially for the thalamus, which has distinct nuclei involved in sleep functions.

      We appreciate your insight regarding the use of anatomically defined ROIs and their potential limitations in detecting sleep rhythm-specific activity within sub-regions, particularly in the thalamus. Given the distinct functional roles of thalamic nuclei in sleep processes, we acknowledge that using a single, large thalamic ROI may reduce sensitivity to localized activity patterns. To address this, we will discuss this limitation in the revised manuscript, acknowledging that our approach prioritizes whole-structure effects but may not fully capture nucleus-specific contributions.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      (9) The study reports SO & spindle amplitudes & densities, as well as SO+spindle coupling, to be larger during N2/3 sleep compared to N1 and REM sleep, which is trivial but can be seen as a sanity check of the data. However, the amount of SOs and spindles reported for N1 and REM sleep is concerning, as per definition there should be hardly any (if SOs or spindles occur in N1 it becomes by definition N2, and the interval between spindles has to be considerably large in REM to still be scored as such). Thus, on the one hand, the report of these comparisons takes too much space in the main manuscript as it is trivial, but on the other hand, it raises concerns about the validity of the scoring.

      We appreciate your concern regarding the reported presence of SOs and spindles in N1 and REM sleep and the potential implications. Our detection method for detecting SO, spindle, and coupling were originally designed only for N2&N3 sleep data based on the characteristics of the data itself, and this method is widely recognized and used in the sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). While, because the detection methods for SO and spindle are based on percentiles, this method will always detect a certain number of events when used for other stages (N1 and REM) sleep data, but the differences between these events and those detected in stage N23 remain unclear. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

      Methods, Page 25 Lines 515-524

      “We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

      (10) Why was electrode F3 used to quantify the occurrence of SOs and spindles? Why not a midline frontal electrode like Fz (or a number of frontal electrodes for SOs) and Cz (or a number of centroparietal electrodes) for spindles to be closer to their maximum topography?

      We appreciate your suggestion regarding electrode selection for SO and spindle quantification. Our choice of F3 was primarily based on previous studies (Massimini et al., 2004; Molle et al., 2011), where bilateral frontal electrodes are commonly used for detecting SOs and spindles. Additionally, we considered the impact of MRI-related noise and, after a comprehensive evaluation, determined that F3 provided an optimal balance between signal quality and artifact minimization. We also acknowledge that alternative electrode choices, such as Fz for SOs and Cz for spindles, could provide additional insights into their topographical distributions.

      (11) Functional connectivity (hippocampus -> thalamus -> cortex (mPFC)) is reported to be increased during SO-spindle coupling and interpreted as evidence for coordination of hippocampo-neocortical communication likely by thalamic spindles. However, functional connectivity was only analysed during coupled SO+spindle events, not during isolated SOs or isolated spindles. Without the direct comparison of the connectivity patterns between these three events, it remains unclear whether this is specific for coupled SO+spindle events or rather associated with one or both of the other isolated events. The PPIs need to be conducted for those isolated events as well and compared statistically to the coupled events.

      We appreciate your critical perspective on our functional connectivity analysis and the interpretation of hippocampus-thalamus-cortex (mPFC) interactions during SO-spindle coupling. We acknowledge that, in the current analysis, functional connectivity was only examined during coupled SO-spindle events, without direct comparison to isolated SOs or isolated spindles. To address this concern, we have conducted PPI analyses for all three ROIs(Hippocampus, Thalamus, mPFC) and all three event types (SO-spindle couplings, isolated SOs, and isolated spindles). Our results indicate that neither isolated SOs nor isolated Spindles yielded significant connectivity changes in all three ROIs, as all failed to survive multiple comparison corrections. This suggests that the observed connectivity increase is specific to SO-spindle coupling, rather than being independently driven by either SOs or spindles alone.

      Results, Page 14 Lines 248-255

      “Crucially, the interaction between FC and SO-spindle coupling revealed that only the functional connectivity of hippocampus -> thalamus (ROI analysis, t<sub>(106)</sub> = 1.86, p = 0.0328) and thalamus -> mPFC (ROI analysis, t<sub>(106)</sub> = 1.98, p = 0.0251) significantly increased during SO-spindle coupling, with no significant changes in all other pathways (Fig. 4e). We also conducted PPI analyses for the other two events (SOs and spindles), and neither yielded significant connectivity changes in the three ROIs, as all failed to survive whole-brain FWE correction at the cluster level (p < 0.05). Together, these findings suggest that the thalamus, likely via spindles, coordinates hippocampal-cortical communication selectively during SO-spindle coupling, but not isolated SOs or spindle events alone.”

      (12) The limited temporal resolution of fMRI does indeed not allow for easily distinguishing between fMRI activation patterns related to SO-up- vs. SO-down-states. For this, one could try to extract the amplitudes of SO-up- and SO-down-states separately for each SO event and model them as two separate parametric modulators (with the risk of collinearity as they are likely correlated).

      We appreciate your insightful comment regarding the challenge of distinguishing fMRI activation patterns related to SO-up vs. SO-down states due to the limited temporal resolution of fMRI. While our current analysis does not differentiate between these two phases, we acknowledge that separately modeling SO-up and SO-down states using parametric modulators could provide a more refined understanding of their distinct neural correlates. However, as you notes, this approach carries the risk of collinearity, and there is indeed a high correlation between the two amplitudes across all subjects in our results (r=0.98). Future studies could explore more on leveraging high-temporal-resolution techniques. While implementing this in the current study is beyond our scope, we will acknowledge this limitation in the Discussion section.

      Discussion, Page 17 Lines 308-322

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.

      (13) L327: "It is likely that our findings of diminished DMN activity reflect brain activity during the SO DOWN-state, as this state consistently shows higher amplitude compared to the UP-state within subjects, which is why we modelled the SO trough as its onset in the fMRI analysis." This conclusion is not justified as the fact that SO down-states are larger in amplitude does not mean their impact on the BOLD response is larger.

      We appreciate your concern regarding our interpretation of diminished DMN activity reflecting the SO down-state. We acknowledge that the current expression is somewhat misleading, and our interpretation of it is: it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. And we will make this clear in the Discussion section.

      Discussion, Page 17 Lines 308-322

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      (14) Line 77: "In the current study, while directly capturing hippocampal ripples with scalp EEG or fMRI is difficult, we expect to observe hippocampal activation in fMRI whenever SOs-spindles coupling is detected by EEG, if SOs- spindles-ripples triple coupling occurs during human NREM sleep". Not all SO-spindle events are associated with ripples (Staresina et al., 2015), but hippocampal activation may also be expected based on the occurrence of spindles alone (Bergmann et al., 2012).

      We appreciate your clarification regarding the relationship between SO-spindle coupling and hippocampal ripples. We acknowledge that not all SO-spindle events are necessarily accompanied by ripples (Staresina et al., 2015). However, based on previous research, we found that hippocampal ripples are significantly more likely to occur during SO-spindle coupling events. This suggests that while ripple occurrence is not guaranteed, SO-spindle coupling creates a favorable network state for ripple generation and potential hippocampal activation. To ensure accuracy, we will revise the manuscript to delete this misleading sentence in the Introduction section and acknowledge in the Discussion that our results cannot conclusively directly observe the triple coupling of SO, spindle, and hippocampal ripples.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      Reviewer #2 (Public review):

      In this study, Wang and colleagues aimed to explore brain-wide activation patterns associated with NREM sleep oscillations, including slow oscillations (SOs), spindles, and SO-spindle coupling events. Their findings reveal that SO-spindle events corresponded with increased activation in both the thalamus and hippocampus. Additionally, they observed that SO-spindle coupling was linked to heightened functional connectivity from the hippocampus to the thalamus, and from the thalamus to the medial prefrontal cortex-three key regions involved in memory consolidation and episodic memory processes.

      This study's findings are timely and highly relevant to the field. The authors' extensive data collection, involving 107 participants sleeping in an fMRI while undergoing simultaneous EEG recording, deserves special recognition. If shared, this unique dataset could lead to further valuable insights. While the conclusions of the data seem overall well supported by the data, some aspects with regard to the detection of sleep oscillations need clarification.

      The authors report that coupled SO-spindle events were most frequent during NREM sleep (2.46 [plus minus] 0.06 events/min), but they also observed a surprisingly high occurrence of these events during N1 and REM sleep (2.23 [plus minus] 0.09 and 2.32 [plus minus] 0.09 events/min, respectively), where SO-spindle coupling would not typically be expected. Combined with the relatively modest SO amplitudes reported (~25 µV, whereas >75 µV would be expected when using mastoids as reference electrodes), this raises the possibility that the parameters used for event detection may not have been conservative enough - or that sleep staging was inaccurately performed. This issue could present a significant challenge, as the fMRI findings are largely dependent on the reliability of these detected events.

      Thank you very much for your thorough and encouraging review. We appreciate your recognition of the significance and relevance of our study and dataset, particularly in highlighting how simultaneous EEG-fMRI recordings can provide complementary insights into the temporal dynamics of neural oscillations and their associated spatial activation patterns during sleep. In the sections that follow, we address each of your comments in detail. We have revised the text and conducted additional analyses wherever possible to strengthen our argument, clarify our methodological choices. We believe these revisions improve the clarity and rigor of our work, and we thank you for helping us refine it.

      We appreciate your insightful comments regarding the detection of sleep oscillations. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

      Regarding the reported SO amplitudes (~25 µV), during preprocessing, we applied the Signal Space Projection (SSP) method to more effectively remove MRI gradient artifacts and cardiac pulse noise. While this approach enhances data quality, it also reduces overall signal power, leading to systematically lower reported amplitudes. Despite this, our SO detection in NREM sleep (especially N2/N3) remain physiologically meaningful and are consistent with previous fMRI studies using similar artifact removal techniques. We appreciate your careful evaluation and valuable suggestions.

      In addition, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (2)Density of detected SOs; (3)Density of detected spindles; (4)Density of detected SO-spindle coupling events.

      Methods, Page 25 Lines 515-524

      “We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

      Supplementary Materials, Page 42-54, Table S1-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      Reviewer #3 (Public review):

      Summary:

      Wang et al., examined the brain activity patterns during sleep, especially when locked to those canonical sleep rhythms such as SO, spindle, and their coupling. Analyzing data from a large sample, the authors found significant coupling between spindles and SOs, particularly during the upstate of the SO. Moreover, the authors examined the patterns of whole-brain activity locked to these sleep rhythms. To understand the functional significance of these brain activities, the authors further conducted open-ended cognitive state decoding and found a variety of cognitive processing may be involved during SO-spindle coupling and during other sleep events. The authors next investigated the functional connectivity analyses and found enhanced connectivity between the hippocampus, the thalamus, and the medial PFC. These results reinforced the theoretical model of sleep-dependent memory consolidation, such that SO-spindle coupling is conducive to systems-level memory reactivation and consolidation.

      Strengths:

      There are obvious strengths in this work, including the large sample size, state-of-the-art neuroimaging and neural oscillation analyses, and the richness of results.

      Weaknesses:

      Despite these strengths and the insights gained, there are weaknesses in the design, the analyses, and inferences.

      Thank you for your detailed and thoughtful review of our manuscript. We are delighted that you recognize our advanced analysis methods and rich results of neuroimaging and neural oscillations as well as the large sample size data. In the following sections, we provide detailed responses to each of your comments. And we have revised the text and conducted additional analyses to strengthen our arguments and clarify our methodological choices. We believe these revisions enhance the clarity and rigor of our work, and we sincerely appreciate your thoughtful feedback in helping us refine the manuscript.

      (1) A repeating statement in the manuscript is that brain activity could indicate memory reactivation and thus consolidation. This is indeed a highly relevant question that could be informed by the current data/results. However, an inherent weakness of the design is that there is no memory task before and after sleep. Thus, it is difficult (if not impossible) to make a strong argument linking SO/spindle/coupling-locked brain activity with memory reactivation or consolidation.

      We appreciate your suggestion regarding the lack of a pre- and post-sleep memory task in our study design. We acknowledge that, in the absence of behavioral measures, it is hard to directly link SO-spindle coupling to memory consolidation in an outcome-driven manner. Our interpretation is instead based on the well-established role of these oscillations in memory processes, as demonstrated in previous studies. We sincerely appreciate this feedback and will adjust our Discussion accordingly to reflect a more precise interpretation of our findings.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      (2) Relatedly, to understand the functional implications of the sleep rhythm-locked brain activity, the authors employed the "open-ended cognitive state decoding" method. While this method is interesting, it is rather indirect given that there were no behavioral indices in the manuscript. Thus, discussions based on these analyses are speculative at best. Please either tone down the language or find additional evidence to support these claims.

      Moreover, the results from this method are difficult to understand. Figure 3e showed that for all three types of sleep events (SO, spindle, SO-spindle), the same mental states (e.g., working memory, episodic memory, declarative memory) showed opposite directions of activation (left and right panels showed negative and positive activation, respectively). How to interpret these conflicting results? This ambiguity is also reflected by the term used: declarative memory and episodic memories are both indexed in the results. Yet these two processes can be largely overlapped. So which specific memory processes do these brain activity patterns reflect? The Discussion shall discuss these results and the limitations of this method.

      We appreciate your critical assessment of the open-ended cognitive state decoding method and its interpretational challenges. Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7.

      Due to the complexity of memory-related processes, we acknowledge that distinguishing between episodic and declarative memory based solely on this approach is not straightforward. We will revise the Supplementary Materials to explicitly discuss these limitations and clarify that our findings do not isolate specific cognitive processes but rather suggest general associations with memory-related networks.

      Discussion, Page 17-18 Lines 323-332

      “To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potenial functional claims.”

      (3) The coupling strength is somehow inconsistent with prior results (Hahn et al., 2020, eLife, Helfrich et al., 2018, Neuron). Specifically, Helfrich et al. showed that among young adults, the spindle is coupled to the peak of the SO. Here, the authors reported that the spindles were coupled to down-to-up transitions of SO and before the SO peak. It is possible that participants' age may influence the coupling (see Helfrich et al., 2018). Please discuss the findings in the context of previous research on SO-spindle coupling.

      We appreciate your concern regarding the temporal characteristics of SO-spindle coupling. We acknowledge that the SO-spindle coupling phase results in our study are not identical to those reported by Hahn et al. (2020); Helfrich et al. (2018). However, these differences may arise due to slight variations in event detection parameters, which can influence the precise phase estimation of coupling. Notably, Hahn et al. (2020) also reported slight discrepancies in their group-level coupling phase results, highlighting that methodological differences can contribute to variability across studies. Furthermore, our findings are consistent with those of Schreiner et al. (2021), further supporting the robustness of our observations.

      That said, we acknowledge that our original description of SO-spindle coupling as occurring at the "transition from the lower state to the upper state" was not entirely precise. The -π/2 phase represents the true transition point, while our observed coupling phase is actually closer to the SO peak rather than strictly at the transition. We will revise this statement in the manuscript to ensure clarity and accuracy in describing the coupling phase.

      Discussion, Page 16 Lines 283-291

      “Our data provide insights into the neurobiological underpinnings of these sleep rhythms. SOs, originating mainly in neocortical areas such as the mPFC, alternate between DOWN- and UP-states. The thalamus generates sleep spindles, which in turn couple with SOs. Our finding that spindle peaks consistently occurred slightly before the UP-state peak of SOs (in 83 out of 107 participants), concurs with prior studies, including Schreiner et al. (2021). Yet it differs from some results suggesting spindles might peak right at the SO UP-state (Hahn et al., 2020; Helfrich et al., 2018). Such discrepancies could arise from differences in detection algorithms, participant age (Helfrich et al., 2018), or subtle variations in cortical-thalamic timing. Nonetheless, these results underscore the importance of coordinated SO-spindle interplay in supporting sleep-dependent processes.”

      (4) The discussion is rather superficial with only two pages, without delving into many important arguments regarding the possible functional significance of these results. For example, the author wrote, "This internal processing contrasts with the brain patterns associated with external tasks, such as working memory." Without any references to working memory, and without delineating why WM is considered as an external task even working memory operations can be internal. Similarly, for the interesting results on SO and reduced DMN activity, the authors wrote "The DMN is typically active during wakeful rest and is associated with self-referential processes like mind-wandering, daydreaming, and task representation (Yeshurun, Nguyen, & Hasson, 2021). Its reduced activity during SOs may signal a shift towards endogenous processes such as memory consolidation." This argument is flawed. DMN is active during self-referential processing and mind-wandering, i.e., when the brain shifts from external stimuli processing to internal mental processing. During sleep, endogenous memory reactivation and consolidation are also part of the internal mental processing given the lack of external environmental stimulation. So why during SO or during memory consolidation, the DMN activity would be reduced? Were there differences in DMN activity between SO and SO-spindle coupling events?

      We appreciate your concerns regarding the brevity of the discussion and the need for clearer theoretical arguments. We will expand this section to provide more in-depth interpretations of our findings in the context of prior literature. Regarding working memory (WM), we acknowledge that our phrasing was ambiguous. We will modify this statement in the Discussion section.

      For the SO-related reduction in DMN activity, we recognize the need for a more precise explanation. This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state.

      To address your final question, we have conducted the additional post hoc comparison of DMN activity between isolated SOs and SO-spindle coupling events. Our results indicate that

      DMN activation during SOs was significantly lower than during SO-spindle coupling (t<sub>(106)</sub> = -4.17, p < 1e-4). This suggests that SO-spindle coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. We appreciate your constructive feedback and will integrate these expanded analyses and discussions into our revised manuscript.

      Results, Page 11 Lines 199-208

      “Spindles were correlated with positive activation in the thalamus (ROI analysis, t<sub>(106)</sub> = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t<sub>(106)</sub> \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t<sub>(106)</sub> \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t<sub>(106)</sub> \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

      Discussion, Page 17-18 Lines 308-332

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

      Reviewing Editor Comment:

      The reviewers think that you are working on a relevant and important topic. They are praising the large sample size used in the study. The reviewers are not all in line regarding the overall significance of the findings, but they all agree the paper would strongly benefit from some extra work, as all reviewers raise various critical points that need serious consideration.

      We appreciate your recognition of the relevance and importance of our study, as well as your acknowledgment of the large sample size as a strength of our work. We understand that there are differing perspectives regarding the overall significance of our findings, and we value the constructive critiques provided. We are committed to addressing the key concerns raised by all reviewers, including refining our analyses, clarifying our interpretations, and incorporating additional discussions to strengthen the manuscript. Below, we address your specific recommendations and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We believe that these revisions will significantly enhance the rigor and impact of our study, and we sincerely appreciate your thoughtful feedback in helping us improve our work.

      Reviewer #1 (Recommendations for the authors):

      (1) The phrase "overnight sleep" suggests an entire night, while these were rather "nocturnal naps". Please rephrase.

      Thank you for pointing this out. We have revised the phrasing in our manuscript to "nocturnal naps" instead of "overnight sleep" to more accurately reflect the duration of the sleep recordings.

      (2) Sleep staging results (macroscopic sleep architecture) should be provided in more detail (at least min and % of the different sleep stages, sleep onset latency, total sleep duration, total recording duration), at least mean/SD/range.

      Thank you for this suggestion. We will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics. This information will help provide a clearer overview of the macroscopic sleep architecture in our dataset.

      Supplementary Materials, Page 42, Table S1

      Author response table 1.

      Descriptive results of demographic information and sleep characteristics. Note: The total recorded time is equal to the awake time plus the total sleep time. The sleep onset latency is the time taken to reach the first sleep epoch. The Sleep Efficiency is the ratio of actual sleep time to total recording time.

      Reviewer #2 (Recommendations for the authors):

      In order to allow for a better estimation of the reliability of the detected sleep events, please:

      (1) Provide densities and absolute numbers of all detected SOs and spindles (N1, NREM, and REM sleep).

      Thank you for pointing this out. We will provide comprehensive tables in the supplementary materials, contains detailed information about sleep waves at each sleep stage for all 107 subjects (Table S2-S4), listing for each subject:1) Different sleep stage duration; 2) Number of detected SOs; 3) Number of detected spindles; 4) Number of detected SO-spindle coupling events; 5) Density of detected SOs; 6) Density of detected spindles; 7) Density of detected SO-spindle coupling events.

      Supplementary Materials, Page 43-54, Table S2-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      (2) Show ERPs for all detected SOs and spindles (per sleep stage).

      Thank you for the suggestion. We will provide ERPs for all detected SOs and spindles, separated by sleep stage (N1, N2&N3, and REM) in supplementary Fig. S2-S4. These ERP waveforms will help illustrate the characteristic temporal profiles of SOs and spindles across different sleep stages.

      Methods, Page 25, Line 525-532

      “Event-related potentials (ERP) analysis. After completing the detection of each sleep rhythm event, we performed ERP analyses for SOs, spindles, and coupling events in different sleep stages. Specifically, for SO events, we took the trough of the DOWN-state of each SO as the zero-time point, then extracted data in a [-2 s to 2 s] window from the broadband (0.1–30 Hz) EEG and used [-2 s to -0.5 s] for baseline correction; the results were then averaged across 107 subjects (see Fig. S2a). For spindle events, we used the peak of each spindle as the zero-time point and applied the same data extraction window and baseline correction before averaging across 107 subjects (see Fig. S2b). Finally, for SO-spindle coupling events, we followed the same procedure used for SO events (see Fig. 2a, Figs. S3–S4).”

      Supplementary Materials, Page 36-38, Fig. S2-S4

      Author response image 1.

      ERPs of SOs and spindles coupling during different sleep stages across all 107 subjects. a. ERP of SOs in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the trough of the DOWN-state of each SO at time zero (see Methods for details). The orange line represents the SO ERP in the N1 stage, the black line represents the SO ERP in the N2&N3 stage, and the green line represents the SO ERP in the REM stage. b. ERP of spindles in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the peak of each spindle at time zero (see Methods for details). The color scheme is the same as in panel a.

      Author response image 2.

      ERP and time-frequency patterns of SO-spindle coupling in the N1 stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, following the same procedure as in Fig. 2a, but for N1 stage.

      Author response image 3.

      ERP and time-frequency patterns of SO-spindle coupling in the REM stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, again following the same procedure as in Fig. 2a, but for REM stage.

      (3) Provide detailed info concerning sleep characteristics (time spent in each sleep stage etc.).

      Thank you for this suggestion. Same as the response above, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics.

      Supplementary Materials, Page 42, Table S1 (same as above)

      (4) What would happen if more stringent parameters were used for event detection? Would the authors still observe a significant number of SO spindles during N1 and REM? Would this affect the fMRI-related results?

      Thank you for this suggestion. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).

      Furthermore, in order to explore the impact of this on our fMRI results, we conducted an additional sensitivity analysis by applying different detection parameters for SOs. Specifically, we adjusted amplitude percentile thresholds for SO detection (the parameter that has the greatest impact on the results). We used the hippocampal activation value during N2&N3 stage SO-spindle coupling as an anchor value and found that when the parameters gradually became stricter, the results were similar to or even better than the current results. However, when we continued to increase the threshold, the results began to gradually decrease until the threshold was increased to 80%, and the results were no longer significant. This indicates that our results are robust within a specific range of parameters, but as the threshold increases, the number of trials decreases, ultimately weakening the statistical power of the fMRI analysis.

      Thank you again for your suggestions on sleep rhythm event detection. We will add the results in Supplementary and revise our manuscript accordingly.

      Results, Page 11, Line 199-208

      “Spindles were correlated with positive activation in the thalamus (ROI analysis, t<sub>(106)</sub> = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t<sub>(106)</sub> \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t<sub>(106)</sub> \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t<sub>(106)</sub> \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

      Supplementary Materials, Page 40, Fig. S6

      Author response image 4.

      Influence of the percentile threshold for SO detection on hippocampal activation (ROI) during SO-spindle coupling. We changed the percentile threshold for SO event detection in the EEG data analysis and then reconstructed the GLM design matrix based on the SO events detected at each threshold. The brain-wide activation pattern of SO-spindle couplings in the N2/3 stage was extracted using the same method as shown in Fig. 3. The gray horizontal line represents the significant range (71%–80%). * p < 0.05.

      Finally, we sincerely thank all again for your thoughtful and constructive feedback. Your insights have been invaluable in refining our analyses, strengthening our interpretations, and improving the clarity and rigor of our manuscript. We appreciate the time and effort you have dedicated to reviewing our work, and we are grateful for the opportunity to enhance our study based on your recommendations.

      References:

      Bergmann, T. O., Mölle, M., Diedrichs, J., Born, J., & Siebner, H. R. (2012). Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations. NeuroImage, 59(3), 2733-2742.

      Buzsáki, G. (2015). Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning. Hippocampus, 25(10), 1073-1188.

      Caporro, M., Haneef, Z., Yeh, H. J., Lenartowicz, A., Buttinelli, C., Parvizi, J., & Stern, J. M. (2012). Functional MRI of sleep spindles and K-complexes. Clinical neurophysiology, 123(2), 303-309.

      Coulon, P., Budde, T., & Pape, H.-C. (2012). The sleep relay—the role of the thalamus in central and decentral sleep regulation. Pflügers Archiv-European Journal of Physiology, 463, 53-71.

      Crunelli, V., Lőrincz, M. L., Connelly, W. M., David, F., Hughes, S. W., Lambert, R. C., Leresche, N., & Errington, A. C. (2018). Dual function of thalamic low-vigilance state oscillations: rhythm-regulation and plasticity. Nature Reviews Neuroscience, 19(2), 107-118.

      Czisch, M., Wehrle, R., Stiegler, A., Peters, H., Andrade, K., Holsboer, F., & Sämann, P. G. (2009). Acoustic oddball during NREM sleep: a combined EEG/fMRI study. PloS one, 4(8), e6749.

      Diba, K., & Buzsáki, G. (2007). Forward and reverse hippocampal place-cell sequences during ripples. Nature Neuroscience, 10(10), 1241.

      Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews Neuroscience, 11(2), 114-126.

      Fogel, S., Albouy, G., King, B. R., Lungu, O., Vien, C., Bore, A., Pinsard, B., Benali, H., Carrier, J., & Doyon, J. (2017). Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles. PloS one, 12(4), e0174755.

      Hahn, M. A., Heib, D., Schabus, M., Hoedlmoser, K., & Helfrich, R. F. (2020). Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence. Elife, 9, e53730.

      Halassa, M. M., Siegle, J. H., Ritt, J. T., Ting, J. T., Feng, G., & Moore, C. I. (2011). Selective optical drive of thalamic reticular nucleus generates thalamic bursts and cortical spindles. Nature Neuroscience, 14(9), 1118-1120.

      Hale, J. R., White, T. P., Mayhew, S. D., Wilson, R. S., Rollings, D. T., Khalsa, S., Arvanitis, T. N., & Bagshaw, A. P. (2016). Altered thalamocortical and intra-thalamic functional connectivity during light sleep compared with wake. NeuroImage, 125, 657-667.

      Helfrich, R. F., Lendner, J. D., Mander, B. A., Guillen, H., Paff, M., Mnatsakanyan, L., Vadera, S., Walker, M. P., Lin, J. J., & Knight, R. T. (2019). Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans. Nature Communications, 10(1), 3572.

      Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T., & Walker, M. P. (2018). Old brains come uncoupled in sleep: slow wave-spindle synchrony, brain atrophy, and forgetting. Neuron, 97(1), 221-230. e224.

      Horovitz, S. G., Fukunaga, M., de Zwart, J. A., van Gelderen, P., Fulton, S. C., Balkin, T. J., & Duyn, J. H. (2008). Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG‐fMRI study. Human brain mapping, 29(6), 671-682.

      Huang, Q., Xiao, Z., Yu, Q., Luo, Y., Xu, J., Qu, Y., Dolan, R., Behrens, T., & Liu, Y. (2024). Replay-triggered brain-wide activation in humans. Nature Communications, 15(1), 7185.

      Ilhan-Bayrakcı, M., Cabral-Calderin, Y., Bergmann, T. O., Tüscher, O., & Stroh, A. (2022). Individual slow wave events give rise to macroscopic fMRI signatures and drive the strength of the BOLD signal in human resting-state EEG-fMRI recordings. Cerebral Cortex, 32(21), 4782-4796.

      Laufs, H. (2008). Endogenous brain oscillations and related networks detected by surface EEG‐combined fMRI. Human brain mapping, 29(7), 762-769.

      Laufs, H., Walker, M. C., & Lund, T. E. (2007). ‘Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study’—its limitations and an alternative approach. Brain, 130(7), e75.

      Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., & Petrides, M. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574-12579.

      Massimini, M., Huber, R., Ferrarelli, F., Hill, S., & Tononi, G. (2004). The sleep slow oscillation as a traveling wave. Journal of Neuroscience, 24(31), 6862-6870.

      Moehlman, T. M., de Zwart, J. A., Chappel-Farley, M. G., Liu, X., McClain, I. B., Chang, C., Mandelkow, H., Özbay, P. S., Johnson, N. L., & Bieber, R. E. (2019). All-night functional magnetic resonance imaging sleep studies. Journal of neuroscience methods, 316, 83-98.

      Molle, M., Bergmann, T. O., Marshall, L., & Born, J. (2011). Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep, 34(10), 1411-1421.

      Ngo, H.-V., Fell, J., & Staresina, B. (2020). Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples. Elife, 9, e57011.

      Picchioni, D., Horovitz, S. G., Fukunaga, M., Carr, W. S., Meltzer, J. A., Balkin, T. J., Duyn, J. H., & Braun, A. R. (2011). Infraslow EEG oscillations organize large-scale cortical– subcortical interactions during sleep: a combined EEG/fMRI study. Brain research, 1374, 63-72.

      Schabus, M., Dang-Vu, T. T., Albouy, G., Balteau, E., Boly, M., Carrier, J., Darsaud, A., Degueldre, C., Desseilles, M., & Gais, S. (2007). Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep. Proceedings of the National Academy of Sciences, 104(32), 13164-13169.

      Schreiner, T., Kaufmann, E., Noachtar, S., Mehrkens, J.-H., & Staudigl, T. (2022). The human thalamus orchestrates neocortical oscillations during NREM sleep. Nature communications, 13(1), 5231.

      Schreiner, T., Petzka, M., Staudigl, T., & Staresina, B. P. (2021). Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes. Nature Communications, 12(1), 3112.

      Singh, D., Norman, K. A., & Schapiro, A. C. (2022). A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation. Proceedings of the National Academy of Sciences, 119(44), e2123432119.

      Spoormaker, V. I., Schröter, M. S., Gleiser, P. M., Andrade, K. C., Dresler, M., Wehrle, R., Sämann, P. G., & Czisch, M. (2010). Development of a large-scale functional brain network during human non-rapid eye movement sleep. Journal of Neuroscience, 30(34), 11379-11387.

      Staresina, B. P., Bergmann, T. O., Bonnefond, M., van der Meij, R., Jensen, O., Deuker, L., Elger, C. E., Axmacher, N., & Fell, J. (2015). Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nature Neuroscience, 18(11), 1679-1686.

      Staresina, B. P., Niediek, J., Borger, V., Surges, R., & Mormann, F. (2023). How coupled slow oscillations, spindles and ripples coordinate neuronal processing and communication during human sleep. Nature Neuroscience, 1-9.

      Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8), 665-670.

      Yeshurun, Y., Nguyen, M., & Hasson, U. (2021). The default mode network: where the idiosyncratic self meets the shared social world. Nature Reviews Neuroscience, 1-12.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1 (Public reviews):

      Summary

      Howard et al. performed deep mutational scanning on the MC4R gene, using a reporter assay to investigate two distinct downstream pathways across multiple experimental conditions. They validated their findings with ClinVar data and previous studies. Additionally, they provided insights into the application of DMS results for personalized drug therapy and differential ligand responses across variant types.

      Strengths

      They captured over 99% of variants with robust signals and investigated subtle functionalities, such as pathway-specific activities and interactions with different ligands, by refining both the experimental design and analytical methods.

      Weaknesses

      While the study generated informative results, it lacks a detailed explanation regarding the input library, replicate correlation, and sequencing depth for a given number of cells. Additionally, there are several questions that it would be helpful for authors to clarify.

      (1) It would be helpful to clarify the information regarding the quality of the input library and experimental replicates. Are variants evenly represented in the library? Additionally, have the authors considered using long-read sequencing to confirm the presence of a single intended variant per construct? Finally, could the authors provide details on the correlation between experimental replicates under each condition?

      Are variants evenly represented in the library?

      We strive to achieve as evenly balanced library as possible at every stage of the DMS process (e.g., initial cloning in E. coli through integration into human cells). Below is a representative plot showing the number of barcodes per amino acid variant at each position in a given ~60 amino acid subregion of MC4R, which highlights how evenly variants are represented at the E. coli cloning stage.

      Author response image 1.

      We also make similar measurements after the library is integrated into HEK293T cell lines, and see similarly even coverage across all variants, as shown in the plot below:

      Author response image 2.

      Additionally, have the authors considered using long-read sequencing to confirm the presence of a single intended variant per construct?

      We agree long-read sequencing would be an excellent way to confirm that our constructs contain a single intended variant. However, we elected for an alternate method (outlined in more detail in Jones et al. 2020) that leverages multiple layers of validation. First, the oligo chip-synthesized portions of the protein containing the variants are cloned into a sequence-verified plasmid backbone, which greatly decreases the chances of spuriously generating a mutation in a different portion of the protein. We then sequence both the oligo portion and random barcode using overlapping paired end reads during barcode mapping to avoid sequencing errors and to help detect DNA synthesis errors. At this stage, we computationally reject any constructs that have more than one variant. Given this, the vast majority of remaining unintended variants would come from somatic mutations introduced by the E. coli cloning or replication process, which should be low frequency. We have used our in-house full plasmid sequencing method, OCTOPUS, to sample and spot check this for several other DMS libraries we have generated using the same cloning methods. We have found variants in the plasmid backbone in only ~1% of plasmids in these libraries. Our statistical model also helps correct for this by accounting for barcode-specific variation. Finally we believe this provides further motivation for having multiple barcodes per variant, which dilutes the effect of any unintended additional variants.

      Finally, could the authors provide details on the correlation between experimental replicates under each condition?

      Certainly! In general, the Gs reporter had higher correlation between replicates than the Gq system (r ~ 0.5 vs r ~ 0.4). The plots below, which have been added as a panel to Supplementary Figure 1, show two representative correlations at the RNA-seq stage of read counts for barcodes between the low a-MSH conditions.

      We added the following text to reference this panel:

      (see Methods > Sequence processing for barcode expression): “The correlation (r) of barcode readcounts between replicates was ~0.5 and ~0.4 for the Gs and Gq assays, respectively (Supplementary Fig. 1E).”

      One important advantage of our statistical model is that it’s able to leverage information from barcodes regardless of the number of replicates they appear in.

      (2) Since the functional readout of variants is conducted through RNA sequencing, it seems crucial to sequence a sufficient number of cells with adequate sequencing saturation. Could the authors clarify the coverage depth used for each RNA-seq experiment and how this depth was determined? Additionally, how many cells were sequenced in each experiment?

      The text has been added in the manuscript as follows:

      (in Methods > Running DMS Assays): “Given the seeding density (~17x10<sup>6</sup> cells per 150 mm replicate dish), time from seeding to collection, and doubling time of HEK293T cells, approximately 25.5x10<sup>6</sup> cells were collected per replicate. This translates to approximately 30-60x cellular coverage per amino acid variant in each replicate.”

      (in Methods > Sequence processing for barcode expression): “Total mapped reads per replicate at the RNA-seq stage were as follows:

      - Gs/CRE: 9.1-18.2 million mapped reads, median=12.3

      - Gq/UAS: 8.6-24.1 million mapped reads, median=14.5

      - Gs/CRE+Chaperone: 6.4-9.5 million mapped reads, median=7.5”

      The median read counts per sample per barcode were 8, 10, and 6 reads for Gs/CRE, Gq/UAS, and Gs/CRE+Chaperone assays, respectively. The median number of barcodes per variant across all samples (the “median of medians”) were 56 for Gs/CRE, 28 for Gq/UAS, and 44 for Gs/CRE+Chaperone.”

      (3) It appears that the frequencies of individual RNA-seq barcode variants were used as a proxy for MC4R activity. Would it be important to also normalize for heterogeneity in RNA-seq coverage across different cells in the experiment? Variability in cell representation (i.e., the distribution of variants across cells) could lead to misinterpretation of variant effects. For example, suppose barcode_a1 represents variant A and barcode_b1 represents variant B. If the RNA-seq results show 6 reads for barcode_a1 and 7 reads for barcode_b1, it might initially appear that both variants have similar effect sizes. However, if these reads correspond to 6 separate cells each containing 1 copy of barcode_a1, and only 1 cell containing 7 copies of barcode_b1, the interpretation changes significantly. Additionally, if certain variants occupy a larger proportion of the cell population, they are more likely to be overrepresented in RNA sequencing.

      We account for this heterogeneity in several ways. First, as shown above (see Response to Reviewer 1, Question 1), we aim to have even representation of variants within our libraries. Second, we utilize compositional control conditions like forskolin or unstimulated conditions to obtain treatment-independent measurements of barcode abundance and, consequently, of mutant-vs-WT effects that are due to compositional rather than biological variability. We expect that variability observed under these controls is due to subtle effects of molecular cloning, gene expression, and stochasticity. Using these controls, we observe that mutant-vs-WT effects are generally close to zero in these normalization conditions (e.g., in untreated Gq, see Supplementary Figure 3) as compared to treated conditions. For example, pre-mature stops behave similar to WT in normalization conditions. This indicates that mutant abundance is relatively homogenous. Where there are barcode-dependent effects on abundance, we can use information from these conditions to normalize that effect. Finally, our mixed-effect model accounts for barcode-specific deviations from the expected mutant effect (e.g., a “high count” barcode consistently being high relative to the mean).

      (4) Although the assay system appears to effectively represent MC4R functionality at the molecular level, we are curious about the potential disparity between the DMS score system and physiological relevance. How do variants reported in gnomAD distribute within the DMS scoring system?

      Figure 2D shows DMS scores (variant effect on Gs signaling) relative to human population frequency for all MC4R variants reported in gnomAD as of January 8, 2024.

      (5) To measure Gq signaling, the authors used the GAL4-VPR relay system. Is there additional experimental data to support that this relay system accurately represents Gq signaling?

      The full Gq reporter uses an NFAT response element from the IL-2 promoter to regulate the expression of the GAL4-VPR relay. In this system, the activation of Gq signaling results in the activation of the NFAT response element, and this signal is then amplified by the GAL4-VPR relay. The NFAT response element has been previously well-validated to respond to the activation of Gq signaling (e.g., Boss, Talpade, and Murphy 1996). We will have added this reference to the text (see Results> Assays for disease-relevant mechanisms) to further support the use of the Gq assay.

      (6) Identifying the variants responsive to the corrector was impressive. However, we are curious about how the authors confirmed that the restoration of MC4R activity was due to the correction of the MC4R protein itself. Is there a possibility that the observed effect could be influenced by other factors affected by the corrector? When the corrector was applied to the cells, were any expected or unexpected differential gene expression changes observed?

      While we do not directly measure whether Ipsen-17 has effects on other signaling processes, previous work has shown that Ipsen-17 treatment does not indirectly alter signaling kinetics such as receptor internalization (Wang et al., 2014). Furthermore, our analysis methods inherently account for this by normalizing variant effects to WT signaling levels. Any observed rescue of a given variant inherently means that the variant is specifically more responsive to Ipsen-17 than WT, and the fact that different variants exhibit different levels of rescue is reassuring that the mechanism is on target to MC4R. Lastly, Ipsen-17 is known to be an antagonist of alpha-MSH activity and is thought to bind directly to the same site on MC4R (Wang et al., 2014).

      We have revised text in the Methods section as follows (see Running DMS Assays) to better articulate this : “For chaperone experiments, cells were washed 3x with 10 mL DMEM to remove Ipsen 17 prior to agonist stimulation as it has been shown to be an antagonist of α-MSH activity and is thought to bind directly to the same site on MC4R (Wang et al. 2014).”

      (7) As mentioned in the introduction, gain-of-function (GoF) variants are known to be protective against obesity. It would be interesting to see further studies on the observed GoF variants. Do the authors have any plans for additional research on these variants?

      We agree this would be an excellent line of inquiry, but due to changes in company priorities we unfortunately do not have any plans for additional research on these variants.

      Reviewer 2 (Public reviews):

      Overview

      In this manuscript, the authors use deep mutational scanning to assess the effect of ~6,600 protein-coding variants in MC4R, a G protein-coupled receptor associated with obesity. Reasoning that current deep mutational scanning approaches are insufficiently precise for some drug development applications, they focus on articulating new, more precise approaches. These approaches, which include a new statistical model and innovative reporter assay, enable them to probe molecular phenotypes directly relevant to the development of drugs that target this receptor with high precision and statistical rigor.

      They use the resulting data for a variety of purposes, including probing the relationship between MC4R's sequence and structure, analyzing the effect of clinically important variants, identifying variants that disrupt downstream MC4R signaling via one but not both pathways, identifying loss of function variants are amenable to a corrector drug and exploring how deep mutational scanning data could guide small molecule drug optimization.

      Strengths

      The analysis and statistical framework developed by the authors represent a significant advance. In particular, the study makes use of barcode-level internally replicated measurements to more accurately estimate measurement noise.

      The framework allows variant effects to be compared across experimental conditions, a task that is currently hard to do with rigor. Thus, this framework will be applicable to a large number of existing and future deep mutational scanning experiments.

      The authors refine their existing barcode transcription-based assay for GPCR signaling, and develop a clever "relay" new reporter system to boost signaling in a particular pathway. They show that these reporters can be used to measure both gain of function and loss of function effects, which many deep mutational scanning approaches cannot do.

      The use of systematic approaches to integrate and then interrogate high-dimensional deep mutational scanning data is a big strength. For example, the authors applied PCA to the variant effect results from reporters for two different MC4R signaling pathways and were able to discover variants that biased signaling through one or the other pathway. This approach paves the way for analyses of higher dimensional deep mutational scans.

      The authors use the deep mutational scanning data they collect to map how different variants impact small molecule agonists activate MC4R signaling. This is an exciting idea, because developing small-molecule protein-targeting therapeutics is difficult, and this manuscript suggests a new way to map small-molecule-protein interactions.

      Weaknesses

      The authors derive insights into the relationship between MC4R signaling through different pathways and its structure. While these make sense based on what is already known, the manuscript would be stronger if some of these insights were validated using methods other than deep mutational scanning.

      Likewise, the authors use their data to identify positions where variants disrupt MC4R activation by one small molecule agonist but not another. They hypothesize these effects point to positions that are more or less important for the binding of different small molecule agonists. The manuscript would be stronger if some of these insights were explored further.

      Impact

      In this manuscript, the authors present new methods, including a statistical framework for analyzing deep mutational scanning data that will have a broad impact. They also generate MC4R variant effect data that is of interest to the GPCR community.

      Recommendations for the authors:

      (1) Page 7 - the Gq reporter relay system is clever. Could the authors include the original data showing that the simpler design didn't work at all, or at least revise the text to say more precisely what "not suitable due to weak SNR" means?

      We added a panel (D) to Supplementary Figure 2 showing that the native NFAT reporter was ~10x weaker than the CRE reporter, and the relay system amplified the NFAT signal to be comparable to the CRE reporter:

      (2) Page 7 - Even though the relay system gives some signal, it's clearly less sensitive/higher background than Gs. How does that play out in the quantitative analysis?

      —AND—

      (4) Page 10 - The Gq library had fewer barcodes per variant, and, as noted above, the Gq reporter doesn't work quite as well as the Gs one. It would be nice if the authors could comment on how these aspects of the Gq experiments affected data quality/power to detect effects.

      Due to the reviewer's excellent suggestion, we updated Supplementary Figure 2B to better contextualize the quantitative effects of the difference in signal to noise ratio of the Gq versus the Gs reporter system (see changes below). These distributions show the Z-statistic for testing either each stop mutation (red) or all possible coding variants against WT. Thus, a |Z| > 1.96 corresponds to a p = 0.05 in a two-sided Wald Test. We can see that in the Gs reporter, 95% of the stops are nominally significantly different from WT (visualized above with the majority of the red distribution being < -1.96). Alternatively, only 64% of stops are nominally significantly different from WT in Gq. This implies that it will be more difficult to detect effects in the Gq system, especially those less severe than stops.

      In addition to the overall signal to noise ratio being less in the Gq system, there were also less barcodes per variant (28 vs 56 barcodes per variant on average for Gq vs Gs). As demonstrated in Supplementary Figure 2C, the error bars on our estimates are related to the number of barcodes per variant (Standard Error ~ 1 / sqrt(Number of Barcodes), as shown in the plot below). This suggests that our estimates of mutant effects will be less certain in the Gq library than the Gs library. For example, the average standard error in the Gq library was 0.260 which was ~1.58 times larger than the Gs library's 0.165. Finally, we believe this further reiterates the power of our statistical framework, as it naturally enables formalized hypothesis testing that takes these errors into account when making comparisons both within reporters and across reporters.

      (3) Page 9 - it would be nice to see the analysis framework applied to a few existing datasets from other types of assays, to really judge its performance. That's not the main point of this paper, and it's fine, but it would be lovely!

      We agree with the reviewer and hope others apply our framework to their problems to further refine its utility and applicability! To that end, we’ve open-sourced it under a permissive license to help encourage the community to use it. Part of the challenge in applying it to other existing datasets is that few DMS experiments leverage variant-level replication through barcodes. While we re-analyzed an older DMS data from Jones et al. 2020 to produce the distributions in Supplementary Figure 2b, a more thorough comparison is outside the scope of this paper. That said, we have two additional manuscripts in preparation that leverage this framework to analyze DMS data in different proteins and assay types.

      (5) Page 10 - In discussing the relationship of the data to ClinVar and AM, the authors use qualitative comparisons like "majority" and "typically." Just giving numbers would better help the reader appreciate how the data compare.

      We added specific proportions for these statements to the text for the ClinVar and AlphaMissense comparisons as follows:

      (See Results > Comprehensive Deep Mutational Scanning of MC4R): “For example, the majority (63.3%, 31/49) of human MC4R variants classified as pathogenic or likely pathogenic in ClinVar (Landrum et al., 2014) lead to a significant reduction of Gs signaling under low α-MSH stimulation conditions (significance threshold: false discovery rate (FDR) < 1%; Fig. 2C). Variants that are significantly loss-of-function in this condition are rarer in the human population, and more common human variants have no significant effect on MC4R function (significance threshold: FDR < 1%; Fig. 2D). Loss-of-function variants by our DMS assay are also typically (e.g., AlphaMissense: 93.4%, 1894/2028) predicted to be deleterious by commonly used variant effect predictors like AlphaMissense (Cheng et al., 2023) and popEVE (Orenbuch et al., 2023) (Supplementary Fig. 5).”

      (6) Pages 10-12, Figures 2C, E. The data look really nice, but the correlation with clinvar and the Huang data is not perfect (e.g. many pathogenic variants are classified as WT and partial LoF variants too). Can the authors comment on this discrepancy? For ClinVar, they should say when ClinVar was accessed and also how they filtered variants. I would recommend using variants with at least 1 star. Provided they did use high-quality clinical classifications, do they think the classifications are wrong, or their data? The same goes for Huang.

      —AND—

      (7) Page 13 - similar to previous comments, I'm curious about the 5 path/likely path ClinVar variants that are not LoF in the assay. Are they high noise/fewer barcodes? Or does the assay just miss some aspect of human biology?

      ClinVar data was accessed on January 5, 2024 (see Methods: Comparison to human genetics data and variant effect predictors). No annotation quality filtering was performed, and we have revised the text as follows to clarify this:

      (see Methods > Comparison to Human Genetics Data and Variant Effect Predictors): “Pathogenicity classifications of MC4R missense and nonsense variants were obtained from ClinVar (Landrum et al., 2014) on January 5, 2024, and all available annotations were included in the analysis regardless of ClinVar review status metric.”

      A substantial proportion of the discrepancy between our data and ClinVar is, as the reviewer suggests, likely due to low quality ClinVar annotations. Of the five variants that the reviewer notes were reported as pathogenic/likely pathogenic but did not result in loss of protein function in any of our DMS assays, two (V50M and V166I) have been reclassified in ClinVar to uncertain or conflicting interpretation since we accessed annotations in early 2024. An additional two of the five discrepant variants (Q43K and S58C) currently have 0 star ratings to support their pathogenic/likely pathogenic annotation. The remaining discrepant variant (S94N) has a 1 star rating supporting an annotation of “likely pathogenic.

      The Huang et al. paper did an admirably thorough job of aggregating variant annotations from more than a dozen primary literature sources that each reported functional validation data for small panels of variants. However, one inherent limitation of this approach is that the resulting annotation classes are based on experiments that were carried out using inconsistent methods and/or scoring criteria. For example, classifications in the Huang et al. paper are based on an inconsistent mix of functional assay types (e.g., Gs signaling, Gq signaling, protein cell surface expression, etc.), and different variants were tested in different cell types (e.g., HEK293T, CHO, Cos-7, etc.). In principle, DMS assays should provide a more accurate assessment of the relative quantitative differences between alleles since each variant was tested using identical experimental conditions and analysis parameters.

      That being said, while very good, our assays are likely missing or only indirectly reporting on at least some aspects of MC4R biology. For example, in addition to Gs and Gq signaling, MC4R interfaces with β-arrestin. Variants that are protective against obesity-related phenotypes have been shown to increase recruitment of β-arrestin to MC4R, and we did not directly assess this function.

      (8) Page 15, Fig 3C - The three variants they highlight all have paradoxical changes in bias as a-MSH dose is increased (e.g. the bias inverts). I'm not a GPCR expert, but this seems interesting and a little weird. Perhaps the authors could comment on it?

      We agree this is an interesting observation that deserves further study, but unfortunately is outside the scope of our priorities at the moment. As noted, all three highlighted variants in this region have a biased basal activity, and this bias inverts upon stimulation. While we don’t have a good explanation for why this would be the case, this phenomenon has been previously observed for 158R (Paisdzior et al., 2020). Our DMS data emphasizes how diverse biased effects can be and further highlights the importance of characterizing these effects. It would be interesting if further studies could elucidate the mechanistic basis for this behavior and how it may be related to G protein coupling in this region.

      (9) Page 16 - I'm not familiar with the A21x1 formalism. For the general reader, maybe the authors could introduce this formalism.

      Given the shared structural topology of GPCRs, others have developed a variety of numbering schemes to refer to where various variants are to allow more direct comparisons between different GPCRs. We use the GPCRDB.org numbering scheme (e.g., F202<sup>5x4</sup>) as it takes experimentally determined structures into account. Roughly speaking, the number preceding the “x” corresponds to which transmembrane domain (one through seven) or region the residue is located in. The numbers following the “x” correspond to where that residue is located in that region relative to a structurally conserved residue that is always assigned 50. For example F202<sup>5x48</sup> means that F202 is located in the 5th transmembrane helix and is 2 residues before the most conserved M204<sup>5x50</sup>. We updated the text to clarify this accordingly:

      (see Results > Structural Insights into Biased Signaling): “Upon ligand binding, W258 (W258<sup>6x48</sup> in https://gpcrdb.org/ nomenclature, where 6 corresponds to the 6th transmembrane helix and 48 denotes 258 is 2 residues before the most conserved residue in that helix (Isberg et al., 2015)) of the conserved CWxP motif undergoes a conformational rearrangement that is translated to L133<sup>3x36</sup> and I137<sup>3x40</sup>, of the conserved PIF motif (MIF in melanocortin receptors).”

      (10) Page 17, Figure 3A - Since 137, 254, and 140 are not picked out on the structure, I have no idea where they are. If the authors want to show readers these residues, perhaps they could be annotated or a panel added. Since ~1 entire page of the manuscript is dedicated to this cascade, it might make sense to add a panel. Just amplifying the comment above as regards position 79, others were discussed in that paragraph but not highlighted.

      We updated Supplementary Fig. 6C,D to label all of the listed residues on the protein structure for easy reference.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes an important study of the giant virus Jyvaskylavirus. The characterisation presented is solid, although, in the current form, it is not clear to what extent these findings change our perception of how giant viruses, especially those isolated from a cold environment, function. The work will be of interest to virologists working on giant viruses as well as those working with other members of the PRD1/Adenoviridae lineage.

      Thank you for the revision and positive comments. We decided to submit our revised version of the manuscript with changes made in light of the comments made by the editorial team and the reviewers. We hope that now the manuscript is in a better shape and satisfies all comments received. Major changes made were:

      - We changed the author order considering reviewer 2 comments (point 11). Note that no author was added or removed, we just rearranged the order of authorship.

      - We included a new supplementary table with the Jyvaskylavirus genome annotation. This is now supplementary table 2.

      - We included a supplementary figure 9 to support our changes based on reviewer 2 comments (point 6).

      - Figures 2,5,6,7 and the supplementary figure 2 were updated to accommodate our answers to different reviewer comments.

      - Three new references were added to support some of our changes.

      Below you will find our responses to each specific point raised by the reviewers.

      Public Reviews:

      Reviewer #1 (Public review):

      This study presents Jyvaskylavirus, a new member of the Marseilleviridae family, infecting Acanthamoeba castellanii. The study provides a detailed and comprehensive genomic and structural analysis of Jyvaskylavirus. The authors identified ORF142 as the capsid penton protein and additional structural proteins that comprise the virion. Using a combination of imaging techniques the authors provide new insights into the giant virus architecture and lifecycle. The study could be improved by providing atomic coordinates and refinement statistics, comparisons with available giant virus structures could be expanded, and the novelty in terms of the first isolated example of a giant virus from Finland could be expounded upon.

      The study contributes new structural and genomic diversity to the Marseilleviridae family, hinting at a broader distribution and ecological significance of giant viruses than previously thought.

      Thank you for your constructive comments. We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly. By following your specific comments, we improved the manuscript regarding atomic coordinates, refinement statistics and novelty of finding a Finnish marseillevirus. Details are provided in the specific answers to your points.

      Reviewer #2 (Public review):

      Summary:

      This paper describes the molecular characterisation of a new isolate of the giant virus Jyvaskylavirus, a member of the Marseilleviridae family infecting Acanthamoeba castellanii. The isolate comes from a boreal environment in Finland, showcasing that giant viruses can thrive in this ecological niche. The authors came up with a non-trivial isolation procedure that can be applied to characterise other members of the family and will be beneficial for the virology field. The genome shows typical Marseilleviridae features and phylogenetically belongs to their clade B. The structural characterisation was performed on the level of isolated virion morphology by negative stain EM, virions associated with cells either during the attachment or release by helium microscopy, the visualisation of the virus assembly inside cells using stained thin sections, and lastly on the protein secondary structure level by reconstructing ~6 A icosahedral map of the massive virion using cryoEM. The cryoEM density combined with gene product structure prediction enabled the identification and functional assessment of various virion proteins.

      Strengths:

      The detailed description of the virus isolation protocol is the largest strength of the paper and this reviewer believes it can be modified for isolating various viruses infecting small eukaryotes. The cryoEM map allows us to understand how exceptionally large virions of these viruses are stabilised by minor capsid proteins and nicely demonstrates the integration of medium-resolution cryoEM with protein structure prediction in deciphering virion protein function. The visualisation of ongoing virus assembly inside virus factories brings interesting hypotheses about the process that; however, needs to be verified in the next studies.

      Weaknesses:

      The conclusions from helium microscopy images are overinterpreted, as the native membrane structure cannot be preserved in a fixed and dehydrated sample. In the image, there are many other parts of the curved membrane and a lot of virions, to me it seems the specific position of the highlighted virion could arise by a random chance. The claim that the cells were imaged in the near-original state by this method should be therefore omitted. Also, no mass spectrometry data are presented that would supplement and confirm the identity of virion proteins which predicted models were fitted into the cryoEM density. For a general virology reader outside of the giant virus field, the results presented in the current state might not have enough influence and the section should be rewritten to better showcase the novelty of findings.

      Thank you for your constructive comments. We thank reviewer #2 for highlighting these weaknesses, giving us the opportunity to improve our study. We have removed the claim that the cells were imaged in a near-original state. Additionally, we agree that the positions of the virions on the cell surface could result from a random distribution. However, the specific virion in panel 3C is situated halfway into a crevice, and it cannot be ruled out that this particular one could be in the process of being endocytotically uptaken. This is why we used the term "probably" while referring to this finding. Regarding the mass spectrometry data, while we understand that MS data would provide an additional layer of evidence to validate the specific proteins present in the virion, they would not confirm the precise location or role of these proteins within the virion.

      We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I have only minor comments which should be relatively simple to address:

      (1) Atomic coordinates should be deposited in the PDB, and refinement statistics for the models provided, for example by expanding Table S2.

      We thank reviewer #1 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7). The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (2) The results section 'Jyvaskylavirus three-dimensional architecture' could be expanded to compare and contrast with other giant virus structures, in terms of T-number, diameter, and features on and inside the capsid. This is not essential but would help focus claims of novelty with regard to structure.

      We have added a few lines as indicated by reviewer#1 to contextualize in morphological terms Jyvaskylavirus with other NCLDV viruses as follows:

      “Both the capsid organization and virion size are similar to those of other Marseilleviruses, such as Melbournevirus and Tokyovirus. Pacmanvirus, considered to be at the crossroads between Asfarviridae and Faustoviruses, also possesses the same T number (309) and a comparable diameter to Jyvaskylavirus. In contrast, other giant viruses, such as African swine fever virus (ASFV), representative of the Asfarviridae family, have a T number of 277 and a diameter of approximately 2,100 Å, while PBCV-1, a member of the Phycodnaviridae family, has a T number of 169 and an average diameter of 1,900 Å. All of the above-mentioned viruses have been shown to possess a major capsid protein with a vertical double jelly-roll fold that composes the capsid shell, along with an internal membrane bilayer. Minor capsid proteins have been identified and structurally modelled for the smaller virions ASFV and PBCV-1 (Wang et al. 2019; Shao et al. 2022).”

      (3) The authors highlight one of the main novelties of the virus as being the first to be isolated from Finland. The first isolation of a giant virus from the region is indeed a success but reported isolation experiments for giant viruses are still relatively few. To help shed light on the likely distribution of Jyvaskylavirus-like viruses in the region, and further afield, the genome of Jyvaskylavirus could be searched against relevant available metagenomes.

      In the last decade the interest on finding giant viruses by metagenomics has increased. However, the focus has been on marine environments, where these viruses are shown to be prevalent. Besides the few isolates from the Northern hemisphere mentioned in the manuscript, northern giant viruses were detected in metagenome datasets from glacier samples, epishelf lakes, the permafrost, the Nordic seas and in a deep-sea hydrothermal vent. Most of the genomic hits are for mimivirus-like or phycodnavirus-like sequences. A few marseilleviruses were found in the Loki’s castle deep sea vent, and we have already included these sequences in the analysis shown by the supplementary figure 3. In this case the deep-sea vent viruses clusters outside the conventional clades of the marseilleviridae family, evidencing their uniqueness.

      In response to the suggestion of exploring the distribution of Jyvaskylavirus, we utilized the MGnify-database to search for DNA polymerase (DNApol) and major capsid protein (MCP) sequences. Our findings revealed multiple hits with significantly low E-values (< 1e-80), where both DNApol and MCP were detected from the same studies, indicating the presence of similar virus-like particles (VLPs) globally. Of particular interest was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland (https://www.ebi.ac.uk/metagenomics/studies/MGYS00005650#overview). We have acknowledged this in the manuscript and cited the appropriated references, as follows:

      Results: “Searching the Jyvaskylavirus major capsid protein and DNA polymerase sequences in the MGnify-database (Richardson et al 2023) yields multiple hits with significantly low E-values (< 1e-80), as expected from the apparent ubiquity of marseilleviruses. Of note was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland, evidencing that marseilleviruses are prevalent but still unexplored in this region (Tiwari et al 2022)”.

      Discussion: “Marseillevirus DNA polymerase sequences are present in metagenomes from Finnish drinking water distribution systems (Tiwari et al 2022), hinting to a wide distribution of these viruses and still unknown ecological role in Central and Eastern Finland.”

      Reviewer #2 (Recommendations for the authors):

      Apart from the major comments in the weaknesses section, I have these additional minor comments to the authors:

      (1) I do not understand why the authors emphasized the uniqueness of isolating a giant virus from Finland. I think the manuscript would benefit if they rather emphasize that the virus comes from a boreal environment.

      The first giant virus, APMV, was described in 2003. In the following years the apparent ubiquity of these viruses was evidenced by two fronts. Metagenomics made clear that giant viruses are found almost everywhere, biased towards the oceans. Isolation efforts brought new virus groups in evidence but has been so far biased towards central Europe and South America samples. The closest isolated giant viruses to Jyvaskylavirus would be either an uncharacterized Swedish cedratvirus or a few microalgae-infecting mimivirus-like and phycodnaviruses-like isolates from Norway. Among marseilleviruses, Jyvaskylavirus is the northernmost isolate so far. Other marseilleviruses from the northern hemisphere were found in France, India, Japan and Algeria only.

      We still believe that finding a giant virus in Finland is relevant, considering that no other is known to date, be as an isolate or detected by genomics. We have made these observations clearer in the manuscript, giving emphasis to the boreal environment as well.

      (2) All discussed AlphaFold models should be added as Supplementary PDB data.

      We thank reviewer #2 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7).

      The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (3) Figure 2A: Could ORFs that encode structural proteins discussed in the paper, be somehow highlighted?

      We have updated Figure2A to include this information.

      (4) Figure 2C: Could be somehow highlighted from these members on which there was conducted structural characterisation (e.g. by some symbol next to the name)?

      We have updated Figure2C to include this information.

      (5) Figure 5A: Could the central bid be shown in a lower threshold (you can retain the threshold for the protein shell)? It would be interesting to see some details of the interior, rather than a massive blob.

      We have decreased the threshold level of the map as suggested.

      (6) Figure 6: the density corresponding to MCPs, minor capsid, and penton proteins respectively could be colour-zoned in Chimera(X). This would better visualise where each entity lies.

      About ORF142 - what other virus protein possesses this fold? Is it similar to the penton protein in other PRD1/Adenoviridae viruses? Maybe some comparison could be presented?

      We have incorporated the feedback from reviewer_#_2 by modifying the corresponding panel A in Figure 6. We have colour-zoned the penton (ORF142), some of the density region corresponding to the MCPs (ORF184) and to the minor cap proteins (ORF121). We have kept in grey the density corresponding to other minor proteins, and those we were able to identify are logically introduced later and shown as individual coloured cartoon tube models fitted into the density in panel A of Figure 7.

      Regarding ORF142, we have included a reference in the Discussion section to a new Supplementary Figure 9, where we provide a side-by-side comparison of the predicted Jyvaskylavirus penton protein model with experimentally derived penton protein models of PRD1 and HCIV-1. In light of this comparison, we have also added a brief clarification in the Discussion as follows:

      “However, in ORF142, the CHEF strands are predicted to be tilted relative to the BIDG strands, with an estimated angle of approximately 60° based on visual inspection (Supplementary Figure 9).”

      (7) Figure 7B: Could the density around the protein be zoned (rather than side view clipped), as this would better showcase how it fits the density?

      Initially, we presented a side view of the clipped surface to highlight the correspondence between the wall-shaped density, characteristic of a low-resolution beta-barrel, and the beta-barrel of the predicted model. Following the Reviewer’s suggestion, we have now surface-zoned the density and provided a stereo view of the density with the model fitted into the map using ChimeraX. While we recognize that stereo views are no longer commonly used in main text figures, we believe they remain valuable for visually assessing the overall match in low-resolution 3D density maps.

      (8) The authors did not try to reconstruct the asymmetric feature of the virion by classifying pentons, which may have identified a special vertex, one they claim might be required for genome packaging in "open particles". I understand the number of particles is low, but even low-resolution classification in C5 might be of interest in the field.

      We thank reviewer #2 for this valuable comment. The potential existence of a unique vertex in Marseilleviruses remains an open and intriguing question. Further investigations, including a significant increase in the number of particles, may help clarify this issue, and we plan to explore this topic in future structural studies.

      (9) Supplementary Figure 2: It would be interesting how the titre changes after the 12 hours, will it plateau? Could you add a bar showing the original titre to the chart showing stability after 109 days? I like the data in this figure and think it should be transferred to the main text.

      The titre at the 12h time point is very close to the titre we often get in our stocks, indicating that indeed it is close to peaking. For comparison: the titre of the 12-hour time point was 10<sup>11.55</sup> TCID50/ml, whereas our stock has a titre of 10<sup>11.66</sup> TCID50/ml. Our growth curve had more time points up to 48h and we lost the later time points due to a higher viral load than predicted, which led to us not being able to count these time points with the dilutions used. Showing the first 12 hours was enough for our initial purpose, which was to show a quick replication cycle for Jyvaskylavirus, in accordance with the other marseilleviruses in which the timing of the replication cycle was observed (see the answer for point 10 below).

      We have added a bar representing the original titre of the stock used for the stability experiment as suggested.

      While preparing the draft we were divided into having the growth and stability figure in the main text or in the supplementary material. Our decision was to move this data to the supplementary material and keep the focus of the main text on the discovery, genome analysis and structural data, as these are the main findings of our work. The specifics regarding stability, growth and other uncharacterized VLPs went to the supplementary material for those in the field who are interested in looking deeper. That being said, we will decide to keep this data as supplementary material if you and the editor agrees.

      (10) In the Discussion, the authors should focus on how our perception of giant viruses changes by this study - compare with other growth curves, stability assays, and structures of giant viruses, showcasing how prevalent those stabilising minor capsid proteins are, etc. My impression is that in the current form, it is just not clear if/how substantial these findings are and such a comparison and putting the results in a bigger picture would considerably increase the impact of the paper.

      Our comparisons with other marseilleviruses were based on genomic and structural characteristics, the two fronts we had data from the literature and databases to compare to. Sadly there is not too much information regarding stability and growth of other isolates that could be used for an in-depth comparison. For example: although marseilleviruses are known to have a fast replication cycle, this has been measured by DAPI staining of DNA inside infected cells to evaluate viral factory formation (Boyer et al 2009), or by time-series observations of viral cycle stages by electron microscopy (Fabre et al 2017), and not by viral titration as done here. We included a mention to these references in the results:

      “A fast replication cycle is a feature also shown for other marseilleviruses (Boyer et al 2009 ; Fabre et al 2017).”

      The literature also does not show virion stability of other isolates, making it impossible to have a comparison with jyvaskylavirus. A comparative study testing different isolates side by side is definitely of relevance and interest, but this would be difficult to be done in a short time due to obtaining other isolates. We believe the results in this manuscript might set some parameters to be used for comparing with other marseilleviruses, by our groups and others, in the future.

      Regarding the prevalence of the minor capsid proteins, we have expanded and clarified the identification of ORFs in Melbournevirus in the ‘Results’ and ‘Discussion’ sections. The revised Supplementary Table 4 has been updated accordingly and referenced in the results to clarify that the identification of Melbourne ORFs was carried out in BLASTp by querying the Jyvaskylavirus minor protein sequences exclusively against the Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1). BLASTp was then performed against the full sequence database, and homologous sequences were primarily retrieved from other marseillaviruses. These results have been compiled in a new Supplementary Table 5.

      However, Supplementary Table 5 also shows that the hits for Melbournevirus are not ranked at the top, and in some cases, they do not appear among the top hits.

      The ‘Results’ section now contains the following text:

      “To this end, we identified the corresponding Jyvaskylavirus ORFs in Melbournevirus through sequence comparison with Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1) (Supplementary Table 34). However, when the identified Jyvaskylavirus ORF sequences were analyzed using BLASTp without restricting the search to the Melbournevirus reference, many hits were observed in other giant viruses, primarily marseillevirus. Remarkably, some of these hits scored higher than those for Melbournevirus, supporting the presence of homologous proteins in these viruses (Supplementary Table 5).”

      The ‘Discussion’ section now contains the following text:

      “Additionally, the observation that the identified Jyvaskylavirus minor capsid protein sequences are shared across other marseillaviruses supports their essential structural and stabilizing roles in these viruses.”

      At the same time, we have modified the ‘Materials and Methods’ section to include a reference to Supplementary Figure 5, where the use of ModelAngelo is mentioned. Additionally, a new Supplementary Figure 10 has been included to clarify how the residues built into the Melbournevirus density using ModelAngelo (without prior knowledge of any sequence) are subsequently matched with the Jyvaskylavirus sequences.

      (11) Based on the author's statement, Iker Arriaga did all the cryoEM experiments. It is strange to me they are not placed higher on the author's list.

      We thank you for this observation and agree with your comment. This manuscript has been in preparation for a few years, and the first draft had the author order defined before the structural data collection and analyses were completed. Iker participation was indeed important and substantial from the first draft to the submitted version and he definitely deserves a better author placement. We have modified the author order to accommodate this. Note that only the author order changed and that no author has been included or removed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors provide strong evidence that the cell surface E3 ubiquitin ligases RNF43 and ZNRF3, which are well known for their role in regulating cell surface levels of WNT receptors encoded by FZD genes, also target EGFR for degradation. This is a newly identified function for these ubiquitin ligases beyond their role in regulating WNT signaling. Loss of RNF43/ZNRF3 expression leads to elevated EGFR levels and signaling, suggesting a potential new axis to drive tumorigenesis, whereas overexpression of RNF43 or ZNRF3 decreases EGFR levels and signaling. Furthermore, RNF43 and ZNRF3 directly interact with EGFR through their extracellular domains.

      Strengths:

      The data showing that RNF43 and ZNRF3 interact with EGFR and regulate its levels and activity are thorough and convincing, and the conclusions are largely supported.

      Weaknesses:

      While the data support that EGFR is a target for RNF43/ZNRF3, some of the authors' interpretations of the data on EGFR's role relative to WNT's roles downstream of RNF43/ZNRF3 are overstated. The authors, perhaps not intentionally, promote the effect of RNF43/ZNRF3 on EGFR while minimizing their role in WNT signaling. This is the case in most of the biological assays (cell and organoid growth and mouse tumor models). For example, the conclusion of "no substantial activation of Wnt signaling" (page 14) in the prostate cancer model is currently not supported by the data and requires further examination. In fact, examination of the data presented here indicates effects on WNT/b-catenin signaling, consistent with previous studies.

      Cancers in which RNF43 or ZNRF3 are deleted are often considered to be "WNT addicted", and inhibition of WNT signaling generally potently inhibits tumor growth. In particular, treatment of WNT-addicted tumors with Porcupine inhibitors leads to tumor regression. The authors should test to what extent PORCN inhibition affects tumor (and APC-min intestinal organoid) growth. If the biological effects of RNF43/ZNRF3 loss are mediated primarily or predominantly through EGFR, then PORCN inhibition should not affect tumor or organoid growth.

      We thank the reviewer’s appreciation of the key strength of our study. We fully agree with the reviewer that RNF43/ZNRF3 play key roles in restraining WNT signaling and their deletions activate WNT signaling that leads  to cancer promotion, as discussed and cited in our manuscript (Hao et al, 2012; Koo et al, 2012). We have revised the language in this manuscript to avoid any confusion or appearance of downplaying this known signaling pathway in cancer progression.

      What we would like to highlight in this work is that our study uncovered an effect of RNF43/ZNRF3 on EGFR, leading to biological impact in multiple model systems. In particular, we included the APC-mutated human cancer cell line HT29 and Apc min mouse intestinal tumor organoids. In the context of APC mutations, β-catenin stabilization and the activation of WNT target genes are essentially decoupled from upstream WNT ligand binding to WNT receptors, thus we could primarily focus on the effect of RNF43/ZNRF3 on EGFR. Our statement of “no substantial activation of WNT signaling” as cited by the reviewer was made in describing the data in Fig. 7E where we did not observe β-catenin accumulation in the nucleus and reasoned no substantial activation of canonical WNT signaling. We agree that further examination would help strengthen the conclusion and appreciate the reviewer’s suggestion of PORCN inhibition experiments. While PORCN inhibition is a valuable experiment in models with abundance of WNT ligands/receptors and non-mutationally activated regulators of WNT signaling (Yu et al, 2020), in biological scenarios with existing APC mutations, another group has previously demonstrated that PORCN inhibition had no observable effect on WNT signaling in APC-deficient cells (PMID: 29533772). In our initial submission, we confirmed this predicted low response to manipulation of WNT signaling components upstream of a mutated APC. We showed that addition of RSPO1 in Apc min mouse intestinal tumor organoids failed to further activate WNT target expression (Fig. 6G). Furthermore, in this revised manuscript, we added new data on EGFR inhibition and PORCN inhibition in WT and Znrf3 KO MEFs (Fig. 6L). PORCN inhibition had no impact on cell growth in neither WT nor Znrf3 KO MEFs, suggesting that Znrf3 KO promoting MEF growth is WNT independent. In contrast, inhibition of EGFR downstream signaling components (Fig. 6L) significantly blocked MEF growth and abolished the impact of Znrf3 KO in MEF growth. This new evidence further supports our main conclusion that RNF43/ZNRF3 controls EGFR signaling to regulate cell growth.

      Reviewer #2 (Public Review):

      Using proteogenomic analysis of human cancer datasets, Yu et al, found that EGFR protein levels negatively correlate with ZNFR3/RNF43 expression across multiple cancers. Interestingly, they found that CRC harbouring the frequent RNF43 G659Vfs*41 mutation exhibits higher levels of EGFR when compared to RNF43 wild-type tumors. This is highly interesting since this mutation is generally not thought to influence Frizzled levels and Wnt-bcatenin pathway activity. Using CRISPR knockouts and overexpression experiments, the authors show that EGFR levels are modulated by ZNRF3/RNF43. Supporting these findings, modulation of ZNRF3/RNF43 activity using Rspondin also leads to increased EGFR levels. Mechanistically, the authors, show that ZNRF3/RNF43 ubiquitinate EGFR and leads to degradation. Finally, the authors present functional evidence that loss of ZNRF3/RNF43 unleashes EGFR-mediated cell growth in 2D culture and organoids and promotes tumor growth in vivo.

      Overall, the conclusions of the manuscript are well supported by the data presented, but some aspects of the mechanism presented need to be reinforced to fully support the claims made by the authors. Additionally, the title of the paper suggests that ZNRF3 and RNF43 loss leads to the hyperactivity of EGFR and that its signalling activity contributes to cancer initiation/progression. I don't think the authors convincingly showed this in their study.

      We thank the reviewer commenting that our “conclusions of the manuscript are well supported by the data presented.”  We address the concerns raised by this reviewer in an itemized way as detailed below:

      Major points:

      (1) EGFR ubiquitination. All of the experiments supporting that ZNFR3/RNF43 mediates EGFR ubiquitination are performed under overexpression conditions. A major caveat is also that none of the ubiquitination experiments are performed under denaturing conditions. Therefore, it is impossible to claim that the ubiquitin immunoreactivity observed on the western blots presented in Figure 4 corresponds to ubiquitinated-EGFR species. Another issue is that in Figure 4A, the experiments suggest that the RNF43-dependent ubiquitination of EGFR is promoted by EGF. However, there is no control showing the ubiquitination of EGFR in the absence of EGF but under RNF43 overexpression. According to the other experiments presented in Figures 4B, 4C, and 4F, there seems to be a constitutive ubiquitination of EGFR upon overexpression. How do the authors reconcile the role of ZNRF3/RNF43 vs c-cbl?

      We agree with this reviewer of the limitation of overexpression experiments. In this manuscript, we actually leveraged both overexpression and knockout systems to demonstrate that ZNRF3/RNF43 regulates EGFR ubiquitination: in Fig 4A, we showed that overexpression of RNF43 increased EGFR ubiquitination; in Fig 4B&C and Fig S3A, we showed that RNF43 knockout decreased EGFR ubiquitination; in Fig 4F, we showed that overexpression of ZNRF3 WT increased EGFR ubiquitination but overexpression of ZNRF3 RING domain deletion mutant failed to increase EGFR ubiquitination.

      We also appreciate the rigor with which the reviewer has approached our methodology. We acknowledge that denaturing conditions can provide additional validation, but the technical challenges associated with denaturing conditions include the potential disruption of epitope structures recognized by these antibodies. Our methodology was chosen to balance the need for accurate detection with the preservation of protein structure and function, which are crucial for understanding the biological implications of EGFR ubiquitination. Moreover, our immunoprecipitation and subsequent Western blotting were stringent with high SDS and 2-ME, optimized to minimize non-specific binding and enhance the specificity of detection. We believe that the data presented are robust and contribute significantly to the existing body of knowledge on EGFR ubiquitination.

      CBL is a well-known E3 ligase of EGFR, and it induces EGFR ubiquitination upon EGF ligand stimulation. Therefore, in order to have a fair comparison of RNF43 and CBL on EGFR ubiquitination, we designed Fig 4A and related experiments in the setting of EGF stimulation. We observed that RNF43 overexpression increased EGFR ubiquitination as potently as CBL did. Following this result, we further demonstrated that knockout of RNF43 decreased endogenous ubiquitinated EGFR level in the unstimulated/basal condition (Fig 4B) as well as in the EGF-stimulated condition (Fig 4C). We acknowledge the importance and interest in fully understanding how ZNRF3/RNF43 interplays with the functions of CBL in regulating EGFR ubiquitination. This line of investigation indeed holds the potential to uncover novel regulatory mechanisms in detail. However, the primary focus of the current study was to establish a foundational understanding of ZNRF3/RNF43 role in regulating EGFR ubiquitination. We look forward to exploring further in future work.

      (2) EGFR degradation vs internalization. In Figure 3C, the authors show experiments that demonstrate that RNF43 KO increases steady-state levels of EGFR and prevents its EGF-dependent proteolysis. Using flow cytometry they then present evidence that the reduction in cell surface levels of EGFR mediated by EGF is inhibited in the absence of RNF43. The authors conclude that this is due to inhibition of EGF-induced internalization of surface EGF. However, the experiments are not designed to study internalization and rather merely examine steady-state levels of surface EGFR pre and post-treatment. These changes are an integration of many things (retrograde and anterograde transport mechanisms presumable modulated by EGF). What process(es) is/are specifically affected by ZNFR3/RNF43? Are these processes differently regulated by c-cbl? If the authors are specifically interested in internalization/recycling, the use of cell surface biotinylation experiments and time courses are needed to examine the effect of EGF in the presence or absence of the E3 ligases.

      We agree that our study design primarily assesses EGFR levels on the cell surface before and after EGF treatment and does not comprehensively measure the whole internalization process. In response to the reviewer’s comments, we have revised the relevant sections of manuscript to clarify that our current findings are focused on changes in cell surface EGFR and do not extend to the detailed mechanisms of EGF-induced internalization or recycling.

      (3) RNF43 G659fs*41. The authors make a point in Figure 1D that this mutant leads to elevated EGFR in cancers but do not present evidence that this mutant is ineffective in mediated ubiquitination and degradation of EGFR. As this mutant maintains its ability to promote Frizzled ubiquitination and degradation, it would be important to show side by side that it does not affect EGFR. This would perhaps imply differential mechanisms for these two substrates.

      Fig 1D is based on bioinformatic analysis of colon cancer patient samples, showing that RNF43 G659Vfs*41 mutant tumors exhibited significantly higher levels of EGFR protein compared to RNF43 WT tumors. Following this lead, we investigated whether this RNF43 G659fs*41 hotspot mutation lost its role in downregulating EGFR. To this end, we transfected the same amount of control vector, RNF43 WT, RING deletion mutant, G659fs*41 mutant DNA into 293T cells and measured the level of EGFR (co-transfected). As shown in Author response image 1, overexpression of RNF43 WT decreased EGFR level while overexpression of RING deletion mutant had no impact on EGFR level as compared with the Vector group, which is consistent with our findings in the manuscript. Cells transfected with the RNF43 G659Vfs*41 mutant exhibited nearly normal levels of EGFR; however, we also observed that RNF43 G659Vfs*41 was less expressed than WT, even though the same amounts of DNA were transfected. Therefore, the insubstantial impact on EGFR levels could be attributed to both functional loss or compromised stability of RNF43 G659Vfs*41 mRNA or protein. Further investigation on RNF43 G659Vfs*41 mRNA and protein stability vs. RNF43 G659Vfs*41 protein function is needed to draw a solid conclusion.

      Author response image 1.

      (4) "Unleashing EGFR activity". The title of the paper implies that ZNRF3/RNF43 loss leads to increased EGFR expression and hence increased activity that underlies cancer. However, I could find only one direct evidence showing that increased proliferation of the HT29 cell line mutant for RNF43 could be inhibited by the EGFR inhibitor Erlotinib. All the other evidence presented that I could find is correlative or indirect (e.g. RPPA showing increased phosphorylation of pathway members upon RNF43 KO, increased proliferation of a cell line upon ZNRF3/ RNF43 KO, decreased proliferation of a cell line upon ZNRF3/RNF43 OE in vitro or in xeno...). Importantly, the authors claim that cancer initiation/ progression in ZNRF3/RNF43 mutants may in some contexts be independent of their regulation of Wnt-bcatenin signaling and relying on EGFR activity upregulation. However, this has not been tested directly. Could the authors leverage their znrf3/RNF43 prostate cancer model to test whether EGFR inhibition could lead to reduced cancer burden whereas a Frizzled or Wnt inhibitor does not?

      More broadly, if EGFR signaling were to be unleashed in cancer, then one prediction would be that these cells would be more sensitive to EGFR pathway inhibition. Could the authors provide evidence that this is the case? Perhaps using isogenic cell lines or a panel of patient-derived organoids (with known genotypes).

      We appreciate the reviewer’s suggestion to provide more direct evidence demonstrating the importance of the ZNRF3/RNF43-EGFR axis in cancer cell proliferation.   In this revised manuscript, we further studied this issue in the WT vs. Znrf3 KO MEF cells. We observed that treatment with the EGFR inhibitor erlotinib did not affect WT MEF but stunted the growth advantage of Znrf3 KO MEF cells (Fig. 6L). On the other hand, treatment with the porcupine inhibitor C59 did not impact either WT or Znrf3 KO MEF cells (Fig. 6L), suggesting a more important role of the ZNRF3/RNF43-EGFR axis in mediating the enhanced cell growth of MEF caused by Znrf3 knockout. Furthermore, considering EGFR is often mutated in human cancer, to increase the clinical relance of our study, we also tested the effect of RNF43 knockout on EGFR L858R (Fig. 2D), a common oncogenic EGFR mutant, and found that RNF43 knockout in HT29 boosted levels of this EGFR mutant detected by its FLAG tag, suggesting that RNF43 degrades both WT and mutated EGFR and its loss can enhance signaling of both WT EGFR and its oncogenic mutant .  However, we emphasize again that this manuscript is in no way written to diminish the proven importance of ZNRF3/RNF43-WNT-β-catenin axis in cancer and development.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The main conclusion that EGFR is targeted for degradation by RNF43 and ZNRF3 is well supported and documented. Figures 1-5 and associated supplemental figures contain largely convincing data. Figures 6 and 7, however, require some modifications, as follows in order of appearance:

      Figure 6C: Growth of intestinal tumor organoids from Apcmin mice does not require Rspo, however, the authors show that these organoids grow larger in the presence of Rspo, an effect they attribute to increased EGFR activity, rather than increased WNT activity. While this conclusion may be correct, the authors should address this possibility by treating the organoids with PORCN inhibitor. The prediction would be that Rspo treatment still increases organoid size in the presence of PORCN inhibition. A further prediction would be that blocking EGFR (e.g. with Cetuximab) will abrogate the RSPO1 effect.

      Yes, we attributed the impact of Rspo on Apc min organoid growth to enhanced EGFR activity because we observed increased EGFR levels (Fig 6F) but no detectable increase in eight WNT target genes assayed. We agree that further pharmacologic experiments would further boost our conclusion, but our few attempts at treating organoids encountered technical difficulties. Hence, we switched to testing PORCN inhibition vs EGFR inhibition in WT and Znfr33 KO MEFs. As shown in the revised Fig. 6L, EGFR inhibition significantly reversed the growth advantage caused by Znrf3 KO but C59 did not.

      Figure 6G: It is unclear why the authors provide "8-day RSPO1 treatment" data. Here, EGFR mRNA appears to be elevated 2-fold (perhaps not statistically significant), and the Wnt targets Lef1 and Axin2 are decreased, as indicated by the statistical significance. What point is being made here?

      Our observation of increased size of APC min mouse intestinal tumor organoids and increased the EGFR protein levels were at 8 days of RSPO1 treatment. Therefore, we measured mRNA levels at the same time point with the 2-day time point also included for comparison. The goal of this qPCR experiment was to detect the contribution of WNT signaling, and we did not detect an increased transcriptional readout. We included EGFR mRNA levels for comparison, and we did not detect a statistically significant increase, consistent with our experiments concluding that ZNRF3/RNF43 regulate EGFR at the protein level. As stated in the preceding response, these data led us to attribute the impact of Rspo on Apc min organoid growth to enhanced EGFR activity.

      Figure 7A: This requires quantitation. How many mice were used per cell line? The data shown is not particularly convincing, with ZNRF3 overexpressing HT29 cells growing detectably. Showing representative mice is fine, but this should be supplemented with quantitation of all mice.

      We had provided this data. The BLI signal quantification was shown below the representative BLI images. Seven mice were used per cell line, as annotated at the top of the graph.

      Figure 7B: The authors assert that "canonical WNT signaling, based on levels of active-β-Catenin (non-phosphorylated at Ser33/37/Thr41; Figure 7B), remained unaffected". As shown, 2 of the 3 Myc-Znrf3 tumors have increased active-b-catenin signal over the GFP tumors. This indicates to me that canonical Wnt signaling was affected. The authors either need to present quantitative data that supports this claim or modify their conclusions. As presented, I don't think it is appropriate to decouple the effect of Znrf3 overexpression on EGFR from its effect on WNT.

      As requested, we have quantified the level of non-phospho β-Catenin at Ser33/37/Thr41 and found no significant differences (p > 0.05) between the control group vs. ZNRF3 overexpression group. We once again note that our manuscript was not meant to dispute the proven signaling and biological significance of WNT signaling regulation by ZNRF3/RNF43, and we have proof-read the manuscript multiple times to ensure that we did not make any generalized or misleading statements in this aspect.

      Author response image 2.

      Figure 7E: Here the authors assert that "no substantial activation of canonical Wnt signaling" in the Z&R KO tumors, however, the figure shows a substantial increase in active b-catenin staining. The current resolution is insufficient to claim that there is no increase in nuclear b-catenin. The authors' claim that WNT signaling is not involved here is not supported by the data presented here. One way to demonstrate that this effect is through EGFR activation and not through WNT activation is to treat mice with PORCN inhibitor. WNT-addicted tumors, such as by Rnf43 or Znrf3 deletion, regress upon PORCN inhibition. In this case, if the effect of Z&R KO is mediated through EGFR rather than WNT, then there should be no effect on tumor growth upon PORCN inhibition. This is a critical experiment in order to make this point.

      We appreciate the reviewer’s comments and suggestion of experiments. We based our initial statement on insubstantial nuclear β-catenin staining, but we agree that immunohistochemical staining lacks the resolution suitable for quantification. We could not generate the adequate number of KO animals for these in vivo experiments in the window of time planned for this revision. Rather, as shown in the newly added Fig. 6L, we tested EGFR inhibition and PORCN inhibition in Znrf3 KO MEFs and obtained strong data further supporting EGFR in mediating Znrf3 KO promotion of MEF growth. Notwithstanding, we have carefully revised our description of the in vivo data in Fig 7E to avoid any confusion or over-interpretation.

      Minor points:

      Figure 2A: provide quantitation of this immunoblot.

      We have revised manuscript with quantification result shown next to the immunoblot.

      Figure 2B: provide more detail in the figure legend and in the Materials and Methods section on how the KO MEFs were generated. Confirmation that Znrf3 (or in cases of Rnf43 KO) expression is lost in KO would be advisable.

      We have confirmed Znrf3 KO by genotyping and RNF43 KO by immunofluorescent staining. We have also tested multiple commercial anti-ZNRF3 antibodies and anti-RNF43 antibodies for Western blotting, but they all failed.

      Figure 4C is a little misleading. The schematic indicates that ECD-TM and TM-ICD truncations were analyzed for both ZNRF3 and RNF43. However, Figure 4 only shows data for ZNRF3, and the corresponding Figure S4 lacks data for the TM-ICD of Rnf43. A recommendation is to show only those schematics for which data is presented in that figure. On a related topic, the results using the deltaRING constructs (Figure S5) are not mentioned/described in the text.

      We think that the reviewer meant Fig 5C. We have revised the Fig 5C by removing the RNF43 label, and we confirm that  Results section does include the data in Fig S5.

      Figure S4A: Only ZNRF3 is indicated in this figure. Please explain why RNF43 is not represented here. Also, indicate what is plotted along the x-axis.

      We only detected the endogenous ZNRF3-EGFR interaction, possibly because the RNF43 protein level is relatively low in the cell line we used for the mass spec experiment. X-axis is the proteins ordered based on Y-axis values as detailed in the figure legend  -- each data point was arranged along the x axis based on the fold change of iBAQ of EGFR-associated proteins identified in EGF-stimulated vs. control in the log2 scale, from low to high (from left to right on x axis). We have added the phrase “Proteins detected by Mass-Spec” for X-axis.

      Reviewer #2 (Recommendations For The Authors):

      Minor Points.

      (1) In Figure 2B, the authors claim that Znrf3 KO enhanced both EGFR and p-EGFR levels both in the absence and presence of EGF. Although it is clear in the presence of EGF, the increased in p-EGFR in the absence of EGF is less than clear.

      We have revised the manuscript to more clearly state the result in Fig 2B.

      (2) Importantly the authors validated their findings using three independent RNF43 gRNA (fig S2D) but they do not show the editing efficiency obtained with the gRNA.

      We did not include RNF43 IB in this Figure due to lack of specific antibodies for detecting RNR43 in IB. We have no reasons to doubt adequate efficiency of knockout since EGFR was increased compared to the control group. As a result, we did not perform deep sequencing to validate knockout efficacy.

      (3) In S2E, the authors show that KO of either ZNRF3 or RNF43 enhance HER2 levels. This suggests that there is no redundancy between these E3 ligases, at least in this context. How do the authors reconcile that?

      The reviewer raised an interesting issue. Due to the lack of WB antibodies for these two proteins, we would not easily assess the feedback impact of knockout of either gene on the protein levels of the other gene. We speculate that there may be a threshold level of the sum of the two proteins that is needed for adequate degradation of HER2, leading to HER2 increase when either gene is knocked out. Detailed studies of this issue is beyond the scope of this current work.

      (4) Experiments performed in Fig 3C are performed in only one clone. The authors need to repeat in an additional clone or rescue this phenotype using a RNF43 cDNA.

      Our RNF43 KO HT29 line is a pool of KO cells, not a single clone.

      (5) In Figure 7E, the authors suggest that the absence of nuclear bcatenin means that canonical Wnt signaling is unaffected. It is widely known that nuclear bcatenin is often not correlating with pathway activity.

      As stated above, we have revised the manuscript to avoid confusion and misinterpretation.

      (6) What is the nature of the error bars in Fig 3c? Are the differences statistically significant?

      As mentioned in the figure legend, the error bars are SEM. The result is statistically significant, and p-value is noted in the graph.

      (7) In the Figure legends, it should be stated clearly how many biological replicates were performed for each experiment and single data points should be plotted where applicable (e.g. qPCR data). It would be helpful if the uncropped and unprocessed Western blot membranes and replicates that are not shown would be accessible to allow the reader a more comprehensive view of the acquired data, especially for blots that were quantified (e.g. Figure 2F, Figure 3C, there is clearly some defect on the blot).

      For WB representation, it would be helpful to include more size markers on the Western blots (especially on the Ips that show ubiquitin smear) and in general to use a reference protein (GAPDH, Actin, Vinculin) that is closer to the protein being accessed.

      More details should be added in the Methods section to explain how protocols were performed in detail. For example, it should be explained how the viruses used for infecting cells were produced (which plasmids were transfected using which transfection reagent, how long was the virus collected for, etc). Then, it should be stated how long the cells were undergoing selection before being harvested. Because the expression of the viral constructs potentially has an effect on cell proliferation through EGFR, this information is quite relevant. This is just an example, there are details missing in nearly every section (Flow: washing protocols, gating protocols (Live/dead stain?), WB: RIPA lysis buffer composition? How much protein was loaded on blots? How was protein quantification done? IP: how were washes performed and how often repeated?)

      Missing: antibody dilutions for IF, IHC, and WB, plasmid backbones, sequences and availability, qPCR primer sequences from Origene.

      Incucyte experiments are not described.

      We have revised the relevant sections to include more details.

      (8) Line 141: revise text: 2x mRNA abundance in the same sentence.

      Line 162: define intermediate expression better.

      Line 197/198: revise text ('the predominant one'?).

      Line 218/219: revise text (Internalisation of surface EGFR?).

      Line 245: clarify in text that it is endogenous EGFR that is being pulled down.

      Line 264: typo: conserved instead of conservative.

      Line 324: revise text (What does 'unknown significance' mean).

      Line 396/397: revise text: 2x Co-IP in the same sentence.

      Figure 3 D/E: more details on the Method in the figure legend.

      We have revised them accordingly.

    1. Reviewer #1 (Public review):

      Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

      (1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

      (2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation. As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

      (3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

      (4) There is a need to better explain why "focality" is important, and justify the measure used.

      (5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM"<br /> The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

      (6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)"<br /> This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

      (7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

      (8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

      (9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

    2. Author response:

      eLife Assessment

      The conclusions of this work are based on valuable simulations of a detailed model of striatal dopamine dynamics. Establishing that a lower dopamine uptake rate can lead to a 'tonic' level of dopamine in the ventral but not dorsal striatum, and that dopamine concentration changes at short delays can be tracked by D1 but not D2 receptor activation, is of value and will be of interest to dopamine aficionados. However, the simulations are incomplete, providing only partial support for the key claims. Several things can be done to strengthen the conclusions, including, for example, but not exclusively, a demonstration of how the results would change as a function of changes in D2 affinity.

      We sincerely thank the Editors and Reviewers for their insightful comments on our manuscript. We are pleased that our simulations are recognized as interesting, sophisticated and valuable. Moreover, we fully agree that many of the findings will be of particular interest to dopamine aficionados. While we maintain that our simulations provide a solid basis for the key claims, we acknowledge that the conclusions can be further strengthened by the revisions suggested below.

      Reviewer #1 (Public review):

      Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

      (1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

      We appreciate that the reviewer finds our work interesting and useful to the community. However, we acknowledge that in the revised version we to need to better describe how our conclusions are different from those reached based on previous models.

      We will also carry out new simulations across a range of D2R affinities to assess how this will affect the finding that even a long pause in DA firing has little effect on DR2 receptor occupancy. As also suggested, the results will be highlighted and further discussed.

      (2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation.

      We will improve our definitions and descriptions relating to nanoclustering of DAT in the revised version of the manuscript. We fully agree that the spatial resolution of the main model is a limitation and, ideally, that the nanoclustering should be combined with the large-scale release simulations. Unfortunately, this would require many orders of magnitude more computational power than currently available.

      As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

      Thank you for raising this important point. While it is true that DAT clustering increases heterogeneity in DA distribution at the microscopic level, the diffusion rate is, in most circumstances, too fast to permit concentration differences on a spatial scale relevant for nearby receptors. Accordingly, we propose that the primary effect of DAT nanoclustering is to decrease the overall uptake capacity, which in turn increases overall extracellular DA concentrations. Thus, homogeneous changes in extracellular DA concentrations can arise from regulating heterogenous DAT distribution. An exception to this would be the circumstance where the receptor is located directly next to a dense cluster – i.e. within nanometers. In such cases, local DA availability may be more directly influenced by clustering effects. This will be further discussed in the revised manuscript.

      (3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

      We fully agree that this typically is outside the physiological range. The values are included to showcase what extreme situations would look like.

      (4) There is a need to better explain why "focality" is important, and justify the measure used.

      We will expand on the intention of this measure in the revised manuscript. Thank you for pointing out this lack of clarification.

      (5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM" The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

      We agree that these assumptions are critical. Simulations on effective off-rates across a range of EC50 values will be included in the revised version.

      (6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)”

      This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

      We sincerely thank the reviewer for highlighting this important point. We fully recognize the fundamental importance of absolute and relative DA receptor kinetics for modeling DA actions and acknowledge that differences in affinity estimates from sensor-based measurements highlight the inherent uncertainty in selecting receptor kinetics parameters. While we have based our modeling decisions on what we believe to be the most relevant available data, we acknowledge that the choice of receptor kinetics is a topic of ongoing debate. Importantly, we are making our model available to the research community, allowing others to test their own estimates of receptor kinetics and assess their impact on the model’s behavior. In our revised manuscript, we will further discuss the rationale behind our parameter choices, including: Our selection of a Kd value of 1000 nM for D1R (based on the observed affinities for D1R sensors) and an extrapolated Koff of 19.5 s<sup>-1</sup> (Labouesse & Patriarchi, 2021). Our use of a Kd value of 7 nM and an extrapolated Koff of 0.2 s<sup>-1</sup> for D2R, consistent with recent binding studies (Ågren et al., 2021).

      (7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

      We fully agree that this is a limitation of FSCV. However, most of the cited papers attempt to correct for this by way of fitting the output to a multi-parameter model for DA kinetics. If newer literature brings the Vmax values estimated into question, we have made the model publicly available to rerun the simulations with new parameters.

      (8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

      The original paper cited does not specify which region the values are measured in. However, a separate paper estimates the rat cerebellum has a comparable tortuosity index (Nicholson and Phillips, J Physiol. (1981)), suggesting it may be a rather uniform value across brain regions.

      (9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

      As part of our revision, we will expand the current discussion of our finding in the context of previous models in the manuscript

      Reviewer #2 (Public review):

      The work presents a model of dopamine release, diffusion, and reuptake in a small (100 micrometer^2 maximum) volume of striatum. This extends previous work by this group and others by comparing dopamine dynamics in the dorsal and ventral striatum and by using a model of immediate dopamine-receptor activation inferred from recent dopamine sensor data. From their simulations, the authors report two main conclusions. The first is that the dorsal striatum does not appear to have a sustained, relatively uniform concentration of dopamine driven by the constant 4Hz firing of dopamine neurons; rather that constant firing appears to create hotspots of dopamine. By contrast, the lower density of release sites and lower rate of reuptake in the ventral striatum creates a sustained concentration of dopamine. The second main conclusion is that D1 receptor (D1R) activation is able to track dopamine concentration changes at short delays but D2 receptor activation cannot.

      The simulations of the dorsal striatum will be of interest to dopamine aficionados as they throw some doubt on the classic model of "tonic" and "phasic" dopamine actions, further show the disconnect between dopamine neuron firing and consequent release, and thus raise issues for the reward-prediction error theory of dopamine.

      There is some careful work here checking the dependence of results on the spatial volume and its discretisation. The simulations of dopamine concentration are checked over a range of values for key parameters. The model is good, the simulations are well done, and the evidence for robust differences between dorsal and ventral striatum dopamine concentration is good.

      However, the main weakness here is that neither of the main conclusions is strongly evidenced as yet. The claim that the dorsal striatum has no "tonic" dopamine concentration is based on the single example simulation of Figure 1 not the extensive simulations over a range of parameters. Some of those later simulations seem to show that the dorsal striatum can have a "tonic" dopamine concentration, though the measurement of this is indirect. It is not clear why the reader should believe the example simulation over those in the robustness checks, for example by identifying which range of parameter values is more realistic.

      We appreciate that the reviewer finds our work interesting and carefully performed. The reviewer is correct that DA dynamics, including the presence and level of tonic DA, are parameter-dependent in both the dorsal striatum (DS) and ventral striatum (VS). Indeed, our simulations across a broad range of biological parameters were intended to help readers understand how such variation would impact the model’s outcomes, particularly since many of the parameters remain contested. Naturally, altering these parameters results in changes to the observed dynamics. However, to derive possible conclusions, we selected a subset of parameters that we believe best reflect the physiological conditions, as elaborated in the manuscript. This is eventually required in computational modelling of biological systems. In response to the reviewer’s comment, we will place greater emphasis on clarifying which parameter regimes produce a "tonic" versus "non-tonic" DA state in the DS. Additionally, we will underscore that the distinction between tonic and non-tonic states is not a binary outcome but a parameter-dependent continuum—one that our model now allows researchers to explore systematically. Finally, we will highlight how our simulations across parameter space not only capture this continuum but also identify the regimes that produce the most heterogeneous DA signaling, both within and across striatal regions.

      The claim that D1Rs can track rapid changes in dopamine is not well supported. It is based on a single simulation in Figure 1 (DS) and 2 (VS) by visual inspection of simulated dopamine concentration traces - and even then it is unclear that D1Rs actually track dynamics because they clearly do not track rapid changes in dopamine that are almost as large as those driven by bursts (cf Figure 1i).

      We would like to draw the attention also to Fig. S1, where the claim that D1R track rapid changes is supported in more depth. According to this figure, upon coordinated burst firing, the D1R occupancy rapidly increased as diffusion no longer equilibrated the extracellular concentrations on a timescale faster than the receptors – and D1R receptor occupancy closely tracked extracellular DA with a delay on the order of tens of milliseconds. Note that the brief increases in [DA] from uncoordinated stochastic release events from tonic firing in Fig. 1i are too brief to drive D1 signaling, as the DA concentration diffuses into the remaining extracellular space on a timescale of 1-5 ms. This is faster than the receptors response rate, and does not lead to any downstream signaling according to our simulations. This means D1 kinetics are rapid enough to track coordinated signaling on a ~50 ms timescale and slower, but not fast enough to respond to individual release events from tonic activity. In our revised manuscript we will expand the discussion of this topic to provide greater clarity.

      The claim also depends on two things that are poorly explained. First, the model of binding here is missing from the text. It seems to be a simple bound-fraction model, simulating a single D1 or D2 receptor. It is unclear whether more complex models would show the same thing.

      We realize that this is not made clear in the methods and, accordingly, we will update the method section to elaborate on how we model receptor binding. The model simulates occupied fraction of D1R and D2R in every single voxel of the simulation space.

      Second, crucial to the receptor model here is the inference that D1 receptor unbinding is rapid; but this inference is made based on the kinetics of dopamine sensors and is superficially explained - it is unclear why sensor kinetics should let us extrapolate to receptor kinetics, and unclear how safe is the extrapolation of the linear regression by an order of magnitude to get the D1 unbinding rate.

      We chose to use the sensors because it was possible to estimate precise affinities/off-rates from the fluorescent measurements. Although there might some variation in affinities that could be attributable to the mutations introduced in the sensors, the data clearly separated D1R and D2R with a D1R affinity of ~1000 nM and a D1R affinity of ~7 nM (Labouesse & Patriarchi, 2021) consistent with earlier predictions of receptor affinities. From our assessment of the literature we found that this was the most reasonable way to estimate affinities and thereby off-rates. Importantly, the model has been made publicly available, so should new measurements arise, the simulations can be rerun with tweaks to the input parameters.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reply to the Reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Referee #1

      1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      Response: The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of the CTCF-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-245, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

      1. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Response: Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      1. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      Response: As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      1. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      Response: According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      Response: I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      1. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Response: Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

      Referee #2

      1. Introduction, line 95: CTCF appears two times, it seems redundant.

      Response: On lines 91-93, I deleted the latter CTCF from the sentence "and examined the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      1. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Response: Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      1. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      Response: On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      1. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      Response: On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      1. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      Response: The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      1. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      Response: As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      1. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Response: Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      1. Do the authors think that the identified DBPs could work in that way as well?

      Response: The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      1. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Response: Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      1. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Response: Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 - 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops. To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Referee #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      Response: When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      1. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      Response: As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 - 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      1. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Response: Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 - 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 - 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      1. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      Response: The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      Minor comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      Response: The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      1. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      Response: In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

    2. Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Reviewer #1

      1) I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      As the reviewer pointed out, a wet experimental validation of the results of this study would give an opportunity for more biological researchers to have an interest in the study. I plan to promote the wet experimental analysis in collaboration with biological experimental researchers as a next step of this study. The same analysis in this study can be performed in immortalized cells for CRISPR experiment (e.g. Guo Y et al. Cell 2015).

      2) Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      3) Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality is their overall tendency, and it may be difficult to notice the directionality from each binding site.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      4) The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value < 0.05). The comparison between the splice sites of both ends of first and last introns and those of other introns showed the similar statistical significance of enrichment and number of splice sites with the insulator-associated DNA-binding proteins (Table 2 and Table S9).

      5) Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      6) Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2’s comments.

      Reviewer #2

      1) Introduction, line 95: CTCF appears two times, it seems redundant.

      On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      2) Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      3) Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      4) Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines”. On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      5) Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      6) Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      I added the following sentence on lines 561-569: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      7) In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      8) Do the authors think that the identified DBPs could work in that way as well?

      The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      9) Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      10) Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 – 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

      To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Reviewer #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 22 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 – 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 – 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 – 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      1.PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g.,https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

    1. This demonstrates how, once again, no design choice is neutral, and serves all people equally well.

      I found this passage interesting because it echoes a concept form our previous readings. That no design choice is ever truly neutral, and there will always be a group of people who are left out. This is a reminder of the responsibility designers have to be mindful of the impact their choices have on different groups. It’s important to remember that every decision designers make can affect people in different ways, and to consider how our designs may unintentionally exclude or disadvantage certain users. As we work on our project, we need to think about this by using typical page layouts that people are used to and a color palette that works for visually impaired people.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms. Response: Thank you very much for your insightful comments. 1) To address "what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment1. We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Figure R1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with anti-DO-1 (mouse) antibody (Figure R1). The latter detects both endogenous wild-type p53 and the V5-tagged FLp53 since the antibody epitope is within the N-terminus (aa 20-25). This result supports the reviewer's comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments. (Figure R1 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      In summary, in line with the reviewer's comment that 'under normal physiological conditions p53 expression is usually low,' we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Figures R1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.

      2) We agree with the reviewer that 'It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario'. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

      Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

      Response: Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Figure R1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

      3. On similar lines, authors described:

      "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

      Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

      Response: Thank you for raising this point regarding the physiological relevance of the ratios used in our study. 1) In the revised manuscript (lines 193-195), we added in this direction that "The elevated Δ133p53 protein modulates p53 target genes such as miR34a and p21, facilitating cancer development2, 3. To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10." This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

      2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p534, 5. Additionally, ∆133TP53 is a p53 target gene6, 7 and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing8. Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp538. These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies3, 4, 9, 10.

      3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Figure R1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Response: Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). "The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

      One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death11. To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis12. The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope."

      **Referees cross-commenting**

      I think the comments from the other reviewers are very much reasonable and logical.

      Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Response: Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53. To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation.

      Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure R3). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions.

      Reviewer #1 (Significance (Required)):

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

      Response: Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

        • Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.* Response: Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): "Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer13, 14, 15. Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines16. Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells17. Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions."

      2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

      Response: Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure R3, lower panel), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation__.__ However, no FLp53 aggregates were observed when it was expressed alone (Figure R2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation.

      (Figure R2 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

      Response: We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa). Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation18, 19. Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)20, 21, 22, potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

      Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

      Reviewer #2 (Significance (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      Response: We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

      First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

      Response: Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): "For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript."

      One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

      • Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599.
      • Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.
      • Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10.
      • Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.
      • Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.
      • Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.
      • Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. Response: Thank you very much for your comment and for highlighting these important studies.

      We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. 'pro-oncogenic activity' with 'dominant-negative effect' in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

      After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases. Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

      • Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.
      • Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.
      • Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.
      • Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.
      • Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.
      • Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed non-cancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. "Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes."
      • Gong, 2016: Suggested that Δ133p53 promotes cell survival under low-level oxidative stress, but its role under different stress conditions remains uncertain. We have revised the Introduction to provide a more balanced discussion of Δ133p53's dule role (lines 62-73):

      "The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations23, 24, where it promotes cell survival in a non-oncogenic manner25, 26, especially under low oxidative stress27. Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 4, 6. The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis28 and in melanoma cells' aggressiveness10. Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a2, 29 by dominant-negative effect, the exact mechanism is not known."

      On the figures presented in this manuscript, I have three major concerns:

      *1- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. *

      Response: Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

      First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

      To further rule out any tag-related artifacts, we conducted a co-immunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5-tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAG-tagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone.

      In the revised paper, we added the following sentences (Lines 146-152): "To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAG-tagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other."

      Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We've added this result in the revised manuscript (lines 236-245): "To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation."

      We've also discussed this in the Discussion section (lines 349-356): "In our study, we primarily utilized an overexpression strategy involving FLAG/V5-tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitope-containing proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins."

      In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

      2- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

      Response: Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions3, 4, 9. For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue4. Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells3. Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:19. These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

      Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

      3-The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

      Response: Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)4, 29, 30, 31. Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p534. Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure below. The discrepancy may be caused by a potentially confusing statement in that paper4

      (The Figure from Bourdon JC et al. (2005) is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      The localization of p53 is governed by multiple factors, including its nuclear import and export32. The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)4 . However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import.

      Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence33. We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy34. This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

      In the revised manuscript, we discussed this in Discussion (lines 328-335): "Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B33. High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy34. Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems."

      Minor concerns:

      - Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

      Response: Thank you! The revised Figure 1A has been created in the revised paper.

      • Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

      Response: Thank you for this suggestion. We've changed the image and the new Figure 2 has been shown in the revised paper.

      • Figure 3C: what ratio of FLp53/Delta isoform was used?

      Response: We have added the ratio in the figure legend of Figure 3C (lines 845-846) "Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio."

      • Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

      Response: Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53.

      These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53's anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominant-negative effects.

      We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

      • Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

      Response: Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated. Changes have been made in lines 214-215: "The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B)."

      **Referees cross-commenting**

      I agree that the system needs to be improved to be more physiological.

      Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

      Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

      To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

      Response: Thank you for these comments. We've addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not 'overwhelmingly high'.

      First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development2, 3, 4, 9. Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We've discussed this in the Results section (lines 254-269).

      Reviewer #3 (Significance (Required)):

      Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

      The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

      The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

      This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

      My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

      Response: Thank you very much for your positive and critical comments. We've included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73).

      References

      1. Pitolli C, Wang Y, Candi E, Shi Y, Melino G, Amelio I. p53-Mediated Tumor Suppression: DNA-Damage Response and Alternative Mechanisms. Cancers 11, (2019).

      Fujita K, et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nature cell biology 11, 1135-1142 (2009).

      Fragou A, et al. Increased Δ133p53 mRNA in lung carcinoma corresponds with reduction of p21 expression. Molecular medicine reports 15, 1455-1460 (2017).

      Bourdon JC, et al. p53 isoforms can regulate p53 transcriptional activity. Genes & development 19, 2122-2137 (2005).

      Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Molecular and cellular biology 24, 7987-7997 (2004).

      Aoubala M, et al. p53 directly transactivates Δ133p53α, regulating cell fate outcome in response to DNA damage. Cell death and differentiation 18, 248-258 (2011).

      Marcel V, et al. p53 regulates the transcription of its Delta133p53 isoform through specific response elements contained within the TP53 P2 internal promoter. Oncogene 29, 2691-2700 (2010).

      Zhao L, Sanyal S. p53 Isoforms as Cancer Biomarkers and Therapeutic Targets. Cancers 14, (2022).

      Nutthasirikul N, Limpaiboon T, Leelayuwat C, Patrakitkomjorn S, Jearanaikoon P. Ratio disruption of the ∆133p53 and TAp53 isoform equilibrium correlates with poor clinical outcome in intrahepatic cholangiocarcinoma. International journal of oncology 42, 1181-1188 (2013).

      Tadijan A, et al. Altered Expression of Shorter p53 Family Isoforms Can Impact Melanoma Aggressiveness. Cancers 13, (2021).

      Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell death and differentiation 25, 104-113 (2018).

      Ghorbani N, Yaghubi R, Davoodi J, Pahlavan S. How does caspases regulation play role in cell decisions? apoptosis and beyond. Molecular and cellular biochemistry 479, 1599-1613 (2024).

      Petronilho EC, et al. Oncogenic p53 triggers amyloid aggregation of p63 and p73 liquid droplets. Communications chemistry 7, 207 (2024).

      Forget KJ, Tremblay G, Roucou X. p53 Aggregates penetrate cells and induce the co-aggregation of intracellular p53. PloS one 8, e69242 (2013).

      Farmer KM, Ghag G, Puangmalai N, Montalbano M, Bhatt N, Kayed R. P53 aggregation, interactions with tau, and impaired DNA damage response in Alzheimer's disease. Acta neuropathologica communications 8, 132 (2020).

      Arsic N, et al. Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells. Nature communications 12, 5463 (2021).

      Melo Dos Santos N, et al. Loss of the p53 transactivation domain results in high amyloid aggregation of the Δ40p53 isoform in endometrial carcinoma cells. The Journal of biological chemistry 294, 9430-9439 (2019).

      Mestrom L, et al. Artificial Fusion of mCherry Enhances Trehalose Transferase Solubility and Stability. Applied and environmental microbiology 85, (2019).

      Kaba SA, Nene V, Musoke AJ, Vlak JM, van Oers MM. Fusion to green fluorescent protein improves expression levels of Theileria parva sporozoite surface antigen p67 in insect cells. Parasitology 125, 497-505 (2002).

      Snapp EL, et al. Formation of stacked ER cisternae by low affinity protein interactions. The Journal of cell biology 163, 257-269 (2003).

      Jain RK, Joyce PB, Molinete M, Halban PA, Gorr SU. Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells. The Biochemical journal 360, 645-649 (2001).

      Campbell RE, et al. A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America 99, 7877-7882 (2002).

      Hofstetter G, et al. Δ133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. British journal of cancer 105, 1593-1599 (2011).

      Bischof K, et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Scientific reports 9, 5244 (2019).

      Gong L, et al. p53 isoform Δ113p53/Δ133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell research 25, 351-369 (2015).

      Gong L, et al. p53 isoform Δ133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Scientific reports 6, 37281 (2016).

      Gong L, Pan X, Yuan ZM, Peng J, Chen J. p53 coordinates with Δ133p53 isoform to promote cell survival under low-level oxidative stress. Journal of molecular cell biology 8, 88-90 (2016).

      Candeias MM, Hagiwara M, Matsuda M. Cancer-specific mutations in p53 induce the translation of Δ160p53 promoting tumorigenesis. EMBO reports 17, 1542-1551 (2016).

      Horikawa I, et al. Δ133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell death and differentiation 24, 1017-1028 (2017).

      Mondal AM, et al. Δ133p53α, a natural p53 isoform, contributes to conditional reprogramming and long-term proliferation of primary epithelial cells. Cell death & disease 9, 750 (2018).

      Joruiz SM, Von Muhlinen N, Horikawa I, Gilbert MR, Harris CC. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell death & disease 15, 454 (2024).

      O'Brate A, Giannakakou P. The importance of p53 location: nuclear or cytoplasmic zip code? Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 6, 313-322 (2003).

      Horikawa I, et al. Autophagic degradation of the inhibitory p53 isoform Δ133p53α as a regulatory mechanism for p53-mediated senescence. Nature communications 5, 4706 (2014).

      Lee H, et al. IRE1 plays an essential role in ER stress-mediated aggregation of mutant huntingtin via the inhibition of autophagy flux. Human molecular genetics 21, 101-114 (2012).

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.
      2. Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
      3. On similar lines, authors described: "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)." Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Referees cross-commenting

      I think the comments from the other reviewers are very much reasonable and logical. Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Significance

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Hippocampal place cells display a sequence of firing activities when the animal travels through a spatial trajectory at a behavioral time scale of seconds to tens of seconds. Interestingly, parts of the firing sequence also occur at a much shorter time scale: ~120 ms within individual cycles of theta oscillation. These so-called theta sequences are originally thought to naturally result from the phenomenon of theta phase precession. However, there is evidence that theta sequences do not always occur even when theta phase precession is present, for example, during the early experience of a novel maze. The question is then how they emerge with experience (theta sequence development). This study presents evidence that a special group of place cells, those tuned to fast-gamma oscillations, may play a key role in theta sequence development.

      The authors analyzed place cells, LFPs, and theta sequences as rats traveled a circular maze in repeated laps. They found that a group of place cells were significantly tuned to a particular phase of fast-gamma (FG-cells), in contrast to others that did not show such tunning (NFG-cells). The authors then omitted FG-cells or the same number of NFG-cells, in their algorithm of theta sequence detection and found that the quality of theta sequences, quantified by a weighted correlation, was worse with the FG-cell omission, compared to that with the NFG-cell omission, during later laps, but not during early laps. What made the FG-cells special for theta sequences? The authors found that FG-cells, but not NFG-cells, displayed phase recession to slow-gamma (25 - 45 Hz) oscillations (within theta cycles) during early laps (both FG- and NFG-cells showed slow-gamma phase precession during later laps). Overall, the authors conclude that FG-cells contribute to theta sequence development through slow-gamma phase precession during early laps.

      How theta sequences are formed and developed during experience is an important question, because these sequences have been implicated in several cognitive functions of place cells, including memory-guided spatial navigation. The identification of FG-cells in this study is straightforward. Evidence is also presented for the role of these cells in theta sequence development. However, given several concerns elaborated below, whether the evidence is sufficiently strong for the conclusion needs further clarification, perhaps, in future studies.

      We thank the reviewer for these positive comments.

      (1) The results in Figure 3 and Figure 8 seems contradictory. In Figure 8, all theta sequences displayed a seemingly significant weighted correlation (above 0) even in early laps, which was mostly due to FG-cell sequences but not NFG-cell sequences (correlation for NFG-sequences appeared below 0). However, in Figure 3H, omitting FG-cells and omitting NFG-cells did not produce significant differences in the correlation. Conversely, FG-cell and NFG-cell sequences were similar in later laps in Figure 8 (NFG-cell sequences appeared even better than FG-cell sequences), yet omitting NFG-cells produced a better correlation than omitting FG-cells. This confusion may be related to how "FG-cell-dominant sequences" were defined, which is unclear in the manuscript. Nevertheless, the different results are not easy to understand.

      We thank the reviewer for pointing out this important problem.  The potential contradictory can be interpreted by different sequence dataset included in Fig3 and Fig8, described as follows.

      (1) In Fig 3, all sequences decoded without either FG or NFG cells were included, defined as exFG-sequences and exNFG sequences, so that we couldn’t observe sequence development at early phase and thus the weighted correlation was low.  (2) In Fig8, however, the sequences with either FG or NFG cells firing across at least 3 slow gamma cycles were included, defined as FG-cell sequences and NFG-cell sequences.  This criterion ensures to investigate the relationship between sequence development and slow gamma phase precession, so that these sequences were contributed by cells likely to show slow gamma phase precession.  These definitions have been updated to the “Theta sequences detection” section of the Methods (Line 606-619).

      At early phase, there’s still no difference of weighted correlation between FG-cell sequences and NFG-cell sequences (Author response image 1A, Student’s t test, t(65)=0.2, p=0.8, Cohen's D=0.1), but the FG-cell sequences contained high proportion of slow gamma phase precession (Fig8F).  At late phase, both FG-cell sequences and NFG-cell sequences exhibited slow gamma phase precession, so that their weighted correlation were high with no difference (Author response image 1B, Student’s t test, t(62)=-1.1, p=0.3, Cohen's D=0.3).  This result further indicates that the theta sequence development requires slow gamma phase precession, especially for FG cells during early phase.

      Author response image 1.

      (2) The different contributions between FG-cells and NFG-cells to theta sequences are supposed not to be caused by their different firing properties (Figure 5). However, Figure 5D and E showed a large effect size (Cohen's D = 07, 0.8), although not significant (P = 0.09, 0.06). But the seemingly non-significant P values could be simply due to smaller N's (~20). In other parts of the manuscript, the effect sizes were comparable or even smaller (e.g. D = 0.5 in Figure 7B), but interpreted as positive results: P values were significant with large N's (~480 in Fig. 7B). Drawing a conclusion purely based on a P value while N is large often renders the conclusion only statistical, with unclear physical meaning. Although this is common in neuroscience publications, it makes more sense to at least make multiple inferences using similar sample sizes in the same study.

      We thank the reviewer for this kind suggestion.  We made multiple inferences using similar sample sizes as much as possible.  In Fig7B, we did the statistical analysis with sessions as samples, and we found the significant conclusion was maintained.  These results have been updated to the revised manuscript (Lines 269-270).and the Fig7B has been replaced correspondingly.

      (3) In supplementary Figure 2 - S2, FG-cells displayed stronger theta phase precession than NFG-cells, which could be a major reason why FG-cells impacted theta sequences more than NFG cells. Although factors other than theta phase precession may contribute to or interfere with theta sequences, stronger theta phase precession itself (without the interference of other factors), by definition, can lead to stronger theta sequences.

      This is a very good point.  The finding that FG-cells displayed stronger theta phase precession than NFG-cells was consistent with the finding of Guardamagna et al., 2023 Cell Rep, that the theta phase precession pattern emerged with strong fast gamma.  Since slow gamma phase precession occurred within theta cycles, it is hard to consider the contribution of these factors to theta sequences development, without taking theta phase precession into account.  But one should be noted that the theta sequences could not be developed even if theta phase precession existed from the very beginning of the exploration (Feng et al., 2025 J Neurosci).  These findings suggest that theta phase precession, together with other factors, impact theta sequence development.  However, the weight of each factor and their interaction still need to be further investigated.  We have discussed this possibility in the Discussion section (Lines 361- 373).

      (4) The slow-gamma phase precession of FG-cells during early laps is supposed to mediate or contribute to the emergence of theta sequences during late laps (Figure 1). The logic of this model is unclear. The slow-gamma phase precession was present in both early and late laps for FG-cells, but only present in late laps for NFG-cells. It seems more straightforward to hypothesize that the difference in theta sequences between early and later laps is due to the difference in slow-gamma phase precession of NFG cells between early and late laps. Although this is not necessarily the case, the argument presented in the manuscript is not easy to follow.

      We thank the reviewer for pointing this out.  The slow gamma phase precession was first found in my previous publication (Zheng et al., 2016 Neuron), which indicates a temporally compressed manner for coding spatial information related to memory retrieval.  In this case, we would expect that slow gamma phase precession occurred in all cells during late laps, because spatial information was retrieved when rats have been familiar with the environment.  However, during early laps when novel information was just encoded, there would be balance between fast gamma and slow gamma modulation of cells for upcoming encoding-retrieval transition.  A possibility is that FG-cells support this balance by receiving modulation of both fast gamma and slow gamma, but with distinct phase-coding modes (fast gamma phase locking and slow gamma phase precession) in a temporally coordinated manner.  We have discussed this possibility in the Discussion section (Lines 415- 428).

      (5) There are several questions on the description of methods, which could be addressed to clarify or strengthen the conclusions.

      (i) Were the identified fast- and slow-gamma episodes mutually exclusive?

      Yes, the fast- and slow-gamma episodes are mutually exclusive. We have added descriptions in the “Detection of gamma episodes” section in the Methods part (Lines 538-550).

      (ii) Was the task novel when the data were acquired? How many days (from the 1st day of the task) were included in the analysis? When the development of the theta sequence was mentioned, did it mean the development in a novel environment, in a novel task, or purely in a sense of early laps (Lap 1, 2) on each day?

      We thank the reviewer for pointing this out.  The task was not novel to rats in this dataset, because only days with good enough recording quality for sequence decoding were included in this paper, which were about day2-day10 for each rat.  However, we still observed the process of sequence formation because of the rat’s exploration interest during early laps.  Thus, when the development of the theta sequence was mentioned, it meant a sense of early laps on each day.

      (iii) How were the animals' behavioral parameters equalized between early and later laps? For example, speed or head direction could potentially produce the differences in theta sequences.

      This is a very good point.  In terms of the effect of running speed on theta sequences, we quantified the running speeds during theta sequences across trials 1-5.  We found that the rats were running at stable running speed, which has been reported in Fig.3F.  In terms of the effect of head direction on theta sequences, we measured the angle difference between head direction and running direction.  We found that the angle difference for each lap was distributed around 0, with no significant difference across laps (Fig.S3, Watson-Williams multi-sample test, F(4,55)=0.2, p=0.9, partial η<sup>2</sup>= 0.01).  These results indicate that the differences in theta sequences across trials cannot be interpreted by the variability of behavioral parameters.  We have updated these results and corresponding methods in the revised manuscript (Lines 172-175, Lines 507-511, with a new Fig.S3).

      Reviewer #2 (Public Review):

      This manuscript addresses an important question that has not yet been solved in the field, what is the contribution of different gamma oscillatory inputs to the development of "theta sequences" in the hippocampal CA1 region? Theta sequences have received much attention due to their proposed roles in encoding short-term behavioral predictions, mediating synaptic plasticity, and guiding flexible decision-making. Gamma oscillations in CA1 offer a readout of different inputs to this region and have been proposed to synchronize neuronal assemblies and modulate spike timing and temporal coding. However, the interactions between these two important phenomena have not been sufficiently investigated. The authors conducted place cell and local field potential (LFP) recordings in the CA1 region of rats running on a circular track. They then analyzed the phase locking of place cell spikes to slow and fast gamma rhythms, the evolution of theta sequences during behavior, and the interaction between these two phenomena. They found that place cells with the strongest modulation by fast gamma oscillations were the most important contributors to the early development of theta sequences and that they also displayed a faster form of phase precession within slow gamma cycles nested with theta. The results reported are interesting and support the main conclusions of the authors. However, the manuscript needs significant improvement in several aspects regarding data analysis, description of both experimental and analytical methods, and alternative interpretations, as I detail below.

      • The experimental paradigm and recordings should be explained at the beginning of the Results section. Right now, there is no description whatsoever which makes it harder to understand the design of the study.

      We thank the reviewer for this kind suggestion.  The description of experimental paradigm and recordings has been added to the beginning of the results section (Lines 114-119).

      • An important issue that needs to be addressed is the very small fraction of CA1 cells phased-locked to slow gamma rhythms (3.7%). This fraction is much lower than in many previous studies, that typically report it in the range of 20-50%. However, this discrepancy is not discussed by the authors. This needs to be explained and additional analysis considered. One analysis that I would suggest, although there are also other valid approaches, is to, instead of just analyzing the phase locking in two discrete frequency bands, compute the phase locking will all LFP frequencies from 25-100 Hz. This will offer a more comprehensive and unbiased view of the gamma modulation of place cell firing. Alternative metrics to mean vector length that is less sensitive to firing rates, such as pairwise phase consistency index (Vinck et a., Neuroimage, 2010), could be implemented. This may reveal whether the low fraction of phase-locked cells could be due to a low number of spikes entering the analysis.

      We thank the reviewer for this constructive suggestion.  A previous work also on Long-Evans rats showed that the proportion of slow gamma phase-locked cells during novelty exploration was ~20%, however it dropped to ~10% during familiar exploration (Fig.4E, Kitanishi et al., 2015 Neuron).  This suggests that the proportion of slow gamma phase-locked cells may decreased with familiarity of the environment, which supports our data.  In addition, we also calculated the pairwise phase consistency index in terms of the effect of spike counts on MVL.  We could observe that the tendency of PPC (Author response image 2A) and MVL (Author response image 2B) along frequency bands were consistent across different subsets of cells, suggesting that the determination of cell subsets by MVL metric was not biased by the low number of spikes.  These results further shed light to the contribution of slow gamma phase precession of place cells to theta sequence development.

      Author response image 2.

      • From the methods, it is not clear to me whether the reference LFP channel was consistently selected to be a different one that where the spikes analyzed were taken. This is the better practice to reduce the contribution of spike leakage that could substantially inflate the coupling with faster gamma frequencies. These analyses need to be described in more detail.

      We thank the reviewer for pointing this out.  In the main manuscript, we used local LFPs as the cells were recorded from the same tetrode.  In addition, we selected an individual tetrode which located at stratum pyramidale and at the center of the drive bundle for each rat.  We detected a similar proportion of FG-cells by using LFPs on this tetrode, compared with that using local LFPs (Author response image 3A-B, Chi-squared test, χ<sup>2</sup>= 0.9, p=0.4, Cramer V=0.03).  We further found that the PPC measurement of FG- and NFG-cells were different at fast gamma band by using central LFPs (Author response image 3D), consistent with that by using local LFPs (Author response image 3C).  Therefore, these results suggest that the findings related to fast gamma was not due to the contribution of spike leakage in the local LFPs.  We have updated the description in the manuscript (Lines 553-557, 566-568).

      Author response image 3.

      • The initial framework of the authors of classifying cells into fast gamma and not fast gamma modulated implies a bimodality that may be artificial. The authors should discuss the nuances and limitations of this framework. For example, several previous work has shown that the same place cell can couple to different gamma oscillations (e.g., Lastoczni et al., Neuron, 2016; Fernandez-Ruiz et al., Neuron, 2017; Sharif et al., Neuron,2021).

      We thank the reviewer for this kind suggestion.  We have cited these references and discussed the possibility of bimodal phase-locking in the manuscript (Lines 430-433).

      • It would be useful to provide a more thorough characterization of the physiological properties of FG and NFG cells, as this distinction is the basis of the paper. Only very little characterization of some place cell properties is provided in Figure 5. Important characteristics that should be very feasible to compare include average firing rate, burstiness, estimated location within the layer (i.e., deep vs superficial sublayers) and along the transverse axis (i.e., proximal vs distal), theta oscillation frequency, phase precession metrics (given their fundamental relationship with theta sequences), etc.

      We thank the reviewer for this constructive suggestion.  In addition to the characterizations shown in Fig5, we also analyzed firing rate, anatomical location and theta modulation to compare the physiological properties of FG- and NFG-cells.

      In terms of the firing properties of both types of cells, we found that the mean firing rate of FG-cell was higher than NFG-cell (Fig. 5A, Student's t-test, t(22) = 2.1, p = 0.04, Cohen's D = 0.9), which was consistent with the previous study that the firing rate was higher during fast gamma than during slow gamma (Zheng et al., 2015 Hippocampus).  However, the spike counts of excluded FG- and NFG-cells for decoding were similar (Fig. 5B, Student's t-test, t(22) = 1.2, p = 0.3, Cohen's D = 0.5), suggesting that the differences found in theta sequences cannot be accounted for by different decoding quality related to spike counts.  In addition, we measured the burstiness based on the distribution of inter-spike-intervals, and we found that the bursting probability of spikes was not significantly different between FG and NFG cells (Author response image 4A, Student's t-test, t(22) = 0.6, p=0.5, Cohen's d=0.3).

      In terms of theta modulation of cells, we first compared the theta frequency related to the firing of FG and NFG cells.  We detected the instantaneous theta frequency at each spike timing of FG and NFG cells, and found that it was not significantly different between cell types (Author response image 4B, Student's t-test, t(22) = -0.5, p=0.6, Cohen's d=0.2).  In addition, we found the proportion of cells with significant theta phase precession was greater in FG-cells than in NFG-cells (Fig. S2E).  However, the slope and starting phase of theta phase precession was not significantly different between FG and NFG cells (Author response image 4C, Student's t-test, t(21) = 0.3, p=0.8, Cohen's d=0.1; Author response image 4D, Watson-Williams test, F(1,21)=0.5, p=0.5, partial η<sup>2</sup>=0.02).

      In terms of the anatomical location of FG and NFG cells, we identified tetrode traces in slices for each cell.  We found that both FG and NFG cells were recorded from the deep layer of dorsal CA1, with no difference of proportions between cell types (Author response image 4E, Chi-squared test, χ<sup>2</sup>=0.5, p=0.5, Cramer V=0.05).  The distribution of FG-cells he NFG-cells along the transverse axis was also similar between cell types (Author response image 4F, χ<sup>2</sup>=0.08, p=0.8, Cramer V=0.02).

      Author response image 4.

      • It is not clear to me how the analysis in Figure 6 was performed. In Figure 6B I would think that the grey line should connect with the bottom white dot in the third panel, which would be the interpretation of the results.

      We thank the reviewer for raising this good point.  The grey line was just for intuitional observation, not a quantitative analysis.  We have removed the grey lines from all heat maps in Fig.6.

      Reviewer #3 (Public Review):

      [Editors' note: This review contains many criticisms that apply to the whole sub-field of slow/fast gamma oscillations in the hippocampus, as opposed to this particular paper. In the editors' view, these comments are beyond the scope of any single paper. However, they represent a view that, if true, should contextualise the interpretation of this paper and all papers in the sub-field. In doing so, they highlight an ongoing debate within the broader field.]

      Summary:

      The authors aimed to elucidate the role of dynamic gamma modulation in the development of hippocampal theta sequences, utilizing the traditional framework of "two gammas," a slow and a fast rhythm. This framework is currently being challenged, necessitating further analyses to establish and secure the assumed premises before substantiating the claims made in the present article.

      The results are too preliminary and need to integrate contemporary literature. New analyses are required to address these concerns. However, by addressing these issues, it may be possible to produce an impactful manuscript.

      We thank the reviewer for raising these important questions in the hippocampal gamma field.  We have done a lot of new analyses according to the comments to strengthen our manuscript.

      I. Introduction

      Within the introduction, multiple broad assertions are conveyed that serve as the premise for the research. However, equally important citations that are not mentioned potentially contradict the ideas that serve as the foundation. Instances of these are described below:

      (1) Are there multiple gammas? The authors launched the study on the premise that two different gamma bands are communicated from CA3 and the entorhinal cortex. However, recent literature suggests otherwise, offering that the slow gamma component may be related to theta harmonics:

      From a review by Etter, Carmichael and Williams (2023)

      "Gamma-based coherence has been a prominent model for communication across the hippocampal-entorhinal circuit and has classically focused on slow and fast gamma oscillations originating in CA3 and medial entorhinal cortex, respectively. These two distinct gammas are then hypothesized to be integrated into hippocampal CA1 with theta oscillations on a cycle-to-cycle basis (Colgin et al., 2009; Schomburg et al., 2014). This would suggest that theta oscillations in CA1 could serve to partition temporal windows that enable the integration of inputs from these upstream regions using alternating gamma waves (Vinck et al., 2023). However, these models have largely been based on correlations between shifting CA3 and medial entorhinal cortex to CA1 coherence in theta and gamma bands. In vivo, excitatory inputs from the entorhinal cortex to the dentate gyrus are most coherent in the theta band, while gamma oscillations would be generated locally from presumed local inhibitory inputs (Pernía-Andrade and Jonas, 2014). This predominance of theta over gamma coherence has also been reported between hippocampal CA1 and the medial entorhinal cortex (Zhou et al., 2022). Another potential pitfall in the communication-through-coherence hypothesis is that theta oscillations harmonics could overlap with higher frequency bands (Czurkó et al., 1999; Terrazas et al., 2005), including slow gamma (Petersen and Buzsáki, 2020). The asymmetry of theta oscillations (Belluscio et al., 2012) can lead to harmonics that extend into the slow gamma range (Scheffer-Teixeira and Tort, 2016), which may lead to a misattribution as to the origin of slow-gamma coherence and the degree of spike modulation in the gamma range during movement (Zhou et al., 2019)."

      And from Benjamin Griffiths and Ole Jensen (2023)

      "That said, in both rodent and human studies, measurements of 'slow' gamma oscillations may be susceptible to distortion by theta harmonics [53], meaning open questions remain about what can be attributed to 'slow' gamma oscillations and what is attributable to theta."

      This second statement should be heavily considered as it is from one of the original authors who reported the existence of slow gamma.

      Yet another instance from Schomburg, Fernández-Ruiz, Mizuseki, Berényi, Anastassiou, Christof Koch, and Buzsáki (2014):

      "Note that modulation from 20-30 Hz may not be related to gamma activity but, instead, reflect timing relationships with non-sinusoidal features of theta waves (Belluscio et al., 2012) and/or the 3rd theta harmonic."

      One of this manuscript's authors is Fernández-Ruiz, a contemporary proponent of the multiple gamma theory. Thus, the modulation to slow gamma offered in the present manuscript may actually be related to theta harmonics.

      With the above emphasis from proponents of the slow/fast gamma theory on disambiguating harmonics from slow gamma, our first suggestion to the authors is that they A) address these statements (citing the work of these authors in their manuscript) and B) demonstrably quantify theta harmonics in relation to slow gamma prior to making assertions of phase relationships (methodological suggestions below). As the frequency of theta harmonics can extend as high as 56 Hz (PMID: 32297752), overlapping with the slow gamma range defined here (25-45 Hz), it will be important to establish an approach that decouples the two phenomena using an approach other than an arbitrary frequency boundary.

      We agree with the reviewer that the theta oscillations harmonics could overlap with higher frequency bands including slow gamma, as the above reviews discussed.  In order to rule out the possibility of theta harmonics effects in this study, we added new analyses in this letter (see below).

      (2) Can gammas be segregated into different lamina of the hippocampus? This idea appears to be foundational in the premise of the research but is also undergoing revision.

      As discussed by Etter et al. above, the initial theory of gamma routing was launched on coherence values. However, the values reported by Colgin et al. (2009) lean more towards incoherence (a value of 0) rather than coherence (1), suggesting a weak to negligible interaction. Nevertheless, this theory is coupled with the idea that the different gamma frequencies are exclusive to the specific lamina of the hippocampus.

      Recently, Deschamps et al. (2024) suggested a broader, more nuanced understanding of gamma oscillations than previously thought, emphasizing their wide range and variability across hippocampal layers. This perspective challenges the traditional dichotomy of gamma sub-bands (e.g., slow vs. medium gamma) and their associated cognitive functions based on a more rigid classification according to frequency and phase relative to the theta rhythm. Moreover, they observed all frequencies across all layers.

      Similarly, the current source density plots from Belluscio et al. (2012) suggest that SG and FG can be observed in both the radiatum and lacunosum-moleculare.

      Therefore, if the initial coherence values are weak to negligible and both slow and fast gamma are observed in all layers of the hippocampus, can the different gammas be exclusively related to either anatomical inputs or psychological functions (as done in the present manuscript)? Do these observations challenge the authors' premise of their research? At the least, please discuss.

      We thank the reviewer for raising this point, which I believe still remains controversial in this field.  We also thank the reviewer for providing detailed proofs of existence forms of gamma rhythms.  The reviewer was considering 2 aspects of gamma: 1) the reasonability of dividing slow and fast gamma by specific frequency bands; 2) the existence of gamma across all hippocampal layers, which challenged the functional significance of different types of gamma rhythms.  Although the results in Douchamps et al., 2024 challenged the idea of rigid gamma sub-bands, we still could see separate slow and fast gamma components exclusively occurred along time course, with central frequency of slow gamma lower than ~60Hz and central frequency of fast gamma higher than ~60Hz (Fig.1b of Douchamps et al., 2024).  This was also seen in the rat dataset of this reference (Fig. S3).  Since their behavioral test required both memory encoding and retrieval processes, it was hard to distinguish the role of different gamma components as they may dynamically coordinate during complex memory process.  Thus, although the behavioral performance can be decoded from broad range of gamma, we still cannot deny the existence of difference gamma rhythms and their functional significance during difference memory phases.

      (3) Do place cells, phase precession, and theta sequences require input from afferent regions? It is offered in the introduction that "Fast gamma (~65-100Hz), associated with the input from the medial entorhinal cortex, is thought to rapidly encode ongoing novel information in the context (Fernandez-Ruiz et al., 2021; Kemere, Carr, Karlsson, & Frank, 2013; Zheng et al., 2016)".

      CA1 place fields remain fairly intact following MEC inactivation include Ipshita Zutshi, Manuel Valero, Antonio Fernández-Ruiz , and György Buzsáki (2022)- "CA1 place cells and assemblies persist despite combined mEC and CA3 silencing" and from Hadas E Sloin, Lidor Spivak, Amir Levi, Roni Gattegno, Shirly Someck, Eran Stark (2024) - "These findings are incompatible with precession models based on inheritance, dual-input, spreading activation, inhibition-excitation summation, or somato-dendritic competition. Thus, a precession generator resides locally within CA1."

      These publications, at the least, challenge the inheritance model by which the afferent input controls CA1 place field spike timing. The research premise offered by the authors is couched in the logic of inheritance, when the effect that the authors are observing could be governed by local intrinsic activity (e.g., phase precession and gamma are locally generated, and the attribution to routed input is perhaps erroneous). Certainly, it is worth discussing these manuscripts in the context of the present manuscript.

      We thank the review for this discussion.  The main purpose of our current study is to investigate the mechanism of theta sequence development along with learning, which may or may not dependent on theta phase precession of single place cells as it remains controversial in this field.  Also, there is a limitation in this study that all gamma components were recorded from stratum pyramidale, thus we cannot make any conclusion on the originate of gamma in modulating sequence development.

      II. Results

      (1) Figure 2-

      a. There is a bit of a puzzle here that should be discussed. If slow and fast frequencies modulate 25% of neurons, how can these rhythms serve as mechanisms of communication/support psychological functions? For instance, if fast gamma is engaged in rapid encoding (line 72) and slow gamma is related to the integration processing of learned information (line 84), and these are functions of the hippocampus, then why do these rhythms modulate so few cells? Is this to say 75% of CA1 neurons do not listen to CA3 or MEC input?

      The proportion ~25% was the part of place cells phase-locked to either slow or fast gamma.  However, one of the main findings in this study was that most cells were modulated by slow gamma as they fired at precessed slow gamma phase within a theta cycle (Figs 6-8), which would promote information compression for theta sequence development.  Therefore, we didn’t mean that only a small proportion of cells were modulated by gamma rhythms and contributed to this process.

      b. Figure 2. It is hard to know if the mean vector lengths presented are large or small. Moreover, one can expect to find significance due to chance. For instance, it is challenging to find a frequency in which modulation strength is zero (please see Figure 4 of PMID: 30428340 or Figure 7 of PMID: 31324673).

      i. Please construct the histograms of Mean Vector Length as in the above papers, using 1 Hz filter steps from 1-120Hz and include it as part of Figure 2 (i.e., calculate the mean vector length for the filtered LFP in steps of 1-2 Hz, 2-3 Hz, 3-4 Hz,... etc). This should help the authors portray the amount of modulation these neurons have relative to the theta rhythm and other frequencies. If the theta mean vector length is higher, should it be considered the primary modulatory influence of these neurons (with slow and fast gammas as a minor influence)?

      We thank the review for this suggestion.  We measured the mean vector length at 5Hz step (equivalent to 1Hz step), and we found that the FG-cells were phase-locked to fast gamma rhythms even stronger than that to theta (Author response image 2B, mean MVL of theta=0.126±0.007, mean MVL of theta=0.175±0.006, paired t-test, t(112)=-5.9, p=0.01, Cohen's d=0.7).  In addition, in some previous studies with significant fast gamma phase locking, the MVL values were around 0.15 by using broad gamma band (Kitanishi et al., 2015 Neuron, Lasztóczi et al., 2016 Neuron, Tomar et al., 2021 Front Behav Neurosci, and Asiminas et al., 2022 Molecular Autism), which was consistent with the value in this study.  Therefore, we don’t believe that fast gamma was only a minor influence of these neurons.

      ii. It is possible to infer a neuron's degree of oscillatory modulation without using the LFP. For instance, one can create an ISI histogram as done in Figure 1 here (https://www.biorxiv.org/content/10.1101/2021.09.20.461152v3.full.pdf+html; "Distinct ground state and activated state modes of firing in forebrain neurons"). The reciprocal of the ISI values would be "instantaneous spike frequency". In favor of the Douchamps et al. (2024) results, the figure of the BioRXiV paper implies that there is a single gamma frequency modulate as there is only a single bump in the ISIs in the 10^-1.5 to 10^-2 range. Therefore, to vet the slow gamma results and the premise of two gammas offered in the introduction, it would be worth including this analysis as part of Figure 2.

      By using suggested method, we calculated the ISI distribution on log scale for FG-cells and NFG-cells during behavior (Author response image 5).  We could observe that the ISI distribution of FG-cells had a bump in the 10<sup>-1.5</sup>= to 10<sup>-2</sup>= range (black bar), in particular in the fast gamma range (10<sup>-2</sup>= to 10<sup>-1.8</sup>=).

      Author response image 5.

      c. There are some things generally concerning about Figure 2.

      i. First, the raw trace does not seem to have clear theta epochs (it is challenging to ascertain the start and end of a theta cycle). Certainly, it would be worth highlighting the relationship between theta and the gammas and picking a nice theta epoch.

      We thank the review for this suggestion.  We've updated this figure with a nice theta epoch in the revised manuscript.

      ii. Also, in panel A, there looks to be a declining amplitude relationship between the raw, fast, and slow gamma traces, assuming that the scale bars represent 100uV in all three traces. The raw trace is significantly larger than the fast gamma. However, this relationship does not seem to be the case in panel B (in which both the raw and unfiltered examples of slow and fast gamma appear to be equal; the right panels of B suggest that fast gamma is larger than slow, appearing to contradict the A= 1/f organization of the power spectral density). Please explain as to why this occurs. Including the power spectral density (see below) should resolve some of this.

      We thank the review for pointing this out.  The scales of y-axis of LFPs tracs in Fig.2B was not consistent, which mislead the comparison of amplitude between slow and fast gamma.  We have unified y axis scales across different gamma types in the revised manuscript.  Moreover, we also have replaced these examples with more typical ones (also see the response below).

      iii. Within the example of spiking to phase in the left side of Panel B (fast gamma example)- the neuron appears to fire near the trough twice, near the peak twice, and somewhere in between once. A similar relationship is observed for the slow gamma epoch. One would conclude from these plots that the interaction of the neuron with the two rhythms is the same. However, the mean vector lengths and histograms below these plots suggest a different story in which the neuron is modulated by FG but not SG. Please reconcile this.

      We thank the review for pointing this out.  We found that the fast gamma phase locking was robust across FG-cells with fast gamma peak as the preferred phase.  Therefore, we have replaced these examples with more typical ones, so that the examples were consistent with the group effect.

      iv. For calculating the MVL, it seems that the number of spikes that the neuron fires would play a significant role. Working towards our next point, there may be a bias of finding a relationship if there are too few spikes (spurious clustering due to sparse data) and/or higher coupling values for higher firing rate cells (cells with higher firing rates will clearly show a relationship), forming a sort of inverse Yerkes-Dodson curve. Also, without understanding the magnitude of the MVL relative to other frequencies, it may be that these values are indeed larger than zero, but not biologically significant.

      - Please provide a scatter plot of Neuron MVL versus the Neuron's Firing Rate for 1) theta (7-9 Hz), 2) slow gamma, and 3) fast gamma, along with their line of best fit.

      - Please run a shuffle control where the LFP trace is shifted by random values between 125-1000ms and recalculate the MVL for theta, slow, and fast gamma. Often, these shuffle controls are done between 100-1000 times (see cross-correlation analyses of Fujisawa, Buzsaki et al.).

      - To establish that firing rate does not play a role in uncovering modulation, it would be worth conducting a spike number control, reducing the number of spikes per cell so that they are all equal before calculating the phase plots/MVL.

      We thank the review for raising this point.  Beside of the MVL value, we also calculated the pairwise phase consistency (PPC) as suggested by Reviewer2, which is not sensitive to the spike counts.  We found that the phase locking strength to either rhythm (theta or gamma) was comparable between MVL and PPC measurements (Author response image 2).  Moreover, we quantified the relationship between MVL and mean firing rate, as suggested.  We found that the MVL value for theta, slow gamma and fast gamma was negatively correlated with mean firing rate (Author response image 6, Pearson correlation, theta: R<sup>2</sup>= 0.06, Pearson’s r=-0.3, p=1.3×10<sup>-8</sup>=; slow gamma: R<sup>2</sup>= 0.1, Pearson’s r=-0.4, p=2.4×10<sup>-17</sup>=; fast gamma: R<sup>2</sup>= 0.03, Pearson’s r=-0.2, p=4.3×10<sup>-5</sup>=).  These results help us rule out the concerns of the effect of spikes counts on the phase modulation measurement.

      Author response image 6.

      (2) Something that I anticipated to see addressed in the manuscript was the study from Grosmark and Buzsaki (2016): "Cell assembly sequences during learning are "replayed" during hippocampal ripples and contribute to the consolidation of episodic memories. However, neuronal sequences may also reflect preexisting dynamics. We report that sequences of place-cell firing in a novel environment are formed from a combination of the contributions of a rigid, predominantly fast-firing subset of pyramidal neurons with low spatial specificity and limited change across sleep-experience-sleep and a slow-firing plastic subset. Slow-firing cells, rather than fast-firing cells, gained high place specificity during exploration, elevated their association with ripples, and showed increased bursting and temporal coactivation during postexperience sleep. Thus, slow- and fast-firing neurons, although forming a continuous distribution, have different coding and plastic properties."

      My concern is that much of the reported results in the present manuscript appear to recapitulate the observations of Grosmark and Buzsaki, but without accounting for differences in firing rate. A parsimonious alternative explanation for what is observed in the present manuscript is that high firing rate neurons, more integrated into the local network and orchestrating local gamma activity (PING), exhibit more coupling to theta and gamma. In this alternative perspective, it's not something special about how the neurons are entrained to the routed fast gamma, but that the higher firing rate neurons are better able to engage and entrain their local interneurons and, thus modulate local gamma. However, this interpretation challenges the discussion around the importance of fast gamma routed from the MEC.

      a. Please integrate the Grosmark & Buzsaki paper into the discussion.

      b. Also, please provide data that refutes or supports the alternative hypothesis in which the high firing rate cells are just more gamma modulated as they orchestrate local gamma activity through monosynaptic connections with local interneurons (e.g., Marshall et al., 2002, Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge). Otherwise, the attribution to a MEC routed fast gamma routing seems tenuous.

      c. It is mentioned that fast-spiking interneurons were removed from the analysis. It would be worth including these cells, calculating the MVL in 1 Hz increments as well as the reciprocal of their ISIs (described above).

      We thank the review for this suggestion.  Because we found the mean firing rate of FG-cells was higher than that of NFG-cells, it would be possible that the FG-cells are mainly overlapped with fast-firing cells (rigid cells) in Grosmark et al., 2016 Science.  Actually, in this study, we aimed to investigate how fast and slow gamma rhythms modulated neurons dynamically during learning, rather than defining new cell types.  Thus, we don’t think this work was just a replication of the previous publication.  We have added this description in the Discussion part (Lines 439-441).  In addition, we don’t have enough number of interneurons to support the analysis between interneurons and place cells.  Therefore, we couldn’t make any statement about where was the fast gamma originated (CA1 locally or routed from MEC) in this study.

      (3) Methods - Spectral decomposition and Theta Harmonics.

      a. It is challenging to interpret the exact parameters that the authors used for their multi-taper analysis in the methods (lines 516-526). Tallon-Baudry et al., (1997; Oscillatory γ-Band (30-70 Hz) Activity Induced by a Visual Search Task in Humans) discuss a time-frequency trade-off where frequency resolution changes with different temporal windows of analysis. This trade-off between time and frequency resolution is well known as the uncertainty principle of signal analysis, transcending all decomposition methods. It is not only a function of wavelet or FFT, and multi-tapers do not directly address this. (The multitaper method, by using multiple specially designed tapers -like the Slepian sequences- smooths the spectrum. This smoothing doesn't eliminate leakage but distributes its impact across multiple estimates). Given the brevity of methods and the issues of theta harmonics as offered above, it is worth including some benchmark trace testing for the multi-taper as part of the supplemental figures.

      i. Please spectrally decompose an asymmetric 8 Hz sawtooth wave showing the trace and the related power spectral density using the multiple taper method discussed in the methods.

      ii. Please also do the same for an elliptical oscillation (perfectly symmetrical waves, but also capable of casting harmonics). Matlab code on how to generate this time series is provided below:

      A = 1; % Amplitude

      T = 1/8; % Period corresponding to 8 Hz frequency

      omega = 2*pi/T; % Angular frequency

      C = 1; % Wave speed

      m = 0.9; % Modulus for the elliptic function (0<m<1 for cnoidal waves)

      x = linspace(0, 2*pi, 1000); % temporal domain

      t = 0; % Time instant

      % Calculate B based on frequency and speed

      B = sqrt(omega/C);

      % Cnoidal wave equation using the Jacobi elliptic function

      u = A .* ellipj(B.*(x - C*t), m).^2;

      % Plotting the cnoidal wave

      figure;

      plot(x./max(x), u);

      title('8 Hz Cnoidal Wave');

      xlabel('time (x)');

      ylabel('Wave amplitude (u)');

      grid on;

      The Symbolic Math Toolbox needs to be installed and accessible in your MATLAB environment to use ellipj. Otherwise, I trust that, rather than plotting a periodic orbit around a circle (sin wave) the authors can trace the movement around an ellipse with significant eccentricity (the distance between the two foci should be twice the distance between the co-vertices).

      We thank the review for this suggestion.  In the main text of manuscript, we only applied Morlet's wavelet method to calculate the time varying power of rhythms.  Multitaper method was used for the estimation of power spectra across running speeds, which was shown in the manuscript.  Therefore, we removed the description of Multitaper method and updated the Morlet's wavelet power spectral analysis in the Methods (Lines 541-544).

      As suggested, we estimated the power spectral densities of 8 Hz sawtooth and elliptical oscillation by using these methods, and compared them with the results from FFT.  We found that both the Multitaper's and Morlet's wavelet methods could well capture the 8Hz oscillatory components (Author response image 7).  However, we could observe harmonic components from FFT spectrum.

      Author response image 7.

      iii. Line 522: "The power spectra across running speeds and absolute power spectrum (both results were not shown).". Given the potential complications of multi-taper discussed above, and as each convolution further removes one from the raw data, it would be the most transparent, simple, and straightforward to provide power spectra using the simple fft.m code in Matlab (We imagine that the authors will agree that the results should be robust against different spectral decomposition methods. Otherwise, it is concerning that the results depend on the algorithm implemented and should be discussed. If gamma transience is a concern, the authors should trigger to 2-second epochs in which slow/fast gamma exceeds 3-7 std. dev. above the mean, comparing those resulting power spectra to 2-second epochs with ripples - also a transient event). The time series should be at least 2 seconds in length (to avoid spectral leakage issues and the issues discussed in Talon-Baudry et al., 1997 above).

      Please show the unmolested power spectra (Y-axis units in mV2/Hz, X-axis units as Hz) as a function of running speed (increments of 5 cm/s) for each animal. I imagine three of these PSDs for 3 of the animals will appear in supplemental methods while one will serve as a nice manuscript figure. With this plot, please highlight the regions that the authors are describing as theta, slow, and fast gamma. Also, any issues should be addressed should there be notable differences in power across animals or tetrodes (issues with locations along proximal-distal CA1 in terms of MEC/LEC input and using a local reference electrode are discussed below).

      As suggested, we firstly estimated the power spectra as a function of running speeds in each running lap, and showed them separately for each rat, by using the multitaper spectral analysis (Author response image 8).  In addition, to achieve unmolested power spectra, the short-time Fourier transform (STFT) was used for this analysis at the same frequency resolution (Author response image 9).  We could see that the power spectra were consistent between these two methods.  Notably, there seems no significant theta harmonic component in the slow gamma band range.

      The multitaper spectral analysis was performed as follows.  The power spectra were measured across different running speeds as described previously (Ahmed et al., 2012 J Neurosci; Zheng et al., 2015 Hippocampus; Zheng et al., 2016 eNeuro).  Briefly, the absolute power spectrum was calculated for 0.5s moving window and 0.2s step size of the LFPs recordings each lap, using the multitaper spectral analysis in the Chronux toolbox (Mitra and Bokil, 2008, http://chronux.org/) and STFT spectral analysis in Matlab script stft.m.  In the multitaper method, the time-bandwidth product parameter (TW) was set at 3, and the number of tapers (K) was set at 5.  In the STFT method, the FFT length was set at 2048, which was equivalent with the parameters used in multitaper method.  Running speed was calculated (see “Estimation of running speed and head direction” section in the manuscript) and averaged within each 0.5s time window corresponding to the LFP segments.  Then, the absolute power at each frequency was smoothed with a Gaussian kernel centered on given speed bin.  The power spectral as a function of running speed and frequency were plotted in log scale.  Also, the colormap was in log scale, allowing for comparisons across different frequencies that would otherwise be difficult due to the 1/f decay of power in physiological signals.

      Author response image 8.

      Author response image 9.

      iv. Schomberg and colleagues (2014) suggested that the modulation of neurons in the slow gamma range could be related to theta harmonics (see above). Harmonics can often extend in a near infinite as they regress into the 1/f background (contributing to power, but without a peak above the power spectral density slope), making arbitrary frequency limits inappropriate. Therefore, in order to support the analyses and assertions regarding slow gamma, it seems necessary to calculate a "theta harmonic/slow gamma ratio". Aru et al. (2015; Untangling cross-frequency coupling in neuroscience) offer that: " The presence of harmonics in the signal should be tested by a bicoherence analysis and its contribution to CFC should be discussed." Please test both the synthetic signals above and the raw LFP, using temporal windows of greater than 4 seconds (again, the large window optimizes for frequency resolution in the time-frequency trade-off) to calculate the bicoherence. As harmonics are integers of theta coupled to itself and slow gamma is also coupled to theta, a nice illustration and contribution to the field would be a method that uses the bispectrum to isolate and create a "slow gamma/harmonic" ratio.

      We thank the reviewer for providing the method regarding on the theta harmonics.  We firstly measured the theta harmonics on the synthesized signal by using the biphasic coherence method, and we could clearly observe the nonlinear coupling between theta rhythm and its harmonics (Author response image 10).

      Author response image 10.

      In addition, we also measured the bicoherence on raw traces during slow gamma episodes.  We did not see nonlinear coupling between slow gamma and theta bands in this real data (mean bicoherence=0.1±0.0002) compared with that in the synthesized signal (mean bicoherence=0.7 for elliptical waves and 0.5 for sawtooth waves), suggesting that the slow gamma detected in this study was not pure theta harmonic (Author response image 11C, F, I, in red boxes).  Therefore, we believe that the contribution of theta harmonic in slow gamma is not significant.

      Author response image 11.

      (4) I appreciate the inclusion of the histology for the 4 animals. Knerim and colleagues describe a difference in MEC projection along the proximal-distal axis of the CA1 region (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866456/)- "There are also differences in their direct projections along the transverse axis of CA1, as the LEC innervates the region of CA1 closer to the subiculum (distal CA1), whereas the MEC innervates the region of CA1 closer to CA2 and CA3 (proximal CA1)" From the histology, it looks like some of the electrodes are in the part of CA1 that would be dominated by LEC input while a few are closer to where the MEC would project.

      a. How do the authors control for these differences in projections? Wouldn't this change whether or not fast gamma is observed in CA1?

      b. I am only aware of one manuscript that describes slow gamma in the LEC which appeared in contrast to fast gamma from the MEC (https://www.science.org/doi/10.1126/science.abf3119). One would surmise that the authors in the present manuscript would have varying levels of fast gamma in their CA1 recordings depending on the location of the electrodes in the Proximal-distal axis, to the extent that some of the more medial tetrodes may need to be excluded (as they should not have fast gamma, rather they should be exclusively dominated by slow gamma). Alternatively, the authors may find that there is equal fast gamma power across the entire proximal-distal axis. However, this would pose a significant challenge to the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz et al. and require reconciliation/discussion.

      c. Is there a difference in neuron modulation to these frequencies based on electrode location in CA1?

      We thank the reviewer for this concern, which was also raised by Reviewer2.  We aligned the physical location of LFP channels in the proximal-distal axis based on histology.  In our dataset, only 2 rats were recorded from both distal and proximal hippocampus, so we calculated the gamma power from both sites in these rats.  We found that slow power was higher from proximal tetrodes than that from distal tetrodes (Author response image 12, repeated measure ANOVA, F(1,7)=10.2, p=0.02, partial η <sup>2</sup>=0.8).  However, fast gamma power were similar between different recording sites (F(1,7)=0.008, p=0.9, partial η <sup>2</sup>=0.001).  These results are partially consistent with the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz’s work.  The main reason would be that all LFPs were recorded from tetrodes in stratum pyramidale, deep layer in particular (Author response image 4E), so that it was hard to precisely identify their distance to distal/proximal apical dendrites.

      Author response image 12.

      In terms of the anatomical location of FG and NFG cells, we identified tetrode traces in slices for each cell.  We found that both FG and NFG cells were recorded from the deep layer of dorsal CA1, with no difference of proportions between cell types (Author response image 4E, Chi-squared test, χ<sup>2</sup>=0.5, p=0.5, Cramer V=0.05).  The distribution of FG-cells he NFG-cells along the transverse axis was also similar between cell types (Author response image 4F, χ<sup>2</sup>=0.08, p=0.8, Cramer V=0.02).

      (5) Given a comment in the discussion (see below), it will be worth exploring changes in theta, theta harmonic, slow gamma, and fast gamma power with running speed as no changes were observed with theta sequences or lap number versus. Notably, Czurko et al., report an increase in theta and harmonic power with running speed (1999) while Ahmed and Mehta (2012) report a similar effect for gamma.

      a. Please determine if the oscillations change in power and frequency of the rhythms discussed above change with running speed using the same parameters applied in the present manuscript. The specific concern is that how the authors calculate running speed is not sensitive enough to evaluate changes.

      We thank the reviewer for this suggestion.  The description of running speed quantification has been updated in the Method (see “Estimation of running speed and head direction” section, Lines 501-511).  Overall, the sample frequency of running speed was25Hz which would be sensitive enough to evaluate the behavioral changes.

      By measuring the rhythmic power changing as a function of running speed (Author response image 8 and Author response image 9), we could observe that theta power was increased as running speed getting higher.  Consistent with the results in (Ahmed and Mehta, 2012) and our previous study (Zheng et al., 2015), the fast gamma power was increasing and slow gamma power was decreasing when running speed was getting high.

      In addition, we also estimated the rhythmic frequency as a function of running speed in the slow and fast episodes respectively.  We found that fast gamma frequency was increased with running speed (Author response image 13, linear regression, R<sup>2</sup>=0.4, corr=0.6, p=9.9×10<sup>-15</sup>), whereas slow gamma frequency was decreased with running speed (R<sup>2</sup>=0.2, corr=-0.4, p=8.8×10<sup>-6</sup>).  Although significant correlation was found between gamma frequency and running speed, consistent with the previous studies, the frequency change (~70-75Hz for fast gamma and ~30-28Hz for slow gamma) was not big enough to affect the sequence findings in this study.  In additiontheta frequency was maintained in either slow episodes (R<sup>2</sup>=0.02, corr=-0.1, p=0.1) or fast episodes (R<sup>2</sup>=0.004, corr=0.06, p=0.5), consistent with results in Fig.1G of Kropff et al., 2021 Neuron.

      Author response image 13.

      b. It is astounding that animals ran as fast as they did in what appears to be the first lap (Figure 3F), especially as rats' natural proclivity is thigmotaxis and inquisitive exploration in novel environments. Can the authors expand on why they believe their rats ran so quickly on the first lap in a novel environment and how to replicate this? Also, please include the individual values for each animal on the same plot.

      We thank the reviewer for pointing this out.  The task was not brand new to rats in this dataset, because only days with good enough recording quality for sequence decoding were included in this paper, which were about day2-day10 for each rat.  However, we still observed the process of sequence formation because of the rat’s exploration interest during early laps.  Thus, in terms exploration behaviors, the rats ran at relative high speeds across laps (Author response image 14, each gray line represents the running speed within an individual session).

      Author response image 14.

      c. Can the authors explain how the statistics on line 169 (F(4,44)) work? Specifically, it is challenging to determine how the degrees of freedom were calculated in this case and throughout if there were only 4 animals (reported in methods) over 5 laps (depicted in Figure 3F. Given line 439, it looks like trials and laps are used synonymously). Four animals over 5 laps should have a DOF of 16.

      This statistic result was performed with each session/day as a sample (n=12 sessions/days).  The statistics were generated by repeated measures ANOVA on 5 trials in 12 sessions, with a DOF of 44.

      (6) Throughout the manuscript, I am concerned about an inflation of statistical power. For example on line 162, F(2,4844). The large degrees of freedom indicate that the sample size was theta sequences or a number of cells. Since multiple observations were obtained from the same animal, the statistical assumption of independence is violated. Therefore, the stats need to be conducted using a nested model as described in Aarts et al. (2014; https://pubmed.ncbi.nlm.nih.gov/24671065/). A statistical consult may be warranted.

      We thank the reviewer for this suggestion.  We have replaced this statistic result by using generalized linear mixed model with ratID being a covariate.  These results have been updated in the revised manuscript (Lines 164-167).

      (7) It is stated that one tetrode served as a quiet recording reference. The "quiet" part is an assumption when often, theta and gamma can be volume conducted to the cortex (e.g., Sirota et al., 2008; This is often why laboratories that study hippocampal rhythms use the cerebellum for the differential recording electrode and not an electrode in the corpus callosum). Generally, high frequencies propagate as well as low frequencies in the extracellular milieu (https://www.eneuro.org/content/4/1/ENEURO.0291-16.2016). For transparency, the authors should include a limitation paragraph in their discussion that describes how their local tetrode reference may be inadvertently diminishing and/or distorting the signal that they are trying to isolate. Otherwise, it would be worth hearing an explanation as to how the author's approach avoids this issue.

      In terms of the locations of references, we had 2 screws above the cerebellum in the skull connected to the recording drive ground, and 1 tetrode in a quiet area of the cortex serving as the recording reference.  We agree that the theta and gamma can be volume conducted to the cortex which may affect the power of these rhythms in the stratum pyramidale.  However, we didn’t mean to measure or compare the absolute theta or gamma power in this study, as we only cared about the phase modulation of gamma to place cells.  Therefore, we believe the location of recording reference would not make significant effect on our conclusion.

      Apologetically, this review is already getting long. Moreover, I have substantial concerns that should be resolved prior to delving into the remainder of the analyses. e.g., the analyses related to Figure 3-5 assert that FG cells are important for sequences. However, the relationship to gamma may be secondary to either their relationship to theta or, based on the Grosmark and Buzsaki paper, it may just be a phenomenon coupled to the fast-firing cells (fast-firing cells showing higher gamma modulation due to a local PING dynamic). Moreover, the observation of slow gamma is being challenged as theta harmonics, even by the major proponents of the slow/fast gamma theory. Therefore, the report of slow gamma precession would come as an unsurprising extension should they be revealed to be theta harmonics (however, no control for harmonics was implemented; suggestions were made above). Following these amendments, I would be grateful for the opportunity to provide further feedback.

      III. Discussion.

      a. Line 330- it was offered that fast gamma encodes information while slow gamma integrates in the introduction. However, in a task such as circular track running (from the methods, it appears that there is no new information to be acquired within a trial), one would guess that after the first few laps, slow gamma would be the dominant rhythm. Therefore, one must wonder why there are so few neurons modulated by slow gamma (~3.7%).

      The proportion of ~3.7% was the part of place cells phase-locked to slow gamma.  However, we aimed to find that the slow gamma phase precession of place cells promoted the theta sequence development.  We would not expect the cells phase-locked to slow gamma if phase precession occurred.

      b. Line 375: The authors contend that: "...slow gamma, related to information compression, was also required to modulate fast gamma phase-locked cells during sequence development. We replicated the results of slow gamma phase precession at the ensemble level (Zheng et al., 2016), and furthermore observed it at late development, but not early development, of theta sequences." In relation to the idea that slow gamma may be coupled to - if not a distorted representation of - theta harmonics, it has been observed that there are changes in theta relative to novelty.

      i. A. Jeewajee, C. Lever, S. Burton, J. O'Keefe, and N. Burgess (2008) report a decrease in theta frequency in novel circumstances that disappears with increasing familiarity.

      ii. One could surmise that this change in frequency is associated with alterations in theta harmonics (observed here as slow gamma), challenging the author's interpretation.

      iii. Therefore, the authors have a compelling opportunity to replicate the results of Jeewajee et al., characterizing changes of theta along with the development of slow gamma precession, as the environment becomes familiar. It will become important to demonstrate, using bicoherence as offered by Aru et al., how slow gamma can be disambiguated from theta harmonics. Specifically, we anticipate that the authors will be able to quantify A) theta harmonics (the number, and their respective frequencies and amplitudes), B) the frequency and amplitude of slow gamma, and C) how they can be quantitatively decoupled. Through this, their discussion of oscillatory changes with novelty-familiarity will garner a significant impact.

      We think we have demonstrated that the slow gamma observed in this study was not purely theta harmonics.  We didn’t focus on the frequency change of slow gamma or theta rhythms in this study.  Further investigation will be carried out on this topic in the future.

      c. Broadly, it is interesting that the authors emphasize the gamma frequency throughout the discussion. Given that the power spectral density of the Local Field Potential (LFP) exhibits a log-log relationship between amplitude and frequency, as described by Buzsáki (2005) in "Rhythms of the Brain," and considering that the LFP is primarily generated through synaptic transmembrane currents (Buzsáki et al., 2012), it seems parsimonious to consider that the bulk of synaptic activity occurs at lower frequencies (e.g., theta). Since synaptic transmission represents the most direct form of inter-regional communication, one might wonder why gamma (characterized by lower amplitude rhythms) is esteemed so highly compared to the higher amplitude theta rhythm. Why isn't the theta rhythm, instead, regarded as the primary mode of communication across brain regions? A discussion exploring this question would be beneficial.

      We thank the reviewer for this deep thinking.  When stating the conclusion on gamma rhythms, we didn’t mean to weaken the role of theta rhythm.  Conversely, the fast or slow gamma episodes were detected riding on theta rhythms, and we believe that the information compression should occur at a finer scale within a theta cycle scale.  More investigation will be carried out on this topic in the future.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) It is helpful to clearly define "FG-cell sequences" before the relevant results are described in the Results section. More importantly, the seemingly conflicting results between Figure 3 and Figure 8 may need to be clarified.

      The “exFG-sequences and exNFG sequences”, “FG-cell sequences and NFG-cell sequences” have been defined clearly in the revised manuscript.  Moreover, the seemingly conflicting results between Figure 3 and Figure 8 have been interpreted properly.

      (2) It is helpful to clearly state the N and what defines a sample whenever a result is described.

      In each statistical results, the N and what defines a sample have been clarified in the revised manuscript.

      (3) Addressing the questions regarding the methods (#5) would clarify some of the results.

      The questions regarding the Methods part has addressed in the revised manuscript.

      (4) Line #244: "successful" should be "successive"?

      Fixed.

      Reviewer #2 (Recommendations For The Authors):

      - The writing of the manuscript can be substantially improved.

      The manuscript can be substantially revised and updated.

      - I noticed that the last author of the manuscript is not the lead or corresponding and has only provided a limited contribution to this work (according to the detailed author contributions). The second to last author seems to be the main senior intellectual contributor and supervisor, together with the third to last author. This speaks of potential bad academic practices where a senior person whose intellectual contribution to the study is relatively minor takes the last author position, against the standard conventions on authorship worldwide. I strongly suggest that this is corrected.

      We thank the reviewer for raising this problem.  The last author Dr. Ming was also a senior author and supervised this project with large contribution.  We have fixed his role as a co-corresponding author in the revised manuscript.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, the reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The main conclusion of the paper is that it is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rates. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.

      Strengths:

      The strength of the paper is the demonstration that a more parsimonious model explains abrupt transitions in the reorientation rate.

      Weaknesses:

      (1) Use of the Gillespie algorithm is not well justified. A conventional model with a fixed dt and an exponentially decaying reorientation rate would be adequate and far easier to explain. It would also be sufficiently accurate - given the appropriate choice of dt - to support the main claims of the paper, which are merely qualitative. In some respects, the whole point of the paper - that discrete transitions are an epiphenomenon of stochastic behavior - can be made with the authors' version of the model having a constant reorientation rate (Figure 2f).

      We apologize, but we are not sure what the reviewer means by “fixed dt”. If the reviewer means taking discrete steps in time (dt), and modeling whether a reorientation occurs, we would argue that the Gillespie algorithm is a better way to do this because it provides floating-point precision time resolution, rather than a time resolution limited by dt, which we hopefully explain in the comments below.

      The reviewer is correct that discrete transitions are an epiphenomenon of stochastic behavior as we show in Figure 2f. However, abrupt stochastic jumps that occur with a constant rate do not produce persistent changes in the observed rate because it is by definition, constant. The theory that there are local and global searches is based on the observation that individual worms often abruptly change their rates. But this observation is only true for a fraction of worms. We are trying to argue that the reason why this is not observed for all, or even most worms is because these are the result of stochastic sampling, not a sudden change in search strategy.

      (2) In the manuscript, the Gillespie algorithm is very poorly explained, even for readers who already understand the algorithm; for those who do not it will be essentially impossible to comprehend. To take just a few examples: in Equation (1), omega is defined as reorientations instead of cumulative reorientations; it is unclear how (4) follows from (2) and (3); notation in (5), line 133, and (7) is idiosyncratic. Figure 1a does not help, partly because the notation is unexplained. For example, what do the arrows mean, what does "*" mean?

      We apologize for this, you are correct,  is cumulative reorientations, and we will edit the text as follows:

      Experimentally, reorientation rate is measured as the number of reorientation events that occurred in an observational window. However, these are discrete stochastic events, so we should describe them in terms of propensity, i.e. the probability of observing a transitional event (in this case, a reorientation) is:

      Here, P(W+1,t) is the probability of observing a reorientation event at time t, and a<sub>1</sub> is the propensity for this event to occur. Observationally, the frequency of reorientations observed decays over time, so we can define the propensity as:

      Where α is the initial propensity at t=0.

      We can model this decay as the reorientation propensity coupled to a decaying factor (M):

      Where the propensity of this event (a<sub>2</sub>) is:

      Since M is a first-order decay process, when integrated, the cumulative M observed is:

      We can couple the probability of observing a reorientation to this decay by redefining (a<sub>1</sub> as:

      So that now:

      A critical detail should be noted. While reorientations are modeled as discrete events, the amount of M at time t\=0 is chosen to be large (M<sub>0</sub>←1,000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      To model both processes, we can create the master equation:

      Since these are both Poisson processes, the probability density function for a state change i occurring in time t is:

      The probability that an event will not occur in time interval t is:

      The probability that no events will occur for ALL transitions in this time interval is:

      We can draw a random number (r<sub>1</sub> ∈[0,1]) that represents the probability of no events in time interval t, so that this time interval can be assigned by rearranging equation 11:

      where:

      This is the time interval for any event (W+1 or M-1) happening at t + t. The probability of which event occurs is proportional to its propensity:

      We can draw a second number (r<sub>2</sub> ∈[0,1]) that represents this probability so that which event occurs at time t + t is determined by the smallest n that satisfies:

      so that:

      The elegant efficiency of the Gillespie algorithm is two-fold. First, it models all transitions simultaneously, not separately. Second, it provides floating-point time resolution. Rather than drawing a random number, and using a cumulative probability distribution of interval-times to decide whether an event occurs at discrete steps in time, the Gillespie algorithm uses this distribution to draw the interval-time itself. The time resolution of the prior approach is limited by step size, whereas the Gillespie algorithm’s time resolution is limited by the floating-point precision of the random number that is drawn.

      We are happy to add this text to improve clarity.

      We apologize for the arrow notation confusion. Arrow notation is commonly used in pseudocode to indicate variable assignment, and so we used it to indicate variable assignment updates in the algorithm.

      We added Figure 2a to help explain the Gillespie algorithm for people who are unfamiliar with it, but you are correct, some notation, like probabilities, were left unexplained. We will address this to improve clarity.

      (3) In the model, the reorientation rate dΩ⁄dt declines to zero but the empirical rate clearly does not. This is a major flaw. It would have been easy to fix by adding a constant to the exponentially declining rate in (1). Perhaps fixing this obvious problem would mitigate the discrepancies between the data and the model in Figure 2d.

      You are correct that the model deviates slightly at longer times, but this result is consistent with Klein et al. that show a continuous decline of reorientations. However, we could add a constant to the model, since an infinite run length is likely not physiological.

      (4) Evidence that the model fits the data (Figure 2d) is unconvincing. I would like to have seen the proportion of runs in which the model generated one as opposed to multiple or no transitions in reorientation rate; in the real data, the proportion is 50% (Lopez). It is claimed that the "model demonstrated a continuum of switching to non-switching behavior" as seen in the experimental data but no evidence is provided.

      We should clarify that the 50% proportion cited by López-Cruz was based on an arbitrary difference in slopes, and by assessing the data visually. We sought to avoid this subjective assessment by plotting the distribution of slopes and transition times produced by the method used in López-Cruz. We should also clarify by what we meant by “a continuum of switching and non-switching” behavior. Both the transition time distributions and the slope-difference distributions do not appear to be the result of two distributions. This is unlike roaming and dwelling on food, where two distinct distributions of behavioral metrics can be identified based on speed and angular speed (Flavell et al, 2009, Fig S2a). We will add a permutation test to verify the mean differences in slopes and transition times between the experiment and model are not significant.

      (5) The explanation for the poor fit between the model and data (lines 166-174) is unclear. Why would externally triggered collisions cause a shift in the transition distribution?

      Thank you, we should rewrite the text to clarify this better. There were no externally triggered collisions; 10 animals were used per experiment. They would occasionally collide during the experiment, but these collisions were excluded from the data that were provided. However, worms are also known to increase reorientations when they encounter a pheromone trail, and it is unknown (from this dataset) which orientations may have been a result of this phenomenon.

      (6) The discussion of Levy walks and the accompanying figure are off-topic and should be deleted.

      Thank you, we agree that this topic is tangential, and we will remove it.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors build a statistical model that stochastically samples from a time-interval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, and is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".

      Strengths:

      This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling, are well described and the model fits the exponential function (Equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study is able to explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.

      Weaknesses:

      This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references, for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) is distinct from the behavior being studied. In this manuscript, the distribution being sampled is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

      Thank you, we apologize that this was not clearer. In the Lotka-Volterra model, the density of predators and prey are being modeled, with the underlying assumption that rates of birth and death are inherently stochastic. In our model, the number of reorientations are being modeled, with the assumption (based on the experiments), that the occurrence of reorientations is stochastic, just like the occurrence (birth) of a prey animal is stochastic. However, the decay in M is phenomenological, and we speculate about the nature of M later in the manuscript.

      You are absolutely right that the decay function for M was fitted to the population average of reorientations and then sampled to generate the distributions of the reorientation data. This was intentional to show that the parameters chosen to match the population average would produce individual trajectories with comparable stochastic “switching” as the experimental data. All we’re trying to show really is that observed sudden changes in reorientation that appear persistent can be produced by a stochastic process without resorting to binary state assignments. In Calhoun, et al 2014 it is reported all animals produced switch-like behavior, but in Klein et al, 2017 it is reported that no animals showed abrupt transitions. López-Cruz et al seem to show a mix of these results, which can be easily explained by an underlying stochastic process.

      The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides. Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening? The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: these are basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed is so variable in dynamics and meaning, that their explanation of what governs M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

      Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

      Thank you, these are all excellent points. We should clarify that in López-Cruz et al, they claim that only 50% of the animals fit a local/global search paradigm. We are simply proposing there is no need for designating local and global searches if the data don’t really support it. The underlying behavior is stochastic, so the sudden switches sometimes observed can be explained by a stochastic process where the underlying rate is slowing down, thus producing the persistently slow reorientation rate when an apparent “switch” occurs. What we hope to convey is that foraging doesn’t appear to follow a decision paradigm, but instead a gradual change in reorientations which for individual worms, can occasionally produce reorientation trajectories that appear switch-like.

      As for M, you are correct, we should be more explicit. A decay in reorientation rate, rather than a sudden change, is consistent with observations made by López-Cruz et al.  They found that the neurons AIA and ADE redundantly suppress reorientations, and that silencing either one was sufficient to restore the large number of reorientations during early foraging. The synaptic output of AIA and ADE was inhibited over long timescales (tens of minutes) by presynaptic glutamate binding to MGL-1, a slow G-Protein coupled receptor expressed in AIA and ADE. Their results support a model where sensory neurons suppress the synaptic output of AIA and ADE, which in turn leads to a large number of reorientations early in foraging. As time passes, glutamatergic input from the sensory neurons decrease, which leads to disinhibition of AIA and ADE, and a subsequent suppression of reorientations.

      The sensory inputs into AIA and ADE are sequestered into two separate circuits, with AIA receiving chemosensory input and ADE receiving mechanosensory input. Since the suppression of either AIA or ADE is sufficient to increase reorientations, the decay in reorientations is likely due to the synaptic output of both of these neurons decaying in time. This correlates with an observed decrease in sensory neuron activity as well, so the timescale of reorientation decay could be tied to the timescale of sensory neuron activity, which in turn is influencing the timescale of AIA/ADE reorientation suppression. This implies that our factor “M” is likely the sum of several different sensory inputs decaying in time.

      The molecular basis of which sensory neuron signaling factors contribute to decreased AIA and ADE activity is made more complicated by the observation that the glutamatergic input provided by the sensory neurons was not essential, and that additional factors besides glutamate contribute to the signaling to AIA and ADE. In addition to this, it is simply not the sensory neuron activity that decays in time, but also the sensitivity of AIA and ADE to sensory neuron input that decays in time. Simply depolarizing sensory neurons after the animals had starved for 30 minutes was insufficient to rescue the reorientation rates observed earlier in the foraging assay. This observation could be due to decreased presynaptic vesicle release, and/or decreased receptor localization on the postsynaptic side.

      In summary, there are two neuronal properties that appear to be decaying in time. One is sensory neuron activity, and the other is decreased potentiation of presynaptic input onto AIA and ADE. Our factor “M” is a phenomenological manifestation of these numerous decaying factors.

      Reviewer #3 (Public review):

      Summary:

      This intriguing paper addresses a special case of a fundamental statistical question: how to distinguish between stochastic point processes that derive from a single "state" (or single process) and more than one state/process. In the language of the paper, a "state" (perhaps more intuitively called a strategy/process) refers to a set of rules that determine the temporal statistics of the system. The rules give rise to probability distributions (here, the probability for turning events). The difficulty arises when the sampling time is finite, and hence, the empirical data is finite, and affected by the sampling of the underlying distribution(s). The specific problem being tackled is the foraging behavior of C. elegans nematodes, removed from food. Such foraging has been studied for decades, and described by a transition over time from 'local'/'area-restricted' search'(roughly in the initial 10-30 minutes of the experiments, in which animals execute frequent turns) to 'dispersion', or 'global search' (characterized by a low frequency of turns). The authors propose an alternative to this two-state description - a potentially more parsimonious single 'state' with time-changing parameters, which they claim can account for the full-time course of these observations.

      Figure 1a shows the mean rate of turning events as a function of time (averaged across the population). Here, we see a rapid transient, followed by a gradual 4-5 fold decay in the rate, and then levels off. This picture seems consistent with the two-state description. However, the authors demonstrate that individual animals exhibit different "transition" statistics (Figure 1e) and wish to explain this. They do so by fitting this mean with a single function (Equations 1-3).

      Strengths:

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Weaknesses:

      (1) The authors claim that only about half the animals tested exhibit discontinuity in turning rates. Can they automatically separate the empirical and model population into these two subpopulations (with the same method), and compare the results?

      Thank you, we should clarify that the observation that about half the animals exhibit discontinuity was not made by us, but by López-Cruz et al. The observed fraction of 50% was based on a visual assessment of the dual regression method we described. To make the process more objective, we decided to simply plot the distributions of the metrics they used for this assessment to see if two distinct populations could be observed. However, the distributions of slope differences and transition times do not produce two distinct populations. Our stochastic approach, which does not assume abrupt state-transitions, also produces comparable distributions. To quantify this, we will perform permutation tests on the means and variances differences between experimental and model data.

      (2) The equations consider an exponentially decaying rate of turning events. If so, Figure 2b should be shown on a semi-logarithmic scale.

      We are happy to add this panel as well.

      (3) The variables in Equations 1-3 and the methods for simulating them are not well defined, making the method difficult to follow. Assuming my reading is correct, Omega should be defined as the cumulative number of turning events over time (Omega(t)), not as a "turn" or "reorientation", which has no derivative. The relevant entity in Figure 1a is apparently <Omega (t)>, i.e. the mean number of events across a population which can be modelled by an expectation value. The time derivative would then give the expected rate of turning events as a function of time.

      Thank you, you are correct. Please see response to Reviewer #1.

      (4) Equations 1-3 are cryptic. The authors need to spell out up front that they are using a pair of coupled stochastic processes, sampling a hidden state M (to model the dynamic turning rate) and the actual turn events, Omega(t), separately, as described in Figure 2a. In this case, the model no longer appears more parsimonious than the original 2-state model. What then is its benefit or explanatory power (especially since the process involving M is not observable experimentally)?

      Thank you, yes we see how as written this was confusing. In our response to Reviewer #1, we added an important detail:

      While reorientations are modeled as discrete events, which is observationally true, the amount of M at time t\=0 is chosen to be large (M<sub>0</sub>←1,000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      However you are correct that if M was chosen to have a binary value of 0 or 1, then this would indeed be the two state model. Adding this as an additional model would be a good idea to compare how this matches the experimental data, and we are happy to add it.

      (5) Further, as currently stated in the paper, Equations 1-3 are only for the mean rate of events. However, the expectation value is not a complete description of a stochastic system. Instead, the authors need to formulate the equations for the probability of events, from which they can extract any moment (they write something in Figure 2a, but the notation there is unclear, and this needs to be incorporated here).

      Thank you, yes please see our response to Reviewer #1.

      (6) Equations 1-3 have three constants (alpha and gamma which were fit to the data, and M0 which was presumably set to 1000). How does the choice of M0 affect the results?

      Thank you, this is a good question. We will test this down to a binary state of M as mentioned in comment #4.

      (7) M decays to near 0 over 40 minutes, abolishing omega turns by the end of the simulations. Are omega turns entirely abolished in worms after 30-40 minutes off food? How do the authors reconcile this decay with the leveling of the turning rate in Figure 1a?

      Yes, reviewer #1 recommended adding a baseline reorientation rate which is likely more biologically plausible. However, we should also note that in Klein et al they observed a continuous decay over 50 minutes.

      (8) The fit given in Figure 2b does not look convincing. No statistical test was used to compare the two functions (empirical and fit). No error bars were given (to either). These should be added. In the discussion, the authors explain the discrepancy away as experimental limitations. This is not unreasonable, but on the flip side, makes the argument inconclusive. If the authors could model and simulate these limitations, and show that they account for the discrepancies with the data, the model would be much more compelling. To do this, I would imagine that the authors would need to take the output of their model (lists of turning times) and convert them into simulated trajectories over time. These trajectories could be used to detect boundary events (for a given size of arena), collisions between individuals, etc. in their simulations and to see their effects on the turn statistics.

      Thank you, we will add error bars and perform a permutation test on the mean and variance differences between experiment and model over the 40 minute window.

      (9) The other figures similarly lack any statistical tests and by eye, they do not look convincing. The exception is the 6 anecdotal examples in Figure 2e. Those anecdotal examples match remarkably closely, almost suspiciously so. I'm not sure I understood this though - the caption refers to "different" models of M decay (and at least one of the 6 examples clearly shows a much shallower exponential). If different M models are allowed for each animal, this is no longer parsimonious. Are the results in Figure 2d for a single M model? Can Figure 2e explain the data with a single (stochastic) M model?

      Thank you, yes, we will perform permutation tests on the mean and variance differences in the observed distributions in figure 2d. We certainly don’t want the panels in Figure 2e to be suspicious! These comparisons were drawn from calculating the correlations between all model traces and all experimental traces, and then choosing the top hits. Every time we run the simulation, we arrive at a different set of examples. Since it was recommended we add a baseline rate, these examples will be a completely different set when we run the simulation, again.

      We apologize for the confusion regarding M. Since the worms do not all start out with identical reorientation rates, we drew the initial M value from a distribution centered on M0 and a variance to match the initial distribution of observed experimental rates.

      (10) The left axes of Figure 2e should be reverted to cumulative counts (without the normalization).

      Thank you, we will add this. We want to clarify that we normalized it because we chose these examples based on correlation to show that the same types of sudden changes in search strategy can occur with a model that doesn’t rely on sudden rate changes.

      (11) The authors give an alternative model of a Levy flight, but do not give the obvious alternative models:

      a) the 1-state model in which P(t) = alpha exp (-gamma t) dt (i.e. a single stochastic process, without a hidden M, collapsing equations 1-3 into a single equation).

      b) the originally proposed 2-state model (with 3 parameters, a high turn rate, a low turn rate, and the local-to-global search transition time, which can be taken from the data, or sampled from the empirical probability distributions). Why not? The former seems necessary to justify the more complicated 2-process model, and the latter seems necessary since it's the model they are trying to replace. Including these two controls would allow them to compare the number of free parameters as well as the model results. I am also surprised by the Levy model since Levy is a family of models. How were the parameters of the Levy walk chosen?

      Thank you, we will remove this section completely, as it is tangential to the main point of the paper.

      (12) One point that is entirely missing in the discussion is the individuality of worms. It is by now well known that individual animals have individual behaviors. Some are slow/fast, and similarly, their turn rates vary. This makes this problem even harder. Combined with the tiny number of events concerned (typically 20-40 per experiment), it seems daunting to determine the underlying model from behavioral statistics alone.

      Thank you, yes we should have been more explicit in the reasoning behind drawing the initial M from a distribution (response to comment #9). We assume that not every worm starts out with the same reorientation rate, but that some start out fast (high M) and some start out slow (low M). However, we do assume M decays with the same kinetics, which seems sufficient to produce the observed phenomena.

      (13) That said, it's well-known which neurons underpin the suppression of turning events (starting already with Gray et al 2005, which, strangely, was not cited here). Some discussion of the neuronal predictions for each of the two (or more) models would be appropriate.

      Thank you, yes we will add Gray et al, but also the more detailed response to Reviewer #2.

      (14) An additional point is the reliance entirely on simulations. A rigorous formulation (of the probability distribution rather than just the mean) should be analytically tractable (at least for the first moment, and possibly higher moments). If higher moments are not obtainable analytically, then the equations should be numerically integrable. It seems strange not to do this.

      Thank you for suggesting this, we will add these analyses.

      In summary, while sample simulations do nicely match the examples in the data (of discontinuous vs continuous turning rates), this is not sufficient to demonstrate that the transition from ARS to dispersion in C. elegans is, in fact, likely to be a single 'state', or this (eq 1-3) single state. Of course, the model can be made more complicated to better match the data, but the approach of the authors, seeking an elegant and parsimonious model, is in principle valid, i.e. avoiding a many-parameter model-fitting exercise.

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Thank you, we agree that this is a generic phenomenon, which is partly why we did this. The data from López-Cruz seem to agree in part with Calhoun et al, that claim abrupt transitions occur, and Klein et al, which claim they do not occur. Since the underlying phenomenon is stochastic, we propose the mixed observations of sudden and gradual changes in search strategy are simply the result of a stochastic process, which can produce both phenomena for individual observations.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1 (Public Review):

      O’Neill et al. have developed a software analysis application, miniML, that enables the quantification of electrophysiological events. They utilize a supervised deep learned-based method to optimize the software. miniML is able to quantify and standardize the analyses of miniature events, using both voltage and current clamp electrophysiology, as well as optically driven events using iGluSnFR3, in a variety of preparations, including in the cerebellum, calyx of held, Golgi cell, human iPSC cultures, zebrafish, and Drosophila. The software appears to be flexible, in that users are able to hone and adapt the software to new preparations and events. Importantly, miniML is an open-source software free for researchers to use and enables users to adapt new features using Python.

      Overall this new software has the potential to become widely used in the field and an asset to researchers. However, the authors fail to discuss or even cite a similar analysis tool recently developed (SimplyFire), and determine how miniML performs relative to this platform. There are a handful of additional suggestions to make miniML more user-friendly, and of broad utility to a variety of researchers, as well as some suggestions to further validate and strengthen areas of the manuscript:

      (1) miniML relative to existing analysis methods: There is a major omission in this study, in that a similar open source, Python-based software package for event detection of synaptic events appears to be completely ignored. Earlier this year, another group published SimplyFire in eNeuro (Mori et al., 2024; doi: 10.1523/eneuro.0326-23.2023). Obviously, this previous study needs to be discussed and ideally compared to miniML to determine if SimplyFire is superior or similar in utility, and to underscore differences in approach and accuracy.

      We thank the reviewer for bringing this interesting publication to our attention. We have included SimplyFire in our benchmarking for comprehensive comparison with miniML. The approach taken by SimplyFire differs from miniML in a number of ways. Our results show that miniML provides higher recall and precision than SimplyFire (revised Figure 3). We appreciate that SimplyFire provides a user-interface similar to the commonly used MiniAnalysis software. In addition, the peak-finding-based approach of SimplyFire makes it relatively robust to event shape, which facilitates analysis of diverse data. However, we noted a strong threshold-dependence and long run time of SimplyFire (revised Figure 3 and Figure 3—figure supplement 1). In addition, SimplyFire is not robust against various types of noise typically encountered in electrophysiological recordings. Our extended benchmark analysis thus indicates that AI-based event detection is superior to existing algorithmic approaches, including SimplyFire.

      (2) The manuscript should comment on whether miniML works equally well to quantify current clamp events (voltage; e.g. EPSP/mEPSPs) compared to voltage clamp (currents, EPSC/mEPSCs), which the manuscript highlights. Are rise and decay time constants calculated for each event similarly?

      miniML works equally well for current- and voltage events (Figure 5, Figure 9). In general, events of opposite polarity can be analyzed by simply inverting the data. Transfer learning models may further improve the detection.

      For each detected event, independent of data/recording type, rise times are calculated as 10–90% times (baseline–peak), and decay times are calculated as time to 50% of the peak. In addition, event decay time constants are calculated from a fit to the event average. With miniML being open-source, researchers can adapt the calculations of event statistics to their needs, if desired. In the revised manuscript, we have expanded the Methods section that describes the quantification of event statistics (Methods, Quantification).

      (3) The interface and capabilities of miniML appear quite similar to Mini Analysis, the free software that many in the field currently use. While the ability and flexibility for users to adapt and adjust miniML for their own uses/needs using Python programming is a clear potential advantage, can the authors comment, or better yet, demonstrate, whether there is any advantage for researchers to use miniML over Mini Analysis or SimplyFire if they just need the standard analyses?

      Following the reviewer’s suggestion, we developed a graphical user interface (GUI) for miniML to enhance its usability (Figure 2—figure supplement 2), which is provided on the GitHub repository. Our comprehensive benchmark analysis demonstrated that miniML outperforms existing tools such as MiniAnalysis and SimplyFire. The main advantages are (i) increased reliability of results, which eliminates the need for visual inspection; (ii) fast runtime and easy automation; (iii) superior detection performance as demonstrated by higher recall in both synthetic and real data; (iv) open-source Python-based design. We believe that these advantages make miniML a valuable tool for researchers recording various types of synaptic events, offering a more efficient and reliable solution compared to existing methods.

      (4) Additional utilities for miniML: The authors show miniML can quantify miniature electrophysiological events both current and voltage clamp, as well as optical glutamate transients using iGluSnFR. As the authors mention in the discussion, the same approach could, in principle, be used to quantify evoked (EPSC/EPSP) events using electrophysiology, Ca2+ events (using GCaMP), and AP waveforms using voltage indicators like ASAP4. While I don’t think it is reasonable to ask the authors to generate any new experimental data, it would be great to see how miniML performs when analysing data from these approaches, particularly to quantify evoked synaptic events and/or Ca2+ (ideally postsynaptic Ca2+ signals from miniature events, as the Drosophila NMJ have developed nice approaches).

      In the revised manuscript, we have extended the application examples of miniML. We applied miniML to detect mEPSPs recorded with the novel voltage-sensitive indicator ASAP5 (Figure 9 and Figure 9—figure supplement 1). We performed simultaneous recordings of membrane voltage through electrophysiology and ASAP5 voltage imaging in rat cultured neurons at physiological temperature. Data were analyzed using miniML, with electrophysiology data being used as ground-truth for assessing detection performance in imaging data. Our results demonstrate that miniML robustly detects mEPSPs in current-clamp, and can localize corresponding transients in imaging data. Furthermore, we observed that miniML performs better than template matching and deconvolution on ASAP5 imaging data (Figure 9 and Figure 9—figure supplement 2).

      Reviewer 2 (Public Review):

      This paper presents miniML as a supervised method for the detection of spontaneous synaptic events. Recordings of such events are typically of low SNR, where state-of-the-art methods are prone to high false positive rates. Unlike current methods, training miniML requires neither prior knowledge of the kinetics of events nor the tuning of parameters/thresholds.

      The proposed method comprises four convolutional networks, followed by a bi-directional LSTM and a final fully connected layer which outputs a decision event/no event per time window. A sliding window is used when applying miniML to a temporal signal, followed by an additional estimation of events’ time stamps. miniML outperforms current methods for simulated events superimposed on real data (with no events) and presents compelling results for real data across experimental paradigms and species. Strengths:

      The authors present a pipeline for benchmarking based on simulated events superimposed on real data (with no events). Compared to five other state-of-the-art methods, miniML leads to the highest detection rates and is most robust to specific choices of threshold values for fast or slow kinetics. A major strength of miniML is the ability to use it for different datasets. For this purpose, the CNN part of the model is held fixed and the subsequent networks are trained to adapt to the new data. This Transfer Learning (TL) strategy reduces computation time significantly and more importantly, it allows for using a substantially smaller data set (compared to training a full model) which is crucial as training is supervised (i.e. uses labeled examples).

      Weaknesses:

      The authors do not indicate how the specific configuration of miniML was set, i.e. number of CNNs, units, LSTM, etc. Please provide further information regarding these design choices, whether they were based on similar models or if chosen based on performance.

      The data for the benchmark system was augmented with equal amounts of segments with/without events. Data augmentation was undoubtedly crucial for successful training.

      (1) Does a balanced dataset reflect the natural occurrence of events in real data? Could the authors provide more information regarding this matter?

      In a given recording, the event frequency determines the ratio of event-containing vs. nonevent-containing data segments. Whereas many synapses have a skew towards non-events, high event frequencies as observed, e.g., in pyramidal cells or Purkinje neurons, can shift the ratio towards event-containing data.

      For model training, we extracted data segments from mEPSC recordings in cerebellar granule cells, which have a low mEPSC frequency (about 0.2 Hz, Delvendahl et al. 2019). Unbalanced training data may complicate model training (Drummond and Holte 2003; Prati et al. 2009; Tyagi and Mittal 2020). We therefore decided to balance the training dataset for miniML by down-sampling the majority class (i.e., non-event segments), so that the final datasets for model training contained roughly equal amounts of events and non-events.

      (2) Please provide a more detailed description of this process as it would serve users aiming to use this method for other sub-fields.

      We thank the reviewer for raising this point. In the revised manuscript, we present a systematic analysis of the impact of imbalanced training data on model training (Figure 1—figure supplement 2). In addition, we have revised the description of model training and data augmentation in the Methods section (Methods, Training data and annotation).

      The benchmarking pipeline is indeed valuable and the results are compelling. However, the authors do not provide comparative results for miniML for real data (Figures 4-8). TL does not apply to the other methods. In my opinion, presenting the performance of other methods, trained using the smaller dataset would be convincing of the modularity and applicability of the proposed approach.

      Quantitative comparison of synaptic detection methods on real-world data is challenging because the lack of ground-truth data prevents robust, quantitative analyses. Nevertheless, we compared miniML to common template-based and finite-threshold based methods on four different types of synapses. We noted that miniML generally detects more events, whereas other methods are susceptible to false-positives (Figure 4—figure supplement 1). In addition, we analyzed the performance of miniML on voltage imaging data (Figure 9). Simultaneous recordings of electrophysiological and imaging data allowed a quantitative comparison of detection methods in this dataset. Our results demonstrate that miniML provides higher recall for optical minis recorded using ASAP5 (Figure 9 and Figure 9—figure supplement 2; F1 score, Cohen’s d 1.35 vs. template matching and 5.1 vs. deconvolution).

      Impact:

      Accurate detection of synaptic events is crucial for the study of neural function. miniML has a great potential to become a valuable tool for this purpose as it yields highly accurate detection rates, it is robust, and is relatively easily adaptable to different experimental setups.

      Additional comments:

      Line 73: the authors describe miniML as "parameter-free". Indeed, miniML does not require the selection of pulse shape, rise/fall time, or tuning of a threshold value. Still, I would not call it "parameter-free" as there are many parameters to tune, starting with the number of CNNs, and number of units through the parameters of the NNs. A more accurate description would be that as an AI-based method, the parameters of miniML are learned via training rather than tuned by the user.

      We agree that a deep learning model is not parameter-free, and this term may be misleading. We have therefore changed this sentence in the introduction as follows: "The method is fast, robust to threshold choice, and generalizable across diverse data types [...]"

      Line 302: the authors describe miniML as "threshold-independent". The output trace of the model has an extremely high SNR so a threshold of 0.5 typically works. Since a threshold is needed to determine the time stamps of events, I think a better description would be "robust to threshold choice".

      To detect event localizations, a peak search is performed on the model output, which uses a minimum peak height parameter (or threshold). Extreme values for this parameter do indeed have a small impact on detection performance (Figure 3J). We have changed the description in the introduction and discussion according to the reviewer’s suggestion.

      Reviewer 3 (Public Review):

      miniML as a novel supervised deep learning-based method for detecting and analyzing spontaneous synaptic events. The authors demonstrate the advantages of using their methods in comparison with previous approaches. The possibility to train the architecture on different tasks using transfer learning approaches is also an added value of the work. There are some technical aspects that would be worth clarifying in the manuscript:

      (1) LSTM Layer Justification: Please provide a detailed explanation for the inclusion of the LSTM layer in the miniML architecture. What specific benefits does the LSTM layer offer in the context of synaptic event detection?

      Our model design choice was inspired by similar approaches in the literature (Donahue et al. 2017; Islam et al. 2020; Passricha and Aggarwal 2019; Tasdelen and Sen 2021; Wang et al. 2020). Convolutional and recurrent neural networks are often combined for time-series classification problems as they allow learning spatial and temporal features, respectively. Combining the strengths of both network architectures can thus help improve the classification performance. Indeed, a CNN-LSTM architecture proved to be superior in both training accuracy and detection performance (Figure 1—figure supplement 2). Further, this architecture requires fewer free parameters than comparable model designs using fully connected layers instead. The revised manuscript shows a comparison of different model architectures (Figure 1—figure supplement 2), and we added the following description to the text (Methods, Deep learning model architecture):

      "The combination of convolutional and recurrent neural network layers helps to improve the classification performance for time-series data. In particular, LSTM layers allow learning temporal features."

      (2) Temporal Resolution: Can you elaborate on the reasons behind the lower temporal resolution of the output? Understanding whether this is due to specific design choices in the model, data preprocessing, or post-processing will clarify the nature of this limitation and its impact on the analysis.

      When running inference on a continuous recording, we choose to use a sliding window approach with stride. Therefore, the model output has a lower temporal resolution than the raw data, which is determined by the stride length (i.e., how many samples to advance the sliding window). While using a stride is not required, it significantly reduces inference time (cf. Figure 2—figure supplement 1). We recommend a stride of 20 samples, which does not impact the detection of events. Any subsequent quantification of events (amplitude, area, risetimes, etc.) is performed on raw data. Based on the reviewer’s comment, we have adapted the code to resample the prediction trace to the sampling rate of the original data. This maintains temporal precision and avoids confusion.

      The Methods now include the following statement:

      "To maintain temporal precision, the prediction trace is resampled to the sampling frequency of the raw data."

      (3) Architecture optimization: how was the architecture CNN+LSTM optimized in terms of a number of CNN layers and size?

      We performed a Bayesian optimization over a defined range of hyperparameters in combination with empirical hyperparameter tuning. We now describe this in the Methods section as follows:

      "To optimise the model architecture, we performed a Bayesian optimisation of hyperparameters. Hyperparameter ranges were chosen for the free parameters of all layers. Optimisation was then performed with a maximum number of trials of 50. Models were evaluated using the validation dataset. Because higher number of free parameters tended to increase inference times, we then empirically tuned the chosen hyperparameter combination to achieve a trade-off between number of free parameters and accuracy."

      Recommendations For The Authors

      Reviewing Editor (Recommendations For The Authors):

      Overall suggestions to the authors:

      (1) Directly compare miniML with SimplyFire (which was not cited or discussed in the original manuscript), with both idealized and actual data. Discuss the pros/cons of each software.

      We have conducted an extensive comparison between miniML and SimplyFire using both simulated and actual experimental data. This analysis is now presented in the revised Figure 3, Figure 3—figure supplement 1, and Figure 4—figure supplement 1. In addition, we have included relevant citations for SimplyFire in our manuscript. These additions provide a more comprehensive and balanced view of the available tools in the field, positioning our work within the broader context of existing solutions.

      (2) Generate a better user interface akin to MiniAnalysis or SimplyFire.

      We thank the editor and reviewers for the suggestion to improve the user interface. We have created a user-friendly graphical user interface (GUI) for miniML that is available on our GitHub repository. This GUI is now showcased in Figure 2—figure supplement 2 of the manuscript. The new interface allows users to load and analyze data through an intuitive point-and-click system, visualize results in real-time, and adjust parameters easily without coding knowledge. We have incorporated user feedback to refine the interface and improve user experience. These improvements significantly enhance the accessibility of miniML, making it more user-friendly for researchers with varying levels of programming expertise.

      Reviewer 1 (Recommendations For The Authors):

      Related to point (1) of the Public Review, we have taken the liberty to compare electrophysiological data using miniAnalysis, SimiplyFire, and miniML. In our comparison, we note the following in our experience:

      (1.1) In contrast to both SimplyFire and miniAnalysis, miniML does not currently have a user-friendly interface where the user can directly control or change the parameters of interest, nor does miniML have a user control center, so the user cannot simply type or select the mini manually. Rather, if any parameter needs to be changed, the user needs to read, understand, and change the original source code to generate the preferred change. This level of "activation energy" and required user coding expertise in computer science, which many researchers do not have, renders miniML much less accessible when directly compared to SimplyFire and miniAnalysis. Hence, unless miniML’s interface can be made more user-friendly, this is a major disadvantage, especially when compared to SimplyFire, which has many of the same features as miniML but with a much easier interface and user controls.

      As suggested by the reviewer, we have created a graphical user interface (GUI) for miniML. The GUI allows easy data loading, filtering, analysis, event inspection, and saving of results without the need for writing Python code. Figure 2—figure supplement 2 illustrates the typical workflow for event analysis with miniML using the GUI and a screenshot of the user interface. Code to use miniML via the GUI is now included in the project’s GitHub repository. The GUI provides a simple and intuitive way to analyze synaptic events, whereas running miniML as Python script allows for more customization and a high degree of automatization.

      (1.2) We compared electrophysiological miniature events between miniML, SimplyFire, and miniAnalysis. All three achieved similar mean amplitudes in "wild type" conditions, and conditions in which mini events were enhanced and diminished, so the overall means and utilities are similar, with miniML and SimplyFire being preferred given the flexibility and much faster analysis. We did note a few differences, however. SimplyFire tends to capture a high number of mini-events over miniML, especially in conditions of diminished mini amplitude (e.g., miniML found 76 events, while SimplyFire 587). The mean amplitudes, however, were similar. It seems that in data with low SNR, SimplyFire captures many more events as real minis that are probably noise, while miniML is more selective, which might be an advantage in miniML. That being said, we found SimplyFire to be superior in many respects, not least of which the user interface and experience.

      We appreciate the reviewer’s thorough comparison of miniML, SimplyFire, and MiniAnalysis. While we acknowledge SimplyFire’s user-friendly interface, our study highlights several advantages of AI-based event analysis over conventional algorithmic approaches. Our updated benchmark analysis revealed better detection performance of miniML compared with SimplyFire (revised Figure 3), which had similar performance to deconvolution. As already noted by the reviewer, high false positive rates are a major issue of the SimplyFire approach. Although a minimum amplitude cutoff can partially resolve this problem, detection performance is highly sensitive to threshold setting (revised Figure 3). Another apparent disadvantage of SimplyFire is its relatively slow runtime (Figure 3—figure supplement 1). Finally, we have enhanced miniML’s accessibility by providing a graphical user interface that is easy to use and provides additional functionality.

      Some technical comments:

      (1) Improvements to the dependence version of miniML: There is a need to clarify the dependence version of the python and tensor flow used in this study and in the GitHub. We used Python version 3.8.19 to load the miniML model. However, if Python versions >=3.9, as described on the GitHub provided, it is difficult to have a matched h5py version installed. It is also inaccurate to say using Python >=3.9, because tensor flow version for this framework needs to be around 2.13. However, if using Python >=3.10, it will only allow 2.16 version tensor flow to be the download choice. Therefore, as a Python framework, the dependency version needs to be specified on GitHub to allow researchers to access the model using the entire work.

      Thank you for highlighting this issue. We have now included specific version numbers in the requirements to avoid version conflicts and to ensure proper functioning of the code.

      (2) Due to the intrinsic characteristics of the trained model, every model is only suitable for analyzing data with similar attributes. It is hard for researchers without a strong computer science background to train a new model themselves for their specific data. Therefore, it would be preferred if there were more available transfer learning models on GitHub accessible for researchers to adapt to their data.

      We would like to thank the reviewer for this feedback. Trained models (such as the default model) can often be used on different data (see, e.g., Figure 4, where data from four distinct synaptic preparations were analyzed with the base model, and Figure 5—figure supplement 1). However, changes in event waveform and/or noise characteristics may necessitate transfer learning to obtain optimal results with miniML. We have revised the description and tutorial for model training on the project’s GitHub repository to provide more guidance in this process. In addition, we now provide a tutorial on how to use existing models on out-of-sample data with distinct kinetics, using resampling. We hope these updates to the miniML GitHub repository will facilitate the use of the method.

      Following the suggestion by the reviewer, we have provided the transfer learning models used for the manuscript on the project’s GitHub repository to increase the number of available machine learning models for event detection. In addition, users of miniML are encouraged to supply their custom models. We hope that this will facilitate model exchange between laboratories in the future.

      Reviewer 3:

      I congratulate all authors for the convincing demonstration of their methodology, I do not have additional recommendations.

      We would like to thank the reviewer for the positive assessment of our manuscript.

      References

      Delvendahl, I., Kita, K., & Müller, M. (2019). Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse. Proceedings of the National Academy of Sciences, 116(47), 23783–23789. https://doi.org/10.1073/pnas.1909675116

      Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017). Long-term recurrent convolutional networks for visual recognition and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 677–691. https://doi.org/10.1109/tpami.2016.2599174

      Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. https: //api.semanticscholar.org/CorpusID:204083391

      Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using x-ray images. Informatics in Medicine Unlocked, 20, 100412. https://doi.org/10.1016/j.imu.2020.100412

      Passricha, V., & Aggarwal, R. K. (2019). A hybrid of deep CNN and bidirectional LSTM for automatic speech recognition. Journal of Intelligent Systems, 29(1), 1261–1274. https://doi.org/10.1515/jisys-2018-0372

      Prati, R. C., Batista, G. E. A. P. A., & Monard, M. C. (2009). Data mining with imbalanced class distributions: Concepts and methods. Indian International Conference on Artificial Intelligence. https://api.semanticscholar.org/CorpusID:16651273

      Tasdelen, A., & Sen, B. (2021). A hybrid CNN-LSTM model for pre-miRNA classification. Scientific Reports, 11(1). https://doi.org/10. 1038/s41598-021-93656-0

      Tyagi, S., & Mittal, S. (2020). Sampling approaches for imbalanced data classification problem in machine learning. In P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar, & S. Tanwar (Eds.), Proceedings of icric 2019 (pp. 209–221). Springer International Publishing.

      Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., An, Y., Wang, K., & Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and Communication Networks, 2020, 1–12. https://doi.org/10.1155/2020/ 2132138

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This is a new and important system that can efficiently train mice to perform a variety of cognitive tasks in a flexible manner. It is innovative and opens the door to important experiments in the neurobiology of learning and memory.

      Strengths:

      Strengths include: high n's, a robust system, task flexibility, comparison of manual-like training vs constant training, circadian analysis, comparison of varying cue types, long-term measurement, and machine teaching.

      Weaknesses:

      I find no major problems with this report.

      (1) Line 219: Water consumption per day remained the same, but number of trails triggered was more as training continued. First, is this related to manual-type training? Also, I'm trying to understand this result quantitatively, since it seems counter-intuitive: I would assume that with more trials, more water would be consumed since accuracy should go up over training (so more water per average trial). Am I understanding this right? Can the authors give more detail or understanding to how more trials can be triggered but no more water is consumed despite training?

      Thanks for the thoughtful comment. We would like to clarify the phenomenon described in Line 219: As the training advanced, the number of trials triggered by mice per day decreased (rather than increased as you mentioned in the comment) gradually for both manual and autonomous groups of mice (Fig. 2H left). The performance as you mentioned, improved over time, leading to an increased probability of obtaining water and thus relatively stable daily water intake (Fig. 2H left). We believe the stable daily intake is the minimum amount of water required by the mice under circumstance of autonomous behavioral training.

      (2) Figure 2J: The X-axis should have some label: at least "training type". Ideally, a legend with colors can be included, although I see the colors elsewhere in the figure. If a legend cannot be added, then the color scheme should be explained in the caption.

      (3) Figure 2K: What is the purple line? I encourage a legend here. The same legend could apply to 2J.

      (4) Supplementary Figure S2 D: I do not think the phrase "relying on" is correct. Instead, I think "predicted by" or "correlating with" might be better.

      We thank the reviewer for the valuable suggestion. We will address all these points and make the necessary revisions in the next version of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Yu et al. describes a novel approach for collecting complex and different cognitive phenotypes in individually housed mice in their home cage. The authors report a simple yet elegant design that they developed for assessing a variety of complex and novel behavioral paradigms autonomously in mice.

      Strengths:

      The data are strong, the arguments are convincing, and I think the manuscript will be highly cited given the complexity of behavioral phenotypes one can collect using this relatively inexpensive ($100/box) and high throughput procedure (without the need for human interaction). Additionally, the authors include a machine learning algorithm to correct for erroneous strategies that mice develop which is incredibly elegant and important for this approach as mice will develop odd strategies when given complete freedom.

      Weaknesses:

      (1) A limitation of this approach is that it requires mice to be individually housed for days to months. This should be discussed in depth.

      Thank you for raising this important point. We agree that the requirement for individual housing of mice during the training period is a limitation of our approach, and we appreciate the opportunity to discuss this in more depth. In the revised manuscript, we will add a dedicated section to the Discussion to address this limitation, including the potential impact of individual housing on the mice, the rationale for individual housing in our study, and efforts or alternatives made to mitigate the effects of individual housing.

      (2) A major issue with continuous self-paced tasks such as the autonomous d2AFC used by the authors is that the inter-trial intervals can vary significantly. Mice may do a few trials, lose interest, and disengage from the task for several hours. This is problematic for data analysis that relies on trial duration to be similar between trials (e.g., reinforcement learning algorithms). It would be useful to see the task engagement of the mice across a 24-hour cycle (e.g., trials started, trials finished across a 24-hour period) and approaches for overcoming this issue of varying inter-trial intervals.

      Thank you for your insightful comment regarding the variability in inter-trial intervals and its potential impact on data analysis. We agree that this is an important consideration for continuous self-paced tasks like the autonomous d2AFC paradigm used in our study. In the original manuscript, we have showed the general task engagement across 24-hour cycle (Fig. 2K). The distribution of inter-trial interval was also illustrated (Fig. S3H), which actually shows that most of trials have short intervals (though with extreme long ones). We will include more detailed analysis and discuss the challenges for data analysis.

      Regarding the approaches to mitigate the issue of varying inter-trial interval, we will also discuss strategies to account for and mitigate the effects, including: trial selection, incorporating engagement period (e.g., open only during a fixed 2-hour period each day), etc.

      (3) Movies - it would be beneficial for the authors to add commentary to the video (hit, miss trials). It was interesting watching the mice but not clear whether they were doing the task correctly or not.

      Thanks for the reminder. We will add subtitles to the videos in the next version.

      (4) The strength of this paper (from my perspective) is the potential utility it has for other investigators trying to get mice to do behavioral tasks. However, not enough information was provided about the construction of the boxes, interface, and code for running the boxes. If the authors are not willing to provide this information through eLife, GitHub, or their own website then my evaluation of the impact and significance of this paper would go down significantly.

      Thanks for this important comment. We would like to clarify that the construction methods, GUI, code for our system, PCB and CAD files (newly uploaded) have already been made publicly available on https://github.com/Yaoyao-Hao/HABITS. Additionally, we have open-sourced all the codes and raw data for all training protocols (https://doi.org/10.6084/m9.figshare.27192897). We will continue to maintain these resources in the future.

      Minor concerns:

      Learning rate is confusing for Figure 3 results as it actually refers to trials to reach the criterion, and not the actual rate of learning (e.g., slope).

      Thanks for pointing this out. We will make the revision in the next version.

      Reviewer #3 (Public review):

      Summary:

      In this set of experiments, the authors describe a novel research tool for studying complex cognitive tasks in mice, the HABITS automated training apparatus, and a novel "machine teaching" approach they use to accelerate training by algorithmically providing trials to animals that provide the most information about the current rule state for a given task.

      Strengths:

      There is much to be celebrated in an inexpensively constructed, replicable training environment that can be used with mice, which have rapidly become the model species of choice for understanding the roles of distinct circuits and genetic factors in cognition. Lingering challenges in developing and testing cognitive tasks in mice remain, however, and these are often chalked up to cognitive limitations in the species. The authors' findings, however, suggest that instead, we may need to work creatively to meet mice where they live. In some cases, it may be that mice may require durations of training far longer than laboratories are able to invest with manual training (up to over 100k trials, over months of daily testing) but the tasks are achievable. The "machine teaching" approach further suggests that this duration could be substantially reduced by algorithmically optimizing each trial presented during training to maximize learning.

      Weaknesses:

      (1) Cognitive training and testing in rodent models fill a number of roles. Sometimes, investigators are interested in within-subjects questions - querying a specific circuit, genetically defined neuron population, or molecule/drug candidate, by interrogating or manipulating its function in a highly trained animal. In this scenario, a cohort of highly trained animals that have been trained via a method that aims to make their behavior as similar as possible is a strength.

      However, often investigators are interested in between-subjects questions - querying a source of individual differences that can have long-term and/or developmental impacts, such as sex differences or gene variants. This is likely to often be the case in mouse models especially, because of their genetic tractability. In scenarios where investigators have examined cognitive processes between subjects in mice who vary across these sources of individual difference, the process of learning a task has been repeatedly shown to be different. The authors do not appear to have considered individual differences except perhaps as an obstacle to be overcome.

      The authors have perhaps shown that their main focus is highly-controlled within-subjects questions, as their dataset is almost exclusively made up of several hundred young adult male mice, with the exception of 6 females in a supplemental figure. It is notable that these female mice do appear to learn the two-alternative forced-choice task somewhat more rapidly than the males in their cohort.

      Thank you for your insightful comments and for highlighting the importance of considering both within-subject and between-subject questions in cognitive training and testing in rodent models.

      We acknowledge that our study primarily focused on highly controlled within-subject questions. However, the datasets we provided have showed some evidences for the ‘between-subject’ questions. For example, the large variability in learning rates among mice observed in Fig. 2I, the overall learning rate difference between male and female subjects (Fig. 2D vs. Fig. S2G, as the reviewer already mentioned), the varying nocturnal behavioral patterns (Fig. 2K), etc. While our primary focus was on highly controlled within-subjects questions, we recognize the value of exploring between-subjects differences. In the revised version, we will discuss these points more systematically.

      (2) Considering the implications for mice modeling relevant genetic variants, it is unclear to what extent the training protocols and especially the algorithmic machine teaching approach would be able to inform investigators about the differences between their groups during training. For investigators examining genetic models, it is unclear whether this extensive training experience would mitigate the ability to observe cognitive differences, or select the animals best able to overcome them - eliminating the animals of interest. Likewise, the algorithmic approach aims to mitigate features of training such as side biases, but it is worth noting that the strategic uses of side biases in mice, as in primates, can benefit learning, rather than side biases solely being a problem. However, the investigators may be able to highlight variables selected by the algorithm that are associated with individual strategies in performing their tasks, and this would be a significant contribution.

      Thank you for the insightful comments. We acknowledge that the extensive training experience, particularly through the algorithmic machine teaching approach, could potentially influence the ability to observe cognitive differences between groups of mice with relevant genetic variants. However, our study design and findings suggest that this approach can still provide valuable insights into individual differences and strategies used by the animals during training. First, the behavioral readout (including learning rate, engagement pattern, etc.) as mentioned above, could tell certain number of differences among mice. Second, detailed modelling analysis (with logistical regression modelling) could further dissect the strategy that mouse use along the training process (Fig. S2B). We have actually highlighted some variables selected by the regression that are associated with individual strategies in performing their tasks (Fig. S2C) and these strategies could be different between manual and autonomous training groups (Fig. S2D). We will discuss these points more in the next version of the manuscript.

      (3) A final, intriguing finding in this manuscript is that animal self-paced training led to much slower learning than "manual" training, by having the experimenter introduce the animal to the apparatus for a few hours each day. Manual training resulted in significantly faster learning, in almost half the number of trials on average, and with significantly fewer omitted trials. This finding does not necessarily argue that manual training is universally a better choice because it leads to more limited water consumption. However, it suggests that there is a distinct contribution of experimenter interactions and/or switching contexts in cognitive training, for example by activating an "occasion setting" process to accelerate learning for a distinct period of time. Limiting experimenter interactions with mice may be a labor-saving intervention, but may not necessarily improve performance. This could be an interesting topic of future investigation, of relevance to understanding how animals of all species learn.

      Thank you for your insightful comments. We agree that the finding that manual training led to significantly faster learning compared to self-paced training is both intriguing and important. One of the possible reasons we think is due to the limited duration of engagement provided by the experimenter in the manual training case, which forced the mice to concentrate more on the trails (thus with fewer omitting trials) than in autonomous training. Your suggestion that experimenter interactions might activate an "occasion setting" process is particularly interesting. In the context of our study, we could actually introduce, for example, a light, serving as the cue that prompt the animals to engage; and when the light is off, the engagement was not accessible any more for the mice to simulate the manual training situation. We agree that this could be an interesting topic for future investigation that might create a more conducive environment for learning, thereby accelerating the learning rate.

    1. Author response:

      Reviewer #1 (Public review):

      Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

      Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

      An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

      Our immunostaining experiments showed that three dopamine receptors, DopR1, Dop2R, and DopEcR were expressed in Gr5a<sup>+</sup> neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that DopR1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest different internal states (courtship failure vs. starvation) modulate peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We will further discuss these possibilities in the revised manuscript.

      The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

      (1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

      We think this is a valid concern. We will conduct courtship conditioning in the absence of food and test if courtship failure can still suppress sugar sensitivity in the revised manuscript.

      (2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose in the revised manuscript. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

      (3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

      We think this is also a valid suggestion. We will directly examine whether activating TH<sup>+</sup> neurons in sexually conditioned males would enhance sugar responses of Gr5a<sup>+</sup> neurons in sexually failed males. We will also add in statistical analysis.

      Nevertheless, we would still argue our current experiments using Naive males (Figure 3, S1C-D) are adequate to show a functional link between TH<sup>+</sup> neurons and Gr5a<sup>+</sup> neurons. Combining with the results that these neurons form active synapses (Figure 3, S1B) and that the activity of TH<sup>+</sup> neurons was dampened in sexually failed males (Figure 3G-I), our current data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuity.

      (4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

      We will add detailed information of statistical analysis in each figure legend.

      (5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

      We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. In fact, we have also tested the role of Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a<sup>+</sup> neurons.

      (6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

      We agree that there might be some potential discrepancies. However, our current data are not adequate for the clarification given the experiments shown in Figure 6E-F and the apparent control (Figure 3C) were not conducted under identical settings at the same (that’s why we did not directly compare these results). One way to address the issues is to conduct these calcium imaging experiments again with a head-to-head comparison with the control group (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi). We will conduct the experiments and present the data in the revised manuscript.

      (7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

      We will change our expressions in the revised manuscript. In brief, we think that these manipulations (suppressing Dop1R1<sup>+</sup> and Dop2R<sup>+</sup> neurons) have two consequences: suppressing the overall sweet sensitivity and eliminating the effect of sexual failure.

      Reviewer #2 (Public review):

      Summary:

      The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

      Strengths:

      The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

      Weaknesses:

      The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

      We agree with the reviewer that in the current study, we did not examine how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

      We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds, Figure 1, S1B-C) upon sexual failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We will further discuss this possibility in the revised manuscript.

      Reviewer #3 (Public review):

      Summary

      In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

      Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

      Concern

      My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose instead. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

    1. Friedman is correct, for example when he points out the importance of changes like the rise of India or China, the spatial fragmentation of the production process through offshoring or the lowering of transaction costs that makes more and more services tradable.

      This made me start to rethink whether globalization is a process of "flattening". Indeed, phenomena such as the rise of India and China, as well as production outsourcing, may seem like globalization has increased opportunities for everyone, but I think this is only superficial. There are actually many imbalances behind it, such as some places where cheap labor has become the bottom of the supply chain, while large companies in developed countries have taken the lead.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This valuable study combined whole-head magnetoencephalography (MEG) and subthalamic (STN) local field potential (LFP) recordings in patients with Parkinson's disease undergoing deep brain stimulation surgery. The paper provides solid evidence that cortical and STN beta oscillations are sensitive to movement context and may play a role in the coordination of movement redirection.

      We are grateful for the expert assessment by the editor and the reviewers. Below we provide pointby-point replies to both public and private reviews. We have tried to keep the answers in the public section short and concise, not citing the changed passages unless the point does not re-appear in the recommendations. There, we did include all of the changes to the manuscript, such that the reviewers need not go back and forth between replies and manuscript.

      The reviewer comments have not only led to numerous improvements of the text, but also to new analyses, such as Granger causality analysis, and to methodological improvements e.g. including numerous covariates in the statistical analyses. We believe that the article improved substantially through the feedback, and we thank the reviewers and the editor for their effort.

      Public Reviews

      Reviewer #1 (Public review):

      Summary:

      Winkler et al. present brain activity patterns related to complex motor behaviour by combining wholehead magnetoencephalography (MEG) with subthalamic local field potential (LFP) recordings from people with Parkinson's disease. The motor task involved repetitive circular movements with stops or reversals associated with either predictable or unpredictable cues. Beta and gamma frequency oscillations are described, and the authors found complex interactions between recording sites and task conditions. For example, they observed stronger modulation of connectivity in unpredictable conditions. Moreover, STN power varied across patients during reversals, which differed from stopping movements. The authors conclude that cortex-STN beta modulation is sensitive to movement context, with potential relevance for movement redirection.

      Strengths:

      This study employs a unique methodology, leveraging the rare opportunity to simultaneously record both invasive and non-invasive brain activity to explore oscillatory networks.

      Weaknesses:

      It is difficult to interpret the role of the STN in the context of reversals because no consistent activity pattern emerged.

      We thank the reviewer for the valuable feedback to our study. We agree that the interpretation of the role of the STN during reversals is rather difficult, because reversal-related STN activity was highly variable across patients. Although there seem to be consistent patterns in sub-groups of the current cohort, with some patients showing event-related increases (Fig. 3b) and others showing decreases, the current dataset is not large enough to substantiate or even explain the existence of such clusters. Thus, we limit ourselves to acknowledging this limitation and discussing potential reasons for the high variability, namely variability in electrode placement and insufficient spatial resolution for the separation of specialized cell ensembles within the STN (see Discussion, section Limitations and future directions).

      Reviewer #2 (Public review):

      Summary:

      This study examines the role of beta oscillations in motor control, particularly during rapid changes in movement direction among patients with Parkinson's disease. The researchers utilized magnetoencephalography (MEG) and local field potential (LFP) recordings from the subthalamic nucleus to investigate variations in beta band activity within the cortex and STN during the initiation, cessation, and reversal of movements, as well as the impact of external cue predictability on these dynamics. The primary finding indicates that beta oscillations more effectively signify the start and end of motor sequences than transitions within those sequences. The article is well-written, clear, and concise.

      Strengths:

      The use of a continuous motion paradigm with rapid reversals extends the understanding of beta oscillations in motor control beyond simple tasks. It offers a comprehensive perspective on subthalamocortical interactions by combining MEG and LFP.

      Weaknesses:

      (1) The small and clinically diverse sample size may limit the robustness and generalizability of the findings. Additionally, the limited exploration of causal mechanisms reduces the depth of its conclusions and focusing solely on Parkinson's disease patients might restrict the applicability of the results to broader populations.

      We thank the reviewer for the insightful feedback. We address these issues one by one in our responses to points 2, 4 and 6, respectively.

      (2) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size and cohort stratification. We now acknowledge this in the revised discussion (section Limitations and future directions). Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (Discussion, section Limitations and future directions). Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      (3) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for the suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up t-tests.

      (4) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band. The detailed results can be viewed on p. 16 in the section on Granger causality. For statistical testing, we conducted an rmANCOVA, similar to those for power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6-figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      (5) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      (6) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS in our centre makes this an unfeasible option in practical terms. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication and dopamine has been found to normalize pathological alterations of beta activity. Further, the general pattern of movement-related beta and gamma oscillations reported here has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants.

      Reviewer #3 (Public review):

      Summary:

      The study highlights how the initiation, reversal, and cessation of movements are linked to changes in beta synchronization within the basal ganglia-cortex loops. It was observed that different movement phases, such as starting, stopping briefly, and stopping completely, affect beta oscillations in the motor system.

      It was found that unpredictable cues lead to stronger changes in STN-cortex beta coherence. Additionally, specific patterns of beta and gamma oscillations related to different movement actions and contexts were observed. Stopping movements was associated with a lack of the expected beta rebound during brief pauses within a movement sequence.

      Overall, the results underline the complex and context-dependent nature of motor-control and emphasize the role of beta oscillations in managing movement according to changing external cues.

      Strengths:

      The paper is very well written, clear, and appears methodologically sound.

      Although the use of continuous movement (turning) with reversals is more naturalistic than many previous button push paradigms.

      Weaknesses:

      The generalizability of the findings is somewhat curtailed by the fact that this was performed perioperatively during the period of the microlesion effect. Given the availability of sensing-enabled DBS devices now and HD-EEG, does MEG offer a significant enough gain in spatial localizability to offset the fact that it has to be done shortly postoperatively with externalized leads, with an attendant stun effect? Specifically, for paradigms that are not asking very spatially localized questions as a primary hypothesis?

      We appreciate the reviewer’s feedback and acknowledge the valid point raised on the timing of our measurements. Indeed, sensing-enabled devices offer a valid alternative to peri-operative recordings, circumventing the stun effect. We acknowledge this in the revised discussion, section Limitations and future directions (p. 23): “Additionally, future research could capitalize on sensingcapable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).” This alternative strategy, however, was not an option here because we did not have a sufficient number of patients implanted with sensing-enabled devices at the time when the data collection was initialized.

      That being said, we would like to highlight that in the present study, our goal was not to study pathology related to Parkinson’s disease. Rather, we aimed to learn about motor control in general. The stun effect may have facilitated motor performance in our patients, which is actually beneficial to the research goals at hand.

      Further investigation of the gamma signal seems warranted, even though it has a slightly lower proportional change in amplitude in beta. Given that the changes in gamma here are relatively wide band, this could represent a marker of neural firing that could be interestingly contrasted against the rhythm account presented.

      We appreciate the reviewer’s interest and we have extended the investigation of gamma oscillations. We now provide statistics regarding the influence of predictability on gamma power and gamma coherence (no significant effects) and explore Granger causality in the gamma (and beta) band (see comment 4 of reviewer 2). Unfortunately, we cannot measure spiking via the DBS electrode, and therefore we cannot investigate correlations between gamma oscillatory activity and action potentials. We do agree with the reviewer, however, that action potentials rather than oscillations form the basis of motor control in the brain. This view of ours is now reflected in the revised discussion, section Limitations and future directions (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This is a well-conducted study and overall the results are clear. I only have one minor suggestion for improvement of the manuscript. I found the order of appearance of the results somewhat confusing, switching from predictability-related behavioral effects to primarily stopping and reversal-related neurophysiological effects, back to predictability but starting with coherence. I would suggest that the authors try to follow a systematic order focused on the questions at hand. E.g. perhaps readability could be improved if the results section is split into reversal vs. stopping related effects, reporting behavior, power, and coherence in this order, followed by a predictability section, again reporting behavior, power, and coherence. Obviously, this is an optional suggestion. Apart from that, I just missed a more direct message related to the absence of statistical significance related to STN power changes during reversal. I think this could be made more clear in the text.

      We thank the reviewer for the feedback to our study. In order to ease reading, we modified the order and further added additional sub-titles to the results section. We start with Behavior (p. 4) and then move on to Power (general movement effects on power – movement effects on STN power – movement effects on cortical power – predictability effects on power). Next, we move on to Connectivity (movement effects on connectivity – predictability effects on connectivity – Granger causality). We hope that these adaptations will help guide the reader.

      Additionally, we thank the reviewer for noting that we did not explicitly mention the lack of statistical significance of reversal-related beta power modulations in the STN. We have adapted the section on modulation of STN beta power associated with reversals (p. 8) to: “In the STN, reversals were associated with a brief modulation of beta power, which was weak in the group-average spectrum and did not reach significance (Fig. 3a).”

      Reviewer #2 (Recommendations for the authors):

      (1) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size. We now acknowledge this in the revised discussion, section Limitations and future directions (p. 20):

      “Invasive measurements of STN activity are only possible in patients who are undergoing or have undergone brain surgery. Studies drawing from this limited pool of candidate participants are typically limited in terms of sample size and cohort stratification, particularly when carried out in a peri-operative setting. Here, we had a sample size of 20, which is rather high for a peri-operative study, but still low in terms of absolute numbers.”

      Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (p. 21):

      “Additionally, future research could capitalize on sensing-capable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).”

      Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      Revised article

      Methods, Statistical analysis:

      “To account for their potential influence on brain activity, we added age, pre-operative UPDRS score, and disease duration as covariates to all ANOVAs. Covariates were standardized by means of zscoring.”

      (2) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for this useful suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up _t-_tests.

      (3) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band, but no directed interactions in the gamma band. For statistical testing, we conducted an rmANCOVA, similar to the analysis of power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6 figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      Revised article

      Methods Section, Granger Causality Analysis

      “We computed beta and gamma band non-parametric Granger causality (Dhamala, Rangarajan, & Ding, 2008) between cortical ROIs and the STN in the hemisphere contralateral to movement for the post-event time windows (0 – 2 s with respect to start, reversal, and stop). Because estimates of Granger causality are often biased, we compared the original data to time-reversed data to suppress non-causal interactions. True directional influence is reflected by a higher causality measure in the original data than in its time-reversed version, resulting in a positive difference between the two, the opposite being the case for a signal that is “Granger-caused” by the other. Directionality is thus reflected by the sign of the estimate (Haufe, Nikulin, Müller, & Nolte, 2013). Because rmANCOVA results indicated no significant effects for predictability and movement type, and post-hoc tests did not detect significant differences between hemispheres, we averaged Granger causality estimates over movement types, hemispheres and predictability conditions in Figure 6-figure supplement 2.”

      Results, Granger causality

      “In general, cortex appeared to drive the STN in the beta band, regardless of the movement type and predictability condition. This was reflected in a main effect of ROI on Granger causality estimates (F<sub>ROI</sub>(7,9) = 3.443, p<sub>ROI</sub> = 0.044, η<sub>p</sub><sup2</sup> = 0.728; refer to Supplementary File 4 for the full results of the ANOVA). In the hemisphere contralateral to movement, follow-up t-tests revealed significantly higher Granger causality estimates from M1 to the STN (t = 3.609, one-sided p < 0.001, d = 0.807) and from MSMC to the STN (t = 2.051, one-sided p < 0.027, d = 0.459) than the other way around. The same picture emerged in the hemisphere ipsilateral to movement (M1 to STN: t = 3.082, one-sided p = 0.003, d = 0.689; MSMC to STN: t \= 1.833, one-sided p < 0.041, d = 0.410). In the gamma band, we did not detect a significant drive from one area to the other (F<sub>ROI</sub>(7,9) = 0.338, p<sub>ROI</sub> = 0.917, η<sub>p</sub><sup2</sup> = 0.208, Supplementary File 6). Figure 6-figure supplement 2 demonstrates the differences in Granger causality between original and time-reversed data for the beta and gamma band.”

      Discussion, The dynamics of STN-cortex coherence

      “Considering the timing of the increase observed here, the STN’s role in movement inhibition (Benis et al., 2014; Ray et al., 2012) and the fact that frontal and prefrontal cortical areas are believed to drive subthalamic beta activity via the hyperdirect pathway (Chen et al., 2020; Oswal et al., 2021) it seems plausible that the increase of beta coherence reflects feedback of sensorimotor cortex to the STN in the course of post-movement processing. In line with this idea, we observed a cortical drive of subthalamic activity in the beta band.”

      (4) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      Revised article

      Discussion, Limitations and Future Directions

      “Further, most of our participants were older than 60 years. To diminish any confounding effects of age on movement-related modulations of neural oscillations, such as beta suppression and rebound (Bardouille & Bailey, 2019; Espenhahn et al., 2019), we included age as a covariate in the statistical analyses.”

      (5) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS makes this an unfeasible option. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication for the study and dopamine has been found to normalize pathological alterations of beta activity. More importantly, the general pattern of movement-related beta and gamma oscillations has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants. Thus, it is not unlikely that the new aspects discovered here are also general features of motor processing.

      Revised article

      Discussion, Limitations and future directions

      “Furthermore, we cannot be sure to what extent the present study’s findings relate to PD pathology rather than general motor processing. We suggest that our approach at least approximates healthy brain functioning as patients were on their usual dopaminergic medication. Dopaminergic medication has been demonstrated to normalize power within the STN and globus pallidus internus, as well as STN-globus pallidus internus and STN-cortex coherence (Brown et al., 2001; Hirschmann et al., 2013). Additionally, several of our findings match observations made in other patient populations and healthy participants, who exhibit the same beta power dynamics at movement start and stop (Alegre et al., 2004) that we observed here. Notably, our finding of enhanced cortical involvement in face of uncertainty aligns well with established theories of cognitive processing, given the cortex' prominent role in managing higher cognitive functions (Altamura et al., 2010). Yet, transferring our approach and task to patients with different disorders, e.g. obsessive compulsive disorder, or examining young and healthy participants solely at the cortical level, could contribute to elucidating whether the synchronization dynamics reported here are indeed independent of PD and age.”

      Reviewer #3 (Recommendations for the authors):

      Despite the strengths of the "rhythm" account of cognitive processes, the paper could possibly be improved by making it less skewed to rhythms explaining all of the movement encoding.

      Thank you for this comment - the point is well taken. There is a large body of literature relating neural oscillations to spiking in larger neural populations, which itself is likely the most relevant signal with respect to motor control. In our eyes, it is this link that justifies the rhythm account, i.e. we agree with the reviewer that action potentials are the basis of movement encoding in the brain, not oscillations. Unfortunately, we cannot measure spiking with the method at hand.

      To better integrate this view into the current manuscript, we make the following suggestion for future research in the Limitations and future directions section (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      In Figure 5 - is the legend correct? Is it really just a 0.2% change in power only? That would be a very surprisingly small effect size.

      We thank the reviewer for noting this. Indeed, the numbers on the scale quantify relative change (post - pre)/pre and should be multiplied by 100 to obtain %-change. We have adjusted the color bars accordingly.

      The dissociation between the effects of unpredictable cues in coherence versus raw power is interesting and could potentially be directly contrasted further in the discussion (here they are presented separately with separate discussions, but this seems like a pretty important and novel finding as beta coherence and power usually go in the same direction).

      We appreciate the reviewer’s interest in our findings on the predictability of movement instructions. In case of coherence, the difference between pre- and post-event was generally more positive in the unpredictable condition, meaning that suppressions (negative pre-post difference) were diminished whereas increases (positive pre-post difference) were enhanced. With respect to power, we also observed less suppression in the unpredictable condition at movement start. Therefore, the direction of change is in fact the same. We made this clearer in the revised version by adapting the corresponding sections of the abstract, results and discussion (see below).

      The only instance of coherence and power diverging (on a qualitative level) was observed during reversals: here, we noted post-event increases in coherence and post-event decreases in M1 power in the group-average spectra. However, when comparing the pre- and post-event epochs statistically by means of permutation testing, the coherence increase did not reach significance. Hence, we did not highlight this aspect.

      Revised version

      Abstract

      “… Event-related increases of STN-cortex beta coherence were generally stronger in the unpredictable than in the predictable condition. … “

      Results, Effects of predictability on beta power  

      “With respect to the effect of predictability of movement instructions on beta power dynamics (research aim 2), we observed an interaction between movement type and condition (F<sub>cond*mov</sub> (2,14) = 4.206, p<sub>cond*mov</sub> = 0.037, η<sub>p</sub><sup2</sup> = 0.375), such that the beta power suppression at movement start was generally stronger in the predictable (M = -0.170, SD = 0.065) than in the unpredictable (M \= -0.154, SD = 0.070) condition across ROIs (t = -1.888, one-sided p \= 0.037, d = -0.422). We did not observe any modulation of gamma power by the predictability of movement instructions (F<sub>cond</sub> (1,15) = 0.792, p<sub>cond</sub> = 0.388, η<sub>p</sub><sup2</sup> = 0.050, Supplementary File 5).”

      Effects of predictability on STN-cortex coherence

      “With respect to the effect of predictability of movement instructions on beta coherence (research aim 2), we found that the pre-post event differences were generally more positive in the unpredictable condition (main effect of predictability condition; F<sub>cond</sub>(1,15) = 8.684, p<sub>cond</sub> = 0.010, η<sub>p</sub><sup2</sup> = 0.367; Supplementary File 3), meaning that the suppression following movement start was diminished and the increases following stop and reversal were enhanced in the unpredictable condition (Fig. 6a). This effect was most pronounced in the MSMC (Fig. 6b). When comparing regionaverage TFRs between the unpredictable and the predictable condition, we observed a significant difference only for stopping (t<sub>clustersum</sub> = 142.8, p = 0.023), suggesting that the predictability effect was mostly carried by increased beta coherence following stops. When repeating the rmANCOVA for preevent coherence, we did not observe an effect of predictability (F<sub>cond</sub>(1,15) = 0.163, p<sub>cond</sub> = 0.692, η<sub>p</sub><sup2</sup> = 0.011), i.e. the effect was most likely not due to a shift of baseline levels. The increased tendency for upward modulations and decreased tendency for downward modulations rather suggests that the inability to predict the next cue prompted intensified event-related interaction between STN and cortex. STN-cortex gamma coherence was not modulated by predictability (F<sub>cond</sub>(1,15) = 0.005, p<sub>cond</sub> = 0.944, η<sub>p</sub><sup2</sup> = 0.000, Supplementary File 5).”

      Discussion, Beta coherence and beta power are modulated by predictability

      “In the present paradigm, patients were presented with cues that were either temporally predictable or unpredictable. We found that unpredictable movement prompts were associated with stronger upward modulations and weaker downward modulations of STN-cortex beta coherence, likely reflecting the patients adopting a more cautious approach, paying greater attention to instructive cues. Enhanced STN-cortex interactions might thus indicate the recruitment of additional neural resources, which might have allowed patients to maintain the same movement speed in both conditions. […]”

      With respect to power, we observed reduced beta suppression in the unpredictable condition at movement start, consistent with the effect on coherence, likely demonstrating a lower level of motor preparation.

      Given that you have a nice continuous data task here - the turning of the wheel, it might be interesting to cross-correlate the circular position (and separately - velocity) of the turning with the envelope of the beta signal. This would be a nice finding if you could also show that the beta is modulated continuously by the continuous movements. In the natural world, we rarely do a continuous movement with a sudden reversal, or stop, most of the time we are in continuous movement. Look at this might also be a strength of your dataset.

      We could not agree more. In fact, having a continuous behavioral output was a major motivation for choosing this particular task. We are very interested in state space models such as preferential subspace identification (Sani et al., 2021), for example. These models relate continuous brain signals to continuous behavioral target variables and should be of great help for questions such as: do oscillations relate to moment-by-moment adaptations of continuous movement? Which frequency bands and brain areas are important? Is angular position encoded by different brain areas/frequency bands than angular speed? These analyses are in fact ongoing. This project, however, is too large to fit into the current article.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study is an important follow-up to their prior work - Wong et al. (2019), starting with clear questions and hypotheses, followed by a series of thoughtful and organized experiments. The method and results are convincing. Experiment 1 demonstrated the sensory preconditioned fear with few (8) or many (32) sound-light pairings. Experiments 2A and 2B showed the role of PRh NMDA receptors during conditioning for online integration, revealing that this contribution is present only after a few sound-light pairings, not after many sound-light pairings. Experiments 3A and 3B showed the contribution of PRh-BLA communication to online integration, again only after a few but not after many. Contrary to Experiments 3A and 3B, Experiments 4A and 4B showed the contribution of PRh-BLA communication to integration at test only after many but not few sound-light pairings.

      Strengths:

      Throughout the manuscript, the methods and results are clearly organized and described, and the use of statistics is solid, all contributing to the overall clarity of the research. The discussion section was also well-written, effectively comparing the current research with the prior work and offering insightful interpretations and potential future directions for this line of research. I have only a limited amount of concerns about some results and some details of experiments/statistics.

      We thank the reviewer for their positive assessment.

      Weaknesses:

      Could you provide further interpretation regarding line 171: the observation that sensory preconditioned fear increased with the number of sound-light pairings? Was this increase due to better sound-light association learning during Stage 1? Additionally, were there any experimental differences between Experiment 1 and the other experiments that might explain why freezing was higher in the P32 group compared to the P8 group? This pattern seemed to be absent in the other experiments. If we consider the hypothesis that the online integration mechanism is more active with fewer pairings and the chaining mechanism at the test is more prominent with many pairings, we wouldn't expect a difference between the P8 and P32 groups. Given the relatively small sample size in Experiment 1, the authors might consider conducting a cross-experiment analysis or something similar to investigate this further.

      We appreciate the reviewer’s point and thank them for the question. The heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment (Group P32) may reflect the combined effects of both mediated learning and chaining at test. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated in the subsequent experiments: i.e., the levels of freezing to the sensory preconditioned stimulus at test were almost identical among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B; and this was also true in Experiments 3A and 3B, and again in Experiments 4A and 4B. A key difference between the initial and subsequent experiments is that, in contrast to the initial experiment, rats in subsequent experiments underwent surgery for one reason or another (implantation of cannulas, lesion of the perirhinal cortex). The implication is that surgical interventions in the perirhinal cortex and/or basolateral amygdala might affect the way that rats integrate the sound-light and light-shock associations in sensory preconditioning: i.e., they may force rats to rely on one type of integration strategy or the other. This is, of course, purely speculative – it will be addressed in future research.

      Reviewer #2 (Public review):

      This manuscript builds on the authors' earlier work, most recently Wong et al. 2019, in which they showed the importance of the perirhinal cortex (PRh) during the first-order conditioning stage of sensory preconditioning. Sensory preconditioning requires learning between two neutral stimuli (S2-S1) and subsequent development of a conditioned response to one of the neutral stimuli after pairing of the other stimulus with a motivationally relevant unconditioned stimulus (S1-US). One highly debated question regarding the mechanisms of learning of sensory preconditioning has been whether conditioned responses evoked by the indirectly trained stimulus (S2) occur through a mediated representation at the time of the first-order US training, or whether the conditioned responses develop through a chained evoked representation (S2--> S1 --> US) at the time of test. The authors' prior findings provided strong evidence for PRh being involved in mediated learning during the first-order training. They showed that protein synthesis was required during the first-order S1-US learning to support the conditioned response to the indirectly trained stimulus (S2) at the test.

      One question remaining following the previous paper was whether certain conditions may promote a chaining mechanism over mediated learning, as there is some evidence for chained representations at the time of the test. In this paper, the authors directly address this important question and find unambiguous results that the extent of training during the preconditioning stage impacts the involvement of PRh during the first-order conditioning or stage 2. They show that putative blockade of synaptic changes in PRh, using an NMDA antagonist, disrupts responding to the preconditioned cue at test during shorter duration preconditioning training (8 trials), but not during extended training (32 trials). They also show that this is the case for communication between the PRh and BLA during the same stage of training using a contralateral inactivation approach. This confirms their previous findings in 2019 of connectivity between these regions for the short-duration training, while they observe here for the first time that this is not the case for extended training. Finally, they show that with extended training, communication between BLA and the PRh is required at the final test of the preconditioned stimulus, but not for the short duration training.

      The results are clear and extremely consistent across experiments within this paper as well as with earlier work. The experiments here are thorough, and well-conceived, and address an important and highly debated question in the field regarding the neural and psychological mechanisms underlying sensory preconditioning. This work is highly impactful for the field as the debate over mediated versus chaining mechanisms has been an important topic for more than 70 years.

      We thank the reviewer for their kind assessment.

      Reviewer #3 (Public review):

      The authors tested whether the number of stimulus-stimulus pairings alters whether preconditioned fear depends on online integration during the formation of the stimulus-outcome memory or during the probe test/mobilization phase, when the original stimulus, which was never paired with aversive events, elicits fear via chaining of stimulus-stimulus and stimulus-outcome memories. They found that sensory preconditioning was successful with either 8 or 32 stimulus-stimulus pairings. Perirhinal cortex NMDA receptor blockade during stimulus-outcome learning impaired preconditioning following 8 but not 32 pairings during preconditioning. Therefore, perirhinal cortex NMDA activity is required for online integration or mediated learning. Perirhinal-basolateral amygdala had nearly identical effects with the same interpretation: these areas communicate during stimulus-outcome learning, and this online communication is required for later expressing preconditioned fear. Disconnection prior to the probe test, when chaining might occur, had different effects: it impaired the expression of preconditioned fear in rats that received 32, but not 8, pairings during preconditioning. The study has several strengths and provides a thoughtful discussion of future experiments. The study is highly impactful and significant; the authors were successful in describing the behavioral and neurobiological mechanisms of mediated learning versus chaining in sensory preconditioning, which is often debated in the learning field. Therefore this study will have a significant impact on the behavioral neurobiology and learning fields.

      Strengths:

      Careful, rigorous experimental design and statistics.

      The discussion leaves open questions that are very much worth exploring. For example - why did perirhinal-amygdala disconnection prior to the probe have no effect in the 8-pairing group, when bilateral perirhinal inactivation did (in Wong et al, 2019)? The authors propose that perirhinal cortex outputs bypass the amygdala during the probe test, which is an excellent hypothesis to test.

      The authors provide evidence that both mediated learning and chaining occur.

      Thank you for the positive assessment – we fully intend to identify the circuitry that regulates retrieval/expression of sensory preconditioned fear when it is based on mediated learning in stage 2.

      Weaknesses:

      This is inherent to all neural interference and behavioral experiments: biological/psychological functions do not typically operate binarily. There is no single clear number or parameter at which mediated learning or chaining happens, and both probably happen to some extent. Addressing this is even more difficult given behavioral variability across subjects, implant sites, etc. Thus, this is not so much a weakness particular to this study as much as an existential problem, which the authors were able to work around with careful experimental design and appropriate controls.

      We completely agree with the point raised here and thank the reviewer for their assessment.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) It appears that the method description for Sensory Preconditioning was copied from their previous Wong et al. (2019) paper, which is fine, but in the current research, the authors use 8 or 32 presentations, which is not reflected in the description.

      Thank you for bringing this to our attention. This is now addressed in the method section on page 27 (beginning at line 655):

      “Rats received either eight presentations of the sound and eight of the light in a single session, or 32 presentations of the sound and 32 of the light across four daily sessions. On Day 3, all rats received eight presentations of the sound and eight of the light. Each presentation of the sound was 30 s in duration and each presentation of the light was 10 s in duration. The first stimulus presentation occurred five min after rats were placed into the chambers. The offset of one stimulus co-occurred with the onset of the other stimulus for groups that received paired presentations of the sound and the light, while these stimuli were presented separately for groups that received explicitly unpaired presentations. The interval between each paired presentation was five min while the interval between each separately presented stimulus was 150 s. After the last stimulus presentation, rats remained in the chambers for an additional one min. They were then returned to their home cages. This training was repeated on Days 4-6 for rats that received 32 presentations of the sound and 32 of the light. All rats proceeded to first-order conditioning (details below) the day after their final session of sound and light exposures, which was Day 4 for rats exposed to eight presentations of the sound and light and Day 7 for rats exposed to 32 presentations of the sound and light.”

      (2) Line 148: Could the authors clarify how the "significant linear increase" was assessed? From similar descriptions in later experiments, it seems it was based on a comparison of freezing across the four presentations, but the F(1,26) statistic suggests there seemed to be a half-split test. The same questions exist in all the experiments. Please clarify.

      Conditioning data were analysed using contrasts with repeated measures in ANOVA. The repeated measures (or within-subject) factor was “trial” as all rats were exposed to four light-shock pairings in this stage of training. We examined whether there was a significant linear increase in freezing across trials using a standard within-subject contrast. The specific coefficients for this contrast, given the four trials, were -3, -1, 1, and 3. The reason that the degrees of freedom remain 1 and 26 in this analysis is because the within-subject contrast is part of a set of planned orthogonal contrasts. That is, in any planned analysis of the sort conducted here, the df1 will always be 1, indicating the very nature of the analysis. There was no splitting of the data, or comparisons between the split halves.

      (3) Line 154: Could the authors clarify what is meant by "other main effects and their interactions"? It is not clearly inferable from the context.

      Apologies for the confusion here. “Other main effects” refer to the two between-subject factors in isolation: i.e., the overall comparison of freezing to the light (averaged across the four trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1 à main effect 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2 à main effect 2). “Their interaction” refers to the assessment of whether the overall difference in freezing to the light (averaged across the four trials) between Groups P8 and U8 differs from the overall difference in freezing to the light (averaged across the four trials) between Groups P32 and U32. We have edited the text near line 153 to indicate that:

      “The overall comparisons of freezing to the light (averaged across the four conditioning trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2), were not significant (Fs < .45, p > .508). The interaction between these two between-subject factors was also not significant (F < .45, p > .508).”

      (4) The use of sound and light as preconditioned and conditioned cues are counterbalanced. Was there any difference in the increase of freezing during conditioning depending on the type of conditioned cues? Was there any difference in the preconditioned fear? While it is hard to assess statistical significance due to the sample size limit, even observing a trend could be interesting.

      We examined whether the levels of freezing to the conditioned and preconditioned stimuli depend on their physical identity. In general, there was a slight trend towards more freezing to the preconditioned stimulus when it was a tone, and less freezing to the conditioned stimulus when it was a tone. These are, however, simply indications. None of the statistical comparisons between rats for which the preconditioned stimulus was the tone (and, thereby, conditioned stimulus was the light) and rats for which the preconditioned stimulus was the light (and, thereby, conditioned stimulus was the tone) reached the conventional level of significance.

      (5) General suggestion on reporting non-significant statistics: the authors reported a small F statistic value a few times to suggest non-significance. But without clearly specifying degrees of freedom, it is hard to get a sense of statistical significance (e.g. Line 227, largest F<3.10). I recommend adding p values alongside the F statistics and reporting exact statistics whenever possible.

      Apologies for the omission. The p values have now been included alongside all non-significant F statistics.

      (6) Another general suggestion is to use non-parametric statistical testing with such small sample sizes. I recommend using the Kruskal-Wallis H test (the non-parametric equivalent of F-statistic) to replace the ANOVA result. Also, given many tests only involve comparing two independent groups, using Mann-Whitney U test (the non-parametric equivalent of independent t-test) would be sufficient.

      We understand that small sample sizes can occasionally lead to unequal variances between groups, which necessitates the use of non-parametric statistics. However, as non-parametric statistics raise a different set of issues for data analysis (e.g., power) and interpretation, our general view for the type of data collected in this study is that parametric analyses are appropriate and should be retained (particularly in the absence of unequal variances between groups). We hold this view for two reasons. First, the hypotheses tested in the present series were derived from past work in which parametric analyses revealed meaningful patterns of results at the same level of statistical power. Second, the application of these analyses then yielded results consistent with our hypotheses: for the most part, we observed between-group differences where we expected there to be such differences and did not observe between-group differences where we did not expect there to be such differences. As such, we have not switched from a parametric to non-parametric analysis strategy. We do, however, appreciate the suggestion and will apply a non-parametric approach where it is warranted in our future work.

      Reviewer #2 (Recommendations for the authors):

      I have a few very minor comments for the authors regarding the discussion and interpretation of the very nice experimental results.

      (1) In Figures 4 and 5, the authors provide a schematic of the experiment. It's very clearly indicated whether the BLA inactivation is ipsi- or contralateral, but the unilateral PRh lesion isn't mentioned. I'd recommend including that here so that someone reading through the figures can more easily understand the experiment. The hypothesis is clear and the experiment is so well designed that a read through of the figures can relay most information to an experienced reader.

      Thank you for this suggestion – we have included information about the unilateral PRh lesion in the schematic for Figures 4 and 5.

      (2) The authors have an extended description of backward conditioning in the discussion. It seems like the authors are suggesting this as an important future direction, but they never explicitly say this, resulting in a bit of confusion as to what this section refers to. Also, Ward-Robinson and Hall 1996 showed backward sensory preconditioning using a serial auditory-visual association and argued for a mediated solution based on their results. It may be worth citing that paper here.

      Apologies for the lack of clarity. We have revised this point in the discussion (page 18, beginning line 434) and referenced Ward-Robinson and Hall (1996):

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993). This order hypothesis is consistent with evidence showing that the way in which animals represent an audio-visual compound changes across repeated compound exposures (e.g., Bellingham & Gillette, 1981; Holmes & Harris, 2009). It can be tested using a so-called “backward” sensory preconditioning protocol, which reverses the order of stimulus presentations in stage 1 (e.g., Ward-Robinson & Hall, 1996). That is, rather than rats being exposed to the “forward” sound-light pairings used here and by Wong et al. (2019), rats in a backward protocol are exposed to light-sound pairings. Increasing the number of light-sound pairings in this protocol should result in rats learning that the light is followed by the sound (light→sound) and that the sound is followed by nothing (sound→nothing). Hence, during the session of light-shock pairings in stage 2, the light should continue to activate the memory of the sound, resulting in formation of the mediated sound-shock association (e.g., Ward-Robinson & Hall, 1996). That is, if our order hypothesis is correct, increasing the number of light-sound pairings in the backward protocol should preserve the likelihood of mediated learning in stage 2 and, if anything, diminish the likelihood of chaining at test in stage 3 (as the sound is never followed by a light). Hence, PRh manipulations that fail to affect fear of the sound when administered after many sound-light pairings (e.g., infusion of DAP5) should disrupt that fear when administered after many light-sound pairings in the backward protocol. This will be assessed in future work.”

      (3) Line 467 in the discussion suggests that the results are surprising that PRh-BLA communication is not needed at test when learning putatively occurs through a mediated mechanism during first-order conditioning. I was a bit surprised by this comment since I was under the assumption that only BLA was required at this point after consolidation of the mediated learning. Holmes et al., 2013 showed that BLA is required for extinction to S2 after first-order conditioning. In that experiment they inactivated BLA during S2- presentations (typically considered the extinction test), and showed that reduction to S2 did not occur the subsequent day, indicating the memory was stored in BLA and may not necessarily require PRh-BLA communication.

      The result noted here was somewhat surprising as our past studies showed that silencing activity in the PRh prior to testing attenuates freezing to a sensory preconditioned stimulus (i.e., an S2). We took this to mean that the PRh is necessary for retrieval/expression of fear to S2 and supposed that this retrieval/expression would be achieved through communication between the PRh and BLA. However, the results of the PRh-BLA disconnection at test show that this communication is not required, leaving us to speculate that retrieval/expression of fear to S2 may be achieved through communication between the PRh and CeA.

      We have edited the opening of the relevant paragraph to clarify why the result noted here was surprising (page 20, beginning line 485):

      “While the PRh and BLA clearly communicate to support mediated learning about the sound, this communication is not required for retrieval/expression of the mediated sound-shock association at the time of testing. This result is somewhat surprising as activity in the PRh is needed for expression of fear to the sound (Holmes et al., 2013; Wong et al., 2019) and raises the question: how does the PRh-dependent sound-shock association come to be expressed in fear responses?”

      (4) The authors reference Holland 1981 and 1998, yet there's not much discussion of these findings. I think there should be a bit more emphasis on these studies since they show how mediated learning greatly depends on the extent of training. Also, it may be worth considering Holland's theory of why mediated conditioning is more effective with shorter training. His theory may be consistent with the authors, but I believe he suggests that early in training a stronger mediated representation is evoked which tends to dissipate with time. I think this is a valid hypothesis to consider in this paper.

      The Holland papers show that rats form mediated associations (Holland, 1981) and that the likelihood of them doing so decreases with the amount of training (Holland, 1998). These findings are paralleled by those reported in the present series of experiments. However, the protocols used by Holland were very different to those used in the present study; and the explanation for his 1998 findings (which is the more relevant of the two papers) simply does not apply to the case of sensory preconditioning.

      To be clear: Holland (1998) exposed rats to either “few” or “many” tone-food pairings in stage 1, tone-lithium chloride pairings in stage 2 and, finally, tested rats with the food alone in stage 3. He predicted and showed that those exposed to few tone-food pairings showed an aversion to the food at test (i.e., they consumed less of the food than controls) whereas those exposed to many tone-food pairings showed no such aversion (i.e., they consumed the same amount of food as the controls). This was taken to mean that, across the series of tone-lithium pairings, the tone activated the memory of food among rats in the few condition, resulting in a mediated food-lithium association; but failed to do so among rats in the many condition, resulting in no food-lithium association. According to Holland, the tone failed to activate the memory of food in the many condition because, by the end of training in stage 1, it was not needed for them to know what to do when the tone was presented: they simply had to run to the magazine to collect the food when delivered. That is, the tone eventually associated with the responses that rats emitted in the training situation, thereby obviating any need for activation of the food memory.

      While this explanation is both elegant and interesting, it cannot be applied to the results obtained in the present study where the initial stage of training involved few or many sound-light pairings. That is, unlike in the Holland study where rats in the many condition eventually learned a stimulus-“run to magazine” association that maintained performance in the absence of any mental image of food, in the present study, any stimulus-response association acquired in stage 1 (e.g., orienting responses towards the sources of the auditory and visual stimuli) cannot have contributed to the expression of sensory preconditioned fear at test. Hence, stimulus-response learning in the many condition cannot be invoked to explain the pattern of results in the present study, even if it adequately explains what-appears-to-be a similar finding in the Holland study.

      Nonetheless, we have included a reference to the general style of explanation that was considered and rejected by Holland in his 1998 and 2005 papers. This appears on page 18 (beginning line 434) and reads:

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      (5) There is also a Holland 2005 paper in which he tests whether extended training of the initial stimulus associations may result in a reduced associability of those stimuli. This would potentially result in lower mediated learning due to a decreased associability of the mediated representation, thereby explaining why extended training reductions in mediated learning occur. Using a probabilistic design, Holland shows that this reduction in mediated learning is likely not due to a change in associability.

      We appreciate the note re Holland (2005) and have included a reference to it in our General Discussion. We agree with Holland that the reduction in mediated learning across extended training is not due to reduced associability of the retrieved stimulus representation. If this were the case, it would remain to explain why stimulus representations continue to be activated at test, which must occur for successful chaining of the sound-light and light-shock associations upon presentations of the sound alone. This is included in the modified text on page 18 (beginning line 434), which is part of our response to point 4.

      Reviewer #3 (Recommendations for the authors):

      (1) I think the 4th intro paragraph is essentially saying that more pairings during preconditioning encourage chaining as opposed to mediated learning - I might recommend clarifying this a bit. It took me a while to put it together.

      Apologies for the confusion. We have clarified the argument at this point in the Introduction with the following insertion on page 4 (beginning line 84):

      “That is, increasing the number of sound-light pairings may allow rats to encode information about stimulus order in stage 1 and, thereby, shift the locus of integration from mediated conditioning in stage 2 to chaining at test in stage 3 (Holmes et al., 2022).”

      (2) In analyzing test data I am assuming percent freezing is the average of the entire 30s or 10s CS period - could this be clarified?

      This is correct and has been clarified in the section for ‘Scoring and Statistics’ on page 29 (beginning line 708):

      “Freezing data were collected using a time-sampling procedure in which each rat was scored as either ‘freezing’ or ‘not freezing’ every two seconds by an observer blind to the rat’s group allocation. A percentage score was then calculated by dividing the number of samples scored as freezing by the total number of samples. The baseline level of freezing was established by scoring the first two min at the start of each experimental session: i.e., we divided the total number of samples scored as freezing by the total number of observed samples, which was 60. The levels of freezing to the 10 s conditioned stimulus and 30 s preconditioned stimulus were established in a similar manner: we scored the entire period of each stimulus presentation and divided the number of samples scored as freezing by the total number of observed samples, which was 5 for each presentation of the conditioned stimulus and 15 for each presentation of the preconditioned stimulus.”

      (3) Complementary to the above - during the probe test is there a difference during the first/last 2s of the CS? This would be interesting with respect to understanding the associative structure encoded.

      We have previously examined whether freezing responses change across the duration of a 30 s preconditioned stimulus and a 10 s conditioned stimulus. We have never seen any such changes: in our past work and in the present series of experiments, the expression of freezing is largely uniform across each presentation of a preconditioned or conditioned stimulus.

      (4) It is sort of unclear to me why more CS-CS pairings produced stronger preconditioned fear - is it that both mediated learning and chaining occur and giving 32 pairings permits both processes more than 8 pairings?

      This is a very reasonable explanation for the heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated across subsequent experiments in the series: i.e., the levels of freezing to the sensory preconditioned stimulus at test were largely the same among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B, and again in Experiments 3A and 3B as well as Experiments 4A and 4B.

      (5) I would suggest individual data points overlaid on the bars, violin plots, or box and whisker plots to provide a better visualization of the data.

      We appreciate the suggestion – these have been included overlaid on bars in each histogram_._

      (6) There are other citations that would strengthen arguments for the idea that unidirectional/temporal associative structure can be acquired during (appetitive) sensory preconditioning: Leising 2007 Learning and Behavior, Hart 2022 Current Biology, for example.

      Thank you for these citations. We have included references to the Leising et al (2007) and Hart et al (2022) papers in our discussion on page 18-19 (beginning line 442):

      “Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      Editor's note:

      We agree with the suggestions about full statistical reporting for non-significant results and about putting individual data points, perhaps coded to identify sex, on top of the bar graphs. Both will increase the transparency of the rigor of the work for readers.

      We thank the editors and authors for their suggestions. We have included full statistical reporting for non-significant results and overlaid individual data points on the bars in each histogram.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Corso-Diaz et al, focus on the NRL transcription factor (TF), which is critical for retinal rod photoreceptor development and function. The authors profile NRL's protein interactome, revealing several RNA-binding proteins (RBPs) among its components. Notably, many of these RBPs are associated with R-loop biology, including DHX9 helicase, which is the primary focus of this study. R-loops are three-stranded nucleic acid structures that frequently form during transcription. The authors demonstrate that R-loop levels increase during photoreceptor maturation and establish an interaction between NRL TF and DHX9 helicase. The association between NRL and RBPs like DHX9 suggests a cooperative regulation of gene expression in a cell-type-specific manner, an intriguing discovery relevant to photoreceptor health. Since DHX9 is a key regulator of R-loop homeostasis, the study proposes a potential mechanism where a cell-type-specific TF controls the expression of certain genes by modulating R-loop homeostasis. This study also presents the first data on R-loop mapping in mammalian retinas and shows the enrichment of R-loops over intergenic regions as well as genes encoding neuronal function factors. While the research topic is very important, there is some concern regarding the data presented: there are substantial data supporting the interaction between NRL and DHX9, including pull-down experiments and proximity labeling assay (PLA), however, the data showing an interaction between NRL and DDX5, another R-loop-associated helicase, are inadequate. Importantly, the data supporting the claim that NRL interacts with R-loops are absolutely insufficient and at best, correlative. The next concerns are regarding the R-loop mapping data analysis and visualization.

      Strengths:

      There is compelling evidence that the NRL transcription factor interacts with several RNA binding proteins, and specifically, sufficient data supporting the interaction of NRL with DHX9 helicase.

      A major strength is the use of the single-stranded R-loop mapping method in the mouse retina.

      Weaknesses:

      (1) Figure S1A: There is a strong band in GST-IP (control IP) for either HNRNPUI1 or HNRNPU, although the authors state in their results that there is a strong interaction of these two RBPs with NRL.

      Under our experimental conditions, most RNA-binding proteins displayed higher binding to glutathione beads (Fig. S1A). However, GST-NRL purifications showed much stronger signals for respective RBPs. In the case of HNRNPU and HNRNPUl1, white bands that are indicative of substrate depletion due to higher protein levels are observed in GST-NRL lanes. Additionally, in Figures 1B and 1C, there is a clear enrichment of HNRNPU and HNRNPUl1 above the background signal. We added this to the text. See page 5.

      Both DHX9 and DDX5 samples have a faint band in the GST-IP.

      RNA-binding proteins may display some background as observed in other studies (e.g. PMID: 32704541). We think that showing the raw data without decreasing the exposure time is useful and that there is a clear enrichment compared to controls.  In addition, we tested the interaction in multiple systems.

      There is an extremely faint band for HNRNPA2B1 in the GST-NRL IP lane. Given this is a pull-down with added benzonase treatment to remove all nucleic acids, these data suggest, that previously observed NRL interactions with these particular RBPs are mediated via nucleic acids. Similarly, there is a loss of band signal for HNRNM in this assay, although it was identified as an NRL-interacting protein in three assays, which again suggests that nucleic acids mediate the interaction.

      Thank you for highlighting this point. We mention in the manuscript that the interaction between HNRNPM and A1 depends on nucleic acids, as noted by the reviewer, since there is no obvious band after the pull-down. We have now added that the interaction of NRL with HNRNPA1B1 is likely dependent on nucleic acids as well, given its weak signal. See page 5.

      (2) The data supporting NRL-DDX5 interaction in rod photoreceptor nuclei is very weak. In Figure 2D, the PLA signal for DDX5-NRL is very weak in the adult mouse retina and is absent in the human retina, as shown in Figure 2H.

      We agree with the reviewer. We think that the signal for DDX5 is weak, and we addressed this in the text. We noted on page 7: “Taken together, these findings suggest a strong interaction between NRL and DHX9 throughout the nuclear compartment in the retina and that a transient and/or more regulated interaction of NRL with DDX5 may require additional protein partners.”  We have modified this sentence to add that the data also suggest transient interaction or the requirement of additional protein partners for stable interaction. See page 7.

      Given that there is no NRL-KO available for the human PLA assay, the control experiments using single-protein antibodies should be included in the assay. Similarly, the single-protein antibody control PLA experiments should be included in the experimental data presented in Figure 2J.

      Thank you for the suggestion. We performed PLAs using both DHX9 and IgG in the human retina and observed no specific amplification signal. Some background is observed outside the nucleus and in the extracellular space. We added these results to the text and to the supplementary information. See page 7 and Fig.S2B.

      (3) The EMSA experiment using a probe containing NRL binding motif within the DHX9 promoter should include incubation with retina nuclear extracts depleted for NRL as a control.

      In EMSA experiments, we used bovine retina to obtain enough protein quantities. As suggested by the reviewer, using NRL depleted extract would increase the specificity of observed gel shift and complement our pre-immune serum as a negative control. However, removal of all the NRL protein using the antibodies available was not feasible. In the future, we will use enough mice to obtain large quantities of protein for this experiment and will collect retinas from Nrl knockout as negative control.

      (4) There is a reduced amount of DHX9 pulled down in NRL-IP in HEK293 cells, but there is no statistically significant difference in the reciprocal IP (DHX9-IP and blotting for NRL) (Figure 4C).

      We believe the reviewer is referring to the data in Figure 4C showing that RNase H treatment led to significantly reduced pulldown of DHX9 as compared to control, but the reciprocal IP in Figure 4D showed no statistical significance between control and RNase H treatment. In Figure 4D, we hypothesize that NRL may account for only a small proportion of DHX9’s interactome, so the change in NRL levels could not be detected due to the sensitivity of our assay. DHX9 likely constitutes a large proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious when pulling down with NRL. We added this information to the results. See page 8.

      (5) The only data supporting the claim that NRL interacts with R-loops are presented in Figure 5A.

      Additional evidence that NRL interacts with R-loops comes from DRIP-Seq experiments where signals from R-loops overlap with NRL ChIP-Seq signals (Figure 7A). This shows that R-loops and NRL co-occur on multiple genomic regions. In addition, indirect evidence of NRL and R-loops’ interaction is shown in pull down experiments and PLA assays where R-loops influence DHX9 and NRL binding. We clarified this in the discussion. See page 14.

      This is a co-IP of R-loops and then blotting for NRL, DHX9, and DDX5. Here, there is no signal for DDX5, quantification of DHX9 signal shows no statistically significant difference between RNase H treated and untreated samples, while NRL shows a signal in RNase H treated sample. These data are not sufficient to make the statement regarding the interaction of NRL with R-loops.

      Thank you for this comment. We respectfully disagree as we observe statistically significant enrichment for both NRL and DHX9 in these experiments (See Fig5A). Some NRL continues to bind to DNA that is pulled down nonspecifically, which may be expected since NRL is a transcription factor. See for example R-loop binding by the transcription factor Sox2 (PMID: 32704541). However, binding to R-loops is evidenced by an enrichment compared to RNase H-treated sample. We clarified this in Results section (See page 9).

      (6) Regarding R-loop mapping, the data analysis is quite confusing. The authors perform two different types of analyses: either overall narrow and broad peak analysis or strand-specific analysis. Given that the authors used ssDRIP-seq, which is a method designed to map R-loops strand specifically, it is confusing to perform different types of analyses.

      Thank you for highlighting this point. This has enhanced the clarity of the methods and enriched the discussion. We aimed to identify R-loops as accurately as possible. We conducted two types of analyses to capture different aspects of R-loops: one that looks at overall patterns (narrow and broad peaks) and another that focuses on specific strands of DNA.

      Using ssDRIP-seq, which is designed to map R-loops on specific strands, allowed us to examine R-loops formed in only one strand and those formed on both strands. To identify strand-specific R-loops, we filtered our RNase-H enriched peaks for those enriched on one strand compared to the opposite strand. We clarified the analysis in the results section, and Figure 6B. See page 10 and methods section page 25.

      Next, the peak analysis is usually performed based on the RNase H treated R-loop mapping; what does it mean then to have a pool of "Not R-loops", see Figure 6B?

      The “Not R-loop” group refers to peaks called using the opposite strand that are not observed when calling peaks using RNase H as control. We modified this figure for clarity (Figure 6B).

      In that regard, what does the term "unstranded" R-loops mean? Based on the authors' definition, these are R-loops that do not fall within the group of strand-specific R-loops. The authors should explain the reasons behind these types of analyses and explain, what the biological relevance of these different types of R-loops is.

      Thank you for helping us clarify this point. Unstranded R-loops are DNA regions containing DNA:RNA hybrids on both plus and minus strands and possibly representing bidirectional transcription by Pol II. We observed that unstranded R-loops are enriched only in intergenic regions, H3K9me3 regions, and downstream of the transcriptional termination site (TTS). We added to the discussion the possible implications of these enrichments, including regulation of Pol II termination and transcription of long genes.  See Page 13.

      (7) It would be more useful to show the percent distribution of R-loops over the different genomic regions, instead of showing p-value enrichment, see Figure 6C.

      Since most of the genome is non-coding, plotting the distribution as a proportion was not informative since the vast proportion of the data falls in intergenic regions. However, we created a new figure showing observed vs. expected ratio that seems to be more informative and moved the current p-value figure to the supplement in revised version. See Figure 6C and S6D.

      (8) Based on the model presented, NRL regulates R-loop biology via interaction with RBPs, such as DHX9, a known R-loop resolution helicase. Given that the gene targets of NRL TF are known, it would be useful to then analyze the R-loop mapping data across this gene set.

      Thank you for this suggestion. We performed an analysis of R-loops on NRL-regulated genes. Interestingly, NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B). We added a table containing all NRL-regulated genes we used for this analysis (table S5) and a figure showing this result (Fig. S7B).

      Reviewer #2 (Public review):

      Summary:

      The authors utilize biochemical approaches to determine and validate NRL protein-protein interactions to further understand the mechanisms by which the NRL transcription factor controls rod photoreceptor gene regulatory networks. Observations that NRL displays numerous protein-protein interactions with RNA-binding proteins, many of which are involved in R-loop biology, led the authors to investigate the role of RNA and R-loops in mediating protein-protein interactions and profile the co-localization of R-loops with NRL genomic occupancy.

      Strengths:

      Overall, the manuscript is very well written, providing succinct explanations of the observed results and potential implications. Additionally, the authors use multiple orthogonal techniques and tissue samples to reproduce and validate that NRL interacts with DHX9 and DDX5. Experiments also utilize specific assays to understand the influence of RNA and R-loops on protein-protein interactions. The authors also use state-of-the-art techniques to profile R-loop localization within the retina and integrate multiple previously established datasets to correlate R-loop presence with transcription factor binding and chromatin marks in an attempt to understand the significance of R-loops in the retina.

      Weaknesses:

      In general, the authors provide superficial interpretations of the data that fit a narrative but fail to provide alternative explanations or address caveats of the results. Specifically, many bands are present in interaction studies either in control lanes (GST controls) of Westerns or large amounts of background in PLA experiments.

      We have added additional information to the text regarding the presence of background signals in pull downs. We wish to note that experimental samples always exceeded background signals.  We believe that reporting these raw findings (rather than showing shorter exposures) is valuable for the scientific community. We did not observe any background in the proximity ligation assay (PLA) that exceeded what is typically expected, and the signals were clearly discernible. Cases where signals are weaker, such as with DDX5, have been highlighted. In addition, we added a DHX9-IgG negative control for the human PLA experiment. See page 5 and Fig. S2B.

      Additionally, the lack of experiments testing the functional significance of Nrl interactions or R-loops within the developing retina fails to provide novel biological insights into the regulation of gene regulatory networks other than, 'This could be a potentially important new mechanism'.

      We agree that functional experiments are necessary to understand the molecular mechanisms behind R-loop regulation in the retina; however, we believe it goes beyond the scope of this initial characterization (as this is the first report on R-loops in the retina). We are currently pursuing these studies.

      We performed new analysis on NRL-regulated genes as suggested by reviewer 1. We show that NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B), providing further evidence of the functional  interaction between NRL and R-loops. See table S5 and Fig. S7B, and discussion.

      Additionally, the authors test the necessity of RNA for NRL/DHX9 interactions but don't show RNA binding of NRL or DHX9 or the sufficiency of RNA to interfere/mediate protein-protein interactions. Recent work has highlighted the prevalence of RNA binding by transcription factors through Arginine Rich Motifs that are located near the DNA binding domains of transcription factors.

      We agree that the role of RNA in these complexes is very exciting, and we are currently pursuing these studies. However, we believe that they fall outside the scope of this initial report on R-loops in the retina.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      There are a couple of minor comments:

      (1) Unfinished sentence; page 11, the end of the first paragraph.

      Thank you for catching this error. We removed the unfinished text.

      (2) Page 6: Figure S2A should be Figure S2.

      In general, the manuscript would benefit from a deeper explanation of the biological relevance of R-loop formation and the connection to NRL TF and the expression of genes regulated by NRL. In this regard, a more substantial description of the model would be useful.

      We have modified the discussion for clarity and included new ideas on possible roles of R-loops in gene regulation of photoreceptors.

      Reviewer #2 (Recommendations for the authors):

      (1) The specificity of interactions needs to be addressed:

      - Figure 1B - HNRNPUI1 bands present in GST control.

      - Figure 1C - Bands present in the Empty Vector control IP for HNRNPU and DHX9.

      - Supplemental Figure 1A - most proteins are present in GST control suggesting prevalent binding to GST and lack of specificity for other interactions.

      Thank you for your comment. RNA-binding proteins can have more background as observed in other studies (e.g. PMID: 32704541) but there is always a higher signal in experimental samples compared to controls. While we agree that we can enhance the conditions for immunoprecipitation (IP) by optimizing washing buffers, exposure and other parameters, we believe the current methods tell the story. We have added additional text explaining this. See page 5.

      (2) Use of the term 'Strongest' interaction - IPs don't directly address the strength of interaction, but depend on levels of expression AND affinity. The strength of interaction should be tested using techniques like an OCTET or SPR assay. One can also quantify the effect that RNA would have in such an assay.

      Thank you for your suggestion. We replaced the term 'stronger' with “higher signal” and “robust” at most places. The source of protein lysates is the same for experiments and controls, thus the amount of protein is consistent in both conditions, and not dependent on level of gene expression.

      (3) In supplemental tables, please use the proper gene names, not the UniProt peptide name. For example, there are no genes named ELAV1-ELAV4. These should be ELAVL1-ELAVL4. A short glance identifies >10 gene name errors.

      Thank you for the suggestion. We updated current gene names in all tables.

      (4) Please provide the rationale for the choice of DNA sequence for the DHX9 nucleotide sequence used for EMSA assays. In the human DHX9 locus, the NRL ChIP-seq peak looks to be contained in Intron1 whereas the NRL ChIP-seq peak in mouse DHX9 looks to be in the proximal upstream promoter. Did the authors choose an evolutionarily conserved sequence in the promoter region that contained the NRL motif or does the probe sequence arise from the sequence that has known NRL binding as assayed by NRL ChIP-seq? A zoomed-in image of the NRL ChIP-seq pile-ups in the DHX9 locus in each species would be beneficial.

      Thank you for this suggestion. The probe was chosen by scanning for NRL binding motifs on the Chip-Seq peak at the human DHX9 promoter. We added a Zoom-in image of the ChIP-Seq or CUT&RUN reads for NRL on both human and mouse retinas. Figure 3D shows NRL binding in both species in regions containing the homologous motif. The sequence is partially conserved and shown in the figure.

      (5) Normalization in RNaseH/RNaseA Co-IP experiments. Why does RNAseH treatment result in increased NRL IP (increased NRL expression?) or does RNaseA treatment cause reduced IP of DHX9? These differences seem to cause a 'denominator' effect, leading the Authors to conclude decreased co-IP of DHX9 with NRL when R-loops are inhibited or increased co-IP of NRL with DHX9 when RNA is degraded. An alternate interpretation would be that inhibiting the R-loop binding of NRL unmasks the epitope for antibody recognition. The authors should test NRL binding to RNA and determine if RNA binding affects the co-IP of NRL with DHX9.

      We agree that removing total RNA by RNase A or R-loops by RNase H may alter the accessibility of our antibodies to the epitopes, resulting in the differences in the level of total protein pulled down. However, we quantified the relative level of the associating protein to the total protein and confirmed, in reciprocal assays, that RNase A treatment led to increased interaction between NRL and DHX9. However, the quantification was not consistent between the reciprocal IPs upon RNase H treatment. We reason that in Figure 4D, as NRL may account for only a small proportion of DHX9’s interactome, the change in NRL level could not be detected due to the sensitivity of our assay. However reciprocally, DHX9 can constitute a larger proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious. We added this information to the text. See page 8.

      (6) Figure 7 - Malat1 - there doesn't seem to be an overlap of NRL with Stranded R-loop peaks in this image. Nrl seems to flank the region of R-loops.

      We changed Malat1 for Mplkip that shows a direct overlap of Nrl binding and R-loops. See Figure 7C.

      (7) Results end with 'A Model'. Seems like some concluding remarks and references to Figure 8 were mistakenly left out.

      Thank you for catching this typo. We removed the misplaced text.

      (8) Model and Discussion - authors should show raw data for RHO with respect to NRL binding and R-loops. No evidence was provided regarding R-loops (or lack thereof) in the Rhodopsin locus. Additionally, conclusions stating that "R-loops... are specifically depleted from genes, such as Rhodopsin, with high expression levels" go against Figures 7B and 7C. Malat1 is one of the highest expressed genes in the retina and contains R-loops.

      Thank you for helping us clarify our hypothesis. We added a genome browser view of Rhodopsin showing the absence of R-loops (Fig. S8). We hypothesize that R-loops could interfere with achieving higher rates of transcription, however we did not mean to say that all high expressed genes lack R-loops. We have rephrased the discussion to clarify this point.

      (9) Neuronal genes, particularly those involved in synaptic transmission are known to be, on average, longer than most genes (Gabel, 2015; PMID: 25762136). Is it possible that R-loops are detected at genes involved in synaptic function/structure solely because of transcript length, as it takes longer for transcription termination to resolve in genes that are longer? A plot showing R-loop enrichment and transcript length would address this.

      We added a plot showing gene length in relation to R-loops and expression levels. We observed that R-loops are more common over long genes regardless of their expression levels. We also observed that the concomitant presence of stranded and unstranded R-loops is restricted to the longest genes in most cases. We added this to Figure 7D.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors investigate the role of BEND2, a novel regulator of meiosis, in both male and female fertility. Huang et al have created a mouse model where the fulllength BEND2 transcript is depleted but the truncated BEND2 version remains. This mouse model is fertile, and the authors used it to study the role of BEND2 on both male and female meiosis. Overall, the full-length BEND2 appears dispensable for male meiosis. The more interesting phenotype was observed in females. Females exhibit a lower ovarian reserve suggesting that full-length BEND2 is involved in the establishment of the primordial follicle pool.

      Strengths:

      The authors generated a mouse model that enabled them to study the role of BEND2 in meiosis. The role of BEND2 in female fertility is novel and enhances our knowledge of genes involved in the establishment of the primordial follicle pool.

      Weaknesses:

      The manuscript extensively explores the role of BEND2 in male meiosis; however, a more interesting result was obtained from the study of female mice. Only a few experiments were performed using female mice, therefore, more experiments should be performed to complete the story of the role of BEND2 on female fertility. In addition, the title and abstract of the manuscript do not align with the story, as female fertility is only a small portion of the data compared to the male fertility section.

      We appreciate the reviewer’s thoughtful summary, recognition of the strengths of our study, and constructive feedback. In the revised manuscript, we have performed additional experiments to enhance our understanding of the role of BEND2 in female gametogenesis. These new experiments provide further insights into the establishment of the ovarian reserve and the role of BEND2 in female fertility.

      Additionally, we have rewritten the title, abstract, and introduction to better align with the content of the manuscript and to reflect the balance between the male and female fertility results. We believe these changes address the reviewer’s concerns and improve the overall clarity and focus of the manuscript.

      Reviewer #1 (Recommendations For The Authors):

      • I recommend that the authors re-organize their abstract and introduction to accurately reflect the manuscript's primary focus on male fertility. Right now, the title of the manuscript is misleading. The manuscript does not investigate reproductive aging; rather, it primarily describes the depletion of primordial follicle number. The mechanism behind this depletion and whether this phenotype accelerates reproductive aging, are not explored. Clarifying these points will help align the title and content of the manuscript more accurately.

      We thank the reviewer for this suggestion. We agree that the original title and abstract did not fully capture the focus of the study. In response, we have rewritten the title, abstract, and introduction to better align with the results presented, focusing more clearly on the implications of the effects of the full-length BEND2 depletion for spermatogenesis and oogenesis. These revisions ensure that the title, the abstract, and the manuscript's introduction are now more accurately reflective of the work performed.

      • Figure 1: I couldn't find the validation of the polyclonal antibody against BEND2 that the authors generated.

      Regarding this query about the validation of the polyclonal antibody against BEND2, we apologize for any confusion. We would like to clarify that this validation is indeed presented in Figure 2 of our manuscript. To ensure this information is easily accessible, we have revised the text to explicitly mention the validation in Figure 2.

      • Figure 2A: Could you provide the actual numbers for the weight of the mice testis?

      In response to this question regarding Figure 2A and the weights of the mice testis, we have now included this data in a graph in Fig 2A and Table S1 and added this information in the results section.

      • Figure 2C and D: I am confused by the fact that in the WB we can appreciate a high expression of the p75 protein, but the signal is very low in the IF (Figure 2D).

      We thank the reviewer for raising this point. We acknowledge the apparent discrepancy between the strong p75 signal observed in the Western blot (Fig. 2C) and the weaker signal seen in the immunofluorescence (Fig. 2D). We think several factors could contribute to this difference, such as differences in sensitivity and detection methods, epitope accessibility, protein localization or differences in sample preparation, antibody affinity, and experimental conditions between Western blot and IF.

      • In the same figure, the authors also mention that the p75 protein is functional. On what basis do they rely on reaching this conclusion?

      We acknowledge that we cannot definitively confirm the functionality of the p75 protein. Our assumption was based on the observed fertility of the male mice and existing literature indicating that BEND2 is essential for completing meiosis (Ma et al., 2022). However, we understand the importance of clarity in our claims. To avoid any potential confusion, we have revised the sentence to read: "The p75 BEND2 protein—likely corresponding to an exon 11-skipped transcript—is present and might be functional in our mutant testis, based on the observed phenotype (see below)."

      • The phenotype in females is very interesting. The authors conclude that BEND2 influences primordial follicle formation, oocyte quality, fertility, and reproductive aging by (1) performing follicle counts, (2) analyzing the litter size, and (3) analyzing meiotic progression. Given that the authors build their story around these experiments, I strongly encourage them to expand the section on female fertility, or reorganize the manuscript, or be more cautious with some of their conclusions. They might consider performing additional experiments such as:

      - Oocyte quality: To determine whether BEND2 impacts oocyte quality, mice should be stimulated with hormones and oocyte quality should be analyzed (GV, MI, MII progression, spindle morphology and/or fertilization, and embryo development). Does the decrease in primordial follicles correlate with the number of ovulated oocytes, or is the impact only on oocyte quality?

      We appreciate the reviewer's suggestion to assess the impact of BEND2 on oocyte quality. Following the reviewer’s recommendation, we stimulated three control and three mutant mice. We analyzed the number of ovulated oocytes, their fertilization rate, and the percentage of embryos that developed to the blastocyst stage. These new results are included in the revised manuscript (see Results section and new Table 1). Our analyses indicate that for all parameters assessed, control and mutant oocytes behaved similarly. Specifically, there were no significant differences in the number of ovulated oocytes, fertilization rates, or the ability of embryos to progress to the blastocyst stage between the control and mutant groups. These findings suggest that mutant oocyte quality is comparable to control mice of a similar age. We have incorporated these new results into the manuscript.

      - Reproductive aging: A fertility trial would provide more information on whether BEND2 depletion triggers an acceleration of reproductive aging. In addition, the oldest mice used by the authors are 9 months old, and at this point, fertility has not declined yet.

      We appreciate the reviewer's suggestion regarding the assessment of reproductive aging. However, we respectfully disagree with the assertion that fertility has not declined by 9 months of age. In our colony, we have observed a significant decline in fertility around 10 months of age. Specifically, out of 18 10-month-old female mice placed in breeding cages, we observed only three pregnancies within the first 30 days (N.N. and I.R., data not published). Based on these observations, we determined that fertility begins to decline around this age in our colony, which informed our decision to use 9-month-old mice as the oldest age group for our analysis. Thus, this age is appropriate for evaluating the potential effects of BEND2 depletion on reproductive aging in our specific mouse population.

      - The observation that the primordial follicle pool is already diminished in mice that are 1 week old is very interesting. Some experiments that the authors could perform to figure out the mechanism are: (1) Analyzing apoptosis. Are the primordial follicles dying during the pool's establishment, or is this an ongoing apoptotic process throughout the mice's lifespan? (2) If the authors still have ovaries from mice younger than 1 week of age (when the primordial pool is forming), they could perform DDX4 staining and quantify the number of oocytes in follicles and the total number of oocytes. These experiments would provide mechanistic insights into whether BEND2 impacts the formation of the primordial follicle pool or if the pool forms but is then depleted.

      We appreciate the reviewer's suggestion to further explore the mechanism behind the reduced primordial follicle pool. In response, we have analyzed the number of DDX4positive cells (DDX4 labels oocytes) in newborn mutant and wild-type animals. Our results show that mutant ovaries contain significantly fewer oocytes compared to controls (see new Fig. 5). This finding supports the hypothesis that BEND2 is critical for the establishment of a normal ovarian reserve. We are grateful for this suggestion, as these additional data reinforce our conclusion that BEND2 is required to determine a normal ovarian reserve in mice.

      • What is the red signal in Supplementary Figure 1C?

      This image depicts the BEND2 staining pattern in 16 days post-coitum (dpc) wild-type mouse ovaries. To clarify this and prevent any confusion, we have updated the figure legend to explicitly state that the sample shown is from a wild-type mouse.

      • Please spell out the full term of all the acronyms.

      We apologize for the oversight in not fully spelling out some acronyms in the original manuscript. We have carefully reviewed the entire manuscript and have ensured that all acronyms are now spelled out in full upon their first use in the revised version. We want to thank the reviewer for bringing this to our attention.

      • Is Line-1 also dysregulated in the ovary? This was one of the main findings from the male part. It would be interesting to perform the same analysis in the ovary since Line1 has a role in establishing the ovarian reserve (PDMI: 31949138).

      We thank the reviewer for this insightful suggestion. We have analyzed the number of LINE1 and SYCP3-positive cells in wild-type and mutant newborn ovaries (new Fig. S4). Our results show no significant difference between the two genotypes, suggesting that LINE-1 is not dysregulated in newborn Bend2 mutant oocytes. These findings indicate that, at least in the context of the newborn ovary, LINE-1 does not appear to be affected by BEND2 depletion.

      Reviewer #2 (Public Review):

      In their manuscript entitled "BEND2 is a crucial player in oogenesis and reproductive aging", the authors present their findings that full-length BEND2 is important for repair of meiotic double strand break repair in spermatocytes, regulation of LINE-1 elements in spermatocytes, and proper oocyte meiosis and folliculogenesis in females. The manuscript utilizes an elegant system to specifically ablate the full-length form of BEND2 which has been historically difficult to study due to its location on the X chromosome and male sterility of global knockout animals.

      While the manuscript is an overall excellent addition to the field, it would significantly benefit from a few additional experiments, as well as some additional clarification/elaboration.

      The claim that BEND2 is required for ovarian reserve establishment is not supported, as the authors only look at folliculogenesis and oocyte abundance starting at one week of age, after the reserve is formed. Analysis of earlier time points would be much more convincing and would parse the role of BEND2 in the establishment vs. maintenance of this cell population. In spermatocytes, the authors demonstrate a loss of nuclear BEND2 in their mutant but do not comment on the change in localization (which is now cytoplasmic) of the remaining protein in these animals. This may have true biological significance and a discussion of this should be more thoroughly explored.

      We thank the reviewer for their thoughtful feedback and constructive suggestions to improve our manuscript.

      In response to the comment regarding the establishment of the ovarian reserve, we have now analyzed Bend2 mutant and control newborn ovaries. Our results show a significant reduction in the number of DDX4-positive cells in mutant ovaries compared to controls. These findings demonstrate that BEND2 is required for the establishment of the ovarian reserve, as the reduction is evident at birth.

      Regarding the cytoplasmic staining of BEND2 in mutant spermatocytes, we did perform secondary-antibody-only controls using goat anti-rabbit Cy3 to address the specificity of the signal. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the cytoplasmic signal is nonspecific. Therefore, we do not believe this represents a meaningful change in the localization of BEND2 protein in the mutants. We have clarified this in the revised manuscript to address this point.

      We hope these additional experiments and clarifications strengthen the manuscript and address the reviewer’s concerns.

      Reviewer #2 (Recommendations For The Authors):

      Major points:

      (1) The title of the manuscript does not accurately capture the content of the work. The vast majority of the data presented here is from the male, which is not reflected at all in the title - perhaps considering revising it?

      Thank you for your valuable suggestion. We agree that the original title did not fully reflect the focus of the manuscript. In response, we have revised the title, along with the abstract and introduction, to more accurately capture the content of the study and the emphasis on the male data. These changes ensure that the manuscript more clearly aligns with the results presented.

      (2) In Figure 2D, the authors demonstrate that WT BEND2 expression and localization are lost in the mutant, but staining is still apparent, just in the cytoplasm. Did the authors perform secondary-antibody-only controls to determine if this was background staining or real staining? If real, can they comment on the change in localization of the protein?

      We thank the reviewer for this insightful question. We have indeed performed secondary antibody-only controls using goat anti-rabbit Cy3. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the signal in the cytoplasm is not specific. Therefore, we do not believe this staining represents any real or meaningful expression of the BEND2 protein in the mutants.

      (3) In Figure S2A, the authors show Ku70 staining and describe that it is similar between the genotypes, but - to my eye - it looks quite distinctly different. It appears to stain in patches in WT SYCP3+ spermatocytes, versus staining in patches in the more mature, SYCP3- germ cells closer to the lumen in the mutant. Can the authors please clarify, or provide arrows to point which foci they are referring to?

      We apologize for the confusion caused by the image provided in the original submission. Upon review, we realized that the mutant image was not fully representative of the staining pattern observed in the majority of mutant samples. We have replaced this image with a new one in the revised manuscript, which more accurately reflects the similarity in Ku70 staining between wild-type and mutant testis. In this updated Figure S2, we have also included arrowheads to indicate the relevant foci, making it clearer to the reader. We have updated the figure legend to correspond with these changes as well.

      (4) The authors state that BEND2 is "required to establish the ovarian reserve during oogenesis" but this has not been demonstrated. The authors do show a reduced density of primordial follicles at one week of age. While this is compelling data, the ovarian reserve is established earlier in the mouse, around postnatal days 0-1, so it is not clear from this manuscript whether BEND2 is required for the maintenance of this population after PND1, leading to reduced numbers by 1 week of age, OR if it is required for the establishment of this population, which would result in reduced numbers of oocytes around the time of birth. This is a critical experiment that should be performed in order to determine which of these possibilities is likely the case. Ideally, looking at embryonic through early postnatal time points during ovarian development would be very helpful.

      We thank the reviewer for raising this important point. As mentioned earlier in response to Reviewer 1, we have performed the experiment suggested by Reviewer 2 and analyzed the number of DDX4-positive cells in newborn ovaries. Our results show that Bend2 mutant ovaries have fewer oocytes at birth than wild-type controls (Fig. 5H). This finding reinforces our conclusion that BEND2 is indeed required to establish the ovarian reserve, as the reduction in oocyte number is evident at the time of birth. We agree that this additional data strengthens our original claim, so we have included these results in the revised manuscript.

      Reviewer #3 (Public Review):

      Summary:

      Huang et al. investigated the phenotype of Bend2 mutant mice which expressed a truncated isoform. This mutant male showed increasing apoptosis due to unrepaired double-strand breaks. However, this mutant male has fertility, and this enabled them to analyze Bend2 function in females. They revealed that Bend2 mutation in females showed decreasing follicle numbers which leads to loss of ovarian reserve.

      Strengths:

      Since their Bend2 mutant males were fertile, they were able to analyze the function of Bend2 in females and they revealed that loss of Bend2 causes less follicle formation.

      Weaknesses:

      Why the phenotype of their mutant male is different from previous work (Ma et al.) is not clear enough although they discuss it.

      We appreciate the reviewer’s comment regarding the differences between our Bend2 mutant male phenotype and the previously reported phenotype by Ma et al., 2022. We believe this discrepancy is due to the fact that the Bend2 locus encodes two BEND2 isoforms: p140 and p80. In contrast to the previous study, where both proteins were ablated by mutation employed (the deletion of exons 12 and 13), our exon 11 deletion specifically ablates p140 expression while allowing the expression of p80 in the testis.

      Based on the distinct phenotypes observed in the two Bend2 mutant mouse models, we hypothesize that p80 is sufficient to fulfill BEND2’s roles in meiosis, which could explain why our Bend2 mutant males remain fertile. We have rewritten the relevant sections in the results and discussion to better articulate this hypothesis and clarify the potential mechanisms behind the observed phenotypic differences.

      We hope these clarifications and additional details adequately address the reviewer’s concerns.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors showed that Bend2 mutant females had decreased fertility. This may be due to decreased ovarian reserve. Did the authors check if the mutant mice decreased or lost fertility faster than WT? If the authors have the data, please refer to it in the manuscript.

      We followed the breeding performance of a small number of control and Bend2 mutant females, and preliminary observations suggested no clear differences between the two groups. However, due to the limited sample size, we felt that these data were not conclusive enough to be included in the manuscript. We agree that a more thorough analysis of fertility decline over time would be valuable, and we plan to address this question in a future study.

      (2) In Figure 1 A, there is no exon1 in the upper figure.

      We thank the reviewer for pointing this out. We have revised Figure 1A to include exon 1 and ensure the schematic is accurate. The updated figure is included in the revised version of the manuscript.

      (3) Figure 3A, it would be nice to show several tubules of the testis section as well as an enlarged one.

      Following the reviewer's advice, we have revised Figure 3A to include new images showing several tubules and an enlarged view of one section of a tubule. These updates are included in the revised manuscript to better represent the testis sections.

      (4) Please be consistent with the format of the graph, especially Supplemental figures 2C and 4D.

      We have revised the figures, including Supplemental Figures 2C and 4D, to ensure consistency in the format throughout the manuscript. We have made modifications to the figures to align them more closely and improve the overall presentation.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Yue et al. re-processed publicly available DNA methylation data (published in 2012 and 2017 from the Meissner lab) from pre- and post-implantation mouse embryos. Against the global wave of genome-wide reduction of DNA methylation occurring during pre-implantation development, they detected a slight increase (~1% on average) of DNA methylation at gene promoter regions during the transition from 8-cell to blastocyst stage. They claim that many such promoters are located in the X chromosome. Subsequently, they knocked down Dnmt3b (presumably because of its upregulation during the transition from the 8-cell to blastocyst stage) and detected the aberrant patterning of H3K27me3 in the mutant female embryos. Based on this observation, they claim that imprinted X-chromosome inactivation is impaired in the Dnmt3b-Kd pre-implantation embryos. Finally, they propose a model where such an increase of DNA methylation together with H3K27me3 regulates imprinted X-chromosome inactivation in the pre-implantation embryos. While their observation is of potential interest, the current version of the work fails to provide enough evidence to support their conclusions. Below are suggestions and comments on the manuscript.

      Major issues:

      (1) Sex of the embryos of the genome-wide bisulfite-sequencing data

      The authors re-analyzed publicly available genome-wide DNA methylation data from the Meissner lab published in 2012 and 2017. The former used reduced representation bisulfite sequencing (RRBS) and the latter used whole-genome bisulfite sequencing (WGBS). Based mainly on the RRBS data, Yue et al. detected de novo DNA methylated promoters during the transition from 8-cell to blastocyst against the global wave of genome-wide DNA demethylation. They claim that such promoter regions are enriched at the "inactive" X chromosome. However, it would be difficult to discuss DNA methylation at inactive X-chromosomes as the RRBS data were derived from a mixture of male and female embryos. It would also be notable that the increase of DNA methylation at these promoter regions is ~1% on average. Such a slight increase in DNA methylation during pre-implantation development could also be due to the developmental variations between the embryos or between the sexes of embryos.

      Thanks so much for your insightful comments. Whether de novo DNA methylation occurs in a sex-dimorphic manner would be of significance for our study. Based on your comments, we have added a reanalysis based on a publicly available single cell multi-omics sequencing (COOL-seq) data of mouse early embryos (Guo et al., 2017). The results showed that both male and female embryonic cells gain DNA methylation during the transition from the 8-cell to ICM (Figure 1—figure supplement 1C-D; Lines 112-115 in the revised manuscript).

      With regards to the increase in the promoter region, many previous studies have revealed that promoter and overlapping CGI regions, especially high CpG promoters, always showed low levels of DNA methylation (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). The relatively lower basal levels make the increase seem relatively slight. Thus, we added relevant statements to clarify this information and rewritten the sentences in the revised manuscript (Lines 116-118, 125-127 in the revised manuscript).

      In addition, using the single cell COOL-seq data, we also specifically reanalyzed the DNA methylation changes on the X chromosome in female embryos. The X chromosome showed a more notable increase than that on autosomes, and the female X chromosome showed a higher DNA methylation level than that of the male (Figure 3—figure supplement 2A-B; Lines 203-206 in the revised manuscript).

      Thanks again for your insightful and constructive comments that significantly strengthen our evidence. We have added these results in the revised manuscript.

      (2) Imprinted X-chromosome inactivation and evaluation of H3K27me3 (related to Figures 2C, D; 3F; Figure2-supplement 2 F, G; Figure3-supplement 3G)

      Based on the slight change in the H3K27me3 signals in the Dnmt3b-Kd blastocysts, the authors claim that imprinted X-chromosome inactivation is impaired in the mutant embryo. It would be not easy to reach this conclusion from such a rough analysis of H3K27me3 presented in Figure 2C, D. Rigorous quantification/evaluation of the H3K27me3 signals in the Dnmt3b-Kd embryos should be considered. Additional evidence for the impairment of H3K27me3 in the mutant embryos should also be provided (expression of a subset of X-linked genes by RNA-FISH or RT-PCR etc.). Though technically challenging, high-resolution genome-wide approach such as ChIP-seq of H3K27me3 in the Dnmt3b-kd female embryos (with traceable SNPs between maternal and paternal X chromosome to distinguish inactive and active X-chromosome) could more precisely evaluate regions that lose H3K27me3 in the X-chromosome (de novo DNA methylated promoters from 8-cell to blastocyst, for example).

      Thanks so much for your insightful comments that make our results more convincing. The H3K27me3 domain is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, in the present study, we have performed immunostaining for H3K27me3 domains to evaluate the iXCI status in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). Base on your comments, we have added another statistical method to quantify the establishment of iXCI, i.e. the percentage of H3K27me3-positive and -negative cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not. The result also indicated that Dnmt3b knockdown led to a significant loss of H3K27me3 domains from total trophoblast cells. Similarly, new data based on statistical analyses of total trophoblast cells, has also been added in the results of Dnmt3b knockout and 5-aza-dC (Figure 3F; Figure 3—figure supplement 3D, H in the revised manuscript).

      To clarify the significance and reliability of detecting H3K27me3 domains, we have added a schematic diagram depicting the process of iXCI initiation and establishment, as well as the experimental design and work flows, to make our results easier to be understood (Figure 3C in the revised manuscript).

      In addition, we agree with your comments that additional evidence will benefit the conclusion. Thus, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that Dnmt knockout-induced chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal X chromosome (specifically inactivated in iXCI), along with a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome.

      We have added this result in the revised manuscript (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript).

      (3) Analysis of the developmental potential of Dnmt3b-kd embryos

      While the authors claim that Dnmt3b-mediated de novo DNA methylation plays an important role in imprinted X-chromosome inactivation, it remains unclear whether the analysis presented in Figure 4 is derived from "female" embryos. This analysis seemed confusing as the authors claim that de novo DNA methylation in the promoter regions during the transition from 8-cell to blastocyst regulates imprinted X-chromosome inactivation, but this should not happen in the male embryos. Was the impairment of embryonic proliferation and differentiation observed in both male and female embryos? Or is this specific to the female embryos? We think that the sex of the embryos would be critical for the analysis presented in Figure 4.

      Thanks so much for your constructive comments to make our results smoother and clearer. The Figure 4 mainly presents the developmental role of minor de novo methylation based on the integrated analysis of DNA methylation and gene expression dynamics from the 8-cell to ICM. Because our data indicated that both male and female embryos undergo minor de novo methylation (Figure 1—figure supplement 1C-D in the revised manuscript). This section mainly focused on genome wide and general changes, but not on sex dimorphic consequence.

      To avoid the possible confusion, we have reorganized the RESULTS AND DISCUSSION section and presented this section as Figure 2 in the revised manuscript, before the chromosomal distribution analysis and subsequent detection relevant to iXCI.

      Reviewer #2 (Public Review):

      Summary:

      Here, Yue et al. set out to determine if the low DNMT3B expression that is observed prior to de novo DNA methylation (before the blastocyst stage) has a function. Re-analyzing existing DNA methylation data from Smith et al. (2012) they find a small DNA methylation gain over a subset of promoters and gene bodies, occurring between the 8-cell and blastocyst stages, and refer to this as "minor de novo DNA methylation". They attempt to assess the relevance/functionality of this minor DNA methylation gain, and report reduced H3K27me3 in Dnmt3b knockdown (KD) trophoblast cells that normally undergo imprinted X-chromosome inactivation (iXCI) before the blastocyst stage. In addition, they assess the proliferation, differentiation, metabolic function, implantation rate, and live birth rate of Dnmt3b KD blastocysts.

      Strengths:

      Working with early embryos is technically demanding, making the well-designed experiments from this manuscript useful to the epigenetics community. Particularly, the DNMT3B expression and 5-mC staining at different embryonic stages.

      Thanks for your positive evaluation, we have revised manuscript based on your comments, and the items need to be addressed in detail are explained in the point-by-point response to each comment.

      Weaknesses:

      - Throughout the manuscript, please represent DNA methylation changes as delta DNA methylation instead of fold change.

      Thanks so much for your constructive comments. We have represented DNA methylation changes as “ΔDNA methylation” (Figure 2—figure supplement 1A; Figure 3—figure supplement 1A; Figure 3—figure supplement 3I in the revised manuscript).

      - Detailed methods on the re-analysis of the DNA methylation data from Smith et al. 2012 are missing from the materials and methods section. Was a minimum coverage threshold used?

      Thanks so much for your reminder. We have added relevant statements and provided the detail of the coverage criteria in the subsection of Bioinformatics analysis in the Materials and methods section as follows: RRBS data of mouse embryos (2-cell embryos, 4-cell embryos, 8-cell embryos, ICM, and E6.5 embryos) were downloaded from the published article by Smith et al (Smith et al., 2012) (accession number: GSE34864). The methylation level was calculated as the number of “methylated” reads (reporting as C), divided by the total number of “methylated” and “unmethylated” read, which reporting as C or T. The genomic region information was downloaded from the mm9 Repeat Masker. As described in the published article, promoters were defined as 1 kb up- and downstream of the TSS and classified into high-density CpG promoter (HCP), intermediate-density CpG promoter (ICP) and low-density CpG promoter (LCP). Only CpG sites with at least fivefold coverage were included in the methylation analysis. We have added relevant information in the revised manuscript (Lines 462-470 in the revised manuscript).

      - Detailed methods on the establishment and validation of Dnmt3b KO blastocysts and 5-aza-dC treated blastocysts are missing (related to Figure 2).

      Thanks so much for your detailed reminder. In the present study, we used a well-established Dnmt3b-deficient mouse model (Okano et al., 1999) to validate the role of minor de novo DNA methylation in iXCI establishment. Heterozygous Dnmt3b<sup>+/-</sup> mice that carry one mutant locus of Dnmt3b, were obtained from the Mutant Mouse Resource & Research Centers (MMRRC, NIH). Homozygous embryos were obtained by intercrossing Dnmt3b<sup>+/-</sup> male and female mice. Genotyping assays of collected embryos was performed by PCR using primers that were designed based on the gene targeting strategy following the MMRRC genotyping protocol (https://www.med.unc.edu/mmrrc/genotyping-protocols/mmrrc-center-protocol-29886/). We have provided the detailed methods in the revised manuscript (Lines 350-354; 391-393 in the revised manuscript). In addition, we added a schematic diagram depicting the processes of embryo collection and detection (Figure 3—figure supplement 3A in the revised manuscript).

      Similarly, we have provided relevant details of 5-aza-dC supplementation in the revised manuscript (Lines 412-415 in the revised manuscript) and added a schematic diagram depicting the details of experimental design and processes (Figure 3—figure supplement 3E in the revised manuscript).

      - Detailed methods on the re-analysis of the ChIPseq data from Liu et al. 2016 are missing from the materials and methods section.

      Thank you for pointing this out. The bigwig files of H3K27me3 ChIP-seq data were downloaded from the published article by Liu et al (Liu et al., 2016)(accession number: GSE73952). These signal tracks were generated using the MACS2 (v2.0.10.20131216) pileup function and normalized to 1 million reads for visualization, as described in the original publication. We have added relevant information to the MATERIALS AND METHODS section in the revised manuscript (Lines 474-479 in the revised manuscript).

      - Some of the data represented in bar graphs does not look convincing/significant. Maybe this data can be better represented differently, such as in box plots or violin plots, which would better represent the data.

      Thanks so much for your comments that improve our result presentation, relevant results have been changed into box plots in the revised manuscript (Figure 3E; Figure 3—figure supplement 3C; Figure 3—figure supplement 3G in the revised manuscript). In addition, to strengthen our evidence, we have added alternative statistical method to quantify the establishment of iXCI, i.e. the percentage of H3K27me3-positive and -negative cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not. (Figure 3F; Figure 3—figure supplement 3D, H in the revised manuscript).

      - The relevance and rationale for experiments using 5-aza-dC treatment is unclear.

      Thanks so much for reminding us to make our results more informative and convincing. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and thus has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005).

      In our study, to validate the function of minor de novo DNA methylation in iXCI, we take advantage of 5-aza-dC-induced DNMT inhibition, which allows us, despite its inhibitory effect common to various DNMTs, to transiently treat embryos specifically during the window of minor de novo DNA methylation (from the 8-cell to blastocyst stage). We have added these statements, as well as a schematic diagram depicting the experimental design, in the revised manuscript to make our experiments more rational and easier to be understood (Lines 183-188; Figure 3—figure supplement 3E in the revised manuscript).

      References

      Auclair, G., Guibert, S., Bender, A. and Weber, M. (2014). Ontogeny of CpG island methylation and specificity of DNMT3 methyltransferases during embryonic development in the mouse. Genome Biol. 15, 545.

      Borgel, J., Guibert, S., Li, Y., Chiba, H., Schubeler, D., Sasaki, H., Forne, T. and Weber, M. (2010). Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093-1100.

      Chen, Z., Yin, Q., Inoue, A., Zhang, C. and Zhang, Y. (2019). Allelic H3K27me3 to allelic DNA methylation switch maintains noncanonical imprinting in extraembryonic cells. Sci Adv 5, eaay7246.

      Chow, J. and Heard, E. (2009). X inactivation and the complexities of silencing a sex chromosome. Curr. Opin. Cell Biol. 21, 359-366.

      Dahlet, T., Argueso Lleida, A., Al Adhami, H., Dumas, M., Bender, A., Ngondo, R. P., Tanguy, M., Vallet, J., Auclair, G., Bardet, A. F., et al. (2020). Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity. Nat Commun 11, 3153.

      Fukuda, A., Tomikawa, J., Miura, T., Hata, K., Nakabayashi, K., Eggan, K., Akutsu, H. and Umezawa, A. (2014). The role of maternal-specific H3K9me3 modification in establishing imprinted X-chromosome inactivation and embryogenesis in mice. Nat Commun 5, 5464.

      Galupa, R. and Heard, E. (2015). X-chromosome inactivation: new insights into cis and trans regulation. Curr. Opin. Genet. Dev. 31, 57-66.

      Gontan, C., Mira-Bontenbal, H., Magaraki, A., Dupont, C., Barakat, T. S., Rentmeester, E., Demmers, J. and Gribnau, J. (2018). REX1 is the critical target of RNF12 in imprinted X chromosome inactivation in mice. Nat Commun 9, 4752.

      Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L. and Tang, F. (2017). Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967-988.

      Heard, E., Chaumeil, J., Masui, O. and Okamoto, I. (2004). Mammalian X-chromosome inactivation: an epigenetics paradigm. Cold Spring Harb. Symp. Quant. Biol. 69, 89-102.

      Huynh, K. D. and Lee, J. T. (2005). X-chromosome inactivation: a hypothesis linking ontogeny and phylogeny. Nat. Rev. Genet. 6, 410-418.

      Inoue, K., Kohda, T., Sugimoto, M., Sado, T., Ogonuki, N., Matoba, S., Shiura, H., Ikeda, R., Mochida, K., Fujii, T., et al. (2010). Impeding Xist expression from the active X chromosome improves mouse somatic cell nuclear transfer. Science 330, 496-499.

      Liu, X. Y., Wang, C. F., Liu, W. Q., Li, J. Y., Li, C., Kou, X. C., Chen, J. Y., Zhao, Y. H., Gao, H. B., Wang, H., et al. (2016). Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 537, 558-562.

      Maslov, A. Y., Lee, M., Gundry, M., Gravina, S., Strogonova, N., Tazearslan, C., Bendebury, A., Suh, Y. and Vijg, J. (2012). 5-aza-2'-deoxycytidine-induced genome rearrangements are mediated by DNMT1. Oncogene 31, 5172-5179.

      Oka, M., Meacham, A. M., Hamazaki, T., Rodic, N., Chang, L. J. and Terada, N. (2005). De novo DNA methyltransferases Dnmt3a and Dnmt3b primarily mediate the cytotoxic effect of 5-aza-2'-deoxycytidine. Oncogene 24, 3091-3099.

      Okano, M., Bell, D. W., Haber, D. A. and Li, E. (1999). DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247-257.

      Schulz, E. G. and Heard, E. (2013). Role and control of X chromosome dosage in mammalian development. Curr. Opin. Genet. Dev. 23, 109-115.

      Smith, Z. D., Chan, M. M., Mikkelsen, T. S., Gu, H. C., Gnirke, A., Regev, A. and Meissner, A. (2012). A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339-344.

      Tan, K., An, L., Miao, K., Ren, L., Hou, Z., Tao, L., Zhang, Z., Wang, X., Xia, W., Liu, J., et al. (2016). Impaired imprinted X chromosome inactivation is responsible for the skewed sex ratio following in vitro fertilization. Proc. Natl. Acad. Sci. U. S. A. 113, 3197-3202.

      Reviewer #1 (Recommendations For The Authors):

      Title

      It would be hard to understand what "co"-regulates means. Does this mean DNA methylation and H3K27me3 co-regulate imprinted X- X-chromosome inactivation? If so, the title can be reworded.

      Thanks for your insightful comments, the title has been corrected into “A wave of minor de novo DNA methylation initiates in mouse 8-cell embryos and co-regulates imprinted X- chromosome inactivation with H3K27me3” (Line 2 in the revised manuscript).

      Text

      (1) As DNA methylation analysis is a primary part of this study, how they processed DNA methylation data can be added to the "Bioinformatics analysis" in the MATERIALS AND METHODS section.

      Thanks for your kind reminder. We have added relevant information in the Materials and methods section in the revised manuscript (Lines 462-474 in the revised manuscript).

      (2) It seems that recent literature has not been cited in the manuscript. Specifically, none of the papers after 2018 were cited. Recent relevant papers should also be cited throughout the manuscript.

      Thanks so much for your reminder. We have added more recent literature to update the relevant information, such as the evidence supporting the causal role between DNA methylation and XCI (Lines 225-228, 264-265 in the revised manuscript); the concurrent enrichment of DNA methylation and H3K27me3 in genes subject to XCI (Lines 301-303 in the revised manuscript); the dominant role of de novo methylation in X chromosome (Lines 253-256 in the revised manuscript), etc.

      (3) Line 56: The first report that describes the dynamics of DNMT3B expression in pre-implantation embryonic development (Hirasawa et al., 2007) is missing. This paper should be cited.

      Sorry for our carelessness, we have added relevant references and rewritten the sentence in the revised manuscript (Lines 56-57 in the revised manuscript). I think you meant the report by Hirasawa et al in 2008, in which presented expression and subcellular localization of Dnmt3a and Dnmt3b in mouse oocytes and preimplantation embryos.

      (4) Line 98: It would be good to mention that the data were derived from reduced representation bisulfite sequencing as the authors used whole-genome bisulfite sequencing data from the same research group as well.

      Thanks for your kind reminder. As you have suggested, we have added the description in the revised manuscript to emphasize that these data were derived from reduced representation bisulfite sequencing, while another data were derived from whole-genome bisulfite sequencing, respectively. (Lines 98-99, 111 in the revised manuscript).

      (5) Line 101: We first... "the preferential target of DNMT3B (Auclair et al., 2014; Borgel et al., 2010)". More recent literature (Baubec et al., 2016, Duymich et al., 2016, for example) showed that the preferential target of DNMT3B is not a promoter but a gene body. This sentence should be reworded.

      Thanks so much for your detailed reminder. As you have pointed out, “preferential target” seems to be an inaccurate statement. Besides of promoters, gene bodies and other elements also undergo de novo DNA methylation (Auclair et al., 2014; Dahlet et al., 2020; Duymich et al., 2016).

      We have rewritten the sentence as follows in the revised manuscript: “Promoter regions are important target sites of DNMT3B (Choi et al., 2011). The acquisition of DNA methylation in promoters, especially in intermediate and low CpG promoters, during implantation is largely dependent on DNMT3B and plays an important role in regulating developmental genes (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). Thus, among genomic regions that may undergo de novo DNA methylation, we initially focused our analysis on DNA methylation dynamics of promoters...” (Lines 100-106 in the revised manuscript)

      (6) Lines 108-109: It would be good to mention that these data were derived from whole-genome bisulfite sequencing.

      Thanks for your kind reminder. As aforementioned, we have added a description in the revised manuscript to distinguish between data derived from reduced representation bisulfite sequencing and whole-genome bisulfite sequencing (Lines 98-99, 111 in the revised manuscript).

      (7) Line 141: rXCI should be defined.

      Thanks for your kind reminder. We have added full descriptions and more necessary information about iXCI and rXCI, to make our statements clearer and easier to be understood (Lines 210-213 in the revised manuscript). In addition, we carefully checked the relevant descriptions throughout the manuscript, and each abbreviation (such as “ICM”) has been defined at its first occurrence. Additionally, we have replaced abbreviations that appears only once in the manuscript with their full terms (Lines 122, 212 in the revised manuscript).

      (8) Lines 145-149: The role of DNA methylation for imprinted X-inactivation has already been reported (Chiba et al., 2008). The relevant sentences should be reworded.

      Thanks so much for reminding us the important earlier literature that explores the relationship between DNA methylation and XCI. However, the primary aim and hypothesis of the study by Chiba et al. are different from those of our study. Chiba et al focused on whether DNA methylation is the imprinting mark responsible for monoallelic expression of Xist (the initiation event of iXCI), while our study focused on the role of DNA methylation in achieving X chromosomal heterochromatinization (the late event of iXCI).

      In detail, the study by Chiba et al. mainly focused on exploring why Xist is specifically expressed from paternal allele and iXCI occurs specifically on the paternal X chromosome in mouse preimplantation embryos. Because Previous studies have suggested that genomic imprinting of Xist is established during oogenesis (Oikawa et al., 2014; Tada et al., 2000), Chiba et al. wanted to test whether the DNA methylation imprinting established during oogenesis is responsible for the monoallelic expression of Xist in preimpantaiton embryos. Analyses of DNA methyltransferase maternal knockout embryos revealed that oocyte DNA methylation is dispensable for Xist imprinting (Chiba et al., 2008). Follow-up study by Inoue et al. identified a broad H3K27me3 enrichment within the Xist 5’region established during oocyte growth and persists through preimplantation development, as the imprinting mark of Xist (Inoue et al., 2017). These series of studies are very important and allows us to understand the mechanism underlying paternal allele-specific iXCI in mouse preimplantation embryos and extraembryonic tissues.

      However, the hypothesis is different in our study. Based on the finding of minor de novo DNA methylation and its preferential distribution on the X chromosome, we have speculated that the minor de novo methylation, which occurs from the 8-cell to blastocyst stage, may participate in achieving X chromosomal heterochromatinization. Although DNA methylation is essential for maintaining X chromosome-wide transcriptional silence of rXCI, its role in iXCI remains controversial and it is even plausibly thought that DNA methylation is not required for achieving iXCI because preimplantation embryos undergo global and massive DNA demethylation.

      We have reorganized this paragraph, relevant statements have been added to make the background and discussion clearer and easier to be understood. (Lines 217-234 in the revised manuscript)

      (9) Lines 164-165: Information regarding Dnmt3b KO is missing. Did the authors generate an original KO line or use an already published one? It should be explicitly stated.

      Thank you so much for your kind reminder. The Dnmt3b heterozygous mice were obtained from the Mutant Mouse Resource & Research Centers (MMRRC), and Dnmt3b knockout (KO) embryos were generated by mating Dnmt3b heterozygous females with heterozygous males. The genotyping of Dnmt3b KO embryos was performed by PCR following the MMRRC genotyping protocol (https://www.med.unc.edu/mmrrc/genotyping-protocols/mmrrc-center-protocol-29886/). The relevant information has been added to the MATERIALS AND METHODS section in the revised manuscript (Lines 350-354; 391-393 in the revised manuscript).

      (10) Line 165: chemical-induced inhibition of DNMT3B. As 5-aza-dC also blocks DNMT3A and DNMT1, this sentence should be reworded.

      Thank you for your valuable comments. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005). Thus, despite its inhibitory effect common to various DNMTs, chemical-induced inhibition of DNMTs has the advantage of allowing us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage). We have rewritten the relevant sentences in the revised manuscript (Lines 183-188 in the revised manuscript).

      (11) Lines 171-174: "The role of de novo methylation in iXCI...". This possibility was already tested in the previous study from the Sasaki lab (Chiba et al., 2008).

      As mentioned above, the primary aim and hypothesis of the study by Chiba et al. are different from those of our study. Chiba et al. mainly focused on exploring why Xist is specifically expressed from paternal allele and iXCI occurs specifically on the paternal X chromosome in mouse preimplantation embryos, so they tested whether the DNA methylation imprinting established during oogenesis is responsible for this monoallelic expression of Xist in preimplantation embryos (the initiation event of iXCI).

      By contrast, based on the finding of minor de novo DNA methylation and its preferential distribution on X chromosome, our study has speculated that the minor de novo DNA methylation, which occurs from the 8-cell to blastocyst stage, may participate in achieving X chromosomal heterochromatinization (the late event of iXCI).

      Thanks so much for reminding us this important literature, to make our discussion more informative. We have reorganized this paragraph by rewriting or adding relevant statements to make the background and discussion clearer and easier to be understood (Lines 217-231 in the revised manuscript). In addition, to avoid repeated statement and make our discussion more concise, we have removed the similar sentences at the end of this paragraph.

      (12) Lines 198-200: "Given DNA methylation...". These citations mention a general relationship between DNA methylation and H3K27me3 in cells in culture. As I believe the authors focus on X-chromosome inactivation in the female embryos, more relevant papers that discuss the order of the events for the establishment of H3K27me3 and DNA methylation in the inactive X-chromosome can be cited.

      Thanks so much for your comment to improve our discussion. It has been thought that during the late phase of rXCI in fully differentiated cells, gene silencing is achieved by PRC2 complex-induced H3K27me3, and then is further stably maintained by the redundant action of multiple layers of epigenetic modifications, including DNA methylation, to reach the maximum level of chromatin compaction (Chow and Heard, 2009; Heard et al., 2004; Pintacuda and Cerase, 2015). In line with this, a recent multifaceted analysis showed that DNA methylation and H3K27me3 are concurrently enriched in genes subject to XCI (Balaton and Brown, 2021). We have added these statements in the revised manuscript (Lines 295-303 in the revised manuscript).

      (13) Line 241: As 5-aza-dC blocks both de novo and maintenance DNA methylation, this sentence should be reworded.

      Thank you for your kind reminder. As you have mentioned above, 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005). Thus, despite its inhibitory effect common to various DNMTs, chemical-induced inhibition of DNMTs has the advantage of allowing us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage). We have rewritten the relevant sentences in the revised manuscript (Lines 183-188 in the revised manuscript).

      Figures

      (1) Figure 1C, D: Do the rows in C and D show the corresponding genes?

      Figure 1C and D represent the DNA methylation changes of promoters (C) and gene bodies (D) respectively, during the transition from the 8-cell to blastocyst stage. Two data were analyzed independently, and rows did not show the corresponding genes. Since we have focused on the minor de novo methylation in promoter regions, to avoid confusion, the results of the gene body have been removed from the revised manuscript.

      (2) Figure 1G: Yy2 promoter gained DNA methylation during the transition from 8-cell to the blastocyst stage. Is this a representative locus for the de novo methylated promoters that are shown in Figure 1F where an increase of DNA methylation is about ~1% on average? Another representative locus could be shown instead of this gene promoter.

      Thanks so much for you detailed reminder. The inconsistency between the global methylation change and bisulfite sequencing analysis of Yy2, may be due to the details of methodologies, such C-T conversion efficiency, the number of picked colonies, etc. Since we have confirmed the presence of minor de novo DNA methylation using different publicly available data, to avoid ambiguity, we have removed this result in revised manuscript.

      (3) Figures 2C and 3A: It would be helpful to mention what the arrowheads mean.

      Thanks so much for you detailed reminder. In Figure 2C, the arrowhead indicates the H3k27me3 domain and the blank arrowhead indicates the blastomere without the H3k27me3 domain. In Figure 3A, the arrowhead indicates Xist RNA domain and the blank arrowhead indicates the blastomere without Xist RNA domain. We have added the information in the revised manuscript (Lines 736-738, 747-749 in the revised manuscript).

      (4) Figure 3-figure supplement 2B: It would be hard to see whether H3K27me3 is enriched at the promoter regions of presented genes. It would be helpful to show the values for the Y-axis as in panel A.

      Thanks for your helpful reminder. We have added the scales to the figure to improve the result presentation (Figure 4—figure supplement 2B in the revised manuscript).

      (5) Figure 4-figure supplement 2: 5-aza-dC blocks not only the activity of DNMT3B but also DNMT1, and DNMT3A (all these DNMTs are expressed during pre-implantation embryos, see Hirasawa et al., 2007). This part can be omitted from the manuscript.

      Thanks for your insightful comments. As you have mentioned above, the relevance and rationale for experiments using 5-aza-dC treatment should be clarified. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and thus has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005).

      In our study, to validate the function of minor de novo DNA methylation in iXCI and blastocyst development, we take advantage of 5-aza-dC-induced DNMT inhibition, which allows us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage), despite its non-specificity to various DNMTs.

      Based on these considerations, we hope to retain this result, and wish to get your understanding.

      We have added these statements in the revised manuscript to make our experiments more rational and easier to be understood (Lines 183-188 in the revised manuscript) and added a schematic diagram depicting the experimental design (Figure 3—figure supplement 3E in the revised manuscript).

      Reviewer #2 (Recommendations For The Authors):

      Recommendations/concerns in the text:

      - Line 106, it is unclear what is meant by "in line with this"? Gene body DNA methylation is a characteristic of active transcription, so why would a gain in DNA methylation at promoters be in line with a gain in DNA methylation over gene bodies?

      Thank you so much for your comments that pointed out our ambiguous statement. We meant both the promoter and gene body regions, albeit accounting for small proportions, gain DNA methylation during the transition from the 8-cell to blastocyst stage. Based on the comment by Reviewer#1, since we have focused on the minor de novo methylation in promoter regions, to avoid confusion, the results of the gene body have been removed from the revised manuscript.

      - Line 111 & 114, can 6% DNA methylation really be considered "relatively hypermethylated" compared to 3% DNA methylation that is referred to as "more hypomethylated"?

      We apologize for our unclear and ambiguous statements. Here we focused on the promoter regions. Many previous studies have revealed that compared with gene bodies and other genome elements, promoter and overlapping CGI regions, especially high CpG promoters, always showed low levels of DNA methylation. We have added relevant statements to clarify this information, and rewritten the sentences in the revised manuscript (Lines 100-106, 116-118, 121, 124 in the revised manuscript).

      - Line 124, there are a number of processes identified, why only mention one in the text? Suggest changing writing to be more accurate, indicating what was included for the GO analysis and using the words "enriched for ... processes". Saying it may be linked to a process is an overstatement and not supported by further experiments/data.

      Thank you so much for your detailed comments that make our results more informative. We have checked the relevant description and addressed your suggestions as follows: By performing gene ontology enrichment analysis of genes that undergo minor or major de novo DNA methylation respectively, we noticed that besides of many important basic processes common to two waves of de novo DNA methylation, genes subject to minor de novo DNA methylation were enriched in processes such as organic substance transport, chromosome organization, and cell fate specification (Lines 129-134 in the revised manuscript).

      - Lines 149 - 152: sentence/message unclear.

      We apologize for the ambiguous description. We have corrected the relevant descriptions as follows: To identify the biological function of minor de novo DNA methylation in iXCI, we knocked down Dnmt3b in preimplantation embryos by microinjecting Dnmt3b siRNA into zygotes (Lines 234-236 in the revised manuscript).

      - Lines 162-164: the data in Figure 2C/D does not support this statement, as it does not show H3K27me3 loss specifically at the inactive X-chromosome.

      Thanks so much for your insightful comments. Despite the global enrichment of H3K27me3, the H3K27me3 domain detected by immunostaining is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, we have used immunostaining for H3K27me3 domains to evaluate the iXCI establishment in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). To make our results more convincing, we have added another statistical method to quantify the establishment of iXCI, i.e., the percentage of H3K27me3-positive and -negative trophoblast cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not.

      In addition, we have added a schematic diagram depicting the process of iXCI initiation and establishment, as well as the experimental design and work flows, to make the result easier to be understood.

      In addition, we agree with your comments that additional evidence will benefit the conclusion. To strengthen the evidence, and test whether DNA methylation loss leads to a prolonged effect on iXCI, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), along with a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome. (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript)

      - Lines 169-174: sentence/message unclear.

      As aforementioned, we have reorganized this paragraph by rewriting or adding relevant statements relevant to the DNA methylation and XCI, to make the background and discussion clearer and easier to be understood (Lines 217-234 in the revised manuscript). In addition, to avoid repeated statement and make our discussion more concise, we have removed the similar sentences at the end of this paragraph.

      - Lines 177-179: this statement is too bold. The data does not support "direct evidence".

      Thank you for your detailed reminder. We have rewritten the sentence to avoid confusion and overstatement (Lines 262-268 in the revised manuscript).

      - Line 198: these are not all enzymes, but could be referred to as chromatin modifiers.

      We apologize for the ambiguous description. As you suggested, we have corrected “enzymes” to “chromatin modifiers” (Lines 284, 287 in the revised manuscript).

      - Line 199: this statement is not correct in all contexts. There are many studies showing antagonism between DNA methylation and H3K27me3.

      Thanks so much for you careful reviewing. As you have pointed out, the relationship of DNA methylation and H3K27me3 are divergent and largely controversial among studies. Under certain circumstances, DNA methylation shows antagonistic effect to H3K27me3 at promoters, via excluding the binding of PRC2 (the main complex responsible for H3K27me3 deposition) components to their targets (Bartke et al., 2010; Jermann et al., 2014), while other studies have presented alternative evidence that PRC2 (the main complex responsible for H3K27me3 deposition) and DNA methylation cooperate to achieve silencing (Hagarman et al., 2013; Vire et al., 2006). Thus, it has been thought that the relationship between DNA and methylation and histone modifications is complex, possibly in a cell-type and/or genomic region-specific manner. Both antagonism and coordination can be observed in different regulatory elements in mouse ES cells (King et al., 2016).

      We apologize our incomplete statement because we mainly focused on their synergistic relationship. We have refined this section by rewriting relevant sentences and adding necessary statements (Lines 288-303 in the revised manuscript).

      - Lines 228-230: the developmental significance of DNA methylation homeostasis is already well-established. Please reference relevant papers showing this here.

      Thank you for this helpful suggestion. We have reorganized this section. Relevant references that highlight the developmental significance of DNA methylation homeostasis have added. The sentence has been rewritten and moved to the end of this paragraph, in the revised manuscript (Lines 159-161 in the revised manuscript).

      - Line 238: an explanation/rationale for looking at energy metabolism is lacking.

      Thank you for your comments to make our results earlier to be understood. The detection of energy metabolism is mainly based on the integrated analysis of DNA methylation and gene expression from the 8-cell embryos to ICM, to test the potential short-and long-term developmental consequences of minor de novo DNA methylation. Bioinformatic analysis suggested that many basic processes, such as cell differentiation, cell cycle and metabolic regulation, may be regulated by minor de novo DNA methylation. Among the enriched genes, several are related energy metabolism. In addition, because energy metabolism is crucial for supporting embryo differentiation and development, and oxidative phosphorylation (OXPHOS) metabolism is highly activated during the blastocyst stage (Zhao et al., 2021), we next examined the energy metabolism, particularly OXPHOS activity, of Dnmt3b-KD embryos. We have refined the section by rewritten relevant sentence and added necessary statements (Lines 175-179 in the revised manuscript).

      - Lines 246-248: Looking at the data in Figure 2 figure supplement 2, this statement is simply not true with regards to DNMT3B protein, and also global DNA methylation level is reduced in the Dnmt3b KD blastocyst, which could lead to defective major de novo DNA methylation.

      Thanks for your careful reviewing, we have rewritten the sentence to make our statement more accurate and avoid overstatement (Lines 188-190 in the revised manuscript).

      Recommendations/concerns relating to figures:

      Figure 1:

      - Of all genic promoters, how many were included in the analysis (contained sufficient coverage)? What cut-off/thresholds were used to consider DNA methylation gain at a promoter?

      Thanks for your comments. In total, 11662 promoters were analyzed. Given that promoter methylation is generally at low level, particularly at the 8-cell stage at which minor de novo methylation is just initiated. The relatively lower basal levels make the increase before the blastocyst, seem considerably slight. To capture the slight changes, we have used the relaxed threshold based on ΔDNA methylation. Only CpG sites with at least fivefold coverage were included in the methylation analysis based on data from Smith et al. (Smith et al., 2012)., ΔDNA methylation greater or less than 0 was defined as gain or loss of DNA methylation. We have added this information in the revised manuscript (Lines 462-470 in the revised manuscript).

      - Does an average methylation level of 0.02 represent 2% DNA methylation? Presuming yes, is the average 1.5% DNA methylation gain at promoters real? And meaningful? Especially compared to the gain in DNA methylation that takes place between ICM and E6.5 (Figure 1 Figure Supplement 1 D)

      As you have pointed out, an average methylation level of 0.02 represent 2% DNA methylation. As aforementioned, promoters exhibited an average of 1.5% DNA methylation gain during the transition from 8-cell stage to ICM. The slight increase may be mainly due to the relatively lower basal levels. As you expected, compared with the comprehensive de novo DNA methylation during implantation, preimplantation de novo methylation occurs more slightly, at a small proportion of promoter regions, so designated it as minor de novo DNA methylation. It should be also mentioned that a proportion of these promoters continue to gain massive DNA methylation during implantation. We have refined the relevant sentences to provide more detailed information of our results (Lines 125-127 in the revised manuscript).

      - Why is there a focus on promoters (which are not the preferential target of DNMT3B)?

      Thanks so much for your detailed reminder. As you have pointed out, “preferential target” seems to be an inaccurate statement. besides of promoters, gene bodies and other elements also undergo de novo DNA methylation (Auclair et al., 2014; Dahlet et al., 2020; Duymich et al., 2016). We have focused on the promoter regions based on the following considerations: (1) Promoter regions are important target sites of DNMT3B (Choi et al., 2011); (2) The acquisition of DNA methylation in promoters, especially in intermediate and low CpG promoters, during implantation is largely dependent on DNMT3B and plays an important role in regulating developmental genes (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). We have rewritten the relevant sentence in the revised manuscript (Lines 100-106 in the revised manuscript).

      - Figure 1H shows that promoters that gain DNA methylation during the "minor de novo DNA methylation" continue to gain DNA methylation during "de novo DNA methylation". Is the ~1.5% DNA methylation gain just the slow start of the main de novo DNA methylation wave?

      Your comments is very helpful to improve the description of our results. In the present study, our analysis indicated that a small proportion of promoters initially gain methylation during the transition from the 8-cell to ICM. The finding challenges current knowledge: (1) de novo DNA methylation occurs during implantation, by which globally hypomethylated blastocysts acquire genome-wide DNA methylation (Borgel et al., 2010; Dahlet et al., 2020; Smith et al., 2012); (2) during preimplantation development, embryos undergo massive and global DNA demethylation.

      To distinguish the current knowledge of the timing and dynamics of DNA methylation during the early development, we have designated our finding during the transition from the 8-cell to blastocyst stage, as minor de novo DNA methylation.

      We agree with your notion that among the promoters undergoing minor de novo methylation, most of them continue to gain DNA methylation during implantation, as revealed in Fig. 1F. We have added refine the relevant statement in revised manuscript (Lines 125-127 in the revised manuscript).

      - The GO analysis performed for Figure 1H, what was used as input? Promoters of genes that gain DNA methylation as identified in 1C?

      Thank you for your comments. For the GO analysis shown in Figure 1H, we used genes with promoter regions that gained or lost DNA methylation during the transition from the 8-cell to ICM respectively (identified in Figure 1C, as input), respectively. This information has been clarified in the revised manuscript to ensure accuracy (Lines 129-134 in the revised manuscript).

      - Figure 1 figure supplement 1, is there only a fold change as threshold or also a calculated significance (eg. p-value/FDR)?

      Thanks for your valuable comments. Considering the relatively low DNA methylation levels at promoter regions, and the slightly changes occurring during the preimplantation embryo development, we used the relaxed threshold based on ΔDNA methylation. Only CpG sites with at least fivefold coverage were included in the methylation analysis based on data from Smith et al. (Smith et al., 2012), ΔDNA methylation greater or less than 0 was defined as gain or loss of DNA methylation. We have replaced relevant figures and added this information in the revised manuscript (Figure 1—figure supplement 1D-E; Lines 125-127 in the revised manuscript).

      - To confirm DNMT3B is responsible for the DNA methylation gain: DNMT3B KD/KO followed by promoter DNA methylation analysis to confirm the promoters that gain DNA methylation between 8 cell and ICM don't gain DNA methylation in the absence of DNMT3B.

      We agree with your comments that additional evidence will benefit the conclusion. To strengthen the evidence, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), which showed a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome. We have added this result in the revised manuscript (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript).

      Figure 2:

      - Figure 2A: label missing for what the numbers on the y-axis represent.

      Thank you for pointing this out. We apologize for the oversight. We have added the label of y-axis in Figure 2A to clarify what the numbers represent, making it easier to be understood (Figure 3A in the revised manuscript).

      - Figure 2B: y-axis is % of methylated promoters compared to all promoters?

      Thank you for your suggestion. The y-axis in Figure 2B indeed represents the percentage of de novo methylated promoters relative to all promoters. As you have suggested, we have clarified this labeling in the revised manuscript (Figure 3B in the revised manuscript).

      - What is the delta DNA methylation gain specifically for X-linked promoters?

      Thanks so much for your reminder. To provide more convincing evidence. We have reanalyzed a single cell COOL-seq data, we also specifically reanalyzed the DNA methylation changes on the X chromosomal promoter in female embryos. The X chromosome showed a more notable increase in the de novo methylated promoters than that on autosomes, and the female X chromosome showed higher DNA methylation levels than that of the male (Figure 3—figure supplement 2A-B; Lines 203-206 in the revised manuscript).

      - Figure 2C: include representative images of separate channels to better see the signal of CDX2 and H3K27me3. Quantification would be better represented with box plots.

      Thank you for your helpful suggestions. We have added separate channel images in the revised manuscript. Additionally, we have adjusted the quantification to be represented as box plots, as you have suggested, to improve the accuracy and interpretability of the data presentation (Figure 3D-F in the revised manuscript).

      - Figure 2C: Does the H3K27me3 signal overlap with the location of the inactive X-chromosome (is there maybe denser DAPI or do IF combined with Xist RNA-FISH)?

      Thanks so much for your insightful comments. Despite the global enrichment of H3K27me3, the H3K27me3 domain detected by immunostaining is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, we have used immunostaining for H3K27me3 domains to evaluate the iXCI establishment in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). We have taken effort to perform co-staining of H3K27me3 IF and Xist FISH, but was hindered by the technical challenge, we wish to get your understanding. However, as we aforementioned, H3K27me3 is a well-accepted maker to clarify the XCI status.

      In addition, to make our results more convincing, we have added an alternative statistical method to quantify the establishment of iXCI, i.e., the percentage of H3K27me3-positive and -negative trophoblast cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not (Figure 3F; Lines 243-244 in the revised manuscript)

      - Figure 2 figure supplement 2A: relative expression of Dnmt3b?

      Thanks for your detailed reminder. The data represent the relative expression level of Dnmt3b, as noted in the original figure legend. Based on your comments, we have added the gene name in the label of the Y-axis. Similarly, the protein name has been also added to make the results more informative (Figure 2 figure supplement 2A, C, E in the revised manuscript).

      - Figure 2 figure supplement 2B/C: in the text, line 153, it is stated that "Dnmt3b mRNA and protein levels were significantly reduced in morulae, but not in blastocysts compared to those of negative control (NC) group". These figures do not support that statement. The IF images show a loss of DNMT3B in the Dnmt3b KD blastocysts. The IF quantification seems to have fewer datapoints for the blastocyst, and looking at the bar graphs, there seems to be a trend towards reduced DNMT3B in both the morula and blastocyst, which would also explain the reduction in DNA methylation in both stages as shown in Figure 2 figure supplement 2D/E.

      Thanks so much for your careful reviewing that makes our statements more accurate. We have rewritten the sentence in the revised manuscript as follows: Dnmt3b mRNA and protein levels were significantly reduced in morulae, and tended to be lower in blastocysts compared to those of the negative control (NC) group. In addition, we have removed “transient” from the original statement “The transient inhibition of Dnmt3b” (Lines 168-170 in the revised manuscript).

      - Figure 2 figure supplement 2F/G: include representative IF images with separation of all channels and the merged image.

      Thank you for your suggestion. We have added the representative immunofluorescence (IF) images with separate channels and merged image in the revised manuscript (Figure 3—figure supplement 3B, F in the revised manuscript).

      - Figure 2 figure supplement 2H: Instead of showing log2FC in methylation levels, delta methylation would be more informative. Are these genes already inactivated at the 8-cell stage? Or are they active and become inactivated by the gain in DNA methylation? Doing qPCR for these genes, or looking at published RNAseq data would be informative. What happens to the expression of these genes in the Dnmt3b KD?

      Thanks for your suggestions. We have represented DNA methylation changes as “ΔDNA methylation”. During mouse preimplantation development, iXCI is initiated in earlier cleavage female embryos dependent on Xist upregulation around 4-8-cell stage, and then Xist specifically coats paternal X chromosome and finally leads to chromosome-wide silencing via heterochromatinization in early blastocysts. Thus, these non-escaping genes, which are subject to XCI, would not be inactivated at 8-cell stage

      Author response image 1.

      The processes of iXCI initiation and establishment (left panel), and dynamics of total expression levels of X chromosome in male and female preimplantation embryos (right panel, note that X-dosage is balanced between sexes until the early blastocyst stage).

      As you expected, most of these representative non-escaping is downregulated upon the transition of 8-cell to blastocyst stage, consistent with their gain of DNA methylation. Additionally, since preimplantation iXCI status maintains extraembryonic cells (Galupa and Heard, 2015; Schulz and Heard, 2013), we further reanalyzed the published RNA-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent DNA methyltransferase knockout (Chen et al., 2019). The results showed that chromosome-wide loss of DNA methylation led to a chromosome-wide transcriptional upregulation, including the locus of these non-escaping genes, on paternal X chromosome. We have added this result in the revised manuscript (Figure 3—figure supplement 3J; Figure 3—figure supplement 4A-B; Lines 253-261 in the revised manuscript).

      Figure 3:

      - Figure 3 figure supplement 1: representative IF image missing.

      Thanks for your kind reminder. We have added the representative IF images in the revised manuscript to provide a clearer illustration of the data (Figure 4—figure supplement 1A in the revised manuscript).

      - Figure 3 figure supplement 2B: scales are missing for the H3K27me3 ChIP-seq data (are the 8-cell and ICM tracks set to the same scale?). It looks like the ICM track is cut off at the top (peaks not fully displayed) and the data looks very sparse. A more informative analysis would be to do peak calling over promoters and compare 8-cell with ICM.

      Thanks for your detailed reminder. We apologize for the missing of scale bars in the H3K27me3 ChIP-seq data. The 8-cell and ICM tracks were set to the same scale, and we have now added scales to the figure in the revised manuscript to improve the result presentation. As you have speculated, the visual effect of the flatted peak is not caused by track cutting off, but rather by zooming into a specific region in the extended IGV files.

      These results are based on the reanalysis of publicly available data of pooled embryos, which just provided suggestive but not direct evidence to support the role of DNA methylation in promoting X-linked H3K27me3 enrichment in iXCI.

      To provide more convincing evidence. we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 female embryos that underwent Dnmt3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that Dnmt knockout led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), which showed a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome (Figure 3—figure supplement 4 in the revised manuscript). We have added these results in the revised manuscript.

      - Figure 3E: Given all tested proteins give a positive signal, it would have been good to include a negative control chromatin protein that is known to not interact with DNMT3B. Given both PRC2 and DNMT3B are chromatin-binding proteins, can the signal be a result of close proximity instead of a direct interaction?

      In the present study, to test the interaction between DNMT3B and PRC2 core components, we have used in situ proximity ligation assay (PLA), an increasingly popular technique for detecting the close proximity of two proteins in fixed samples using two primary antibodies (Alsemarz et al., 2018).

      Author response image 2.

      Schematic diagram of the principle of the in situ PLA.

      Compared with classical co-Immunoprecipitation (Co-IP) method, in situ PLA has advantages in (1) detecting low input samples or proteins expressed at low levels, which is extremely difficult using Co-IP; (2) providing in situ or subcellular information of protein-protein interaction. However, it should be noted that the maximal distance allowing this reaction is 40 nm, which is not quite small enough to demonstrate a physical interaction between the two antigens, but sufficient to support a very close “proximity”.

      In our study, in situ PLA, including the experimental design of negative control, was performed in the accordance with the manufacturer’s instruction of Duolink® In Situ Red Starter Kit (MilliporeSigma): “Technical negative controls included incubation with each primary antibody separately and no primary antibody”. We have refined the relevant sentence in the revised manuscript (Lines 308-310 in the revised manuscript)

      - Figure 3G: It would have been good to include a negative control, and DNase/benzonase to exclude DNA/RNA-mediated protein interaction.

      - (Of note, there have been previous studies reporting an interaction between PRC2 and DNMT3B in other cell types, such as in Weigert et al. 2023, but unfortunately, they don't seem to use DNase/benzonase either).

      The Co-IP analysis of DNMT3B and PRC2 core components in differentiated female ES cells was presented as additional supportive evidence. Because the Co-IP analysis is extremely difficult for preimplantation embryos, we have used in situ PLA to detect their interaction. However, the maximal distance allowing in situ PLA reaction is 40 nm, which is not quite small enough to demonstrate a physical interaction (Alsemarz et al., 2018). Thus, we have added a Co-IP analysis using differentiated female ES cells, in which rXCI occurs upon the differentiation.

      Based on this consideration of the importance and contribution of this result, we have moved this result from the main figure, to the supplemental figure (Figure 4—figure supplement 3H in the revised manuscript).

      - Figure 3 figure supplement 3G: what were the ESCs differentiated into? Did the Dnmt3b KO or Dnmt3a/b DKO show any differentiation defect?

      The mouse ESC line PGK12.1 was a well-established ex vivo model of rXCI. Under the standard culture condition, PGK12.1 is normally fated to neuroectodermal commitment.

      Author response image 3.

      Immunostaining of NESTIN, a neuroectodermal stem cell marker molecule, and NANOG in undifferentiated and differentiated PGK12.1 ESCs respectively.

      No differentiation defects have been observed in either Dnmt3b KO or Dnmt3a/3b DKO ESCs in our study. Dnmt KO/DKO/TKO ES cell lines have been successfully used as the model of interaction of DNA methylation and H3K27me3 deposition (King et al., 2016).

      Figure 4:

      - Figure 4B: Is there an explanation for seeing similar total cell numbers in Figure 4B, but showing decreased proliferation in Figure 4A?

      Thank you for your insightful comments. The EdU cell proliferation assays labels cells during the S phase of cell cycle, as the 5-ethynyl 2´-deoxyuridine (EdU) is incorporated into newly synthesized DNA. This labeling identifies cells undergoing DNA synthesis, but these cells may not have completed mitosis at the time of detection. As a result, the total cell number may not immediately reflect the decrease in proliferation observed in the treated group. To address this point, we have rewritten the sentences in the revised manuscript (Lines 174-175 in the revised manuscript).

      References

      Alsemarz, A., Lasko, P. and Fagotto, F. J. B. (2018). Limited significance of the in situ proximity ligation assay. bioRxiv, 411355.

      Auclair, G., Guibert, S., Bender, A. and Weber, M. (2014). Ontogeny of CpG island methylation and specificity of DNMT3 methyltransferases during embryonic development in the mouse. Genome Biol. 15, 545.

      Balaton, B. P. and Brown, C. J. (2021). Contribution of genetic and epigenetic changes to escape from X-chromosome inactivation. Epigenetics Chromatin 14, 30.

      Bartke, T., Vermeulen, M., Xhemalce, B., Robson, S. C., Mann, M. and Kouzarides, T. (2010). Nucleosome-interacting proteins regulated by DNA and histone methylation. Cell 143, 470-484.

      Borgel, J., Guibert, S., Li, Y., Chiba, H., Schubeler, D., Sasaki, H., Forne, T. and Weber, M. (2010). Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093-1100.

      Chen, Z., Yin, Q., Inoue, A., Zhang, C. and Zhang, Y. (2019). Allelic H3K27me3 to allelic DNA methylation switch maintains noncanonical imprinting in extraembryonic cells. Sci Adv 5, eaay7246.

      Chiba, H., Hirasawa, R., Kaneda, M., Amakawa, Y., Li, E., Sado, T. and Sasaki, H. (2008). De novo DNA methylation independent establishment of maternal imprint on X chromosome in mouse oocytes. Genesis 46, 768-774.

      Choi, S. H., Heo, K., Byun, H. M., An, W., Lu, W. and Yang, A. S. (2011). Identification of preferential target sites for human DNA methyltransferases. Nucleic Acids Res. 39, 104-118.

      Chow, J. and Heard, E. (2009). X inactivation and the complexities of silencing a sex chromosome. Curr. Opin. Cell Biol. 21, 359-366.

      Dahlet, T., Argueso Lleida, A., Al Adhami, H., Dumas, M., Bender, A., Ngondo, R. P., Tanguy, M., Vallet, J., Auclair, G., Bardet, A. F., et al. (2020). Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity. Nat Commun 11, 3153.

      Duymich, C. E., Charlet, J., Yang, X. J., Jones, P. A. and Liang, G. N. (2016). DNMT3B isoforms without catalytic activity stimulate gene body methylation as accessory proteins in somatic cells. Nat Commun 7, 11453.

      Fukuda, A., Tomikawa, J., Miura, T., Hata, K., Nakabayashi, K., Eggan, K., Akutsu, H. and Umezawa, A. (2014). The role of maternal-specific H3K9me3 modification in establishing imprinted X-chromosome inactivation and embryogenesis in mice. Nat Commun 5, 5464.

      Galupa, R. and Heard, E. (2015). X-chromosome inactivation: new insights into cis and trans regulation. Curr. Opin. Genet. Dev. 31, 57-66.

      Gontan, C., Mira-Bontenbal, H., Magaraki, A., Dupont, C., Barakat, T. S., Rentmeester, E., Demmers, J. and Gribnau, J. (2018). REX1 is the critical target of RNF12 in imprinted X chromosome inactivation in mice. Nat Commun 9, 4752.

      Hagarman, J. A., Motley, M. P., Kristjansdottir, K. and Soloway, P. D. (2013). Coordinate regulation of DNA methylation and H3K27me3 in mouse embryonic stem cells. PLoS One 8, e53880.

      Heard, E., Chaumeil, J., Masui, O. and Okamoto, I. (2004). Mammalian X-chromosome inactivation: an epigenetics paradigm. Cold Spring Harb. Symp. Quant. Biol. 69, 89-102.

      Huynh, K. D. and Lee, J. T. (2005). X-chromosome inactivation: a hypothesis linking ontogeny and phylogeny. Nat. Rev. Genet. 6, 410-418.

      Inoue, A., Jiang, L., Lu, F. and Zhang, Y. (2017). Genomic imprinting of Xist by maternal H3K27me3. Genes Dev. 31, 1927-1932.

      Inoue, K., Kohda, T., Sugimoto, M., Sado, T., Ogonuki, N., Matoba, S., Shiura, H., Ikeda, R., Mochida, K., Fujii, T., et al. (2010). Impeding Xist expression from the active X chromosome improves mouse somatic cell nuclear transfer. Science 330, 496-499.

      Jermann, P., Hoerner, L., Burger, L. and Schubeler, D. (2014). Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation. Proc. Natl. Acad. Sci. U. S. A. 111, E3415-3421.

      King, A. D., Huang, K., Rubbi, L., Liu, S., Wang, C. Y., Wang, Y., Pellegrini, M. and Fan, G. (2016). Reversible Regulation of Promoter and Enhancer Histone Landscape by DNA Methylation in Mouse Embryonic Stem Cells. Cell Rep. 17, 289-302.

      Maslov, A. Y., Lee, M., Gundry, M., Gravina, S., Strogonova, N., Tazearslan, C., Bendebury, A., Suh, Y. and Vijg, J. (2012). 5-aza-2'-deoxycytidine-induced genome rearrangements are mediated by DNMT1. Oncogene 31, 5172-5179.

      Oikawa, M., Inoue, K., Shiura, H., Matoba, S., Kamimura, S., Hirose, M., Mekada, K., Yoshiki, A., Tanaka, S., Abe, K., et al. (2014). Understanding the X chromosome inactivation cycle in mice: a comprehensive view provided by nuclear transfer. Epigenetics-Us 9, 204-211.

      Oka, M., Meacham, A. M., Hamazaki, T., Rodic, N., Chang, L. J. and Terada, N. (2005). De novo DNA methyltransferases Dnmt3a and Dnmt3b primarily mediate the cytotoxic effect of 5-aza-2'-deoxycytidine. Oncogene 24, 3091-3099.

      Pintacuda, G. and Cerase, A. (2015). X Inactivation Lessons from Differentiating Mouse Embryonic Stem Cells. Stem Cell Rev Rep 11, 699-705.

      Schulz, E. G. and Heard, E. (2013). Role and control of X chromosome dosage in mammalian development. Curr. Opin. Genet. Dev. 23, 109-115.

      Smith, Z. D., Chan, M. M., Mikkelsen, T. S., Gu, H. C., Gnirke, A., Regev, A. and Meissner, A. (2012). A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339-344.

      Tada, T., Obata, Y., Tada, M., Goto, Y., Nakatsuji, N., Tan, S., Kono, T. and Takagi, N. (2000). Imprint switching for non-random X-chromosome inactivation during mouse oocyte growth. Development 127, 3101-3105.

      Tan, K., An, L., Miao, K., Ren, L., Hou, Z., Tao, L., Zhang, Z., Wang, X., Xia, W., Liu, J., et al. (2016). Impaired imprinted X chromosome inactivation is responsible for the skewed sex ratio following in vitro fertilization. Proc. Natl. Acad. Sci. U. S. A. 113, 3197-3202.

      Vire, E., Brenner, C., Deplus, R., Blanchon, L., Fraga, M., Didelot, C., Morey, L., Van Eynde, A., Bernard, D., Vanderwinden, J. M., et al. (2006). The Polycomb group protein EZH2 directly controls DNA methylation. Nature 439, 871-874.

      Zhao, J., Yao, K., Yu, H., Zhang, L., Xu, Y., Chen, L., Sun, Z., Zhu, Y., Zhang, C., Qian, Y., et al. (2021). Metabolic remodelling during early mouse embryo development. Nat Metab 3, 1372-1384.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment 

      This study presents valuable finding regarding the role of life history differences in determining population size and demography. The evidence for the claims is still partially incomplete, with concerns about generation times and population structure. Nonetheless, the work will be of considerable interest to biologists thinking about the evolutionary consequences of life history changes.  

      Thank you. We have addressed the generation time and population structure issues in detail in our revision and hope that you, like us, find them to be of sufficiently low concern (i.e., they are not driving the results) that they do not overshadow the main findings and conclusions.

      The opportunity to make in-depth revisions also helped the manuscript in two ways unanticipated by both us and the reviewers. First, KW made a mistake in the original analysis of phylogenetic signal, and catching that error simplifies that aspect of the study (there is none in our measured variables). Second, in June 2024 Hilgers et al. (2024; https://doi.org/10.1101/2024.06.17.599025) posted an important manuscript to bioRxiv noting the possibility of false population size peaks in PSMC analyses using the standard default settings. Our results had three of those, which we have eliminated. N<sub>e</sub>ither of these issues affect the overall conclusions, but their resolution improves the work.  

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      This interesting study applies the PSMC model to a set of new genome sequences for migratory and nonmigratory thrushes and seeks to describe differences in the population size history among these groups. The authors create a set of summary statistics describing the PSMC traces - mean and standard deviation of N<sub>e</sub>, plus a set of metrics describing the shape of the oldest N<sub>e</sub> peak - and use these to compare across migratory and resident species (taking single samples sequenced here as representative of the species). The analyses are framed as supporting or refuting aspects of a biogeographic model describing colonization dynamics from tropical to temperate North and South America. 

      Strengths: 

      At a technical level, the sequencing and analysis up through PSMC looks good and the paper is engaging and interesting to read as an introduction to some verbal biogeographic models of avian evolution in the Pleistocene.

      The core findings - higher and more variable N<sub>e</sub> in migratory species - seem robust, and the biogeographic explanation is plausible.  

      Thanks. We thought so as well. Our analyses go beyond being simply descriptive and test some simple hypotheses, including a biogeographic+ecological expansion opportunity gained in some lineages through the adoption of a seasonal migration life-history strategy.  

      Weaknesses: 

      I did not find the analyses particularly persuasive in linking specific aspects of clade-level PSMC patterns causally to evolutionary driving forces. To their credit, the authors have anticipated my main criticism in the discussion. This is that variation in population size inferred by methods like PSMC is in "effective" terms, and the link between effective and census population size is a morass of bias introduced by population structure and selection so robustly connecting specific aspects of PSMC traces to causal evolutionary forces is somewhere between extremely difficult and impossible.  

      As R1 notes, we do not attempt to link effective population sizes and census sizes (though we do discuss this), and we are also careful to discuss correlated rather than causative factors when going beyond the overarching hypotheses regarding life-history strategy.

      Population structure is the most obvious force that can generate large N<sub>e</sub> changes mimicking the census-sizefocused patterns the authors discuss. The authors argue in the discussion that since they focus on relatively deep time (>50kya at least, with most analyses focusing on the 5mya - 500kya range) population structure is "likely to become less important", and the resident species are usually more structured today (true) which might bias the findings against the observed higher N<sub>e</sub> in migrants.  

      To clarify, the patterns we discuss are entirely related to effective population size, not census size. But, yes, this is why we’ve given population structure its own section in the Discussion.

      But is structure really unimportant in driving PSMC results at these specific timescales? There is no numerical analysis presented to support the claim in this paper. The biogeographic model of increased temperate-latitude land area supporting higher populations could yield high N<sub>e</sub> via high census size, but shifts in population structure (for example, from one large panmictic population to a series of isolated refugial populations as a result of glaciation-linked climate changes) could plausibly create elevated and more variable N<sub>e</sub>. Is it more land area and ecological release leading to a bigger and faster initial N<sub>e</sub> bump, or is it changes in population connectivity over time at expanding range edges, or is the whole single-bump PSMC trace an artifact of the dataset size, or what? The authors have convinced me that the N<sub>e</sub> history of migratory thrushes is on average very different from nonmigrant thrushes, but beyond that it's unclear what exactly we've learned here about the underlying process.  

      We do not argue that population structure is unimportant, only that it is less important as one goes into deeper time. Further, we agree with the reviewer’s observation above that structure is more likely to bias nonmigrant estimates of N<sub>e</sub>. In other words, following Li & Durbin’s (2011) simulations, we interpret that an inflated N<sub>e</sub> due to structure should occur more often among residents. We have clarified this in the revision. We also agree that what we’ve learned about the underlying process is not entirely clear, but as we stated, population structure does not seem to be the main driver, and there is evidence that both biogeographic and ecological factors are involved. With this being the first time that these questions have been asked, we think we’ve made an important advance and that we’ve opened a number of avenues for future study.

      It also important to consider the time scales involved and the sampling regime. Glacial-interglacial cycles averaged ~100 Kyr back to 0.74 Mya and then averaged ~41 Kyr from then back to 2.47 Mya; about 50-60 of these cycles occurred (Lisiecki & Raymo 2005: fig. 4). This probably caused a lot of population structuring and mixing in these lineages. In addition, in the PSMC output from one of our lineages, C. ustulatus swainsonii, we find that there are 54 time segments sampled for the Pleistocene, indicating the inadequacy of this method to reflect fine-scale changes and suggesting that each estimate is capturing a lot of both phenomena, structuring and mixing. We have added this to the revision.

      I generally agree with the authors that "at present there is no way to fully disentangle the effects of population structure and geographic space on our results". But given that, I think there are two options - either we can fully acknowledge that oversimplified demographic models like PSMC cannot be interpreted as supporting evidence of any particular mechanistic or biogeographic hypothesis and stop trying to use them to do that, or we have to do our best to understand specifically which models can be distinguished by the analyses we're employing. 

      Short of developing some novel theory deep in the PSMC model, I think readers would need to see simulations showing that the analyses employed in this paper are capable of supporting or refuting their biogeographic hypothesis before viewing them as strongly supporting a specific biogeographic model. Tools like msprime and stdpopsim can be used to simulate genome-scale data with fairly complex biogeographic models. Running simulations of a thrush-like population under different biogeographic scenarios and then using PSMC to differentiate those patterns would be a more convincing argument for the biogeographic aspects of this paper. The other benefit of this approach would be to nail down a specific quantitative version of the taxon cycles model referenced in the abstract, and it would allow the authors to better study and explain the motivation behind the specific summary statistics they develop for PSMC posthoc analysis.  

      These could very well be fruitful pursuits for future work, but they are beyond the scope of this paper. The impossibility of reconstructing ranges through deep time makes anything other than the very general biogeographic hypothesis we’ve posed an uncertain pursuit. Also, a purely biogeographic approach neglects the likelihood of ecological expansion also being involved. We get at the importance of the latter in the “Geography and evolutionary ecology” section of the Discussion. Below, the editor states that discussions among reviewers indicate that simulations are not warranted at this time. We agree that the complexities involved are substantial, to the point of making direct relevance to this empirical study uncertain (especially in such an among-lineage context). Regarding taxon cycles, we merely point out that that conceptual framework seems relevant given our findings. This was not even remotely anticipated at the outset of the study, so we are reluctant to do anything more than point out its possible relevance in several aspects of the results. Finally, the motivation for the study’s summary statistics were entirely driven by the hypotheses, as given in Methods, and due to an earlier error (noted above), there are no post-hoc analyses in the revision. Sorry for the needless confusion.

      Reviewer #2 (Public Review): 

      Summary: 

      Winker and Delmore present a study on the demographic consequences of migratory versus resident behavior by contrasting the evolutionary history of lineages within the same songbird group (thrushes of the genus Catharus). 

      Strengths: 

      I appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools. 

      Weaknesses: 

      The current version of the paper, with the case study chosen, the results, and the relative discussion, is not satisfying enough to support or reject the hypotheses here considered.  

      Given the stated strengths, the weaknesses noted seem a little incongruous, but we understand from the comments below that the reviewer would like to see the study redesigned and expanded.  

      The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

      Because the questions are about the migratory life history strategy and the best way to get at this is in a phylogenetic framework, we’re not sure how we could effectively add species “regardless of their migratory behavior.” Further, we know that migration causes lineages to experience variable ecological conditions that include breeding, migration, and wintering conditions. Obligate migrants are going to have different breeding ranges from their close relatives, and the more distantly related species are, the less likely it is that they respond to particular ecological conditions the same way. So we do not think that an approach that included miscellaneous species from northern and southern regions would strengthen this study. Here, the comparative framework of closely related lineages that possess or lack the trait of interest is a study design strength. We do agree, however, that future work is needed that does encompass more lineages (we would argue in a phylogenetic context), and that disentangling the effects of geography and ecology will also be an important future endeavor. 

      As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.  

      We do recognize that timing estimates vary among studies. Differences among studies in important variables like markers, methods, generation time, and mutation or substitution rates create much of this uncertainty. Also, we are not confident in prior dating efforts in this group, largely because of gene flow and its effects on bringing estimates closer to the present. As we point out (line 485), differences among studies on these issues do not detract from the strengths here for within-study, among-lineage contrasts. In short, the timing could be off in an among-study context (and likely is with prior work, given gene flow), but relative performance of among-lineage N<sub>e</sub> differences is less susceptible to these factors. This was shown fairly well in Li & Durbin’s initial use of the method among human populations. Regarding substantial admixture, PSMC curves often unite at their origins with sister lineages (when they were the same lineage). A good example is with the two C. guttatus E & W curves in Fig. S3, which still have substantial gene flow today (they are subspecies and in contact), yet they show remarkably different N<sub>e</sub> curves through their history. It is not possible to mark a cutoff point for each lineage that represents the cessation of admixture with another lineage (e.g., Everson et al. 2019 showed substantial admixture between three full species in this group); that period can be very long (Price et al. 2008), varies among lineages, and will not be available for deeper lineage divergences in the phylogeny. We therefore chose to use all of the time intervals retrievable from the genomic data in each lineage, considering that this uniform treatment is the best approach for our among-lineage comparison. And note that we were careful to label these as “the lineages’ PSMC inception” (line 190).  

      Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.  

      The complexities of such modeling in a system like this are daunting. The different degrees of structuring among all of these lineages across just a single glacial-interglacial cycle would necessitate a lot of guesswork; projecting that back across 50-60 such cycles just in the Pleistocene would probably end up being fiction. Disentangling the effects of structure versus changes in N<sub>e</sub> in a system like this would probably not be possible with that approach and these data. As noted above and below, there was agreement among reviewers and the editor that simulations in this case are not warranted for revision. We have added the nature of the glacialinterglacial cycles and the PSMC sampling time segments to help readers understand this better (see above in response to R1, and lines 272-278).

      Additionally, I have struggled to understand if migratory behaviour in birds is considered to be acquired to relieve species competition, or as a consequence of expanded range (i.e., birds expand their range but their feeding ground is kept where speciation occurred as to exploit a ground with higher quality and abundance of seasonal local resources).  

      The origins of migration have been a struggle for researchers since the subject was taken up. But how the trait was acquired among these species does not really matter for our study. Here, migratory lineages possess different biogeographic+ecological attributes than their close relatives that are sedentary. Our focus is on the presence and absence of this life-history trait.

      The points raised above could be considered to improve the current version of the paper. 

      Thank you. We appreciate the opportunity to guide our revision using your comments.  

      Reviewer #3 (Public Review): 

      Summary: 

      This paper applies PSMC and genomic data to test interesting questions about how life history changes impact long-term population sizes. 

      Strengths: 

      This is a creative use of PSMC to test explicit a priori hypotheses about season migration and N<sub>e</sub>. The PSMC analyses seem well done and the authors acknowledge much of the complexity of interpretation in the discussion. 

      Weaknesses: 

      The authors use an average generation time for all taxa, but the citations imply generation time is known for at least some of them. Are there differences in generation time associated with migration? I am not a bird biologist, but quick googling suggests maybe this is the case (https://doi.org/10.1111/1365-2656.13983). I think it important the authors address this, as differences in generation time I believe should affect estimates of N<sub>e</sub> and growth.  

      Good point. The study cited by the reviewer encompasses a much higher degree of variation in body size and thus generation time. Differences in generation time in similarly sized close relatives, as in our study, should be small, and our approach has been to average those that are known. Unfortunately, generation times are not known for all of these species, but given their similarity in size we can have reasonable confidence in their being similar. We used data from the life-history research available (as cited) to obtain our average; there are not appropriate data for the residents, though. However, there is thought to be a generation time cost to seasonal migration in birds, and Bird et al. (2020) included this in their estimates to provide modeled values for all of the lineages we studied. We’re leery of using modeled values where good data for the nonmigrants in this group don’t exist (and the basis for quantifying this cost is tiny), but we recognize that this second approach is available and could leave some doubt in our results if not pursued. So we re-did everything with the modeled generation times of Bird et al. (2020). As expected, most of the differences are time-related. Importantly, our overall results are not different. We present them as Table S2 and have added the details on this to the Methods.

      The writing could be improved, both in the introduction for readers not familiar with the system and in the clarity and focus of the discussion.  

      We have added a phylogeny (new Fig. 1) to help readers better understand the system, and we’ve re-worked the Discussion to make it clearer what is clarified by our results and what remains unclear.  

      Recommendations for the authors:

      Reviewing Editor comment: 

      I note that discussion among the reviewers made clear that simulations are probably not the right answer given the complexity of the modeling required.  

      We appreciate this conclusion, with which we agree.  

      Reviewer #2 (Recommendations For The Authors): 

      Apologies for the delay with the review, which came at a very busy time. I hope you will find my comments helpful.

      Thanks. Your comments are helpful, and we fully understand how reviews (and our revisions!) have to wait until more pressing needs are addressed.

      I enjoyed reading the manuscript but I believe that the discussion sections could be heavily rewritten for better clarity. The discussion is sometimes redundant and lacks some flow/clarity. In a nutshell, I had the feeling that a bit of everything is thrown in the discussion but clear conclusions are not made.  

      Yes, the Discussion has been difficult to write, because more issues arose in the Results than we anticipated at the outset. We feel that discussing them is relevant, but we agree that much remains unclear. This coupling of paleodemographics with geography and ecology is a new area, which opens some important new (and relevant) areas to consider. So clarity is not possible in some areas. We’ve revised to point out where we do have clarity (e.g., in migrant lineages having different paleodemographic attributes than nonmigrants) and where only further study can provide clarity (e.g., in the roles of geography versus ecology). The journal format does not seem to have secondary subheaders, but we’ve used bold in one place to highlight ‘ecological mechanisms’ to offset that section, one of the more complex. We’ve also added a paragraph in the conclusions to clarify where we have clear takeaways and where uncertainties remain. 

      Reviewer #3 (Recommendations For The Authors): 

      The introduction should engage the reader with biology, not the use of demographic methods or genomics (both of which have been around for more than a decade). I would drop the first paragraph and considerably expand the second. What has previous research on ecology/behavior/genetics found regarding the demographic effects of seasonal migration?

      There are two important aspects to our study: 1) using paleodemographic methods to test hypotheses about adoption of a major life-history trait—an important biological question regardless of system, and so far (surprisingly) unaddressed; and 2) using this novel approach to study the effects of one such trait, seasonal migration. At these timescales, nothing exists on this subject, so there is really nothing to expand with. If there is relevant literature that we’ve missed, we’d be happy to add it.

      What is the missing bit of information or angle the current study addresses (other than just doing it larger and fancier with genomics)?  

      The effects of major life-history traits on paleodemographics has not been addressed before, to our knowledge. The whole context is new, so we’re not doing something “larger and fancier” with genomics. We are doing something that has not been done before: testing hypotheses about the effects of a major life-history trait on population sizes in evolutionary time. We’re not sure how this can be made clearer. To us this seems like a very engaging biological question with wide applicability. We hope that this study is just the first of many to come, in a diversity of biological systems.

      A figure showing the phylogenetic relationships of these taxa which are migratory would help the reader immensely. Although this is shown in Fig S3 I think it might be nice to have a map of the species and their ranges alongside a phylogeny as a main figure early on.  

      Thank you. This is a good suggestion. We can’t fit a phylogeny and all the distribution maps (Fig. S1) onto a page, but we can include a phylogeny as one of the main figures with nonmigrants highlighted. We’ve inserted this as a new Fig. 1. 

      If I understand correctly, the authors' arguments for why migratory species should show more growth hinge on large range size and geographic expansion. Yet they argue in the discussion that these forces are unlikely to be important (L226). I found the discussion on this confusing (e.g. L231 then says maybe it does matter). I think more clarity here would be helpful.

      Our argument and predictions are based both on geographic and ecological expansion. This was clearly stated as our third prediction “3) early population growth would be higher as seasonal migration opens novel ecological and geographic space…” We have gone back through and reiterated the coupling of these two factors. The line mentioned concludes the first paragraph in the section ‘Geography and evolutionary ecology,’ which focuses on the difficulty of decoupling these in this system. As the paragraph relates, geography alone does not seem to be driving our results (we do not argue that it is unimportant). 

      I also would have liked more time in the discussion addressing why variation in N<sub>e</sub> may be higher in migratory lineages.

      In addition to re-clarifying this in the Introduction, we have touched back on this now at line 221: “We attribute the higher variation in N<sub>e</sub> among migrants to be the result of the relative instability of northern biomes compared with tropical ones through glacial-interglacial cycles (e.g., Colinvaux et al., 2000; Pielou, 1991).”

      Minor comments: 

      L 62: Presumably PSMC is limited by the coalescent depth of the genelaogy, which may be younger or older than population "origins" depending on the history of colonization, lineage splitting, gene flow, etc.  

      We were careful to phrase these as “the lineages’ PSMC inception” (line 190), and responded to this issue in more detail above in response to R2’s public review. 

      L 338: I think a few more details on PSMC would be helpful. Was no maskfile used?  

      We did not use a maskfile, choosing instead to generate data of decent coverage and aligning reads to a single closely related relative. 

      Did the consensus fasta include all species?  

      No, we used a single reference high-quality fasta of Catharus ustulatus , as reported (lines 434-37). We have added that “Identical treatment of all lineages in these respects should provide a strong foundation for a comparative study like this among close relatives.” 

      L 361: Fair to assume the authors used a weighted average of N<sub>e</sub> from the output, rather than just averaging the N<sub>e</sub> values from each time segment?  

      No – we used all the values of N<sub>e</sub> produced by PSMC output. The PSMC method uses nonoverlapping portions of the genome in its analyses (which we’ve added to make that clear), and portions in juxtaposition will often provide data for very different periods in the time segments. Further, time segments are uneven within and among taxa, so it is not clear how a uniform and comparable weighting scheme could be implemented. We consider a uniform approach to be of primary importance, including for future comparisons among studies. 

      L 383 "delta" typo

      Thank you for catching this.

      L 93: I'd be tempted to present the questions (how does seasonal migration affect population size trajectory, means, and variation) and rationale before presenting the hypotheses. I found myself reading the hypotheses and wondering "why?"  

      We’ve tried this change in the revision. It makes the hypotheses a little harder to pull out (they are no longer numbered in a short sequence), but it is shorter and solves this concern.  

      L 337 read depth is usually expressed as X (e.g. "23X") rather than bp.

      Changed.

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      Reply to the reviewers

      Reply to the Reviewers

      We sincerely appreciate your insightful and constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript.

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

      2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

      Response:

      Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

      2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

      Response:

      As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

      The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

      As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 7).

      3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

      Response:

      In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 12). Therefore, by E21, the sound localization circuit is largely established.

      On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume.

      Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

      4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Response:

      In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

      As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

      Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

      Response:

      This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the post-functional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

      __ __

      Reviewer #2

      Evidence, reproducibility and clarity

      Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

      Major points, detailed below, need to be addressed to overcome some limitations of the study.

      Major comments:

      1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

      Response:

      eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

      One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

      Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 {plus minus} 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 5).

      2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

      Response:

      Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 {plus minus} 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

      On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

      To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we plan to add a new Supplementary Figure. Specifically, we will evaluate the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. Preliminary data indicate that, unlike the results in Figure 6D, sparse eTeNT expression did not contribute to an increase in heminodes and unmyelinated segments. This result would further support our argument that the increase in unmyelinated segments by A3V-eTeNT was due to a disruption of synaptic transmission between NM axons and NL neurons, which in turn altered the pericellular microenvironment at the NL region.

      3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

      __Response: __

      Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

      3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

      __Response: __

      We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

      3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

      __Response: __

      In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

      3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

      __Response: __

      We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman's rank correlation coefficient (rₛ) was used for evaluation.

      3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

      __Response: __

      We have now clarified what "n" represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

      In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

      To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592-019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

      If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

      3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

      __Response: __

      We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

      4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

      __Response: __

      The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 32).

      5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

      __Response: __

      As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

      The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

      In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

      6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

      6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

      6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

      6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812). 6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

      __Response: __

      Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

      Minor comments:

      7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

      __Response: __

      Thank you for your suggestion. We have corrected the units in Figure 1G to µm

      8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

      __Response: __

      Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

      Significance

      In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

      __Response: __

      The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density.

      __ __

      Reviewer #____3

      Evidence, reproducibility and clarity

      The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

      1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

      __Response: __

      Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

      Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

      However, a key issue to be discussed in this study is not merely the presence or absence of pre-nodal clusters, but rather whether pre-nodal clusters-if present-would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression. If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

      2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

      __Response: __

      As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision.

      In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions.

      3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

      __Response: __

      The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

      Significance

      The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1. Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
      2. Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1. The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
      2. In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
      3. The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
      4. The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      Regarding the manuscript's clarity, the sentence on page 5, "We also stained VTA sections for Tyrosine hydroxylase (TH) to estimate the rate of ChR2 colocalization with DA neurons," reads awkwardly. Removing the word "rate" could improve clarity.

      We have made the recommended clarifying edit (page 5, lines 30-31).

      Additionally, the anatomical data and findings are largely non-quantitative in nature. However, solid microscopy images are presented to support each claim. Additional quantification would strengthen the paper, specifically the quantification of projection density for each population and the proportion of each subpopulation that projects to their regions of interest.

      To rigorously quantify the projection density of each subpopulation would require a level of exhaustivity our study was not designed for. This is because during microscopy we focused efforts on imaging regions containing dense signals but did not exhaustively image regions receiving apparently weak or no input. While we considered including a semi-quantitative table of projection density, based on the data available we could not discriminate with confidence between, e.g., regions recipient of minimal input versus no input from VTA populations. Thus, while we stand by our descriptive statements we do not expand on those further.

      The authors should consider discussing the possibility that subpopulations of these cells could still be true interneurons especially if cells were looked at the single neuron level of resolution.

      We agree that some of the VTA populations we studied could include subpopulations that are bona fide interneurons. The identification of alternate markers or combinations of markers, or use of single-cell imaging approaches may indeed support this possibility in future. This is discussed in the context of currently available evidence on page 5 lines 32-34, page 11 lines 2-4, page 12 lines 2-11, and page 12 lines 15-16.

      Overall, the paper is well-written and important for the field and beyond.

      Thank you!

      Reviewer #2:

      Weaknesses:

      While the authors use several Cre driver lines to identify GABAergic projection neurons, they then use wild-type mice to show that projection neurons synapse onto neighboring cells within the VTA. This does not seem to lend evidence to the idea that previously described "interneurons" are projection neurons that collateralize within the VTA.

      We think the use of WT mice is a strength because it allows us to measure both GABA and non-GABA synapses made by VTA projections on to the same cells within VTA. However, we have also done this experiment targeting NAc-projecting VTA VGAT-Cre neurons, and VP-projecting VTA MOR-Cre neurons. Consistent with the WT dataset, we find that these defined projection neurons also make intra-VTA synapses. These data are now included as Figure 7.

      More broadly. Our review of the literature finds very little evidence to support the notion of a VTA interneuron as we define it: VTA neurons that makes only local connections. But the absence of evidence need not imply evidence of absence, thus we do not claim that all VTA neurons previously presumed to be interneurons must be projection neurons. We do express confidence in our findings that VTA projection neurons (that include GABA-releasing neurons) make local synapses in VTA. We argue that in the absence of compelling positive evidence for the existence of VTA interneurons, such as a selective marker, “we”, “the field”, should not presume their existence.

      Other suggestions:

      (1) While the authors present evidence that some projection neurons also synapse locally, there is no quantification as to the proportion of each neuronal subtype that collateralizes within the VTA. This would be a useful analysis.

      We agree this would be useful information. But our experiments were not designed to answer this question. Indeed, we have not conceived of a feasible method to discriminate between collateralizing and non-collateralizing VTA projection neurons at the single-cell level, thus we do not know how we would calculate such proportions.

      (2) There is significant interest in the molecular heterogeneity and spatial topography of the VTA. Additional analyses of the spatial topography of labeled projectors would be useful. For example, knowing if Pvalb+ projection neurons are distributed throughout the VTA or located along the midline would be a useful analysis.

      Prior studies and public databases (e.g., Allen brain atlas, GENSAT) allow one to visualize the location of VTA neurons positive for Pvalb and the other markers we investigated (Olson & Nestler, 2007). However, these label the entire population of neurons and thereby include those that project to any of the various projection targets. There are also studies that have used retrograde labeling approaches to map the distribution of labeled VTA cells projecting to one or another target (Beier et al., 2015; Lammel et al., 2008; Margolis et al., 2006). For example, finding that LHb-projecting neurons (a major target of Pvalb+ VTA neurons) are enriched in medial VTA (Root et al., 2014). From this evidence we might infer that Pvalb+ VTA neurons that project to LHb are likely to be medially biased. Future studies may more carefully map the intersection of specific projection targets for each VTA subpopulation.  

      Reviewer #3 (Recommendations For The Authors):

      Weaknesses:

      This study has a few modest shortcomings, of which the first is likely addressable with the authors' existing data, while the latter items will likely need to be deferred to future studies:

      (1) Some key anatomical details are difficult to discern from the images shown. In Figure 1, the low-magnification images of the VTA in the first column, while essential for seeing what overall section is being shown, are not of sufficient resolution to distinguish soma from processes. A supplemental figure with higher-resolution images could be helpful.

      We uploaded a higher resolution file for figure 1.

      Also, where are the insets shown in the second column obtained from? There is not a corresponding marked region on the low-magnification images. Is this an oversight, or are these insets obtained from other sections that are not shown?

      This was an oversight, we added the corresponding marked region to the low-magnification images.

      Lastly, there is a supplemental figure showing the NAc injection sites corresponding to Figure 5, but not one showing VP or PFC injection sites in Figure 6. Why not?

      We added a figure with histology examples for the VP and the PFC injection sites as done for Figure 5, included as Supplemental Figure 3.

      (2) Because multiple ChR2 neurons are activated in the optogenetic experiments, it is not clear how common is it for any specific projection neuron to make local connections. Are the observed synaptic effects driven by just a few neurons making extensive local collateralizations (while other projection neurons do not), or do most VTA projection neurons have local collaterals? I realize this is a complex question, that may not have an easy answer.

      This is a great question but, indeed, we don’t know the answer. As mentioned in response to Reviewer #2, we are not convinced there is a currently feasible way to discriminate between collateralizing and non-collateralizing cells at the single cell level.

      (3) There is something of a conceptual disconnect between the early and later portions of this paper. Whereas Figures 1-4 examine forebrain projections of genetic subtypes of VTA neurons, the optogenetic studies do not address genetic subtypes at all. I do realize that is outside of the scope of the author's intent, but it does give the impression of somewhat different (but related) studies being stitched together. For example, the MOR-expressing neurons seem to project strongly to the VP, but it is not addressed whether these are also the ones making local projections. Also, after showing that PV neurons project to the LHb, the opto experiments do not examine the LHb projection target at all.

      This too was raised by Reviewer #2. While addressing this question for all the populations we investigated feels redundant, we now include optogenetic data showing that NAc-projecting VTA VGAT-Cre and VP-projecting VTA MOR-Cre neurons also make local collaterals (Figure 7). We think this allows us to connect the two approaches to a greater degree. Based on our findings using a dual virus approach to express Syn:Ruby in each population of VTA projection neuron, we think it very likely that we’d continue to find similar results using optogenetics-assisted slice electrophysiology for each population.

      Other suggestions:

      (1) I appreciated the extensive and high-quality anatomical figures shown in Figures 2-4. However, the layout was sometimes left-to-right, and sometimes right-to-left, which felt distracting. At some point, the text refers to "Fig. 3KJ", i.e. with the letters being in backward alphabetical order, and Figures 3I and 3L do not appear mentioned anywhere in the main text, leading me to wonder if that text was intended to read "Fig. 3I-L".

      Thank you for noting this. We have harmonized the layout of Figures 2-4 and adjusted the in-text Figure call-outs.

      Also, the inset in Figure 3J appears to show local collaterals of NTS neurons in the VTA, since there is no soma in that inset. This is interesting, and worth reporting, but is not explained in either the main text or Figure legend.

      We added a more complete description in the result section (page 6 line 25-30).

      (2) Perhaps I missed it, but I could not find any mention of the intensity of the LED light delivered during the optogenetic experiments. While acknowledging that this can be variable, do the authors have at least a rough range?

      We have added this information to the methods, page 17 line 8.

      Editor's Note:

      Should you choose to revise your manuscript, please double check that you have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      We confirm that we have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      Note to Editor and Readers

      While reanalyzing our data for resubmission, we discovered that some of the short-latency optogenetic evoked postsynaptic currents (oPSCs) we detected were erroneously categorized. Specifically, some VTA cells that showed large outward currents (oIPSCs) when held at 0 mV, also had small inward currents when held at -60 mV. These small inward currents were initially categorized as oEPSCs, suggesting these VTA cells received input from populations of VTA projection neurons that released GABA and/or glutamate. However, the kinetics of these small inward currents were slow and aligned with the within-cell kinetics of the oIPSCs, indicating that these were very likely mediated by GABA<SUB>A</SUB> receptors. In one case the opposite was apparent, with a small PSC initially miscategorized as an oIPSC. These miscategorized oEPSCs and oIPSC were presumably detected because our holding potentials were not precisely identical to the reversal potentials for GABA<SUB>A</SUB> and AMPA receptors, respectively. For this reason, we removed these 14 oEPSCs and 1 oIPSCs from our analyses in the revised version. The revised dataset suggests that VTA glutamate projection neurons may be less likely to collateralize widely within VTA compared to GABA projection neurons. But, importantly, this correction does not affect any of our conclusions.

      Citations:

      Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015). Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping. Cell, 162(3), 622-634. https://doi.org/10.1016/j.cell.2015.07.015

      Lammel, S., Hetzel, A., Hackel, O., Jones, I., Liss, B., & Roeper, J. (2008). Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron, 57(5), 760-773. https://doi.org/10.1016/j.neuron.2008.01.022

      Margolis, E. B., Lock, H., Chefer, V. I., Shippenberg, T. S., Hjelmstad, G. O., & Fields, H. L. (2006). Kappa opioids selectively control dopaminergic neurons projecting to the prefrontal cortex. Proc Natl Acad Sci U S A, 103(8), 2938-2942. https://doi.org/10.1073/pnas.0511159103

      Olson, V. G., & Nestler, E. J. (2007). Topographical organization of GABAergic neurons within the ventral tegmental area of the rat. Synapse, 61(2), 87-95. https://doi.org/10.1002/syn.20345

      Root, D. H., Mejias-Aponte, C. A., Zhang, S., Wang, H. L., Hoffman, A. F., Lupica, C. R., & Morales, M. (2014). Single rodent mesohabenular axons release glutamate and GABA. Nat Neurosci, 17(11), 1543-1551. https://doi.org/10.1038/nn.3823

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      Reply to the reviewers

      Manuscript number: RC-2024-02831

      Corresponding author(s): Charisios Tsiairis

      1. General Statements [optional]

      We are very pleased that all three reviewers found our work to be solid, well-supported by the data, and free of major flaws. It is particularly gratifying that they did not request additional experimental work to support our conclusions. Instead, their comments focused on clarifications, textual improvements, and refinements in data presentation, which we have carefully addressed.

      • *

      We have made revisions to improve the clarity of the manuscript, incorporating insightful suggestions from the reviewers. These include refining key explanations, adjusting figure annotations, and modifying the structure of certain sentences. Additionally, we have addressed specific points regarding statistical significance, genome assembly references, and phylogenetic comparisons, ensuring that all aspects of our study are as precise and informative as possible.

      • *

      We are confident that these revisions have strengthened the manuscript.

      2. Point-by-point description of the revisions

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes. *

      • *

      *General Points: *

      • *

      • The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood. *

      • *

      The observed differences in upregulation are primarily technical. Expression levels are measured relative to unperturbed tissue, and in the control, Zic4 expression in the foot is detected only at noise levels (see figure 2C). As a result, any increase in Zic4 expression upon Gata3 knockdown appears relatively high when normalized to the minimal control levels. In contrast, Gata3 is already present at detectable levels in control samples from the upper body, head, and tentacles (See Fig 2D). Therefore, while its upregulation following Zic4 RNAi appears more modest, we interpret this as a qualitative indication of increased gene expression in the absence of the opposing transcription factor. That said, we acknowledge the possibility of additional regulatory layers contributing to these differences.

      • *

      • Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced. *

      • *

      This is a valid point; however, the interpretation is complicated by the technical limitations of RNAi-based knockdown rather than a complete knockout of Gata3. Over time, the effect of RNAi diminishes, and we have observed that GFP expression returns within four weeks following GFP RNAi, indicating a temporal limit to RNAi-mediated knockdown. Therefore, while an extended time course would be informative, the transient nature of the knockdown makes it challenging to definitively track long-term cell replacement dynamics.

      • *

      • The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers? *

      • *

      We appreciate the reviewer’s comment. In our previous publication (Vogg et al., 2022), we provided evidence supporting this phenomenon. As demonstrated in our data published there, markers of the peduncle, rather than the basal disc—such as manacle (gene ID 100212761) (Bridge et al., 2000) and Bmp5-8 (gene ID 100206618) (Reinhardt et al., 2004)—are also upregulated, suggesting a transition towards a peduncle-like state. However, we opted not to elaborate on this aspect in the current manuscript to maintain focus and avoid redundancy with previously published findings.

      • *

      *Specific Points: *

      • *

      *Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles. *

      • *

      Our intention was to illustrate the general presence of Zic4 in the oral domain, but we acknowledge the reviewer’s point that this could be misleading regarding its precise expression pattern. To address this concern, we have updated the figure panels to more accurately reflect the available in situ hybridization and scRNA-seq data.

      • *

      *Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend. *

      • *

      We thank the reviewer for catching this error. We have now corrected the references, replacing the numbering with the first authors' last names and publication dates.

      • *

      Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

      • *

      We appreciate the reviewer’s observation. The label refers to the budding zone, and we acknowledge our omission in specifying this. We have now updated the figure and its legend to clarify this.

      • *

      *Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper? *

      • *

      We appreciate the reviewer’s request for clarification. These conditions are indeed similar to those in the previously published Vogg et al. paper. The boxes in the figure represent proximal and distal tentacle regions, each with four replicates. We have now updated the figure and its legend to make this explicit.

      • *

      *Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative. *

      • *

      *We added the percentage of cells expressing Zic4, Gata3 and both genes on the panel. *

      • *

      *Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control. *

      • *

      To make the figure clearer, we modified it and grouped each experimental sample with its corresponding control.

      • *

      *Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups. *

      • *

      We have now expanded the statistical analyses, ensuring that significance tests are presented in all relevant instances. However, we note that while statistical significance is important, it should be interpreted alongside other factors such as the magnitude of the effect, consistent trends across replicates, and biological relevance. Additionally, high standard deviations in certain conditions may influence absolute p-values, and we encourage consideration of the broader context of the data when interpreting these results.

      • *

      *Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway). *

      • *

      We understand the reviewer’s concern. The p-value for the qPCR data is slightly above 0.05, primarily due to high standard deviation. As the reviewer notes, qPCR on RNAi samples can be noisy, so the data should be interpreted in context. Importantly, the consistent qualitative increase in Gata3 levels after Zic4 knockdown aligns with the RNA-seq results, which, as the reviewer correctly points out, provide a more reliable measurement. Additionally, qPCR samples include a broader portion of head tissue, likely diluting the Gata3 signal from the tentacles and contributing to the observed variability.

      • *

      *Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A. *

      • *

      We appreciate the reviewer’s suggestion. However, we believe that displaying co-expression UMAPs for Zic4 would be redundant. Additionally, for genes with greater positional overlap, such as FoxI1 and Nfat5, co-expression UMAPs make visualization more challenging. To ensure clarity and optimize the interpretability of the data, we have chosen to present the expression profiles of each gene separately.

      • *

      *Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly. *

      • *

      Indeed, the study does not focus on the functional role of Gata3 in these neurons. We have revised the sentence, replacing "involvement of Gata3 in specifying neurons" with "expression expression of Gata3 in neurons emerging*" to more accurately reflect the study’s findings. *

      • *

      *Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences. *

      • *

      We appreciate the reviewer’s concern. However, we have chosen to use the Hydra_RP_1.0 assembly for Figure 1 to maintain consistency with previously published data, which were also mapped to this assembly. Since these publications predate the newer assembly, using the same reference ensures comparability in our analysis. Importantly the assembly used is still downloadable and accessible to every researcher. That said, for the phylogenetic analysis in Figure 2, we have used the latest available genome assemblies and annotations for all species, including Hydra. We have now clarified this in the Methods section.

      • *

      *The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper). *

      • *

      We appreciate the reviewer’s suggestion and have updated the references accordingly to include Siebert et al., 2019, for data collection and Cazet et al., 2023, for the analysis used in this paper.

      • *

      Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

      • *

      We agree with the reviewer that the nematocyte marker HCR provides a more specific identification of nematocytes. This is why we have also used the nematocilin marker in separate samples. However, actin staining adds important information on the morphology of the surrounding epithelial cells, which become indistinguishable from battery cells in Gata3 KDs. Unfortunately, combining actin staining with HCR is technically challenging, as the tissue preparation protocols for these two approaches are not compatible, and we have therefore decided to show both stainings next to each other.

      • *

      *Minor Wording Issues: *

      • *

      *Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear. *

      • *

      We have made the suggested change.

      • *

      *Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset. *

      • *

      We are grateful for the suggestion, we have changed the text accordingly.

      • *

      *p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed. *

      • *

      We are referring to the upregulation of gata3 expression in tentacles upon Zic4 knockdown. To make this clearer, we have revised the wording to: “Q-PCR performed on dissected oral and aboral body regions confirmed the upregulation of gata3 upon Zic4 knockdown (Fig. 2C-D).”

      • *

      *Reviewer #1 (Significance (Required)): *

      • *

      *This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination. *

      • *

      *We appreciate the reviewer’s thoughtful summary and recognition of our study’s significance. *

      • *

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Summary: *

      *The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia. *

      • *

      *Major comments: *

      *- Are the key conclusions convincing? *

      *The key conclusions are convincing. *

      • *

      *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? *

      *The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. *

      • *

      *- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. *

      *No. *

      *- Are the data and the methods presented in such a way that they can be reproduced? *

      *Yes, *

      *- Are the experiments adequately replicated and statistical analysis adequate? *

      *Yes. *

      • *

      *Minor comments: *

      *- Specific experimental issues that are easily addressable. *

      *None. *

      *- Are prior studies referenced appropriately? *

      *Yes. *

      *- Are the text and figures clear and accurate? *

      *Yes. The figures are very nice. *

      • *

      *- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *

      • *

      *1) Move the phylogenetic comparisons to the end *

      *2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results. *

      • *

      We thank the reviewer for their positive assessment and constructive suggestions. Below, we comment on each point:

      • Placement of cross-species comparison: This suggestion concerns the emphasis and structure of the manuscript. We appreciate the reviewer's interest in the evolutionary aspects of our work. However, we believe that moving this analysis to the end would dilute the main message, which is reinforced by the schematic in Figure 4E-F. We aim to conclude with the experimental results demonstrating the minimization of phenotypic consequences when both factors are knocked down. Therefore, we have chosen to retain the cross-species comparison in its current position to emphasize the conservation of the double-negative interaction before presenting the functional consequences of its perturbation.
      • Reordering of Gata3 KD results: We understand the rationale behind this suggestion. However, our sequencing is guided by the fact that foot regeneration deficiency under Gata3 kd has already been documented and presented in previous work (Ferenc et al., 2021). For this reason, we begin with that reference, then build upon it with a deeper examination of the phenotype.
      • *

      We are grateful for the reviewer’s feedback and for recognizing the clarity of our figures and analysis.

      • *

      ***Referee cross-commenting** *

      • *

      *I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1. *

      • *

      *Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity. *

      • *

      We appreciate Reviewer’s #3 remark about the nuance of transcription factor homology. The situation is exactly as described here by Reviewer #2 - The gene names in Figure 1 are based on the results of NCBI automated homology annotation, which we have now clarified in a note in the legend of Figure 1.

      • *

      *Reviewer #2 (Significance (Required)): *

      • *

      *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. *

      *This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. *

      *- Place the work in the context of the existing literature (provide references, where appropriate). *

      *The manuscript does a good job of placing the work into the appropriate context. *

      • *

      *- State what audience might be interested in and influenced by the reported findings. *

      *Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. *

      • *

      *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. *

      *Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution *

      • *

      We greatly appreciate the reviewer’s positive feedback and recognition of our study's focus on gene expression in cell fate decisions. We're pleased that our findings on the mutual knockdown and the broader context were well received. Thank you for highlighting the relevance of our work to gene regulation and cell specification.

      • *

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two. *

      • *

      *A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry. *

      • *

      The naming of the gene as Sry was indeed based on the NCBI automated homology annotation, and we have clarified this in the revised manuscript. Since we did not pursue further analysis of this gene, we believe that a deeper phylogenetic analysis may not be necessary and could potentially divert attention from the main focus of our study on Gata3's role.

      • *

      *Reviewer #3 (Significance (Required)): *

      • *

      *This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers. *

      • *

      We thank the reviewer for the thoughtful assessment of our work. We appreciate their feedback and the recognition of the quality and significance of our findings.

      • *
    1. This suffers from a sufficient formalisation of the concept of "similarity". Everything is either so similar that characterisation as "identical", similar or different or very different, depending on the frame of reference. By pointing out some resemblense, you cannot make a justified judgement about the similarity or difference of anything. I would suggest that Luhmann didn't write more about his method himself because it would have been generally fruitless for him as everyone around him was doing exactly the same thing. I asked ca. two dozen professors at the very university about their method (btw. at the very university that Luhmann was a professor at). NONE had anything remotely resembling a Luhmann-Zettelkasten. During his lifetime there was quite some interest in his Zettelkasten, hence the visitors, hence the disappointment of the visitors (people made an effort to review his Zettelkasten): (9/8,3) Geist im Kasten? Zuschauer kommen. Sie bekommen alles zu sehen, und nichts als das – wie beim Pornofilm. Und entsprechend ist die Enttäuschung. - From his own Zettelkasten So: The statement that his practice was basically common place (or even a common place book) is not based on sound reasoning (sufficiently precise in the use of the concept "similarity") There is empirical evidence that it was very uncommon. (Which is obvious if you think about the his theoretical reasoning about his Zettelkasten as heavily informed by the very systems theory that he developed. So, a reasoning unique to him)

      Reply to u/FastSascha at https://old.reddit.com/r/Zettelkasten/comments/1ilvvnc/you_need_to_first_define_the_zettlekasten_methoda/mc01tsr/

      The primary and really only "innovation" for Luhmann's system was his numbering and filing scheme (which he most likely borrowed and adapted from prior sources). His particular scheme only serves to provide specific addresses for finding his notes. Regardless of doing this explicitly, everyone's notes have a physical address and can be cross referenced or linked in any variety of ways. In John Locke's commonplacing method of 1685/1706 he provided an alternate (but equivalent method) of addressing and allowing the finding of notes. Whether you address them specifically or not doesn't change their shape, only the speed by which they may be found. This may shift an affordance of using such a system, but it is invariant from the form of the system. What I'm saying is that the form and shape of Luhmann's notes is identical to the huge swath of prior art within intellectual history. He was not doing something astoundingly new or different. By analogy he was making the same Acheulean hand axe everyone else was making; it's not as if he figured out a way to lash his axe to a stick and then subsequently threw it to invent the spear.

      When I say the method was commonplace at the time, I mean that a broad variety of people used it for similar reasons, for similar outputs, and in incredibly similar methods. You can find a large number of treatises on how to do these methods over time and space, see a variety of examples I've collected in Zotero which I've mentioned several times in the past. Perhaps other German professors weren't using the method(s) as they were slowly dying out over the latter half of the 20th century with the rise and ultimate ubiquity of computers which replaced many of these methods. I'll bet that if probed more deeply they were all doing something and the something they were doing (likely less efficiently and involving less physically evident means) could be seen to be equivalent to Luhmann's.

      This also doesn't mean that these methods weren't actively used in a variety of equivalent forms by people as diverse as Aristotle, Cicero, Quintilian, Seneca, Boethius, Thomas Aquinas, Desiderius Erasmus, Rodolphus Agricola, Philip Melancthon, Konrad Gessner, John Locke, Carl Linnaeus, Thomas Harrison, Vincentius Placcius, Gottfried Wilhelm Leibniz, S. D. Goitein, Gotthard Deutsch, Beatrice Webb, Sir James Murray, Marcel Mauss, Claude Lévi-Strauss, Mortimer J. Adler, Niklas Luhmann, Roland Barthes, Umberto Eco, Jacques Barzun, Vladimir Nabokov, George Carlin, Twyla Tharp, Gertrud Bauer, and even Eminem to name but a few better known examples. If you need additional examples to look at, try searching my Hypothesis account for tag:"zettelkasten examples". Take a look at their examples and come back to me and tell me that beyond the idiosyncrasies of their individual use that they weren't all doing the same thing in roughly the same ways and for roughly the same purposes. While the modalities (digital or analog) and substrates (notebooks, slips, pen, pencil, electrons on silicon, other) may have differed, the thing they were doing and the forms it took are all equivalent.

      Beyond this, the only thing really unique about Luhmann's notes were that he made them on subjects that he had an interest, the same way that your notes are different from mine. But broadly speaking, they all have the same sort of form, function, and general topology.

      If these general methods were so uncommon, how is it that all the manuals on note taking are all so incredibly similar in their prescriptions? How is it that Marbach can do an exhibition in 2013 featuring 6 different zettelkasten, all ostensibly different, but all very much the same?

      Perhaps the easier way to see it all is to call them indexed databases. Yours touches on your fiction, exercise, and nutrition; Luhmann's focuses on sociology and systems theory; mine looks at intellectual history, information theory, evolution, and mathematics; W. K. Kellogg's 640 drawer system in 1906 focused on manufacturing, distributing and selling Corn Flakes; Jonathan Edwards' focused on Christianity. They all have different contents, but at the end of the day, they're just indexed databases with the same forms and functionalities. Their time periods, modalities, substrates, and efficiencies have differed, but at their core they're all far more similar in structure than they are different.

      Perhaps one day, I'll write a deeper treatise with specific definitions and clearer arguments laying out the entire thing, but in the erstwhile, anyone saying that Luhmann's instantiation is somehow more unique than all the others beyond the meaning expressed by Antoine de Saint-Exupéry in The Little Prince is fooling themselves. Instead, I suspect that by realizing you're part of a longer, tried-and-true tradition, your own practice will be far easier and more useful.

      The simplicity of the system (or these multiply-named methods) allows for the rise of a tremendous amount of complexity. This resultant complexity can in turn hide the simplicity of the root system.

      “To me, you are still nothing more than a little boy who is just like a hundred thousand other little boys. And I have no need of you. And you, on your part, have no need of me. To you, I am nothing more than a fox like a hundred thousand other foxes. But if you tame me, then we shall need each other. To me, you will be unique in all the world. To you, I shall be unique in all the world..."

      I can only hope people choose to tame more than Luhmann.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary

      The authors describe a method for gastruloid formation using mouse embryonic stem cells (mESCs) to study YS and AGM-like hematopoietic differentiation. They characterise the gastruloids during nine days of differentiation using a number of techniques including flow cytometry and single-cell RNA sequencing. They compare their findings to a published data set derived from E10-11.5 mouse AGM. At d9, gastruloids were transplanted under the adrenal gland capsule of immunocompromised mice to look for the development of cells capable of engrafting the mouse bone marrow. The authors then applied the gastruloid protocol to study overexpression of Mnx1 which causes infant AML in humans.

      In the introduction, the authors define their interpretation of the different waves of hematopoiesis that occur during development. 'The subsequent wave, known as definitive, produces: first, oligopotent erythro-myeloid progenitors (EMPs) in the YS (E8-E8.5); and later myelo-lymphoid progenitors (MLPs - E9.5-E10), multipotent progenitors (MPPs - E10-E11.5), and hematopoietic stem cells (HSCs - E10.5-E11.5), in the aorta-gonadmesonephros (AGM) region of the embryo proper.' Herein they designate the yolk sac-derived wave of EMP hematopoiesis as definitive, according to convention, although paradoxically it does not develop from intraembryonic mesoderm or give rise to HSCs.

      The apparent perplexity of the Reviewer with our definition of primitive and definitive waves is somewhat surprising, as it is widely used in the field (e.g. PMID: 18204427; PMID: 28299650; PMID: 33681211). Definitive haematopoiesis, encompassing EMP, MLP, MPP and HSC, highlights their origin from haemogenic hendothelium, generation of mature cells with adult characteristics from progenitors with multilineage potential and direct and indirect developmental contributions to the intra-embryonic and time-restricted generation of HSCs.

      General comments

      The authors make the following claims in the paper:

      (1) The development of a protocol for hemogenic gastruloids (hGx) that recapitulates YS and AGM-like waves of blood from HE.

      (2) The protocol recapitulates both YS and EMP-MPP embryonic blood development 'with spatial and temporal accuracy'.

      (3) The protocol generates HSC precursors capable of short-term engraftment in an adrenal niche.

      (4) Overexpression of MNX1 in hGx transforms YS EMP to 'recapitulate patient transcriptional signatures'.

      (5) hGx is a model to study normal and leukaemic embryonic hematopoiesis.

      There are major concerns with the manuscript. The statements and claims made by the authors are not supported by the data presented, data is overinterpreted, and the conclusions cannot be justified. Furthermore, the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis. the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis.

      Specific points

      (1) It is claimed that HGxs capture cellularity and topography of developmental blood formation. The hGx protocol described in the manuscript is a modification of a previously published gastruloid protocol (Rossi et al 2022). The rationale for the protocol modifications is not fully explained or justified. There is a lack of novelty in the presented protocol as the only modifications appear to be the inclusion of Activin A and an extension of the differentiation period from 7 to 9 days of culture. No direct comparison has been made between the two versions of gastruloid differentiation to justify the changes.

      The Reviewer paradoxically claims that the protocol is not novel and that it differs from a previous publication in at least 2 ways – the patterning pulse and the length of the protocol. Of these, the patterning pulse is key. As documented in Fig. S1, we cannot obtain Flk1-GFP expression in the absence of Activin A. Expression of Flk1 is a fundamental step in haemato-endothelial specification and, accordingly, we do not see CD41 or CD45+ cells in the absence of Activin A. Also, in our hands, there is a clear time-dependent progression of marker expression, with sequential acquisition of CD41 and CD45, with the latter not detectable until 192h (Fig. 1C-D), another key difference relative to the Rossi et al (2022) protocol. The 192h-timepoint, we argue in the manuscript, and present further evidence for in this rebuttal, corresponds to the onset of AGM-like haematopoiesis. We have empirically extended the protocol to maximise the CD45+ cell output (Fig. S1B-D).

      The inclusion of Activin A at high concentration at the beginning of differentiation would be expected to pattern endoderm rather than mesoderm. BMP signaling is required to induce Flk1+ mesoderm, even in the presence of Wnt.

      Again, we call the Reviewer’s attention to Fig. S1 which clearly shows that Activin A (with no BMP added) is required for induction of Flk1 expression, in the presence of Wnt. Activin A in combination with Wnt, is used in other protocols of haemato-endothelial differentiation from pluripotent cells, with no BMP added in the same step of patterning and differentiation (PMID: 39227582; PMID: 39223325). In the latter protocol, we also call the Reviewer’s attention to the fact that a higher concentration of Activin A precludes the need for BMP4 addition. Finally, one of us has recently reported that Activin A, on its own, will induce FLK1, as well as other anterior mesodermal progenitors (https://www.biorxiv.org/content/10.1101/2025.01.11.632562v1)..) In addressing the Reviewer’s concerns with the dose of Activin A used, we titrated its concentration against activation of Flk1, confirming optimal Flk1-GFP expression at the 100ng/ml dose used in the manuscript.

      Author response image 1.

      Dose-dependent requirement of Activin A for induction of Flk1 expression in haemogenic gastruloids. Composite GFP and brightfield live imaging of Flk1-GFP haemogenic gastruloids at 96h. Images were acquired using a Cytation5 instrument (Thermo). Images are representative of 12 gastruloids per condition.

      FACS analysis of the hGx during differentiation is needed to demonstrate the co-expression of Flk1-GFP and lineage markers such as CD34 to indicate patterning of endothelium from Flk1+ mesoderm. The FACS plots in

      Fig. 1 show c-Kit expression but very little VE-cadherin which suggests that CD34 is not induced. Early endoderm expresses c-Kit, CXCR4, and Epcam, but not CD34 which could account for the lack of vascular structures within the hGx as shown in Fig. 1E.

      We were surprised by the Reviewer’s comment that there are no endothelial structures in our gastruloids. The presence of a Flk1-GFP+ network is visible in the GFP images in Fig.1B, from 144h onwards, also shown in Author response image 2A. In addition, our single-cell RNA-seq data, included in the manuscript, confirms the presence of endothelial cells with a developing endothelial, including arterial, programme. This can be seen in Fig. 2B, F of the manuscript and is represented in Author response image 2B. In contrast with the Reviewer’s claims that no endothelial cells are formed, the data show that Kdr (Flk1)+ cells co-express Cdh5/VE-Cadherin and indeed Cd34, attesting to the presence of an endothelial programme. Arterial markers Efnb2, Flt1, and Dll4 are present. A full-blown programme, which also includes haemogenic markers including Sox17, Esam, Cd44 and Mecom is clear at early (144h) and, particularly at late (192h) timepoints in cells sorted on detection of surface c-Kit (Author response image 2B). Further to the data shown in B, already present in the manuscript, we also document co-expression of Flk1-GFP and CD34 by flow cytometry (Author response image 2C).

      Author response image 2.

      Haemogenic gastruloids have a branched vascular network. A. Whole-mount confocal imaging of 144h-haemogenic gastruloids. B. Differentiation of an arterial endothelial programme in haemogenic gastruloids; singlecell RNA-seq data of differentiating haemogenic gastruloids, sorted on cell surface expression of c-Kit at 144 and 192h; gene expression colour scale from yellow (low) to orange (high); grey = no detectable expression. C. Flow cytometry plots of 216h-haemogenic gastruloids showing detection of haemato-endothelial marker CD34.

      (2) The protocol has been incompletely characterised, and the authors have not shown how they can distinguish between either wave of Yolk Sac (YS) hematopoiesis (primitive erythroid/macrophage and erythro-myeloid EMP) or between YS and intraembryonic Aorta-Gonad-Mesonephros (AGM) hematopoiesis. No evidence of germ layer specification has been presented to confirm gastruloid formation, organisation, and functional ability to mimic early development. Furthermore, differentiation of YS primitive and YS EMP stages of development in vitro should result in the efficient generation of CD34+ endothelial and hematopoietic cells. There is no flow cytometry analysis showing the kinetics of CD34 cell generation during differentiation. Benchmarking the hGx against developing mouse YS and embryo data sets would be an important verification.

      The Reviewer is correct that we have not provided detailed characterisation of the different germ layers, as this was not the focus of the study. In that context, we were surprised by the earlier comment assuming co-expression of c-kit, Cxcr4 and Epcam, which we did not show, while overlooking the endothelial programme reiterated above, which we have presented.

      Given our focus on haemato-endothelial specification, we have started the single-cell RNA-seq characterisation of the haemogenic gastruloid at 120h and have not looked specifically at earlier timepoints of embryo patterning.

      This said, we show the presence of neuroectodermal cells in cluster 9; on the other hand, cluster 7 includes hepatoblast-like cells, denoting endodermal specification. We are happy to include this characterisation, to the extent that it is present, in a revised version of the manuscript. However, in the absence of earlier timepoints and given the bias towards mesodermal specification, we expect that specification of ectodermal and endodermal programmes may be incomplete.

      In respect of the contention regarding the capture of YS-like and AGM-like haematopoiesis, we have presented evidence in the manuscript that haemogenic cells generated during gastruloid differentiation, particularly at late 192h and 216h timepoints project onto highly purified c-Kit+ CD31+ Gfi1-expressing cells from mouse AGM (PMID: 38383534), providing support for the recapitulation of the corresponding developmental stage. In distinguishing between YS-like and AGM-like haematopoiesis, we call the Reviewer’s attention to the replotting of the single-cell RNA-seq data already in the manuscript, which we provided in response to point 1 (Author response image 2B), which highlights an increase in Sox17, but not Sox18, expression in the 192h haemogenic endothelium, which suggests an association with AGM haematopoiesis (PMID: 20228271). A significant association of Cd44 and Procr expression with the same time-point (Fig. 2F in the manuscript), further supports an AGM-like endothelial-to-haematopoietic transition at the 192h timepoint.

      Following on the Reviewer’s comments about CD34, we also inspected co-expression of CD34 with CD41 and CD45, the latter co-expression present in, although not necessarily exclusive to, AGM haematopoiesis.

      Reassuringly, we observed clear co-expression with both markers (Author response image 3), in addition to a CD41+CD34-population, which likely reflects YS EMP-independent erythropoiesis. Interestingly, marker expression is responsive to the levels of Activin A used in the patterning pulse, with the 100ng/ml Activin A used in our protocol superior to 75ng/ml.

      Author response image 3.

      Association of CD34 with CD41 and CD45 expression is Activin A-responsive and supports the presence of definitive haematopoiesis. A. Flow cytometry analysis of CD34 and CD41 expression in 216h-haemogenic gastruloids; two doses of Activin A were used in the patterning pulse with CHI99021 between 48-72h. FMO controls shown. B. Flow cytometry analysis of CD34 and CD45 at 216h in the same experimental conditions.

      We agree that it remains challenging to identify markers exclusive to AGM haematopoiesis, which is operationally equated with generation of transplantable haematopoietic stem cells. While HSC generation is a key event characteristic of the AGM, not all AGM haematopoiesis corresponds to HSCs, an important point in evaluating the data presented in the manuscript, and indeed acknowledged by us.

      Author response image 4.

      Clustering of haemogenic gastruloid cells sorted on the basis of haemato-endothelial surface markers CD41, C-Kit and CD45. A. Leiden clustering to single-cell RNA-seq data. B. Time stamps of sorted haemogenic gastruloid cells in A. C. Surface marker stamps of cells in A.

      Given the centrality of this point in comments by all the Reviewers, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346).

      Focusing the analysis on the subsets of haemogenic gastruloid cells sorted as CD41+ (144h) CKit+ (144h and 192h) and CD45+ (192h and 216h) (Author response image 4AC), we show:

      (1) That a subset of haemato-endothelial cells from haemogenic gastruloids at 144h to 216h project onto intra-embryonic cells spanning E8.25 to E10 (Author response image 5A-B). This is in agreement with our interpretation that 216h are no later than the MPP/pre-HSC state of embryonic development, requiring further maturation to generate long-term engrafting HSC.

      (2) That haemogenic gastruloids contain YS-like (including EMP-like) and AGM-like haematopoietic cells (Author response image 6A-B). Significantly, some of the cells, particularly c-Kit-sorted cells with a candidate endothelial and HE-like signature project onto AGM pre-HE and HE, as well as IAHC, and later, predominantly 216h cells, have characteristics of MPP/LMPP-like cells from the FL.

      Altogether, the data support the notion that haemogenic gastruloids capture YS and AGM haematopoiesis until E10, as suggested by us in the manuscript. We thought it was important to share this preliminary data with the Editors at an early stage, and we will incorporate a deeper analysis in a revised version of the manuscript.

      Single-cell RNA sequencing was used to compare hGx with mouse AGM. The authors incorrectly conclude that ' ..specification of endothelial and HE cells in hGx follows with time-dependent developmental progression into putative AGM-like HE..' And, '...HE-projected hGx cells.......expressed Gata2 but not Runx1, Myb, or Gfi1b..' Hemogenic endothelium is defined by the expression of Runx1 and Gfli1b is downstream of Runx1.

      As a hierarchy of regulation, Gata2 precedes and drives Runx1 expression at the specification of HE (PMID: 17823307; PMID: 24297996), while Runx1 drives the EHT, upstream of Gfi1b in haematopoietic clusters (PMID: 34517413).

      Author response image 5.

      Projection of sorted haemogenic gastruloid cells onto Hou et al dataset (PMID: 32203131) analysing development of mouse intra-embryonic haematopoiesis. A. Time signatures of Hou et al data. B. Projection of Leiden clusters in Author response image 4A. Methodology as described in our manuscript; 68% gastruloid cells projected.

      Author response image 6.

      Projection of sorted haemogenic gastruloid cells onto Zhu et al dataset (PMID: 32392346), capturing arterial endothelial and haemogenic endothelial development, in reference to YS, AGM and FL haematopoietic progenitors. A. Functional cluster classification as per Zhu et al. B. Projection of Leiden clusters in Author response image 4A. Methodology as detailed in our manuscript; 58% gastruloid cells projected. Haematopoietic clusters annotated as in A.

      (3) The hGx protocol 'generates hematopoietic SC precursors capable of short-term engraftment' is not supported by the data presented. Short-term engraftment would be confirmed by flow cytometric detection of hematopoietic cells within the recipient bone marrow, spleen, thymus, and peripheral blood that expressed the BFP transgene. This analysis was not provided. PCR detection of transcripts, following an unspecified number of amplification cycles, as shown in Figure 3G (incorrectly referred to as Figure 3F in the legend) is not acceptable evidence for engraftment.

      We provide the full flow cytometry analysis of spleen engraftment in the 5 mice which received implantation of 216h-haemogenic gastruloids in the adrenal gland; an additional (control) animal received adrenal injection of PBS (Author response image 7). The animals were analysed at 4 weeks. In this experiment, the bone marrow collection was limiting, and material was prioritised for PCR.

      We had previously provided only representative plots of flow cytometry analysis of bone marrow and spleen in Fig. S4E, which we described as low-level engraftment. The analysis was complemented with genomic DNA PCR, where detection was present in only some of the replicates tested per animal. We confirm that PCR analysis used conventional 40 cycles; the sensitivity was shown in Fig. S4F. As shown in Fig. 3 A-C, no more than 7 CD45+CD144+ multipotent cells are present per haemogenic gastruloid, with 3 haemogenic gastruloids implanted in the adrenal gland of each transplanted animal. We argue that the low level of cytometric and molecular engraftment at 4 weeks, from haemogenic gastruloid-derived progenitors that have not progressed beyond a stage equivalent to E10 Author response image 5A-B) and that we have described as requiring additional maturation in vivo, are not surprising.

      Author response image 7.

      BFP engraftment of Nude recipient mice 4 weeks after unilateral adrenal implantation of 216h-haemogenic gastruloids. Flow cytometry analysis of spleen engraftment. Genomic PCR analysis is shown in Fig. 3G of the manuscript.

      Transplanted hGx formed teratoma-like structures, with hematopoietic cells present at the site of transplant only analysed histologically. Indeed, the quality of the images provided does not provide convincing validation that donor-derived hematopoietic cells were present in the grafts.

      As stated in the text, the images mean to illustrate that the haemogenic gastruloids developed in situ. The observation of donor-derived blood cells in the implanted haemogenic gastruloids would not correspond to engraftment, as we have amply demonstrated that they have generated blood cells in vitro. There is no evidence that there are remaining pluripotent cells in the haemogenic gastruloid after 9 days of differentiation, and it is therefore not clear that these are teratomas

      There is no justification for the authors' conclusion that '... the data suggest that 216h hGx generate AGM-like pre-HSC capable of at least short-term multilineage engraftment upon maturation...'. Indeed, this statement is in conflict with previous studies demonstrating that pre-HSCs in the dorsal aorta of the mouse embryo are immature and actually incapable of engraftment.

      We have clearly stated that we do not see haematopoietic engraftment through transplantation of dissociated haemogenic gastruloids, which reach the E10 state containing pre-HSC (Author response image 5). Instead, we observed rare myelo-erythroid (in the manuscript) and myelo-lymphoid (Author response image 9 below, in response to Reviewer 2) engraftment upon in vivo maturation of haemogenic gastruloids with preserved 3D organisation. These statements are not contradictory.

      The statement '...low-level production of engrafting cells recapitulates their rarity in vivo, in agreement with the embryo-like qualities of the gastruloid system....' is incorrect. Firstly, no evidence has been provided to show the hGx has formed a dorsal aorta facsimile capable of generating cells with engrafting capacity. Secondly, although engrafting cells are rare in the AGM, approximately one per embryo, they are capable of robust and extensive engraftment upon transplantation.

      We are happy to rephrase the statement to simply say that “…the data suggest that 216h haemogenic gastruloids contain candidate AGM-like progenitors with some short-term engraftment potential but incomplete functional maturation.” To be clear, with our existing statement we meant to highlight that the production of definitive AGM-like haematopoietic progenitors (not all of which are engrafting) in haemogenic gastruloids does not correspond to non-physiological single-lineage programming. We did not claim that we achieved production of HSC, which would be long-term engrafting.

      (4) Expression MNX1 transcript and protein in hematopoietic cells in MNX1 rearranged acute myeloid leukaemia (AML) is one cause of AML in infants. In the hGX model of this disease, Mnx1 is overexpressed in the mESCs that are used to form gastruloids. Mnx1 overexpression seems to confer an overall growth advantage on the hGx and increase the serial replating capacity of the small number of hematopoietic cells that are generated. The inefficiency with which the hGx model generates hematopoietic cells makes it difficult to model this disease. The poor quality of the cytospin images prevents accurate identification of cells. The statement that the kit-expressing cells represent leukemic blast cells is not sufficiently validated to support this conclusion. What other stem cell genes are expressed? Surface kit expression also marks mast cells, frequently seen in clonogenic assays of blood cells. Flow cytometric and gene expression analyses using known markers would be required.

      The haemogenic gastruloid model generates haematopoietic and haemato-endothelial cells. MNX1 expands Kit+ cells at 144h, which we show to have a haemato-endothelial signature (manuscript Fig. 2B, which we replotted in Author response image 2B).

      Serial replating of CFC assays is a conventional in vitro assay of leukaemia transformation. Critically, colony replating is not maintained in EV control cells, attesting to the transformation potential of MNX1.

      Although we have not fully-traced the cellular hierarchy of MNX1-driven transformation in the haemogenic gastruloid system, the in vitro replating expands a Kit+ cell (Fig. 5E), which reflects the surface phenotype of the leukaemia, also recapitulated in the mouse model initiated by MNX1-overexpressing FL cells. Importantly, it recapitulates the transcriptional profile of MNX1-leukaemia patients (Fig. 6C), which is uniquely expressed by MNX1144h and replated colony cells, but not to MNX1 216h gastruloid cells, arguing against a generic signature of MNX1 overexpression (Fig. 6B). Importantly, the MNX1-transformation of haemogenic gastruloid cells is superior to the FL leukaemia model at capturing the unique transcriptional features of MNX1-driven leukaemia, distinct from other forms of AML in the same age group (Fig S7). It is possible that this corresponds to a preleukaemia event, and we will explore this in future studies, which are beyond the proof-of-principle nature of this paper.

      (5) In human infant MNX1 AML, the mutation is thought to arise at the fetal liver stage of development. There is no evidence that this developmental stage is mimicked in the hGx model.

      We never claim that the haemogenic gastruloid model mimics the foetal liver. We propose that susceptibility to MNX1 is at the HE-to-EMP transition. Moreover, and importantly, contrary to the Reviewer’s statement, there is no evidence in the literature that the mutation arises in the foetal liver stage, just that the mutation arises before birth (PMID: 38806630), which is different. In a mouse model of MNX1 overexpression, the authors achieve leukaemia engraftment upon MNX1 overexpression in foetal liver, but not in bone marrow cells (PMID: 37317878). This is in agreement with a vulnerability of embryonic / foetal, but not adult cells to the MNX1 expression caused by the translocation. However, haematopoietic cells in the foetal liver originate from YS and AGM precursors, so the origin of the MNX1-susceptible cells can be in those locations, rather than the foetal liver itself.

      Reviewer #2 (Public review):<br /> Summary:<br /> In this manuscript, the authors develop an exciting new hemogenic gastruloid (hGX) system, which they claim reproduces the sequential generation of various blood cell types. The key advantage of this cellular system would be its potential to more accurately recapitulate the spatiotemporal emergence of hematopoietic progenitors within their physiological niche compared to other available in vitro systems. The authors present a large set of data and also validate their new system in the context of investigating infant leukemia.<br /> Strengths:<br /> The development of this new in vitro system for generating hematopoietic cells is innovative and addresses a significant drawback of current in vitro models. The authors present a substantial dataset to characterize this system, and they also validate its application in the context of investigating infant leukemia.<br /> Weaknesses:<br /> The thorough characterization and full demonstration that the cells produced truly represent distinct waves of hematopoietic progenitors are incomplete. The data presented to support the generation of late yolk sac (YS) progenitors, such as lymphoid cells, and aortic-gonad-mesonephros (AGM)-like progenitors, including pre-hematopoietic stem cells (pre-HSCs), by this system are not entirely convincing. Given that this is likely the manuscript's most crucial claim, it warrants further scrutiny and direct experimental validation. Ideally, the identity of these progenitors should be further demonstrated by directly assessing their ability to differentiate into lymphoid cells or fully functional HSCs. Instead, the authors primarily rely on scRNA-seq data and a very limited set of markers (e.g., Ikzf1 and Mllt3) to infer the identity and functionality of these cells. Many of these markers are shared among various types of blood progenitors, and only a well-defined combination of markers could offer some assurance of the lymphoid and pre-HSC nature of these cells, although this would still be limited in the absence of functional assays.<br /> The identification of a pre-HSC-like CD45⁺CD41⁻/lo c-Kit⁺VE-Cadherin⁺ cell population is presented as evidence supporting the generation of pre-HSCs by this system, but this claim is questionable. This FACS profile may also be present in progenitors generated in the yolk sac such as early erythro-myeloid progenitors (EMPs). It is only within the AGM context, and in conjunction with further functional assays demonstrating the ability of these cells to differentiate into HSCs and contribute to long-term repopulation, that this profile could be strongly associated with pre-HSCs. In the absence of such data, the cells exhibiting this profile in the current system cannot be conclusively identified as true pre-HSCs.

      At this preliminary response stage, we present 2 additional pieces of evidence to support our claims that we capture YS and AGM stages of haematopoietic development. In future experiments, we can complement these with functional assays, including co-culture with OP9 and OP9-DL stroma.

      Author response image 8.

      EZH2 inhibition affects CD41+ cellular output in haemogenic gastruloids at 144, but not 216h. A. Flow cytometry analysis of CD41 expression in 144h-haemogenic gastruloid treated with 0.5μM EZH2 inhibitor GSK126 from 120h. DMSO (0.05%), vehicle. 1 of 2 independent experiments (average CD41+: DMSO, 21.20%; GSK126, 12.10%; CD45 not detected). B. Flow cytometry analysis of CD41 and CD45 expression in 216h gastruloids, treated with DMSO or GSK216. (DMSO: average CD41+, 15.28%; average CD45+ 0.46%. GSK126: average CD41+, 23.78%; average CD45+, 2.08%).

      In Author response images 5 and 6, we project our single-cell RNA-seq data onto (1) developing intra-embryonic pSP and AGM between E8 and E11 (Author response image 5) and (2) a single-cell RNA-seq study of HE development which combines haemogenic and haematopoietic cells from the YS, the developing HE and IAHC in the AGM, and FL (Author response image 6). Our data maps E8.25-E10 (Author response image 5) and captures YS EMP and erythroid and myeloid progenitors, as well as AGM pre-HE, HE and IAHC, with some cells matching HSPC and LMPP (Author response image 6), as suggested by the projection onto the Thambyrajah et al data set (Fig. S3 in the manuscript).

      Given the difficulty in finding markers that specifically associate with AGM haematopoiesis, we inspected the possibility of capturing different regulatory requirements at different stages of gastruloid development mirroring differential effects in the embryo. Polycomb EZH2 is specifically required for EMP differentiation in the YS, but does not affect AGM-derived haematopoiesis; it is also not required for primitive erythroid cells (PMID: 29555646; PMID: 34857757). We treated haemogenic gastruloids from 120h onwards with either DMSO (0.05%) or GSK126 (0.5μM), and inspected the cellularity of gastruloids at 144h, which we equate with YS-EMP, and 216h – putatively AGM haematopoiesis (Author response image 8). We show that EZH2 inhibition / GSK126 treatment specifically reduces %CD41+ cells at 144h (Author response image 8A), but does not reduce %CD41+ or %CD45+ cells at 216h (Author response image 8B).

      Although preliminary, these data, together with the scRNA-seq projections described, provide evidence to our claim that 144h haemogenic gastruloids capture YS EMPs, while CD41+ and CD45+ cells isolated at 216h reflect AGM progenitors. We cannot conclude as to the functional nature of the AGM cells from this experiment.

      The engraftment data presented are also not fully convincing, as the observed repopulation is very limited and evaluated only at 4 weeks post-transplantation. The cells detected after 4 weeks could represent the progeny of EMPs that have been shown to provide transient repopulation rather than true HSCs.

      We clearly state that there is low level engraftment and do not claim to have generated HSC. We describe cells with short-term engraftment potential. Although the cells we show in the manuscript at 4 weeks could be EMPs (Author response image 7 and Fig. 3 and S3), we now have 8-week analysis of implant recipients, in which we observed, again low-level, engraftment of the recipient bone marrow in 1:3 animals (Author response image 9). This engraftment is myeloid-lymphoid and therefore likely to have originated in a later progenitor. To be clear, we do not claim that this corresponds to the presence of HSC. It nevertheless supports the maturation of progenitors with engraftment potential.

      Author response image 9.

      Flow cytometry BFP engraftment of recipient bone marrow 8-weeks post implantation of 216hhaemogenic gastruloids in the adrenal gland of Nude mice. 1:3 animals show BFP CD45+ engraftment in the myeloid (Mac1+) and B-lymphoid (B220+) lineages. 3 haemogenic gastruloids were implanted unilaterally in the adrenal gland of each animal. A. Engrafted animal, showing CD45+ BFP cells of myeloid (CD11b) and B-lymphoid affiliation (B220). B. Non-engrafted mouse recipient of haemogenic gastruloid implants.

      Reviewer #3 (Public review):<br /> In this study, the authors employ a mouse ES-derived "hemogenic gastruloid" model which they generated and which they claim to be able to deconvolute YS and AGM stages of blood production in vitro. This work could represent a valuable resource for the field. However, in general, I find the conclusions in this manuscript poorly supported by the data presented. Importantly, it isn't clear what exactly are the "YS" and the "AGM"-like stages identified in the culture and where is the data that backs up this claim. In my opinion, the data in this manuscript lack convincing evidence that can enable us to identify what kind of hematopoietic progenitor cells are generated in this system. Therefore, the statement that "our study has positioned the MNX1-OE target cell within the YS-EMP stage (line 540)" is not supported by the evidence presented in this study. Overall, the system seems to be very preliminary and requires further optimization before those claims can be made.<br /> Specific comments below:<br /> (1) The flow cytometric analysis of gastruloids presented in Figure 1 C-D is puzzling. There is a large % of c-Kit+ cells generated, but few VE-Cad+ Kit+ double positive cells. Similarly, there are many CD41+ cells, but very few CD45+ cells, which one would expect to appear toward the end of the differentiation process if blood cells are actually generated. It would be useful to present this analysis as consecutive gating (i.e. evaluating CD41 and CD45 within VE-Cad+ Kit+ cells, especially if the authors think that the presence of VE-Cad+ Kit+ cells is suggestive of EHT). The quantification presented in D is misleading as the scale of each graph is different.

      Fig. 1C-D provide an overview of haemogenic markers during the timecourse of haemogenic gastruloid differentiation, and does indeed show a late up-regulation of CD45, as the Reviewer points out would be expected. The %CD45+ cells is indeed low. However, we should point out that the haemogenic gastruloid protocol, although biased towards mesodermal outputs, does not aim to achieve pure haematopoietic specification, but rather place it in its embryo-like context. Consecutive gating at the 216h-timepoint is shown and quantified in Fig. 3A-B. We refute that the scale is misleading. It is a necessity to represent the data in a way that is interpretable by the reader: the gates (in C) are truly representative and annotated, as are the plot axes (in D).

      (2) The imaging presented in Figure 1E is very unconvincing. C-Kit and CD45 signals appear as speckles and not as membrane/cell surfaces as they should. This experiment should be repeated and nuclear stain (i.e. DAPI) should be included.

      We include the requested images below (Author response image 10).

      Author response image 10.

      Confocal images of haematopoietic production in haemogenic gastruloids. Wholemount, cleared haemogenic gastruloids were stained for CD45 (pseudo-coloured red) and c-Kit antigens (pseudo-coloured yellow) with indirect staining, as described in the manuscript. Flk1-GFP signal is shown in green. Nuclei are contrasted with DAPI. (A) 192h. (B) 216h.

      (3) Overall, I am not convinced that hematopoietic cells are consistently generated in these organoids. The authors should sort hematopoietic cells and perform May-Grunwald Giemsa stainings as they did in Figure 6 to confirm the nature of the blood cells generated.

      It is factual that the data are reproducible and complemented by functional assays shown in Fig. 3, which clearly demonstrate haematopoietic output. The single-cell RNA-seq data also show expression of a haematopoietic programme. Nevertheless, we include Giemsa-Wright’s stained cytospins obtained at 216h to illustrate haematopoietic output (Reviewer Fig. 11). Inevitably, the cytospins will be inconclusive as to the presence of endothelial-to-haematopoietic transition or the generation of haematopoietic stem/progenitor cells, as these cells do not have a distinctive morphology.

      Author response image 11.

      Cytospin of dissociated haemogenic gastruloids at 216h. Cytospins were stained with Giemsa-Wright’s stain and are visualised with a 40x objective. Annotated are cells in the monocytic (dashed open arrow), granulocytic (solid open arrow), megakaryocytic (solid arrow) and erythroid (asterisk) lineages; arrowheads indicate cells with a non-specific blast-like morphology. Representative image.

      (4) The scRNAseq in Figure 2 is very difficult to interpret. Specific points related to this:<br /> - Cluster annotation in Figure 2a is missing and should be included.<br /> - Why do the heatmaps show the expression of genes within sorted cells? Couldn't the authors show expression within clusters of hematopoietic cells as identified transcriptionally (which ones are they? See previous point)? Gene names are illegible.<br /> - I see no expression of Hlf or Myb in CD45+ cells (Figure 2G). Hlf is not expressed by any of the populations examined (panels E, F, G). This suggests no MPP or pre-HSC are generated in the culture, contrary to what is stated in lines 242-245. (PMID 31076455 and 34589491).<br /> Later on, it is again stated that "hGx cells... lacked detection of HSC genes like Hlf, Gfi1, or Hoxa9" (lines 281-283). To me, this is proof of the absence of AGM-like hematopoiesis generated in those gastruloids.

      Author response image 12.

      Expression of endothelial, haemogenic and haematopoietic genes in haemogenic gastruloid cells sorted at 144h, 192h and 216h. UMAP as in Author response image 4. Pecam (CD31) and CD34 represent endothelial genes also detected in haemogenic endothelium. CD44 is specifically enriched at the endothelial-to-haemogenic transition. Mecom is detected in haemogenic endothelium and haematopoietic progenitors. Mllt3 and Runx1 are haematopoietic markers. Hoxa9 and Hlf are associated with haematopoietic stem and progenitor cells and their detection is rare in haemogenic gastruloids at 216h.

      For a combination of logistic and technical reasons, we performed single-cell RNA-seq using the Smart-Seq2 platform, which is inherently low throughput. We overcame the issue of cell coverage by complementing whole-gastruloid transcriptional profiling at successive time-points with sorting of subpopulations of cells based on individual markers documented in Fig. 1. We clearly stated which platform was used as well as the number and type of cells profiled (Fig. S2A and lines 172-179 of the manuscript), and our approach is standard. We will review our representation of the data in a revised manuscript. Nevertheless, at this stage, we provide plots of the expression of key haematopoietic markers over UMAPs of haemogenic gastruloid timecourse (Author response image 12). We also show preliminary qRT-PCR data with increased Hlf expression upon extension of the protocol to 264h (Author response image 13), further confirming haematopoietic specification, including of candidate definitive progenitor cells, in the haemogenic gastruloid model.

      Author response image 13.

      Hlf expression is up-regulated in late stage haemogenic gastruloids. Quantitative RT-PCR analysis of Hlf expression in unfractionated haemogenic gastruloids cultured for 264h. From 168h onwards, haemogenic gastruloids were cultured in N2B27 in the presence of VEGF, SCF, FLT3L and TPO, all recombinant mouse cytokines, as described in the manuscript. Shown are mean±standard deviation of n=5 replicates from 2 mouse ES cell lines, respectively Flk1-GFP and Rosa26-BFP::Flk1-GFP, reported in the manuscript; 2-tailed unpaired t-test with Welch correction.

      (5) Mapping of scRNA-Seq data onto the dataset by Thambyrajah et al. is not proof of the generation of AGM HE. The dataset they are mapping to only contains AGM cells, therefore cells do not have the option to map onto something that is not AGM. The authors should try mapping to other publicly available datasets also including YS cells.

      We have done this and the data are presented in Author response image 5 and 6. As detailed in response to Reviewer 1, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131) (Author response image 5), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346) (Author response image 6). Specifically in answering the Reviewers’ point, we show that different subsets of haemogenic gastruloid cells sorted on haemogenic surface markers c-Kit, CD41 and CD45 cluster onto pre-HE and HE, intra-aortic clusters and FL progenitor compartments, and to YS EMP and erythroid and myeloid progenitors. This lends support to our claim that the haemogenic gastruloid system specifies both YS-like and AGM-like cells.

      (6) Conclusions in Figure 3, named "hGx specify cells with preHSC characteristics" are not supported by the data presented here. Again, I am not convinced that hematopoietic cells can be efficiently generated in this system, and certainly not HSCs or pre-HSCs.

      We have provided evidence, both in the manuscript and in this response to Reviewers, that there is haematopoietic specification, including of progenitor cells, in the haemogenic gastruloid system (Fig. 3 and Author response image 7,9). We have added data in this response that supports the specification of YS-like and AGM-like cells (Author response image 5-6, 8). Importantly, we have never claimed that haemogenic gastruloids generate HSC. We accept the Reviewer’s comment that we have not provided sufficient evidence for the specification of pre-HSC-like cells. We will re-phrase Fig. 3 conclusion as “Haemogenic gastruloids specify cells with characteristics of definitive haematopoietic progenitors”.

      - FACS analysis in 3A is again very unconvincing. I do not think the population identified as c-Kit+ CD144+ is real. Also, why not try gating the other way around, as commonly done (e.g. VE-Cad+ Kit+ and then CD41/CD45)?

      There is nothing unconventional about our gating strategy, which was done from a more populated gate onto the less abundant one to ensure that the results are numerically more robust. In the case of haemogenic gastruloids, unlike the AGM preparations the Reviewer may be referring to, CD41 and CD45+ cells are more abundant as there is no circulation of more differentiated haematopoietic cells away from the endothelial structures. This said, we did perform the gating as suggested (Author response image 14), indeed confirming that most VE-cad+ Kit+ cells are CD45+. Interestingly VE-cad+Kit- are predominantly CD41+, reinforcing the true haematopoietic nature of these cells.

      Author response image 14.

      Flow cytometry analysis of VE-cadherin+ cells in haemogenic gastruloids at 216h of the differentiation protocol, probing co-expression of CD45, CD41 and c-Kit.

      - The authors must have tried really hard, but the lack of short- or long-engraftment in a number of immunodeficient mouse models (lines 305-313) really suggests that no blood progenitors are generated in their system. I am not familiar with the adrenal gland transplant system, but it seems like a very non-physiological system for trying to assess the maturation of putative pre-HSCs. The data supporting the engraftment of these mice, essentially seen only by PCR and in some cases with a very low threshold for detection, are very weak, and again unconvincing. It is stated that "BFP engraftment of the Spl and BM by flow cytometry was very low level albeit consistently above control (Fig. S4E)" (lines 337-338). I do not think that two dots in a dot plot can be presented as evidence of engraftment.

      We have presented the data with full disclosure and do not deny that the engraftment achieved is low-level and short-term, indicating incomplete maturation of definitive haematopoietic progenitors in the current haemogenic gastruloid system. However, we call the Reviewer’s attention to the fact that detection of BFP+ cells by PCR and flow cytometry in the recipient animals at 4 weeks is consistent between the 2 methods (Author response image 7).

      Furthermore, we have now also been able to detect low-level myelo-lymphoid engraftment in the bone marrow 8 weeks after adrenal implantation, again suggesting the presence of a small number of definitive haematopoietic progenitors that potentially mature from the 3 haemogenic gastruloids implanted (Author response image 9).

      (7) Given the above, I find that the foundations needed for extracting meaningful data from the system when perturbed are very shaky at best. Nevertheless, the authors proceed to overexpress MNX1 by LV transduction, a system previously shown to transform fetal liver cells, mimicking the effect of the t(7;12) AML-associated translocation. Comments on this section:<br /> - The increase in the size of the organoid when MNX1 is expressed is a very unspecific finding and not necessarily an indication of any hematopoietic effect of MNX1 OE.

      We agree with the Reviewer on this point; it is nevertheless a reproducible observation which we thought relevant to describe for completeness and data reproducibility.

      - The mild increase of cKit+ cells (Figure 4E) at the 144hr timepoint and the lack of any changes in CD41+ or CD45+ cells suggests that the increase in Kit+ cells % is not due to any hematopoietic effect of MNX1 OE. No hematopoietic GO categories are seen in RNA seq analysis, which supports this interpretation. Could it be that just endothelial cells are being generated?

      The Reviewer is correct that the MNX1-overexpressing cells have a strong endothelial signature, which is present in the patients (Fig. 4A). We investigated a potential link with c-Kit by staining cells from the replating colonies during the process of in vitro transformation with CD31. We observed that 40-50% of c-Kit+ cells (20-30% total colony cells) co-expressed CD31(Author response image 15), at least at early plating. These cells co-exist with haematopoietic cells, namely Ter119+ cells, as expected from the YS-like erythroid and EMP-like affiliation of haematopoietic output from 144h-haemogenic gastruloids (Fig. 5F).

      Author response image 15.

      Endothelial affiliation of MNX1-oe replating cells from haemogenic gastruloid. A. Representative flow cytometry plot of plate 1 CFC from MNX1-overexpressing haemogenic gastruloids at 144h. B. Quantification of the proportion of CD31+c-Kit+ cells in plates 1 and 2 of MNX1-oe-driven in vitro transformation.

      (8) There seems to be a relatively convincing increase in replating potential upon MNX1-OE, but this experiment has been poorly characterized. What type of colonies are generated? What exactly is the "proportion of colony forming cells" in Figures 5B-D? The colony increase is accompanied by an increase in Kit+ cells; however, the flow cytometry analysis has not been quantified.

      Given the inability to replate control EV cells, there is not a population to compare with in terms of quantification. The level of c-Kit+ represented in Fig. 5E is achieved at plate 2 or 3 (depending on the experiment), both of which are significantly enriched for colony-forming cells relative to control (Fig. 5B, D).

      (9) Do hGx cells engraft upon MNX1-OE? This experiment, which appears not to have been performed, is essential to conclude that leukemic transformation has occurred.

      For the purpose of this study, we are satisfied with confirmation of in vitro transformation potential of MNX1 haemogenic gastruloids, which can be used for screening purposes. Although interesting, in vivo leukaemia engraftment from haemogenic gastruloids is beyond the scope of this study.

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      Reply to the reviewers

      What follows is our revision Plan.

      Manuscript number: RC-2024-02794

      Corresponding author(s): Jo Morris

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

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      If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      We feel the reviewers understood the paper well and made many reasonable points for improvement.

      In response to Reviewer three's concern about the reliance on SAE2 over-expression, in the 'Significance' section "One limitation is the strong reliance on the use of an actyl-mimicking mutant". We were minded not to rely on the mutant. Hence, the paper contains considerable data onthe HDCAC6 deacteylase, responsible for SEA2 deacetylation. We show that HDAC6 inhibition phenocopies SAE2-K164Q expression and, moreover, that the approaches which rescue the mitotic defects of SAE2-K164Q expression cells also rescue the defects of HDCA6 inhibited cells. These observations, we believe, overcome the concern.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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      Revisions.

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      R1: As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check: a. Whether this form is indeed SUMO1-NuMA b. Whether it decreases upon expression of the SAE2K164Q variant.

      R2: Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.

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      Response:These are important points. We intend to perform the suggested experiments to address which isoform NuMa is modified by, and what the impact of the variant is.

      R2:Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.

      R2: Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?

      Response: We intend to repeat these assays, as suggested by the reviewer, reducing the enzyme concentrations and using low-concentration DTT. With the relevant controls and blots to show the identity of the RanGAP-SUMO2 product. Further, we will show control experiments with and without DTT that demonstrate the sensitivity of the SAE2~SUMO band to the reducing agent.

      R2: Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments?

      Response: We will attempt to show free SUMO levels in mitotic cells.

      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We will examine the localisation of the SAE2 variants (see section below for the SUMO proteins).

      R3: It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.

      Response: We intend to perform the first approach suggested, making and examining the double mutant's activity as suggested.

      R3: The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.

      Response. This is an excellent suggestion. We intend to make the constructs suggested and perform this experiment for our revision.

      R3. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures.

      Response. We will attempt to gain a view of SAE2 acetylation over the cell cycle, which requires the precipitation of endogenous SAE following synchronisation.

      R3: Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.

      Response: We will change this.

      R3: The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.

      Response: We have considered: "HDAC6-Dependent Deacetylation of SAE2 enhances SUMO1 Conjugation for Mitotic Integrity", we have not changed it on the current manuscript so as not to confuse the reader - we will change it at the journal level.

      R3: Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.

      Response: The data will be normalised to the WT condition (1 instead of 0), and raw data shown.

      R3: Fig 2c: Please also show representative raw data.

      Response: Representative images will be shown.

      R3: Fig 2d,f: Again, the legend should explain what the plots were normalized to.

      Response: Inserted in the legend for Fig. 2d&f: 'The RanGAP1-SUMO1 products are normalised to the WT SAE1:SAE2:SUMO1-only condition (top) and the RanGAP1-SUMO2 products are normalised to the WT SAE1:SAE2:SUMO2-only condition (bottom).'

      R3 Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Response: Agreed. The figure will be redrawn, and the possibility will be discussed.

      R1 Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?

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      Response: We believe this arose in measuring background density in the blots in error. We will re-assess the method used.

      R3: Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.

      Response: We will change one of the colours.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

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      R1:2. Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).

      Response: Inserted before first result using doxycycline: 'Furthermore, we generated U2OS with a doxycycline-inducible (wild-type) WT FLAG-SAE2 or a FLAG-SAE2-K164R mutant.'

      R1.3. Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?

      Response: Fig. 4f now annotated with 'Untransfected' and 'GFP-NuMA transfected'.

      R1.4. ML972- should read ML792 on pg 8.

      Response: Corrected.

      R3: All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed?

      Response: Supplemental Figure 4a illustrates that with the exception of the C173G mutant, the remainder WT, and K164-mutants are all expressed at near WT-levels and not over-expressed. The C-G-mutant is highly expressed.

      R3: On page 3, the authors could introduce a justification of why they tested IR treatment.

      Response: now justified.

      R3: The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.

      Response: Agreed. Done.

      R3: Page 8: "confirmation" should be "conformation".

      Response: Done.

      R3:Page 8: "While we find a little role for..." - delete "a"

      Response: Done.

      R2: Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates fractions pooled. Figure updated with relevant recombinant protein preps generated for updated in vitro experiments. The additional SAE1:SAE2-K164R peak which appeared in the previous manuscript Supp. Fig. 2a eluted in the void volume and so we think it comprised aggregated SAE1:SAE2 protein, more recent preparations do not show it.

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      R3: The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?

      Response: Yes, good point. Now included:

      R3: Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?

      Response: Comment included.

      R3: Fig 3h: typo ("Trasfect")

      Response: Done.

      R3: Fig 4f: The DAPI signal is hardly visible - better contrast would help.

      Response: Improved.

      R3: Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates the fractions pooled.

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      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

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      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We have investigated SUMO isoform location. However, in our hands, using a range of SUMO antibodies, we do not see the previously reported localisations in mitotic wild-type cells, and thus, we are not able to assess the impact of the SAE variants. As our phenotypes are restricted to mitosis, we do not consider it worthwhile to look at interphase.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors report on an interesting regulatory mechanism that influences the balance between conjugation of the different SUMO isoforms, SUMO1 versus SUMO2/3. The authors describe that acetylation of a specific residue, K164, in the SUMO activating enzyme (E1) subunit, SAE2, biases the E1's preference towards SUMO2/3. Specifically, they use an acetylation-mimicking K164Q mutation to show that the acetylation state of SAE2 likely affects the affinity of the E1 to SUMO and the rate of thioester formation. With an antibody, they demonstrate the acetylation of SAE2 in cells. Mechanistically, they locate the cause of the isoform bias to a residue in the C-terminus of SUMO in proximity to K164 or SAE2, where SUMO1 carries glutamate, while SUMO2/3 has glutamine. Switching these residues between the SUMO isoforms reverses the isoform preference of the E1. Phenotypically, the SAE2 K164Q mutant induces mitotic problems that the authors attribute to the SUMOylation of the NuMA complex. They assign the deacetylation of SAE1 to HDAC6 and report that deacetylation occurs during mitosis. These results are consistent with a model that SUMO1 modification of the NuMA complex in mitosis is important for mitotic fidelity and that the cell cycle-dependent changes in the acetylation status of SAE2 promote this. Accordingly, fusion of SUMO1 to a NuMA subunit partially overcomes the problems induced by the K164Q mutant or the inhibition of HDAC6.

      Major comments:

      The experiments are largely performed in a well-controlled manner, and overall, the study is very convincing. I would like to suggest a few experiments that would strengthen the authors' conclusions, and there are a few minor issues with some of the figures.

      1. It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.
      2. The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.
      3. The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?
      4. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures. All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed? Is is not possible to work with endogenous SAE2?

      Minor comments:

      • The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.
      • On page 3, the authors could introduce a justification of why they tested IR treatment.
      • The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.
      • Page 8: "confirmation" should be "conformation".
      • Page 8: "While we find a little role for..." - delete "a"
      • Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.
      • Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.
      • Fig 2c: Please also show representative raw data.
      • Fig 2d,f: Again, the legend should explain what the plots were normalized to.
      • Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?
      • Fig 3h: typo ("Trasfect")
      • Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.
      • Fig 4f: The DAPI signal is hardly visible - better contrast would help.
      • Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.
      • Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Significance

      The results presented here are interesting and novel. Importantly, the authors provide a molecular model for a new mechanism of how the SUMO system achieves isoform specificity, which is a still very poorly understood phenomenon. The manuscript makes a significant advance by contributing an important new aspect of how the SUMO conjugation machinery chooses between isoforms. The manuscript is strong by providing very good evidence for its conclusions. One limitation is the strong reliance on the use of an actyl-mimicking mutant; this limitation could be overcome by placing a bit more emphasis on detecting endogenous SAE2 acetylation.

      Audience: The study should be relevant to a broad audience, given the impact of the SUMO system on cellular regulation; after all, the study addresses a very fundamental problem in the field. In addition, it should be of interest to researchers studying regulation of mitosis.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      While CRISPR/Cas technology has greatly facilitated the ability to perform precise genome edits in Leishmania spp., the lack of a non-homologous DNA end-joining (NHEJ) pathway in Leishmania has prevented researchers from performing large-scale Cas-based perturbation screens. With the introduction of base editing technology to the Leishmania field, the Beneke lab has begun to address this challenge (Engstler and Beneke, 2023).

      In this study, the authors build on their previously published protocols and develop a strategy that:

      (1) allows for very high editing efficiency. The cell editing frequency of 1 edit per 70 cells reported in this study represents a 400-fold improvement over the previously published protocol,

      (2) reduces the negative effects of high sgRNA levels on parasite growth by using a weaker T7 promoter to drive sgRNA transcription.

      The combination of these two improvements should open the door to exciting large-scale screens and thus be of great interest to researchers working with Leishmania and beyond.

      We thank reviewer #1 for these encouraging comments.

      Reviewer #2 (Public Review):

      Summary:

      Previously, the authors published a Leishmania cytosine base editor (CBE) genetic tool that enables the generation of functionally null mutants. This works by utilising a CAS9-cytidine deaminase variant that is targeted to a genetic locus by a small guide RNA (sgRNA) and causes cytosine to thymine conversion. This has the potential to generate a premature stop codon and therefore a loss of function mutant.

      CBE has advantages over existing CAS-based knockout tools because it allows the targeting of multicopy gene families and, potentially, the easier generation of pooled loss of function mutants in complex population experiments. Although successful, the first generation of this genetic tool had several limitations that may have prevented its wider adoption, especially in complex genome-wide screens. These include nonspecific toxicity of the sgRNAs, low transfection efficiencies, low editing efficiencies, a proportion of transfectants that express multiple different sgRNAs, and insufficient effectivity in some Leishmania species.

      Here, the authors set out to systematically solve each of these limitations. By trialling different transfection conditions and different CAS12a cut sites to promote sgRNA expression cassette integration, they increase the transfection efficiency 400-fold and ensure that only a single sgRNA expression cassette integrates that edits with high efficiencies. By trialling different T7 promoters, they significantly reduce the non-specific toxicity of sgRNA expression whilst retaining high editing efficiencies in several Leishmania species (Leishmania major, L. mexicana and L. donovani). By improving the sgRNA design, the authors predict that null mutants will be more efficiently produced after editing.

      This tool will find adoption for producing null mutants of single-copy genes, multicopy gene families, and potentially genome-wide mutational analyses.

      Strengths:

      This is an impressive and thorough study that significantly improves the previous iteration of the CBE. The approach is careful and systematic and reflects the authors' excellent experience developing CRISPR tools. The quality of data and analysis is high and data are clearly presented.

      Weaknesses:

      Figure 4 shows that editing of PF16 is 'reversed' between day 6 and day 16 in L. mexicana WTpTB107 cells. The authors reasonably conclude that in drug-selected cells there is a mixed population of edited and non-edited cells, possibly due to mis-integration of the sgRNA expression construct, and non-edited cells outcompete edited cells due to a growth defect in PF16 loss of function mutants. However, this suggests that the CBE tool will not work well for producing mutants with strong fitness phenotypes without incorporating a limiting dilution cloning step (at least in L. mexicana and quite possibly other Leishmania species). Furthermore, it suggests it will not be possible to incorporate genes associated with a growth defect into a pooled drop-out screen as described in the paper. This issue is not well explored in the paper and the authors have not validated their tool on a gene associated with a severe growth defect, or shown that their tool works in a mixed population setting.

      We would like to thank reviewer #2 for this helpful comment and valid point. We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      In addition, we would like to re-iterate our initial public response to this comment. We believe that escapes or reversals of mutant phenotypes can be observed also with other genetic tools used for loss-of-function screening, including lentiviral CRISPR approaches in mammalian systems and RNAi in Trypanosoma brucei (e.g. Ariyanayagam et al., 2005 and Schlecker et al., 2005). Notably, in lentiviral delivered CRISPR screens, sgRNA expression cassettes are integrated in random places within the genome and multiple cassettes can be integrated depending on the viral titre. In these type of screens, cells can escape phenotypes through various mechanisms, such as promoter silencing or selection of non-deleterious mutations. Additionally, not every CRISPR guide is efficient in generating a mutant phenotype, and RNAi constructs can also vary in their effectiveness. Despite these challenges, genome-wide loss-of-function screens have been successfully carried out in mammalian cells and Trypanosoma parasites. Therefore, we believe that the observed escape of one mutant phenotype does not preclude the detection of growth-associated or other phenotypes in pooled screens. Moreover, we did not observe a reversal of the mutant phenotype in L. mexicana, L. donovani, and L. major parasites expressing tdTomato from an expression cassette integrated into the 18S rRNA SSU locus (Figure 4). Our now included small scale fitness screen (Figure 5) confirms these assumptions and shows that we can detect “strong” growth associated phenotypes. We would also like to point out that we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation).

      We have included a discussion of these points under section “Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates” and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” in our revised manuscript.

      Although welcome, the improvements to the crRNA CBE design tool are hypothetical and untested.

      We agree that the improvements to the CBE sgRNA design are currently hypothetical. We plan to systematically test our guide design principles in future studies. Since this will require testing hundreds of guides to draw robust conclusions, we believe that this aspect is beyond the scope of the current study. In section “Improved CBE sgRNA design to prioritize edits resulting only in STOP codons” of our revised manuscript we now discuss these future plans.

      The Sanger and Oxford Nanopore Technology analyses on integration sites of the sgRNA expression cassette integration will not detect the mis-integration of the sgRNA expression construct into an entirely different locus.

      We have now re-analysed our ONT data and have extracted all ONT contigs that match the CBE sgRNA expression cassette. All extracted contigs align to the 18S rRNA SSU locus, showing integration of the cassette into this locus. It is important to note that here a population was sequenced and not a clone. Despite this, no contigs could be found that would link the CBE sgRNA expression cassettes to another locus. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Reviewer #3 (Public Review):

      Genetic manipulation of Leishmania has some challenges, including some limitations in the DNA repair strategies that are present in the organism and the absence of RNA interference in many species. The senior author has contributed significantly to expanding the available routes towards Leishmania genetic manipulation by developing and adapting CRISPR-Cas9 tools to allow gene manipulation via DNA double-strand break repair and, more recently, base modification. This work seeks to improve on some limitations in the tools previously described for the latter approach of base modification leading to base change.

      The work in the paper is meticulously described, with solid evidence for most of the improvements that are claimed: Figure1 clearly describes reduced impairment in the growth of parasites expressing sgRNAs via changes in promoters; Figures 2 and 3 compellingly document the usefulness of using AsCas12a for integration after transformation; and Figures 1 and 4 demonstrate the capacity of the combined modifications to efficiently edit a gene in three different Leishmania species. There is little doubt these new tools will be adopted by the Leishmania community, adding to the growing arsenal of approaches for genetic manipulation.

      There are two weaknesses the authors may wish to address, one smaller and one larger.

      (1) The main advance claimed here is in this section title: 'Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates', with the evidence for this presented in Figure 4. It is hard work in the submission to discern what direct evidence there is for editing rates being improved relative to earlier, Cas9-based approaches. Did they directly compare the editing by the new and old approach? If not, can they more clearly explain how they are able to make this claim, either by adding text or a new figure? A side-by-side comparison would emphasise the advance of the new approach more clearly.

      We would like to thank reviewer #3 for this helpful comment. We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and our here presented new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      In addition, we also feel that our title might have been misleading in a sense that we claim that Cas12a editing is more efficient than other Cas9 based approaches, which is something that we don’t want to state here. Given that we have now included a small scale CRISPR screen and given that we generally show improved base editing compared to our previous method (improved in terms of less toxicity, more editing in shorter time, higher transfection rates and less species specific variation), we have rephrased our title to: “Improved base editing and functional screening in Leishmania via co-expression of the AsCas12a ultra, a T7 RNA Polymerase, and a cytosine base editor”. 

      (2) The ultimate, stated goal of this work is (abstract) to 'enable a variety of loss-of-function screens', as the older approach had some limitations. This goal is not tested for the new tools that have been developed here; the experiment in Figure 5 merely shows that they can, not unexpectedly, make a gene mutant, which was already possible with available tools. Thus, to what extent is this paper describing a step forward? Why have the authors not run an experiment - even the same one that was described previously in Engstler and Beneke (2023) - to show that the new approach improves on previous tools in such a screen, either in scale or accuracy?

      We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”. We believe that this underscores our claims made here and believe therefore that our updated toolbox will indeed enable a variety of loss-of-function screens.

      As pointed out in the comment to reviewer #2, we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation). Without the improvements presented here, such as the higher transfection and base editing rates, these genome-wide screens could have not been carried out.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I would like to compliment Tom Beneke and his lab on their continued efforts to develop tools to facilitate genome editing in Leishmania.

      I have no doubt that the toolkit presented in this study will be very useful for the community. The submitted paper is very well written and contains all the necessary controls to support the author's claims. There is only one point that left me a bit concerned if this strategy is to be used for large-scale screens, and that is the potential for integration of multiple sgRNA expression cassettes in a single cell.

      We would like to thank reviewer 1 for helpful comments. We have addressed the major concern raised by including a small-scale loss-of-function screen in our revised manuscript. By targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs, this approach successfully detected growth-associated phenotypes in a pooled format (see section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” and Figure 5). Regarding the point of multiple sgRNA expression cassette integration, please see the next comment below.

      Major points:

      Integration of multiple sgRNA expression cassettes:

      While Illumina-based gDNA-seq is well suited to determine changes in ploidy, I don't think it is sensitive enough to draw conclusions about possible double integration in a small percentage of cells. In fact, the data shown in Figure 4 S1D show a normalized coverage >1.5 for sgRNA cassette and NeoR, suggesting that they may have integrated >1 times in some cells.

      To verify that the integration of the CBE sgRNA expression cassette is specific, we have re-analysed our ONT results and confirmed that only ONT contigs can be detected that link the CBE sgRNA expression to the 18S rRNA locus. No other integration sites can be found. We also do not detect any contigs containing multiple CBE sgRNA expression cassettes. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Nevertheless, it is a valid concern that the sequencing depth is not sufficient to detect small percentage of cells that have integrated the CBE sgRNA expression multiple times. However, in this case we also like to make the point that this small percentage of cells within a screen is likely to be not relevant and we therefore now added a small scale pooled loss-of-function screen, targeting essential genes, to the manuscript (see new Figure 5) to proof our claim. If the integration of multiple sgRNAs into one cell would have any measurable combinatorial effect, the non-targeting controls in our screen would have been depleted as well. However, there is no detectable difference between all 15 included controls in our small-scale screen.

      We have addressed all points in sections “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant“ and “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      To avoid double integration, wouldn't it be easiest to just create an allele-specific "landing pad" on one chromosome? I believe that a double integration rate of ~20% could severely complicate the analysis of any large-scale screen later on.

      We thank the reviewer for this suggestion but we have tried to use an allele-specific "landing pad" and described this already in our first manuscript version (see section “DSBs introduced by AsCas12a ultra increase integration rates of donor DNA constructs”). Specifically, we integrated CBE sgRNA expression cassettes into the neomycin resistance marker contained in the tdTomato expression cassette (Figure 2 S1D, Cas12a crRNA-5 and 6) but this resulted in lower transfection rates (Figure 2F: crRNA-5 1 in ~47,000; crRNA-6 1 in ~32,000) then when using a Cas12a crRNA that targets the 18S rRNA locus directly (Figure 2F: crRNA-4 1 in ~2,000). As we believe a high transfection rate is key for pooled large-scale screens, we therefore pursued further experiments with crRNA-4. However, since a different crRNA can be easily selected for our tool, simply by just changing the Cas12a crRNA during transfection, users can chose a different integration site or other “landing pads” if they want to. We have updated section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant” to clarify these details.

      Also, it is not clear to me how the integration of tdTomato could affect the integration of the sgRNA expression cassette 400 bp downstream.

      As said above, our ONT data clearly shows that we can only see integration into one locus (Figure 4 S1 and S2). Given that the recognition site of crRNA-4 is contained in the homology flank used to integrate tdTomato into the 18S rRNA locus, this may contribute to the effect we observe. But since the homology sequences match the original sequences within the locus, the reasons to why this affects integration of the CBE sgRNA expression cassettes remain also elusive to us. We try to discuss this better now in the section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Data accessibility:

      The Illumina and ONT data should be made publicly available.

      ONT and Illumina fastq reads are now available at the European Nucleotide Archive (ENA Accession Number: PRJEB83088)

      Minor point:

      Line 30: It would be easier for readers if the authors could briefly explain what bar-seq is.

      We have added more details:[…] and bar-seq screens, which involve individually deleting, barcoding, and pooling mutants for analysis, have facilitated […].

      Lines 114, 120: I think the authors are referring to Figures 1E and F, not Figures 2E and F.

      Many thanks for picking this up, we have corrected the Figure reference.

      Reviewer #2 (Recommendations For The Authors):

      This has the potential to be a valuable tool for the community if it is efficiently distributed. If the authors have not yet done so they should make their plasmids available to the community via Addgene.

      We have started the deposit process with Addgene and plasmids will be available soon. In the meantime, all plasmid maps are available on our website www.leishbaseedit.net and can be requested for shipment from our lab.

      Line 162-165, 400-401: The potential for using AsCAS12a's intrinsic RNase activity for "multiplexing" would benefit from a little more explanation (i.e. how this would work, and what multiplexing means in this context).

      We have added further details on multiplexing with Cas12a and point out potential applications.

      “For example, Cas12a crRNA arrays with four or more guides can be assembled and transfected to introduce multiple DSBs within one gene. Since Cas12a generates sticky DNA ends that facilitate recombination via microhomology-mediated end joining and homologous recombination (Zhang et al., 2021), this approach could effectively disrupt target genes without requiring the addition of donor DNA and this may provide an alternative approach to our here presented base editing method in the future. Moreover, CBE sgRNAs could be multiplexed by interspacing them with Cas12a direct repeats (DRs), enabling simultaneous targeting of multiple genes in one cell.”

      Line 193-194: can the authors offer an explanation for the reduction in mNG editing observed with 30nt homology flanks?

      We assume this is caused by imprecise recombination events in some cells and have revised the original sentence.

      In several places in the manuscript, it is unclear if an analysis has been done on an individual clone or a population derived from multiple transfected cells. If on mixed population, clarify this and calculate the number of clones that the mixture represents. E.g. lines 195-196 and 221-223 (Sanger sequencing of integration site); Line 333-352 (ONT analysis of CBE expression cassette integration).

      Only when we tested whether multiple CBE sgRNAs are integrated, we generated and analysed clones (Figure 4 S3). In all other experiments we analysed parasite populations. For better clarity, we have where possible indicated this in the revised manuscript (e.g. at the lines requested). 

      Line 259: "site by site" should presumably be "side by side".

      Many thanks for pointing this out. We have changed this typo.

      Lines 315-317: Clarify why the mis-integration of the CBE sgRNA expression cassette might cause a lack of editing (e.g. lack of expression?).

      We have added: “This could potentially result in the silencing of the CBE sgRNA expression or even lead to the deletion of the guide cassette”

      Line 364 - 367: it is unlikely there is the statistical power to state that 2/10 represents lower than the previously observed 38% of double integrants.

      We agree that the statistical power is low and have therefore changed our phrasing to an overall estimation.

      Reviewer #3 (Recommendations For The Authors):

      I suggest that the authors make clearer to the reader the evidence for improved editing efficiency in the new CBE system described here relative to the system described in Engstler and Beneke, 2023. Such clarification could be as simple as an extra paragraph or figure, clearly comparing the editing rates with the two systems in, as far as possible, equivalent conditions.

      We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      The significance of this work would be improved by running the type of loss of fitness screen described previously in Engstler and Beneke (2023), thereby showing that the new approach improves on previous tools. Without such data, questions remain about potential confounding effects that might not be anticipated from the targeted experiments provided in the current manuscript.

      We thank the reviewer for this suggestion. The requested experiment is now presented in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment

      This important study provides empirical evidence of the effects of genetic diversity and species diversity on ecosystem functions across multi-trophic levels in an aquatic ecosystem. The support for these findings is solid, but a more nuanced interpretation of the results could make the conclusions more convincing. The work will be of interest to ecologists working on multi-trophic relationships and biodiversity.

      Thanks for this new assessment. Here below we reply to the comments that you and the reviewer have made. We understand the critics related to the issue of the interpretation of causal relationships from observational data. We now added an entire paragraph (in the second paragraph of the Discussion) that explicitly call for a cautionary interpretation of our results. We also tried to refrain the use of certain words (e.g., “we demonstrate”) when we think it is hard to conclude. This a tricky exercise as on the one hand we gathered a large and strong database (which had been underlined by the reviewers) that should supposedly strengthen statistical inferences, but on the other hands, the inferences we’ve made are based from observational data, which obviously comes from biases (even if partially controlled statistically). We hope that you’ll find our adding appropriate to find the good balance between a strong dataset and fragile interpretation.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results stated that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. Additionally, these effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.<br /> Strengths:

      The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes genetic diversity of the dominant species in each trophic level, biomass production, decomposition rates, and environmental data. The writing is logical and easy to follow.

      Weaknesses:

      The two main conclusions-(1) species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects, and (2) these effects were observed only within each trophic level, not across the three levels-are overly generalized. Analysis of the raw data shows that species and genetic diversity have different effects depending on the ecosystem function. For example, neither affected invertebrate biomass, but species diversity positively influenced fish biomass, while genetic diversity had no effect. Furthermore, Table S2 reveals that only four effect sizes were significant (P < 0.05): one positive genetic effect, one negative genetic effect, and two negative species effects, with two effects within a trophic level and two across trophic levels. Additionally, using a P < 0.2 threshold to omit lines in the SEMs is uncommon and was not adequately justified. A more cautious interpretation of the results, with acknowledgment of the variability observed in the raw data, would strengthen the manuscript.

      There is actually no objective justification for having chosen p<0.20. This is a subjective threshold that has been chosen to simplify the visual interpretation of causal graphs while highlighting the most biologically relevant links. We have now added a sentence stating explicitly the subjective nature of the threshold. We understand the point you raised regarding the cautionary interpretation of the results. We have now added a paragraph (just before the detailed discussion) explicitly calling for a cautionary interpretation of the results (see l. 414-424). We think this paragraph prevails for the entire discussion. Our message in this paragraph is that inferences that we’ve made can arise from both a biological reality and statistical artefacts. We can not really tease apart at this stage, and our interpretation of the results therefore has to be taken with care. We hope you’ll find the statement adequate.  We prefer advertising the readers from the start rather than including cautionary note all over the discussion. We feel it was more logical and comfortable. We have also modified the text from place to place to avoid strong statement such as “we demonstrated” when we think the demonstration can not be considered as solid.

      Recommendations for the authors:

      Reviewing Editor:

      In addition to the comments from the reviewer, we have the following comments on your paper:

      (1) It would be important to clarify that there could be different interpretations about one of the major findings: for within-trophic BEF relationships, genetic and species diversity have the opposite effects on ecosystem functions (i.e., positive and negative effects for genetic and species diversity, respectively). (1) One possibility is that for each specific ecosystem function, genetic and species diversity have the opposite effects. (2) The other possibility is that genetic diversity has positive effects on some functions, while species diversity has negative effects on other functions. These two possibilities can have quite different implications about the generalizability of the conclusion, mechanisms involved, and practices for ecosystem management. Therefore, it would be important to clarify that the findings from this paper are more about the second rather than the first possibility both in the discussion and conclusion sections.

      Yes, true, this is an important distinction and we agree with your conclusion. We have added a section in the Discussion (l. 537-545) and a note in the Conclusion (l. 625-627).

      (2) Please take special caution when comparing the findings from this observational study vs. previous experimental works. (1) The different ranges of diversity in the observational vs. experimental works, together with the nonlinear nature of the BEF relationship challenge the direct comparisons of their results. That is, even if their true BEF relationship are identical, focusing on different sections of a nonlinear curve can give us different results of the estimated BEF relationships. This challenge is further aggravated when involving both genetic and species diversity because these two facets have different biological meanings as the authors have already noted. Using standardized effect size or explained variance, as this paper did, may partially get around but not truly resolve this issue. It would be important to add clarifications to make the comparisons between genetic and species diversity effects more understandable in a biological or ecological context. One possibility could be to state that both genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem, so that the standardized effect sizes quantify how these natural diversity gradients associate with ecosystem functions. This further points to the issue about the representatives of the genetic diversity sampled from up to 32 individuals for each species per site, which would also need clarification. We suggest the authors to identify these challenges in the discussion, so that future studies can be aware of these or even find alternative solutions. (2) The species diversity effects have quite different meanings between this study and previous observational and experimental studies. The negative effects are for the biomass of one target species from this study, while the species diversity effects are usually for the biomass of all species within a community. These two scenarios are not directly comparable. The negative relationship between species diversity and a target species' biomass can simply arise from a sampling process, for example, given the same community biomass, the more species occur in a community, the less biomass allocated to a single species, without assuming any biological interactions or species differences. And this study cannot exclude this possibility. Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448. To avoid possible mis-interpretation, we suggest the authors to revise or remove the comparison appearing in the paragraph starting from line 515.

      Thanks for these comments. Although we agree with the two points raised by the Editor, we must admit that we found them difficult to answer properly.  See our detailed responses hereafter.

      Point (1): this is true that comparisons with previous studies is tricky, especially when these comparisons also include both genetic and species components. This is a problem (a limit) for almost all comparisons in biology. We added a few lines to warn readers that these comparisons are not without any limits (see l. 414-424). Regarding the fact that « genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem »: all is about scales. The genetic and species diversity measured in this study are obviously representative of communities and populations of the upstream (piedmont) part of the Garonne River basin as our sampling design covers all the east-west gradient. On the other hand, these communities and populations are not representative of the entire Garonne River basin, as we lack all the downstream part of the network. We added a sentence to specify that the sampling communities are specific of this specific ecosystem (rivers from the piedmont, see l. 224-226). Regarding « the issue about the representatives of the genetic diversity sampled from up to 32 individuals », we must admit that we are surprised by this comment as it is a very classical way for estimating genomic diversity. Although there is no clear rule, 30 individuals per site is generally assumed (and has been shown) to be an appropriate sample size (especially given that we used here a genome-wide approach). We added a reference to justify the sample size.

      Point (2): We understand the point raised by the Editors. Regarding your note “Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448.”: this is true, we rephrase this sentence to be more neutral. Regarding the paragraph starting l. 515 (now 550), we refrained to remove this paragraph as it provides some mechanistic explanation for underlying patterns, which we think is important even if incomplete or speculative. The confusion probably arises because here we discuss all type of negative BEFs, including the effect of species diversity on the biomass of the community, on the biomass of focal species (including those from other trophic levels) and the litter degradation. Our discussion is very general, whereas you seem to focus on a specific case of negative species-BEFs. To highlight this further and warn readers about possible conclusions, we added the following sentence: “Given the empirical nature of our study and the fact that our meta-regressive approach includes several types of BEFs (e.g., species richness acting either on the biomass of a single focal species or on the biomass of an entire focal community), it is hard to tease apart specific and underlying mechanisms” (l. 573-576).

      (3) Please clarify how you derived the 95% CI in Fig. 5. For example, how did you involve the uncertainties of each raw effect size (e.g. each black triangle in Fig. 5a) when calculating their mean and 95% CI in each group (e.g., the red triangles and error bars in Fig. 5a)?

      Estimates and 95%-CI from Figure 5 are derived from the mixed-effect models described from l. 314. They are hence marginal effects derived from the models, and 95%-CI include all error terms (fixed and random). We now specify in the Figure caption that estimates and 95%-CI are marginal effects derived from the mixed-effect models.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper is an incremental follow-up to the authors' recent paper which showed that Purkinje cells make inhibitory synapses onto brainstem neurons in the parabrachial nucleus which project directly to the forebrain. In that precedent paper, the authors used a mouse line that expresses the presynaptic marker synaptophysin in Purkinje cells to identify Purkinje cell terminals in the brainstem and they observed labeled puncta not only in the vestibular and parabrachial nuclei, as expected, but also in neighboring dorsal brainstem nuclei, prominently the central pontine grey. The present study, motivated by the lack of thorough characterization of PC projections to the brainstem, uses the same mouse line to anatomically map the density and a PC-specific channelrhodopsin mouse line to electrophysiologically assess the strength of Purkinje cell synapses in dorsal brainstem nuclei. The main findings are (1) the density of Purkinje cell synapses is highest in vestibular and parabrachial nuclei and correlates with the magnitude of evoked inhibitory synaptic currents, and (2) Purkinje cells also synapse in the central pontine grey nucleus but not in the locus coeruleus or mesencephalic nucleus.

      Strengths:

      The complementary use of anatomical and electrophysiological methods to survey the distribution and efficacy of Purkinje cell synapses on brainstem neurons in mouse lines that express markers and light-sensitive opsins specifically in Purkinje cells is the major strength of this study. By systematically mapping presynaptic terminals and light-evoked inhibitory postsynaptic currents in the dorsal brainstem, the authors provide convincing evidence that Purkinje cells do synapse directly onto pontine central grey and nearby neurons but do not synapse onto trigeminal motor or locus coeruleus neurons. Their results also confirm previously documented heterogeneity of Purkinje cell inputs to the vestibular nucleus and parabrachial neurons.

      Weaknesses:

      Although the study provides strong evidence that Purkinje cells do not make extensive synapses onto LC neurons, which is a helpful caveat given previous reports to the contrary, it falls short of providing the comprehensive characterization of Purkinje cell brainstem synapses which seemed to be the primary motivation of the study. The main information provided is a regional assessment of PC density and efficacy, which seems of limited utility given that we are not informed about the different sources of PC inputs, variations in the sizes of PC terminals, the subcellular location of synaptic terminals, or the anatomical and physiological heterogeneity of postsynaptic cell types. The title of this paper would be more accurate if "characterization" were replaced by "survey".

      Several of the study's conclusions are quite general and have already been made for vestibular nuclei, including the suggestions in the Abstract, Results, and Discussion that PCs selectively influence brainstem subregions and that PCs target cell types with specific behavioral roles.

      We agree that we did not provide an in-depth characterization of PC synapses onto all identified types of brainstem neurons. With so many types of neurons in the brainstem, this would be a monumental task. Despite this limitation we prefer to keep our original title, since our study makes the following advances:

      • We provide a comprehensive map of all PC synaptic boutons across the brainstem, and corresponding maps of PC synaptic input sizes. The input sizes vary widely, but are often multiple nanoamps, indicating that the cerebellum is an important regulator of activity in these regions. These maps will be indispensable for future investigations of cerebellar outputs.

      • We find that PC projections and the synapses they make are spatially restricted within most target nuclei such as the vestibular and parabrachial nuclei. This suggests that the influence of the cerebellum is spatially segregated within these nuclei, and likely allows the cerebellum to regulate specific behaviors.  While some aspects of these gradients have been described previously, our study is comprehensive, and has a higher degree of specificity than can be achieved with immunohistochemistry. 

      • We discover that PCs form functional synapses in the pontine central grey and nearby nuclei. Much of this region’s function is unknown, but certain subregions are important for micturition and valence. PCs make large synapses onto a small fraction of cells in this region, which suggests that PCs may target specific cell types to control novel nonmotor behaviors.

      • We provide clarification regarding PC projections to the locus coeruleus. Multiple high-profile, highly influential studies using rabies tracing (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019; and others) described a prominent PC input to the locus coeruleus. We showed that this projection is essentially nonexistent, both anatomically and functionally. We previously addressed this issue, but the PC-specific optogenetic approach we used here provides the most compelling evidence against a prominent PC-LC connection. This is an important finding for the cerebellum and a cautionary tale for conclusions based solely on viral tracing methods. We will expand on this issue in response to the comments of reviewer #3.

      Reviewer #2 (Public review):

      Summary:

      While it is often assumed that the cerebellar cortex connects, via its sole output neuron, the Purkinje cell, exclusively to the cerebellar nuclei, axonal projections of the Purkinje cells to dorsal brainstem regions have been well documented. This paper provides comprehensive mapping and quantification of such extracerebellar projections of the Purkinje cells, most of which are confirmed with electrophysiology in slice preparation. A notable methodological strength of this work is the use of highly Purkinje cell-specific transgenic strategies, enabling selective and unbiased visualization of Purkinje terminals in the brainstem. By utilizing these selective mouse lines, the study offers compelling evidence challenging the general assumption that Purkinje cell targets are limited to the cerebellar nuclei. While the individual connections presented are not entirely novel, this paper provides a thorough and unambiguous demonstration of their collective significance. Regarding another major claim of this paper, "characterization of direct Purkinje cell outputs (Title)", however, the depth of electrophysiological analysis is limited to the presence/absence of physiological Purkinje input to postsynaptic brainstem neurons whose known cell types are mostly blinded. Overall, conceptual advance is largely limited to confirmatory or incremental, although it would be useful for the field to have the comprehensive landscape presented.

      Strengths:

      (1) Unsupervised comprehensive mapping and quantification of the Purkinje terminals in the dorsal brainstem are enabled, for the first time, by using the current state-of-the-art mouse lines, BAC-Pcp2-Cre and synaptophysin-tdTomato reporter (Ai34).

      (2) Combinatorial quantification with vGAT puncta and synaptophysin-tdTomato labeled Purkinje terminals clarifies the anatomical significance of the Purkinje terminals as an inhibitory source in each dorsal brainstem region.

      (3) Electrophysiological confirmation of the presence of physiological Purkinje synaptic input to 7 out of 9 dorsal brainstem regions identified.

      (4) Pan-Purkinje ChR2 reporter provides solid electrophysiological evidence to help understand the possible influence of the Purkinje cells onto LC.

      Weaknesses:

      (1) The present paper is largely confirmatory of what is presented in a previous paper published by the author's group (Chen et al., 2023, Nat Neurosci). In this preceding paper, the author's group used AAV1-mediated anterograde transsynaptic strategy to identify postsynaptic neurons of the Purkinje cells. The experiments performed in the present paper are, by nature, complementary to the AAV1 tracing which can also infect retrogradely and thus is not able to demonstrate the direction of synaptic connections between reciprocally connected regions. Anatomical findings are all consistent with the preceding paper. The likely absence of robust physiological connections from the Purkinje to LC has also been evidenced in the preceding paper by examining c-Fos response to Purkinje terminal photoinhibition at the PBN/LC region.

      We agree that we previously dealt with the issue of PC-LC synapses (Chen et al., 2023, Nat Neurosci), but our conclusions differed from several high-profile publications (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019), and still met considerable resistance. We felt that the optogenetic approach provided the most definitive means of evaluating the presence and strength of PC-LC synapse that will hopefully settle this issue. These experiments also set a standard for future studies assessing the presence of PC synapses onto other target neurons in the brainstem.

      (2) Although the authors appear to assume uniform cell type and postsynaptic response in each of the dorsal brainstem nuclei (as noted in the Discussion, "PCs likely function similarly to their inputs to the cerebellar nuclei, where a very brief pause in firing can lead to large and rapid elevations in target cell firing"), we know that the responses to the Purkinje cell input are cell type dependent, which vary in neurotransmitter, output targets, somata size, and distribution, in the cerebellar and vestibular nuclei (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci). This consideration impacts the interpretation of two key findings: (a) "Large ... PC-IPSCs are preferentially observed in subregions with the highest densities of PC synapses (Abstract)". For example, we know that the terminal sparse regions reported in the present paper do contain Floccular Targeted Neurons that are sparse yet have dense somatic terminals with profound postinhibitory rebound (Shin et al.). Despite their sparsity, these postsynaptic neurons play a distinct and critical role in proper vestibuloocular reflex. Therefore, associating broad synaptic density with "PC preferential" targets, as written in the Abstract, may not fully capture the behavioral significance of Purkinje extracerebellar projections. (b) "We conclude ... only a small fraction of cell. This suggests that PCs target cell types with specific behavioral roles (Abstract, the last sentence)". Prior research has already established that "PCs target cell types with specific behavioral roles in brainstem regions". Also, whether 23 % (for PCG), for example, is "a small fraction" would be subjective: it might represent a numerically small but functionally important cell type population. The physiological characterization provided in the present cell type-blind analysis could, from a functional perspective, even be decremental when compared to existing cell typespecific analyses of the Purkinje cell inputs in the literature.

      We now cite the papers suggested by the reviewer (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci) and add to the discussion.

      (3) The quantification analyses used to draw conclusions about

      (a) the significance of PC terminals among all GABAergic terminals and the fractions of electrophysiologically responsive postsynaptic brainstem neurons may have potential sampling considerations:.

      (a.i) this study appears to have selected subregions from each brainstem nucleus for quantification (Figure 2). However, the criteria for selecting these subregions are not explicitly detailed, which could affect the interpretation of the results.

      Additional explanation has been added to results in the section, “Quantification of PC synapses in the brainstem.”  

      (a.ii) the mapping of recorded cells (Figure 3) seems to show a higher concentration in terminal-rich regions of the vestibular nuclei.

      In Figure 3, we strived to record in an unbiased manner. However, there may have been a slight bias to recordings in areas of lower myelination where patching is easier. We now clarify this issue in the text.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Chen and colleagues explores the connections from cerebellar Purkinje cells to various brainstem nuclei. They combine two methods - presynaptic puncta labeling as putative presynaptic markers, and optogenetics, to test the anatomical projections and functional connectivity from Purkinje cells onto a variety of brainstem nuclei. Overall, their study provides an atlas of sorts of Purkinje cell connectivity to the brainstem, which includes a critical analysis of some of their own data from another publication. Overall, the value of this work is to both provide neural substrates by which Purkinje cells may influence the brainstem and subsequent brain regions independent of the deep cerebellar nuclei and also, to provide a critical analysis of viral-based methods to explore neuronal connectivity.

      Strengths:

      The strengths lie in the simplicity of the study, the number of cells patched, and the relationship between the presence of putative presynaptic puncta and electrophysiological results. This type of study is important and should provide a foundation for future work exploring cerebellar inputs and outputs. Overall, I think that the critique of viral-based methods to define connectivity, and a more holistic assessment of what connectivity is and how it should be defined is timely and warranted, as I think this is under-appreciated by many groups and overall, there is a good deal of research being published that do not properly consider the issues that this manuscript raises about what viral-based connectivity maps do and do not tell us.

      We thank the reviewer for highlighting this important aspect of this work, and for agreeing with our thesis concerning viral-based connectivity maps.

      Weaknesses:

      While I overall liked the manuscript, I do have a few concerns that relate to interpretation of results, and discussion of technological limitations. The main concerns I have relate to the techniques that the authors use, and an insufficient discussion of their limitations. The authors use a Cre-dependent mouse line that expresses a synaptophysin-tomato marker, which the authors confidently state is a marker of synapses. This is misleading. Synaptophysin is a vesicle marker, and as such, labels axons, where vesicles are present in transit, and likely cell bodies where the protein is being produced. As such, the presence of tdtomato should not be interpreted definitively as the presence of a synapse. The use of vGAT as a marker, while this helps to constrain the selection of putative pre-synaptic sites, is also a vesicle marker and will likely suffer the same limitations (though in this case, the expression is endogenous and not driven by the ROSA locus). A more conservative interpretation of the data would be that the authors are assessing putative pre-synaptic sites with their analysis. This interpretation is wholly consistent with their findings showing the presence of tdtomato in some regions but only sparse connectivity - this would be expected in the event that axons are passing through. If the authors wish to strongly assert that they are specifically assessing synapses, a marker better restricted to synapses and not vesicles may be more appropriate.

      We agree that synaptophysin-tdTomato is an imperfect marker, although it is vastly superior to cytosolic tdTomato.  We found that viral expression of synaptophysin-GFP gives much more punctate labelling, but an appropriate synaptophysin-GFP line is not available. We carefully point out this issue, and threshold the images to avoid faint labeling associated with fibers of passage.  The intersection of VGAT labelling and of the synaptophysin-tdTomato labelling provides us with superior identification of PC boutons.  We will add additional clarification to point out that these are putative presynaptic boutons, but that alone this does not establish the existence or the strength of functional synapses.

      Similarly, while optogenetics/slice electrophysiology remains the state of the art for assessing connectivity between cell populations, it is not without limitations. For example, connections that are not contained within the thickness of the slice (here, 200 um, which is not particularly thick for slice ephys preps) will not be detected. As such, the absence of connections is harder to interpret than the presence of connections. Slices were only made in the coronal plane, which means that if there is a particular topology to certain connections that is orthogonal to that plane, those connections may be under-represented. As such, all connectivity analyses likely are under-representations of the actual connectivity that exists in the intact brain. Therefore, perhaps the authors should consider revising their assessments of connections, or lack thereof, of Purkinje cells to e.g., LC cells. While their data do make a compelling case that the connections between Purkinje cells and LC cells are not particularly strong or numerous, especially compared to other nearby brainstem nuclei, their analyses do indicate that at least some such connections do exist. Thus, rather than saying that the viral methods such as rabies virus are not accurate reflections of connectivity - perhaps a more circumspect argument would be that the quantitative connectivity maps reported by other groups using rabies virus do not always reflect connectivity defined by other means e.g., functional connections with optogenetics. In some cases, the authors do suggest this (e.g."Together, these findings indicate that reliance on anatomical tracing experiments alone is insufficient to establish the presence and importance of a synaptic connection"), but in other cases, they are more dismissive of viral tracing results (e.g. "it further suggests that these neurons project to the cerebellum and were not retrogradely labeled"). Furthermore, some statements are a bit misleading e.g., mentioning that rabies methods are critically dependent on starter cell identity immediately following the citation of studies mapping inputs onto LC cells. While in general, this claim has merit, the studies cited (19-21) use Dbh-Cre to define LC-NE cells which does have good fidelity to the cells of interest in the LC. Therefore, rewording this section in order to raise these issues generally without proximity to the citations in the previous sentence may maintain the authors' intention without suggesting that perhaps the rabies studies from LC-NE cells that identified inputs from Purkinje cells were inaccurate due to poor fidelity of the Cre line. Overall, this manuscript would certainly not be the first report indicating that the rabies virus does not provide a quantitative map of input connections. In my opinion, this is still under-appreciated by the broad community and should be explicitly discussed. Thus, an acknowledgment of previous literature on this topic and how their work contributes to that argument is warranted.

      We have a different take on connectivity and the use of optogenetics.  Based on our years of experience studying synapses in brain slice, axons survive very well even when they are cut. It is not necessary to preserve intact axons that extend for long distances. It is also true that activation of these axons, with either extracellular electrical stimulation or with optogenetics, is sufficient to evoke synaptic inputs. Robust synaptic responses are evoked with optogenetic activation regardless of the slice orientation. We thank the reviewer for raising this issue, and we have added a couple of sentences to clarify this point under the section “Characterization of functional properties of PC synapses in the brainstem.”

      The discussion on starter cell specificity was not referring to the specificity of cre in transgenic animals, but the TVA/G helper proteins that are introduced by AAV and used in conjunction with the rabies virus. The issues related to this have recently been discussed in Elife (Beier, 2022) in addition to citations 58 and 59 in the manuscript. We have more explicitly highlighted this issue in the revised manuscript in the section “Lack of significant PC inputs to LC neurons.”

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Methods need detail to be replicable, particularly in how PC synapses were identified and automatically counted. It is not clear what was the variation within subregions across mice. How were neurons selected or rejected for recordings and analyses? Was each subregion sampled at equal spacing? Methods for anatomy should mention sagittal sections.

      Wording in Methods section, “Anatomy” was changed to better reflect how PC synapses were identified as colabeled segments of vGAT and tdTomato labeling. 

      Each datapoint in Figure 2D-F was quantification of a region for each section and each mouse. The color of the data point indicates the anterior posterior location of the section. The violin plot quantifies the median and quartile value for all points across sections and mice. The variability captured by the violin point reflects variability across the anterior-posterior axis. 

      Neurons were mostly randomly selected in each slice, and rejected based on unstable holding current or access resistance. Cell locations were recorded and updated with each experiment so that we minimized oversampling easier to patch regions.

      Sagittal sections were added in methods.

      (2) Figure 2D-F what is the black line and grey region?

      Additional text was added in the caption for Figure 2D-F

      (3) MEV is confusing given LAV stands for lateral vestibular - perhaps call it ME5?

      We will remain consistent with the abbreviations in the Allen Brain Reference Atlas.

      Reviewer #2 (Recommendations for the authors):

      (1) What are the criteria for distinguishing large, small, and non-responders?

      Large are in the nA range, small are in the hundreds of pA, and non-responders are effectively zero. Manual curation of these responses indicated that a current amplitude threshold of 45 pA clearly separated non-responders from responders. To be clear, the average response (as stated in text and displayed in Figure 3D) includes all cells.

      (2) p1. "Unexpectedly": it would not be unexpected, rather, expected, because it was reported in Chen et al., 2023, Nat Neurosci.

      The PCG was hinted at, but an actual functional, anatomical connection was not reported in our previous manuscript.

      (3) p1. "We combined electrophysiological recordings with immunohistochemistry to assess the molecular identities of these PC targets": please clarify "these" here. It could be read that it refers to "pontine central gray and nearby subnuclei" but it doesn't make sense. Immuno has only been performed for MeV and LC.

      Corrected

      (4) p1. "but only inhibit a small fraction of cells in many nuclei": as far as I read Fig.3, it seems that ~50% for PBN/VN and ~25% for PCG: would this be "a small fraction"?

      The small fraction of cells was in reference to subnuclei within the PCG, but we agree this statement is too broad to be useful and have eliminated it.

      (5) p2. "conventional tracer": viral tracer is becoming a standard, so dye tracer could be better here.

      Corrected

      (6) p3. "rostral/cauda": typo.

      Corrected.  

      (7) p3. Quantification of PC synapses in the brainstem: it would be helpful to introduce why synapto-tdT alone is not sufficient, and the purpose of adding vGAT immunostaining.

      We have added more on vGAT labeling putative presynaptic sites and quantifying only synaptic labeling instead of axonal tdTomato in the Results, “Quantification of PC synapses in the brainstem.” In addition, vGAT staining allows us to examine the PC contribution to total inhibition in each region.

      (8) p7. "PB and are": typo.

      Corrected. And all instances of PBN were changed to PB

      (9) p7. "they are likely a mix of excitatory and inhibitory inputs 54,55": Bagnall et al., 2009, J Neurosci, would be critically relevant here.

      Added, thank you

      (10) Figures 2-3: Yellow/Blue color scheme is hard to distinguish, and having two colors could be read as implying two distinct regions.

      We are unsure what the reviewer is referring to exactly here, but the colors refer to the sections in 2C (see the color bar on the bottom right of each atlas schematic). The points represent an individual section that was quantified, and thus do represent distinct samples from distinct regions.

      (11) Figure 2D-F: what is indicated by each point?

      Each data point is the number of PC bouton (D), density of bouton (E), or percentage of synaptophysin/vGAT (F) quantified for each region per section. Each color represents a coronally distinct section of a region. Additional text was added into the captions to clarify this and point 10.

      (12) Figure 3E, right: what is the correlation coefficient?

      The correlation coefficient was found to be 0.74

      Reviewer #3 (Recommendations for the authors):

      Some minor grammatical errors and typos need to be cleaned up (e.g. "To quantifying the densities...", "The medial-ventral region of the PBN...have extensive...".

      These errors have been corrected

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Insulin is crucial for maintaining metabolic homeostasis, and its release is regulated by various pathways, including blood glucose levels and neuromodulatory systems. The authors investigated the role of neuromodulators in regulating the dynamics of the adult Drosophila IPC population. They showed that IPCs express various receptors for monoaminergic and peptidergic neuromodulators, as well as synaptic neurotransmitters with highly heterogeneous profiles across the IPC population. Activating specific modulatory inputs, e.g. dopaminergic, octopaminergic or peptidergic (Leucokinin) using an optogenetic approach coupled with in vivo electrophysiology unveiled heterogeneous responses of individual IPCs resulting in excitatory, inhibitory or no responses. Interestingly, calcium imaging of the entire IPC population with or without simultaneous electrophysiological recording of individual cells showed highly specific and stable responses of individual IPCs suggesting their intrinsic properties are determined by the expressed receptor repertoire. Using the adult fly connectome they further corroborate the synaptic input of excitatory and inhibitory neuronal subsets of IPCs. The authors conclude that the heterogeneous modulation of individual IPC activity is more likely to allow for flexible control of insulin release to adapt to changes in metabolic demand and environmental cues.

      Strengths:

      This study provides a comprehensive, multi-level analysis of IPC properties utilizing single-nucleus RNA sequencing, anatomical receptor expression mapping, connectomics, electrophysiological recordings, calcium-imaging and an optogeneticsbased 'intrinsic pharmacology' approach. It highlights the heterogeneous receptor profiles of IPCs, demonstrating complex and differential modulation within the IPC population. The authors convincingly showed that different neuromodulatory inputs exhibit varied effects on IPC activity and simultaneous occurrence of heterogeneous responses in IPCs with some populations exciting a subset of IPCs while inhibiting others, showcasing the intricate nature of IPC modulation and diverse roles of IPC subgroups. The temporal dynamic of IPC modulation showed that polysynaptic and neuromodulatory connections play a major role in IPC response. The authors demonstrated that certain neuromodulatory inputs, e.g. dopamine, can shift the overall IPC population activity towards either an excited or inhibited state. The study thus provides a fundamental entry point to understanding the complex influence of neuromodulatory inputs on the insulinergic system of Drosophila.

      We thank the reviewer for endorsing our study as a fundamental entry point to understanding the complex neuromodulation of the insulin system.

      Weakness:

      GPCRs are typically expressed at low levels and while the transcriptomic and reporter expression analysis was comprehensive, both approaches have the caveat that they do not allow validating protein level expression. Thus, some receptors might have been missed while others might be false positives. The authors acknowledged the challenges in accurately accessing receptor expression in complex modulatory systems indicating there are limitations in full understanding of the receptor profiles of IPCs.

      We agree with the reviewer and acknowledge that both the transcript and protein expression need to be examined in order to obtain higher confidence in receptor expression profiles. The T2A-GAL4 lines used in our anatomical analyses do in fact provide insights into which of the receptor transcripts are translated. We added the following statement to the discussion section to clarify this approach “The singlenucleus transcriptome analysis reveals which receptor transcripts are expressed whereas the T2A-GAL4 lines used in our anatomical analyses provide insights on which of the receptor transcripts are translated. This is based on the fact that T2A peptides induce ribosome skipping during translation. Therefore, GAL4 protein is only produced when the receptor protein is produced(42,88).”

      While this study provides valuable insights into the heterogeneity of IPC responses and receptor expression, it will require future studies to elucidate how these modulatory inputs affect insulin release and transcriptional long-term changes. The authors further analyzed male and female snRNAseq data and claimed that the differences in receptor expression were minimal. The experimental analyses used mated females only and while the study is very complete in this respect, it would have been extremely interesting to compare male flies in terms of their response profiles.

      We thank the reviewer for acknowledging that long-term effects on release and transcript levels go beyond the scope of this study and agree that these questions should be addressed in future investigations. Concerning the differences between females and males: we did not find significant differences in the snRNAseq data between the two sexes. Moreover, a parallel study from our lab found no differences between males and females in IPC baseline activity (Bisen et al. 2024, eLife https://doi.org/10.7554/eLife.98514.1). We therefore did not follow this path for the present study. We explained our reasoning in the results section of our paper, by adding: “Since there were little differences in receptor expression between males and females (Fig. S1C), we used the transcriptomes from both sexes for all subsequent analyses.” in the transcriptome section, and “Since baseline recordings from IPCs, in addition to our transcriptomic analysis, revealed no significant difference between male and female flies(26), we only used mated females for our physiological experiments.” in the transition to the physiology section of our manuscript.

      Lastly as also pointed out by the authors, their approach of using optogenetically driven excitation of modulatory neuronal subsets limits the interpretation of the results due to the possibly confounding direct or indirect effect of fast synaptic transmission on IPC excitation/inhibition, and the broad expression of some neuromodulatory lines used in this analysis.

      We agree that our results are limited to general effects of neuronal populations rather than individual neurons or specific inputs, and that it is generally hard to untangle effects of fast transmitters from those of modulatory inputs. However, we believe that we are careful in presenting and interpreting our results in this regard.

      Overall, however, the conclusions of this study are well supported by the data provided by the authors. Moreover, their detailed and thorough analysis of IPC modulation will have a significant impact on the field of metabolic regulation to understand the complex regulatory mechanism of insulin release, which can now be studied further to provide insight about metabolic homeostasis and neural control of metabolic processes.

      We thank the referee kindly for these comments!

      Reviewer #2 (Public review):

      Summary:

      Held et al. investigated the distinct activities of Insulin-Producing Cells (IPCs) by electrophysiological recordings and calcium imaging. In the brain of the fruit fly Drosophila melanogaster, there are approximately 14 IPCs that are analogous to mammalian pancreatic beta cells and provide a good model system for monitoring their activities in vivo. The authors performed single-nucleus RNA sequencing analysis to examine what types of neuromodulatory inputs are received by IPCs. A variety of neuromodulatory receptors are expressed heterogeneously in IPCs, which would explain the distinct activities of IPCs in response to the activations of neuromodulatory neurons. The authors also conducted the connectome analysis and G-protein prediction analysis to strengthen their hypothesis that the heterogeneity of IPCs may underlie the flexible insulin release in response to various environmental conditions.

      Strengths:

      The authors succeeded patch-clamp recordings and calcium imaging of individual IPCs in living animals at a single-cell resolution, which allows them to show the heterogeneity of IPCs precisely. They measured IPC activities in response to 9 types of neurons in patch-clamp recordings and 5 types of neurons in calcium imaging, comparing the similarities and differences in activities between two methods. These results support the idea that the neuromodulatory system affects individual IPC activities differently in a receptor-dependent manner.

      We thank the reviewer for emphasizing how our in vivo experiments allow for a precise characterization of the IPC responses to modulatory inputs.

      Weaknesses:

      One concern is how much extent the heterogeneity of IPC activities in a short time scale is relevant to the net output, a release of insulin-like peptides in response to metabolic demands in a relatively longer time scale. The authors can test their hypothesis by manipulating the heterogeneous expressions of receptor genes in IPCs and examining IPC activities on a longer time scale. Moreover, while the authors focus on IPC activities, they did not show the activation of the neuromodulatory inputs and the net output of insulin levels in the data. The readers might want to know which neurons are indeed activated to send signals to IPCs and how IPC activities result in the secretion of insulin peptides.

      We agree with the reviewer that the two experiments described, manipulating receptor expression before long-term recordings and measuring insulin levels after activating modulatory inputs, would deliver exciting insights into the interplay of modulatory inputs, IPC population activity, and insulin release. However, currently available methods for monitoring insulin release do not allow us to perform these experiments with a temporal resolution that would match the sensitivity and time resolution of our physiological experiments and are therefore not suited for a direct comparison. We also acknowledge that it would be extremely exciting to characterize the modulatory populations providing input to IPCs in terms of their sensitivity to internal state changes and external inputs. However, this clearly goes beyond the scope of our study. Essentially, one would have to perform experiments on a similar scale and breadth as we have done for IPCs here for the other populations. We aim to perform some of these experiments in follow up projects to this work.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors used a 5% expression cutoff initially, which seems arbitrary. Can you explain the rationale for using this cutoff? If I interpret the authors' logic correctly and given there are 14 IPCs per animal, at 5% there is a 70% chance that 1 cell expresses that receptor.

      We used a 5% cutoff to reduce false positives in our transcriptomic analysis. This threshold translates to expression in 0.8 out of 16 IPCs found in an individual fly on average. Hence, this cutoff ensures that receptors are expressed in at least 1 cell. Based on 392 IPC transcriptomes used in our analysis, our 5% threshold means that any receptor expressed in less than 20 transcriptomes will be deemed to be absent. At the population level, this ensures that our expression analysis is based on cells from at least two flies. However, we expect the actual number of flies from which the IPC transcriptomes were derived from to be much higher. We added the following statement to the methods section to clarify this point: “To determine if a transcript is present in the IPC transcriptomes, we used a 5% cutoff to reduce false positives. This cutoff is equivalent to expression in 0.8 IPCs out of 16 on average in an individual fly, and hence less than one IPC in the entire population. Since we used 392 IPC transcriptomes in our analysis, this cutoff means that expression in less than 20 IPCs will be deemed false positive”

      (2) Were male and female brains examined separately and tested for divergent expression of T2A-reporter signals? While there were not many strong differences in the snRNAseq dataset, based on some discrepancies with the reporters it might be worthwhile to assess sex-specific differences that might account for the observed expression/non-expression of some receptors.

      We did not investigate sex-specific differences using anatomical mapping, since our scRNA analysis pointed against that being a major factor. We clarified our reasoning in the results section by adding “Since there were little differences in receptor expression between males and females (Fig. S1C), we used the transcriptomes from both sexes for all subsequent analyses.” in the transcriptome section, and “Since baseline recordings from IPCs, in addition to our transcriptomic analysis, revealed no significant difference between male and female flies(26), we only used mated females for our physiological experiments.” in the transition to the physiology section of our manuscript.

      (3) The anatomical reporter and transcriptome data for neuromodulatory receptor expression do not fully complement each other, e.g. in Fig1D Lkr is expressed only in one cluster but anatomical expression is observed in most IPCs. Ultimately, visualizing receptor expression at the protein level and functional analysis with genetic perturbation of the respective receptors is needed to draw strong conclusions.

      We agree with the reviewer that visualizing receptor expression at protein level could help clarify some of these differences since neuropeptide GPCR transcripts tend to be less abundant whereas we expect protein expression to be more stable. However, out of the 14 receptors examined in our study, antibodies are only available for two: DH31R and LKR. Since our DH31R-T2A-GAL4 line does not drive expression in IPCs, we did not pursue this further. We did perform preliminary experiments to validate LKR protein expression in IPCs. Unfortunately, we found that the LKR antibody labels cells in the pars intercerebralis in both the wild type and LKR mutants (see Author response image 1 below). Therefore, we do not think it suitable to monitor LKR protein expression. Thus, additional investigations must await future generations of neuropeptide receptor antibodies. One biological reason for the discrepancies could be that anatomical quantification is based on cumulative expression while transcriptomic analysis captures a brief snapshot. We included “One explanation for the discrepancies could be that transcriptomic analysis provides a single snapshot, whereas anatomical data is based on cumulative expression. Fluorescent markers persist long after transcription and translation has terminated. Therefore, a higher likelihood for receptor expression can be expected when it is quantified via anatomical techniques.” in our results part to give the readers more context.

      Author response image 1.

      (4) In Fig1E, As Dop2R reporter signal is not colocalizing with IPC whereas dop2R is expressed in all four clusters.

      We tested if additional transcript variants with different C-termini are the cause for the discrepancy between transcriptome data and anatomical mapping. However, using a Trojan-GAL4 line for Octa2R that should account for other transcript variants did also not show any expression. At this point, with the tools we have, we cannot conclusively determine what the cause of this discrepancy is. Since we only see them with Dop2R and Octa2R, a mismatch caused by more general differences,

      e.g. sex-specific differences, seems unlikely. A more plausible reason could be that for those lines, inadequate transgenes lead to failed expressions. We added “Hence, inadequate transgenes for Dop2R and Octα2R or the lack of protein translation are the likely cause for the discrepancy between transcriptome analysis and anatomical mapping.“ to our results part as a possible explanation for the discrepancy.

      (5) Moving the AstANs expression images to the main figure (Fig 1E) would make sense as the authors focus on AstAN rather than MsRT or Dop2R in the later parts of their work.

      We thank the reviewer for this suggestion and replaced the LKR image with an AstAR2 image, as suggested. We kept the other two receptors in the main figure as additional examples.

      (6) Have the authors considered gap junction coupling of IPCs, which might explain the simultaneous responses in some cases?

      We have indeed considered this exciting idea, as gap junctions between IPCs could potentially synchronize activity in connected IPC subpopulations. To test if gap junctions are a major factor in the IPC population, we performed experiments with patch-clamp recordings from a single IPC while performing calcium imaging of the IPC population (as demonstrated in Fig. 4J). In some of these experiments, we injected current into individual IPCs and tested for activity changes in the other IPCs. However, the preliminary data we acquired did not indicate that the current-induced train of action potentials was transmitted to others IPCs. Hence, it is unlikely that the IPCs are directly coupled by gap junctions. Given the challenging nature of these experiments, and the discouraging preliminary results, we have not followed up on the idea any further.

      Reviewer #2 (Recommendations for the authors):

      (1) Figure 3D was not described in the text.

      We thank the reviewer for pointing out this mistake, we included the panel in Figure 3C and added the reference in the text describing the results from multiple animals shown in the panel.

      (2) In Figure 4B, a scale of heat map is required. There is a blue spot with no ROI setting on the left side. On the right side of the photos, the ROI No.6 seemed to turn blue after activation. However, Figure 4D shows the ROI No.6 was inhibited.

      We are now using a simplified heatmap in Figure 4B and added a scalebar. We also changed the example images to avoid any confusion. Previously, we used a random snapshot from before LED onset, now we used a snapshot from the actual time window to which we normalized the traces. Regarding the spot where no ROI is depicted but a response is visible: in this area, a trachea made it difficult to clearly delimit the cell body underneath, and we therefore excluded this ROI. Occlusions by trachea are one reason why we can typically not image the entire IPC population in a single animal.

      (3) In Figure 4F, the regions of gray bars (baseline) contain blue and red colors to some extent, which makes me confused. Moreover, the description "within one cluster, the response seemed homogeneous, e.g., in fly #4 during the activation of DANs (Fig. 4F)." was not clear to me. How about fly #1, #2, and #3? It seems that the responses changed excitedly and inhibitory within a cluster. Although the authors tend to raise some consistent results with examples, it would not be so effective if I can see there are other counter-examples and exceptions in the results.

      We apologize for the confusion we caused. The gray bars indicate the time window we used for baseline subtraction: The median activity of each IPC in this window was subtracted from the activity of that IPC. Hence, the median activity in this window is zero, but individual frames can have positive or negative values.

      We thank the reviewer for pointing out the confusion about the homogeneous responses in one cluster. We clarified this part in the results, by adding “Recording from multiple IPCs at the same time uncovered that the activity of IPCs within a cluster was synchronized in some cases. For example, in fly #1 in the DAN activation experiment, the baseline activity pattern of the excited IPC cluster was already synchronized before the first activation (fly #1, cells 3-8). Furthermore, the excitation onset and duration during the activation of DANs was highly uniform in this cluster. However, in other flies, e.g. #2 and #3 in the DAN activation experiments, we did not observe this synchronicity. While all IPCs in the excited cluster displayed an excitatory response to the DAN activation in these flies, the onset and duration differed between individual IPCs. In addition, the IPCs also showed more variability in their baseline activity (Fig. 4F). These findings point towards a shared input that can lead to the synchronization of IPC activity in some clusters and time windows. One known such input is the behavioral state – flight strongly inhibits the activity of all IPCs with very short delays(22). The flies in our experiments were not flying, but this example illustrates the presence of strong, state-dependent inputs that can synchronize the IPC population activity.”

      (4) In Figure 4J, no explanations of arrowheads, gray boxes, or asterisks are available in the legend.

      We thank the reviewer for pointing out this omission. We added the missing information to the figure legend.

      (5) "IPCs form distinct clusters." Is this cluster located closely each other or distant from one another?

      We did not encounter a location-dependent relationship between the IPCs of one cluster in calcium imaging experiments, nor did the anatomical receptor mapping data or connectomics analysis give any indication for anatomical clusters. The location of individual IPC cell bodies is not stereotypical across flies. We clarified this point in the results by adding “IPCs form distinct functional clusters” and “However, we found no evidence in our anatomical data, calcium imaging experiments, or in the fly brain EM volume that these clusters are distinguishable based on IPC soma location in the pars intercerebralis.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Abstract

      I don't think you need the first two sentences of the abstract. This is not a grant and your results are exciting enough to justify a full basic science-based approach.

      We fully understand this perspective.  However, we prefer to introduce the work in the broader context of sleep medicine.  This manuscript is part of our long-standing efforts to develop cavefish as a model for sleep disorders and we believe this provides important context.

      Last sentence of the abstract: the subject is missing. "That have developed..." who has developed?

      Thank you. We have corrected this error, the sentence now reads “...these findings suggest that cavefish have developed resilience to sleep loss...”

      Introduction

      First paragraph. Worth explaining in a sentence what is the link between DNA damage and ROS.

      We now state ‘Further, chronic sleep loss results in elevated reactive oxygen species (ROS), a known mediatior of DNA damage, in the gut and/or brain that contribute to mortality in Drosophila and mice [11,16].’

      "A. mexicanus exists as blind cave populations and an extant surface population that are interfertile". This needs rephrasing. As it is, it sounds like the surface population is infertile.

      We have rephrased for clarity; the line now reads: “while the surface and cave populations are geographically isolated, they remain interfertile and capable of hybridization in nature as well as laboratory settings”.

      "Further, the evolved differences in DNA repair genes, including links between mechanisms regulating sleep, light responsiveness, and DNA repair across all three cave populations studied to date [27,29]" This sentence is incomplete.

      We have corrected the phrasing, which now reads “...evolved differences in DNA repair genes have been identified across all three cave populations studied to date, including links between mechanisms regulating sleep, light responsiveness, and DNA repair”:

      Figure 1

      I recommend improving the legibility of the figure copying some of the information provided in the legend directly within the figure itself.

      A, B: label in the panel itself what is blue and what is green.

      Thank you, we have made this change.

      C: Make it clear in the figure itself that you are measuring yH2AX. Also, probably you have enough room in the figure to avoid abbreviations for Rhomb, mes, and tele. It may also help if you could add a little cartoon that explains what those three brain regions are.

      We have added text to the y axis indicating that yH2AX fluorescence is being measured, and replaced the abbreviations with eh full names of the regions.

      G: again, explain that DHE is being measured here. And perhaps pick a different colour choice to highlight the difference from C?

      We have added clarifiaction to the y-axis of the figure, but have retained the color scheme for consistency; in all surface-cave comparisons in the manuscript, gray is used for surface fish and red for cavefish.

      In the text: I would recommend adding some quantitative reminder of what is the difference in sleep amount between the two species (cave vs surface).

      We have added the following to highlight the magnitude of the difference in sleep: “Strikingly, cavefish sleep as little as 1-2 hours per day, in contrast to their surface counterparts, which sleep as much as 6-10 hours a day”

      "Together, these findings fortify the notion that cellular stress is elevated in the gut of cavefish relative to surface fish." Were the two populations fed the same diet and raised in the same lab conditions? If this is pinpointed to sleep amount, it's worth ruling out possible confounding factors.

      We have added a sentence to the results underlining this point: “Prior to imaging, both surface and cavefish had been reared in a temperature-controlled incubator, and relied solely on their yolk sac for nutrients; so, differences in gut ROS cannot be attributed to differences in rearing or feeding conditions.”

      Figure 2

      Spell out, somewhere in the figure itself, that the 30s and 60s refer to UV treatment protocols.

      We have added X-axis titles to clarify this in Fig 2 and supp. Fig 1.

      It would be worth providing a cartoon of the experimental setup that shows for instance what time of the day UV was given (it's only specified in the text) and which subsequent sleep period was selected for comparisons.

      We have added arrows to all sleep plots indicating the time of UV treatment, and brackets indicating the time period used for statistical comparisons, as well as text in the figure legends indicating this.

      Figure 3

      A. I don't think this is needed, to be honest, and if you want to keep it, it needs a better legend.

      We have edited the figure legend to increase clarity.

      B. I would make it clear in the figure that this refers to transcriptomics analysis. Perhaps you could change the order and show C, D, and then B.

      We have added text to the figure legend and the results text to more explicitly state that the PCA plot is of transcriptional response. We have however retained the original figure order, as well feel this figure is important to establish that both populations have strong, but distinct responses to the UV treatment.

      Figure 4

      A. Spell it out in the figure itself that you're staining for CPD.

      Thank you, we have made this change.

      B. You are using the same colour combination you had in Figure 1 but for yet another pairing. This is a bit confusing.

      Thank you for bringing this to our attention.  We have added descriptions of the colors in the figure legend.

      Discussion

      "Beyond the Pachón cavefish population, all three other cavefish populations have been found to have reduced sleep (Cite)." Citation missing here.

      Thank you.  We have now clarified this sentence and included a citation.

      Reviewer #2 (Recommendations For The Authors):

      Consideration of Environmental Conditions:

      Evaluate whether the lab conditions, which may more closely resemble surface environments, could influence the observed increase in neuronal DNA damage and gut ROS levels in cavefish. Adjusting these conditions or discussing their potential impact in the manuscript would strengthen the findings.

      We are very excited about these experiments.  We have a paper that will be submitted to BioRxiv this week where we record wild-caught fish, as well as fish in caves.  The conclusion is that sleep loss is present in both populations.  This field work took over 10 years to come together and still lacks the power of the lab based assays.  Nevertheless, we can conclusively say that the phenotypes we have observed for the last ~15 years in the lab are present in a natural setting.  We have included a statement about the need for future work to test these findings in a natural setting.

      Alternative Stressors:

      Given that cavefish are albino and blind (to my knowledge), consider using alternative sources of genotoxic stress beyond UV-induced damage. This could include chemical agents or other forms of environmental stress to provide a more comprehensive assessment of DDR.

      We agree and are enthusiastic about looking more generally at stress.  We note that we have previously found that cavefish rebound following sleep deprivation (McGaugh et al, 2020) suggesting that they are responsive to sleep disruption.  This will be a major research focus area moving forward.

      Broader Stress Responses:

      Investigate whether other forms of stress, such as dietary changes or temperature fluctuations, elicit similar differences in sleep patterns and DDR responses. This could provide additional insights into the robustness of the observed phenomena.

      We fully agree.  This will be the primary focus of this research area moving forward. We hypothesize that cavefish are generally less responsive to their environment.  Unpublished data reveals that temperature stress, circadian changes, and aging (presented here) to little to impact gene expression in surface fish.  We would like to test the hypothesis that transcriptional stability of cavefish contributes to their longevity.

      Potential Protective Mechanisms:

      Discuss the possibility that lower levels of gamma-H2AX in cavefish might be protective, as DDR can lead to cellular senescence or cancer. This perspective could add depth to the interpretation of the results.

      This was the hypothesis underlying this manuscript.  However, we found elevated levels of gamma-H2AX.  We believe there may be additional protective mechanisms that have evolved in cavefish, but cannot identify them to date.  Our hope is future functional studies by our group, as well as other groups’ access to this published work, may help address these questions.

      Strengthening the Sleep-DNA Damage Link:

      Further experiments are needed to directly link sleep differences to the observed variations in DNA damage and DDR. This could involve manipulating sleep patterns in surface fish and cavefish to observe corresponding changes in DNA repair mechanisms.

      We agree.  We have referenced work that conclusively showed this relationship in zebrafish. Our current methods for limiting sleep involves shaking, and this has too many confounds.  We are working on developing genetic tools, and applying the gentle rocking methods used previously in zebrafish to address these questions.

      Clarification of Causal Directionality:

      Address the potential that sleep patterns and DDR responses may both be downstream effects of a common cause or independent adaptations to the cave environment. Clarifying this in the manuscript would provide a more nuanced understanding of the evolutionary adaptations.

      Thank you for this suggestion.  We have now added a paragraph describing how these experiments (and the ones described above) are necessary for understanding the relationship between sleep and DDR.

      Clarification and Presentation:

      Fix the many typos, and improve the clarity of the figures and their legends to ensure they are easily interpretable. Additional context in the discussion section would help readers understand the significance and potential implications of the findings.

      Thank you, we have now included this.

      Reviewer #3 (Recommendations For The Authors):

      There are a number of suggestions that I have made in the public review, but there are a few things that I would like to add here.

      The methods section is missing many important details, for instance, the intensity of the illumination used in the UV exposure in larvae is not reported but is vital for the interpretation/replication of these experiments. In general, this section should be redone with a greater effort to include all important information. Similarly, the figure legends could be greatly improved, with important details like n-number and definition of significance thresholds defined (e.g. see Figures 1, C, and G.)

      We have added greater detail to the methods section to specify the spectral peak and power output of the bulbs used.

      There are a number of passages in the manuscript that do not make sense, which suggests that a future version of record should be carefully proofread. I know that this can be a case of reading multiple versions of a manuscript so many times that one doesn't really see it anymore, but, for example, phrases like "To differentiate between these two possibilities" are confusing to the reader when there has been no introduction of alternate possibilities.

      Thank you for this comment.  We have fixed this mistake and proofread the manuscript.

      Additionally, there are multiple examples of errors in citations/references. A few examples are below:

      "Further, chronic sleep loss results in elevated reactive oxygen species (ROS) in the gut and/or brain that contribute to mortality in Drosophila and mice [11, 16]". Reference 16 does not include mice at all, and reference 11 is Vaccaro et al. 2020, where Drosophila mortality is assessed, but mouse mortality is not.

      We have added the appropriate citations and revised this sentence.

      References 13 and 15 are the same.

      Thank you, we have fixed.

      References 24 and 26 are the same.

      Thank you, we have fixed.

      Public Reviews:

      Reviewer #1 (Publc Review):

      Summary:

      Lloyd et al employ an evolutionary comparative approach to study how sleep deprivation affects DNA damage repair in Astyanax mexicanus, using the cave vs surface species evolution as a playground. The work shows, convincingly, that the cavefish population has evolved an impaired DNA damage response both following sleep deprivation or a classical paradigm of DNA damage (UV).

      Strengths:

      The study employs a thorough multidisciplinary approach. The experiments are well conducted and generally well presented.

      Weaknesses:

      Having a second experimental mean to induce DNA damage would strengthen and generalise the findings.

      Overall, the study represents a very important addition to the field. The model employed underlines once more the importance of using an evolutionary approach to study sleep and provides context and caveats to statements that perhaps were taken a bit too much for granted before. At the same time, the paper manages to have an extremely constructive approach, presenting the platform as a clear useful tool to explore the molecular aspects behind sleep and cellular damage in general. The discussion is fair, highlighting the strengths and weaknesses of the work and its implications.

      We fully agree with this assessment.  We are currently performing experiments to test the effects of additional DNA damaging agents.  We hope to extend these studies beyond DNA-damage agents to look more generally at how animals respond to stress including ROS, sleep deprivation, and high temperature.  This will be a major direction of the laboratory moving forward.

      The manuscript investigates the relationship between sleep, DNA damage, and aging in the Mexican cavefish (Astyanax mexicanus), a species that exhibits significant differences in sleep patterns between surface-dwelling and cave-dwelling populations. The authors aim to understand whether these evolved sleep differences influence the DNA damage response (DDR) and oxidative stress levels in the brain and gut of the fish.

      Summary of the Study:

      The primary objective of the study is to determine if the reduced sleep observed in cave-dwelling populations is associated with increased DNA damage and altered DDR. The authors compared levels of DNA damage markers and oxidative stress in the brains and guts of surface and cavefish. They also analyzed the transcriptional response to UV-induced DNA damage and evaluated the DDR in embryonic fibroblast cell lines derived from both populations.

      Strengths of the Study:

      Comparative Approach:

      The study leverages the unique evolutionary divergence between surface and cave populations of A. mexicanus to explore fundamental biological questions about sleep and DNA repair.

      Multifaceted Methodology:

      The authors employ a variety of methods, including immunohistochemistry, RNA sequencing, and in vitro cell line experiments, providing a comprehensive examination of DDR and oxidative stress.

      Interesting Findings:

      The study presents intriguing results showing elevated DNA damage markers in cavefish brains and increased oxidative stress in cavefish guts, alongside a reduced transcriptional response to UV-induced DNA damage.

      Weaknesses of the Study:

      Link to Sleep Physiology:

      The evidence connecting the observed differences in DNA damage and DDR directly to sleep physiology is not convincingly established. While the study shows distinct DDR patterns, it does not robustly demonstrate that these are a direct result of sleep differences.

      We agree with this assessment.  We are currently working to apply tools developed in zebrafish to examine the physiology of sleep.  While this is important, and our results our promising, we will note that functional analysis of sleep physiology in fish has been limited to zebrafish.  We hope future studies will allow us to integrate approaches that examine the physiology of sleep.

      Causal Directionality:

      The study fails to establish a clear causal relationship between sleep and DNA damage. It is possible that both sleep patterns and DDR responses are downstream effects of a common cause or independent adaptations to the cave environment.

      We agree, however, we note that this could be the case for all animals in which sleep has been linked to DNA damage.  We believe the most likely explanation for Astyanax and other animals studied, is that sleep is that sleep and DDR are downstream/interface with the sleep homeostat.

      Environmental Considerations:

      The lab conditions may not fully replicate the natural environments of the cavefish, potentially influencing the results. The impact of these conditions on the study's findings needs further consideration.

      This is correct. We have considered this carefully.  After nearly a decade of effort,  we have completed analysis of sleep in the wild.  These will be uploaded to BioRxiv within the next week.

      Photoreactivity in Albino Fish:

      The use of UV-induced DNA damage as a primary stressor may not be entirely appropriate for albino, blind cavefish. Alternative sources of genotoxic stress should be explored to validate the findings.

      We have addressed this above.  Future work will examine additional stressors. Both fish are transparent at 6dpf and so it is unlikely that albinism impacts the amount of UV that reaches the brain.

      Assessment of the Study's Achievements:

      The authors partially achieve their aims by demonstrating differences in DNA damage and DDR between surface and cavefish. However, the results do not conclusively support the claim that these differences are driven by or directly related to the evolved sleep patterns in cavefish. The study's primary claims are only partially supported by the data.

      Impact and Utility:

      The findings contribute valuable insights into the relationship between sleep and DNA repair mechanisms, highlighting potential areas of resilience to DNA damage in cavefish. While the direct link to sleep physiology remains unsubstantiated, the study's data and methods will be useful to researchers investigating evolutionary biology, stress resilience, and the molecular basis of sleep.

      Reviewer #3 (Public Review):

      Lloyd, Xia, et al. utilised the existence of surface-dwelling and cave-dwelling morphs of Astyanax mexicanus to explore a proposed link between DNA damage, aging, and the evolution of sleep. Key to this exploration is the behavioural and physiological differences between cavefish and surface fish, with cavefish having been previously shown to have low levels of sleep behaviour, along with metabolic alterations (for example chronically elevated blood glucose levels) in comparison to fish from surface populations. Sleep deprivation, metabolic dysfunction, and DNA damage are thought to be linked and to contribute to aging processes. Given that cavefish seem to show no apparent health consequences of low sleep levels, the authors suggest that they have evolved resilience to sleep loss. Furthermore, as extended wake and loss of sleep are associated with increased rates of damage to DNA (mainly double-strand breaks) and sleep is linked to repair of damaged DNA, the authors propose that changes in DNA damage and repair might underlie the reduced need for sleep in the cavefish morphs relative to their surface-dwelling conspecifics.

      To fulfill their aim of exploring links between DNA damage, aging, and the evolution of sleep, the authors employ methods that are largely appropriate, and comparison of cavefish and surface fish morphs from the same species certainly provides a lens by which cellular, physiological and behavioural adaptations can be interrogated. Fluorescence and immunofluorescence are used to measure gut reactive oxygen species and markers of DNA damage and repair processes in the different fish morphs, and measurements of gene expression and protein levels are appropriately used. However, although the sleep tracking and quantification employed are quite well established, issues with the experimental design relate to attempts to link induced DNA damage to sleep regulation (outlined below). Moreover, although the methods used are appropriate for the study of the questions at hand, there are issues with the interpretation of the data and with these results being over-interpreted as evidence to support the paper's conclusions.

      This study shows that a marker of DNA repair molecular machinery that is recruited to DNA double-strand breaks (γH2AX) is elevated in brain cells of the cavefish relative to the surface fish and that reactive oxygen species are higher in most areas of the digestive tract of the cavefish than in that of the surface fish. As sleep deprivation has been previously linked to increases in both these parameters in other organisms (both vertebrates and invertebrates), their elevation in the cavefish morph is taken to indicate that the cavefish show signs of the physiological effects of chronic sleep deprivation.

      It has been suggested that induction of DNA damage can directly drive sleep behaviour, with a notable study describing both the induction of DNA damage and an increase in sleep/immobility in zebrafish (Danio rerio) larvae by exposure to UV radiation (Zada et al. 2021 doi:10.1016/j.molcel.2021.10.026). In the present study, an increase in sleep/immobility is induced in surface fish larvae by exposure to UV light, but there is no effect on behaviour in cavefish larvae. This finding is interpreted as representing a loss of a sleep-promoting response to DNA damage in the cavefish morph. However, induction of DNA damage is not measured in this experiment, so it is not certain if similar levels of DNA damage are induced in each group of intact larvae, nor how the amount of damage induced compares to the pre-existing levels of DNA damage in the cavefish versus the surface fish larvae. In both this study with A. mexicanus surface morphs and the previous experiments from Zada et al. in zebrafish, observed increases in immobility following UV radiation exposure are interpreted as following from UV-induced DNA damage. However, in interpreting these experiments it is important to note that the cavefish morphs are eyeless and blind. Intense UV radiation is aversive to fish, and it has previously been shown in zebrafish larvae that (at least some) behavioural responses to UV exposure depend on the presence of an intact retina and UV-sensitive cone photoreceptors (Guggiana-Nilo and Engert, 2016, doi:10.3389/fnbeh.2016.00160). It is premature to conclude that the lack of behavioural response to UV exposure in the cavefish is due to a different response to DNA damage, as their lack of eyes will likely inhibit a response to the UV stimulus.

      We believe that in A. mexicanus, like in zebrafish, it is highly unlikely that the effects of UV are mediated through visual processing. Even if this were the case, the timeframe of UV activation is very short compared to the time-scale of sleep measurements so this is unlikely to be a confound.

      Indeed, were the equivalent zebrafish experiment from Zada et al. to be repeated with mutant larvae fish lacking the retinal basis for UV detection it might be found that in this case too, the effects of UV on behaviour are dependent on visual function. Such a finding should prompt a reappraisal of the interpretation that UV exposure's effects on fish sleep/locomotor behaviour are mediated by DNA damage.

      We prefer not to comment on Zada et al, as that is a separate manuscript.

      An additional note, relating to both Lloyd, Xia, et al., and Zada et al., is that though increases in immobility are induced following UV exposure, in neither study have assays of sensory responsiveness been performed during this period. As a decrease in sensory responsiveness is a key behavioural criterion for defining sleep, it is, therefore, unclear that this post-UV behaviour is genuinely increased sleep as opposed to a stress-linked suppression of locomotion due to the intensely aversive UV stimulus.

      We understand this concern and are working on improved methodology for measuring sleep.  However, behavioral measurements are the standard for almost every manuscript that has studied sleep in zebrafish, flies, and worms to date. 

      The effects of UV exposure, in terms of causing damage to DNA, inducing DNA damage response and repair mechanisms, and in causing broader changes in gene expression are assessed in both surface and cavefish larvae, as well as in cell lines derived from these different morphs. Differences in the suite of DNA damage response mechanisms that are upregulated are shown to exist between surface fish and cavefish larvae, though at least some of this difference is likely to be due to differences in gene expression that may exist even without UV exposure (this is discussed further below).

      UV exposure induced DNA damage (as measured by levels of cyclobutene pyrimidine dimers) to a similar degree in cell lines derived from both surface fish and cave fish. However, γH2AX shows increased expression only in cells from the surface fish, suggesting induction of an increased DNA repair response in these surface morphs, corroborated by their cells' increased ability to repair damaged DNA constructs experimentally introduced to the cells in a subsequent experiment. This "host cell reactivation assay" is a very interesting assay for measuring DNA repair in cell lines, but the power of this approach might be enhanced by introducing these DNA constructs into larval neurons in vivo (perhaps by electroporation) and by tracking DNA repair in living animals. Indeed, in such a preparation, the relationship between DNA repair and sleep/wake state could be assayed.

      Comparing gene expression in tissues from young (here 1 year) and older (here 7-8 years) fish from both cavefish and surface fish morphs, the authors found that there are significant differences in the transcriptional profiles in brain and gut between young and old surface fish, but that for cavefish being 1 year old versus being 7-8 years old did not have a major effect on transcriptional profile. The authors take this as suggesting that there is a reduced transcriptional change occurring during aging and that the transcriptome of the cavefish is resistant to age-linked changes. This seems to be only one of the equally plausible interpretations of the results; it could also be the case that alterations in metabolic cellular and molecular mechanisms, and particularly in responses to DNA damage, in the cavefish mean that these fish adopt their "aged" transcriptome within the first year of life.

      This is indeed true.  However, one could also interpret this as a lack of aging.  If the profile does not change over time, the difference seems largely semantic.

      A major weakness of the study in its current form is the absence of sleep deprivation experiments to assay the effects of sleep loss on the cellular and molecular parameters in question. Without such experiments, the supposed link of sleep to the molecular, cellular, and "aging" phenotypes remains tenuous. Although the argument might be made that the cavefish represent a naturally "sleep-deprived" population, the cavefish in this study are not sleep-deprived, rather they are adapted to a condition of reduced sleep relative to fish from surface populations. Comparing the effects of depriving fish from each morph on markers of DNA damage and repair, gut reactive oxygen species, and gene expression will be necessary to solidify any proposed link of these phenotypes to sleep.

      We agree this would be beneficial.  We note that relatively few papers have sleep deprived fish.  While we done have this before in A. mexicanus the assay is less than ideal and likely induces generalizable stress.  We are working on adapting more recently developed methods in zebrafish.

      A second important aspect that limits the interpretability and impact of this study is the absence of information about circadian variations in the parameters measured. A relationship between circadian phase, light exposure, and DNA damage/repair mechanisms is known to exist in A. mexicanus and other teleosts, and differences exist between the cave and surface morphs in their phenomena (Beale et al. 2013, doi: 10.1038/ncomms3769). Although the present study mentions that their experiments do not align with these previous findings, they do not perform the appropriate experiments to determine if such a misalignment is genuine. Specifically, Beale et al. 2013 showed that white light exposure drove enhanced expression of DNA repair genes (including cpdp which is prominent in the current study) in both surface fish and cavefish morphs, but that the magnitude of this change was less in the cave fish because they maintained an elevated expression of these genes in the dark, whereas the darkness suppressed the expression of these genes in the surface fish. If such a phenomenon is present in the setting of the current study, this would likely be a significant confound for the UV-induced gene expression experiments in intact larvae, and undermine the interpretation of the results derived from these experiments: as samples are collected 90 minutes after the dark-light transition (ZT 1.5) it would be expected that both cavefish and surface fish larvae should have a clear induction of DNA repair genes (including cpdp) regardless of 90s of UV exposure. The data in Supplementary Figure 3 is not sufficient to discount this potentially serious confound, as for larvae there is only gene expression data for time points from ZT2 to ZT 14, with all of these time points being in the light phase and not capturing any dynamics that would occur at the most important timepoints from ZT0-ZT1.5, in the relevant period after dark-light transition. Indeed, an appropriate control for this experiment would involve frequent sampling at least across 48 hours to assess light-linked and developmentally-related changes in gene expression that would occur in 5-6dpf larvae of each morph independently of the exposure to UV.

      We agree that this would be useful, however, frequent sampling is not feasible given the experiments presented here and the challenges of working with an emerging model.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      Again, see comment above.  The goal was to identify whether there are differences in DNA Damage response between A. mexcicanus. Extending on this to examine interactions with the circadian system could be a useful path to pursue in the future.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      The main weaknesses of the paper are a lack of significance in key findings, and relatedly, concluding effects from insignificant findings. Additional elements could be improved to help strengthen this overall well-rounded and intriguing set of results.

      In the original manuscript, we reported that chemogenetic silencing of POA-social neurons (previously called POA-iso neurons; more details on rationale for renaming below in our responses to reviewer recommendations) tended to reduce mounting in both single-housed female and single-housed male mice, although these effects were non-significant. We have added samples to both datasets and now report that chemogenetic silencing of POA-social neurons significantly reduces the proportion of trials with mounting in both sexes (Fig. 2C and Fig. 6G). 

      We have also included new analyses to test whether optogenetic activation of POAsocial neurons in group-housed females promotes social investigation (in addition to USV production, as reported in the original manuscript). We now report that optogenetic activation of POA-social neurons significantly increases the probability of social investigation (Fig. 4E-F) and significantly increases the duration of social investigation bouts (Fig. 4G). 

      Additional recommendations from the reviewer are addressed in detail below. Thank you for your critical and insightful feedback.

      Reviewer 2:

      All the activity-dependent labeling experiments with TRAP mice, including the subsequent neural activity manipulation experiments (Figures 2, 3, 4, 5E-F), were conducted by labeling neurons only in socially isolated animals, not group-housed animals. The authors labeled neurons after 30-minute social interactions, raising the possibility that the labeled neurons simply represent a "social interaction/behavior population" (mediating mounting and USVs in females and males) rather than a set of neurons specific to social isolation.

      I strongly recommend including experimental groups that involve labeling neurons after 30minute social interactions in group-housed female or male mice and inhibit TRAPed neurons after social isolation or activate TRAPed neurons after group housing. If manipulating the grouphoused TRAP neurons has similar effects to manipulating the isolated TRAP neurons, it would suggest the current labeling paradigm is not isolating neurons specific to the effect of social isolation per se. Rather, the neurons may mediate more general social interaction or motivationrelated activities. Given the known role of POA in male mating behavior, a group-housed TRAP experiment in males with a female visitor is especially important for understanding the selectivity of the labeled cells.

      Without proper controls, referring to the labeled neurons as "POAiso" neurons is potentially misleading. The data thus far suggests these neurons may predominantly reflect a "POA social behavior" population rather than a set of cells distinctly responsive to isolated housing.

      We agree with the reviewer that the POA neurons we are studying regulate the production of social behaviors in females and males, rather than representing a set of cells distinctly responsive to single housing. To more clearly reflect our thinking, we have changed the name of the neurons from “POA-iso neurons” to “POA-social neurons”. Thank you for this helpful criticism.

      Our Fos data are consistent with the idea that the POA may regulate social behaviors in group-housed females (not just single-housed females). Namely, we found that counts of Fospositive POA neurons are significantly related to rates of social investigation (p = 0.01) and tend to be related to USV rates (p = 0.05) in group-housed females that engaged in same-sex interactions (Fig. S1C). We now include two new sets of experiments aimed at further testing the idea this idea. 

      First, we include 2 control groups in which TRAPing sessions were performed in grouphoused females following same-sex interactions. We find that chemogenetic silencing of grouphoused-TRAPed POA neurons fails to reduce social behaviors in females that are subsequently single-housed and given a same-sex social interaction (Fig. 5A-D), and that optogenetic activation of group-housed-TRAPed POA neurons fails to promote female social behavior (Fig. 5E-H). At face value, these findings do not support the idea that the POA contains neurons that regulate social behaviors in group-housed females.

      However, one important caveat is that group-housed females engage in low rates of social behaviors (low investigation time, no mounting, and few USVs), and thus TRAP-based labeling may not work efficaciously in these mice. There may be POA neurons that regulate social behaviors in group-housed females but that do not upregulate Fos following production of relatively low rates of social behaviors. To test this idea, we also include females in which POA neurons are chemogenetically silenced using a viral strategy that does not depend on activitydependent labeling. In this new experiment, we report that silencing of POA neurons significantly reduces USV production in group-housed females (Fig. 5J-L) and significantly reduces social investigation, mounting, and USV production when these same females are retested following single-housing (Fig. 5M-O). Together, these experiments suggest that the POA may regulate the production of social behaviors during same-sex interactions in group-housed females, but that these effects may be difficult to detect in some cases given the low rates at which group-housed females engage in social behaviors during same-sex interactions relative to single-housed females.

      Finally, we want to highlight an additional new dataset that supports the idea that POAsocial neurons regulate social behaviors, rather than encoding the “state” of social isolation. We now include a control group for the chemogenetic silencing of female POA-social neurons, in which females were single-housed but were not given a social interaction prior to 4-OHT treatment (N = 5 non-social controls). Rates of social behaviors were subsequently unaffected following CNO delivery in these females (Fig. S2D-G). These new data support the conclusion that POA-social neurons regulate the production of social behaviors, rather than encoding the state of social isolation. 

      Reviewer 3:

      While the authors should be commended for performing and reporting multiple circuit perturbation experiments (e.g., chemogenetics, ablation), the conflicting effects on behavior are hard to interpret without additional experiments. For example, chemogenetic silencing of the POA neurons (using DREADDs) attenuated all three behavioral measures but the ablation of the same POA neurons (using CASPACE) decreased mounting duration without impacting social investigation or USV production. Similarly, optogenetic activation of POA neurons was sufficient to generate USV production as reported in earlier studies but mounting or social investigation remained unaffected. 

      Do these discrepancies arise due to the efficiency differences between DREADD-mediated silencing vs. Casp3 ablation? Or does the chemogenetic result reflect off-manifold effects on downstream circuitry whereas a more permanent ablation strategy allows other brain regions to compensate due to redundancy? It is important to resolve whether these arise due to technical reasons or whether these reflect the underlying (perhaps messy) logic of neural circuitry. Therefore, while it is clear that POA neurons likely contribute to multiple behavioral readouts of social isolation, understanding their exact roles in any greater detail will require further experiments.

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this interesting finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.  

      The majority of the females that we used in our TRAP2-based ablation experiments were heterozygous for TRAP2 (N = 11 of 15 POA-social-caspase subjects were TRAP2;Ai14 females), whereas all females used in our chemogenetic silencing experiments were homozygous for TRAP2. To test whether a more effective ablation of POA-social neurons might drive decreases in social investigation and USV production, we set up additional TRAP2 homozygous POA-social-caspase females and directly compare the effects of ablation between the two genotypes (Fig. S3; N = 11 hets in total and N = 9 homozygotes in total). These experiments revealed that effects on mounting were more pronounced following POA-social ablation in TRAP2 homozygotes vs. heterozygotes, but that neither group exhibited decreased social investigation or USV production following 4-OHT treatment.

      To ask whether caspase-mediated ablation in TRAP2 homozygotes was effective in eliminating neural activity associated with social behaviors in females, we performed Fos immunostaining in a subset of the POA-social-caspase TRAP2 homozygotes following a samesex interaction. We found that POA Fos expression was robustly reduced in these females relative to control group-housed and control single-housed females that also engaged in samesex interactions, down to levels seen in group-housed and single-housed females that did not engage in a social interaction (comparison shown in Fig. S3D; control female data same as in Fig. 1). Moreover, the remaining POA Fos in these TRAP2 homozygotes was no longer positively correlated to social investigation or USV production (Fig. S3E-F). Together, these findings lead us to favor the interpretation suggested by the reviewer below, that permanent ablation of POA-social neurons leads to compensation from other brain regions due to redundancy. In addition, our finding that optogenetic activation of POA-social neurons promotes both USV production and social investigation supports the idea that POA-social neurons directly regulate these behaviors. We agree with the reviewer that additional work is needed to understand the complex sex- and context-dependent role played by the POA in the regulation of mouse social behaviors.

      Recommendations for the Authors:

      Reviewer 1 Recommendations:

      (1) The largest issue is that many of the stated "key" behavioral findings are not statistically significant.

      (1a) Figure 2C is not significant and Figure 5G is not significant

      We have added N = 5 POA-social-hM4Di females, N = 3 POA-social-hM4Di males, and N = 3 POA-social-GFP males to the dataset. The decrease in mounting following chemogenetic silencing of POA-social neurons is now statistically significant in both sexes (p < 0.05 for both; see current Figs. 2C and 6G). We also simplified our statistical analysis of mounting in these experiments to consider the proportion of trials with and without resident-initiated mounting on saline vs. CNO days, using McNemar’s test for paired proportions. 

      (1b) Mounting graphs are completely omitted in Figure 4. 

      Given that mounting was only observed infrequently in POA-social-ChR2 females, we simply report this information in the Results text (lines 382-388). In our prior summary of the mounting results, we reported that mounting was observed in a total of 3 trials from 2 females, but we inadvertently included information from a duplicate trial from one of the POA-socialChR2 females in this summary (all other analyses of the POA-social-ChR2 females included one trial per female). We have corrected that error and now report that we observed mounting following laser stimulation in 1 trial from 1 POA-social-ChR2 female. We have expanded our consideration of potential effects of optogenetic activation of POA-social neurons on social investigation and include these new analyses as part of Figure 4 (Fig. 4E-G), following the existing analyses of USV production.

      (1c) Figure 3C shows a reduction of mounting following the ablation of POA (although no stats on the graph to denote significance), but this ablation approach can't resolve whether POA is required to encode the state produced by the short period of isolation, and/or whether it needs to be online at test.

      We have now added an asterisk in Fig. 3C to denote a p value less than 0.05. Thank you for catching our oversight.

      We designed our activity-dependent labeling experiments to TRAP and express viruses in POA neurons that increase their activity in conjunction with the production of social behaviors in single-housed females. We believe our findings our most consistent with the conclusion that these neurons regulate the production of social behaviors, rather than encoding the state of social isolation, and we have renamed these neurons as “POA-social” neurons to better reflect our thinking.

      We also now include control experiments (albeit chemogenetic inhibition, not caspase ablation) in which the TRAP2 strategy is used to express hM4Di in the POA of single-housed females that do not experience a social interaction prior to 4-OHT delivery (non-social controls, Fig. S2D-G). We report that chemogenetic inhibition of these neurons does not decrease social behavior in single-housed females during a subsequent same-sex interaction (p > 0.05 for saline vs. CNO rates of social investigation, mounting, and USVs). These additional findings support the idea that the activity of POA-social neurons is related to the production of social behaviors rather than to the state of social isolation. 

      The reviewer is correct that our ablation approach cannot resolve the question of whether POA-social neuronal activity is required online during testing, but our reversible chemogenetic inhibition experiments provide evidence that the activity of POA-social neurons is required online at the time of testing to regulate social behavior.

      (1d) A similar issue is seen regarding investigation (a general lack of significance with most of the LOF and GOF manipulations).

      As reported in the original manuscript, we find that chemogenetic inhibition of POAsocial neurons reduces social investigation in females, while caspase-mediated ablation of female POA-social neurons does not. Our original caspase dataset used mostly but not all TRAP2 heterozygous females (N = 11 TRAP2 heterozygotes (TRAP2;Ai14), generated by crossing TRAP2 mice with Ai14 mice, for the purpose of visualizing the absence of tdTomato labeling to estimate spread of the caspase virus; and N = 4 TRAP2 homozygotes). By adding to the TRAP2 homozygous caspase dataset and comparing the effects on female social behavior of ablation of POA-social neurons in TRAP2 heterozygous vs. TRAP2 homozygous females, we

      now provide evidence that the attenuation of mounting is more efficacious in TRAP2 homozygous females than in heterozygotes (Fig. S3B). Nonetheless, we fail to see effects on social investigation and USV production, even when caspase ablation of POA-social neurons is performed in TRAP2 homozygous females (Fig. S3A,C). 

      In spite of the lack of effect on these behaviors, we show that caspase-mediated ablation of POA-social neurons in TRAP2 homozygous females leads to a dramatic reduction in social interaction-induced Fos expression in the POA. POA Fos expression in these caspase females is reduced to the levels seen in control group-housed and single-housed females that are not given social interactions and are significantly lower than Fos expression in group-housed and single-housed females that are given a same-sex interaction (Fig. S3D). Moreover, the remaining POA Fos expression in the caspase females is no longer related to rates of social investigation (Fig. S3E), as is normally the case in group-housed and single-housed control females (Fig. S1C, left). Together, these data support the idea that some type of neuronal compensation outside of the POA is occurring following ablation of POA-social neurons, and this compensation permits normal levels of USV production and social investigation.

      As in the original manuscript, we report that chemogenetic inhibition of POA-social neurons in male mice reduces mounting but does not reduce social investigation (or USV production). We now include quantification of social behaviors produced by male and female POA-social-hM4Di mice in the TRAPing sessions that preceded 4-OHT delivery (Fig. S5). These measurements show that males spent significantly more time than females engaged in mounting, and we speculate that this bias in TRAPing session behavior might have led to a bias in TRAP-mediated viral labeling of male POA neurons that regulate mounting, at the expense of male POA neurons that regulate social investigation (or USV production).

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this encouraging finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.

      (2) In Figure 1 and elsewhere, the authors use a Mann-Whitney U test, which should be used for non-parametric data, but in other places, they use statistical tests for normally distributed data. Why? How was the normality of distributions tested?

      We tested the normality of data distributions using the Shapiro-Wilk test. Parametric tests were used for analyses that contained normally distributed data, and non-parametric tests were used for analyses that contained non-normally distributed data. This information is included in the Methods (lines 997-1000), and full details of statistical analyses can be found in Table S1.

      (3) The method for "trapping" neurons that are part of the short-term isolation ensemble has some caveats that have not been adequately addressed. First, 4-OHT was administered after social interaction, but before 24 hours of isolation, making it unclear exactly WHAT is being trapped.

      i) Is it neurons that encode the recent 3-day iso experience? (seems unlikely, as this would have been hours after the end of that iso window)

      We now include a group of control females to directly test this possibility (Fig. S2D-G). These TRAP2 females were single-housed for 3 days but were not given a social interaction prior to 4-OHT treatment (N = 5 non-social controls). Presumably, POA neurons TRAPed in these females might encode the experience of short-term isolation. However, we found that chemogenetic inactivation of these TRAPed neurons during a subsequent same-sex interaction failed to decrease social behaviors in single-housed females (Fig. S2E-G; p > 0.05 for CNO vs. saline rates of social investigation, mounting, and USV production). These control experiments support the idea that we are TRAPing neurons whose activity is related to the production of social behaviors, and we have renamed the neurons as “POA-social” neurons to reflect this thinking.

      ii) Is it neurons that encode the recent behavior impacted by the 3-day iso? (this seems to be the goal, but the authors do not provide evidence that the time course of their injection is efficient enough to recruit the recently activated neurons, nor do they provide evidence that opening the trapping window directly after the behavior is better than directly before)

      We opted to perform IP injections of 4-OHT immediately following the behavior session, rather than behavior, due to concern that handling the mice and delivering IP injections prior to behavior sessions would stress the mice, leading to lower rates of social behaviors. The nonsocial female hM4Di experiments described above support the idea that we are TRAPing neurons related to the production of social behaviors, as the reviewer suggests. 

      iii) Is it trapping neurons active during the subsequent 24 hours of isolation? (seems possible, but this would mean that the authors are looking at a different population of neurons than they claim).

      If chemogenetic silencing of POA neurons that were TRAPed following 3-days of social isolation but in the absence of a social interaction (N = 5 non-social controls, Fig. S2D-G) does not alter social behaviors, there is no compelling reason to hypothesize that TRAPing POA neurons activated following the 24 hours of social isolation that follow a social interaction would do so. Moreover, in the original study characterizing the TRAP2 mice (DeNardo et al., 2019), the authors performed experiments to characterize the time course of TRAPing relative to 4-OHT treatment and concluded that the majority of TRAPing occurs within a 6-hour window centered around the 4-OHT injection.

      (4) Relatedly, the authors seem to find a fair bit of variability in their TRAP-mediated experiments. This begs the question - are the effects of their GOF and LOF approaches

      i) dependent on the iso-behaviors that were "trapped" for each animal (in other words, how does behavior at test 1 correlate with behavior at test 2)? 

      To test the reviewer’s idea, we compared rates of TRAPing session behaviors for the POA-social-hM4Di females to the subsequent effects of neuronal silencing on these behaviors (calculated as (CNO behavior – saline behavior). These correlations are shown in Fig. S2A-C and are all non-significant. We also include below for the reviewer the same types of correlations for the other datasets in our study (loss-of-function experiments: female POAsocial-caspase, male POA-social-hM4Di; and gain-of-function experiments: female POA-socialChR2).

      Author response image 1.

      The only loss-of-function experiment comparison in the above figure that reveals a negative and significant correlation is the mounting comparison for the POA-social-hM4Di males (time spent mounting during TRAPing session vs. (CNO time spent mounting -saline time spent mounting). This significant correlation likely reflects that fact that (1) no males mounted in the CNO session and (2) that mounting rates for individual males are relatively consistent over time (in comparison to female mounting, which is more variable; see Author response image 2 below of TRAPing session vs. saline mounting in male vs. female POA-social-hM4Di experiments). The correlation between TRAPing session and testing session mounting is significant for the POA-social-ChR2 females, but despite the significant correlation, we would want to see more instances of optogenetically-elicited mounting to make any claim about its relationship to TRAPing session behavior.

      Author response image 2.

      Nonetheless, we agree with the reviewer’s intuition that one would expect the effects of POA activity manipulations on different behaviors to scale with rates at which these behaviors were performed during the TRAPing session. We speculate that variability in the TRAPing process might have obscured such a relationship. There is inevitable variability in the exact body cavity placement of IP injections, which can affect drug absorption, and another point is that we delivered a fixed volume of 4-OHT (10 mg/mL 4-OHT in 150 uL filtered corn oil) to all mice in the study, regardless of their weight, which likely added variability in TRAPing efficacy from animal to animal. This detail was reported inaccurately in the Methods, and that error has been corrected (line 920). With regard to our male POA-social-hM4Di dataset, we find that these males spend more time mounting during their TRAPing sessions than female POA-socialhM4Di (Fig. S5; males also spent less time investigating and tended to produce fewer USVs than females), a fact that we hypothesize may have led to a bias toward TRAPing mountingrelated POA neurons in male subjects. In addition, however, the fact that male mice typically weigh more than females and would have received a slightly lower effective dosage of 4-OHT may also have contributed to the weaker effects on behavior in the male POA-social-hM4Di experiments relative to the female POA-social-hM4Di experiments.

      We also want to highlight that interpreting correlations for females between time spent mounting during the TRAPing session and time spent mounting during the test sessions can be complicated. For example, we see 2 cases in the female POA-social-hM4Di dataset in which the female did not mount in the TRAPing session, and then mounted on the saline day (12s and 10s total mounting for those 2 females) but not on the CNO day. One interpretation of the data from these 2 females is that mounting on the TRAPing day is not required to attenuate mounting on the later test days. However, female mounting behavior itself is variable, both across different females and across different tests of a given female, as noted above. If we consider all singlehoused females included in our dataset for which we quantified control behavioral data (i.e., behavior trials from unmanipulated females and TRAPing sessions from females that were later manipulated), we find that mounting is not observed in ~30% of the females (24 of 83). In ongoing behavioral experiments not included in this manuscript, we are investigating factors that regulate female mounting following single-housing. In that dataset, we also see little evidence that female mounting in one social interaction predicts mounting in a subsequent interaction

      (i.e., there don’t appear to stable “high mounters” and “low mounters” following single housing). Thus, the small number of cases in which females did not mount in the TRAPing session and then displayed mounting on the CNO only day are difficult to interpret. 

      Two additional considerations are that TRAPing may not be equally efficacious for POA neurons that regulate different behaviors, and that different behaviors may be differentially sensitive to perturbations of the POA. Previous elegant calcium imaging work has shown that different subsets of Esr1+ POA neurons exhibit activity that is “tuned” to specific behaviors (sniffing vs. mounting in males interacting with females; Yang et al., 2023). However, it is possible that these subsets of neurons display differential levels of Fos expression following the production of their preferred behavior and that some behavior-related subsets may thus be more easily TRAPed than others. It may also be the case that some behaviors are more easily disrupted by POA activity manipulations than others (e.g., perturbation in a smaller percentage of behavior-related POA neurons may be required to disrupt some behaviors relative to others). 

      Despite these caveats, we have two lines of evidence that the effects of chemogenetic silencing of POA-social neurons depends on the behaviors produced during the TRAPing sessions.

      (1) Social behavior is required during the TRAPing session to see subsequent effects on social behavior following chemogenetic silencing of TRAPed POA neurons. In control females that were single-housed but were not given a social interaction prior to 4OHT treatment, social behaviors are not reduced by chemogenetic silencing of TRAPed POA neurons (Figs. S2D-G).

      (2) To directly test whether mounting in the TRAPing session is required to see attenuation of mounting during subsequent chemogenetic silencing of POA-social neurons, we performed control experiments in which single-housed females interacted with a female visitor that was placed under a cup during the TRAPing session prior to 4-OHT treatment. Mounting was not possible in this context, and we also found that females produced lower rates of USVs during the TRAPing session relative to single-housed females engaged in free social interaction. However, subject females spent more time engaged in social investigation of the visitor relative to single-housed females engaged in free social interactions (see Author response image 3 below).

      Author response image 3.

      Unfortunately, none of the experimental females in this cohort displayed mounting in the CNO or saline sessions. Given that we could use this dataset to address the intended question, we did not include it in the manuscript. However, it is quite interesting that female subjects displayed higher than normal social investigation and lower than normal USV production in their TRAPing sessions (relative to single-housed females engaged in free interactions), and subsequently, chemogenetic inhibition of TRAPed POA neurons decreased social investigation but did not decrease USV production (Author response image 4 below). 

      Author response image 4.

      Together, we think our data support the idea that the POA neurons that are TRAPed are related to the social behaviors performed by the animals, but these relationships may be complex and difficult to detect from comparisons across animals within a single experimental group.

      And/or are they

      ii) influenced by the spread or amount of virus for each animal? These correlations could help shed light on what exactly is being trapped - is it specific behaviors or is it the "state" of shortterm isolation?

      Our control experiments with females that were single-housed but did not receive a social interaction prior to 4-OHT treatment provide evidence that the production of social behaviors is required to see subsequent effects on behavior following chemogenetic inhibition of TRAPed POA neurons (Figs. S2D-G).

      The same volume of virus was injected across all activity manipulation experiments (200 nL). Because of the trajectory of our POA viral injections (performed at a slight rostral angle relative to vertical), we did sometimes see viral labeling that spread into the AH caudal to the POA. For this reason, we included the AH TRAPed control group (Fig. 2), to rule out the possibility that viral spread into the AH could account for the effects of chemogenetic silencing of POA-social neurons on female social behaviors. Also because of the injection angle used, we don’t see substantial viral spread rostral to our injection coordinates. In short, there isn’t systematic variability in the targeting or spread of our POA viral injections that can account for variability in the effects on USV production and social investigation of our LOF and GOF manipulations (female hM4Di and female ChR2 experiments).

      In older lesion studies in male rodents and birds, there is some support for the idea that rostral vs. caudal POA neurons differentially regulate appetitive vs. consummatory sexual behaviors (as reviewed in Balthazart and Ball, 2007). However, all of our viral injections were placed in what that review paper would have considered ‘caudal’ POA. We also note that more recent imaging studies have reported that subsets of POA neurons are differentially tuned to male sniffing vs. male mounting (Yang et al.,2023), and these subsets must be relatively co-localized given that they are imaged in the same field of view. Whether distinct subsets of POA neurons regulate the production of different female social behaviors, and if so, how these subsets are localized within the POA, remains an important question for future study.

      (5) The authors label their region of interest as the "POA" but images throughout (e.g. their fos image, Figure 1E), look more like the MPO. Why label it POA?

      The POA neurons in our study are found in a band that spans the medial POA, as well as a bit of the lateral POA. To avoid over-specifying, we call this region the POA more generally.

      (6) In all the experiments, mice are isolated and then re-group housed with siblings. Do all the siblings in the group belong to the same experimental group, or are siblings naïve? This may be critical to help determine whether some of the effects observed may be "group" effects.

      In general, multiple (although not always all) mice in a cage belonged to the same experimental group. In our inhibitory DREADDs experiments, it is unclear how that could drive our observed effects on behavior, given that home cage behavior would only be expected to differ for a given mouse in the time period following their CNO session. 

      For the female POA-social-caspase mice, we cannot rule out the possibility that their home cage behaviors differed in the time period following 4-OHT treatment and re-grouphousing and prior to post-4-OHT behavior measurements. However, given that the only social behavior affected by ablation of POA-social neurons was mounting, and that rates of mounting would be expected to be very low in group-housed females within home cages, it is unclear how our experimental result could be attributed to group effects.

      If by “group” effects the reviewer means “litter” effects, we include a plot below that shows the CNO vs. saline behaviors for the POA-social-hM4Di females, separated by cage ID. There is no evidence that the effects of chemogenetic silencing of POA-social-hM4Di females are being driven by only certain cages (only social investigation and USVs are shown, because mounting was uniformly low (1 of 17 females mounted) in the CNO session).

      Author response image 5.

      (7) For chemogenetic experiments, the authors state that CNO and Saline were given in a counterbalanced order (eg line 189). Did the authors see any order effects?

      We did not see order effects, and we can include plots of those data below for the female and male POA-social-hM4Di groups, with mice plotted according to which treatment they received first.

      Author response image 6.

      (8) In the control experiments in Figure 2 where VMH or AH are chemogenetically silenced, it isn't clear whether these groups include mice that were subjected to 3 days of isolation. Please clarify.

      Yes, these female groups were also subjected to 3 days of isolation (first prior to the TRAPing session, and for a second time prior to the onset of the CNO/saline testing sessions). That information has been clarified in the Results section (line 214) and in the Methods (lines 935-938).

      (9) Line 312. The title for this section, "POA neurons increase their activity....." is somewhat misleading. It sounds like the authors imaged trapped neurons. I think what they mean is that more POA neurons are activated following opposite-sex interactions with males.

      Thanks for this catch. We have modified the section title, as well as the title of the first results sub-section.

      (10) Figure 5A, right panels. The authors fail to find an increase in the investigation of male-male pairs following the short-term isolation of one. This contrasts with the main finding in Matthews et al., 2016 Cell, where short periods of isolation are said to promote pro-social behaviors. The authors could comment on this discrepancy in their discussion (eg difference in testing apparatus/test type? Difference in the number of days of isolation? etc.).

      In current Fig. 6A, there is no significant interaction between the two main effects, but each main effect is significant: single-housed males spend more time investigating partners than group-housed males, and males spend more time investigating female partners than male partners. The significant main effect of housing condition is consistent with the findings of Matthews et al., 2016 and is included within the Results (lines 486-492). 

      (11) Figure 5F, the authors seem to have a main effect of virus (more overall investigation in dreadds mice). Nothing about this is addressed.

      We sometimes see differences in social behavior between cohorts of males when they are tested at different times and, correspondingly, with different groups of female social partners. Our POA-social-hM4Di and POA-social-GFP males were set-up and tested at largely non-overlapping times. We have added a brief note to the Results section to include this information (lines 535-539).

      Reviewer 2 Recommendations:

      (1) (C)ritical control experiments are missing to support this claim (that a population of preoptic hypothalamic neurons contribute to the effects of short-term social isolation on the social behaviors of female mice).  

      (1a) All the activity-dependent labeling experiments with TRAP mice, including the subsequent neural activity manipulation experiments (Figures 2, 3, 4, 5E-F), were conducted by labeling neurons only in socially isolated animals, not group-housed animals. The authors labeled neurons after 30-minute social interactions, raising the possibility that the labeled neurons simply represent a "social interaction/behavior population" (mediating mounting and USVs in females and males) rather than a set of neurons specific to social isolation behaviors of mice)… The data thus far suggests these neurons may predominantly reflect a "POA social behavior" population rather than a set of cells distinctly responsive to isolated housing.

      We agree with the reviewer that the POA neurons we are studying regulate the production of social behaviors in females and males, rather than representing a set of cells distinctly responsive to single housing. To more clearly reflect our thinking, we have changed the name of the neurons from “POA-iso neurons” to “POA-social neurons”. Thank you for this helpful criticism.

      Our Fos data are consistent with the idea that the POA may regulate social behaviors in group-housed females (not just single-housed females). Namely, we found that counts of Fospositive POA neurons are significantly related to rates of social investigation (p = 0.01) and tend to be related to USV rates (p = 0.05) in group-housed females that engaged in same-sex interactions (Fig. S1C). We now include two new sets of experiments aimed at further testing the idea this idea. 

      First, we include 2 control groups in which TRAPing sessions were performed in grouphoused females following same-sex interactions. We find that chemogenetic silencing of these group-housed-TRAPed POA neurons fails to reduce social behaviors in females that are subsequently single-housed and given a same-sex social interaction (Fig. 5A-D; GH-TRAPed POA hM4Di females), and that optogenetic activation of group-housed-TRAPed POA neurons fails to promote female social behavior (Fig. 5E-H; GH-TRAPed POA ChR2 females). At face value, these findings do not support the idea that the POA contains neurons that regulate social behaviors in group-housed females.

      However, one important caveat is that group-housed females engage in low rates of social behaviors (low investigation time, no mounting, and few USVs), and thus TRAP-based labeling may not work efficaciously in these mice. There may be POA neurons that regulate social behaviors in group-housed females but that do not upregulate Fos following production of relatively low rates of social behaviors. To test this idea, we also include females in which POA neurons are chemogenetically silenced using a viral strategy that does not depend on activitydependent labeling. In this new experiment, we report that silencing of POA neurons significantly reduces USV production in group-housed females (Fig. 5J-L) and significantly reduces social investigation, mounting, and USV production when these same females are retested following single-housing (Fig. 5M-O).

      (2) Please add strain background information of subject animals in the methods.

      This information has been added to the Animals section within the Methods (lines 788802).

      Responses to Reviewer 3 Recommendations:

      (1a) (T)he conflicting effects on behavior are hard to interpret without additional experiments….Similarly, optogenetic activation of POA neurons was sufficient to generate USV production as reported in earlier studies but mounting or social investigation remained unaffected. 

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this interesting finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.

      (1b) Do these discrepancies (between hM4Di and caspase) arise due to the efficiency differences between DREADD-mediated silencing vs. Casp3 ablation? Or does the chemogenetic result reflect off-manifold effects on downstream circuitry whereas a more permanent ablation strategy allows other brain regions to compensate due to redundancy? It is important to resolve whether these arise due to technical reasons or whether these reflect the underlying (perhaps messy) logic of neural circuitry.  

      The possibility that the difference in effects on behavior between chemogenetic silencing and caspase ablation at face value seems inconsistent with the findings of previous experiments, in which ablation of large numbers of POA neurons failed to reduce USV production in male mice (POA lesions in Bean et al., 1981; ablation of VGAT+ POA neurons by Gao et al., 2018). These findings stand in contrast to those using chemogenetic silencing of large numbers of POA neurons, which report reduced USV production in male mice (VGAT+/Esr1+ in Karigo et al., 2021; Esr1+ in Chen et al., 2021).

      However, it is the case that the majority of the females that we used in our TRAP2-based ablation experiments were heterozygous for TRAP2 (N = 11 of 15 POA-social-caspase subjects were TRAP2;Ai14 females), whereas all females used in our chemogenetic silencing experiments were homozygous for TRAP2. To test whether a more effective ablation of POAsocial neurons might drive decreases in social investigation and USV production, we set up additional TRAP2 homozygous POA-social-caspase females and directly compare the effects of ablation between the two genotypes (Fig. S3; N = 11 hets in total and N = 9 homozygotes in total). These experiments revealed that effects on mounting were more pronounced following POA-social ablation in TRAP2 homozygotes vs. heterozygotes, but that neither group exhibited decreased social investigation or USV production following 4-OHT treatment.

      To ask whether caspase-mediated ablation in TRAP2 homozygotes was effective in eliminating neural activity associated with social behaviors in females, we performed Fos immunostaining in a subset of the POA-social-caspase TRAP2 homozygotes following a samesex interaction. We found that POA Fos expression was robustly reduced in these females relative to control group-housed and control single-housed females that also engaged in samesex interactions, down to levels seen in group-housed and single-housed females that did not engage in a social interaction (comparison shown in Fig. S3D; control female data same as in Fig. 1). Moreover, the remaining POA Fos in these TRAP2 homozygotes was no longer positively correlated to social investigation or USV production (Fig. S3E-F). Together, these findings lead us to favor the interpretation suggested by the reviewer below, that permanent ablation of POA-social neurons leads to compensation from other brain regions due to redundancy.

      Given the negative results above, we favor this possibility and indicate so in our Discussion. In addition, our finding that optogenetic activation of POA-social neurons promotes both USV production and social investigation supports the idea that POA-social neurons directly regulate these behaviors. We agree with the reviewer that additional work is needed to understand the complex sex- and context-dependent role played by the POA in the regulation of mouse social behaviors.

      (2) L 49: Please define Mesolimbic circuitry the first time it is mentioned.

      We have added a definition (lines 52-53).

      (3) L 210: In Figure 2C, the mounting duration baseline (saline) distribution seems lower than the same experimental baseline in Figures 1C and 3C. Does this reflect natural variability in the behavioral assay and might this be mitigated by additional sampling of animals?

      Yes, there is substantial variability in the display of mounting behavior by single-housed females, including in the proportion of trials with mounting as well as in the total duration of mounting. In the revised manuscript, we have simplified our analysis of mounting in our TRAPbased experiments to quantify the proportion of trials with mounting, rather than considering the total time spent mounting. After adding N = 5 additional females to the POA-social-hM4Di dataset, we now report a statistically significant decrease in the proportion of trials with mounting following chemogenetic silencing of POA-social neurons (Fig. 2C; McNemar’s test for paired proportions). 

      (4) L 310: The authors claim that "These findings suggest that a subset of POAiso neurons overlap with GABAergic, PAG-projecting POA neurons that have been demonstrated in previous work to promote USVs via disinhibition of excitatory PAG neurons important to USV production (Chen et al., 2021; Michael et al., 2020)." I think the data reported suggests the opposite since only 18.3% of all POA->PAG neurons are cFos+. Perhaps better rephrased as "A subset (18.3%) of POA->PAG neurons are labelled by cFos and that is sufficient to drive the production of USVs". Is it surprising?

      We modified the phrasing (lines 468-469), but a bit differently than suggested above, because although we suspect that optogenetic activation of the PAG-projecting neurons within the larger population of POA-social neurons is responsible for eliciting USV production, we did not technically demonstrate this to be the case in the current dataset. 

      We do find it surprising that so few (only ~20%) of PAG-projecting POA neurons upregulate Fos following female-female interactions marked by high rates of USV production. Even though optogenetic activation of PAG-projecting POA neurons elicits USV production, our finding suggests that the majority of PAG-projecting POA neurons may not play a role in regulating vocalization. In future work, it may be useful to apply an intersectional approach to further understand how the POA regulates USV production (for example, measure or manipulate activity selectively in projection-defined subsets of POA-social neurons).

      (5) Given the considerable prior evidence of POA->PAG circuit in promoting USVs, it is hard to understand why chemogenetic inactivation of POA neurons in males affects mounting but not USV production (Figures 5F-H). Any potential explanation for this discrepancy?

      We have two ideas about this surprising result. First, we examined the TRAPing session social behaviors of female and male POA-social-hM4Di mice. We found that male POA-socialhM4Di mice spent more time than female subjects mounting during the TRAPing sessions, and conversely, males spent less time investigating visitors and tended to produce fewer USVs than female subjects (Fig. S5). Given that our labeling method is activity-dependent, one possibility is that this bias in behavior is reflected in a bias toward labeling of POA neurons related to mounting.  

      Second, each mouse in the TRAP2-based hM4Di datasets received an IP injection of the same amount of 4-OHT (150 nL of 10 mg/mL 4-OHT in filtered corn oil) not adjusted for weight of the mouse. This information was not reported accurately in the Methods, and we have adjusted that section accordingly (line 920). As a result, because male mice typically weigh more than females and would have received a lower effective dosage of 4-OHT, another possibility is that TRAPing in males was less efficient than in females and accounts for the less complete effects on social behaviors. We have added language to the Results to discuss these possibilities (lines 540-560).

      (6) L 472: Typo. "we found that short-term isolation exerts more robust on the effects of male behavior during subsequent interactions with females than during interactions with males."

      Thank you for catching this mistake.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi", which are stimuli that enhance other canonical tastes, increasing essentially the hedonic attributes of these other stimuli; the mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model.

      Strengths:

      The data show the effects of ornithine on taste: in two-bottle and briefer intake tests, adding ornithine results in a higher intake of most, but not all, stimuli tests. Bilateral nerve cuts or the addition of GPRC6A antagonists decrease this effect. Small effects of ornithine are shown in whole-nerve recordings.

      Weaknesses:

      The conclusion seems to be that the authors have found evidence for ornithine acting as a taste modifier through the GPRC6A receptor expressed on the anterior tongue. It is hard to separate their conclusions from the possibility that any effects are additive rather than modulatory. Animals did prefer ornithine to water when presented by itself. Additionally, the authors refer to evidence that ornithine is activating the T1R1-T1R3 amino acid taste receptor, possibly at higher concentrations than they use for most of the study, although this seems speculative. It is striking that the largest effects on taste are found with the other amino acid (umami) stimuli, leading to the possibility that these are largely synergistic effects taking place at the tas1r receptor heterodimer.

      We would like to thank Reviewer #1 for the valuable comments. Our basis for considering ornithine as a taste modifier stems from our observation that a low concentration of ornithine (1 mM), which does not elicit a preference on its own, enhances the preference for umami substances, sucrose, and soybean oil through the activation of the GPRC6A receptor. Notably, this receptor is not typically considered a taste receptor. The reviewer suggested that the enhancement of umami taste might be due to potentiation occurring at the TAS1R receptor heterodimer. However, we propose that a different mechanism may be at play, as an antagonist of GPRC6A almost completely abolished this enhancement. In the revised manuscript, we will endeavor to provide additional information on the role of ornithine as a taste modifier acting through the GPRC6A receptor.

      Reviewer #2 (Public review):

      Summary:

      The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

      Strengths:

      The authors examined a new and exciting taste enhancer (ornithine). They used a variety of experimental approaches in rats to document the impact of ornithine on taste preference and peripheral taste nerve recordings. Further, they provided evidence pointing to a potential receptor for ornithine.

      Weaknesses:

      The authors have not established that the rat is an appropriate model system for studying kokumi. Their measurements do not provide insight into any of the established effects of kokumi on human flavor perception. The small study on humans is difficult to compare to the rat study because the authors made completely different types of measurements. Thus, I think that the authors need to substantially scale back the scope of their interpretations. These weaknesses diminish the likely impact of the work on the field of flavor perception.

      We would like to thank Reviewer #2 for the valuable comments and suggestions. Regarding the question of whether the rat is an appropriate model system for studying kokumi, we have chosen this species for several reasons: it is readily available as a conventional experimental model for gustatory research; the calcium-sensing receptor (CaSR), known as the kokumi receptor, is expressed in taste bud cells; and prior research has demonstrated the use of rats in kokumi studies involving gamma Glu-Val-Gly (Yamamoto and Mizuta, Chem. Senses, 2022).

      We acknowledge that fundamentally different types of measurements were conducted in the human psychophysical study and the rat study. Kokumi can indeed be assessed and expressed in humans; however, we do not currently have the means to confirm that animals experience kokumi in the same way that humans do. Therefore, human studies are necessary to evaluate kokumi, a conceptual term denoting enhanced flavor, while animal studies are needed to explore the potential underlying mechanisms of kokumi. We believe that a combination of both human and animal studies is essential, as is the case with research on sugars. While sugars are known to elicit sweetness, it is unclear whether animals perceive sweetness identically to humans, even though they exhibit a strong preference for sugars. In the revised manuscript, we will incorporate additional information to address the comments raised by the reviewer. We will also carefully review and revise our previous statements to ensure accuracy and clarity.

      Reviewer #3 (Public review):

      Summary:

      In this study, the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein-coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste.

      Strengths:

      The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants, including inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl); citric acid and quinine hydrochloride. Robust effects of ornithine were observed in the cases of IMP, MSG, MPG, and sucrose, and little or no effects were observed in the cases of sodium chloride, citric acid, and quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. The inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify the role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally, they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

      Weaknesses:

      The researchers undertook what turned out to be largely confirmatory studies in rats with respect to their previously published work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

      The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9). They miss an opportunity to outline the experimental results from the study that favor their preferred interpretation that ornithine is a taste enhancer rather than a tastant.

      At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). While the experimental results as a whole favor the authors' interpretation that C6A mediates the Ornithine responses, they do not make clear either the nature of the 'receptor identification problem' in the Introduction or the way in which they approached that problem in the Results and Discussion sections. It would be helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response. In addition, while they showed that C6A-positive cells were clearly distinct from gustducin-positive, and thus T1R-positive cells, they missed an opportunity to clearly differentiate C6A-expressing taste cells and CaSR-expressing taste cells in the rat tongue sections.

      It would have been helpful to include a positive control kokumi substance in the two-bottle preference experiment (e.g., one of the known gamma-glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

      The results demonstrate that enhancement of the chorda tympani nerve response to MSG occurs at substantially greater Ornithine concentrations (10 and 30 mM) than were required to observe differences in the two bottle preference experiments (1.0 mM; Figure 2). The discrepancy requires careful discussion and if necessary further experiments using the two-bottle preference format.

      We would like to thank Reviewer #3 for the valuable comments and helpful suggestions. We propose that ornithine has two stimulatory actions: one acting on GPRC6A, particularly at lower concentrations, and another on amino acid receptors such as T1R1/T1R3 at higher concentrations. Consequently, ornithine is not preferable at lower concentrations but becomes preferable at higher concentrations. For our study on kokumi, we used a low concentration (1 mM) of ornithine. The possibility mentioned in the Discussion that 'the umami substances may enhance the taste response to ornithine' is entirely speculative. We will reconsider including this description in the revised version. As the reviewer suggested, in addition to GPRC6A, ornithine may bind to CaSR and/or T1R1/T1R3 heterodimers. However, we believe that ornithine mainly binds to GPRC6A, as a specific inhibitor of this receptor almost completely abolished the enhanced response to umami substances, and our immunohistochemical study indicated that GPRC6A-expressing taste cells are distinct from CaSR-expressing taste cells (see Supplemental Fig. 3). We conducted essentially the same experiments using gamma-Glu-Val-Gly in Wistar rats (Yamamoto and Mizuta, Chem. Senses, 2022) and compared the results in the Discussion. The reviewer may have misunderstood the chorda tympani results: we added the same concentration (1 mM) used in the two-bottle preference test to MSG (Fig. 5-B). Fig. 5-A shows nerve responses to five concentrations of plain ornithine. In the revised manuscript, we will strive to provide more precise information reflecting the reviewer’s comments.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The behavioral effects found with the CPRC6A antagonists are not entirely convincing, as the antagonist is seemingly just mixed up in the solution with the stimuli. There are no control experiments demonstrating that the antagonists do not have a taste themselves.

      We mixed the antagonists into both liquids used in the two-bottle preference test to eliminate any potential taste effects of the antagonists themselves. In the electrophysiological experiments, the antagonist was incorporated into the solution after confirming that it did not elicit any appreciable response in the taste nerve.

      (2) The effects of ornithine found with quinine did not have a satisfying explanation - if there is some taste cell-taste cell modulation that accounts for the taste enhancement, why is the quinine less aversive? Why is it not enhanced like the other compounds?

      The effects of ornithine on quinine responses remain difficult to explain. A previous study (Tokuyama et al., Chem Pharm Bull, 2006) proposed that ornithine prevents bitter substances from binding to bitter receptors, although this hypothesis lacks definitive evidence. In the present study, our findings suggest that the binding of quinine to bitter receptors is essential, as another agonist, gallate, also enhanced the preference for quinine, but this effect was abolished by EGCG, a GPRC6A antagonist (see Supplemental Fig. 2).

      (3) Unless I am missing something, there appears to be no quantitative analysis of the immunocytochemical data, just assertions.

      We have made quantitative analyses in the revised text, and the following sentences have been added: “Approximately 11% of GPRC6A-positive cells overlapped with IP3R3 (9 double-positive cells/80 GPRC6A-positive cells), while approximately 8.3% of IP3R3-positive cells expressed GPRC6A (9 double-positive /109 IP3R3-positive cells). In addition, GPRC6A-positive cells were unlikely to colocalize with a-gustducin, another marker for a subset of type II cells, in single taste cells (0 double-positive cell/93 GPRC6A-positive cells). Regarding type III cell markers, GPRC6A-positive cells were unlikely to colocalize with 5-HT in single taste cells (0 double-positive cell/75 GPRC6A-positive cells).”

      (4) The hallmarks of Kokumi taste include descriptors such as "thickness", and "mouthfeel", which sound like potential somatosensory attributes. Perhaps the authors should consider this possibility for at least some of the effects found.

      The term kokumi, a Japanese word, refers to a phenomenon in which the flavor of complexly composed food is enhanced through certain processes, making them more delicious. To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      (5) I don't think the human experiment (S1) belongs to the paper, even as a supplementary bit of data. It's only 17 subjects, they are all female, and we don't know anything about how they were selected, even though it states they are all students/staff at Kio. Were any of them lab members? Were they aware of the goals of the experiment? Could simply increasing the amount of solute in the soup make it seem thicker? This (sparse) data seems to have been shoehorned into the paper without enough detail/justification.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (6) The introduction could be more concise - for example, when describing Kokumi stimuli such as ornithine and its possible receptors, the authors do not need to add the detail about how this stimulus was deduced from adding clams to the soup. Details like this can be reserved for the discussion.

      Thank you for this comment. We have tried to shorten the Introduction.

      (7) Line 86: awkward phrasing - this doesn't need to be a rhetorical question.

      We have deleted the sentence.

      (8) Supplementary Figure 1: The labels on the figure say "Miso soup in 1 mM Orn" when the Orn is dissolved into the soup.

      Thank you for pointing out our mistake. We have changed the description, such as “1 mM Orn in miso soup”.

      Reviewer #2 (Recommendations for the authors):

      Major concerns

      (1) The impact of "kokumi" taste ligands on food perception appears to be profound in humans. This observation is fascinating because it implies that molecules like ornithine impact a variety of flavor perceptions, some of which are non-gustatory in nature (e.g., spread, mouthfulness and harmony). What remains unclear is whether "kokumi" ligands produce analogous sensations in rodents. If they don't, then rodents are an inappropriate model system for studying the impact of kokumi on flavor perceptions. The authors fail to address this key issue, and uncritically assume that kokumi ligands produce sensations like thickness, mouthfulness, and continuity in rodents. For this reason, the authors' reference to GPRC6A as a kokumi receptor is inappropriate.

      Thank you very much for the valuable comments. The term kokumi refers to a phenomenon in which the flavor of complexly composed foods is enhanced through certain processes, making them more delicious. It is an important concept in the field of food science, which studies how to make prepared dishes more enjoyable. Kokumi is also considered a higher-order, profound cognitive function evaluated by humans who experience a wide variety of foods. However, it is unclear whether animals, particularly experimental animals, can perceive kokumi in the same way humans do.

      To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      Rodents are thought to possess basic taste functions similar to humans, such as the expression of taste receptors, including kokumi receptors, in taste cells. Regardless of whether rodents can perceive kokumi, findings from studies on rodents may provide insights into aspects of the kokumi concept as experienced by humans.

      Indeed, the results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (2) A related concern is that the authors did not make any measurements that model kokumi sensations documented in the literature. For example, they would need to develop behavioral/electrophysiological measurements that reflect the known effects of kokumi ligands on flavor perception (i.e., increases in intensity, spread, continuity, richness, harmony, and punch). For example, ornithine is thought to produce more "punch" (i.e., a more rapid rise in intensity). This could be manifested as a more rapid rise in peripheral taste response or a more rapid fMRI response in the taste cortex. Alternatively, ornithine is thought to increase "continuity" (i.e., make the taste response more persistent). This response would presumably be manifested as a peripheral taste response that adapts more slowly or a more persistent fMRI response. As it stands, the authors have documented that ornithine increases (i) the preference of rats for some chemical stimuli, but not others; and (ii) the response of the CT nerve to some but not all taste stimuli.

      In animal experiments, it is challenging to examine each attribute of kokumi. The increase of complex tastes can be investigated through behavioral experiments and neural activity recordings. However, phenomena such as spread or harmony, which arise from profound human judgments, are difficult to validate in animal studies.

      While it was possible to examine persistence through neural responses to tastants, all stimuli were rinsed at 30 seconds after onset of stimulation, so the exact duration of persistence was not investigated. However, since the MSG response was enhanced approximately 1.5 times with the addition of ornithine, it is strongly suggested that the duration might also have been prolonged.

      Regarding punch, no differences were observed in the neural responses when ornithine was added, likely because the phasic response already had a rapid onset.

      In the context of fMRI studies, there has been a report that adding glutathione to mixtures of umami and salt solutions increases responses (Goto et al. Chem Senses, 2016). However, research specifically examining the attributes of kokumi has not yet been reported.

      (3) The quality of the SNAP-25 immunohistochemistry is poor (see Figure 7D), with lots of seemingly nonspecific staining in and outside the taste bud.

      The quality of the SNAP-25 is not poor. It is known that SNAP-25 labels not only type III cells but also the dense network of intragemmal nerve fibers (Tizzano et al., Immunohistochemical Analysis of Human Vallate Taste Buds. Chem Senses.40:655-60, 2015). Therefore, lots of seemingly nonspecific staining is due to intense SNAP-25-immunoreactivity of the nerve fibers.

      (4) The authors need to drastically scale back the scope of their conclusions. What they can say is that ornithine appears to enhance the taste responses of rats to a variety of taste stimuli and that this effect appears to be mediated by the GPRC6A receptor. They cannot use their data to address kokumi effects in humans, as they have not attempted to model any of these effects. Given the known problems with pharmacological blocking agents (e.g., nonspecificity), the authors would significantly strengthen their case if they could generate similar results in a GPRC6A knockout mouse.

      Our research approach begins with confirming in humans that the addition of ornithine to complex foods (such as miso soup) induces kokumi. Based on this confirmation, we conduct fundamental studies using animal models to investigate the peripheral taste mechanisms underlying the expression of kokumi.

      It is possible that the key to kokumi expression lies in the enhancement of desirable tastes (particularly umami) and the suppression of unpleasant tastes. Moving forward, we will deepen our fundamental research on the action of ornithine mediated through GPRC6A, including studies using knockout mice.

      (5) The introduction is too long. Much of the discussion of kokumi perception in humans should either be removed or shortened considerably.

      Following the reviewer’s suggestion, the introduction has been shortened.

      (6) I recommend that the authors break up the Methods and Results sections into different experiments. This would enable the authors to provide separate rationales for each procedure. For instance, the authors conducted a variety of different behavioral procedures (e.g., long- and short-term preference tests, and preference tests with and without GPRC6A receptor antagonists).

      Rather than following the reviewer’s suggestion, we have added subheadings to describe the purpose of each experiment. This approach would help readers better understand the experimental flow, as each experiment is relatively straightforward.

      (7) The inclusion of the human data is odd for two reasons. First, the measurements used to assess the impact of ornithine on flavor perception in humans were totally different than those used in rats. This makes it impossible to compare the human and rat datasets. Second, the human study was rather limited in scope, had small effect sizes, and had a lot of individual variation. For these reasons, the human data are not terribly helpful. I recommend that the authors remove the human data from this paper, and publish them as part of a more extensive study on humans.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role. The considerable variation in the scores suggests that evaluating the three kokumi attributes is challenging and likely influenced by differences in judgment criteria among participants.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (8) While the use of English is generally good, there are many instances where the English is a bit awkward. I recommend that the authors ask a native English speaker to edit the text.

      Thank you for this comment. The text has been edited by a native English speaker.

      Minor concerns

      (1) Lines 13-14: The authors state that "the concept of 'kokumi' has garnered significant attention in gustatory physiology and food science." This is an exaggeration. Kokumi has generated considerable interest in food science but has yet to generate much interest in gustatory physiology.

      We have rewritten this part: “The concept of “kokumi” has generated considerable interest in food science but kokumi has not been well studied in gustatory physiology.”

      (2) Line 20: The use of "specific taste" is unclear in this context. The authors indicate (in Figure 5A) that 1 mM ornithine generates a CT nerve response. They also reveal (in Figure 1A) that rats do not prefer 1 mM ornithine over water. The results from a preference test do not provide insight into whether a solution can be tasted; they merely demonstrate a lack of preference for that solution. Based on these data, the authors cannot infer that 1 mM ornithine cannot be tasted.

      We agree with the reviewer’s comment. Ornithine at 1 mM concentration may have a weak taste because this solution elicited a small neural response (Fig. 5-A). We have rewritten the text: “… at a concentration without preference for this solution.”

      (3) Line 44: Sensory information from foods enters the oral and the nasal cavity.

      The nasal cavity has been added.

      (5) Lines 59: The terms "thickness", "mouthfulness" and "continuity" are not intuitive in English, and may reflect, at least in part, a failure in translation. The word thickness implies a tactile sensation (e.g., owing to high viscosity), but the authors use it to indicate a flavor that is more intense and onsets more quickly. The word mouthfulness is supposed to indicate that a flavor is experienced throughout the oral cavity. The problem here is that this happens with all tastants, independent of the presence of substances like ornithine. Indeed, taste buds occur in a limited portion of the oral epithelium, but we nevertheless experience tastes throughout the oral cavity, owing to a phenomenon called tactile referral (see the following reference: Todrank and Bartoshuk, 1991, A taste illusion: taste sensation localized by touch" Physiology & Behavior 50:1027-1031). The word continuity does not imply that the taste is long-lasting or persistent.

      These three attributes were originally introduced by Ueda et al. (1990), who translated Japanese terms describing the profound characteristics of kokumi, which are deeply rooted in Japanese culinary culture. However, these simply translated terms have caused global misunderstanding and confusion, because they sound like somatosensory rather than gustatory descriptions. Therefore, to clarify that kokumi attributes are inherently gustatory, in the revised version we use the terms “intensity of whole complex tastes (rich flavor with complex tastes)” instead of thickness, “mouthfulness (spread of taste and flavor throughout the oral cavity),” and “persistence of taste (lingering flavor)” instead of continuity.

      The results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (6) Figure legends: The authors provide results of statistical comparisons in several of the figures. They need to explain what statistical procedures were performed. As it stands, it is impossible to interpret the asterisks provided.

      We have explained statistical procedures in each Figure legend.

      (7) I did not see any reference to the sources of funding or any mention of potential conflicts of interest.

      We have added the following information:

      Funding: JSPS KAKENHI Grant Numbers JP17K00935 (to TY) and JP22K11803(to KU).

      Declaration of interests: The authors declare that they have no competing interests.

      Reviewer #3 (Recommendations for the authors):

      (1) I suggest that the authors increase their level of interest in glutathione and gamma-glutamyl peptides. This might include an appropriate gamma-glutamyl control substance in the two-bottle preference study (see Public Review). It might also include more careful attention to the work that identified glutathione as an activator of the CaSR (Wang et al., JBC 2006) and the nature of its binding site on the CaSR which overlaps with its site for L-amino acids (Broadhead et al., JBC 2011). This latter article also identified S-methyl glutathione, in which the free-SH group is blocked, as a high-potency activator of the CaSR. It would be expected to show comparable potency to gamma-glu-Val-Gly in assays of kokumi taste.

      We have appropriately referenced glutathione and gamma-Glu-Val-Gly, potent agonists of CaSR, where necessary. In our previous study (Yamamoto and Mizuta, Chem Senses, 2022), we examined the additive effects of these substances on basic taste stimuli in rodents, and the results were compared in greater detail with those obtained from the addition of ornithine in the present study. We have also discussed the potential binding of ornithine to other receptors, including CaSR and T1R1/T1R3 heterodimers.

      (2) Figures:

      -None of the figures were labelled with their Figure numbers. I have inferred the Figure numbers from the legends and their positions in the pdf.

      We are sorry for this inconvenience.

      - The labelling of Figure 1 and Figure 2 are problematic. In Figure 1 it should be made clear that the horizontal axes refer to the Ornithine concentration. In Figure 2 it should be made clear that the horizontal axes refer to the tastant concentrations (MSG, IMP, etc) and that the Ornithine concentrations were fixed at either zero or 1.0 mM.

      We are sorry for the lack of information about the horizontal axes. We have explained the horizontal axes in figure legends in Figs. 1 and 2. The labelling of both figures has also been modified to make this clear.

      - Figure 3B: 'Control' should appear at the top of this panel since the panels that follow all refer to it.

      Following the reviewer’s suggestion, we have added ‘Control’ at the top of Figure 3B.

      - Figure 5A. Provide a label for the test substance, presumably Ornithine.

      Yes, we have added ‘Ornithine’.

      - Figure 7 would be strengthened by the inclusion of immunohistochemistry analyses of the CaSR.

      We are sorry that we did not analyze immunohistochemistry for the CaSR because a previous study precisely had analyzed the CaSR expression on taste cells in rats. We have analyzed co-expression of GPRC6A and CaSR (see Supplemental Figure 3).

      (3) Other Matters:

      - Line 38: list the five basic taste modalities here.

      Yes, we have included the five basic taste modalities here.

      - Line 107: 'even if ... kokumi ... is less developed in rodents' - if there is evidence that kokumi is less developed in rodents it should be cited here.

      We cannot cite any references here because no studies have compared the perception of kokumi between humans and rodents.

      - Line 308: 'recently we conducted experiments in rats using gallate ...' - the authors appear to imply that they performed the research in Reference 43, however, I was unable to find an overlap between the two lists of authors.

      We are not doing a similar study as the research in Reference 43 (40 in the revised paper). Following the result that gallate is an agonist of GPRC6A as shown by Reference 43, we were interested in doing similar behavioral experiments using gallate instead of ornithine.

      The sentences have been rewritten to avoid misunderstanding.

      - Line 506: the sections are said to be 20 mm thick - should this read 20 micrometers?

      Thank you. We have changed to 20 micrometers.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.

      *We thank the reviewer for their appreciation of our work, and the constructive comments.

      *

      Major points:

      1. Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.

      *We have now highlighted, in Fig.1D, the requested RNAseq results from Table S1, showing a very limited reduction of expression of Nr2f1 in Sox2 mutant and of Sox2 in Nr2f1 mutants. We further added ISH results confirming this data (Fig. 4A). *

      The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.

      We previously reported in Mercurio et al 2019 (ref. 7) that Sox2 is highly expressed in the dorsal thalamus (precursor to the sensory thalamic nuclei) at least from E15.5 and is later expressed in all the sensory thalamic nuclei, though not in surrounding regions (Mercurio et al 2019 Fig.1). A similar expression pattern was previously reported for Nr2f1 in Chou et al 2013 (ref. 6). A brief mention of this point is now present in Introduction.

      Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.

      *We agree on the interest of this point. We note that SOX2 binding sites in neurons (but not in stem cells), as detected by CUT&RUN, are enriched for SOX2 and RORA/NRF binding sites. The co-presence of SOX and NRF potential binding motifs (Fig. 2F-G), suggests the possibility of direct physical interaction between SOX2 and NR2F1 mediating joint binding to DNA. This is interesting and will be experimentally addressed in a follow up study. *

      Minor points: 1. Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.

      We clarified the figure legend, better explaining how the plotted were computed and their meaning.

      Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.

      *In fact, we propose that downregulation of RORa in layer 4 maybe caused by reduced thalamic afferents to layer 4, possibly also acting through a reduced delivery of VGF to the cortex; in fact, as the reviewer correctly states Sox2 itself is not expressed in the cortex. *

      Why did the authors use two types of Sox2 antibodies in Fig.4A?

      We strive to replicate our CUT&RUN data such that we can rely only on the reproducible binding events. We have often noted that – being CUT&RUN a “challenging” application for antibodies – different antibodies yield non-fully overlapping binding profiles. While we do not have a clear explanation for this, we consider more robust converging on those binding events that are obtained by two independent antibodies, when such tools are available. This, in our opinion and experience, drastically decreases the chance of stumbling upon false positive hits.

      Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.

      The requested quantification has been added in Fig. S1A and in Fig. 4C.

      In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).

      *We corrected this. *

      Reference 14 is identical to 44.

      *We corrected this. *

      Reviewer #1 (Significance (Required)):

      This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise. My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.

      The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.

      We thank the reviewer for pointing out contributions as well as limitations of our work. We are convinced that our work does indeed open up " new avenues for future investigation", reporting for the first time hundreds of targets of SOX2 and NR2F1 as well as hundreds of direct SOX2 binding sites in dLGN neurons that will contribute to future investigations.

      Major points: 1. Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;

      We now validated the downregulation VGF and Sox5 at the RNA level by ISH confirming SOX5 downregulation by IF. These data are presented in the new Fig. 4, in results page 5 and discussion page 7.

      Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.

      The experiment suggested by the reviewer would undoubtly be interesting to address Sox5 contribution to the mutant phenotype; unfortunately, this is too demanding for the present paper.

      However, for the sake of data interpretation, we propose that the mutant phenotypes observed rather result from the global deregulation of a set of genes, not just of a single gene. Indeed, we discuss the potential contribution of several different genes, among those co-regulated by SOX2 and NR2F1. From this point of view, we don't necessarily expect the contribution of a specific gene to be prominent. In fact, we believe an interesting result emerging from our work is the identification of a rather numerous set of genes collectively responding to both Sox2 and Nr2f1 mutation, many of which may contribute to the shared phenotypes of the two mutants.

      Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.

      We have now modified Fig. 3 as requested and moved the raw data to the Supplementary material (Table S4).

      The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.

      We are not sure we understand the reviewer's question. The " dLGN unique (non-neurogenesis) targets" that we report are not the results of a bioinformatics prediction, but of the CUT&RUN experiment itself including the dLGN peaks near Vgf and Sox5. In addition, we experimentally validated the downregulation of Vgf and Sox5 by in situ hybridization in the new Figure 4.

      Minor points: For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex;

      This is now mentioned in results in agreement with the results in Chou et al. 2013 and Mercurio et al. 2019.

      Chou et al mentions explicitly absence of Rora Cre activity in the cortex and this is also in agreement with our own results in Mercurio et al. 2019. As to the retina, we reported not observing any retinal phenotypes in Sox2 mutants in agreement with the absence of any Sox2 deletion within the retina, that would have caused a drastic phenotype as reported in Taranova et al. 2006.

      (2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.

      The onset of Rora Cre activity is at a stage in which dLGN neurogenesis is completed and most if not all cells are postmitotic as reported in Chou et al. 2013. This point is now more explicitly mentioned in results.

      Reviewer #2 (Significance (Required)):

      The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.

      • General assessment: The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.

      We now added histological validation of selected targets as requested in the new Fig. 4.

      • Advance: The advance of the study is limited, though the experiments were carefully launched.

      • Audience: Very limited audience with a specialty in transcription factors in visual system development.

      The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.

      The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.

      Major comments:

      Issues with the statistical logic

      -Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knock-outs.

      The meaning of statistical testing and significance in this context is assessing if, and how much, the observed changes in expression in RNA-Seq estimated transcript levels can be due only to experimental variability (not significant) or, vice versa, if there is an additional biological factor (the knock-out of Sox2 or Nr2f1, in this case) behind the changes observed. Clearly, the more “significant” (lower) are the p-value/FDR values associated with changes observed for a gene, the more likely is that the gene transcript levels are affected by the knock outs. Vice versa, if the change is reported to be “not significant”, there isn’t enough evidence – at least from a statistical point of view - that the observed changes in transcript levels are due to the effect of the knock outs. Three replicates per condition are required in order to estimate variance – which is gene specific and estimates what is the “natural” range of variability of each gene due only to experimental variability (and not generated by the knock-outs).

      We now report the RNAseq data for Sox2 and Nr2f1 in Fig. 1D and complete them with ISH data in the new Fig. 4. The results are consistent with a limited reduction Nr2f1 in the Sox2 mutants and Sox2 in the Nr2f1 mutants. Though we cannot rule out that they might contribute to some extent to the mutant phenotype, we document a stronger downregulation, in both mutants, of a vast set of other genes (Fig. 1C) onto which our analysis focuses.

      -Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.

      See previous point. The more “significant” are the changes of transcript levels according to statistical testing, the “stronger” the effect of the knock out on them, where by “strong” we mean a more relevant variation of transcript levels. However, since we realized that this term could cause confusion in the reader, we rephrased the relevant parts. Variance is taken into account in the computation of pvalues/FDRs, so the same difference in mean TPM values for two different genes can result to more/less significant according to the estimated variance of the values.

      -The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.

      *We appreciate the concern from the reviewer. CUT&RUN, especially when performed in vivo versus cell lines, has a high intrinsic variability between experiments, and even between technical replicates (DOI: 10.1093/nar/gkae180). While it would be possible to, for example, run DiffBind (built for ChIP-seq), on the dLGN data versus the NS data, these are not, in our opinion, directly comparable as they were not performed in the same batch, on the same type of material (dissected mouse tissue versus cultured cells) or even with the same batches of reagents. Thus, to quantify them in terms of signal at specific loci, without taking into account things like global background, local background, and overall signal to noise ratio, we do not believe is correct. There are many attempts in the field to better quantify CUT&RUN data (spike-in yeast or E. coli DNA at different moments, spike-in drosophila nuclei, etc.) but there remains to be determined a general consensus on what is best or trustworthy. The best way we could do the comparison, with our data as it was generated, was as pointed out above, by comparing the statistically significant events in the dLGN versus those in the NS, that way each dataset is considered independently before the overlap is performed. To help alleviate the reviewers concerns, we have provided here, for the reviewer, signal profiles and heatmaps of the dLGN only regions in both dLGN and NS CUT&RUN. *

      Non-quantitative issues:

      -It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.

      The timepoint of the RNAseq analyses was chosen to precede any phenotypic changes detected in the dLGN based on our previous analyses reported in Mercurio et al. 2019 as stated in Results page 3.

      * * -The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.

      We agree with the Reviewer. The possible contribution of VGF has been rephrased considering a possible wider contribution of thalamic afferents in general.

      -Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.

      The time of this analysis was chosen to precede dLGN size reduction in mutants, as clearly shown in our previous work Mercurio et al. 2019 and further confirmed by the new ISH for Sox2 and Nr2f1 presented in the new Fig. 4.

      The sample size is n=4 as reported in the Figure legend.

      Minor

      Introduction:

      -Writing could be improved.

      -Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.

      We revised the wording for this point.

      RESULTS

      -What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?

      -Why are there two groups of 3 control mice each and not one group of 6?

      As reported in Materials and Methods " RNA sequencing was performed on three independent samples for both mutant and control dLGN. Each sample was composed of dLGNs from three animals of the same genotype pooled together."

      *Thalami from 3 mice represent an adequate amount of RNA to perform a single experiment of RNAseq. 3 x 3 represents a biological triplicate for the RNAseq experiment. * Section 2

      -For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.

      Genes were selected with the same FDR thresholds in both experiments. The assumption is anyway more simple: the probability of a gene to have a significant change (passing the FDR threshold) in one experiment does not influence its probability to change also in the other, and vice versa. That is, we compute the probability to have a given number of up- or down-regulated genes in common in the two experiments assuming that the two experiments were independent from one another. From another point of view, this is the usual strategy employed in order to assess whether the overlap between two gene sets obtained by two different genome-wide experiments can be considered to be random or not, that is, if the number of genes in the overlap is close to random expected values they can be considered to be independent from one another.

      Section 3

      -I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?

      The graph shown represents the most significantly enriched functional annotations (GO annotations, pathways, etc.) among the deregulated genes as computed by Enrichr, one of the many tools developed for this task. And as for all the tools performing this analysis, the p-value means “the probability of having the same number of genes sharing the same functional annotation in a set of genes chosen at random”, computed with the same strategy employed for the overlap between the two deregulated gene sets described before.

      Section "Deconvolution..."

      -It is great that results for each replicate is presented.

      We thank the reviewer.

      * * -There are too many significant digits in Fig 3A given the variance.

      This has been adjusted as suggested.

      -Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?

      *The graphical presentation of the data in Fig. 3 has been improved, and the numerical data (former panel A) have been moved to the supplementary materials (Table S4) as recommended. *

      Controls for Nr2f1 and Sox2 mutants have similar values for excitatory neurons, as expected, see Table S4. Fig. 3 shows the variation between each knock-out and its respective control experiments, and although excitatory neurons are reduced in both mutants the extent of reduction is greater in the Sox2 mutant.

      Section "CUT&RUN..."

      -How many overlaps (Figure 4B) would you expect by chance?

      *This is an extremely difficult number to calculate. It is possible to, for example, generate a random set of genomic fragments of similar length, and check how many of them overlap. This would however be extremely unfair, as CUT&RUN is naturally biased towards open chromatin, and thus would preferentially contain these types of regions in a “randomly” digested set. Additionally, data analysis and mapping biases further increase what overlaps would often occur. To circumvent this, we i) use an IgG control, which should identify and remove regions that are nonspecifically digested and sequenced during the experiment, and ii) performed our analysis after first removing sets of known artifact regions (Nordin et al 2023, ref. 43). *

      -Fig 4J needs more description. What does the first full pie represent?

      *We have added more description in the figure legend, it now reads: *

      1. *Schematic depiction of CUT&RUN and RNA-seq overlap, showing Sox2 peak associated genes that are transcribed ( > 5 TPM, 784/1102) and those that are differentially expressed (DEG) in Sox2 mutant dLGN (FDR -Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.

      We apologize but we don't understand this comment.

      Reviewer #3 (Significance (Required)):

      The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.

      I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript.

      Performing a RNA-Seq analysis in replicates is common practice, and as we detailed in our replies to the reviewer’s comments the goal of replicates is to have reliable estimations of the parameters needed (mean, variance of each gene) for the subsequent statistical analyses. So, we leveraged the information obtained from the replicates in order to identify with high confidence with genes could be considered to be affected by the knock-outs.

      Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.

      As discussed in the previous responses, the statistical evaluations usually performed on estimated transcript levels and their variance can be translated into a more qualitative evaluation of the effect of the knock-outs performed – the larger is the impact on transcript levels of a gene with respect to its estimated variance (variability) the stronger the effect is assumed to be. Confidence intervals are not usually employed in this context – the “confidence” with which the experimental setting can be assumed to affect gene expression is summarized by the p-values and the subsequent FDR values.

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      I think that the importance of understanding how much is needed to succeed is more important than wanting to succeed. The reason I believe this is that many times I think we as individuals believe we need a grand solution or something very impressive to solve an issue when in reality it may be a small adjustment. What I mean by that is that it's important to understand the scope, because it can save you time, and also help you get to the solution. That's why reinventing the wheel is not such a big deal.

    1. Others involve getting us to ask ourdoctors about these conditions and drugs and developing relationships with us so that we keep taking our meds

      I think many doctors use medications as a default 'fix' to a problem which may (or may not) be related to them trying to push us to take more medications. It is possible but in my opinion I don't think they are pushing drugs for the sake of it. I agree that the chronic use of medicine is on the rise and has been for years, but I also think that this push for medicine has to do with our life expectancy growing. Many of these professionals are trying to maximize our lifespans, so the idea of 'getting ahead' of these known ailments is the priority.

    2. The pharmaceutical industry is a massive elephant. Like the blind men of the famous parable, we each catch hold of a tiny piece of it—leg, tail, trunk—and think we have a handle on it: it is strong and solid, it is hairy, it moves like a snake. From about $880 billion dollars of sales for 2011, the industry is expected to growapproximately 5 percent a year in the future.

      This analogy presents the pharmaceutical industry as a powerful but multifaceted entity that different stakeholders perceive differently. The industry's financial growth suggests continued expansion, but the metaphor subtly implies the challenges of incomplete understanding and differing priorities among those interacting with it. Scientists may focus on research and innovation, while policymakers see regulation, and patients experience cost and accessibility concerns.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The model of phosphotransfer from Y169 IKK to S32 IkBa is compelling and an important new contribution to the field. In fact, this model will not be without controversy, and publishing the work will catalyze follow-up studies for this kinase and others as well. As such, I am supportive of this paper, though I do also suggest some shortening and modification.

      We appreciate the reviewers candid response on the difficulty of this study and the requirement of follow-up studies to confirm a direct transfer of the phosphate. We also have edited the manuscript to make it shorter.

      Generally, the paper is well written, but several figures should be quantified, and experimental reproducibility is not always clear. The first 4 figures are slow-going and could be condensed to show the key points, so that the reader gets to Figures 6 and 7 which contain the "meat" of the paper.

      We have indicated the experimental reproducibility in the methodology section against each assay. We have shortened the manuscript corresponding to sections describing figures 1-4. However, when we talked to some of our colleagues whose expertise do not align with kinases and IKK, we realized that some description were necessary to introduce them to the next figures. Additionally, we added Fig. S6 indicating that the radiolabelled phospho-IKK2 Y169F is unable to transfer its own phosphate group(s) to the substrate IkBa.

      Reviewer #2 (Public Review):

      Phosphorylation of IκBα is observed after ATP removal, although there are ambiguous requirements for ADP.

      We agree with the reviewer that this observation is puzzling. We hypothesize that ADP is simultaneously regulating the transfer process likely through binding to the active site.

      It seems that the analysis hinges on the fidelity of pan-specific phosphotyrosine antibodies.

      We agree with the reviewer. To bolster our conclusion, we used antibodies from two different sources. These were Monoclonal mouse anti-Phospho-Tyrosine (catalogue number: 610000) was from BD Biosciences or from EMD Millipore (catalogue no. 05-321X).

      The analysis often returns to the notion that tyrosine phosphorylation(s) (and critical active site Lys44) dictate IKK2 substrate specificity, but evidence for this seems diffuse and indirect. This is an especially difficult claim to make with in vitro assays, omitting the context of other cellular specificity determinants (e.g., localization, scaffolding, phosphatases).

      We agree with the concerns that the specificity could be dependent on other cellular specificity determinants and toned down our claims where necessary. However, we would like to point out that the specificity of IKK2 towards S32 and S36 of IkBa in cells in response to specific stimuli is well-established. It is also well-established that its non-catalytic scaffolding partner NEMO is critical in selectively bringing IkBa to IKK from a large pool of proteins. The exact mechanism of how IKK2 choose the two serines amongst many others in the substrate is not clear.

      Multiple phosphorylated tyrosines in IKK2 were apparently identified by mass spectrometric analyses, but the data and methods are not described. It is common to find non-physiological post-translational modifications in over-expressed proteins from recombinant sources. Are these IKK2 phosphotyrosines evident by MS in IKK2 immunoprecipitated from TNFa-stimulated cells? Identifying IKK2 phosphotyrosine sites from cells would be especially helpful in supporting the proposed model.

      Mass spectrometric data for identification of phosphotyrosines from purified IKK2 is now incorporated (Figure S3A). Although we have not analyzed IKK2 from TNF-a treated cells in this study, a different study of phospho-status of cellular IKK2 indicated tyrosine phosphorylation (Meyer et al 2013).

      Reviewer #3 (Public Review):

      The identity and purity of the used proteins is not clear. Since the findings are so unexpected and potentially of wide-reaching interest - this is a weakness. Similar specific detection of phospho-Ser/Thr vs phospho-Tyr relies largely on antibodies which can have varying degrees of specificity.

      We followed a stringent purification protocol of several steps (optimized for the successful crystallization of the IKK2) that removed most impurities (PMID: 23776406, PMID: 39227404). The samples analysed with ESI MS did not show any significant contaminating kinase from the Sf9 cells.

      Sequence specific phospho-antibodies used in this study are very well characterized and have been used in the field for years (Basak et al 2007, PMID: 17254973). We agree on the reviewer’s concerns on the pan-specific phospho-antibodies. Since phospho-tyrosine detection is the crucial aspect of this study, we minimized such bias by using pan-specific phosphotyrosine antibodies from two independent sources.

      Reviewer #1 (Recommendations For The Authors):

      I understand that Figure 3 shows that K44M abolishes both S32/26 phosphorylation and tyrosine phosphorylation, but not PEST region phosphorylation. This suggests that autophosphorylation is reflective of its known specific biological role in signal transduction. But I do not understand why "these results strongly suggest that IKK2-autophosphorylation is critical for its substrate specificity". That statement would be supported by a mutant that no longer autophosphorylates, and as a result shows a loss of substrate specificity, i.e. phosphorylates non-specific residues more strongly. Is that the case? Maybe Darwech et al 2010 or Meyer et al 2013 showed this.

      Later figures seem to address this point, so maybe this conclusion should be stated later in the paper.

      We have now clarified this in the manuscript and moved the comment to the next section. We have consolidated the results in Figure 3 and 4 in the previous version into a single figure in Figure. The text has also been modified accordingly.

      Page 10: mentions DFG+1 without a proper introduction. The Chen et al 2014 paper appears to inform the author's interest in Y169 phosphorylation, or is it just an additional interesting finding? Does this publication belong in the Introduction or the Discussion?

      The position of Y169 at the DFG+1 was intriguing and the 2014 article Chen et al further bolstered our interest in this residue to be investigated. We think this publication is important in both sections. 

      To understand the significance of Figure 4D, we need a WT IKK2 control: or is there prior literature to cite? This is relevant to the conclusion that Y169 phosphorylation is particularly important for S32 phosphorylation.

      We have now added a new supplementary figure where activities of WT and Y169F IKK2 towards WT and S32/S36 mutants are compared (Figure S3F). At a similar concentration, the activity of WT-IKK2 is many fold higher than that of YtoF mutants (Fig. 4C). The experiments were performed simultaneously, although samples were loaded on different gels but otherwise processed in a similar way. The corresponding data is now included in the manuscript as Figure S3F.

      The cold ATP quenching experiment is nice for testing the model that Y169 functions as a phospho sink that allows for a transfer reaction. However, there is only a single timepoint and condition, which does not allow for a quantitative analysis. Furthermore, a positive control would make this experiment more compelling, and Y169F mutant should show that cold ATP quenching reduces the phosphorylation of IkBa.

      We thank the reviewer for appreciating our experimental design, and pointing out the concerns. We kept the ATP-time point as the maximum of the non-competition experiment. Also, we took 50mM ATP to compare its competition with highest concentration of ADP used. The idea behind using the maximum time and ATP (comparable to ADP) was to capture the effect of competitive-effect of ATP, if any, that would be maximal in the given assay condition in comparison with the phospho-transfer set up in absence of cold ATP. We agree that finer ranges of ATP concentration and time points would have enabled more quantitative analyses. We have now included data where different time intervals are tested (Figure S5D).

      Why is the EE mutant recognized by anti-phospho-serine antibodies? In Figure 2F.

      We anticipate Serine residues besides those in the activation loop to be phosphorylated when IKK2 is overexpressed and purified from the Sf9 cells. Since Glu (E) mimics phospho-Ser, the said antibody cross reacts with the IKK2-EE that mimics IKK2 phosphorylated at Ser177 and 181.

      Figure 7B is clear, but 7C does not add much.

      We have now removed the Fig. 7C in the current version. Figure 7 is now renumbered as Figure 6 that does not contain the said cartoon.  

      Reviewer #2 (Recommendations For The Authors):

      Regarding the specificity arguments (see above in public review), the authors note that NEMO is very important in IKK specificity, and - if I'm understanding correctly - most of these assays were performed without NEMO. Would the IKK2-NEMO complex change these conclusions?

      NEMO is a scaffolding protein whose action goes beyond the activation of the IKK-complex. In cells, NEMO brings IkBa from a pool of thousands of proteins to its bonafide kinase when the cells encounter specific signals. In other words, NEMO channels IKK-activity towards its bonafide substrate IkBa at that moment. Though direct proof is lacking, it is likely that NEMO present IkBa in the correct pose to IKK such that the S32/S36 region of IkBa is poised for phosphorylation. The proposed mechanism in the current study further ensures the specificity and fidelity of that phosphorylation event. We believe this mechanism will be preserved in the IKK-NEMO complex unless proven otherwise. As shown below, IKK2 undergoes tyrosine autophosphorylation in presence of NEMO.

      Author response image 1.

      The work primarily focuses on Y169 as a candidate target for IKK autophosphorylation. This seems reasonable given the proximity to the ATP gamma phosphate. However, Y188F more potently disrupted IκBα phosphorylation. The authors note that this could be due to folding perturbations, but this caveat would also apply to Y169F. A test for global fold perturbations for both Tyr mutants would be helpful.

      Y188 is conserved in S/T kinases and that in PKA (Y204) has been studied extensively using structural, biochemical and biophysical tools. It was found in case of PKA that Y204 participates in packing of the hydrophobic core of the large lobe. Disruption of this core structure by mutation allosterically affect the activity of the kinase. We also observed similar engagement of Y188 in IKK2’s large lobe, and speculated folding perturbations in analogy with the experimental evidence observed in PKA. What we meant was mutation of Y188 would allosterically affect the kinase activity. Y169 on the other hand is unique at that position, an no experimental evidence on the effect of phospho-ablative mutation of this residue exist in the literature. Hence, we refrained from speculating its effect on the folding or conformational allostery, however, such a possibility cannot be ruled out. 

      I struggled to follow the rationalization of the results of Figure 4D, the series of phosphorylation tests of Y169F against IκBα with combinations of phosphoablative or phosphomimetic variants at Ser32 and Ser36. This experiment is hard to interpret without a direct comparison to WT IKK2.

      We agree with the reviewer’s concerns. Through this experiment we wanted to inform about the importance of Tyr-phosphorylation of IKK2 in phosphorylating S32 of IκBα which is of vital importance in NF-kB signaling. We have now provided a comparison with WT-IKK2 in the supplementary Figure S3F. We hope this will help bring more clarity to the issue.

      MD simulations were performed to compare structures of unphosphorylated vs. Ser-phosphorylated (p-IKK2) vs. Ser+Tyr-phosphorylated (P-IKK2) forms of IKK2. These simulations were performed without ATP bound, and then a representative pose was subject to ADP or ATP docking. The authors note distortions in the simulated P-IKK2 kinase fold and clashes with ATP docking. Given the high cellular concentration of ATP, it seems more logical to approach the MD with the assumption of nucleotide availability. Most kinase domains are highly dynamic in the absence of substrate. Is it possible that the P-IKK2 poses are a result of simulation in a non-physiological absence of bound ATP? Ultimately, this MD observation is linked to the proposed model where ADP-binding is required for efficient phospho-relay to IκBα. Therefore, this observation warrants scrutiny. Perhaps the authors could follow up with binding experiments to directly test whether P-IKK2 binds ADP and fails to bind ATP.

      We thank that reviewer for bringing up this issue. This is an important issue and we must agree that we don’t fully understand it yet. We took more rigorous approach this time where we used three docking programs: ATP and ADP were docked to the kinase structures using LeDock and GOLD followed by rescoring with AutoDock Vina. We found that ATP is highly unfavourable to P-IKK2 compared to ADP. To further address these issues, we performed detailed MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) analyses after MD-simulation to estimate binding free energies and affinities of ADP and ATP for each of the three differently phosphorylated states of IKK2. These analyses (Figure S4 E and F) clearly indicate that phosphorylated IKK2 have much higher preference for ADP over ATP. However, it does not negate ATP-binding by P-IKK2 in a different pose that may not support kinase activity.

      We could not perform any binding experiment because of the following reason. We incubated FL IKK2 WT with or without cold ATP for 30mins, and then incubated these samples with <sup>32</sup>P-ATP and analysed the samples by autoradiography after resolving them on a 10% SDS-PAGE. We found that even after pre-incubation of the kinase with excess cold ATP it still underwent autophosphorylation when radioactive ATP was added as shown below. This prevented us from doing direct binding experiment with ATP as it would not represent true binding event. We also noticed that after removal of bulk ATP post autophosphorylation, phosphorylated IKK2 is capable of further autophosphorylation when freshly incubated with ATP. We have not been able to come up with a condition that would only account for binding of ATP and not hydrolysis. 

      Author response image 2.

      The authors could comment on whether robust phosphorylation of NEMO was expected (Figure 1D). On a related note, why is NEMO a single band in the 1D left panel and double bands on the right?

      No, we did not expect robust phosphorylation of NEMO. However, robust phosphorylation of NEMO is observed only in the absence of IκBα. In presence of IκBα, phosphorylation of NEMO goes down drastically. These were two different preparations of NEMO. When TEV-digestion to remove His-tag is incomplete it gives two bands as the tagged and untagged versions cannot be separated in size exclusion chromatography which is the final step.

      Page 14, line 360. "...observed phosphorylation of tyrosine residue(s) only upon fresh ATP-treatment..." I'm not sure I understand the wording here (or the relevance of the citation). Is this a comment on unreported data demonstrating the rapid hydrolysis of the putative phosphotyrosine(s)? If so, that would be helpful to clarify and report in the supporting information.

      In our X-ray crystallographic studies with phosphorylated IKK2 we failed to observe any density of phosphate moiety. Furthermore, this IKK2 showed further autophosphorylation when incubated with fresh ATP. These two observations lead us to believe that some of the autophosphorylation are transient in nature. However, quantitative kinetic analyses of this dephosphorylation have not been performed.

      Figure S3 middle panel: The PKA substrate overlaid on the IKK2 seems sterically implausible for protein substrate docking. Is that just a consequence of the viewing angle? On a related note, Figure S3 may be mislabeled as S4 in the main text).

      It is a consequence of the viewing angle. Also, we apologize for this inadvertent mislabelling. It has been corrected in the current version.

      Reviewer #3 (Recommendations For The Authors):

      The detection of phosphorylated amino acids relies largely on antibodies which can have a varying degree of specificity. An alternative detection mode of the phospho-amino acids for example by MS would strengthen the evidence.

      We agree with the concern of specificity bias of antibodies. We tried to minimize such bias by using two different p-Tyr antibodies as noted previously and also in the methodology section. We were also able to detect phospho-tyrosine residues by MS/MS analyses, representative spectra are now added (Figure S3A).

      IKK2 purity - protocol states "desired purity". What was the actual purity and how was it checked? MS would be useful to check for the presence of other kinases.

      Purity of the recombinantly purified IKK2s are routinely checked by silver staining. A representative silver stained SDS-PAGE is shown (Figure S1C). It may be noted that, there’s a direct correlation of expression level and solubility, and hence purification yield and quality with the activity of the kinase. Active IKK2s express at much higher level and yields cleaner prep. In our experience, inactive IKKs like K44M give rise to poor yield and purity. We analysed K44M by LC MS/MS to identify other proteins present in the sample. We did not find any significant contaminant kinase the sample (Figure S1D). The MS/MS result is attached.

      Figure 1C&D: where are the Mw markers? What is the size of the band? What is the MS evidence for tyrosine phosphorylation?

      We have now indicated MW marker positions on these figures.

      MS/MS scan data for the two peptides containing pTyr169 and pTyr188 are shown separately (Figure S3A).

      Figure 2F: Why is fresh ATP necessary? Why was Tyr not already phosphorylated? The kinetics of this process appear to be unusual when the reaction runs to completion within 5 minutes ?

      As stated earlier, we believe some of the autophosphorylation are transient in nature. We think the Tyr-phosphorylation are lost due to the action of cellular phosphatases. We agree with the concern of the reviewer that, the reaction appears to reach completion within 5 minutes in Fig 2F. We believe it is probably due to the fact that the amount of kinase used in this study exceeds the linear portion of the dynamic range of the antibody used. Lower concentration of the kinase do show that reaction does not reach completion until 60mins as shown in Fig. 2A.

      Figure 3: Can the authors exclude contamination with a Tyr kinase in the IKK2-K44M prep? The LC/MS/MS data should be included.

      We have reanalysed the sample on orbitrap to check if there’s any Tyr-kinase or any other kinase contamination. We used Spodoptera frugiperda proteome available on the Uniprot website for this analysis. These analyses confirmed that there’s no significant kinase contaminant present in the fraction (Figure S1D).

      What is the specificity of IKK-2 Inhibitor VII? Could it inhibit a contaminant kinase?

      This inhibitor is highly potent against IKK2 and the IKK-complex, and to a lesser extent to IKK1. No literature is available on its activity on other kinases. In an unrelated study, this compound was used alongside MAPK inhibitor SB202190 wherein they observed completely different outcomes of these two inhibitors (Matou-Nasri S, Najdi M, AlSaud NA, Alhaidan Y, Al-Eidi H, Alatar G, et al. (2022) Blockade of p38 MAPK overcomes AML stem cell line KG1a resistance to 5-Fluorouridine and the impact on miRNA profiling. PLoS ONE 17(5):e0267855. https://doi.org/10.1371/journal.pone.0267855). This study indirectly proves that IKK inhibitor VII does not fiddle with the MAPK pathways. We have not found any literature on the non-specific activity of this inhibitor.

      Figure 6B: the band corresponding to "p-IkBa" appears to be similar in the presence of ADP (lanes 4-7) or in the absence of ADP but the presence of ATP (lane 8).

      Radioactive p-IκBα level is more when ADP is added than in absence of ADP. In presence of cold ATP, radioactive p-IκBα level remains unchanged. This result strongly indicate that the addition of phosphate group to IκBα happens directly from the radioactively labelled kinase that is not competed out by the cold ATP.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, Harpring et al. investigated divisome assembly in Chlamydia trachomatis serovar L2 (Ct), an obligate intracellular bacterium that lacks FtsZ, the canonical master regulator of bacterial cell division. They find that divisome assembly is initiated by the protein FtsK in Ct by showing that it forms discrete foci at the septum and future division sites. Additionally, knocking down ftsK prevents divisome assembly and inhibits cell division, further supporting their hypothesis that FtsK regulates divisome assembly. Finally, they show that MreB is one of the last chlamydial divisome proteins to arrive at the site of division and is necessary for the formation of septal peptidoglycan rings but does not act as a scaffold for division assembly as previously proposed.

      Strengths:

      The authors use microscopy to clearly show that FtsK forms foci both at the septum as well as at the base of the progenitor cell where the next septum will form. They also show that the Ct proteins PBP2, PBP3, MreC, and MreB localize to these same sites suggesting they are involved in the divisome complex.

      Using CRISPRi the authors knock down ftsK and find that most cells are no longer able to divide and that PBP2 and PBP3 no longer localized to sites of division suggesting that FtsK is responsible for initiating divisome assembly. They also performed a knockdown of pbp2 using the same approach and found that this also mostly inhibited cell division. Additionally, FtsK was still able to localize in this strain, however PBP3 did not, suggesting that FtsK acts upstream of PBP2 in the divisome assembly process while PBP2 is responsible for the localization of PBP3.

      The authors also find that performing a knockdown of ftsK also prevents new PG synthesis further supporting the idea that FtsK regulates divisome assembly. They also find that inhibiting MreB filament formation using A22 results in diffuse PG, suggesting that MreB filament formation is necessary for proper PG synthesis to drive cell division.

      Overall the authors propose a new hypothesis for divisome assembly in an organism that lacks FtsZ and use a combination of microscopy and genetics to support their model that is rigorous and convincing. The finding that FtsK, rather than a cytoskeletal or "scaffolding" protein is the first division protein to localize to the incipient division site is unexpected and opens up a host of questions about its regulation. The findings will progress our understanding of how cell division is accomplished in bacteria with non-canonical cell wall structure and/or that lack FtsZ.

      Weaknesses:

      No major weaknesses were noted in the data supporting the main conclusions. However, there was a claim of novelty in showing that multiple divisome complexes can drive cell wall synthesis simultaneously that was not well-supported (i.e. this has been shown previously in other organisms). In addition, there were minor weaknesses in data presentation that do not substantially impact interpretation (e.g. presenting the number of cells rather than the percentage of the population when quantifying phenotypes and showing partial western blots instead of total western blots).

      We agree with the weaknesses identified by the reviewer. We removed the statements in the Results and Discussion that multiple independent divisome complexes can simultaneously direct PG synthesis. We presented the data in Figs. 3-5 as % of the cells in the population, and complete western blots are shown in Supp. Fig. S1.

      Reviewer #2 (Public review):

      Summary:

      Chlamydial cell division is a peculiar event, whose mechanism was mysterious for many years. C. trachomatis division was shown to be polar and involve a minimal divisome machinery composed of both homologues of divisome and elongasome components, in the absence of an homologue of the classical division organizer FtsZ. In this paper, Harpring et al., show that FtsK is required at an early stage of the chlamydial divisome formation.

      Strengths:

      The manuscript is well-written and the results are convincing. Quantification of divisome component localization is well performed, number of replicas and number of cells assessed are sufficient to get convincing data. The use of a CRISPRi approach to knock down some divisome components is an asset and allows a mechanistic understanding of the hierarchy of divisome components.

      Weaknesses:

      The authors did not analyse the role of all potential chlamydial divisome components and did not show how FtsK may initiate the positioning of the divisome. Their conclusion that FtsK initiates the assembly of the divisome is an overinterpretation and is not backed by the data. However, data show convincingly that FtsK, if perhaps not the initiator of chlamydial division, is definitely an early and essential component of the chlamydial divisome.

      The following statement has been included in the Discussion (pg. 16 of the revised manuscript)  “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.”

      We will soon be submitting another manuscript that addresses how FtsK specifies the site of divisome assembly. This work is too extensive to be included in this manuscript.

      Reviewer #3 (Public review):

      Summary:

      The obligate intracellular bacterium Chlamydia trachomatis (Ct) divides by binary fission. It lacks FtsZ, but still has many other proteins that regulate the synthesis of septal peptidoglycan, including FtsW and FtsI (PBP3) as well as divisome proteins that recruit and activate them, such as FtsK and FtsQLB. Interestingly, MreB is also required for the division of Ct cells, perhaps by polymerizing to form an FtsZ-like scaffold. Here, Harpring et al. show that MreB does not act early in division and instead is recruited to a protein complex that includes FtsK and PBP2/PBP3. This indicates that Ct cell division is organized by a chimera between conserved divisome and elongasome proteins. Their work also shows convincingly that FtsK is the earliest known step of divisome activity, potentially nucleating the divisome as a single protein complex at the future division site. This is reminiscent of the activity of FtsZ, yet fundamentally different.

      Strengths:

      The study is very well written and presented, and the data are convincing and rigorous. The data underlying the proposed localization dependency order of the various proteins for cell division is well justified by several different approaches using small molecule inhibitors, knockdowns, and fluorescent protein fusions. The proposed dependency pathway of divisome assembly is consistent with the data and with a novel mechanism for MreB in septum synthesis in Ct.

      Weaknesses:

      The paper could be improved by including more information about FtsK, the "focus" of this study. For example, if FtsK really is the FtsZ-like nucleator of the Ct divisome, how is the Ct FtsK different sequence-wise or structurally from FtsK of, e.g. E. coli? Is the N-terminal part of FtsK sufficient for cell division in Ct like it is in E. coli, or is the DNA translocase also involved in focus formation or localization? Addressing those questions would put the proposed initiator role of FtsK in Ct in a better context and make the conclusions more attractive to a wider readership.

      We will be submitting another manuscript soon that details the conserved domain organization of FtsK from different bacteria, and the role of the various domains of chlamydial FtsK (including the N-terminus and the C-terminal translocase domain) in directing its localization in dividing Chlamydia. We have added text to the discussion (pg. 16 of the revised manuscript) that describes the sequence homology of chlamydial FtsK to FtsK from E. coli.

      Another weakness is that the title of the paper implies that FtsK alone initiates divisome assembly. However, the data indicate only that FtsK is important at an early stage of divisome assembly, not that it is THE initiator. I suggest modifying the title to account for this--perhaps "FtsK is required to initiate....".

      We agree with the reviewer and modified the title to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis”. We have also modified the text throughout to indicate that FtsK is required for the assembly of the hybrid divisome of Chlamydia

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Suggestions for improvement (mostly minor):

      (1) For several of the graphs, the authors plot the number of cells with a given phenotype on the y-axis, but then describe percentages of cells in the text. It would make it clearer if all the graphs had the percentage of cells on the y-axis instead.

      We have modified the figures to indicate the percentage of cells on the y-axis with a given phenotype.

      (2) In Figures 3, 4, and 5 the authors show separate graphs for plus/minus drug or inducer. These should be on the same graph as they are directly comparing these two different conditions. Having them on separate graphs makes it less clear whether these differences are significant between the two conditions

      We modified Fig. 4 to show +/- inducer in ftsk and pbp2 knockdown strains in the same graph.  Regarding Figures 3 and 5, we believe the figures in the original submission effectively demonstrate the +/- drug conditions, so these figures remain unchanged in the revised manuscript.

      (3) In Figure 2 the authors show microscopy of the colocalization of FtsK with several other divisome proteins from Ct. Quantification of the colocalization of FtsK with these other proteins would provide a more holistic understanding of their colocalization and help further support their argument that FtsK initiates the assembly of the divisome.

      Supp. Fig. S4A of the revised manuscript contains images showing the colocalization of FtsK with the fusions at the septum and the base of dividing cells, and the colocalization of FtsK with the fusions that are only at the base of dividing cells. Supp. Fig. S4B quantified the percentage of dividing cells where FtsK overlaps the localization of each of the fusions at the septum, at the septum and the base, and at the base alone.

      (4) In Figure 6 the authors mention that the PG ring was at a slight angle relative to the MOMP-stained septum. What is the significance of this? The authors mention it several times but do not explain its relevance to divisome assembly. It is not really evident in the images presented.

      We mention in the discussion pgs. 17-18 of the revised manuscript that “The relevance of the angled orientation of PG and MreC rings relative to the MOMP-stained septum in division intermediates is unclear. However, it appears to be a conserved feature of the cell division process and may arise because the divisome proteins are often positioned slightly above or below the plane of the MOMP-stained septum. The positioning of divisome proteins above or below the septum is indicated in Figs. 1 and 2.

      We included cartoons in Fig. 6C of the revised manuscript to assist the reader in visualizing the angled orientation of the PG ring relative to the MOMP-stained septum.

      (5) In line 270 the authors claim that "these are the first data in any system to suggest that septal PG synthesis/modification is simultaneously directed by multiple independent divisome complexes." However, their experiments do not demonstrate that multiple divisome complexes are active at the same time. They show that multiple foci of FtsK etc. are present at sites where PG synthesis has occurred, but that does not necessarily mean that each focus/complex was actively synthesizing PG at the same time. Moreover, similar approaches were used to support a claim that septal PG synthesis is directed by multiple discrete divisome complexes previously (e.g. in Figure 1 of Bisson-Filho et al. 2017 (PMID: 28209898) in Bacillus subtilis and in Perez et al 2021 (PMID: 33269494) in Streptococcus pneumoniae). This claim is not central to the main conclusions of the study and could just be removed.

      This statement has been removed from the Results and the Discussion.

      (6) In Figure 6B the authors see three distinct FtsK foci. Why is this the only place in the manuscript where they see three foci? They mentioned previously that they saw foci at the septum and at the base of the progenitor mother cell, but why are there three foci here?

      The vast majority of dividing cells displayed one foci at the septum and/or the base.  Representative images were chosen that reflected the localization profiles observed in the majority of cells. While we observed cells with  multiple foci, as shown in Figure 6C, these cells were relatively rare   (~2% of cells for all the divisome proteins in 3 independent experiments).  Since  the number of cells with multiple foci were relatively rare, we chose to group these cells with the cells that had single foci at the septum, the septum and base, or base alone categories in the quantification shown in Fig. 2C. This is stated in the legend of Fig. 2 of the revised manuscript.

      (7) The Discussion section is lacking a couple of things that would put the data in a broader context. Can the authors speculate on how FtsK knows how to find the division site? I.e. what might be upstream of FtsK localization? Additionally, the authors do not talk about the FtsK sequence or domains at any point in the paper. Does Ct FtsK have a similar sequence/structure to FtsKs from other bacteria? Are there any differences in sequence/structure that might tell us about its function in Ct?

      We will be submitting another manuscript soon that examines how the site of assembly of the divisome is defined in dividing Chlamydia. This manuscript will also define the localization of the different sub-domains of chlamydial FtsK during cell division.  For this manuscript, we added a paragraph in the Discussion (pg. 16 of the revised manuscript) that states the domain organization is conserved in FtsK proteins from different bacteria. This paragraph includes information regarding the % sequence identity of the C-terminus and the N-terminus of chlamydial FtsK when compared to E. coli FtsK.

      (8) For Supplementary Figure S1B-C. The authors should show the full blots rather than just the single band of the protein of interest to show that the antibodies are specific. Additionally, the authors should include a loading control to show that they loaded the same amount of protein for each sample.

      We have included the full blots in Supp. Fig. S1 of the revised manuscript. We do not see the need for including a loading control for these blots because we are not making arguments about the relative level of the proteins that were assayed. We only use the blots to show that the fusion proteins are primarily a single species of the predicted molecular mass.

      (9) In Supplementary Figure S4A the authors use RT-qPCR to measure ftsK and pbp2 transcript levels. Since they have antibodies against these proteins, they should also include Western blots to show that the proteins are not being produced when targeted using CRISPRi.

      We have included data in Supp. Fig. S5E of the resubmission that indicates foci of FtsK and PBP2 could not be detected following the knockdown of ftsk and pbp2. We feel that these data support our conclusion that the induced expression of dCas12 in the the ftsk and pbp2 knockdown strains results in the downregulation of the endogenous FtsK and PBP2 polypeptides.

      (10) In lines 261-262 the authors say that "PG organization was the same or differed at the septum." What is the PG organization being compared to? Same or different from what?

      We agree with the reviewer that the text in lines 261-262 in the original submission was confusing.  The text has been modified.

      (11) Lines 201-215 the authors refer to Supplementary Figure S3 throughout this section, but they should refer to Supplementary Figure S4.

      This has been corrected.

      Reviewer #2 (Recommendations for the authors):

      I am not convinced that this paper shows that FtsK initiates the assembly of the divisome since the authors did not analyse the role and localization of all other chlamydial divisome components. Out of the ten homologues of divisome and elongasome components encoded by C. trachomatis genome, only five are investigated in this study. There is no explanation about how these five were chosen.

      We state on pg. 16 of the revised manuscript that “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.

      Results convincingly indicate that FtsK is an early divisome component, but proofs are lacking to indicate that it initiates the divisome formation. Indeed, the authors do not show how FtsK would be the first protein to selectively accumulate at a given location to initiate the divisome formation. For this reason, the model they propose at the end of their study is not backed by sufficient data, to my opinion.

      We agree with the reviewer that our data does not show that FtsK initiates divisome assembly. The title of the manuscript has been modified to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis” and the text throughout has been modified to indicate that FtsK is the first protein we assayed that associates with nascent divisomes at the base of dividing cells. We will soon be submitting another manuscript that details how FtsK is recruited to a specific site to initiate nascent divisome assembly, This work is too extensive to be included in this manuscript.

      There are also discrepancies in the number of cells analysed to quantify the localization of divisome components, ranging from 50 to 250 cells. The authors could better explain why there are such variations.

      There were differences in the number of cells analyzed in the various experiments, but in every instance the effect of inhibitors (A22 and mecillinam) or ftsk and pbp2 knockdown on divisome assembly was statistically significant.

      There are a few mistakes in the text regarding figure numbering (Figure S4 is mentioned as S3 in the text). Figures 5B and D are not specifically cited.

      These mistakes have been corrected in the revised manuscript.

      Line 261-262: the sentence starting "Our imaging analysis.." is not clear to me.

      We agree with the reviewer that the text in lines 261-262 was confusing.  The text has been modified (pg. 14 of the revised manuscript).

      Line 270-271: there are insufficient proofs to say that there are multiple independent divisome complexes. This is in my opinion an overinterpretation of the data, since there is no proof that these complexes are independent.

      This statement has been removed from the text.

      A few details are lacking in the figure legends:

      Figure 2C: when was the expression of the different mCherry and 6xHis constructs induced?

      The onset and length of the induction of the fusions have been included in the legend of Fig. 2.

      Bars are sometimes mentioned as uM and should be um. Bars sizes, number of replicates, and/or meaning of the error bars are lacking in legends of Figures S2, S3, and S4

      This has been corrected in the revised manuscript.

      The consistency of Figures could be improved between Figures 3A, 4A, B, and 5A. The results of treated cells could be always shown as dark grey. It would help the reader.

      We have used consistent coloring in Figs. 3-5 to indicate the treated cells.

      Reviewer #3 (Recommendations for the authors):

      (1) Lines 113-118: do Ct cells increase in size as they get closer to starting division? If so, could a pseudo-time course (demograph) be done to bolster the evidence that the base foci formed mainly in predivisional cells and not newborn cells? This evidence might be more convincing than the data in Figures 1F and G.

      Chlamydial cells in the population were heterogeneous in size at the timepoint we are studying. This observation is consistent with previous reports in the literature (Liechti et al.,2021). While we agree that a pseudo-time course could potentially bolster the evidence about when FtsK foci appear, we believe our current analysis sufficiently demonstrates that basal foci of FtsK appear prior to the appearance of new buds at the base of dividing cells.

      (2) Figure 3E: It looks like MreC localization to foci doesn't strictly require MreB polymerization. Is this known for E. coli or other species?

      To our knowledge, MreC assembly into a filament has not been shown to be dependent upon MreB in other bacteria.  In Caulobacter crescentus, MreC forms a helical structure that is not dependent upon MreB or MreB filament formation (Dye et al., 2005. PNAS; Divakaruni et al., 2005. PNAS).

      (3) Figure 5E: why is nearly half of PBP2 and PBP3 still localized to foci at the membrane even after treatment with mecillinam? This suggests, as the authors mention, that mecillinam reduces the efficiency of localization to the divisome but does not eliminate it. Any ideas why?

      At this time, we do not know why inhibiting the catalytic activity of PBP2 with mecillinam does not fully prevent the association of PBP2 with the chlamydial divisome. We have included a statement in the Results (pg. 13 of the revised manuscript) that inhibiting the catalytic activity of PBP2 prevents it from efficiently associating with or maintaining its association with polarized divisome complexes.

      (4) Line 262-263: This sentence is confusing-please rephrase. The same as what? Differed from what?

      We agree with the reviewer. The wording in lines 262-263 of the original submission has been modified.  

      (5) Lines 265-267 and Figure 6: Adding cartoon schematics might help readers visualize cell orientations in Fig. 6 (especially 6B).

      Cartoons have been added to Fig. 6C (Fig. 6B in the original submission) to orient the reader.

      (6) Line 294-298: Do the authors think that the residual 5-10% of PG foci after FtsK knockdown is due to the ability of residual FtsK to organize divisomes?

      We show that knockdown of FtsK is not complete, and while we cannot be certain, it is likely, that the PG foci detected in FtsK knockdown cells is due to the ability of the residual FtsK to organize divisomes that direct PG synthesis.

      (7) Do the authors have any evidence that FtsK foci are mobile like treadmilling FtsZ?

      We have not performed real-time imaging studies, and we currently have no evidence that FtsK foci are mobile.

      (8) FtsK foci here are reminiscent of mobile foci formed by the FtsK-like SpoIIIE at the Bacillus subtilis sporulation septum. This might be a good idea to mention in the Discussion. Is it possible that Ct FtsK is also involved in coordinating chromosome partitioning through the developing septum? (That is another reason why it would be useful to know if the translocase domain was dispensable for localization/activity).

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile and whether the division defect in ftsk knockdown cells can be suppressed by specific subdomains of FtsK. This manuscript not only will include these data, it will also include experiments that address how the site of polarized budding is defined. In the revised manuscript, we have included a description of how the domain organization of chlamydial FtsK is similar to E. coli FtsK (pg. 16 of revision). Chlamydial FtsK also has a similar domain organization as SpoIIIE from B. subtilis. The C-terminal catalytic domain of SpoIIIE is 45% identical to chlamydial FtsK. The N-terminus of SpoIIIE is predicted to encode 4 transmembrane spanning helices, like chlamydial FtsK. However, the N-terminus of SpoIIIE shares no sequence homology with the N-terminus of chlamydial FtsK.  We have not included the similar domain organization of SpoIIIE and chlamydial FtsK in the revised manuscript.

      (9) It seems that FtsK foci localize to a particular spot opposite from the active septum, although how this spot is specified is not clear. Is there any geometric clue for FtsK's localization like there is for Min-specified FtsZ localization?

      As mentioned above, we are currently preparing another manuscript that documents our efforts to understand how the site of polarized budding is defined.  This analysis is too extensive to include in this study.

      (10) As mentioned in the Summary, do the authors know whether the N-terminal membrane binding part of FtsK (FtsKn) sufficient for localization/divisome assembly in Ct as it is in other species? Oullette et al. 2012 showed that FtsKn could interact with MreB in BACTH.

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile.

      (11) The previous BACTH result with MreB and FtsKn implies that this interaction is direct, yet the current data suggest that this is not the case. Can the authors comment on this? Is this due to bridging effects inherent in the BACTH system?

      We have not presented any data to indicate that FtsK and MreB do not interact. We have only shown that FtsK localization is not dependent upon MreB filament formation (Fig. 3).

      (12) The FtsZ-independent role of FtsK in nucleating the divisome suggests that Ct FtsK may differ from other FtsKs structurally - can this be explored, perhaps with AlphaFold 3?

      As mentioned above, we have included a paragraph in the discussion of the revised manuscript (pg. 16 of the revised manuscript) that states the domain organization of chlamydial FtsK is similar to E.coli FtsK. This conserved domain organization is evident when we view the structures of the proteins using Alphafold.

      (13) Typo on line 559: should be HeLa.

      This has been corrected.

    1. Just as the technology of printing altered and reduced the power of medieval guilds and the social power structure, so too will cryptologic methods fundamentally alter the nature of corporations and of government interference in economic transactions. Combined with emerging information markets, crypto anarchy will create a liquid market for any and all material which can be put into words and pictures. And just as a seemingly minor invention like barbed wire made possible the fencing-off of vast ranches and farms, thus altering forever the concepts of land and property rights in the frontier West, so too will the seemingly minor discovery out of an arcane branch of mathematics come to be the wire clippers which dismantle the barbed wire around intellectual property. Arise, you have nothing to lose but your barbed wire fences!

      digital IP as crypto backed media... NFTs didnt really take off, but i believe thats because its hard to not reproduce digital media. which leads to the dissoultion of IP, especially market backed IP. I keep saying all digial media should be freely accesible. maybe opening access enables a digial footprint that is similar to DCMA. Is there an open source version of DMCA. how does that tech work.

      I also dont think people are much concerned with privacy in optimal conditions. there isnt much i do in private or public that warrents true privacy. most systems i contribute to digitally are not op secret.

      i think this manifesto reminds me of the radical versions of left/right concepts thomas sowell probs surface in a conflict of visions. it assumes that the avg person cares or benefits from this.

      when it comes to tool usse, i think e should remember this is a specialized tool. for example the avg person probs wont ever use a beat machine, but it does allow the newbie to create loops and digi beats.

      i say that to say its unlikely the avg person will build a beat machine. i certainly might. but most wont. and most dont actually benefit from the capability to do so. the same can be said of the plethora of yogurt brands that democratize access to whatever flavor you want.

      that is a bit silly of an example. but we should know now that technology does not solve human collaboration. and while the mediums of our conceptual thinking are two way forces having more yogurt flavors doesnt really mean much.

      i am curious to see if crypto lands outside of the market based domains, and more into civilian infrastructure tech, warehouse tech, etc. Its just hard to believe that tools communicate anything other than their capapbilities as devloped by humans.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The Bagnat and Rawls groups' previous published work (Park et al., 2019) described the kinetics and genetic basis of protein absorption in a specialized cell population of young vertebrates termed lysosome-rich enterocytes (LREs). In this study they seek to understand how the presence and composition of the microbiota impacts the protein absorption function of these cells and reciprocally, how diet and intestinal protein absorption function impact the microbiome.

      Strengths of the study include the functional assays for protein absorption performed in live larval zebrafish, which provides detailed kinetics on protein uptake and degradation with anatomic precision, and the gnotobiotic manipulations. The authors clearly show that the presence of the microbiota or of certain individual bacterial members slows the uptake and degradation of multiple different tester fluorescent proteins.

      To understand the mechanistic basis for these differences, the authors also provide detailed single-cell transcriptomic analyses of cells isolated based on both an intestinal epithelial cell identity (based on a transgenic marker) and their protein uptake activity. The data generated from these analyses, presented in Figures 3-5, are valuable for expanding knowledge about zebrafish intestinal epithelial cell identities, but of more limited interest to a broader readership. Some of the descriptive analysis in this section is circular because the authors define subsets of LREs (termed anterior and posterior) based on their fabp2 expression levels, but then go on to note transcriptional differences between these cells (for example in fabp2) that are a consequence of this initial subsetting.

      Inspired by their single-cell profiling and by previous characterization of the genes required for protein uptake and degradation in the LREs, the authors use quantitative hybridization chain reaction RNA-fluorescent in situ hybridization to examine transcript levels of several of these genes along the length of the LRE intestinal region of germ-free versus mono-associated larvae. They provide good evidence for reduced transcript levels of these genes that correlate with the reduced protein uptake in the mono-associated larval groups.

      The final part of the study (shown in Figure 7) characterized the microbiomes of 30-day-old zebrafish reared from 6-30 days on defined diets of low and high protein and with or without homozygous loss of the cubn gene required for protein uptake. The analysis of these microbiomes notes some significant differences between fish genotypes by diet treatments, but the discussion of these data does not provide strong support for the hypothesis that "LRE activity has reciprocal effects on the gut microbiome". The most striking feature of the MDS plot of Bray Curtis distance between zebrafish samples shown in Figure 7B is the separation by diet independent of host genotype, which is not discussed in the associated text. Additionally, the high protein diet microbiomes have a greater spread than those of the low protein treatment groups, with the high protein diet cubn mutant samples being the most dispersed. This pattern is consistent with the intestinal microbiota under a high protein diet regimen and in the absence of protein absorption machinery being most perturbed in stochastic ways than in hosts competent for protein uptake, consistent with greater beta dispersal associated with more dysbiotic microbiomes (described as the Anna Karenina principle here: https://pubmed.ncbi.nlm.nih.gov/28836573/). It would be useful for the authors to provide statistics on the beta dispersal of each treatment group.

      Overall, this study provides strong evidence that specific members of the microbiota differentially impact gene expression and cellular activities of enterocyte protein uptake and degradation, findings that have a significant impact on the field of gastrointestinal physiology. The work refines our understanding of intestinal cell types that contribute to protein uptake and their respective transcriptomes. The work also provides some evidence that microbiomes are modulated by enterocyte protein uptake capacity in a diet-dependent manner. These latter findings provide valuable datasets for future related studies.

      We thank the Reviewer for their thorough and kind assessment. We appreciate the suggestion for edits and for pointing out areas that needed further clarification.

      One point in need of further explanation is the use fabp6 (referred to as fabp2 by the reviewer) to define anterior LREs and their gene expression pattern, which includes high levels of fabp6, something that was deemed a “circular argument” by the reviewer.  The rationale for using fabp6 as a reference is that we were able to define its spatial pattern in relation to other LRE markers and the neighboring ileocyte population using transgenic markers (Lickwar et al., 2017; Wen et al., 2021). Thus, far from being a circular argument, using fabp6 allowed us to identify other markers that are differentially expressed between anterior and posterior LREs, which share a core program that we highlight in our study. In the revised manuscript, we clarified this point (lines 166 – 169).

      We followed the Reviewer’s suggestion to test if LRE activity and dietary protein affected beta dispersal. Our analyses revealed that beta dispersion was not significantly different between our experimental conditions. We added details about this analysis (lines 384 – 386) and a new supplemental figure panel (Figure S7C).

      Reviewer #2 (Public review):

      Summary:

      The authors set out to determine how the microbiome and host genotype impact host protein-based nutrition.

      Strengths:

      The quantification of protein uptake dynamics is a major strength of this work and the sensitivity of this assay shows that the microbiome and even mono-associated bacterial strains dampen protein uptake in the host by causing down-regulation of genes involved in this process rather than a change in cell type.

      The use of fluorescent proteins in combination with transcript clustering in the single cell seq analysis deepens our understanding of the cells that participate in protein uptake along the intestine. In addition to the lysozome-rich enterocytes (LRE), subsets of enteroendocrine cells, acinar, and goblet cells also take up protein. Intriguingly, these non-LRE cells did not show lysosomal-based protein degradation; but importantly analysis of the transcripts upregulated in these cells include dab2 and cubn, genes shown previously as being essential to protein uptake.

      The derivation of zebrafish mono-associated with single strains of microbes paired with HCR to localize and quantify the expression of host protein absorption genes shows that different bacterial strains suppress these genes to variable extents.

      The analysis of microbiome composition, when host protein absorption is compromised in cubn-/- larvae or by reducing protein in the food, demonstrates that changes to host uptake can alter the abundance of specific microbial taxa like Aeramonas.

      Weaknesses:

      The finding that neurons are positive for protein uptake in the single-cell data set is not adequately discussed. It is curious because the cldn:GFP line used for sorting does not mark neurons and if the neurons are taking up mCherry via trans-synaptic uptake from EECs, those neurons should be mCherry+/GFP-; yet methods indicate GFP+ and GFP+/mCherry+ cells were the ones collected and analyzed.

      We thank the Reviewer for the kind and positive assessment of our work, for suggestions to improve the accessibility and clarity of the manuscript, and for pointing out an issue related to a neuronal population that needed further clarification.

      It turns out that there is a population of neurons that express cldn15la. They are not easily visualized by microscopy because IECs express this gene much more highly. However, the endogenous cldn15la transcripts can be found in neurons as shown in a recently published dataset (PMID: 35108531) as well as in this study We added a discussion point to clarify this issue (lines 463 – 465).

      Reviewer #3 (Public review):

      Summary:

      Childers et al. address a fundamental question about the complex relationship within the gut: the link between nutrient absorption, microbial presence, and intestinal physiology. They focus on the role of lysosome-rich enterocytes (LREs) and the microbiota in protein absorption within the intestinal epithelium. By using germ-free and conventional zebrafishes, they demonstrate that microbial association leads to a reduction in protein uptake by LREs. Through impressive in vivo imaging of gavaged fluorescent proteins, they detail the degradation rate within the LRE region, positioning these cells as key players in the process. Additionally, the authors map protein absorption in the gut using single-cell sequencing analysis, extensively describing LRE subpopulations in terms of clustering and transcriptomic patterns. They further explore the monoassociation of ex-germ-free animals with specific bacterial strains, revealing that the reduction in protein absorption in the LRE region is strain-specific.

      Strengths:

      The authors employ state-of-the-art imaging to provide clear evidence of the protein absorption rate phenotype, focusing on a specific intestinal region. This innovative method of fluorescent protein tracing expands the field of in vivo gut physiology.

      Using both conventional and germ-free animals for single-cell sequencing analysis, they offer valuable epithelial datasets for researchers studying host-microbe interactions. By capitalizing on fluorescently labelled proteins in vivo, they create a new and specific atlas of cells involved in protein absorption, along with a detailed LRE single-cell transcriptomic dataset.

      Weaknesses:

      While the authors present tangible hypotheses, the data are primarily correlative, and the statistical methods are inadequate. They examine protein absorption in a specific, normalized intestinal region but do not address confounding factors between germ-free and conventional animals, such as size differences, transit time, and oral gavage, which may impact their in vivo observations. This oversight can lead to bold conclusions, where the data appear valuable but require more nuance.

      The sections of the study describing the microbiota or attempting functional analysis are elusive, with related data being overinterpreted. The microbiome field has long used 16S sequencing to characterize the microbiota, but its variability due to experimental parameters limits the ability to draw causative conclusions about the link between LRE activity, dietary protein, and microbial composition. Additionally, the complex networks involved in dopamine synthesis and signalling cannot be fully represented by RNA levels alone. The authors' conclusions on this biological phenomenon based on single-cell data need support from functional and in vivo experiments.

      We thank the Reviewer for their assessment and for pointing out some areas that needed to be explained better and/or discussed.

      The Reviewer mentions some potential confounding factors (ie., size differences, transit time, oral gavage) in the gnotobiology experiments. We would like to convey that these aspects have been addressed in our experimental design and are now clarified in the revised manuscript: 1- larval sizes were recorded and found to be similar between GF and monoassociated larvae (Figure S6A); 2- while intestinal transit time may be affected by microbes and is a topic of interest, in our assay luminal mCherry cargo is present at high levels throughout the gut and is not limiting at any point during the experiment; 3- gavage, which is necessary for quantitative assays, is indeed an experimental manipulation that may somehow alter the subjects (the same is true for microscopy and virtually any research method). However, it cannot explain differences between GF and CV or alter our conclusions via microbial or dietary effects. We now elaborate the former point in the revised discussion (line 426). A new panel has been added for Fig.S6 to show that standard length was similar in GF and monoassociated larvae (Figure S6A).

      We are aware that microbial community composition is often highly variable between experiments and this necessitates adequately high biological replication and inclusion of internal controls to allow conclusions to be drawn. Nevertheless, studies evaluating the utility of 16S rRNA gene sequencing have found that this analysis reveals important impacts of environmental factors on the gut microbiome (PMIDs: 21346791, 31409661, 31324413). Our results provide further evidence that 16S rRNA gene sequencing remains a useful method to detect perturbations to the zebrafish gut microbiome. Reproducing previous findings, we detected many of the core zebrafish microbiota strains in our samples that have been identified by other studies (PMIDs: 26339860, 21472014, 17055441). To ensure the robustness of our results, we included several biological replicates for each condition, co-housed genotypes and included large sample sizes to minimize environmental variability between groups. In response to this reviewer concern, we have added a supplemental beta diversity plot and statistical analyses showing that the microbiomes in our larvae were significantly different from the diets or tank water (Figure S7A). This analysis shows that the host environment influenced microbial community composition (lines 376 – 378). We also added an additional supplemental panel and performed analysis showing that the experimental replicates (i.e., different tanks) were not a significant source of variation in this study (lines 378 – 380) (Figure S7B). This result underscores that the microbiota in these larvae were influenced by both the host and diet.

      Regarding dopamine pathways, we acknowledge that it involves complex biology that will require dedicated studies. In this work, we simply point out gene expression patterns we find interesting as they may inform future studies.

      Finally, the Reviewer mentions the use of inadequate statistical methods for some analyses without specifying or indicating alternative analyses, only the need to justify the use of two-way ANOVA is made explicit. In this point, we respectfully disagree and would like to emphasize that we use statistical methods that are standard in the field (PMID: 37707499). We nevertheless added a justification for the use of two-way ANOVA where appropriate (lines 635-637, 653-654, 773-776). The two-way ANOVA test was to compare fluorescence profiles of gavages cargoes or HCR probes along the length of the LRE region. This test accounts for differences in fluorescence between experimental conditions in segments (30 μm) along the LRE region (~300 μm). This allows us to capture differences in fluorescence between experimental conditions while accounting for heterogeneity in the LRE region. Please see our comment below for more information about our use of the 2-way ANOVA.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Please provide in the materials and methods the strain identifiers and sources of the bacteria used in the study.

      Thank you for the suggestions. Strain identifiers and source information were added to the methods (lines 576-579).

      Reviewer #2 (Recommendations for the authors):

      (1) This is a very satisfying and thorough analysis of the reciprocal influence of diet, microbiome, and host genotype on protein absorption by the host. Below I make suggestions that mainly relate to making the paper more accessible to a broader audience.

      (2) Line 233 Starts a section that reports the findings of the scRNA dataset. The writing is inconsistent with respect to how the genes are listed: whether abbreviation only or spelled out followed by abbreviation. I prefer the latter. For example, slc10a2 is a bile acid Na cotransporter but for those not in the know, they would have to look this up. Perhaps adding a supplementary table that provides a gene list of those discussed in the text with abbreviation/spelled-out, and KEGG terms.

      Thank you for pointing out inconsistent gene labeling. We have revised the text with spelled out gene names followed by abbreviations.

      (3) Line 461 Where did the neurons come from when you were sorting cldn+ cells?

      Neuronal expression of cldn15la was detected in our data and other published datasets (PMID: 37995681, 35108531). We added a note to the text clarifying that neuronal cells can express cldn15la (lines 463-465).

      (4) Line 561 1x tricaine should be converted to percentage in solution or concentration throughout.

      The tricaine concentration was 0.2 mg/mL. We added this detail to the methods (line 596).

      (5) Line 612 Please clarify how normalizations are carried out: is it to the peak value in the germ-free condition? CV never reaches 1.

      AUC values were normalized to the peak value in the GF condition at 60 minutes PG. We clarified this step in the methods (lines 618-619).

      (6) Line 654-663 I think mCherry here should be mTourquoise?

      Thank you for catching this typo. We corrected it in the text.

      (7) In Figure 1 Please consider adding a color so that magenta does not represent BOTH germ-free AND mCherry.

      Due to the many colors of fluorescent proteins and HCR probes in this paper, we were not able to find an alternative plot line color to represent GF.

      (8) In Figure 2 I suggest consistency with respect to the order you present GF/CV

      Figure 1 GF->CV

      Figure 2 CV->GF

      My preference is GF->CV

      Images in Figure 2 were re-ordered following reviewer’s recommendation.

      Here, 20 minute time point also appears qualitatively different between GF and CV.

      There can be slight differences in LREs between individuals. These images were selected because they represented the average differences in the amount of mTurquoise degradation activity that occurred between 20 – 60 minutes post-flushing in the GF and CV conditions.

      In Figure 3E Figure legend refers to being able to see BSA in vacuoles. The image should be modified to show this- currently too small.

      In response, we enlarged the confocal microscopy images showing DQ red BSA in the LRE region (Figure 3E). We added a panel with confocal microscopy images of the LREs in 6 dpf larva gavaged with DQ red BSA (Figure S3F). These images show that DQ red BSA fluorescence was localized to the LRE lysosomal vacuole.

      In Figure 5D, Posterior LRE should be pink not green in the key to the right of the heatmap.

      Thank you for catching this error. We have corrected the colors (Figure 5D).

      Reviewer #3 (Recommendations for the authors):

      (1) Introduction and context:

      Expand the introduction to include more background on microbial-mediated protein absorption, with references to relevant findings in Drosophila. This will provide a stronger foundation for the study's contributions to the field.

      Thank you for this suggestion. We added information about microbe-mediated amino acid harvest in Drosophila to the introduction (lines 49-53).

      (12) Methodological suggestions:

      Measure and report differences between germ-free (GF) and conventional (CV) animals, such as transit time, to account for potential confounding factors in protein absorption dynamics.

      We respectfully assert that a transit assay is not required for this study and could actually create confusion as an effect in transit time could be interpreted as a contributing factor when it is in fact not the case due to the experimental design. This is because the concentration of luminal protein was equivalent in GF and CV larvae (Figure S1E), so the LREs had equal saturating access to those proteins in both conditions. Furthermore, we showed the microbiota did not degrade fluorescent protein (Figure S1F). Therefore, we feel confident that there was lower protein uptake in the LREs of CV larvae because the microbiome exerted regulatory effects on LRE activity.

      Provide detailed information on the gating strategy used for single-cell sorting to enhance the dataset's utility and support claims about cell changes.

      The methods we used for sorting cells were previously described (PMID: 31474562). In this manuscript, we describe them under the heading “Fluorescence activated cell sorting for single cell RNA-sequencing.”

      Explain the "GeneRatio" metric in figure legends for clarity.

      The GeneRatio is the ratio of genes associated with each individual GO term to the number of genes associated with the domain. An explanation was added to the caption (Figure S3C).

      (13) Visual and statistical improvements:

      Include images of labeled peptidases within lysosome-rich enterocytes (LREs) to reinforce findings.

      Thank you for the suggestion. We added images of labeled peptidases in the LRE region (Figure S6E-D).

      For Panels 4-F and 5-D, consider using violin plots of selected genes to improve clarity and emphasize major ideas.

      In Figure 4F, the heatmap shows multiple genes were upregulated in mCherry-positive cells. We tried the plotting suggested by the reviewer and felt that violin plots could not convey this message as clearly. Likewise, the heatmap in Figure 5D effectively shows the gradient of expression between ileocytes, anterior and posterior LREs.

      Strengthen statistical analysis by employing more rigorous methods and justifying their selection, such as using two-way ANOVA where appropriate.

      The two-way ANOVA was used to quantify protein uptake or HCR probe fluorescence along the length of the LRE region. This statistical test allowed us to compare differences in fluorescence between experimental conditions in multiple LRE segments (see Authoer response image 1 below for example). As our assays show, the LRE region is heterogenous with segments showing different levels of activity and gene expression. The two-way ANOVA is appropriate because it allows us to account for this heterogeneity by comparing fluorescence across multiple segments.

      Author response image 1.

      Our figures display these fluorescent levels in line plots (above, left) rather than bar plots (above, right). The results are easier to visualize interpret in line plots, and they display the fluorescence profiles in greater detail.

      (14) Technical corrections:

      Correct figure references: Figure 5 about tryptophan metabolism should be 5A, S5G-S5H.

      We corrected the figure references.

      Line 518: Spell out "heterozygotes" instead of using "gets".

      We changed the term from “hets” to “heterozygotes.”

      (15) Revise Figure S2 citation to match the actual figure labeling.

      We corrected the text to indicate “Figure S2” rather than “Figure S2A.”

      Additional manuscript modification

      · Figure panels 3B-C, S3A-B, 4A-C: Two cluster were relabeled with improved descriptors based on our updated annotations. The clusters “Pharynx-esophagus-cloaca 1” (PEC1) and PEC2 were relabeled as “Pharynx-cloaca 1” and “Pharynx-cloaca 2.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

      Strengths:

      The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

      Thank you for your positive evaluation.

      Weaknesses:

      The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

      (1) We fully agree with the reviewer that reuniens likely does not generate neither slow waves nor spindles. We do not make such claim, which we clearly stated in the discussion (lines 319-324). We propose that Reuniens neurons mediate different forms of activity. In the model, we introduced MD nucleus only because without MD we were unable to generate spindles. While the slow waves and spindles are generated in other thalamocortical regions, the REU neurons show these rhythms due to long-range projections from these regions to REU as has been shown in the model.

      (2) Definitely, we cannot exclude some influence of volume conductance on obtained LFP recordings in REU nucleus. However, we show modulation of spiking activity within REU by spindles. Spike modulation cannot be explained by volume conductance but can be explained by either synaptic drive (likely the case here) or some intrinsic neuronal processes (like T-current).

      (3) In our REU recordings for spike identification we used tetrode recordings. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

      Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

      (1) Thalamus of majority of mammals (definitely primates and carnivores, including cats) contain local circuit interneurons (about 30 % of all neurons). A vast majority of studies in rodents (except LGN nucleus) report either absence or extremally low (i.e. Jager P, Moore G, Calpin P, et al. Dual midbrain and forebrain origins of thalamic inhibitory interneurons. eLife. 2021; 10: e59272.) number of thalamic interneurons. Therefore, studies on other species than rodents are necessary, and bring new information, which is impossible to obtain in rodents.

      (2) Cats’ brain is much larger than the brain of mice or rats, therefore, the effects of volume conductance from cortex to REU are much smaller, if not negligible. The distance between REU and closest cortical structure (ectosylvian gyrus) in cats is about 15 mm.

      (3) Indeed, there is much less anatomical data on cats as opposed to rodents. This is why, we performed experiments shown in the figure 1. This figure contains functional anatomy data. Antidromic responses show that recorded structure projects to stimulated structure. Orthodromic responses show that stimulated structure projects to recorded structure.

      Reviewer #2 (Public Review):

      Summary:

      The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

      Strengths:

      An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

      We thank this reviewer for very positive evaluation of our study.

      Weaknesses:

      Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

      Thank you for this comment. In the revised version, we will do our best to simplify presentation and more clearly explain our findings.

      Reviewing Editor (Recommendations for Authors):

      Please discuss the ability of reuniens to generate spindles?

      We briefly discussed this in previous version. We now extended the discussion (p. 18).

      For population data, how many cats were used in acute and chronic experiments, where does the population data originate in Fig. 2? How repeatable were the findings across animals? Was histology verified in each animal?

      As previously stated in the beginning of method section we totally used 20 cats: 16 anesthetized (or acute) and 4 non-anesthetized (or chronic). We added number of cats in appropriate places in the result section. Population data in figure 2 comes from 48, 49 or 52 recording sessions (depending on the type of analysis, and indicated in the figure legend) from 4 chronic cats; we clarified this information in the legend. Results were highly repeatable across animals. Histology was verified in all chronic and acute animals, we added a sentence in the method section.

      Explanation of figures is very poor, values in figures should be reported in results so they can be compared in the context of the description.

      In this revised version, we report most numbers present in figures and their legend to the main text (result section).

      The depth of the recording tungsten electrodes are meaningless without the AP and ML coordinates given how heterogenous mPFC is. What is the ventromedial wall of the mPFC in the cat?

      We added the ML and AP coordinates in the method section. We corrected ventromedial wall for ventroposterior part of the mPFC.

      What are the two vertical lines in 1F?

      This was an error while preparing the figure. The panel was corrected.

      Line 90 mean +-SD of what? There are no numbers.

      Thanks, we now indicate the values.

      Panel 2L does not show increased spindling in reuniens prior to PFC as indicated in the results, please explain. It does show SWR in the hippocampus prior to spindles, what is the meaning of such a time relationship?

      Panel 2L did show an increased spindling reuniens prior to mPFC, but indeed at the time scale shown, it was not very clear. In this revised manuscript, we added an inset zooming around time zero to make this point clearer.

      Panel 2L indeed show an increase in SWR prior to the increase in spindle in both Reuniens and mPFC.

      As stated in the discussion, ‘We found that hippocampal SWRs trigger thalamic firing and precede the onset of reuniens and mPFC spindles, which points to SWRs as one of candidate events for spindle initiation.’

      It is unclear what the slow waves of PFC mean, these represent filtered PFC lfp, but is this a particular oscillation? They continue to occur during the spindle, while the slow waves supposedly trigger the spindle. Please explain and clarify.

      We recently published a review article involving several scientists studying both human and animal sleep that has inserted Box. 1 (Timofeev I, Schoch S, LeBourgeois M, Huber R, Riedner B, Kurth S. Spatio-temporal properties of sleep slow waves and implications for development. Current Opinion in Physiology. 2020; 15: 172–182). In this box among other terms, we provide current definition of slow waves vs slow oscillation. Briefly, if slow waves are repeated with a given rhythm, they typically form slow oscillation. However, if they occur in isolation or are not rhythmic, they remain slow waves, but cannot be called slow oscillation.

      Regarding relation of spindles and slow oscillation. We are currently systematically analyzing data on spindles and slow waves obtained from head-restrained and freely behaving cats. One of the main findings is that a majority of ‘cortical’ spindles are local. Local to the extent that spindles can occur in alternation in two neighboring cortical cells. Largely, LFP sleep spindles occur more or less synchronously within suprasylvian gyrus of cats where indeed a large majority of them was triggered by slow waves. The synchrony between LFP spindles in suprasylvian vs other other cortical areas is much less clear. So, it is not surprizing that spindles in one bran region can occur when there is a slow wave present in some other brain region. Something of a kind was also shown in human (Mölle M, Bergmann TO, Marshall L, Born J. Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep. 2011; 34 (10): 1411-1421).

      In this regard, we are not ready to include modifications in the manuscript.

      Line 134, where is spindle amplitude shown? Plots report power within the spindle frequency band, which obviously captures more than just spindles.

      No, plots of figure 3 B, C show the phase-amplitude coupling (PAC) strength. These were calculated with detected spindles, therefore, while we cannot exclude some false spindle detections, we are confident that the false spindle detections are at a negligible level. We modified text and instead of spindle amplitude, we describe SW-spindle amplitude coupling. This reflects our analysis with exactitude.

      The discussion must include the medio dorsal nucleus which is the largest thalamic input to the prefrontal cortex and also receives input from the hippocampus. In particular, the case must be made for why reuniens would play a more important or different role than MD? (For example: Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus - PMC (nih.gov)).

      We cited the suggested study. We cannot say whether reuniens plays a more or less important role. What is clear is that hippocampal ripples at the onset of spindles trigger increased firing in both MD and reuniens. Our extracellular recordings (Fig. 4, K) suggest that the increased firing is associated with spike-bursts. We also have a parallel unpublished study done on anesthetized mice showing SWR triggered inhibitory potentials in both reuniens and MD that reverses around -65mV - -70 mV. Because the majority of SWR occurred at the onset of cortical up state, a relative role of cortico-thalamic vs hippocampo-thalamic drive is not easy to separate. We hope, we will convincingly do this in our forthcoming study, with the limitation that it was done on anesthetized mice.

      Reviewer #1 (Recommendations For The Authors):

      I strongly encourage the authors to perform current source density analyses on the LFP signals recorded in the nucleus reuniens to make sure that the observed oscillations are indeed locally generated. So far, the anatomical organisation in reuniens cannot support the local generation of oscillations, such as spindles and slow wave. At least in rodents (the cat reuniens does not seem too different, until shown differently), there were no oscillators found in reuniens, and at least not arranged like in cortical areas, allowing the summation in time, and particularly space, of rhythmic input currents. Bipolar recordings with pairs of twisted electrodes might also be useful to assess the local existence of spindles and slow waves.

      Current source density calculation is possible when one knows the exact distance between recording sites. As we used tetrodes made with 4 twisted platinum-iridium wires, we know more or less the range of distance between recording sites, but not the exact distance between any given pair of electrodes.

      Then, the physical distance between the reuniens and any cortical structure is about 8-9 mm. Therefore, with such distances, volume conductance is expected to be negligible. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

      Below, we plotted the voltage of one channel of the tetrode versus another channel of the same tetrode. If the signal was simply volume conducted, one would expect to see the vast majority of points on the x=y line (red).

      Author response image 1.

      Below is a segment of mPFC LFP recording (upper black trace), mPFC LFP filtered for spindle frequency (7-15 Hz) and the spindle detected (black lines above the filtered trace. Then two LFP traces from a tetrode in the Reuniens (orange and light blue) are overlayed. The second trace (Blue) from bottom represents the substraction of Reuniens 1 minus Reuniens 2 channel, and just below (lower Blue trace) is this susbtraction trace filtered for spindle frequency (7-15 Hz) showing clear voltage difference in the spindle range between the two electrodes. Note also that around time 179-179.5 s, there is clear spindle oscillation in the mPFC recording which is not present in the Reuniens recordings.

      Author response image 2.

      Therefore, we are convinced that in our recordings, volume conductance did not play any significant role.

      Another concern regarding delays between events, like slow waves, measured between two regions (as exemplified by Figure 3). It appears that the delays were calculated from the filtered signal. Figure 3G shows a delay between the peak of the mPFC slow wave between the raw and the filtered signal, which might be artifactual of the processing. It is though not (or less) visible for the reuniens recording. Such mismatch might explain the observed differences in delays.

      Thanks for this comment. We recomputed the analysis using the original signal (smoothed) and obtained very similar results. Panels H and I of figure 3 were updated using the new analysis performed on original signal.

      The overall analyses of LFP-triggered reuniens MUA activity lack of statistics (at least z-scored firing to normalise the firings).

      Fig. 2 H and I are representative examples for histograms; statistical data are shown in circular plots as explained in the legend. Fig. 2 L, shows populational data and we provide now standard error. Fig. 4 C and D show individual example. Fig. 4 E shows histograms of activity of all identified putative single units. Units that show significant modulation are displayed above white line. Fig. 4 F shows populational data for significantly modified units.  

      A last point of detail in the model, which surprisingly shows reuniens to excitatory hippocampal cells' connectivity. Recent literature reports that reuniens only connect hippocampal interneurons, and not principal cells (at least in rodents, I could not find any report in cats). I wonder how changing this parameter would affect the results of the computational investigation, particularly the results shown in Figure 6.

      There are several studies in the literature showing a direct excitation from the Reuniens to pyramidal cells in the CA1, here are three of them:

      Goswamee, P., et al. (2021). "Nucleus Reuniens Afferents in Hippocampus Modulate CA1 Network Function via Monosynaptic Excitation and Polysynaptic Inhibition." Frontiers in Cellular Neuroscience 15.

      Dolleman-Van der Weel MJ, Lopes da Silva FH, Witter MP (1997) Nucleus Reuniens Thalami Modulates Activity in Hippocampal Field CA1 through Excitatory and Inhibitory Mechanisms. The Journal of Neuroscience 17:5640.

      Dolleman-van der Weel MJ, Lopes da Silva FH, Witter MP (2017) Interaction of nucleus reuniens and entorhinal cortex projections in hippocampal field CA1 of the rat. Brain Structure and Function 222:2421-2438.

      Because this is not a review paper, we opted to not cite all the papers describing connectivity between mPFC, hippocampus and thalamus.

      Reviewer #2 (Recommendations For The Authors):

      I respectively suggest that the earlier (public) comments listed above should be addressed. In addition, it would be useful to make it clearer when non-rapid eye movement sleep was being addressed and when rapid eye movement was being addressed. Is it of value to use a single term instead of adding "slow wave sleep" or else clarify when either term is used? The addition of more subheadings might help. Moreover, the relative contribution/value of evidence from these two sleep states was not addressed or was not very clear.

      We tried to make it clearer when NREM and when REM was analysed.

      We replaced slow-wave sleep with NREM sleep in the figure 5 title.

      We added several subheadings in the discussion.

      Relative contribution of NREM vs REM sleep was not addressed? Sorry but we do not clearly understand your question. Figs. 2 and 3 deal mainly with NREM sleep (Fig 2.B has an example of REM sleep). Fig. 4 essentially describes results obtained during REM sleep.

      I was not sure if the Abstract summarised the key take-home messages from the large amount of evidence provided. Some choices are needed, of course, but "evidence of bidirectional connectivity" struck me as less novel than other evidence provided. Given the huge amount of findings provided, which is commendable, it is still useful to present it perhaps in a more digestible fashion. For example, the headings or the first sentence(s) below headings could indicate the aim or the outcome of the specific method/analysis/findings.

      We rewrote abstract and we also added some conclusion to highlight major findings and their meaning.

      It is more common to use NRe or Re, rather than REU.

      We avoided using RE as, for decades, we used RE to abbreviate the thalamic reticular nucleus in several publications. In this revised version, we spell at full - Reuniens.

      Line 49 mentions "short-term" memory. Please specify this more clearly as it is otherwise ambiguous. Also, line 303.

      We rephrased the sentence: In particular, the hierarchical coupling of slow waves, spindles and SWRs is thought to play a key role in memory consolidation.

      Line 303 was likely about the ventromedial wall: we corrected that sentence.

      Line 62: the word, "required" (for memory function) is too strong because there is evidence that it is not always required.

      We modified the sentence for plays a major role.

      The focus within the medial prefrontal cortex could be specified more clearly / earlier.

      The mPFC is mentioned in the second sentence of the abstract and in the first sentence of the introduction.

      Line 134: The heading states "determine" and then mentions modulation. These terms may not be interchangeable or they need clarification.

      We changed it to slow wave-spindle amplitude coupling. This represents exactly our analysis.

      Line 204: Does "cortical network" mean prefrontal cortex network"?

      Yes, as described in lines 192-193, the two cortical networks (N1 and N2) of the model represent the mPFC layer 5 and 6 respectively.

      Lines 283 to 289: These were not very clear to me.

      These lines described the potential mechanisms for the responses to hippocampal and reuniens stimulation recorded intracellularly (results in figure 1). We modified this paragraph for clarity.

      Line 296: Specify the "claim".

      We modified the sentence for “[…] provides supporting evidence for this claim that nucleus Reuniens might synchronize the activity of ventral hippocampus and mPFC.”

      The discussion naturally focuses on the thalamic nucleus reuniens, but also occasionally mentions the thalamic mediodorsal nucleus. The distinction, assuming this is highly relevant, could be expressed more clearly (direct comparison with their previous papers).

      We never published a study on the mediodorsal nucleus. We do have some unpublished results from recordings in the MD nucleus and they reveal the presence of an inhibitory component at the beginning of cortical active states, therefore behaving in a similar way to first order nuclei. It is then possible that spindles recorded in the reuniens are actually generated in the MD nucleus and then transmitted to Reuniens through the thalamic reticular nucleus, as both MD and reuniens are connected to the rostral thalamic reticular nucleus. We added some discussion about this.

      Figure 1B: Do the authors have any additional evidence of the placements in the reuniens, because the photo provided suggests a large area beyond the reuniens boundary. Also, please confirm is the CEM between Rh and Re in the cat (I think the Rh and Re are adjacent in the rat).

      Figure 1B is from an electrolytic lesion, which is necessarily bigger than the tip of the electrode. Therefore the center of the electrolytic lesion indicates where the electrode tip was located which is well within the reuniens nucleus.

      Also, yes CE (Nucleus centralis thalami, pars medialis) is located between the reuniens and rhomboid in cats. This can be found in two cat atlas:  

      Reinoso-Suárez, F. (1961). Topographischer Hirnatlas der Katze für experimental-physiologische Untersuchungen (Merck).

      Berman AL, Jones EG (1982) The Thalamus and Basal Telencephalon of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates: University of Wisconsin Press.

      The first mention of hippocampus in the figure legends should remind the reader by stating "ventral hippocampus".

      In this revised version, we added “ventral” in several instances both in the main text and in figure legend.

      Figure 2: It seems unusual to mention "unusually short NREM". Presumably, things are the same otherwise - if so, perhaps mention that, especially if some of the effects reflect an "unusual" episode.

      We display this particular segment because we want to show continuous recording in which still individual elements characterizing specific states are still visible.

      Some effects look like they are strong and others perhaps weaker. If so, how do these impact the final conclusions?

      Sorry, we did not understand clearly what is meant here by the reviewer. In general, if any effect has statistically significant difference (old fashion 0.05) we consider it as significant. Any other cases are described on individual basis.

      Perhaps "MAD" should be in full on the first occasion, if not already.

      It was spelled out at line 659, but we now spell it out also in the results section and in figure 2 legend.

      Methods: the key question is the use of rodent recordings to classify cat recordings. It would be good to have a reference indicating that this can be directly used for cats, which may have different sleep cycles and patterns compared to rats.

      We did not use rodent recordings to classify cat recordings, however we did used a state detection script that was developed with rodent recordings. As mentioned in the method section, we adapted the script to cat mPFC recordings and then manual corrections were made to correctly detect REM episodes. Respectfully, our lab investigates sleep-wake in non-anesthetized animals for a few decades; we developed state detection algorithm in mice, cats, marmosets when needed (to analyse months of recordings), and we have an extensive expertise in identifying states of vigilance from electrophysiological recordings.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      (1) The overall conclusion, as summarized in the abstract as "Together, our study documents the diversification of locomotor and oculomotor adaptations among hunting teleost larvae" is not that compelling. What would be much more interesting would be to directly relate these differences to different ecological niches (e.g. different types of natural prey, visual scene conditions, height in water column etc), and/or differences in neural circuit mechanisms. While I appreciate that this paper provides a first step on this path, by itself it seems on the verge of stamp collecting, i.e. collecting and cataloging observations without a clear, overarching hypothesis or theoretical framework.

      There are limited studies on the prey capture behaviors of larval fishes, and ours is the first to compare multiple species systematically using a common analysis framework. Our analysis approach could have uncovered a common set of swim kinematics and capture strategies shared by all species; but instead, we found that medaka used a monocular strategy rather than the binocular strategy of cichlids and zebrafish. Our analysis similarly could have revealed first-feeding larvae of all species go through a “bout” stage, which was previously proposed as important for sensorimotor decision making (Bahl et al., 2019), but instead we found that medaka and some cichlids have more continuous swimming from an early life stage. Finally, the rate at which prey capture kinematics evolves is not known. Our approach could have revealed rapid diversification of feeding strategies in cichlids (similarly to how adult feeding behavior evolves), but instead we found smaller differences within cichlids than between cichlids and medaka.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - It would be helpful to include previously published data from zebrafish for comparison.

      We appreciate the suggestion. Mearns et al. (2020) provided a comprehensive account of prey capture in zebrafish larvae in an almost identical setup with similar analyses. We do not feel it is necessary to recount all the findings in that paper here. There are many studies on prey capture in zebrafish from the past 20 years, and reproducing these here would not add anything to that extensive pre-existing literature.

      - Justification is required for why it is meaningful to compare hunting strategies when both fish species and prey species are being varied. For instance, artemia and paramecia are different sizes and have different movement statistics.

      We added text explaining why different food was chosen for medaka/cichlids. There is no easy way to stage match fishes as evolutionarily diverged as cichlids, medaka, and zebrafish. Size is a reasonable metric within a species, but there is no guarantee that sizematched larvae of two different species are at the same level of maturity. Therefore, we thought the most appropriate stage to address is when larvae first start feeding, as this enables us to study innate prey capture behavior before any learning or experience-dependent changes have taken place. Given that zebrafish, medaka and cichlid larvae are different sizes when they first start feeding, it was necessary to study their hunting behavior to different prey items.

      - It would be helpful in Figure 1A to add the abbreviations used elsewhere in the paper. I found it slightly distracting that the authors switch back and forth in the paper between using "OL" and "medaka" to refer to the same species: please pick one and then remain consistent.

      Medaka is the common name for the japanese rice fish, O. latipes. Cichlilds do not have common names are only referred to by their scientific names. Since readers are more likely to be familiar with the common name, medaka, we now use medaka (OL) throughout the manuscript, which we hope makes the text clearer.

      - The conceptual meaning of behavioral segmentation is somewhat unclear. For zebrafish, the bouts already come temporally segmented. However in medaka for instance, swimming is more continuous, and the segmentation is presumably more in terms of "behavioral syllables" as have been discussed for example mouse or drosophila behavior (in the last row of Figure S1 it is not at all obvious why some of the boundaries were placed at their specific locations). It's not clear whether it's meaningful to make an equivalence between syllables and bouts, and so whether for instance Figure 1H is making an apples-to-apples comparison.

      We clarified the text to say we are comparing syllables, rather than bouts.

      - The interpretation of 1H is that "medaka exhibited significantly longer swims than cichlids"; however this is not supported by the appropriate statistical test. The KS test only says that two probability distributions are different; to say that one quantity is larger than another requires a comparison of means.

      Updated Fig 1H; boostrap test (difference of medians) and re plotted data as violin plots.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - I think the evidence that there are qualitatively different patterns of eye convergence between species is weak. In Figure 2A I admire the authors addressing this using BIC, and the distributions are clearly separated in LA (the Hartigan dip test could be a useful additional test here). However for LO, NM, and AB the distributions only have one peak, and it's therefore unclear why it's better to fit them with two Gaussians rather than e.g. a gamma distribution. Indeed the latter has fewer parameters than a two-gaussian model, so it would be worthwhile to use BIC to make that comparison. The positions of the two Gaussians for LO, NM, and AB are separated by only a handful of degrees (cf LA, where the separation is ~20 degrees), which further supports the idea that there aren't really two qualitatively different convergence states here.

      Added explanation to text.

      - Figure S2 is unfortunately misleading in this regard. I don't claim the authors aimed to mislead, but they have made the well-known error of using colors with very different luminances in a plot where size matters (see e.g.

      https://nam12.safelinks.protection.outlook.com/?url=https%3A %2F%2Fwww.r-project.org%2Fconferences%2FDSC2003%2FProceedings%2FIhaka.pdf&data=05%7C02%7Cdme arns%40princeton.edu%7C17ae2b44f0f246f15ddd08dc9b8e2 01c%7C2ff601167431425db5af077d7791bda4%7C0%7C0%7

      C638556282750568814%7CUnknown%7CTWFpbGZsb3d8ey

      JWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJ XVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Ll4J4Xo39JEtKb %2FNnRWNoyedZAu5aAOMq0lHJCwsfXI%3D&reserved=0).

      Thus, to the eye, it appears there's a big valley between the red and blue regions, but actually, that valley is full of points: it's really just one big continuous blob.

      Kernel density estimation of eye convergence angles were added to Figure S2. The point we wish to make is that there is higher density when both eyes are rotated invwards (converged) in cichlids, but not medaka (O. latipes). The valley between converged and unconverged states being full of points is due to (1) slight variation with placement of key points in SLEAP, which blurs the boundary between states and (2) the eye convergence angle must pass through the valley in order to become converged, so necessarily there are points in between the two extremes of eye convergence.

      - In Figure 2D please could the authors double-check the significance of the difference between LO and NM: they certainly don't look different in the plot.

      Thank for for flagging this. We realize the way we previously reported the stats was open to misinterpretation. We have updated figure 2C, D and F to use letters to indicate statistical groupings, which hopefully makes it clearer which species are statistically different from each other.

      - In Figure 2G it's not clear why AB is not included. It is mentioned that the artemia was hard to track in the AB videos, but the supplementary videos provided do not support this.

      The contrast of the artemia in the AB videos is sufficiently different from the other cichlid videos that our pre-trained YOLO model fails. Retraining the model would be a lot of extra work and we feel like a comparison of three species is sufficient to address the sensorimotor transformations that occur over the course of prey capture in cichlids.

      - The statement "Zebrafish larvae have a unique swim repertoire during prey capture, which is distinct from exploratory swim bouts" is not supported by the work of others or indeed the authors' own work. In Figure 4F all types of bouts can occur at any time, it's just the probability at which they occur that varies during prey capture versus other times (see also Mearns et al (2020) Figure S4B).

      The point is well taken that there probably is not a hard separation between spontaneous and prey capture swims based on tail kinematics alone, which is also shown in Marques et al. (2018). However, we think that figure 2I of Mearns et al., which plots the probability of swims being drawn from different parts of the behavior space during prey capture (eyes converged) or not (eyes unconverged), shows that the repertoire of swims during the two states is substantially different. Points are blue or red; there are very few pale blue/pale red points in that figure panel. Figure S4B is showing clustered data, and clustering is a notoriously challenging problem for which there exists no perfect solution (Kleinberg, 2002). The clusters in Mearns et al. incorporated information about transition structure, as this was necessary for obtaining interpretable clusters for subsequent analyses. However, a different clustering approach could have yielded different boundaries, which may have shown more (or less) separation of bout types during prey capture/exploratory swimming. Therefore, we have updated the text to say that zebrafish perferentially perform different swim types during prey capture and exploration, and re-interpreted the behavior of cichlids similarly.

      - More discussion is warranted of the large variation in the number of behavioral clusters found between species (11-32). First, how much is this variation really to be trusted? I appreciate the affinity propogation parameters were the same in all cases, but what parameters "make sense" is somewhat dependent on the particular data set. Second, if one does believe this represents real variation, then why? This is really the key question, and it's unsatisfying to merely document it without trying to interpret it.

      Extended paragraph with more interpretation.

      - What is the purpose of "hovers"? Why not stay motionless? Could it be a way of reducing the latency of a subsequent movement? Is this an example of the scallop theorem?

      Added a couple of sentences speculating on function.

      - I'm not sure "spring-loaded" is a good term here: the tension force of a coiled tail is fairly negligible since there's little internal force actively trying to straighten it.

      Rewrote this part to highlight that fish spring toward the prey, without the implication that tension forces in the tail are responible for the movement. However, we are not aware of any literature measuring passive forces within the tail of fishes. Presumably the notochord is relatively stiff and may provide an internal force trying to straighten the tail.

      - There are now several statements for which no direct evidence is presented. We shouldn't have to rely on the author's qualitative impressions of what they observed: show us quantitative analysis.

      * "often hover"

      * "cichlids often alternate between approaches and hover swims"

      * "over many hundreds of milliseconds"

      * "we have also observed suction captures and ram-like attacks"

      * "may swim backwards"

      * "may expel prey from their mouth"

      * "cichlid captures often occur in two phases"

      Added references to supplementary videos with timestamps to highlight these behaviors.

      - I don't find it plausible that sated fish continue hunting prey that they know they're not going to eat just for the practice.

      Removed the speculation.

      - In Figure 3 is it not possible to include medaka, based on the hand-tracked paramecia?

      The videos are recorded at high frame rate, so it would be a lot of additional work to track these manually. Furthermore, earlier in prey capture it is very difficult to tell by watching videos which prey the medaka are tracking, especially as single paramecia can drift in and out of focus in the videos. Since there is no eye convergence, it is very difficult to ascertain for certain when tracking a given prey begins. In Fig 4, it was only possible to track paramecia by hand since it is immediately prior to the strike and from the video it is possible to see which paramecium the fish targeted. Our analyses of heading changes was performed over the 200 ms prior to a strike, which we think is a conservative enough cutoff to say that fish were probably pursuing prey in this window (it is shorter than the average behavioral syllable duration in medaka).

      - Figure 3 (particularly 3D) suggests the interesting finding that LA essentially only hunt prey that is directly in front of them (unlike LO and NM, the distribution of prey azimuth actually seems to broaden slightly over the duration of hunting events).

      This is worthy of discussion.

      We offer a suggestion for the many instances of prey capture being initiated in the central visual field in LA later in the manuscript when we discuss spitting behavior. We have added text to make this point earlier in the manuscript. The increase in azimuthal range at the end of prey capture may be due to abort swims (e.g. supp. vid. 1, 00:21). The widening of azimuthal angles is present in LO and NM also and is not unique to LA.

      - The reference Ding et al (2016) is not in the reference list.

      Wrong paper was referenced. Should be Ding 2019, which has been added to bibliography.

      - I am not convinced that medaka exhibit a unique side-swing behavior. I agree there is this tendency in the example movie, however, the results of the quantification (Figure 4) are underwhelming. First, cluster 5 in 4K appears to include a proportion of cases from LA and AB. These proportions may be small, but anything above zero means this is not unique to medaka. Second, the heading angle (4N) starts at 4 degrees for LA and 8 degrees for medaka. This difference is genuine but very small, much smaller than what's drawn in the schematic (4M). I'm not sure it's justifiable to call a difference of 4 degrees a qualitatively different strategy.

      We have changed the text to highlight that side swing is highly enriched in medaka. Comparing 4J to 3B we would argue that there is a qualitative difference in the strategy used to capture prey in the cichlid larvae we study here and medaka. We agree that further work is required to understand distance estimation behaviors in different species. In this manuscript, we use heading angle as a proxy for how prey position might change on the retina over a hunting sequence. But as the heading and distance are changing over time, the actual change in angle on the retina for prey may be much larger than the ~8 degree shift reported here. The actual position of the prey is also important here, which, for reasons mentioned above, we could not track. Given the final location of prey in the visual field prior to the strike (Fig 4J), the most parsimonious explanation of the data is that the prey is always in the monocular visual field. In cichlids, the prey is more-or-less centered in the 200 ms preceding the strike. While it is true theat the absolute difference in heading is 4 degrees, when converted to an angular velocity (4N, right), the medaka (OL) effectively rotate twice as fast as LA (20 deg/s vs 40 deg/s), which we think is a substantial difference and evidence of a different targeting strategy.

      - 4K: This is referred to in the caption as a confusion matrix, which it's not.

      Fixed.

      - 4N right panel: how many fish contributed to the points shown?

      Added to figure legend (n=113, LA; n=36, OL). Same data in left and right panels.

      - In the Discussion it is hypothesized that medaka use their lateral line in hunting more than in other species. Testing this hypothesis (even just compared to one other species) would be fairly straightforward, and would add significant interest to the paper overall.

      We agree that this is an interesting experiment for follow up studies, but it is beyond the scope of the current manuscript as we do not have the appropriate animal license for this experiment.

      Reviewer 2:

      The paper is rather descriptive in nature, although more context is provided in the discussion. Most figures are great, but I think the authors could add a couple of visual aids in certain places to explain how certain components were measured.

      Added new supplemental figure (Supp Fig 2)

      Figure 1B- it could be useful to add zebrafish and medaka to the scientific names (I realize it's already in Figure A but I found myself going back and forth a couple of times, mostly trying to confirm that O. latipes is medaka).

      Added common names to 1B, sprinkled reminders of OL/medaka throughout text.

      Figure 1G. I wasn't sure how to interpret the eye angle relative to the midline. Can they rotate their eyes or is this due to curvature in the 'upper' body of the fish? Adding a schematic figure or something like that could help a reader who is not familiar with these methods. Related to this, I was a bit confused by Figure 2A. After reading the methods section, I think I understand - but I little cartoon to describe this would help. It also reminds the reader (especially if they don't work with fish) that fish eyes can rotate. I also wanted to note that initially, I thought convergence was a measure of how the two eyes were positioned relative to the prey given the emphasis given on binocular vision, and only after reading certain sections again did I realize convergence was a measure of eye rotation/movement.

      New supplemental figure explaining how eye tracking is performed

      Figure 3. It was not immediately clear to me what onset, middle, and end represented - although it is explained in the caption. I think what tripped me up is the 'eye convergence' title in the top right corner of Figure 3A.

      Updated figure with schematic illustrating that time is measured relative to eye convergence onset and end.

      The result section about attack swim, S-strike, capture spring, etc. was a bit confusing to read and could benefit from a couple of concise descriptions of these behaviors. For example, I am not familiar with the S strike but a couple of paragraphs into this section, the reader learns more about the difference between S strike vs. attack swim. This can be mentioned in the first paragraph when these distinct behaviors are mentioned.

      Added description of behavior earlier in text.

      Figure 4. Presents lots of interesting data! I wonder if using Figure 1E could help the reader better understand how these measurements were taken.

      New supplemental figure added, explaining how tail tracking is performed.

      I probably overlooked this, but I wonder why so many panels are just focused on one species.

      Added explanation to the text.

      Is the S-shaped capture strategy the same as an S strike?

      Clarified in text to say "S-strike-like". This is a description of prey capture from adult largemouth bass in New et al. (2002). From the still frames shown in that paper, the kinematics looks similar to an S-strike or capture spring. The important point we wish to make is that tail is coiled in an S-shape prior to a strike, which indicates this that a kinematically similar behavior exists fishes beyond just larval cichlids and zebrafish.

      At the end of the page, when continuous swimming versus interrupted swimming is discussed, please remind the reader that medaka shows more continuous swimming (longer bouts).

      Added "while medaka swim continuously with longer bouts ("gliding")".

      After reading the discussion, it looks like many findings are unique. For example, given that medaka is such a popular model species in biology, it strikes me that nobody has ever looked into their hunting movements before. If their findings are novel, perhaps they should state so it is clear that the authors are not ignoring the literature.

      We have highlighted what we believe to be the novelty of our findings (first description of prey capture in larval cichlids and medaka). To our knowledge, we are first to describe hunting in medaka; but there is an extensive literature on medaka dating back to the early 20th century, some of which is only published in Japanese. We have done our best to review the literature, but we cannot rule out that there are papers that we missed. No English language article or review we found mentions literature on hunting behavior in medaka larvae.

      Reviewer 3:

      More evidence is needed to assess the types of visual monocular depth cues used by medaka fish to estimate prey location, but that is beyond the scope of this compelling paper. For example, medaka may estimate depth through knowledge of expected prey size, accommodation, defocus blur, ocular parallax, and/or other possible algorithms to complement cues from motion parallax.

      Added sentence to discussion highlighting that other cues may also contribute to distance estimation in cichlids and medakas. Follow-up studies will require new animal license.

      None. It's quite nice, timely, and thorough work! For future work, one could use 3D pose estimation of eye and prey kinematics to assess the dynamics of the 2D image (prey and background) cast onto the retina. This sort of representation could be useful to infer which monocular depth cues may be used by medaka during hunting.

      Great suggestion for follow up studies. Bolton et al. and Mearns et al. both find changes in z associated with prey capture, and it would be interesting to see how other fish species use the full 3-dimensional water column during prey capture, especially considering the diversity of hunting strategies in adult cichlids (ranging from piscivorous species, like LA, to algar grazers).

      In Figure 4N, you use "change in heading leading up to a strike as a proxy for the change in visual angle of the prey for cichlids and medaka." This proxy makes sense, but you also have the eye angles and (in some cases) the prey positions. One could estimate the actual change in visual angle from this information, which would also allow one to measure whether the fish are trying to stabilize the position of the prey on a high-acuity patch of the retina during the final moments of the hunt. This information may also shed light on which monocular depth cues are used.

      As addressed in comment to reviewer 1, this would require actually manually tracking individual paramecia over hundreds of frames. It is not possible to determine exactly when hunting begins in medaka, and it is prone to errors if medaka switch between targets over the course of a hunting episode. This question is better addressed with psychophysics experiments in embedded animals where it is possible to precisely control the stimulus, but this requires new animal licenses and is beyond the scope of this paper.

      In Figure 5, you could place the prey object a little farther from the D. rerio fish for the S-strike diagram.

      Fixed.

      Figure 4F legend should read "...at the peak of each bout."

      Fixed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Thank you for your constructive feedback and recognition of our work. We followed your suggestion and improved the accuracy of the language used to interpret some of our findings. 

      Summary:

      The present study by Mikati et al demonstrates an improved method for in-vivo detection of enkephalin release and studies the impact of stress on the activation of enkephalin neurons and enkephalin release in the nucleus accumbens (NAc). The authors refine their pipeline to measure met and leu enkephalin using liquid chromatography and mass spectrometry. The authors subsequently measured met and leu enkephalin in the NAc during stress induced by handling, and fox urine, in addition to calcium activity of enkephalinergic cells using fiber photometry. The authors conclude that this improved tool for measuring enkephalin reveals experimenter handling stress-induced enkephalin release in the NAc that habituates and is dissociable from the calcium activity of these cells, whose activity doesn't habituate. The authors subsequently show that NAc enkephalin neuron calcium activity does habituate to fox urine exposure, is activated by a novel weigh boat, and that fox urine acutely causes increases in met-enk levels, in some animals, as assessed by microdialysis.

      Strengths:

      A new approach to monitoring two distinct enkephalins and a more robust analytical approach for more sensitive detection of neuropeptides. A pipeline that potentially could help for the detection of other neuropeptides.

      Weaknesses:

      Some of the interpretations are not fully supported by the existing data or would require further testing to draw those conclusions. This can be addressed by appropriately tampering down interpretations and acknowledging other limitations the authors did not cover brought by procedural differences between experiments.

      We have taken time to go through the manuscript ensuring we are more detailed and precise with our interpretations as well as appropriately acknowledging limitations. 

      Reviewer #2 (Public Review):

      Thank you for your constructive and thorough assessment of our work. In our revised manuscript, we adjusted the text to reflect the references you mentioned regarding the methionine oxidation procedure. Additionally, we expanded the methods section to include the key details of the statistical tests and procedures that you outlined. 

      Summary:

      The authors aimed to improve the detection of enkephalins, opioid peptides involved in pain modulation, reward, and stress. They used optogenetics, microdialysis, and mass spectrometry to measure enkephalin release during acute stress in freely moving rodents. Their study provided better detection of enkephalins due to the implementation of previously reported derivatization reaction combined with improved sample collection and offered insights into the dynamics and relationship between Met- and Leu-Enkephalin in the Nucleus Accumbens shell during stress.

      Strengths:

      A strength of this work is the enhanced opioid peptide detection resulting from an improved microdialysis technique coupled with an established derivatization approach and sensitive and quantitative nLC-MS measurements. These improvements allowed basal and stimulated peptide release with higher temporal resolution, lower detection thresholds, and native-state endogenous peptide measurement.

      Weaknesses:

      The draft incorrectly credits itself for the development of an oxidation method for the stabilization of Met- and Leu-Enk peptides. The use of hydrogen peroxide reaction for the oxidation of Met-Enk in various biological samples, including brain regions, has been reported previously, although the protocols may slightly vary. Specifically, the manuscript writes about "a critical discovery in the stabilization of enkephalin detection" and that they have "developed a method of methionine stabilization." Those statements are incorrect and the preceding papers that relied on hydrogen peroxide reaction for oxidation of Met-Enk and HPLC for quantification of oxidized Enk forms should be cited. One suggested example is Finn A, Agren G, Bjellerup P, Vedin I, Lundeberg T. Production and characterization of antibodies for the specific determination of the opioid peptide Met5-Enkephalin-Arg6-Phe7. Scand J Clin Lab Invest. 2004;64(1):49-56. doi: 10.1080/00365510410004119. PMID: 15025428.

      Thank you for highlighting this. It was not our intention to imply that we developed the oxidation method, rather that we were able improve the detection of metenkephalin by oxidation of the methionine without compromising the detection resolution of leu-enkephalin, enabling the simultaneous detection of both peptides. We have addressed this is the manuscript and included the suggested citation. 

      Another suggestion for this draft is to make the method section more comprehensive by adding information on specific tools and parameters used for statistical analysis:

      (1) Need to define "proteomics data" and explain whether calculations were performed on EIC for each m/z corresponding to specific peptides or as a batch processing for all detected peptides, from which only select findings are reported here. What type of data normalization was used, and other relevant details of data handling? Explain how Met- and Leu-Enk were identified from DIA data, and what tools were used.

      Thank you for pointing out this source of confusion. We believe it is because we use a different DIA method than is typically used in other literature. Briefly, we use a DIA method with the targeted inclusion list to ensure MS2 triggering as opposed to using large isolation widths to capture all precursors for fragmentation, as is typically done with MS1 features. For our method, MS2 is triggered based on the 4 selected m/z values (heavy and light versions of Leu and Met-Enkephalin peptides) at specific retention time windows with isolation width of 2 Da; regardless of the intensity of MS1 of the peptides. 

      (2) Simple Linear Regression Analysis: The text mentions that simple linear regression analysis was performed on forward and reverse curves, and line equations were reported, but it lacks details such as the specific variables being regressed (although figures have labels) and any associated statistical parameters (e.g., R-squared values). 

      Additional detail about the linear regression process was added to the methods section, please see lines 614-618. The R squared values are also now shown on the figure. 

      ‘For the forward curves, the regression was applied to the measured concentration of the light standard as the theoretical concentration was increased. For plotting purposes, we show the measured peak area ratios for the light standards in the forward curves. For the reverse curves, the regression was applied to the measured concentration of the heavy standard, as the theoretical concentration was varied.’

      (3) Violin Plots: The proteomics data is represented as violin plots with quartiles and median lines. This visual representation is mentioned, but there is no detail regarding the software/tools used for creating these plots.

      We used Graphpad Prism to create these plots. This detail has been added to the statistical analysis section. See line 630.

      (4) Log Transformation: The text states that the data was log-transformed to reduce skewness, which is a common data preprocessing step. However, it does not specify the base of the logarithm used or any information about the distribution before and after transformation.

      We have added the requested details about the log transformation, and how the data looked before and after, into the statistical analysis section. We followed convention that the use of log is generally base 10 unless otherwise specified as natural log (base 2) or a different base. See lines 622-625

      ‘The data was log10 transformed to reduce the skewness of the dataset caused by the variable range of concentrations measured across experiments/animals. Prior to log transformation, the measurements failed normality testing for a Gaussian distribution. After the log transformation, the data passed normality testing, which provided the rationale for the use of statistical analyses that assume normality.’

      (5) Two-Way ANOVA: Two-way ANOVA was conducted with peptide and treatment as independent variables. This analysis is described, but there is no information regarding the software or statistical tests used, p-values, post-hoc tests, or any results of this analysis.

      Information about the two-way ANOVA analysis has been added to the statistical analysis section. Additionally, more detailed information has been added to the figure legends about the statistical results. Please see lines 625-628.

      ‘Two-way ANOVA testing with peptide (Met-Enk or Leu-Enk) and treatment (buffer or stress for example) as the two independent variables. Post-hoc testing was done using Šídák's multiple comparisons test and the p values for each of these analyses are shown in the figures (Figs. 1F, 2A).’ 

      (6) Paired T-Test: A paired t-test was performed on predator odor proteomic data before and after treatment. This step is mentioned, but specific details like sample sizes, and the hypothesis being tested are not provided.

      The sample size is included in the figure legend to which we have included a reference. We have also included the following text to highlight the purpose of this test. See lines 628-630

      A paired t-test was performed on the predator odor proteomic data before and after odor exposure to test that hypothesis that Met-Enk increases following exposure to predator odor  (Fig. 3F). These analyses were conducted using Graphpad Prism.

      (7) Correlation Analysis: The text mentions a simple linear regression analysis to correlate the levels of Met-Enk and Leu-Enk and reports the slopes. However, details such as correlation coefficients, and p-values are missing.

      We apologize for the use of the word correlation as we think it may have caused some confusion and have adjusted the language accordingly. Since this was a linear regression analysis, there is no correlation coefficient. The slope of the fitted line is reported on the figures to show the fitted values of Met-Enk to Leu-Enk. 

      (8) Fiber Photometry Data: Z-scores were calculated for fiber photometry data, and a reference to a cited source is provided. This section lacks details about the calculation of zscores, and their use in the analysis. 

      These details have been added to the statistical analysis section. See lines 634-637

      ‘For the fiber photometry data, the z-scores were calculated as described in using GuPPy which is an open-source python toolbox for fiber photometry analysis. The z-score equation used in GuPPy is z=(DF/F-(mean of DF/F)/standard deviation of DF/F) where F refers to fluorescence of the GCaMP6s signal.’

      (9) Averaged Plots: Z-scores from individual animals were averaged and represented with SEM. It is briefly described, but more details about the number of animals, the purpose of averaging, and the significance of SEM are needed.

      We have added additional information about the averaging process in the statistical analysis section. See lines 639-643.

      ‘The purpose of the averaged traces is to show the extent of concordance of the response to experimenter handling and predator odor stress among animals with the SEM demonstrating that variability. The heatmaps depict the individual responses of each animal. The heatmaps were plotted using Seaborn in Python and mean traces were plotted using Matplotlib in Python.’

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper.

      We have taken this opportunity to improve our manuscript following comments from all the reviewers that we hope has resulted in a manuscript with a more objective interpretation of results. 

      Reviewer #3 (Public Review):

      Thank you for your thoughtful review of our work. To clarify some of the points you raised, we revised the manuscript to include more detail on how we distinguish between the oxidized endogenous and standard signal, as well as refine the language concerning the spatial resolution. We also edited the manuscript regarding the concentration measurements. We conducted technical replicates, so we appreciate you raising this point and clarify that in the main text. 

      Summary:

      This important paper describes improvements to the measurement of enkephalins in vivo using microdialysis and LC-MS. The key improvement is the oxidation of met- to prevent having a mix of reduced and oxidized methionine in the sample which makes quantification more difficult. It then shows measurements of enkephalins in the nucleus accumbens in two different stress situations - handling and exposure to predator odor. It also reports the ratio of released met- and leu-enkephalin matching what is expected from the digestion of proenkephalin. Measurements are also made by photometry of Ca2+ changes for the fox odor stressor. Some key takeaways are the reliable measurement of met-enkephalin, the significance of directly measuring peptides as opposed to proxy measurements, and the opening of a new avenue into the research of enkephalins due to stress based on these direct measurements.

      Strengths:

      -Improved methods for measurement of enkephalins in vivo.

      -Compelling examples of using this method.

      -Opening a new area of looking at stress responses through the lens of enkephalin concentrations.

      Weaknesses:

      (1) It is not clear if oxidized met-enk is endogenous or not and this method eliminates being able to discern that.

      We clarified our wording in the text copied below to provide an explanation on how we distinguish between the two. Even after oxidation, the standard signal has a higher m/z ratio due to the presence of the Carbon and Nitrogen isotopes as described in the Chemicals section of the methods ‘For Met Enkephalin, a fully labeled L-Phenylalanine (<sup>13</sup>C<sub>9</sub>, <sup>15</sup>N) was added (YGGFM). The resulting mass shift between the endogenous (light) and heavy isotope-labeled peptide are 7Da and 10Da, respectively.’, so they can still be differentiated from the endogenous signal. We have clarified the language in the results section. See lines 82-87. 

      ‘After each sample collection, we add a consistent known concentration of isotopically labeled internal standard of Met-Enk and Leu-Enk of 40 amol/sample to the collected ISF for the accurate identification and quantification of endogenous peptide. These internal standards have a different mass/charge (m/z) ratio than endogenous Met- and Leu-Enk. Thus, we can identify true endogenous signal for Met-Enk and Leu-Enk (Suppl Fig. 1A,C) versus noise, interfering signals, and standard signal (Suppl. Fig. 1B,D).’

      (2) It is not clear if the spatial resolution is really better as claimed since other probes of similar dimensions have been used.

      Apologies for any confusion here. To clarify we primarily state that our approach improves temporal resolution and in a few cases refer to improved spatiotemporal resolution, which we believe we show. The dimensions of the microdialysis probe used in these experiments allow us to target the nucleus accumbens shell and as well as being smaller – especially at the membrane level - than a fiber photometry probe. 

      (3) Claims of having the first concentration measurement are not quite accurate.

      Thank you for your feedback. To clarify, we do not claim that we have the first concentration measurements, rather we are the first to quantify the ratio of Met-Enk to Leu-Enk in vivo in freely behaving animals in the NAcSh. 

      (4) Without a report of technical replicates, the reliability of the method is not as wellevaluated as might be expected.

      We have added these details in the methods section, please see lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      • The authors should provide appropriate citations of a study that has validated the Enkephalin-Cre mouse line in the nucleus accumbens or provide verification experiments if they have any available.

      Thank you for your comment. We have added a reference validating the Enk-Cre mouse line in the nucleus accumbens to the methods section and is copied here. 

      D.C. Castro, C.S. Oswell, E.T. Zhang, C.E. Pedersen, S.C. Piantadosi, M.A. Rossi, A.C. Hunker, A. Guglin, J.A. Morón, L.S. Zweifel, G.D. Stuber, M.R. Bruchas, An endogenous opioid circuit determines state-dependent reward consumption, Nature 2021 598:7882 598 (2021) 646–651. https://doi.org/10.1038/s41586-02104013-0.

      • Better definition of the labels y1,y2,b3 in Figures 1 and S1 would be useful. I may have missed it but it wasn't described in methods, results, or legends.

      Thank you for this comment. We have added this information to Fig.1 legend ‘Y1, y2, b3 refer to the different elution fragments resulting from Met-Enk during LC-MS.

      • It is interesting that the ratio of KCl-evoked release is what changes differentially for Met- vs Leu. Leu enk increases to the range of met-enk. There is non-detectable or approaching being non-detectable leu-enk (below the 40 amol / sample limit of quantification) in most of the subjects that become apparent and approach basal levels of met-enkephalin. This suggests that the K+ evoked response may be more pronounced for leu-enk. This is something that should be considered for further analysis and should be discussed.

      Thank you for this astute observation, and you make a great point. We have added some discussion of this finding in the results and discussion sections see lines 111112 and lines 253-257. 

      ‘Interestingly, Leu-Enk showed a greater fold change compared to baseline than did Met-Enk with the fold changes being 28 and 7 respectively based on the data in Fig.1F.’

      ‘We also noted that Leu-Enk showed a greater fold increase relative to baseline after depolarization with high K+ buffer as compared to Met-Enk. This may be due to increased Leu-Enk packaging in dense core vesicles compared to Met-Enk or due to the fact that there are two distinct precursor sources for Leu-Enk, namely both proenkephalin and prodynorphin while Met-Enk is mostly cleaved from proenkephalin (see Table 1 [48]).’

      • For example in 2E, it would be helpful to label in the graph axis what samples correspond to the manipulation and also in the text provide the reader with the sample numbers. The authors interpret the relationship between the last two samples of baseline and posthandling stress as the following in the figure legend "the concentration released in later samples is affected; such influence suggests that there is regulation of the maximum amount of peptide to be released in NAcSh. E. The negative correlation in panel d is reversed by using a high K+ buffer to evoke Met-Enk release, suggesting that the limited release observed in D is due to modulation of peptide release rather than depletion of reserves." However, the correlations are similar between 2D and E and it appears that two mice are mediating the difference between the two groups. The appropriate statistical analysis would be to compare the regressions of the two groups. Statistics for the high K+ (and all other graphs where appropriate) need to be reported, including the r2 and p-value.

      Thank you for your constructive critique. To elucidate the effect of high K+, we have plotted the regression line and reported the slope for Fig. 2E. Notably, the slope is reduced by a factor of 2 and appears to be driven by a large subset of the animals. The statistics for the high K+ graph are shown on the figure (Fig 1F) which test the hypothesis of whether high K+ leads to the release of Leu-Enk and Met-Enk respectively compared to baseline with aCSF. We have added the test statistics to the figure legend for additional clarity. Fig. 1G has no statistics because it is only there to elucidate the ratio between Met-Enk and Leu-Enk in the same samples. We did not test any hypotheses related to whether there are differences between their levels as that is not relevant to our question. The correlation on the same data is depicted in Fig. 1H, and we have added the R<sup>2</sup> value per your request. 

      • The interpretation that handling stress induces enkephalin release from microdialysis experiments is also confounded by other factors. For instance, from the methods, it appears that mice were connected and sample collection started 30 min after surgery, therefore recovery from anesthesia is also a confounding variable, among other technical aspects, such as equilibration of the interstitial fluid to the aCSF running through the probe that is acting as a transmitter and extracellular molecule "sink". Did the authors try to handle the mice post hookup similar to what was done with photometry to have a more direct comparison to photometry experiments? This procedural difference, recording from recently surgerized animals (microdialysis) vs well-recovered animals with photometry should be mentioned in addition to the other caveats the authors mention.

      Thank you for your comment. We are aware of this technical limitation, and it is largely why we sought to conduct the fiber photometry experiments to get at the same question. As you requested, we have included additional language in the discussion to acknowledge this limitation and how we chose to address it by measuring calcium activity in the enkephalinergic neurons, which would presumably be the same cell population whose release we are quantifying using microdialysis. See lines 262-273.  

      ‘Our findings showed a robust increase in peptide release at the beginning of experiments, which we interpreted as due to experimenter handling stress that directly precedes microdialysis collections. However, there are other technical limitations to consider such as the fact that we were collecting samples from mice that were recently operated on. Another consideration is that the circulation of aCSF through the probe may cause a sudden shift in oncotic and hydrostatic forces, leading to increased peptide release to the extracellular space. As such, we wanted to examine our findings using a different technique, so we chose to record calcium activity from enkephalinergic neurons - the same cell population leading to peptide release. Using fiber photometry, we showed that enkephalinergic neurons are activated by stress exposure, both experimenter handling and fox odor, thereby adding more evidence to suggest that enkephalinergic neurons are activated by stress exposure which could explain the heightened peptide levels at the beginning of microdialysis experiments.’

      • The authors should provide more details on handling stress manipulation during photometry. For photometry what was the duration of the handling bout, what was the interval between handling events, and can the authors provide a description of what handling entailed? Were mice habituated to handling days before doing photometry recording experiments?

      Thank you for your suggestion. We have addressed all of your points in the methods section. See lines 564-570. 

      ‘The handling bout which mimicked traditional scruffing lasted about 3-5 seconds. The mouse was then let go and the handling was repeated another two times in a single session with a minimum of 1-2 minutes between handling bouts. Mice were habituated to this manipulation by being attached to the fiber photometry rig, for 3-5 consecutive days prior to the experimental recording. Additionally, the same maneuver was employed when attaching/detaching the fiber photometry cord, so the mice were subjected to the same process several times.’

      • For the novel weigh boat experiments, the authors should explicitly state when these experiments were done in relation to the fox urine, was it a different session or the same session? Were they the same animals? Statements like the following (line 251) imply it was done in the same animals in the same session but it should be clarified in the methods "We also showed using fiber photometry that the novelty of the introduction of a foreign object to the cage, before adding fox odor, was sufficient to activate enkephalinergic neurons."

      As shown in supplementary figure 4, individual animal data is shown for both water and fox urine exposure (overlaid) to depict whether there were differences in their responses to each manipulation – in the same animal. And yes, you are correct, the animals were first exposed to water 3 times in the recording session and then exposed to fox urine 3 times in the same session. We have added that to the methods section describing in vivo fiber photometry. See lines 575-576.  

      • Statistical testing would be needed to affirm the conclusions the authors draw from the fox urine and novel weigh boat experiments. For example, it shows stats that the response attenuates, that it is not different between fox urine and novel (it looks like the response is stronger to the fox urine when looking at the individual animals), etc. These data look clear but stats are formally needed. Formal statistics are also missing in other parts of the manuscript where conclusions are drawn from the data but direct statistical comparisons are not included (e.g. Fig 2.G-I).

      The photometry data is shown as z-scores which is a formal statistical analysis. ANOVA would be inappropriate to run to compare z-scores. We understand that this is erroneously done in fiber photometry literature, however, it remains incorrect. The z-scores alone provide all the information needed about the deviation from baseline. We understand that this is not immediately clear to readers, and we thank you for allowing us to explain why this is the case. We have added test statistics to figure legends where hypothesis testing was done and p-values were reported. 

      • Did the authors try to present the animals with repeated fox urine exposure to see if this habituates like the photometry?

      No, we did not do that experiment due to the constrained timing within which we had to run our microdialysis/LC-MS timeline, but it is a great point for future exploration. 

      • It would be useful to present the time course of the odor experiment for the microdialysis experiment.

      The timeline is shown in Fig.1a and Fig.3e. To reiterate, each sample is 13 minutes long.

      • Can the authors determine if differences in behavior (e.g. excessive avoidance in animals with with one type of response) or microdialysis probe location dictate whether animals fall into categories of increased release, no release, or no-detection? From the breakdown, it looks like it is almost equally split into three parts but the authors' descriptions of this split are somewhat misleading (line 210). " The response to predator odor varies appreciably: although most animals show increased Met-Enk release after fox odor exposure, some show continued release with no elevation in Met-Enk levels, and a minority show no detectable release".

      Thank you for your constructive feedback. We do not believe the difference in behavior is correlated with probe placement. The hit map can be found in suppl. Fig 3 and shows that all mice included in the manuscript had probes in the NAcSh. We purposely did not distinguish between dorsal and ventral because of our 1 mm membrane would make it hard to presume exclusive sampling from one subregion. That is a great point though, and we have thought about it extensively for future studies. We have edited the language to reflect the almost even split of responses for Met-Enk and appreciate you pointing that out. 

      • Overall, given the inconsistencies in experimental design and overall caveats associated, I think the authors are unable to draw reasonable conclusions from the repeated stressor experiments and something they should either consider is not trying to draw strong conclusions from these observations or perform additional experiments that provide the grounds to derive those conclusions.

      We have included additional language on the caveats of our study, and our use of a dual approach using fiber photometry and microdialysis was largely driven by a

      desire to offer additional support of our conclusions. We expected pushback about our conclusions, so we wanted to offer a secondary analysis using a different technique to test our hypothesis. To be honest the tone of this comment and content is not particularly constructive (especially for trainees) nor does it offer a space to realistically address anything. This work took multiple years to optimize, it was led by a graduate student, and required a multidisciplinary team. As highlighted, we believe it offers an important contribution to the literature and pushes the field of peptide detection forward.  

      Reviewer #2 (Recommendations For The Authors):

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper. The manuscript contains statements like "we are the first to confirm," which can be challenging to substantiate and may not significantly enhance the paper. It's essential to ensure that novelty statements are well-founded. For example, the release of enkephalins from other brain regions after stress exposure is well-documented but not addressed in the paper. Similarly, the role of the NA shell in stress has been extensively studied but lacks coverage in this manuscript.

      We have edited the language to reflect your feedback. We have also included relevant literature expanding on the demonstrated roles of enkephalins in the literature. We would like to note that most studies have focused on chronic stress, and we were particularly interested in acute stress. See lines 129-134.

      ‘These studies have included regions such as the locus coeruleus, the ventral medulla, the basolateral nucleus of the amygdala, and the nucleus accumbens core and shell. Studies using global knockout of enkephalins have shown varying responses to chronic stress interventions where male knockout mice showed resistance to chronic mild stress in one study, while another study showed that enkephalin-knockout mice showed delayed termination of corticosteroid release. [33,34]’ 

      Finally, not a weakness but a clarification suggestion: the method description mentions the use of 1% FA in the sample reconstitution solution and LC solvents, which is an unusually high concentration of acid. If this concentration is intentional for maintaining the peptides' oxidation state, it would be beneficial to mention this in the text to assist readers who might want to replicate the method.

      This is correct and has been clarified in the methods section

      Reviewer #3 (Recommendations For The Authors):

      -The Abstract should state the critical improvements that are made. Also, quantify the improvements in spatiotemporal resolution.

      Thank you for your comment. We have edited the abstract to reflect this. 

      - The use of "amol/sample" as concentration is less informative than an SI units (e.g., pM concentration) and should be changed. Especially since the volume used was the same for in vivo sampling experiments.

      Thank you for your comment. We chose to report amol/sample because we are measuring such a small concentration and wanted to account for any slight errors in volume that can make drastic differences on reported concentrations especially since samples are dried and resuspended.  

      -Please check this sentence: "After each collection, the samples were spiked with 2 µL of 12.5 fM isotopically labeled Met-Enkephalin and Leu-Enkephalin" This dilution would yield a concentration of ~2 fM. In a 12 uL sample, that would be ~0.02 amol, well below the detection limit. (note that fM would femtomolar concentration and fmol would be femtomoles added).

      -"liquid chromatography/mass spectrometry (LC-MS) [9-12]"... Reference 9 is a RIA analysis paper, not LC-MS as stated.

      Thank you for catching these. We have corrected the unit and citation. 

      -Given that improvements in temporal resolution are claimed, the lack of time course data with a time axis is surprising. Rather, data for baseline and during treatment appear to be combined in different plots. Time course plots of individuals and group averages would be informative.

      Due to the expected variability between individual animal time course data, where for example, we measure detectable levels in one sample followed by no detection, it was very difficult to combine data across time. Therefore, to maximize data inclusion from all animals that showed baseline measurements and responses to individual manipulations, we opted to report snapshot data. Our improvement in temporal resolution refers to the duration of each sample rather than continuous sampling, so those two are unrelated. Thank you for your feedback and allowing us to clarify this.

      - I do not understand this claim "We use custom-made microdialysis probes, intentionally modified so they are similar in size to commonly used fiber photometry probes to avoid extensive tissue damage caused by traditional microdialysis probes (Fig. 1B)." The probes used are 320 um OD and 1 mm long. This is not an uncommon size of microdialysis probes and indeed many are smaller, so is their probe really causing less damage than traditional probes?

      Thank you for your comment. We are only trying to make the point that the tissue damage from these probes is comparable to commonly used fiber photometry probes. We only point that out because tissue damage is used as a point to dissuade the usage of microdialysis in some literature, and we just wanted to disambiguate that. We have clarified the statement you pointed out.  

      -The oxidation procedure is a good idea, as mentioned above. It would be interesting to compare met-enk with and without the oxidation procedure to see how much it affects the result (I would not say this is necessary though). It is not uncommon to add antioxidants to avoid losses like this. Also, it should be acknowledged that the treatment does prevent the detection of any in vivo oxidation, perhaps that is important in met-enk metabolism?

      The comparison between oxidized and unoxidized Met-Enk detection is in figure 1C. 

      -It would be a best practice to report the standard deviation of signal for technical replicates (say near in vivo concentrations) of standards and repeated analysis of a dialysate sample to be able to understand the variability associated with this method. Similarly, an averaged basal concentration from all rats.

      Thank you for your comment. We have included a table showing example quality control standard injections from 4 randomly selected experiments included in the manuscript that were run before and after each experiment and descriptive statistics associated with these technical replicates. We also added some detail to the methods section to describe how quality control is done. See lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      EDITORS NOTE

      Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

      Thank you for your suggestion. We have included more detail about statistical analysis in the figure legends per this comment and reviewer comments.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Responses to Reviewer #1:

      We thank the reviewer for these additional comments, and more generally for their extensive engagement with our work, which is greatly appreciated. Here, we respond to the three points in their latest review in turn.

      The results of these experiments support a modest but important conclusion: If sub-optimal methods are used to collect retrospective reports, such as simple yes/no questions, inattentional blindness (IB) rates may be overestimated by up to ~8%.

      It is true, of course, that we think the field has overstated the extent of IB, and we appreciate the reviewer characterizing our results as important along these lines. Nevertheless, we respectfully disagree with the framing and interpretation the reviewer attaches to them. As explained in our previous response, we think this interpretation — and the associated calculations of IB overestimation ‘rates’ — perpetuates a binary approach to perception and awareness which we regard as mistaken.

      A graded approach to IB and visual awareness 

      Our sense is that many theorists interested in IB have conceived of perception and awareness as ‘all or nothing’: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. This is implicit in the reviewer’s characterization of our results as simply indicating that fewer subjects fail to see the critical stimulus than previously assumed. To think that way is precisely to assume the orthodox binary position about perception, i.e., that any given subject can neatly be categorized into one of two boxes, saw or didn’t see.

      Our perspective is different. We think there can be degraded forms of perception and awareness that fall neatly into neither of the categories “saw the stimulus perfectly clearly” or “saw nothing at all”. On this graded conception, the question is not: “What proportion of subjects saw the stimulus?” but: “What is the sensitivity of subjects to the stimulus?” This is why we prefer signal detection measures like d′ over % noticing and % correct. This powerful framework has been successful in essentially every domain to which it has been applied, and we think perception and visual awareness are no exception. We understand that the reviewer may not think the same way about this foundational issue, but since part of our goal is to promote a graded approach to perception, we are keen to highlight our disagreement here and so resist the reviewer’s interpretation of our results (even to the extent that it is a positive one!).

      Finally, we note that given this perspective, we are correspondingly inclined to reject many of the summary figures following below in Point (1) by the reviewer. These calculations (given in terms of % noticing and not noticing) make sense on the binary conception of awareness, but not on the SDT-based approach we favor. We say more about this below. 

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who were correct on the 2AFC above and beyond the pure guess rate. If these 29 subjects are moved from the non-noticer to the noticer group, the corrected rate of IB for Experiment 1 is 20.86% instead of the original 28.61% rate that would have been obtained if only the Y/N question was used. In other words, relying only on the "Y/N did you notice anything" question led to an overestimate of IB rates by 7.75% in Experiment 1.

      In the revised version of their manuscript, the authors provided the data that was missing from the original submission, which allows this same exercise to be carried out on the other 4 experiments.  

      (To briefly interject: All of these data were provided in our public archive since our original submission and remain available at https://osf.io/fcrhu. The difference now is only that they are included in the manuscript itself.)

      Using the same logic as above, i.e., calculating the pure-guess rate on the 2AFC, moving the number of subjects above this pure-guess rate to the non-noticer group, and then re-calculating a "corrected IB rate", the other experiments demonstrate the following:

      Experiment 2: IB rates were overestimated by 4.74% (original IB rate based only on Y/N question = 27.73%; corrected IB rate that includes the 2AFC = 22.99%)

      Experiment 3: IB rates were overestimated by 3.58% (original IB rate = 30.85%; corrected IB rate = 27.27%)

      Experiment 4: IB rates were overestimated by ~8.19% (original IB rate = 57.32%; corrected IB rate for color* = 39.71%, corrected IB rate for shape = 52.61%, corrected IB rate for location = 55.07%)

      Experiment 5: IB rates were overestimated by ~1.44% (original IB rate = 28.99%; corrected IB rate for color = 27.56%, corrected IB rate for shape = 26.43%, corrected IB rate for location = 28.65%)

      *note: the highest overestimate of IB rates was from Experiment 4, color condition, but the authors admitted that there was a problem with 2AFC color guessing bias in this version of the experiment which was a main motivation for running experiment 5 which corrected for this bias.

      Taken as a whole, this data clearly demonstrates that even with a conservative approach to analyzing the combination of Y/N and 2AFC data, inattentional blindness was evident in a sizeable portion of the subject populations. An important (albeit modest) overestimate of IB rates was demonstrated by incorporating these improved methods.

      We appreciate the work the reviewer has put into making these calculations. However, as noted above, such calculations implicitly reflect the binary approach to perception and awareness that we reject. 

      Consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about this is that the subject fully and clearly sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses completely blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a ‘high threshold’ approach to the problem, in contrast to an SDT approach. On an SDT approach — an approach with tremendous evidential support — on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials, they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no nonarbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to the subjects in our experiments (and specifically to our ‘super subject’). Instead of calculating the proportion of subjects who saw or failed to see the stimulus (with some characterized as aware and some as unaware), we think the best way to characterize our results is that, across subjects (and so trials also), there was differential graded access to information from the stimulus, and this is best represented in terms of the group-level sensitivity parameter d′. This is why we frame our results as demonstrating that subjects traditionally considered inattentionally blind exhibit significant residual visual sensitivity to the critical stimulus.

      (2) One of the strongest pieces of evidence presented in this paper was the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result can most likely be explained by measurement error.

      In their revised paper, the authors reported data that was missing from their original submission: the confidence ratings on the 2AFC judgments that followed the initial Y/N question. The most striking indication that this data is likely due to measurement error comes from the number of subjects who indicated that they were highly confident that they didn't notice anything on the critical trial, but then when asked to guess the location of the stimulus, indicated that they were highly confident that the stimulus was on the left (or right). There were 18 subjects (8.82% of the high-confidence non-noticer group) who responded this way. To most readers, this combination of responses (high confidence in correctly judging a stimulus feature that one is highly confident in having not seen at all) indicates that a portion of subjects misunderstood the confidence scales (or just didn't read the questions carefully or made mistakes in their responses, which is common for experiments conducted online).

      In the authors' rebuttal to the first round of peer review, they wrote, "it is perfectly rationally coherent to be very confident that one didn't see anything but also very confident that if there was anything to be seen, it was on the left." I respectfully disagree that such a combination of responses is rationally coherent. The more parsimonious interpretation is that a measurement error occurred, and it's questionable whether we should trust any responses from these 18 subjects.

      In their rebuttal, the authors go on to note that 14 of the 18 subjects who rated their 2AFC with high confidence were correct in their location judgment. If these 14 subjects were removed from analysis (which seems like a reasonable analysis choice, given their contradictory responses), d' for the high-confidence non-noticer group would most likely fall to chance levels. In other words, we would see a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. This corrected Figure 3e would then provide a very nice evidence-based justification for including confidence ratings along with Y/N questions in future inattentional blindness studies.

      We appreciate the reviewer’s highlighting of this particular piece of evidence as amongst our strongest. (At the same time, we must resist its characterization as a “single data point”: it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment.) We also appreciate their raising the issue of measurement error.

      Specifically, the reviewer contends that our finding that even highly confident non-noticers exhibit significant sensitivity is “most likely … explained by measurement error” due to subjects mistakenly inverting our confidence scale in giving their response. In our original reply, we gave two reasons for thinking this quite unlikely; the reviewer has not addressed these in this revised review. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. More importantly, however, we reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the large number of high-confidence “yes” subjects (N = 2677), thus providing a robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low confidence (a pattern which might, though need not, suggest measurement error), we found that the number was tiny. Only 28/2677 (1.05%) of high-confidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. For these reasons, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      The reviewer is correct to note that 18/204 (9%) subjects reported both being highly confident that they didn't notice anything and highly confident in their 2afc judgment, although only 14/18 were correct in this judgment. Should we exclude these 14? Perhaps if we agree with the reviewer that such a pattern of responses is not “rationally coherent” and so must reflect a misconstrual of the scale. But such a pattern is in fact perfectly and straightforwardly intelligible. Specifically, in a 2afc task, two stimuli can individually fall well below a subject’s single interval detection criterion — leading to a high confidence judgment that nothing was presented in either interval. Quite consistent with this, the lefthand stimulus may produce a signal that is much higher than the right-hand stimulus — leading to a high confidence forced-choice judgment that, if something was presented, it was on the left. (By analogy, consider how a radiologist could look at a scan and say the following: “We’re 95% confident there’s no tumor. But even on the 5% chance that there is, our tests completely rule out that it’s a malignant one, so don’t worry.”) 

      (3) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials, one cannot conclude that attention was manipulated in the first place.

      In their rebuttal to the first round of peer review, the authors provided weak justification for not including such a control condition. They cite one paper that argues such control conditions are often used to exclude subjects from analysis (those who fail to notice the stimulus on the control trial are either removed from analysis or replaced with new subjects) and such exclusions/replacements can lead to underestimations of inattentional blindness rates. However, the inclusion of a partial or full attention condition as a control does not necessitate the extra step of excluding or replacing subjects. In the broadest sense, such a control condition simply validates the attention manipulation, i.e., one can easily compare the percent of subjects who answered "yes" or who got the 2AFC judgment correct during the critical trial versus the control trial. The subsequent choice about exclusion/replacement is separate, and researchers can always report the data with and without such exclusions/replacements to remain more neutral on this practice.

      If anyone were to follow-up on this study, I highly recommend including a partial or full attention control condition, especially given the online nature of data collection. It's important to know the percent of online subjects who answer yes and who get the 2AFC question correct when the critical stimulus is attended, because that is the baseline (in this case, the "ceiling level" of performance) to which the IB rates on the critical trial can be compared.

      We agree with the reviewer that future studies could benefit from including a partial or full attention condition. They are surely right that we might learn something additional from such conditions. 

      Where we differ from the reviewer is in thinking of these conditions as “controls” appropriate to our research question. This is why we offered the justification we did in our earlier response. When these conditions are used as controls, they are used to exclude subjects in ways that serve to inflate the biases we are concerned with in our work. For our question, the absence of these conditions does not impact the significance of the findings, since such conditions are designed to answer a question which is not the one at the heart of our paper. Our key claim is that subjects who deny noticing an unexpected stimulus in a standard inattentional blindness paradigm nonetheless exhibit significant residual sensitivity (as well as a conservative bias in their response to the noticing question); the presence or absence of partial- or full-attention conditions is orthogonal to that question.

      Moreover, we note that our tasks were precisely chosen to be classic tasks widely used in the literature to manipulate attention. Thus, by common consensus in the field, they are effective means to soak up attention, and have in effect been tested in partial- and full-attention control settings in a huge number of studies. Second, we think it very doubtful that subjects in a full-attention trial would not overwhelmingly have detected our critical stimuli. The reviewer worries that they might have been “too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.” But consider E5 where the stimulus was a highly salient orange or green shape, present on the screen for 5 seconds. The reviewer also suggests that subjects in the full-attention control might not have detected the stimulus because they “weren't paying enough attention overall”. But evidently if they weren’t paying attention even in the full-attention trial this would be reason for thinking that there was inattentional blindness even in this condition (a point made by White et al. 2018) and certainly not a reason for thinking there was not an attentional effect in the critical trial. Lastly, the reviewer suggests that a full-attention condition would have helped ensure that subjects were following instructions. But we ensured this already by (as per our pre-registration) excluding subjects who performed poorly in the relevant primary tasks.

      Thus, both in principle and in practice, we do not see the absence of such conditions as impacting the interpretation of our findings, even as we agree that future work posing a different research question could certainly learn something from including such conditions.

      Responses to Reviewer #2:

      We note that this report is unchanged from an earlier round of review, and not a response to our significantly revised manuscript. We believe our latest version fully addresses all the issues which the reviewer originally raised. The interested reader can see our original response below. We again thank the reviewer for their previous report which was extremely helpful.

      —-

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study presents valuable findings to the field interested in inattentional blindness (IB), reporting that participants indicating no awareness of unexpected stimuli through yes/no questions, still show above-chance sensitivity to specific properties of these stimuli through follow-up forced-choice questions (e.g., its color). The results suggest that this is because participants are conservative and biased to report not noticing in IB. The authors conclude that these results provide evidence for residual perceptual awareness of inattentionally blind stimuli and that therefore these findings cast doubt on the claim that awareness requires attention. Although the samples are large and the analysis protocol novel, the evidence supporting this interpretation is still incomplete, because effect sizes are rather small, the experimental design could be improved and alternative explanations have not been ruled out.

      We are encouraged to hear that eLife found our work “valuable”. We also understand, having closely looked at the reviews, why the assessment also includes an evaluation of “incomplete”. We gave considerable attention to this latter aspect of the assessment in our revision. In addition to providing additional data and analyses that we believe strengthen our case, we also include a much more substantial review and critique of existing methods in the IB literature to make clear exactly the gap our work fills and the advance it makes. (Indeed, if it is appropriate to say this here, we believe one key aspect of our work that is missing from the assessment is our inclusion of ‘absent’ trials, which is what allows us to make the crucial claims about conservative reporting of awareness in IB for the first time.) Moreover, we refocus our discussion on only our most central claims, and weaken several of our secondary claims so that the data we’ve collected are better aligned with the conclusions we draw, to ensure that the case we now make is in fact complete. Specifically, our two core claims are (1) that there is residual sensitivity to visual features for subjects who would ordinarily be classified as inattentionally blind (whether this sensitivity is conscious or not), and (2) that there is a tendency to respond conservatively on yes/no questions in the context of IB. We believe we have very compelling support for these two core claims, as we explain in detail below and also through revisions to our manuscript.

      Given the combination of strengthened and clarified case, as well as the weakening of any conclusions that may not have been fully supported, we believe and hope that these efforts make our contribution “solid”, “convincing”, or even “compelling” (especially because the “compelling” assessment characterizes contributions that are “more rigorous than the current state-of-the-art”, which we believe to be the case given the issues that have plagued this literature and that we make progress on).

      Reviewer #1 (Public review):

      Summary:

      In the abstract and throughout the paper, the authors boldly claim that their evidence, from the largest set of data ever collected on inattentional blindness, supports the views that "inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing", "subjects retain awareness of stimuli they fail to report", and "these data...cast doubt on claims that awareness requires attention." If their results were to support these claims, this study would overturn 25+ years of research on inattentional blindness, resolve the rich vs. sparse debate in consciousness research, and critically challenge the current majority view in cognitive science that attention is necessary for awareness.

      Unfortunately, these extraordinary claims are not supported by extraordinary (or even moderately convincing) evidence. At best, the results support the more modest conclusion: If sub-optimal methods are used to collect retrospective reports, inattentional blindness rates will be overestimated by up to ~8% (details provided below in comment #1). This evidence-based conclusion means that the phenomenon of inattentional blindness is alive and well as it is even robust to experiments that were specifically aimed at falsifying it. Thankfully, improved methods already exist for correcting the ~8% overestimation of IB rates that this study successfully identified.

      We appreciate here the reviewer’s recognition of the importance of work on inattentional blindness, and the centrality of inattentional blindness to a range of major questions. We also recognize their concerns with what they see as a gap between our data and the claims made on their basis. We address this in detail below (as well as, of course, in our revised manuscript). However, from the outset we are keen to clarify that our central claim is only the first one the reviewer mentions — and the one which appears in our title — namely that, as a group, participants can successfully report the location, color, and shape of stimuli they deny noticing, and thus that there is “Sensitivity to visual features in inattentional blindness”. This is the claim that we believe is strongly supported by our data, and all the more so after revising the manuscript in light of the helpful comments we’ve received.

      By contrast, the other claims the reviewer mentions, concerning awareness (as opposed to residual sensitivity–which might be conscious or unconscious) were intended as both secondary and tentative. We agree with the referee that these are not as strongly supported by our data (and indeed we say so in our manuscript), whereas we do think our data strongly support the more modest — and, to us central — claim that, as a group, inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing. 

      We also feel compelled to resist somewhat the reviewer’s summary of our claims. For example, the reviewer attributes to us the claim that “subjects retain awareness of stimuli they fail to report”; but while that phrase does appear in our abstract, what we in fact say is that our data are “consistent with an alternative hypothesis about IB, namely that subjects retain awareness of stimuli they fail to report”. We do in fact believe that our data are consistent with that hypothesis, whereas earlier investigations seemed not to be. We mention this only because we had used that careful phrasing precisely for this sort of reason, so that we wouldn’t be read as saying that our results unequivocally support that alternative.

      Still, looking back, we see how we may have given more emphasis than we intended to some of these more secondary claims. So, we’ve now gone through and revised our manuscript throughout to emphasize that our main claim is about residual sensitivity, and to make clear that our claims about awareness are secondary and tentative. Indeed, we now say precisely this, that although we favor an interpretation of “our results in terms of residual conscious vision in IB … this claim is tentative and secondary to our primary finding”. We also weaken the statements in the abstract that the reviewer mentions, to better reflect our key claims.

      Finally, we note one further point: Dialectically, inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if (as we agree) they do not provide decisive evidence that awareness survives inattention. In other words, we think our data call that claim into question, such that it’s now genuinely unclear whether awareness does or does not survive inattention. We have adjusted our claims on this point accordingly as well.

      Comments:

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate, if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who may have indeed actually seen the location of the stimulus. If these 29 subjects are moved to the noticer group, the corrected rate of IB for experiment 1 is 21% instead of 29%. In other words, relying only on the "Y/N did you notice anything" question leads to an overestimate of IB rates by 8%. This modest level of inaccuracy in estimating IB rates is insufficient for concluding that "subjects retain awareness of stimuli they fail to report", i.e. that inattentional blindness does not exist.

      In addition, this 8% inaccuracy in IB rates only considers one side of the story. Given the data reported for experiment 1, one can also calculate the number of subjects who answered "yes, I did notice something unusual" but then reported the incorrect location of the critical stimulus. This turned out to be 8 subjects (or 3% of the "noticer" group). Some would argue that it's reasonable to consider these subjects as inattentionally blind, since they couldn't even report where the critical stimulus they apparently noticed was located. If we move these 8 subjects to the non-noticer group, the 8% overestimation of IB rates is reduced to 6%.

      The same exercise can and should be carried out on the other 4 experiments, however, the authors do not report the subject numbers for any of the other experiments, i.e., how many subjects answered Y/N to the noticing question and how many in each group correctly answered the stimulus feature question. From the limited data reported (only total subject numbers and d' values), the effect sizes in experiments 2-5 were all smaller than in experiment 1 (d' for the non-noticer group was lower in all of these follow-up experiments), so it can be safely assumed that the ~6-8% overestimation of IB rates was smaller in these other four experiments. In a revision, the authors should consider reporting these subject numbers for all 5 experiments.

      We now report, as requested, all these subject numbers in our supplementary data (see Supplementary Tables 1 and 2 in our Supplementary Materials).

      However, we wish to address the larger question the reviewer has raised: Do our data only support a relatively modest reduction in IB rates? Even if they did, we still believe that this would be a consequential result, suggesting a significant overestimation of IB rates in classic paradigms. However, part of our purpose in writing this paper is to push back against a certain binary way of thinking about seeing/awareness. Our sense is that the field has conceived of awareness as “all or nothing”: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. Our perspective is different: We think there can be degraded forms of awareness that fall into neither of those categories. For that reason, we are disinclined to see our results in the way that the reviewer suggests, namely as simply indicating that fewer subjects fail to see the stimulus than previously assumed. To think that way is, in our view, to assume the orthodox binary position about awareness. If, instead, one conceives of awareness as we do (and as we believe the framework of signal detection theory should compel us to), then it isn’t quite right to think of the proportion of subjects who were aware, but rather (e.g.) the sensitivity of subjects to the relevant stimulus. This is why we prefer measures like d′ over % noticing and % correct. We understand that the reviewer may not think the same way about this issue as we do, but part of our goal is to promote that way of thinking in general, and so some of our comments below reflect that perspective and approach.

      For example, consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about that is that the subject sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a “high threshold” approach to the problem, in contrast to an SDT approach. On an SDT approach (an approach with tremendous evidential support), on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no non-arbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to our super subject. Instead of saying that some subjects saw/failed to see the stimuli, instead we suggest that the best way to characterize our results is that across subjects (and so trials also) there was differential graded access to information from the stimulus best represented in terms of the group-level sensitivity parameter d′.

      We acknowledge that (despite ourselves) we occasionally fell into an all-too-natural binary/high threshold way of thinking, as when we suggested that our data show that “inattentionally blind subjects consciously perceive these stimuli after all” and “the inattentionally blind can see after all." (p.17) We have removed such problematic phrasing as well as other problematic phrasing as noted below.

      (2) Because classic IB paradigms involve only one critical trial per subject, the authors used a "super subject" approach to estimate sensitivity (d') and response criterion (c) according to signal detection theory (SDT). Some readers may have issues with this super subject approach, but my main concern is with the lack of precision used by the authors when interpreting the results from this super subject analysis.

      Only the super subject had above-chance sensitivity (and it was quite modest, with d' values between 0.07 and 0.51), but the authors over-interpret these results as applying to every subject. The methods and analyses cannot determine if any individual subject could report the features above-chance. Therefore, the following list of quotes should be revised for accuracy or removed from the paper as they are misleading and are not supported by the super subject analysis: "Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning" (p.6)

      "In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location." (p.6)

      "Even subjects who answer "no" under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all." (p.8)

      "Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?" (p.8)

      "we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli." (p.14)

      "our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them." (p.16)

      "In other words, the inattentionally blind can see after all." (p.17)

      We thank the reviewer for pointing out how these quotations may be misleading as regards our central claim. We intended them all to be read generically as concerning the group, and not universally as claiming that all subjects could report above-chance/see the stimuli etc. We agree entirely that the latter universal claim would not be supported by our data. In contrast, we do contend that our super-subject analysis shows that, as a group, subjects traditionally considered intentionally blind exhibit residual sensitivity to features of stimuli (color, shape, and location) that they had all claimed not to notice, and likewise that as a group they could succeed at our forced-choice questions. 

      To ensure this claim is clear throughout the paper, and that we are not interpreted as making an unsupported universal claim we have revised the language in all of the quotations above, as follows, as well as in numerous other places in the paper.

      “Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning” (p.6) => “Altogether this approach reveals that as a group subjects can report above-chance the features of stimuli (color, shape, and location) that they had all claimed not to notice under traditional yes/no questioning” (p.6)

      “Even subjects who answer “no” under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all.” (p.8) => “... even subjects who answer “no” under traditional questioning can, as a group, still correctly report various features of the stimuli they just reported not having noticed, indicating significant group-level sensitivity to visual features. Moreover, these results are even consistent with an alternative hypothesis about IB, that as a group, subjects who would traditionally be classified as inattentionally blind are in fact at least partially aware of the stimuli they deny noticing.” (p.8)

      “Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?” (p.8) => “Why, if subjects could succeed at our forcedresponse questions as a group, did they all individually claim not to have noticed anything?” (p.8)

      “we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli.” (p.14) => “we found that groups of observers could successfully report a variety of features of unattended stimuli, even when they all individually claimed not to have noticed those stimuli.” (p.14)

      “our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them.” (p.16) => “our results just as easily raise an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects may retain a degree of awareness of these stimuli after all.” (p.16) Here deleting: “they show sensitivity to IB stimuli because they can see them.”

      “In other words, the inattentionally blind can see after all.” (p.17) => “In other words, as a group, the inattentionally blind enjoy at least some degraded or partial sensitivity to the location, color and shape of stimuli which they report not noticing.” (p.17)

      In one case, we felt the sentence was correct as it stood, since it simply reported a fact about our data:

      “In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location.” (p.6)

      After all, if subjects were entirely blind and simply guessed, it would be true to say that 50% of subjects would be able to correctly report the stimulus location (by guessing).

      In addition to these and numerous other changes, we also added the following explicit statement early in the paper to head-off any confusion on this point: “Note that all analyses reported here relate to this super subject as opposed to individual subjects”. 

      (3) In addition to the d' values for the super subject being slightly above zero, the authors attempted an analysis of response bias to further question the existence of IB. By including in some of their experiments critical trials in which no critical stimulus was presented, but asking subjects the standard Y/N IB question anyway, the authors obtained false alarm and correct rejection rates. When these FA/CR rates are taken into account along with hit/miss rates when critical stimuli were presented, the authors could calculate c (response criterion) for the super subject. Here, the authors report that response criteria are biased towards saying "no, I didn't notice anything". However, the validity of applying SDT to classic Y/N IB questioning is questionable.

      For example, with the subject numbers provided in Box 1 (the 2x2 table of hits/misses/FA/CR), one can ask, 'how many subjects would have needed to answer "yes, I noticed something unusual" when nothing was presented on the screen in order to obtain a non-biased criterion estimate, i.e., c = 0?' The answer turns out to be 800 subjects (out of the 2761 total subjects in the stimulus-absent condition), or 29% of subjects in this condition.

      In the context of these IB paradigms, it is difficult to imagine 29% of subjects claiming to have seen something unusual when nothing was presented. Here, it seems that we may have reached the limits of extending SDT to IB paradigms, which are very different than what SDT was designed for. For example, in classic psychophysical paradigms, the subject is asked to report Y/N as to whether they think a threshold-level stimulus was presented on the screen, i.e., to detect a faint signal in the noise. Subjects complete many trials and know in advance that there will often be stimuli presented and the stimuli will be very difficult to see. In those cases, it seems more reasonable to incorrectly answer "yes" 29% of the time, as you are trying to detect something very subtle that is out there in the world of noise. In IB paradigms, the stimuli are intentionally designed to be highly salient (and unusual), such that with a tiny bit of attention they can be easily seen. When no stimulus is presented and subjects are asked about their own noticing (especially of something unusual), it seems highly unlikely that 29% of them would answer "yes", which is the rate of FAs that would be needed to support the null hypothesis here, i.e., of a non-biased criterion. For these reasons, the analysis of response bias in the current context is questionable and the results claiming to demonstrate a biased criterion do not provide convincing evidence against IB.

      We are grateful to the reviewer for highlighting this aspect of our data. We agree with several of these points. For example, it is indeed striking that — given the corresponding hit rate — a false alarm rate of 29% would be needed to obtain an unbiased criterion. At the same time, we would respectfully push back on other points above. In our first experiment that uses the super-subject analysis, for example, d′ is 0.51 and highly significant; to describe that figure, as the reviewer does, as “slightly above zero” seemed not quite right to us (and all the more so given that these experiments involve very large samples and preregistered analysis plans). 

      We also respectfully disagree that our data call into question the validity of applying SDT to classic yes/no IB questioning. The mathematical foundations of SDT are rock solid, and have been applied far more broadly than we have applied them here. In fact, in a way we would suggest that exactly the opposite attitude is appropriate: rather than thinking that IB challenges an immensely well-supported, rigorously tested and broadly applicable mathematical model of perception, we think that the conflict between our SDT-based model of IB and the standard interpretation constitutes strong reason to disfavor the standard interpretation. Several points are worth making here.

      First, it is already surprising that 11.03% of our subjects in E2 (46/417) and 7.24% of our subjects in E5 (200/2761) E5 reported noticing a stimulus when no stimulus was present. But while this may have seemed unlikely in advance of inquiry, this is in fact what the data show and forms the basis of our criterion calculations. Thus, our criterion calculations already factor in a surprising but empirically verified high false alarm rate of subjects answering “yes” when no stimulus was presented and were asked about their noticing. (We also note that the only paper we know of to report a false alarm rate in an IB paradigm, though not one used to calculate a response criterion, found a very consistent false alarm rate of 10.4%. See Devue et al. 2009.)

      Second, while the reviewer is of course correct that a common psychophysical paradigm involves detection of a “threshold-level”/faint stimulus in noise, it is widely recognized that SDT has an extremely broad application, being applicable to any situation in which two kinds of event are to be discriminated (Pastore & Scheirer 1975) and being “almost universally accepted as a theoretical account of decision making in research on perceptual detection and recognition and in numerous extensions to applied domains” quite generally (Estes 2002, see also: Wixted 2020). Indeed, cases abound in which SDT has been successfully applied to situations which do not involve near threshold stimuli in noise. To pick two examples at random, SDT has been used in studying acceptability judgments in linguistics (Huang and Ferreira 2020) and the assessment of physical aggression in childstudent interactions (Lerman et al. 2010; for more general discussion of practical applications, see Swets et al. 2000). Given that the framework of SDT is so widely applied and well supported, and that we see no special reason to make an exception, we believe it can be relied on in the present context.

      Finally, we note that inattentional blindness can in many ways be considered analogous to “near threshold” detection since inattention is precisely thought to degrade or even abolish awareness of stimuli, meaning that our stimuli can be construed as near threshold in the relevant sense. Indeed, our relatively modest d′ values suggest that under inattention stimuli are indeed hard to detect. Thus, even were SDT more limited in its application, we think it still would be appropriate to apply to the case of IB.

      (4) One of the strongest pieces of evidence presented in the entire paper is the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result hinges on a single data point, it was not replicated in any of the other 4 experiments, and it can be explained by methodological limitations. I strongly encourage the authors (and other readers) to follow up on this result, in an in-person experiment, with improved questioning procedures.

      We agree that our finding that even the super-subject group that rated their non-noticing as “highly confident” had a d' score significantly above zero is an especially strong piece of evidence, and we thank the reviewer for highlighting that here. At the same time, we note that while the finding is represented by a single marker in Figure 3e, it seemed not quite right to call this a “single data point”, as the reviewer does, given that it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment. It would of course be tremendous to follow up on this result – and we certainly hope our work inspires various follow-up studies. That said, we note that recruiting the necessary numbers of in person subjects would be an absolutely enormous, career-level undertaking – it would involve bringing more than the entire undergraduate population at our own institution, Johns Hopkins, into our laboratory! While those results would obviously be extremely valuable, we wouldn’t want to read the reviewer’s comments as implying that only an experiment of that magnitude — requiring thousands upon thousands of in-person subjects — could make progress on these issues. Indeed, because every subject can only contribute one critical trial in IB, it has long been recognized as an extremely challenging paradigm to study in a sufficiently well-powered and psychophysically rigorous way. We believe that our large preregistered online approach represents a major leap forward here, even if it involves certain trade-offs.

      In the current Experiment 3, the authors asked the standard Y/N IB question, and then asked how confident subjects were in their answer. Asking back-to-back questions, the second one with a scale that pertains to the first one (including a tricky inversion, e.g., "yes, I am confident in my answer of no"), may be asking too much of some subjects, especially subjects paying half-attention in online experiments. This procedure is likely to introduce a sizeable degree of measurement error.

      An easy fix in a follow-up study would be to ask subjects to rate their confidence in having noticed something with a single question using an unambiguous scale:

      On the last trial, did you notice anything besides the cross?

      (1): I am highly confident I didn't notice anything else

      (2): I am confident I didn't notice anything else

      (3): I am somewhat confident I didn't notice anything else

      (4): I am unsure whether I noticed anything else

      (5): I am somewhat confident I noticed something else

      (6): I am confident I noticed something else

      (7): I am highly confident I noticed something else

      If we were to re-run this same experiment, in the lab where we can better control the stimuli and the questioning procedure, we would most likely find a d' of zero for subjects who were confident or highly confident (1-2 on the improved scale above) that they didn't notice anything. From there on, the d' values would gradually increase, tracking along with the confidence scale (from 3-7 on the scale). In other words, we would likely find a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. In the current online study with the successive (and potentially confusing) retrospective questioning, a handful of subjects could have easily misinterpreted the confidence scale (e.g., inverting the scale) which would lead to a mixture of genuine high-confidence ratings and mistaken ratings, which would result in a super subject d' that falls between zero and the other extreme of the scale (which is exactly what the data in Fig 3e shows).

      One way to check on this potential measurement error using the existing dataset would be to conduct additional analyses that incorporate the confidence ratings from the 2AFC location judgment task. For example, were there any subjects who reported being confident or highly confident that they didn't see anything, but then reported being confident or highly confident in judging the location of the thing they didn't see? If so, how many? In other words, how internally (in)consistent were subjects' confidence ratings across the IB and location questions? Such an analysis could help screen-out subjects who made a mistake on the first question and corrected themselves on the second, as well as subjects who weren't reading the questions carefully enough.

      As far as I could tell, the confidence rating data from the 2AFC location task were not reported anywhere in the main paper or supplement.

      We are grateful to the reviewer for raising this issue and for requesting that we report the confidence rating data from our 2afc location task in Experiment 3. We now report all this data in our Supplementary Materials (see Supplementary Table 3).

      We of course agree with the reviewer’s concern about measurement error, which is a concern in all experiments. What, then, of the particular concern that some subjects might have misunderstood our confidence question? It is surely impossible in principle to rule out this possibility; however, several factors bear on the plausibility of this interpretation. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. However, we accept that it is a possibility that certain subjects might nonetheless have been confused in some other way.

      So, we also took a second approach. We examined the confidence ratings on the 2afc question of subjects who reported being highly confident that they didn't notice anything.

      Reassuringly, the large majority of these high confidence “no” subjects (~80%) reported low confidence of 0 or 1 on the 2afc question, and the majority (51%) reported the lowest confidence of 0. Only 18/204 (9%) subjects reported high confidence on both questions. 

      Still, the numbers of subjects here are small and so may not be reliable. This led us to take a third approach. We reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the much larger number of highconfidence “yes” subjects (N = 2677), thus providing a much more robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low-confidence, we found that the number was tiny. Only 28/2677 (1.05%) of highconfidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. In this light, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      What should we make of the 18 subjects who were highly confident non-noticers but then only low-confidence on the 2afc question? Importantly, we do not think that these 18 subjects necessarily made a mistake on the first question and so should be excluded. There is no a priori reason why one’s confidence criterion in a yes/no question should carry over to a 2afc question. After all, it is perfectly rationally coherent to be very confident that one didn’t see anything but also very confident that if there was anything to be seen, it was on the left. Moreover, these 18 subjects were not all correct on the 2afc question despite their high confidence (4/18 or 22% getting the wrong answer). 

      Nonetheless, and again reassuringly, we found that the above-chance patterns in our data remained the same even excluding these 18 subjects. We did observe a slight reduction in percent correct and d′ but this is absolutely what one should expect since excluding the most confident performers in any task will almost inevitably reduce performance.

      In this light, we consider it unlikely that measurement error fully explains the residual sensitivity found even amongst highly confident non-noticers. That said, we appreciate this concern. We now raise the issue and the analysis of high confidence noticers which addresses it in our revised manuscript. We also thank the reviewer for pressing us to think harder about this issue, which led directly to these new analyses that we believed have strengthened the paper.

      (5) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial (or set of trials) is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials one cannot conclude that attention was manipulated.

      It is puzzling why the authors decided not to include any control trials with partial or full attention in their five experiments, especially given their online data collection procedures where stimulus size, intensity, eccentricity, etc. were uncontrolled and variable across subjects. Including such trials could have actually helped them achieve their goal of challenging the IB hypothesis, e.g., excluding subjects who failed to see the stimulus on the control trials might have reduced the inattentional blindness rates further. This design decision should at least be acknowledged and justified (or noted as a limitation) in a revision of this paper.

      We acknowledge that other studies in the literature include divided and full attention trials, and that they could have been included in our work as well. However, we deliberately decided not to include such control trials for an important reason. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials.

      (For example, as Most et al. 2001 write: “Because observers should have seen the object in the full-attention trial (Mack & Rock, 1998), we used this trial as a control … Accordingly, 3 observers who failed to see the cross on this trial were replaced, and their data were excluded from the analyses.") As the reviewer points out, excluding such subjects would very likely have ‘helped' us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any such exclusions, and for that reason decided not to include divided/full attention trials. 

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      (6) In the discussion section, the authors devote a short paragraph to considering an alternative explanation of their non-zero d' results in their super subject analyses: perhaps the critical stimuli were processed unconsciously and left a trace such that when later forced to guess a feature of the stimuli, subjects were able to draw upon this unconscious trace to guide their 2AFC decision. In the subsequent paragraph, the authors relate these results to above-chance forced-choice guessing in blindsight subjects, but reject the analogy based on claims of parsimony.

      First, the authors dismiss the comparison of IB and blindsight too quickly. In particular, the results from experiment 3, in which some subjects adamantly (confidently) deny seeing the critical stimulus but guess a feature at above-chance levels (at least at the super subject level and assuming the online subjects interpreted and used the confidence scale correctly), seem highly analogous to blindsight. Importantly, the analogy is strengthened if the subjects who were confident in not seeing anything also reported not being confident in their forced-choice judgments, but as mentioned above this data was not reported.

      Second, the authors fail to mention an even more straightforward explanation of these results, which is that ~8% of subjects misinterpreted the "unusual" part of the standard IB question used in experiments 1-3. After all, colored lines and shapes are pretty "usual" for psychology experiments and were present in the distractor stimuli everyone attended to. It seems quite reasonable that some subjects answered this first question, "no, I didn't see anything unusual", but then when told that there was a critical stimulus and asked to judge one of its features, adjusted their response by reconsidering, "oh, ok, if that's the unusual thing you were asking about, of course I saw that extra line flash on the left of the screen". This seems like a more parsimonious alternative compared to either of the two interpretations considered by the authors: (1) IB does not exist, (2) super-subject d' is driven by unconscious processing. Why not also consider: (3) a small percentage of subjects misinterpreted the Y/N question about noticing something unusual. In experiments 4-5, they dropped the term "unusual" but do not analyze whether this made a difference nor do they report enough of the data (subject numbers for the Y/N question and 2AFC) for readers to determine if this helped reduce the ~8% overestimate of IB rates.

      Our primary ambition in the paper was to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived). We were evidently not clear about this, however, leading to two referees coming away with an impression of our claims that is different than we intended. We have revised our manuscript throughout to address this. But we also want to emphasize here that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has solid support in our data.

      We do in the discussion section offer one reason for believing that there is residual awareness in the group of subjects who are traditionally classified as inattentionally blind. However, we acknowledge that this is controversial and now emphasize in the manuscript that this claim “is tentative and secondary to our primary finding”. We also emphasize that part of our point is dialectical: Inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if they do not provide decisive evidence (as we agree) that awareness survives inattention. (Cf. here, Hirshhorn et al. 2024 who take up a common suggestion in the field that awareness is best assessed by using both subjective and objective measures, with claims about lack of awareness ideally being supported by both; our data suggest at a minimum that in IB objective measures do not neatly line up with subjective measures.)

      We hope this addresses the referee’s concern that we dismiss the “the comparison of IB and blindsight too quickly”. We do not intend to dismiss that comparison at all, indeed we raise it because we consider it a serious hypothesis. Our aim is simply to raise one possible consideration against it. But, again, our main claim is quite consistent with sensitivity in IB being akin to “blindsight”.

      We also agree with the referee that a possible explanation of why some subjects say they do not notice something unusual in IB paradigms, is not because they didn’t notice anything but because they didn’t consider the unexpected stimulus sufficiently unusual. However, the reviewer is incorrect that we did not mention this interpretation; to the contrary, it was precisely the kind of concern which led us to be dissatisfied with standard IB methods and so motivated our approach. As we wrote in our main text: “However, yes/no questions of this sort are inherently and notoriously subject to bias…   For example, observers might be under-confident whether they saw anything (or whether what they saw counted as unusual); this might lead them to respond “no” out of an excess of caution.” On our view, this is exactly the kind of reason (among other reasons) that one cannot rely on yes/no reports of noticing unusual stimuli, even though the field has relied on just these sorts of questions in just this way.

      We do not, however, think that this explanation accounts for why all subjects fail to report noticing, nor do we think that it accounts for our finding of above-chance sensitivity amongst non-noticers. This is for two critical reasons. First, whereas the word “unusual” did appear in the yes/no question in our Experiments 1-3, it did not appear in our Experiments 4 and 5 on dynamic IB. (In both cases, we used the exact wording of such questions in the experiments we were basing our work on.) And, of course, we still found significant residual sensitivity amongst non-noticers in Experiments 4 and 5. Second, in relation to our confidence experiment, we think it unlikely that subjects who were highly confident that they did not notice anything unusual only said that because they thought what they had seen was insufficiently unusual. Yet even in this group of subjects who were maximally confident that they did not notice anything unusual, we still found residual sensitivity.

      (7) The authors use sub-optimal questioning procedures to challenge the existence of the phenomenon this questioning is intended to demonstrate. A more neutral interpretation of this study is that it is a critique on methods in IB research, not a critique on IB as a manipulation or phenomenon. The authors neglect to mention the dozens of modern IB experiments that have improved upon the simple Y/N IB questioning methods. For example, in Michael Cohen's IB experiments (e.g., Cohen et al., 2011; Cohen et al., 2020; Cohen et al., 2021), he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them. In other experiments (e.g., Hirschhorn et al., 2024; Pitts et al., 2012), researchers not only ask the Y/N question but then follow this up by presenting examples of the critical stimuli so subjects can see exactly what they are being asked about (recognition-style instead of free recall, which is more sensitive). These follow-up questions include foil stimuli that were never presented (similar to the stimulus-absent trials here), and ask for confidence ratings of all stimuli. Conservative, pre-defined exclusion criteria are employed to improve the accuracy of their IB-rate estimates. In these and other studies, researchers are very cautious about trusting what subjects report seeing, and in all cases, still find substantial IB rates, even to highly salient stimuli. The authors should consider at least mentioning these improved methods, and perhaps consider using some of them in their future experiments.

      The concern that we do not sufficiently discuss the range of “improved” methods in IB studies is well-taken. A similar concern is raised by Reviewer #2 (Dr. Cohen). To address the concern, we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have four main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude people from the IB group but to include people in the IB group. Thus, Most et al. 2001 asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and answered their follow ups incorrectly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to see its color, say, would be treated as inattentionally blind. This has the purpose of inflating IB rates, in exactly the way our paper is intended to critique. So, in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in Cohen’s studies which the reviewer mentions, it is certainly true that “he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them.” We agree that this improves over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      This same basic issue affects the follow up question used in Pitts et al. 2012 which the reviewer mentions. Pitts et al. write: “If a participant reported not seeing any patterns and rated their confidence in seeing the square pattern (once shown the sample) as a 3 or less (1 = least confident, 5 = most confident), she or he was placed in Group 1 and was considered to be inattentionally blind to the square patterns.” The confidence rating follow-up question here remains subject to bias. Moreover, and strikingly, the inclusion criterion used means that subjects who were moderately confident that they saw the square pattern when shown (i.e. answered 3) were counted as inattentionally blind (!). We do not think this is an appropriate inclusion criterion.

      The third problem is that follow up questions are often free/open-response. For instance, Most et al. (2005) ask the follow up question: "If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess." This is a much more difficult and to that extent less sensitive question than our binary forced-response/2afc questions. For this reason, we believe our follow up questions are more suitable for ascertaining low levels of sensitivity.

      The fourth and final issue is that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is in fact not true of n_afc questions in general, nor is it true in general of _delayed n-alternative match to sample designs. Thus, even when limited response options are given, they are not immune to response biases and so require SDT analysis. Moreover, some such tasks can involve decision spaces which are often poorly understood or difficult to analyze without making substantial assumptions about observer strategy. 

      This last point (as well as the first) is relevant to Hirshhorn et al. 2024. Hirshhorn et al. write that they “used two awareness measures. Firstly, participants were asked to rate stimulus visibility on the Perceptual Awareness Scale (PAS, a subjective measure of awareness: Ramsøy & Overgaard, 2004), and then they were asked to select the stimulus image from an array of four images (an objective measure: Jakel & Wichmann, 2006).”

      While certainly an improvement on simple yes/no questioning, the PAS remains subject to response bias. On the other hand, we applaud Hirshhorn et al.’s use of objective measures in the context of IB which of course our design implements. However, while Hirshhorn et al. 2024 suggest that their task is a spatial 4afc following the recommendation of this design by Jakel & Wichmann (2006), it is strictly a 4-alternative delayed match to sample task, so it is doubtful if it can be considered a preferred psychophysical task for the reasons Jakel & Wichmann offer. Regardless, the more crucial point is that observers in such a task might be biased towards one alternative as opposed to another. Thus, use of d′ (as opposed to percent correct as in Hirshhorn et al. 2024) is crucial in assessing performance in such tasks.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much more clearly, and we thank the reviewer for prompting these improvements.

      Reviewer #2 (Public review):

      In this study, Nartker et al. examine how much observers are conscious of using variations of classic inattentional blindness studies. The key idea is that rather than simply asking observers if they noticed a critical object with one yes/no question, the authors also ask follow-up questions to determine if observers are aware of more than the yes/no questions suggest. Specifically, by having observers make forced choice guesses about the critical object, the authors find that many observers who initially said "no" they did not see the object can still "guess" above chance about the critical object's location, color, etc. Thus, the authors claim, that prior claims of inattentional blindness are mistaken and that using such simple methods has led numerous researchers to overestimate how little observers see in the world. To quote the authors themselves, these results imply that "inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them."

      Before getting to a few issues I have with the paper, I do want to make sure to explicitly compliment the researchers for many aspects of their work. Getting massive amounts of data, using signal detection measures, and the novel use of a "super subject" are all important contributions to the literature that I hope are employed more in the future.

      We really appreciate this comment and that the reviewer found our work to make these important contributions to the literature. We wrote this paper expecting not everyone to accept our conclusions, but hoping that readers would see the work as making a valuable contribution to the literature promoting an underexplored alternative in a compelling way. Given that this reviewer goes on to express some skepticism about our claims, it is especially encouraging to see this positive feedback up top!

      Main point 1: My primary issue with this work is that I believe the authors are misrepresenting the way people often perform inattentional blindness studies. In effect, the authors are saying, "People do the studies 'incorrectly' and report that people see very little. We perform the studies 'correctly' and report that people see much more than previously thought." But the way previous studies are conducted is not accurately described in this paper. The authors describe previous studies as follows on page 3:

      "Crucially, however, this interpretation of IB and the many implications that follow from it rest on a measure that psychophysics has long recognized to be problematic: simply asking participants whether they noticed anything unusual. In IB studies, awareness of the unexpected stimulus (the novel shape, the parading gorilla, etc.) is retroactively probed with a yes/no question, standardly, "Did you notice anything unusual on the last trial which wasn't there on previous trials?". Any subject who answers "no" is assumed not to have any awareness of the unexpected stimulus.

      If this quote were true, the authors would have a point. Unfortunately, I do not believe it is true. This is simply not how many inattentional blindness studies are run. Some of the most famous studies in the inattentional blindness literature do not simply as observes a yes/no question (e.g., the invisible gorilla (Simons et al. 1999), the classic door study where the person changes (Simons and Levin, 1998), the study where observers do not notice a fight happening a few feet from them (Chabris et al., 2011). Instead, these papers consistently ask a series of follow-up questions and even tell the observers what just occurred to confirm that observers did not notice that critical event (e.g., "If I were to tell you we just did XYZ, did you notice that?"). In fact, after a brief search on Google Scholar, I was able to relatively quickly find over a dozen papers that do not just use a yes/no procedure, and instead as a series of multiple questions to determine if someone is inattentionally blind. In no particular order some papers (full disclosure: including my own):

      (1) Most et al. (2005) Psych Review

      (2) Drew et al. (2013) Psych Science

      (3) Drew et al. (2016) Journal of Vision

      (4) Simons et al. (1999) Perception

      (5) Simons and Levin (1998) Perception

      (6) Chabris et al. (2011) iPerception

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      (8) Most et al. (2001) Psych Science

      (9) Todd & Marois (2005) Psych Science

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      (11) New and German (2015) Evolution and Human Behaviour

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      (13) Mack et al. (2016) Consciousness and cognition

      (14) Devue et al. (2009) Perception

      (15) Memmert (2014) Cognitive Development

      (16) Moore & Egeth (1997) JEP:HPP

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      (18) Cohen et al. (2011) Psych Science

      This is a critical point. The authors' key idea is that when you ask more than just a simple yes/no question, you find that other studies have overestimated the effects of inattentional blindness. But none of the studies listed above only asked simple yes/no questions. Thus, I believe the authors are mis-representing the field. Moreover, many of the studies that do much more than ask a simple yes/no question are cited by the authors themselves! Furthermore, as far as I can tell, the authors believe that if researchers do these extra steps and ask more follow-ups, then the results are valid. But since so many of these prior studies do those extra steps, I am not exactly sure what is being criticized.

      To make sure this point is clear, I'd like to use a paper of mine as an example. In this study (Cohen et al., 2020, Proc Natl Acad Sci USA) we used gaze-contingent virtual reality to examine how much color people see in the world. On the critical trial, the part of the scene they fixated on was in color, but the periphery was entirely in black and white. As soon as the trial ended, we asked participants a series of questions to determine what they noticed. The list of questions included:

      (1) "Did you notice anything strange or different about that last trial?"

      (2) "If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?"

      (3) "If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?"

      (4) "Did you notice anything different about the colors in the last scene?"

      (5) We then showed observers the previous trial again and drew their attention to the effect and confirmed that they did not notice that previously.

      In a situation like this, when the observers are asked so many questions, do the authors believe that "the inattentionally blind can see after all?" I believe they would not say that and the reason they would not say that is because of the follow-up questions after the initial yes/no question. But since so many previous studies use similar follow-up questions, I do not think you can state that the field is broadly overestimating inattentional blindness. This is why it seems to me to be a bit of a strawman: most people do not just use the yes/no method.

      We appreciate this reviewer raising this issue. As he (Dr. Cohen) states, his “primary issue” concerns our discussion of the broader literature (which he worries understates recent improvements made to the IB methodology), rather than, e.g., the experiments we’ve run. We take this concern very seriously and address it comprehensively here.

      A very similar issue is identified by Reviewer #1, comment (7). To review some of what we say in reply to them: To address the concern we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have three main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude subjects from the IB group but to include subjects in the IB group. Thus, Most et al. (2001) asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and failed to answer their follow ups correctly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to describe it in these respects would be treated as inattentionally blind. This is problematic since failure to describe a feature (e.g., color, shape) does not imply a complete lack of information concerning that feature; and even if a subject did lack all information concerning these features of an object, this would not imply a complete failure to see the object. Similarly, Pitts et al. (2012) asked subjects to rate their confidence in their initial yes/no response from 1 = least confident to 5 = most confident, and used these ratings to include in the IB group those who rated their confidence in seeing at 3 or less. This is evidently problematic, since there is a large gap between being under confident that one saw something and being completely blind to it. More generally, using follows up to inflate IB rates in such ways raises precisely the kinds of issues our paper is intended to critique. So in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in the reviewer’s own studies (Cohen et al. 2020, 2011; see also: Simons et al., 1999; Most et al., 2001, 2005; Drew et al., 2013; Memmert, 2014) a series of follow up questions are used to try and ensure that subjects who noticed the critical stimuli are given the maximum opportunity to report doing so, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      We certainly agree that such follow up questions improve over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, intrinsically subject to response bias. Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      It is also important to recognize that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is not true of n_afc nor delayed _n-alternative match to sample designs in general. Performance in such tasks thus requires SDT analysis – which itself may be problematic if the decision space is not properly understood or requires making substantial assumptions about observer strategy.

      Third, and finally, many follow up questions are insufficiently sensitive (especially with small sample sizes). For instance, Todd, Fougnie & Marois (2005) used a 12-alternative match-tosample task (see similarly: Fougnie & Marois, 2007; Devue et al., 2009). And Most et al. (2005) asked an open-response follow-up: “If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess.” These questions are more difficult and to that extent less sensitive than binary forced-response/2afc questions of the sort we use in our own studies – a difference which may be critical in uncovering degraded perceptual sensitivity.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach of using 2afc or forced-response questions combined with signal detection analysis is an important improvement on prior methods and contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much clearer.

      Other studies that improve on the standard methodology

      This reviewer adds something else, however: A very helpful list of 18 papers which include follow ups and that he believes overcome many of the issues we raise in our paper. To just state our reaction bluntly: We are familiar with every one of these papers (indeed, one of them is a paper by one of us!), and while we think these are all very valuable contributions to the literature, it is our view that none of these 18 papers resolves the worries that led us to conduct our work.  

      Here we briefly comment on the relevant pitfalls in each case. We hope this serves to underscore the importance of our methodological approach.

      (1) Most et al. (2005) Psych Review

      Either a 2-item or 5-item questionnaire was used. The 2-item questionnaire ran as follows:

      (1) On the last trial, did you see anything other than the 4 circles and the 4 squares (anything that had not been present on the original two trials)? Yes No 

      (2) If you did see something on the last trial that had not been present during the original two trials, please describe it in as much detail as possible.

      This clearly does not substantially improve on the traditional simple yes/no question. Moreover, the second question (as well as being open-ended) was used to include additional subjects in the IB group, in that participants were counted as having seen the object only if they responded “yes” to Q1 and in addition “were able to report at least one accurate detail” in response to Q2. In other words, either a subject says “no” (and is treated as unaware), or says “yes” and then is asked to prove their awareness, as it were. If anything, this intensifies the concerns we raise, by inflating IB rates. 

      The 5-item questionnaire looked like this: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2) If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional y/n question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the 2-item questionnaire, these responses were not used to exclude people from the IB group but to include people in it. So again, this approach does not in any way address the issues we are concerned about, and if anything, only makes them worse. 

      (2)  Drew et al. (2013) Psych Science

      All follow ups were yes/no: “we asked a series of questions to determine whether they noticed the gorilla: ‘Did the final trial seem any different than any of the other trials?’, ‘Did you notice anything unusual on the final trial?’, and, finally, ‘Did you see a gorilla on the final trial?’”. So, this paper essentially implements the standard methodology we mention (and criticize). 

      (3)  Drew et al. (2016) Journal of Vision

      Follow up questions were used, but the reported procedure does not provide sufficient details to evaluate them (we are only told: “After the final trial, they were asked: ‘On that last trial of the task, did you notice anything that was not there on previous trials?’ They then answered questions about the features of the unexpected stimulus on a separate screen (color, shape, movement, and direction of movement).”). It is not clear that these follow ups were used to exclude any subjects from the analysis. Finally, given that the unexpected object could be the same color as the targets/distractors, it is clear that biases would have been introduced which would need to be considered (but which were not).

      (4)  Simons & Chabris (1999) Perception

      All follow ups were yes/no: “observers were … asked to provide answers to a surprise series of additional questions. (i) While you were doing the counting, did you notice anything unusual on the video? (ii) Did you notice any- thing other than the six players? (iii) Did you see anyone else (besides the six players) appear on the video? (iv) Did you see a gorilla [woman carrying an umbrella] walk across the screen? After any “yes'' response, observers were asked to provide details of what they noticed. If at any point an observer mentioned the unexpected event, the remaining questions were skipped.” As noted previously, the analyses in fact did not use these questions to exclude subjects since answers were so consistent.

      (5)  Simons and Levin (1998) Perception

      This is a change detection paradigm, not a study of inattentional blindness. And in any case, one yes/no follow up was used: “Did you notice that I'm not the same person who approached you to ask for directions?”

      (6)  Chabris et al. (2011) iPerception

      Two yes/no questions were asked: “we asked whether the subjects had seen anything unusual along the route, and then whether they had seen anyone fighting.” It seems that follow up questions (a request to describe the fight) were asked only of those who said yes.

      This is in fact a common procedure – follow up questions only being asked of the “yes” group. As discussed, it is sometimes used to increase rates of IB, compounding the problem we identify in our paper. So this is another example of a follow-up question that makes the problem we identify worse, not better.

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      Two yes/no questions were used: “...observers were asked whether they noticed ‘anything … that was different from the first three trials’ — and if so, to describe what was different. They were then shown the gray cross and asked if they had noticed it—and if so, to describe where it was and how it moved. Only observers who explicitly reported not noticing the cross were counted as ‘nonnoticers’ to be included in the final sample (N = 100).” In each case, combining the traditional noticing question with a request to describe and identify may have induced conservative response biases in the noticing question, since a subject might consider being able to describe or identify the unexpected stimulus a precondition of giving a positive answer to the noticing question.

      (8) Most et al. (2001) Psych Science

      The same 5-item questionnaire discussed above in relation to Most et al. (2005) was used: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2)   If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional yes/no question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the two item questionnaire in Most et al. 2005, these responses were not used to exclude people from the IB group but to include people in it. So again this approach does not in any way address the issues we are concerned about, and if anything only makes them worse.

      (9) Todd, Fougnie & Marois (2005) Psych Science

      “participants were probed with three questions to determine whether they had detected the critical stimulus ... .The first question assessed whether subjects had seen anything unusual during the trial; they responded ‘‘yes’’ or ‘‘no’’ by pressing the appropriate key on the keyboard. The second question asked participants to select which stimulus they might have seen among 12 possible objects and symbols selected from MacIntosh font databases. The third question asked participants to select the quadrant in which the critical stimulus may have appeared by pressing one of four keys, each of which corresponded to one of the quadrants.”

      These follow ups were used to include people in the IB group: “In keeping with previous studies (Most et al., 2001), participants were considered to have detected the critical stimulus successfully if they (a) reported seeing an unexpected stimulus and (b) correctly selected its quadrant location.” In line with our third point about sensitivity, the object identity test transpired to be “too difficult even under full-attention conditions … Thus, performance with this question was not analyzed further.”

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      Same exact methods and problems as with Todd & Marois (2005) Psych Science, just discussed.

      (11) New and German (2015) Evolution and Human Behaviour

      “After the fourth trial containing the additional experimental stimulus, the participant was asked, “Did you see anything in addition to the cross on that trial?” and which quadrant the additional stimulus appeared in. They were then asked to identify the stimulus in an array which in Experiment 1 included two variants chosen randomly from the spider stimuli and the two needle stimuli. Participants in Experiment 2 picked from all eight stimuli used in that experiment.”

      Our second concern about response biases and the need for appropriate SDT analysis of the 4/8 alternative tasks applies to all these questions. We also note that analyses were only performed on groups separately (those who detected/failed to detect, those who located/failed to locate, and those who identified/failed to identify) and on the group which did all three/failed to do any one of the three. Especially in light of the fact that some subjects could clearly detect the stimulus without being able to identity it (e.g.), the most stringent test given our concerns (which were not obviously New and German’s comparative concerns), would be to consider the group which could not detect, identify or localize.

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      This is a very interesting example of a follow-up which used a 3-AFC recognition test:

      “participants were immediately asked, ‘‘which display looks most like what you just saw?’ from 3 alternatives”. However, though such an objective test is definitely to be preferred in our view to an open-ended series of probes, the 3-AFC test administered clearly had issues with response biases, as discussed, and actually yielded significantly below chance performance in one of the experiments.

      (13) Mack et al. (2016) Consciousness and cognition

      The follow ups here were essentially yes/no combined with an assessment of surprise. Participants were asked to enter letters into a box, and if they did so “were immediately asked by the experimenter whether they had noticed anything different about the array on this last trial and if they did not, they were told that there had been no letters and their responses to that news were recorded. Clearly, if they expressed surprise, this would be compelling evidence that they were unaware of the absence of the letters. Those observers who did not enter letters and realized there were no letters present were considered aware of the absence.” So, this again has all of the same problems we identify, considering subjects unaware because they expressed surprise.

      (14) Devue et al. (2009) Perception

      An 8-alternative task was used. The authors were primarily interested in a comparative analysis and so did not use this task to exclude subjects. We note that an 8 alternative task is very demanding – compare the 12-alternative task used in Todd, Fougnie & Marois (2005). There was an attempt to investigate biases in a separate bias trial, however SDT measures were not used.

      (15) Memmert (2014) Cognitive Development

      “After watching the video and stating the number of passes, participants answered four questions (following Simons & Chabris, 1999): (1) While you were counting, did you perceive anything unusual on the video? (2) Did you perceive anything other than the six players? (3) Did you see anyone else (besides the six players) appear on the video? (4) Did you notice a gorilla walk across the screen? After any “yes” reply, children were asked to provide details of what they noticed. If at any point a child mentioned the unexpected event, the remaining questions were omitted.” All of these follow-up questions are yes/no judgments, used to determine awareness in exactly the way we critique as problematic.

      (16) Moore & Egeth (1997) JEP:HPP

      This study (which includes one of us, Egeth, as author) did use forced choice questions. In one case, the question was 2-alternative, in the other it was 4-alternative. In the latter case, SDT would have been appropriate but was not used. In the former case, it may have been that a larger sample would have revealed evidence of sensitivity to the background pattern (as it stood 55% answered the 2-alternative question correctly). Although these results have been replicated, unfortunately the replication in Wood and Simons 2019 used a 6-alternative recognition task and this was not analyzed using SDT. We also note that the task is rather difficult in this study. Wood and Simons report: “Exclusion rates were much higher than anticipated, primarily due to exclusions when subjects failed to correctly report the pattern on the full-attention trial; we excluded 361 subjects, or 58% of our sample.”

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      While this paper improves over a simple yes/no question in some ways, especially in that it used the follow up questions to exclude subjects from the unaware (IB) group, the follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions can produce remarkable consistency despite their rather different wording. 

      (18) Cohen et al. (2011) Psych Science

      Here are the probes used in this study:

      (1) Did you notice anything different on that trial?

      (2) Did you notice something different about the background stream of images?

      (3) Did you notice that a different type of image was presented in the background that was unique in some particular way?

      (4) Did you see an actual photograph of a natural scene in that stream?

      (5) If I were to tell you that there was a photograph in that stream, can you tell me what it was a photograph of?

      Qs 1-4 are yes/no. Q5 is yes/no with an open-ended response. After this, a 5 or 6-alternative recognition test was administered. So again, this faces the same issues, since y/n questions are subject to bias in the way we have described, and many-alternative tests are more problematic than 2afc tests.

      In summary

      We really appreciate the care that went into compiling this list, and we agree that these papers and the improved methods they contain are relevant. But as hopefully made clear above, the approaches in each of these papers simply don’t solve the foundational issues our critique is aimed at (though they may address other issues). This is why we felt our new approach was necessary. And we continue to feel this way even after reading and incorporating these comments from Dr. Cohen.

      Nevertheless, there is clearly lots for us to do in light of these comments. And so as noted earlier we have now added a very substantial new section to our discussion section to more fairly and completely portray the state of the art in this literature. This is really to our benefit in the end, since we now not only better acknowledge the diverse approaches present, but also set up ourselves to make our novel contribution exceedingly clear.

      Main point 2: Let's imagine for a second that every study did just ask a yes/no question and then would stop. So, the criticism the authors are bringing up is valid (even though I believe it is not). I am not entirely sure that above chance performance on a forced choice task proves that the inattentionally blind can see after all. Could it just be a form of subliminal priming? Could there be a significant number of participants who basically would say something like, "No I did not see anything, and I feel like I am just guessing, but if you want me to say whether the thing was to the left or right, I will just 100% guess"? I know the literature on priming from things like change and inattentional blindness is a bit unclear, but this seems like maybe what is going on. In fact, maybe the authors are getting some of the best priming from inattentional blindness because of their large sample size, which previous studies do not use.

      I'm curious how the authors would relate their studies to masked priming. In masked priming studies, observers say the did not see the target (like in this study) but still are above chance when forced to guess (like in this study). Do the researchers here think that that is evidence of "masked stimuli are truly seen" even if a participant openly says they are guessing?

      We’re grateful to the reviewer for raising this question. As we say in response to Reviewer #1, our primary ambition in the paper is to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived, or what the reviewer here suggests may be happening in masked priming). Since we were evidently insufficiently clear about this we have revised our manuscript in several places to clarify that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has much more solid support in our data than our secondary and tentative suggestion about awareness.

      This said, we do consider masked priming studies to be susceptible to the critique that performance may reflect degraded conscious awareness which is unreported because of conservative response criteria. There is good evidence that response criteria tend to be conservative near threshold (Björkman et al. 1993; see also: Railo et al. 2020), including specifically in masked priming studies (Sand 2016, cited in Phillips 2021). So, we consider it a perfectly reasonable hypothesis that subjects who say they feel they are guessing in fact have conscious access to a degraded signal which is insufficient to reach a conservative response criterion but nonetheless sufficient to perform above chance in 2afc detection. Of course, we appreciate that this hypothesis is controversial, so it is not one we argue for in our paper (though we are happy to share our feelings about it here).

      Main point 3: My last question is about how the authors interpret a variety of inattentional blindness findings. Previous work has found that observers fail to notice a gorilla in a CT scan (Drew et al., 2013), a fight occurring right in front of them (Chabris et al., 2011), a plane on a runway that pilots crash into (Haines, 1991), and so forth. In a situation like this, do the authors believe that many participants are truly aware of these items but simply failed to answer a yes/no question correctly? For example, imagine the researchers made participants choose if the gorilla was in the left or right lung and some participants who initially said they did not notice the gorilla were still able to correctly say if it was in the left or right lung. Would the authors claim "that participant actually did see the gorilla in the lung"? I ask because it is difficult to understand what it means to be aware of something as salient as a gorilla in a CT scan, but say "no" you didn't notice it when asked a yes/no question. What does it mean to be aware of such important, ecologically relevant stimuli, but not act in response to them and openly say "no" you did not notice them?

      Our view is that in such cases, observers may well have a “degraded” percept of the relevant feature (gorilla, plane, fight etc.). But crucially we do not suggest that this percept is sufficient for observers to recognize the object/event as a gorilla, plane, fight etc. Our claim is only that, in our studies at least, observers (as a group) do have enough information about the unexpected stimuli to locate them, and discriminate certain low level features better than chance. Crudely, it may be that subjects see the gorilla simply as a smudge or the plane as a shadowy patch etc. (One of us who is familiar with the gorilla CT scan stimuli notes that the gorilla is in fact rather hard to see even when you know which slide it is on, suggesting that they are not as “salient” as the reviewer suggests!) 

      More precisely, in the paper we write that in our view perhaps “...unattended stimuli are encoded in a partial or degraded way. Here we see a variety of promising options for future work to investigate. One is that unattended stimuli are only encoded as part of ensemble representations or summary scene statistics (Rosenholtz, 2011; Cohen et al., 2016). Another is that only certain basic “low-level” or “preattentive” features (see Wolfe & Utochkin, 2019 for discussion) can enter awareness without attention. A final possibility consistent with the present data is that observers can in principle be aware of individual objects and higher-level features under inattention but that the precision of the corresponding representations is severely reduced. Our central aim here is to provide evidence that awareness in inattentional blindness is not abolished. Further work is needed to characterize the exact nature of that awareness.” We hope this sheds light on our perspective while still being appropriately cautious not to go too far beyond our data.

      Overall: I believe there are many aspects of this set of studies that are innovative and I hope the methods will be used more broadly in the literature. However, I believe the authors misrepresent the field and overstate what can be interpreted from their results. While I am sure there are cases where more nuanced questions might reveal inattentional blindness is somewhat overestimated, claims like "the inattentionally blind can see after all" or "Inattentionally blind subjects consciously perceive thest stimuli after all" seem to be incorrect (or at least not at all proven by this data).

      Once again, we would like to thank this reviewer for his feedback, which obviously comes from a place of tremendous expertise on these issues. We appreciate his assessment that our studies are innovative and that our methodological advances will be of use more broadly. We also hear the reviewer loud and clear about the passages in question, which on reflection we agree are not as central to our case as the other claims we make (regarding residual sensitivity and conservative responding), and so we have now edited them accordingly to refocus our discussion on only those claims that are central and supported. Thank you for making our paper stronger!

      Reviewer #3 (Public review):

      Summary:

      Authors try to challenge the mainstream scientific as well as popularly held view that Inattentional

      Blindness (IB) signifies subjects having no conscious awareness of what they report not seeing (after being exposed to unexpected stimuli). They show that even when subjects indicate NOT having seen the unexpected stimulus, they are at above chance level for reporting features such as location, color or movement of these stimuli. Also, they show that 'not seen' responses are in part due to a conservative bias of subjects, i.e. they tend to say no more than yes, regardless of actual visibility. Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.

      We just thought to say that we felt this was a very accurate summary of our claims, and in ways underscore the modesty we had hoped to convey. This is especially true of the reviewer’s final sentence: “Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.”; as we noted in response to other reviewers, our claim is not that IB doesn’t exist, that subjects are always conscious of the stimulus, etc.; it is only that the cohort of IB subjects show sensitivity to the unattended stimulus in ways that suggest they are not as blind as traditionally conceived. Thank you for reading us as intended!

      Strengths:

      A huge pool of (25.000) subjects is used. They perform several versions of the IB experiments, both with briefly presented stimuli (as the classic Mack and Rock paradigm), as well as with prolonged stimuli moving over the screen for 5 seconds (a bit like the famous gorilla version), and all these versions show similar results, pointing in the same direction: above chance detection of unseen features, as well as conservative bias towards saying not seen.

      We’re delighted that the reviewer appreciated these strengths in our manuscript!

      Weaknesses:

      Results are all significant but effects are not very strong, typically a bit above chance. Also, it is unclear what to compare these effects to, as there are no control experiments showing what performance would have been in a dual task version where subjects have to also report features etc for stimuli that they know will appear in some trials

      The backdrop to the experiments reported here is the “consensus view” (Noah & Mangun, 2020) according to which inattention completely abolishes perception, such that subjects undergoing IB “have no awareness at all of the stimulus object” (Rock et al., 1992) and that “one can have one’s eyes focused on an object or event … without seeing it at all” (Carruthers, 2015). In this context, we think our findings of significant above-chance sensitivity (e.g., d′ = 0.51 for location in Experiment 1; chance, of course, would be d′ = 0 here) are striking and constitute strong evidence against the consensus view. We of course agree that the residual sensitivity is far lower than amongst subjects who noticed the stimulus. For this reason, we certainly believe that inattention has a dramatic impact on perception. To that extent, our data speak in favor of a “middle ground” view on which inattention substantially degrades but crucially does not abolish perception/explicit encoding. We see this as an importantly neglected option in a literature which has overly focused on seen/not seen binaries (see our section ‘Visual awareness as graded’).

      Regarding the absence of a control condition, we think those conditions wouldn’t have played the same role in our experiments as they typically play in other experiments. As Reviewer #1 comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. As Reviewer #1 points out, excluding such subjects would very likely have ‘helped’ us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any exclusions, and for that reason decided not to include divided/full attention trials.

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      There are quite some studies showing that during IB, neural processing of visual stimuli continues up to high visual levels, for example, Vandenbroucke et al 2014 doi:10.1162/jocn_a_00530 showed preserved processing of perceptual inference (i.e. seeing a kanizsa illusion) during IB. Scholte et al 2006 doi: 10.1016/j.brainres.2005.10.051 showed preserved scene segmentation signals during IB. Compared to the strength of these neural signatures, the reported effects may be considered not all that surprising, or even weak.

      We agree that such evidence of neural processing in IB is relevant to — and perhaps indeed consistent with — our picture, and we’re grateful to the reviewer for pointing out further studies along those lines. Previously, we mentioned a study from Pitts et al., 2012 in which, as we wrote, “unexpected line patterns have been found to elicit the same Nd1 ERP component in both noticers and inattentionally blind subjects (Pitts et al., 2012).” We have added references to both the studies which the reviewer mentions – as well as an additional relevant study – to our manuscript in this context. Thank you for the helpful addition.

      We do however think that our studies are importantly different to this previous work. Our question is whether processing under IB yields representations which are available for explicit report and so would constitute clear evidence of seeing, and perhaps even conscious experience. As we discuss, evidence for this kind of processing remains wanting: “A handful of prior studies have explored the possibility that inattentionally blind subjects may retain some visual sensitivity to features of IB stimuli (e.g., Schnuerch et al., 2016; see also Kreitz et al., 2020, Nobre et al., 2020). However, a recent meta-analysis of this literature (Nobre et al., 2022) argues that such work is problematic along a number of dimensions, including underpowered samples and evidence of publication bias that, when corrected for, eliminates effects revealed by earlier approaches, concluding “that more evidence, particularly from well-powered pre-registered experiments, is needed before solid conclusions can be drawn regarding implicit processing during inattentional blindness” (Nobre et al., 2022).” Our paper is aimed at addressing this question which evidence of neural processing can only speak to indirectly.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Please report all of the data, especially the number of subjects in each experiment that answered Y/N and the numbers of subjects in each of the Y and N groups that guessed a feature correctly/incorrectly on the 2AFC tasks. And also the confidence ratings for the 2AFC task (for comparison with the confidence ratings on the Y/N questions).

      We now report all this data in our (revised) Supplementary Materials. We agree that this information will be helpful to readers.

      (2) Consider adding a control condition with partial attention (dual task) or full attention (single task) to estimate the rates of seeing the critical stimulus when it's expected.

      This is the only recommendation we have chosen not to implement. The reason, as we explain in detail above (especially in response to Reviewer #1 comment 5), is that this would not in fact be a “control condition” in our studies, and indeed would only inflate the biases we are concerned with in our work. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. And the practice is controversial: Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness" (emphasis added). So, our choice not to have such conditions ensures an especially stringent test of our central claim. Not having those conditions (and their accompanying exclusions) makes our results more interpretable, not less interpretable, and so the absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials in our experiments. (For even fuller discussion, see our response to Reviewer #1’s comment 5 above.)

      (3) Consider revising the interpretations to be more precise about the distinction between the super subject being above chance versus each individual subject who cannot be at chance or above chance because there was only a single trial per subject.

      We have now done this throughout the manuscript, as discussed above. We have also added a substantive additional discussion to our “Design and analytical approach” section discussing what should be said about individual subjects in light of our group level data.

      This was a very helpful point, and greatly clarifies the claims we wish to make in the paper. Thank you for this comment, which has certainly made our paper stronger.

      Reviewer #2 (Recommendations for the authors):

      I would be curious to hear the authors' response to two points:

      (1) What do they have to say about prior studies that do more than just ask yes/no questions (and ask several follow-ups)? Are those studies "valid"?

      A very substantial new discussion of this important point has been added. As you will see above, we comment on every one of the 18 papers this reviewer raised (as well as the general argument made); we contend that while many of these papers improve on past methodology in various ways, most in fact do “just ask yes/no questions”, and none of them makes the methodological advance we offer in our manuscript. However, this discussion has helped us clarify that very advance, and so working through this issue has really helped us improve our paper and make its relation to existing literature that much clearer. Thank you for raising this crucial point.

      (2) Do the authors think it is possible that in many cases, people are just guessing about a critical item's location or color and this is at least in part a form of priming?

      We have clarified our discussion in numerous places to further emphasize that our main point concerns above-chance sensitivity, not awareness. Given this, we take very seriously the hypothesis that something like priming of a kind sometimes proposed to occur in cases of blindsight or other putative cases of unconscious perception could be what is driving the responses in non-noticers.

      Reviewer #3 (Recommendations for the authors):

      (1) Control dual task version with expected stimuli would be nice

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials, which would not in fact be a “control” task in our experiments. For full discussion, see our response to Reviewer 3 above, as well as our summary here in the Recommendations for Authors section in responding to Reviewer 1, recommendation (2).

      (2) Please do a better job in discussing and introducing experiments about neural signatures during IB.

      A discussion of Vandenbroucke et al. 2014 and Scholte et al. 2006 has been added to our discussion of neural signatures in IB, as well as an additional reference to an important early study of semantic processing in IB (Rees et al., 1999). Thank you for these very helpful suggestions!

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      (1) The notion of a “root” causal gene - which the authors define based on a graph theoretic notion of topologically sorting graphs - requires a graph that is directed and acyclic. It is the latter that constitutes an important weakness here - it simply is a large simplification of human biology to draw out a DAG including hundreds of genes and a phenotype Y and to claim that the true graph contains no cycles.

      We agree that real causal graphs in biology often contain cycles. We now include additional experimental results with cyclic directed graphs in the Supplementary Materials. RCSP outperformed the other algorithms even in this setting, but we caution the reader that the theoretical interpretation of the RCS score may not coincide with a root causal effect when cycles exist:

      “We also evaluated the algorithms on directed graphs with cycles. We generated a linear SEM over ρ + 1 = 1000 variables in . We sampled the coefficient matrix β from a Bernoulli (1/(p − 1)) distribution but did not restrict the non-zero coefficients to the upper triangular portion of the matrix. We then proceeded to permute the variable ordering and weight each entry as in the Methods for the DAG. We repeated this procedure 30 times and report the results in Supplementary Figure 3.

      RCSP again outperformed all other algorithms even in the cyclic case. The results suggest that conditioning on the surrogate ancestors also estimates the RCS well even in the cyclic case. However, we caution that an error term E<sub>i</sub> can affect the ancestors of when cycles exist. As a result, the RCS may not isolate the causal effect of the error term and thus not truly coincide with the notion of a root causal effect in cyclic causal graphs.”

      (2) I also encourage the authors to consider more carefully when graph structure learned from Perturb-seq can be ported over to bulk RNA-seq. Presumably this structure is not exactly correct - to what extent is the RCSP algorithm sensitive to false edges in this graph? This leap - from cell line to primary human cells - is also not modeled in the simulation. Although challenging - it would be ideal for the RCSP to model or reflect the challenges in correctly identifying the regulatory structure.

      We now include additional experimental results, where we gradually increased the incongruence between the DAG modeling the Perturb-seq and the DAG modeling the bulk RNA-seq using a mixture of graphs. The performance of RCSP degraded gradually, rather than abruptly, with increasing incongruence. We therefore conclude that RCSP is robust to differences between the causal graphs representing Perturb-seq and bulk RNA-seq:

      “We next assessed the performance of RCSP when the DAG underlying the Perturb-seq data differs from the DAG underlying the bulk RNA-seq data. We considered a mixture of two random DAGs in bulk RNA-seq, where one of the DAGs coincided with the Perturb-seq DAG and second alternate DAG did not. We instantiated and simulated samples from each DAG as per the previous subsection. We generated 0%, 25%, 50%, 75%, and 100% of the bulk RNA-seq samples from the alternate DAG, and the rest from the Perturb-seq DAG. We ideally would like to see the performance of RCSP degrade gracefully, as opposed to abruptly, as the percent of samples derived from the alternate DAG increases.

      We summarize results in Supplementary Figure 4. As expected, RCSP performed the best when we drew all samples from the same underlying DAG for Perturb-seq and bulk RNA-seq. However, the performance of RCSP also degraded slowly as the percent of samples increased from the alternate DAG. We conclude that RCSP can accommodate some differences between the underlying DAGs in Perturb-seq and bulk RNA-seq with only a mild degradation in performance.”

      (3) It should also be noted that in most Perturb-seq experiments, the entire genome is not perturbed, and frequently important TFs (that presumably are very far “upstream” and thus candidate “root” causal genes) are not expressed highly enough to be detected with scRNA-seq. In that context - perhaps slightly modifying the language regarding RCSP’s capabilities might be helpful for the manuscript - perhaps it would be better to describe it as an algorithm for causal discovery among a set of genes that were perturbed and measured, rather than a truly complete search for causal factors. Perhaps more broadly it would also benefit the manuscript to devote slightly more text to describing the kinds of scenarios where RCSP (and similar ideas) would be most appropriately applied - perhaps a well-powered, phenotype annotated Perturb-seq dataset performed in a disease relevant primary cell.

      We now clarify that Perturb-seq can only identify root causal genes among the perturbed set of genes in the Discussion:

      “Modern genome-wide Perturb-seq datasets also adequately perturb and measure only a few thousand, rather than all, gene expression levels. RCSP can only identify root causal genes within this perturbed and measured subset.”

      We now also describe the scenario where RCSP can identify root causal genes well in the Introduction:

      “Experiments demonstrate marked improvements in performance, when investigators have access to a large bulk RNA-seq dataset and a genome-wide Perturb-seq dataset from a cell line of a disease-relevant tissue.”

      Reviewer 2:

      (1) The process from health-to-disease is not linear most of the time with many checks along the way that aim to prevent the disease phenotype. This leads to a non-deterministic nature of the path from health-to-disease. In other words, with the same root gene perturbations, and depending on other factors outside of gene expression, someone may develop a phenotype in a year, another in 10 years and someone else never. Claiming that this information is included in the error terms might not be sufficient to address this issue. The authors should discuss this limitation.

      The proposed approach accommodates the above non-deterministic nature. The error terms of model factors that are outside of gene expression. We model the relation from gene expression to Y as probabilistic rather than deterministic because , where E<sub>Y</sub> introduces stochasticity. Thus, two individuals with the same instantiations of the root causes may develop disease differently. We now clarify this in Methods:

      “The error terms model root causes that are outside of gene expression, such as genetic variation or environmental factors. Moreover, the relation from gene expression to Y is stochastic because , where E<sub>Y</sub> introduces the stochasticity. Two individuals may therefore have the exact same error term values over but different instantiations of Y.”

      (2) The paper assumes that the network connectivity will remain the same after perturbation. This is not always true due to backup mechanisms in the cells. For example, suppose that a cell wants to create product P and it can do it through two alternative paths: Path #1: ABP, Path #2: ACP. Now suppose that path #1 is more efficient, so when B can be produced, path #2 is inactive. Once the perturbation blocks element B from being produced, the graph connectivity changes by activation of path #2. I did not see the authors taking this into consideration, which seems to be a major limitation in using Perturb-seq results to infer conductivities.

      We agree that backup mechanisms can exist and therefore now include additional experimental results, where we gradually increased the incongruence between the DAG modeling the Perturb-seq and the DAG modeling the bulk RNA-seq using a mixture of graphs. The performance of RCSP degraded gradually, rather than abruptly, with increasing incongruence. We therefore conclude that RCSP is robust to differences between the causal graphs representing Perturb-seq and bulk RNA-seq:

      “We next assessed the performance of RCSP when the DAG underlying the Perturb-seq data differs from the DAG underlying the bulk RNA-seq data. We considered a mixture of two random DAGs in bulk RNA-seq, where one of the DAGs coincided with the Perturb-seq DAG and second alternate DAG did not. We generated 0%, 25%, 50%, 75%, and 100% of the bulk RNA-seq samples from the alternate DAG, and the rest from the Perturb-seq DAG. We ideally would like to see the performance of RCSP degrade gracefully, as opposed to abruptly, as the percent of samples derived from the alternate DAG increases.

      We summarize results in Supplementary Figure 4. As expected, RCSP performed the best when we drew all samples from the same underlying DAG for Perturb-seq and bulk RNA-seq. However, the performance of RCSP also degraded slowly as the percent of samples increased from the alternate DAG. We conclude that RCSP can accommodate some differences between the underlying DAGs in Perturb-seq and bulk RNA-seq with only a mild degradation in performance.”

      (3) There is substantial system heterogeneity that may cause the same phenotype. This goes beyond the authors claim that although the initial gene causes of a disease may differ from person to person, at some point they will all converge to changes in the same set of “root genes.” This is not true for many diseases, which are defined based on symptoms and lab tests at the patient level. You may have two completely different molecular pathologies that lead to the development of the same symptoms and test results. Breast cancer with its subtypes is a prime example of that. In theory, this issue could be addressed if there is infinite sample size. However, this assumption is largely violated in all existing biological datasets.

      The proposed method accommodates the above heterogeneity. We do not assume that the root causes affect the same set of root causal genes. Instead the root causes and root causal genes may vary from person to person. We write in the Introduction:

      “The problem is further complicated by the existence of complex disease, where a patient may have multiple root causal genes that differ from other patients even within the same diagnostic category... We thus also seek to identify patient-specific root causal genes in order to classify patients into meaningful biological subgroups each hopefully dictated by only a small group of genes.”

      The root causal genes may further affect different downstream genes at the patient-specific level. However root causal genes tend to have many downstream effects so that virtually every gene expression level becomes correlated with Y. We now clarify this by describing the omnigenic root causal model in the Introduction as follows:

      “Finally, application of the algorithm to two complex diseases with disparate pathogeneses recovers an omnigenic root causal model, where a small set of root causal genes drive pathogenesis but impact many downstream genes within each patient. As a result, nearly all gene expression levels are correlated with the diagnosis at the population level.”

      (4) Were the values of the synthetic variables Z-scored?

      Yes, all variables were z-scored. We now clarify this in Methods:

      “We also standardized all variables before running the regressions to prevent gaming of the marginal variances in causal discovery (Reisach et al., 2021; Ng et al., 2024).”

      (5) The algorithm seems to require both RNA-seq and Perturb-seq data (Algorithm 1, page 14). Can it function with RNA-seq data only? What will be different in this case?

      The algorithm cannot function with observational bulk RNA-seq data only. We included Perturb-seq because causal discovery with observational RNA-seq data alone tends to be inaccurate and unstable, as highlighted by the results of CausalCell. We further emphasize that we do not rely on d-separation faithfulness in Methods, which is typically required for causal discovery from observational data alone:

      “We can also claim the backward direction under d-separation faithfulness. We however avoid making this additional assumption because real biological data may not arise from distributions obeying d-separation faithfulness in practice.”

      (6) Synthetic data generation: how many different graphs (SEMs) did they start from? (30?) How many samples per graph? Did they test different sample sizes?

      We now clarify that we generate 30 random SEMs, each associated with a DAG. We used 200 samples for the bulk RNA-seq to mimic a relatively large but common sample size. We also drew 200 samples for each perturbation or control in the Perturb-seq data. We did not consider multiple sample sizes due to the time required to complete each run. Instead, we focused on a typical scenario where investigators would apply RCSP. We now write the following in the Methods:

      “We drew 200 samples for the bulk RNA-seq data to mimic a large but common dataset size. We introduced knockdown perturbations in Perturb-seq by subtracting an offset of two in the softplus function: . We finally drew 200 samples for the control and each perturbation condition to generate the Perturb-seq data. We repeated the above procedure 30 times.” We also include the following in Results:

      “We obtained 200 cell samples from each perturbation, and another 200 controls without perturbations. We therefore generated a total of 2501 × 200 = 500,200 single cell samples for each Perturb-seq dataset. We simulated 200 bulk RNA-seq samples.”

      (7) The presentation of comparative results (Supplementary Figures 4 and 7) is not clear. No details are given on how these results were generated. (what does it mean “The first column denotes the standard deviation of the outputs for each algorithm?”) Why all other methods have higher SD differences than RCSP? Is it a matter of scaling? Shouldn’t they have at least some values near zero since the authors “added the minimum value so that all histograms begin at zero?”

      Each of these supplementary figures contains a 6 by 3 table of figures. By the first column, we mean column one (with rows 1 through 6) of each figure. The D-RCS and D-SD scores represent standard deviations of the RCS and SD scores from zero of each gene, respectively. We can similarly compute the standard deviation of the outputs of the algorithms. We now clarify this in the Supplementary Materials:

      “The figure contains 6 rows and 3 columns. Similar to the D-RCS, we can compute the standard deviation of the output of each algorithm from zero for each gene. The first column in Supplementary Figure 7 denotes the histograms of these standard deviations across the genes.”

      Many histograms do not appear to start at zero because the bars are too small to be visible. We now clarify this in the Supplementary Materials as well:

      “Note that the bars at zero are not visible for many algorithms, since only a few genes attained standard deviations near the minimum.”

      (8) Why RCSP results are more like a negative binomial distribution and every other is kind of normal?

      All other methods have higher standard deviations than RCSP because they fail to compute an accurate measure of the root causal effect. Recall that, just like a machine has a few root causal problems, only a few root casual genes have large root causal effects under the omnigenic root causal model. The results of RCSP look more like a negative binomial distribution because most RCS scores are concentrated around zero and only a few RCS scores are large – consistent with the omnigenic root causal model. The other algorithms fail to properly control for the upstream genes and thus attain large standard deviations for nearly all genes. We now clarify these points in the Supplementary Materials as follows:

      “If an algorithm accurately identifies root causal genes, then it should only identify a few genes with large conditional root causal effects under the omnigenic root causal model. The RCSP algorithm had a histogram with large probability mass centered around zero with a long tail to the right. The standard deviations of the outputs of the other algorithms attained large values for nearly all genes. Incorporating feature selection and causal discovery with CausalCell introduced more outliers in the histogram of ANM. We conclude that only RCSP detected an omnigenic root causal model.”

      (9) What is the significance of genes changing expression “from left to right” in a UMAP plot? (e.g., Fig. 3h and 3g)

      The first UMAP dimension captured the variability of the RCS scores for most root causal genes. As a result, we could focus our analysis on the black cluster in Figure 3 (g) with large RCS scores in the subsequent pathway enrichment analysis summarized in Figure 3 (j). If two dimensions were involved, then we would need to analyze at least two clusters (e.g., black and pink), but this was not the case. We now clarify this in Results:

      “The RCS scores of most of the top genes exhibited a clear gradation increasing only from the left to the right hand side of the UMAP embedding; we plot an example in Figure 3 (h). We found three exceptions to this rule among the top 30 genes (example in Figure 3 (i) and see Supplementary Materials). RCSP thus detected genes with large RCS scores primarily in the black cluster of Figure 3 (g). Pathway enrichment analysis within this cluster alone yielded supra-significant results on the same pathway detected in the global analysis...”

      (10) The authors somewhat overstate the novelty of their algorithm. Representation of GRNs as causal graphs dates back in 2000 with the work of Nir Friedman in yeast. Other methods were developed more recently that look on regulatory network changes at the single sample level which the authors do not seem to be aware (e.g., Ellington et al, NeurIPS 2023 workshop GenBio and Bushur et al, 2019, Bioinformatics are two such examples). The methods they mention are for single cell data and they are not designed to connect single sample-level changes to a person’s phenotype. The RCS method needs to be put in the right background context in order to bring up what is really novel about it.

      We agree that many methods already exist for uncovering associational, predictive (Markov, neighborhood) and causal gene regulatory networks. We now cite the above papers. However, the novelty in our manuscript is not causal graph discovery, but rather estimation of root causal effects, detection of root causal genes, and the proposal of the omnigenic root causal model. We now clarify this in the

      Introduction:

      “Many algorithms focus on discovering associational or predictive relations, sometimes visually represented as gene regulatory networks (Costa et al., 2017; Ellington et al., 2023). Other methods even identify causal relations (Friedman et al., 2000; Wang et al., 2023; Wen et al., 2000; Buschur et al., 2000), but none pinpoint the first gene expression levels that ultimately generate the vast majority of pathogenesis. Simply learning a causal graph does not resolve the issue because causal graphs do not summarize the effects of unobserved root causes, such as unmeasured environmental changes or variants, that are needed to identify all root causal genes. We therefore define the Root Causal Strength (RCS) score...”

      Reviewer 3:

      (1) Several assumptions of the method are problematic. The most concerning is that the observational expression changes are all causally upstream of disease. There is work using Mendelian randomization (MR) showing that the opposite is more likely to be true: most differential expression in disease cohorts is a consequence rather than a cause of disease (Porcu et al., 2021). Indeed, the oxidative stress of AMD has known cellular responses including the upregulation of p53. The authors need to think carefully about how this impacts their framework. Can the theory say anything in this light? Simulations could also be designed to address robustness.

      Strictly speaking, we believe that differential expression in disease most likely has a cyclic causal structure: gene expression causes a diagnosis or symptom severity, and a diagnosis or symptom severity lead to treatments and other behavioral changes that perturb gene expression. For example, revTMWR in Porcu et al. (2021) uses trans-variants that are less likely to directly cause gene expression and instead directly cause a phenotype. However, TWMR as proposed in Porcu et al. (2019) instead uses cis-eQTLs and finds many putative causal relations from gene expression to phenotype. Thus, both causal directions likely hold.

      RCSP uses disease-relevant tissue believed to harbor gene expression levels that cause disease. However, RCSP theoretically cannot handle the scenario where Y is a non-sink vertex and is a parent of a gene expression level because modern Perturb-seq datasets usually do not perturb or measure Y. We therefore empirically investigated the degree of error by running experiments, where we set Y to a non-sink vertex, so that it can cause gene expression. We find that the performance of RCSP degrades considerably for gene expression levels that contain Y as a parent. Thus RCSP is sensitive to violations of the sink target assumption:

      “We finally considered the scenario where Y is a non-sink (or non-terminal) vertex. If Y is a parent of a gene expression level, then we cannot properly condition on the parents because modern Perturbseq datasets usually do not intervene on Y or measure Y . We therefore empirically investigated the degradation in performance resulting from a non-sink target Y, in particular for gene expression levels where Y is a parent. We again simulated 200 samples from bulk RNA-seq and each condition of Perturbseq with a DAG over 1000 vertices, an expected neighborhood size of 2 and a non-sink target Y . We then removed the outgoing edges from Y and resampled the DAG with a sink target. We compare the results of RCSP for both DAGs in gene expression levels where Y is a parent. We plot the results in Supplementary Figure 5. As expected, we observe a degradation in performance when Y is not terminal, where the mean RMSE increased from 0.045 to 0.342. We conclude that RCSP is sensitive to violations of the sink target assumption.”

      (2) A closely related issue is the DAG assumption of no cycles. This assumption is brought to bear because it is required for much classical causal machinery, but is unrealistic in biology where feedback is pervasive. How robust is RCSP to (mild) violations of this assumption? Simulations would be a straightforward way to address this.

      We agree that real causal graphs in biology often contain cycles. We now include additional experimental results with cyclic directed graphs in the Supplementary Materials. RCSP outperformed the other algorithms even in this setting, but we caution the reader that the theoretical interpretation of the RCS score may not coincide with a root causal effect when cycles exist:

      “We also evaluated the algorithms on directed graphs with cycles. We generated a linear SEM over p + 1 = 1000 variables in . We sampled the coefficient matrix β from a Bernoulli (1/(p − 1)) distribution but did not restrict the non-zero coefficients to the upper triangular portion of the matrix. We then proceeded to permute the variable ordering and weight each entry as in the Methods for the DAG. We repeated this procedure 30 times and report the results in Supplementary Figure 3.

      RCSP again outperformed all other algorithms even in the cyclic case. The results suggest that conditioning on the surrogate ancestors also estimates the RCS well even in the cyclic case. However, we caution that an error term E<sub>i</sub> can affect the ancestors of , when cycles exist. As a result, the RCS may not isolate the causal effect of the error term and thus not truly coincide with the notion of a root causal effect in cyclic causal graphs.”

      (3) The authors spend considerable effort arguing that technical sampling noise in X can effectively be ignored (at least in bulk). While the mathematical arguments here are reasonable, they miss the bigger picture point that the measured gene expression X can only ever be a noisy/biased proxy for the expression changes that caused disease: 1) Those events happened before the disease manifested, possibly early in development for some conditions like neurodevelopmental disorders. 2) bulk RNA-seq gives only an average across cell-types, whereas specific cell-types are likely “causal.” 3) only a small sample, at a single time point, is typically available. Expression in other parts of the tissue and at different times will be variable.

      We agree that many other sources of error exist. The causal model of RNA-expression in Methods corresponds to a single snapshot in time for each sample. We now clarify this in the Methods as follows:

      “We represent a snapshot of a biological causal process using an SEM over obeying Equation (3).”

      We thus only detect the root causal genes in a single snapshot in time for each sample in bulk RNA-seq. If we cannot detect the root causal effect in a gene due to the signal washing out over time as in (1), or if the root causal effect in different cell types cancel each other out to exactly zero in bulk as in (2), then we cannot detect those root causal genes even with an infinite sample size.

      (4) While there are connections to the omnigenic model, the latter is somewhat misrepresented. The authors refer to the “core genes” of the omnigenic model as being at the end (longitudinal) of pathogenesis. The omnigenic model makes no statements about temporal ordering: in causal inference terminology the core genes are simply the direct causes of disease.

      We now clarify that we use the word pathogenesis to mean the causal cascade from root causes to the diagnosis. In this case, the direct causes of the diagnosis correspond to the end of pathogenesis, while the root causes correspond to the beginning. For example, if , with Y a diagnosis, then X<sub>1</sub> is a root causal gene while X<sub>2</sub> is a core (direct causal) gene. We now clarify this in the Introduction:

      Root causes of disease correspond to the most upstream causes of a diagnosis with strong causal effects on the diagnosis. Pathogenesis refers to the causal cascade from root causes to the diagnosis. Genetic and non-genetic factors may act as root causes and affect gene expression as an intermediate step during pathogenesis. We introduce root causal gene expression levels – or root causal genes for short – that correspond to the initial changes to gene expression induced by genetic and non-genetic root causes that have large causal effects on a downstream diagnosis (Figure 1 (a)). Root causal genes differ from core genes that directly cause the diagnosis and thus lie at the end, rather than at the beginning, of pathogenesis (Boyle et al., 2017).”

      (5) A key observation underlying the omnigenic model is that genetic heritability is spread throughout the genome (and somewhat concentrated near genes expressed in disease relevant cell types). This implies that (almost) all expressed genes, or their associated (e)SNPs, are “root causes”.

      We now clarify that genetic heritability can be spread throughout the genome in the omnigenic root causal model as well in the Discussion:

      “Further, each causal genetic variant tends to have only a small effect on disease risk in complex disease because the variant can directly cause Y or directly cause any causal gene including those with small root causal effects on Y ; thus, all error terms that cause Y can model genetic effects on Y. However, the root causal model further elaborates that genetic and non-genetic factors often combine to produce a few root causal genes with large root causal effects, where non-genetic factors typically account for the majority of the large effects in complex disease. Many variants may therefore cause many genes in diseases with only a few root causal genes.”

      We finally add Figure 5 into the Discussion as a concrete example illustrating the omnigenic root causal model:

      (6) The claim that root causal genes would be good therapeutic targets feels unfounded. If these are highly variable across individuals then the choice of treatment becomes challenging. By contrast the causal effects may converge on core genes before impacting disease, so that intervening on the core genes might be preferable. The jury is still out on these questions, so the claim should at least be made hypothetical.

      We clarify that we do not claim that root causal genes are better treatment targets than core genes in terms of magnitudes of causal effects on the phenotype. For example, in the common cold with a virus as the root cause, giving a patient an antiviral will eliminate fever and congestion, but so will giving a decongestant and an antipyretic. We only claim that treating root causal genes can eliminate disease near its pathogenic onset, just like giving an antiviral can eliminate the viral load and stop pathogenesis. We write the following the Introduction:

      “Treating root causal genes can modify disease pathogenesis in its entirety, whereas targeting other causes may only provide symptomatic relief... Identifying root causal genes is therefore critical for developing treatments that eliminate disease near its pathogenic onset.”

      We also further clarify in the Discussion that root causal genes account for deleterious causal effects not captured by the diagnosis Y:

      “We finally emphasize that the root causal model accounts for all deleterious effects of the root causal genes, whereas the core gene model only captures the deleterious effects captured by the diagnosis Y. For example, the disease of diabetes causes retinopathy, but retinopathy is not a part of the diagnostic criteria of diabetes. As a result, the gene expression levels that cause retinopathy but not the diagnosis of diabetes are not core genes, even though they are affected by the root causal genes.”

      We do agree that root causal genes may differ substantially between patients, although it is unclear if the heterogeneity is too great to develop treatments.

      (7) The closest thing to a gold standard I believe we have for “root causal genes” is integration of molecular QTLs and GWAS, specifically coloc/MR. Here the “E” of RCSP are explicitly represented as SNPs. I don’t know if there is good data for AMD but there certainly is for MS. The authors should assess the overlap with their results. Another orthogonal avenue would be to check whether the root causal genes change early in disease progression.

      Colocalization and Mendelian randomization unfortunately cannot identify root causal effects because they all attempt, either heuristically (colocalization) or rigorously (MR), to identify variants that cause each gene expression level rather than variants that directly cause each gene expression level and thus make up the error terms. We therefore need new methods that can identify direct causal variants in order to assess overlap.

      We checked whether root causal genes change early in disease progression using knowledge of pathogenesis. In particular, oxidative stress induces pathogenesis in AMD, and RCSP identified root causal genes involved in oxidative stress in AMD:

      “The pathogenesis of AMD involves the loss of RPE cells. The RPE absorbs light in the back of the retina, but the combination of light and oxygen induces oxidative stress, and then a cascade of events such as immune cell activation, cellular senescence, drusen accumulation, neovascularization and ultimately fibrosis (Barouch et al., 2007). We therefore expect the root causal genes of AMD to include genes involved in oxidative stress during early pathogenesis. The gene MIPEP with the highest D-RCS score in Figure 3 (d) indeed promotes the maturation of oxidative phosphorylation-related proteins (Shi et al., 2011). The second gene SLC7A5 is a solute carrier that activates mTORC1 whose hyperactivation increases oxidative stress via lipid peroxidation (Nachef et al., 2021; Go et al., 2020). The gene HEATR1 is involved in ribosome biogenesis that is downregulated by oxidative stress (Turi et al., 2018). The top genes discovered by RCSP thus identify pathways known to be involved in oxidative stress.”

      Similarly, T cell infiltration across the blood brain barrier initiates pathogenesis in MS, and RCSP identified root causal genes involved in this infiltration:

      “Genes with the highest D-RCS scores included MNT, CERCAM and HERPUD2 (Figure 4 (d)). MNT is a MYC antagonist that modulates the proliferative and pro-survival signals of T cells after engagement of the T cell receptor (Gnanaprakasam et al., 2017). Similarly, CERCAM is an adhesion molecule expressed at high levels in microvessels of the brain that increases leukocyte transmigration across the blood brain barrier (Starzyk et al., 2000). HERPUD2 is involved in the endoplasmic-reticulum associated degradation of unfolded proteins (Kokame et al., 2000). Genes with the highest D-RCS scores thus serve key roles in known pathogenic pathways of MS.”

      (8) The available Perturb-seq datasets have limitations beyond on the control of the authors. 1) The set of genes that are perturbed. The authors address this by simply sub-setting their analysis to the intersection of genes represented in the perturbation and observational data. However, this may mean that a true ancestor of X is not modeled/perturbed, limiting the formal claims that can be made. Additionally, some proportion of genes that are nominally perturbed show little to no actual perturbation effect (for example, due to poor guide RNA choice) which will also lead to missing ancestors.

      We now clarify that Perturb-seq can only identify root causal genes among the adequately perturbed set of genes in the Discussion:

      “Modern genome-wide Perturb-seq datasets also only adequately perturb and measure a few thousand, rather than all, gene expression levels. RCSP can only identify root causal genes within this perturbed and measured subset.”

      (9) The authors provide no mechanism for statistical inference/significance for their results at either the individual or aggregated level. While I am a proponent of using effect sizes more than p-values, there is still value in understanding how much signal is present relative to a reasonable null.

      We now explain that RCSP does not perform statistical inference in Methods because it is not clear how to define the appropriate cut-off for the RCS score under the null distribution:

      “We focus on statistical estimation rather than statistical inference because Φ<sub>i</sub> > 0 when E<sub>i</sub> causes Y under mild conditions, so we reject the null hypothesis that Φ<sub>i</sub> \= 0 for many genes if many gene expression levels cause Y. However, just like a machine typically breaks down due to only one or a few root causal problems, we hypothesize that only a few genes have large RCS scores Φ<sub>i</sub> ≫ 0 even in complex disease.”

      (10) I agree with the authors that age coming out of a “root cause” is potentially encouraging. However, it is also quite different in nature to expression, including being “measured” exactly. Will RCSP be biased towards variables that have lower measurement error?

      We tested the above hypothesis by plotting sequencing depth against the D-RCS scores of each gene. We observed a small negative correlation between sequencing depth and D-RCS scores, indicating the D-RCS scores are slightly biased upwards with low sequencing depth. However, genes with the largest D-RCS scores exhibited a wide variety of sequencing depths in both MS and AMD, suggesting that sequencing depth has minimal effect on the largest D-RCS scores. We now explain these results for AMD in the Supplementary Materials:

      “Theorem 1 states that RCS scores may exhibit bias with insufficient sequencing depth. The genes with large D-RCS scores may therefore simply have low sequencing depths. To test this hypothesis, we plotted sequencing depth against D-RCS scores. Consistent with Theorem 1, we observed a small negative correlation between D-RCS and sequencing depth (ρ \= −0.16, p=2.04E-13), and D-RCS scores exhibited greater variability at the lowest sequencing depths (Supplementary Figure 8). However, genes with the largest D-RCS scores had mean sequencing depths interspersed between 20 and 3000. We conclude that genes with the largest D-RCS scores had a variety of sequencing depths ranging from low to high.”

      We also report the results for MS:

      “We plot sequencing depth against the D-RCS scores of each gene similar to the AMD dataset. We again observed a small negative correlation (ρ \= −0.136, p_<_2.2E-16), indicating that genes with low sequencing depths had slightly higher D-RCS scores on average (Supplementary Figure 12). However, genes with the largest D-RCS scores again had a variety of sequencing depths. We conclude that sequencing depth has minimal correlation with the largest D-RCS scores.”

      (11) Finally, it’s a stretch to call K562 cells “lymphoblasts.” They are more myeloid than lymphoid.

      We now clarify that K562 cells are undifferentiated blast cells that can be induced to differentiate into lymphoblasts in Results:

      “We next ran RCSP on 137 samples collected from CD4+ T cells of multiple sclerosis (MS; GSE137143) as well as Perturb-seq data of 1,989,578 undifferentiated blast cells that can be induced to differentiate into lymphoblasts, or the precursors of T cells and other lymphocytes.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      The authors assess the effectiveness of electroporating mRNA into male germ cells to rescue the expression of proteins required for spermatogenesis progression in individuals where these proteins are mutated or depleted. To set up the methodology, they first evaluated the expression of reporter proteins in wild-type mice, which showed expression in germ cells for over two weeks. Then, they attempted to recover fertility in a model of late spermatogenesis arrest that produces immotile sperm. By electroporating the mutated protein, the authors recovered the motility of ~5% of the sperm, although the sperm regenerated was not able to produce offspring using IVF.

      We actually did not write that “sperm regenerated was not able to produce offspring using IVF” but rather that IVF was not attempted because the number of rescued sperm was too low. To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.

      This is a comprehensive evaluation of the mRNA methodology with multiple strengths. First, the authors show that naked synthetic RNA, purchased from a commercial source or generated in the laboratory with simple methods, is enough to express exogenous proteins in testicular germ cells. The authors compared RNA to DNA electroporation and found that germ cells are efficiently electroporated with RNA, but not DNA. The differences between these constructs were evaluated using in vivo imaging to track the reporter signal in individual animals through time. To understand how the reporter proteins affect the results of the experiments, the authors used different reporters: two fluorescent (eGFP and mCherry) and one bioluminescent (Luciferase). Although they observed differences among reporters, in every case expression lasted for at least two weeks. 

      The authors used a relevant system to study the therapeutic potential of RNA electroporation. The ARMC2-deficient animals have impaired sperm motility phenotype that affects only the later stages of spermatogenesis. The authors showed that sperm motility was recovered to ~5%, which is remarkable due to the small fraction of germ cells electroporated with RNA with the current protocol. The 3D reconstruction of an electroporated testis using state-of-the-art methods to show the electroporated regions is compelling. 

      The main weakness of the manuscript is that although the authors manage to recover motility in a small fraction of the sperm population, it is unclear whether the increased sperm quality is substantial to improve assisted reproduction outcomes. The quality of the sperm was not systematically evaluated in the manuscript, with the endpoints being sperm morphology and sperm mobility. 

      We would like to thank the reviewers for their comments. As previously stated above, we produced additional rescue experiments and performed CASA, morphology observation, IVF and ICSI with the rescued sperm. The rescued ARMC2 sperm exhibited normal morphology (new figure 11 and Supp Fig 8), motility (figure 11), and fecundity (figure 12).  Whereas sperm from untreated KO males were unable to fertilize egg by IVF, the rescued sperm fertilized eggs in vitro at a significant level (mean 62%, n=5), demonstrating that our strategy improves the sperm quality and assisted reproduction outcome (from 0 to 62%). 

      Some key results, such as the 3D reconstruction of the testis and the recovery of sperm motility, are qualitative given the low replicate numbers or the small magnitude of the effects. The presentation of the sperm motility data could have been clearer as well. For example, on day 21 after Armc2-mRNA electroporation, only one animal out of the three tested showed increased sperm motility. However, it is unclear from Figure 11A what the percentage of sperm motility for this animal is since the graph shows a value of >5% and the reported aggregate motility is 4.5%. It would have been helpful to show all individual data points in Figure 11A. 

      We provide now in figure 11A, a graph showing the percentage of rescued sperm for all animals. (scatter dot plot). Moreover, we performed additional CASA experiments to analyze in detail sperm motility (Figure 11A2-A3). Individual CASA parameters for motile sperm cells were extracted as requested by reviewer 3 and represented in a new graph (Fig 11 A2). 

      The expression of the reporter genes is unambiguous; however, better figures could have been presented to show cell type specificity. The DAPI staining is diffused, and it is challenging to understand where the basement membranes of the tubules are. For example, in Figures 7B3 and 7E3, the spermatogonia seems to be in the middle of the seminiferous tubule. The imaging was better for Figure 8. Suboptimal staining appears to lead to mislabeling of some germ cell populations. For example, in Supplementary Figure 4A3, the round spermatid label appears to be labeling spermatocytes. Also, in some instances, the authors seem to be confusing, elongating spermatids with spermatozoa, such as in the case of Supplementary Figures 4D3 and D4.

      Thanks for the comments, some spermatogenic cells were indeed mislabeled as you mentioned. We have therefore readjusted the labeling accordingly. We also changed spermatozoa to mature spermatids. The new sentence is now: “At the cellular level, fluorescence was detectable in germ cells (B1-B3) including Spermatogonia (Sg), Spermatocytes (Scytes),round Spermatids (RStids), mature spermatids (m-Sptids) and Sertoli cells (SC)”. Moreover, to indicate the localization of the basal membrane, we have also labelled myoid cells.

      The characterization of Armc2 expression could have been improved as well. The authors show a convincing expression of ARMC2 in a few spermatids/sperm using a combination of an anti-ARMC2 antibody and tubules derived from ARMC2 KO animals. At the minimum, one would have liked to see at least one whole tubule of a relevant stage.  

      Thanks for the remark. 

      We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.

      Overall, the authors show that electroporating mRNA can improve spermatogenesis as demonstrated by the generation of motile sperm in the ARMC2 KO mouse model. 

      Thank you

      Reviewer #2 (Public Review): 

      Summary: 

      Here, the authors inject naked mRNAs and plasmids into the rete testes of mice to express exogenous proteins - GFP and later ARMC2. This approach has been taken before, as noted in the Discussion to rescue Dmc1 KO infertility. While the concept is exciting, multiple concerns reduce reviewer enthusiasm. 

      Strengths: 

      The approach, while not necessarily novel, is timely and interesting.  Weaknesses: 

      Overall, the writing and text can be improved and standardized - as an example, in some places in vivo is italicized, in others it's not; gene names are italicized in some places, others not; some places have spaces between a number and the units, others not. This lack of attention to detail in the preparation of the manuscript is a significant concern to this reviewer - the presentation of the experimental details does cast some reasonable concern with how the experiments might have been done. While this may be unfair, it is all the reviewers have to judge. Multiple typographical and grammatical errors are present, and vague or misleading statements. 

      Thanks for the comment, we have revised the whole manuscript to remove all the mistakes. We have also added new experiments/figures to strengthen the message. Finally, we have substantially modified the discussion.

      Reviewer #3 (Public Review):

      Summary: 

      The authors used a novel technique to treat male infertility. In a proof-of-concept study, the authors were able to rescue the phenotype of a knockout mouse model with immotile sperm using this technique. This could also be a promising treatment option for infertile men. 

      Strengths: 

      In their proof-of-concept study, the authors were able to show that the novel technique rescues the infertility phenotype in vivo. 

      Weaknesses: 

      Some minor weaknesses, especially in the discussion section, could be addressed to further improve the quality of the manuscript. 

      We have substantially modified the discussion, following the remarks of the reviewers.

      It is very convincing that the phenotype of Armc2 KO mice could (at least in part) be rescued by injection of Armc2 RNA. However, a central question remains about which testicular cell types have been targeted by the constructs. From the pictures presented in Figures 7 and 8, this issue is hard to assess. Given the more punctate staining of the DNA construct a targeting of Sertoli cells is more likely, whereas the more broader staining of seminiferous tubules using RNA constructs is talking toward germ cells. Further, the staining for up to 119 days (Figure 5) would point toward an integration of the DNA construct into the genome of early germ cells such as spermatogonia and/or possibly to Sertoli cells. 

      Thanks for the comment. We would like to recall the peculiar properties of the non-insertional Enhanced Episomes Vector (EEV) plasmid, which is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). It allows the persistence of the plasmid for long period of time without integration. Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an EpsteinBarr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

      The 641-amino-acid EBNA1 protein contains numerous domains. The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018). It is notable that EEV is maintained at a rate of 90-95% per cell division.

      Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

      Of note, the specificity of EEV was already indicated in the introduction (lines 124-128 clean copy). Nevertheless, we have added more information about EEV to help the readers.  

      Given the expression after RNA transfection for up to 21 days (Figure 4) and the detection of motile sperm after 21 days (Figure 11), this would point to either round spermatids or spermatocytes.  These aspects need to be discussed more carefully (discussion section: lines 549-574).

      We added a sentence to highlight that spermatids are transfected and protein synthetized at this stage and this question is discussed in details (see lines 677-684 clean copy).

      It would also be very interesting to know in which testicular cell type Armc2 is endogenously expressed (lines 575-591)

      Thanks for the remarks. We present now new images showing the full seminiferous tubules as requested by reviewer 1 (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that Armc2 is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 570-579 clean copy).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      The article is well-structured and easy to read. Nonetheless, there are typos and mistakes in some places that are distracting to the reader, such as the capitalization of the word "Oligo-" in the title of the manuscript, the use of the word "Materiel" in the title of the Materials and methods and the presence of space holders "Schorr staining was obtained from Merck (XXX)".  Thank you, we corrected the misspelling of "Materials and Methods" and corrected our error: "obtained from Merck (Darmstadt, Germany)". We also carefully corrected the manuscript to remove typos and mistakes.

      The discussion is too lengthy, with much repetition regarding the methods used and the results obtained. For example, these are two sentences from the discussion. "The vector was injected via the rete testis into the adult Armc2 KO mice. The testes were then electroporated." I would recommend shortening these passages.

      Thanks for your comments, we removed the sentences and we have substantially modified the discussion, following the remarks of the reviewers.

      The work is extensive, and many experiments have been done to prove the points made. However, a more in-depth analysis of critical experiments would have benefited the manuscript significantly. A more thorough analysis of sperm mobility and morphology using the CASA system would have been an initial step.

      In response to the observations made, additional CASA experiments and sperm motility analysis were conducted, as illustrated in Figure 11 (A2-A3). Individual CASA parameters for motile sperm cells were extracted as suggested and represented in a new graph (Fig 11 A2). We have observed significant differences between WT and rescued sperm. In particular, the VSL and LIN parameters were lower for rescued sperm. Nevertheless, these differences were not sufficient to prevent IVF, maybe because the curvilinear velocity (VCL) was not modified.

      In the case of ARMC2 localization, an analysis of the different stages of spermatogenesis to show when ARMC2 starts to be expressed. 

      Thanks for the remarks. This is an important remark pointed out by all reviewers. As explained above, we have performed more experiments. We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatid layers. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 575579 clean copy).

      Finally, exploring additional endpoints to understand the quality of the sperm generated, such as the efficiency of ICSI or sperm damage, could have helped understand the degree of the recovery.

      This point was underlined in public review. We paste here our answer: “To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.”

      Reviewer #2 (Recommendations For The Authors):

      38,74 intracellular

      Thanks, we changed it accordingly: "Intracytoplasmic sperm injection (ICSI) is required to treat such a condition, but it has limited efficacy and has been associated with a small increase in birth defects" and "such as intracytoplasmic sperm injection (ICSI)".

      39 "limited efficacy" Versus what? And for what reason? "small increase in birth defects" - compared to what? 

      We changed to “… but it is associated with a small increase in birth defect with comparison to pregnancies not involving assisted conception.”

      40 Just thinking through the logic of the argument thus far - the authors lay out that there are people with OAT (true), ICSI must be used (true), ICSI is bad (not convincing), and therefore a new strategy is needed... so is this an alternative to ICSI? And this is to restore fertility, not "restore spermatogenesis"

      - because ICSI doesn't restore spermatogenesis. This logic flow needs to be cleaned up some

      Thanks we changed it accordingly: “restore fertility.”

      45 "mostly"?

      Thank you, we removed the word: “We show that mRNA-coded reporter proteins are detected for up to 3 weeks in germ cells, making the use of mRNA possible to treat infertility.”

      65 Reference missing. 

      We added the following reference Kumar, N. and A. K. Singh (2015). "Trends of male factor infertility, an important cause of infertility: A review of literature." J Hum Reprod Sci 8(4): 191-196.

      68 Would argue meiosis is not a reduction of the number of chromosomes - that happens at the ends of meiosis I and II - but the bulk of meiosis is doubling DNA and recombination; would re-word; replace "differentiation" with morphogenesis, which is much more commonly used:

      Thank you, we have changed the sentence accordingly: "proliferation (mitosis of spermatogonia), reduction of the number of chromosomes (meiosis of spermatocytes), and morphogenesis of sperm (spermiogenesis)".

      70 "almost exclusively" is an odd term, and a bit of an oxymoron - if not exclusively, then where else are they expressed? Can you provide some sense of scale rather than using vague words like "large", "almost", "several", "strongly" and "most...likely" - need some support for these claims by being more specific: 

      Thanks for the comment, we changed the sentence: "The whole process involves around two thousand genes, 60% of which are expressed exclusively in the testes."

      73 "severe infertility" is redundant - if they are infertile, is there really any more or less about it? I think what is meant is patients with immotile sperm can be helped by ICSI - so just be more specific... 

      We changed the transition : “Among infertility disorders, oligo-astheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991); it is likely to be of genetic origin. Spermatocytograms of OAT patients show a decrease in sperm concentration, multiple morphological defects and defective motility. Because of these combined defects, patients are infertile and can only conceive by IntraCytoplasmic Sperm Injection (ICSI). IntraCytoplasmic Sperm Injection (ICSI) can efficiently overcome the problems faced. However, there are …”

      75 "some" is vague - how many concerns, and who has them? Be specific!

      Thanks for the comment, we removed the word.

      76-7 Again, be specific - "real" has little meaning - what is the increased risk, in % or fold? This is likely a controversial point, so make sure you absolutely support your contention with data .

      77 "these"? There was only one concern listed - increased birth defects; and "a number" is vague - what number, 1 or 1,000,000? A few (2-3), dozens, hundreds? 

      Thanks for the comment, we have reworded the sentence: “Nevertheless, concerns persist regarding the potential risks associated with this technique, including blastogenesis defect, cardiovascular defect, gastrointestinal defect, musculoskeletal defect, orofacial defect, leukemia, central nervous system tumors, and solid tumors. Statistical analyses of birth records have demonstrated an elevated risk of birth defects, with a 30–40% increased likelihood in cases involving ICSI, and a prevalence of birth defects between 1% and 4%.” We have added a list of references to support these claims.

      79-81 So, basically transgenesis? Again, vague terms "widely" - I don't think it's all that widely used yet... and references are missing to support the statement that integration of DNA into patient genomes is widely used. Give specific numbers, and provide a reference to support the contention. 

      Thanks for the comment, we removed the word widely and add references.

      81-5 Just finished talking about humans, but now it appears the authors have switched to talking about mice - got to let the readers know that! Unless you're talking about the Chinese group that deleted CCR5 in making transgenic humans? 

      Your feedback is greatly appreciated. In response to your comments, the sentence in question has been amended to provide a more comprehensive understanding. Indeed, the text refers to experiences carried in mice. The revised wording is as follows: “Given the genetic basis of male infertility, the first strategy, tested in mice, was to overcome spermatogenic failure associated with monogenic diseases by delivery of an intact gene to deficient germ cells (Usmani, Ganguli et al. 2013). 

      84-5 "efficiently" and "high" - provide context so the reader can understand what is meant - do the authors mean the experiments work efficiently, or that a high percentage of cells are transfected? And give some numbers or range of numbers - you're asking the readers to take your word for things when you choose adjectives - instead, provide values and let the readers decide for themselves.

      Thanks for the comment, we have reworded the sentence: Gene therapy is effective in germ cells, as numerous publications have shown that conventional plasmids can be transferred into spermatogonia in several species with success, allowing their transcription in all cells of the germinal lineage (Usmani, Ganguli et al. 2013, Michaelis, Sobczak et al. 2014, Raina, Kumar et al. 2015, Wang, Liu et al. 2022).

      93 Reference at the end of the sentence "most countries"

      Thanks, we changed the sentence and added the reference: the new sentence is "… to avoid any eugenic deviations, transmissible changes in humans are illegal in 39 countries (Liu 2020)” (Liu, S. (2020). "Legal reflections on the case of genomeedited babies." Glob Health Res Policy 5: 24

      93-4 Odd to say "multiple" and then list only one. 

      Thanks for the comment, we have reworded the sentence: “Furthermore, the genetic modification of germ cell lines poses biological risks, including the induction of cancer, off-target effects, and cell mosaicism. Errors in editing may have adverse effects on future generations. It is exceedingly challenging to anticipate the consequences of genetic mosaicism, for instance, in a single individual. (Sadelain, Papapetrou et al. 2011, Ishii 2017).”

      97 Is this really a "small" change? Again, would use adjectives carefully - to this reviewer, this is not a small change, but a significant one! And "should be" is not altogether convincing

      Thanks for the comment, we have reworded the sentence: “Thanks to this change, the risk of genomic insertion is avoided, and thus there is no question of heritable alterations.”

      What chance is there of retrotransposition? Is there any data in the literature for that, after injecting millions of copies of RNA one or more might be reverse transcribed and inserted into the genome?

      This is certainly possible and is the putative origin for multiple intronless spermatid-expressed genes: 

      The expert poses an interesting question, but one that unfortunately remains unanswered at present. Most papers on mRNA therapy state that there is no risk concerning genomic integration, but no reference is given (for instance see mRNA-based therapeutics: looking beyond COVID-19 vaccines. Lancet. 2024 doi: 10.1016/S0140-6736(23)02444-3). This is an important question, which deserves to be evaluated, but is beyond the scope of this manuscript. Nevertheless is remaining very debating (Igyarto and Qin 2024).

      98 Odd to say "should be no risk" and then conclude with "there is no question" - so start the sentence with 'hedging', and then end with certainty - got to pick one or the other.

      Thanks for the comment, we have reworded the sentence

      99 "Complete" - probably not, would delete:

      We removed the word: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice”

      101-2 Reference missing, as are numbers - what % of cases? 

      Thank you, we changed the sentence and added the reference: “Among infertility disorders, oligoastheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991)” Thonneau, P., S. Marchand, A. Tallec, M. L. Ferial, B. Ducot, J. Lansac, P. Lopes, J. M. Tabaste and A. Spira (1991). "Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988-1989)." Hum Reprod 6(6): 811-816.

      103 Once again, the reference is missing:

      We have added these references: (Colpi, Francavilla et al. 2018) (Cavallini 2006)

      104-5 Awkward transition.

      Thanks, we changed the transition: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice. The second part is to apply the protocol to a preclinical mouse model of OAT.”

      105 Backslash is odd - never seen it used in that way before

      Removed

      108 "completely infertile" is redundant;

      Thank you, we changed it accordingly: “Patients and mice carrying mutations in the ARMC2 gene present a canonical OAT phenotype and are infertile”.

      and is a KO mouse really "preclinical"? 

      The definition of preclinical research, is research involving the use of animals to ascertain the potential efficacy of a drug, procedure, or treatment. Preclinical studies are conducted prior to any testing in humans. Our KO mouse model has been shown to mimic human infertility. Indeed Armc2-/-mice exhibit a phenotype that is identical to that observed in humans. Our study is in line with this definition. For this reason, we have decided to maintain our current position and to use the term "preclinical" in the article. 

      110  Delete "sperm".

      Thank you, we changed it accordingly: “The preclinical Armc2 deficient (Armc2 KO) mouse model is therefore a valuable model to assess whether in vivo injection of naked mRNA combined with electroporation can restore spermatogenesis”

      111  "Easy"? Really? 

      We changed it accordingly: “We chose this model for several reasons: first, Armc2 KO mice are sterile and all sperm exhibit short, thick or coiled flagella [13].”

      112-3 "completely immobile" is redundant - either they are immobile or not.

      Thank you, we changed it accordingly: “As a result, 100 % of sperm are immobile, thus it should be easy to determine the efficacy of the technique by measuring sperm motility with a CASA system.”

      108-33 Condense this lengthy text into a coherent few sentences to give readers a sense of what you sought to accomplish, broadly how it was done, and what you found. This reads more like a Results section

      Thanks for the comment, we shortened the text.

      Materials and Methods 

      The sections appear to have been written by different scientists - the authors should standardize so that similar detail and formatting are used - e.g., in some parts the source is in parentheses with catalog number, in others not, some have city, state, country, others do not... the authors should check eLife mandates for this type of information and provide. 

      We are grateful for your feedback. We standardized the text, and if we had missed some, as outlined on the E-Life website, we can finish to format the article once it has been accepted for publication in the journal before sending the VOR.

      134 Misspelling

      We corrected the misspelling  

      142 Just reference, don't need to spell it out.

      Thanks, we changed it accordingly: “and the Armc2 KO mouse strain obtained by CRISPR-Cas9 (Coutton, Martinez et al. 2019). Experiments”

      150 What is XXX?

      We would like to express our gratitude for bringing this error to our attention. We have duly rectified the issue: “obtained from Merck (Darmstadt, Germany).”

      157-60 Are enough details provided for readers to repeat this if necessary? Doesn't seem so to this reviewer; if kits were followed, then can say "using manufacturer's protocol", or refer to another manuscript - but this is too vague. 

      Thanks, we change it accordingly: After expansion, plasmids were purified with a NucleoBond Xtra Midi kit (740410-50; Macherey-Nagel, Düren, Germany) using manufacturer's protocol.”

      165 Again, too few details - how was it purified? What liquid was it in?

      Thanks for the comment, the EEV plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” 

      170 Seems some words are missing - and will everyone know Dr. Conti by last name alone? Would spell out, and the details of the plasmid must either be provided or a reference given; how was amplification done? Purification? What was it resuspended in? 

      Thank for the remark, the mcherry plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD, UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation”

      175 Again, for this plasmid provide more information - catalog number, reference, etc; how amplified and purified, what resuspension buffer?

      Thank you for the remark, as We mentioned, we add this sentence for the preparation: “All plasmids, EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid (given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOMS017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” and we add these sentence “The EEV-Armc2-GFP plasmid used for in vivo testes microinjection and electroporation was synthesized and customized by Trilink (CUSTOM-S017188-R2-3,San Diego, USA).”

      183 What sequence, or isoform was used? Mouse or human? 

      Thanks, we changed accordingly: “This non-integrative episome contains the mice cDNA sequences of Armc2 (ENSMUST00000095729.11)”

      186-7 Provide sequence or catalog number; what was it resolubilized in?

      Thanks we changed accordingly “the final plasmid concentration was adjusted to 9 μg μL-1 in water.” We provided the sequence of EEV-Armc2-GFP in supp data 6.

      207-219 Much better, this is how the entire section needs to be written! 

      237-240 Font

      Thanks for the comment, we changed it accordingly

      246 Cauda, and sperm, not sperm cells

      Thanks for the comment, we changed it accordingly

      255-6 Which was done first? Would indicate clearly.

      Thanks for the comment, we changed the sentence: “Adult mice were euthanized by cervical dislocation and then transcardiac perfused  with 1X PBS”

      281-2 Provide source for software - company, location, etc: 

      We changed it accordingly: FIJI software (Opened source software) was used to process and analyze images and Imaris software (Oxford Instruments Tubney Woods, Abingdon, Oxon OX13 5QX, UK) for the 3D reconstructions.  

      323 um, not uM. 

      Thanks for the comment, we changed our mistake: “After filtration (100 µm filter)”

      Results 

      369 Weighed.  

      Thanks for the comment, we changed our mistake: “the testes were measured and weighed”

      371 No difference in what, specifically?

      Thanks for the comment, we changed the sentence to: “No statistical differences in length and weight were observed between control and treated testes”

      375 "was respected"? What does this mean?

      Thanks for the comment, we changed the sentence to “The layered structure of germ cells were identical in all conditions”

      378  This is highly unlikely to be true, as even epididymal sperm from WT animals are often defective - the authors are saying there were ZERO morphological defects? Or that there was no difference between control and treated? Only showing 2-3 sperm for control vs treatment is not sufficient.

      Your observation that the epididymal spermatozoa from wild-type animals exhibited defective morphology is indeed true. The prevalence of these defects varies by strain, with an average incidence of 20% to 40% (Kawai, Hata et al., 2006; Fan, Liu et al., 2015). To provide a more comprehensive representation, we conducted a Harris-Shorr staining procedure and included a histogram of the percentage of normal sperm in each condition (new figure 2F4). Furthermore, Harris-Shorr staining of the epididymal sperm cells revealed that there were no discernible increases in morphological defects when mRNA and EEV were utilized, in comparison with the control. We add the sentence “At last, Harris-Shorr staining of the epididymal sperm cells demonstrated that there were no increases in morphological defects when mRNA and EEV were used in comparison with the control”.

      379  "safe" is not the right word - better to say "did not perturb spermatogenesis". 

      Thanks, we changed it accordingly: “these results suggest that in vivo microinjection and electroporation of EEV or mRNA did not perturb spermatogenesis”

      382-3 This sentence needs attention, doesn't make sense as written: 

      Thanks for the remark, we changed the sentence to: “No testicular lesions were observed on the testes at any post injection time”

      389  How long after injection? 

      Thanks for the comment, we changed the sentence to: “It is worth noting that both vectors induced GFP expression at one day post-injection”

      390  Given the duration of mouse spermatogenesis (~35 days), for GFP to persist past that time suggests that it was maintained in SSCs? How can the authors explain how such a strong signal was maintained after such a long period of time? How stable are the episomally-maintained plasmids, are they maintained 100% for months? And if they are inherited by progeny of SSCs, shouldn't they be successively diluted over time? And if they are inherited by daughter cells such that they would still be expressed 49 days after injection, shouldn't all the cells originating from that SSC also be positive, instead of what appear to be small subsets as shown in Fig. 3H2? Overall, this reviewer is struggling to understand how a plasmid would be inherited and passed through spermatogenesis in the manner seen in these results. 

      Thanks for the comment. 

      This point was already underlined in public review. We paste here our answer: “The non-insertional Enhanced Episomes Vector (EEV) plasmid is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an Epstein-Barr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

      The 641-amino-acid EBNA1 protein contains numerous domains.The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013a). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018a). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018b). It is notable that EEV is maintained at a rate of 90-95% per cell division.”

      Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

      Of note, the specificity of EEV was already indicated in the introduction. Nevertheless, we have added more information about it to help the readers (lines 124-128 clean copy)  

      398 Which "cell types"? 

      Your feedback is greatly appreciated, and the sentence in question has been amended to provide a more comprehensive understanding. The revised wording is as follows: These results suggest that GFPmRNA and EEV-GFP targeted different seminiferous cell types, such as Sertoli cells and all germline cells, or that there were differences in terms of transfection efficiency.

      409 Why is it important to inject similar copies of EEV and mRNA? Wouldn't the EEV be expected to generate many, many more copies of RNA per molecule than the mRNAs when injected directly?? 

      We removed the word importantly. 

      415 How is an injected naked mRNA stably maintained for 3 weeks? What is the stability of this mRNA?? Wouldn't its residence in germ cells for 21 days make it more stable than even the most stable endogenous mRNAs? Even mRNAs for housekeeping genes such as actin, which are incredibly stable, have half-lives of 9-10 hours.

      We appreciate your inquiry and concur with your assessment that mRNA stability is limited.  It is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the expression of the GFP protein induced by the mRNA. To draw the reader's attention to this point, we have added the following sentence to the text “It is important to underline that the signal measured is the fluorescence emitted by the GFP. This signal is dependent of both the half-lives of the plasmid/mRNA and the GFP. Therefore, the kinetic of the signal persistence (which is called here expression) is a combination of the persistence of the vector and the synthetized protein. See lines 469-472 clean copy. 

      This being said, it is difficult to compare the lifespan of a cellular mRNA with that of a mRNA that has been modified at different levels, including 5’Cap, mRNA body, poly(A)tail modifications, which both increase mRNA stability and translation (see The Pivotal Role of Chemical Modifications in mRNA Therapeutics  (2022) https://doi.org/10.3389/fcell.2022.901510). This question is discussed lines 687698 clean copy

      467 "safely" should be deleted

      Thanks, we removed the word: “To validate and confirm the capacity of naked mRNA to express proteins in the testes after injection and electroporation”

      470  Except that apoptotic cells were clearly seen in Figure 2:

      We would like to thank the reviewer for their comment. We agree that the staining of the provided sections were of heterogenous quality. To address the remark, we carried out additional HE staining for all conditions, and we now present testis sections correctly stained obtained in the different condition in Fig. 2 and Supp. 7. Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      471  "remanence"?

      We appreciate your feedback and have amended the sentence to provide clear meaning. The revised wording is as follows: “The assessment of the temporal persistence of testicular mCherry fluorescent protein expression revealed a robust red fluorescence from day 1 post-injection, which remained detectable for at least 15 days (Fig. Supp. 3 B2, C2, and D2).”

      489 IF measures steady-state protein levels, not translation; should say you determined when ARMC2 was detectable. 

      Thanks for the remark, we changed the sentence to: “ By IF, we determined when ARMC2 protein was detectable during spermatogenesis.”

      491 Flagella

      Thanks for the comment, we changed our mistake: “in the flagella of the elongated spermatids (Fig 9A)”

      Discussion 

      The Discussion is largely a re-hashing of the Methods and Results, with additional background.

      Message stability must be addressed - how is a naked mRNA maintained for 21 days?

      As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the synthetized GFP protein. This point and the stability of protein in the testis is now discussed lines 677-684 (clean copy).

      556 How do the authors define "safe"?

      Thanks for the comment, we changed the sentence to be clearer: “Our results also showed that the combination of injection and electroporation did not perturb spermatogenesis when electric pulses are carefully controlled”

      563 Synthesized

      Thanks, we changed it accordingly

      602 Again, this was not apparent, as there were more apoptotic cells in Fig. 2 - data must be provided to show "no effect".

      As previously stated, we carried out additional HE staining for all conditions, as can be observed in Fig. 2 . Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      629-30 This directly contradicts the authors' contention in the Introduction that ICSI was unsafe - how is this procedure going to be an advancement over ICSI as proposed, if ICSI needs to be used?? Why not just skip all this and do ICSI then?? Perhaps if this technique was used to 'repair' defects in spermatogonia or spermatocytes, then that makes more sense. But if ICSI is required, then this is not an advancement when trying to rescue a sperm morphology/motility defect.

      In light of the latest findings (Fig 12), we have revised this part of the discussion and this paragraph no longer exist.

      Nevertheless, to address specifically the reviewer’s remark, we would like to underline that ICSI with sperm from fertile donor is always more efficient than ICSI with sperm from patient suffering of OAT condition. Our strategy, by improving sperm quality, will improve the efficiency of ICSI and at the end will increase the live birth rate resulting from the first fresh IVF cycle.

      640-2 What is meant by "sperm organelles" And what examples are provided for sperm proteins being required at or after fertilization? 

      This paragraph was also strongly modified and the notion of protein persistence during spermatogenesis was discussed in the paragraph on fluorescent signal duration. See lines 698-705.

      651 "Dong team"??

      Thanks for the comment, we added the references. 

      Figure 2D2 - tubule treated with EEV-GFP appears to have considerably more apoptotic cells - this reviewer counted ~10 vs 0 in control; also, many of the spermatocytes appear abnormal in terms of their chromatin morphology - the authors must address this by staining for markers of apoptosis - not fair to conclude there was no difference when there's a very obvious difference! 

      We would like to thank the reviewer for their comment. This point was already addressed. As previously stated, we provide now new testis sections for all condition (see Fig. 2). Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      Figure 2D3 staining is quite different than D1-2, likely a technical issue - looks like no hematoxylin was added? Need to re-stain so results can be compared to the other 2 figures 

      As previously stated, we carried out additional HE staining for all conditions, and new images are provided, with similar staining. 

      Figure 3 - the fluorescent images lack any context of tubule structure so it is nearly impossible to get a sense of what cells express GFP, or whether they're in the basal vs adluminal compartment - can the authors outline them? Indicate where the BM and lumen are. 

      We would like to thank the reviewer for their comment. This figure provides actually a global view of the green fluorescent protein (GFP) expression at the surface of the testis. The entire testis was placed under an inverted epifluorescence microscope, and a picture of the GFP signal was recorded. For this reason, it is impossible to delineate the BM and the lumen. It should be noted that the fluorescence likely originates from different seminiferous tubules.

      Author response image 1.

      So, for Figure 3 if the plasmid is being uptaken by cells and maintained as an episome, is it able to replicate? Likely not. 

      Yes! it is the intrinsic property of the episome, see the detailed explanation provided above about the EEV plasmid

      So, initially, it could be in spermatogonia, spermatocytes, and spermatids. As time progressed those initially positive spermatids and then spermatocytes would be lost - and finally, the only cells that should be positive would be the progeny of spermatogonia that were positive - but, as they proliferate shouldn't the GFP signal decline? 

      Because EEV is able  to replicate in a synchronous manner with the host genome and to segregate into daughter cells at a level of 90% of the mother cell, the expected decline is very slow.

      And, since clones of germ cells are connected throughout their development, shouldn't the GFP diffuse through the intercellular bridges so entire clones are positive? Was this observed? 

      We did not perform IF experiments further than 7 days after injection, a time too short to observe what the reviewer suggested. Moreover, if at 1 day after injection, GFP synthesized from injected EEV was found in both germ cells and Sertoli cells (Fig 7), after one week, the reporter proteins were only observable in Sertoli cells. This result suggests that EEV is maintained only in Sertoli cells, thus preventing the observation of stained clones.

      Can these sections be stained for the ICB TEX14 so that clonality can be distinguished? Based on the apparent distance between cells, it appears some are clones, but many are not... 

      We thank the reviewer for this suggestion but we are not able to perform testis sectioning and costaining experiments because the PFA treatment bleaches the GFP signal. We also tested several GFP antibodies, but all failed.  

      Nevertheless, we were able to localize and identify transfected cells thank to the whole testis optical clearing, combined with a measure of GFP fluorescence and three-dimensional image reconstructions. 

      For Figure 4, with the mRNA-GFP, why does the 1-day image (which looks similar to the plasmidtransfected) look so different from days 7-21? 

      And why do days 7-21 look so different from those days in Fig 3? 

      Thank you for your feedback. It is an excellent question. Because of the low resolution of the whole testis epifluorescences imaging and light penetration issue, we decided to carry-out whole testis optical clearing and three-dimensional image reconstructions experiments, in order to get insights on the transfection process. At day 1, GFP synthesized from EEV injection was found in spermatogonia, spermatocytes and Sertoli cells (Fig 7).  After one week, the reporter protein synthesized from injected EEV was only observable in Sertoli cells.

      In contrast, for mRNA, on day 1 and day 7 post-injection, GFP fluorescent signal was associated with both Sertoli cells and germ cells. This explains why patterns between mRNA-GFP and EEV-GFP are similar at day 1 and different at day 7 between both conditions. 

      Why do the authors think the signal went from so strong at 21 to undetectable at 28? What changed so drastically over those 7 days?

      What is the half-life of this mRNA supposed to be? It seems that 21 days is an unreasonably long time, but then to go to zero at 28 seems also odd... Please provide some explanation, and context for whether the residence of an exogenous mRNA for 21 days is expected. 

      As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the GFP protein produced by the mRNA. The time of observation of the reporter proteins expressed by the respective mRNA molecules (mCherry, luciferase, or GFP) ranged from 15 to 21 days. Proteins have very different turnover rates, with half-lives ranging from minutes to days. Half-lives depend on proteins but also on tissues. As explained in the discussion, it has been demonstrated that proteins involved in spermatogenesis exhibit a markedly low turnover rate and this explains the duration of the fluorescent signal. 

      The authors should immunostain testis sections from controls and those with mRNA and plasmid and immunostain with established germ cell protein fate markers to show what specific germ cell types are GFP+

      Thank you for your feedback. As previously mentioned, we were unable to perform testis sectioning and co-staining because the PFA treatment bleaches the GFP signal and because we were unable to reveal GFP with an GFP antibody, for unknown reasons.

      For the GFP signal to be maintained past 35 days, the plasmid must have integrated into SSCs - and for that to happen, the plasmid would have to cross the blood-testis-barrier... is this expected? 

      We are grateful for your observation. 

      First, as explained above, we do not think that the plasmid has been integrated. 

      Concerning the blood-testing barrier.  It bears noting that electroporation is a technique that is widely utilized in biotechnology and medicine for the delivery of drugs and the transfer of genes into living cells (Boussetta, Lebovka et al. 2009). This process entails the application of an electric current, which induces the formation of hydrophilic pores in the lipid bilayer of the plasma membrane (Kanduser, Miklavcic et al. 2009). The pores remain stable throughout the electroporation process and then close again once it is complete. Consequently, as electroporation destabilizes the cell membrane, it can also destabilize the gap junctions responsible of the blood-testis barrier. This was actually confirmed by several studies, which have observed plasmid transfection beyond the blood-testis barrier with injection into rete testis following electroporation (Muramatsu, Shibata et al. 1997, Kubota, Hayashi et al. 2005, Danner, Kirchhoff et al. 2009, Kanduser, Miklavcic et al. 2009, Michaelis, Sobczak et al. 2014).

      Figure 9 - authors should show >1 cell - this is insufficient; also, it's stated it's only in the flagella, but it also appears to be in the head as well. And is this just the principal piece?? And are the authors sure those are elongating vs condensing spermatids? Need to show multiple tubules, at different stages, to make these claims

      We have partly answered to this question in the public review; We pastehere  our answer

      “We present now new images showing the full seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.”

      Concerning the localization of the protein in the head, we confirm that the base of the manchette is stained but we have no explanation so far. This point is now indicated in the manuscript.

      Figure 10B2 image - a better resolution is necessary

      We are grateful for your feedback. We concede that the quality of the image was not optimal. Consequently, We have replaced it with an alternative.

      Figure 11 - in control, need to show >1 sperm; and lower-mag images should be provided for all samples to show population-wide effects; showing 1 "normal" sperm per group (white arrows) is insufficient: 

      We are grateful for your feedback. We conducted further experiments and provide now additional images in Supp. figure 8.

      Reviewer #3 (Recommendations For The Authors)

      In this study, Vilpreux et al. developed a microinjection/electroporation method in order to transfect RNA into testicular cells. The authors studied several parameters of treated testis and compared the injection of DNA versus RNA. Using the injection of Armc2 RNA into mice with an Armc2 knockout the authors were able to (partly) rescue the fertility phenotype. 

      Minor points. 

      Figure 6 + lines 553+554: might it be that the staining pattern primarily on one side of the testis is due to the orientation of the scissor electrode during the electroporation procedure and the migration direction of negatively charged RNA molecules (Figure 6)? 

      Your input is greatly appreciated. We concur that the observed peripheral expression is due to both the electroporation and injection. Accordingly, we have amended the sentence as follows: "The peripheral expression observed was due to the close vicinity of cells to the electrodes, and to a peripheral dispersal of the injected solution, as shown by the distribution of the fluorescent i-particles NIRFiP-180."

      Discussion of the safety aspect (lines 601-608): The authors state several times that there are no visible tissue changes after the electroporation procedure. However, in order to claim that this procedure is "safe", it is necessary to examine the offspring born after microinjection/electroporation. 

      Your input is greatly appreciated. Consequently, the term "safe" has been replaced with "did not perturb spermatogenesis" in accordance with the provided feedback. Your assertion is correct; an examination of the offspring born would be necessary to ascertain the safety of the procedure. Due to the quantity of motile sperm obtained, it was not possible to produce offspring through natural mating. However, novel Armc2-/--rescued sperm samples have been produced and in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) experiments have been conducted. The results demonstrate that the Armc2-/--rescued sperm can successfully fertilize eggs and produce two-cell embryos by IVF and blastocysts by ICSI. These outcomes are visually represented in Figure 12. The development of embryos up to the blastocyst stage is a step in the right direction.

      The discussion section could be shortened. Lines 632-646 are largely a repetition of the introductory section. In addition, the Dong paper (ref. 25) may be interesting; however, this part could also be shortened (lines 647-676). This reviewer would prefer the authors to focus on the technique (different application sites and applied nucleotides) and proof of concept for (partial) phenotype rescue in the knockout mice. 

      Your contribution is highly valued. In light of your observations and the latest findings, we have substantially revised the discussion accordingly.

      Line 63: oocytes rather than eggs.

      We are grateful for your input, but we have decided to retain our current position and to use the term "eggs" rather than "oocytes" in our writing because the definition of an oocyte is a female gametocyte or germ cell involved in reproduction. In other words, oocyte corresponds to a germ cell inside the ovary and after ovulation become an egg.  

      Boussetta, N., N. Lebovka, E. Vorobiev, H. Adenier, C. Bedel-Cloutour and J. L. Lanoiselle (2009). "Electrically assisted extraction of soluble matter from chardonnay grape skins for polyphenol recovery." J Agric Food Chem 57(4): 1491-1497.

      Cavallini, G. (2006). "Male idiopathic oligoasthenoteratozoospermia." Asian J Androl 8(2): 143-157.

      Colpi, G. M., S. Francavilla, G. Haidl, K. Link, H. M. Behre, D. G. Goulis, C. Krausz and A. Giwercman (2018). "European Academy of Andrology guideline Management of oligo-asthenoteratozoospermia." Andrology 6(4): 513-524.

      Coutton, C., G. Martinez, Z. E. Kherraf, A. Amiri-Yekta, M. Boguenet, A. Saut, X. He, F. Zhang, M. Cristou-Kent, J. Escoffier, M. Bidart, V. Satre, B. Conne, S. Fourati Ben Mustapha, L. Halouani, O. Marrakchi, M. Makni, H. Latrous, M. Kharouf, K. Pernet-Gallay, M. Bonhivers, S. Hennebicq, N. Rives, E. Dulioust, A. Toure, H. Gourabi, Y. Cao, R. Zouari, S. H. Hosseini, S. Nef, N. Thierry-Mieg, C. Arnoult and P. F. Ray (2019). "Bi-allelic Mutations in ARMC2 Lead to Severe Astheno-Teratozoospermia Due to Sperm Flagellum Malformations in Humans and Mice." Am J Hum Genet 104(2): 331-340.

      Danner, S., C. Kirchhoff and R. Ivell (2009). "Seminiferous tubule transfection in vitro to define postmeiotic gene regulation." Reprod Biol Endocrinol 7: 67.

      Gan, H., L. Wen, S. Liao, X. Lin, T. Ma, J. Liu, C. X. Song, M. Wang, C. He, C. Han and F. Tang (2013). "Dynamics of 5-hydroxymethylcytosine during mouse spermatogenesis." Nat Commun 4: 1995. Igyarto, B. Z. and Z. Qin (2024). "The mRNA-LNP vaccines - the good, the bad and the ugly?" Front Immunol 15: 1336906.

      Ishii, T. (2017). "Germ line genome editing in clinics: the approaches, objectives and global society." Brief Funct Genomics 16(1): 46-56.

      Kanduser, M., D. Miklavcic and M. Pavlin (2009). "Mechanisms involved in gene electrotransfer using high- and low-voltage pulses--an in vitro study." Bioelectrochemistry 74(2): 265-271.

      Kubota, H., Y. Hayashi, Y. Kubota, K. Coward and J. Parrington (2005). "Comparison of two methods of in vivo gene transfer by electroporation." Fertil Steril 83 Suppl 1: 1310-1318.

      Michaelis, M., A. Sobczak and J. M. Weitzel (2014). "In vivo microinjection and electroporation of mouse testis." J Vis Exp(90).

      Muramatsu, T., O. Shibata, S. Ryoki, Y. Ohmori and J. Okumura (1997). "Foreign gene expression in the mouse testis by localized in vivo gene transfer." Biochem Biophys Res Commun 233(1): 45-49.

      Raina, A., S. Kumar, R. Shrivastava and A. Mitra (2015). "Testis mediated gene transfer: in vitro transfection in goat testis by electroporation." Gene 554(1): 96-100.

      Sadelain, M., E. P. Papapetrou and F. D. Bushman (2011). "Safe harbours for the integration of new DNA in the human genome." Nat Rev Cancer 12(1): 51-58.

      Thonneau, P., S. Marchand, A. Tallec, M. L. Ferial, B. Ducot, J. Lansac, P. Lopes, J. M. Tabaste and A. Spira (1991). "Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988-1989)." Hum Reprod 6(6): 811-816.

      Usmani, A., N. Ganguli, H. Sarkar, S. Dhup, S. R. Batta, M. Vimal, N. Ganguli, S. Basu, P. Nagarajan and S. S. Majumdar (2013). "A non-surgical approach for male germ cell mediated gene transmission through transgenesis." Sci Rep 3: 3430.

      Wang, L., C. Liu, H. Wei, Y. Ouyang, M. Dong, R. Zhang, L. Wang, Y. Chen, Y. Ma, M. Guo, Y. Yu, Q. Y. Sun and W. Li (2022). "Testis electroporation coupled with autophagy inhibitor to treat nonobstructive azoospermia." Mol Ther Nucleic Acids 30: 451-464.

    1. Annotation #1: Thoughts

      "World inequality, however, cannot be explained by climate or diseases, or any version of the geography hypothesis. Just think of Nogales. What separates the two parts is not climate, geography, or disease environment, but the U.S.-Mexico border." In this passage the author is debunking various aspects of the geographic theory, which attributes difference in economic success to geographic conditions. The author is essentially saying that while theories about climate and disease impacting a countries productivity and consequently economic development may seem plausible. When looking at actual events in history, we can see that even countries with exactly the same geographies still face completely different circumstances economically. This connects to Singapore as we can see this phenomenon occur here as well, with Singapore being significantly better off economically than its neighbours such as Thailand, Cambodia, etc.

      Annotation #2: Question “But mostly no, because those aspects of culture often emphasized—religion, national ethics, African or Latin values—are just not important for understanding how we got here and why the inequalities in the world persist.”

      This passage is introducing the culture theory, a theory that cites cultural differences as the source of inequality in economic growth across the world. This specific quote elaborates on why Robinson doesn’t believe that culture is a significant cause of the difference in economic growth across the world, stating that aspects of culture such as “religion, national ethics and African or Latin values” are not important. This idea made me wonder about the impact a societies intrinsic cultural values can have when looked at from a larger scale: How do a society’s intrinsic cultural values influence its long-term economic growth and development on a global scale? When connecting this to Singapore and it’s own rapid economic development, I wonder if certain cultural values such as value of education in many asian cultures or respect for the law, influenced the way Singapore as a country was able to develop economically on a global scale.

      **Annotation #3: Epiphanies ** “China, despite many imperfections in its economic and political system, has been the most rapidly growing nation of the past three decades. Chinese poverty until Mao Zedong’s death had nothing to do with Chinese culture; it was due to the disastrous way Mao organized the economy and conducted politics.”

      This passage further analyzing why the culture theory doesn’t properly explain the economic growth of certain countries over others.This specific quote shifts the focus from cultural explanations of poverty to highlighting how government policies and political decisions can drastically impact economic outcomes. It made me think a lot more about the significant influence that historical and political events have on countries, and how decisions from decades ago can still have lasting impact on countries. What interested me most was how some countries, like China, were able to recover and achieve significant growth, while others, such as certain African nations, continue to struggle. This made me reflect on how institutions and governance propel a country towards prosperity, and connecting it to Singapore made me wonder the unique aspects of it's institutions that lead it to be so economically successful.

    2. Annotation 1: "History illustrates that there is no simple or enduring connection between climate or geography and economic success."

      The authors here are arguing that geographical factors like climate and location do not provide a reliable or enduring explanation for economic success or failure. This emphasises that history demonstrates other factors, like colonisation and political institutions play a much larger role in shaping economic outcomes. While geography may influence certain conditions, it is not the primary factor in explaining economic growth or development.

      Annotation 2: "Though it is not politically correct to articulate in public, many people still maintain that Africans are poor because they lack a good work ethic, still believe in witchcraft and magic, or resist new Western technologies."

      Why do certain negative cultural perceptions persist, despite evidence showing that these beliefs are not the underlying causes of poverty? How will these misunderstandings about different cultures chance in the future, especially if these perceptions have historically shaped policies and economic outcomes?

      Annotation 3: "To understand world inequality we have to understand why some societies are organised in very inefficient and socially undesirable ways. Nations sometimes do manage to adopts efficient instituations and achieve prosperity, but alas, these are the rare cases. Most economists and policymakers have focused on "getting it right,"whole what is really needed is an explanation for why poor nations "get it wrong." Getting it wrong is mostly not about ignorance or culture. As we will show, poor countries are poor because those in power make choices that create poverty. They get it wrong not by mistake or ignorance but on purpose."

      The author is telling us that poverty is often the result of a strategic decision made by elites who benefit from the status quo. This makes me think about the role of politics and power in economic development, rather than focusing purely on economic policies or advice. Corruption and poor governance in certain nations create economic inequality and prevent sustainable growth, strengthening the notion that improving economic growth is not just about providing better economic advice or policies but also addressing the deep rooted political issues that prevent those policies from being implemented effectively. The real challenge lies in political reform and ensuring that those in power are incentivised to act in the interests of economic prosperity for all.

      Corruption is a Global Problem for Development

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Hahn et al use bystander BRET, NanoBiT assays, and APEX2 proteomics to investigate endosomal signaling of CCR7 by two agonists, CCL19 and CCL21. The authors suggest that CCR7 signals from early endosomes following internalisation. They use spatial proteomics to try to identify novel interacting partners that may facilitate this signaling and use this data to specifically enhance a Rac1 signaling pathway. Many of the results in the first few figures showing simultaneous recruitment of Barr and G proteins by CCR7 have been shown previously (Laufer et al, 2019, Cell Reports), as has signaling from endomembranes, and Rac1 activation at intracellular sites. The new findings are the APEX2 proteomics studies, which could be useful to the scientific community. Unfortunately, the authors only follow up on a single finding, and the expansion of this section would improve the manuscript.

      First of all, we would like to thank the reviewer for helping with the manuscript. The summary is mostly accurate except for the statement that simultaneous recruitment of barr and G protein to CCR7 has been shown before. It should also be noted that it has not been demonstrated that CCR7 activates G proteins from endosomes previously nor has the functional role of this signaling mechanism. However, that CCR7 activity at endomembranes is associated with Rac1 signaling was demonstrated in the Laufer et al. study as the reviewer correctly points out.

      Strengths:

      (1) The APEX2 resource will be valuable to the GPCR and immunology community. It offers many opportunities to follow up on findings and discover new biology. The resource could also be used to validate earlier findings in the current manuscript and in previous manuscripts. Was there enrichment of early endosomal markers, Barr and Gi as this would provide further evidence for their earlier claims regarding endosomal signaling? Previous studies have suggested signaling from the TGN, so it is possible that the different ligands also direct to different sites. This could easily be investigated using the APEX2 data.

      Thank you for your comment. We do in fact observe enrichment of TGN/Golgi markers in response to chemokine stimulation, which we now have highlighted in the manuscript (fourth paragraph on page 7).

      (2) The results section is well written and can be followed very easily by the reader.

      We are glad that the reviewer found the results section very readable.

      (3) Some findings verify previous studies (e.g. endomembrane signalling). This should be acknowledged as this shows the validity of the findings of both studies.

      This is correct. We have now included more discussion of previous work related to CCR7 signaling at endomembranes (thirdparagraph on page 10).

      Weaknesses:

      (1) The findings are interesting although the studies are almost all performed in HEK293 cells. I understand that these are commonly used in GPCR biology and are easy to transfect and don't express many GPCRs at high concentrations, but their use is still odd when there are many cell-lines available that express CCR7 and are more reflective of the endogenous state (e.g. they are polarised, they can perform chemotaxis/ migration). Some of the findings within the study should also be verified in more physiologically relevant cells. At the moment only the final figure looks at this, but findings need to be verified elsewhere.

      We thank the reviewer for raising this point and giving us an opportunity to elaborate in further detail. The major goal of our study was to investigate whether CCR7 activates G protein from endosomes, the underlying mechanism, and functions of this potential signaling mechanism. The reason we chose CCR7 as our model receptor was that it belongs to a group of GPCRs, the chemokine receptors, that most often have features associated with the ability to promote endosomal G protein activation (phosphorylation site clusters in the C-terminal region).

      Specific detection of G protein activation at distinct subcellular compartments is currently very challenging in truly endogenous systems despite new innovative biosensors that are available (not just related to CCR7, but GPCRs in general). To our knowledge, most if not all studies that detect direct activation of G protein at a specific compartment whether at the plasma membrane, endosome, Golgi, or other compartments, have overexpressed either the receptor, G protein, or both. This is why we choose the HEK293 cell system for most of our experiments, which are easy to manipulate. That being said, we did confirm major findings in an indirect manner using Jurkat T-cells, which express CCR7 endogenously and are physiological relevant. Our hope is that in the future we will be able to use highly sensitive biosensors to directly confirm our findings in such a cell system as the reviewer wisely suggests.

      (2) The authors acknowledge that the kinetic patterns of the signals at the early endosome are not consistent with the rates of internalisation. They mention that this could be due to trafficking elsewhere. This could be easily looked at in their APEX2 data. Is there evidence of proximity to markers of other membranes? Perhaps this could be added to the discussion. Similarly, previous studies have shown that CCR7 signaling may involve the TGN. Was there enrichment of these markers? If not, this could also be an interesting finding and should be discussed. It is also possible that the Rab5 reporter is just not as efficient as the trafficking one, especially as in later figures the very convincing differences in the two ligands are not as robust as the differences in trafficking.

      Excellent point. We have now highlighted the possibility of CCR7 being further trafficked to the trans-Golgi network (TGN) as possible explanation for the transient translocation of activated CCR7 to the early endosome in Fig. 1G-H (second paragraph on page 3).

      Furthermore, in the APEX2 experiment we observe enrichment of proteins involved in lysosomal trafficking (LAMP1, VPS16, VAMP7, WDR91, and PP4P1) by CCL19 stimulation at 25 min, and recycling endosomes/TGN markers (SNX6, RAB7L, and GGA) by CCL21 stimulation at 25 min. In addition to this, several markers of TGN/Golgi (SNX3, COG5, YIF1A, SC22B, and AP3S1) were enriched as well in response to both CCL19 and CCL21 stimulation. We have now included a statement in the manuscript, which describes the likely trafficking of CCR7 to the TGN/Golgi in response to CCL19 and CCL21 stimulation (fourth paragraph on page 7).

      (3) In the final sentence of paragraph 2 of the results the authors state that the internalisation is specific to CCR7 as there isn't recruitment to V2R. I'm not sure this is the best control. The authors can only really say it doesn't recruit to unrelated receptors. The authors could have used a different chemokine receptor which does not respond to these ligands to show this.

      The point with this control experiment was to demonstrate that the loss of NanoBiT signal in response to CCL19 in CCR7-SmBiT/LgBiT-CAAX expressing cells, but not in V2R-SmBiT/LgBiT-CAAX expressing cells, was a result of bona fide CCR7 internalization rather than potential artifactual effects of CCL19 on the NanoBiT system. Our intent was not to demonstrate specificity of CCL19 among chemokine receptors, which already has been thoroughly tested in previous studies. We have now modified the sentence (second paragraph on page 3) “Moreover, CCL19/CCL21-stimulation of receptor internalization to endosomes is specific to CCR7 as none of the chemokines promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct (Fig. S1E-F)” to “Moreover, CCL19/CCL21-stimulation did not promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct, which validates that these chemokines act specifically via the CCR7-SmBiT system (Fig. S1E-F).”

      (4) The miniGi-Barr1 and imaging showing co-localisation could be more convincing if it was also repeated in a more physiological cell line as in the final figure. Imaging of CCR7, miniGi, and Barr1 would also provide further evidence that the receptor is also present within the complex.

      We agree with the reviewer’s assessment. However, as mentioned above it is currently extremely challenging to detect endogenous G protein coupling/activation to endogenous receptors. In addition, we are not sure if overexpressing fluorophore-tagged receptor, miniG, and barr1 in a physiological-relevant cell line would provide truly physiological conditions as the expression of these proteins still would be artificially high. This is why we chose to conduct these mechanistic experiments in HEK293 cells and then indirectly verify key findings in an endogenous and physiological-relevant cell line.

      (5) The findings regarding Rac1 are interesting, although an earlier paper found similar results (Laufer et al, 2019, Cell Reports), so perhaps following up on another APEX2-identified protein pathway would have been more interesting. The authors' statement that Rac1 is specifically activated, and RhoA and Cdc42 are not, is unconvincing from the current data. Only a single NanoBiT assay was used, and as raw values are not reported it is difficult for the reader to glean some essential information. The authors should show evidence that these reporters work well for other receptors (or cite previous studies) and also need evidence from an independent (i.e. non-NanoBiT or BRET) assay.

      The major focus of the study was to investigate whether CCR7 can activate G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and to dissect out potential functions of said endosomal G protein signaling. To do this, we used CCL19 and CCL21 which stimulate G protein to the same extent but differ in their ability of promote barr recruitment and receptor internalization with CCL19 being superior to CCL21. To this end, we found that CCL19 also promote endosomal G protein activation to a greater extent than CCL21, and therefore, we specifically looked for proteins enriched by CCL19 in our APEX experiment. This led us to some Rho GTPase regulators that were differentially enriched by CCL19 and CCL21. We agree that there were other interesting effectors related to CCR7 biology identified in the APEX experiment such as EYA2, GRIP2, and EI24. However, those proteins were enriched similar by CCL19 and CCL21 challenge, and thus, do not seem to be activated specifically at endosomes. Following the same argument, we also did not observe any difference in the activity of RhoA or Cdc42 when stimulated with CCL19 or CCL21, so we cannot conclude that these signaling proteins are activated specifically in endosomes. On the other hand, Rac1 was stimulated to a larger degree by CCL19 than CCL21, its activity was inhibited by the Gi/o inhibitor PTX and endocytosis inhibitors Dyngo-4a and PitStop2. CCR7-mediated Rac1 signaling was also inhibited by expression of a dominant negative dynamin mutant that inhibits receptor internalization, and Rac1 was not activated by an internalization-deficient CCR7-DS/T mutant. Finally, the involvement of Rac1 in CCR7 mediated chemotaxis of Jurkat T cells was also demonstrated. We believe that these findings together provide strong basis for the claim that endosomal Gi/o protein signaling by CCR7 activates Rac1.

      Following the reviewer’s suggestion, we have now included experiments to show that the activation of RhoA, Rac1, and Cdc42 by CXCR4 also can be detected by the NanoBiT biosensors (Fig. S7D-F). We have also added the appropriate references to the original studies where these biosensors were developed in the results section (first paragraph on page 8).

      (6) At present, the studies in Figure 7 do not go beyond those in the previous Laufer et al study in which they showed blocking endocytosis affected Rac1 signalling. The authors could show that Rac1 signalling is from early endosomes to improve this, otherwise, it could be from the TGN as previously reported.

      The major purpose of Figure 7 was to indirectly confirm findings from HEK293 cells experiments and to tie them to physiological functions. Our experiments using Jurkat T-cells show that CCL19 promote stronger chemotactic response than CCL21 despite similar Gi/o response. In addition, we showed that CCR7-mediated Gi/o activation, receptor endocytosis, as well as Rac1 activity, are required to drive chemotaxis. The Laufer et al. study did not investigate whether CCR7 activates G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and thus, did not focus on functional outcomes of this signaling mechanism. Based on this, we believe our work provides new and valuable knowledge to the field.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript describes a comprehensive analysis of signalling downstream of the chemokine receptor CCR7. A comprehensive dataset supports the authors' hypothesis that G protein and beta-arrestin signalling can occur simultaneously at CCR7 with implications for continued signalling following receptor endocytosis.

      We would like to thank the reviewer for helping with the manuscript. We agree on all points made and have now updated the manuscript accordingly.

      Strengths:

      The experiments are well controlled and executed, employing a wide range of assays using - in the main - CCR7 transfectants. Data are well presented, with the authors' claims supported by the data. The paper also has an excellent narrative which makes it relatively easy to follow. I think this would certainly be of interest to the readership of the journal.

      We appreciate the positive assessment of strengths.

      Weaknesses:

      Since the authors show a differential enrichment of RhoGTPases by CCR7 stimulation with CCL19 versus CCL21, I think that they also need to show that the Gi/o coupling of HEK-292-CCR7-APEX2 cells to both CCL19 and CCL21 is not perturbed by the modification. Currently, the authors only show data for CCL19 signalling, which leaves the potential for a false negative finding in terms of CCL21 signalling being selectively impaired. This should be relatively easy to do and should strengthen the authors' conclusions.

      We agree with the reviewer and have now included experiments to show that both CCL19- and CCL21-mediated CCR7-APEX2 stimulation leads to Gi/o activation (Fig. S4C). In addition, our proteomics experiments show strong effects of both CCL19 and CCL21 stimulation, which suggest that the receptor is activated by both ligands.

      The authors conclude the discussion by suggesting that their findings highlight endosomal signalling as a general mechanism for chemokine receptors in cell migration. I think this is an overreach. The authors chose several studies of CXC chemokine receptors to support their argument that C-terminal truncation or mutation of the C-terminal phosphorylation sites impairs endocytosis and chemotaxis (refs 40-42). However, in some instances e.g. at the related chemokine receptor CCR4, C-terminal removal of these sites impairs endocytosis but promotes chemotaxis (Nakagawa et al, 2014); Anderson et al, 2020). I therefore think that either the final statement needs to be tempered down or the counterargument discussed a little.

      We appreciate the reviewer highlighting this point. We have now modified the concluding sentence from “Thus, the findings from our study highlight endosomal G protein signaling by chemokine receptors as a potential general mechanism that regulates key aspects of cell migration” to “Thus, the findings from our study highlight endosomal G protein signaling by some chemokine receptors as a potential mechanism that regulates key aspects of cell migration.” We hope that the temper level of this sentence is more appropriate.

      References:

      Anderson, C. A. et al. A degradatory fate for CCR4 suggests a primary role in Th2 inflammation. J Leukocyte Biol 107, 455-466 (2020).

      Nakagawa, M. et al. Gain-of-function CCR4 mutations in adult T cell leukaemia/lymphoma. Journal of Experimental Medicine 211, 2497-2505 (2014).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The results section is well written, although the introduction needs more information on what is known about CCR7 trafficking and endomembrane signaling. I understand this is because the authors wanted to focus on GPCR signaling, but the study will equally be of interest to researchers in the immunology and chemokine fields, and therefore more CCR7-focussed discussion in the introduction would be useful. Similarly, the discussion would benefit from more discussion of previous studies of CCR7 trafficking and endomembrane signaling (in particular the Laufer et al paper) to acknowledge that many of the findings within this paper verify previous studies.

      We have now included additional immunology/endomembrane background information about CCR7 at the place where the receptor is introduced (first paragraph on page 3). We have also expanded our discussion of our work in relation to the Laufer et al. study (third paragraph on page 10).

      (2) On page 5, the authors state that 'The response to chemokine stimulation was not observed in mock transfected HEK293 cells'. Figure S4D does not have a legend so it is difficult to see what they mean by mock transfected. Do they mean not transfecting with anything or not with the receptor? The better control would be transfecting the reporters but not the receptor. This may have been done, but the wording needs clarifying and S4D needs a legend.

      Thanks for pointing this out. We believe the reviewer refers to Figure S2D and we have now highlighted/clarified the legend better. Mock transfected conditions refer to HEK293 cells transfected with the reporter, but not the receptor. This is written in the legend as “(D) Change in luminescence signal generated between SmBiT-barr1 and LgBiT-miniGi in response to 100 nM CCL19 or 100 nM CCL21 in mock transfected HEK293 cells (no CCR7)”, which we believe should be clear to the audience.

      (3) The validation of the APEX2 receptor construct relies on a single assay with one ligand. The authors should show that the receptor expresses at the cell surface, is internalised normally, and that both ligands activate the receptor.

      We have now included additional data to show that (1) the receptor is expressed at the cell surface, (2) that the CCR7-APEX2 recruits barr1 to the plasma membrane, (3) that this association leads to barr1 translocation to the early endosomes as an indirect measurement of receptor internalization, and (4) that both CCL19- and CCL21-stimulation inhibit forskolin induced cAMP production (Fig.S4A-C, and described in fifth paragraph on page 6).

      (4) The APEX2 section is very short, especially as this is novel data. It lacks some important information, e.g. when the authors state that 'we identified a total of 579 proteins', is this in total for both ligands, separately or were some shared? More information on each ligand separately and combined would make this clearer.

      We have now specified that the identified total proteins enriched from our APEX2 approach is when the cells are stimulated with either CCL19 or CCL21 (third paragraph on page 7). Furthermore, we have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those were shared at different time points.

      (5) The discussion would benefit from some further work. The current first two paragraphs just reiterate the introduction and don't discuss the current paper so could be removed completely. The Laufer et al study needs much more discussion as they report many of the findings of the current paper (signaling following endocytosis, Rac1 endomembrane signaling) five years ago. The APEX2 findings that are discussed, though interesting, are not followed up by further experimental evidence and there is little discussion of why the two ligands have different responses or what the physiological effects could be.

      We appreciate the reviewer’s effort in helping with the discussion. To this end, we have now expanded our discussion of the mentioned paper further as suggested (third paragraph on page 10). We agree that the findings from our APEX experiment are interesting, but the focus of this study relates to proteins enriched specifically at endosomes. Several of the most enriched proteins did not show this localization bias, which is why these proteins were not further investigated.

      Minor changes:

      (1) The authors should remove the word 'recent' at the start of the first sentence of the third paragraph. Endosomal signaling by GPCRs was described 15 years ago so cannot really be seen as recent anymore.

      We have now adjusted the manuscript accordingly.

      (2) Tukey defaulted to Turkey in some places.

      We thank the reviewer for pointing out these typos, which now have been corrected.

      Reviewer #2 (Recommendations For The Authors):

      Minor Points:

      (1) ACKRs do not couple to G proteins so it is peculiar to see them in this table. I would limit the table to the conventional CCR1-10, CXCR1-6 and XCR1. The ligand for XCR1 is XCL1 which is absent from the table.

      We have now modified the table accordingly.

      (2) CCL19 (formerly known as ELC) has been long known to be a more efficacious and potent ligand in chemotaxis assays (Bardi et al, 2001). This earlier reference should be added to the citations in the preceding statement on page 10.

      This is an important study showing that CCL19 is more efficacious than CCL21 in promoting chemotaxis and that this has been known for decades. We have now included the reference accordingly (reference 59 in second paragraph on page 11).

      (3) Figure 6, Panel Q. I think the legends for CCR7 and CCR7 delta ST might be flipped.

      We thank the reviewer for pointing out this error. We have now corrected the figure panel.

      (4) Figure S5 (or 5) might benefit from simple Venn diagrams showing the numbers of differentially enriched proteins following treatment with the two ligands at different time points.

      We have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those where shared.

      Reference:

      Bardi, G., Lipp, M., Baggiolini, M. & Loetscher, P. The T cell chemokine receptor CCR7 is internalized on stimulation with ELC, but not with SLC. European Journal of Immunology 31, 3291-3297 (2001).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Understanding the mechanisms of how organisms respond to environmental stresses is a key goal of biological research. Assessment of transcriptional responses to stress can provide some insights into those underlying mechanisms. The researchers quantified traits, fitness, and gene expression (transcriptional) response to salinity stress (control vs stress treatments) for 130 accessions of rice (three replicates for each accession), which were grown in the field in the Philippines. This experimental design allowed for many different types of downstream analyses to better understand the biology of the system. These analyses included estimating the strength of selection imposed on transcription in each environment, evaluating possible trade-offs in gene expression, testing whether salinity induces transcriptional decoherence, and conducting various eQTL-type analyses.

      Strengths:

      The study provides an extensive analysis of gene expression responses to stress in rice and offers some insights into underlying mechanisms of salinity responses in this important crop system. The fact that the study was conducted under field conditions is a major plus, as the gene expression responses to soil salinity are more realistic than if the study was conducted in a greenhouse or growth chamber. The preprint is generally well-written and the methods and results are mostly well-described.

      Weaknesses:

      While the study makes good use of analyzing the dataset, it is not clear how the current work advances our understanding of gene regulatory evolution or plant responses to soil salinity generally. Overall, the results are consistent with other prior studies of gene expression and studies of selection across environmental conditions. Some of the framing of the paper suggests that there is more novelty to this study than there is in reality. That said, the results will certainly be useful for those working in rice and should be interesting to scientists interested in how gene expression responses to stress occur under field conditions. I detail other concerns I had about the preprint below:

      The abstract on lines 33-35 illustrates some of my concerns about the overstatement of the novelty of the current study. For example, is it really true that the role of gene expression in mediating stress response and adaptation is largely unexplored? There have been numerous studies that have evaluated gene expression responses to stresses in a wide range of organisms. Perhaps, I am missing something critically different about this study. If so, I would recommend that the authors reword this sentence to clarify what gap is being filled by this study. Further, is it really the case that none of them have evaluated how the correlational structure of gene expression changes in response to stresses in plants, as implied in lines 263-265? Don't the various modules and PC analyses of gene expression get at this question?

      We have re-worded these sentences, and highlighted the novelty of our work.

      There were some places in the methods of the preprint that required more information to properly evaluate. For example, more information should be provided on lines 664-668 about how G, E, and GxE effects were established, especially since this is so central to this study. What programs/software (R? SAS? Other?) were used for these analyses? If R, how were the ANOVAs/models fit? What type of ANOVA was used? How exactly was significance determined for each term? Which effects were considered fixed and which were random? If the goal was to fit mixed models, why not use an approach like voom-limma (Law et al. 2014 Genome Biology)? More details should also be added to lines 688-709 about these analyses, including what software/programs were used for these analyses.

      We have added more details in the methods. Also, although we could in priciple use voom-limma to fit our mixed model, to be able to partition variance into G, E and G×E, we need to use the function fitExtractVarPartModel (from package VariancePartition) which requires all categorical variables to be modeled as random effects. Therefore, we couldn’t model environment as a fixed effect.

      One thing that I found a bit confusing throughout was the intermixing of different terms and types of selection. In particular, there seemed to be some inconsistencies with the usage of quantitative genetics terms for selection (e.g. directional, stabilizing) vs molecular evolution terms for selection (e.g. positive, purifying). I would encourage the authors to think carefully about what they mean by each of these terms and make sure that those definitions are consistently applied here.

      We have defined the selection terms used in the study and used these terms consistently throughout the manuscript.

      It would be useful to clarify the reasons for the inherent bias in the detection of conditional neutrality (CN) and antagonistic pleiotropy (AP; Lines 187-196). It is also not clear to me what the authors did to deal with the bias in terms of adjusting P-value thresholds for CN and AP the way it is currently written. Further, I found the discussion of antagonistic pleiotropy and conditional neutrality to be a bit confusing for a couple of reasons, especially around lines 489-491. First of all, does it really make sense to contrast gene expression versus local adaptation, when lots of local adaptation likely involves changes in gene expression? Second, the implication that antagonistic pleiotropy is more common for local adaptation than the results found in this study seems questionable. Conditional neutrality appears to be more common for local adaptation as well: see Table 2 of Wadgymar et al. 2017 Methods in Ecology and Evolution. That all said, it is always difficult to conclude that there are no trade-offs (antagonistic pleiotropy) for a particular locus, as the detecting trade-offs may only manifest in some years and not others and can require large sample sizes if they are subtle in effect.

      We have now explained the cause of the inherent bias in the detection of CN, and also elaborated on how we deal with this bias. Also, we have edited our discussion and added relevant citations to indicate both conditional neutrality and antagonistic pleiotropy can lead to local adaptations and added the caveat regarding detecting antagonistic pleiotropy.

      Reviewer #2 (Public Review):

      The authors investigate the gene expression variation in a rice diversity panel under normal and saline growth conditions to gain insight into the underlying molecular adaptive response to salinity. They present a convincing case to demonstrate that environmental stress can induce selective pressure on gene expression, which is in agreement to their earlier study (Groen et al, 2020). The data seems to be a good fit for their study and overall the analytic approach is robust.

      (1) The work started by investigating the effect of genotype and their interaction at each transcript level using 3'-end-biased mRNA sequencing, and detecting a wide-spread GXE effect. Later, using the total filled grain number as a proxy of fitness, they estimated the strength of selection on each transcript and reported stronger selective pressure in a saline environment. However, this current framework relies on precise estimation of fitness and, therefore can be sensitive to the choice of fitness proxy.

      We now acknowledge this caveat in the discussion.

      (2) Furthermore, the authors decomposed the genetic architecture of expression variation into cis- and trans-eQTL in each environment separately and reported more unique environment-specific trans-eQTLs than cis-. The relative contribution of cis- and trans-eQTL depends on both the abundance and effect size. I wonder why the latter was not reported while comparing these two different genetic architectures. If the authors were to compare the variation explained by these two categories of eQTL instead of their frequency, would the inference that trans-eQTLs are primarily associated with expression variation still hold?

      We have now also reported the effect sizes for both cis- and trans-eQTLs in the two environments and showed that the trans-eQTLs have higher effect sizes as compared to cis-eQTLs, indicating that they are able to explain higher proportion of variation in transcript abundances in the two environments.

      (3) Next, the authors investigated the relationship between cis- and trans-eQTLs at the transcript level and revealed an excess of reinforcement over the compensation pattern. Here, I struggle to understand the motivation for testing the relationship by comparing the effect of cis-QTL with the mean effect of all trans-eQTLs of a given transcript. My concern is that taking the mean can diminish the effect of small trans-eQTLs potentially biasing the relationship towards the large-effect eQTLs.

      We wanted to estimate compensating vs reinforcing effects, which essentially entails identifying genes that have opposing directionality of cis and trans-effects. To get the total trans-effect we decided to take the mean effect of trans-eQTLs. This mean was only used to identify the compensating/reinforcing genes and although the mean effects diminishes the effect of small trans-eQTLs, this mean was not used in downstream analyses.

      Reviewer #3 (Public Review):

      In this work, the authors conducted a large-scale field trial of 130 indica accessions in normal vs. moderate salt stress conditions. The experiment consists of 3 replicates for each accession in each treatment, making it 780 plants in total. Leaf transcriptome, plant traits, and final yield were collected. Starting from a quantitative genetics framework, the authors first dissected the heritability and selection forces acting on gene expression. After summarizing the selection force acting on gene expression (or plant traits) in each environment, the authors described the difference in gene expression correlation between environments. The final part consists of eQTL investigation and categorizing cis- and trans-effects acting on gene expression.

      Building on the group's previous study and using a similar methodology (Groen et al. 2020, 2021), the unique aspect of this study is in incorporating large-scale empirical field works and combining gene expression data with plant traits. Unlike many systems biology studies, this study strongly emphasizes the quantitative genetics perspective and investigates the empirical fitness effects of gene expression data. The large amounts of RNAseq data (one sample for each plant individual) also allow heritability calculation. This study also utilizes the population genetics perspective to test for traces of selection around eQTL. As there are too many genes to fit in multiple regression (for selection analysis) and to construct the G-matrix (for breeder's equation), grouping genes into PCs is a very good idea.

      Building on large amounts of data, this study conducted many analyses and described some patterns, but a central message or hypothesis would still be necessary. Currently, the selection analysis, transcript correlation structure change, and eQTL parts seem to be independent. The manuscript currently looks like a combination of several parallel works, and this is reflected in the Results, where each part has its own short introduction (e.g., 185-187, 261-266, 349-353). It would be great to discuss how these patterns observed could be translated to larger biological insights. On a related note, since this and the previous studies (focusing on dry-wet environments) use a similar methodology, one would also wonder what the conclusions from these studies would be. How do they agree or disagree with each other?

      We acknowledge that the manuscript currently presents some analyses in a somewhat independent manner. Although it would be ideal to have a central hypothesis/message, our study is meant to broadly outline the various responses and fitness effects of salinity stress in rice. Throughout the manuscript, we have also included comparisons between our findings and that of our previous studies on drought stress to highlight any consistent themes or novel insights.

      Many analyses were done separately for each environment, and results from these two environments are listed together for comparison. Especially for the eQTL part, no specific comparison was discussed between the two environments. It would be interesting to consider whether one could fit the data in more coherent models specifically modeling the X-by-environment effects, where X might be transcripts, PCs, traits, transcript-transcript correlation, or eQTLs.

      We do plan to consider fitting models that explicitly incorporate X-by-environment interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      As stated, grouping genes into PCs is a good idea, but although in theory, the PCs are orthogonal, each gene still has some loadings on each PC (ie. each PC is not controlled by a completely different set of genes). Another possibility is to use any gene grouping method, such as WGCNA, to group genes into modules and use the PC1 of each module. There, each module would consist of completely different sets of genes, and one would be more likely to separate the biological functions of each module. I wonder whether the authors could discuss the pros and cons of these methods.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses.

      Reviewer #4 (Public Review):

      The manuscript examines how patterns of selection on gene expression differ between a normal field environment and a field environment with elevated salinity based on transcript abundances obtained from leaves of a diverse panel of rice germplasm. In addition, the manuscript also maps expression QTL (eQTL) that explains variation in each environment. One highlight from the mapping is that a small group of trans-mapping regulators explains some gene expression variation for large sets of transcripts in each environment. The overall scope of the datasets is impressive, combining large field studies that capture information about fecundity, gene expression, and trait variation at multiple sites. The finding related to patterns indicating increased LD among eQTLs that have cis-trans compensatory or reinforcing effects is interesting in the context of other recent work finding patterns of epistatic selection. However, other analyses in the manuscript are less compelling or do not make the most of the value of collected data. Revisions are also warranted to improve the precision with which field-specific terminology is applied and the language chosen when interpreting analytical findings.

      Selection of gene expression:

      One strength of the dataset is that gene expression and fecundity were measured for the same genotypes in multiple environments. However, the selection analyses are largely conducted within environments. The addition of phenotypic selection analyses that jointly analyze gene expression across environments and or selection on reaction norms would be worthwhile.

      We do plan to consider fitting models that explicitly incorporate G×E interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      Gene expression trade-offs:

      The terminology and possibly methods involved in the section on gene expression trade-offs need amendment. I specifically recommend discontinuing reference to the analysis presented as an analysis of antagonistic pleiotropy (rather than more general trade-offs) because pleiotropy is defined as a property of a genotype, not a phenotype. Gene expression levels are a molecular phenotype, influenced by both genotype and the environment. By conducting analyses of selection within environments as reported, the analysis does not account for the fact that the distribution of phenotypic values, the fitness surface, or both may differ across environments. Thus, this presents a very different situation than asking whether the genotypic effect of a QTL on fitness differs across environments, which is the context in which the contrasting terms antagonistic pleiotropy and conditional neutrality have been traditionally applied. A more interesting analysis would be to examine whether the covariance of phenotype with fitness has truly changed between environments or whether the phenotypic distribution has just shifted to a different area of a static fitness surface.

      We recognize that pleiotropy is a property of a genotype, and not phenotype, but since our phenotype (gene expression) is strongly coupled with the genotype, we choose to call trade-offs as antagonistic pleiotropy. That being said, we did test whether the covariance of gene expression with phenotype significantly varies between environments, and found that to indeed be the case.

      Biological processes under selection / Decoherence: PCs are likely not the most ideal way to cluster genes to generate consolidated metrics for a selection gradient analysis. Because individual genes will contribute to multiple PCs, the current fractional majority-rule method applied to determine whether a PC is under direct or indirect selection for increased or decreased expression comes across as arbitrary and with the potential for double-counting genes. A gene co-expression network analysis could be more appropriate, as genes only belong to one module and one can examine how selection is acting on the eigengene of a co-expression module. Building gene co-expression modules would also provide a complementary and more concrete framework for evaluating whether salinity stress induces "decoherence" and which functional groups of genes are most impacted.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses. But again as pointed out by the reviewer, our PCs did contain contribution (even negligible) of each gene, so to identify the ‘primary’ biological processes represented by the PCs, we chose the majority rule. As for testing decoherence, we agree that a co-expression module analyses would have provided additional support to the specific test performed in our manuscript, but since it would just be additional support, we choose to not add it in the manuscript.

      But based on the recommendation of the reviewer(s), we did perform a WGCNA analyses and found a total of 14 and 13 modules in normal and saline conditions, of which 0 and 2 modules (with no significant GO enrichment) were under directional selection. This supports our reasoning of potentially missing on identification of processes under selection.

      Selection of traits:

      Having paired organismal and molecular trait data is a strength of the manuscript, but the organismal trait data are underutilized. The manuscript as written only makes weak indirect inferences based on GO categories or assumed gene functions to connect selection at the organismal and molecular levels. Stronger connections could be made for instance by showing a selection of co-expression module eigengene values that are also correlated with traits that show similar patterns of selection, or by demonstrating that GWAS hits for trait variation co-localize to cis-mapping eQTL.

      We did perform a GWAS for all the traits collected in both normal and saline environment, and only found significant hits for fecundity (in both normal and saline environment) and chlorophyll_a content (in the saline environment). But these regions did not overlap with any candidate genes or cis-mapping eQTL. Hence we choose to mention it in the manuscript. Additionally, using the WGCNA modules, we found that the only two module under selection in the saline environment were not significantly correlated with any of the traits measured.

      Genetic architecture of gene expression variation:

      The descriptive statistics of the eQTL analysis summarize counts of eQTLs observed in each environment, but these numbers are not broken down to the molecular trait level (e.g., what are the median and range of cis- and trans-eQTLs per gene). In addition, genetic architecture is a combination of the numbers and relative effect sizes of the QTLs. It would be useful to provide information about the relative distributions of phenotypic variance explained by the cis- vs. trans- eQTLs and whether those distributions vary by environment. The motivation for examining patterns of cis-trans compensation specifically for the results obtained under high salinity conditions is unclear to me. If the lines sampled have predominantly evolved under low salinity conditions and the hypothesis being evaluated relates to historical experience of stabilizing selection, then my intuition is that evaluating the eQTL patterns under normal conditions provides the more relevant test of the hypothesis.

      We have added the median number of eQTLs per gene in each environment. Additionally, we recognize that genetic architecture is a combination id numbers and effect size, and we have added information regarding the effect sizes of eQTLs by type and by environment as recommend by another reviewer. We did explore the distributions of phenotypic variance explained by the cis- vs. trans- eQTLs as recommended here, and found that trans-eQTLs explain more phenotypic variance than cis-eQTLs in both environments and that the distribution of either type of eQTL does not vary by environment. We are choosing to not add this in the main text due to space limitations. Lastly, we examined the patterns of cis-trans compensation/reinforcement under both normal and salinity conditions and have compared and contrasted the results from both in the main text.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Lines 126: I would recommend citing those who originally developed the 3' end targeted RNA sequencing methods (e.g. Meyer et al 2011 Molecular Ecology).

      We have cited the recommended paper.

      Lines 128-130: It would be useful to include a description here of what models were fit to the data to partition out G, E, and GxE effects.

      Due to space limitations, we have in brief added a sentence to this effect.

      Line 139: I would suggest changing "found little" to "no" since the test was not significant.

      The sentence has been modified to say no evidence.

      Line 313: I think you mean directional selection instead of positive selection.

      We have corrected the text

      Lines 362-363: Would the authors also expect an enrichment of reinforcing genes for most scenarios where that has been divergent selection, such as local adaptation among populations?

      Based on our hypothesis, we would indeed expect an enrichment of reinforcing genes for scenarios of local adaptation where different alleles are maintained in different populations due to local adaptation.

      Reviewer #3 (Recommendations For The Authors):

      Figures 1d-e are not mentioned in the Results.

      The figures have been referenced in appropriate places.

      Lines 41-45: Terms such as reinforcement and compensation need to be explained in this specific context. Also "different selection regimes" is a bit broad and vague.

      Due to word-count limitation, we are choosing to not elaborate the terms reinforcement and compensation in the abstract (since these are commonly used in the literature, and we have also defined these in the main text). Additionally, we now explicitly state the selection pressures associated with cis and trans eQTLs.

      Table 1: Please explain S and C in the footnote.

      We have added the recommended footnote

      Figures: Some panel labels (a, b, c...) are mingled with the graphs.

      We are re-made our figure such that the panel labels do not mingle with the plots.

      Lines 588-591: font.

      Modified

      Lines 620-633: Please describe how these RNAseq libraries were allocated/pooled into different sequencing lanes to avoid potential batch effects among sequencing lanes.

      The sequencing was performed on the same Illumina NextSeq 500 machine and we have added the sequencing libraries/pool plan in the methods (lines 688-689). 

      Lines 690-692: At the beginning of this paragraph, it was mentioned that the un-standardized coefficients were estimated. But here, it seems like the transcript data were already standardized in the data preparation step. What do lines 687-688 refer to? Further standardizing those estimated coefficients so that the whole distribution has mean=0 and sd=1?

      Thank you pointing out our oversight. We checked our scripts and data preparation did not include transcript standardization, and we have removed the above line from the manuscript.

      Lines 705-711: Please explain why assigning the positive/negative selection status for each gene is important. "Positive selection" here is defined as genes whose increased expression also increases fitness, but traditionally positive selection was defined as "the derived state is favored over the ancestral state". For a gene whose ancestral expression is high but lower expression increases fitness in this experiment, could we also say this gene is under positive selection? Given that we don't know the ancestral state here, maybe the authors could explain whether this definition is necessary. Also, given that many genes positively or negatively regulate each other in a pathway, it is also unclear whether it is necessary to assign the positive/negative status for a PC using the majority rule (lines 710-711).

      We have now defined the different selection terms with respect to our study and use them consistently throughout the manuscript.

      Lines 711-715: If I understand correctly, PCs were used as traits, and by definition PCs should all be orthogonal. Is this section saying only retaining PCs whose correlation < 0.6 with each other? What is the rationale?

      PCA were performed on transcript abundance and the resulting orthogonal PCs explaining over 0.5% variance were all retained for selection analyses.

      We also performed selection analyses on the functional traits measured in the field, but since these functional traits are correlated (and as such would not satisfy the independent variable requirement of regression analyses), we retained only those functional traits which had a Pearson correlation coefficient < 0.6.

      Line 729: Please briefly describe what CLIP is doing.

      We have added the required description.

      Lines 736-741: The accession numbers do not add up to 125.

      Thank you for catching our oversight. We have edited the text, and now the numbers add upto 125.

      Line 796: Please remind readers where these 247k SNPs come from. Supposedly all accessions have been whole-genome sequenced, so the total number of SNPs should be larger than this.

      We have detailed method detailing how the SNPs were obtained and processed in the lines preceding this. Indeed the number of SNPs would have been much bigger, but the stringent cutoffs and linkage disequilibrium pruning reduced our dataset to about 247k SNPs.

      Lines 154-160: This is a bit confusing. The authors first mentioned, for the raw selection differentials, the mean and variance differ between environments, meaning they are misleading (why?). The next sentence then says non-standardized selection differentials will be used.

      The mean and variance for transcript abundances vary between the two environments. Because traits are usually measured in different scales, it is recommended to standardize trait values using variance or mean before estimating selection coefficients. Multiplying this variance (or mean) standardized selection differential with heritability gives the expected response to selection in standard deviation (or mean) units. But if the trait variance (or mean) varies between traits or environments, it leads to a conflation between the standardized selection differential and trait variance (or mean), which can be misleading. So to avoid this, and given that our traits (transcript abundance in this case) were all measured on the same scale, we chose to not standardize our trait values and estimated raw selection differentials.

      Figure 1 c-e: Please explain how the horizontal axis values were obtained. Is it assuming these selection differentials have a normal distribution of mean=0 & sd=1?

      Yes, horizontal axis represents theorical quantile for selection differential assuming they have a normal distribution with mean=0 and sd=1. This has been added to the figure legend.

      Line 162-168: Please clarify this part. What does “general trend towards stronger positive compared to negative selection on gene expression” mean? Does it mean the whole distribution of S is significantly different from 0, the difference in the number of genes in the S>0 vs S<0 category, or the a-bit-higher median |S| in the S>0 vs S<0 category? If it is the last one, are the small differences biological meaningful (0.053 vs. 0.047 for control & 0.051 vs. 0.050 for salt conditions), given that the authors defined |S|<0.1 as neutral?

      By “general trend towards stronger positive compared to negative selection on gene expression”, we mean that more transcripts were under positive directional selection as compared to negative directional selection. We have also clarified this in the text now.

      Line 177-178: This sentence implies disruptive selection is more important than stabilizing selection in the saline environment, but the test was not significant (line 176).

      Although there was no significant difference in the magnitude of stabilizing vs disruptive selection within the saline environment, the number of transcripts experiencing stronger disruptive selection in the saline condition was greater than the number of transcripts experiencing disruptive selection in the normal conditions. And so comparing between conditions, disruptive selection plays an important role in the saline conditions.

      Line 188-190: How CN vs. AP was statistically defined was not mentioned in the Methods section.

      We have added in the main text within the Results section.

      Line 203-214: How do these results fit with the previous observations that almost all transcripts have significant heritability?

      Although we do find that all but three transcripts have a have significant genetic effect (and thus have significant heritability), the median broad-sense heritability for 51 antagonistically pleiotropic genes is 0.23. Give that, we would only be able to detect SNPs regulating gene expression with high effect size since our sample size is n=130. Additionally, we used a very stringent criteria (FDR < 0.001) to define eQTLs. These two factors in combination could lead to us not being able to detect significant eQTLs for AP genes.

      Line 246-250: Please explain why the current conclusion would be opposite from the previous study. Supposedly the PCA, G matrix, and breeder’s equation were done for each environment separately. It makes sense that the G matrix and response to selection could be different between saline and drought treatments, but for the control treatments in the two studies, do they still differ? Why? Also in Table S7, it would be nice to show the % variation explained by each PC.

      Although both our studies had largely overlapping samples, about 20% samples were unique to each study. Additionally, although the site where the study was performed was the same across the two studies, we found significant temporal differences in gene expression due to micro-environmental differences. Both these factors can lead to changes in direct and indirect selection and its response, and we are examining these differences as part of a separate study. We also highlight these caveats in our discussion.

      Information on percent explained by each PCs is given in Table S5.

      Figure 2b: The vertical axis was labeled as “selection gradient”, but I think the responses to selection (D, I, T) have different units.

      We have re-labeled the vertical axis as “selection”.

      Reviewer #4 (Recommendations For The Authors):

      The manuscript mixes terminology for selection from quantitative genetics with that from population genetics. This is problematic, and the adjectives positive and negative should be replaced as descriptors of selection by instead rewording, for example, positive directional selection as directional selection for higher transcript abundance.

      Lines 193-196: The phrasing here reads as if the selection is solely acting on the presence/absence of expression rather than on quantitative variation in expression. During revision, it would be worth considering including an analysis of genes that parses genes that show the presence/absence of variation of expression within or across environments separately from genes that are expressed to non-trivial levels in both environments.

      We have modified the sentence in question now. Also, we pre-processed RNA-seq data to remove all transcripts with low expression signals (sigma signal < 20), and further retained only transcripts that had non-trivial expression in at least 10% of the population, which we believe represents presence/absence of variation of expression within or across environments.

      Lines 216-231: Is this analysis solely for directional selection? Not clear since previous sections examined both directional and stabilizing selection.

      Yes, we performed this analysis for only directional selection, and have clarified this in the text too.

      Lines 224-226: The meaning of this sentence is unclear and should be written more concretely.

      We have rephrased the sentence to be more clear.

      Lines 232-241: The description of the scientific logic here could be read as implying that genes interacting in networks are the sole source of indirect selection. I recommend revising the language to indicate this cause is one of several potential causes.

      We have reworded the sentence such that we indicate selection acting on interacting genes is just one of the causes of indirect selection.

      The strength of the conclusions of the decoherence analysis should be evaluated in light of caveats with such analyses (see Cai and Des Marais New Phytologist 2023).

      We have added the caveat with relevant citation in the manuscript.

      Rename this section as "Selection on Organismal Traits", as the previous sections have also been investigating selection on traits, just molecular traits.

      We have renamed the section as recommended

      Lines 314-318: Rewrite for clarity. Most environments select for an optimal phenotype; it is just the case here that the phenotypic distribution in the high salinity environment overlaps with the optimum.

      We have rephrased and clarified the statement.

      Lines 343-345: Rephrase to "These results indicate that natural variation in gene regulation under..."

      Rephrased.

      Line 354: "most" reads as too strong a descriptor here if the majority is ~60%.

      We have reworded the sentence to read “more than half”

      Lines 359-361: It is unclear to me how this interpretation follows from the above analysis.

      We have reworded the sentence so that the claim follows our analysis.

      Line 372: Is the expectation here more specifically one of epistatic selection? Other processes could stochastically lead to the genetic fixation of compensatory/reinforcing variants, but I think only epistasis for fitness would cause the interesting patterns of LD observed.

      The expectation here is that certain cis and trans variants only exists to compensate/reinforce, potentially through epistasis. We have clarified this in the text.

      Line 405: Change "adaptive organismal responses of organisms" to "organismal responses." As written, the sentence reads as being about plasticity rather than evolutionary responses, which are by populations, not organisms. None of the analyses included the manuscript test specifically test for adaptive plasticity.

      Rephrased.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, a chromosome-level genome of the rose-grain aphid M. dirhodum was assembled with high quality, and A-to-I RNA-editing sites were systematically identified. The authors then demonstrated that: 1) Wing dimorphism induced by crowding in M. dirhodum is regulated by 20E (ecdysone signaling pathway); 2) an A-to-I RNA editing prevents the binding of miR-3036-5p to CYP18A1 (the enzyme required for 20E degradation), thus elevating CYP18A1 expression, decreasing 20E titer, and finally regulating the wing dimorphism of offspring.

      Strengths:

      he authors present both genome and A-to-I RNA editing data. An interesting finding is that a A-to-I RNA editing site in CYP18A1 ruin the miRNA binding site of miR-3036-5p. And loss of miR-3036-5p regulation lead to less 20E and winged offspring.

      Weaknesses:

      How crowding represses the miR-3036-5p is still unclear.

      Reviewer #2 (Public Review):

      Summary:

      Environmental influences on development are ubiquitous, affecting many phenotypes in organisms. However molecular genetic and cellular mechanisms transducing environmental signals are still only barely understood. This study examines part of one such intracellular mechanism in a polyphenic (or dimorphic) aphid.

      Strengths:

      While other published reports have linked phenotypic plasticity to RNA editing before, this study reports such an interaction in insects. The study uses a wide array of molecular tools to identify connections upstream and downstream of the RNA editing to elucidate the regulatory mechanism, which is illuminating.

      Weaknesses:

      While this system is intriguing, this report does not foster confidence in its conclusions. Many of the analyses seem based on very small sample sizes. It is itself problematic that sample sizes are not obvious in most figures, although based on Methods section covering RNAseq, they seem to be either 3, 6 or 9, depending on whether stages were pooled, but that point is not made clear. With such small sample sizes, statistical tests of any kind are unreliable. Besides the ambiguity on sample sizes, it's unclear what error bars or whiskers show in plots throughout this study. When sample sizes are small estimates of variance are not reliable. Student's t-test is not appropriate for comparisons with such small sample sizes. Presently, it is not possible to replicate the tests shown in Figures 3, 4 and 6. (Besides the HT-seq reads, other data should also be made publicly available, following the journal's recommendations.) Regardless, effect sizes in some comparisons (Fig 3J, 4A-C, 6E, H) are clearly not large, making confidence in conclusions low. The authors should be cautious about over-interpreting these data.

      We appreciate very much for the reviewers’ time spent on our manuscript and the referees for the valuable suggestions and comments.

      To Reviewer #1:

      At present, researches on miRNAs mainly focus on its role in gene regulation by binding to the mRNA of target genes, “how miRNAs are regulated” has received less attention.

      Recent researches indicated that the expression of miRNAs is also regulated at the transcriptional or post transcriptional level. Transcriptional regulation including changes in the promoter of microRNA genes, and post-transcriptional mechanisms such as changes in miRNA processing and stability can both affect the final expression level of miRNAs.

      This article did not address how crowding treatment regulates miRNA expression. But this will be a very interesting issue, and we will pay attention to it in our future research.

      Thank you for this suggestion.

      To Reviewer #2:

      (1) “Transgenerational wing dimorphism was observed in M. dirhodum in which crowding of the parent (100 mother aphids in a 10 cm³ tube) increased the winged offspring (Fig 3E).” In this experiment, over 250 offsprings were used to calculate the proportion of winged and wingless individuals in normal (277), crowding (255) and crowding+20E (272) groups, respectively.

      “The RNAi-mediated knockdown of CYP18A1 and ADAR2 can significantly increase the titer of 20E (Fig. 4E) and reduce the number of winged offspring by 29.6% and 24.4% (Fig. 4F), respectively.” In this experiment, over 245 offsprings were used to calculate the proportion of winged and wingless individuals in dsEGFP (273), dsCYP18A1(248), and dsADAR2 (250) groups, respectively.

      “miR-3036-5p agomir and antagomir treatments could affect the proportion of winged offspring under normal conditions (Fig. 6F), but have no effect on the wing dimorphism of offspring under crowded conditions (Fig. 6L).” In this experiment, over 235 offsprings were used to calculate the proportion of winged and wingless individuals in each group, respectively.

      So I think our conclusion that crowding treatment, A-to-I RNA editing, and miRNAs could affect the wing dimorphism of offspring in M. dirhodum is very reliable. Because the number of aphids we use to count the results is sufficient.

      (2) The quantitative PCR method is used to detect changes in gene expression levels of CYP18A1 and ADAR2 after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3J, 4A-C, 5B, 6B, H, respectively. 5 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample, which might be sufficient for qPCR experiments. And among these biological replicates, the differences in gene expression levels are relatively small.

      (3) The titer of 20E was detected after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3I, 4E, 6E, K, respectively. 8 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample.

      The number of biological replicates used in each analysis and the number of aphids included in each biological replicate have been added in the Materials and Methods section. Thank you very much for pointing out this important issue.

      Reviewer #1 (Recommendations For The Authors):

      Several questions:

      (1) This study was conducted on the rose-grain aphid M. dirhodum. However, pea aphid Acyrthosiphon pisum seems to be a better object in wing dimorphism and development studies. Have the authors also identified the A-to-I RNA editing on pea aphids or other aphids?

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      We didn’t identify the A-to-I RNA editing on pea aphids or other aphids. A recent study has shown that editing events are poorly conserved across different Xenopus species. Even sites that are detected in both X. laevis and X. tropicalis show largely divergent editing levels or developmental profiles. In protein-coding regions, only a small subset of sites that are found mostly in the brain are well conserved between frogs and mammals. The conservation of RNA editing in aphids is still unknown, and we will continue to pay attention to this issue in our future research works.

      Reference: Nguyen TA, Heng JWJ, Ng YT, Sun R, Fisher S, Oguz G, Kaewsapsak P, Xue S, Reversade B, Ramasamy A, Eisenberg E, Tan MH. Deep transcriptome profiling reveals limited conservation of A-to-I RNA editing in Xenopus. BMC Biology. 2023, 21(1):251.

      (2) "Two miRNA-target prediction software programs, miRanda and RNAhybrid, were used to identify the miRNAs that potentially act on CYP18A1. The results showed that miR-3036-5p could bind to the sequence containing edited position (editing site 528) of CYP18A1 in M. dirhodum." Is there any other miRNA that can also act on CYP18A1, thereby regulating its expression?

      The predicted results indicate that there are several other miRNAs can act on CYP18A1, but none of them can bind to this editing site (editing site 528). Therefore, we did not pay attention to other miRNAs.

      (3) 11678 A-to-I RNA-editing sites were systematically identified in M. dirhodum. Does that mean RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes? Please clarify.

      It is of course possible that RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes. A-to-I RNA editing was also observed in 5 other genes that involved in 20E biosynthesis and signaling pathway, but no evident difference was identified for the RNA editing and expression levels of these 5 genes after crowding treatment (Fig. S2, Table S5). That means the A-to-I RNA editing of CYP18A1 might be crucial in 20E-mediated wing dimorphism in M. dirhodum.

      (4) It is interesting that "the transcriptional level of ADAR2 was 2.19 fold higher in the crowding+20E treatment parent than that in the normal group, but no significant difference was identified between the crowding and normal groups". ADAR2 can be induced by 20E, rather than crowding. How should the author explain? It seems that 20E induction can also cause many RNA editing events.

      20-hydroxyecdysone (20E) can affect the growth and development, molting, metamorphosis, and reproductive processes of insects. According to this result, 20E induction can also cause RNA editing events by regulating the expression of ADAR2, and which may provide valuable references for the future study on 20E. Meanwhile, we will also continue to pay attention to this issue in our future research works.

      (5) Authors provided a lot of text to describe the genome assembly. I don't think it's necessary, authors can make appropriate deletions.

      Thank you for this suggestion. This is the first high-quality chromosome-level genome of M. dirhodum, which will be very helpful for the cloning, functional verification, and evolutionary analysis of genes in this important species or even other Hemiptera insects. Therefore, I think it is necessary to provide a detailed description. We will also make appropriate deletions in the “Result and Discussion” sections.

      Reviewer #2 (Recommendations For The Authors):

      Additional concerns

      - With an existing genome sequence available for the peas aphid *Acyrthosiphon pisum*, why have these authors chosen to use the rose-grain aphid for this study? It would be helpful to address any limitations in *Acyrthosiphon pisum* or advantages in *Metopolophium dirhodum* that explain that decision.

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      - In Figure 5E, what anatomy is being shown in FISH? Moreover, this represents a single sample. It would be preferable to include a supplemental figure with comparable images from at least 3 additional specimens.

      It is the whole aphid body, and we have already uploaded additional 2 FISH images to the supplementary material Fig. S5. Thank you for this suggestion.

      - L190: Conservation alone seems inadequate to conclude that a chromosome functions as a sex chromosome. It would be fine to note the homology between Chr1 and the X of other Aphidini, but there are other explanations for that. Inference that Chr 1 is a sex chromosome might come from observations in karyotypes (by relative size comparisons or ideally from FISH) or from comparison of reads mapped to the chromosomes, suggesting Chr1 is hemizygous in males.

      Karyotype analysis experiment was not conducted in this research, so here the sex chromosome was determined based on chromosome homology between M. dirhodum and A. pisum genome. We have made appropriate modifications to the description in the article. Thank you for this suggestion.

      - L205: It's unclear to me how to interpret RNA editing results, based on RNAseq data, that map to "intergenic regions", especially when this is such a large fraction (37.3%) of the total result. Does this suggest a fundamental problem with the analysis, that so much RNAseq data maps to parts of the genome that are not annotated as genes?

      Non-coding RNA regions often account for a large proportion in the genome, and this RNAseq data is mapped to non-coding RNA transcription regions (37.3%) between protein-coding genes (intergenic regions).

      - L288-290: What degrees of confidence are attached to the predictions of these miRNA targets?

      There is no clear research indicating the accuracy of miRNA target prediction software. However, by comprehensively utilizing multiple prediction tools and experimental verification, the accuracy and reliability of prediction can be significantly improved.

      Actually, the prediction of miRNA targets is only a preliminary identification step, and we have subsequently demonstrated that miR-3036-5p can act on CYP18A1 through dual-luciferase reporter assay, RNA immunoprecipitation and FISH, etc.

      - L296-298: The mechanism proposed in this study seems to imply that miR-3036-5p should be absent (not expressed) in aphids under crowded conditions. Therefore, relative realtime PCR is not particularly useful here. Finding that the miR relative expression is reduced by 48.8% is meaningless, because in *relative* expression, zero has no special meaning. In this case, absolute quantitative PCR, measuring actual transcript numbers, would be far more informative.

      miR-3036-5p is not absent in aphids under crowded conditions. Only a significant decrease of miR-3036-5p in expression level under crowded conditions was identified compared to normal feeding conditions (Fig. 5B). So it should be reasonable to use relative quantitative methods for expression level analysis.

      - L361: Isn't alternative mRNA splicing a more common post-transcriptional modification?

      I'm very sorry, this sentence has been modified to “A-to-I RNA editing is one of the most prevalent forms of posttranscriptional modification in animals, plants, and other organisms.” Thank you for this suggestion.

      - L372: "Functional wing polymorphism is commonly observed in insects as a form of adaptation and a source of variation for natural selection (14)." The relationship between plastic phenotypic variation and natural selection is complex, and there is a large theoretical literature in evolutionary biology and evo-devo on this topic, but it is not a focus in the cited review by Zhang et al.. It would be helpful if the authors could expand on this idea with reference to some of this literature (e.g. Levins 1968; Harrison 1980; Moran 1992; Roff 1996; West-Eberhard 2003; Zera 2009).

      I have changed the citation and expanded on this idea. “Wing polymorphism is commonly observed in insects, resulting from variation in both genetic factors and environmental factors (Zera 2009).”

      - L404: Use the word "accurate" seems inappropriate in this context. Both morphs are equally "accurate".

      This sentence has been modified to “resulting in the alteration of CYP18A1 expression and wing dimorphism of offspring regulated by miR-3036-5p”, Thank you for this suggestion.

      - L412: Reference 67 seems irrelevant to this point.

      References have been changed and added.

      67. E.J. Duncan, C.B. Cunningham, P.K. Dearden. Phenotypic plasticity: what has DNA methylation got to do with it? Insects. 13(2):110 (2022).

      68. K.J. Rangan, S.L. Reck-Peterson, RNA recoding in cephalopods tailors microtubule motor protein function. Cell 186, 2531-2543 (2023).

      - L443: Is this referring to "mixed stage" aphids?

      Yes. To make it clearer, this sentence has been modified to “Approximately 200 mg of fresh M. dirhodum with mixed stages (including first- to fourth-instar nymphs and winged and wingless adults)”.

      - L483: What mass or number of individual aphids was used? I assume multiple individuals were pooled?

      Each sample contains approximately 200 aphids.

      - L499: Why was k = 17 used? The default is k = 21.

      The selection of k is usually an odd number between 15 and 21, which ensures that the types of k-mers can cover the genome while being small enough to avoid erroneous effects. Therefore, using 17 is very reasonable.

      - L574: what does it mean "multiple editing types"? What different types are possible? Are you referring to things other than A-to-I editing?

      That means besides A-to-I, this locus may also have other editing situations, such as A-to-C. If this situation occurs, it will be discarded.

      - L635: Which luciferase construct or plasmid has been used in this experiment? Citation to that source is necessary.

      PmirGLO vector (Promega, Leiden, Netherlands) was used in this experiment, and a reference has been added.

      B. Zhu, L. Li, R. Wei, P. Liang, X. Gao. Regulation of GSTu1-mediated insecticide resistance in Plutella xylostella by miRNA and lncRNA. PLoS Genetics. 17(10), e1009888 (2021).

      - L644: Did cDNA synthesis employ random primers or a poly-dT primer?

      This kit provides mixed primers, including random and poly-dT primers. (PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time), Takara Biotechnology, Dalian, China).

      - Fig 4D: Seems like this panel should be divided to cover the two sites, as in Fig 3F. Right now the x-axis labels seem redundant.

      Done. Thank you for this suggestion.

      - Fig 7: Consider adding ADAR2 to this figure.

      Done. Thank you for this suggestion.

      - Table 1: It would be helpful to represent this data in a figure where the phylogenetic relationships among the species can be shown.

      The phylogenetic relationships among the species were shown in Fig. 1D, and the table here may present genome information in more detail.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      “This work presents valuable data demonstrating that a camelid single-domain antibody can selectively inhibit a key glycolytic enzyme in trypanosomes via an allosteric mechanism. The claim that this information can be exploited for the design of novel chemotherapeutics is incomplete and limited by the modest effects on parasite growth, as well as the lack of evidence for cellular target engagement in vivo.”

      We agree with this assessment. In this re-worked version, we implemented the textual changes suggested by the reviewers and performed additional in silico work. The reviewers also presented valuable suggestions for additional experiments. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional “wet lab” experiments at this stage. We have thus not included new experimental “wet lab” data. Finally, the claim that our results may be exploited for the design of novel chemotherapeutics perhaps came across stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence. We have softened this statement by removing it from the abstract and have edited the discussion to end as follows.

      “Based on the presented results, we propose that sdAb42 may pinpoint a site of vulnerability on trypanosomatid PYKs that could potentially be exploited for the design of novel chemotherapeutics. Indeed, antibodies (or fragments thereof) are valuable drug discovery tools. Antibodies (and camelid sdAbs especially) are known for their ability to "freeze out" specific conformations of highly dynamic antigens, thereby exposing target sites of interest, which could be exploited for rational drug design (the development of so-called "chemo-superiors", (Lawson, 2012; Khamrui et al., 2013; van Dongen et al., 2019)). While the design of a "chemo-superior" inspired on the sdAb42-mediated allosteric inhibition mechanism will require further investigation, the results presented here provide a foundation to fuel such an endeavour.”

      REVIEWER 1:

      Summary:

      The authors identified nanobodies that were specific for the trypanosomal enzyme pyruvate kinase in previous work seeking diagnostic tools. They have shown that a site involved in the allosteric regulation of the enzyme is targeted by the nanobody and using elegant structural approaches to pinpoint where binding occurs, opening the way to the design of small molecules that could also target this site.

      Strengths:

      The structural work shows the binding of a nanobody to a specific site on Trypanosoma congolense pyruvate kinase and provides a good explanation as to how binding inhibits enzyme activity. The authors go on to show that by expressing the nanobodies within the parasites they can get some inhibition of growth, which albeit rather weak, they provide a case on how this could point to targeting the same site with small molecules as potential trypanocidal drugs.

      Weaknesses:

      The impact on growth is rather marginal. Although explanations are offered on the reasons for that, including the high turnover rate of the expressed nanobody and the difficulty in achieving the high levels of inhibition of pyruvate kinase required to impact energy production sufficiently to kill parasites, this aspect of the work doesn't offer great support to developing small molecule inhibitors of the same site.

      Recommendations for authors:

      Generally, the paper is very well written and the figures and their legends are clear.

      Comment 1.1: I thought the Introduction could give more focus to the need for new drugs for veterinary trypanosomiasis. The reality is that with fexinidazole now available and acoziborole soon to be available, with <1,000 cases of human African trypanosomiasis in each of the last five years, the case for needing new drugs is difficult to make. For Animal trypanosomiasis, however, the need for novel drugs is much more pressing.

      We agree with this comment and have included an additional section in the Introduction’s second paragraph, which reads as follows.

      “Hence, there is a need for alternative compounds, preferably with novel modes of action and/or designed based on mechanistic insights of the target’s structure-function relationship (Field et al., 2017; De Rycker et al., 2018). This need is especially pressing for AAT, which strongly impedes sustainable livestock rearing in Sub-Saharan Africa. AAT results in drastic reductions of draft power, meat, and milk production by the infected animals (small and large ruminants), and its control relies mainly on vector control and chemotherapy, with only few drugs currently available. The lack of routine field diagnosis has resulted in the misuse of trypanocidal drugs, thereby accelerating the rise of parasite resistance and further exacerbating the problem (Richards et al., 2021). As such, AAT-inflicted annual losses are estimated at around $5 billion (and the necessity to invest another $30 million each year to control AAT through chemotherapy), thereby having a devastating impact on the socio-economic development of Sub-Saharan Africa (Fetene et al., 2021). In contrast, HAT is perceived as a minor threat as it has reached a post-elimination phase as a public health problem with less than 1,000 yearly documented cases (Franco et al., 2022). In addition, new and effective drugs for HAT treatment have recently become available (De Rycker et al., 2023). HAT control currently relies on case detection and treatment, and vector control (Büscher et al., 2017).”

      Comment 1.2: A few pedantic things can be tidied up too, for example on line 61 it is stated tsetse flies are part of the life cycle for all trypanosomes while some veterinary species e.g. T. evansi and some T.vivax strains use other biting flies for transmission. I'd also add in the Introduction that pyruvate kinase is not a glycosomal enzyme (it is covered in the legend to figure 1 but I think it is quite important to clarify in the Introduction too so as to assure readers aren't wondering if "intrabodies" can get targeted there.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph to expand on the life cycles of African trypanosomes, which reads as follows.

      “African trypanosomes are extracellular parasites that have a bipartite life cycle involving insect vectors and mammals as hosts (Radwanska et al., 2018). Most HAT (T. brucei gambiense and T. b. rhodesiense) and AAT (T. b. brucei and T. congolense) causing trypanosomes are uniquely vectored by tsetse flies (Glossina spp.) and are confined to Sub-Saharan Africa. T. b. evansi and T. vivax (both causative agents of AAT) have expanded beyond the tsetse belt due to their ability to be mechanically transmitted by a variety of biting flies (Glossina, Stomoxys, and Tabanus spp.). Finally, T. b. equiperdum infects equids and represents an exception as it is transmitted directly from animal to animal through sexual contact.”

      The introduction now also explicitly mentions that pyruvate kinase is not a glycosomal enzyme.

      Comment 1.3: The introduction would also be a good place to include some more information on what is known about the allosteric effectors of pyruvate kinase in trypanosomes, and emphasize where gaps in knowledge exist too.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph, which reads as follows.

      “Pyruvate kinase (PYK) represents another attractive glycolytic target. This non-glycosomal enzyme catalyses the last step of the glycolysis (the irreversible conversion of phosphoenolpyruvate (PEP) to pyruvate; Figure 1A). The importance of this reaction is two-fold: i) the generation of ATP through the transfer of a phosphoryl group from PEP to ADP and ii) the formation of pyruvate, a crucial metabolite of the central metabolism. Like most PYKs, trypanosomatid PYKs are homotetramers. The PYK monomer is a ∼55 kDa protein organized into four domains termed ’N’, ’A’, ’B’, and ’C’ (Figure 1B). The A domain constitutes the largest part of the PYK monomer and is characterized by an (𝛼/𝛽)8-TIM barrel fold that contains the active site. Together with the N-terminal domain, it is also involved in the formation of the PYK tetramer AA’ dimer interfaces. The B domain is known as the flexible ’lid’ domain that shields the active site during enzyme-mediated phosphotransfer. Finally, the C domain harbors the binding pocket for allosteric effectors and stabilizes the PYK tetramer by formation of CC’ dimer interfaces. Because of their role in ATP production and distribution of fluxes into different metabolic branches, the activity of trypanosomatid PYKs is tightly regulated through an allosteric mechanism known as the "rock and lock" model (Morgan et al., 2010, 2014; Pinto Torres et al., 2020). In this model (which is detailed in Figure 1C), the binding of substrates and/or effectors (and analogs thereof) to the active and effector sites, respectively, trigger a conformational change from the enzymatically inactive T state to the catalytically active R state. Known effector molecules for trypanosomatid PYKs are fructose 2,6-bisphosphate (F26BP), fructose 1,6-bisphosphate (F16BP) and sulfate (SO<sub>4</sub><sup>2-</sup>), with F26BP being the most potent one (van Schaftingen et al., 1985; Callens and Opperdoes, 1992; Ernest et al., 1994; Tulloch et al., 2008). Interestingly, trypanosomatid PYKs seem to be largely unresponsive to the allosteric regulation of enzyme activity by free amino acids (Callens et al., 1991), which contrasts with human PYKs (Chaneton et al., 2012; Yuan et al., 2018). Known trypanosomatid PYK inhibitors impair enzymatic activity through occupation of the PYK active site (Morgan et al., 2011).”

      In the Results, although I am not qualified to analyse the structural data in detail I am confident in the ability of the authors to do so.

      Comment 1.4: Differences in nanobody binding kinetics to the T. congolense enzyme when compared to T. brucei and Leishmania enzymes are attributed to the relatively few amino acid differences in those sites. It is desirable to test site-directed mutagenesis of those residues.

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage.

      Comment 1.5: In the section on slow-binding inhibition kinetics (lines 194-220) I found it difficult to follow whether it was just the R<>T transition that slowed nanobody inhibition, or whether competition with effectors at the site would also impact on those inhibition kinetics. Can this be clarified?

      Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely the observed “slow-binding inhibition” kinetics are the result of a competition between sdAb42 on one hand and substrates and/or effectors on the other for enzyme binding. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. To clarify this matter, we slightly reformulated the discussion as indicated below. We also included a small discussion on the observation that there is a 400-fold difference between the Kd and the IC50.

      “Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely that the observed “slow-binding inhibition” kinetics are the result of a direct competition between sdAb42 and substrates and/or effectors. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. An additional observation in this context, is the 400-fold difference between the K<sub>D</sub> and IC<sub>50</sub> values. Although we currently do not have a mechanistic explanation, similar differences have been observed for the sdAb-mediated allosteric inhibition of other kinases (Singh et al., 2022).”

      For the intrabody expression work, the reference cited on line 230 actually points to a growing ability to genetically modify T. congolense. However, it is justifiable to work on T.brucei given the much wider availability and advanced status of the genetic tools.

      The growth inhibition data shown in Figure 7 is weak, albeit significant and the case is made as to why that might be.

      Comment 1.6: The authors do point to the fact that inhibiting other parts of the glycolytic pathway might be helpful in getting a better growth inhibitory effect. It would be useful, in this regard, to test the ability of the PFK inhibitors in the Macnae et al. paper in the intrabody expressing line, and possibly other inhibitors e.g. 2-deoxy-D-glucose to see if these combinations do have the desired impacts. Also, looking at the metabolome of the intrabody expressors under induction could also give some further insights into changes in flux (although perhaps not on its own given the weak effects on the growth seen).

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage. We would like to point out that, in our experience, studying the effect of enzyme inhibition on the metabolome is usually only useful shortly after adding the onset of inhibition. The system adapts to the lowered flux and relevant changes are mostly transient. Since the induced expression of sdAb42 is almost certainly slow, we expect the metabolic changes will be minimal.

      REVIEWER 2:

      Summary:

      In this work, the authors show that the camelid single-chain antibody sdAb42 selectivity inhibits Trypanosome pyruvate kinase (PYK) but not human PYK. Through the determination of the crystal structure and biophysical experiments, the authors show that the nanobody binds to the inactive T-state of the enzyme, and in silico analysis shows that the binding site coincides with an allosteric hotspot, suggesting that nanobody binding may affect the enzyme active site. Binding to the T-state of the enzyme is further supported by non-linear inhibition kinetics. PYK is an important enzyme in the glycolytic pathway, and inhibition is likely to have an impact on organisms such a trypanosomes, that heavily rely on glycolysis for their energy production. The nanobody was generated against Trypanosoma congolense PYK, but for technical reasons the authors progressed to testing its impact on cell viability in Trypanosoma brucei brucei. First, they show that sdA42 is able to inhibit Tbb PYK, albeit with lower potency. Cell-based experiments next show that expression of sdA42 has a modest, and dose-dependent effect on the growth rate of Tbb. The authors conclude that their data indicates that targeting this allosteric site affects cell growth and is a valuable new option for the development of new chemotherapeutics for trypanosomatid diseases.

      Strengths:

      The work clearly shows that sdA42A inhibits Trypanosome and Leishmania PYK selectively, with no inhibition of the human orthologue. The crystal structure clearly identifies the binding site of the nanobody, and the accompanying analysis supports that the antibody acts as an allosteric inhibitor of PYK, by locking the enzyme in its apo state (T-state).

      Weaknesses:

      (1) The most impactful claim of this work is that sdAb42-mediated inhibition of PYK negatively affects parasite growth and that this presents an opportunity to develop novel chemotherapeutics for trypanosomatid diseases. For the following reasons I think this claim is not sufficiently supported:

      Comment 2.1: The authors do not provide evidence of target-engagement in cells, i.e. they do not show that sdA42A binds to, or inhibits, Tbb PYK in cells and/or do not provide a functional output consistent with PYK inhibition (e.g. effect on ATP production). Measuring the extent of target engagement and inhibition is important to draw conclusions from the modest effect on growth.

      The authors do not explore the selectivity of sdA42A in cells. Potentially sdA42A may cross-react with other proteins in cells, which would confound interpretation of the results.

      We understand the reviewer’s concern. While it is theoretically possible that sdAb42 may non-specifically (cross-)react with other proteins in the cell, this would be highly unlikely based on the following arguments. First, many studies have employed sdAbs as intrabodies and reported on specific sdAb-mediated effects (outstanding reviews on the topic are Cheloha et al. (PMID 32868455) and Soetens et al. (PMID 33322697)). Second, it has been demonstrated that selecting sdAbs from an immune library through phage display or “bacteriomatch” (a bacterial system similar to yeast two hybrid) yields highly similar results (Pellis et al., PMID 22583807), thereby indicating that sdAbs interact specifically with their target antigens in an intracellular environment. Third, we identified TcoPYK as the target for sdAb42 by employing sdAb42 as bait in a pull-down from a parasite whole cell lysate (Pinto Torres et al., PMID 29899344). The pull-down fractions were analysed by SDS-PAGE and we observed a clear prominent band, which was further analysed by mass spectrometry and revealed TcoPYK as the target with great certainty. Even though the affinity of sdAb42 for TbrPYK is lower, it still remains high (nM affinity) and we expect it to bind TbrPYK with high specificity.

      Regarding measuring the effect on ATP production, we would like to state that such experiments are not obvious. Instead of measuring ATP levels, one should measure ATP turnover as ATP levels may not necessarily be decreased. The latter was observed to be the case for the specific inhibition of trypanosomal PFK (Nare et al. PMID 36864883). The specific trypanosomal PFK inhibitor inhibits motility (and growth) of T. congolense IL3000 at concentrations that only slightly affect ATP levels. One could think of repeating the sdAb42 experiments in a T. congolense model. However, T. congolense BSF metabolism is more complicated than that of T. brucei BSF. First, the T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Second, pyruvate is not excreted but further metabolised, in part in the mitochondrion. Steketee et al. (PMID 34310651) have shown that T. congolense also takes up pyruvate from the medium. One can thus check if (increased) external pyruvate (partially) rescues the growth inhibition by sdAb42. It will not provide proof, but maybe an indication. As mentioned above, we are currently unable to perform such additional experiments due to lack of dedicated hands and funding.

      Comment 2.2: sdA42A only affects minor growth inhibition in Tbb. The growth defect is used as the main evidence to support targeting this site with chemotherapeutics, however based on the very modest effect on the parasites, one could reasonably claim that PYK is actually not a good drug target. The strongest effect on growth is seen for the high expressor clone in Figure 4a, however here the uninduced cells show an unusual profile, with a sudden increase in growth rate after 4 days, something that is not seen for any of the other control plots. This unexplained observation accentuates the growth difference between induced and uninduced, and the growth differences seen in all other experiments, including those with the highest expressors (clones 54 and 55) are much more modest. The loss of expression of sdA42A over time is presented as a reason for the limited effect, and used to further support the hypothesis that targeting the allosteric site is a suitable avenue for the development of new drugs. However, strong evidence for this is missing.

      We agree that the growth effect of sdAb42 expression is modest, and we have provided several explanations as to why this could be the case. In addition, as mentioned at the start of this rebuttal, the claim that our results may be exploited for the design of novel chemotherapeutics was perhaps expressed stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence as mentioned by the reviewer.

      We, however, disagree that PYK would not be a good drug target. Its potential to serve as a drug target is related to its fundamentally important role in trypanosomal glycolysis and not to the features of sdAb42. Steketee et al. (PMID 34310651) have shown that glycolysis is essential for T. congolense BSF, despite a lower glycolytic flux than in T. brucei BSF. The T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Also here, PYK is thus almost certainly essential and from that perspective a good drug target.

      Comment 2.3: For chemotherapeutic interventions to be possible, a ligandable site is required. There is no analysis provided of the antibody binding site to indicate that small molecule binding is indeed feasible.

      We agree with the reviewer’s comment and have included APOP analysis on the TcoPYK T state crystal structure (see also reply to Comment 3.1). Briefly, APOP works by detecting pockets and then perturbing each pocket in the protein's elastic network (GNM) by adding stiffer springs between the surrounding residues. The pockets are scored and ranked based on the calculated shifts in the eigenvalues of the global GNM modes and their local hydrophobic densities, thereby also considering the pocket’s surface accessibility, which renders it suitable for the identification of allosteric (and druggable) pockets. The APOP analysis identifies pockets overlapping with the sdAb42 epitope as highly ranking allosteric ligand binding pockets. The data have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed APOP analysis in the Materials and Methods section.

      Comment 2.4: The authors comment on the modest growth inhibition, and refer to the need to achieve over 88% reduction in Vmax of PYK to see a strong effect, something that may or may not be achieved in the cell-based model (no target-engagement or functional readout provided). The slow binding model and switch of species are also raised as potential explanations. While these may be plausible explanations, they are not tested which leaves us with limited evidence to support targeting the allosteric site on PYK.

      In our understanding of this remark, we believe it be related to Comments 2.1 and 2.2 and thus refer to our answers formulated above.

      Comment 2.5: The evidence to support an allosteric mechanism is derived from structural studies, including the in silico allosteric network predictions. Unfortunately, standard enzyme kinetics mode of inhibition studies are missing. Such studies could distinguish uncompetitive from non-competitive behaviour and strengthen the claim that sdAb42 locks the enzyme complex in the apo form.

      We agree with the referee that a thorough kinetic analysis could distinguish between uncompetitive (i.e., sdAb only binds to the enzyme if substrate is bound) or non-competitive (i.e., sdAb can bind to apo enzyme and substrate-bound enzyme) inhibition. In both cases, however, this would correspond to an allosteric mechanism of inhibition. Although such a thorough kinetic analysis would be interesting in its own right, we would like to argue that this type of very detailed kinetics is outside the scope of this paper. This is especially the case taking into account that this analysis could be complicated by the slow-onset inhibition behavior.

      Comment 2.6: As general comment, the graphical representation of the data could be improved in line with recent recommendations: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128, https://elifesciences.org/inside-elife/5114d8e9/webinar-report-transforming-data-visualisation-to-improve-transparency-and-reproducibility.

      - Bar-charts for potency are ideally presented as dot plots, showing the individual data points, or box plots with datapoints shown.

      - Images in Figure 7 show significant heterogeneity of nanobody expression, but the extent of this can not be gleaned from Figure 7B. It would be much better to use box plots or violin plots for each cell line on this figure panel. The same applies to Figure 10.

      We thank the reviewer for these suggestions but have taken the decision not to act upon these as the other reviewers explicitly mentioned that our figures are very clear.

      Recommendations for authors:

      Please find below some minor comments:

      Comment 2.7: Line 24: "increasing number of drug failures": This does not really reflect the current situation for human African trypanosomiasis, with NECT treatment retaining efficacy, fexinidazole now being registered, and acoziborole progressing towards registration. It may be worth considering focusing the introduction more on Nagana, as all Trypanosome species used in the paper are animal infective, and the nanobody was discovered for T. congolense.

      We refer to our answer formulated in response to Comment 1.1.

      Comment 2.8: Line 55: "alarming number of reports describing ..." While resistance is a big problem, this mainly applies to malaria, bacterial and fungal diseases. For kinetoplastids, the number of reports describing resistance in the clinic is pretty limited. However, the drug discovery pipeline for these diseases is sparse, so I definitely agree there is a need to develop new compounds with differentiated mechanisms.

      We agree with the reviewer and have slightly adapted our wording here as follows.

      “Unfortunately, a number of reports describe treatment failure or parasite resistance to the currently available drugs (De Rycker et al., 2018).”

      Comment 2.9: This manuscript is about pyruvate kinase, but the enzyme is not properly introduced. I suggest a short paragraph introducing PYK at line 77 (without duplicating Figure 1), covering its role in glycolysis, the importance of pyruvate, any essentiality data from the literature, and any known inhibitors.

      We refer to our answer formulated in response to Comment 1.3.

      Comment 2.10: Figure 6: For the top insets it would be useful to somehow show the increasing antibody concentration, either by using a changing intensity or size for each line.

      We thank the reviewer for this suggestions, but decided not to act upon it as we found that the inclusion of this information in the figure made it “too crowded”, which is why we opted to provide this information in the figure legend.

      “Only a subset of the traces is shown for the sake of clarity. The following curves are shown (from bottom to top): TcoPYK (0.15 nM sdAb42, 500 nM sdAb42, 750 nM sdAb42, 1000 nM sdAb42, 1500 nM sdAb42, 2000 nM sdAb42, no enzyme control), LmePYK (5 nM sdAb42, 750 nM sdAb42, 1250 nM sdAb42, 1500 nM sdAb42, 2500 nM sdAb42, 3000 nM sdAb42, no enzyme control), and TbrPYK (1 nM sdAb42, 1000 nM sdAb42, 1750 nM sdAb42, 2000 nM sdAb42, 3500 nM sdAb42, 4000 nM sdAb42, no enzyme control).”

      Comment 2.11: You refer to the curves as biphasic, but they look like 1st order kinetics, and there are no clear 1st and 2nd phases (or at least they are not marked). It may be more appropriate to label these as non-linear.

      We agree that the term “biphasic” is potentially an over-simplification of the actual situation. What we mean is that the formation of product as a function of time ([P] versus [t] curve) is not linear at short time ranges but evolves from an initial “weakly inhibited” rate (v<sub>0</sub>) to a “strongly inhibited” steady-state rate (v<sub>ss</sub>). This conversion from v<sub>0</sub> to v<sub>ss</sub> indeed occurs in a fashion following single exponential behavior. With the term “biphasic” we thus meant a non-linear phase (before v<sub>ss</sub> is reached) followed by a linear phase (after v<sub>ss</sub> is reached). To avoid any confusion, we replaced the term “biphasic” by “non-linear”.

      Comment 2.12: IC50s - would be useful to provide a comparison with IC50s generated in the pre-incubation experiments - is the antibody less potent without pre-incubation? I could not find IC50s for the pre-incubation experiments shown in Figure 2.

      We determined an IC50 value for sdAb42 against TcoPYK under pre-incubation conditions, but initially decided not to include this into the manuscript. We agree with the reviewer that a comparison between IC50 values determined under pre- and post-incubation conditions would be of interest, and have therefore included the pre-incubation IC50 data for TcoPYK in Figure 2 (panel B). The data indeed show that sdAb42 far more efficiently inhibits an enzyme that is not continuously cycling between R and T states (IC50 values of 15 nM and 359 nM under pre- and post-incubation conditions, respectively). This is now discussed in the results section of the manuscript. We did not determine IC50 values for sdAb42 against TbrPYK and LmePYK under pre-incubation conditions, but suspect that a similar observation will be made upon comparing these values to IC50 under post-incubation conditions.

      REVIEWER 3:

      Summary:

      Out of the 20 Neglected Tropical Diseases (NTD) highlighted by the WHO, three are caused by members of the trypanosomatids, namely Leishmanaisis, Trypanosomiasis, and Chagas disease. Trypanosomal glycolytic enzymes including pyruvate kinase (PyK) have long been recognised as potential targets. In this important study, single-chain camelid antibodies have been developed as novel and potent inhibitors of PyK from the T, congolense. To gain structural insight into the mode of action, binding was further characterised by biophysical and structural methods, including crystal structure determination of the enzyme-nanobody complex. The results revealed a novel allosteric mechanism/pathway with significant potential for the future development of novel drugs targeting allosteric and/or cryptic binding sites.

      Strengths:

      This paper covers an important area of science towards the development of novel therapies for three of the Neglected Tropical Diseases. The manuscript is very clearly written with excellent graphics making it accessible to a wide readership beyond experts. Particular strengths are the wide range of experimental and computational techniques applied to an important biological problem. The use of nanobodies in all areas from biophysical binding experiments and X-ray crystallography to in-vivo studies is particularly impressive. This is likely to inspire researchers from many areas to consider the use of nanobodies in their fields.

      Weaknesses:

      There is no particular weakness, but I think the computational analysis of allostery, which basically relies on a single server could have been more detailed.

      Recommendations for authors:

      Overall an excellent paper, there are just a couple of points that the authors could consider, if time allows.

      Comment 3.1: As mentioned above the computational analysis of allostery appears to be based on a single server based on coordinates alone with no in-depth analysis. It would be extremely interesting to see if more sophisticated methods based on elastic network model and/or molecular dynamics simulation gave similar results. I realize that this would require quite a lot of work though.

      We agree with the reviewer’s comment and have complemented the perturbation analysis (previously presented in the manuscript) with dGNM and APOP analyses to identify allosteric communication pathways and allosteric binding pockets, respectively. dGNM, which is based on transfer entropy, allowing for a detailed characterization of the dynamic coupling and information transfer between residues. Meanwhile, APOP employs a perturbation-based approach to detect and rank allosteric pockets. The findings are in good agreement with the previously presented perturbation data and have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed transfer entropy and APOP analyses in the Materials and Methods section.

      Comment 3.2: The figures are excellent and really help the reader - with the exception of the screenshots (Figure 8). Using pymol or chimera (or any other more expensive commercial package) would really help the reader and will not take much time.

      We agree with the referee that this is not the most beautiful figure. However, we find the quality and clarity of the figure to be adequate for its purpose (i.e., a supplemental figure).

      Comment 3.3: Finally, I would have liked to see at least the PDB validation files. This is a highly regarded and experienced team, nevertheless, the resolution is rather mediocre. As the crystal coordinates were used as input for the computational, any experimental inaccuracies will affect the computational results.

      We agree with the reviewer that we could have provided the validation report together with the submitted manuscript and we apologise for the inconvenience. The validation reports will be released together with the structures following final manuscript publication. Regarding the resolution of the crystal structures, we agree with the reviewer’s comment, but we obviously employed data sets from our best diffracting crystals and could not obtain a higher resolution despite our best efforts.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary and Strengths:

      The ability of Wolbachia to be transmitted horizontally during parasitoid wasp infections is supported by phylogenetic data here and elsewhere. Experimental analyses have shown evidence of wasp-to-wasp transmission during coinfection (eg Huigins et al), host to wasp transmission (eg Heath et al), and mechanical ('dirty needle') transmission from host to host (Ahmed et al). To my knowledge this manuscript provides the first experimental evidence of wasp to host transmission. Given the strong phylogenetic pattern of host-parasitoid Wolbachia sharing, this may be of general importance in explaining the distribution of Wolbachia across arthropods. This is of interest as Wolbachia is extremely common in the natural world and influences many aspects of host biology.

      Weaknesses:

      The first observation of the manuscript is that the Wolbachia strains in hosts are more closely related to those in their parasitoids. This has been reported on multiple occasions before, dating back to the late 1990s. The introduction cites five such papers (the observation is made in other studies too that could be cited) but then dismisses them by stating "However, without quantitative tests, this observation could simply reflect a bias in research focus." As these studies include carefully collected datasets that were analysed appropriately, I felt this claim of novelty was rather strong. It is unclear why downloading every sequence in GenBank avoids any perceived biases, when presumably the authors are reanalysing the data in these papers.

      Thank you for bringing this to our attention. In this study, we downloaded all wsp sequences from GenBank and conducted a systematic analysis. We acknowledge that there could still be a bias in research focus, but a systematic analysis, compared to a limited dataset, may reduce this bias. We agree with the reviewer's point, and we have revised this statement to make it more accurate. Now the new sentence reads: "However, there is still a lack of systematic statistical analyses to support this hypothesis." (Lines 69–70 in the revised manuscript)

      I do not doubt the observation that host-parasitoid pairs tend to share related Wolbachia, as it is corroborated by other studies, the effect size is large, and the case study of whitefly is clearcut. It is also novel to do this analysis on such a large dataset. However, the statistical analysis used is incorrect as the observations are pseudo-replicated due to phylogenetic non-independence. When analysing comparative data like this it is essential to correct for the confounding effects of related species tending to be similar due to common ancestry. In this case, it is well-known that this is an issue as it is a repeated observation that related hosts are infected by related Wolbachia. However, the authors treat every pairwise combination of species (nearly a million pairs) as an independent observation. Addressing this issue is made more complex because there are both the host and symbiont trees to consider. The additional analysis in lines 123-124 (including shuffling species pairs) does not explicitly address this issue.

      We agree with your point about the non-independence of data due to phylogenetic relationships. In the analysis of species traits, a conventional phylogenetic correction assumes that traits follow a Brownian motion model (Felsenstein, 1985). The variance of the trait values for a species i is given by:

      Var[Yi]=σ2Ti,

      Where Ti represents the time from the root to the tip for species i. Consequently, the covariance between traits of species i and j is:

      Cov[Yij,Yj]=σ<sup>2</sup>Tii,

      where Tij is the time from the root to the most recent common ancestor (MRCA) of species i and j. Linear model analysis incorporates the covariance matrix to correct for the effects of non-independence. Mathematically, this method is equivalent to the independent contrasts approach (Felsenstein, 1985).

      In our analysis, we treat the minimum interspecific wsp distance between two species as a trait for the species pair (i, j). Similarly, for any two pairs of species (i, j) and (k, l), we postulate that the covariance between their traits is given by:

      Cov[Y<sub>ij</sub>,Y<sub>kl</sub>]=σ2⋅(T<sub>ik</sub>+T<sub>jl</sub>),

      where Tik denotes the time from the root to the MRCA of species i and k, and Tjl represents the time from the root to the MRCA of species j and l. This covariance matrix is then incorporated into our linear model analysis to account for the effects of phylogenetic non-independence.

      However, when extending trait analysis to pairs of species, the computational demands increase substantially. For instance, with a dataset of 1,377 species, forming all possible pairs yields 947,376 unique species combinations. Consequently, constructing a covariance matrix for these pairs would necessitate storing 897,521,285,376 entries, a requirement that far exceeds the memory capabilities of standard computing systems.

      To address this, we randomly sampled 1,000 pairs from the total of 947,376 species pairs within the 'Others' category, thereby reducing the computational load without compromising the representativeness of our analysis. Ultimately, even after accounting for phylogenetic correction using covariance, the effect of parasitism remains highly significant (p < 0.0001).

      We have added a “Phylogenetic correction” section to Materials and Methods (Lines 392–405 in the revised manuscript). The corresponding results are described on lines 120–121 and in supplementary Note 1. The data and scripts for this analysis are available at https://doi.org/10.6084/m9.figshare.24718119.

      REFERENCE

      Felsenstein J, 1985. Phylogenies and the comparative method. The American Naturalist, 125(1), 1-15.

      The sharing of Wolbachia between whitefly and their parasitoids is very striking, although this has been reported before (eg the authors recently published a paper entitled "Diversity and Phylogenetic Analyses Reveal Horizontal Transmission of Endosymbionts Between Whiteflies and Their Parasitoids"). In Lines 154-164 it is suggested that from the tree the direction of transfer between host and parasitoid can be inferred from the data. This is not obvious to me given the poor resolution of the tree due to low sequence divergence. There are established statistical approaches to test the direction of trait changes on a tree that could have been used (a common approach is to use the software BEAST).

      We thank the reviewer for this constructive feedback on our interpretation of Wolbachia transfer between whiteflies and their parasitoids. Inspired by the reviewer's comments, we have now incorporated a trait-based approach, using the taxonomic order of the source species of the wsp gene as a discrete trait for ancestral state reconstruction on the wsp tree. The estimated ancestral trait state for one clade, which clusters wsp sequences from whiteflies and parasitoids, is Hymenoptera, suggesting that within this clade, the direction of Wolbachia transfer may have been from parasitoids to hosts. Conversely, in another clade characterized by the ancestral trait state of Hemiptera, the inferred direction of transfer appears to be from hosts to parasitoids. We have added a “Ancestral state reconstruction” section to Materials and Methods (Lines 406–412 in the revised manuscript). The corresponding results are described on lines 159–163 and 167–168. The data and script for this analysis is available at https://doi.org/10.6084/m9.figshare.24718119.

      Reviewer #2 (Public Review):

      The paper by Yan et al. aims to provide evidence for horizontal transmission of the intracellular bacterial symbiont Wolbachia from parasitoid wasps to their whitefly hosts. In my opinion, the paper in its current form consists of major flaws.

      Weaknesses:

      The dogma in the field is that although horizontal transmission events of Wolbachia occur, in most systems they are so rare that the chances of observing them in the lab are very slim.

      For the idea of bacteria moving from a parasitoid to its host, the authors have rightfully cited the paper by Hughes, et al. (2001), which presents the main arguments against the possibility of documenting such transmissions. Thus, if the authors want to provide data that contradict the large volume of evidence showing the opposite, they should present a very strong case.

      In my opinion, the paper fails to provide such concrete evidence. Moreover, it seems the work presented does not meet the basic scientific standards.

      We are grateful for your critical perspective on our work. Nonetheless, we are confident in the credibility of our findings regarding the horizontal transmission of Wolbachia from En. formosa to B. tabaci. Our study has documented this phenomenon through phylogenetic tree analyses, and we have further substantiated our observations with rigorous experiments in both cages and petri dishes. The horizontal transfer of Wolbachia was confirmed via PCR, with the wsp sequences in B. tabaci showing complete concordance with those in En. formosa. Additionally, we utilized FISH, vertical transmission experiments, and phenotypic assays to demonstrate that the transferred Wolbachia could be vertically transmitted and induce significant fitness cost in B. tabaci. All experiments were conducted with strict negative controls and a sufficient number of replicates to ensure reliability, thereby meeting basic scientific standards. The collective evidence we present points to a definitive case of Wolbachia transmission from the parasitoid En. formosa to the whitefly B. tabaci.

      My main reservations are:

      - I think the distribution pattern of bacteria stained by the probes in the FISH pictures presented in Figure 4 looks very much like Portiera, the primary symbiont found in the bacterium of all whitefly species. In order to make a strong case, the authors need to include Portiera probes along with the Wolbachia ones.

      We thank you for your critical evaluation regarding the specificity of FISH in our study. We assure the reliability of our FISH results based on several reasons.

      (1) We implemented rigorous negative controls which exhibited no detectable signal, thereby affirming the specificity of our hybridization. (2) The central region of the whitefly nymphs is a typical oviposition site for En. formosa. Post-parasitism, we observed FISH signals around the introduced parasitoid eggs, distinct from bacteriocyte cells which are rich in endosymbionts including Portiera (Fig 3e-f). This observation supports the high specificity of our FISH method. (3) In the G3 whiteflies, we detected the presence of Wolbachia in bacteriocytes in nymphs and at the posterior end of eggs in adult females (Fig. 4). This distribution pattern aligns with previously reported localizations of Wolbachia in B. tabaci (Shi et al., 2016; Skaljac et al., 2013). Furthermore, the distribution of Wolbachia in the whiteflies does indeed exhibit some overlap with that of Portiera (Skaljac et al., 2013; Bing et al., 2014). 4) The primers used in our FISH assays have been widely cited (Heddi et al., 1999) and validated in studies on B. tabaci and other systems (Guo et al., 2018; Hegde et al., 2024; Krafsur et al., 2020; Rasgon et al., 2006; Uribe-Alvarez et al., 2019; Zhao et al., 2013).

      Taking all these points into consideration, we stand by the reliability of our FISH results.

      REFERENCES

      Bing XL, Xia WQ, Gui JD, et al., 2014. Diversity and evolution of the Wolbachia endosymbionts of Bemisia (Hemiptera: Aleyrodidae) whiteflies. Ecol Evol, 4(13):2714-37.

      Guo Y, Hoffmann AA, Xu XQ, et al., 2018. Wolbachia-induced apoptosis associated with increased fecundity in Laodelphax striatellus (Hemiptera: Delphacidae). Insect Mol Biol, 27:796-807.

      Heddi A, Grenier AM, Khatchadourian C, Charles H, Nardon P, 1999. Four intracellular genomes direct weevil biology: nuclear, mitochondrial, principal endosymbiont, and Wolbachia. Proc Natl Acad Sci USA, 96:6814-6819.

      Hegde S, Marriott AE, Pionnier N, et al., 2024. Combinations of the azaquinazoline anti-Wolbachia agent, AWZ1066S, with benzimidazole anthelmintics synergise to mediate sub-seven-day sterilising and curative efficacies in experimental models of filariasis. Front Microbiol, 15:1346068.

      Krafsur AM, Ghosh A, Brelsfoard CL, 2020. Phenotypic response of Wolbachia pipientis in a cell-free medium. Microorganisms, 8.

      Rasgon JL, Gamston CE, Ren X, 2006. Survival of Wolbachia pipientis in cell-free medium. Appl Environ Microbiol, 72:6934-6937.

      Shi P, He Z, Li S, et al., 2016. Wolbachia has two different localization patterns in whitefly Bemisia tabaci AsiaII7 species. PLoS One, 11: e0162558.

      Skaljac M, Zanić K, Hrnčić S, et al., 2013. Diversity and localization of bacterial symbionts in three whitefly species (Hemiptera: Aleyrodidae) from the east coast of the Adriatic Sea. Bull Entomol Res, 103(1):48-59.

      Uribe-Alvarez C, Chiquete-Félix N, Morales-García L, et al., 2019. Wolbachia pipientis grows in Saccharomyces cerevisiae evoking early death of the host and deregulation of mitochondrial metabolism. MicrobiologyOpen, 8: e00675.

      Zhao DX, Zhang XF, Chen DS, Zhang YK, Hong XY, 2013. Wolbachia-host interactions: Host mating patterns affect Wolbachia density dynamics. PLoS One, 8: e66373.

      - If I understand the methods correctly, the phylogeny presented in Figure 2a is supposed to be based on a wide search for Wolbachia wsp gene done on the NCBI dataset (p. 348). However, when I checked the origin of some of the sequences used in the tree to show the similarity of Wolbachia between Bemisia tabaci and its parasitoids, I found that most of them were deposited by the authors themselves in the course of the current study (I could not find this mentioned in the text), or originated in a couple of papers that in my opinion should not have been published to begin with.

      We appreciate your meticulous examination of the sources for our sequence data. All the sequences included in our phylogenetic analysis were indeed downloaded from the NCBI database as of July 2023. The sequences used to illustrate the similarity of Wolbachia between B. tabaci and its parasitoids include those from our previously published study (Qi et al., 2019), which were sequenced from field samples. Additionally, some sequences were also obtained from other laboratories (Ahmed et al., 2009; Baldo et al., 2006; Van Meer et al., 1999). We acknowledge that in our prior research (Qi et al., 2019), the sequences were directly submitted to NCBI and, regrettably, we did not update the corresponding publication information after the article were published. It is not uncommon for sequences on NCBI, with some never being followed by a published paper (e.g., FJ710487- FJ710511 and JF426137-JF426149), or not having their associated publication details updated post-publication (for instance, sequences MH918776-MH918794 from Qi et al., 2019, and KF017873-KF017878 from Fattah-Hosseini et al., 2018). We recognize that this practice can lead to confusion and apologize for the oversight in our work.

      REFERENCES

      Ahmed MZ, Shatters RG, Ren SX, Jin GH, Mandour NS, Qiu BL, 2009. Genetic distinctions among the Mediterranean and Chinese populations of Bemisia tabaci Q biotype and their endosymbiont Wolbachia populations. J Appl Entomol, 133:733-741.

      Baldo L, Dunning Hotopp JC, Jolley KA, et al., 2006. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl Environ Microbiol. 72(11):7098-110.

      Fattah-Hosseini S, Karimi J, Allahyari H, 2014. Molecular characterization of Iranian Encarsia formosa Gahan populations with natural incidence of Wolbachia infection. J Entomol Res Soc, 20(1):85–100.

      Qi LD, Sun JT, Hong XY, Li YX, 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2):894-905.

      Van Meer MM, Witteveldt J, Stouthamer R, 1999. Phylogeny of the arthropod endosymbiont Wolbachia based on the wsp gene. Insect Mol Biol, 8(3):399-408.

      - The authors fail to discuss or even acknowledge a number of published studies that specifically show no horizontal transmission, such as the one claimed to be detected in the study presented.

      Thank you for bringing this to our attention. We have made corresponding modifications to the discussion section (Lines 256271 in the revised manuscript) and have discussed the published studies that report no evidence of horizontal transmission (Lines 260263 in the revised manuscript). The added sentences read: “Experimental confirmations of Wolbachia horizontal transfer remain relatively rare, with only a limited number of documented cases (24, 27, 37, 38). Additionally, some experiments have found no evidence of horizontal transmission of Wolbachia (39-42).” (Lines 260263 in the revised manuscript)

      Reviewer #3 (Public Review):

      This is a very ordinary research paper. The horizontal of endosymbionts, including Wolbachia, Rickettsia etc. has been reported in detail in the last 10 years, and parasitoid vectored as well as plant vectored horizontal transmission is the mainstream of research. For example, Ahmed et al. 2013 PLoS One, 2015 PLoS Pathogens, Chiel et al. 2014 Enviromental Entomology, Ahmed et al. 2016 BMC Evolution Biology, Qi et al. 2019 JEE, Liu et al. 2023 Frontiers in Cellular and Infection Microbiology, all of these reported the parasitoid vectored horizontal transmission of endosymbiont. While Caspi-Fluger et al. 2012 Proc Roy Soc B, Chrostek et al. 2017 Frontiers in Microbiology, Li et al. 2017 ISME Journal, Li et al. 2017 FEMS, Shi et al. 2024 mBio, all of these reported the plant vectored horizontal transmission of endosymbiont. For the effects of endosymbiont on the biology of the host, Ahmed et al. 2015 PLoS Pathogens explained the effects in detail.

      Thank you for the insightful comments and for highlighting the relevant literature in the field of horizontal transmission of endosymbionts, including Wolbachia and Rickettsia. After careful consideration of the studies mentioned in the commences, we believe that our work presents significant novel contributions to the field. 1) Regarding the parasitoid-mediated horizontal transmission of Wolbachia, most of the cited articles, such as Ahmed et al. 2013 in PLoS One and Ahmed et al. 2016 in BMC Evolutionary Biology, propose hypotheses but do not provide definitive evidence. The transmission of Wolbachia within the whitefly cryptic species complex (Ahmed et al. 2013) or between moths and butterflies (Ahmed et al. 2016) could be mediated by parasitoids, plants, or other unknown pathways. 2) Chiel et al. 2014 in Environmental Entomology reported “no evidence for horizontal transmission of Wolbachia between and within trophic levels” in their study system. 3) The literature you mentioned about Rickettsia, rather than Wolbachia, indirectly reflects the relative scarcity of evidence for Wolbachia horizontal transmission. For example, the evidence for plant-mediated transmission of Wolbachia remains isolated, with Li et al. 2017 in the ISME Journal being one of the few reports supporting this mode of transmission. 4) While the effects of endosymbionts on their hosts are not the central focus of our study, the effects of transgenerational Wolbachia on whiteflies are primarily demonstrated to confirm the infection of Wolbachia into whiteflies. Furthermore, the effects we report of Wolbachia on whiteflies are notably different from those reported by Ahmed et al. 2015 in PLoS Pathogens, likely due to different whitefly species and Wolbachia strains. 6) More importantly, our study reveals a mechanism of parasitoid-mediated horizontal transmission of Wolbachia that is distinct from the mechanical transmission suggested by Ahmed et al. 2015 in PLoS Pathogens. Their study implies transmission primarily through dirty needle, without Wolbachia infection of the parasitoid, suggesting host-to-host transmission at the same trophic level, where parasitoids serve as phoretic vectors. In contrast, our findings demonstrate transmission from parasitoids to hosts through unsuccessful parasitism, which represents cross-trophic level transmission. To our knowledge, this is the first experimental evidence that Wolbachia can be transmitted from parasitoids to hosts. We believe these clarifications and the novel insights provided by our research contribute valuable knowledge to the field.

      REFERENCES

      Ahmed MZ, De Barro PJ, Ren SX, Greeff JM, Qiu BL, 2013. Evidence for horizontal transmission of secondary endosymbionts in the Bemisia tabaci cryptic species complex. PLoS One, 8(1):e53084.

      Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM, Greeff JM, Qiu BL, 2015. The intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog, 10(2):e1004672.

      Ahmed MZ, Breinholt JW, Kawahara AY, 2016. Evidence for common horizontal transmission of Wolbachia among butterflies and moths. BMC Evol Biol, 16(1):118.

      Caspi-Fluger A, Inbar M, Mozes-Daube N, Katzir N, Portnoy V, Belausov E, Hunter MS, Zchori-Fein E, 2012. Horizontal transmission of the insect symbiont Rickettsia is plant-mediated. Proc Biol Sci, 279(1734):1791-6.

      Chiel E, Kelly SE, Harris AM, Gebiola M, Li X, Zchori-Fein E, Hunter MS, 2014. Characteristics, phenotype, and transmission of Wolbachia in the sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), and its parasitoid Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae). Environ Entomol, 43(2):353-62.

      Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL, 2017. Horizontal transmission of intracellular insect symbionts via plants. Front Microbiol, 8:2237.

      Li SJ, Ahmed MZ, Lv N, Shi PQ, Wang XM, Huang JL, Qiu BL, 2017. Plant-mediated horizontal transmission of Wolbachia between whiteflies. ISME J, 11(4):1019-1028.

      Li YH, Ahmed MZ, Li SJ, Lv N, Shi PQ, Chen XS, Qiu BL, 2017. Plant-mediated horizontal transmission of Rickettsia endosymbiont between different whitefly species. FEMS Microbiol Ecol, 93(12).

      Liu Y, He ZQ, Wen Q, Peng J, Zhou YT, Mandour N, McKenzie CL, Ahmed MZ, Qiu BL, 2023. Parasitoid-mediated horizontal transmission of Rickettsia between whiteflies. Front Cell Infect Microbiol, 12:1077494.

      Qi LD, Sun JT, Hong XY, Li YX, 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2):894-905.

      Shi PQ, Wang L, Chen XY, Wang K, Wu QJ, Turlings TCJ, Zhang PJ, Qiu BL, 2024. Rickettsia transmission from whitefly to plants benefits herbivore insects but is detrimental to fungal and viral pathogens. mBio, 15(3):e0244823.

      Weaknesses:

      In the current study, the authors downloaded the MLST or wsp genes from a public database and analyzed the data using other methods, and I think the authors may not be familiar with the research progress in the field of insect symbiont transmission, and the current stage of this manuscript lacking sufficient novelty.

      We appreciate your critical perspective on our study. However, we respectfully disagree with the viewpoint that our manuscript lacks sufficient novelty.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The data and scripts from the experimental section of the paper are not made publicly available. This would be good practice. It may well be a requirement for this journal too, but I have not read the journal policy on this matter.

      Thank you for the kind reminder, we have uploaded the data and scripts to the public database at https://doi.org/10.6084/m9.figshare.24718119.

      • Line 16 should read 'intertrophic' not 'intertropical'.

      Corrected.

      • Line 50 should not say 'the most infectious' as this is an incorrect use of the word 'infectious'. Maybe 'common'? Should also add something like 'likely' here.

      Corrected. The new sentence reads “Together, these characteristics make Wolbachia likely the most common microbe on Earth in terms of the number of species it infects (7, 8).” (Lines 47–49 in the revised manuscript).

      • Line 54 These references are all about mosquito disease vectors, not pests. More generally, in this paragraph, the research interest in Wolbachia relates overwhelmingly to blocking arbovirus transmission and not controlling pest populations.

      To enhance consistency with our statements, we have revised the supporting references as follows:

      X. Zheng et al., "Combined incompatible and sterile insect techniques eliminate mosquitoes," Nature 572, 56-61 (2019).

      A. A. Hoffmann et al., "Wolbachia establishment in Aedes populations to suppress dengue transmission," Nature 476, 454-457 (2011).

      J. T. Gong, T. P. Li, M. K. Wang, X. Y. Hong, "Prospects of Wolbachia in agricultural Pest Control," Current Opinion in Insect Science 57, 101039 (2023).J. T. Gong et al., "Stable integration of plant-virus-inhibiting Wolbachia into planthoppers for rice protection," Current Biology 30, 4837-4845.e4835 (2020).

      Regarding the content of the articles:

      Zheng et al. (2019) detail the successful suppression of wild mosquito populations through the release of male mosquitoes artificially infected with Wolbachia.

      Gong et al. (2020) present the potential of releasing Wolbachia-infected brown planthoppers to inhibit plant viruses and control pest populations.

      Gong et al. (2023) provide a comprehensive review on the application and future of Wolbachia in managing agricultural pests.

      • Line 60-61. This sentence seems poorly supported by theory or data. I suggest it is deleted. Why should CI cause extinction, and why would it have a major effect on genetic diversity beyond mtDNA?

      We have deleted the statements about extinction or genetic diversity. Now the sentence reads “It may also spread to nontarget organisms, potentially disrupting their population dynamics.” (Lines 57–58 in the revised manuscript)

      • Line 66. Reword to make clear these routes are not an exhaustive list.

      We have reworded these sentences. The new sentences now read “Similar to other symbionts, Wolbachia host shifts may occur through three main routes: parasitism, predation, and shared plant or other food sources (17). However, it is important to note that these are not the only routes through which transmission may occur, and the specific contributions of each to the overall process of host shift are not yet fully understood.” (Lines 62–66 in the revised manuscript).

      • Line 77-79. This could do with mentioning studies of parasitoid-to-host transmission like Ahmedd et al given that it is common knowledge that insects commonly survive parasitoid attacks.

      We have added sentences acknowledging the common occurrence of insects surviving parasitoid attacks and referenced and described the Ahmed et al. 2015 study. The added sentences read:

      “However, it is common in nature for hosts to survive parasitoid attacks (27-29). For example, whiteflies can survive after attacks of Eretmocerus parasitoids (27). These parasitoids can act as phoretic vectors, facilitating the spread of Wolbachia within whitefly populations through the contamination of their mouthparts and ovipositors with Wolbachia during the probing process (27).” (Lines 77–82 in the revised manuscript).

      • Line 173. Mention that there are three replicates of each cage. In Figures 2C and D, it is better to show each replicate as a separate line to see how consistent they are.

      In accordance with the reviewer's suggestion, we have included a statement highlighting the replication of our experiments: “Notably, each cage setup was replicated three times to ensure experimental rigor.” (Lines 179–180 in the revised manuscript).

      Regarding Figures 2C and D, we have revised the figures to display each replicate as a separate line, as suggested. However, we have encountered a visual clutter that may detract from the clarity of the figures. Additionally, in Figure C, the three black lines, all representing zero values, do not allow for the distinction of individual trends. Therefore, we recommend retaining the original figure format. In accordance with eLife's data policy, we have also provided the source data for all figures, ensuring that readers can access to the detailed data, thus balancing the need for visual simplicity with the provision of comprehensive data.

      Author response image 1.

      • The GloBI database is central to the phylogenetic analysis and it would be helpful to have a few words in the results stating where this information comes from.

      The revised sentence now reads: “To investigate potential horizontal transmission of Wolbachia, we retrieved 4685 wsp sequences from the NCBI database, and species interaction relationships were extracted from the GloBI database (for details, see Methods and Materials).” (Lines 94–96 in the revised manuscript).

      Reviewer #3 (Recommendations For The Authors):

      To improve the quality of this manuscript, I have some questions and suggestions.

      Introduction:

      Line 41-42, I don't agree with this statement, as mentioned above, the ways of insect symbiont transmission have been studied in the last 10 years.

      According to the reviewer’s suggestion, we have deleted this statement.

      Line 75-76, Again, the statement is not correct, many studies have clearly revealed and confirmed that Wolbachia CAN be transferred from parasitoid to their insect hosts including whitefly Bemisia tabaci.

      Thank you for your insightful comments. After careful consideration of the studies you have mentioned above, none of these articles provided definitive evidence supporting the transfer of Wolbachia from parasitoids to their insect hosts. A closely related study is Ahmed et al. (2015) in PLoS Pathogens. This article demonstrates that parasitoid wasps can act as phoretic vectors mediating the transmission of Wolbachia between whiteflies. However, Wolbachia did not infect the parasitoid wasps themselves. Therefore, this study does not provide evidence for intertrophic transmission of Wolbachia from parasitoids to their hosts. To avoid confusion, we have cited the Ahmed et al. (2015) reference following this statement and described its findings accordingly. (Lines 88-92 in revised manuscript).

      Results:

      Line 133-134, Ahmed et al. 2016 BMC Evolution Biology, clearly revealed and confirmed the "common horizontal transmission of Wolbachia between butterflies and moths".

      We thank you for guiding us to the relevant study. Ahmed et al. 2016 BMC Evolution Biology suggested common horizontal transmission of Wolbachia between butterflies and moths and proposed that this horizontal transmission might be caused by parasitoid wasps. Here, we present the potential Wolbachia transfer between Trichogramma and their lepidopteran hosts (Lines 135–136 in revised manuscript). Integrating the results from Ahmed et al. 2016, our result also suggests that Trichogramma wasps may be the vectors for horizontal transmission of Wolbachia among lepidopteran hosts. We have discussed this point in the discussion section and cited Ahmed et al. 2016 BMC Evolution Biology (Lines 239–246 in revised manuscript).

      Line 176-177, as we know Wolbachia in Encarsia formosa is a strain of parthenogenesis, why did it reduce the female ratio of whitefly progeny after it was transmitted to whitefly B. tabaci, it needs a convincing explanation.

      Wolbachia induces parthenogenesis in En. formosa. However, we observed that Wolbachia from En. formosa failed to induce parthenogenesis in B. tabaci, possibly due to the requirement for host gene compatibility. Additionally, we noted a reduced female ratio in B. tabaci infected with En. formosa Wolbachia. We speculate that this might result from the burden imposed by En. formosa Wolbachia on the new host, potentially reducing fertilization success rates and indirectly leading to a decrease in the female ratio. Similarly, we observed a decline in female fecundity, egg hatching rate, and immature survival rate in B. tabaci infected with En. formosa Wolbachia. The mechanisms underlying these fitness costs remain unclear and warrant further in-depth research.

      Line 189-190, do the authors have convincing evidence that the 60Gy irradiation only has effects on the reproduction of En. formosa, but does not have any negative effects on the activity of Wolbachia? I think there may be.

      We observed that after irradiation, the titer of Wolbachia within En. formosa significantly decreased (Fig S3). We agree that the irradiation may cause other negative effects on Wolbachia which is worth of close investigation. However, even with a significant reduction in Wolbachia titer, irradiation increased the infection rate of Wolbachia in surviving B. tabaci after wasp attacks (Fig 3C). We speculate that this may be due to irradiation of En. formosa increasing the rate of parasitic failure. While the full extent of the effects of irradiation on Wolbachia is not yet clear in our experiments, it does not alter our conclusion that Wolbachia can be transmitted from En. formosa to whitefly hosts through failed parasitism.

      Discussion:

      Line 289-290, I don't understand, why the authors think from parasitoid Eretmocerus to whitefly, and from Trichogramma to moth, are the same trophic level, they are indeed two different trophic levels.

      Thank you for your feedback. We have conducted a thorough search but were unable to locate the specific statement you are referring to. If there has been any ambiguity in our manuscript that has led to confusion, we sincerely apologize for any misunderstanding it may have caused. We agree with your perspective and have always considered the parasitoid Eretmocerus and whitefly, as well as Trichogramma and moth, to be at different trophic levels. However, in the context of specific references, such as Ahmed et al. 2015 in PLoS Pathogens, we believe that Wolbachia is transmitted within the same trophic level without infecting the parasitoid Eretmocerus, merely serving as a phoretic vector to facilitate the spread of Wolbachia among whitefly hosts. Similarly, in the case of Huigens et al. 2000 in Nature, Wolbachia uses lepidopteran hosts as vectors to promote its transmission among Trichogramma without the need to infect the lepidopteran hosts themselves.

      Materials and Methods

      Line 348, what is tblastn?

      We have corrected tblastn to TBLASTN. We are grateful to the reviewer for pointing this out. Here, we utilized TBLASTN instead of BLASTN, to avoid missing the rapidly evolving wsp sequences. Because alignment at the protein level is generally more sensitive than at the nucleotide level. TBLASTN is a bioinformatics tool within the BLAST (Basic Local Alignment Search Tool) suite used for comparing a protein query sequence against a nucleotide database. Specifically, TBLASTN aligns a given protein sequence with nucleotide sequences in a database by translating the nucleotide sequences into all possible protein sequences (considering different reading frames) and comparing them to the query protein sequence.

      Line 383, how was the Wolbachia-free line of B. tabaci established, by antibiotics? If so, how do we ensure the antibiotic does not have any negative to other symbionts in whitefly B. tabaci?

      The Wolbachia-free line of B. tabaci was collected from field, without the treatment of antibiotics. We have made revisions in the Materials and Methods section to clarify this, stating, "An iso-female line of B. tabaci, which is naturally Wolbachia-free and has not been treated with antibiotics, was established." (Lines 417–418 in the revised manuscript)

      Line 419-421 as I mentioned before, the irradiation may have negative effects on Wolbachia too, so change the biology of both Encarsia and whitefly host.

      We observed that after irradiation, the titer of Wolbachia within En. formosa significantly decreased (Fig S3). However, even with a significant reduction in Wolbachia titer, irradiation increased the infection rate of Wolbachia in surviving B. tabaci after wasp attacks (Fig 3C). We speculate that this may be due to irradiation of En. formosa increasing the rate of parasitic failure. While the full extent of the effects of irradiation on Wolbachia is not yet clear in our experiments, it does not alter our conclusion that Wolbachia can be transmitted from En. formosa to whitefly hosts through failed parasitism.

      Line 452-453, From egg to eclosion, it needs about 21 days to understand suitable temperature and other conditions, during this period, the egg and nymphs can not move, so how to keep the cut-leaf fresh enough in a Petri dish for 21 days?

      We apologize for not clearly describing the materials and methods. By using wet cotton to wrap the end of petiole of the leaf, we can keep the leaves fresh for up to a month. We have included this detail in the materials and methods to enhance the reproducibility of the experiment. “A single irradiated wasp was subsequently introduced into a Petri dish, which contained a tomato leaf infested with Wolbachia-free third or fourth instar whitefly nymphs, and wet cotton was used to wrap the end of the leaf petiole to keep the leaf fresh.” (Lines 455–458 in the revised manuscript)

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Reviews):

      Summary:

      This study examines to what extent this phenomenon varies based on the visibility of the saccade target. Visibility is defined as the contrast level of the target with respect to the noise background, and it is related to the signal-to-noise ratio of the target. A more visible target facilitates the oculomotor behavior planning and execution, however, as speculated by the authors, it can also benefit foveal prediction even if the foveal stimulus visibility is maintained constant. Remarkably, the authors show that presenting a highly visible saccade target is beneficial for foveal vision as the detection of stimuli with an orientation similar to that of the saccade target is improved, the lower the saccade target visibility, the less prominent the effect.  

      Strengths:

      The results are convincing and the research methodology is technically sound.

      Weaknesses:

      Discussion on how this phenomenon may unfold in natural viewing conditions when the foveal and saccade target stimuli are complex and are constituted by different visual properties is lacking. Some speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified and not clearly supported by the data.

      We thank the reviewer for their comment. In general, we tried to address conceptual points only briefly in this Research Advance if we had discussed them in depth in our main article which this advance will be linked to (Kroell & Rolfs, 2022: https://elifesciences.org/articles/78106). However, the reviews showed us that this rendered our theoretical reasoning in the current manuscript appear incomplete. In the revised Discussion section, we have elaborated on several conceptual questions. In particular, we expand on the transferability of our findings to natural viewing conditions:

      “Foveal prediction in natural visual environments

      As noted above, human observers typically move their eyes towards the most conspicuous objects in their environment (‘t Hart, Schmidt, Roth, & Einhäuser, 2013). Foveal prediction seems to benefit from this strategy as the strength of the predicted signal increases with the conspicuity of the eye movement target. Nonetheless, natural visual environments as well as naturalistic viewing behavior pose several challenges for the foveal prediction mechanism (see Kroell & Rolfs, 2022, for an initial discussion). 

      First, naturalistic saccade target stimuli will likely exhibit complex shapes and, more often than not, will include feature conjunctions rather than isolated features. Previous findings suggest that the foveal feedback mechanism is capable of operating at this level of complexity: High-level peripheral information such as the category of novel, rendered objects (Williams et al., 2008) has been successfully decoded from activation in foveal retinotopic cortex. If, indeed, temporal objectspecific areas such as area TE send feedback, the foveal prediction mechanism may even be specialized for the transfer of complex visual properties.

      Second, foveal input will often be of high contrast in natural visual environments. If fed-back predictive signals can influence foveal perception in the presence of high-contrast feedforward input remains to be established. In our main investigation (Kroell & Rolfs, 2022; Figure 2B) as well as in previous studies (Hanning & Deubel, 2022b), pre-saccadic foveal detection performance decreased markedly in the course of saccade preparation, presumably because visuospatial attention gradually shifted towards the saccade target and away from the foveal location. This presaccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.

      Third, while foveal and peripheral information was congruent on 50% of all ‘probe present’ trials in our investigation, peripheral and foveal features will often be weakly correlated or even uncorrelated in natural environments (see Samonds, Geisler, & Priebe, 2018). Again, the presaccadic attenuation of foveal feedforward processing may allow fed-back peripheral signals to influence perception even if they are uncorrelated with foveal information. Moreover, in piloting variations of our paradigm, we observed that the subjective impression of perceiving the saccade target at the pre-saccadic foveal location is most pronounced if the foveal noise region is replaced with a black Gaussian blob at certain time points before saccade onset (unpublished phenomenological accounts). In consequence, fed-back signals do not seem to require correlated feedforward input to influence perception. Quantitative evidence, however, remains to be established.

      Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 1D), however, the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the fixation location. Despite this spatial unpredictability, congruency effects peaked at the presaccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. On a similar note, the orientation of the saccade target is irrelevant to the behavioral task in our design, mirroring naturalistic situations: The eye movement can be planned and executed based on local contrast variations alone, and observers are never required to report on the orientation of the peripheral target stimulus. Ultimately, however, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring overt responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and electrophysiological studies, foveal predictions should manifest in early visually evoked potentials (e.g., Creel, 2019) and increased firing rates of featureselective foveal neurons in early visual areas, respectively. In conclusion, previous findings (Williams et al., 2008), the assumed properties of the neuronal feedback mechanism (Williams et al., 2008; Bullier, 2001) and characteristics of our current and previous experimental paradigms collectively suggest that foveal feature predictions are likely to transfer to naturalistic environments and viewing situations. Experimental evidence remains to be established.”

      We have furthermore modified the Abstract to emphasize the connection of the current manuscript to the main article.

      With respect to the reviewer’s point that “speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified”: 

      Again, we understand that we should have elaborated on our theoretical reasoning in this Research Advance. The assumption that our initial findings rely on neuronal feedback to foveal retinotopic cortex is derived from Williams et al.’s (2008) seminal findings: In an fMRI study, the category of peripherally presented objects could be decoded from voxels in foveal retinotopic cortex, suggesting that peripheral visual information was available to neurons with strictly foveal receptive fields. We extended these findings to saccade preparation, suggesting that feedback from higher-order, non-retinotopically organized visual areas may transmit information without the requirement of efference copies (see Kroell, 2023; Dissertation; https://doi.org/10.18452/27204, pp. 54-59): Irrespective of the vector of the upcoming saccade, the features of the attended saccade target would invariably be relayed to foveal retinotopic cortex. Ultimately, only anatomical and functional studies in non-human primates can conclusively establish the role of feedback connections in the observed foveal prediction effects. At present, however, this parsimonious model could account for all of our current and previous findings, that is, a temporally, spatially and feature-specific anticipation of saccade target properties in the presaccadic center of gaze. Nonetheless, we are open to considering any other mechanism that may account for our findings, and have integrated the explanation provided by the reviewer into the paragraph on potential thalamic mechanisms (see the reviewer’s Major Point 1).

      Concerning the point that the “some speculations regarding feedforward vs feedback neural processing […] and the speed of the feedforward signal in relation to the visibility of the target are not well justified and not clearly supported by the data”: 

      Theoretical considerations on the impact of peripheral target contrast on feedforward processing speed were a main motivation for the current study. We apologize if our theoretical reasoning was incomplete and have added additional references and elaborations to the Introduction: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee, Elepfandt, & Virsu, 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997; Carandini, Heeger, & Senn, 2002), and anterior superior temporal sulcus (STSa; Oram, Xiao, Dritschel, & Payne, 2002; van Rossum, van der Meer, Xiao, & Oram, 2008)—influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Concerning the interpretation of the observed time courses, and regarding the reviewer’s Major points 3 & 6, we substantially revised the Results and Discussion section. In brief, we deemphasized the claim/interpretation of faster enhancement with increasing target opacity and instead focus on describing the oscillatory pattern mentioned by the reviewer. We provide a more temporally resolved pre-saccadic time course using a moving-window analysis and discuss all suggested and further alternative explanations (i.e., saccade-locked perceptual or attentional oscillations, longer signal accumulation intervals for low-contrast information, oscillatory nature of feedback signaling). Details and full revised paragraphs are provided in the response to this reviewer’s Major points 3 & 6.

      Unfortunately, there is no line numbering in the manuscript version I downloaded so I cannot refer to the specific lines of text here.

      We apologize for the inconvenience and have added line numbers.

      Major:

      (1) The authors speculate that the phenomenon of pre-saccadic foveal prediction arises from feedback connections from higher-order visual areas, which relay relevant saccade target features to the foveal retinotopic cortex. These feedback signals are then presumably combined with feedforward foveal input to the early visual cortex and facilitate the detection of target-congruent features at the center of gaze. This interpretation is sensible, however, it may not be the only plausible scenario. The thalamus receives copies of feedforward and feedback connections between all visual areas and is a likely candidate hub for combining information across visual space. In this latter case, the phenomenon of pre-saccadic foveal prediction may not arise from feedback from higher-order visual areas, but rather from a combination of signals occurring at the level of the thalamus. The authors should either acknowledge this possibility and the fact that this phenomenon is not necessarily the result of a feedback loop, or they should explain their rationale for excluding this scenario.

      We thank the reviewer for their highly thoughtful suggestion, and for alerting us to relevant literature. We have added the following paragraph to the Discussion section. In brief, we discuss the thalamic pulvinar as either an intermediate modulatory region or as the final receiver of the fed-back signal. Yet, we assume that—to solve the combinatorial issue associated with a transfer of feature information before saccades with any possible direction and amplitude—the contribution of non-retinotopic, higherorder object processing areas is likely required. 

      “Neural implementation of foveal prediction

      Based on the body of our findings as well as previous literature, we suggested a parsimonious feedback mechanism to underly the observed effects: the preparation of a saccadic eye movement, and the concomitant shift of pre-saccadic attention (e.g., Kowler, Anderson, Dosher, & Blaser, 1995; Deubel & Schneider, 1996), selects the peripheral target stimulus among competing information. Higher-order visual areas feed selected feature input back to early retinotopic areas— specifically, to neurons with foveal receptive fields. Fed-back feature information combines with congruent, foveal feedforward input, resulting in the enhancement effects we observe. Especially in the context of active vision, this feedback mechanism is appealing as it resolves a combinatorial issue associated with feature-specific information transfer before saccades. Consider a simplified case in which, right before a saccadic eye movement, the activation of a feature-selective neuron that encodes a certain retinal location is transferred to a neuron within the same brain area that will encode said retinal location after saccade landing. For this mechanism to function for any possible saccade direction and amplitude, most neurons would need to be connected to most other neurons (or, in a simplified version, to neurons with foveal receptive fields) in a given brain area. Assuming an information transmission via feedback rather than horizontal connections significantly reduces this dimensionality: Higher-order visual areas that encode object properties (largely) detached from retinotopic or spatiotopic reference frames selectively transfer feature information to neurons with foveal receptive fields, irrespective of the vector of the upcoming saccade. This parsimonious mechanism would have shortcomings. In particular, foveal feedback should become less effective during saccade sequences where several peripheral targets are simultaneously attended. Feature information at both attended target locations may be fed back in temporal succession or weighted and erroneously combined into a single fed-back signal. In most cases, however, foveal feedback may reasonably achieve what established transsaccadic mechanisms struggle to explain: An anticipation of the features of a single saccade target—which typically constitutes the currently most relevant object in the visual field—in foveal vision. 

      While direct feedback connections from higher-order to early visual areas would constitute the most straightforward implementation, it is conceivable that feedback signals are relayed through and modulated by subcortical areas. In particular, the thalamic pulvinar has been identified as a connection hub for visual processing that receives copies of feedforward and feedback connections from different visual areas and may even combine information across visual space (Cortes, Ladret, Abbas-Farishta, & Casanova, 2024). In the case of foveal prediction, thalamic neurons may receive fed-back signals from higher-order areas and enhance those signals before passing them on to cortical neurons with foveal receptive fields. Perhaps, a modification of foveal activation within the thalamic pulvinar itself is sufficient to influence perception. To the best of our understanding, however, the fed-back signal must originate in non-retinotopic, higher-order object processing areas to reduce the number of necessary neuronal connections.”

      (2) The results presented are very compelling. I wonder to which extent they generalize to situations in which the foveal input and the peripheral input are more heterogenous (e.g., faces or complex objects composed of many different features, orientations, and other visual properties). I think the current research raises a number of interesting questions. In general, it would be important for the readers to elaborate more on how the mechanism of pre-saccadic foveal prediction may play out in normal viewing conditions or in conditions in which the foveal input is completely irrelevant to the task.

      We agree and have reiterated this point in the current manuscript (see our first reply to “Weaknesses”). We also explicitly refer to Kroell & Rolfs (2022) for an extensive initial discussion of this question.

      (3) On page 10 the authors state that their data suggest that foveal enhancement emerges in earlier stages of saccade preparation as target opacity increases. However, this is not clear from the figures, when performance is locked to saccade onset (Fig 3 C), for the highest opacity targets performance seems to oscillate, however, the authors do not comment on that. There is literature showing how saccades can reset perceptual oscillations, and maybe what is observed here is just a stronger performance oscillation when the saccade target is more visible. Why would performance drop systematically 75 ms before saccade onset and then increase again 25 ms before the onset? Can the authors elaborate more on this?

      In response to this comment, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials (with no constraints on parameters) to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). Throughout, we highlight that we are aware that our analysis approach is purely descriptive and that the potential explanations we give are speculative.

      “Moreover, foveal congruency effects appear to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedbackgenerating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attentional processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      In the revised Abstract, we removed our claim on an earlier emergence of enhancement at higher opacities and have added this summary instead:

      “Second, the time course of foveal enhancement appeared to show an oscillatory pattern that was particularly pronounced at higher target opacities. Interestingly, the temporal frequency of these oscillations corresponded to the frequency range typically associated with neural feedback signaling.”

      (4) What was the average difference in latency between short and long latencies? It would be good to report it in the main text.

      We apologize for the oversight. The difference was 61 ms, with latencies of md = 247±18 ms for short- and md = 308±18 ms for long-latency saccades. We have added this information to the main text.

      (5) From the saccade latency graphs in Figure S1 it seems there is some variability in the latency of saccades across subjects, I wonder if there is a correlation between saccade latency and the magnitude of the foveal prediction effect across subjects.

      We had inspected a connection between saccade latency and congruency in our first investigation (Kroell & Rolfs, 2022; not reported) and observed that participants with lower latencies tended to show more enhancement, albeit non-significantly. Likewise, we observed a non-significant negative correlation between the median saccade latency and the mean foveal prediction effect (across opacities and time points) in the current investigation, r \= -0.22, p \= .572. While our study involved a small number of observers (n = 9), the analysis approach illustrated in Figure 2 A-C instead makes use of the large number of trials collected per participant (mean n = 2841 trials per observer) and demonstrates a reliable influence of saccade latency on an individual-observer level.

      (6) Page 14, the authors state that their findings suggest that the feedforward processing of the peripheral saccade target is accelerated when it is presented at high contrast. I find this a bit too speculative, both in terms of assuming that there is a feedforward vs a feedback process (see my point 1) and in terms of speculating that the feedforward process is accelerated as I do not see a clear hint of this in the data (see my point 3) and it is a bit of a stretch to speculate on delays or accelerations of neural processing. It is possible that the feedforward signal is always delivered at the same speed but it is weaker in one case and the effect needs more time to build up.

      We fully agree and hope to have addressed the reviewer’s arguments in the sections preceding this point. We included the reviewer’s last sentence in the Discussion section as well: 

      “Alternatively, or in addition, it is conceivable that weaker feedforward signals require a longer accumulation interval before the feedback process can be initiated.”

      Minor:

      (1) I think the description of the linear mixed-effects model can go in the supplemental methods, if possible, and its results can be briefly mentioned in the text.

      In previous work, we have been asked to move linear mixed-effects model descriptions from supplemental to main method (or even results) sections for clarity. We have followed this suggestion ever since and, due to the relevance of the models for the interpretation of the presented results, would like to keep their description in the methods section.

      (2) This is just a minor point, but I would suggest using a different word instead of opacity (maybe visibility?).

      We had gone back and forth on this. We decided to use the term ‘conspicuity’ when we discuss our findings conceptually and the term ‘opacity’ when we refer to the experimental manipulation (since we directly manipulate the transparency, i.e., 1-opacity, of the target patch against the background). To compute the slopes in Figures 2 and 5, we ordered observers’ performances by the linearly spaced opacity conditions. Since the term ‘opacity’ is closest to both the experimental manipulation and the variable entered into analysis, we would like to adhere to this terminology. However, we have added an explicit note to the end of our introduction to avoid confusion: 

      “Throughout the paper, we use the term ‘opacity’ when we refer to the experimental manipulation (that is, a variation of the transparency, i.e., 1-opacity of the target patch against the background noise) and the term ‘conspicuity’ when we discuss our findings conceptually.”

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors ran a dual task. Subjects monitored a peripheral location for a target onset (to generate a saccade to), and they also monitored a foveal location for a foveal probe. The foveal probe could be congruent or incongruent with the orientation of the peripheral target. In this study, the authors manipulated the conspicuity of the peripheral target, and they saw changes in performance in the foveal task. However, the changes were somewhat counterintuitive.

      Strengths:

      The authors use solid analysis methods and careful experimental design.

      Weaknesses:

      I have some issues with the interpretation of the results, as explained below. In general, I feel that a lot of effects are being explained by attention and target-probe onset asynchrony etc, but this seems to be against the idea put forth by the authors of "foveal prediction for visual continuity across saccades". Why would foveal prediction be so dependent on such other processes? This needs to be better clarified and justified.

      We address the described weaknesses in the respective sections below. In general, as we point out in response to Reviewer 1 as well, the current submission is a Research Advance article meant to supplement our main article (Kroell & Rolfs, 2022, https://doi.org/10.7554/eLife.78106). To comply with the eLife recommendations for Research Advance submissions, we addressed conceptual points only briefly, especially if they had been explained in detail in our main article. To make the nature and format of the current submission as explicit as possible, and to emphasize its connection to our previous work, we refer to the submission format in our abstract and introduction now.

      Specifics:

      The explanation of decreased hit rates with increased peripheral target opacity is not convincing. The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)? And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery? What is the function of foveal prediction? What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      We will address these comments one by one:

      The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)?

      We consider these observations to rely on separate processes. Already in the main publication (Kroell & Rolfs, 2022), we had observed a continuous decrease of target-congruent and target-incongruent foveal Hit Rates (HRs) during saccade preparation, and suggested that this decrease (similarly observed in Hanning & Deubel, 2022b is likely caused by the pre-saccadic shift of visuospatial attention to the target. In other words, as attentional resources shift towards the periphery, foveal detection performance is hampered, irrespective of peripheral and foveal feature (in-)congruency. In the current investigation, we again observed a pronounced pre-saccadic decrease of foveal HRs, irrespective of foveal probe orientation. Our argument that high-contrast peripheral saccade targets attract more attention relies on the clear observation that this decrease becomes more pronounced as the contrast of the saccade target increases. To the best of our judgment and experience with doing the task ourselves, this interpretation appears very conceivable. We explain this rationale in the Abstract and the Results sections of the manuscript (see below).

      Our hypotheses and interpretations concerning the time course of foveal prediction refer to the difference between target-congruent and target-incongruent foveal HRs (i.e., to predictive foveal feature enhancement). Irrespective of the general, feature-unspecific decrease of foveal detection performances, we had hypothesized that the peripheral target is processed faster if it exhibits a high contrast. This assumption is based on temporal processing properties of many visual neurons that we have expanded on in our revision: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee et al., 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini et al., 1997, 2002; Carandini and Heeger, 1994), and anterior superior temporal sulcus (STSa; Oram et al., 2002; van Rossum et al., 2008)— influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Of note, both reviewers asked us to explore the oscillatory nature of the difference between targetcongruent and target-incongruent HRs. We will post our changes in response to the reviewer’s remark below.

      And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery?

      We hope that our previous reply has cleared up that the opposite is true: In general, and irrespective of the feature congruency of target and foveal probe, foveal HRs decrease as target contrast increases. As we have stated in our Abstract and Results, “foveal Hit Rates for target-congruent and incongruent probes decreased as target opacity increased, presumably since attention was increasingly drawn to the target the more salient it became. Crucially, foveal enhancement defined as the difference between congruent and incongruent Hit Rates increased with opacity”. This finding did not appear counterintuitive to us and was, in fact pre-registered as a main hypothesis (see https://osf.io/wceba). 

      We are unsure if this goes beyond the reviewer’s concern but we, in fact, speculate in the revised Discussion section as well as in our original eLife article that the overall, feature-unspecific decrease in foveal detection performances may aid feature-specific foveal prediction: 

      “This pre-saccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.”

      What is the function of foveal prediction?

      Please refer to the section ‘What is the function of foveal prediction?’ in our main article. We have pasted this paragraph below for the reviewer’s convenience. 

      “What is the function of foveal prediction?

      As stated above, previous investigations on foveal feedback required observers to make peripheral discrimination judgments. We, in contrast, did not ask observers to generate a perceptual judgment on the orientation of the saccade target. Instead, detecting the target was necessary to perform the oculomotor task. While the identification of local contrast changes would have sufficed to direct the eye movement, the orientation of the target enhanced foveal processing of congruent orientations. The automatic nature of foveal enhancement showcases that perceptual and oculomotor processing are tightly intertwined in active visual settings: planning an eye movement appears to prioritize the features of its target; commencing the processing of these features before the eye movement is executed may accelerate post- saccadic target identification and ultimately provide a head start for corrective gaze behavior (Deubel et al., 1982; Ohl and Kliegl, 2016; Tian et al., 2013).”

      What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      Alternative explanations in general: In our main article, we ruled out—either through direct experimentation or by considering relevant properties of our findings—the following alternative explanations: i) spatially global feature-based attention to the target orientation, ii) a multiplicative combination of spatial and feature-based attention, and iii) shifts of decision criterion. While dual tasks (i.e., simultaneous oculomotor planning and perceptual detection) are standard in psychophysical investigations of active vision, we acknowledge the potential influence of an explicit foveal task in the revised manuscript, and in response to both reviewers: 

      “Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 2D), the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the screen center. Despite this spatial unpredictability, however, congruency effects peaked at the pre-saccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. Ultimately, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring keyboard responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and neurophysiological studies, foveal predictions should manifest in early visual evoked potentials (e.g., Creel, 2019) and increased firing rates of feature-selective foveal neurons in early visual areas, respectively.”

      Difficulty of the task: Concerning the perceptual detection task, every experimental session was preceded by an adaptive staircase procedure that adjusted the transparancy of the foveal probe—and, thus, task difficulty—depending on the respective observer’s performance (see Methods for details). Concerning the oculomotor task, observers were able to perform accurate saccades with typical movement latencies for all target opacity conditions (see Results, Supplements & Figure S1). In general, we are unsure how high task difficulty could produce a feature-, temporally and spatially specific enhancement of both filtered and incidental target-congruent foveal orientation information. In fact, a main finding of our current submission is that foveal HRs decrease as the target becomes easier to see and the oculomotor task thus becomes easier to perform.

      Perceptual confusion of target and probe stimulus: We observe a specific increase in HRs for foveal probes that exhibit the same orientation as the peripheral saccade target. Just like in our main article, a response is defined as a ‘Hit’ if a foveal probe is presented and the observer generates a ‘present’ judgment. To our understanding, the suggestion that a confusion of target and probe stimuli may account for these effects necessarily implies that this confusion hinges on the congruency between peripheral and foveal feature inputs. In other words, peripheral and foveal signals should be more readily “confused” if they exhibit similar features. We assume that peripheral feature information is fed back to neurons with foveal receptive field and combines with feature-congruent feedforward input. Whether this combination of signals can be described as low-level perceptual “confusion” likely depends on individual linguistic judgments (it would certainly be a novel description of feedback-feedforward interactions). Perhaps a defining difference between the reviewer’s concern and our assumed mechanism is the spatial specificity of the resulting congruency effects. We suggest that only neurons with foveal receptive fields receive feature information via feedback. And indeed, we demonstrate a clear spatial specificity of congruency effects around the pre-saccadic foveal location, even after parafoveal performances had been raised to a foveal level by an adaptive increase in probe opacity (see Kroell & Rolfs, 2022; Figure 2C & Figure 3). In other words, observers’ perception is altered in their pre-saccadic center of gaze while the target is presented peripherally. We struggle to conceive a

      scenario in which a confusion of signals should be feature-specific as well as specific to an interaction between peripheral and foveal signals without being meaningful at the same time. If the reviewer is referring to confusions on the response or decision level, we would like to point them towards the Discussion section ‘Can our findings be explained by established mechanisms other than foveal prediction?’ in our main article. In this paragraph, we provide detailed arguments for a dissociation between our findings and shifts in decision criterion that would exceed the scope of a Research Advance. 

      When the peripheral target is easier to see, then the foveal hit rate drops.

      We agree. Target-congruent and incongruent foveal HRs decreased as the contrast of the probe increased. However, and as we stated in response to the reviewer’s first comment, the difference between target-congruent and target-incongruent foveal HRs (and, thus, foveal enhancement of the target orientation) increased with peripheral target contrast.

      The analyses of Fig. 3C appear to be overly convoluted. They also imply an acknowledgment by the authors that target-probe temporal difference matters. Doesn't this already negate the idea that the foveal effects are associated with the saccade generation process itself? If the effect is related to target onset, how is it interpreted as related to a foveal prediction that is associated with the saccade itself? 

      We indeed conducted analyses that can reveal an influence of target presentation duration at probe onset, the saccade preparation stage at probe offset, as well as a combination of both factors. The fact that target presentation duration may have an influence on foveal prediction would not negate a simultanous influence of saccade preparation and vice versa. In the main article, we directly investigated the influence of saccade preparation on foveal enhancement by introducing a passive fixation condition (Kroell & Rolfs, 2022; Figure 5). At identical target-probe offset durations, pre-saccadic foveal enhancement was significantly more pronounced and accelerated compared to enhancement during passive fixation. We have added a purely saccade-locked time course (uncorrected by targetprobe interval) to our Results section and to Figure 3 (second row). We still believe that the target-locked, saccade-locked and combined analysis are informative for future investigations and would like to present them all for completeness.

      Also, the oscillatory nature of the effect in Fig. 3C for 59% and 90% opacity is quite confusing and not addressed. The authors simply state that enhancement occurs earlier before the saccade for higher contrasts. But, this is not entirely true. The enhancement emerges then disappears and then emerges again leading up to the saccade. Why would foveal prediction do that?

      In response to this comment and a suggestion by Reviewer 1, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). We are aware, and acknowledge in the manuscript, that our analysis approach is purely descriptive, and that the potential explanations we give are speculative. 

      “Moreover, foveal congruency effects appeared to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedback-generating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attention processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback  signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      The interpretation of Fig. 4 is also confusing. Doesn't the longer latency already account for the lapse in attention, such that visual continuity can proceed normally now that the saccade is actually eventually made? In all results, it seems that the effects are all related to the dual nature of the task and/or attention, rather than to the act of making the saccade itself. Why should visual continuity (when a saccade is actually made, whether with short or long latency) have different "fidelity"? And, isn't this disruptive to the whole idea of visual continuity in the first place?

      We are unsure if we grasp the unifying concern behind these remarks. For the reviewer’s point on the dual-task nature of our paradigm, please consider our answer above. Perhaps it is important to note that we do not (and would never) claim that foveal prediction is the only mechanism underlying visual continuity. We believe that multiple mechanisms, including but not limited to pre-saccadic shifts of attention, predictive remapping of attention pointers and the perception of intra-saccadic signals interact and jointly contribute to visual continuity. It appears highly conceivable that, like most processes in biological systems, motor and perceptual performances are subject to fluctuations. We argue that saccade latencies as well as the magnitude of foveal prediction constitute read-outs of these variations. We also suggest that those read-outs are innately correlated beyond their common moderator of, perhaps, attentional state; we have previously presented clear evidence for a link between eye movement preparation and foveal prediciton (Kroell & Rolfs, 2022; Figure 2). To the best of our judgment, we consider it reasonable that the effectiveness of movement-contingent perceptual processes varies with the effectiveness (in programming or execution) of the very movement motivating them. We present evidence for this assumption in our submission. We would also like to make clear that we do not assume our vision to fail entirely, even if every single well-known mechanism of visual continuity were to break down at once. Upon saccade landing, the visual system receives reliable visual input. Nonetheless, the visual system has undeniably developed mechanisms to optimize this process. We believe foveal prediciton to rank among them.

      Small question: is it just me or does the data in general seem to be too excessively smoothed?

      We did not apply any smoothing to either the analysis or visualization of our data in the initial manuscript.

      Every observer completed a large number of trials (mean n = 2841 trials per observer; total trial number > 25,500), which likely contributes to the clarity of our data. To inspect the oscillatory pattern of enhancement in a more temporally resolved fashion (in response to the reviewer’s point above), we applied a moving window analysis in this revision. Due to overlapping window borders, this analysis introduces a certain degree of smoothing. Nonetheless, data patterns are comparable to the time course with only few non-overlapping time bins (Figure 3B; second row). In general, we have described all steps of our analysis routine extensively in the Methods section and will make our data publicly available upon publication of the Reviewed Preprint. 

      General comment: it is important to include line numbers in manuscripts, to help reviewers point to specific parts of the text when writing their comments. Otherwise, the peer review process is rendered unnecessarily complicated for the reviewers.

      We apologize and have added line numbers.

    1. may not have any correlation to the world as humans understand it. Like a good bullshitter, it’s better at form and style than substance

      we can see how this happens over time leading to inaccuracies against the truth "from people", I think this leads to a shift in how we talk as we all try to become more human.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      The analysis of the dormancy rates is interesting and offers some intriguing questions related to the higher dormancy rate found for the L2 isolates and lower for the L3 ones. It will be interesting in the future to expand the data generated in this advanced in vitro plaAorm to in vivo studies.

      Indeed, an increased dormancy propensity of L2 isolates was previously reported in broth culture and associated to specific genetic polymorphisms. The opposite phenotype observed in the L3 isolates is indeed particularly intriguing and was not described to date. Hence, we fully agree that it would be very interesting to find out whether these phenotypes are also observed in vivo.

      The authors propose that ‘strains exhibiting greater proliferative capacity are more prone to induce macrophage apoptosis, thereby contributing to the extent of the granulomatous response.’ It would be interesting to know what happens if the macrophage apoptotic response is blocked.

      This is an interesting suggestion that would deserve a dedicated comprehensive investigation covering other cell death pathways. Even though the trend is significant, the correlation coefficient is rather low in this interaction, which looks a fortiori due to substantial inter-host variability in the apoptotic propensity of macrophages from individual donors to a given strain. In addition, such blocking experiments may require performing isolated macrophage infections that would fall outside of the scope of this study, or considering the extent and the contribution of the apoptosis of other cell subsets. 

      In contrast to macrophage apoptosis, T cell activation correlated with less replicative bacteria. Are these two findings related, ie, are the granulomas showing more (apoptotic) macrophages the ones with a lower percentage of activated T cells? This would shed light on what distinguishes granulomas that are protective from those that support bacterial growth. 

      Indeed, a significant negative correlation between macrophage apoptosis induction and T cell activation can be observed, specifically with activated CD4 T cells expressing CD38 (rS \= -0.36, p < 0.05) or CD69 (rS = -0.40, p < 0.01). We have added this additional result in the manuscript text (line 217).

      It would also be interesting to know the functional impact of blocking early CXCL9 or IL1b on the outcome of granulomatous response/bacteria growth.

      We have performed the suggested early blocking experiments and added the expected negative effect on granuloma formation upon neutralization of IL-1b (current Fig. 6E) in the revised version of the manuscript, and furthermore discussed the null effect on bacterial growth of the treatment with an anti-CXCL-9 specific antibody (current Fig. 6H).

      The authors acknowledge the absence of neutrophils in this model. However, this could be discussed in more detail, as neutrophils play an important part in TB pathogenesis as shown in different models of infection and human TB. 

      We concur and have expanded the importance of neutrophils in TB pathogenesis (including references) in the discussion section (line 260). 

      Related to neutrophils and TB pathogenesis, another important player is type I IFN. The multiplex assay used included IFN-alpha, was this molecule detected? If so, was there any difference in the levels of type I IFN detected among the different infections?

      We agree and that is why we had originally included IFN-α in our screen. However, this cytokine remained under the limit of quantification at both studied time points, preventing us to draw conclusions on the effect of Mtb strain diversity on the secretion of type I IFNs in in vitro granulomas.

      Reviewer #2:

      In Figure 1b/c, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 2a/b, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 3a, again it is not clear what comparisons are being made to give the p-value annotation.

      The p-values formerly present on the upper le] corner of the panels were resulting from either Friedman (Figures 1C, 2A and 3A) or Kruskal-Wallis (Figures 1B and 2B) tests and indicated whether there was a significant difference between the analyzed groups overall. To avoid confusion, those values have been removed to only leave the post-test comparison between specific groups.  

      In the results narrative related to Figure 1 (lines 93-103), the authors refer to lineage heterogeneity without providing any objective quantification of this - I suggest they do so, by providing variance or standard deviations. 

      Thank you very much for this relevant suggestion, we have now included the coefficients of variation as a quantitative measure of the within-lineage heterogeneity in the manuscript (line 97). 

      I also suggest the authors explain what the data points actually represent in this figure - do I assume each data point = cfu from a well of 'granuloma'? Are they all from the same donor PBMC? What is the sample N for each lineage? If the data are not from the same donor PBMC, I think more informative to present the results of paired statistical analyses, stratified by donor cells. In addition, the authors should include a summary table of the demographic characteristics of the donors (at least sex, ethnicity, and age). If the data are derived from a single donor, I'd advocate providing data from at least one further donor.

      In the new supplementary figure requested by Reviewer 3 Figure 1—figure supplement 1 (actual CFU data on days 1 and 8 p.i. used to calculate the growth rate) it is now indicated that bacterial load was quantified as CFU per well.

      Regarding the number of donors used, as stated in the Material and Methods section (current line 418) and depicted by the four different shapes used when data are grouped by individual infecting strain, all figures in our manuscript have been generated using PBMCs from 4 independent donors. For greater clarity, “n = 4” has now been included in the figure legends. Regarding the statistical analyses, paired statistical analyses stratified by donor were already performed in the original version of the manuscript whenever appropriate. 

      As stated in the methods section, the buffy coats used for PBMC isolation are anonymized so demographic data are unavailable.

      The premise of the analysis in Figure tic and the results narrative ("This finding suggests that an increased ability to enter dormancy is not necessarily associated with a more pronounced growth phenotype", line 132) is not clear to me. Why would increased dormancy relate to increased growth in the same context? I suggest this analysis be removed.

      We apologize for the confusion in our original statement. We now rephrased it as “This finding suggests that an increased tendency to remain in a metabolically active state is not necessarily associated with a more pronounced growth phenotype”.

      In Figure 3b, I think it may be more informative if the data points from the same donor were linked. Likewise in Figure 3c, I'd like to see a donor-paired statistical analysis.

      For all figures, the choice of using individual symbols to identify data points from the same donor but not connecting lines was made to provide a neater image. Nevertheless, we have now modified the figure linking the data points from the same donor. The statistical analysis performed is always donor-paired whenever appropriate. 

      The casual inference suggested in the results narrative between ‘macrophage apoptosis’ and granulomatous response line 173-175) is not tested directly by the experiment – I suggest the authors exclude this statement.

      Fair point, the statement has been removed.

      To what extent have the authors considered whether variation in T cell responses between lineages may be confounded by variation in Mtb reactive T cell frequencies in donor PBMC. Can this be disentangled at all? This should be acknowledged as a potential limitation of the study.

      We did characterize the presence of mycobacterial antigen-specific reactive T cells in the PBMCs from the investigated donors. To do so, we performed in vitro stimulations with purified protein derivative (PPD) or an ESAT-6/CFP-10 peptide pool and quantified the frequency of IFN-γ-positive CD4 T cells by flow cytometry. The percentage of IFN-γg-positive CD4 T cells recalled by PPD stimulation ranged from 0.02% to 0.13%, while no ESAT6/CFP-10 reactive T cells were detected. As such, we can akest that the PBMC donors never encountered Mtb even though some levels of memory recalled by PPD may be due to cross-reactivity with BCG or pre-exposure to non-tuberculous mycobacteria. We have now added a panel in Figure 5—figure supplement 2 representing the frequency of mycobacteria-specific CD4 T cells and, as suggested, discussed the impact on the extent of the T cell responses observed in granulomas in the revised version of the manuscript.  Nevertheless, the observed MTBC strain-specific trends are consistent across the donors, as depicted in Figure 5B and Figure 5—figure supplement 2A-B.

      Moreover, the experimental design does not really test cause and effect for the relationship between T cell proliferation/activation and bacterial growth. What is the impact of T-cell depletion from PBMC on bacterial growth?

      The increased TB susceptibility of HIV patients demonstrated that T cells play a critical part in the control of Mtb infection. We agree and did envisage such a depletion experiment. However, depleting T cells from PBMCs would imply removing up to 70% of the cells present in the specimen, which would lead to a situation from which results cannot be compared to the original sample and therefore would not be interpretable. 

      Reviewer #3:

      Data presentation:

      - In Figure 1 (replication rate), actual cumulative CFU means from each strain for both days 1 and 8 with statistical analysis should be presented as panels in this figure.

      Agreed. We are providing the requested representation of the data and the corresponding paired statistical analysis as supplementary material Figure 1—figure supplement 1.

      - In Figure 2 (dormancy), a panel comparing the mean number of bacteria that are single positive for either Auramine-O, Nile Red, or are double positive should be included for each strain, with statistical analysis. Representative photomicrographs of phenotypes from the staining should also be included. Electron microscopy could be conducted to compare the presence of intermediate lipid inclusions within organoidbound mycobacteria.

      As requested, percentages of single stained as well as double positive bacilli in each sample are now represented in Figure 2—figure supplement 1. In addition, we have now also followed the request and included a photomicrograph picturing representative Mtb staining phenotypes. Lastly, it would certainly be very elegant to visualize the presence of Mtb lipid inclusions within cellular aggregates by electron microscopy. However, we do not currently have the means for such investigations and the implementation of such a protocol under BSL3 conditions appears unrealistic in the context of this study.  

      - In Figure 3 (granulomatous response), the number, circularity, and size of immune aggregates are presented as "granuloma score" in which the mean ratio of size to circularity is divided by the number of inclusions. To their credit, in Supplementary Figure 2, the authors provide the data in a straighAorward manner. However, the granuloma score metric is reduced as the number of observed "granulomas" increases, which is counterintuitive. Additionally, circularity is not a definitive aspect of human granulomas (Wells et al., Am J Respir Crit Care Med, 2021, PMID: 34015247). I am skeptical that the "granuloma score" is an accurate predictor granulomatous inflammation. Is there precedent for this metric in the literature? If so, a reference should be provided. A high magnification inset of 1 representative granuloma from each strain should be included in Figure 3A.

      As requested, insets of a representative average granuloma for each strain have been included in Figure 3A. The formulation of the “granuloma score” has no precedent and cannot be referenced. By doing so, we meant to integrate within one single parameter the visual differences represented in the current Figure 3— figure supplement 2. We intentionally sought to assign the highest score to the massive aggregation that some strains may promote unlike some that trigger several small, dispersed and diffused aggregates.

      - In Figure 4 (macrophage apoptosis), a panel showing the percentage of dual Annexin V and 7-AAD positive cells should be included to provide the reader with the relative scope of ongoing apoptotic vs necrotic/secondary necrotic death in the model. If the data is readily available, including a control of uninfected PBMCs would also allow the reader to evaluate donor-dependent differences of in vitro cell death at baseline.

      No significant differences were observed in the percentage of dual Annexin V- and 7-AAD-positive macrophages (necrosis/secondary necrosis) between the MTBC strains at this time-point. Nevertheless, we have disclosed this result in the revised manuscript as Figure 4—figure supplement 2.

      - In Figures 5 and 6 (lymphocyte activation and soluble mediator secretion), panels showing unscaled data should be included. Panels depicting the unscaled immunoassay protein readings (pg/mL) by strain for CXCL9, granzyme B, and TNF with statistical analysis should be included in Figure 6.

      As requested, unscaled lymphocyte activation and soluble mediator data have been included as Figure 5— figure supplement 2 and Figure 6—figure supplement 1, respectively (replacing former supplementary figures 5 and 7). In addition, updated Figure 6G panel now depicts correlation analysis with the unscaled cytokine concentrations.

      The DosR-regulon:

      The authors hypothesize that differences in the prevalence of the dormancy metrics (acid-fastness or lipid inclusion prevalence, are due to strain-specific increases in expression of the DosR regulon within the model's hypoxic conditions (lines 107-114, 126-127). The claim that their model is equipped to evaluate dosR-dependent mycobacterial phenotypes was also previously proposed (Arbués et el., 2021) and should be tested. A comparison of the dosR-dependent gene expression of each strain in PBMC aggregates and broth culture by qRT-PCR would test this idea at a very basic level.

      We agree. Actually, a similar request was made during the revision of our first in vitro granuloma study for which such qPCR data were generated and presented in Fig. 1 D (PMID: 32069329). In addition, the work of Kapoor et al., who originally developed the in vitro granuloma model also demonstrated the induction of most of the DosR regulated genes by qPCR (PMID: 23308269). We trust that the reviewer will agree that this does not need to be repeated.

      The modern Beijing lineage strain L2C:

      The authors claim (Line 101-102) that the results of Figure 1 "confirm the higher virulence propensities of strains from modern lineages". From the data presented, it appears that strain L2C (Modern-Beijing) dominates the modern vs ancestral and inter/intra-lineage phenotypes of replication, dormancy, and apoptosis. Are significant differences between modern and ancestral lineages or between strains simply a facet of the distinct profile of L2C? Do the statistical differences disappear when the L2C group is excluded?

      Indeed, among the modern lineages’ isolates, L2C exhibits a hypervirulent profile in terms of bacterial replication. However, the difference between modern and ancestral strains remains statistically significant when L2C is excluded from the analysis (p = 0.002). That is also the case when we analyze the proportion of dormant bacteria. Exclusion of L2C strain results in a Kruskal-Wallis overall p = 0.005, and p = 0.0002 when we compare L2 vs. L3. Lastly, regarding the percentage of apoptotic macrophages, if we use L2B (instead of L2C) to compare, the difference is still significant vs. L1A (p = 0.008) although there is no longer a trend for L2A (p = 0.1).

      "Dormancy":

      Dormancy is definitively a non-replicative state, where bacterial growth is absent. The authors' findings and claims appear to be incompatible with that definition, which they acknowledge (Lines 130-135). The lack of correlation between growth and dormancy in their model is supported with reference to Figure 2C, a Spearman's analysis of dormancy ratio with growth rate (inclusive of all strains under consideration). The figure supports a model where "dormancy" and "growth rate" are disjunct but also appears to show high "dormancy" accompanying increasing "growth" in the L2C group. How are strains able to grow if they are in a non-replicative state? Are the "growth rate" assays actually measures of survival? Are there different rates of infectivity? Are the bacteria growing cellularly in the serum-rich ECM, etc. etc? We need to see the hard CFU and Nile Red, and Auramine-O data to contextualize these findings. Alternatively, could the accumulation of inclusions in the model not be a reliable dormancy metric (Fines et al., BioRxiv [Preprint], 2023, PMID: 37609245)?

      We fully agree. The Nile red profiles are always relative and only depict the proportion of the population that has entered a dormant state. Nevertheless, dormancy can be dynamic and bacteria may swi]ly resuscitate in that model. Furthermore, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli still remains metabolically active and in a replicative state. The referred preprint is very interesting and we will follow it up closely.

      Specificity of responses to PBMC aggregation:

      The authors claim that their results "reveal a broad spectrum of granulomatous responses" (Line 73) but do not show any aggregation specificity of PBMC responses beyond the model's intrinsic metrics of area and circularity. To establish that their phenotypes such as lymphocyte activation, cytokine release, cell death, or mycobacterial acid-fastness/lipid inclusion prevalence, are aspects of the granulomatous response the authors could infect PBMCs from the same donors with the same strains and perform the same assays using established Mtb-PBMC models in which the cells do not aggregate. This would answer many important questions, for example, does the rate of macrophage infection account for variability in apoptosis percentage? Phagocytosis assay and quantification of stained intracellular mycobacteria within recently infected PBMCs could be conducted to determine if phenotypes are an aspect of granulomatous aggregation or due to strain-specific differences in cellintrinsic macrophage immunity. It would also be very informative to know what percentage of PBMCs and mycobacteria are granuloma-bound in the ECM.

      We are not aware of Mtb-PBMC models in which the cells do not aggregate. We previously compared PBMC infection models in the presence or absence of the collagen matrix and cells also spontaneously coalesced around infection foci (PMID: 34603299). Regarding the last point, the melting step of the collagen matrix requires enzymatic digestion and pipetting that dislocate the aggregates. Accordingly, we cannot distinguish the bacteria that would remain within the matrix compared to those replicating within cellular aggregates. However, we did resolve this question by demonstrating that the bacteria were not able to grow in the absence of cells in this culture condition (Supplementary material, PMID: 34603299)

      Minor recommendations

      - The term TNF-a should be replaced with TNF throughout the manuscript.

      We acknowledge that the term TNF-a can be interchangeable with TNF. However, we chose to use the TNFα terminology to differentiate it from lymphotoxin α, which is also referred to as TNF-β.

      - The authors cite studies conducted in murine and NHP models to support the claim that "understanding of immune protective traits in TB remains insufficient and yet dominated by data from mouse and non-human primate studies" (Lines 63-64) but ignore an abundance of data from other in vivo and in vitro models that have provided numerous valuable insights in the field of TB immunology. This line should be revised or omired.

      For us, the term “dominate” implies that these models are widely used, not that they are the only ones. Other models indeed provided additional relevant data. We are citing the lung-on-chip model of McKinney’lab and the in vitro granuloma model of Elkigton’s lab (line 66). We would be very happy to include more references upon further specifications even though we cannot build an extensive review here.

      - The authors claim that their model "encompasses, with the exception of neutrophils, all immune cell types involved in TB" (Lines 67-68). To support this claim, they should provide additional references or data demonstrating that the PBMC aggregates include, eosinophils, mast cells, dendritic cells, yolk-sac-derived alveolar macrophages, and Langhan's giant cells.

      With the aim of providing a more accurate and detailed information regarding the cell types present in the model, the sentence has been reformulated as: “The model encompasses all PBMC-derived cell types involved in TB immune responses, but lacks granulocytes (i.e. neutrophils, eosinophils, basophils and mast cells)” (line 260). Noteworthy, the presence of multinucleated giant cells was reported in Kapoor’s paper describing the in vitro granuloma model for the first time (PMID: 23308269).

      -  As an additional note, the title can be improved and made more broadly accessible by revising the use of the acronyms CXCL9, granzyme B, and TNF-α.

      To render the title more broadly accessible we propose to replace the listed acronyms by “soluble immune mediators”, but we remain opened to more appropriate and specific suggestions.

      Answers to the reviewers’ public comments

      Reviewer #1:

      First of all, we would like to thank the reviewers for their feedback and suggestions to improve our manuscript. To strengthen the findings of our study, we have performed and added results from IL-1b and CXCL9 blocking experiments evaluating the impact on the granulomatous response and bacterial load, respectively. In the revised version of the manuscript, while we discuss the null effect on bacterial growth of the treatment with an anti-CXCL-9 antibody and the potential reason behind it, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1b that the correlation analysis had initially suggested.

      Reviewer #2:

      The revised version of our manuscript incorporates now all the points detailed in the private answers to the reviewer, including clarifications on the statistical tests performed, additional supplementary materials to transparently disclose the raw data behind the normalization approach, as well as flow cytometry data on the immune memory status of the blood donors. In addition, and as stated in the answer to reviewer #1, to test causal relationship between some host and pathogen traits, we have now performed and provided data and interpretation of IL-1b and CXCL9 blocking experiments.

      Reviewer #3:

      We are thankful and concur with these constructive comments and insights. We have now consistently revisited the statistics in the figures to improve clarity and included new supplementary figures reporting the raw data that were missing in the initial version of the manuscript. In addition, and as mentioned in the answers to reviewers #1 and #2, we have now performed and added IL-1β and CXCL9 blocking experiments to test causal relationship between specific host and pathogen traits. In particular, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1β that the correlation analysis had initially suggested.

      More specifically, regarding the point that our method for bacterial collection calls into question whether all Mtb plated for CFU assay resided within granulomatous aggregates, we previously reported that Mtb growth strictly required the presence of human cells in our culture conditions (Supplementary material, Arbués et al, 2021, PMID: 34603299). In the presence of cells, our microscopy read-out does allow us to observe extra-cellular growth if infections are carried on beyond an 8-day limit, which we applied in the current study to exclude this particular caveat. 

      Concerning the apparently conflicting observation that those strains displaying an increased tendency to enter a dormant-like state are the ones exhibiting the highest replication rates, we would like to point out that a considerable population of bacilli still remains metabolically active and in a replicative state. For instance, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli does remain metabolically active. Moreover, dormancy can be dynamic and bacteria may swi]ly resuscitate.

      Regarding the mentioned limitations of our study that we have discussed in the revised version of our manuscript, we fully concur that PBMC-based in vitro granuloma models lack tissue structure as well as some important stromal and immune cellular players. Nevertheless, we and others demonstrated the particular relevance of the 3-dimensional infection approach within a matrix of collagen and fibronectin by providing mechanistical insights into Mtb resuscitation previously associated to treatment with various immunomodulatory drugs (Arbués et al., 2020, PMID: 32069329; Tezera et al., 2020, PMID: 32091388).

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      Reply to the reviewers

      1. General Statements [optional]

      Our manuscript initially entitled “Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription” investigates the regulation of the initial steps of ribosome biogenesis – the synthesis of large ribosomal RNA precursor by RNA polymerase I.

      In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anti-cancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.

      We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.

      All three reviewers acknowledged the increased processivity of SuperPol compared to its wild-type counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:

      “Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.

      Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA. This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity.

      We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.

      2. Description of the planned revisions

      Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic rate-limiting steps (see David Schneider's review on this topic for further details).

              Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased error-rate. We already started to address this point. Preliminary results from *in vitro* experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity *in vitro* and *in vivo*, and we agree that this might be at the cost of a decreased fidelity.
      

      Regulatory aspect of the process:

      To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.

      We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.

      __ __

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Some very important modifications have now been incorporated:



      Statistical Analyses and CRAC Replicates:

      Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.

      CRAC vs. Net-seq:

      Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.

      CRAC vs. Other Methods:

      Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and co-transcriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.

      Pol I ChIP Signal Comparison:

      The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.

      BMH-21 Effects:

      As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.

      We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.

      Minor points:

      Reviewer #1:

      • In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: On page 14: "SuperPol accumulation is decreased (compared to Pol I)". • *On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." *

      We clarified and increased the global writing style according to reviewer comment.

      • *There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references. *

      This was corrected in this version of the manuscript.

      • *In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? *

      Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.

      • Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.

      The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

      Y-axis is figure 7D is now correctly labelled

      • *In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? *

      The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.

      Reviewer #2:

      • *pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" *

      According to reviewer, statement has been simplified in this version of the manuscript.


      • *Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. *

      We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.


      • *A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? *

      Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively.


      • *pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. *

      According to reviewer suggestion, we referenced these studies.


      • *p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. *

      This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.


      • *p. 23: "The released of the premature transcript" - this phrase contains a typo *

      This is now corrected.


      Reviewer #3:

      • *Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. *

      The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.


      • *Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. *

      __We corrected Figure 1A. __


      • *Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. *

      We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.


      • *"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. *

      Thanks for pointing out this mistake. it has been corrected.


      • *Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. *

      We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.


      • *Figure 4: should probably be provided as supplementary material. *

      As lmentioned earlier (see comments), ____the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.


      • *"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. *

      We agree with this comment and we modified the text accordingly.


      • *Figure 7D: the legend of the y-axis is missing as well as the title of the plot. *

      Legend of the y-axis and title of the plot are now present.


      • The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.

      This was discussed previously. See comment above.


      • *Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. *

      Bibliography is now fixed

      __ __

      4. Description of analyses that authors prefer not to carry out

      Does SuperPol mutant produces more functional rRNAs ?

      As Reviewer 1 requested, we agree that this point requires clarification. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)-rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.

      __Is premature termination coupled with rRNA processing __

      We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.

      Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a Premature Transcription Termination (PPT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII), and which is suggested to be an important step for the quality control of rRNA transcripts. SuperPol is proposed to lack such a regulatory mechanism, due to an increased processivity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.

      Overall, the experiments are performed with rigor and include the appropriate controls and statistical analysis. Both the figures and the text present the data clearly. The Material and Methods section is detailed enough. The reported results are interesting; however, I am not fully convinced of the existence of PPT of RNAPI, and even less of its utmost importance. The existence of PPT of RNAPI would entail an intended regulatory mechanism. The authors propose that PPT could serve as quality control step for the UTP-A complex loading on the rRNA 5'-end. While this hypothesis is enticing and cautiously phrased by the authors, the lack of evidence showing a specific regulatory function (such as UTP-A loading checkpoint or else) limits these termination events to possibly abortive actions of unclear significance. The auhors may want to consider comparisons to other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al, 2006) or the G1136S allele of E. coli RNAP (Bar-Nahum et al., 2005). While clearly mechanistically distinct, these mutations result in similarly processive enzymes that achieve more robust transcription, possibly at the cost of decreased fidelity. Indeed, an alternative possibility explaining these transcripts could be that they originate from unsuccessful resumption of transcription after misincorporation (see below).

      I suggest reconsidering the study's main conclusions by limiting claims about the regulatory function of these termination events (the title of the manuscript should be changed accordingly). Alternatively, the authors should provide additional investigation on their regulatory potential, for example by assessing if indeed this quality control is linked to the correct assembly of the UTP-A complex. The expectation would be that SuperPol should rescue at least to some extent the defects observed in the absence of UTP-A components. Moreover, the results using the clv3 substrate suggest the possibility that SuperPol might simply be more able to tolerate mismatches, thus be more processive in transcribing, because not subjected to proof-reading mechanisms, similarly to what observed in Schwank et al., 2022. This could explain many of the observations, and I think it is worth exploring by assessing the fidelity of the enzyme, especially in the frame of suggesting a regulatory function for these termination events.

      Minor comments

      1. Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes.
      2. Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly.
      3. Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture.
      4. "Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C.
      5. Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region.
      6. Figure 4: should probably be provided as supplementary material.
      7. "While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected.
      8. Figure 6B: can the authors explain why most of bands detected in their Pol I TMA assay in Figure 6B are unchanged? It is unclear to me why only the 18S and 25S bands are decreased following BMH-21 treatment. Moreover, this experiment lacks the corresponding quantification and statistical tests.
      9. Figure 7D: the legend of the y-axis is missing as well as the title of the plot.
      10. The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.
      11. Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b.

      Significance

      Azouzi and co-authors' work builds on their previous study (Darrière et al, 2019) of RPA135-F301S (SuperPol), a mutant allele of the second largest RNAPI subunit, which was shown to compensate for Rpa49 loss, potentially by increasing rRNA production. The work advances the mechanistic understanding of the the SuperPol allele, demonstrating the increased processivity of this enzyme compared to its wild-type counterpart. Such increased processivity "desensitizes" RNAPI from abortive transcription cycles, the existence of which is clearly shown, though the biological significance of this phenomenon remains unclear. The lack of evidence for a regulatory mechanism behind these early termination events is, in my opinion, a limitation of this study, as it does not allow for differentiation between an intended regulatory process and a byproduct of an imperfect system.

      This work is of interest for researchers studying transcription regulation, particularly those interested in understanding RNAPI's role and fidelity. Demonstrating PPT as a regulatory quality control for RNAPI could point to common strategies in between RNAPI and RNAPII regulation, where premature termination has been extensively documented. However, without evidence of a specific regulatory function, these findings may currently be limited to descriptive insights.

      My expertise lies is RNAPII transcription, transcription termination, and genomic approaches to studying transcription.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes the impact of modulating signaling by a key regulatory enzyme, Dual Leucine Zipper Kinase (DLK), on hippocampal neurons. The results are interesting and will be important for scientists interested in synapse formation, axon specification, and cell death. The methods and interpretation of the data are solid, but the study can be further strengthened with some additional studies and controls.

      We greatly appreciate the thorough review and thoughtful suggestions from the reviewers and editors on our original manuscript. We provide point-to-point response below.  We added new studies on P10 mice and controls as suggested, and made revision of figures and texts for clarification. The revised manuscript includes three new supplemental figures; major text revision is copied under response.

      Reviewer #1 (Public Review):

      Summary:

      In this work, Ritchie and colleagues explore functional consequences of neuronal over-expression or deletion of the MAP3K DLK that their labs and others have strongly implicated in both axon degeneration, neuronal cell death, and axon regeneration. Their recent work in eLife (Li, 2021) showed that inducible over-expression of DLK (or the related LZK) induces neuronal death in the cerebellum. Here, they extend this work to show that inducible over-expression in Vglut1+ neurons also kills excitatory neurons in hippocampal CA1, but not CA3. They complement this very interesting finding with translatomics to quantify genes whose mRNAs are differentially translated in the context of DLK over-expression or knockout, the latter manipulation having little to no effect on the phenotypes measured. The authors note that several genes and pathways are differentially regulated according to whether DLK is over-expressed or knocked out. They note DLK-dependent changes in genes related to synaptic function and the cytoskeleton and ultimately relate this in cultured neurons to findings that DLK over-expression negatively impacts synapse number and changes microtubules and neurites, though with a less obvious correlation.

      Strengths:

      This work represents a conceptual advance in defining DLK-dependent changes in translation. Moreover, the finding that DLK may differentially impact neuronal death will become the basis for future studies exploring whether DLK contributes to differential neuronal susceptibility to death, which is a broadly important topic.

      We thank the reviewer for the comments on the value of our work.

      Weaknesses:

      This seems like two works in parallel that the authors have not yet connected. First is that DLK affects the translation of an interesting set of genes, and second, that DLK(OE) kills some neurons, disrupts their synapses, and affects neurite growth in culture.

      Specific questions:

      (1) Is DLK effectively knocked out? The authors reference the floxxed allele in their 2016 work (PMID: 27511108), however, the methods of this paper say that the mouse will be characterized in a future publication. Has this ever been published? The major concern is that here the authors show that Cre-mediated deletion results in a smaller molecular weight protein and the maintenance of mRNA levels.

      We apologize for out-of-date citation of the DLK(cKO)<sup>fl/fl</sup> mice.  The DLK(cKO)<sup>fl/fl</sup> mice have been published in (Li et al., 2021; Saikia et al., 2022); excision of the flox-ed exon was verified using several Cre drivers (Pv-Cre, AAV-Cre, and VGlut1-Cre in this study).  The flox-ed exon contains the initiation ATG and 148 amino acids.  By western blot analysis using antibodies against C-terminal peptides of DLK on cerebellar extracts (in Li et al., 2021) and hippocampal extracts (this study), the full-length DLK protein was significantly reduced (Fig 1A-B); DLK is expressed in other hippocampal cells, in addition to glutamatergic neurons, explaining remaining full-length DLK detected. 

      Our Ribo-seq of VGlut1-Cre; DLK(cKO)<sup>fl/fl</sup> detected remaining Dlk mRNAs lacking the floxed exon (Fig.S1C), which has several candidate ATG at amino acid 223 and after (Fig.S1C1). We detected a very faint band for smaller molecular weight proteins on western blots, only when the membrane was exposed under 5X longer exposure using Pico PLUS Chemiluminescent Substrate (Thermo Scientific, 34580) and a Licor Odyssey XF Imager (revised Fig. S1B). This smaller molecular weight protein might be produced using any candidate ATGs, but would represent an N-terminal truncated DLK protein lacking the ATP binding site and ~1/4 of the kinase domain, i.e. not a functional kinase. 

      The revised manuscript has updated citation for DLK(cKO)<sup>fl/fl</sup>. Revised Fig.S1B includes images of a western blot under normal exposure vs longer exposure of western blots using anti-DLK antibodies. New Fig.S1C1 shows effects of floxed exon on DLK.

      (2) Why does DLK(OE) not kill CA3 neurons? The phenomenon is clear but there is no link to gene expression changes. In fact, the highlighted transcript in this work, Stmn4, changes in a DLK-dependent manner in CA3.

      We agree that this is a very interesting question not answered by our gene expression analysis.  While we verified Stmn4 expression levels to correlate to the levels of DLK, we do not think that increased Stmn4 per se in DLK(iOE) is a major factor accounting for CA1 death vs CA3 survival. Several published studies have also reported regulation of Stmn4 mRNAs in other cell types, in the contexts of cell death (Watkins et al., 2013; Le Pichon et al., 2017) and axon regeneration and cytoskeleton disruption (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019;  Shin et al., 2019). As Stmns have significant expression and function redundancy, conventional knockdown or overexpression of individual Stmn generally does not lead to detectable effects on cellular function. As CA3 neurons are widely known for their dense connections and show resilience to NMDA-mediated neurotoxicity (Sammons et al., 2024; Vornov et al., 1991), we speculate that the differential vulnerability of CA1 and CA3 under DLK(iOE) is a reflection of both the intrinsic property, such as gene expression, and also their circuit connection. 

      In the revised manuscript, we have included following statement on pg 18:

      ‘While our data does not pinpoint the molecular changes explaining why CA3 would show less vulnerability to increased DLK, we may speculate that DLK(iOE) induced signal transduction amplification may differ in CA1 vs CA3. CA1 genes appear to be more strongly regulated than CA3 genes, consistent with our observation that increased c-Jun expression in CA1 is greater than that in CA3. Other parallel molecular factors may also contribute to resilience of CA3 neurons to DLK(iOE), such as HSP70 chaperones, different JNK isoforms, and phosphatases, some of which showed differential expression in our RiboTag analysis of DLK(iOE) vs WT (shown in File S2. WT vs DLK(iOE) DEGs). Together with other genes that show dependency on DLK, the DLK and Jun regulatory network contributes to the regional differences in hippocampal neuronal vulnerability under pathological conditions.’

      Further we state in ‘Limitation of our study’ on pg 20:

      ‘Our analysis also does not directly address why CA3 neurons are less vulnerable to increased DLK expression. Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      We hope our data will stimulate continued interests for testable hypothesis in future studies.

      (3) Why are whole hippocampi analyzed to IP ribosome-associated mRNAs? The authors nicely show a differential effect of DLK on CA1 vs CA3, but then - at least according to their methods ¬- lyse whole hippocampi to perform IP/sequencing. Their data are therefore a mix of cells where DLK does and does not change cell death. The key issue is whether DLK does/does not have an effect based on the expression changes it drives.

      At the time of planning the Ribo-Tag experiment several years ago, we focused on the hippocampal glutamatergic neurons. Due to technical difficulty in micro-dissecting individual hippocampal regions from this early timepoint, we opted to use whole hippocampi to isolate ribosome-associated mRNAs. We agree with the reviewer that it is important to sort out DLK-dependent general gene expression changes vs those specific to a particular cell type where DLK impacts its survival. With emerging CA1, CA3 and other cell-type specific Cre drivers and advanced RNAseq technology, we hope that our work will stimulate broad interest in these questions in future studies. 

      In the revised manuscript, we have included new analysis comparing our Vglut1-RiboTag profiling (P15) with CamK2-RiboTag (for CA1) and Grik4-RiboTag (for CA3) (P42) published in Traunmüller et al., 2023 (GSE209870). We find that >80% of the top ranked genes in their CamK2-RiboTag (for CA1) and Girk4-RiboTag (for CA3) were detected in our VGlut1-RiboTag (revised methods and Supplemental Excel File S3). CA1-enriched genes tended to be expressed higher in DLK(cKO), compared to control, whereas CA3-enriched genes showed less significant correlation to DLK expression levels. Additionally, many genes known to specify CA1 fate do not show significant downregulation in DLK(iOE). This analysis, along with other data in our manuscript, is consistent with an idea that DLK does not regulate neuronal fate.

      In the revised manuscript, we presented this additional analysis in Fig. S6K-L, and expanded text description on page 9:

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration’.

      (4) Is the subtle decrease in synapse number (Basson/Homer co-loc.) in the DLK (OE) simply a function of neurons (and their synapses, presumably) having died? At the P15 time point that the authors choose because cell death is minimal, there is still a ~25% reduction in CA1 thickness (Figure 2B), which is larger than the ~15% change in synapses (Figure 5H) they describe.

      We thank reviewer for the question. To address this, we have analyzed synapses in the CA1 region at P10 in DLK(iOE) mice when there was no detectable loss of neurons. At P10, we did not detect significant changes in Bassoon, Homer1, or colocalized puncta in CA1 (Fig.S11A-F). In P15 DLK(iOE) mice, Homer1 puncta were slightly smaller (Fig.5L) and showed a significant decrease in CA1 SR (Fig.5I).

      In the revised manuscript we have also redone our statistical analysis of synapses, using mice rather than ROIs (revised Fig. 5), as recommended by R3. We also analyzed synapses in CA3, and found no significant differences in P10 or P15 (Fig.S12).  We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Reviewer #2 (Public Review):

      This manuscript describes the impact of deleting or enhancing the expression of the neuronal-specific kinase DLK in glutamatergic hippocampal neurons using clever genetic strategies, which demonstrates that DLK deletion had minimal effects while overexpression resulted in neurodegeneration in vivo. To determine the molecular mechanisms underlying this effect, ribotag mice were used to determine changes in active translation which identified Jun and STMN4 as DLK-dependent genes that may contribute to this effect. Finally, experiments in cultured neurons were conducted to better understand the in vivo effects. These experiments demonstrated that DLK overexpression resulted in morphological and synaptic abnormalities.

      Strengths:

      This study provides interesting new insights into the role of DLK in the normal function of hippocampal neurons. Specifically, the study identifies:

      (1) CA1 vs CA3 hippocampal neurons have differing sensitivity to increased DLK signaling.

      (2) DLK-dependent signaling in these neurons is similar to but distinct from the downstream factors identified in other cell types, highlighted by the identification of STMN4 as a downstream signal.

      (3) DLK overexpression in hippocampal neurons results in signaling that is similar to that induced by neuronal injury.

      The study also provides confirmatory evidence that supports previously published work through orthogonal methods, which adds additional confidence to our understanding of DLK signaling in neurons. Taken together, this is a useful addition to our understanding of DLK function.

      We thank the reviewer for careful reading and positive comments.

      Weaknesses:

      There are a few weaknesses that limit the impact of this manuscript, most of which are pointed out by the authors in the discussion. Namely:

      (1) It is difficult to distinguish whether the changes in the translatome identified by the authors are DLK-dependent transcriptional changes, DLK-dependent post-transcriptional changes or secondary gene expression changes that occur as a result of the neurodegeneration that occurs in vivo. Additional expression analysis at earlier time points could be one method to address this concern.

      We appreciate the reviewer’s comment, and have performed new analysis on c-Jun and p-c-Jun levels in CA1, CA3, and DG in P10 DLK(OE) mice. Our data suggest that in CA3 elevations in p-c-Jun and c-Jun occur separately from cell death in a DLK-dependent manner, though the high elevation of both p-c-Jun and c-Jun in CA1 correlates with cell death.

      The data is presented in revised Fig.S7A,B, and described in revised text on pg 9-10:

      ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis.’

      Also, on pg.10:

      In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      (2) Related to the above, it is difficult to conclusively determine from the current data whether the changes in synaptic proteins observed in vivo are a secondary result of neuronal degeneration or a primary impact on synapse formation. The in vitro studies suggest this has the potential to be a primary effect, though the difference in experimental paradigm makes it impossible to determine whether the same mechanisms are present in vitro and in vivo.

      We appreciate the comment, which is related to R1 point 4. We have performed further analysis and revised the text on pg.12 with the following text:

      ‘To assess effects of DLK overexpression on synapses, we immunostained hippocampal sections from both P10 and P15, with age-matched littermate controls. Quantification of Bassoon and Homer1 immunostaining revealed no significant differences in CA1 SR and CA3 SR and SL in P10 mice of _<_i>Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> and control (Fig.S11A-F, S12A-J). In P15, Bassoon density and size in CA1 SR were comparable in both mice (Fig 5G, H, K), while Homer1 density and size were reduced in DLK(iOE) (Fig.5G,I, L). Overall synapse number in CA1 SR was similar in DLK(iOE) and control mice (Fig.5J). Similar analysis on CA3 SR and SL detected no significant difference from control (Fig.S12M-V).’

      We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Additionally, to address whether the same mechanisms are present in vitro, we have performed further analysis on cultured hippocampal neurons. As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      (3) The phenotype of DLK cKO mice is very subtle (consistent with previous reports) and while the outcome of increased DLK levels is interesting, the relevance to physiological DLK signaling is less clear. What does seem possible is that increased DLK may phenocopy other neuronal injuries but there are no real comparisons to directly address this in the manuscript. It would be helpful for the authors to provide this analysis as well as a table with all of the translational changes along with fold changes.

      Thank you for the suggestion. The fold changes of genes showing significantly altered expression in DLK(cKO) and DLK(iOE) are provided in the excel files (Supplementary excel File S1 WT vs DLK(cKO) DEGs and File S2. WT vs DLK(iOE) DEGs, highlighted columns B and F).  

      On pg 6, we revised the text as following to include comparison of DLK levels in other physiological conditions and our mice:

      ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion, we state (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      (4) For the in vivo experiments, it is unclear whether multiple sections from each animal were quantified for each condition. More information here would be helpful and it is important that any quantification takes multiple sections from each animal into account to account for natural variability.

      We apologize this was unclear in the original manuscript.

      In the revised methods, under Confocal imaging and quantification (pg 33), we stated: “For brain tissue, three sections per mouse were imaged with a minimum of three mice per genotype for data analysis.”

      In revised figure legends, we made it clear that multiple sections from each animal have been used for quantification in all instances, i.e. “Each dot represents averaged thickness from 3 sections per mouse, N≥4 mice/genotype per timepoint.” 

      In Fig.1F-H: “Each dot represents averaged intensity from 3 sections per mouse”

      In Fig.S3B “Data points represent individual mice, averages taken across 3 sections per mouse”

      Reviewer #3 (Public Review):

      Dr Jin and colleagues revisit DLK and its established multifactorial roles in neuronal development, axonal injury, and neurodegeneration. The ambitious aim here is to understand the DLK-dependent gene network in the brain and, to pursue this, they explore the role of DLK in hippocampal glutamatergic neurons using conditional knockout and induced overexpression mice. They produce evidence that dorsal CA1 and dentate gyrus neurons are vulnerable to elevated expression of DLK, while CA3 neurons appear unaffected. Then they identify the DLK-dependent translatome featured by conserved molecular signatures and cell-type specificity. Their evidence suggests that increased DLK signaling is associated with possible STMN4 disruptions to microtubules, among else. They also produce evidence on cultured hippocampal neurons showing that expression levels of DLK are associated with changes in neurite outgrowth, axon specification, and synapse formation. They posit that downstream translational events related to DLK signaling in hippocampal glutamatergic neurons are a generalizable paradigm for understanding neurodegenerative diseases.

      Strengths

      This is an interesting paper based on a lot of work and a high number of diverse experiments that point to the pervasive roles of DLK in the development of select glutamatergic hippocampal neurons. One should applaud the authors for their work in constructing sophisticated molecular cre-lox tools and their expert Ribotag analysis, as well as technical skill and scholarly treatment of the literature. I am somewhat more skeptical of interpretations and conclusions on spatial anatomical selectivity without stereological approaches and also going directly from (extremely complex) Ribotag profiling patterns to relevance based on immunohistochemistry and no additional interventions to manipulate (e.g. by knocking down or blocking) their top Ribotag profile hits. Also, it seems to this reviewer that major developmental claims in the paper are based on gene translational profiling dependent on DLK expression, not DLK activation, despite some evidence in the paper that there is a correlation between the two. Therefore, observed patterns and correlations may or may not be physiologically or pathologically relevant. Generalizability to neurodegenerative diseases is an overreach not justified by the scope, approach, and findings of the paper.

      We thank the reviewer for the encouraging and constructive comments on the manuscript.

      Weaknesses and Suggestions:

      The authors state that the rationale for the translatomic studies is to "to gain molecular understanding of gene expression associated with DLK in glutamatergic neurons" and to characterize the "DLK-dependent molecular and cellular network", However, a problem with the experimental design is the selection of an anatomical region at a time point featured by active neurodegeneration. Therefore, it is not straightforward that the differentially expressed genes or pathways caused by DLK overexpression changes could be due to processes related to neurodegeneration. Indeed, the authors find enrichment of signals related to pathways involved in extracellular matrix organization, apoptosis, unfolded protein responses, the complement cascade, DNA damage responses, and depletion of signals related to mitochondrial electron transport, etc., all of which could be the consequence of neurodegeneration regardless of cause. A more appropriate design to discover DLK-dependent pathways might be to look at a region and/or a time point that is not confounded by neurodegeneration.

      We appreciate reviewer’s comment. We included our thoughts in ‘Limitation of the study’ (pg 20):

      ‘Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      In a related vein, the authors ask "if the differentially expressed genes associated with DLK(iOE) might show correlation to neuronal vulnerability" and, to answer this question, they select the set of differentially expressed genes after DLK overexpression and assess their expression patterns in various regions under normal conditions. It looks to me that this selection is already confounded by neurodegeneration which could be the cause for their downregulation. Therefore, such gene profiles may not be directly linked to neuronal vulnerability. A similar issue also relates to the conclusion that "...the enrichment of DLK-dependent translation of genes in CA1 suggests that the decreased expression of these genes may contribute to CA1 neuron vulnerability to elevated DLK".

      We agree with the reviewer’s concern that it is difficult to separate neurodegenerative consequences from changes caused by DLK solely based on our translatomics studies on P15 DLK(iOE) mice.  As responded to reviewer 1 (point 4) and reviewer 2 (point 1), we have included new analysis of P10 mice (Fig.S7A,B) when neurons did not show detectable sign of degeneration.

      We consider several lines of evidence supporting that some differentially expressed genes in DLK(iOE) vs control may likely be specific for increased DLK signaling.

      First, the genes identified in DLK(iOE) vs control represent a small set of genes (260), which is comparable to other DLK dependent datasets (Asghari Adib et al., 2024) but shows cell-type specificity.

      Second, our analysis using rank-rank hypergeometric overlap (RRHO) detects a significant correlation between upregulated genes from DLK(iOE) vs downregulated genes in DLK(cKO), and vice versa, suggesting that expression of a similar set of genes is depended on DLK (Fig.3C, S6C-E). Consistently, GO term analysis using the list of genes coordinately regulated by DLK, derived from our RRHO analysis, leads to identification of similar GO terms related to up- and downregulated genes as using DLK(iOE)-RiboTag data alone. SynGO analysis of DLK(iOE) regulated genes and DLK(cKO) regulated genes also identified similar synaptic processes regulated by significantly regulated genes (Fig.3F and S6J).  

      Third, we performed additional analysis comparing our Vglut1-RiboTag dataset with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We observed >80% overlap among the top ranked genes (revised Methods). We described this analysis on pg 9 and Fig. S6K-L (and Supplemental Excel File S3):

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration.’

      To understand the role and relevance of the DLK overexpression model, there should be a discussion of to what extent it corresponds to endogenous levels of DLK expression or DLK-MAPK pathway activation under baseline or pathological conditions.

      We appreciate the suggestion, which is similar to R2 point 3. We have revised the text and discussion to include how DLK levels may be altered in other physiological conditions vs our mice.

      Pg. 6: ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      The authors posit that "dorsal CA1 neurons are vulnerable to elevated DLK expression, while neurons in CA3 appear largely resistant to DLK overexpression". This statement assumes that DLK expression levels start at a similar baseline among regions. Do the authors have any such data? Ideally, they should show whether DLK expression and p-c-Jun (as a marker of downstream DLK signaling) are the same or different across regions in both WT and overexpression mice. For example, what are the DLK/p-c-Jun expression levels in regions other than CA1 in Supplementary Figures 2-3 and how do they compare with each other? Normalization to baseline for each region does not allow such a comparison. Also, in Supplementary Figure 6, analyses and comparisons between regions are done at a time point when degeneration has already started. Ideally, these should be done at P10.

      We thank the reviewer for raising these points. In the revised manuscript we have included protein expression analysis of DLK (Fig S3), c-Jun, and p-c-Jun at P10 (Fig. S7).

      We provided a quantification of DLK immunostaining intensity in CA1 and CA3 in Fig.S3D,E and find roughly comparable levels between regions.

      Pg. 6: ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      We provided our quantifications without normalization to baseline in each region for c-Jun and p-c-Jun, and revised the text accordingly:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Illustration of proposed selective changes in hippocampal sector volume needs to be very carefully prepared in view of the substantial claims on selective vulnerability. In 2A under P15 and especially P60, it is difficult to see the difference - this needs lower magnification and a lot of care that anteroposterior levels are identical because hippocampal sector anatomy and volumes of sectors vary from level to level. One wonders if the cortex shrinks, too. This is important.

      Thank you for raising the point. We have provided images to view the anteroposterior level in Fig.S2A-C. We have noticed cortex in DLK(OE) mice to become thinner, along with expansion of ventricles in some animals at later timepoints (Fig.S2C).

      One cannot be sure that there is selective death of hippocampal sectors with DLK overexpression versus, say, rearrangement of hippocampal architecture. One may need stereological analysis, otherwise this substantial claim appears overinterpreted.

      We appreciate the comment.

      In the revised manuscript, we included a new supplemental figure (Fig. S2) showing lower magnification images of coronal sections, and used cautionary wording, such as ‘CA3 is less vulnerable, compared to CA1’, to minimize the impression of over-interpretation.  By NeuN staining, at P10, P15, P60, we did not observe detectable difference in overall hippocampus architecture, apart from noted cell death of CA1 and DG and associated thinning of each of the layers. At 46 weeks, some animals showed differences in the overall shape of dorsal hippocampus, though this appeared to reflect a disproportionately large CA3 region compared to other regions (Fig S2). Increased GFAP staining (Fig.S5A-C) was detected in CA1 but not in CA3, and microglia by IBA1 staining (Fig.S5E) also displayed less reactivity in CA3, compared to CA1. Thus, based on NeuN staining, GFAP staining, IBA1 staining and analysis of the differentially regulated genes, we infer that the effect of DLK(iOE) in CA1 is different than the effect on CA3.

      Is the GFAP excess reflective of neuroinflammation? What do microglial markers show? The presence of neuroinflammation does not bode well with apoptosis. Speaking of which, TUNEL in one cell in Supplementary Figure 4E is not strong evidence of a more widespread apoptotic event in CA1.

      We have included staining data for the microglia marker IBA1. Both GFAP and IBA1 showed evidence of reactivity particularly in the CA1 region (S5A-E), supporting the differential vulnerability in different regions, though whether cell death is primarily due to apoptosis is unclear.

      We agree that our data of sparse TUNEL staining at P15 (Fig S5F,G) do not rule out whether other mechanisms of cell death may also occur.  We have included this in our limitations (pg.20) “While we find evidence for apoptosis, other forms of cell death may also occur.”

      In several places in the paper (as illustrated in Figure 4B, Supplementary Figure 2B, etc.): the unit of biological observation in animal models is typically not a cell, but an organism, in which averaged measures are generated. This is a significant methodological problem because it is not easy to sample neurons without involving stereological methods. With the approach taken here, there is a risk that significance may be overblown.

      We appreciate the reviewer’s point. We used same region for quantification of RNAscope, genotype-blind when possible. We revised the graphs to show mean values for individual mice in Fig.4B, 4C, and Fig.S3B (previously Fig.S2B).

      Other Comments and Questions:

      Supplementary Figure 9: The authors state that data points are shown for individual ROIs - ideally, they should also show averages for biological replicates. Can the authors confirm that statistical analyses are based on biological replicates (mice) and not ROIs?

      We have revised the graphs to show averages from individual mice in Fig.5B-D, F5E-F (previously Fig.S9G-I), Fig.5H-J, and Fig.5K-L (previously Fig.S9J-L)  and Fig.S10B,C,E,F (previously Fig.S9B,C, E,F). The statistical analyses are based on biological replicates of mice.

      For in vitro experiments, what is the effect of DLK overexpression on neuronal viability and density? Could these variables confound effects on synaptogenesis/synapse maturation?

      As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      We cannot rule out whether variable factors in our cultures may confound effects on synaptogenesis/synapse maturation, and would hope future studies will shed clarity.

      Correlations between c-jun expression and phosphorylation are extremely important and need to be carefully and convincingly documented. I am a bit concerned about Supplementary Figure 6 images, especially 6B-CA1 (no difference between control and KO, too small images) and 6D (no p-c-Jun expression at all anywhere in the hippocampus at P15?).

      At P10, P15, and P60 we stained for p-c-Jun using the Rabbit monoclonal p-c-Jun (Ser73) (D47G9) antibody from Cell Signaling (cat# 3270) at a 1:200 dilution and imaged using an LSM800 confocal microscope with a 20x objective. We observed p-c-Jun to be quite low generally in control animals. We have replaced the images in Fig.S7F (previously S6D), and adjusted the brightness/contrast to enable better visualization of the low signal in Fig.S7B,D,F (previously Fig.S6B,D).

      We revised our text to present the data carefully as stated above:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Recommendations for the authors:

      Several major and minor reservations were raised. The major issues are the need for more information about the over-expression of DLK and a need to extrapolate to an in vivo condition with DLK. A considerable amount of useful information is presented with some very nicely done experiments but it is not yet a coherent or integrated story. The lack of impact of DLK overexpression in some neurons is perhaps the most impactful observation of the study and would be great to have more information around the differential transcriptional/signaling response in these cell types. There is also a need for more experimental details and to address several questions about the mouse genetic and translatome analysis. They are valid concerns that require attention by the authors.

      We thank the editors and reviewers for their thoughtful evaluation and suggestions.  We hope that the editors and reviewers find that the new data and text changes in our revised manuscript, along with above point-to-point response, have addressed the concerns and strengthened our findings.

      Minor points:

      (1)The authors state that deletion of DLK has no effect on CA1 at 1yr, however, the image of CA1 in Figure S1D shows substantially fewer NeuN+ neurons. Is this a representative field of view?

      We have re-examined images, and observed no effect on hippocampal morphology at 1 yr. We now included representative images in the revised Fig S1D.

      (2) Is the DLK protein section staining in Figure 2C a real signal? The staining looks like speckles and is purely somatic. Axonal staining is widely expected based on the literature and the authors' own work. There should be a specificity control.

      To our knowledge, axonal staining of DLK reported in the literature is mostly based on cultured DRG neurons. In addition to the reported axonal localization, DLK is present in the cell soma, near the golgi (Hirai et al., 2002), and in the post-synaptic density (Pozniak et al., 2013).

      In the revised manuscript, we addressed this point by including controls with no primary antibody, and using an antibody against the closely related kinase, LZK. These additional data are shown in (Fig.S3C,D) (previously Fig.S2C), supporting that DLK protein staining represents real signal.  At P10 and P15, DLK immunostaining around CA3 showed axonal staining of the mossy fibers, as well as in the soma and dendritic layers (Fig.S3C,D). A similar pattern was also seen in primary cultured neurons (Fig 6A).

      (3) The protein expression of DLK in the transgenic overexpressor (Figure S7C) looks, to the resolution of this blot, to be at least 50kD heavier than 'WT' DLK. Can the authors explain this discrepancy?

      The Cre-induced DLK(iOE) transgene has T2A and tdTomato in-frame to C-terminus of DLK. It is known that T2A ‘self-cleavage’ is often incomplete. DLK-T2A-tdTomato would be about 50 kD bigger than WT DLK. We now include the transgene design in revised Fig S1D, and also stated in figure legend of Fig.S8C (previously S7C) that ‘Larger molecular weight band of DLK in Vglut1<sup>Cre/+</sup>;H11-DLKiOE/+ would match the predicted molecular weight of DLK-T2A-tdTomato if T2A-peptide induced ‘self-cleavage’ due to ribosomal skipping is ineffective (Fig.S1D).’

      (4) Expression changes in DLK affect various aspects of neurites in CA1 cultures (Figure 6), and changes in DLK also modestly affect STMN4 (and 2, perhaps indirectly) levels (Figure S7C), but there is no indication that DLK acts via STMN4 to cause these changes. It is not clear what to make of these data. Of note, Stmn4 levels change in response to DLK in CA3, without DLK affecting cell death in this region.

      We appreciate and agree with the comment. Other studies (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019; Larhammar et al., 2017; Le Pichon et al., 2017; Shin et al., 2019; Watkins et al., 2013) reported expression changes in Stmn4 mRNAs in other cell types and cellular contexts, which appeared to depend on DLK. Hippocampal neurons express multiple Stmns (Fig.S8A). While we present our analysis on the effects of DLK dosage on Stmn4, and also Stmn2, we do not think that DLK-induced changes of Stmn4 expression per se is a major factor underlying CA1 cell death vs CA3 survival.

      In the revised manuscript, we addressed this point in ‘Limitation of our study’ (pg 20):

      ‘Additional experiments will be needed to elucidate in vivo roles of STMN4 and its interaction with other STMNs’.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      • Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary:

      In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; diKerent types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.

      The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

      Major comment:

      [Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta- Notch signaling, could the authors analyze the eKect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

      Response: We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression aKects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 20- 22. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is suKicient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

      Minor comments:

      • PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

      Response: We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

      • page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

      Response: We thank the reviewer for spotting this. This has now been corrected.

      • Figure 3C: Description of black crosses in the panels is required in the figure legend.

      Response: Thank you for spotting this. The legend has now been corrected.

      • Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

      Response: We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

      • In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

      Response: These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a diKerent seed value. This is now reflected in the text on page 12 and in figure 4.

      • The authors analyze the eKect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

      Response: We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in figure 4 supplementary figure 1 and are referred to in the text on page 11.

      • The authors analyze the eKect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

      Response: We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

      • I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

      Response: We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

      • Figure 5-figure supplement 2: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Figure 5-figure supplement 3: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Reviewer #1 (Significance (Required)):

      Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

      Response: We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too. In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

      • Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY

      The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

      Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes aKect the segmentation clock.

      Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

      Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

      MAJOR COMMENTS

      1. The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

      Response: We thank the reviewer for their positive comments about our work

      1. This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

      Response: We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space.

      1. The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

      Response: We support open source coding practices.

      1. In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'oK' phase of the clock will ingressing cells be in-phase with their neighbours."

      However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853])

      Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

      Response: We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence.

      However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

      MINOR COMMENTS:

      1. The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

      Response: We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

      The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

      Response: We are very glad to see that the reviewer found that the manuscript was clearly presented.

      1. The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

      Response: Thank you

      Minor suggestions:

      1. Page 26: In the Cell addition sub-section of the Methods section, correct all

      instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

      Response: We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

      1. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

      Response: Thank you for the suggstion, we have now done this (see table 1 page 36).

      **Referee Cross-commenting**

      I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.

      • On reviewer #1's Comments:

      I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.

      Response: As per reviewer #1’s suggestion, we have now included this analysis (figure 8).

      In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.

      Response: As per reviewer #1’s suggestion, we have now included such an analysis (figure 4 supplements 2 and 4) and agree with both reviewers that they improve the communication of our results.

      The remaining minor comments are useful and relevant to improving the manuscript.

      • On reviewer #3's Comments:

      Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.

      Response: We thank Reviewer #2 for this assessment, which we agree with. Nonetheless we have sought to streamline our writing throughout to increase clarity without reducing the content.

      Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.

      Response: We thank Reviewer #2 for their comments here and refer them to our responses to Reviewer #3.

      I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.

      Response: We have now addressed these concerns in our response to Reviewer #3. Please see below.

      I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be diKerent for diKerent organisms.

      Response: We now justify this assumption in the methods on page 32.

      I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.

      Response: We agree with Reviewer #2 and have included this in our response to Reviewer #3.

      Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.

      Response: We thank Reviewer #2 for their comments.

      • Reviewer #2 (Significance (Required)): GENERAL ASSESSMENT

      • This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

      ADVANCE

      • This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.

      However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

      • The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

      AUDIENCE

      • Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

      Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular

      mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from diKerent vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

      MY EXPERTISE

      My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.

      I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.

      • Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to diKerent biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:

      1. The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the eKects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the eKect of diKerent parameters in greater detail.

      Response: One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with diKering cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM thatcaptures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available.

      While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting eKect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model.

      Furthermore, it is unclear how one would simulate processes such as compaction- extension using a one-dimensional model. The two diKerent scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively diKerent than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

      I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

      Response: Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the x- axis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

      While studying the eKect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes aKected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

      Response: We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the diKerent modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve. While interesting, this work is however outside the scope of the current manuscript.

      While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

      Response: We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression.

      Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

      Response: We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

      The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

      Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript.

      Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

      Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

      The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

      Response: We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31.

      Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016).

      We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

      Figure 3 and the associated text shows no eKect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

      Response: Thank you for the suggestion. However, we would argue that the lack of eKect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

      • Reviewer #3 (Significance (Required)):

      The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

      My research interests are building physics and engineering based models of cell and tissue scale biological phenomena.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Zanetti et al use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

      Strengths:

      Support previously published claims that VP3 associates with early endosome membrane, potentially through binding to PI3P. The finding that mutating a single residue (R200) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

      Weaknesses:

      The manuscript is relatively narrowly focused: the specifics of the bi-molecular interaction between the VP3 of an unusual avian virus and a host cell lipid (PIP3). Further, the affinity of this interaction is low and its specificity relative to other PIPs is not tested, leading to questions about whether VP3-PI3P binding is relevant.

      Regarding the manuscript’s focus, we challenge the notion that studying a single bi-molecular interaction makes the scope of the paper overly narrow. This interaction—between VP3 and PI3P—plays a critical role in the replication of the birnavirus, which is the central theme of our work. Moreover, identifying and understanding such distinct interactions is a fundamental aspect of molecular virology, as they shed light on the precise mechanisms that viruses exploit to hijack the host cell machinery. Consequently, far from being narrowly focused, we believe our work contributes to the broader understanding of host-pathogen interactions.

      As for the low affinity of the VP3-PI3P interaction, we argue that this is not a limitation but rather a biologically relevant feature. As discussed in the manuscript, the moderate strength of this interaction is likely critical for regulating the turnover rate of VP3/endosomal PI3P complexes, which in turn could optimize viral replication efficiency. A stronger affinity might trap VP3 on the endosomal membrane, whereas weaker interactions might reduce its ability to efficiently target PI3P. Thus, the observed affinity may reflect a fine-tuned balance that supports the viral life cycle.

      With regard to specificity, we emphasize that in the context of the paper, we refer to biological specificity, which is not necessarily the same as chemical specificity. The binding of PI3P to early endosomes is “biologically” preconditioned by the distribution of PI3P within the cell. PI3P is predominantly localized in endosomal membranes, which “biologically precludes” interference from other PIPs due to their distinct cellular distributions. Moreover, while early endosomes also contain other anionic lipids, our work demonstrates that among these, PI3P plays a distinctive role in VP3 binding. This highlights its functional relevance in the context of early endosome dynamics.

      Reviewer #3 (Public review):

      Summary:

      Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2. This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

      Strengths:

      Detailed characterization of the interaction between VP3 and PIP3 identified R200D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

      We thank the reviewer for the feedback.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Zanetti et al. use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that the association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

      Strengths:

      Supports previously published claims that VP3 may associate with early endosomes and bind to PI3P-containing membranes. The claim that mutating a single residue (R<sub>200</sub>) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

      Weaknesses:

      The manuscript is relatively narrowly focused: one bimolecular interaction between a host cell lipid and one protein of an unusual avian virus (VP3-PI3P). Aspects of this interaction have been described previously. Additional data would strengthen claims about the specificity and some technical issues should be addressed. Many of the core claims would benefit from additional experimental support to improve consistency.

      Indeed, our group has previously described aspects of the VP3-PI3P interaction, as indicated in lines 100-105 from the manuscript. In this manuscript, however, we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3—the R<sub>200</sub>—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

      We have now incorporated new experimental and simulation data; and have carefully revised the manuscript in accordance with the reviewers’ recommendations. We are confident that these improvements have further strengthened the manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Birnavirus replication factories form alongside early endosomes (EEs) in the host cell cytoplasm. Previous work from the Delgui lab has shown that the VP3 protein of the birnavirus strain infectious bursal disease virus (IBDV) interacts with phosphatidylinositol-3-phosphate (PI3P) within the EE membrane (Gimenez et al., 2018, 2020). Here, Zanetti et al. extend this previous work by biochemically mapping the specific determinants within IBDV VP3 that are required for PI3P binding in vitro, and they employ in silico simulations to propose a biophysical model for VP3-PI3P interactions.

      Strengths:

      The manuscript is generally well-written, and much of the data is rigorous and solid. The results provide deep knowledge into how birnaviruses might nucleate factories in association with EEs. The combination of approaches (biochemical, imaging, and computational) employed to investigate VP3-PI3P interactions is deemed a strength.

      Weaknesses:

      (1) Concerns about the sources, sizes, and amounts of recombinant proteins used for co-flotation: Figures 1A, 1B, 1G, and 4A show the results of co-flotation experiments in which recombinant proteins (control His-FYVE v. either full length or mutant His VP3) were either found to be associated with membranes (top) or non-associated (bottom). However, in some experiments, the total amounts of protein in the top + bottom fractions do not appear to be consistent in control v. experimental conditions. For instance, the Figure 4A western blot of His-2xFYVE following co-flotation with PI3P+ membranes shows almost no detectable protein in either top or bottom fractions.

      Liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the experimental conditions are homogeneous, it is not surprising to observe differences in the protein level between different experiments. Indeed, the revised version of the manuscript includes membranes with more similar band intensities, as depicted in the new versions of Figures 1 and 4.

      Reading the paper, it was difficult to understand which source of protein was used for each experiment (i.e., E. coli or baculovirus-expressed), and this information is contradicted in several places (see lines 358-359 v. 383-384). Also, both the control protein and the His-VP3-FL proteins show up as several bands in the western blots, but they don't appear to be consistent with the sizes of the proteins stated on lines 383-384. For example, line 383 states that His-VP3-FL is ~43 kDa, but the blots show triplet bands that are all below the 35 kDa marker (Figures 1B and 1G). Mass spectrometry information is shown in the supplemental data (describing the different bands for His-VP3-FL) but this is not mentioned in the actual manuscript, causing confusion. Finally, the results appear to differ throughout the paper (see Figures 1B v. 1G and 1A v. 4A).

      Thank you for pointing out these potentially confusing points in the previous version of the manuscript. Indeed, we were able to produce recombinant VP3 from the two sources: Baculovirus and Escherichia coli. Initially, we opted for the baculovirus system, based on evidence from previous studies showing that it was suitable for ectopic expression of VP3. Subsequently, we successfully produced VP3 using Escherichia coli. On the other side, the fusion proteins His-2xFYVE and GST-2xFYVE were only produced in the prokaryotic system, also following previous reported evidence. We confirmed that VP3, produced in either system, exhibited similar behavior in our co-flotation and bio-layer interferometry (BLI) assays. However, the results of co-flotation and BLI assays shown in Figs. 1 and 4 were performed using the His-VP3 FL, His-VP3 FL R<sub>200</sub>D and His-VP3 FL DCt fusion proteins produced from the corresponding baculoviruses. We have clarified this in the revised version of our manuscript. Please, see lines 430-432.

      Additionally, we have made clear that the His-VP3 FL protein purification yielded four distinct bands, and we confirmed their VP3 identity through mass spectrometry in the revised version of the manuscript. Please, see lines 123-124.

      Finally, we replaced membranes for Figs. 4A and 1G (left panel) with those with more similar band intensities. Please, see the new version of Figures 1 and 4.

      (2) Possible "other" effects of the R<sub>200</sub>D mutation on the VP3 protein. The authors performed mutagenesis to identify which residues within patch 2 on VP3 are important for association with PI3P. They found that a VP3 mutant with an engineered R<sub>200</sub>D change (i) did not associate with PI3P membranes in co-floatation assays, and (ii) did not co-localize with EE markers in transfected cells. Moreover, this mutation resulted in the loss of IBDV viability in reverse genetics studies. The authors interpret these results to indicate that this residue is important for "mediating VP3-PI3P interaction" (line 211) and that this interaction is essential for viral replication. However, it seems possible that this mutation abrogated other aspects of VP3 function (e.g., dimerization or other protein/RNA interactions) aside from or in addition to PI3P binding. Such possibilities are not mentioned by the authors.

      The arginine amino acid at position 200 of VP3 is not located in any of the protein regions associated with its other known functions: VP3 has a dimerization domain located in the second helical domain, where different amino acids across the three helices form a total of 81 interprotomeric close contacts; however, R<sub>200</sub> is not involved in these contacts (Structure. 2008 Jan;16(1):29-37, doi:10.1016/j.str.2007.10.023); VP3 has an oligomerization domain mapped within the 42 C-terminal residues of the polypeptide, i.e., the segment of the protein composed by the residues at positions 216-257 (J Virol. 2003 Jun;77(11):6438–6449, doi: 10.1128/jvi.77.11.6438-6449.2003); VP3’s ability to bind RNA is facilitated by a region of positively-charged amino acids, identified as P1, which includes K<sub>99</sub>, R<sub>102</sub>, K<sub>105</sub>, and K<sub>106</sub> (PLoS One. 2012;7(9):e45957, doi: 10.1371/journal.pone.0045957). Furthermore, our findings indicate that the R<sub>200</sub>D mutant retains a folding pattern similar to the wild-type protein, as shown in Figure 4B. All these lead us to conclude that the loss of replication capacity of R<sub>200</sub>D viruses results from impaired, or even loss of, VP3-PI3P interaction.

      We agree with the reviewer that this is an important point and have accordingly addressed it in the Discussion section of the revised manuscript. Please, see lines 333-346.

      (3) Interpretations from computational simulations. The authors performed computational simulations on the VP3 structure to infer how the protein might interact with membranes. Such computational approaches are powerful hypothesis-generating tools. However, additional biochemical evidence beyond what is presented would be required to support the authors' claims that they "unveiled a two-stage modular mechanism" for VP3-PI3P interactions (see lines 55-59). Moreover, given the biochemical data presented for R<sub>200</sub>D VP3, it was surprising that the authors did not perform computational simulations on this mutant. The inclusion of such an experiment would help tie together the in vitro and in silico data and strengthen the manuscript.

      We acknowledge that the wording used in the previous version of the manuscript may have overstated the "unveiling" of the two-stage binding mechanism of VP3. Our intention was to propose a potential mechanism, that is consistent both with the biophysical experiments and the molecular simulations. In the revised version of the manuscript, we have tempered these claims and framed them more appropriately.

      Regarding the simulations for the R<sub>200</sub>D VP3 mutant, these simulations were indeed performed and included in the original manuscript as part of Figure S14 in the Supplementary Information. However, we realize that this was not sufficiently emphasized in the main text, an oversight on our part. We have now revised the manuscript to highlight these results more clearly.

      Additionally, to further strengthen the connection between experimental and simulation trends, we have now included a new figure in the Supplementary Information (Figure S15). This figure depicts the binding energy of VP3 ΔNt and two of its mutants, VP3 ΔNt R<sub>200</sub>D and VP3 ΔNt P2 Mut, as a function of salt concentration. The results show that as the number of positively charged residues in VP3 is systematically reduced, the binding of the protein to the membrane becomes weaker. The effect is more pronounced at lower salt concentrations, which highlights the weight of electrostatic forces on the adsorption of VP3 on negatively charged membranes. Please, see Supplementary Information (Figure S15).

      Reviewer #3 (Public Review):

      Summary:

      Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2.

      This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

      Strengths:

      Detailed characterization of the interaction between VP3 and PIP3 identified R<sub>200</sub>D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

      Weaknesses:

      The work does not directly show that the identified R<sub>200</sub> residues are directly involved in VP3-early endosome recruitment during infection. The majority of work is done with transfected VP3 protein (or in vitro) and not in virus-infected cells. Additional controls such as the use of PIP3 antagonizing drugs in infected cells together with a colocalization study of VP3 with early endosomes would strengthen the study.

      In addition, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow ruling out any unspecific binding that might not be detected on WB.

      The authors also do not propose how their findings could be translated into drug development that could be applied to protect poultry during an outbreak. The title of the manuscript is broad and would improve with rewording so that it captures what the authors achieved.

      In previous works from our group, we demonstrated the crucial role of the VP3 P2 region in targeting the early endosomal membranes and for viral replication, including the use of PI3K inhibitors to deplete PI3P, showing that both the control RFP-2xFYVE and VP3 lost their ability to associate with the early endosomal membranes and reduces the production of an infective viral progeny (J Virol. 2018 May 14;92(11):e01964-17, doi: 10.1128/jvi.01964-17; J Virol. 2021 Feb 24;95(6):e02313-20, doi: 10.1128/jvi.02313-20). In the present work, to further characterize the role of R<sub>200</sub> in binding to early endosomes and for viral replication, we show that: i) the transfected VP3 R<sub>200</sub>D protein loses the ability to bind to early endosomes in immunofluorescence assays (Figure 2E and Figure 3); ii) the recombinant His-VP3 FL R<sub>200</sub>D protein loses the ability to bind to liposomes PI3P(+) in co-flotation assays (Figure 4A); and, iii) the mutant virus R<sub>200</sub>D loses replication capacity (Figure 4C).

      Regarding the cryo-electron microscopy observation, we verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

      We have replaced the title of the manuscript by a more specific one. Thus, our current is " On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

      Regarding the question of how our findings could be translated into drug development, indeed, VP3-PI3P binding constitutes a good potential target for drugs that counteract infectious bursal disease. However, we did not mention this idea in the manuscript, first because it is somewhat speculative and second because infected farms do not implement any specific treatment. The control is based on vaccination.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Critical issues to address:

      (1) The citations in the important paragraph on lines 101-5 are not identifiable. These references are described as showing that VP3 is associated with EEs via P2 and PI3P, which is basically what this paper also shows. The significant advance here is unclear.

      We apologize for this mistake. These citations are identifiable in the revised version of the manuscript (lines 100-105). As mentioned before, in this manuscript we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3 P2—the R<sub>200</sub>—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

      (2) Even if all the claims were to be clearly supported through major revamping, authors should make the significance of knowing that this protein binds to early endosomes through PI3P more clear?

      Thank you for the recommendation, which aligns with a similar suggestion from Reviewer #2. In response, we have revised the significance paragraph to emphasize the mechanistic aspects of our findings. Please refer to lines 62–67 in the revised manuscript.

      (3) Flotation assay shows binding, but this is not quantitative. An estimate of a Kd would be useful. BLI experiments suggest that half of the binding disappears at 0.5 mM, implying a very low binding affinity.

      We agree with the reviewer that our biophysical and molecular simulation results suggest a specific but weak interaction of VP3 with PI3P bearing membranes. Indeed, our previous version of the manuscript already contained a paragraph in this regard. Please, see lines 323-332 in the revised version of the manuscript.

      From a biological point of view, a low binding affinity of VP3 for the endosomes may constitute an advantage for the virus, in the sense that its traffic through the endosomes may be short lived during its infectious cycle. Indeed, VP3 has been demonstrated to be a "multifunctional" protein involved in several processes of the viral cycle (detailed in lines 84-90), and in our laboratory we have shown that the Golgi complex and the endoplasmic reticulum are organelles where further viral maturation occurs. Taking all of this into account, a high binding affinity of VP3 for endosomes could result in the protein becoming trapped on the endosomal membrane, potentially hindering the progression of the viral infection within the host cell.

      (4) There are some major internal inconsistencies in the data: Figure 1B quantifies VP3-FL T/B ratio ~4 (which appears inconsistent with the image shown, as the T lanes are much lighter than the B) whereas apparently the same experiment in Figure 1G shows it to be ~0.6. With the error bars shown, these results would appear dramatically different from each other, despite supposedly measuring the same thing. The same issue with the FYVE domain between Figures 1A and 4A.

      We appreciate the reviewer’s comment, as it made us aware of an error in Figure 1B. There, the mean value for the VP3-FL Ts/B ratio is 3.0786 for liposomes PI3P(+) and 0.4553 for liposomes PI3P(-) (Please, see the new bar graph on Figure 1B). This may have occurred because, due to the significance of these experiments, we performed multiple rounds of quantification in search of the most suitable procedure for our observations, leading to a mix-up of data sets. Anyway, it’s possible that these corrected values still seem inconsistent given that T lanes are much lighter than the B for VP3-FL in the image shown. Flotation assays are quite labor-intensive and, at least in our experience, yield fairly variable results in terms of quantification. To illustrate this point, the following image shows the three experiments conducted for Figure 1B, where it is clear that, despite producing visually distinct images, all three yielded the same qualitative observation. For Figure 1B, we chose to present the results from experiment #2. However, all three experiments contributed to a Ts/B ratio of 3.0786 for His-VP3 FL, which may account for the apparent inconsistency when focusing solely on the image in Figure 1B.

      Author response image 1.

      We acknowledge that, at first glance, some inconsistencies may appear in the results, and we have thoroughly discussed the best approach for quantification. However, we believe the observations are robust in terms of reproducibility and reliable, as the VP3-PI3P interaction was consistently validated by comparison with liposomes lacking PI3P, where no binding was observed.

      (5) Comparison of PA (or PI) to PI3P at the same molar concentration is inappropriate because PI3P has at least double charge. The more interesting question about specificity would be whether PI45P2 (or even better PI35P2) binds or not. Without this comparison, no claim to specificity can be made.

      For us, "specificity" refers to the requirement of a phosphoinositide in the endosomal membrane for VP3 binding. Phosphoinositides have a conspicuous distribution among cellular compartments, and knowing that VP3 associates with early endosomes, our specificity assays aimed to demonstrate that PI3P is strictly required for the binding of VP3. To validate this, we used PI (lacking the phosphate group) and PA (lacking the inositol group) despite their similar charges. In spite of the potential chemical interactions between VP3 and various phosphoinositides, our experimental results suggest that the virus specifically targets endosomal membranes by binding to PI3P, a phosphoinositide present only in early endosomes.

      That said, we agree with the reviewer’s point and consider adequate to smooth our specificity claim in the manuscript as follows: “We observed that His-VP3 FL bound to liposomes PI3P(+), but not to liposomes PA or PI, reinforcing the notion that a phosphoinositide is required since neither a single negative charge nor an inositol ring are sufficient to promote VP3 binding to liposomes (SI Appendix, Fig. S2)” (Lines 136-139).

      (6) In the EM images, many of the gold beads are inside the vesicles. How do they cross the membranes?

      They do not cross the membrane. Our EM images are two-dimensional projections, meaning that the gold particles located on top or beneath the plane appear to be inside the liposome.

      (7) Images in Figure 2D are very low quality and do not show the claimed difference between any of the mutants. All red signal looks basically cytosolic in all images. It is not clear what criteria were used for the quantification in Figure 2E. The same issue is in Figure 2E, where no red WT puncta are observable at all. Consistently, there is minimal colocalization in the quantification in Figure S3, which appears to show no significant differences between any of the mutants, in direct contradiction to the claim in the manuscript.

      We apologize for the poor quality of panels in Figures 2D and 2E. Unfortunately, this was due to the PDF conversion of the original files. Please, check the high-quality version of Figure 2. As suggested by reviewers #2 and #3, we have incorporated zoomed panels, which help the reader to better see the differences in distribution.

      As mentioned in the legend to Figure 2, the quantification in Figure 2D was performed by calculating the percentage of cells with punctuated fluorescent red signal (showing VP3 distribution) for each protein. The data were then normalized to the P2 WT protein, which is the VP3 wild type.

      Figure S3 certainly shows a tendency which positively correlates with the results shown in Figure 3, where we used FYVE to detect PI3P on endosomes and observed significantly less co-localization when VP3 bears its P2 region all reversed or lacks the R<sub>200</sub>

      (8) The only significant differences in colocalization are in Figure 3B, whose images look rather dramatically different from the rest of the manuscript, leading to some concern about repeatability. Also, it is unclear how colocalization is quantified, but this number typically cannot be above 1. Finally, it is unclear what is being colocalized here: with three fluorescent components, there are 3 possible binary colocalizations and an additional ternary colocalization.

      We thank the reviewer for pointing out those aspects related to Figure 3. The experiments performed for Figure 3B were conducted by a collaborator abroad handling the purified GST-2xFYVE, which recognizes endogenous PI3P, while the rest of the cell biology experiments were conducted in our laboratory in Argentina. This is why they are aesthetically different. We have made an effort in homogenizing the way they look for the revised version of the manuscript. Please, see the new version of Figure 3.

      For quantification of the co-localization of VP3 and EGFP-2xFYVE (Figure 3A), the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment. The M2 coefficient, which reflects co-localization of signals, is defined as the ratio of the total intensities of magenta image pixels for which the intensity in the blue channel is above zero to the total intensity in the magenta channel. JACoP plugin was utilized to determine M2. For VP3 puncta co-distributing with EEA1 and GST-FYVE (Figure 3B), the number of puncta co-distributing for the three signals was manually determined out of approximately 40 cells per construct and experiment per 200 µm². We understand that Manders or Pearson coefficients, typically ranging between 0 and 1, is the most commonly used method to quantify co-localizing immunofluorescent signals; however, this “manual” method has been used and validated in previous published manuscripts [Figures 3 and 7 from (Morel et al., 2013); Figure 7 in (Khaldoun et al., 2014); and Figure 4 in (Boukhalfa et al., 2021)].

      (9) SegA/B plasmids are not introduced, and it is not clear what these are or how this assay is meant to work. Where are the foci forming units in the images of Figure 4C? How does this inform on replication? Again, this assay is not quantitative, which is essential here: does the R<sub>200</sub> mutant completely kill activity (whatever that is here)? Or reduce it somewhat?

      We apologize for the missing information. Segments A and B are basically the components of the IBDV reverse genetics system. For their construction, we used a modification of the system described by Qi and coworkers (Qi et al., 2007), in which the full length sequences of the IBDV RNA segments A and B, flanked by a hammerhead ribozyme at the 5’-end and the hepatitis delta ribozyme at the 3’-end, were expressed under the control of an RNA polymerase II promoter within the plasmids pCAGEN.Hmz.SegA.Hdz (SegA) and pCAGEN.Hmz.SegB.Hdz (SegB). For this specific experiment we generated a third plasmid, pCAGEN.Hmz.SegA.R<sub>200</sub>D.Hdz (SegA.R<sub>200</sub>D), harboring a mutant version of segment A cDNA containing the R<sub>200</sub>D substitution. Then, QM7 cells were transfected with the plasmids SegA, SegB or Seg.R<sub>200</sub>D alone (as controls) or with a mixture of plasmids SegA+SegB (wild type situation) or SegA.R<sub>200</sub>D+SegB (mutant situation). At 8 h post transfection (p.t.), when the new viruses have been able to assemble starting from the two segments of RNA, the cells were recovered and re-plated onto fresh non-transfected cells for revealing the presence (or not) of infective viruses. At 72 h post-plating, the generation of foci forming units (FFUs) was revealed by Coomassie staining. As expected, single-transfections of SegA, SegB or Seg.R<sub>200</sub>D did not produce FFUs and, as shown in Figure 4C, the transfection of SegA+SegB produced detectable FFUs (the three circles in the upper panel) while no FFUs (the three circles in the lower panel) were detected after the transfection of SegA.R<sub>200</sub>D+SegB (Figure 4C). This system is quantitative, since the FFUs detected 72 h post-plating are quantifiable by simply counting the FFUs. However, since no FFUs were detected after the transfection of SegA.R<sub>200</sub>D+SegB, evidenced by a complete monolayer of cells stained blue, we did not find any sense in quantifying. In turn, this drastic observation indicates that viruses bearing the VP3 R<sub>200</sub>D mutation lose their replication ability (is “dead”), demonstrating its crucial role in the infectious cycle.

      We agree with the reviewer that a better explanation was needed in the manuscript, so we have incorporated a paragraph in the results section of our revised version of the manuscript (lines 209-219).

      (10) Why pH 8 for simulation?

      The Molecular Theory calculations were performed at pH 8 for consistency with the experimental conditions used in our biophysical assays. These biophysical experiments were also performed at pH 8, following the conditions established in the original study where VP3 was first purified for crystallization (DOI: 10.1016/j.str.2007.10.023).

      (11) There is minimal evidence for the sequential binding model described in the abstract. The simulations do not resolve this model, nor is truly specific PI3P binding shown.

      In response to your concerns, we would like to emphasize that our simulations provide robust evidence supporting the two more important aspects of the sequential binding model: 1) Membrane Approach: In all simulations, VP3 consistently approaches the membrane via its positively charged C-terminal (Ct) region. 2) PI3P Recruitment: Once the protein is positioned flat on the membrane surface, PI3P is unequivocally recruited to the positively charged P2 region. The enrichment of PI3P in the proximity to the protein is clearly observed and has been quantified via radial distribution functions, as detailed in the manuscript and supplementary material.

      While we understand that opinions may vary on the sufficiency of the data to fully validate the model, we believe the results offer meaningful insights into the proposed binding mechanism. That said, we acknowledge that the specificity of VP3 binding may not be restricted solely to PI3P but could extend to phosphoinositides in general. To address this, we performed the new set of co-flotation experiments which are discussed in detail in our response to point 5.

      Reviewer #2 (Recommendations For The Authors):

      (1) Line 1: Consider changing the title to better reflect the mostly biochemical and computational data presented in the paper: "Mechanism of Birnavirus VP3 Interactions with PI3P-Containing Membranes". There are no data to show hijacking by a virus presented.

      We appreciate this recommendation, which was also expressed by reviewer #3. Additionally, we thank for the suggested title. We have replaced the title of the manuscript by a more specific one. Thus, our current is

      "On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

      (2) Lines 53-54 and throughout: Consider rephrasing "demonstrate" to "validate" to give credit to Gimenez et al., 2018, 2022 for discovery.

      Thanks for the suggestion. We have followed it accordingly. Please see line 52 from our revised version of the manuscript.

      (3) Line 56-59 and throughout: Consider tempering and rephrasing these conclusions that are based mostly on computational data. For example, change "unveil" to "suggest" or another term.

      We have now modified the wording throughout the manuscript.

      (4) The abstract could also emphasize that this study sought to map the resides within VP3 that are important for P13P interaction.

      Thanks for the suggestion. We have followed it accordingly. Please, see lines 53-55 from our revised version of the manuscript.

      (5) Lines 63-69: This Significance paragraph seems tangential. The findings in this paper aren't at all related to the evolutionary link between birnaviruses and positive-strand RNA viruses. The significance of the work for me lies in the deep biochemical/biophysical insights into how a viral protein interacts with membranes to nucleate its replication factory.

      We have re-written the significance paragraph highlighting the mechanistic aspect of our findings. Please, see lines 62-67 in our revised version of the manuscript.

      (6) Line 74: Please define "IDBV" abbreviation.

      We apologize for the missing information. We have defined the IBDV abbreviation in our revised version of the manuscript (please, see line 73).

      (7) Line 88: Please define "pVP2" abbreviation.

      We apologize for the missing information. We have defined the pVP2 abbreviation in our revised version of the manuscript (please, see line 87).

      (8) Lines 101-105: Please change references (8, 9, 10) to be consistent with the rest of the manuscript (names, year).

      We apologize for this mistake. These citations are identifiable and consistent in the revised version of the manuscript (lines 100-105).

      (9) Line 125: For a broad audience, consider explaining that recombinant His-2xFYVE domain is known to exhibit PI3P-binding specificity and was used as a positive control.

      Thanks for the recommendation. We have incorporated a brief explanation supporting the use of His-2xFYVE as a positive control in our revised version of the manuscript. Please, see lines 127-129.

      (10) Lines 167-171: The quantitative data in Figure S3 shows that there was a non-significant co-localization coefficient of the R<sub>200</sub>D mutant. For transparency, this should be stated in the Results section when referenced.

      We agree with this recommendation. We have clearly mentioned it in the revised version of the manuscript. Please, see lines 177-179. Also, we have referred this fact when introducing the assays performed using the purified GST-2xFYVE, shown in Figure 3. Please, see lines 182-184.

      (11) Lines 156 and 173: These Results section titles have nearly identical wording. Consider rephrasing to make it distinct.

      We agree with the reviewer’s observation. In fact, we sought to do it on purpose as for them to be a “wordplay”, but we understand that could result in a awkwarded redundancy. So, in the revised version of the manuscript, both titles are:

      Role of VP3 P2 in the association of VP3 with the EE membrane (line 163).

      VP3 P2 mediates VP3-PI3P association to EE membranes (line 182).

      (12) Line 194: Is it alternatively possible that the R<sub>200</sub>D mutant lost its capacity to dimerize, and that in turn impacted PI3P interaction?

      Thanks for the relevant question. VP3 was crystallized and its structure reported in (Casañas et al., 2008) (DOI: 10.1016/j.str.2007.10.023). In that report, the authors showed that the two VP3 subunits associate in a symmetrical manner by using the crystallographic two-fold axes. Each subunit contributes with its 30% of the total surface to form the dimer, with 81 interprotomeric close contacts, including polar bonds and van der Waals contacts. The authors identified the group of residues involved in these interactions, among which the R<sub>200</sub> is not included. Addittionally, the authors determined that the interface of the VP3 dimer in crystals is biologically meaningful (not due to the crystal packing).

      To confirm that the lack of binding was not due to misfolding of the mutant, we compared the circular dichroism spectra of mutant and wild type proteins, without detecting significant differences (shown in Figure 4B). These observations do not exclude the possibility mentioned by the reviewer, but constitute solid evidences, we believe, to validate our observations.

      (13) Lines 231-243: Consider changing verbs to past tense (i.e., change "is" to "was") for the purposes of consistency and tempering.

      Thanks for the recommendation, we have proceeded as suggested. Please, see lines 249-262 in our revised version of the manuscript.

      (14) Lines 306-308: Is there any information about whether it is free VP3 (v. VP3 complexed in RNP) that binds to membrane? I am just trying to wrap my head around how these factories form during infection.

      Thanks for pointing this out. We first observed that in infected cell, all the components of the RNPs [VP3, VP1 (the viral polymerase) and the dsRNA] were associated to the endosomes. Since by this moment it had been already elucidated that VP3 "wrapped" de dsRNA within the RNPs (Luque et al., 2009) (DOI: 10.1016/j.jmb.2008.11.029), we sought that VP3 was most probably leading this association. We answered yes after studying its distribution, also endosome-associated, when ectopically expressed. These results were published in (Delgui et al., 2013) (DOI: 10.1128/jvi.03152-12).

      Thus, in our subsequent studies, we have worked with both, the infection-derived or the ectopically expressed VP3, to advance in elucidating the mechanism by which VP3 hijacks the endosomal membranes and its relevancy for viral replication, reported in this current manuscript.

      (15) Lines 320-334: This last paragraph discussing evolutionary links between birnaviruses and positive-strand RNA viruses seems tangential and distracting. Consider reducing or removing.

      Thanks for highlighting this aspect of our work. Maybe difficult to follow, but in the context of other evidences reported for the Birnaviridae family of viruses, we strongly believe that there is an evolutionary aspect in having observed that these dsRNA viruses replicate associated to membranous organelles, a hallmark of +RNA viruses. However, we agree with the reviewer that this might not be the main point of our manuscript, so we reduced this paragraph accordingly. Please, see lines 358-367 in our revised version of the manuscript.

      (16) Lines 322-324: Change "RdRd" to "RdRp" if keeping paragraph.

      Thanks. We have corrected this mistake in lines 360 and 361.

      (17) Figures 1A, 1B, and throughout: Again, please check and explain protein sizes and amounts. This would improve the clarity of the manuscript.

      All our flotation assays were performed using 1 mM concentration of purified protein in a final volume of 100 mL (mentioned in M&M section). The complete fusion protein His-2xFYVE (shown in Figs. 1A and 4A left panel) is 954 base pairs-long and contains 317 residues (~35 kDa). The complete fusion protein His-VP3 FL (shown in Figs. 1B and 1G left panel) is 861 base pairs-long and contains 286 residues (~32 kDa). The complete fusion protein His-VP3 DCt (shown in Fig. 1G, right panel) is 753 bp-long and contains 250 residues (~28 kDa). The complete fusion protein His-VP3 FL R<sub>200</sub>D (shown in Fig. 4A right panel) is 861 bp-long and contains 286 residues (~32 kDa). This latter information was incorporated in our revised version of the manuscript. Please, see lines 381-382, 396-397 and 399-400 from the M&M section, and lines in the corresponding figure legends.

      (18) Figures 1B and 1G show different results for PI3P(+) membranes. I see protein associated with the top fraction in 1B, but I don't see any such result in 1G.

      As already mentioned, liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the conditions are homogeneous, it’s not surprising to observe differences in the protein level between each one. Indeed, the revised version of the manuscript include a membrane for Figure 1G, were His-VP3 FL associated with the top fraction is more clear. Please, see the new version of Figure 1G.

      (19) Figure 1C: Please include cryo-EM images of the liposome PI3P(-) variables to assess the visual differences of the liposomal membranes under these conditions.

      Thanks for the recommendation. it has been verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

      (20) Figures 2D, 2E, and 3A: The puncta are not obvious in these images. Consider adding Zoomed panels.

      We apologize for this aspect of Figures 2 and 3, also highlighted by reviewer #1. We believe that this was due to the low quality resulting from the PDF conversion of the original files. For Figure 3A, we have homogenized its aspect with those from 3B. Regarding Figure 2, we have incorporated zoomed panels, as suggested. Please, see the revised versions of both Figures.

      (21) Figure 4A: There is almost no protein in the control PI3P(+) blot. Why? Also, the quantification shows no significant membrane association for this control. This result is different from Figure 1A and very confusing (and concerning).

      We apologize for the confusion. We replaced membranes for Figure 4A (left panel) with more similar band intensities to that shown in Figure 1A. Please, visit our new version of Figure 4. The quantification shows no significant difference in the association to liposomes PI3P(+) compared to liposomes PI3P(+); it’s true and this is due to, once more, the intrinsically lack of homogeneity of co-flotation assays. However, this one shown in Figure 4A is a redundant control (has been shown in Figure 1A) and we believe that the new membrane is qualitative eloquent.

      Reviewer #3 (Recommendations For The Authors):

      (1) Overall, the title is general and does not summarize the study. I recommend making the title more specific. The current title is better suited for a review as opposed to a research article. This study provides further biophysical details on the interaction. This should be reflected in the title.

      We appreciate this recommendation, which was also expressed by reviewer #2. We have chosen a new title for the manuscript: “On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting”.

      (2) References 8,9,10 are important but they were not correctly cited in the work, this should be corrected.

      We apologize for this mistake. These citations are identifiable in our revised version of the manuscript. See lines 100-105.

      (3) Flotation experiments and cryo-EM convincingly show that VP3 binds to membranes in a PIP3-dependent manner. However, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow us to rule out any unspecific binding that might not be detected on WB.

      Thanks for the advice, also given by reviewer #2. We confirmed that no gold particles were bound on liposomes PI3P(-) even when incubated with the Ni-NTA reagent alone or pre-incubated with His-2xFYVE of His-VP3 FL. We have incorporated a new panel to Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of the manuscript and see the revised version of Figure 1C.

      (4) It is not clear what is the difference between WB in B and WB in G. Figure 1G seems to show the same experiment as shown in B, is this a repetition? In both cases, plots next to WBs show quantification with bars, do they represent STD or SEM? Legend A mentions significance p>0.01 (**) but the plot shows ***. This should be corrected.

      The Western blot membrane in Figure 1B shows the result of co-flotation assay using His-VP3 FL protein, while the Western blot membrane in Figure 1G (left panel) shows a co-flotation assay using His-VP3 FL protein as a positive control. In another words, in 1B the His-VP3 FL protein is the question while in 1G (left panel) it’s the co-flotation positive control for His-VP3 DCt. The bar plots next to Western blots show quantification, the mean and the STD. Thanks for highlighting this inconsistency. We have now corrected it on the revised version of the manuscript.

      (5) It would be useful to indicate positively charged residues and P2 on the AF2 predicted structure in Fig 1.

      These are indicated in panels A and B of Figure 2.

      (6) Figure 1 legend: Change cryo-fixated liposomes to cryo-fixation or better to "liposomes were vitrified". There is a missing "o" in the cry-fixation in the methods section.

      Thanks for the recommendation. We have modified Figure 1. legend to "liposomes were vitrified" (line 758), and fixed the word cryo-fixation in the methods section (line 512).

      (7) Figure 2B. It is not clear how the punctated phenotype was unbiasedly characterized (Figure 2D). I see no difference in the representative images. Magnified images should be shown. This should be measured as colocalization (Pearson's and Mander's coefficient) with an early endosomal marker Rab5. Perhaps this figure could be consolidated with Figure 3.

      Unfortunately, the lack of clarity in Figure 2D was due to the PDF conversion of the original files. Please, observe the high-quality original image above in response to reviewer #1, where we have additionally included zoomed panels, as also suggested by the other reviewers. For quantification of the co-localization of VP3 and either EGFP-Rab5 orEGFP-2xFYVE, the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment and were shown in Figure S3 and Figure 3A, respectively, in our previous version of the manuscript.

      (8) PIP3 antagonist drugs should be used to further substantiate the results. If PIP3 specifically recruits VP3, this interaction should be abolished in the presence of PIP3 drug and VP3 should show a diffused signal.

      We certainly agree with this point. These experiments were performed and the results were reported in (Gimenez et al., 2020). Briefly, in that work, we blocked the synthesis of PI3P in QM7 cells in a stable cell line overexpressing VP3, QM7-VP3, with either the pan-PI3Kinase (PI3K) inhibitor LY294002, or the specific class III PI3K Vps34 inhibitor Vps34-IN1. In Figure 4, we showed that 98% of the cells treated with these inhibitors had the biosensor GFP-2FYVE dissociated from EEs, evidencing the depletion of PI3P in EEs (Figure 4A). In QM7-VP3 cells, we showed that the depletion of PI3P by either inhibitor caused the dissociation of VP3 from EEs and the disaggregation of VP3 puncta toward a cytosolic distribution (Figure 4B). Moreover, since this observation was crucial for our hipothesis, these results were further confirmed with an alternative strategy to deplete PI3P in EEs. We employed a system to inducibly hydrolyze endosomal PI3P through rapamycin-induced recruitment of the PI3P-myotubularin 1 (MTM1) to endosomes in cells expressing MTM1 fused to the FK506 binding protein (FKBP) and the rapamycin-binding domain fused to Rab5, using the fluorescent proteins mCherry-FKBP-MTM1 and iRFP-FRB-Rab5, as described in (Hammond et al., 2014). These results, shown in Figures 5, 6 and 7 in the same manuscript, further reinforced the notion that PI3P mediates and is necessary for the association of VP3 protein with EEs.

      (9) The authors should show the localization of VP3 in IBDV-infected cells and treat cells with PI3P antagonists. The fact that R<sub>200</sub> is not rescued does not necessarily mean that this is because of the failed interaction with PI3P. As the authors wrote in the discussion: VP3 bears multiple essential roles during the viral life cycle (line 305).

      Indeed, after having confirmed that the VP3 lost its localization associated to the endosomes after the treatment of the cells with PI3P antagonists, we demonstrated that depletion of PI3P significantly reduced the production of IBDV progeny. For this aim, we used two approaches, the inhibitor Vps34-IN1 and an siRNA against VPs34. In both cases, we observed a significantly reduced production of IBDV progeny (Figures 9 and 10). Specifically related to the reviewer’s question, the localization of VP3 in IBDV-infected cells and treated with PI3P antagonists was shown and quantified in Figure 9a.

      (10) Could you provide adsorption-free energy profiles and MD simulations also for the R<sub>200</sub> mutant?

      Following the reviewer’s suggestion, we have added a new figure to the supplementary information (Figure S15). Instead of presenting a full free-energy profile for each protein, we focused on the adsorption free energy (i.e., the minimum of the adsorption free-energy profile) for VP3 ΔNt and its mutants, VP3 ΔNt R<sub>200</sub>D and VP3 ΔNt P2 Mut, as a function of salt concentration. The aim was to compare the adsorption free energy of the three proteins and evaluate the effect of electrostatic forces on it, which become increasingly screened at higher salt concentrations. As shown in the referenced figure, reducing the number of positively charged residues from VP3 ΔNt to VP3 ΔNt P2 Mut systematically weakens the protein’s binding to the membrane. This effect is particularly pronounced at lower salt concentrations, underscoring the importance of electrostatic interactions in the adsorption of the negatively charged VP3 onto the anionic membrane.

      (11) Liposome deformations in the presence of VP3 are interesting (Figure 6G), were these also observed in Figure 1C?

      Good question. The liposome deformations in the presence of VP3 shown in Figure 6G were a robust observation since, as mentioned, it was detectable in 36% of the liposomes PI3P(+), while they were completely absent in PI3P(-) liposomes. However, and unfortunately, the same deformations were not detectable in experiments performed using gold particles shown in Figure 1C. In this regard, we think that it might be possible that the procedure of gold particles incubation itself, or even the presence of the gold particles in the images, would somehow “mask” the deformations effect.

      Bibliography

      Boukhalfa A, Roccio F, Dupont N, Codogno P, Morel E. 2021. The autophagy protein ATG16L1 cooperates with IFT20 and INPP5E to regulate the turnover of phosphoinositides at the primary cilium. Cell Rep 35:109045. doi:10.1016/j.celrep.2021.109045

      Casañas A, Navarro A, Ferrer-Orta C, González D, Rodríguez JF, Verdaguer N. 2008. Structural Insights into the Multifunctional Protein VP3 of Birnaviruses. Structure 16:29–37. doi:10.1016/j.str.2007.10.023

      Delgui LR, Rodriguez JF, Colombo MI. 2013. The Endosomal Pathway and the Golgi Complex Are Involved in the Infectious Bursal Disease Virus Life Cycle. J Virol 87:8993–9007. doi:10.1128/JVI.03152-12

      Gimenez MC, Issa M, Sheth J, Colombo MI, Terebiznik MR, Delgui LR. 2020. Phosphatidylinositol 3-Phosphate Mediates the Establishment of Infectious Bursal Disease Virus Replication Complexes in Association with Early Endosomes. J Virol 95:e02313-20. doi:10.1128/jvi.02313-20

      Hammond GRV, Machner MP, Balla T. 2014. A novel probe for phosphatidylinositol 4-phosphate reveals multiple pools beyond the Golgi. J Cell Biol 205:113–126. doi:10.1083/jcb.201312072

      Khaldoun SA, Emond-Boisjoly MA, Chateau D, Carrière V, Lacasa M, Rousset M, Demignot S, Morel E. 2014. Autophagosomes contribute to intracellular lipid distribution in enterocytes. Mol Biol Cell 25:118. doi:10.1091/mbc.E13-06-0324

      Luque D, Saugar I, Rejas MT, Carrascosa JL, Rodríguez JF, Castón JR. 2009. Infectious Bursal Disease Virus: Ribonucleoprotein Complexes of a Double-Stranded RNA Virus. J Mol Biol 386:891–901. doi:10.1016/j.jmb.2008.11.029

      Morel E, Chamoun Z, Lasiecka ZM, Chan RB, Williamson RL, Vetanovetz C, Dall’Armi C, Simoes S, Point Du Jour KS, McCabe BD, Small SA, Di Paolo G. 2013. Phosphatidylinositol-3-phosphate regulates sorting and processing of amyloid precursor protein through the endosomal system. Nature Communications 2013 4:1 4:1–13. doi:10.1038/ncomms3250

      Qi X, Gao Y, Gao H, Deng X, Bu Z, Wang Xiaoyan, Fu C, Wang Xiaomei. 2007. An improved method for infectious bursal disease virus rescue using RNA polymerase II system. J Virol Methods 142:81–88. doi:10.1016/j.jviromet.2007.01.021

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      General Statements<br /> The reviewer comments helped us improve the paper by including new computations, figures, and analyses related to vasopressin, drug dosages, and treatment cessation. We have also removed confusing terminology from the text. We believe that the paper is now more comprehensive, clear, and rigorous.

      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The authors address the question of lowering long-term elevated cortisol levels by affecting the parameters in a previously published mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis. The parameters are related to various pathways. The elevation in cortisol levels is related to diseases e.g. mood disorders and Cushing's syndrome.<br /> The authors conducted a systematic in silico analysis of various points of intervention in the HPA axis. They found that only two interventions targeting corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, they identify potential drug targets, including CRH-neutralizing antibodies and CRH synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress.<br /> The method used is in silico investigations of the mathematical model.<br /> The draft is well written with a single typo in line 270. I have no further comments!

      Response: The typo is fixed.

      Reviewer #1 (Significance):

      In silico predictions without direct use of data is a weakness but the conducted analysis is convincing. An improved understanding of why some drugs work and others do not is important and is postulated to agree with clinical evidence.

      Response: We thank the reviewer for this endorsement.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary<br /> The authors utilise a mathematical model of the hypothalamic-pituitary-adrenal axis to address the utility of interventions altering its various outputs (CRH, ACTH and cortisol) to ameliorate axis disruption in response to chronic stress. They show that a lowering of circulating CRH by either blocking its synthesis or increasing its clearance is effective at returning the HPA axis to basal activity at all levels. In contrast, interventions altering ACTH or cortisol production, their circulating levels or actions are ineffective in the model. This is consistent with data on the long-term efficacy of drugs reducing excess corticosteroids in patients and animal models. The use of mathematical models to describe complex interactions in endocrine systems is a valuable advance in our understanding of potential mechanisms and therapies and this is an excellent example.

      Response: We thank the reviewer for this endorsement.

      Major comments<br /> 1. The model of the HPA axis that the authors have described previously is a little simplistic when considering the known physiology. Specifically, this model ignores the contribution of vasopressin to the axis, which has been described as being the primary hypothalamic factor driving HPA axis activity in chronic stress (see doi.org/10.1016/S0079-6123(08)00403-2). Including this may be beyond the scope of the current model, however it should be considered and at least commented on. It is notable that the model fits the clinical and animal model data, which may suggest that the contribution of vasopressin in the long term may be overestimated, possibly as a result of differential effects of the two hypothalamic factors, with CRH driving ACTH release and POMC gene expression, whilst vasopressin only increases ACTH release without augmenting POMC expression. This is worthy of discussion.

      Response: We thank the reviewer for this comment which helped us discuss vasopressin. We agree that adding it as a variable in the model is beyond the scope of the current study. We describe its effects in the introduction and discussion sections. Interestingly, when one considers the best characterized effect of vasopressin, namely enhancing CRH-dependent ACTH release, one can use this model to investigate the effects of inhibiting vasopressin. We predict that vasopressin inhibition is unlikely to be an effective strategy for lowering long-term cortisol and alleviating stress-related mental disorders, as evidenced by the failure of clinical trials.

      In the introduction we add:<br /> 1. “CRH stimulates the secretion of adrenocorticotropic hormone (ACTH) by corticotroph cells in the anterior pituitary, an effect enhanced by vasopressin (Aguilera et al, 2008; Antoni, 2017).” (lines 35-37)<br /> 2. Clinical trials for two vasopressin 1b receptor antagonist candidates, SSR149415 and TS-121, in the table of HPA-related clinical trials (Table 1)

      In the discussion we add (lines 398-409): ”One important factor not explicitly considered in the model is the contribution of vasopressin to the axis. Vasopressin potentiates the CRH-dependent release of ACTH from pituitary corticotrophs by acting on the V1b receptor (V1bR) (Aguilera et al, 2008; Antoni, 2017). Including this hormone explicitly is beyond the current scope. However, a simple analysis indicates that the effect of elevated vasopressin can be modeled by increasing the ACTH secretion parameter b2. This suggests that vasopressin V1b receptor antagonists should have effects similar to inhibitors of ACTH production. As such, vasopressin receptor antagonists should be compensated by the HPA axis without long-term effects on cortisol. Accordingly, V1bR antagonists did not show statistically significant efficacy in clinical trials for major depressive disorder and generalized anxiety disorder (Griebel et al, 2012; Chaki, 2021; Kamiya et al, 2020). However, vasopressin may have additional relevant effects on the HPA axis and the central nervous system which warrant a more detailed modeling analysis.”

      1. The model that this study relies on is dependent on slow changes in the various levels of the endocrine axis and the authors have focused on alterations in cell number as the process leading to a prolongation of their dysfunction. For the stress axis, the evidence for changes in corticotroph cell number is weak and the recent paper of Lopez et al (DOI: 10.1126/sciadv.abe44) suggests that chronic stress, at least over a period of 3 weeks does not lead to an alteration in the number of corticotrophs, despite cell population changes in the adrenal gland. There are other processes which could lead to prolonged alteration of corticotroph output and it would be better to focus (as the authors have in places) on functional mass, rather than cell number which may suggest it is not the trophic effect of CRH that is important for increased functional mass.

      Response: We thank the reviewer for this. We now refer only to functional mass changes. We corrected all places in which hyperplasia of corticotrophs is mentioned. We also state in lines 125-126 that the model is agnostic as to whether growth in functional mass is due to hyperplasia or hypertrophy.<br /> We also added a citation for Lopez et al. 2021 (line 86) to support the growth of cortisol-secreting cells in the zona fasciculata of the adrenal gland under stress conditions.

      1. The parameters in the model for interventions are described as simply being less than or greater than one- to what extent are the effects of these interventions dependent on their specific value? For example, presumably if the I1 value is close to zero, then the CRH-synthesis inhibitor would be ineffective. Likewise, if it were close to 1 then there would be negligible release of CRH in response to stress, and the preservation of a response to acute stress would be lost. Can the authors show the range of values for I1, C1 and A1 where the interventions are effective at normalising HPA axis function whilst (for I1 and A1) still preserving the acute stress response?

      Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):

      CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).

      At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.

      We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).

      Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”

      In the supplemental information:

      Cortisol dose response to HPA-targeting drugs

      Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.

      Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”

      1. In the models that the authors describe with CRH interventions, what is the impact of stopping the intervention on axis output in the short and long-term? Presumably ceasing the use of CRH antagonists would lead to much more severe axis dysregulation than CRH neutralising antibodies or CRH synthesis inhibitors.

      Response: We have now added new analysis on drug cessation (new figure 4E-F, Figure S4). After a 50 day treatment, sudden cessation caused a rapid return to hypercortisolemia:<br /> We added in lines 277-278: “We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F).”

      Reviewer #2 (Significance):

      Whilst the study builds on the use of a previously described mathematical model, its utility in identifying potential targets for therapy within the important area of chronic stress makes it an important example of the value of the modelling approach to decisions on appropriate targets for therapy. The model does not include important known factors which have been described as being important in the HPA axis response to chronic stress and would be considerably improved if these could be incorporated.<br /> The study builds on conceptual insights into the role a delayed or slow functional mass change might play in dysregulation of endocrine axes and this could be applied to other physiological systems and will be of interest to modellers and physiologists alike. The authors are leaders in this field and there are few other modellers considering systems level interactions over this timescale.

      Response: We thank the reviewer for this endorsement.

      As a pituitary physiologist, my review has focused on the interactions between the various players in HPA axis function, I do not have the expertise to comment on mathematical modelling aspects.

      Reviewer #3 (Evidence, reproducibility and clarity):

      This extremely interesting paper asks why various attempts to treat depression and bipolar disorder with glucocorticoid antagonists or cortisol synthesis inhibitors have failed. The starting point for their analysis is a simple computational model that, importantly, includes the facts that CRH stimulates not only ACTH release but also corticotroph growth and ACTH stimulates not only cortisol production but also the growth of cells in the adrenal cortex. They call this the "gland mass model". According to the model, if the hypothalamus receives a continuous stress input, all of the HPA hormones will be elevated-CRH transiently and the others in a sustained fashion. Adding a sufficient dose of a CRH inhibitor (decreasing the rate constant b1 in the model) or a CRH antibody (increasing the rate constant a1) normalizes the hormone levels, whereas blocking cortisol function or production does not. This is demonstrated by numerical simulations and backed up by deriving analytical expressions for the hormone concentrations at steady state. The paper provides a plausible explanation for why past therapeutic efforts have failed and points to a couple of approaches that might succeed. These conclusions are hypotheses-they haven't been tested experimentally and we really don't know how accurately the system is described by this nice, simple model-but they are really intriguing hypotheses that could lead to therapeutic breakthroughs. I strongly recommend publication.

      Response: We thank the reviewer for this endorsement.

      My only criticisms are minor:

      1. The authors should specify what exact change in the model's parameters they are making to implement their therapeutic interventions. E.g. in Fig 1B top left and 2A, what is the change in the value of b1 that corresponds to the addition of a CRH-synthesis inhibitor? (I'd guess it's being dropped to zero, but if this is stated, I missed it)

      Response: We thank the reviewer for that comment which helped us to clarify what is the required parameter change to normalize cortisol. We have now added in lines 173-175: “According to equation (1), as a general guideline, treating cortisol levels that are x-fold higher than baseline requires a drug dose that alters the relevant parameter (e.g., CRH production or removal rate) by a similar x-fold.”

      1. I think it would also be useful to show a dose-response relationship for the various interventions.

      Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):

      CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).

      At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.

      We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).

      Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”

      In the supplemental information:

      Cortisol dose response to HPA-targeting drugs

      Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.

      Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”

      *Referees cross-commenting*

      It looks like we are all enthusiastic about this work.

      Response: Thank you.

      Reviewer #3 (Significance):

      Strengths: It's a beautiful new insight on a really important topic, extracted from a simple, understandable mathematical model of the HPA axis.

      Weaknesses: It is based on a model and the model could be wrong. This does not however diminish my enthusiasm for this provocative work.

      Advance: It is highly original.

      Audience: I hope attracts a wide audience--modelers, endocrinologists, psychiatrists, drug developers.

      My expertise: I am a systems biologist, have taught psychopharmacology to medical students, and have an interest in endocrine signaling.

    1. Those with disabilities often find ways to cope with their disability, that is, find ways to work around difficulties they encounter and seek out places and strategies that work for them (whether realizing they have a disability or not). Additionally, people with disabilities might change their behavior (whether intentionally or not) to hide the fact that they have a disability, which is called masking and may take a mental or physical toll on the person masking, which others around them won’t realiz

      People with disabilities often feel difficult due to various challenges caused by their conditions. Therefore we should do as much as possible to make their lives easier. For example, I think it is very warm to have parking spots for disabled in parking lot so that they can park in a nearby place without moving around.

    2. 10.2. Accessible Design# There are several ways of managing disabilities. All of these ways of managing disabilities might be appropriate at different times for different situations. 10.2.1. Coping Strategies# Those with disabilities often find ways to cope with their disability, that is, find ways to work around difficulties they encounter and seek out places and strategies that work for them (whether realizing they have a disability or not). Additionally, people with disabilities might change their behavior (whether intentionally or not) to hide the fact that they have a disability, which is called masking and may take a mental or physical toll on the person masking, which others around them won’t realize. For example, kids who are nearsighted and don’t realize their ability to see is different from other kids will often seek out seats at the front of classrooms where they can see better. As for us two authors, we both have ADHD and were drawn to PhD programs where our tendency to hyperfocus on following our curiosity was rewarded (though executive dysfunction with finishing projects created challenges)[1]. This way of managing disabilities puts the burden fully on disabled people to manage their disability in a world that was not designed for them, trying to fit in with “normal” people. 10.2.2. Modifying the Person# Another way of managing disabilities is assistive technology [j13], which is something that helps a disabled person act as though they were not disabled. In other words, it is something that helps a disabled person become more “normal” (according to whatever a society’s assumptions are). For example: Glasses help people with near-sightedness see in the same way that people with “normal” vision do Walkers and wheelchairs can help some disabled people move around closer to the way “normal” people can (though stairs can still be a problem) A spoon might automatically balance itself [j14] when held by someone whose hands shake Stimulants (e.g., caffeine, Adderall) can increase executive function in people with ADHD, so they can plan and complete tasks more like how neurotypical people do. Assistive technologies give tools to disabled people to help them become more “normal.” So the disabled person becomes able to move through a world that was not designed for them. But there is still an expectation that disabled people must become more “normal,” and often these assistive technologies are very expensive. Additionally, attempts to make disabled people (or people with other differences) act “normal” can be abusive, such as Applied Behavior Analysis (ABA) therapy for autistic people [j15], or “Gay Conversion Therapy” [j16]. 10.2.3. Making an environment work for all# Another strategy for managing disability is to use Universal Design [j17], which originated in architecture. In universal design, the goal is to make environments and buildings have options so that there is a way for everyone to use it[2]. For example, a building with stairs might also have ramps and elevators, so people with different mobility needs (e.g., people with wheelchairs, baby strollers, or luggage) can access each area. In the elevators the buttons might be at a height that both short and tall people can reach. The elevator buttons might have labels both drawn (for people who can see them) and in braille (for people who cannot), and the ground floor button may be marked with a star, so that even those who cannot read can at least choose the ground floor. In this way of managing disabilities, the burden is put on the designers to make sure the environment works for everyone, though disabled people might need to go out of their way to access features of the environment. 10.2.4. Making a tool adapt to users# When creating computer programs, programmers can do things that aren’t possible with architecture (where Universal Design came out of), that is: programs can change how they work for each individual user. All people (including disabled people) have different abilities, and making a system that can modify how it runs to match the abilities a user has is called Ability based design [j18]. For example, a phone might detect that the user has gone from a dark to a light environment, and might automatically change the phone brightness or color scheme to be easier to read. Or a computer program might detect that a user’s hands tremble when they are trying to select something on the screen, and the computer might change the text size, or try to guess the intended selection. In this way of managing disabilities, the burden is put on the computer programmers and designers to detect and adapt to the disabled person. 10.2.5. Are things getting better?# We could look at inventions of new accessible technologies and think the world is getting better for disabled people. But in reality, it is much more complicated. Some new technologies make improvements for some people with some disabilities, but other new technologies are continually being made in ways that are not accessible. And, in general, cultures shift in many ways all the time, making things better or worse for different disabled people.

      The comparison between assistive technology and universal design also made me reflect on how differently society perceives accommodations. Glasses, for example, are a widely accepted assistive tool, to the point where people forget that nearsightedness is technically a disability. Meanwhile, other assistive devices, like wheelchairs or ADHD medication, can sometimes carry stigma, even though they serve the same purpose—helping people function in a world that isn’t designed for them. T

  4. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. A disability is an ability that a person doesn’t have, but that their society expects them to have.[1] For example: If a building only has staircases to get up to the second floor (it was built assuming everyone could walk up stairs), then someone who cannot get up stairs has a disability in that situation. If a physical picture book was made with the assumption that people would be able to see the pictures, then someone who cannot see has a disability in that situation. If tall grocery store shelves were made with the assumption that people would be able to reach them, then people who are short, or who can’t lift their arms up, or who can’t stand up, all would have a disability in that situation. If an airplane seat was designed with little leg room, assuming people’s legs wouldn’t be too long, then someone who is very tall, or who has difficulty bending their legs would have a disability in that situation. Which abilities are expected of people, and therefore what things are considered disabilities, are socially defined [j1]. Different societies and groups of people make different assumptions about what people can do, and so what is considered a disability in one group, might just be “normal” in another. There are many things we might not be able to do that won’t be considered disabilities because our social groups don’t expect us to be able to do them. For example, none of us have wings that we can fly with, but that is not considered a disability, because our social groups didn’t assume we would be able to. Or, for a more practical example, let’s look at color vision: Most humans are trichromats, meaning they can see three base colors (red, green, and blue), along with all combinations of those three colors. Human societies often assume that people will be trichromats. So people who can’t see as many colors are considered to be color blind [j2], a disability. But there are also a small number of people who are tetrachromats [j3] and can see four base colors[2] and all combinations of those four colors. In comparison to tetrachromats, trichromats (the majority of people), lack the ability to see some colors. But our society doesn’t build things for tetrachromats, so their extra ability to see color doesn’t help them much. And trichromats’ relative reduction in seeing color doesn’t cause them difficulty, so being a trichromat isn’t considered to be a disability. Some disabilities are visible disabilities that other people can notice by observing the disabled person (e.g., wearing glasses is an indication of a visual disability, or a missing limb might be noticeable). Other disabilities are invisible disabilities that other people cannot notice by observing the disabled person (e.g., chronic fatigue syndrome [j4], contact lenses for a visual disability, or a prosthetic for a missing limb covered by clothing). Sometimes people with invisible disabilities get unfairly accused of “faking” or “making up” their disability (e.g., someone who can walk short distances but needs to use a wheelchair when going long distances). Disabilities can be accepted as socially normal, like is sometimes the case for wearing glasses or contacts, or it can be stigmatized [j5] as socially unacceptable, inconvenient, or blamed on the disabled person. Some people (like many with chronic pain) would welcome a cure that got rid of their disability. Others (like many autistic people [j6]), are insulted by the suggestion that there is something wrong with them that needs to be “cured,” and think the only reason autism is considered a “disability” at all is because society doesn’t make reasonable accommodations for them the way it does for neurotypical [j7] people. Many of the disabilities we mentioned above were permanent disabilities, that is, disabilities that won’t go away. But disabilities can also be temporary disabilities, like a broken leg in a cast, which may eventually get better. Disabilities can also vary over time (e.g., “Today is a bad day for my back pain”). Disabilities can even be situational disabilities, like the loss of fine motor skills when wearing thick gloves in the cold, or trying to watch a video on your phone in class with the sound off, or trying to type on a computer while holding a baby. As you look through all these types of disabilities, you might discover ways you have experienced disability in your life. Though please keep in mind that different disabilities can be very different, and everyone’s experience with their own disability can vary. So having some experience with disability does not make someone an expert in any other experience of disability. As for our experience with disability, Kyle has been diagnosed with generalized anxiety disorder [j8] and Susan has been diagnosed with depression [j9]. Kyle and Susan also both have: near sightedness [j10]: our eyes cannot focus on things far away (unless we use corrective lenses, like glasses or contacts) ADHD [j11]: we have difficulty controlling our focus, sometimes being hyperfocused and sometimes being highly distracted and also have difficulties with executive dysfunction [j12]. [1]

      This made me think about how I’ve encountered situational disabilities in my own life. For example, trying to use a smartphone in bright sunlight when the screen becomes unreadable is a form of situational disability. Similarly, being in a loud space where I can't hear a conversation well might resemble the experience of someone with hearing loss, even if it's only temporary. It’s a reminder that disability is fluid and context-dependent, not just a fixed identity that applies to a specific group of people.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This is a comprehensive study that clearly and deeply investigates the function of GATA6 in human early cardiac development. 

      Strengths: 

      This study combines hESC engineering, differentiation, detailed gene expression, genome occupancy, and pathway modulation to elucidate the role of GATA6 in early cardiac differentiation. The work is carefully executed and the results support the conclusions. The use of publicly available data is well integrated throughout the manuscript. The RIME experiments are excellent. 

      Weaknesses: 

      Much has been known about GATA6 in mesendoderm development, and this is acknowledged by the authors. 

      We appreciate the comments and have tried to highlight both the early role of GATA6 in cardiac progenitor biology as well as the haploinsufficiency for relevance to human congenital heart disease, which we believe adds value to other recent published work, among others Sharma et al. eLife 2020.

      Reviewer #2 (Public review): 

      Summary: 

      This manuscript by Bisson et al describes the role of GATA6 to regulate cardiac progenitor cell (CPC) specification and cardiomyocyte (CM) generation using human embryonic stem cells (hESCs). The authors found that GATA6 loss-of-function hESC exhibits early defects in mesendoderm and lateral mesoderm patterning stages. Using RNA-seq and CUT&RUN assays the genes of the Wnt and BMP programs were found to be affected by the loss of GATA6 expression. Modulating Wnt and BMP during early cardiac differentiation can partially rescue CPC and CM defects in GATA6 hetero- and homozygous mutant hESCs. 

      Strengths: 

      The studies performed were rigorous and the rationale for the experimental design was logical. The results obtained were clear and supported the conclusions that the authors made regarding the role of GATA6 on Wnt and BMP pathway gene expression. 

      Weaknesses: 

      Given the wealth of studies that have been performed in this research area previously, the amount of new information provided in this study is relatively modest. Nevertheless, the results and quite clear and should make a strong contribution to the field. 

      Likewise for reviewer 2, we appreciate the comments and have tried to highlight both the early role of GATA6 in cardiac progenitor biology as well as the haploinsufficiency for relevance to human congenital heart disease.

      Reviewer #3 (Public review): 

      In this study, Bison et al. analyzed the role of the GATA6 transcription factor in patterning the early mesoderm and generating cardiomyocytes, using human embryonic stem cell differentiation assays and patient-derived hiPSCs with heart defects associated with mutations in the GATA6 gene. They identified a novel role for GATA6 in regulating genes involved in the WNT and BMP pathways -findings not previously noted in earlier analyses of GATA6 mutant hiPSCs during early cardiac mesoderm specification (Sharma et al., 2020). Modulation of the WNT and BMP pathways may partially rescue early cardiac mesoderm defects in GATA6 mutant hESCs. These results provide significant insights into how GATA6 loss-of-function and heterozygous mutations contribute to heart defects. 

      I have the following comments: 

      (1) Throughout the manuscript, Bison et al. alternate between different protocols to generate cardiomyocytes, which creates some confusion (e.g., Figure 1 vs. Supplemental Figure 2A). The authors should provide a clear justification for using alternative protocols. 

      We agree and clarified this issue in the revision (p. 6). The reviewer is correct that there are two widely used protocols for directed differentiation of PSCs to cardiac fate. One is a cytokine-based protocol (Fig. 1A) and the other uses small molecules to manipulate the WNT pathway (CHIR protocol, Supplemental Fig. 2B). In our study, we used the CHIR protocol only for experiments in Supplemental Figure 2B-E. Since our data implicated BMP and WNT as mediators of the GATA6-dependent program, we did this mainly to confirm that the phenotype we observed with the cytokine-based protocol was not biased by the differentiation protocol. However, we found the CHIR protocol to be overall relatively inefficient for cardiac differentiation using the parental H1 hESCs and the various isogenic lines. The in vitro cardiac differentiation protocols for hPSCs are known to be variable depending on lines and sometimes require extensive optimization for various media components and concentrations, cell seeding densities, and batch variations for crucial reagents. The cytokine-based protocol we optimized worked most efficiently with our hPSC lines to generate cardiomyocytes, therefore we committed to using it for the bulk of experiments in this study.  

      (2) The authors should characterise the mesodermal identity and cardiomyocyte subtypes generated with the activin/BMP-induction protocol thoroughly and clarify whether defects in the expression of BMP and WNTrelated gene affect the formation of specific cardiomyocyte subtypes in a chamber-specific manner. This analysis is important, as Sharma et al. suggested a role for GATA6 in orchestrating outflow tract formation, and Bison et al. similarly identified decreased expression of NRP1, a gene involved in outflow tract septation, in their GATA6 mutant cells. 

      We agree it is important that the mesodermal identities are quite thoroughly characterized.

      For example, Fig. 2 (K+P+, Brachyury, EOMES), Fig. 3G&H (lateral mesoderm, cardiac mesoderm RNAseq & GSEA comparing datasets from Koh et al.). The capacity of the cytokine-based protocol to generate both FHF and SHF derived sub-types has been rigorously evaluated by Keller and colleagues, which we now cite (Yang et al. 2022). Since the null cells do not generate CMs, chamber specific subtypes cannot be evaluated; whether the GATA6 heterozygous mutants are biased is an interesting question. Indeed, the top GO term identified by CUT&RUN analysis for GATA6 at day 2 of

      differentiation is outflow tract morphogenesis, which is consistent with the interpretation by Sharma et al., but implicates this program at a much earlier developmental stage, long before cardiomyocyte differentiation. We think this is one of the most important findings of our study and appreciate the chance to highlight this in the revision (p. 9, 17). When we evaluated chamber-specificity for differentiated cardiomyocytes, we did not find significant differences, as indicated for the reviewer in the panel below (day 20 of differentiation). Since our study focuses on early stages of progenitor specification rather than cardiomyocyte differentiation, we agree that a more rigorous analysis would be of value, and indicated this as a limitation of our current study (p. 18).

      Author response image 1.

      (3) The authors developed an iPSC line derived from a congenital heart disease (CHD) patient with an atrial septal defect and observed that these cells generate cTnnT+ cells less efficiently. However, it remains unclear whether atrial cardiomyocytes (or those localised specifically at the septum) are being generated using the activin/BMP-induction protocol and the patient-derived iPSC line.

      As indicated above, our study is focused on cardiac progenitor specification, and we found similar differences with the patient-derived iPSC-CMs compared to using hESC heterozygous targeted mutants. While we did not note any major differences in expression of cardiomyocyte markers, whether the mutants show any biases toward sub-types of cardiomyocytes is an interesting question to be pursued in subsequent work.

      (4) The authors should also justify the necessity of using the patient-derived line to further analyse GATA6 function. 

      This is a good point, and as suggested we provided the justification (p. 5-6). This is the first patient-derived iPSC line published with a heterozygous GATA6 mutation along with an isogenic mutation-corrected control generated for cardiac directed differentiation. Patients with congenital heart disease (CHD) associated with GATA6 mutations are typically heterozygous (also true for many other CHD variants; presumably homozygous null embryos would not survive). It is important to query if phenotypes found using targeted mutations in hESCs (or iPSCs) model the human disease, since the patient cells (or the hESCs) likely have additional genetic variants that might interact with the GATA6 mutation. The fact that both types of heterozygous cells (patient-derived iPSCs and targeted hESCs) generate similar defects in CM differentiation provides evidence supporting the use of these human cellular models to study the genetic and cellular basis for congenital heart disease. This is particularly important, since other models, such as heterozygous mice, do not show such phenotypes.

      (5) Figure 3 suggests an enrichment of paraxial mesoderm genes in the context of GATA6 loss-of-function, which is intriguing given the well-established role of GATA6 in specifying cardiac versus pharyngeal mesoderm lineages in model organisms. Could the authors expand their analysis beyond GO term enrichment to explore which alternative fates GATA6 mutant cells may acquire? Additionally, how does the potential enrichment of paraxial mesoderm, rather than pharyngeal mesoderm, relate to the initial mesodermal induction from their differentiation protocol? Could the authors also rule out the possibility of increased neuronal cell fates? 

      We need to interpret our in vitro differentiation data cautiously in relation to what has been shown in vivo, since we are unlikely to be reproducing all the complex signaling taking place in the embryo. Yet we do see modest increases in gene expression levels including signatures of paraxial mesoderm and ECM/mesenchymal at days 2 or 3 of differentiation in the GATA6 mutant cells. Therefore, we now include a heatmap showing enriched paraxial mesoderm gene expression in the mutant cells, new Fig. 3I (see page 10).

      A caveat of this result is that the cells are being differentiated toward cardiac fate, so a bias for alternative fates might be suppressed. We modified the protocol to favor paraxial fate by adding CHIR at day 2 (rather than XAV) and performing qPCR assays at day 3. We found this successfully induced paraxial mesoderm gene expression, but equally comparing wildtype, heterozygous, or null cells, so do not feel it warrants highlighting further. 

      Recommendations for the authors:  

      Reviewing Editor (Recommendations for the authors): 

      Incorporation of marker analysis for various stages of iPSC to CM differentiation (mesoderm, cardiac progenitor, CM subtypes) would increase the significance and support for the findings presented. Further data on the link (direct or indirect) between GATA6 and Wnt/BMP signalling would also add to the significance of this study. A number of textual changes/clarifications are also suggested to improve the manuscript. 

      We appreciate the feedback and provide responses for issues raised for markers, direct or indirect interactions, and textual changes/clarifications in the following sections. As indicated above, we did not find obvious alterations in cardiac subtypes, but since our study is focused on early progenitor specification, this is an interesting question that we think should be more rigorously evaluated in subsequent work.  

      Reviewer #1 (Recommendations for the authors)

      Minor details: 

      (1) On p6 "Principal component analysis (PCA) showed that the cells derived from each genotype were well separated from each other (Supplemental Figure 2C)". All genotypes should be in one PCA plot to better evaluate the three genotypes. 

      We prepared the new plot as suggested, presented as new Supplemental Fig. 2C. 

      (2) p10: "Chia et al.22 and found a significantly decreased enrichment in GATA6-/- cells relative to WT at day 2" decreased enrichment of what? Direct target genes? 

      Thank you for catching this. Yes, the text was changed to indicate a “decreased enrichment in GATA6-/- cells relative to WT at day 2 for putative direct GATA6 target genes.” 

      Reviewer #2 (Recommendations for the authors): 

      Overall, this is an interesting study that addresses the early developmental roles of GATA6 on cardiac differentiation. While the identification of Wnt and BMP pathway genes to be involved in GATA6 regulation is not entirely unexpected, the authors do bring forth some useful knowledge that helps to further elucidate the mechanism of pre-cardiac mesoderm regulation. Some suggestions for improvement are included below - 

      Major points: 

      (1) Since the loss of Gata6 in this study is global (either as heterozygous or homozygous, it is likely that the very early requirement of Gata6 (e.g. mesodermal stage of differentiation) is responsible for the cardiac transcriptional phenotype observed and not due to specific role of Gata6 in the cardiac lineage which would need to be addressed using conditional knock out of Gata6 in hPSC model. The authors should be more explicit when discussing the results as disruption of mesodermal differentiation leading to loss of downstream cardiac lineage cells. For example, I would change the title "GATA6 loss-of-function impairs CM differentiation" to "GATA6 loss-of-function impairs mesodermal (or mesodermal lineage) differentiation" and show the changes in cardiac progenitor cells genes (Isl1, Tbx1, Hand1, and BAF50c/Smarcd3) in addition to cardiomyocyte genes but no change in mesodermal (e.g. Brachyury, T, Eomes, Mesp1/2, etc) genes. 

      We agree with the reviewer’s interpretation. The title for the section was changed as suggested. In Fig. 1, we show changes in cardiac progenitor cell genes (Isl1, Hand1, and BAF50c/Smarcd3) while not seeing changes in mesodermal genes in Fig. 2 (e.g. Brachyury, Eomes, Mesp1/2). We note that the defect may be specific to cardiac (or anterior lateral) mesoderm, as the ability to express paraxial mesoderm markers was not impaired.  

      (2) The use of NKX2.5, TBX5, TBX20, and GATA4 as markers for CPC is not ideal. These markers are also expressed in differentiated cardiomycytes. ISL1 or TBX1 for second heart field progenitors and HAND1 or BAF60c/Smarcd3 for first heart field progenitors would be ideal.  

      As suggested, we included additional day 6 qPCR panel (new Fig. 1E) to evaluate the heart field progenitor markers. 

      (3) Much of the findings described in this study have been known in the field including the requirement of Wnt and BMP to induce mesodermal and subsequently cardiomyocyte differentiation. The key new information here is that Gata6 knockout disrupts Wnt and BMP signaling. It would help to further validate experimentally some of the Wnt and BMP genes as either direct or indirect targets of Gata6 using reporter assays. 

      While reporter assays are feasible and do provide relevant outputs, we feel that the use of any one or even several response elements in a reporter assay adds relatively little value compared to comprehensive analysis of bona fide network components. To address the reviewers concern we have included profiling heat maps for WNT and BMP pathway components to more rigorously and specifically evaluate the disruption in the signaling networks caused by loss of GATA6. Proving direct targets of endogenous genes is challenging, but we mapped many binding peaks for GATA6 to putative enhancers of WNT/BMP pathway genes (based on histone marks). We provide a list of these genes (new Fig. 4F) and distinguish these from WNT/BMP pathway genes that were not bound by GATA6 yet are down-regulated in the GATA6 mutant cells and are likely to be indirect targets (p. 12). 

      Minor points: 

      (1) Figures 1 and 2 - in the figure legend the labels w2, w4, m2, m5, m11, and m14 should be explained as the name of the clones of targeted hESC.  

      The legends were edited to provide this information.  

      (2) Supplemental Figure 3A - the resolution of the FACS plot is suboptimal. 

      We apologize and have corrected the plot resolution in the revised manuscript.  

      (3) Supplemental Table 1 - it's intriguing that amongst all the SWI/SNF factors, the one that is known to be cardiac-specific (SMARCD3) did not come up in the GATA6-RIME-enriched proteins. Is this a reflection of the early stage in which GATA6 plays a role in development (e.g. mesendoderm development but not precardiac mesoderm development when SMARCD3 is expressed)? 

      We agree and have noted this feature in the revised manuscript (p. 17). We note that SMARCD3 is expressed in the RNA-seq data as early as day 2. Although speculative, it may be that GATA6 primarily interacts with SWI/SNF complexes prior to the role for SMARCD3 in cardiac specification.

      Reviewer #3 (Recommendations for the authors): 

      (1) Figures 3G and 3H, as well as others, have resolution issues. The gene names are unreadable, and higherresolution images should be provided. 

      We apologize for the resolution issues and these have been fixed in the revised version. 

      (2) In their early manipulation of the WNT and BMP pathways (Figure 6A), it is unclear whether the activin/BMP protocol shown in Figure 1A was used. If this is the case, the authors should compare their results to a wild-type + DOX EV condition for consistency. 

      We clarified in the revision (Fig. 6A) that all the experiments in Fig. 6 use the cytokine protocol. In the revised figure, we included the wild-type + DOX EV condition as suggested. 

      (3) In Figures 6C and 6D, the authors should include an analysis of a wild-type isogenic line under their new CHIR/LB condition for comparison. 

      As suggested, we included the WT isogenic line in the comparison. For Fig. 6C these are shown on a separate graph because the Y-axis values are very different. Note that the CHIR/LB treatments that improve mutant cell differentiation impact the WT cells in the opposite manner.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:  

      Reviewer #1 (Public Review):  

      Summary:  

      The study by Pudlowski et al. investigates how the intricate structure of centrioles is formed by studying the role of a complex formed by delta- and epsilon-tubulin and the TEDC1 and TEDC2 proteins. For this, they employ knockout cell lines, EM, and ultrastructure expansion microscopy as well as pull-downs. Previous work has indicated a role of delta- and epsilon-tubulin in triplet microtubule formation. Without triplet microtubules centriolar cylinders can still form, but are unstable, resulting in futile rounds of de novo centriole assembly during S phase and disassembly during mitosis. Here the authors show that all four proteins function as a complex and knockout of any of the four proteins results in the same phenotype. They further find that mutant centrioles lack inner scaffold proteins and contain an extended proximal end including markers such as SAS6 and CEP135, suggesting that triplet microtubule formation is linked to limiting proximal end extension and formation of the central region that contains the inner scaffold. Finally, they show that mutant centrioles seem to undergo elongation during early mitosis before disassembly, although it is not clear if this may also be due to prolonged mitotic duration in mutants.  

      Strengths:  

      Overall this is a well-performed study, well presented, with conclusions mostly supported by the data. The use of knockout cell lines and rescue experiments is convincing.  

      Weaknesses:  

      In some cases, additional controls and quantification would be needed, in particular regarding cell cycle and centriole elongation stages, to make the data and conclusions more robust. 

      We thank the reviewer for these comments and have improved our analyses of these as detailed below.

      Reviewer #2 (Public Review):  

      Summary:  

      In this article, the authors study the function of TEDC1 and TEDC2, two proteins previously reported to interact with TUBD1 and TUBE1. Previous work by the same group had shown that TUBD1 and TUBE1 are required for centriole assembly and that human cells lacking these proteins form abnormal centrioles that only have singlet microtubules that disintegrate in mitosis. In this new work, the authors demonstrate that TEDC1 and TEDC2 depletion results in the same phenotype with abnormal centrioles that also disintegrate into mitosis. In addition, they were able to localize these proteins to the proximal end of the centriole, a result not previously achieved with TUBD1 and TUBE1, providing a better understanding of where and when the complex is involved in centriole growth.  

      Strengths:  

      The results are very convincing, particularly the phenotype, which is the same as previously observed for TUBD1 and TUBE1. The U-ExM localization is also convincing:

      despite a signal that's not very homogeneous, it's clear that the complex is in the proximal region of the centriole and procentriole. The phenotype observed in U-ExM on the elongation of the cartwheel is also spectacular and opens the question of the regulation of the size of this structure. The authors also report convincing results on direct interactions between TUBD1, TUBE1, TEDC1, and TEDC2, and an intriguing structural prediction suggesting that TEDC1 and TEDC2 form a heterodimer that interacts with the TUBD1- TUBE1 heterodimer.  

      Weaknesses:  

      The phenotypes observed in U-ExM on cartwheel elongation merit further quantification, enabling the field to appreciate better what is happening at the level of this structure.  

      We thank the reviewer for these comments and have improved our analyses of cartwheel elongation as detailed below.

      Reviewer #3 (Public Review):  

      Summary:  

      Human cells deficient in delta-tubulin or epsilon-tubulin form unstable centrioles, which lack triplet microtubules and undergo a futile formation and disintegration cycle. In this study, the authors show that human cells lacking the associated proteins TEDC1 or TEDC2 have these identical phenotypes. They use genetics to knockout TEDC1 or TEDC2 in p53negative RPE-1 cells and expansion microscopy to structurally characterize mutant centrioles. Biochemical methods and AlphaFold-multimer prediction software are used to investigate interactions between tubulins and TEDC1 and TEDC2.  

      The study shows that mutant centrioles are built only of A tubules, which elongate and extend their proximal region, fail to incorporate structural components, and finally disintegrate in mitosis. In addition, they demonstrate that delta-tubulin or epsilon-tubulin and TEDC1 and TEDC2 form one complex and that TEDC1 TEDC2 can interact independently of tubulins. Finally, they show that the localization of four proteins is mutually dependent.  

      Strengths:  

      The results presented here are mostly convincing, the study is exciting and important, and the manuscript is well-written. The study shows that delta-tubulin, epsilon-tubulin, TEDC1, and TEDC2 function together to build a stable and functional centriole, significantly contributing to the field and our understanding of the centriole assembly process.  

      Weaknesses:  

      The ultrastructural characterization of TEDC1 and TEDC2 obtained by U-ExM is inconclusive. Improving the quality of the signals is paramount for this manuscript.  

      We thank the reviewer for these comments and have improved our imaging of TEDC1 and TEDC2 localization, as detailed below.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):  

      The reviewers agreed that the conclusions are largely supported by solid evidence, but felt that improving the following aspects would make some of the data more convincing:  

      (1) The UExM localizations of TEDC1/2 are not very convincing and the reviewers suggest to complement these with alternative super-resolution approaches (e.g. SIM) and/or different labeling techniques such as pre-expansion labeling using STAR red/orange secondaries (also robust for SIM and STED), use of the Halo tag, different tag antibodies, etc 

      We thank the reviewers for these recommendations and have adapted two of these strategies to improve our imaging of TEDC1 and TEDC2 localization. First, we used an alternative super-resolution approach, a Yokogawa CSU-W1 SoRA confocal scanner (resolution = 120 nm) and imaged cells grown on coverslips (not expanded). We found that TEDC1 and TEDC2 localize to procentrioles and the proximal end of parental centrioles (Fig 2 – Supplementary Figure 1a, b). Second, we used a recently described expansion gel chemistry (Kong et al., Methods Mol Biol 2024) combined with Abberior Star red and orange secondary antibodies. This technique resulted in robust signal at centrosomes and in the cytoplasm and indicated that TEDC1 and TEDC2 localize near the centriole walls of procentrioles and the proximal region of parental centrioles, near CEP44 (Fig 2 – Supplementary Figure 1c, d). These results complement and support our initial observations (Fig 2C, D) and we have edited the text to reflect this (lines 157-163). We also note that these Flag tag and V5 tag primary antibodies are specific and have little background signal in all applications (Fig 2 – Supplementary Fig 1E-J), while other commercially available antibodies against these tags did exhibit non-specific signal. 

      (2) The cell cycle classifications of centrioles would strongly benefit, apart from a better description, from adding quantifications of average centriole length at a given stage based on tubulin staining (not acTub). 

      We thank the reviewers for these recommendations. We have added an improved description of our cell cycle analyses (lines 234-237). We have also added new analyses for centriole length as measured by staining with alpha-tubulin (Fig 4 – Supp 3 and Fig 4 – Supp 4). We find that in all mutants, acetylated tubulin elongates along with alpha-tubulin in a similar way as control centrioles.

      Reviewer #1 (Recommendations For The Authors):  

      Specific points:  

      (1) The introduction is a bit oddly structured. About halfway through it summarizes what is going to be presented in the study, giving the impression that it is about to conclude, but then continues with additional, detailed introduction paragraphs. Overall, the authors may also want to consider making it more concise.

      We thank the reviewer for these suggestions and have shortened and restructured the introduction for clarity and conciseness.

      (2) The text should explain to the non-expert reader why endogenous proteins are not detected and why exogenously expressed, tagged versions are used. Related to this, the authors state overexpression, but what is this assessment based on? Does expression at the endogenous level also rescue? At least by western blot, these questions should be addressed. 

      In the text, we have added clarification about why endogenous proteins were not detected for immunofluorescence (lines 149-151). To quantify the overexpression, we have added Western blots of TEDC1 and TEDC2 to Fig 1 – Supplementary Figure 1E,F. We note that endogenous levels of both proteins are very low, and the rescue constructs are overexpressed 20 to 70 fold above endogenous levels.  

      (3) The figures should clearly indicate when tagged proteins are used and detected.

      Currently, this info is only found in the legends but should be in the figure panels as well. 

      We have made these changes to the figure panels in Fig 2, Fig 2 – Supp 1, and Fig 3.

      (4)  I could not find a description and reference to Figure 2 Supplement 2 and 3. 

      We have replaced these supplements with new supplementary figures for TEDC1 and TEDC2 localization (Fig 2 – Supp 1).

      (5) The multiple bands including unspecific (?) bands should be labeled to guide the reader in the western blots. 

      We have labeled nonspecific bands in our Western blots with asterisks (Fig 1 – Supp 1, Fig 3)

      (6) The alphafold prediction suggests that TUBD1 can bind to the TED complex in the absence of TUBE1 can this be shown? This would be a nice validation of the predicted architecture of the complex. I also missed a bit of a discussion of the predicted architecture. How could it be linked to triplet microtubule formation? Is the latest alphafold version 3 adding anything to this analysis? 

      In our pulldown experiments, we found that TUBD1 cannot bind to TEDC1 or TEDC2 in the absence of TUBE1 (Fig 3C, D, IB: TUBD1). We performed this experiment with three biological replicates and found the same result. It is possible that TUBD1 and TUBE1 form an intact heterodimer, similar to alpha-tubulin and beta-tubulin, and this will be an exciting area of future research.

      We have added new analysis from AlphaFold3 (Fig 3 – Supp 1B). AlphaFold3 predicts a similar structure as AlphaFold Multimer.

      We have also added additional discussion about the AlphaFold prediction to the text (lines 220-222, 365-367). Thanks to the reviewer for pointing out this oversight.

      (7) I suggest briefly explaining in the text how cells and centrioles at different cell cycle stages were identified. I found some info in the legend of Figure 1, but no info for other figures or in the text. Related to this, how are procentrioles defined in de novo formation? There is no parental centriole to serve as a reference. 

      We have added a brief explanation of the synchronization and identification in lines 234237. We have also clarified the text regarding de novo centrioles, and now term these “de novo centrioles in the first cell cycle after their formation” (lines 271-272).

      (8) Related to point 7: using acetylated tubulin as a universal length and width marker seems unreliable since it is a PTM. The authors should use general tubulin staining to estimate centriole dimensions, or at least establish that acetylated tubulin correlates well with the overall tubulin signal in all mutants. 

      We have added two supplementary data figures (Fig 4 – supp 3 and Fig 4 – supp 4) in which we co-stain control and mutant centrioles with alpha-tubulin. We found that acetylated tubulin marked mutant centrioles well and as alpha-tubulin length increased, acetylated tubulin length also increased. 

      (9) Presence and absence of various centriolar proteins. These analyses lack a clear reference for the precise centriole elongation stage. This is particularly problematic for proteins that are recruited at specific later stages (such as inner scaffold proteins). The staining should be correlated with centriole length measurements, ideally using general tubulin staining.  

      As described for point 8, we have added two supplementary data figures in which we costain control and mutant centrioles with alpha-tubulin and found that acetylated tubulin also increases as overall tubulin length increases in all mutants. We note that inner scaffold proteins are absent in all our mutant centrioles at all stages of the cell and centriole cycle, as also previously reported for POC5 in Wang et al., 2017.

      Reviewer #2 (Recommendations For The Authors):  

      Here's a list of points I think could be improved:  

      -  As the authors previously published, the centriole appears to have a smaller internal diameter than mature centrioles. Could the authors measure to see if the phenotype is identical? Is the centriole blocked in the bloom phase (Laporte et al. 2024)? 

      We have added an additional supplementary figure (Fig 4 – supp 5) to show that mutant centrioles have smaller diameters than mature centrioles, as we previously reported for the delta-tubulin and epsilon-tubulin mutant centrioles by EM. We thank the reviewers for the additional question of the bloom phase. Given the comparatively smaller number of centrioles we analyzed in this paper compared to Laporte et al (50 to 80 centrioles per condition here, versus 800 centrioles in Laporte et al), it is difficult to definitively conclude whether there is a block in bloom phase. This would be an interesting area for future research.  

      -  The images of the centrioles in EM are beautiful. Would it be possible to apply a symmetrisation on it to better see the centriolar structures? For example, is the A-C linker present? 

      We thank the reviewer for this excellent suggestion. Using centrioleJ, we find that the A-C linker is absent from mutant centrioles. The symmetrized images have been added to Fig 1 – Supplementary Fig 2, and additional discussion has been added to the text (line 143-144, line 368-374).  

      -  How many EM images were taken? Did the centrioles have 100% A-microtubule only or sometimes with B-MT? 

      For TEM, we focused on centrioles that were positioned to give perfect cross-section images of the centriolar microtubules, and thus did not take images of off-angle or rotated centrioles. Given the difficulty of this experiment (centrioles are small structures within the cell, centrosomes are single-copy organelles, and off-angle centrioles were not imaged), we were lucky to image 3 centrioles that were in perfect cross-section – 2 for Tedc1<sup>-/-</sup> and 1 for Tedc2<sup>-/-</sup>. Our images indicate that these centrioles only have A-tubules (Fig 1 – Supp Fig

      2).

      -  In Figure 2 - it would be preferable to write TEDC2-flag or TEDC1-flag and not TEDC2/1. 

      We have made this change

      -  It seems that Figures 2C and D aren't cited, and some of the data in the supplemental data are not described in the main text. 

      We have replaced these supplements with new supplementary figures for TEDC1 and TEDC2 localization (Fig 2 – Supp 1).

      -  The signal in U-ExM with the anti-Flag antibody is heterogeneous. Did the authors test several anti-FLAG antibodies in U-ExM? 

      We tested several anti-Flag and anti-V5 antibodies for our analyses, and chose these because they have little background signal in all applications (Fig 2 – Supplementary Fig 1E-J). Other commercially available antibodies against these tags did exhibit non-specific signal.

      -  The AlphaFold prediction is difficult to interpret, the authors should provide more views and the PDB file. 

      We have added 2 additional views of the AlphaFold prediction in Fig 3 – Supp 1A.

      -  In general, but particularly for Figure 4: the length doesn't seem to be divided by the expansion factor, it is therefore difficult to compare with known EM dimensions. Can the authors correct the scale bars? 

      We have corrected the scale bars for all figures to account for the expansion factor.

      -  Concerning Gamma-tubulin that is "recruited to the lumen of centrioles by the inner scaffold, had localization defects in mutant centrioles. However, we were unable to reliably detect gamma-tubulin within the lumen of control or de novo-formed centrioles in S or G2-phase (Figure 4 - Supplement 1E), and thus were unable to test this hypothesis". In Laporte et al 2024, Gamma-tubulin arrives later than the inner scaffold and only on mature centrioles, so this result appears to be in line with previous observation. However, the authors should be able to detect a proximal signal under the microtubules of the procentriole, is this the case? 

      We agree that this is an exciting question. However, in our expansion microscopy staining, we frequently observe that gamma-tubulin surrounds centrioles, corresponding to its role in the pericentriolar material (PCM). In our hands, we find it difficult to distinguish between centriolar gamma-tubulin at the base of the A-tubule from gamma-tubulin within the PCM.  

      -  In the signal elongation of SAS-6, STIL, CEP135, CPAP, and CEP44, would it be possible to quantify the length of these signals (with dimensions divided by the expansion factor for comparison with known TEM distances)? 

      We have quantified the lengths of SAS-6 and CEP135 in new Fig 4 – Supp 3 and Fig 4 – Supp 4.  

      -  The authors observe that centrin is present, but only as a SFI1 dot-like localization (which is another protein that would be interesting to look at), and not an inner scaffold localization. Can the authors elaborate? These results suggest that the distal part is correctly formed with only a microtubule singlet. 

      We agree with the reviewer’s interpretation that the centriole distal tip is likely correctly formed with only singlet microtubules, as both distal centrin and CP110 are present. We have added this point to the discussion (line 415).

      -The authors observe that CPAP is elongated, but CPAP has two locations, proximal and distal. Is it distal or proximal elongation? Is the proximal signal of CPAP longer than that of CEP44 in the mutants? The authors discuss that the elongation could come from overexpression of CPAP, but here it seems that the centriole is not overlong, just the structures around the cartwheel. 

      We thank the reviewer for this point. It is difficult for us to conclude whether the proximal or distal region is extended in the mutants, as our mutant centrioles lacks a visible separation between these two regions. It would be interesting to probe this question in the future by testing whether subdomains of CPAP may be differentially regulated in our mutants.

      Reviewer #3 (Recommendations For The Authors):  

      It isn't apparent to me what was counted in Figure 1C. Were all centrioles (mother centrioles and procentrioles) counted? Where is the 40% in control cells coming from? Can this set of data be presented differently? 

      We apologize for the confusion. In this figure, all centrioles were counted. We have updated the figure legend for clarity. We performed this analysis in a similar way as in Wang et al., 2017 to better compare phenotypes.  

      Figure 2C. and the text lines 182-187: The ultrastructural characterization of TEDC1 and TEDC2 suffers from the low quality of the TEDC1 and TEDC2 signals obtained postexpansion. In comparison with robust low-resolution immunosignal, it appears that most of the signal cannot be recovered after expansion. Another sub-resolution imaging method to re-analyze TEDC1 and TEDC22 localization would be essential. The same concern applies to Figures 2 - Supplement 2 and 3. Also, Figure 2 - Supplement 2 and Supplement 3 do not seem to be cited. 

      We thank the reviewer for these recommendations. As also mentioned above, we used an alternative super-resolution approach, a Yokogawa CSU-W1 SoRA confocal scanner (resolution = 120 nm), and found that TEDC1 and TEDC2 localize to procentrioles and the proximal end of parental centrioles (Fig 2 – Supplementary Figure 1a, b). Second, we used a recently described expansion gel chemistry (Kong et al., Methods Mol Biol 2024) combined with Abberior Star red and orange secondary antibodies. This technique resulted in robust signal at centrosomes and in the cytoplasm and indicated that TEDC1 and TEDC2 localize near the centriole walls of procentrioles and the proximal region of parental centrioles, near CEP44 (Fig 2 – Supplementary Figure 1c, d). These stainings complement and support our initial observations (Fig 2C, D) and we have edited the text to reflect this (lines 157-163). We have also removed the supplementary figures that were uncited in the text.

      TUBD1 and TUBE1 form a dimer and TEDC2 and TEDC1 can interact. Any speculation as to why TEDC2 does not pull down both TUBE1 and TUBD1? 

      We apologize for the confusion. TEDC2 does pull down both TUBE1 and TUBD1 (Fig 3D, pull-down, second column, Tedc2-V5-APEX2 rescuing the Tedc2<sup>-/-</sup> cells pulls down TUBD1, TUBE1, and TEDC1).  

      Figure 4A and B. The authors use acetylated tubulin to determine the length of procentrioles in the S and G2 phases. However, procentrioles are not acetylated on their distal ends in these cell phase phases (as the authors also mention further in the text). Why has alpha tubulin not been used since it works well in U-ExM? The average size of the control, G2 procentrioles, seems too small in Figure 4A and not consistent with other imaging data (for instance, in Figure 4 - Supplement 1 C, Cp110, and CPAP staining). There is no statistical analysis in F4A.  

      We have added two supplementary data figures (Fig 4 – supp 3 and Fig 4 – supp 4) in which we co-stain control and mutant centrioles with alpha-tubulin. We found that acetylated tubulin correlates well with overall tubulin signal in all mutants. We have added statistical analysis to the figure legend of Fig 4A.

      Lines 260 - 262: "These results indicate that centrioles with singlet microtubules can elongate to the same length as controls, and therefore that triplet microtubules are not essential for regulating centriole length." It is hard to agree with this statement. Mutant procentrioles show aberrantly elongated proximal signals of several tested proteins. In addition, in lines 326 - 328, the authors state that "Together, these results indicate that centrioles lacking compound microtubules are unable to properly regulate the length of the proximal end."  

      We thank the reviewer and have clarified the statement to state that these results indicate that centrioles with singlet microtubules can elongate to the same overall length as control centrioles in G2 phase.  

      Line 353: The authors suggest that elongated procentriole structure in mitosis may represent intermediates in centriole disassembly. Another interpretation, more in line with the EM data from Wang et al., 2017, would be that these mutant procentrioles first additionally elongate before they disassemble in late mitosis. The aberrant intermediate structure concept would need further exploration. For instance, anti-alpha/beta-tubulin antibodies could be used to investigate centriole microtubules.  

      We apologize for the confusion and have edited this section for clarity (lines 341-343): “We conclude that in our mutant cells, centrioles elongate in early mitosis to form an aberrant intermediate structure, followed by fragmentation in late mitosis.”

      References need to be included in lines 122, 277, 279. 

      We have added these references

      Line 281: Add references PMID: 30559430 and PMID: 32526902.  

      We have added these references (lines 265-266).

      Line 289: "Moreover, our results suggest that centriole glutamylation is a multistep process, in which long glutamate side chains are added later during centriole maturation." This does not seem like an original observation. For instance, see PMID: 32526902.  

      We have added this reference (lines 273-274).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors present a modelling study to test the hypothesis that horizontal gene transfer (HGT) can modulate the outcome of interspecies competition in microbiomes, and in particular promote bistability in systems across scales. The premise is a model developed by the same authors in a previous paper where bistability happens because of a balance between growth rates and competition for a mutual resource pool (common carrying capacity). They show that introducing a transferrable element that gives a "growth rate bonus" expands the region of parameter space where bistability happens. The authors then investigate how often (in terms of parameter space) this bistability occurs across different scales of complexity, and finally under selection for the mobile element (framed as ABR selection).

      Strengths:

      The authors tackle an important, yet complex, question: how do different evolutionary processes impact the ecology of microbial ecosystems? They do a nice job at increasing the scales of heterogeneity and asking how these impact their main observable: bistability.

      We appreciate the reviewer for agreeing with the potential value of our analysis. We are also grateful for the constructive comments and suggestions on further analyzing the influence of the model structure and the associated assumptions. We have fully addressed the raised issues in the updated manuscript and below.

      Weaknesses:

      The author's starting point is their interaction LV model and the manuscript then explores how this model behaves under different scenarios. Because the structure of the model and the underlying assumptions essentially dictate these outcomes, I would expect to see much more focus on how these two aspects relate to the specific scenarios that are discussed. For example:

      A key assumption is that the mobile element conveys a multiplicative growth rate benefit (1+lambda). However, the competition between the species is modelled as a factor gamma that modulates the competition for overall resource and thus appears in the saturation term (1+ S1/Nm + gamma2*S2/Nm). This means that gamma changes the perceived abundance of the other species (if gamma > 1, then from the point of view of S1 it looks like there are more S2 than there really are). Most importantly, the relationship between these parameters dictates whether or not there will be bistability (as the authors state).

      This decoupling between the transferred benefit and the competition can have different consequences. One of them is that - from the point of view of the mobile element - the mobile element competes at different strengths within the same population compared to between. To what degree introducing such a mobile element modifies the baseline bistability expectation thus strongly depends on how it modifies gamma and lambda.

      Thus, this structural aspect needs to be much more carefully presented to help the reader follow how much of the results are just trivial given the model assumptions and which have more of an emergent flavour. From my point of view, this has an important impact on helping the reader understand how the model that the authors present can contribute to the understanding of the question "how microbes competing for a limited number of resources stably coexist". I do appreciate that this changes the focus of the manuscript from a presentation of simulation results to more of a discussion of mathematical modelling.

      We thank the reviewer for the insightful suggestions. We agree with the reviewer that the model structure and the underlying assumptions need to be carefully discussed, in order to understand the generality of the theoretical predictions. In particular, the reviewer emphasized that how HGT affects bistability might depend on how mobile genetic elements modified growth rates and competition. In the main text, we have shown that when mobile genes only influence species growth rates, HGT is expected to promote multistability (Fig. 1 and 2). However, when mobile genes modify species interactions, the effect of HGT on multistability is dependent on how mobile genes change competition strength (Fig. 3a to f). When mobile genes increase competition, HGT promotes multistability (Fig. 3c and e). In contrast, when mobile genes relax competition, HGT is expected to reduce multistability (Fig. 3d and f).

      In light of the reviewer’s comments, we have further generalized the model structure, by accounting for the scenario where mobile genes simultaneously modify growth rates and competition. The effect of mobile genes on growth rates is represented by the magnitude of 𝜆’s, and the influence on competition is described by another parameter 𝛿. By varying these two parameters, we can evaluate how the model structure and the underlying assumptions affect the baseline expectation. We performed additional simulations with broad ranges of 𝜆 and 𝛿 values. In particular, we analyzed whether HGT would promote the likelihood of bistability in two-species communities compared with the scenario without gene transfer (Fig. 3g-i). Our results suggested that: (1) With or without HGT, reducing 𝜆 (increasing neutrality) promotes bistability; (2) With HGT, increasing 𝛿 promotes bistability; (2) Compared with the population without HGT, gene transfer promotes bistability when 𝛿 is zero or positive, while reduces bistability when 𝛿 is largely negative. These results agree with the reviewer’s comment that the baseline bistability expectation depends on how HGT modifies gamma and lambda. In the updated manuscript, we have thoroughly discussed how the model structure and the underlying assumptions can influence the predictions (line 238-253). 

      We further expanded our analysis, by calculating how other parameters, including competition strength, growth rate ranges, and death/dilution rate, would affect the multistability of communities undergoing horizontal gene transfer (Fig. S2, S3, S9, S10, S11, S12, S13, S15). Together with the results presented in the first draft, these analysis enables a more comprehensive understanding of how different mechanisms, including but not limited to HGT, collectively shaped community multistability. In the updated manuscript, the reviewer can see the change of focus from exploring the effects of HGT to a more thorough discussion of the mathematical model. The revised texts highlighted in blue and the supplemented figures reflect such a change.

      Reviewer #2 (Public review):

      Summary:

      In this work, the authors use a theoretical model to study the potential impact of Horizontal Gene Transfer on the number of alternative stable states of microbial communities. For this, they use a modified version of the competitive Lotka Volterra model-which accounts for the effects of pairwise, competitive interactions on species growth-that incorporates terms for the effects of both an added death (dilution) rate acting on all species and the rates of horizontal transfer of mobile genetic elements-which can in turn affect species growth rates. The authors analyze the impact of horizontal gene transfer in different scenarios: bistability between pairs of species, multistability in communities, and a modular structure in the interaction matrix to simulate multiple niches. They also incorporate additional elements to the model, such as spatial structure to simulate metacommunities and modification of pairwise interactions by mobile genetic elements. In almost all these cases, the authors report an increase in either the number of alternative stable states or the parameter region (e.g. growth rate values) in which they occur.

      In my opinion, understanding the role of horizontal gene transfer in community multistability is a

      very important subject. This manuscript is a useful approach to the subject, but I'm afraid that a thorough analysis of the role of different parameters under different scenarios is missing in order to support the general claims of the authors. The authors have extended their analysis to increase their biological relevance, but I believe that the analysis still lacks comprehensiveness.

      Understanding the origin of alternative stable states in microbial communities and how often they may occur is an important challenge in microbial ecology and evolution. Shifts between these alternative stable states can drive transitions between e.g. a healthy microbiome and dysbiosis. A better understanding of how horizontal gene transfer can drive multistability could help predict alternative stable states in microbial communities, as well as inspire novel treatments to steer communities towards the most desired (e.g. healthy) stable states.

      Strengths:

      (1) Generality of the model: the work is based on a phenomenological model that has been extensively used to predict the dynamics of ecological communities in many different scenarios.

      (2) The question of how horizontal gene transfer can drive alternative stable states in microbial communities is important and there are very few studies addressing it.

      We thank the reviewer for the positive comments on the potential novelty and conceptual importance of our work. We are also grateful for the constructive suggestions on the generality and comprehensiveness of our analysis. In particular, we agree with the reviewer that a thorough analysis of the role of different parameter could further improve the rigor of this work. We have fully addressed the raised issues in the updated manuscript and below.

      Weaknesses:

      (1) There is a need for a more comprehensive analysis of the relative importance of the different model parameters in driving multistability. For example, there is no analysis of the effects of the added death rate in multistability. This parameter has been shown to determine whether a given pair of interacting species exhibits bistability or not (see e.g. Abreu et al 2019 Nature Communications 10:2120). Similarly, each scenario is analyzed for a unique value of species interspecies interaction strength-with the exception of the case for mobile genetic elements affecting interaction strength, which considers three specific values. Considering heterogeneous interaction strengths (e.g. sampling from a random distribution) could also lead to more realistic scenarios - the authors generally considered that all species pairs interact with the same strength. Analyzing a larger range of growth rates effects of mobile genetic elements would also help generalize the results. In order to achieve a more generic assessment of the impact of horizontal gene transfer in driving multistability, its role should be systematically compared to the effects of the rest of the parameters of the model.

      We appreciate the suggestions. For each of the parameters that the reviewer mentioned, we have performed additional simulations to evaluate its importance in driving multistability. 

      For the added death rate, we have calculated the bistability feasibility of two-species populations under different values of 𝐷. Our results suggested that (1) varying death rate indeed changed the bistability probability of the system; (2) when the death rate was zero, mobile genetic elements that only modify growth rates would have no effects on system’s bistability. These results highlighted the importance of added death rate in driving multistability (Fig. S2, line 136-142). 

      For the interspecies interaction strength, we first extended our analysis on two-species populations. By calculating the bistability probability under different values of 𝛾, we showed that when interspecies interaction strength was smaller than 1, the influence of HGT on population bistability became weak (Fig. S3, line 143-147). We also considered heterogenous interaction strengths in multispecies communities, by randomly sampling 𝛾<sub>ij</sub> values from uniform distributions. While our results suggested the heterogeneous distribution of 𝛾<sub>ij</sub> didn’t fundamentally change the main conclusion, the mean value and variance of 𝛾<sub>ij</sub> affected the influence of HGT on multistability. The effects of HGT on community multistability becomes stronger when the mean value of 𝛾<sub>ij</sub> gets larger than 1 and the variance of 𝛾<sub>ij</sub> is small (Fig. S12, line 190-196).

      We also analyzed different ranges of growth rates effects of mobile genetic elements. In particular, we sampled 𝜆<sub>ij</sub> values from uniform distributions with given widths. Greater width led to larger range of growth rate effects. We used five-species populations as an example and tested different ranges. Our results suggested that multistability was more feasible when the growth rate effects of MGEs were small. The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13, line 197-205).

      (2) The authors previously developed this theoretical model to study the impact of horizontal gene transfer on species coexistence. In this sense, it seems that the authors are exploring a different (stronger interspecies competition) range of parameter values of the same model, which could potentially limit novelty and generality.

      We appreciate the comment. In a previous work (PMID: 38280843), we developed a theoretical model that incorporated horizontal gene transfer process into the classic LV framework. This model provides opportunities to investigate the role of HGT in different open questions of microbial ecology. In the previous work, we considered one fundamental question: how competing microbes coexist stably. In this work, however, we focused on a different problem: how alternative stable states emerge in complex communities. While the basic theoretical tool that we applied in the two works were similar, the scientific questions, application contexts and the implications of our analysis were largely different. The novelty of this work arose from the fact that it revealed the conceptual linkage between alternative stable states and a ubiquitous biological process, horizontal gene transfer. This linkage is largely unknown in previous studies. Exploring such a linkage naturally required us to consider stronger interspecies competitions, which in general would diminish coexistence but give rise to multistability. We believe that the analysis performed in this work provide novel and valuable insights for the field of microbial ecology. 

      With all the supplemented simulations that we carried out in light of the all the reviewer’s comments, we believe the updated manuscript also provide a unified framework to understand how different biological processes collectively shaped the multistability landscape of complex microbiota undergoing horizontal gene transfer. The comprehensive analyses performed and the diverse scenarios considered in this study also contribute to the novelty and generality of this work.  

      (3) The authors analyze several scenarios that, in my opinion, naturally follow from the results and parameter value choices in the first sections, making their analysis not very informative. For example, after showing that horizontal gene transfer can increase multistability both between pairs of species and in a community context, the way they model different niches does not bring significantly new results. Given that the authors showed previously in the manuscript that horizontal gene transfer can impact multistability in a community in which all species interact with each other, one might expect that it will also impact multistability in a larger community made of (sub)communities that are independent of (not interacting with) each-which is the proposed way for modelling niches. A similar argument can be made regarding the analysis of (spatially structured) metacommunities. It is known that, for smaller enough dispersal rates, space can promote regional diversity by enabling each local community to remain in a different stable state. Therefore, in conditions in which the impact of horizontal gene transfer drives multistability, it will also drive regional diversity in a metacommunity.

      Thanks. Based on the reviewer’s comments, we have move Fig. 3 and 4 to Supplementary Information. In the updated manuscript, we have focused more on analyzing the roles of different parameters in shaping community multistability.

      (4) In some cases, the authors consider that mobile genetic elements can lead to ~50% growth rate differences. In the presence of an added death rate, this can be a relatively strong advantage that makes the fastest grower easily take over their competitors. It would be important to discuss biologically relevant examples in which such growth advantages driven by mobile genetic elements could be expected, and how common such scenarios might be.

      We appreciate the suggestion. Mobile genetic elements can drive large growth rate differences when they encode adaptative traits like antibiotic resistance (line 197-198). 

      We also analyzed different ranges of growth rates effects of mobile genetic elements, by sampling 𝜆<sub>ij</sub> values from uniform distributions with given widths. Our results suggested that multistability was more feasible when the fitness effects of MGEs were small (Fig. S13b). The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13a and b). We discussed these results in line 197-205 of the updated main text.

      Reviewer #3 (Public review):

      Hong et al. used a model they previously developed to study the impact of horizontal gene transfer (HGT) on microbial multispecies communities. They investigated the effect of HGT on the existence of alternative stable states in a community. The model most closely resembles HGT through the conjugation of incompatible plasmids, where the transferred genes confer independent growth-related fitness effects. For this type of HGT, the authors find that increasing the rate of HGT leads to an increasing number of stable states. This effect of HGT persists when the model is extended to include multiple competitive niches (under a shared carrying capacity) or spatially distinct patches (that interact in a grid-like fashion). Instead, if the mobile gene is assumed to reduce between-species competition, increasing HGT leads to a smaller region of multistability and fewer stable states. Similarly, if the mobile gene is deleterious an increase in HGT reduces the parameter region that supports multistability.

      This is an interesting and important topic, and I welcome the authors' efforts to explore these topics with mathematical modeling. The manuscript is well written and the analyses seem appropriate and well-carried out. However, I believe the model is not as general as the authors imply and more discussion of the assumptions would be helpful (both to readers + to promote future theoretical work on this topic). Also, given the model, it is not clear that the conclusions hold quite so generally as the authors claim and for biologically relevant parameters. To address this, I would recommend adding sensitivity analyses to the manuscript.

      We thank the reviewer for the agreeing that our work addressed an important topic and was wellconducted. We are also grateful for the suggestion on sensitivity analysis, which is very helpful to improve the rigor and generality of our conclusion. All the raised issues have been fully addressed in the updated manuscript and below.

      Specific points

      (1) The model makes strong assumptions about the biology of HGT, that are not adequately spelled out in the main text or methods, and will not generally prove true in all biological systems. These include:

      a) The process of HGT can be described by mass action kinetics. This is a common assumption for plasmid conjugation, but for phage transduction and natural transformation, people use other models (e.g. with free phage that adsorp to all populations and transfer in bursts).

      b) A subpopulation will not acquire more than one mobile gene, subpopulations can not transfer multiple genes at a time, and populations do not lose their own mobilizable genes. [this may introduce bias, see below].

      c) The species internal inhibition is independent of the acquired MGE (i.e. for p1 the self-inhibition is by s1).

      These points are in addition to the assumptions explored in the supplementary materials, regarding epistasis, the independence of interspecies competition from the mobile genes, etc. I would appreciate it if the authors could be more explicit in the main text about the range of applicability of their model, and in the methods about the assumptions that are made.

      We are grateful for the reviewer’s suggestions. In main text and methods of the updated manuscript, we have made clear the assumptions underlying our analysis. For point (a), we have clarified that our model primarily focused on plasmid transfer dynamics (line 74, 101, 517). Therefore, the process of HGT can be described by mass action kinetics, which is commonly assumed for plasmid transfer (line 537-538). For point (b), our model allows a cell to acquire more than one mobile genes. Please see our response to point (3) for details. We have also made it clear that we assumed the populations would not lose their own mobile gene completely (line 526-527). For (c), we have also clarified it in the updated manuscript (line 111-112, 527-528). 

      We have also performed a series of additional simulations to show the range of applicability of our model. In particular, we discuss the role of other mechanisms, including interspecies interaction strength, the growth rate effects of MGEs, MGE epistasis and microbial death rates in shaping the multistability of microbial communities undergoing HGT. These results were provided in Fig. S2, S3, S9, S10, S11, S12, S13 and S15.

      (2) I am not surprised that a mechanism that creates diversity will lead to more alternative stable states. Specifically, the null model for the absence of HGT is to set gamma to zero, resulting in pij=0 for all subpopulations (line 454). This means that a model with N^2 classes is effectively reduced to N classes. It seems intuitive that an LV-model with many more species would also allow for more alternative stable states. For a fair comparison, one would really want to initialize these subpopulations in the model (with the same growth rates - e.g. mu1(1+lambda2)) but without gene mobility.

      We appreciate the insightful comments. The reviewer was right that in our model HGT created additional subpopulations in the community. However, with or without HGT, we calculated the species diversity and multistability based on the abundances of the 𝑁 species (s<sub>i</sub> in our model), instead of all the p<sub>ij</sub> subpopulations. Therefore, although there exist more ‘classes’ in the model with HGT, the number of ‘classes’ considered when we calculated community diversity and multistability was equal. In light of the reviewer’s suggestion, we have also performed additional simulations, where we initialized the subpopulations in the model with nonzero abundances. Our results suggested that initializing the p<sub>ij</sub> subpopulations with non-zero abundances didn’t change the main conclusion (Fig. S11, line 188-189).

      (3) I am worried that the absence of double gene acquisitions from the model may unintentionally promote bistability. This assumption is equivalent to an implicit assumption of incompatibility between the genes transferred from different species. A highly abundant species with high HGT rates could fill up the "MGE niche" in a species before any other species have reached appreciable size. This would lead to greater importance of initial conditions and could thus lead to increased multistability.

      This concern also feels reminiscent of the "coexistence for free" literature (first described here http://dx.doi.org/10.1016/j.epidem.2008.07.001 ) which was recently discussed in the context of plasmid conjugation models in the supplementary material (section 3) of https://doi.org/10.1098/rstb.2020.0478 .

      We appreciate the comments. Our model didn’t assume the incompatibility between MGEs transferred from different species. Instead, it allows a cell to acquire more than one MGEs. In our model, p<sub>ij</sub> described the subpopulation in the 𝑖-th species that acquired the MGE from the 𝑗th species. Here, p<sub>ij</sub> can have overlaps with p<sub>ik</sub> (𝑗 ≠ 𝑘). In other words, a cell can belong to p<sub>ij</sub> and p<sub>ik</sub> at the same time. The p<sub>ij</sub> subpopulation is allowed to carry the MGEs from the other species. In the model, we used to describe the influence of the other MGEs on the growth of p<sub>ij</sub>.

      We also thank the reviewer for bringing two papers into our attention. We have cited and discussed these papers in the updated manuscript (line 355-362).

      (4) The parameter values tested seem to focus on very large effects, which are unlikely to occur commonly in nature. If I understand the parameters in Figure 1b correctly for instance, lambda2 leads to a 60% increase in growth rate. Such huge effects of mobile genes (here also assumed independent from genetic background) seem unlikely except for rare cases. To make this figure easier to interpret and relate to real-world systems, it could be worthwhile to plot the axes in terms of the assumed cost/benefit of the mobile genes of each species.

      Thanks for the comments. In the main text, we presented one simulation results that assumed relatively large effects of MGE on species fitness, as the reviewer pointed out. In the updated manuscript, we have supplemented numerical simulations that considered different ranges of fitness effects, including the fitness effect as small as 10% (Fig. S13a). We have also plotted the relationship between community multistability and the assumed fitness effects of MGEs, as the reviewer suggested (Fig. S13b). Our results suggested that multistability was more feasible when the fitness effects of MGEs were small, and changing the range of MGE fitness effects didn’t fundamentally change our main conclusion. These results were discussed in line 197-205 of the updated main text.

      Something similar holds for the HGT rate (eta): given that the population of E. coli or Klebsiella in the gut is probably closer to 10^9 than 10^12 (they make up only a fraction of all cells in the gut), the assumed rates for eta are definitely at the high end of measured plasmid transfer rates (e.g. F plasmid transfers at a rate of 10^-9 mL/CFU h-1, but it is derepressed and considered among the fastest - https://doi.org/10.1016/j.plasmid.2020.102489 ). To adequately assess the impact of the HGT rate on microbial community stability it would need to be scanned on a log (rather than a linear) scale. Considering the meta-analysis by Sheppard et al. it would make sense to scan it from 10^-7 to 1 for a community with a carrying capacity around 10^9.

      We thank the reviewer for the constructive suggestion. We have carried out additional simulations by scanning the 𝜂 value from 10<sup>-7</sup> to 1. The results suggested that increasing HGT rates started to promote multistability when 𝜂 value exceeded 10<sup>-2</sup> per hour (Fig. S9, line 337-346). This corresponds to a conjugation efficiency of 10<sup>-11</sup> cell<sup>-1</sup> ∙ mL<sup>-1</sup>∙ mL when the maximum carrying capacity equals 10<sup>9</sup> cellsmL<sup>-1</sup>, or a conjugation efficiency of 10<sup>-14</sup> cell<sup>-1</sup> ∙ hr<sup>-1</sup>∙ mL when the maximum carrying capacity equals 10<sup>12</sup> cellsmL<sup>-1</sup>.

      (5) It is not clear how sensitive the results (e.g. Figure 2a on the effect of HGT) are to the assumption of the fitness effect distribution of the mobile genes. This is related to the previous point that these fitness effects seem quite large. I think some sensitivity analysis of the results to the other parameters of the simulation (also the assumed interspecies competition varies from figure to figure) would be helpful to put the results into perspective and relate them to real biological systems.

      We appreciate the comments. In light of the reviewer’s suggestion, we have changed the range of the fitness effects and analyzed the sensitivity of our predictions to this range. As shown in Fig. S13, changing the range of MGE fitness effects didn’t alter the qualitative interplay between HGT and community multistability. We have also examined the sensitivity of the results to the strength of interspecies competition strength (Fig. S3, S10, S12). These results suggested that while the strength of interspecies interactions played an important role in shaping community multistability, the relationship between HGT rate and multistability was not fundamentally changed by varying interaction strength. In addition, we examined the role of death rates (Fig. S2). In the updated manuscript, we discussed the sensitivity of our prediction to these parameters in line 136-147, 190205, 335-354.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Please find below a few suggestions that, in my opinion, could help improve the manuscript.

      TITLE

      It might not be clear what I 'gene exchange communities' are. Perhaps it could be rewritten for more specificity (e.g. '...communities undergoing horizontal gene transfer').

      We have updated the title as the reviewer suggested.

      ABSTRACT

      The abstract could also be edited to improve clarity and specificity. Terms like 'complicating factors' are vague, and enumerating specific factors would be better. The results are largely based on simulations, no analytical results are plotted, so I find that the sentence starting with 'Combining theoretical derivation and numerical simulations' can be a bit misleading.

      We appreciate the suggestions. We have enumerated the specific factors and scenarios in the updated abstract (line 18-26). We have also replaced 'Combining theoretical derivation and numerical simulations' with ‘Combining mathematical modeling and numerical simulations’.

      INTRODUCTION

      -  Line 42, please revise this paragraph. The logical flow is not so clear, it seems a bit like a list of facts, but the main message might not be clear enough. Also, it would be good to define 'hidden' states or just rewrite this sentence.

      We appreciate the suggestion. In the updated manuscript, we have rewritten this paragraph to improve the logical flow and clarity (line 46-52).

      -  Line 54, there is little detail about both theoretical models and HGT in this paragraph, and mixing the two makes the paragraph less focused. I suggest to divide into two paragraphs and expand its content. For example, you could explain a bit some relevant implications of MGE.

      We appreciate the suggestion. In the updated manuscript, we have divided this paragraph into two paragraphs, focusing on theoretical models and HGT, respectively (line 55-71). In particular, we have added explanations on the implications of MGEs (line 66-69), as the reviewer suggested.

      -  Line 72, as mentioned in the abstract, it would be better to explicitly mention which confounding factors are going to be discussed.

      Thanks for the suggestion. We have rewritten this part as “We further extended our analysis to scenarios where HGT changed interspecies interactions, where microbial communities were subjected to strong environmental selections and where microbes lived in metacommunities consisting of multiple local habitats. We also analyzed the role of different mechanisms, including interspecies interaction strength, the growth rate effects of MGEs, MGE epistasis and microbial death rates in shaping the multistability of microbial communities. These results created a comprehensive framework to understand how different dynamic processes, including but not limited to HGT rates, collectively shaped community multistability and diversity” (line 75-82).

      RESULTS

      -  The basic concepts (line 77) should be explained with more detail, keeping the non-familiar reader in mind. The reader might not be familiar with the concept of bistability in terms of species abundance. Also, note that mutual inhibition does not necessarily lead to positive feedback, as an interaction strength between 0 and 1 might still be considered inhibition. In any case, in Figure 1 it is not obvious how the positive feedback is represented, the caption should explain it. Note that neither the main text nor the caption explains the metaphor of the landscape and the marble that you are using in Figure 1a.

      We have rewritten this paragraph to provide more details on the basic concepts (line 86-99). We have removed the statement about ‘mutual inhibition’ to avoid being misleading. We have also updated the caption of Fig. 1a to explain the metaphor of the landscape and the marble (line 389396). 

      -  In the classical LV model, bistability does not depend on growth rates, but only on interaction strength. Therefore, I think that much of the results are significantly influenced by the added death rate. I believe that if the death rate is set to zero, mobile genetic elements that only modify growth rates will have no effect on the system's bistability. Because of this, I think that a thorough analysis of the role of the added death (dilution) rate and the distribution of growth rates is especially needed.

      We are grateful for the reviewer’s insightful comments. In the updated manuscript, we have thoroughly analyzed the role of the added death (dilution) rate on the bistability of communities composed of two species (Fig. S2). Indeed, as the reviewer pointed out, if the death rate equals zero, mobile genetic elements that only modify growth rates will have no effect on the system's bistability. We have discussed the role of death rate in line 136-142 of the updated manuscript.

      We have also expanded our analysis on the distribution of growth rates. In particular, we considered different ranges of growth rates effects of mobile genetic elements, by sampling 𝜆<sub>ij</sub> values from uniform distributions with given widths (Fig. S13). Greater width led to larger range of growth rate effects. We used five-species populations as an example and tested different ranges.

      Our results suggested that multistability was more feasible when the growth rate effects of MGEs were small (Fig. S13b). The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13a). These results are discussed in line 197205 of the updated manuscript.

      -  The analysis uses gamma values that, in the absence of an added death rate, render a species pair bistable. Therefore, multistability would be quite expected for a 5 species community. Note that, multistability is possible in communities of more than 2 species even if all gamma values are smaller than 1. Analyzing a wide range of interaction strength distributions would really inform on the relative role of HGT in multistability across different community scenarios.

      We are grateful for the reviewer’s suggestion. In light of the reviewer’s comments, in the updated manuscript, we have performed additional analysis by focusing on a broader range of interaction strengths (Fig. S3, S10, S12), especially the gamma values below 1 (Fig. S10). Our results agreed with the reviewer’s notion that multistability was possible in communities of more than 2 species even if all gamma values were smaller than 1 (Fig. S10). 

      -  I would recommend the authors extend the analysis of the model used for Figures 1 and 2. Figures 3 and 4 could be moved to the supplement (see my point in the public review), unless the authors extend the analysis to explain some non-intuitive outcomes for niches and metacommunities.

      Thanks. In the updated manuscript we have performed additional simulations to extend the analysis in Figure 1 and 2. These results were presented in Fig. S2, S3, S9, S10, S11, S12, and S13. We have also moved Figure 3 and 4 to SI as the reviewer suggested.

      -  The authors seem to refer to fitness and growth rates as the same thing. This could lead to confusion - the strongest competitor in a species pair could also be interpreted as the fittest species despite being the slowest grower. I think there's no need to use fitness if they refer to growth rates. In any case, they should define fitness if they want to use this concept in the text.

      We are grateful for the insightful suggestion. To avoid confusion, we have used ‘growth rate’ throughout the updated manuscript.

      -  Across the text, the language needs some revision for clarity, specificity, and scientific style. In lines 105 - 109 there are some examples, like the use of 'in a lot of systems', and ' interspecies competitions' (I believe they mean interspecies interaction strengths).

      We appreciate the reviewer for pointing them out. We have thoroughly checked the text and made the revisions whenever applicable to improve the clarity and specificity.

      -  Many plots present the HGT rate on the horizontal axis. Could the authors explain why is it that the rate of HGT is relatively important for the number of alternative stable states? I understand how from zero to a small positive number there is a qualitative change. Beyond that, it shouldn't affect bistability too much, I think. If I am right, then other parameters could be more informative to plot in the horizontal axis. If I am wrong, I think that providing an explanation for this would be valuable.

      Thanks. To address the reviewer’s comment, we have systematically analyzed the effects of HGT on community multistability, by scanning the HGT rate from 10<sup>-7</sup> to 10<sup>0</sup>hr<sup>-1</sup> . In communities of two or multiple species, our simulation results showed that multistability gradually increased with HGT rate when HGT rate exceeded 10<sup>2</sup>hr<sup>-1</sup>. These results, presented in Fig. S9 and discussed in line 337-346, provided a more quantitative relationship between multistability and HGT rate.

      While in this work we showed the potential role of HGT in modulating community multistability, our results didn’t exclude the role of the other parameters. Motivated by the comments raised by the reviewers, in the updated manuscript, we have performed additional simulations to analyze the influence of other parameters in shaping community multistability. These parameters include death or dilution rate (Fig. S2), interaction strength (Fig. S3, S9, S10, S11, S12, S14, S15), 𝜆 range (Fig. S13, S15) and 𝛿 value (Fig. 3g, h, i). In many of the supplemented results (Fig. S2b, S3b, S13b, Fig. 3g, 3h and 3i), we have also plotted the data by using these parameters as the x axis. We believe the updated work now provided a more comprehensive framework to understand how different mechanisms, including but not limited to HGT, might shape the multistability of complex microbiota. These points were discussed in line 136-147, 190-205, 238-253, 334-354 of the updated main text. 

      -  My overall thoughts on the case of antibiotic exposure are similar to those of previous sections. Very few of the different parameters of the model are analyzed and discussed. In this case, the authors increased the interaction strength to ~0.4 times higher compared to previous sections. Was this necessary, and why?

      Thanks for the comments. In the previous draft, the interaction strength 𝛾=1.5 was tested as an example. Motivated by the reviewer’s comments, in the updated manuscript, we have examined different interaction strengths, including the strength ( 𝛾 = 1.1 ) commonly tested in other scenarios. The prediction equally held for different 𝛾 values (Fig. S15). We have also analyzed different 𝜆 ranges (Fig. S15). These results, together with the analyses presented in the earlier version of the manuscript, suggested the potential role of HGT in promoting multistability for communities under strong selection. The supplemented results were presented in Fig. S15 and discussed in line 293-295 of the updated manuscript.

      -  Line 195, if a gene encodes for the production of a public good, why would its HGT reduce interaction strength? I can think of the opposite scenario: the gene is a public good, and without HGT there is only one species that can produce it. Let's imagine that the public good is an enzyme that deactivates an antibiotic that is present in the environment, and then the species that produces has a positive interaction with another species in a pairwise coculture. If HGT happens, the second species becomes a producer and does not need the other one to survive in the presence of antibiotics anymore. The interaction can then become more competitive, as e.g. competition for resources could become the dominant interaction.

      We are grateful for pointing it out. In the updated manuscript, we have removed this statement.

      DISCUSSION

      -  L 267 "by comparison with empirical estimates of plasmid conjugation rates from a previous study [42], the HGT rates in our analysis are biologically relevant in a variety of natural environments". The authors are using a normalized model and the relevance of other parameter values is not discussed. If the authors want to claim that they are using biologically relevant HGT, they should also discuss whether the rest of the parameter values are biologically relevant. I recommend relaxing this statement about HGT rates.

      We appreciate the suggestion. We agree with the reviewer that other parameters including the death/dilution rate, interactions strength and 𝜆 ranges are also important in shaping community multistability. We have performed additional analysis to show the effects of these parameters. In light of the reviewer’s suggestion, we have relaxed this statement and thoroughly discussed the context-dependent effect of HGT as well as the roles of different parameters (line 334-354).

      -  Last sentence: "Therefore, inhibiting the MGE spread using small molecules might offer new opportunities to reshape the stability landscape and narrow down the attraction domains of the disease states". It is not clear what procedure/technique the authors are suggesting. If they want to keep this statement, the authors should give more details on how small molecules can be/are used to inhibit MGE.

      We appreciated the comments. Previous studies have shown some small molecules like unsaturated fatty acids can inhibit the conjugative transfer of plasmids. By binding the type IV secretion traffic ATPase TrwD, these compounds limit the pilus biogenesis and DNA translocation. We have provided more details regarding this statement in the updated manuscripts (line 376-379).

      METHODS

      -  Line 439, mu_i should be presented as the maximum 'per capita' growth rate.

      We have updated the definition of 𝜇i following the suggestion (line 529).

      -  Line 444, this explanation is hard to follow, please expand it to provide more details. You could provide an example, like explaining that all individuals from S1 have the MGE1 and therefore they have mu_1 = mu_01 ... After HGT, their fitness changes if they get the plasmid from S2, so a term lambda2 appears.

      Thanks. In the updated manuscript, we have expanded the explanation by providing an example as the reviewer suggested (line 534-537).

      -  The normalization assumes a common carrying capacity Nm (Eqs 1-4) and then it's normalized (Eqs. 5-8). It would be better to start from a more general scenario in which each species has a different carrying capacity and then proceed with the normalization.

      We appreciate the suggestion. In the updated manuscript, we have started our derivation from the scenario where each species has a different carrying capacity before proceeding with the normalization (section 1 of Methods, line 516-554). The same equations can be obtained after normalization.

      -  I think that the meaning of kappa (the plasmid loss rate) is not explained in the text.

      Thanks for pointing it out. We have explained the meaning of kappa in the updated text (line 108, 154, 539-541, 586-587, 607).

      SUPPLEMENT

      -  Figure S4, what are the different colors in panel b?

      In panel b of Fig. S4, the different colors represent the simulation results repeated with randomized growth rates. We have made it clear in the updated SI.

      Reviewer #3 (Recommendations for the authors):

      (1) Please extend your description of the model, so it is easier to understand for readers who have not read the first paper. Especially the choice to describe the model as species and subpopulations, as opposed to writing it as MGE-carrying and MGE-free populations of each species makes it quite complicated to understand which parameters influence each other.

      Thanks for the suggestion. We have extended the model description in the updated manuscript, which provides a more detailed introduction on model configurations and parameter definitions (line 86-99, 101-113, 151-159). We have also updated the Methods to extend the model description.

      (2) Please define gamma_ji in equation 13 and eta_jki in equation 14 (how to map the indices onto the assumed directionality of the interaction).

      We have defined these two parameters in the updated manuscript (line 584-586, 630-632).

      (3)  Line 511: please add at the beginning of this paragraph that you are assuming a grid-like arrangement of patches which will be captured by dispersal term H.

      We have updated this paragraph to make this assumption clear (line 636-637).

      (4)  Line 540: "used in our model" (missing a word).

      We have corrected it in the updated manuscript.

      (5)  Currently the analyses looking at the types of growth effects HGT brings (Figures 5-7) feel very "tacked on". These are not just "confounding factors", but rather scenarios that are much more biologically realistic than the assumption of independent effects. I would introduce them earlier in the text, as I think many readers may not trust your results until they know this was considered (+ how it changes the conclusions).

      We are grateful for the suggestion. We agree with the reviewer that these biologically realistic scenarios should be introduced earlier in the text. In the updated manuscript, we have moved these analyses forward, as sections 3, 4 and 5. We have also avoided the term “confounding factors”. Instead, in the updated manuscript, we have separated these analyses into different sections, and clearly described each scenario in the section title (line 217-218, 254, 275).

      (6)  In some places the manuscript refers to HGT, in others to MGE presence (e.g. caption of Figure 6). These are not generally the same thing, as HGT could also occur due to extracellular vesicles or natural transformation etc. Please standardize the nomenclature and make it clearer which type of processes the model describes.

      We appreciate the comment. The model in this work primarily focused on the process of plasmid transfer. We have made it clear throughout the main text. 

      (7)  In many figures the y-axis starts at a value other than 0. This is a bit misleading. In addition, I would recommend changing the title "Area of bistability region" to "Area of bistability" or perhaps even "Area of multistability" (since more than two species are considered).

      Thanks for the suggestion. We have updated all the relevant figures to make sure that their y-axes start at 0. We have also changed the title “Area of bistability region” to “Area of multistability”, whenever it is applicable.

      (8)  Figure 7: what are the assumed fitness effects of the mobile genes in the simulation? Which distribution were they drawn from? Please add this info to the figure caption here and elsewhere.

      In Figure 7, we explored an extreme scenario of the fitness effects of the mobile genes, where the population was subjected to strong environmental selection and only cells carrying the mobile gene could grow. Therefore, the carriage of the mobile gene changed the species growth rate from 0 to a positive value µ<sub>i</sub>. When calculating the number of stable states in the communities, we randomly drew the µ<sub>i</sub> values from a uniform distribution between 0.3 and 0.7 hr<sup>-1</sup>. We had added this information in the figure caption (line 505-508) and method (line 615-617) of the updated manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their time and thoughtful comments. We believe that the further analyses suggested have made the results clearer and more robust. Below, we briefly highlight the key points addressed in the revision and the new evidence supporting them. Then, we address each reviewer’s critiques point-by-point.

      - Changes in variability with respect to time/experience

      Both reviewers #1 and #3 asked whether the variability in grid properties observed was dependent on time or experience. This is an important point, given that such a dependence on time could lead to interesting hypotheses about the underlying dynamics of the grid code. However, in the new analyses we performed, we do not observe changes in grid variability within a session (Fig S5 of the revised manuscript), suggesting that the grid variability seen is constant within the timescale of the data set.

      - The assumption of constant grid parameters in the literature

      Reviewer #2 pointed out that it had been appreciated by experimentalists that grid properties are variable within a module. We agree that we may have overstated the universality of this assumption in the original manuscript, and we have toned down the language in the revision. However, we note that many previous theoretical studies assumed these properties to be constant, within a given module. We provide some examples below, and have added evidence of this assertion, with citations to the theoretical literature, to the revised manuscript .

      - Additional sources of variability

      Reviewer #3 pointed out additional sources that might explain the variability observed in the paper (beyond time and experience). These sources include: field width, border location, and the impact of conjunctive cells. We have run additional analyses and have found no significant impact on the observed variability from any of these factors. We believe that these are important controls, and have added them to the manuscript (Fig S4-S7 of the revised manuscript)

      - Analysis of computational models

      Reviewer #3 noted that our results could be strengthened by performing similar analyses on the output of computational models of grid cells. This is a good idea. We have now measured the variability of grid properties in a recent normative recurrent neural network (RNN) model that develops grid cells when trained to perform path integration (Sorscher et al., 2019). This model has been shown to develop signatures of a 2D toroidal attractor (Sorscher et al., 2023) and achieves a high accuracy on a simple path integration task. Interestingly, the units with the greatest grid scores also exhibit a range of grid spacings and grid orientations (Fig S8 of the revised manuscript). Furthermore, by decreasing the amount of sparsity (through decreasing the weight decay regularization), we found an increase in the variability of the grid properties. This analysis demonstrates a heretofore unknown similarity between the RNN models trained to perform path integration and recorded grid cells from MEC. It additionally provides a framework for computational analysis of the emergence of grid property variability.

      Reviewer #1:

      (1) Is the variability in grid spacing and orientation that the authors found intrinsically organized or is it shaped by experience? Previous research has shown that grid representations can be modified through experience (e.g., Boccara et al., Science 2019). To understand the dynamics of the network, it would be important to investigate whether robust variability exists from the beginning of the task period (recording period) or whether variability emerges in an experience-dependent manner within a session.

      This is an interesting question that was not addressed in the paper. To test this, we performed additional analysis to resolve whether the variability changes across a session.

      Using a sliding window, we have measured changes in variability with respect to recording time (Fig S5A). To this end, we compute grid orientation and spacing over a time-window whose length is half the total length of the recording. From the population distribution of orientation and spacing values, we compute the standard deviation as a measure of variability. We repeat the same procedure, sliding the window forward until the variability for the second half of the recording is computed.

      We applied this approach to recording ID R12 (the same as in Figs 2-4) given that this recording session was significantly longer than the rest (nearly two hours). Results are shown in Fig S5B-C. For both orientation and spacing, no changes of variability with respect to time can be observed. Similar results were found for other modules (see caption of Fig S5 for statistics).

      We also note that the rats were already familiarized with the environment for 10-20 sessions prior to the recordings, so there may not be further learning during the period of the grid cell recordings. No changes in variability can be seen in Rat R across days (e.g., in Fig 5B R12 and R22 have similar distributions of variability). However, we note that it may be possible that there are changes in grid properties at time-scales greater than the recordings.

      (2) It is important to consider the optimal variability size. The larger the variability, the better it is for decoding. On the other hand, as the authors state in the

      Discussion, it is assumed that variability does not exist in the continuous attractor model. Although this study describes that it does not address how such variability fits the attractor theory, it would be better if more detailed ideas and suggestions were provided as to what direction the study could take to clarify the optimal size of variability.

      We appreciate this suggestion and agree that more discussion is warranted on how our results can be reconciled with previously observed attractor dynamics. To explore this, we studied the recurrent neural network (RNN) model from Sorscher et al. (2019), which develops grid responses when trained on path integration. This network has previously been found to develop signatures of toroidal topology (Sorscher et al., 2023), yet we find its grid responses also contain heterogeneity in grid properties (Fig S8). By decreasing the strength of the weight decay regularization (which leads to denser connectivity in the recurrent layer), we find an increase in the grid property variability. Interestingly, decreasing the weight decay regularization has been previously found to lead to weaker grid responses and worse ability of the RNN to perform path integration on environments larger than it was trained on. This approach not only provides preliminary evidence to our claim that too much variability can lead to weaker continuous attractor structure, but also provides a modeling framework with which future work can explore this question in more detail. We have added discussion of this issue to the manuscript text (Discussion).

      Reviewer #2:

      (1) Even though theoreticians might have gotten the mistaken impression that grid cells are highly regular, this might be due to an overemphasis on regularity in a subset of papers. Most experimentalists working with grid cells know that many if not most grid cells show high variability of firing fields within a single neuron, though this analysis focuses on between neurons. In response to this comment, the reviewers should tone down and modify their statements about what are the current assumptions of the field (and if possible provide a short supplemental section with direct quotes from various papers that have made these assumptions).

      We agree that some experimentalists are aware of variability in the recorded grid response patterns and that this work may not come as a complete surprise to them. We have toned down our language in the Introduction, changing “our results challenge a long-held assumption” to “our results challenge a frequently made assumption in the theoretical literature”. Additionally, we have added a caveat that “experimentalists have been aware” of the observed variability in grid properties.

      We would like to emphasize that the lack of work carefully examining the robustness of this variability has prevented a firm understanding of whether this is an inherent property of grid cells or due to measurement noise. The impact of this can be seen in theoretical neuroscience work where a considerable number of articles (including recent publications) start with the assumption that all grid cells within a module have identical properties, with the exception of phase shift and noise. We have now cited a number of these papers in the Introduction, to provide specific references. To further illustrate the pervasiveness of this assumption being explicitly made in theoretical neuroscience, below we provide quotes from a few important papers:

      “Cells with a common spatial period also share a common grid orientation; their responses differ only by spatial translations, or different preferred firing phases, with respect to their common response period” (Sreenivasan and Fiete, 2011)”

      “Grid cells are organized into discrete modules; within each module, the spatial scale and orientation of the grid lattice are the same, but the lattice for different cells is shifted in space.” (Stemmler et al., 2015)”

      “Recently, it was shown that grid cells are organized in discrete modules within which cells share the same orientation and periodicity but vary randomly in phase” (Wei et al., 2015)”

      “...cells within one module have receptive fields that are translated versions of one another, and different modules have firing lattices of different scales and orientations” (Dorrell et al., 2023)”

      In these works, this assumption is used to derive properties relating to the computational properties of grid cells (e.g., error correction, optimal scaling between grid spacings in different modules).

      In addition, since grid cells are assumed to be identical in the computational neuroscience community, there has been little work on quantifying how much variability a given model produces. This makes it challenging to understand how consistent different models are with our observations. This is illustrated in our analysis of a recent recurrent neural network (RNN) model of grid cells (Fig S8), which does exhibit variability.

      (2) The authors state that "no characterization of the degree and robustness of variability in grid properties within individual modules has been performed." It is always dangerous to speak in absolute terms about what has been done in scientific studies. It is true that few studies have had the number of grid cells necessary to make comparisons within and between modules, but many studies have clearly shown the distribution of spacing in neuronal data (e.g. Hafting et al., 2005; Barry et al., 2007; Stensola et al., 2012; Hardcastle et al., 2015) so the variability has been visible in the data presentations. Also, most researchers in the field are well aware that highly consistent grid cells are much rarer than messy grid cells that have unevenly spaced firing fields. This doesn't hurt the importance of the paper, but they need to tone down their statements about the lack of previous awareness of variability (specific locations are noted in the specific comments).

      We have toned down our language in the Introduction. However, we note that our point that no detailed analysis had been done on measuring the robustness of this variability stands. Thus, for the general community, it has not been clear whether this previously observed variability is noise or a real feature of the grid code.

      (3) The methods section needs to have a separate subheading entitled: How grid cells were assigned to modules" that clearly describes how the grid cells were assigned to a module (i.e. was this done by Gardner et al., or done as part of this paper's post-processing?

      We thank the reviewer for pointing out this missing information. We have added a new subsection in the Materials and Methods section, entitled “Grid module classification” to clarify how the grid cells are assigned to modules. In short, this was done by Gardner et al. (2022) using an unsupervised clustering approach that was viewed as enabling a less biased identification of modules. We did not perform any additional processing steps on module identity.

      Reviewer #3:

      (1) One possible explanation of the dispersion in lambda (not in theta) could be variability in the typical width of the field. For a fixed spacing, wider fields might push the six fields around the center of the autocorrelogram toward the outside, depending on the details of how exactly the position of these fields is calculated. We recommend authors show that lambda does not correlate with field width, or at least that the variability explained by field width is smaller than the overall lambda variability.

      We agree that this option had not been carefully ruled out by our previous analyses. To tackle this question, we compute the field width of a given cell using the value at the minima of its spatial autocorrelogram (Fig S4A-B). For all cells in recording ID R12, there is a non-significant negative linear correlation between grid field width and between-cell variability (Fig S4C) . The variability explained by the width of the field is 4% of the variability, as indicated by the R<sup>2</sup> value of the linear fit. Similar results were found for all other modules (see caption of Fig S4C for statistics). Therefore, we do not think that grid field width explains spacing variability.

      (2) An alternative explanation could be related to what happens at the borders. The authors tackle this issue in Figure S2 but introduce a different way of measuring lambda based on three fields, which in our view is not optimal. We recommend showing that the dispersions in lambda and theta remain invariant as one removes the border-most part of the maps but estimating lambda through the autocorrelogram of the remaining part of the map. Of course, there is a limit to how much can be removed before measures of lambda and theta become very noisy.

      We have performed additional analysis to explore the role of borders in grid property variability. To do so, we have followed the suggestion by the reviewer and have re-analyzed grid properties from the autocorrelogram when the border-most part of the maps are removed (Fig S6A-B). For all modules, we do not see any changes in variability (computed as the standard deviation of the population distribution) for either orientation or spacing. As predicted by the reviewer, after removing about 25% of the border-most part of the environment we start seeing changes in variability, as measures of theta and lambda become noisy and computed over a smaller spatial range. This result holds for all other modules (Fig S6C-D).

      (3) A third possibility is slightly more tricky. Some works (for example Kropff et al, 2015) have shown that fields anticipate the rat position, so every time the rat traverses them they appear slightly displaced opposite to the direction of movement. The amount of displacement depends on the velocity. Maps that we construct out of a whole session should be deformed in a perfectly symmetric way if rats traverse fields in all directions and speeds. However, if the cell is conjunctive, we would expect a deformation mainly along the cell's preferred head direction. Since conjunctive cells have all possible preferred directions, and many grid cells are not conjunctive at all, this phenomenon could create variability in theta and lambda that is not a legitimate one but rather associated with the way we pool data to construct maps. To rule away this possibility, we recommend the authors study the variability in theta and lambda of conjunctive vs non-conjunctive grid cells. If the authors suspect that this phenomenon could explain part of their results, they should also take into account the findings of Gerlei and colleagues (2020) from the Nolan lab, that add complexity to this issue.

      We appreciate the reviewer pointing out the possible role conjunctive cells may play. To investigate how conjunctive cells may affect the observed grid property variability, we have performed additional analyses taking into account if the grid cells included in the study are conjunctive. Comparing within- and between-cell variability of conjunctive vs. non-conjunctive cells in recording R12, we do not see any qualitative differences for either orientation or spacing (Fig S7A-B). When excluding conjunctive cells from the between-variability comparison, we do not see any significant difference compared to when these cells are included (Fig S7C-D). As such, it does not appear that conjunctive cells are the source of variability in the population.

      We further note that the number of putative conjunctive cells varied across modules and recordings. For instance, in recording Q1 and Q2, Gardner et al. (2022) reported 3 (out of 97) and 1 (out of 66) conjunctive cells, respectively. Given that we see variability robustly across recordings (Fig 5), we do not believe that conjunctive cells can explain the presence of variability we observe.

      (4) The results in Figure 6 are correct, but we are not convinced by the argument. The fact that grid cells fire in the same way in different parts of the environment and in different environments is what gives them their appeal as a platform for path integration since displacement can be calculated independently of the location of the animal. Losing this universal platform is, in our view, too much of a price to pay when the only gain is the possibility of decoding position from a single module (or non-adjacent modules) which, as the authors discuss, is probably never the case. Besides, similar disambiguation of positions within the environment would come for free by adding to the decoding algorithm spatial cells (non-hexagonal but spatially stable), which are ubiquitous across the entorhinal cortex. Thus, it seems to us that - at least along this line of argumentation - with variability the network is losing a lot but not gaining much.

      We agree that losing the continuous attractor network (CAN) structure and the ability to path integrate would be a very large loss. However, we do not believe that the variability we observe necessarily destroys either the CAN or path integration. We argue this for two reasons. First, the data we analyzed [from Gardner et al. (2022)] is exactly the data set that was found to have toroidal topology and therefore viewed to be consistent with a major prediction of CANs. Thus, the amount of variability in grid properties does not rule out the underlying presence of a continuous attractor. Second, path integration may still be possible with grid cells that have variable properties. To illustrate this, we analyzed data from Sorscher et al. (2019) recurrent neural network model (RNN) that was trained explicitly on path integration, and found that the grid representations that emerged had variability in spacing and orientation (see point #6 below).

      (5) In Figure 4 one axis has markedly lower variability. Is this always the same axis? Can the authors comment more on this finding?

      We agree that in Fig 4 the first axis has lower variability. We believe that this is specific to the module R12 and does not reflect any differences in axis or bias in the methods used to compute the axis metrics. To test this, we have performed the same analyses for other modules, finding that other recordings do not exhibit the same bias. Results for the modules with the most cells are shown below (Author response image 1).

      Author response image 1.

      Grid propertied along Axis 1 are not less variable for many recorded grid modules. Same as Fig.4C-D, but for four other recorded modules. Note that the variability along each axis is similar.

      (6) The paper would gain in depth if maps coming out of different computational models could be analyzed in the same way.

      We agree with the reviewer that examining computational models using the same approach would strengthen our results and we appreciate the suggestion. To address this, we have analyzed the results from a previous normative model for grid cells [Sorscher et al., (2019)] that trained a recurrent neural network (RNN) model to perform path integration and found that units developed grid cell like responses. These models have been found to exhibit signatures of toroidal attractor dynamics [Sorscher et al. (2023)] and exhibit a diversity of responses beyond pure grid cells, making them a good starting point for understanding whether models of MEC may contain uncharacterized variability in grid properties.

      We find that RNN units in these normative models exhibit similar amounts of variability in grid spacing and orientation as observed in the real grid cell recordings (Fig S8A-D). This provides additional evidence that this variability may be expected from a normative framework, and that the variability does not destroy the ability to path integrate (which the RNN is explicitly trained to perform).

      The RNN model offers possibilities to assess what might cause this variability. While we leave a detailed investigation of this to future work, we varied the weight decay regularization hyper-parameter. This value controls how sparse the weights in the hidden recurrent layer are. Large weight decay regularization strength encourages sparser connectivity, while small weight decay regularization strength allows for denser connectivity. We find that increasing this penalty (and enforcing sparser connectivity) decreases the variability of grid properties (Fig S8E-F). This suggests that the observed variability in the Gardner et al. (2022) data set could be due to the fact that grid cells are synaptically connected to other, non-grid cells in MEC.

      (7) Similarly, it would be very interesting to expand the study with some other data to understand if between-cell delta_theta and delta_lambda are invariant across environments. In a related matter, is there a correlation between delta_theta (delta_lambda) for the first vs for the second half of the session? We expect there should be a significant correlation, it would be nice to show it.

      We agree this would be interesting to examine. For this analysis, it is essential to have a large number of grid cells, and we are not aware of other published data sets with comparable cell numbers using different environments.

      Using a sliding window analysis, we have characterized changes in variability with respect to the recording time (Figure S5A). To do so, we compute grid orientation and spacing over a time-window whose length is half of the total length of the recording. From the population distribution of orientation and spacing values, we compute the standard deviation as a measure of between-cell variability. We repeat the same procedure, sliding the window forward until the variability for the second half of the recording is computed.

      We applied this approach to recording ID R12 (the same as in Figs 2-4) given that this recording session was significantly longer than the rest (almost two hours). Results are shown in Fig S5 B-C. For both orientation and spacing, no systematic changes of variability with respect to time were observed. Similar results were found for other modules (see caption of Fig S5 for statistics).

      We also note that the rats were already familiarized with the environment for 10-20 sessions prior to the recordings, so there may not be further learning during the period of the grid cell recordings. No changes in variability can be seen in Rat R across days (e.g., in Fig 5B R12 and R22 have similar distributions of variability). However, we note that it may be possible that there are changes in grid properties at time-scales greater than the recordings.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review): 

      Hotinger et al. explore the population dynamics of Salmonella enterica serovar Typhimurium in mice using genetically tagged bacteria. In addition to physiological observations, pathology assessments, and CFU measurements, the study emphasizes quantifying host bottleneck sizes that limit Salmonella colonization and dissemination. The authors also investigate the genetic distances between bacterial populations at various infection sites within the host.

      Initially, the study confirms that pretreatment with the antibiotic streptomycin before inoculation via orogastric gavage increases the bacterial burden in the gastrointestinal (GI) tract, leading to more severe symptoms and heightened fecal shedding of bacteria. This pretreatment also significantly reduces between-animal variation in bacterial burden and fecal shedding. The authors then calculate founding population sizes across different organs, discovering a severe bottleneck in the intestine, with founding populations reduced by approximately 10^6-fold compared to the inoculum size. Streptomycin pretreatment increases the founding population size and bacterial replication in the GI tract. Moreover, by calculating genetic distances between populations, the authors demonstrate that, in untreated mice, Salmonella populations within the GI tract are genetically dissimilar, suggesting limited exchange between colonization sites. In contrast, streptomycin pretreatment reduces genetic distances, indicating increased exchange.

      In extraintestinal organs, the bacterial burden is generally not substantially increased by streptomycin pretreatment, with significant differences observed only in the mesenteric lymph nodes and bile. However, the founding population sizes in these organs are increased. By comparing genetic distances between organs, the authors provide evidence that subpopulations colonizing extraintestinal organs diverge early after infection from those in the GI tract. This hypothesis is further tested by measuring bacterial burden and founding population sizes in the liver and GI tract at 5 and 120 hours post-infection. Additionally, they compare orogastric gavage infection with the less injurious method of infection via drinking, finding similar results for CFUs, founding populations, and genetic distances. These results argue against injuries during gavage as a route of direct infection. 

      To bypass bottlenecks associated with the GI tract, the authors compare intravenous (IV) and intraperitoneal (IP) routes of infection. They find approximately a 10-fold increase in bacterial burden and founding population size in immune-rich organs with IV/IP routes compared to orogastric gavage in streptomycin-pretreated animals. This difference is interpreted as a result of "extra steps required to reach systemic organs."

      While IP and IV routes yield similar results in immune-rich organs, IP infections lead to higher bacterial burdens in nearby sites, such as the pancreas, adipose tissue, and intraperitoneal wash, as well as somewhat increased founding population sizes. The authors correlate these findings with the presence of white lesions in adipose tissue. Genetic distance comparisons reveal that, apart from the spleen and liver, IP infections lead to genetically distinct populations in infected organs, whereas IV infections generally result in higher genetic similarity. 

      Finally, the authors investigate GI tract reseeding, identifying two distinct routes. They observe that the GI tracts of IP/IV-infected mice are colonized either by a clonal or a diversely tagged bacterial population. In clonally reseeded animals, the genetic distance within the GI tract is very low (often zero) compared to the bile population, which is predominantly clonal or pauciclonal. These animals also display pathological signs, such as cloudy/hardened bile and increased bacterial burden, leading the authors to conclude that the GI tract was reseeded by bacteria from the gallbladder bile. In contrast, animals reseeded by more complex bacterial populations show that bile contributes only a minor fraction of the tags. Given the large founding population size in these animals' GI tracts, which is larger than in orogastrically infected animals, the authors suggest a highly permissive second reseeding route, largely independent of bile. They speculate that this route may involve a reversal of known mechanisms that the pathogen uses to escape from the intestine. 

      The manuscript presents a substantial body of work that offers a meticulously detailed understanding of the population dynamics of S. Typhimurium in mice. It quantifies the processes shaping the within-host dynamics of this pathogen and provides new insights into its spread, including previously unrecognized dissemination routes. The methodology is appropriate and carefully executed, and the manuscript is well-written, clearly presented, and concise. The authors' conclusions are well-supported by experimental results and thoroughly discussed. This work underscores the power of using highly diverse barcoded pathogens to uncover the within-host population dynamics of infections and will likely inspire further investigations into the molecular mechanisms underlying the bottlenecks and dissemination routes described here.

      Major point:

      Substantial conclusions in the manuscript rely on genetic distance measurements using the Cavalli-Sforza chord distance. However, it is unclear whether these genetic distance measurements are independent of the founding population size. I would anticipate that in populations with larger founding population sizes, where the relative tag frequencies are closer to those in the inoculum, the genetic distances would appear smaller compared to populations with smaller founding sizes independent of their actual relatedness. This potential dependency could have implications for the interpretation of findings, such as those in Figures 2B and 2D, where antibiotic-pretreated animals consistently exhibit higher founding population sizes and smaller genetic distances compared to untreated animals.

      Thank you for raising this important point regarding reliance on cord distances for gauging genetic distance in barcoded populations. The reviewer is correct that samples with more founders will be more similar to the inoculum and thus inherently more similar to other samples that also have more founders. However, creation of libraries containing very large numbers of unique barcodes can often circumvent this issue. In this case, the effect size of chance-based similarity is not large enough to change the interpretation of the data in Figures 2B and 2D. In our case, the library has ~6x10<sup>4</sup> barcodes, and the founding populations in Figure 2B are ~10<sup>3</sup>. Randomly resampling to create two populations of 10<sup>3</sup> cells from an initial population with 6x10<sup>4</sup> barcodes is expected to yield largely distinct populations with very little similarity. Thus, the similarity between streptomycin-treated populations in Figure 2D is likely the result of biology rather than chance.  

      Reviewer #2 (Public review):

      In this paper, Hotinger et. al. propose an improved barcoded library system, called STAMPR, to study Salmonella population dynamics during infection. Using this system, the authors demonstrate significant diversity in the colonization of different Salmonella clones (defined by the presence of different barcodes) not only across different organs (liver, spleen, adipose tissues, pancreas, and gall bladder) but also within different compartments of the same gastrointestinal tissue. Additionally, this system revealed that microbiota competition is the major bottleneck in Salmonella intestinal colonization, which can be mitigated by streptomycin treatment. However, this has been demonstrated previously in numerous publications. They also show that there was minimal sharing between populations found in the intestine and those in the other organs. Upon IV and IP infection to bypass the intestinal bottleneck, they were able to demonstrate, using this library, that Salmonella can renter the intestine through two possible routes. One route is essentially the reverse path used to escape the gut, leading to a diverse intestinal population; while the other, through the bile, typically results in a clonal population. Although the authors showed that the STAMPR pipeline improved the ability to identify founder populations and their diversity within the same animal during infections, some of the conclusions appear speculative and not fully supported.

      (1) It's particularly interesting how the authors, using this system, demonstrate the dominant role of the microbiota bottleneck in Salmonella colonization and how it is widened by antibiotic treatment (Figure 1). Additionally, the ability to track Salmonella reseeding of the gut from other organs starting with IV and IP injections of the pathogen provides a new tool to study population dynamics (Figure 5). However, I don't think it is possible to argue that the proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces have different founder populations for reasons other than stochastic variations. All the barcoded Salmonella clones have the same fitness and the fact that some are found or expanded in one region of the gastrointestinal tract rather than another likely results from random chance - such as being forced in a specific region of the gut for physical or spatial reasons-and subsequent expansion, rather than any inherent biological cause. For example, some bacteria may randomly adhere to the mucus, some may swim toward the epithelial layer, while others remain in the lumen; all will proliferate in those respective sites. In this way, different founder populations arise based on random localization during movement through the gastrointestinal tract, which is an observation, but it doesn't significantly contribute to understanding pathogen colonization dynamics or pathogenesis. Therefore, I would suggest placing less emphasis on describing these differences or better discussing this aspect, especially in the context of the gastrointestinal tract.

      Thank you for helping us identify this area for further clarification. We agree with the reviewer’s interpretation that seeding of proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces with different founder populations is likely caused by stochastic variations, consistent with separate stochastic bottlenecks to establishing these separate niches. To clarify this point we have modified the text in the results section, “Streptomycin treatment decreases compartmentalization of S. Typhimurium populations within the intestine”.

      Change to text:

      “Except for the cecum and colon, in untreated animals the S. Typhimurium populations in different regions of the intestine were dissimilar (Avg. GD ranged from 0.369 to 0.729, 2D left); i.e., there is little sharing between populations in the intestine. These data suggest that there are separate bottlenecks in different regions of the intestine that cause stochastic differences in the identity of the founders. Interestingly, when these founders replicate, they do not mix, remaining compartmentalized with little sharing between populations throughout the intestinal tract (i.e., barcodes found in one region are not in other regions, Figure S3). This was surprising as the luminal contents, an environment presumably conducive to bacterial movement, were not removed from these samples.”

      In this section we are interested in the underlying biology that occurs after the initial bottleneck to preserve this compartmentalization during outgrowth of the intestinal population. In other words, what prevents these separate populations from merging (e.g., what prevents the bacteria replicating in the proximal small intestine from traveling through the intestine and establishing a niche in the distal small intestine)? While we do not explore the mechanisms of compartmentalization, we observe that it is disrupted by streptomycin pretreatment, suggesting a microbiota-dependent biological cause. 

      (2) I do think that STAMPR is useful for studying the dynamics of pathogen spread to organs where Salmonella likely resides intracellularly (Figure 3). The observation that the liver is colonized by an early intestinal population, which continues to proliferate at a steady rate throughout the infection, is very interesting and may be due to the unique nature of the organ compared to the mucosal environment. What is the biological relevance during infection? Do the authors observe the same pattern (Figures 3C and G) when normalizing the population data for the spleen and mesenteric lymph nodes (mLN)? If not, what do the authors think is driving this different distribution?

      Thank you for raising this interesting point. These data indicate that the liver is seeded from the intestine early during infection. The timing and source of dissemination have relevance for understanding how host and pathogen variables control the spread of bacteria to systemic sites. For example, our conclusion (early dissemination) indicates that the immune state of a host at the time of exposure to a pathogen, and for a short period thereafter, are what primarily influence the process of dissemination, not the later response to an active infection. 

      We observe that the liver and mucosal environments within the intestine have similar colonization behaviors. Both niches are seeded early during infection, followed by steady pathogen proliferation and compartmentalization that apparently inhibits further seeding. This results in the identity of barcodes in the liver population remaining distinct from the intestinal populations, and the intestinal populations remaining distinct from each other.

      We observe a similar pattern to the liver in the spleen and MLN (the barcodes in the spleen and MLN are dissimilar to the population in the intestine). To clarify this point, we have modified the text (below) and added this analysis as a supplemental figure (S4).

      Change to text:

      Genetic distance comparison of liver samples to other sites revealed that, regardless of streptomycin treatment, there was very little sharing of barcodes between the intestine and extraintestinal sites (Avg. GD >0.75, Figure 3C). Furthermore, the MLN and spleen populations also lacked similarity with the intestine (Figure S4). These analyses strongly support the idea that S. Typhimurium disseminates to extraintestinal organs relatively early following inoculation, before it establishes a replicative niche in the intestine.

      (3) Figure 6: Could the bile pathology be due to increased general bacterial translocation rather than Salmonella colonization specifically? Did the authors check for the presence of other bacteria (potentially also proliferating) in the bile? Do the authors know whether Salmonella's metabolic activity in the bile could be responsible for gallbladder pathology?

      The reviewer raises interesting points for future work. We did not check whether other bacterial species are translocating during S. Typhimurium infection. The relevance of Salmonella’s metabolic activity is also very interesting, and we hope these questions will be answered by future studies.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Minor points:

      (1) P. 9/10 "... the marked delay in shedding after IP and IV relative to orogastric inoculation suggest that the S. Typhimurium population encounters substantial bottleneck(s) on the route(s) from extraintestinal sites back to the intestine.": Can you conclude that from the data? It could also be possible that there is a biological mechanism (other than chance events) that delays the re-entry to the intestine.

      We propose that the delay in shedding indicates additional obstacles that bacteria face when re-entering the intestine, and that there are likely biological mechanisms that cause this delay. However, these unknown mechanisms effectively act as additional bottlenecks by causing a stochastic loss of population diversity. 

      (2) P. 11 "...both organs would likely contain all 10 barcodes. In contrast, a library with 10,000 barcodes can be used to distinguish between a bottleneck resulting in Ns = 1,000 and Ns = 10,000, since these bottlenecks result in a different number of barcodes in output samples. Furthermore, high diversity libraries reduce the likelihood that two tissue samples share the same barcode(s) due to random chance, enabling more accurate quantification of bacterial dissemination.": I agree with the general analysis, but I find it misleading to talk about the presence of barcodes when the analyses in this manuscript are based on the much more powerful comparison of relative abundance of individual tags instead of their presence or absence.

      The reviewer raises an excellent point, and the distinction between relative abundance versus presence/absence is discussed extensively in the original STAMPR manuscript. Although relative abundance is powerful, the primary metric used in this study (Ns) is calculated principally from the number of barcodes, corrected (via simulations) for the probability of observing the same barcode across distinct founders. Although this correction procedure does rely on barcode abundance, the primary driver of founding population quantification is the number of barcodes.

      (3) P.14 "the library in LB supplemented with SM was not significantly different than the parent strain" and Figure 2C: How was significance tested? How many times were the growth curves recorded? On my print-out, the red color has different shades for different growth curves.

      Significance was tested with a Mann-Whitney and growth curves were performed 5 times. Growth curves are displayed with 50% opacity, and as a result multiple curves directly on top of each other appear darker. The legend to S2 has been modified accordingly.

      (4) P.16: close bracket in the equation for FRD calculation.

      Done

      (5) Figure 2C "Average CFU per founder": I found the wording confusing at first as I thought you divided the average bacterial burden per organ by Ns, instead of averaging the CFU/Ns calculated for each mouse.

      The wording has been clarified. 

      (6) Figure 3B: It would be helpful to include expected genetic distances in the schematic as it is difficult to infer the genetic distance when only two of three, respectively, different "barcode colors" are used. While I find the explanation in the main text intuitive, a graphical representation would have helped me.

      Thank you for the suggestion. Unfortunately, using colors to represent barcodes is imperfect and limits the diversity that can be depicted. We have modified Figure 3B to further clarify. 

      (7) Figure 3C: Why do you compare the genetic distance to the liver, when you discuss the genetic distance of the intestinal population? Is it not possible that the intestinal populations are similar to the extraintestinal organs except the liver?

      For clarity, we chose to highlight exclusively the liver. However, we observed a similar pattern to the liver in other extraintestinal organs. To clarify the generalizability of this point we have added a supplemental figure with comparisons to MLN and Spleen (Supplemental figure S4) as well as further text.

      (8) Figure 3C & S5A: I found "+SM" and "+SM, Drinking" confusing and would have preferred "+SM, Gavage" and "+SM, Drinking" for clarity.

      Done, thank you for the suggestion.

      (9) Figure 3G&H: I find it worthy of discussion that the bacterial burden increases over time, while the founding population decreases. Does that not indicate that replication only occurs at specific sites leading to the amplification of only a few barcodes and thereby a larger change of the relative barcode abundance compared to the inoculum?

      From 5h to 120h the size of the founding population decreases in multiple intestinal sites. This likely indicates that the impact of the initial bottleneck is still ongoing at 5h, although further temporal analysis would be required to define the exact timing of the bottleneck. Notably, the passage time through the mouse intestine is ~5h. Many of the founders observed at 5h could be a population that will never establish a replicative niche, and failing to colonize be shed in the feces, bottlenecking the population between 5h and 120h. To clarify this point we have added the following text:

      Section “S. Typhimurium disseminates out of the intestine before establishing an intestinal replicative niche”.

      “In contrast to the liver, there were more founders present in samples from the intestine (particularly in the colon) at 5 hours versus 120 hours (Figure 3H). These data likely indicate that many of the founders observed in the intestine at 5 hours are shed in the feces prior to establishing a replicative niche, and demonstrates that the forces restricting the S. Typhimurium population in the intestine act over a period of > 5 hours.”  

      (10) Figure S2A: I do not understand this figure. Why are there more than 70.000 tags listed? I was under the impression the barcode library in S. Typhimurium had 55.000 tags while only the plasmid pSM1 had more than 70.000 (but the plasmid should not be relevant here). Why are there distinct lines at approximately 10^-5 and a bit lower? I would have expected continuously distributed barcode frequencies.

      During barcode analysis, each library is mapped to the total barcode list in the barcode donor pSM1, which contains ~70,000 barcodes. This enables consistent analysis across different bacterial libraries. The designation “barcode number” refers to the barcode number in pSM1, meaning many of the barcodes in the Salmonella library are at zero reads. This graph type was chosen to show there was no bias toward a particular barcode, however there is significant overlap of the points, making individual barcode frequencies difficult to see. We have changed the x-axis to state “pSM1 Barcode Number” and clarified in the figure legend.

      Since the y-axes on these graphs is on a log10 scale, the lines represent barcodes with 1 read, 2 reads, 3 reads, etc. As the number of reads per barcode increases linearly, the space between them decreases on logarithmic axes.

      (11) There are a few typos in the figure legends of the supplementary material. For example Figure S2: S. Typhimurium not italicized, ~7x105 no superscript. Fig. S4&5 ", Open circles" is "O" is capitalized.

      Typos have been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This is an interesting manuscript where the authors systematically measure rG4 levels in brain samples at different ages of patients affected by AD. To the best of my knowledge this is the first time that BG4 staining is used in this context and the authors provide compelling evidence to show an association with BG4 staining and age or AD progression, which interestingly indicates that such RNA structure might play a role in regulating protein homeostasis as previously speculated. The methods used and the results reported seems robust and reproducible. There were two main things that needed addressing:

      (1) Usually in BG4 staining experiments to ensure that the signal detected is genuinely due to rG4 an RNase treatment experiment is performed. This does not have to be extended to all the samples presented but having a couple of controls where the authors observe loss of staining upon RNase treatment will be key to ensure with confidence that rG4s are detected under the experimental conditions. This is particularly relevant for this brain tissue samples where BG4 staining has never been performed before.

      (2) The authors have an association between rG4-formation and age/disease progression. They also observe distribution dependency of this, which is great. However, this is still an association which does not allow the model to be supported. This is not something that can be fixed with an easy experiment and it is what it is, but my point is that the narrative of the manuscript should be more fair and reflect the fact that, although interesting, what the authors are observing is a simple correlation. They should still go ahead and propose a model for it, but they should be more balanced in the conclusion and do not imply that this evidence is sufficient to demonstrate the proposed model. It is absolutely fine to refer to the literature and comment on the fact that similar observations have been reported and this is in line with those, but still this is not an ultimate demonstration.

      Comments on current version:

      The authors have now addressed my concerns.

      We thank the reviewer for their support!

      Reviewer #2 (Public review):

      RNA guanine-rich G-quadruplexes (rG4s) are non-canonical higher order nucleic acid structures that can form under physiological conditions. Interestingly, cellular stress is positively correlated with rG4 induction.

      In this study, the authors examined human hippocampal postmortem tissue for the formation ofrG4s in aging and Alzheimer Disease (AD). rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. 21 cases were used in this study (age range 30-92).

      This immunostaining co-localized with hyper-phosphorylated tau immunostaining in neurons. The BG4 staining levels were also impacted by APOE status. rG4 structure was previously found to drive tau aggregation. Based on these observations, the authors propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse.

      This model is interesting, and would explain different observations (e.g., RNA is present in AD aggregates and rG4s can enhance protein oligomerization and tau aggregation).

      Main issue from the previous round of review:

      There is indeed a positive correlation between Braak stage severity and BG4 staining, but this correlation is relatively weak and borderline significant ((R = 0.52, p value = 0.028). This is probably the main limitation of this study, which should be clearly acknowledged (together with a reminder that "correlation is not causality"). Related to this, here is no clear justification to exclude the four individuals in Fig 1d (without them R increases to 0.78). Please remove this statement. On the other hand, the difference based on APOE status is more striking.

      Comments on current version:

      The authors have made laudable efforts to address the criticisms I made in my evaluation of the original manuscript.

      We thank the reviewer for their support!

      Recommendations for the authors:

      Reviewing Editor:

      I would suggest two minor edits:

      - The findings are correlative and descriptive, but the title implies functionality (A New Role for RNA G-quadruplexes in Aging and Alzheimer′s Disease). I would suggest toning down this title).

      - While I understand the limitations in performing additional biochemical experiments to validate the immunofluorescence study, I think this is worth mentioning as a limitation in the text.

      We have made these two changes as requested, altering the title to remove the word Role that may imply more meaning than intended, and adding a line to the discussion on the need for future additional biochemical experiments.

      Reviewer #1 (Recommendations for the authors):

      Thanks for addressing the concerns raised.

      We thank the reviewer for their support!

      Reviewer #2 (Recommendations for the authors):

      Minor point:

      Related to the "correlation is not causality" remark I made in my evaluation of the original manuscript: the authors' answer is reasonable. Still, I would suggest to modify the abstract: "we propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse" => "we propose a model of neurodegeneration in which chronic rG4 formation is linked to proteostasis collapse"

      All other remarks I made have been answered properly.

      We thank the reviewer for their support! We have made the change exactly as requested by the reviewer.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

      Critical issues:

      (1) Lack of Novelty:

      The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

      We agree with the reviewer’s statement regarding the lack of novelty when it comes to our observations of scrambling in the groove of open Ca<sup>2+</sup>-bound TMEM16 structures. However, we feel that the inclusion of closed structures in this study, which attempts to address the yet unanswered question of how scrambling by TMEM16s occurs in the absence of Ca<sup>2+</sup>, offers new observations for the field. In our study we specifically address to what extent the induced membrane deformation, which has been theorized to aid lipids cross the bilayer especially in the absence of Ca<sup>2+</sup>, contributes to the rate of scrambling (see references 36, 59, and 66). There are also several TMEM16F structures solved under activating conditions (bound to Ca<sup>2+</sup> and in the presence of PIP2) which feature structural rearrangements to TM6 that may be indicative of an open state (PDB 6P48) and had not been tested in simulations. We show that these structures do not scramble and thereby present evidence against an out-of-the-groove scrambling mechanism for these states. Although we find a handful of examples of lipids being scrambled by Ca<sup>2+</sup>-free structures of TMEM16 scramblases, none of our simulations suggest that these events are related to the degree of deformation.

      (2) Redundancy Across Systems:

      The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

      Again, we agree with the reviewer’s statement that our results largely confirm those published by other groups and our own. We think there is however value in comparing the scrambling competence of these TMEM16 structures in a consistent manner in a single study to reduce inconsistencies that may be introduced by different simulation methods, parameters, environmental variables such as lipid composition as used in other published works of single family members. The consistency across our simulations and high number of observed scrambling events have allowed us to confirm that the mechanism of scrambling is shared by multiple family members and relies most obviously on groove dilation.

      (3) Discrepancy with Experimental Observations:

      The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

      We thank the reviewer for bringing up the possible inaccuracies introduced by coarse graining our simulations. This is also a concern for us, and we address this issue extensively in our discussion. As the reviewer pointed out above, our CG simulations have largely confirmed existing evidence in the field which we think speaks well to the transferability of observations from atomistic simulations to the coarse-grained level of detail. We have made both qualitative and quantitative comparisons between atomistic and coarse-grained simulations of nhTMEM16 and TMEM16F (Figure 1, Figure 4-figure supplement 1, Figure 4-figure supplement 5) showing the two methods give similar answers for where lipids interact with the protein, including outside of the canonical groove. We do not dispute the possible discrepancy between our simulations and experiment, but our goal is to share new nuanced ideas for the predicted TMEM16 scrambling mechanism that we hope will be tested by future experimental studies.

      (4) Alternative Scrambling Sites:

      The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

      We agree with the reviewer that our observed number of scrambling events in the dimer interface is too low to present it as strong evidence for it being the alternative mechanism for Ca<sup>2+</sup>-independent scrambling. This will require additional experiments and computational studies which we plan to do in future research. However, we are less certain that these are artifacts of the coarse-grained simulation system as we observed a similar event in an atomistic simulation of TMEM16F.

      Conclusion:

      Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

      Reviewer #2 (Public review):

      Summary:

      Stephens et al. present a comprehensive study of TMEM16-members via coarse-grained MD simulations (CGMD). They particularly focus on the scramblase ability of these proteins and aim to characterize the "energetics of scrambling". Through their simulations, the authors interestingly relate protein conformational states to the membrane's thickness and link those to the scrambling ability of TMEM members, measured as the trespassing tendency of lipids across leaflets. They validate their simulation with a direct qualitative comparison with Cryo-EM maps.

      Strengths:

      The study demonstrates an efficient use of CGMD simulations to explore lipid scrambling across various TMEM16 family members. By leveraging this approach, the authors are able to bypass some of the sampling limitations inherent in all-atom simulations, providing a more comprehensive and high-throughput analysis of lipid scrambling. Their comparison of different protein conformations, including open and closed groove states, presents a detailed exploration of how structural features influence scrambling activity, adding significant value to the field. A key contribution of this study is the finding that groove dilation plays a central role in lipid scrambling. The authors observe that for scrambling-competent TMEM16 structures, there is substantial membrane thinning and groove widening. The open Ca<sup>2+</sup>-bound nhTMEM16 structure (PDB ID 4WIS) was identified as the fastest scrambler in their simulations, with scrambling rates as high as 24.4 {plus minus} 5.2 events per μs. This structure also shows significant membrane thinning (up to 18 Å), which supports the hypothesis that groove dilation lowers the energetic barrier for lipid translocation, facilitating scrambling.

      The study also establishes a correlation between structural features and scrambling competence, though analyses often lack statistical robustness and quantitative comparisons. The simulations differentiate between open and closed conformations of TMEM16 structures, with open-groove structures exhibiting increased scrambling activity, while closed-groove structures do not. This finding aligns with previous research suggesting that the structural dynamics of the groove are critical for scrambling. Furthermore, the authors explore how the physical dimensions of the groove qualitatively correlate with observed scrambling rates. For example, TMEM16K induces increased membrane thinning in its open form, suggesting that membrane properties, along with structural features, play a role in modulating scrambling activity.

      Another significant finding is the concept of "out-of-the-groove" scrambling, where lipid translocation occurs outside the protein's groove. This observation introduces the possibility of alternate scrambling mechanisms that do not follow the traditional "credit-card model" of groove-mediated lipid scrambling. In their simulations, the authors note that these out-of-the-groove events predominantly occur at the dimer interface between TM3 and TM10, especially in mammalian TMEM16 structures. While these events were not observed in fungal TMEM16s, they may provide insight into Ca<sup>2+</sup>-independent scrambling mechanisms, as they do not require groove opening.

      Weaknesses:

      A significant challenge of the study is the discrepancy between the scrambling rates observed in CGMD simulations and those reported experimentally. Despite the authors' claim that the rates are in line experimentally, the observed differences can mean large energetic discrepancies in describing scrambling (larger than 1kT barrier in reality). For instance, the authors report scrambling rates of 10.7 events per μs for TMEM16F and 24.4 events per μs for nhTMEM16, which are several orders of magnitude faster than experimental rates. While the authors suggest that this discrepancy could be due to the Martini 3 force field's faster diffusion dynamics, this explanation does not fully account for the large difference in rates. A more thorough discussion on how the choice of force field and simulation parameters influence the results, and how these discrepancies can be reconciled with experimental data, would strengthen the conclusions. Likewise, rate calculations in the study are based on 10 μs simulations, while experimental scrambling rates occur over seconds. This timescale discrepancy limits the study's accuracy, as the simulations may not capture rare or slow scrambling events that are observed experimentally and therefore might underestimate the kinetics of scrambling. It's however important to recognize that it's hard (borderline unachievable) to pinpoint reasonable kinetics for systems like this using the currently available computational power and force field accuracy. The faster diffusion in simulations may lead to overestimated scrambling rates, making the simulation results less comparable to real-world observations. Thus, I would therefore read the findings qualitatively rather than quantitatively. An interesting observation is the asymmetry observed in the scrambling rates of the two monomers. Since MARTINI is known to be limited in correctly sampling protein dynamics, the authors - in order to preserve the fold - have applied a strong (500 kJ mol-1 nm-2) elastic network. However, I am wondering how the ENM applies across the dimer and if any asymmetry can be noticed in the application of restraints for each monomer and at the dimer interface. How can this have potentially biased the asymmetry in the scrambling rates observed between the monomers? Is this artificially obtained from restraining the initial structure, or is the asymmetry somehow gatekeeping the scrambling mechanism to occur majorly across a single monomer? Answering this question would have far-reaching implications to better describe the mechanism of scrambling.

      The main aim of our computational survey was to directly compare all relevant published TMEM16 structures in both open and closed states using the Martini 3 CGMD force field. Our standardized simulation and analysis protocol allowed us to quantitatively compare scrambling rates across the TMEM16 family, something that has never been done before. We do acknowledge that direct comparison between simulated versus experimental scrambling rates is complicated and is best to be interpreted qualitatively. In line with other reports (e.g., Li et al, PNAS 2024), lipid scrambling in CGMD is 2-3 orders of magnitude faster than typical experimental findings. In the CG simulation field, these increased dynamics due to the smoother energy landscape are a well known phenomenon. In our view, this is a valuable trade-off for being able to capture statistically robust scrambling dynamics and gain mechanistic understanding in the first place, since these are currently challenging to obtain otherwise. For example, with all-atom MD it would have been near-impossible to conclude that groove openness and high scrambling rates are closely related, simply because one would only measure a handful of scrambling events in (at most) a handful of structures.

      Considering the elastic network: the reviewer is correct in that the elastic network restrains the overall structure to the experimental conformation. This is necessary because the Martini 3 force field does not accurately model changes in secondary (and tertiary) structure. In fact, by retaining the structural information from the experimental structures, we argue that the elastic network helped us arrive at the conclusion that groove openness is the major contributing factor in determining a protein’s scrambling rate. This is best exemplified by the asymmetric X-ray structure of TMEM16K (5OC9), in which the groove of one subunit is more dilated than the other. In our simulation, this information was stored in the elastic network, yielding a 4x higher rate in the open groove than in the closed groove, within the same trajectory.

      Notably, the manuscript does not explore the impact of membrane composition on scrambling rates. While the authors use a specific lipid composition (DOPC) in their simulations, they acknowledge that membrane composition can influence scrambling activity. However, the study does not explore how different lipids or membrane environments or varying membrane curvature and tension, could alter scrambling behaviour. I appreciate that this might have been beyond the scope of this particular paper and the authors plan to further chase these questions, as this work sets a strong protocol for this study. Contextualizing scrambling in the context of membrane composition is particularly relevant since the authors note that TMEM16K's scrambling rate increases tenfold in thinner membranes, suggesting that lipid-specific or membrane-thickness-dependent effects could play a role.

      Considering different membrane compositions: for this study, we chose to keep the membranes as simple as possible. We opted for pure DOPC membranes, because it has (1) negligible intrinsic curvature, (2) forms fluid membranes, and (3) was used previously by others (Li et al, PNAS 2024). As mentioned by the reviewer, we believe our current study defines a good standardized protocol and solid baseline for future efforts looking into the additional effects of membrane composition, tension, and curvature that could all affect TMEM16-mediated lipid scrambling.

      Reviewer #3 (Public review):

      Strengths:

      The strength of this study emerges from a comparative analysis of multiple structural starting points and understanding global/local motions of the protein with respect to lipid movement. Although the protein is well-studied, both experimentally and computationally, the understanding of conformational events in different family members, especially membrane thickness less compared to fungal scramblases offers good insights.

      We appreciate the reviewer recognizing the value of the comparative study. In addition to valuable insights from previous experimental and computational work, we hope to put forward a unifying framework that highlights various TMEM16 structural features and membrane properties that underlie scrambling function.

      Weaknesses:

      The weakness of the work is to fully reconcile with experimental evidence of Ca²⁺-independent scrambling rates observed in prior studies, but this part is also challenging using coarse-grain molecular simulations. Previous reports have identified lipid crossing, packing defects, and other associated events, so it is difficult to place this paper in that context. However, the absence of validation leaves certain claims, like alternative scrambling pathways, speculative.

      It is generally difficult to quantitatively compare bulk measurements of scrambling phenomena with simulation results. The advantage of simulations is to directly observe the transient scrambling events at a spatial and temporal resolution that is currently unattainable for experiments. The current experimental evidence for the precise mechanism of Ca<sup>2+</sup>-independent scrambling is still under debate. We therefore hope to leverage the strength of MD and statistical rigor of coarse-grained simulations to generate testable hypotheses for further structural, biochemical, and computational studies.

    1. Reviewer #1 (Public review):

      This paper describes a number of patterns of epistasis in a large fitness landscape dataset recently published by Papkou et al. The paper is motivated by an important goal in the field of evolutionary biology to understand the statistical structure of epistasis in protein fitness landscapes, and it capitalizes on the unique opportunities presented by this new dataset to address this problem.

      The paper reports some interesting previously unobserved patterns that may have implications for our understanding of fitness landscapes and protein evolution. In particular, Figure 5 is very intriguing. However, I have two major concerns detailed below. First, I found the paper rather descriptive (it makes little attempt to gain deeper insights into the origins of the observed patterns) and unfocused (it reports what appears to be a disjointed collection of various statistics without a clear narrative. Second, I have concerns with the statistical rigor of the work.

      (1) I think Figures 5 and 7 are the main, most interesting, and novel results of the paper. However, I don't think that the statement "Only a small fraction of mutations exhibit global epistasis" accurately describes what we see in Figure 5. To me, the most striking feature of this figure is that the effects of most mutations at all sites appear to be a mixture of three patterns. The most interesting pattern noted by the authors is of course the "strong" global epistasis, i.e., when the effect of a mutation is highly negatively correlated with the fitness of the background genotype. The second pattern is a "weak" global epistasis, where the correlation with background fitness is much weaker or non-existent. The third pattern is the vertically spread-out cluster at low-fitness backgrounds, i.e., a mutation has a wide range of mostly positive effects that are clearly not correlated with fitness. What is very interesting to me is that all background genotypes fall into these three groups with respect to almost every mutation, but the proportions of the three groups are different for different mutations. In contrast to the authors' statement, it seems to me that almost all mutations display strong global epistasis in at least a subset of backgrounds. A clear example is C>A mutation at site 3.

      1a. I think the authors ought to try to dissect these patterns and investigate them separately rather than lumping them all together and declaring that global epistasis is rare. For example, I would like to know whether those backgrounds in which mutations exhibit strong global epistasis are the same for all mutations or whether they are mutation- or perhaps position-specific. Both answers could be potentially very interesting, either pointing to some specific site-site interactions or, alternatively, suggesting that the statistical patterns are conserved despite variation in the underlying interactions.

      1b. Another rather remarkable feature of this plot is that the slopes of the strong global epistasis patterns seem to be very similar across mutations. Is this the case? Is there anything special about this slope? For example, does this slope simply reflect the fact that a given mutation becomes essentially lethal (i.e., produces the same minimal fitness) in a certain set of background genotypes?

      1c. Finally, how consistent are these patterns with some null expectations? Specifically, would one expect the same distribution of global epistasis slopes on an uncorrelated landscape? Are the pivot points unusually clustered relative to an expectation on an uncorrelated landscape?

      1d. The shapes of the DFE shown in Figure 7 are also quite interesting, particularly the bimodal nature of the DFE in high-fitness (HF) backgrounds. I think this bimodality must be a reflection of the clustering of mutation-background combinations mentioned above. I think the authors ought to draw this connection explicitly. Do all HF backgrounds have a bimodal DFE? What mutations occupy the "moving" peak?

      1e. In several figures, the authors compare the patterns for HF and low-fitness (LF) genotypes. In some cases, there are some stark differences between these two groups, most notably in the shape of the DFE (Figure 7B, C). But there is no discussion about what could underlie these differences. Why are the statistics of epistasis different for HF and LF genotypes? Can the authors at least speculate about possible reasons? Why do HF and LF genotypes have qualitatively different DFEs? I actually don't quite understand why the transition between bimodal DFE in Figure 7B and unimodal DFE in Figure 7C is so abrupt. Is there something biologically special about the threshold that separates LF and HF genotypes? My understanding was that this was just a statistical cutoff. Perhaps the authors can plot the DFEs for all backgrounds on the same plot and just draw a line that separates HF and LF backgrounds so that the reader can better see whether the DFE shape changes gradually or abruptly.

      1f. The analysis of the synonymous mutations is also interesting. However I think a few additional analyses are necessary to clarify what is happening here. I would like to know the extent to which synonymous mutations are more often neutral compared to non-synonymous ones. Then, synonymous pairs interact in the same way as non-synonymous pair (i.e., plot Figure 1 for synonymous pairs)? Do synonymous or non-synonymous mutations that are neutral exhibit less epistasis than non-neutral ones? Finally, do non-synonymous mutations alter epistasis among other mutations more often than synonymous mutations do? What about synonymous-neutral versus synonymous-non-neutral. Basically, I'd like to understand the extent to which a mutation that is neutral in a given background is more or less likely to alter epistasis between other mutations than a non-neutral mutation in the same background.

      (2) I have two related methodological concerns. First, in several analyses, the authors employ thresholds that appear to be arbitrary. And second, I did not see any account of measurement errors. For example, the authors chose the 0.05 threshold to distinguish between epistasis and no epistasis, but why this particular threshold was chosen is not justified. Another example: is whether the product s12 × (s1 + s2) is greater or smaller than zero for any given mutation is uncertain due to measurement errors. Presumably, how to classify each pair of mutations should depend on the precision with which the fitness of mutants is measured. These thresholds could well be different across mutants. We know, for example, that low-fitness mutants typically have noisier fitness estimates than high-fitness mutants. I think the authors should use a statistically rigorous procedure to categorize mutations and their epistatic interactions. I think it is very important to address this issue. I got very concerned about it when I saw on LL 383-388 that synonymous stop codon mutations appear to modulate epistasis among other mutations. This seems very strange to me and makes me quite worried that this is a result of noise in LF genotypes.

    1. 3.5 IPC in Shared-Memory Systems Interprocess communication using shared memory requires communicating processes to establish a region of shared memory. Typically, a shared-memory region resides in the address space of the process creating the shared-memory segment. Other processes that wish to communicate using this shared-memory segment must attach it to their address space. Recall that, normally, the operating system tries to prevent one process from accessing another process's memory. Shared memory requires that two or more processes agree to remove this restriction. They can then exchange information by reading and writing data in the shared areas. The form of the data and the location are determined by these processes and are not under the operating system's control. The processes are also responsible for ensuring that they are not writing to the same location simultaneously. To illustrate the concept of cooperating processes, let's consider the producer–consumer problem, which is a common paradigm for cooperating processes. A producer process produces information that is consumed by a consumer process. For example, a compiler may produce assembly code that is consumed by an assembler. The assembler, in turn, may produce object modules that are consumed by the loader. The producer–consumer problem also provides a useful metaphor for the client–server paradigm. We generally think of a server as a producer and a client as a consumer. For example, a web server produces (that is, provides) web content such as HTML files and images, which are consumed (that is, read) by the client web browser requesting the resource. One solution to the producer–consumer problem uses shared memory. To allow producer and consumer processes to run concurrently, we must have available a buffer of items that can be filled by the producer and emptied by the consumer. This buffer will reside in a region of memory that is shared by the producer and consumer processes. A producer can produce one item while the consumer is consuming another item. The producer and consumer must be synchronized, so that the consumer does not try to consume an item that has not yet been produced. Two types of buffers can be used. The unbounded buffer places no practical limit on the size of the buffer. The consumer may have to wait for new items, but the producer can always produce new items. The bounded buffer assumes a fixed buffer size. In this case, the consumer must wait if the buffer is empty, and the producer must wait if the buffer is full. Let's look more closely at how the bounded buffer illustrates interprocess communication using shared memory. The following variables reside in a region of memory shared by the producer and consumer processes: #define BUFFER_SIZE 10 typedef struct { . . . } item; item buffer[BUFFER_SIZE]; int in = 0; int out = 0; The shared buffer is implemented as a circular array with two logical pointers: in and out. The variable in points to the next free position in the buffer; out points to the first full position in the buffer. The buffer is empty when in == out; the buffer is full when ((in + 1) % BUFFER_SIZE) == out. The code for the producer process is shown in Figure 3.12, and the code for the consumer process is shown in Figure 3.13. The producer process has a local variable next_produced in which the new item to be produced is stored. The consumer process has a local variable next_consumed in which the item to be consumed is stored. item next_produced; while (true) {      /* produce an item in next_produced */      while (((in + 1) % BUFFER_SIZE) == out)        ; /* do nothing */      buffer[in] = next_produced;      in = (in + 1) % BUFFER_SIZE; } Figure 3.12 The producer process using shared memory. item next_consumed; while (true) {      while (in == out)        ; /* do nothing */      next_consumed = buffer[out];      out = (out + 1) % BUFFER_SIZE;      /* consume the item in next_consumed */ } Figure 3.13 The consumer process using shared memory. This scheme allows at most BUFFER_SIZE − 1 items in the buffer at the same time. We leave it as an exercise for you to provide a solution in which BUFFER_SIZE items can be in the buffer at the same time. In Section 3.7.1, we illustrate the POSIX API for shared memory. One issue this illustration does not address concerns the situation in which both the producer process and the consumer process attempt to access the shared buffer concurrently. In Chapter 6 and Chapter 7, we discuss how synchronization among cooperating processes can be implemented effectively in a shared-memory environment.

      Interprocess communication (IPC) in shared-memory systems allows processes to communicate by creating a shared-memory region. Normally, operating systems restrict processes from accessing each other’s memory, but shared memory requires processes to agree to lift this restriction. These processes determine data structure and management without operating system intervention. A common example is the producer–consumer problem, where a producer generates data consumed by a consumer. This paradigm extends to client-server models, such as web servers providing content to browsers. Shared memory enables concurrent execution of producers and consumers through a buffer, which can be either unbounded (allowing unlimited production) or bounded (with a fixed size, requiring synchronization). A bounded buffer, implemented as a circular array, uses two pointers, in and out, to manage data flow. The buffer is empty when in == out and full when ((in + 1) % BUFFER_SIZE) == out. The producer adds items to the buffer, while the consumer removes them. However, simultaneous access by both processes can lead to conflicts, requiring synchronization techniques, discussed in later chapters. This model enhances efficiency by minimizing kernel intervention, but careful synchronization is necessary to avoid issues like race conditions and data inconsistency.

    1. There are many design principles in broad use that are a bit more precise, even though they might not be universally good in all contexts:Simple. This is a design aesthetic that prizes minimalism and learnability. These can be good qualities, reducing how much people have to learn to use an interface and how long it takes to learn. But simplicity isn’t always good. Should moderation tools in social media simple? There’s nothing inherently simple about regulating speech, so they might need to be complicated, to reflect the complexity of preventing hate speech.Novel. In some design cultures (e.g., fashion design), the best design is the new design that pushes boundaries and explores undiscovered territories. Novelty is powerful in that it has the power to surprise and empower in new ways. It also has the power to convey status, because possession of new design suggests knowledge and awareness of the bleeding edge of human creativity, which can have status in some cultures. But novelty trades off against simplicity, because simplicity often requires familiarity and convention66 Norman, D. A. (1999). Affordance, conventions, and design. ACM interactions. .Powerful. This aesthetic values the ability of designs to augment human ability. Take, for example, a graphing calculator. These are exceedingly complex little devices with thousands of functions that can support almost any kind of mathematics. It’s certainly not simple or novel, but it’s extremely powerful. But power isn’t always good. Sometimes power leads to complexity that poses barriers to use and adoption. Powerful designs can also amplify harm; for example, powerful saved searches on Twitter enable trolls to quickly find people to harass by keyword. Is that harm worth whatever other positive might come from that power, such as saved time?Invisible. Some trends in design aesthetics value designs that “get out of the way”, trying to bring a person as close as possible to their activity, their information, and their goals. Designs that achieve invisibility don’t try to be the center of attention, but rather put the attention on the work that a person is doing with the design. Good example of designs that attempt to be invisible are the many intelligent assistants such as Siri and Alexa, which try to provide “natural” interfaces that don’t need to be learned, personalized, or calibrated. All of this may come at the expense of power and control, however, as the mechanisms we often use for invisibility are automated.Universal. The premise of universal design77 Story, M. F. (1998). Maximizing usability: the principles of universal design. Assistive Technology.  as something that all of humanity should be able to access, prizing equality over other values. For example, designing a website that is screen readable so that people who are blind can read it often constrains the type of interactivity that can be used on a site. What’s better: power and novelty or universal access? Maybe there are some types of designs that are so powerful, they should only be used by certain people with certain knowledge and skills. Of course, universal designs are rarely universal; all design exclude somehow.Just. The premise of design justice11 Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. MIT Press.  is the purpose of design should not be to amplify inequities and injustices in the world, but to dismantle them. This might mean that a design that ultimately serves the enrich and empower the wealthy (e.g., Facebook Ads) might be deemed worse than a design that helps dismantle an unjust system (e.g., a social media network for small-business loan networking amongst Black owned businesses)

      This reading talks about different ways to design things, like making them simple, new, powerful, hidden, fair, or useful for everyone. I agree that powerful designs can be both helpful and harmful, like how saved searches on Twitter can be used for good or bad. It made me think about how designers need to be careful about how their work affects people, not just how easy or exciting it is to use.

    1. n 1982 my initial goal was to reacquaint myself with the people of 0 Cruzeiro. We had lost contact with each other for many years. Letter writing was complicated by my friends' illiteracy, and after a few years both sides desisted. And if many of my Alto friends were peripatetic rural migrants, I was even more so during the early years of life "in the academy," when my family and I constantly moved back and forth across the country. Nonethe­less, prior to my return in 1982 I sent dozens of letters to everyone I could think of ... and received no response. I feared returning to a social void and felt that I might as well begin my research anywhere at all as in Born Jesus da Mata, for clearly the social world I once knew had evaporated. But curiosity and my saudades, as Brazilians call the pull of nostalgic longings, led me to persist in the plan to return to Born Jesus. In my letters I had mentioned the approximate date of my arrival in the capital city of Recife but had given no other details. Yet when we stepped off the plane, there in the crowd waving madly to us was my old friend and sometime adversary, Seu Felix, still the reigning prefeito and "boss" of Born Jesus. "Did I forget to send you a reply?" Felix asked in his usual distracted way. I had indeed come home.

      The emotional and practical challenges of sustaining long-term relationships despite distance and literacy obstacles are highlighted in this chapter. A issue pertinent to migration studies, the author's worry of returning to a "social void" is a reflection of larger worries about displacement and the loss of previous relationships. Furthermore, Seu Felix's casual comment about forgetting to react highlights the unpredictable nature of interpersonal relationships—social ties can remain in unexpected ways, even though written correspondence may not. This raises the question of how many cultures manage to stay connected in the face of challenges like migration and illiteracy. In comparable communities, are there non-written means of communication that could take the place of letters?

  5. Jan 2025
    1. The theologian Buber confronted the /1 suspension of the ethical" in accordance with the will and purpose of something "higher," the Divine;

      This phrase the, "suspension of the ethical" stood out to me because of how often we see the people in the book do this. So far, in just the Introduction we have seen many different people suspend their ethics for some outside reason. While their reasoning may be different from the 'divine' its a similar concept. Instead of women mourning the loss of their children, they have learned to expect that most of their kids will die. The way I see it is they are suspending their ethics/morals because that has become the 'norm'. It made me think about how many times we see people suspend their ethics for an outside being even today. Whether it be for a 'divine' reason or for a similar reason. That they can act a certain way because it is 'normal'. In my eyes, this is a way for someone to feel better about not acting or feeling a certain way. The reason these mothers, even fathers, aren't upset with how many losses they've endured is because they can blame it on the society's expectation. Or in a religious concept as described in the passage, Abraham can blame the divine for sacrificing his son. The reason this resonated so much with me is because I feel the need to connect this with everything going on in the United States. How people are passing certain laws because of the need to please a divine being. Or how others are placing blame on certain groups to elevate their status. They're suspending their ethics because of some outside influence. The way I see it is the suspension of ethics is finding a moral scapegoat, and it is a dangerous way of thinking.

    1. hese psychological processes have implications for our communication because when we attribute causality to another person’s personality, we tend to have a stronger emotional reaction and tend to assume that this personality characteristic is stable, which may lead us to avoid communication with the person or to react negatively.

      I never thought of it in these terms, but it really makes sense that if we assume someone has done something because of their personality (as opposed to external factors) we get emotional reactions more strongly, I certain get more emotional if i think someone is late for our meeting due to internal factors (laziness, not thinking the meeting is important etc) vs thinking that their car might be broken down

    2. Race, gender, sexual orientation, class, ability, nationality, and age all affect the perceptions that we make. The schemata through which we interpret what we perceive are influenced by our cultural identities. As we are socialized into various cultural identities, we internalize beliefs, attitudes, and values shared by others in our cultural group. Schemata held by members of a cultural identity group have similarities, but schemata held by different cultural groups may vary greatly. Unless we are exposed to various cultural groups and learn how others perceive us and the world around them, we will likely have a narrow or naïve view of the world and assume that others see things the way we do. Exposing yourself to and experiencing cultural differences in perspective doesn’t mean that you have to change your schema to match another cultural group’s. Instead, it may offer you a chance to better understand why and how your schemata were constructed the way they were.

      This is gold, and i think its one of the most important lessons and attitudes about life. I think I can tell within about 10 minutes of casually chatting with someone if they've been much of a world traveler or not (without overtly asking if a person has travelled). From a lifetime of observation alone, I've found that people who travel extensively for both work and enjoyment tend to be much more openminded and comfortable about other cultures and values. Seeing how people live in relative poverty or in different communities can certainly help a person appreciate where they themselves come from. Travelling can help a person feel more curious and less threatened by new experiences and ideas that are different from the norms back home. Generally I find that people who never left their home states tend to be fearful about the world, unable to understand different religions and value systems, and are very certain that the way things are done 'back home' are the only acceptable way to do things. That's a shame. The world is such a big place, and its much more interesting when you have an open mind and a willing attitude for exploring and experiencing.

    1. Whenever we try to pierce the meanings of lives very different from our own, we face two interpretive risks. On the one hand, we may be tempted to attribute our own ways of thinking and feeling to "other" mothers. Any suggestion of radically different existential premises (such as those, for example, that guide selective neglect in Northeast Brazil) is rejected out of hand as impossible, unthinkable. To describe some poor women as aiding and abetting the deaths of certain of thei r infants can only be seen as "victim blaming." But the alternative is to cast women as passive "victims" of their fate, as powerless, without will, agency, or subjectivity. Part of the difficulty lies in the confusion between causality and blame. There must be a way to look dispassionately at the problem of child survival and conclude that a child died from mortal neglect, even at her or his mother's own hands, without also blaming the mother-that is, without holding her personally and morally accountable.

      I think this challenges the way we often judge people from outside their circumstances. For example, when we see a mother making decisions that seem unthinkable to us, it's easy to assume she's cold or uncaring. But could it be that the lack of resources, along with cultural beliefs about life and death, shapes her actions? What if we shifted our focus from blaming individuals to addressing the systems that lead to these difficult choices? It makes me wonder how many of our assumptions about “right” and “wrong” are influenced by our own privilege, and how different the world would look if we looked deeper into the larger forces that continue to shape people’s lives.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      MacDonald et al., investigated the consequence of double knockout of substance P and CGRPα on pain behaviors using a newly created mouse model. The investigators used two methods to confirm knockout of these neuropeptides: traditional immunolabeling and a neat in vitro assay where sensory neurons from either wildtype or double knock are co-cultured with substance P "sniffer cells", HEK cells stably expressing NKR1 (a substance P receptor), GCaMP6s and Gα15. It should be noted that functional assays confirming CGRPα knockout were not performed. Subsequently, the authors assayed double knockout mice (DKO) and wildtype (WT) mice in numerous behavioral assays using different pain models, including acute pain and itch stimuli, intraplanar injection of Complete Freund's Adjuvant, prostaglandin E2, capsaicin, AITC, oxaliplatin, as well as the spared nerve injury model. Surprisingly, the authors found that pain behaviors did not differ between DKO and WT mice in any of the behavioral assays or pain paradigms. Importantly, female and male mice were included in all analyses. These data are important and significant, as both substance P and CGRPα have been implicated in pain signaling, though the magnitude of the effect of a single knockout of either gene has been variable and/or small between studies.

      The conclusions of the study are largely supported by the data; however, additional experimental controls and analyses would strengthen the authors claims.

      We thank the reviewer for their insightful comments and have answered them below.

      (1) The authors note that single knockout models of either substance P or CGRPα have produced variable effects on pain behaviors that are study-dependent. Therefore, it would have strengthened the study if the authors included these single knockout strains in a side-by-side analysis (in at least some of the behavioral assays), as has been done in prior studies in the field when using double- or triple-knockout mouse models (for example, see PMID: 33771873). If in the authors hands, single knockouts of either peptide also show no significant differences in pain behaviors, then the finding that double knockouts also do not show significant differences would be less surprising.

      In our study, we found no phenotypic differences between WT and DKO mice, suggesting Substance P and CGRPα are largely dispensable for pain behavior. We agree that if we had we observed significant changes in behavior, it would have been interesting to examine the effects of knocking out each gene individually to determine which peptide is responsible for the phenotype. However, given the double deletion had no effect, we can predict that loss of each alone would have no or minor effects. In line with this, a more recent study that comprehensively phenotyped the Calca KO mouse found no deficits in a range of danger related behaviors (PMID: 34376756). Overall, as we are reporting negative data about the Double KO, we do not believe extensive studies of the single KOs is necessary to support the findings of our paper.

      (2) It is unclear why the authors only show functional validation of substance P knockout using "sniffer" cells, but not CGRPα. Inclusion of this experiment would have added an additional layer of rigor to the study.

      Imaging of CGRPα release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We now have succeeded in generating a new stable cell line expressing Calcrl and Ramp1, along with GCaMPs and human Galpha15 and include new data in the revised Figure 1F-H and Figure Supplement 1B. These cells respond robustly to CGRPalpha, but not to SP. In contrast, the existing SP cell line responds to SP but not CGRPalpha. Capsaicin evokes a strong response in these cells in co-culture with DRGs. This response is dramatically reduced in the DKO. This data therefore confirms our mice have a loss of CGRPalpha signaling as indicated by IHC.

      (3) The authors should be a bit more reserved in the claims made in the manuscript. The main claim of the study is that "CGRPα and substance P are not required for pain transmission." However, the authors also note that neuropeptides can have opposing effects that may produce a net effect of no change. In my view, the data presented show that double knockout of substance P and CGRPα do not affect somatic pain behaviors, but do not preclude a role for either of these molecules in pain signaling more generally. Indeed, the authors also note that these neuropeptides could be involved in nociceptor crosstalk with the immune or vascular systems to promote headache. The authors only assayed pain responses to glabrous skin stimulation. How the DKO mice would behave in orofacial pain assays, migraine assays, visceral pain assays, or bone/joint pain assays, for example, was not tested. I do not suggest the authors include these experiments, only that they address the limitations/weaknesses of their study more thoroughly.

      The reviewer makes an important point that we agree with. Our study assesses acute and chronic pain in peptide DKO mice lacking Substance P and CGRPα. Most of our data focuses on the hindpaw as pain in the paw is the gold-standard approach for phenotyping pain targets and numerous well-validated chronic pain models have been developed for this body site.  However, to extend the conclusions to other tissues, we did also look at visceral pain and GI distress using acetic acid and LiCl models (Figure 2J and Figure 2 supplement). We agree with the reviewer that given the utility of CGRP monoclonal antibodies, migraine experiments would be interesting for future studies using these mice, a point we highlight in the discussion. Bone/joint pain is also clearly important from a translational perspective, but outside the scope of the current study.

      (4) A more minor but important point, the authors do not describe the nature of the WT animals used. Are the littermates or a separately maintained colony of WT animals? The WT strain background should be included in the methods section.

      The WT strain are C57/BL6j from Jackson Lab. This has been added to the methods.

      Reviewer #2 (Public Review):

      Summary:

      The paper aimed to examine the effect of co-ablating Substance P and CGRPα peptides on pain using Tac1 and Calca double knockout (DKO) mice. The authors observed no significant changes in acute, inflammatory, and neuropathic pain. These results suggest that Substance P and CGRPα peptides do not play a major role in mediating pain in mice. Moreover, they reveal that the lack of behavioral phenotype cannot be explained by the redundancy between the two peptides, which are often co-expressed in the same neuron

      Strengths:

      The paper uses a straightforward approach to address a significant question in the field. The authors confirm the absence of Substance P and CGRPα peptides at the levels of DRG, spinal cord, and midbrain. Subsequently, they employ a comprehensive battery of behavioral tests to examine pain phenotypes, including acute, inflammatory, and neuropathic pain. Additionally, they evaluate neurogenic inflammation by measuring edema and extravasation, revealing no changes in DKO mice. The data are compelling, and the study's conclusions are well-supported by the results. The manuscript is succinct and well-presented.

      We thank the reviewer for their enthusiasm for the importance of our work.

      Reviewer #3 (Public Review):

      In this study, the authors were assessing the role of double global knockout of substance P and CGPRα on the transmission of acute and chronic pain. The authors first generated the double knockout (DKO) mice and validated their animal model. This is then followed by a series of acute and chronic pain assessments to evaluate if the global DKO of these neuropeptides are important in modulating acute and chronic pain behaviors. Authors found that these DKO mice Substance P and CGRPα are not required for the transmission of acute and chronic pain although both neuropeptides are strongly implicated in chronic pain. This study does provide more insight into the role of these neuropeptides on chronic pain processing, however, more work still needs to be done. (see the comments below).

      We thank the reviewer for their detailed and constructive feedback, and below outline the steps we have taken to answer their concerns.

      (1) In assessing the double KO (result #1), why are different regions of the brains shown for substance P and CGRPα (for example, midbrain for substance P and amygdala for CGRPα)? Since the authors mentioned that these peptides co-expressed in the brain (as in the introduction), shouldn't the same brain regions be shown for both IHC? It would be ideal if the authors could show both regions (midbrain and amygdala) in addition to the DRG and spinal cord for both peptides in their findings.<br /> In addition, since this is double KO, the authors should show more representative IHC-stained brain regions (spanning from the anterior to posterior).

      We could not co-stain both SP and CGRP in the same sections as the DKO mouse has endogenous GFP and RFP fluorescence, limiting us to one channel (far red). Specifically, we use a Calca KO that is a Cre:GRP knock-in/knockout (Chen et al 2018, PMID30344042) and Tac1 KO is a tagRFP knock-in/knockout (Wu et al 2018 PMID29485996). This is why we show different brain sections.

      (2) It is also unclear as to why the authors only assessed the loss of substance P signaling in the double KO mice. Shouldn't the same be done for CGRPα signaling? Either the authors assess this, or the authors have to provide clear explanations as to why only substance P signaling was assessed.

      As noted in our response to Reviewer 1, imaging of CGRP release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We have now generated this cell line and performed the experiment (see revised Figure 1 and Figure 1 Supplement).

      (3) Has these animal's naturalistic behavior been assessed after the double KO (food intake, sleep, locomotion for example)? I think this is important as changes to these naturalistic behaviors can affect pain processes or outcomes.

      We agree that assessment of naturalistic behavior including food intake, sleep and locomotion would be interesting to look at in DKO mice. However, our study is focused on acute and chronic pain behavior of these animals, and therefore a comprehensive phenotypic assessment of naturalistic home-cage behavior is outside the scope of our study.

      (4) Figure 2H: The authors acknowledge that there is a trend to decrease with capsaicin-evoked coping-like responses. However, a close look at the graph suggests that the lack of significance could be driven by 1 mouse. Have the authors run an outlier test? Alternatively, the authors should consider adding more n to these experiments to verify their conclusions.

      We were reluctant to add more animals searching for significance. Instead, we investigated the potential phenotype further by looking at cfos staining in the cord and found no differences (Figure 2, supplement 1). This result suggests loss of the two peptides does not grossly disrupt capsaicin evoked pain signal transmission between the nociceptor and post-synaptic dorsal neurons in the spinal cord.

      (5) Similarly, the values for WT in the evoked cFos activity (Figure 2- Suppl Figure 1) are pretty variable. Considering that the n number is low (n = 5), authors should consider adding more n.<br /> Also, since the n number is low in this experiment (eg. 5 vs 4), does this pass the normality test to run a parametric unpaired t-test? Either the authors increase their n numbers or run the appropriate statistical test.

      As described in the statistical tables, the Shapiro-Wilk test indicates these data do pass the normality test. Therefore, we retain the use of the unpaired t test, which demonstrates no significant difference between the groups.

      (6) In most of the results, authors ran a parametric test despite the low n number. Authors have to ensure that they are carrying out the appropriate statistical test for their dataset and n number.

      We now provide a table of the statistical results, which provides detailed information about all statistical tests performed in this study. For experiments where we make a single comparison between the two distributions (WT vs DKO), we have run a Shapiro-Wilk test. Where the data from both groups pass the normality test, we retain the use of the unpaired t test. Where the Shapiro-Wilk test indicates data from either group are unlikely to be normally distributed, we now use a Mann-Whitney U test to compare the groups, as this non-parametric test makes no assumptions about the underlying distribution.

      Many experiments involved two factors (genotype, and e.g. temperature, drug, time-point). These data were analyzed in the original submission using 2-WAY ANOVA or Repeated Measures 2-WAY ANOVA, followed by post-hoc Sidak’s tests to compute p values adjusted for multiple comparisons. Because there is no widely agreed non-parametric alternative to 2-WAY ANOVA for analyzing data with two factors and that enables us to account for multiple comparisons, we used 2-WAY ANOVA as is typically used in the field for these kinds of experiments. We reasoned sticking with the 2-WAY ANOVA was the best course of action based on information provided by the statistical software used for this study - https://www.graphpad.com/support/faq/with-two-way-anova-why-doesnt-prism-offer-a-nonparametric-alternative-test-for-normality-test-for-homogeneity-of-variances-test-for-outliers/

      We note that regardless of the test, our conclusion that there are no major changes in acute or chronic pain behaviors are clear and strongly supported.

      (7) Along the same line of comment with the previous, authors should increase the n number for DKO for staining (Figure 4) as n number is only 3 and there is variability in the cFos quantification in the ipsilateral side.

      We believe this is not necessary as the finding is clear that there is no difference.

      (8) Authors should provide references for statement made in Line 319-321 as authors mentioned that there are accumulating evidence indicating that secretion of these neuropeptides from nociceptor peripheral terminals modulates immune cells and the vasculature in diverse tissues.

      We now provide several references to primary papers and reviews supporting this statement.

      (9) Authors state that the sample size used was similar to those from previous studies, but no references were provided. Also, even though the sample sizes used were similar, I believe that the right statistic test should be used to analyze the data.

      We have now cited several classic studies phenotyping mouse KOs in pain in the methods that used similar sample sizes. As detailed above, we have taken the reviewer’s feedback on board and performed normality testing to ensure the correct statistical test is used for each experiment.

      (10) In the discussion, the authors noted that knocking out of a gene remains the strongest test of whether the molecule is essential for a biological phenomenon. At the same time, it was acknowledged that Substance P infusion into the spinal cord elicits pain, but it is analgesic in the brain. The authors might want to expand more on this discussion, including how we can selectively assess the role of these neuropeptides in areas of interest. For example, knocking out both Substance P and CGRPα in selected areas instead of the global KO since there are reported compensatory effects.

      This is highlighted in the closing paragraph: “Emerging approaches to image and manipulate these molecules (Girven et al., 2022; Kim et al., 2023), as well as advances in quantitating pain behaviors (Bohic et al., 2023; MacDonald and Chesler, 2023), may ultimately reveal the fundamental roles of neuropeptides in generating our experience of pain.” The Kim preprint (now published, and so the citation has been updated in the text) describes a method of inactivating neuropeptide transmission in select brain regions in a cell-type specific manner.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I do not have any major comments. My minor comments are as follows:

      (1) What was the control group for all behavioral studies? Was it WT from an independent colony or one of the littermates was used for generating controls?

      We used C57/Bl6 mice from Jax. This is now mentioned in methods.

      (2) In Fig. 2H, it seems that the effect will become significant if several mice are added.

      We are reluctant to add mice searching for significance. Sample sizes were determined before we collected the data blind.

      (3) There is no figure 3, but two figures 4.

      Thank you. This has been corrected.

      (4) Multiple typos in the legend for figure 4 (lines 234-254). Line 242 (& n=8 (3M, 3F)), line 243 (swelling and plasma), line 252 ((n=8 for) & n=6 for DKO (4M, 4F)).

      Thank you. This has been corrected.

      (5) In Figure 4 (lines 273-285), the contralateral side is mentioned in B but no images are shown.

      Thank you. We removed the mention.

      (6) Although ligand knockouts cannot be compared directly with receptor inhibition, the readers could benefit from discussing studies of receptor ablation and/or pharmacological inhibition.

      We do discuss the classic studies of receptor KO, and the clinical data on receptor blockers here –

      “However, selective antagonists of the Substance P receptor NKR1 failed to relieve chronic pain in human clinical trials (Hill, 2000). Although CGRP monoclonal antibodies and receptor blockers have proven effective for subsets of migraine patients, their usefulness for other types of pain in humans is unclear (De Matteis et al., 2020; Jin et al., 2018). In line with this, knockout mice deficient in Substance P, CGRPα or their receptors have been reported to display some pain deficits, but the analgesic effects are neither large nor consistent between studies (Cao et al., 1998; De Felipe et al., 1998; Guo et al., 2012; Salmon et al., 2001, 1999; Zimmer et al., 1998).” 

      Reviewer #3 (Recommendations For The Authors):

      Minor comments:

      (1) Figure 1E: What does chambers mean? Additionally, are the 12 chambers equally from the male and female samples (6 from male and 6 from female)?

      We have changed this to well. Each replicate is an individual well from 8 well chamber slide. In all these experiments, the wells are approximately evenly distributed by mouse, because from each mouse we cultured around 8 wells’ worth of DRGs.

      (2) Figure 1D: What does low and high mean in the Hargreaves test?

      These refer to a low and high active intensity of the radiant heat stimulus. Number is now described in the methods. 40 and 55 in the intensity units used by the instrument.

      (3) Figure 2-Suppl Figure 1: Authors should provide a bigger image of the image so that it is clearer to the readers.

      We think the image is of a reasonable size and comparable to the images used elsewhere in the paper.

      (4) Authors should consider labeling their supplementary figures in running numbers or combining supplementary figures together to avoid confusion. For example, Figure 2-Supplementary Figure 1 and Figure 2- Supplementary Figure 2 can be combined as just Supplementary Figure 2.

      We agree with the reviewer this would be clearer, but we have followed eLife’s convention for labelling and numbering supplements.

      (5) Figure 3 is mislabeled as Figure 4.

      Thank you. We have corrected this.

      (6) Only female mice were used in the CFA experiment, which does not go in line with the rest of the results which consist of both sexes.

      We have repeated the experiment with additional male mice. To be consistent with the von frey data, these were followed for 7 days, and so the figure now shows a 7 day time course.

      (7) Typo in line 243. The word "and" is subscript.

      Thank you. We have corrected this.

      (8) There is a typo in the legend for Figure 4 where E is labeled I, G is labeled as F, and J is labeled as J.

      Thank you. We have corrected this.

      (9) Authors should specify what "several weeks" means (Line 263).

      It means three weeks. We tested to 21 days. We will replace with three.

      (10) Authors should specify what "one day" means (Line 267). For example, how many days after the intraplantar oxaliplatin treatment? Also, authors should justify why that specific time point was selected or have a reference for it.

      This means one day after - 24 hours. Please see PMID: 33693512. Two references are provided in them methods.

      (11) Figure 4 legend: authors should again be specific on what "prolonged" entails (Line 277).

      We have replaced prolonged with 30 minutes brushing. Specifically, 3 x 10 min stim period, with 1 min rest between stim. It is in the methods.

      (12) In the methods section, authors state that both male and female mice were used for all experiments. However, only female mice were used in the CFA experiment (see minor comment #6). Authors should verify and correct this.

      This is correct. We only used female mice for one of the groups. We have since repeated with males, now included in the data.

      (13) Authors should be more specific in the methods section on how long the habituation per day, how many days and what were the mice habituation to (experimenter, room, chamber, etc)?

      As noted in the methods, mice are habituated for at least an hour to the chambers, and thus implicitly to the room. We do not perform explicit habituation to the investigator such as repeated handling.

      (14) Authors need to provide more information on the semi-automated procedure they are referring to in Line 397. Also, authors should also provide the criteria for cFos quantification (eg. Intensity, etc). If this has been published before, they should provide the reference.

      We have added this. We used the ‘Find maxima’ and ‘Analyze particles’ functions in FIJI, followed by a manual curation step.

      (15) How much acetone was applied and how was it applied to the paw? (Line 495)

      We used the same applicator (1ml syringe with a well at the top) to generate a droplet of acetone that was used for all mice. This has been added to methods.

      (16) Authors should specify the amount of capsaicin injected in μl (Line 500).

      20 ul. We have added this.

      (17) Authors should explain or reference why they are analyzing the 15 min interval between 5 and 20 minutes for injection (Line507-508).

      Acetic acid behaviour lasts around 30 mins in our hands. We chose the 15 minute interval because it reduces burdensome hand scoring time by 50% versus doing the whole 30 mins. We reasoned that in the first 5 mins post injection the animal behaviour may be contaminated by stress related to handling, injection and return to chamber. Thus, 5 and 20 minutes provided a sensible time-frame for scoring the behavior when it is at its peak.

      (18) Authors have to provide more information/explanation on how they decide on the conditioned taste aversion protocol. Like why they do 30 mins exposure to a single water-containing bottle followed 90 mins exposure to both bottles. If this has been published before, they should provide the reference.

      We read dozens of different published protocols in the literature, and piloted one that was something of an amalgam of some of them with various adaptations of convenience. Because it worked on our first attempt, we stuck to it. The advantage of the CTA assay is it is incredibly robust to changes in the specificities of the paradigm, evincing the clear survival value of learning to avoid tastes that make you sick.

      (19) Authors again should provide more detail in their methods section.

      a. Specify the time frame that they are assessing here (Line 533).

      This can be seen in the Figure. 0 to 120 mins. We have added it to the methods.

      b. How long were the mice allowed to recover post-SNI before mechanical allodynia was assessed (Line 545)?

      This is apparent in the figures. 2 days to 21 days. We have added it to the methods.

      c. How much of the oxaliplatin was injected into the mice?

      40 ug / 40 ul (see PMID:33693512)

      Editors note: Reviewers agreed that addressing the concerns about power, outliers, and statistics, as well as functional validation of CGRPα would raise the strength of evidence to compelling, and inclusion of comparison to single KO would raise it to exceptional.

      Should you choose to revise your manuscript, please check to ensure full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

    1. Reviewer #3 (Public review):

      Summary:

      Overall, this is a clearly written manuscript with nice hypothesis testing in a non-model organism that addresses the mechanism of Wolbachia-mediated male killing. The authors aim to determine how five previously identified male-killing genes (encoded in the prophage region of the wHm Wolbachia strain) impact the native host, Homona magnanima moths. This work builds on the authors' previous studies in which<br /> (1) they tested the impact of these same wHm genes via heterologous expression in Drosophila melanogaster<br /> (2) also examined the activity of other male-killing genes (e.g., from the wFur Wolbachia strain in its native host: Ostrinia furnacalis moths).

      Advances here include identifying which wHm gene most strongly recapitulates the male-killing phenotype in the native host (rather than in Drosophila), and the finding that the Hm-Oscar protein has the potential for male-killing in a diverse set of lepidopterans, as inferred by the cell-culture assays.

      Strengths:

      Strengths of the manuscript include the reverse genetics approaches to dissect the impact of specific male-killing loci, and use of a "masculinization" assay in Lepidopteran cell lines to determine the impact of interactions between specific masc and oscar homologs.

      Weaknesses:

      It is clear from Figure 1 that the combinations of wmk homologs do not cause male killing on their own here. While I largely agree with the author's conclusions that oscar is the primary MK factor in this system, I don't think we can yet rule out that wmk(s) may work synergistically or interactively with oscar in vivo. This might be worth a small note in the discussion. (eg at line 294 'indicating that wmk likely targets factors other than masc." - this could be downstream of the impacts of oscar; perhaps dependent on oscar-mediated impacts on masc first).

      Regarding the perceived male-bias in Figure 2a: I think readers might be interpreting "unhatched" as "total before hatching". You could eliminate ambiguity by perhaps splitting the bars into male and female, and then within a bar, coloring by hatched versus unhatched. But this is a minor point, and I think the updated text helps clarify this.

      The new Figure 4b looks to be largely redundant with the oscar information in Figure 1a.

      Updated statistical comparisons for the RNA-seq analysis are helpful. However these analyses are based on single libraries (albeit each a pool of many individuals), so this is still a weaker aspect of the manuscript.

      The new information on masc similarity is useful (Fig 4d) - if the authors could please include a heatmap legend for the colors, that would be helpful. Also, please avoid green and red in the same figure when key for interpretation.

      Figure 1A "helix-turn-helix" is misspelled. ("tern").

    2. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Insects and their relatives are commonly infected with microbes that are transmitted from mothers to their offspring. A number of these microbes have independently evolved the ability to kill the sons of infected females very early in their development; this male killing strategy has evolved because males are transmission dead-ends for the microbe. A major question in the field has been to identify the genes that cause male killing and to understand how they work. This has been especially challenging because most male-killing microbes cannot be genetically manipulated. This study focuses on a male-killing bacterium called Wolbachia. Different Wolbachia strains kill male embryos in beetles, flies, moths, and other arthropods. This is remarkable because how sex is determined differs widely in these hosts. Two Wolbachia genes have been previously implicated in male-killing by Wolbachia: oscar (in moth male-killing) and wmk (in fly male-killing). The genomes of some male-killing Wolbachia contain both of these genes, so it is a challenge to disentangle the two.

      This paper provides strong evidence that oscar is responsible for male-killing in moths. Here, the authors study a strain of Wolbachia that kills males in a pest of tea, Homona magnanima. Overexpressing oscar, but not wmk, kills male moth embryos. This is because oscar interferes with masculinizer, the master gene that controls sex determination in moths and butterflies. Interfering with the masculinizer gene in this way leads the (male) embryo down a path of female development, which causes problems in regulating the expression of genes that are found on the sex chromosomes.

      We would like to thank you for evaluating our manuscript.

      Strengths:

      The authors use a broad number of approaches to implicate oscar, and to dissect its mechanism of male lethality. These approaches include:

      (1) Overexpressing oscar (and wmk) by injecting RNA into moth eggs.

      (2) Determining the sex of embryos by staining female sex chromosomes.

      (3) Determining the consequences of oscar expression by assaying sex-specific splice variants of doublesex, a key sex determination gene, and by quantifying gene expression and dosage of sex chromosomes, using RNASeq.

      (4) Expressing oscar along with masculinizer from various moth and butterfly species, in a silkmoth cell line.

      This extends recently published studies implicating oscar in male-killing by Wolbachia in Ostrinia corn borer moths, although the Homona and Ostrinia oscar proteins are quite divergent. Combined with other studies, there is now broad support for oscar as the male-killing gene in moths and butterflies (i.e. order Lepidoptera). So an outstanding question is to understand the role of wmk. Is it the master male-killing gene in insects other than Lepidoptera and if so, how does it operate?

      Thank you for your comments. Wolbachia strains often carry wmk genes, but as observed in this study, the homologs in Homona showed no apparent MK ability. These showed strong male lethality in D. melanogaster, but it is still unclear whether the genes are the master male-killing gene in Diptera. It is also possible that the genes show toxicities in other lepidopteran insects as well as in other insect taxa. Further functional validation assays in different insects are warranted to clarify whether wmk shows toxicity in different insect taxa. We have also discussed the functions of wmk in the Discussion section (lines 301-306).

      Weaknesses:

      I found the transfection assays of oscar and masculinizer in the silkworm cell line (Figure 4) to be difficult to follow. There are also places in the text where more explanation would be helpful for non-experts (see recommendations).

      Thank you for your suggestion. We have thoroughly revised the manuscript to address all the questions, comments and suggestions you raised in “recommendations”. In particular, we have revised the section on the transfection assays of Oscar and Masc in Bm-N4 cells (result section “Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes” starts on line 214 and Fig. 4; materials and methods section ”Transfection assays and quantification of BmIMP<sup>M</sup>”, starts on line 483). We have also provided more detailed explanations for non-experts in some contexts (in response to your recommendation). We believe that the resulting revisions have significantly improved the quality and comprehensiveness of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      Wolbachia are maternally transmitted bacteria that can manipulate host reproduction in various ways. Some Wolbachia induce male killing (MK), where the sons of infected mothers are killed during development. Several MK-associated genes have been identified in Homona magnanima, including Hm-oscar and wmk-1-4, but the mechanistic links between these Wolbachia genes and MK in the native host are still unclear.

      In this manuscript, Arai et al. show that Hm-oscar is the gene responsible for Wolbachia-induced MK in Homona magnanima. They provide evidence that Hm-Oscar functions through interactions with the sex determination system. They also found that Hm-Oscar disrupts sex determination in male embryos by inducing female-type dsx splicing and impairing dosage compensation. Additionally, Hm-Oscar suppresses the function of Masc. The manuscript is well-written and presents intriguing findings. The results support their conclusions regarding the diversity and commonality of MK mechanisms, contributing to our understanding of the mechanisms and evolutionary aspects of Wolbachia-induced MK.

      We would like to thank you for evaluating our manuscript.

      Strengths/weaknesses:

      (1) The authors found that transient overexpression of Hm-oscar, but not wmk-1-4, in Wolbachia-free H. magnanima embryos induces female-biased sex ratios. These results are striking and mirror the phenotype of the wHm-t infected line (WT12). However, Table 1 lists the "male ratio," while the text presents the "female ratio" with standard deviation. For consistency, the calculation term should be uniform, and the "ratio" should be listed for each replicate.

      We have revised the first results section (Hm-oscar induces female-biased sex ratios, starting from line 147) accordingly to maintain the consistency in the calculation term. In the revised manuscript, the 'male ratio' is now consistently used, in alignment with Fig. 1. In addition, we have included all sex ratio information (number of males and females) in the supplementary data file for transparency and clarity.

      (2) The error bars in Figure 3 are quite large, and the figure lacks statistical significance labels. The authors should perform statistical analysis to demonstrate that Hm-oscar-overexpressed male embryos have higher levels of Z-linked gene expression.

      The large error bar on each chromosome (Fig.3a-d) likely reflect the overall variation in expression levels across different transcripts. Accordingly, we have included statistical data for Figure 3 based on the Steel-Dwass test for expression levels. However, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Instead, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in wHm-t-infected/Hb-Oscar-injected embryos, we have included the expression data for a Z-linked gene tpi, along with its statistical data in the revised manuscript (Fig. 3e, lines 210-212).

      (3) The authors demonstrated that Hm-Oscar suppresses the masculinizing functions of lepidopteran Masc in BmN-4 cells derived from the female ovaries of Bombyx mori. They should clarify why this cell line was chosen and its biological relevance. Additionally, they should explain the rationale for evaluating the expression levels of the male-specific BmIMP variant and whether it is equivalent to dsx.

      Thank you for your suggestion. We selected BmN-4 cell line because previous studies have established it as a reliable model for investigating the functions of lepidopteran masc genes and the interactions between masc and Oscar genes (Katsuma et al., 2019; 2022). In addition, BmIMP<sup>M</sup> is a male-specific regulator of the male-type dsx, making it an ideal target for assessing the 'maleness' induced by transfection of the masc gene in female-derived BmN-4 cells (Suzuki et al., 2010; Katsuma et al., 2015). We have included more detailed background information in the revised manuscript and have thoroughly revised this section (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, starting at line 214) and Figure 4 for better clarity.

      (4) Although the authors show that Hm-oscar is involved in Wolbachia-induced MK in Homona magnanima and interacts with the sex determination system in lepidopteran insects, the precise molecular mechanism of Hm-oscar-induced MK remains unclear. Further studies are needed to elucidate how Hm-oscar regulates Homona magnanima genes to induce MK, though this may be beyond the scope of the current manuscript.

      Based on our findings and previous studies in Homona, Ostrinia and Bombyx (Arai et al., 2023a; Katsuma et al., 2023; Kiuchi et al., 2014), we hypothesize that the molecular mechanisms underlying _w_Hm-induced MK are likely linked to impaired dosage compensation caused by the inhibition of Masc function by the Hm-Oscar protein. While the precise mechanisms remain unclear, unbalanced Z-linked gene expression due to the impaired dosage compensation (i.e., 2-fold higher Z-linked gene expression compared to normal males) is known to be lethal for lepidopteran males (Kiuchi et al., 2014; Fukui et al., 2015; Visser et al., 2021). We have outlined this hypothesis in the Discussion section (lines 245-254).

      Reviewer #3 (Public review):

      Summary:

      Overall, this is a clearly written manuscript with nice hypothesis testing in a non-model organism that addresses the mechanism of Wolbachia-mediated male killing. The authors aim to determine how five previously identified male-killing genes (encoded in the prophage region of the wHm Wolbachia strain) impact the native host, Homona magnanima moths. This work builds on the authors' previous studies in which:

      (1) They tested the impact of these same wHm genes via heterologous expression in Drosophila melanogaster.

      (2) They examined the activity of other male-killing genes (e.g., from the wFur Wolbachia strain in its native host: Ostrinia furnacalis moths).

      Advances here include identifying which wHm gene most strongly recapitulates the male-killing phenotype in the native host (rather than in Drosophila), and the finding that the Hm-Oscar protein has the potential for male-killing in a diverse set of lepidopterans, as inferred by the cell-culture assays.

      Strengths:

      Strengths of the manuscript include the reverse genetics approaches to dissect the impact of specific male-killing loci, and the use of a "masculinization" assay in Lepidopteran cell lines to determine the impact of interactions between specific masc and oscar homologs.

      We would like to thank you for evaluating our manuscript.

      Weaknesses:

      My major comments are related to the lack of statistics for several experiments (and the data normalization process), and opportunities to make the manuscript more broadly accessible.

      Thank you for your suggestions. We have thoroughly revised the manuscript to provide clearer explanations for non-experts. In addition, we have included more detailed statistical data for Figure 3 and Figure 4 based on the Steel-Dwass tests. For Figure 3a-d, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Therefore, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in w_Hm-t-infected/Hm-Oscar-injected embryos, we have included the expression data for a Z-linked gene _tpi, along with its statistical data in the revised manuscript (Fig.3e, lines 210-212). Regarding Figure 4, we have revised the Figure based on the reviewer’s suggestions, and provided more detailed information on how the expression data were analyzed (Transfection assays and quantification of BmIMP<sup>M</sup>, lines 495-520). We have also included more detailed background information on the assay system (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, lines 215-237). Although we did not observe statistical significance based on the Steel-Dwass test, likely due to limited replicates, the observed changes in the IMP gene expression remain clearly evident.

      The manuscript I think would be much improved by providing more details regarding some of the genes and cross-lineage comparisons. I know some of this is reported in previous publications, but some summary and/or additional analysis would make this current manuscript much more approachable for a broader audience, and help guide readers to specific important findings. For example, a graphic and/or more detail on how the wmk/oscar homologs (within and across Wolbachia strains) differ (e.g., domains, percent divergence, etc) would be helpful for contextualizing some of the results. I recognize the authors discuss this in parts (e.g., lines 223-227), but it does require some bouncing between sections to follow. Similarly, the experiments presented in Figure 4 indicate that Hm-oscar has broad spectrum activity: how similar are the masc proteins from these various lepidopterans? Are they highly conserved? Rapidly evolving? Do the patterns of masc protein evolution provide any hints at how Oscar might be interacting with masc?

      Thank you for your valuable suggestion. To address this, we have included a visualization of the structural differences between the Oscar and wmk homologs in Figure 1a of the revised manuscript. In addition, we have included more detailed information for these genes and revised the introduction (lines 110-114; 124-137) and discussion (lines 255-266) to provide a clearer and more comprehensive overview. We have also described the similarity of the Masc proteins and Oscar proteins that we used, which is now reflected in the revised Figure 4b and 4d. More detailed information on these proteins is available in the supplementary data. Notably, Masc proteins exhibit high sequence variability with conserved domains (Figure 4d). Previous study identified the N-terminal region of Masc as crucial for the Oscar function (Katsuma et al., 2022). The wide spectrum of the actions of Hm-Oscar likely stems from these conserved structures of Masc, but the effects might have undergone evolutionary tuning through interactions with the native host as discussed in lines 293-294.

      It is clear from Figure 1 that the combinations of wmk homologs do not cause male killing on their own. Did the authors test if any of the wmk homologs impact the MK phenotype of oscar? It looks like a previous study tested this in wFur (noted in lines 250-252), but given that the authors also highlight the differences between the wFur-oscar and Hm-oscar proteins, this may be worth testing in this system. Related to this, what is the explanation for why there would be 4 copies of wmk in Hm?

      Thank you for your valuable suggestion. Unfortunately, we have not yet tested the effects of co-expression of wmk and Oscar. Due to a technical issue, the mixing of multiple constructs results in a reduced amount of mRNA (i.e. mixing wmk-3 and Hm-Oscar at the same concentration results in a 2-fold lower concentration in mRNA for both genes compared to mono-injected groups). In addition, we have previously tested injecting mRNA at the twofold higher concentration (i.e. 2 ug/ul mRNA), which resulted in very low hatchability regardless of the genes. Katsuma et al (2022) tested the effect of wmk on the sex determination system, but did not test the effect of co-injection/transfection of wmk and Oscar. Considering the results of this and previous studies (Katsuma et al., 2022; Arai et al., 2023), it is likely that the targets of the wmk and oscar genes are different (as discussed in lines 267-289). Co-injection of wmk and oscar may not produce additive effects. Nevertheless, we would like to test the results in future studies using the Drosophila system as well.

      As you point out, it is an interesting point that the moth-derived MK Wolbachia w_Hm-t encodes four _wmk genes, although they have no apparent effect on host survival. The exact functional relevance of these wmk homologs remains unclear. However, they may play a role in Wolbachia biology as transcriptional regulators, given that they encode HTH domains. Wolbachia generally encode several wmk homologs in their genome, regardless of whether they induce MK. This suggests that the functions of the wmk genes may be 'suppressed' in certain Wolbachia-host systems. The wmk and Hm-oscar genes are located within a prophage region, and some wmk genes are tandemly arrayed (as described in Arai et al., 2023). These wmk homologs may have increased in number by horizontal phage transfer, and the region containing wmk and adjacent sequences may act as a genomic island for virulence. So far, the function of wmk homologs has only been tested in D. melanogaster and H. magnanima, and further studies in other Wolbachia-host systems are highly warranted to test whether wmk exerts MK effects in other insect models. These points have been briefly discussed in the revised manuscript (lines 301-306; 318-320).

      Why are some of the broods male-biased (2/3) rather than ~50:50? (Lines 170-175, Figure 2a). For example, there is a strong male bias in un-hatched oscar-injected and naturally infected embryos, whereas the control uninfected embryos have normal 50:50 sex ratios. It is difficult to interpret the rate of male-killing given that the sex ratios of different sets of zygotes are quite variable.

      The observed male-biased sex ratios in unhatched embryos are due to the occurrence of MK during embryogenesis. In the unhatched groups, the skew towards males reflects that fact that the male embryos were targeted and killed by Wolbachia/Oscar, leading to a surplus of unhatched male embryos. Conversely, hatched individuals show a higher proportion of females because many of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the relevant section (Males are killed mainly at the embryonic stage, lines 179-186) and provided more detailed information to clarify this explanation.

      Figure 2b - it appears there are both male and female bands in the HmOsc male lane. I think this makes sense (likely a partial phenotype due to the nature of the overexpression approach), but this is worth highlighting, especially in the context of trying to understand how much of the MK phenotype might be recapitulated through these methods. Related, there is no negative control for this PCR.

      Thank you for your suggestion. As you noted, a faint dsx-M band is visible in the Hm-oscar treated group in Figure 2b. This is consistent with previous findings by Arai et al. (2023), which reported that male embryos with low-density w_Hm-t showed double bands of _dsx-M and dsx-F, similar to what we observed in this study. This information has been included in the revised manuscript in lines 196-198, as follows:

      “Notably, male embryos expressing Hm-oscar also exhibited weak male-type dsx splicing in addition to the female-type splicing, resembling the previously observed pattern in male embryos infected with low-titer _w_Hm-t (Arai et al., 2023a).”

      Also, we appreciate your comment regarding the missing of negative control. The figure has now been revised as we realised that the negative control lane had been lost during the preparation of the figure. We also included the relevant molecular marker information in both the figure legends and Figure 2b.

      It appears the RNA-seq analysis (Figure 3) is based on a single biological replicate for each condition. And, there are no statistical comparisons that support the conclusions of a shift in dosage compensation. Finally, it is unclear what exactly is new data here: the authors note "The expression data of the wHm-t-infected and non-infected groups were also calculated based on the transcriptome data included in Arai et al. (2023a)" - So, are the data in Figure 3c and 3d a re-print of previous data? The level of dosage compensation inferred by visually comparing the control conditions in 3b and 3d does not appear consistent. With only one biological replicate library per condition, what looks like a re-print of previous data, and no statistical comparisons, this is a weakly supported conclusion.

      Thank you for your suggestion. In this study, we generated the RNA-seq data for the Hm-oscar/GFP-injected groups, but did not sequence the w_Hm-t-infected/NSR lines. Instead, the previously generated RNA-seq data of _w_Hm-t-infected/NSR (Arai et al., 2023) were re-analyzed (rather than simply reprinted) to evaluate whether the expression patterns of _Hm-oscar-injected and w_Hm-t-infected groups are similar. We have revised the Results section (_Hm-oscar impairs dosage compensation in male embryos, lines 200-212), the Materials and methods section (Quantification of Z chromosome-linked genes, lines 454-456), and the figure legends to provide more precise information about this analysis.

      Although we did not perform replicates for the RNA-seq comparisons, it is important to note that each RNA-seq sample contains 50-60 male/female individuals. We believe the results are still robust and clearly indicative of the trends we observe. This was further supported by the quantification of Hmtpi gene expression, which we have visualized in Figure 3e (and lines 210-212). As you noted, the expression patterns in Figure 3b (GFP injected) and Figure 3d (NSR) are not completely identical. This discrepancy may be due to the differences between injection treatments and natural infections. Nevertheless, both treatments are consistent in showing that gene expressions on the Z chromosome (Chr01 and Chr15) are not upregulated.

      We have also added more detailed statistical data for Figure 3 based on the Steel-Dwass tests. For Figure 3a-d, however, showing the statistical significance directly on the whisker plots would create excessive clutter due to the numerous combinations of chromosomes. Instead, we have provided the full statistical data in the supplementary data file. Furthermore, to support/strengthen our conclusion that Z-linked genes are highly expressed in w_Hm-t-infected/_Hm-Oscar-injected embryos, we have included expression data for the Z-linked gene tpi, along with statistical data, in the revised manuscript (Fig. 3e, lines 210-212).

      In Figure 4: There are no statistics to support the conclusions presented here. Additionally, the data have gone through a normalization process, but it is difficult to follow exactly how this was done. The control conditions appear to always be normalized to 100 ("The expression levels of BmImpM in the Masc and Hm-Oscar/Oscar co-transfected cells were normalized by setting each Masc-transfected cell as 100"). I see two problems with this approach:

      (1) This has eliminated all of the natural variation in BmImpM expression, which is likely not always identical across cells/replicates.

      (2) How then was the percentage of BmImpM calculated for each of the experimental conditions? Was each replicate sample arbitrarily paired with a control sample? This can lead to very different outcomes depending on which samples are paired with each other. The most appropriate way to calculate the change between experimental and control would be to take the difference between every single sample (6 total, 3 control, 3 experimental) and the mean of the control group. The mean of the control can then be set at 100 as the authors like, but this also maintains the variability in the dataset and then eliminates the issue of arbitrary pairings. This approach would also then facilitate statistical comparisons which is currently missing.

      Thank you for your suggestion. As you pointed out in (1), the previous analysis did indeed eliminate the natural variation in BmIMP-M expression. In the revised manuscript and Figure 4, we have reanalyzed the data following your suggestion and have described the variation across replicates.

      For (2), the data shown in the previous manuscript were normalized to 100 for each Masc-treated group. In doing so, each replicate sample was arbitrarily paired with a control sample from the same cell lot to account for variations that might occur due to differences in cell lots. However, following your recommendation, we have revised the figure to set the average of the Hm-masc treated group to 100, rather than using arbitrary pairings. More detailed normalization procedures have been provided in the section 'Transfection assays and quantification of BmIMP' (lines 483-520). Additionally, we have provided more detailed background information on the assay system in lines 218-223. Although we did not observe statistical significance based on the Steel-Dwass test, likely due to the limited number of replicates, the differences in IMP gene expression between the Masc-treated and Masc&Hm-oscar-treated groups remain evident.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Line 38: change to: 'Wolbachia are maternally transmitted'.

      Revised accordingly (line 38).

      Line 69: remove 'seemingly'.

      Revised accordingly (line 69).

      Paragraph starting line 123: I don't think this is so clear to a reader who is not familiar with the work and system. It would be helpful to more clearly explain that candidate male-killing genes from Wolbachia that infect Homona were inserted into Drosophila melanogaster, and that their expression was then induced, with interesting patterns (and that it can be a bit difficult to interpret the transgenic expression of genes from a moth male-killer that are inserted into a fly). Also, the sentence about the combined action of cifA and cifB in Drosophila cytoplasmic incompatibility is also confusing to a non-expert. I would suggest removing it.

      Thank you for your suggestion. We have revised the paragraph (lines 124-139) to provide clearer background information, making it easier for non-experts to follow. We have also removed the sentence regarding the combined effect of cifA and cifB to improve the flow and overall clarity.

      Line 170: what is the explanation for the male-biased sex ratio instead of 50-50?

      The male-biased sex ratio occurs because MK happens during embryogenesis. Unhatched embryos include males that were killed by Wolbachia/Oscar, resulting in a higher proportion of unhatched male embryos. Conversely, the hatched individuals display a female bias, as most of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the section “Males are killed mainly at the embryonic stage” (lines 170-186) to include more detailed information explaining this phenomenon.

      Line 190: please explain what are the Z chromosomes in Bombyx and Homona and Lepidoptera in general (chromosomes 1 and 15?), as this is not so clear for a non-expert.

      Thank you for your suggestion. I have revised the section (lines 200-212) to include more precise background information about the chromosome constitutions in lines 202-204 as follows:

      “Unlike other lepidopteran species, Tortricidae, including H. magnanima, generally possess a large Z chromosome that is homologous to B. mori chromosomes 1 (Z) and 15 (autosome).”

      Line 222: please explain oscar diversity and classification in more detail, as this is not so clear for a non-expert.

      Thank you for your suggestion. We have revised the sentences to provide clearer background information on the diversity of oscar genes (lines 255-264).

      Figure 4: I found this difficult to follow. Why are there 2 rows (HmOscar and Oscar)? Does oscar here refer to oscar from Ostrinia? I am also a bit confused about the baseline control of Masc in these cell lines. If I understand Lepidoptera sex determination, then these cell lines are expressing high levels of female-specific piRNAs that suppress Masc. How specific are these piRNAs (i.e. do Bombyx piRNAs suppress Mascs from other Lepidoptera)? How much extra Masc will override endogenous piRNA? Information is lost by setting Masc expression to 100% in each separate comparison.

      Yes, the Oscar indicates the w_Fur-encoded _oscar (Oscar from Ostrinia) that was tested to compare function with the Homona-derived Hm-oscar gene. In addition, following the reviewer's suggestions, we have revised the figure and included more detailed information on how we adjusted the expressions in the M&M section.

      A previous study (Shoji et al., 2017, RNA 23:86–97) demonstrated that the Fem piRNA (29 bp) in Bombyx mori requires a 17 bp complementary sequence from its 5' region for its function. However, in species other than B. mori, no significant homology (i.e., over 17 bp matches) was found between the B. mori Fem piRNA and the masc genes analyzed in this study. Therefore, it is likely that the Fem piRNA expressed in BmN-4 cells is unable to suppress the masculinizing function driven by masc genes in other lepidopteran species. In addition, we did not quantify the levels of piRNA in this system, but the expression levels of masc are probably too high to be suppressed.

      Figure 4 legend: spelling of Spodoptera.

      Revised accordingly.

      Reviewer #2 (Recommendations for the authors):

      In Figure 2, what is the dsx splicing type for the hatched male in the Hm-oscar-injected group and the wHm-t infected line? Dsx-F or dsx-M?

      Thank you for your suggestion. Unfortunately, we have not tested splicing in the hatched male neonates (1st instar larvae), partly due to difficulties in obtaining sufficient material for RNA extraction. Based on the previous publication in the Ostrinia system, where Oscar-bearing w_Sca induces MK, the hatched males (ZZ) exhibit female type _dsx as observed in the male embryos (Herran et al., 2022). The hatched Homona males may show double bands for dsx-M and dsx-F as observed in this study.

      The size of the markers (in kilobase pairs) should be indicated in Figure 2.

      We have accordingly included the marker information in the revised Figure 2b and the figure legends.

      In Figure 3, could the authors identify which genes exhibit higher expression levels in the Hm-oscar-injected group and the wHm-t infected line? Could they provide hints for the possible mechanism of male-killing?

      In the RNA-seq data shown in Figure 3a-d, we observed that both the Hm-oscar-injected and w_Hm-infected groups generally exhibited upregulated expression of Z-linked genes. Rather than the upregulation or downregulation of a specific gene, we consider that global upregulation of Z-linked genes, caused by improper dosage compensation, is lethal for males. The Z chromosome contains various genes involved in key biological processes such as endocrine function and detoxification, and disruption of these processes may contribute to male lethality. Additionally, in this revised manuscript, we have provided more detailed information on the expression level of the Z-linked gene _tpi. We have also discussed the potential mechanisms of MK in the Discussion section (lines 245-254).

      The format of the references should be consistent. Gene and species names should be italicized.

      We have accordingly formatted.

      Reviewer #3 (Recommendations for the authors):

      The authors use the term "upstream" (e.g., Oscar suppressed the function of masculinizer, the upstream male sex determinant...), which was sometimes confusing. In many cases, it reads as though the masculinizer was upstream of oscar, but what I think the authors are trying to convey is that masculinizer is a primary sex-determining factor.

      Thank you for your suggestion. We have accordingly revised the term.

      Line 101: which insect is wFur from?

      It is from Ostrinia furnacalis - line 104 has been revised.

      Figure 1: it would be helpful to indicate the statistical results on the figure.

      Accordingly, we have added statistical data (binominal test) for Figure 1. The data for the Steel-Dwass test have been included in the supplementary data.

      Figure 2b: please label the ladder on the gel.

      Thank you for your suggestion. We have accordingly labeled the DNA ladder on the gel.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper is an elegant, mostly observational work, detailing observations that polysome accumulation appears to drive nucleoid splitting and segregation. Overall I think this is an insightful work with solid observations.

      Thank you for your appreciation and positive comments. In our view, an appealing aspect of this proposed biophysical mechanism for nucleoid segregation is its self-organizing nature and its ability to intrinsically couple nucleoid segregation to biomass growth, regardless of nutrient conditions.

      Strengths:

      The strengths of this paper are the careful and rigorous observational work that leads to their hypothesis. They find the accumulation of polysomes correlates with nucleoid splitting, and that the nucleoid segregation occurring right after splitting correlates with polysome segregation. These correlations are also backed up by other observations:

      (1) Faster polysome accumulation and DNA segregation at faster growth rates.

      (2) Polysome distribution negatively correlating with DNA positioning near asymmetric nucleoids.

      (3) Polysomes form in regions inaccessible to similarly sized particles.

      These above points are observational, I have no comments on these observations leading to their hypothesis.

      Thank you!

      Weaknesses:

      It is hard to state weaknesses in any of the observational findings, and furthermore, their two tests of causality, while not being completely definitive, are likely the best one could do to examine this interesting phenomenon.

      It is indeed difficult to prove causality in a definitive manner when the proposed coupling mechanism between nucleoid segregation and gene expression is self-organizing, i.e., does not involve a dedicated regulatory molecule (e.g., a protein, RNA, metabolite) that we could have depleted through genetic engineering to establish causality. We are grateful to the reviewer for recognizing that our two causality tests are the best that can be done in this context.

      Points to consider / address:

      Notably, demonstrating causality here is very difficult (given the coupling between transcription, growth, and many other processes) but an important part of the paper. They do two experiments toward demonstrating causality that help bolster - but not prove - their hypothesis. These experiments have minor caveats, my first two points.

      (1) First, "Blocking transcription (with rifampicin) should instantly reduce the rate of polysome production to zero, causing an immediate arrest of nucleoid segregation". Here they show that adding rifampicin does indeed lead to polysome loss and an immediate halting of segregation - data that does fit their model. This is not definitive proof of causation, as rifampicin also (a) stops cell growth, and (b) stops the translation of secreted proteins. Neither of these two possibilities is ruled out fully.

      That’s correct; cell growth also stops when gene expression is inhibited, which is consistent with our model in which gene expression within the nucleoid promotes nucleoid segregation and biomass growth (i.e., cell growth), inherently coupling these two processes. This said, we understand the reviewer’s point: the rifampicin experiment doesn’t exclude the possibility that protein secretion and cell growth drive nucleoid segregation. We are assuming that the reviewer is envisioning an alternative model in which sister nucleoids would move apart because they would be attached to the membrane through coupled transcription-translation-protein secretion (transertion) and the membrane would expand between the separating nucleoids, similar to the model proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several observations arguing against this cell elongation/transertion model.

      (1) For this alternative mechanism to work, membrane growth must be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To circumvent the membrane fluidity issue, one could potentially evoke an additional connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid. However, peptidoglycan growth is dispersed early in the cell division cycle when the nucleoid splitting happens in fast growing cells and only appears to be zonal after the onset of cell constriction (PMID: 35705811, 36097171, 2656655).

      (2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we wil clarify this point and provide confirmatory data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate, indicating that it cannot be the main driver.

      (3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in Figure 1H and Figure 1 – figure supplement 3 of the original manuscript but were not highlighted in this context. We will revise the text to clarify this point.

      (4) The asymmetries in nucleoid compaction that we described in our paper are predicted by our model. We do not see how they could be explained by cell growth or protein secretion.

      (5) We also show that polysome accumulation at ectopic sites (outside the nucleoid) results in correlated nucleoid dynamics, consistent with our proposed mechanism. These nucleoid dynamics cannot be explained by cell growth or protein secretion (transertion).

      (1a) As rifampicin also stops all translation, it also stops translational insertion of membrane proteins, which in many old models has been put forward as a possible driver of nucleoid segregation, and perhaps independent of growth. This should at last be mentioned in the discussion, or if there are past experiments that rule this out it would be great to note them.

      It is not clear to us how the attachment of the DNA to the cytoplasmic membrane could alone create a directional force to move the sister nucleoids. We agree that old models have proposed a role for cell elongation (providing the force) and transertion (providing the membrane tether).  Please see our response above for the evidence (from the literature and our work) against it. This was mentioned in the introduction and Results section, but we agree that this was not well explained. We will add experimental data and revise the text to clarify these points.

      (1b) They address at great length in the discussion the possibility that growth may play a role in nucleoid segregation. However, this is testable - by stopping surface growth with antibiotics. Cells should still accumulate polysomes for some time, it would be easy to see if nucleoids are still segregated, and to what extent, thereby possibly decoupling growth and polysome production. If successful, this or similar experiments would further validate their model.

      We reviewed the literature and could not find a drug that stops cell growth without stopping gene expression. Any drug that affects the membrane integrity or potential stops gene expression, which requires ATP.  However, our experiment in which we drive polysome accumulation at ectopic sites decouples polysome accumulation from cell growth. In this experiment, by redirecting most of chromosome gene expression to a single plasmid-encoded gene, we reduce the rate of cell growth but still create a large accumulation of polysomes at an ectopic location. This ectopic polysome accumulation is sufficient to affect nucleoid dynamics in a correlated fashion. In the revised manuscript, we will clarify this point and add model simulations to show that our experimental observations are predicted by our model.

      (2) In the second experiment, they express excess TagBFP2 to delocalize polysomes from midcell. Here they again see the anticorrelation of the nucleoid and the polysomes, and in some cells, it appears similar to normal (polysomes separating the nucleoid) whereas in others the nucleoid has not separated. The one concern about this data - and the differences between the "separated" and "non-separated" nuclei - is that the over-expression of TagBFP2 has a huge impact on growth, which may also have an indirect effect on DNA replication and termination in some of these cells. Could the authors demonstrate these cells contain 2 fully replicated DNA molecules that are able to segregate?

      We will perform the requested experiment.

      (3) What is not clearly stated and is needed in this paper is to explain how polysomes do (or could) "exert force" in this system to segregate the nucleoid: what a "compaction force" is by definition, and what mechanisms causes this to arise (what causes the "force") as the "compaction force" arises from new polysomes being added into the gaps between them caused by thermal motions.

      They state, "polysomes exert an effective force", and they note their model requires "steric effects (repulsion) between DNA and polysomes" for the polysomes to segregate, which makes sense. But this makes it unclear to the reader what is giving the force. As written, it is unclear if (a) these repulsions alone are making the force, or (b) is it the accumulation of new polysomes in the center by adding more "repulsive" material, the force causes the nucleoids to move. If polysomes are concentrated more between nucleoids, and the polysome concentration does not increase, the DNA will not be driven apart (as in the first case) However, in the second case (which seems to be their model), the addition of new material (new polysomes) into a sterically crowded space is not exerting force - it is filling in the gaps between the molecules in that region, space that needs to arise somehow (like via Brownian motion). In other words, if the polysome region is crowded with polysomes, space must be made between these polysomes for new polysomes to be inserted, and this space must be made by thermal (or ATP-driven) fluctuations of the molecules. Thus, if polysome accumulation drives the DNA segregation, it is not "exerting force", but rather the addition of new polysomes is iteratively rectifying gaps being made by Brownian motion.

      We apologize for the understandable confusion. In our picture, the polysomes and DNA (conceptually considered as small plectonemic segments) basically behave as dissolved particles. If these particles were noninteracting, they would simply mix. However, both polysomes and DNA segments are large enough to interact sterically. So as density increases, steric avoidance implies a reduced conformational entropy and thus a higher free energy per particle. We argue (based on Miangolarra et al. PNAS 2021 PMID: 34675077 and Xiang et al. Cell 2021 PMID: 34186018) that the demixing of polysomes and DNA segments occurs because DNA segments pack better with each other than they do with polysomes. This raises the free energy cost associated with DNA-polysome interactions compared to DNA-DNA interactions.  We model this effect by introducing a term in the free energy χ_np, which refer to as a repulsion between DNA and polysomes, though as explained above it arises from entropic effects. At realistic cellular densities of DNA and polysomes this repulsive interaction is strong enough to cause the DNA and polysomes to phase separate.

      This same density-dependent free energy that causes phase separation can also give rise to forces, just in the way that a higher pressure on one side of a wall can give rise to a net force on the wall. Indeed, the “compaction force” we refer to is fundamentally an osmotic pressure difference. At some stages during nucleoid segregation, the region of the cell between nucleoids has a higher polysome concentration, and therefore a higher osmotic pressure, than the regions near the poles. This results in a net poleward force on the sister nucleoids that drives their migration toward the poles. This migration continues until the osmotic pressure equilibrates. Therefore, both phase separation (due to the steric repulsion described above) and nonequilibrium polysome production and degradation (which creates the initial accumulation of polysomes around midcell) are essential ingredients for nucleoid segregation.

      This will be clarified in the revised text, with the support of additional simulation results.

      The authors use polysome accumulation and phase separation to describe what is driving nucleoid segregation. Both terms are accurate, but it might help the less physically inclined reader to have one term, or have what each of these means explicitly defined at the start. I say this most especially in terms of "phase separation", as the currently huge momentum toward liquid-liquid interactions in biology causes the phrase "phase separation" to often evoke a number of wider (and less defined) phenomena and ideas that may not apply here. Thus, a simple clear definition at the start might help some readers.

      Phase separation means that the DNA-polysome steric repulsion is strong enough to drive their demixing, which creates a compact nucleoid. As mentioned in a previous point, this effect is captured in the free energy by the χ_np term, which is an effective repulsion between DNA and polysomes, though as explained above it arises from entropic effects.

      In the revised manuscript, we will illustrate this with our theoretical model by initializing a cell with a diffuse nucleoid and low polysome concentration. For the sake of simplicity, we assume that the cell does not elongate. We observe that the DNA-polysome steric repulsion is sufficient to compact the nucleoid and place it at mid-cell.

      (4) Line 478. "Altogether, these results support the notion that ectopic polysome accumulation drives nucleoid dynamics". Is this right? Should it not read "results support the notion that ectopic polysome accumulation inhibits/redirects nucleoid dynamics"?

      We think that this is correct; the ectopic polysome accumulation drives nucleoid dynamics. In our theoretical model, we can introduce polysome production at fixed sources to mimic the experiments where ectopic polysome production is achieved by high plasmid expression (Fig. 6). The model is able to recapitulate the two main phenotypes observed in experiments. These new simulation results will be added to the revised manuscript.

      (5) It would be helpful to clarify what happens as the RplA-GFP signal decreases at midcell in Figure 1- is the signal then increasing in the less "dense" parts of the cell? That is, (a) are the polysomes at midcell redistributing throughout the cell? (b) is the total concentration of polysomes in the entire cell increasing over time?

      It is a redistribution—the RplA-GFP signal remains constant in concentration from cell birth to division (Figure 1 – Figure Supplement 1E). This will be clarified in the revised text.

      (6) Line 154. "Cell constriction contributed to the apparent depletion of ribosomal signal from the mid-cell region at the end of the cell division cycle (Figure 1B-C and Movie S1)" - It would be helpful if when cell constriction began and ended was indicated in Figures 1B and C.

      Good idea. We will add markers to indicate the start of cell constriction. We will also indicate that cell birth and division correspond to the first and last images/timepoint in Fig. 1B and C, respectively.

      (7) In Figure 7 they demonstrate that radial confinement is needed for longitudinal nucleoid segregation. It should be noted (and cited) that past experiments of Bacillus l-forms in microfluidic channels showed a clear requirement role for rod shape (and a given width) in the positing and the spacing of the nucleoids.

      Wu et al, Nature Communications, 2020 . "Geometric principles underlying the proliferation of a model cell system" https://dx.doi.org/10.1038/s41467-020-17988-7

      Good point. We will add this reference. Thank you.

      (8) "The correlated variability in polysome and nucleoid patterning across cells suggests that the size of the polysome-depleted spaces helps determine where the chromosomal DNA is most concentrated along the cell length. This patterning is likely reinforced through the displacement of the polysomes away from the DNA dense region"

      It should be noted this likely functions not just in one direction (polysomes dictating DNA location), but also in the reverse - as the footprint of compacted DNA should also exclude (and thus affect) the location of polysomes

      We agree that the effects could go both ways at this early stage of the story. We will revise the text accordingly.  

      (9) Line 159. Rifampicin is a transcription inhibitor that causes polysome depletion over time. This indicates that all ribosomal enrichments consist of polysomes and therefore will be referred to as polysome accumulations hereafter". Here and throughout this paper they use the term polysome, but cells also have monosomes (and 2 somes, etc). Rifampicin stops the assembly of all of these, and thus the loss of localization could occur from both. Thus, is it accurate to state that all transcription events occur in polysomes? Or are they grouping all of the n-somes into one group?

      In the discussion, we noted that our term “polysomes” also includes monosomes for simplicity, but we agree that the term should have been defined much earlier. This will be done in the revised manuscript.

      Thank you for the valuable comments and suggestions!

      Reviewer #2 (Public review):

      Summary:

      The authors perform a remarkably comprehensive, rigorous, and extensive investigation into the spatiotemporal dynamics between ribosomal accumulation, nucleoid segregation, and cell division. Using detailed experimental characterization and rigorous physical models, they offer a compelling argument that nucleoid segregation rates are determined at least in part by the accumulation of ribosomes in the center of the cell, exerting a steric force to drive nucleoid segregation prior to cell division. This evolutionarily ingenious mechanism means cells can rely on ribosomal biogenesis as the sole determinant for the growth rate and cell division rate, avoiding the need for two separate 'sensors,' which would require careful coupling.

      Terrific summary! Thank you for your positive assessment.

      Strengths:

      In terms of strengths; the paper is very well written, the data are of extremely high quality, and the work is of fundamental importance to the field of cell growth and division. This is an important and innovative discovery enabled through a combination of rigorous experimental work and innovative conceptual, statistical, and physical modeling.

      Thank you!

      Weaknesses:

      In terms of weaknesses, I have three specific thoughts.

      Firstly, my biggest question (and this may or may not be a bona fide weakness) is how unambiguously the authors can be sure their ribosomal labeling is reporting on polysomes, specifically. My reading of the work is that the loss of spatial density upon rifampicin treatment is used to infer that spatial density corresponds to polysomes, yet this feels like a relatively indirect way to get at this question, given rifampicin targets RNA polymerase and not translation. It would be good if a more direct way to confirm polysome dependence were possible.

      The heterogeneity of ribosome distribution inside E. coli cells has been attributed to polysomes by many labs (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340).  The attribution is also consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875).

      Furthermore, inhibition of translation initiation with kasugamycin treatment, which decreases the pool of polysomes, results in a homogenization of ribosomes and expansion of the nucleoid (see Author response image 1). This further supports the rifampicin experiments. Given that the attribution of ribosome heterogeneity to polysomes is well accepted in the field, we would prefer to not include these kasugamycin data in the revised manuscript because long-term exposure to this drug leads to nucleoid re-compaction (PMID: 25250841 and PMID: 34186018). This secondary effect may possibly be due to a dysregulated increase in synthesis of naked rRNAs (PMID: 14460744, PMID: 2114400, and PMID: 2448483) or ribosome aggregation, which we are currently investigating.

      Author response image 1.

      Effects of kasugamycin treatment on the intracellular distribution of ribosomes and nucleoids. Representative single cell (CJW7323) growing in M9gluCAAT.  Kasugamycin (3 mg/mL) was added at time = 0 min. Show is the early response (0-30 min) to the drug characterized by the homogenization of the ribosomal RplA-GFP fluorescence and the expansion of the HupA-mCherry-labeled nucleoids. For each segmented cell, the RplA-GFP and HupA-mCherry signals were normalized by the average fluorescence.

      Second, the authors invoke a phase separation model to explain the data, yet it is unclear whether there is any particular evidence supporting such a model, whether they can exclude simpler models of entanglement/local diffusion (and/or perhaps this is what is meant by phase separation?) and it's not clear if claiming phase separation offers any additional insight/predictive power/utility. I am OK with this being proposed as a hypothesis/idea/working model, and I agree the model is consistent with the data, BUT I also feel other models are consistent with the data. I also very much do not think that this specific aspect of the paper has any bearing on the paper's impact and importance.

      We appreciate the reviewer’s comment, but the output of our reaction-diffusion model is a bona fide phase separation (spinodal decomposition). So, we feel that we need to use the term when reporting the modeling results. Inside the cell, the situation is more complicated. As the reviewer points out, there likely are entanglements (not considered in our model) and other important factors (please see our discussion on the model limitations). This said, we will revise our text to clarify our terms and proposed mechanism.

      Finally, the writing and the figures are of extremely high quality, but the sheer volume of data here is potentially overwhelming. I wonder if there is any way for the authors to consider stripping down the text/figures to streamline things a bit? I also think it would be useful to include visually consistent schematics of the question/hypothesis/idea each of the figures is addressing to help keep readers on the same page as to what is going on in each figure. Again, there was no figure or section I felt was particularly unclear, but the sheer volume of text/data made reading this quite the mental endurance sport! I am completely guilty of this myself, so I don't think I have any super strong suggestions for how to fix this, but just something to consider.

      We agree that there is a lot to digest. We will add schematics and a didactic simulation. We will also try to streamline the text.

      Reviewer #3 (Public review):

      Summary:

      Papagiannakis et al. present a detailed study exploring the relationship between DNA/polysome phase separation and nucleoid segregation in Escherichia coli. Using a combination of experiments and modelling, the authors aim to link physical principles with biological processes to better understand nucleoid organisation and segregation during cell growth.

      Strengths:

      The authors have conducted a large number of experiments under different growth conditions and physiological perturbations (using antibiotics) to analyse the biophysical factors underlying the spatial organisation of nucleoids within growing E. coli cells. A simple model of ribosome-nucleoid segregation has been developed to explain the observations.

      Weaknesses:

      While the study addresses an important topic, several aspects of the modelling, assumptions, and claims warrant further consideration.

      Thank you for your feedback. Please see below for a response to each concern. 

      Major Concerns:

      Oversimplification of Modelling Assumptions:

      The model simplifies nucleoid organisation by focusing on the axial (long-axis) dimension of the cell while neglecting the radial dimension (cell width). While this approach simplifies the model, it fails to explain key experimental observations, such as:

      (1) Inconsistencies with Experimental Evidence:

      The simplified model presented in this study predicts that translation-inhibiting drugs like chloramphenicol would maintain separated nucleoids due to increased polysome fractions. However, experimental evidence shows the opposite-separated nucleoids condense into a single lobe post-treatment (Bakshi et al 2014), indicating limitations in the model's assumptions/predictions. For the nucleoids to coalesce into a single lobe, polysomes must cross the nucleoid zones via the radial shells around the nucleoid lobes.

      We do not think that the results from chloramphenicol-treated cells are inconsistent with our model. Our proposed mechanism predicts that nucleoids will condense in the presence of chloramphenicol, consistent with experiments. It also predicts that nucleoids that were still relatively close at the time of chloramphenicol treatment could fuse if they eventually touched through diffusion (thermal fluctuation) to reduce their interaction with the polysomes and minimize their conformational energy. Fusion is, however, not expected for well-separated nucleoids since their diffusion is slow in the crowded cytoplasm. This is consistent with our experimental observations: In the presence of a growth-inhibitory concentration of chloramphenicol (70 μg/mL), nucleoids in relatively close proximity can fuse, but well-separated nucleoids condense and do not fuse. Since the growth rate inhibition is not immediate upon chloramphenicol treatment, many cells with well-separated condensed nucleoids divide during the first hour. As a result, the non-fusion phenotype is more obvious in non-dividing cells, achieved by pre-treating cells with the cell division inhibitor cephalexin (50μg/mL). In these polyploid elongated cells, well-separated nucleoids condensed but did not fuse, not even after an hour in the presence of chloramphenicol (as illustrated in Author response image 2).

      In Bakshi et al, 2014, nucleoid fusion was shown for a single cell in which the sister nucleoids were relatively close to each other at the time of chloramphenicol treatment. Population statistics were provided for the relative length and width of the nucleoids, but not for the fusion events. So, it is unclear whether the illustrated fusion was universal or not. Also, we note that Bakshi et al (2014) used a chloramphenicol concentration of 300 μg/mL, which is 20-fold higher than the minimal inhibitory concentration for growth, compared to 70 μg/mL in our experiments.

      Author response image 2.

      Effects of chloramphenicol treatment on the intracellular distribution of ribosomes and nucleoids in non-dividing cells. Exponentially growing cells (M9glyCAAT at 30°C) were pre-treated with cephalexin for one hour before being spotted on an 1% agarose pad for time-lapse imaging. The agarose pad contained M9glyCAAT, cephalexin, and chloramphenicol.  (A) Phase contrast, RplA-GFP fluorescence and HupA-mCherry fluorescence images of a representative single cell. Three timepoints are shown, including the first image after spotting on the agarose pad (at 0 min), 30 minutes and one hour of chloramphenicol treatment. (B) One-dimensional profiles of the ribosomal (RplA-GFP) and nucleoid (HupA-mCherry) fluorescence from the cells shown in panel A. These intensity profiles correspond to the average fluorescence along the medial axis of the cell considering a 6-pixel region (0.4 μm) centered on the central line of the cell. The fluorescence intensity is plotted along the relative cell length, scaled from 0 to 100% between the two poles, illustrating the relative nucleoid length (L<sub>DNA</sub>/L<sub>cell</sub>) that was plotted by Bakshi et al in 2014 (PMID: 25250841).

      (2) The peripheral localisation of nucleoids observed after A22 treatment in this study and others (e.g., Japaridze et al., 2020; Wu et al., 2019), which conflicts with the model's assumptions and predictions. The assumption of radial confinement would predict nucleoids to fill up the volume or ribosomes to go near the cell wall, not the nucleoid, as seen in the data.

      The reviewer makes a good point that DNA attachment to the membrane through transertion likely contributes to the nucleoid being peripherally localized in A22 cells. We will revise the text to add this point. However, we do not think that this contradicts the proposed nucleoid segregation mechanism based on phase separation and out-of-equilibrium dynamics described in our model. On the contrary, by attaching the nucleoid to the cytoplasmic membrane along the cell width, transertion might help reduce the diffusion and thus exchange of polysomes across nucleoids. We will revise the text to discuss transertion over radial confinement.

      (3) The radial compaction of the nucleoid upon rifampicin or chloramphenicol treatment, as reported by Bakshi et al. (2014) and Spahn et al. (2023), also contradicts the model's predictions. This is not expected if the nucleoid is already radially confined.

      We originally evoked radial confinement to explain the observation that polysome accumulations do not equilibrate between DNA-free regions. We agree that transertion is an alternative explanation. Thank you for bringing it to our attention. However, please note that this does not contradict the model. In our view, it actually supports the 1D model by providing a reasonable explanation for the slow exchange of polysomes across DNA-free regions. The attachment of the nucleoid to the membrane along the cell width may act as diffusion barrier. We will revise the text and the title of the manuscript accordingly.

      (4) Radial Distribution of Nucleoid and Ribosomal Shell:

      The study does not account for well-documented features such as the membrane attachment of chromosomes and the ribosomal shell surrounding the nucleoid, observed in super-resolution studies (Bakshi et al., 2012; Sanamrad et al., 2014). These features are critical for understanding nucleoid dynamics, particularly under conditions of transcription-translation coupling or drug-induced detachment. Work by Yongren et al. (2014) has also shown that the radial organisation of the nucleoid is highly sensitive to growth and the multifork nature of DNA replication in bacteria.

      We will discuss the membrane attachment. Please see the previous response.

      The omission of organisation in the radial dimension and the entropic effects it entails, such as ribosome localisation near the membrane and nucleoid centralisation in expanded cells, undermines the model's explanatory power and predictive ability. Some observations have been previously explained by the membrane attachment of nucleoids (a hypothesis proposed by Rabinovitch et al., 2003, and supported by experiments from Bakshi et al., 2014, and recent super-resolution measurements by Spahn et al.).

      We agree—we will add a discussion about membrane attachment in the radial dimension. See previous responses.

      Ignoring the radial dimension and membrane attachment of nucleoid (which might coordinate cell growth with nucleoid expansion and segregation) presents a simplistic but potentially misleading picture of the underlying factors.

      As mentioned above, we will discuss membrane attachment in the revised manuscript.

      This reviewer suggests that the authors consider an alternative mechanism, supported by strong experimental evidence, as a potential explanation for the observed phenomena:

      Nucleoids may transiently attach to the cell membrane, possibly through transertion, allowing for coordinated increases in nucleoid volume and length alongside cell growth and DNA replication. Polysomes likely occupy cellular spaces devoid of the nucleoid, contributing to nucleoid compaction due to mutual exclusion effects. After the nucleoids separate following ter separation, axial expansion of the cell membrane could lead to their spatial separation.

      This “membrane attachment/cell elongation” model is reminiscent to the hypothesis proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several lines of evidence arguing against it as the major driver of nucleoid segregation:

      (Below is a slightly modified version of our response to a comment from Reviewer 1—see page 3)

      (1) For this alternative model to work, axial membrane expansion (i.e., cell elongation) would have to be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation.  Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To go around this fluidity issue, one could potentially evoke a potential connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid to “push” the sister nucleoid apart from each other. However, peptidoglycan growth is dispersed prior to cell constriction (PMID: 35705811, 36097171, 2656655).

      (2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we will provide additional data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate.

      (3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in the original manuscript (Figure 1I and Figure 1 – figure supplement 3) but were not highlighted in this context. We will revise the text to clarify this point.

      (4) The membrane attachment/cell elongation model does not explain the nucleoid asymmetries described in our paper (Figure 3), whereas they can be recapitulated by our model.

      (5) The cell elongation/transertion model cannot predict the aberrant nucleoid dynamics observed when chromosomal expression is largely redirected to plasmid expression. In the revised manuscript, we will add simulation results showing that these nucleoid dynamics are predicted by our model.

      In line of these arguments, we do not believe that a mechanism based on membrane attachment and cell elongation is the major driver of nucleoid segregations. However, we do believe that it may play a complementary role (see “Nucleoid segregation likely involves multiple factors” in the Discussion). We will revise this section to clarify our thoughts and mention the potential role of transertion.

      Incorporating this perspective into the discussion or future iterations of the model may provide a more comprehensive framework that aligns with the experimental observations in this study and previous work.

      As noted above, we will revise the text to mention about transertion.

      Simplification of Ribosome States:

      Combining monomeric and translating ribosomes into a single 'polysome' category may overlook spatial variations in these states, particularly during ribosome accumulation at the mid-cell. Without validating uniform mRNA distribution or conducting experimental controls such as FRAP or single-molecule measurements to estimate the proportions of ribosome states based on diffusion, this assumption remains speculative.

      Indeed, for simplicity, we adopt an average description of all polysomes with an average diffusion coefficient and interaction parameters, which is sufficient for capturing the fundamental mechanism underlying nucleoid segregation. To illustrate that considering multiple polysome species does not change the physical picture, we consider an extension of our model, which contains three polysome species, each with a different diffusion coefficient (D<SUB>P</SUB> = 0.018, 0.023, or 0.028 μm<sup>2</sup>/s), reflecting that polysomes with more ribosomes will have a lower diffusion coefficient. Simulation of this model reveals that the different polysome species have essentially the same concentration distribution, suggesting that the average description in our minimal model is sufficient for our purposes. We will present these new simulation results in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      This study by Wu et al. provides valuable computational insights into PROTAC-related protein complexes, focusing on linker roles, protein-protein interaction stability, and lysine residue accessibility. The findings are significant for PROTAC development in cancer treatment, particularly breast and prostate cancers.

      The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects.

      Strengths:

      (1) Comprehensive computational analysis of PROTAC-related protein complexes.

      (2) Focus on critical aspects: linker role, protein-protein interaction stability, and lysine accessibility.

      Weaknesses:

      (1) Limited examination of lysine accessibility despite its stated importance.

      (2) Use of RMSD as the primary metric for conformational assessment, which may overlook important local structural changes.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. Expand the analysis of lysine accessibility, potentially correlating it with other structural features such as linker length.

      We thank the reviewers for the suggestions! We performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17). We included detailed explanation on page 16:

      “To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.”

      (2) The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects. Consider changing "protein functional dynamics" to "protein dynamics" to more accurately reflect the scope of the study.

      Thanks to the reviewer for the suggestion to use the more accurate terminology! We agreed with the reviewer that if we keep “protein functional dynamics” in the title, we should focus on how the “overall protein dynamic” links to the “function” – The function is directly related to PROTAC-induced structural dynamics which is commonly seen in “protein-structural-function” relationship, but it is not our main focus. Therefore, we changed the title to replace “functional” by “structural”.  

      (3) Incorporate more local and specific characterization methods in addition to RMSD for a more comprehensive conformational assessment.

      We thank the reviewer for the suggestion. We performed time dependent correlation analysis to understand how the rotation of PROTACs can translate to the Lys-Gly interaction. In addition, we performed dihedral entropies analysis for each dihedral angle in the linker of the PROTACs to better examine the flexibility of each PROTAC.

      We included detailed explanation at page 18: “Our dihedral entropies analysis showed that dBET57 has ~0.3 kcal/mol lower entropies than the other three linkers, suggesting dBET57 is less flexible than other PROTACs (Figure S18).”

      Reviewer #2 (Public review):

      Summary:

      The manuscript reports the computational study of the dynamics of PROTAC-induced degradation complexes. The research investigates how different linkers within PROTACs affect the formation and stability of ternary complexes between the target protein BRD4BD1 and Cereblon E3 ligase, and the degradation machinery. Using computational modeling, docking, and molecular dynamics simulations, the study demonstrates that although all PROTACs form ternary complexes, the linkers significantly influence the dynamics and efficacy of protein degradation. The findings highlight that the flexibility and positioning of Lys residues are crucial for successful ubiquitination. The results also discussed the correlated motions between the PROTAC linker and the complex.

      Strengths:

      The field of PROTAC discovery and design, characterized by its limited research, distinguishes itself from traditional binary ligand-protein interactions by forming a ternary complex involving two proteins. The current understanding of how the structure of PROTAC influences its degradation efficacy remains insufficient. This study investigated the atomic-level dynamics of the degradation complex, offering potentially valuable insights for future research into PROTAC degradability.

      Reviewer #2 (Recommendations for the authors):

      (1) Regarding the modeling of the ternary complex, the BRD4 structure (3MXF) is from humans, whereas the CRBN structure in 4CI3 is derived from Gallus gallus. Is there a specific reason for not using structures from the same species, especially considering that human CRBN structures are available in the Protein Data Bank (e.g., 8OIZ, 4TZ4)?

      We appreciate the reviewer’s insightful comment regarding the choice of crystal structures of BRD4 and CRBN structures from two species. Our initial selection of 4CI3 for CRBN structure was based on its high resolution and publication in Nature journal. Furthermore, the Gallus gallus CRBN structure shares high degree of sequence and structural similarity with Homo sapiens CRBN, especially in the ligand binding region. At the time of our study, we were aware of 4TZ4 as Homo sapiens CRBN, however, we did not use this structure since no publication or detailed experimental was associated with it. Additionally, PDB 8OIZ, was not publicly available yet for other researchers to use at the time.

      (2) Based on the crystal structure (PDB ID: 6BNB) discussed in Reference 6, the ternary complex of dBET57 exhibits a conformation distinct from other PROTACs, with CRBN adopting an "open" conformation. Using the same CRBN structure for dBET57 as for other PROTACs might result in inaccurate docking outcomes.

      Thank you for the reviewer’s comment! As noted by the authors in Reference 6, the observed open conformation of CRBN in the dBET57 ternary complex may result from the high salt crystallization conditions, which could drive structural rearrangement, and crystal contacts that may induce this conformation. The authors also mentioned that this open conformation could, in part, reflect CRBN’s intrinsic plasticity. However, they acknowledged that further studies are needed to determine whether this conformational flexibility is a characteristic feature of CRBN that enables it to accommodate a variety of substrates. Despite these observations, we believe that the compatibility of the observed BRD4<sup>BD1</sup> binding conformation with both open and closed CRBN states suggests that these conformational changes are all possible. Therefore, we believe using the same initial CRBN structure for dBET57 as for other PROTACs can still reasonably reveal the dynamic nature of the ternary complex and would not significantly affect the accuracy of our docking outcomes either.

      (3) Figure 2 displays only a single frame from the simulations, which might not provide a comprehensive representation. Could a contact frequency heatmap of PROTAC with the proteins be included to offer a more detailed view?

      We thank the reviewer for the suggestion! We performed the contact map analysis to observe the average distance between PROTACs and BRD4<sup>BD1</sup> over 400ns of MD simulation (new Figure S4 added).

      We included detailed explanation at page 8 and 9: “The residues contact map throughout the 400ns MD simulation also showed different pattern of protein-protein interactions, indicating that the linkers were able to adopt different conformations (Figure S4).”

      (4) The conclusions in Figure 3 and S11 are based on a single 400 ns trajectory. The reproducibility of these results is therefore uncertain.

      We thank the reviewer for the suggestion! We added one more random seed MD simulation for each PROTAC to ensure the reproducibility of the results. The Result is shown in Figure S21 and the details for each MD run are updated in Table 1.

      (5) Figure 4 indicates significant differences between the first and last 100 ns of the simulations. Does this suggest that the simulations have not converged? If so, how can the statistical analysis presented in this paper be considered reliable?

      We thank the reviewers for the question. The simulation was initiated with a 10-15A gap between BRD4 and Ub to monitor the movement of degradation machinery and Lys-Gly interaction. The significant changes in pseudo dihedral in Figure 4 shows that the large-scale movement of the degradation complex can initiate the Lys-Gly binding. It does not relate to unstable sampling because the system remains very stable when BRD4 comes close to Ub.

      (6) In Figure 5, the dihedral angle of dBET57_#9MD1 is marked on a peptide bond. Shouldn't this angle have a high energy barrier for rotation?

      We thank the reviewers for catching the error! Indeed, it was an error that the dihedral angles were marked on the peptide bond. We reworked the figure and double checked our dihedral correlation analysis. The updated correlate dihedral angle selection and the correlation coefficient is shown in Figure 5.

      (7) Given that crystal structures for dBET 70, 23, and 57 are available, why is there a need to model the complex using protein-protein docking?

      We thank the reviewer for the feedback. Only dBET23 has the ternary complex available in a crystal structure, which has the PROTAC and both proteins, while dBET1, dBET57 and dBET70 are not completed as ternary complexes. Although dBET70 has a crystal structure, its PROTAC’s conformation is not resolved, and thus we decided to still perform protein-protein docking with dBET70. 

      We includeed the explanation at page 8: “Only dBET23 crystal structure is available with the PROTAC and both proteins, while the experimentally determined ternary complexes of dBET1, dBET57 and dBET70 are not available. “

      (8) On page 9, it is mentioned that "only one of the 12 PDB files had CRBN bound to DDB1 (PDB ID 4TZ4)." However, there are numerous structures of the DDB1-CRBN complex available, including those used for docking like 4CI3, as well as 4CI1, 4CI2, 8OIZ, etc.

      We thank the reviewers for the comment! We acknowledged the existence of several DDB1-CRBN complex crystal structures, such as PDB IDs 4CI1, 4CI2, 4CI3, and the more recent 8OIZ. For our study, we chose to use 4TZ4 to maintain consistency in complex construction and to align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653), which successfully utilized the same structure for a similar construct. At the time our study was conducted, the 8OIZ structure had not yet been released. We appreciate your suggestion and will consider incorporating alternative structures in future studies to further investigate our findings.

      (9) Table 2 is first referenced on page 8, while Table 1 is mentioned first on page 10. The numbering of these tables should be reversed to reflect their order of appearance in the text.

      We thank the reviewer for catching the error! We switched the order of Table 1 and Table 2.

      Reviewer #3 (Public review):

      The authors offer an interesting computational study on the dynamics of PROTAC-driven protein degradation. They employed a combination of protein-protein docking, structural alignment, atomistic MD simulations, and post-analysis to model a series of CRBN-dBET-BRD4 ternary complexes, as well as the entire degradation machinery complex. These degraders, with different linker properties, were all capable of forming stable ternary complexes but had been shown experimentally to exhibit different degradation capabilities. While in the initial models of the degradation machinery complex, no surface Lys residue(s) of BRD4 were exposed sufficiently for the crucial ubiquitination step, MD simulations illustrated protein functional dynamics of the entire complex and local side-chain arrangements to bring Lys residue(s) to the catalytic pocket of E2/Ub for reactions. Using these simulations, the authors were able to present a hypothesis as to how linker property affects degradation potency. They were able to roughly correlate the distance of Lys residues to the catalytic pocket of E2/Ub with observed DC50/5h values. This is an interesting and timely study that presents interesting tools that could be used to guide future PROTAC design or optimization.

      Reviewer #3 (Recommendations for the authors):

      (1) My most important comment refers to the MM/PBSA analysis, the results of which are shown in Figure S9: binding affinities of -40 to -50 kcal/mol are unrealistic. This would correspond to a dissociation constant of 10^-37 M. This analysis needs to be removed or corrected.

      We thank the reviewer for the comment! MM/PBSA analysis indeed cannot give realistic binding free energy. It does not include the configurational entropy loss which should be a large positive value. In addition, while the implicit PBSA solvent model computes solvation free energy, the absolute values may not be very accurate. However, because this is a commonly used energy calculation, and some readers may like to see quantitative values to ensure that the systems have stable intermolecular attractions, we kept the analysis in SI. We edited the figure legend, moved the Figure S10 in SI page 19, and added sentences to clearly state that the calculations did not include configuration entropy loss “Note that the energy calculations focus on non-bonded intermolecular interactions and solvation free energy calculations using MM/PBSA, where the configuration entropy loss during protein binding was not explicitly included. “.

      (2) I think that the analysis of what in the different dBETx makes them cause different degradation potency is underdeveloped. The dihedral angle analysis (Figure 4B) did not explain the observed behavior in my opinion. Please add additional, clearer analysis as to what structural differences in the dBETx make them sample very different conformations.

      We thank the reviewer for the suggestions! Based on the suggestion, we further performed dihedral entropy analysis for each dihedral angle in the linker part of the PROTAC to examine the flexibility of each PROTAC. Because each PROTAC has a different linker, we now clearly label them in a new Figure S18 in SI page 27. Low dihedral entropies indicate a more rigid structure and thus less flexibility to make a PROTAC more difficult to rearrange and facilitate the protein structural dynamic necessary for ubiquitination.

      We added detailed explanation on page 18: “Our dihedral entropy analysis showed that dBET57 has ~0.3 kcal/mol lower configuration entropies than the other dBETs with three different linkers, suggesting that dBET57 is less flexible than the other PROTACs (Figure S18).”

      (3) "The movement of the degradation machinery correlated with rotations of specific dihedrals of the linker region in dBETs (Figure 5).": this is not sufficiently clear from the figure. Definitely not in a quantitative way.

      We thank the reviewers for the suggestions! To further understand the correlation between PROTACs dihedral angles and the movement of degradation machinery, we performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17).

      We included detailed explanation on page 16:

      “To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.

      (4) Cartoons are needed at multiple stages throughout the paper to enhance the clarity of what the modeled complexes looked like (e.g. which subunits they contained).

      We thank the reviewers for the suggestions. We added and remade several Figures with cartoons to better represent the stages. We also used higher resolution and included clearer labels for each protein system.

      (5) The difference between CRL4A E3 ligase and CRBN E3 ligase is not clear to the non-expert reader.

      Thanks for the reviewer’s comment! To clarify the terms "CRL4A E3 ligase" and "CRBN E3 ligase", which refer to different levels of description for the protein complexes, we added a couple of sentences in the Figure 1 legend. As a result, the non-expert readers can clearly know the differences.

      As illustrated in Figure 1,

      • CRL4A E3 ligase refers to the full E3 ligase complex, which includes all protein components such as CRBN, DDB1, CUL4A, and RBX1.

      • CRBN E3 ligase, on the other hand, is a more colloquial term typically used to describe just the CRBN protein, often in isolation from the full CRL4A complex.

      (6) Figure 1, legend: unclear why it's E3 in A and E2 in B.

      We thank the reviewer for the question! E3 ligase in Figure 1A refers to CRBN E3 ligase, where researchers also simply term it CRBN. We have added a sentence to specify that CRBN E3 ligase is also termed CRBN for simplicity. In Figure 1B, E2 was unclear in the sentences. The full name of E2 should be E2 ubiquitin-conjugating enzyme. Because the name is a bit long, researchers also call it E2 enzyme. We have corrected it and used E2 enzyme to make it clearer. 

      (7) "Although the protein-protein binding affinities were similar, other degraders such as dBET1 and dBET57 had a DC50/5h of about 500 nM". It's unclear what experimental data supports the assertion that the protein-protein binding affinities are similar.

      We thank reviewer for the question. Indeed, the statement is unclear.

      We corrected the sentence in page 6: “Although utilizing the exact same warheads, other degraders such as dBET1 and dBET57 had a DC<sub>50/5h</sub> of about 500 nM.”

      (8) Was the construction of the degradation machinery complex guided by experimental data (maybe cryo-EM or tomography)? If not, what is the accuracy of the starting complex for MD? This may impact the reliability of the obtained results.

      Thank you for your insightful comments! Yes, the construction of the degradation machinery complex was guided by available high-resolution crystal structures, which was selected to maintain consistency and align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653).

      We acknowledged that static crystal structures represent only a single snapshot of the system and may not capture the full conformational flexibility of the complex. To address this limitation, we performed MD simulations using multiple starting structures. This approach allowed us to explore a broader conformational landscape and reduced the dependence on any single starting configuration, thereby enhancing the reliability of the results.

      We hope this clarifies the robustness of our methodology and the steps taken to ensure accuracy in our simulations.

      (9) "With quantitative data, we revealed the mechanism underlying dBETx-induced degradation machinery": I think this may be too strong of an assertion. The authors may have developed a mechanistic hypothesis that can be tested experimentally in the future.

      We thank the reviewer for the suggestion. This is indeed a strong assertion and needs to be modified. We edited the sentence in page 7: “With quantitative data, we revealed the importance of the structural dynamics of dBETx-induced motions, which arrange positions of surface lysine residues of BRD4<sup>BD1</sup> and the entire degradation machinery.”

      (10) Figure S2: are the RMSDs calculated over all residues? Or just the BRD4 residues? Given that the structures are aligned with respect to CRBN, the reported RMSD numbers might be artificially low since there are many more CRBN residues than there are BRD4 residues. Also, why weren't the crystal structures used for dBET 23 and 70 for the modeling? Wouldn't you want to use the most accurate possible structures? Simulations were run for 23. Why not for 70?

      We thank the reviewer for the suggestion. We added a sentence to more clearly explain the RMSD calculations in Figure S2: “The structural superposition is performed based on the backbone of CRBN and RMSD calculation is conducted based on the backbone of BRD4<sup>BD1</sup>.”

      Although dBET70 has crystal structure, its PROTAC structure is not resolved, and thus we decided to still perform protein-protein docking with dBET70.  dBET1 and dBET57 do not have a crystal structure for the ternary complexes.

      We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available. “

      a. And there are no crystal structures available for 1 and 57? If so, please clearly say that. Otherwise please report the RMSD.

      We thank the reviewer for the suggestion. We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available.”

      (11) Table 2 is referenced before Table 1.

      We thank the reviewer for catching the error! We switched the order for Table 1 and Table 2.

      (12) Figure S3 is not referenced in the main paper.

      We thank the reviewer for catching the error! We now referred Figure S3 on page7.

      (13) Minor comments on grammar and sentence structure:

      a. It should be "binding of a ternary complex"

      b. "Our shows the importance": word missing.

      c. "...providing insights into potential orientations for ubiquitination. observe whether the preferred conformations are pre-organized for ubiquitination." Word or words missing.

      We thank reviewer for catching the errors! We corrected grammatical errors and unclear sentences throughout the entire paper and revised the sentences to make them easily understandable for non-expert readers.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      We are grateful to the reviewers for their detailed evaluation and insightful comments, which have improved the clarity and readability of this manuscript. We have addressed all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. Following reviewer 3’s comment, we have repeated the structural refinement of G234A and G234V apo crystal structures without water molecules, which improved the reliability of the data.

      Reviewer #1

      1. Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown." Response: We thank the reviewer for the suggestion to improve clarity. We have revised the indicated sentence and updated the abstract to enhance clarity.

      Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."

      Response: We have corrected the phrase accordingly (line 44).

      Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."

      Response: We appreciate the suggestion for clarification. We have corrected the phrase accordingly (lines 72-75).

      Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."

      Response: We have corrected the phrase accordingly (line 100).

      Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.

      Response: We have corrected this sentence as follows: “These findings suggest that constraints on the neck linker (whether from steric hindrance or interactions with the head or microtubule) are crucial in preventing the tethered head from binding to microtubule” (lines 105-107).

      Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."

      Response: We have corrected the phrase accordingly (line 374-375).

      Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.

      Response: We have reviewed the manuscript and corrected the verb tenses.

      Reviewer #2

      1. Lines 72-73 can be deleted as they are repetitive with lines 95-96. Response: While I acknowledge the reviewer’s point about redundancy, we would like to retain this sentence as it provides an important connection to the opening sentence of the next paragraph, where we explain why the rear-head gating model is required.

      Line 87: The authors should cite Mickolaczyk et al. PNAS 2015 and Sudhakar et al. Science 2021 as these studies also observed that the trailing head takes a sub-step and is located on the right side of the leading head before it moves forward and completes the step.

      Response: We did not cite these two papers as they contradict the statement of this sentence and rather suggest that kinesin waits for ATP-binding in the “two-head-bound” state. We interpreted this discrepancy as follows: 1) Mickolaczyk’s observations likely represent multiple motor-driven movement. Ensuring mono-valency of bead labeling is essential. In optical trapping assays, it is established that >98% of the bead motility is driven by a single motor when less than 50% of beads moved along the microtubule when brought into contact with microtubule using optical trap. The corresponding author has extensive experience preparing monovalent probes for optical trapping bead assays and high-speed single-molecule assays using gold probe (Tomishige et al., J. Cell Biol. 142, 989 (1998)), having established reliable protocols for monovalent labeling of kinesin with gold probes (refer to methods in Isojima et al., Nat. Chem. Biol. 2016 and Niitani et al. biorxiv 2024). The colloidal gold was coated with three SAMs (self-assembled monolayers) in a ratio of 1:10:10 (biotin-SAM:carboxy-SAM:hydroxy-SAM) to reduce surface biotin molecules and non-specific kinesin binding. The gold particles and kinesin-streptavidin complex were mixed at a 1:1 ratio, though this mixing ratio does not guarantee that 100% of the gold particle movements along microtubule are driven by single motors. We established that standard deviations (s.d.) of on- and off-axis displacements (especially that of off-axis) are key indicators for distinguishing between single- and multiple-motor driven motility of the gold probe. Under the above single-molecule conditions, majority of off-axis s.d. traces exhibited clear two-state transitions between microtubule-bound (low s.d.) and -unbound (high s.d.) states of the gold-labeled head, while under multivalent conditions (with higher kinesin:gold ratio and/or higher biotin-SAM ratio on the gold surface), most traces showed sub-steps but lacked these two-state transitions, instead displaying uncorrelated on- and off-axis s.d. traces. In contrast, Mickolajczyk et al. used commercial streptavidin-coated gold nanoparticles mixed with kinesin at a 6:1 motor-to-gold ratio. While their 2016 and 2017 papers did not show s.d. traces, their Biophys. J. 2019 paper (Fig.4) displayed s.d. traces that are characteristic of multivalent bead motility according to the criteria described above. 2) Sudhakar et al.’s interpretation that rapid sub-steps between 8-nms steps represent tethered head movement (illustrated in Fig 4 of their paper) is likely incorrect. The optical trap force acts on the neck linker of the microtubule-bound head, not to the neck linker of the tethered head. Consequently, trailing head detachment should not cause significant displacement of the trapped bead (as illustrated in Fig. 4 of Carter and Cross, Nature 2005). Instead, conformational changes in the neck linker of the microtubule-bound head (i.e., cover-neck bundle formation after ATP binding (Hwang et al. Structure 2008)) would cause bead displacement, supporting that kinesin waits for ATP in the “one-head-bound state”.

      Lines 103: The authors should cite Benoit et al. kinesin14 and Kif1A structures as these studies directly show the conformations of the neck-linkers when both heads are bound to the microtubule.

      Response: We cited the paper (line 105).

      Line 113: There is an extra "e" on "nucleotide".

      Response: We have corrected the typo (line 117).

      Line 118: I would delete "universal" as it is not clear whether all kinesins use a tension-based mechanism.

      Response: We agree with the reviewer’s comment. Further, reviewer 3 noted that recent studies showed that kinesin-3 may not be explained by this mechanism, so we have removed the word “universal” from this sentence as well as from the Abstract and Discussion.

      Line 132: Why did the authors decide to use a cys-lite mutant for X-ray and cryo-EM studies?

      Response: We used the Cys-light mutant to maintain consistency across various experimental techniques in this paper and to enable direct comparison with the nucleotide-free kinesin-1 structures reported by Cao et al. (2014, 2017), who used the same Cys-light construct. To express this, we revised the sentence as follows: “For consistency across experimental techniques and comparison with the previously solved nucleotide-free kinesin-1 structures, we used a cysteine-light mutant kinesin, where surface-exposed cysteines were replaced with either Ala or Ser” (lines 135-138).

      Line 192: The authors refer to Figures 3A and B when they discuss ATP-like and ADP-like conformations. However, these figures refer to open, semi-open, and closed conformations. Things become clear later in the text, but this is confusing, as is. I recommend the authors either show ATP-like and ADP-like classification as a supplemental figure and refer to that figure or not refer to the figure in this sentence.

      Response: To explain the result in this paragraph, we should reference these figures, while we acknowledge the reviewer’s comment about the confusing nomenclature in Fig.3. To address this, Fig. 3A now lists both the old terminology (nucleotide-free, ADP-like, and ATP-like) alongside the new terminology (open, semi-open, and closed).

      Lines 259-260: I would delete "as evidenced by..." and just cite those papers.

      Response: We have corrected this sentence accordingly (line 265-266).

      Lines 262-276: The authors should cite the relevant literature in this paragraph as most of their conclusions here were already shown by previous structural studies.

      Response: Reviewer 3 also noted that this paragraph outlines our current understanding, which seems out of place in the Results and more relevant for the Discussion. Therefore, we have moved this paragraph to the Discussion section and added relevant citations from the literature (lines 390-406).

      Recent biophysical studies claim that neck-linker docking is a two-step process that occurs in ATP binding and ATP hydrolysis. Do the authors agree with this model? Can they comment on why the neck-linker only partially docks during ATP binding, and require ATP hydrolysis to complete the docking? If they disagree with this model, this should be explained in the Discussion.

      Response: This paper focuses on the neck linker’s extensibility in coordinated motility rather than its docking onto the head. The correlation between ATP binding/hydrolysis and neck linker-docking has been examined in a concurrent paper by Niitani et al. (biorxiv 10.1101/2024.09.19.613828) and is discussed in their Discussion section. In this paper, using loose backward constraint on the neck linker, we demonstrated that docking of the initial neck linker segment is sufficient to half-open the gate. Furthermore, extending the neck linker length increased the ATP off-rate of the rear E236A head, indicating that forward neck linker strain plays a crucial role in stabilizing the closed state. These findings support the hypothesis that neck linker docking remains partially unstable in the one-head-bound state and achieves full stabilization only after transitioning to the two-head-bound state.

      Lines 285: The authors should cite Benoit et al. as they showed this clearly in their structure. Benoit et al. showed that, even though both heads are bound to AMP-PNP, the neck linkers are pointed in opposite directions and the rigid body conformations of the trailing and leading heads are different. Do the authors take this into account when they model the Topen-Lopen state? Can they also comment on why the heads can have different rigid body conformations even though they are bound to the same nucleotide? Is this because tension on the neck-linker is too high if both heads are in the open conformation?

      Response: We have added a citation to Benoit et al. 2021. The Topen-Lopen state is an off-pathway conformational state that differs from the on-pathway two-head-bound states (Tclosed-Lopen) studied using cryoEM. Using smFRET, we showed this state appeared only in the neck linker extended mutants, for which no cryoEM observation exist. Therefore, we modeled the Topen-Lopen state by assuming both heads adopt identical conformations in the open state, and showed that this off-pathway transition is suppressed because it would cause an intolerable increase in neck linker tension. Benoit et al.’s finding that the front open head can bind AMPPNP aligns with Niitani et al.’s observation (bioRxiv 2024) that while the front head can bind ATP, it maintains a low ATP affinity state—unlike the rear head, which exhibits high ATP affinity. This suggests that ATP binding (nucleotide state) is not tightly coupled to the open-to-closed conformational transition of the head.

      Line 308: How do the authors estimate the tension on the neck linker? This needs to be explained briefly in the main text as it is central to the conclusions of this work.

      Response: While we briefly described the method to estimate the tension in the text, we did not specify which part of the disordered neck linker was used for this calculation. We have now added this explanation as follows: “To estimate the amount of this tension, we isolated the disordered neck linker segments from both the leading and trailing heads that are stretched between the motor domains without steric hindrance or docking onto the head (Fig. S4 D). Then, we applied a harmonic potential to the Cα atoms at both ends of the stretched region and calculated the tension from the average displacement of the Cα atom from the potential minimum using MD simulations (Fig. 7, A and B)” (lines 300-306)

      Line 308: Calculated tension is a lot higher than the force needed to pull a tubulin out from its tail from the microtubule (Kuo et al. Nat Comms 2022). Even the lowest tension they reported is a lot higher than the estimates made by Clancy et al. and Hyeon and Onuchic. The authors should comment on why this might be the case.

      Response: The neck linker tension between two heads differs from the force applied by the optical trap to the bead attached to the coiled-coil stalk. Because these forces act in different direction and the coiled-coil stalk contains flexible hinges, torques, rather than forces, should be compared, though this is difficult to estimate (as described in Figure S16 in Hwang and Karplus, Structure 16, 62-71 (2008)). Hyeon & Onuchi (2007) and Hariharan & Hancock (2009) calculated the neck linker tension using a worm-like chain model, yielding different results of 12-15 pN and 28 pN, respectively (Clancy et al. cited these results). This discrepancy stems from different end-to-end distances used in their calculations (3.1 nm versus 4 nm). The 4 nm distance used by Hariharan and Hancock likely represents the tension in the two-head-bound state, as it equals half the distance between two heads on adjacent tubulin-binding sites. Using MD simulation, Hariharan and Hancock further estimated the neck linker tension of 15 pN in constraint force mode and 35 pN in force-clamp mode. Our estimated tension (39 pN) in Tclosed-Lopen state is comparable to the upper limit of these calculations. This estimated tension using isolated neck linkers is likely an overestimate, since the stretched neck linker in the presence of the motor domain includes an additional energetic contribution from its direct interaction with the leading head, which will be described in detail in our response to the reviewer 2’s comment #16. To address this, we have included the following sentence: “The tension in the Tclosed-Lopen state is likely an overestimate since this measurement excludes the enthalpic component discussed above, though it is comparable to previous MD measurements and theoretical calculations using a worm-like chain model (Hariharan and Hancock, 2009).” (lines 307-311)

      Line 321: I would also cite Shastry and Hancock here.

      Response: We have cited this paper (line 322).

      Lines 387: "...the transition from one-head-bound to two-head-bound Topen-Lopen state".

      Response: We have corrected the phrase accordingly (lines 387-388).

      Lines 418-428: The authors assume that the neck-linker extension is purely entropic. However, neck linkers are almost fully stretched especially in unfavorable two-head-bound conformations, and they can potentially make contact with the motor domains. Therefore, this process may not be purely entropic and may also involve energetic terms when considering the free energy of neck linker docking.

      Response: We appreciate the reviewer’s comment, as we had overlooked this important point. After examining the simulation movies of neck linker dynamics in Topen-Lopen and Tclosed-Lopen states (Fig. S4B, C and Videos 3, 4), we found that the stretched neck linker region in the Topen-Lopen state was displaced from the head and showed no interaction with the head during the simulation period. However, in the Tclosed-Lopen state, we observed a stable interaction between the K326 residue in the neck linker and the D37 and F48 residues of the leading open head (which can be seen in Video 4). This interaction was not included in our tension estimation (Fig. S4D), which assumed the tension had a purely entropic origin. Therefore, the estimated tension in the Tclosed-Lopen state is likely an overestimate, while the tension in the Topen-Lopen state remains purely entropic. We have added two sentences to describe these observations as follows: “Throughout the simulation, the stretched neck linker remained displaced from the head without any interaction, suggesting that the neck linker behaves as an entropic spring.” (lines 288-290), and “During this simulation, we observed a stable contact between the K326 side chain of the disordered neck linker and the D37 and F48 residues of the leading head (see Video 4), suggesting that the neck linker tension in Tclose-Lopen state includes an energetic component.” (lines 293-296)

      Lines 452-454: I think this sentence summarizes the most significant contribution of this work and should be clearly mentioned in the abstract.

      Response: We thank the reviewer for this suggestion and have incorporated the sentence into the abstract.

      Lines 476-479: This sentence claims that neck linker docking is not necessary. Instead, rotation of the R-sub domain of the motor domain is sufficient to trigger the forward step. I would omit this sentence, as the rationale is not well explained, and it conflicts with a large body of literature on neck-linker docking. This could be an interesting idea to discuss in a perspective article or a topic of future research, but it may unnecessarily confuse the reader at the conclusion of this work.

      Response: We included this sentence because it provides a testable prediction for neck linker-docking independent stepping, and we are preparing a manuscript to experimentally test this hypothesis. However, we agree with the reviewer’s comment that this statement conflicts with the common view in this field, and without additional verification or statement, it would confuse readers. Therefore, we have removed this sentence from the manuscript.

      Reviewer #3

      Major Comments:

      1. The Abstract is not clearly written to distinguish which kinesin head is being discussed.

      Response: We revised the second sentence in the abstract to distinguish between the tethered and microtubule-bound heads and updated the abstract to enhance clarity.

      The authors describe the bulge formed by the terminus if helix 4 as an obstruction that is "creating an intolerable increase in neck linker tension", but could it not simply be that forward head binding is conformationally disfavoured? Perhaps these ideas are not mutually exclusive.

      Response: We agree with the reviewer that in the ATP-waiting state, the tethered head might also be prevented from binding to the tubulin-binding site due to the neck linker requiring a highly stretched configuration—this could occur before the tension increase that accompanies the transition from semi-open to open conformation. While we addressed this possibility in the Discussion section (lines 398-405 of the original version), our explanation was not sufficiently clear. We have therefore revised the sentence to clarify this point as follows: “Therefore, we can only speculate that the tension would lie somewhere between that of the Tclose-Lopen and Topen-Lopen states, and that microtubule binding of the tethered semi-open head may be restricted because the disordered neck linkers would need to adopt highly stretched configurations.” (lines 421-424)

      The term "universal" in describing this tension-based regulation mechanism seems unjustified without examination of other kinesins. They might consider Kif1A as a subject given its shorter and seemingly more entropically-constrained neck linker. Recent structures of Kif1A bound to MTs in two-heads bound states have recently been described by Benoit et al. (Nat Comm. 2024).

      Response: We agree with the reviewer and acknowledge that this tension-based regulation mechanism may not apply to some other kinesin subfamilies, which have different neck linker properties, such as varying neck linker lengths or specific interactions with the motor domain. We removed the word “universal” from the Abstract, Introduction and Discussion and added a final sentence to the Discussion as follows: “Additionally, studies are needed to examine whether this mechanism extends to other kinesin subfamilies with different neck linker properties, such as varying neck linker lengths (kinesin-2: Hariharan and Hancock, 2009; kinesin-3: Benoit et al., 2024) or specific interactions with the motor domain (kinesin-6: Guan et al., 2016; Ranaivoson et al., 2023).” (lines 501-505).

      The authors should consider discussing how having two chains in the asymmetric unit of the APO motor impacts the NL structure.

      Response: The G234A apo and G234V apo crystals share the same asymmetric unit since the G234A crystal was grown from a G234V crystal seed. We inspected the structures near the proximal end of the neck linker (or the C-terminus of the a6 helix connected to neck linker) that could cause steric hindrance or direct interaction with the initial segment of the neck linker. The closest element of the adjacent chain was L5, which was separated by 1.1 nm from the proximal end of the neck linker (K324 residue) and did not interact with it. The proximal ends of the neck linkers of chains A and B face each other, with a cylindrical cavity between them. This cavity in G234V apo allows an antiparallel β-sheet formation between the two stretched neck linkers of chain A and B (Figure S2A). However, we did not observe density corresponding to the antiparallel β-sheet in the cavity of G234A apo, likely due to its slightly smaller cavity size. Notably, this antiparallel β-sheet formation would be geometrically impossible for the two neck linkers in a dimer since their C-termini are joined in parallel by the neck coiled-coil. These explanations have been added to the text (lines 154-156) and the legend of Figure S2.

      At barely 3 angstroms, how are waters modelled and how is it their B-factors are so low? Rfact and Rfree are also quite divergent for the GA mutant (APO) structure.

      Response: To improve the R-factor, we placed water molecules to account for unmodeled and discontinuous electron density peaks that were too small to be interpreted as polypeptides. However, this treatment was likely incorrect and is the primary reason for both the low B-factor and Rfree values, which led to the large discrepancy between Rwork and Rfree. To address this issue, we repeated the structural refinement of G234A and G234V apo structures by removing water molecules placed on unmodeled density peaks. We retained only one water molecule in the nucleotide pocket of chain A in the G234A apo structure due to its well-defined density (Figure S1). This improved refinement significantly reduced the discrepancy between Rwork and Rfree of G234A apo from 20.0/28.1% to 20.7/26.5%. For G234V apo, while the discrepancy remained unchanged, the overall values were improved from 24.4/29.2% to 20.0/25.8%. We updated Table 1 and deposited these refined structures to the Protein Data Bank (PDB# 9L78 and 9L6K) with details provided in the “Data availability” section.

      Lines 262-276: This section describes our current understanding of the mechanism of neck linker docking in accord with NP closure, which seems out of place in the Results and more relevant for the Discussion. Likewise, the two paragraphs before and after the description of the gold nanocluster study describe a re-evaluation and graphical/animated description of others' findings (Figure 4 and videos 1 and 2), rather than analysis of structural data obtained experimentally in this study.

      Response: We acknowledge that this paragraph describes previous findings rather than current results. Therefore, we have relocated it to the Discussion section with appropriate citations from the literatures (lines 390-406). In addition, the paragraph, which precedes the gold nanocluster study, draws from previous research using different subdomain boundaries, so we added the relevant citations accordingly (line 238).

      It is mentioned in the Discussion that the neck linker-docking is not necessary to trigger the forward step after ATP binding, but rather the rotation of the R-domain is sufficient to diminish the steric hindrance that limits tethered head binding. Are they suggesting that the neck linker could be undocked or disordered when making the forward step of a two-headed motor? According to other structural studies, a fully docked neck-linker is required to adopt the closed conformation. Moreover, binding of the leading head to the MT is necessary for complete closure of the nucleotide-binding pocket of the trailing head.

      Response: This sentence was included because it offers a testable prediction for neck linker-docking independent stepping, and we are currently preparing a manuscript to test this hypothesis experimentally. The prediction is supported by Niitani et al.’s finding (biorxiv 10.1101/2024.09.19.613828) that loose neck linker crosslinking, which allows docking of the initial segment of the neck linker onto the head but prevents complete neck docking, reduced ATP-induced microtubule detachment rate by half. However, since this statement challenges the conventional understanding in this field and requires further verification, as noted by reviewer 2, we have removed it to avoid confusion.

      Minor Comments:

      Line 113 - "nucletodiee-free" spelling.

      Response: We have corrected the typo (line 117).

      Lines 118-122 - Final sentence of Introduction needs improvement: "Moderate neck-linker extension"? Terms are not defined/vague.

      Response: To clarify this point, we revised this sentence as follows: “among possible conformational transitions, the one that requires less entropy reduction from stretching the disordered neck linker is favored” (lines 123-125).

      Line 131 - Possible Error: "N-terminal motor domain (1-332 residues)" - should this be 1-322?

      Response: This is our mistakes and we corrected the number of residues (line 134).

      It could be difficult for some readers to follow the naming convention used Tapo-Lapo which is equivalent to Topen-Lopen in the final mechanistic model figure.

      Response: In response to the reviewer’s comment, we have removed the reference to the Tapo-Lapo state from the Introduction and revised the notation in the Result section from Tapo-Lapo to Topen-Lopen.

    1. Old English is so unlike the modern version that it feels like a stretch to think of them as the same language at all

      It may be useful to teach this in schools, granted we have translations. to preserve the language and to continue the tradition of old english. Similar to how Jewish people study and learn Hebrew for their religious traditions, making many ancient texts readable.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors tested whether learning to suppress (ignore) salient distractors (e.g., a lone colored nontarget item) via statistical regularities (e.g., the distractor is more likely to appear in one location than any other) was proactive (prior to paying attention to the distractor) or reactive (only after first attending the distractor) in nature. To test between proactive and reactive suppression the authors relied on a recently developed and novel technique designed to "ping" the brain's hidden priority map using EEG inverted encoding models. Essentially, a neutral stimulus is presented to stimulate the brain, resulting in activity on a priority map which can be decoded and used to argue when this stimulation occurred (prior to or after attending to a distracting item). The authors found evidence that despite learning to suppress the high probability distractor location, the suppression was reactive, not proactive in nature.

      Overall, the manuscript is well-written, tests a timely question, and provides novel insight into a long-standing debate concerning distractor suppression.

      Strengths (in no particular order):

      (1) The manuscript is well-written, clear, and concise (especially given the complexities of the method and analyses).

      (2) The presentation of the logic and results is mostly clear and relatively easy to digest.

      (3) This question concerning whether location-based distractor suppression is proactive or reactive in nature is a timely question.

      (4) The use of the novel "pinging" technique is interesting and provides new insight into this particularly thorny debate over the mechanisms of distractor suppression.

      Weaknesses (in no particular order):

      (1) The authors tend to make overly bold claims without either A) mentioning the opposing claim(s) or B) citing the opposing theoretical positions. Further, the authors have neglected relevant findings regarding this specific debate between proactive and reactive suppression.

      (2) The authors should be more careful in setting up the debate by clearly defining the terms, especially proactive and reactive suppression which have recently been defined and were more ambiguously defined here.

      (3) There were some methodological choices that should be further justified, such as the choice of stimuli (e.g., sizes, colors, etc.).

      (4) The figures are often difficult to process. For example, the time courses are so far zoomed out (i.e., 0, 500, 100 ms with no other tick marks) that it makes it difficult to assess the timing of many of the patterns of data. Also, there is a lot of baseline period noise which complicates the interpretations of the data of interest.

      (5) Sometimes the authors fail to connect to the extant literature (e.g., by connecting to the ERP components, such as the N2pc and PD components, used to argue for or against proactive suppression) or when they do, overreach with claims (e.g., arguing suppression is reactive or feature-blind more generally).

      We thank the reviewer for their insightful feedback and have made several adjustments to address the concerns raised. To provide a balanced discussion, we tempered our claims about suppression mechanisms and incorporated additional references to opposing theoretical positions, including the signal suppression hypothesis, while clarifying the definitions of proactive and reactive suppression based on recent terminology (Liesefeld et al., 2024). We justified methodological choices, such as the slight size differences between stimuli to achieve perceptual equivalence and the randomization of target and distractor colors to mitigate potential luminance biases. We have revised our figure to enhance figure clarity. Lastly, while our counterbalanced design precluded reliable ERP assessments (e.g., N2pc, PD), we discussed their potential relevance for future research and ensured consistency with the broader literature on suppression mechanisms.

      Reviewer #2 (Public Review):

      Summary:

      The authors investigate the mechanisms supporting learning to suppress distractors at predictable locations, focusing on proactive suppression mechanisms manifesting before the onset of a distractor. They used EEG and inverted encoding models (IEM). The experimental paradigm alternates between a visual search task and a spatial memory task, followed by a placeholder screen acting as a 'ping' stimulus -i.e., a stimulus to reveal how learned distractor suppression affects hidden priority maps. Behaviorally, their results align with the effects of statistical learning on distractor suppression. Contrary to the proactive suppression hypothesis, which predicts reduced memory-specific tuning of neural representations at the expected distractor location, their IEM results indicate increased tuning at the high-probability distractor location following the placeholder and prior to the onset of the search display.

      Strengths:

      Overall, the manuscript is well-written and clear, and the research question is relevant and timely, given the ongoing debate on the roles of proactive and reactive components in distractor processing. The use of a secondary task and EEG/IEM to provide a direct assessment of hidden priority maps in anticipation of a distractor is, in principle, a clever approach. The study also provides behavioral results supporting prior literature on distractor suppression at high-probability locations.

      Weaknesses:

      (1) At a conceptual level, I understand the debate and opposing views, but I wonder whether it might be more comprehensive to present also the possibility that both proactive and reactive stages contribute to distractor suppression. For instance, anticipatory mechanisms (proactive) may involve expectations and signals that anticipate the expected distractor features, whereas reactive mechanisms contribute to the suppression and disengagement of attention.

      This is an excellent point. Indeed, while many studies, including our own, have tried to dissociate between proactive and reactive mechanisms, as if it is one or the other, the overall picture is arguably more nuanced. We have added a paragraph to the discussion on page 19 to address this. At the same time, (for more details see our responses to your comments 3 and 5), we have added a paragraph where we provide an alternative explanation of the current data in the light of the dual-task nature of our experiment.

      (2) The authors focus on hidden priority maps in pre-distractor time windows, arguing that the results challenge a simple proactive view of distractor suppression. However, they do not provide evidence that reactive mechanisms are at play or related to the pinging effects found in the present paradigm. Is there a relationship between the tuning strength of CTF at the high-probability distractor location and the actual ability to suppress the distractor (e.g., behavioral performance)? Is there a relationship between CTF tuning and post-distractor ERP measures of distractor processing? While these may not be the original research questions, they emerge naturally and I believe should be discussed or noted as limitations.

      Thank you for raising these important points. While CTF slopes have been shown to provide spatially and temporally resolved tracking of covert spatial attention and memory representations at the group level, to the best of our knowledge, no study to date has found a reliable correlation between CTFs and behavior. Moreover, the predictive value of the learned suppression effect, while also highly reliable at the group level, has been proven to be limited when it comes to individual-level performance (Ivanov et al. 2024; Hedge et al., 2018). Nevertheless, based on your suggestion, we explored whether there was a correlation between the averaged gradient slope within the time window where the placeholder revived the memory representation and the average distance slope in reaction times for the learned suppression effect. This correlation was not significant (r = .236, p = 0.267), which, considering our sample size and the reasons mentioned earlier, is not particularly surprising. Given that our sample size was chosen to measure group level effects, we decided not to include individual differences analysis it in the manuscript.

      Regarding the potential link between the CTF tuning profile and post-distractor ERP measures like N2pc and Pd, our experimental design presented a specific challenge. To reliably assess lateralized ERP components like N2pc or Pd the high probability location must be restricted to static lateralized positions (e.g., on the horizontal midline). Our counterbalanced design (see also our response to comment 9 by reviewer 1), which was crucial to avoid bias in spatial encoding models, precluded such a targeted ERP analysis.

      (3) How do the authors ensure that the increased tuning (which appears more as a half-split or hemifield effect rather than gradual fine-grained tuning, as shown in Figure 5) is not a byproduct of the dual-task paradigm used, rather than a general characteristic of learned attentional suppression? For example, the additional memory task and the repeated experience with the high-probability distractor at the specific location might have led to longer-lasting and more finely-tuned traces for memory items at that location compared to others.

      Thank you for raising these important points. Indeed, a unique aspect of our study that sets it apart from other studies, is that the effects of learned suppression were not measured directly via an index of distractor processing, but rather inferred indirectly via tuning towards a location in memory. The critical assumption here, that we now make explicit on page 18, is that various sources of attentional control jointly determine the priority landscape, and this priority landscape can be read out by neutral ping displays. An alternative however, as suggested by the reviewer, is that memory representations may have been sharper when they remembered location was at the high probability distractor location. We believe this is unlikely for various reasons. First, at the behavioral level there was no evidence that memory performance differed for positions overlapping high and low probability distractor locations (also see our response to reviewer 3 minor comment 4). Second, there was no hint whatsoever that the memory representation already differed during encoding or maintenance (This is now explicitly indicated in the revised manuscript on page 14), which would have been expected if the spatial distractor imbalance modulated the spatial memory representations.

      Nevertheless, as discussed in more detail in response to comment 5, there is an alternative explanation for the observed gradient modulation that may be specific to the dual nature of our experiment.

      (4) It is unclear how IEM was performed on total vs. evoked power, compared to typical approaches of running it on single trials or pseudo-trials.

      Thank you for pointing out that our methods were not clear. We did not run our analysis on single trials because we were interested in separately examining the spatial selectivity of both evoked alpha power (phase locked activity aligned with stimulus onset) and total alpha power (all activity regardless of signal phase). It is only possible to calculate evoked and total power when averaging across trials. Thus, when we partitioned the data into sets for the IEM analysis, we averaged trials for each condition/stimulus location to obtain a measurement of evoked and total power each condition for each set. This is the same approach used in previous work (e.g. Foster et al., 2016; van Moorselaar et al., 2018).

      We reviewed our method section and can see why this was unclear. In places, we had incorrectly described the dimensions of training and test data as electrodes x trials. To address this, we’ve rewritten the “Time frequency analysis”, “Inverted encoding model” sections, and added a new “Training and test data” section. We hope that these sections are easier to follow.

      (5) Following on point 1. What is the rationale for relating decreased (but not increased) tuning of CTF to proactive suppression? Could it be that proactive suppression requires anticipatory tuning towards the expected feature to implement suppression? In other terms, better 'tuning' does not necessarily imply a higher signal amplitude and could be observable even under signal suppression. The authors should comment on this and clarify.

      We appreciate your highlighting of these highly relevant alternative explanations. In response, we have revised a paragraph in the General Discussion on page 18 to explicitly outline our rationale for associating decreased tuning with proactive suppression. However, in doing so, we now also consider the alternative perspective that proactive suppression might actually require enhanced tuning towards the expected feature to implement suppression effectively.

      It's important to note that both of these interpretations – decreased tuning as a sign of suppression and increased tuning as a preparatory mechanism for suppression – diverge significantly from the commonly held model (including our own initial assumptions) wherein weights at the to-be-suppressed location are simply downregulated.

      Minor:

      (1) In the Word file I reviewed, there are minor formatting issues, such as missing spaces, which should be double-checked.

      Thank you! We have now reviewed the text thoroughly and tried our best to avoid formatting issues.

      (2) Would the authors predict that proactive mechanisms are not involved in other forms of attention learning involving distractor suppression, such as habituation?

      Habituation is a form of non-associative learning where the response to a repetitive stimulus decreases over time. As such, we would not characterize these changes as “proactive”, as it only occurs following the (repeated) exposure to the stimulus. 

      (3) A clear description in the Methods section of how individual CTFs for each location were derived would help in understanding the procedure.

      Thank you. We have now added several sentences on page 27 to clarify how individual CTFs in Figure 3 and distance CTFs in Figure 5 are calculated.

      “The derived channel responses (8 channels × 8 location bins) were then used for the following analyses: (a) calculating individual Channel Tuning Functions (CTFs) based on each of the eight physical location bins (e.g., Figure 3C and 3D); (b) grouping responses according to the distance between each physical location and the high-probability distractor location to calculate distance CTFs (e.g., Figure 5); and (c) averaging across location bins to represent the general strength of spatial selectivity in tracking the memory cue, irrespective of its specific location (e.g., Figure 3A and 3B).”

      (4) Why specifically 1024 resampling iterations?

      Thank you for your question. The statistical analysis was conducted using the permutation_cluster_1samp_test function within the MNE package in Python. We have clarified this on page 25. The choice of 1024 permutations reflects the default setting of the function, which is generally considered sufficient for robust non-parametric statistical testing. This number provides a balance between computational efficiency and the precision of p-value estimation in the context of our analyses.

      Reviewer #3 (Public Review):

      Summary:

      In this experiment, the authors use a probe method along with time-frequency analyses to ascertain the attentional priority map prior to a visual search display in which one location is more likely to contain a salient distractor.  The main finding is that neural responses to the probe indicate that the high probability location is attended, rather than suppressed, prior to the search display onset.  The authors conclude that suppression of distractors at high-probability locations is a result of reactive, rather than proactive, suppression.

      Strengths:

      This was a creative approach to a difficult and important question about attention.  The use of this "pinging" method to assess the attentional priority map has a lot of potential value for a number of questions related to attention and visual search. Here as well, the authors have used it to address a question about distractor suppression that has been the subject of competing theories for many years in the field. The paper is well-written, and the authors have done a good job placing their data in the larger context of recent findings in the field.

      Weaknesses:

      The link between the memory task and the search task could be explored in greater detail. For example, how might attentional priority maps change because of the need to hold a location in working memory? This might limit the generalizability of these findings. There could be more analysis of behavioral data to address this question. In addition, the authors could explore the role that intertrial repetition plays in the attentional priority map as these factors necessarily differ between conditions in the current design. Finally, the explanation of the CTF analyses in the results could be written more clearly for readers who are less familiar with this specific approach (which has not been used in this field much previously).

      We appreciate the reviewer's valuable feedback and have made significant revisions to address the concerns raised. To clarify the connection between the memory and search tasks, we conducted additional analyses to explore the effects of spatial distance between the memory cue location and the high-probability distractor location on behavioral performance. We also investigated the potential influence of intertrial repetition effects on the observed results by removing trials with location repetitions. To enhance clarity, we revised the explanation of the CTF analyses in the Results section and improved figure annotations to ensure accessibility for readers unfamiliar with this approach. Collectively, these updates further discuss how the pattern of CTF slopes reflect the interplay between memory and search tasks while addressing key methodological and interpretative considerations.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Suggestions/Critiques (in no particular order)

      (1) The authors discuss the tripartite model (bottom-up, top-down, and selection history) but neglect recent and important discussions of why this trichotomy might be unnecessarily complicated (e.g., Anderson, 2024: Trichotomy revisited: A monolithic theory of attentional control). Simply put, one of the 3 pillars (i.e., selection history) likely does not fall into a unitary construct or "box"; instead, it likely contains many subcomponents (e.g., reward associations, stimulus-response habit learning, statistical learning, etc.). Since the focus of the current study is learned distractor suppression based on the statistical regularities of the distractor, the authors should comment on which aspects of selection history are relevant, perhaps by using this monolithic framework.

      We appreciate the reviewer's insightful suggestion regarding theoretical frameworks of attentional control. While Anderson (2024) proposes a monolithic theory that challenges the traditional tripartite model, our study deliberately maintains a pragmatic approach. The main purpose of our experiment is empirically investigating the mechanisms of learned distractor suppression, rather than adjudicating between competing theoretical models.

      We agree that selection history is not a unitary construct but comprises multiple subcomponents, including reward associations, stimulus-response habit learning, and statistical learning. In this context, our study specifically focuses on statistical learning as a key mechanism of distractor suppression. By explicitly acknowledging the multifaceted nature of selection history and referencing Anderson's monolithic perspective, we invite readers to consider the theoretical implications while maintaining our research's primary focus on empirical investigation. To this end, we have modified the manuscript to read (see page 3):

      "The present study investigates the mechanisms underlying statistical learning, specifically learned distractor suppression, which represents one critical subcomponent of selection history. While theoretical models like the tripartite framework and the recent monolithic theory (Anderson, 2024) offer complementary perspectives on attentional control, our investigation focuses on empirically characterizing the statistical learning mechanisms underlying learned distractor suppression."

      (2) The authors discuss previous demonstrations of location-based and feature-based learned distractor suppression. The authors admit that there have been a large number of studies but seem to mainly cite those that were conducted by the authors themselves (with the exception being Vatterott & Vecera, 2012). For example, there are other studies investigating location-based suppression (Feldmann-Wüstefeld et al., 2021; Sauter et al., 2021), feature-based suppression (Gaspelin & Luck, 2018a; Stilwell et al., 2022; Stilwell & Gaspelin, 2021; Vatterott et al., 2018), or both (Stilwell et al., 2019). The authors do not cite Gaspelin and colleagues at all in the manuscript, despite claiming that singleton-based suppression is not proactive.

      We appreciate your pointing out the need for a more comprehensive citation of the literature on learned distractor suppression, particularly with respect to location-based and feature-based suppression. In response to your comment, we have now expanded the reference list on page 4 to include relevant studies that further support our discussion of both location-based and feature-based suppression mechanisms.

      (3) The authors use the terms "proactive" and "reactive" suppression without taking into consideration the recent terminology paper, which one of the current authors, Theeuwes, helped to write (Liesefeld et al., 2024, see Figure 8). The terms proactive and reactive suppression need to be defined relative to a time point. The authors need to be careful in defining proactive suppression as prior to the first shift of attention, but after the stimuli appear and reactive suppression as after the first shift of attention and after the stimuli appear. Thus, the critical time point is the first shift of attention. Does suppression occur before or after the first shift of attention? The authors could alleviate this by using the term "stimulus-triggered suppression" to refer to "suppression that occurs after the distractor appears and before it captures attention" (Liesefeld et al., 2024).

      Thank you for pointing out that this was insufficiently clear in the previous version. In the revised version we specifically refer to the recent terminology paper on page 5 to make clear that suppression could theoretically occur at three distinct moments in time, and that the present paper was designed to dissociate between suppression before or after the first shift of attention.

      (4) Could the authors justify why the circle stimulus (2° in diameter) was smaller than the diamonds (2.3° x 2.3°)? Are the stimuli equated for the area? Or, for width and height? Doesn't this create a size singleton target on half of all trials (whenever the target is a circle) in addition to the lone circle being a shape singleton? Along these lines, could the authors justify why the colors were used and not equiluminant? This version of red is much brighter than this version of green if assessed by a spectrophotometer. Thus, there are sensory imbalances between the colors. Further, the grey used as the ping is likely not equiluminant to both colors. Thus, the grey "ping" is likely dimmer for red items but brighter for green items. Is this a fair "ping"?

      Thank you for raising these important points. We chose, as is customary in this experimental paradigm (e.g., Huang et al., 2023; Duncan et al., 2023), to make the diamond slightly larger (2.3° x 2.3°) than the circle (2° in diameter) to ensure a better visual match in overall size appearance. If the circle and diamond stimuli were equated strictly in terms of size (both at 2°), the diamond would appear visually smaller due to the differences in geometric shape. By adjusting the dimensions slightly, we aimed to minimize any unintentional differences in perceptual salience.

      As for the colors used in the experiment, the reviewer is right that there might be sensory imbalances between the red and green stimuli, with red appearing brighter than green based on measurements such as spectrophotometry. To ensure that any effects couldn’t be explained by sensory imbalance in the displays, we randomized target and distractor colors across trials, meaning that roughly half the trials had a red distractor and half had a green distractor. This randomization should have mitigated any systematic biases caused by color differences.

      We appreciate your feedback and have clarified these points in method section in the revised manuscript on page 22:

      "Please note that although the colors were not equiluminant, the target and distractor colors were randomized across trials such that roughly half the trials had a red distractor, and half had a green distractor. This randomization process should help mitigate any systematic biases this may cause."

      (5) For the eye movement artifact rejection, the authors use a relatively liberal rejection routine (i.e., allowing for eye movements up to 1.2° visual angle and a threshold of 15 μV). Given that every 3.2 μV deviation in HEOG corresponds to ~ ± 0.1° of visual angle (Lins, et al., 1993), the current oculomotor rejection allows for eye movements between 0.5° and 1.2° visual angle to remain which might allow for microsaccades (e.g., Poletti, 2023) to contaminate the EEG signal (e.g., Woodman & Luck, 2003).

      The reviewer correctly points out that our eye rejection procedure, which is the same as in our previous work (e.g., Duncan et al., 2023), still allows for small, but systematic biases in eye position towards the remembered location and potentially towards or away from the high probability distractor location. While we cannot indefinitely exclude this possibility, we believe this is unlikely for the following reasons. First, although there is a link between microsaccades and covert attention, it has been demonstrated that subtle biases in eye position cannot explain the link between alpha activity and the content of spatial WM (Foster et al., 2016, 2017). Specifically, Foster et al. (2017) found no evidence for a gaze-position-related CTF, while an analysis on that same data yielded clear target related CTFs. Similarly, within the present data set there was no evidence that the observed revival induced by the ping display could be attributed to systematic changes in gaze position, as a multivariate cross-session decoding analysis with x,y positions from the tracker did not yield reliable above-chance decoding of the location in memory.

      Author response image 1.

      (6) The authors claim that "If the statistically learned suppression was spatial-based and feature-blind, one would also expect impaired target processing at the high-probability location." (p. 7, lines 194-195). Why is it important that suppression is feature-blind here? Further, is this a fair test of whether suppression is feature-blind? What about inter-trial priming of the previous trial? If the previous trial's singleton color repeated RTs might be faster than if it switched. In other words, the more catastrophic the interference (the target shape, target color, distractor shape, distractor color) change between trials, the more RTs might slow (compared with consistencies between trials, such that the target and distractor shapes repeat and the target and distractor colors repeat). Lastly, given the variability across both the shape and color dimensions, the claim that this type of suppression is feature-blind might be an artifact of the design promoting location-based instead of feature-based suppression.

      Thank you for raising this point. In the past we have used the finding that learned suppression was not specific to distractors, but also generalized to targets to argue in favor of proactive (or stimulus triggered) suppression. However, we agree that given the current experimental parameters it may be an oversimplification to conclude that the effect was feature-blind based on the impaired target processing as observed here. As this argument is also not relevant to our main findings, we have removed this interpretation and simply report that the effect was observed for both distractor and targets. Nevertheless, we would like to point out that while inter-trial priming could influence reaction times, the features of both target and distractors (shape and color) were randomly assigned on each trial. This should mitigate consistent feature repetitions effects. Additionally, previous research has demonstrated that suppression effects persist even when immediate feature repetitions are controlled for or statistically accounted for (e.g., Wang & Theeuwes 2018 JEP:HPP; Huang et al., 2021 PB&R).

      (7) The authors should temper claims such as "suppression occurs only following attentional enhancement, indicating a reactive suppression mechanism rather than proactive suppression." (p. 15, lines 353-353). Perhaps this claim may be true in the current context, but this claim is too generalized and not supported, at least yet. Further, "Within the realm of learned distractor suppression, an ongoing debate centers around the question of whether, and precisely when, visual distractors can be proactively suppressed. As noted, the idea that learned spatial distractor suppression is applied proactively is largely based on the finding that the behavioral benefit observed when distractors appear with a higher probability at a given location is accompanied by a probe detection cost (measured via dot offset detection) at the high probability distractor location (Huang et al., 2022, 2023; Huang, Vilotijević, et al., 2021)." (p. 15, lines 355-361). Again, the authors should either cite more of the opposing side of the debate (e.g., the signal suppression hypothesis, Gaspelin & Luck, 2019 or Luck et al., 2021) and the many lines of converging evidence of proactive suppression) or temper the claims.

      Thank you for your constructive feedback regarding our statements on suppression mechanisms. We acknowledge that our original claim was intended to reflect our specific findings within the context of this study and was not meant to generalize across all research in the field. To prevent any misunderstanding, we have tempered our claims to avoid overgeneralization by clarifying that our findings suggest a tendency toward reactive suppression within the specific experimental conditions we investigated (see page 17).

      Furthermore, learned distractor suppression is multifaceted, encompassing both feature-based suppression (as proposed by the signal suppression hypothesis) and spatial-based suppression (as examined in the current study). The signal suppression hypothesis provides proactive evidence related to the suppression of specific feature values (Gaspelin et al., 2019; Gaspelin & Luck, 2018b; Stilwell et al., 2019). We have incorporated references to these studies to offer a more comprehensive perspective on the ongoing debate at a broader level (see page 17).

      (8) "These studies however, mainly failed to find evidence in support of active preparatory inhibition (van Moorselaar et al., 2020, 2021; van Moorselaar & Slagter, 2019), with only one study observing increased preparatory alpha contralateral to the high probability distractor location (Wang et al., 2019)." (p. 15, lines 367-370). This is an odd phrasing to say "many studies" have shown one pattern (citing 3 studies) and "only" one showing the opposite, especially given these were all from the current authors' labs.

      Agreed. We have rewritten this text on page 17.

      “These studies however, failed to find evidence in support of active preparatory inhibition as indexed via increased alpha power contralateral to the high probability distractor location  (van Moorselaar et al., 2020, 2021; van Moorselaar & Slagter, 2019; but see Wang et al., 2019).”

      (9) Could the authors comment on why total power was significantly above baseline immediately (without clearer timing marks, ~10-50 ms) after the onset of the cue (Figure 3)? Is this an artifact of smearing? Further, it appears that there is significant activity (as strong as the evoked power of interest) in the baseline period of the evoked power when the memory item is presented on the vertical midline in the upper visual field (this is also true, albeit weaker, for the memory cue item presented on the horizontal midline to the right). This concern again appears in Figure 4 where the Alpha CTF slope was significantly below or above the baseline prior to the onset of the memory cue. Evoked Alpha was already significantly higher than baseline in the baseline period. In Figure 5, evoked power is already higher and different for the hpl than the lpls even at the memory cue (and before the memory cue onsets). There are often periods of differential overlap during the baseline period, or significant activity in the baseline period or at the onset of the critical, time-locked stimulus array. The authors should explain why this might be (e.g., smearing).

      Thank you for pointing this out. As suggested by the reviewer, this ‘unexpected’ pre-stimulus decoding is indeed the result of temporal smearing induced by our 5th order Butterworth filter. The immediate onset of reliable tuning (sometimes even before stimulus onset) is then also a typical aspect of studies that track tuning profiles across time in the lower frequency bands such as alpha (van Moorselaar & Slagter 2019; van Moorselaar et al., 2020; Foster et al., 2016).

      Indeed, visual inspection also suggests that evoked activity tracked items at the top of the screen, an effect that is unlikely to result from temporal smearing as it is temporally interrupted around display onset. However, it is important to note that CTFs by location are based on far fewer trials, making them inherently noisier. The by-location plots primarily serve to show that the observed pattern is generally consistent across locations. In any case, given that the high probability distractor location was counterbalanced across participants it did not systematically influence our results.

      (10) Given that EEG was measured, perhaps the authors could show data to connect with the extant literature. For example, by showing the ERP N2pc and PD components. A strong prediction here is that there should be an N2pc component followed by a PD component if there is the first selection of the singleton before it is suppressed.

      Thank you for your great suggestion regarding the analysis of ERP components such as N2pc and Pd. To reliably assess lateralized ERP components like N2pc or Pd the high probability location must be restricted to static lateralized positions (e.g., on the horizontal midline such as Wang et al., 2019). In contrast, our study was designed to utilize an inverted encoding model to investigate the mechanisms underlying spatial suppression. To avoid bias in training the spatial model toward specific spatial locations (see also the previous comment), we counterbalanced the high-probability location across participants, ensuring an equal distribution of high-probability locations within the sample. Given this counterbalanced design, it was not feasible to reliably assess these components within the scope of the current study. Yet, we agreed with the reviewer that it would be of theoretical interest to examine Pd and N2pc evoked by the search display, particularly in this scenario where suppression has been triggered prior to search onset.

      (11) Figure 2 (behavioral results) is difficult to see (especially the light grey and white bars). A simple fix might be to outline all the bars in black.

      Thank you! We have incorporated your suggestion by outlining all the bars on page 10.

      Reviewer #3 (Recommendations For The Authors):<br /> (1) I'm wondering about the link between the memory task and the search task.  I think the interpretation of the data should include more discussion of the fact that much of the search literature doesn't involve simultaneously holding an unrelated location in memory.  How might that change the results?

      For example - what happens behaviorally on the subset of trials in which the location to be held in memory is near the high probability distractor location?  All the behavioral data is more or less compartmentalized, but I think some behavioral analysis of this and related questions might be quite useful.  I know there are comparisons of behavior in single vs. dual-task cases (for the memory task at least), but I think the analyses could go deeper.

      Thank you for your great suggestion. To investigate the potential interactions between the spatial memory task and the visual search task, we conducted additional analyses on the behavioral data. First, we examined whether memory recall was influenced by the spatial distance (dist0 to dist4) between the memory cue location and the high-probability distractor location. As shown in the figure below, memory recall is not systematically biased either toward or away from the high-probability distractor location (p = .562, ηp<sup>2</sup> = .011).

      We also assessed how the memory task might affect search performance. Specifically, we plotted reaction times as a function of the spatial overlap between the memory cue location and any of the search items, separating trials by distractor-present (match-target, match-distractor, match-neutral) and distractor-absent (match-target, match-neutral) conditions. Although visually the result pattern seems to suggest that search performance was facilitated when the memory cue spatially overlapped with the target and interfered with when it overlapped with the distractor, this pattern did not reach statistical significance (distractor-present: p = .249, ηp<sup>2</sup> = .002; distractor-absent: p = .335, ηp<sup>2</sup> = .002). We have now included these analyses in our supplemental material.

      Beyond additional data analyses, there are also theoretical questions to be asked.  For example, one could argue that in order to maintain a location near or at the high probability distractor location in working memory, the priority map would have to shift substantially. This doesn't necessarily mean that proactive suppression always occurs in search when there is a high probability location. Instead, one could argue that when you need to maintain a high probability location in memory but also know that this location might contain a distractor, the representation necessarily looks quite different than if there were no memory tasks.  Maybe there are reasons against this kind of interpretation but more discussion could be devoted to it in the manuscript. I guess another way to think of this question is - how much is the ping showing us about attentional priority for search vs. attentional priority for memory, or is it simply a combination of those things, and if so, how might that change if we could ping the attentional priority map without a simultaneous memory task?

      Thank you for this valuable suggestion. The aim of our study was to explore how the CTFs elicited by the memory cue were influenced by the search task. We employed a simultaneous memory task because directly measuring CTFs in relation to the search task was not feasible, as the HPL typically does not vary within individual participants. Consequently, CTFs locked to placeholder onsets could reflect arbitrary differences between (subgroups of) participants rather than true differences in the HPL. To address this, we combined the search task with a VWM task, leveraging the fact that location-specific CTFs can reliably be elicited by a memory cue and that the location of this cue relative to the HPL can be systematically varied within participants (Foster et al., 2016, 2017; van Moorselaar et al., 2018). This approach allowed us to examine the CTFs elicited by the memory cue and how these were modulated by their distance from the HPL.

      While it is theoretically possible that the observed changes resulted from alterations in how the memory cue was maintained in memory only, this explanation seems unlikely, for memory performance (recall) did not vary as a function of the cue's distance from the HPL, suggesting that the distance-related changes in the CTFs are reflections of both tasks. Moreover, distractor learning typically occurs without awareness (Gao & Theeuwes 2022; Wang & Theeuwes 2018). It is difficult to understand how such unconscious processes could lead to anticipations in the memory task and subsequently modulate the representation of the consciously remembered memory cue only. We therefore believe that if we would have pinged the attentional priority map without a simultaneous memory task, the results would have been similar to those obtained in the present experiment, indicating stronger tuning at the HPL. Yet, this work still needs to be done.

      To address this comment, we have added a paragraph on p. 18:

      “However, two alternative explanations warrant consideration. First, one could argue that observed modulations in the revived CTFs do not provide insight into the mechanisms underlying distractor suppression but instead reflect changes in the memory representation itself, potentially triggered by the anticipation of the HPL in the search task. According to this view, the changes in the revived CTFs would be unrelated to how search performance (in particular distractor suppression) was achieved. While this is theoretically possible, we believe it to be unlikely. Memory performance (recall) did not vary as a function of the cue's distance from the HPL, whereas the revived CTFs did, indicating that these changes likely reflect contributions from both tasks. Additionally, distractor learning typically occurs without conscious awareness (Gao & Theeuwes 2022; Wang & Theeuwes 2018). It is difficult to conceive how such unconscious processes could produce anticipatory effects in the memory task and selectively modulate the representation of the consciously remembered memory cue. Second, the apparent lack of suppression and the presence of a pronounced tuning at the high-probability distractor location could actually reflect a proactive mechanism that manifests in a way that seems reactive due to the dual-task nature of our experiment.”

      (2) When the distractor appears at a particular location with a high probability it necessarily means that intertrial effects differ between high and low probability distractor locations.  Consecutive trials with a distractor at the same location are far more frequent in the high probability condition.  You may not have enough power to look at this, and I know this group has analyzed this behaviorally in the past, but I do wonder how much that influences the EEG data reported here.  Are CTFs also sensitive to distractors/targets from the most recent trial?  And does that contribute to the overall patterns observed here?

      Thank you for your thoughtful comment. Indeed, Statistical distractor learning studies naturally involve a higher proportion of intertrial effects for high-probability distractors compared to low-probability ones. Previous research, including the present study, has demonstrated that while distractor location improves performance—shown by faster response times (t(23) = 6.32, p < .001, d = 0.33) and increased accuracy (t(23) = 4.21, p < .001, d = 0.86)—intertrial effects alone cannot fully account for the learned suppression effects induced by spatial distractor imbalances. This analysis in now reflected in the revised manuscript on page 9.

      However, as noted by the reviewer, this leaves uncertain to what extent the neural indices of statistical learning, in this case the modulation of channel tuning functions, capture the effects of interest beyond the contributions of intertrial priming. To address this issue, one possible approach is to rerun the CTF analysis after excluding trials with location repetitions. Since the distractor location is unknown to participants at the time the CTF is revived by the placeholder, we removed trials where the memory cue location repeated the distractor location from the preceding trial, rather than trials with distractor location repetitions between consecutive trials. Our analyses indicate that after trials removal (~ 9% of overall trials), the spatial gradient pattern in the CTF slopes remains similar. However, the cluster-based permutation analysis fails to reveal any significant findings, and a one-sample t-test on the slopes averaged within the 100 ms time window of interest yields a p-value of 0.106. While this could suggest that the current pattern is influenced by distractor-cue repetition, it is more likely that the trial removal resulted in an underpowered analysis. To investigate this, we randomly removed an equivalent number of trials (9%), which similarly resulted in insignificant findings, although the overall result pattern remained comparable (p = 0.066 for the one-sample t-test on the slopes average within the interested time window of 100 ms).

      Author response image 2.

      Also, in our previous pinging study we observed that, despite the trial imbalance, decoding was approximately equal between high probability trailing (i.e., location intertrial priming) and non-trailing trials, suggesting that the ping is able to retrieve the priority landscape that build up across longer timescales.

      (3) Maybe there is too much noise in the data for this, but one could look at individual differences in the magnitude of the high probability distractor suppression and the magnitude of the alpha CTF slope.  If there were a correlation here it would bolster the argument about the relationship between priority to the distractor location and subsequent behavior reduction of interference from that distractor.  

      Thank you for this valuable suggestion. We investigated whether there was a correlation between the average gradient slope during the time window in which the placeholder revived the memory representation and the average distance slope in reaction times for the learned suppression effect. This correlation was not significant (r = .236, p = 0.267), which is perhaps expected given the potential noise levels, as noted by the reviewer. Furthermore, while the learned suppression effect is robust at the group level, its predictive value for individual-level performance has been shown to be limited (Ivanov et al., 2024; Hedge et al., 2018). Consequently, we chose not to include this analysis in the manuscript (see also our response to comment 2 by reviewer 2).

      (4) The results sections are a bit dense in places, especially starting at the bottom of page 11.  For readers who are familiar with the general questions being asked but less so with the particular time-frequency analyses and CTF approaches being used (like myself), I think a bit more time could be spent setting up these analyses within the results section to make extra clear what's going on.

      Thank you for your feedback regarding the clarity of our Results section. We have revised this section to make it more understandable and easier to follow, especially for readers who may be less familiar with the specific time-frequency analyses and modeling approaches used in our study. Specifically, we have provided additional interpretations alongside the reported results from page 10 to page 13 to aid comprehension and ensure that the methodology and findings are accessible to a broader audience. Additionally, we have revised the figure notes to further enhance clarity and understanding.

      Other comments:

      Abstract: "a neutral placeholder display was presented to probe how hidden priority map is reconfigured..."  i think the word "the" is missing before "priority map"

      Thank you. We have added the word “the” before “hidden priority map”.

      p. 4, Müller's group also has a number of papers that demonstrate how learned distractor regularities impact search (From the ~2008-2012 range, probably others as well), it might be worth citing a few here.

      Thank you for your suggestion. In the revised manuscript, we have added citations to several key papers from Muller’s group on page 4 as well as other research groups.

      p.5 - Chang et al. (2023) seems highly relevant to the current study (and consistent with its results) - depending on word limits, it might make sense to expand the description of this in the introduction to make clear how the present study builds upon it

      Thank you! We have expanded the discussion of Chang et al. (2023) on page 5 to provide more detailed elaboration of their study and its relevance to our work.

      p. 7 - maybe not for the current study, but I do wonder whether the distortion of spatial memory by the presence of the search task occurs only when there is a relevant regularity in the search task. In other words, if the additional singleton task had completely unpredictable target and distractor locations, would there be memory distortions?  Possibly for the current dataset, the authors could explore whether the behavioral distortion is systematically towards or away from the high probability distractor location.

      Thank you for your insightful suggestion. Following your recommendation, we conducted an additional analysis to examine memory recall as a function of the distance between the memory cue location and the high-probability distractor location. Figure S1A illustrates the results, depicting memory recall deviation across various distances (dist0 to dist4) from the high-probability distractor location.

      Our statistical analysis indicates that memory recall is not systematically biased either towards or away from the high-probability distractor location (p = .562, η<sub>p</sub><sup>2</sup> = .011). This finding suggests that spatial memory recall remains relatively stable and is not heavily influenced by the presence of regularities in the distractor locations.

      p. 7 - in addition to stats it would be helpful to report descriptive statistics for the high probability vs. other distractor location comparisons

      Thank you! We have added descriptive statistics on page 8 and page 9.

      p. 19, "64%" repeated unnecessarily - also, shouldn't it be 65% if it's 5% at each of the other seven locations?

      Thank you. This is now corrected in the revised manuscript.

      p. 20 "This process continued until participants demonstrated a thorough understanding of the assigned tasks" Were there objective criteria to measure this?

      Thank you for pointing out this issue. To clarify, objective criteria were indeed used to assess participants’ readiness to proceed. Specifically:

      For the training phase practice trials, participants were required to achieve an average memory recall deviation of less than 13°.

      For the test phase practice trials, participants needed to demonstrate a minimum of 65% accuracy in the search task. In addition, participants were asked to verbally confirm their understanding of the task goals with the experimenter before proceeding.

      We have revised the manuscript to clearly indicate these criteria on p. 23.

      p. 21 "P-values were Greenhouse-Geiser corrected in case where the..." I think "case" should be "cases"

      Thank you. We have corrected this in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews: 

      Reviewer #1 (Public review): 

      Koren et al. derive and analyse a spiking network model optimised to represent external signals using the minimum number of spikes. Unlike most prior work using a similar setup, the network includes separate populations of excitatory and inhibitory neurons. The authors show that the optimised connectivity has a like-to-like structure, which leads to the experimentally observed phenomenon of feature competition. The authors also examine how various (hyper)parameters-such as adaptation timescale, the excitatory-to-inhibitory cell ratio, regularization strength, and background current-affect the model. These findings add biological realism to a specific implementation of efficient coding. They show that efficient coding explains, or at least is consistent with, multiple experimentally observed properties of excitatory and inhibitory neurons. 

      As discussed in the first round of reviews, the model's ability to replicate biological observations such as the 4:1 ratio of excitatory vs. inhibitory neurons hinges on somewhat arbitrary hyperparameter choices. Although this may limit the model's explanatory power, the authors have made significant efforts to explore how these parameters influence their model. It is an empirical question whether the uncovered relationships between, e.g., metabolic cost and the fraction of excitatory neurons are biologically relevant.

      The revised manuscript is also more transparent about the model's limitations, such as the lack of excitatory-excitatory connectivity. Further improvements could come from explicitly acknowledging additional discrepancies with biological data, such as the widely reported weak stimulus tuning of inhibitory neurons in the primary sensory cortex of untrained animals.

      We thank the Reviewer for their insightful characterization of our paper and for further suggestions on how to improve it. We have now further improved the transparency about model’s limitations and we explicitly acknowledged the discrepancy with biological data about connection probability and about the selectivity of inhibitory neurons (pages 4 and 15).

      Reviewer #2 (Public review): 

      Summary: 

      In this work, the authors present a biologically plausible, efficient E-I spiking network model and study various aspects of the model and its relation to experimental observations. This includes a derivation of the network into two (E-I) populations, the study of single-neuron perturbations and lateral-inhibition, the study of the effects of adaptation and metabolic cost, and considerations of optimal parameters. From this, they conclude that their work puts forth a plausible implementation of efficient coding that matches several experimental findings, including feature-specific inhibition, tight instantaneous balance, a 4 to 1 ratio of excitatory to inhibitory neurons, and a 3 to 1 ratio of I-I to E-I connectivity strength.

      Strengths: 

      While many network implementations of efficient coding have been developed, such normative models are often abstract and lacking sufficient detail to compare directly to experiments. The intention of this work to produce a more plausible and efficient spiking model and compare it with experimental data is important and necessary in order to test these models. In rigorously deriving the model with real physical units, this work maps efficient spiking networks onto other more classical biophysical spiking neuron models. It also attempts to compare the model to recent single-neuron perturbation experiments, as well as some long-standing puzzles about neural circuits, such as the presence of separate excitatory and inhibitory neurons, the ratio of excitatory to inhibitory neurons, and E/I balance. One of the primary goals of this paper, to determine if these are merely biological constraints or come from some normative efficient coding objective, is also important. Lastly, though several of the observations have been reported and studied before, this work arguably studies them in more depth, which could be useful for comparing more directly to experiments.

      Weaknesses: 

      This work is the latest among a line of research papers studying the properties of efficient spiking networks. Many of the characteristics and findings here have been discussed before, thereby limiting the new insights that this work can provide. Thus, the conclusions of this work should be considered and understood in the context of those previous works, as the authors state. Furthermore, the number of assumptions and free parameters in the model, though necessary to bring the model closer to biophysical reality, make it more difficult to understand and to draw clear conclusions from. As the authors state, many of the optimality claims depend on these free parameters, such as the dimensionality of the input signal (M=3), the relative weighting of encoding error and metabolic cost, and several others. This raises the possibility that it is not the case that the set of biophysical properties measured in the brain are accounted for by efficient coding, but rather that theories of efficient coding are flexible enough to be consistent with this regime. With this in mind, some of the conclusions made in the text may be overstated and should be considered in this light.

      Conclusions, Impact, and additional context: 

      Notions of optimality are important for normative theories, but they are often studied in simple models with as few free parameters as possible. Biophysically detailed and mechanistic models, on the other hand, will often have many free parameters by their very nature, thereby muddying the connection to optimality. This tradeoff is an important concern in neuroscientific models. Previous efficient spiking models have often been criticized for their lack of biophysically-plausible characteristics, such as large synaptic weights, dense connectivity, and instantaneous communication. This work is an important contribution in showing that such networks can be modified to be much closer to biophysical reality without losing their essential properties. Though the model presented does suffer from complexity issues which raise questions about its connections to "optimal" efficient coding, the extensive study of various parameter dependencies offers a good characterization of the model and puts its conclusions in context.

      We thank the Reviewer for their thorough and accurate assessment of our paper.  

      Reviewer #3 (Public review): 

      Summary: 

      In their paper the authors tackle three things at once in a theoretical model: how can spiking neural networks perform efficient coding, how can such networks limit the energy use at the same time, and how can this be done in a more biologically realistic way than previous work. 

      They start by working from a long-running theory on how networks operating in a precisely balanced state can perform efficient coding. First, they assume split networks of excitatory (E) and inhibitory (I) neurons. The E neurons have the task to represent some lower dimensional input signal, and the I neurons have the task to represent the signal represented by the E neurons. Additionally, the E and I populations should minimize an energy cost represented by the sum of all spikes. All this results in two loss functions for the E and I populations, and the networks are then derived by assuming E and I neurons should only spike if this improves their respective loss. This results in networks of spiking neurons that live in a balanced state, and can accurately represent the network inputs. 

      They then investigate in depth different aspects of the resulting networks, such as responses to perturbations, the effect of following Dale's law, spiking statistics, the excitation (E)/inhibition (I) balance, optimal E/I cell ratios, and others. Overall, they expand on previous work by taking a more biological angle on the theory and show the networks can operate in a biologically realistic regime.

      Strengths: 

      * The authors take a much more biological angle on the efficient spiking networks theory than previous work, which is an essential contribution to the field

      * They make a very extensive investigation of many aspects of the network in this context, and do so thoroughly

      * They put sensible constraints on their networks, while still maintaining the good properties these networks should have

      Weaknesses: 

      * One of the core goals of the paper is to make a more biophysically realistic network than previous work using similar optimization principles. One of the important things they consider is a split into E and I neurons. While this works fine, and they consider the coding consequences of this, it is not clear from an optimization perspective why the split into E and I neurons and following Dale's law would be beneficial. This would be out of scope for the current paper however.

      * The theoretical advances in the paper are not all novel by themselves, as most of them (in particular the split into E and I neurons and the use of biophysical constants) had been achieved in previous models. However, the authors discuss these links thoroughly and do more in-depth follow-up experiments with the resulting model. 

      Assessment and context: 

      Overall, although much of the underlying theory is not necessarily new, the work provides an important addition to the field. The authors succeeded well in their goal of making the networks more biologically realistic, and incorporate aspects of energy efficiency. For computational neuroscientists this paper is a good example of how to build models that link well to experimental knowledge and constraints, while still being computationally and mathematically tractable. For experimental readers the model provides a clearer link of efficient coding spiking networks to known experimental constraints and provides a few predictions.

      We thank the Reviewer for a positive assessment and for pointing out the merits of our work.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      The authors have addressed my previous concerns, and I agree that the manuscript has improved. However, I believe they could still do more to acknowledge two notable mismatches between the model and experimental data.

      (1) Stimulus selectivity of excitatory and inhibitory neurons 

      In the model, excitatory and inhibitory neurons exhibit similar stimulus selectivity, which appears inconsistent with most experimental findings. The authors argue that whether inhibitory neurons are less selective remains an open question, citing three studies in support. However, only one of these studies (Ranyan) was conducted in primary sensory cortex and it is, to my knowledge, one of the few papers showing this (indeed, it's often cited as an exception). The other two studies (Kuan and Najafi) recorded from the parietal cortex of mice trained on decision making tasks, and therefore seem less relevant to the model.

      In contrast to the cited studies, the overwhelming majority of the work has found that inhibitory neurons in sensory cortex, in particular those expressing Parvalbumin, are less stimulus selective than excitatory cells. And this is indeed the prevailing view, as summarized by the review from Hu et al. (Science, 2014): "PV+ interneurons exhibit broader orientation tuning and weaker contrast specificity than pyramidal neurons." This view emerged from numerous classical studies, including Sohya et al. (J. Neurosci., 2007), Cardin (J. Neurosci., 2007), Nowak (Cereb. Cortex, 2008), Niell et al. ( J. Neurosci., 2008), Liu (J. Neurosci., 2009), Kerlin (Neuron, 2010), Ma et al. (J. Neurosci., 2010), Hofer et al. (Nature Neurosci. 2011), and Atallah et al. (Neuron 2012). Weak inhibitory tuning has been confirmed by recent studies, such as Sanghavi & Kar (biorxiv 2023), Znamenskiy et al. (Neuron 2024), and Hong et al. (Nature, 2024).

      The authors should acknowledge this consensus and cite the conflicting evidence. Failing to do so is cherry picking from the literature. Since training can increase the stimulus selectivity of PV+ neurons to that of Pyr levels, also in primary visual cortex (Khan et al. Neuron 2018), a favourable interpretation of the model is that it represents a highly optimized, if not overtrained, state.

      We have carefully considered the literature cited by the Reviewer. We agree with the interpretation that stimulus selectivity of inhibitory neurons in our model is higher than the stimulus selectivity of Parvalbumin-positive inhibitory neurons in the primary sensory cortex of naïve animals. We have edited the text in Discussion (page 14).

      (2) Connection probability 

      The manuscript claims that "rectification sets the overall connection probability to 0.5, consistent with experimental results (Pala & Petersen; Campagnola et al.)." However, the cited studies, and others, report significantly lower probabilities, except for Pyr-PV (E-I connections in the model). For example, Campagnola et al. measured PV-Pyr connectivity at 34% in L2/3 and 20% in L5.

      It's perfectly acceptable that the model cannot replicate every detail of biological circuits. But it's important to be cautious when claiming consistency with experimental data.

      Here as well, we agree with the Reviewer that the connection probability of 0.5 is consistent with reported connectivity of Pyr-PV neurons, but less so with reported connectivity of PV-Pyr neurons. We have now qualified our claim about compatibility of the connection probability in our model with empirical observations more precise (page 4).

      Reviewer #2 (Recommendations for the authors): 

      I commend the authors for an extremely thorough and detailed rebuttal, and for all of the additional work put in to address the reviewer concerns. For the most part, I am satisfied with the current state of the manuscript. 

      We thank the Reviewer for recognizing our effort to address the first round of Reviews to our best ability.

      Here are some small points still remaining that I think the authors should address: 

      (1) Pg. 8, "We verified the robustness of the model to small deviations from the optimal synaptic weights" - while the authors now cite Calaim et al. 2022 in the discussion, its relevance to several of the results justify its inclusion in other places. Here is one place where the authors test something that was also studied in this previous paper.

      The Reviewer is correct that Calaim et al. (eLife 2022) addressed the robustness of synaptic weights, and we now cited this study when describing our results on jiVering of synaptic connections (page 8).

      (2) Pg. 9, "In our optimal E-I network we indeed found that optimal coding efficiency is achieved in absence of within-neuron feedback or with weak adaptation in both cell types" Pg. 10, "the absence of within-neuron feedback or the presence of weak and short-lasting spike-triggered adaptation in both E and I neurons are optimally efficient solutions" The authors seem to state that both weak adaptation and no adaptation at all are optimal. In contrast to the rest of the results presented, this is very vague and does not give a particular level of adaptation as being optimal. The authors should make this more clear. 

      We agree that the text about optimal level of adaptation was unclear. The optimal solution is no adaptation, while weak and short-lasting adaptation define a slightly suboptimal, yet still efficient, network state, as now stated on page 10.

      (3) Pg. 13, "In summary our analysis suggests that optimal coding efficiency is achieved with four times more E neurons than I neurons and with mean I-I synaptic efficacy about 3 times stronger..." --- claims such as these are still too strong, in my opinion. It is rather the case that the particular ratio of E to I neurons and connections strengths can be made consistent with an optimally efficient regime.

      We agree here as well. We have revised the text (page 13) to beVer explain our results.

      (4) Pg. 14, "firing rates in the 1CT model were highly sensitive to variations in the metabolic constant" (Fig. 8I, as compared to Fig. 6C). This difference between the 1CT and E-I networks is striking, and I would suspect it is due to some idiosyncrasies in the difference between the two models (e.g., the relative amount of delay that it takes for lateral inhibition to take effect, or the fact that E-E connections have not been removed in this model). The authors should ideally back up this result with some justified explanation. 

      We agree with Reviewer that the delay for lateral inhibition in the E-I model is twice that of the 1CT model and that the E-I model gains stability from the lack of E-E connectivity. Furthermore, the tuning is stronger in I compared to E neurons in the E-I model, which contributes to making the E-I network inhibition-dominated (Fig. 1H). In contrast, the average excitation and inhibition in the 1CT model are of exactly the same magnitude. The property of being inhibition-dominated makes the E-I model more stable. We report these observations in the revised text (pages 14-15). 

      Reviewer #3 (Recommendations for the authors): 

      Overall my points were very well responded to and I removed most of my weaknesses.

      I appreciate the authors implementing my suggested analysis change for Figure 8, and I find the result very clear. I would further suggest they add a bit of text for the reader as to why this is done. For a new reader without much knowledge of these networks at first it seems the inhibitory population is very good at representation in fig 8G: so why is it not further considered in fig 8H?

      We thank the reviewer for providing further suggestions. We now clarified in the text why only the excitatory population of the E-I model is considered in E-I vs 1 cell type model comparison (page 14). 

      Thanks for sharing the code. From a quick browse through it looks very manageable to implement for follow up work, although some more guidance for how to navigate the quite complicated codebase and how to reproduce specific paper results would be helpful.

      We have also updated the code repository, where we have included more complete instructions on how to reproduce results of each figure. We renamed the folders with the computer code so that they point to a specific figure in the paper. The repository has been completed with the output of the numerical simulations we run, which allows immediate replot of all figures. We have deposited the repository at Zenodo to have the final version of the code associated with the DOI ttps://doi.org/10.5281/zenodo.14628524. This is mentioned in the section Code availability (page 17).

    1. We also need to consider the story of our data when working with qualitative data, such as quotations, observations, or descriptions.

      nm563 We should also think about the possible outliers or causes of some of the data as well as who the audience is and how it can affect the credibility of the data. How the data was achieved may also affect the credibility.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      The authors constructed a novel HSV-based therapeutic vaccine to cure SIV in a primate model. The novel HSV vector is deleted for ICP34.5. Evidence is given that this protein blocks HIV reactivation by interference with the NF-kB pathway. The deleted construct supposedly would reactivate SIV from latency. The SIV genes carried by the vector ought to elicit a strong immune response. Together the HSV vector would elicit a shock and kill effect. This is tested in a primate model.

      Thank you for your kind comments and suggestions, which are very helpful in improving our manuscript. We have carefully revised our manuscript and performed additional experiments accordingly, and we now think this version has been substantially improved for your reconsideration.

      Strengths and weaknesses:

      (1) Deleting ICP34.5 from the HSV construct has a very strong effect on HIV reactivation. Why is no eGFP readout given in Figure 1C as for WT HSV? The mechanism underlying increased activation by deleting ICP34.5 is only partially explored. Overexpression of ICP34.5 has a much smaller effect (reduction in reactivation) than deletion of ICP34.5 (strong activation); so the story seems incomplete.

      Thank you for your careful review and kind reminder.

      (1) We are sorry for the misunderstanding of Figure 1C. In the experiment of Figue 1C, we used an HSV-1 17 strain containing GFP (HSV-GFP) and HSV-DICP34.5 (recombinant HSV-1 17 strain with ICP34.5 deletion based on HSV-GFP) to reactivate the HIV latency cell line (J-Lat 10.6 cell). Since detecting GFP cannot distinguish between HSV infection and HIV reactivation, we assessed the reactivation by measuring the mRNA levels of HIV LTR upon stimulation with either HSV-GFP or HSV-ΔICP34.5. Actually, in Figure 1B, we had verified the reactivation efficacy by infecting J-Lat 10.6 cells with the HSV-1 17 strain containing GFP (HSV-GFP) and found significant upregulation of mRNA levels of HIV-1 LTR, Tat, Gag, Vif, and Vpr. We have adjusted the corresponding descriptions accordingly in the revised manuscript.

      (2) We agree with your insightful mention that the mechanism underlying increased activation by HSV-ΔICP34.5 is worthy to be further explored in the future study. In this study, we found that ICP34.5 play an antagonistic role with the reactivation of HIV latency by HSV-1 mainly through the modulation of host NF-κB and HSF1 pathways, while HSV-1 (especially HSV-ΔICP34.5) might reactivate HIV latency through NF-κB, HSF1, and other yet-to-be-determined mechanisms. Thus, ICP34.5 overexpression can only a partial effect on the reduction of the HIV latency reactivation by HSV-1. We have mentioned this issue in the revised “Discussion section”. Intriguingly, these findings collectively indicated that ICP34.5 might play an antagonistic role in the reactivation of HIV by HSV-1, and thus our modified HSV-DICP34.5 constructs can effectively reactivate HIV/SIV latency through the release of imprisonment from ICP34.5. However, ICP34.5 overexpression had only a partial effect on the reduction of the HIV latency reactivation, indicating that HSV-DICP34.5-based constructs can also reactivate HIV latency through other yet-to-be-determined mechanisms. (Lines 334 to 340).

      (2) No toxicity data are given for deleting ICP34.5. How specific is the effect for HIV reactivation? An RNA seq analysis is required to show the effect on cellular genes.

      Thank you for your questions and suggestions.

      (1) It’s well known that ICP34.5 is a neurotoxicity factor that can antagonize host immune responses, and previous studies (in gene therapy and oncolytic virotherapy) have shown that the safety of recombinant HSV-based vector can be improved by deleting ICP34.5. In this study, we also found that HSV-DICP34.5 exhibited lower virulence and replication ability than its parental strain (HSV-GFP) (Figure 1D, Figure S1). In addition, HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV-GFP stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5I) and body weight (Figure S9) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-sPD1-SIVgag/SIVenv group (Figure S10). Thus, these data suggest the safety of HSV-DICP34.5 in PLWH might be tolerable. We have added the corresponding description in the revised manuscript.

      (2) In our study, we found both adenovirus and vaccinia virus cannot reactivate HIV latency (Figure S3). In addition, the deletion of ICP0 gene from HSV-1 diminished the reactivation effect of HIV latency by HSV-1 (Figure S4). Thus, these data suggested the reactivation of HIV latency by HSV-1 might be virus-specific. Of course, this might be further investigated in future studies. We have added the corresponding description in the revised manuscript.

      (3) To explore the mechanism of reactivating viral latency by HSV-DICP34.5-based constructs, we performed RNA-seq analysis (Figure S5). We have added the corresponding description accordingly in the revised manuscript.

      (3) The primate groups are too small and the results to variable to make averages. In Figure 5, the group with ART and saline has two slow rebounders. It is not correct to average those with a single quick rebounder. Here the interpretation is NOT supported by the data.

      We agree with you that this is a pilot study with limited numbers of rhesus macaques. Although the number of macaques was relatively limited, these nine macaques were distributed evenly based on the background level of age, sex, weight, CD4 count, and viral load (VL) (Table S2). All SIV-infected macaques used in this study had a long history of SIV infection and had several courses of ART therapy, which mimics treatment of chronic HIV-1 infection in humans. These macaques were infected with SIVmac239 for more than 5 years, and highly pathogenic SIV-infected macaques have been well-validated as a stringent model to recapitulate HIV-1 pathogenesis and persistence during ART therapy in humans. Indeed, in our Chinese rhesus model, ART treatment effectively suppressed SIV infection to undetectable levels in plasma, and upon ART discontinuation, virus rapidly rebounded, which is very similar with that in ART-treated HIV patients. We think the results of this pilot study were very promising for further studies which will be expanded the scale of animals and then to preclinical and clinical study in our next projects. Thank you for your understanding.

      As for your question regarding “the two animals with low VL and slow rebound”, our explanation is following: As mentioned above, these macaques were distributed evenly based on the background level of CD4 count and VL (Table S2), and then there were different change of viral load and viral rebound in different groups. Thus, we think these data can support our interpretation. Moreover, our conclusion can also be supported from at least three evidences.

      (1) The VL in the ART+saline group promptly rebounded after ART discontinuation, with an average 8.63-fold increase in the rebounded peak VL compared with the pre-ART VL (Figure 5A, D and E). However, plasma VL in the ART+HSV-sPD1-SIVgag/SIVenv group exhibited a delayed rebound interval (Figure 5B-D).

      (2) There was a lower rebounded peak VL than pre-ART VL in the ART+HSV-sPD1-SIVgag/SIVenv group (average 12.20-fold decrease), while a higher rebounded peak VL than pre-ART VL in the ART+HSV-empty group (average 2.74-fold increase) (Figure 5E).

      (3) We found significant suppression of total SIV DNA and integrated SIV DNA provirus in the ART+HSV-sPD1-SIVgag/SIVenv group. However, the copies of the SIV DNA provirus were significantly improved in the ART+HSV-empty group and ART+saline group (Figure 5F-G).

      Thank you for your understanding.

      Discussion

      HSV vectors are mainly used in cancer treatment partially due to induced inflammation. Whether these are suitable to cure PLWH without major symptoms is a bit questionable to me and should at least be argued for.

      Thank you for your kind question comment and question. We confirmed the enhanced reactivation of HIV latency by HSV-∆ICP34.5 in primary CD4+ T cells from people living with HIV (PLWH) (Figure S2). As mentioned above, previous studies have shown that the safety of recombinant HSV-based vector can be improved by deleting ICP34.5. In this study, we also found that HSV-DICP34.5 exhibited lower virulence and replication ability than its parental strain (HSV-GFP) (Figure 1D, Figure S1). In addition, HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV-GFP stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5I) and body weight (Figure S9) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-sPD1-SIVgag/SIVenv group (Figure S10). Thus, these data suggest the safety of HSV-DICP34.5 in PLWH might be tolerable. We have added the corresponding description in the revised manuscript.

      Reviewer #2 (Public Review):

      Summary:

      In this article, Wen et. al. describe the development of a 'proof-of-concept' bi-functional vector based on HSV-deltaICP-34.5's ability to purge latent HIV-1 and SIV genomes from cells. They show that co-infection of latent J-lat T-cell lines with an HSV-deltaICP-34.5 vector can reactivate HIV-1 from a latent state. Over- or stable expression of ICP 34.5 ORF in these cells can arrest latent HIV-1 genomes from transcription, even in the presence of latency reversal agents. ICP34.5 can co-IP with- and de-phosphorylate IKKa/b to block its interaction with NF-k/B transcription factor. Additionally, ICP34.5 can interact with HSF1 which was identified by mass-spec. Thus, the authors propose that the latency reversal effect of HSV-deltaICP-34.5 in co-infected JLat cells is due to modulatory effects on the IKKa/b-NF-kB and PP1-HSF-1 pathway.

      Next, the authors cleverly construct a bifunctional HSV-based vector with deleted ICP34.5 and 47 ORFs to purge latency and avoid immunological refluxes, and additionally, expand the application of this construct as a vaccine by introducing SIV genes. They use this 'vaccine' in mouse models and show the expected SIV-immune responses. Experiments in rhesus macaques (RM), further elicit the potential for their approach to reactivate SIV genomes and at the same time block their replication by antibodies. What was interesting in the SIV experiments is that the dual-functional vector vaccine containing sPD1- and SIV Gag/Env ORFs effectively delayed SIV rebound in RMs and in some cases almost neutralized viral DNA copy detection in serum. Very promising indeed, however, there are some questions I wish the authors had explored to get answers to, detailed below.

      Overall, this is an elegant and timely work demonstrating the feasibility of reducing virus rebound in animals, with the potential to expand to clinical studies. The work was well-written, and sections were clearly discussed.

      Strengths:

      The work is well designed, rationale explained, and written very clearly for lay readers.<br /> Claims are adequately supported by evidence and well-designed experiments including controls.

      Thank you for your nice comments regarding our work.

      Weaknesses:

      (1) While the mechanism of ICP34.5 interaction and modulation of the NF-kB and HSF1 pathways are shown, this only proves ICP34.5 interactions but does not give away the mechanism of how the HSV-deltaICP-34.5 vector purges HIV-1 latency. What other components of the vector are required for latency reversal? Perhaps serial deletion experiments of the other ORFs in the HSV-deltaICP-34.5 vector might be revealing.

      Thank you for your valuable suggestion. In fact, we are currently further exploring some potential viral genes of HSV-1 that might play a role in the reactivation of HIV latency. We have found that the deletion of ICP0 gene from HSV-1 diminished the reactivation effect of HIV latency by HSV-1 (Figure S4), showing that ICP0 might play a vital role for the reactivation. Of course, this might be further investigated in future studies. We have added the corresponding description in the revised manuscript.

      (2) The efficacy of the HSV vaccine vectors was evaluated in Rhesus Macaque model animals. Animals were chronically infected with SIV (a parent of HIV), treated with ART, challenged with bi-functional HSV vaccine or controls, and discontinued treatment, and the resulting virus burden and immune responses were monitored. The animals showed SIV Gag and Env-specific immune responses, and delayed virus rebound (however rebound is still there), and below-detection viral DNA copies. What would make a more convincing argument to this reviewer will be data to demonstrate that after the bi-functional vaccine, the animals show overall reduction in the number of circulating latent cells. The feasibility of obtaining such a result is not clearly demonstrated.

      Thank you for your valuable mention. We have now provided more data about this issue. We found significant suppression of total SIV DNA and integrated SIV DNA provirus in the ART+HSV-sPD1-SIVgag/SIVenv group. However, the copies of the SIV DNA provirus were significantly improved in the ART+HSV-empty group and ART+saline group (Figure 5F-G). We have added the corresponding description in the revised manuscript.

      (3) The authors state that the reduced virus rebound detected following bi-functional vaccine delivery is due to latent genomes becoming activated and steady-state neutralization of these viruses by antibody response. This needs to be demonstrated. Perhaps cell-culture experiments from specimens taken from animals might help address this issue. In lab cultures one could create environments without antibody responses, under these conditions one would expect a higher level of viral loads to be released in response to the vaccine in question.

      Thanks for your kind mention and suggestion. We performed the following cell experiment to address this issue. Primary CD4+ T cells from people living with HIV (PLWH) were isolated, and then infected with HSV or HSV-∆ICP34.5 constructs. As expected, we confirmed the enhanced reactivation of HIV latency by HSV-∆ICP34.5 (Figure S2). Thank you.

      (4) How do the authors imagine neutralizing HIV-1 envelope epitopes by a similar strategy? A discussion of this point may also help.

      Thank you for your kind comment. We have added the corresponding discussion in the revised manuscript. “The current consensus on HIV/AIDS vaccines emphasizes the importance of simultaneously inducing broadly neutralizing antibodies and cellular immune responses. Therefore, we believe that incorporating the induction of broadly neutralizing antibodies into our future optimizing approaches may lead to better therapeutic outcomes.” (Lines 384 to 388)

      (5) I thought the empty HSV-vector control also elicited somewhat delayed kinetics in virus rebound and neutralization, can the authors comment on why this is the case?

      Thank you for your careful review and mention. We agree with you that the HSV-1 empty vector does exhibit somewhat a delayed rebound. We think the possible reason is: Although the empty HSV-vector cannot elicit SIV-specific CTL responses, it effectively activates the latent SIV reserviors, and then these activated virions can be partially killed by ART drugs. Therefore, even without carrying HIV/SIV antigens, somewhat delayed kinetics in virus rebound may be observed. Thank you.

      Reviewer #1 (Recommendations For The Authors):

      (1) The authors should provide toxicity data for HSV transduction after deleting ICP34.5 and provide an explanation of why overexpression of ICP34.5 has such a small effect.

      Thank you for your questions and suggestions. As mentioned above, we now provided data for the safety of HSV-DICP34.5-based constructs.

      (1) It’s well known that ICP34.5 is a neurotoxicity factor that can antagonize host immune responses, and previous studies (in gene therapy and oncolytic virotherapy) have shown that the safety of recombinant HSV-based vector can be improved by deleting ICP34.5. In this study, we also found that HSV-DICP34.5 exhibited lower virulence and replication ability than its parental strain (HSV-GFP) (Figure 1D, Figure S1). In addition, HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV-GFP stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5I) and body weight (Figure S9) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-sPD1-SIVgag/SIVenv group (Figure S10). Thus, these data suggest the safety of HSV-DICP34.5 in PLWH might be tolerable. We have added the corresponding description in the revised manuscript.

      (2) We agree with your insightful mention that the mechanism underlying increased activation by HSV-ΔICP34.5 is worthy to be further explored in the future study. In this study, we found that ICP34.5 play an antagonistic role with the reactivation of HIV latency by HSV-1 mainly through the modulation of host NF-κB and HSF1 pathways, while HSV-1 (especially HSV-ΔICP34.5) might reactivate HIV latency through NF-κB, HSF1, and other yet-to-be-determined mechanisms. Thus, ICP34.5 overexpression can only a partial effect on the reduction of the HIV latency reactivation by HSV-1. We have mentioned this issue in the revised “Discussion section”. “Intriguingly, these findings collectively indicated that ICP34.5 might play an antagonistic role in the reactivation of HIV by HSV-1, and thus our modified HSV-DICP34.5 constructs can effectively reactivate HIV/SIV latency through the release of imprisonment from ICP34.5. However, ICP34.5 overexpression had only a partial effect on the reduction of the HIV latency reactivation, indicating that HSV-DICP34.5-based constructs can also reactivate HIV latency through other yet-to-be-determined mechanisms.” (Lines 334 to 340).

      (2) How specific is the effect for HIV reactivation? An RNA seq analysis is required to show the effect on cellular genes.

      Thank you for your questions and suggestions.

      (1) In our study, we found both adenovirus and vaccinia virus cannot reactivate HIV latency (Figure S3). In addition, the deletion of ICP0 gene from HSV-1 diminished the reactivation effect of HIV latency by HSV-1 (Figure S4). Thus, these data suggested the reactivation of HIV latency by HSV-1 might be virus-specific. Of course, this might be further investigated in future studies. We have added the corresponding description in the revised manuscript.

      (2) To explore the mechanism of reactivating viral latency by HSV-DICP34.5-based constructs, we performed RNA-seq analysis (Figure S5). Results showed that there were numerous differentially expressed genes (DEGs) in response to HSV-ΔICP34.5 infection. Among them, 2288 genes were upregulated, and 611 genes were downregulated. GO analysis showed the enrichment of these DEGs in cellular cycle, cellular development, and cellular proliferation, and KEGG enrichment analysis indicated the enrichment in pathways such as cellular cycle and cytokine-cytokine receptor interaction. We have added the corresponding description accordingly in the revised manuscript.

      (3) A comparison in primates has to be given for constructs with or without ICP34.5 to validate cell culture data (what is an empty vector?)

      Thank you for your reminder. In the revised manuscript, we performed the following cell experiment to address this issue. Primary CD4+ T cells from people living with HIV (PLWH) were isolated, and then infected with HSV or HSV-∆ICP34.5 constructs. As expected, we confirmed the enhanced reactivation of HIV latency by HSV-∆ICP34.5 (Figure S2). Thank you.

      (4) Legends should be improved in writing and content.

      Thank you for your kind mention. In the revised version, we have improved both the manuscript content and the legends of all Figures have been carefully revised in writing and content. Thank you.

      (5) The primate groups should be enlarged before any reliable conclusions can be made. Inflammatory/tox data should be provided.

      Thank you for your question.

      (1) As mentioned above, we agree with you that this is a pilot study with limited numbers of rhesus macaques. Although the number of macaques was relatively limited, these nine macaques were distributed evenly based on the background level of age, sex, weight, CD4 count, and viral load (VL) (Table S2). All SIV-infected macaques used in this study had a long history of SIV infection and had several courses of ART therapy, which mimics treatment of chronic HIV-1 infection in humans. These macaques were infected with SIVmac239 for more than 5 years, and highly pathogenic SIV-infected macaques have been well-validated as a stringent model to recapitulate HIV-1 pathogenesis and persistence during ART therapy in humans. Indeed, in our Chinese rhesus model, ART treatment effectively suppressed SIV infection to undetectable levels in plasma, and upon ART discontinuation, virus rapidly rebounded, which is very similar with that in ART-treated HIV patients. We think the results of this pilot study were very promising for further studies which will be expanded the scale of animals and then to preclinical and clinical study in our next projects. Thank you for your understanding.

      (2) As well known, ICP34.5 is a neurotoxicity factor that can antagonize host immune responses, and previous studies have shown that the safety of recombinant HSV-based vector can be improved by deleting ICP34.5. In this study, we also found that HSV-DICP34.5 exhibited lower virulence and replication ability than its parental strain (HSV-GFP) (Figure 1D, Figure S1). In addition, HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV-GFP stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5I) and body weight (Figure S9) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-sPD1-SIVgag/SIVenv group (Figure S10). Thus, these data suggest the safety of HSV-DICP34.5 in PLWH might be tolerable. We have added the corresponding description in the revised manuscript.

      (6) Discuss the potential of inflammatory HSV vaccines to be used in PLWH without clinical symptoms.

      Thank you for your mention. As discussed above, we found that HSV-DICP34.5 exhibited lower virulence and replication ability than its parental strain (Figure 1D, Figure S1), and we also found that HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV-GFP stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5I) and body weight (Figure S9) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-sPD1-SIVgag/SIVenv group (Figure S10). Thus, these data suggest the safety of HSV-DICP34.5 in PLWH might be tolerable. We have added the corresponding description in the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      I think the authors have done due diligence to the experimental system, and collected evidence to show the feasibility of delaying virus rebound in macaques. However, I would encourage the authors to perform experiments that can back up the claim that delayed virus rebound is due to neutralization effects, or perhaps due to a reduction in viral reservoir. I believe insights into this process will add rigor, and push the relevance of the study to the next level.

      Thank you for your nice comment and valuable suggestion. We have now provided more data about this issue. We found significant suppression of total SIV DNA and integrated SIV DNA provirus in the ART+HSV-sPD1-SIVgag/SIVenv group. However, the copies of the SIV DNA provirus were significantly improved in the ART+HSV-empty group and ART+saline group (Figure 5F-G). We also discussed that incorporating the induction of broadly neutralizing antibodies into our future optimizing approaches may lead to better therapeutic outcomes in the revised Discussion section. We have added the corresponding description in the revised manuscript. Thank you.

      Altogether, all of the above comments and suggestions are very helpful in improving our manuscript. We have taken these comments into account seriously and try our best to address these questions point-by-point. After making extensive revisions, we now submit this revised manuscript for your re-consideration. Thank you again for all of your comments and suggestions.

    1. amorphous. What makes a work “artistic”? How do we define “superior” or “lasting”? Let’s break down some of the defining qualities of literature in a bit more detail, starting with the word “artistic.” Consider the following works of art. "Wanderer Above the Sea of Fog" by Caspar David Friedrich (1818) is in the public domain "stick figure self portrait" by marco Links to an external site. on flickr (2019) is licensed CC BY-NC-SA 2.0 Which of these images do you feel is higher quality or more “artistic”? Which is lower-quality or less artistic? Why? So how does this relate to our attempts to define literature? Literature is art, but with words. While the artist uses different colors, paintbrushes, mediums, canvases, and techniques, the writer uses different genres and literary techniques called literary devices Links to an external site.. Just like different types of paint, paintbrushes, and artistic tools, there are literally hundreds of literary devices, but some of the most common are metaphor, simile, personification, and imagery. Genre is the type or style of literature. Each genre has its own conventions. Literary genres include creative nonfiction, fiction, drama, and poetry. Works that are literary tend to masterfully use genre conventions and literary devices to create a world in the mind of the reader. Works that are less literary tend to be for practical and/or entertainment purposes, and the writer dedicates less focused energy towards artfully employing literary devices. However, just because a work is not as literary as another does not mean it cannot be enjoyed. Just like a stick figure or cartoon character might be perfectly fine if intended for a particular audience or purpose, readers can still enjoy People Magazine even though it is not of the same literary quality as Hamlet. So, to use an example from earlier: Hamlet by William Shakespeare People Magazine Has lasted hundreds of years Is written by a master of his craft Covers deep, meaningful concepts like love, loss, war & political corruption Uses many literary devices such as metaphors Is often forgotten by the next issue Is written by a pop culture writer Covers shallow issues like what plastic surgeries a starlet has had Usually does not use literary devices in a masterful way, but merely to capture the attention of an audience While some literature falls into clear designations of literature or not literature, most works are open to debate. Given the sometimes difficult task of determining whether a work falls into one camp or the other, it may be more helpful to think of Literature less as a dichotomy than a spectrum, with popular magazines on one end and works like Hamlet and Beloved on the other, and most written works falling somewhere between the two extremes. The Literary Spectrum This spectrum can be a helpful way to think about literature because it provides a more open-ended way to discuss writing as art than simply labeling works as literary or not. After viewing the above chart, why do you think popular magazines and a Calculus textbook are considered "less literary"? In terms of popular magazines, they do not fit the definition of literature as "lasting" in the sense that they usually fade from relevancy quickly after publication. Additionally, the authors of such magazines are striving for quick entertainment rather than leaving a meaningful impression on the reader. They tend not to use literary devices, such as metaphor, in a masterful way. On the other end, Shakespeare's Hamlet definitely fits the definition of "lasting," in that it has survived hundreds of years. It is full of literary devices used for rhetorical effect and, one would argue, it touches upon deep themes such as death, the afterlife, murder, vengeance, and love, rather than trifling issues such as a starlet's most recent plastic surgery. Certainly, works of literature are up for debate: that is the quintessential question literary scholars might ask. What makes certain literary works survive the test of time? What makes a story, poem, or drama "good"? While literary scholars are less interested in proving a certain work is "good" or not -- and more focused on analyzing the ways to illuminate a given work -- it can be helpful for you to consider what kinds of literature you like and why you like it. What about the way it was written causes you to feel the way you do about it? Who Decides What is Literature? Now that we have at least somewhat clarified the definition of literature, who decides what works are or are not literature? Historically speaking, kings, queens, publishers, literary critics, professors, colleges, and readers (like you!) have decided which works survive and which works do not. Aristotle was one of the first writers to attempt to decide what works fall into the category of literature, and what works do not. While Aristotle was most famous for his contributions to science and philosophy, he is also considered one of the first literary critics. A literary critic is a person who studies and analyzes literature. A literary critic produces scholarship called literary criticism. An example of this would be Aristotle’s Poetics, in which he identifies the defining qualities of a “good” Tragedy. Aristotle’s analysis of Tragedy was so influential that it is still used today, over two thousand years later! When a work is officially decided to constitute literature, it enters something called the Canon. Not to be confused with the large metal tube that shoots bombs popular in the 16th through the 19th centuries (cannon), the Literary Canon is a collection of works that are considered by the powers that be to constitute literature. A work that falls into this designation is called canonical. So, to use an example from Aristotle’s Poetics, Aristotle defined Sophocles’ Oedipus Trilogy as the pinnacle of the Tragic Genre. From there, in part due to Aristotle's influence, Greek society valued Oedipus so much that they kept discussing, reading, referencing, and teaching it. Thus, it became a kind of shining example of the Tragic Canon, one which has lasted thousands of years and continues to be read and lauded to this day. Other tragedies, fairly or not, are often judged on their quality in comparison to Sophocles' works. It seems crazy to think but someone who died thousands of years ago still influences what we consider literature today! Memes and Video Games: Today's Literature? All this talk of thousands-of-years-old texts might seem out of touch. A lot of people think "old and boring" and literature are synonymous. Students are often surprised to hear that comic books and video games can, arguably, be considered literature, too. There are plenty of arguments to be made that comic books, such as Maus by Art Spiegalman (1991) or Fun Home by Alison Bechdel (2006) are literature. Cutting edge literary scholars argue video games like Kentucky Route Zero by Cardboard Computer (2015) can be considered literary. There is also literature that is published in tweets, like Jennifer Egan's "Black Box"  Links to an external site.(2012). Some might even consider memes literature! Generative question: do you think memes can be literary? A meme is an image or video containing cultural values or ideas, often represented through allusion (implied reference to another work, without naming that work or its author). Memes can spread rapidly spreads through social media. Why? Because the best ones are #relatable; that is, they speak to a common human experience. Usually memes take the form of text superimposed on an image. For example, the meme above conveys the dramatic reaction students sometimes give when I assign an essay. This is done primarily through a literary device called hyperbole, or exaggeration for rhetorical effect. It conveys its message comically through certain conventions that come along with the meme genre, such as the syntactic structure "me, a [insert noun]" and asterisks, which convey action. Just like in the Shakespearean drama, the colon indicates what each character (me and the students, in this case) is saying or doing. My chihuahuas' face looks silly and very dramatic. Through this use of image, text, format, and convention, the meaning I intended to convey was that I was making fun of my students for being over-dramatic about what to me seems like a fairly simple assignment. While some might dismiss memes as shallow, when you start to unravel the layers of meaning, they can actually be very complex and even, dare I say, literary! Think about a recent meme you have seen, or your favorite meme of all time. Imagine explaining this meme to someone who has no idea what it means. What is the message or idea behind the meme? What cultural reference points does it use to convey this message? In what ways might this meme be considered literature? How might this compare to a short poem, like a haiku? Not Literature Let's say you come to the conclusion that a meme, a gossip magazine, or the Twilight Series is not literary. Does that mean you have to feel guilty and give up reading it forever? Or that it is not "good"? No! Just because a work is not literary does not mean it is "bad," that it does not have value, or that one cannot enjoy it. Indeed, there are plenty of examples of written works that are on the less literary side of the spectrum but are still fun and enriching to read. Joe Dirt is not on the same artistic level of cinema as Schindler's List, but my husband still loves watching it. Nothing Taylor Swift has produced is as deep as Tupac Shakur's "Changes" Links to an external site. (1992) or Mitski's "Last Words of a Shooting Star" Links to an external site. (2014), but listening to Taylor Swift is my guilty pleasure. This is all to say that whether a text is literary or not is not as important as the methods of analyzing texts. In fact, texts which were excluded from literature are often argued into the literary canon through such analysis. Part of what makes analyzing literature so fun is that it means the definition of literature is always up for debate! This is especially important given the history of the canon. The Problem with the Canon In an ideal world, literature would be celebrated purely based on its artistic merit. Well-written works would last, poorly-written works would wither from public memory. However, that is not always the case. Works often achieve public prominence or survive based on qualities unrelated to skill or aesthetics, such as an author's fame, wealth, connections, or acceptance by the dominant culture. William Wordsworth, for example, was named Poet Laureate of England and has been taught as one of the #Big6 major Romantic-era authors ever since. Indeed, he is accepted as part of the literary canon. One would be hard-pressed to find a Literature anthology that does not feature William Wordsworth. However, how many people have read or heard of Dorothy Wordsworth, William Wordsworth's sister, who arguably depicted Romantic themes with equal skill and beauty? Or James Hogg, a Scottish contemporary of Wordsworth who was a lower-class shepherd? Similarly, while most readers have encountered F. Scott Fitzgerald or Edgar Allen Poe in their high school literature classes, how many have read Frederick Douglass? In short, all artistic skill (arguably) considered equal, why do some authors predominantly feature in the Canon while others do not? Let’s perform an experimental activity. On a scratch piece of paper, write down as many works of literature that you feel constitute “Big L Literature.” Perhaps they are works you read in high school, works which have been made into films, or works you have been taught or told are literary masterworks. Don’t turn the page until you have written them down. Try to think of at least 10, but a larger sample size is better. Once you are finished, continue to the next paragraph. Alright, now look at your list. If you know the author of the literary texts you named, write their name next to the work. If you do not know the author, Google the information and write it down. Continue doing this until you have named the author of each work. Once you are finished, read on to the next paragraph. Now, as uncomfortable as it seems, label the gender/race/age/presumed sexual orientation of the authors you listed. After you have categorized them to the best of your ability, consider the following questions: What percentage of the authors are male? What percentage of the authors are white? What percentage of the authors are old/dead? What patterns do you notice? Why do you think this is? Answer As a cultural relic, similar to art, many scholars suggest literature is a reflection of the society which produces it. This includes positive aspects of society (championing values such as love, justice, and good triumphing over evil), but it can also reflect negative aspects of society (such as discrimination, racism, sexism, homophobia, historical lack of opportunity for marginalized authors). For example, enslaved Africans were often prevented from learning to read and write as a form of control. When Phillis Wheatley published her book of poetry, Poems on Various Subjects, Religious and Moral Links to an external site. (1773) she had to defend the fact that she wrote it, as racist views that slaves were incapable of writing poetry were popularly held. Later, Frederick Douglass wrote about how his masters banned him from reading and writing, as the slaveowners realized "education and slavery were incompatible with each other" (Douglass). He later championed his learning to read and write as the means which conveyed him to freedom. However, even when trying to publish The Narrative of the Life of Frederick Douglass, his publishers were forced to prove that it was, in fact, a slave who wrote the story and not a white man who wrote it for him. Slave owners actively attempted to keep this book from circulation as it threatened the institution of slavery upon which they depended. Indeed, to this day, Douglass' book continues to be banned in some prisons (Darby, Gilroy). How could black writers enter the canon en masse if they were not allowed to read or write? Or if they were forced to spend all of their waking hours working? And if those who had the means to read and write had to jump through absurd hoops just to have their works published? And if even those texts which were published were banned? Similarly, throughout much of Western history, women have been discouraged from pursuing reading and writing, as it distracted from society's expectations for women to focus on motherly and household duties. Until the 1700s, women were not allowed to go to college. Even then, very few went: only the extremely wealthy. It was not until the 19th century that women truly began attending college. Virginia Woolf wrote in A Room of One's Own that if there are fewer works of literature written by women, it is only because society, historically, has not given women the time, education, funding, or space to do so. In this extended essay, she describes an imaginary sister of William Shakespeare who could have been just as great of a writer had she the same opportunities as her brother. I told you in the course of this paper that Shakespeare had a sister; but do not look for her in Sir Sidney Lee's life of the poet. She died young—alas, she never wrote a word. She lies buried where the omnibuses now stop, opposite the Elephant and Castle. Now my belief is that this poet who never wrote a word and was buried at the cross-roads still lives. She lives in you and in me, and in many other women who are not here tonight, for they are washing up the dishes and putting the children to bed. But she lives; for great poets do not die; they are continuing presences; they need only the opportunity to walk among us in the flesh. This opportunity, as I think, it is now coming within your power to give her. Woolf argues that in our time those who have been excluded from literature can now join the canon by adding their voices. The inequity of representation in literature -- which has arguably improved, but in many ways persists today -- can be remedied if more people from a wide array of backgrounds and walks of life are empowered to study and create Literature. That is one reason why the current study of literature is so exciting. As a student and budding literary scholar, you have the power to influence culture through your reading and analysis of literature! Links to an external site. Works Cited Bacon, Katie. "An African Voice."  Links to an external site.The Atlantic, 2000. "Battle of the Authors: Are The Most Popular Rated Fiction Books Written by Men or Women?"  Links to an external site.Wordery, 1 Mar. 2019. Darby, Luke. "Illinois Prison Bans Frederick Douglass's Memoir and Other "Racial" Books." Links to an external site. GQ, 20 August 2019. Douglass, Frederick. The Narrative of the Life of Frederick Douglass. 1845. Friedrich, Caspar David. "Wanderer Above the Sea of Fog." Hamburger Kunsthalle Museum, 1818. Gilroy, Paul. "Banned Books of Guantánamo: 'An American Slave' by Frederick Douglass." Links to an external site. Vice, 14 Nov. 2014. "literature, n.; 3b & 5" OED Online, Oxford University Press, September 2019, www.oed.com/view/Entry/109080. Accessed 6 September 2019. Rollison, David. "Big L vs Little L Literature." Survey of World Literature I. College of Marin, 2008. Lecture. Wheatley, Phillis. Poems on Various Subjects, Religious and Moral Links to an external site.. 1773. Woolf, Virginia. A Room of One's Own. 1929. Contributed by Heather Ringo & Athena Kashyap City College of San Francisco Links to an external site. Sourced from ASCCC Open Educational Resources Initiative Links to an external site.

      amorphous= without form Big L literature has lasting artistic merit Little L literature is anything published

    1. That overconfidence is bad for learning because if we think we already know something, we might study less.

      the hunger for knowledge over time will disappear as some people may not even have a need for learning when they can just google search something and find out without remembering it.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors constructed a novel HSV-based therapeutic vaccine to cure SIV in a primate model. The novel HSV vector is deleted for ICP34.5. Evidence is given that this protein blocks HIV reactivation by interference with the NFkappaB pathway. The deleted construct supposedly would reactivate SIV from latency. The SIV genes carried by the vector ought to elicit a strong immune response. Together the HSV vector would elicit a shock and kill effect. This is tested in a primate model.

      Strengths and weaknesses:

      (1) Deleting ICP34.5 from the HSV construct has a very strong effect on HIV reactivation. The mechanism underlying increased activation by deleting ICP34.5 is only partially explored. Overexpression of ICP34.5 has a much smaller effect (reduction in reactivation) than deletion of ICP34.5 (strong activation); this is acknowledged by the authors that no full mechanistic explanation can be given at this moment.

      Thank you for your comments. We agree with you that the mechanism underlying increased reactivation by deleting ICP34.5 is only partially explored. As you pointed out, the deletion of ICP34.5 leads to a significant reactivation, while the overexpression of ICP34.5 has a relatively weak inhibitory effect on reactivation. This difference prompts us to further contemplate the role of HSV-1 in regulating HIV latency and reactivation. Our data (Figure S4), along with previous literature (Mosca et al., 1987, Nabel et al., 1988), have indicated that the ICP0 protein might play a crucial role in the reactivation of HIV latency. However, we found for the first time that ICP34.5 can play an antagonistic role with this reactivation. This is a very interesting topic for understanding the complicated interactions between host cells and different viruses. We will investigate the deeper insights in future studies, and we have mentioned this limitation in the revised Discussion Section. Thank you!

      (2) No toxicity data are given for deleting ICP34.5. How specific is the effect for HIV reactivation? A RNA seq analysis is required to show the effect on cellular genes.

      A RNA seq analysis was done in the revised manuscript comparing the effect of HSV-1 and deleted vector in J-LAT cells (Fig S5). More than 2000 genes are upregulated after transduction with the modified vector in comparison with the WT vector. Hence, the specificity of upregulation of SIV genes is questioned. Authors do NOT comment on these findings. In my view it questions the utility of this approach.

      Thank you for your mentions.

      (1) As for the toxicity of HSV-ΔICP34.5, it is well known that ICP34.5 is a neurotoxicity factor that can antagonize host immune responses, and thus deleting ICP34.5 is beneficial to improve the safety of HSV-based constructs. As expected, we have demonstrated experimentally that HSV-DICP34.5 exhibited lower virulence and replication ability than wild-type HSV-1 (Figure S1). Importantly, we also observed a significant decrease in the expression of inflammatory factors in PWLH when compared to wild-type HSV-1 (Figure 1I-K). These data suggested that the safety of HSV-DICP34.5 should be more tolerable than wild-type HSV vector.

      (2) The RNASeq analysis is aimed to explore the HSV-ΔICP34.5-induced signaling pathways, but it is not suitable to use this data for assessing the toxicity of HSV-ΔICP34.5 constructs. As for the RNASeq data, we think it is reasonable to observe many upregulated genes (which are involved in a variety of signaling pathways), since HSV-DICP34.5 constructs reactivated HIV latency more effectively than wild-type HSV by modulating the IKKα/β-NF-kB pathway and PP1-HSF1 pathway.

      (3) To further validate whether HSV-ΔICP34.5 can specifically activate the HIV latent reservoir, we conducted additional experiments using vaccinia virus and adenovirus as controls, and results showed that both vaccinia virus and adenovirus cannot effectively reactivate HIV latency (Figure S3). Moreover, the deletion of ICP0 gene from HSV-1 diminished the reactivation effect of HIV latency by HSV-1, and overexpressing ICP0 greatly reactivate the latent HIV (Figure S4, Figure S5), implying that this reactivation should be virus-specific and ICP0 plays an important factor on reversing HIV latency. Interestingly, we herein found that ICP34.5 can act as an antagonistic factor for this reactivation of HIV latency by HSV-1. Thus, after the deletion of ICP34.5, the ability of HSV to reverse HIV latency was significantly enhanced. Our research group will investigate the underlying mechanism in future studies. Thank you for your insightful mention.

      (3) The primate groups are too small and the results to variable to make averages. In Fig 5, the group with ART and saline has two slow rebounders. It is not correct to average those with the single quick rebounder. Here the interpretation is NOT supported by the data.

      Although authors provided some promising SIV DNA data, no additional animals were added. Groups of 3 animals are too small to make any conclusion, especially since the huge variability in response. The average numbers out of 3 are still presented in the paper, which is not proper science.

      No data are given of the effect of the deletion in primates. Now the deleted construct is compared with an empty vector containing no SIV genes. Authors provide new data in Fig S2 on the comparison of WT and modified vector in cells from PLWH, but data are not that convincing. A significant difference in reactivation is seen for LTR in only 2/4 donors and in Gag in 3/4 donors. (Additional question what is meaning of LTR mRNA, do authors relate to genomic RNA??)

      Thank you for your serious review and kind reminder.

      (1) We agree with you that it is not appropriated to use averages for this pilot study with limited numbers of macaques. We are currently unable to conduct another experiment with a larger number of macaques, but we think the results of this pilot study were very promising for further studies. Now, following your kind suggestions, we have removed the averages and now presented the data for each monkey individually in the revised manuscript. We have also modified the corresponding description accordingly (Line 254 to 262). Thank you for your understanding.

      (2) Regarding your comment about the lack of data on the deletion of ICP34.5 from HSV-1, we are sorry for previously unclear description. In fact, the empty vector used in our animal experiments not only does not contain SIV antigens but also has the ICP34.5 deletion. We have revised the corresponding description accordingly (For example, we use HSV-DICP34.5DICP47-empty, HSV-DICP34.5DICP47-sPD1-SIVgag/SIVenv instead of HSV-empty, HSV-sPD1-SIVgag/SIVenv). We hope this revision will address your question.

      (3) As for the reactivation effects observed in PLWH samples, the data may be not perfect, but we think this result (a significant difference in reactivation is seen for LTR in 2/4 donors and for Gag in 3/4 donors, and the purpose of detecting LTR RNA is to evaluate the level of virus replication) is promising to support our conclusion (The enhanced reactivation effect in primary CD4+ T cells by HSV-∆ICP34.5 than wild-type HSV). Of course, we recognize the need for more samples to gain a comprehensive understanding of reactivation effect in different individuals in future study. In addition, we corrected the description of LTR RNA (Lines 99-106 and 115-116). Thank you for the reminder!

      Discussion

      HSV vectors are mainly used in cancer treatment partially due to induced inflammation. Whether these are suitable to cure PLWH without major symptoms is a bit questionable to me and should at least be argued for.

      The RNA seq data add on to this worry and should at least be discussed.

      Thank you for your mention. As mentioned above, the RNASeq analysis is aimed to explore the HSV-ΔICP34.5-induced signaling pathways, but it is not suitable to use this data for assessing the toxicity of HSV-ΔICP34.5 constructs. Actually, ICP34.5 is a neurotoxicity factor that can antagonize innate immune responses, and thus ICP34.5 deletion is beneficial to improve the safety of HSV-based constructs. As expected, our data have demonstrated experimentally that HSV-DICP34.5 exhibited lower virulence and replication ability than wild-type HSV-1 (Figure S1). Importantly, HSV-DICP34.5 induced a lower level of inflammatory cytokines (including IL-6, IL-1β, and TNF-α) in primary CD4+ T cells from PLWH compared to HSV stimulation, likely due to its lower virulence and replication ability (Figure 1I-K). In addition, the CD4+ /CD8+ T cell ratio (Figure 5H) and body weight (Figure S10) after treatment were effectively ameliorated in the SIV-infected macaques of the ART+HSV-DICP34.5DICP47-sPD1-SIVgag/SIVenv group. Our data also demonstrated that there was no significant effect on the cell composition of peripheral blood in the SIV-infected macaques of ART+HSV-DICP34.5DICP47-sPD1-SIVgag/SIVenv group (Figure S11). These data suggested that the safety of HSV-DICP34.5 should be more tolerable than wild-type HSV vector. We have added a more comprehensive description in the revised Discussion (Lines 328-334). Thank you again for all of your kind comments and suggestions.

      Reviewer #2 (Public review):

      Summary:

      In this article Wen et. al., describe the development of a 'proof-of-concept' bi-functional vector based out of HSV-deltaICP-34.5's ability to purge latent HIV-1 and SIV genomes from cells. They show that co-infection of latent J-lat T-cell lines with a HSV-deltaICP-34.5 vector can reactivate HIV-1 from a latent state. Over- or stable expression of ICP 34.5 ORF in these cells can arrest latent HIV-1 genomes from transcription, even in the presence of latency reversal agents. ICP34.5 can co-IP with- and de-phosphorylate IKKa/b to block its interaction with NF-k/B transcription factor. Additionally, ICP34.5 can interact with HSF1 which was identified by mass-spec. Thus, the authors propose that the latency reversal effect of HSV-deltaICP-34.5 in co-infected JLat cells is due to modulatory effects on the IKKa/b-NF-kB and PP1-HSF-1 pathway.

      Next the authors cleverly construct a bifunctional HSV based vector with deleted ICP34.5 and 47 ORFs to purge latency and avoid immunological refluxes, and additionally expand the application of this construct as a vaccine by introducing SIV genes. They use this 'vaccine' in mouse models and show the expected SIV-immune responses. Experiments in rhesus macaques (RM), further elicit potential for their approach to reactivate SIV genomes and at the same time block their replication by antibodies. What was interesting in the SIV experiments is that the dual-functional vector vaccine containing sPD1- and SIV Gag/Env ORFs effectively delayed SIV rebound in RMs and in some cases almost neutralized viral DNA copy detection in serum. Very promising indeed, however there are some questions I wish the authors explored to answer, detailed below.

      Overall, this is an elegant and timely work demonstrating the feasibility of reducing virus rebound in animals, and potentially expand to clinical studies. The work was well written, and sections were clearly discussed.

      Strengths:

      The work is well designed, rationale explained and written very clearly for lay readers.

      Claims are adequately supported by evidence and well designed experiments including controls.

      We appreciate your positive comment for our work.

      Weaknesses:

      (1) It looks like ICP0 is also involved in latency reversal effects. More follow-up work will be required to test if this is in fact true.

      Both our data (Figure S4, Figure S5) and previous literature (Nabel et al., 1988, Mosca et al., 1987) have reported that HSV ICP0 may play a role in reversing HIV latency. However, the exact mechanisms behind this effect have not yet been fully elucidated. Of note, we herein reported for the first time that ICP34.5 can act as an antagonistic factor for this reactivation of HIV latency by HSV-1. Thus, after the deletion of ICP34.5, the ability of HSV to reverse HIV latency was significantly enhanced. Our research group will investigate the underlying mechanism in future studies. Thank you for your insightful mention.

      (2) It is difficult to estimate the depletion of the latent viral reservoir. The authors have tried to address this issue. A more convincing argument to this reviewer will be data to demonstrate that after the bi-functional vaccine, the animals show overall reduction in the number of circulating latent cells. The feasibility to obtain such a result is not clearly demonstrated.

      Thank you for your comment. As you mentioned, we have indeed measured both total DNA and integrated DNA (iDNA) in blood cells (see Figure 5E-F), which can provide support for the reduction of the latent viral reservoir. Thank you for your kind reminder.

      (3) The authors state that the reduced virus rebound detected following bi-functional vaccine delivery is due to latent genomes becoming activated and steady-state neutralization of these viruses by antibody response. This needs to be demonstrated. Perhaps cell-culture experiments from specimen taken from animals might help address this issue. In lab cultures one could create environments without antibody responses, under these conditions one would expect higher level of viral loads being released in response to the vaccine in question.

      Thank you for your valuable suggestion. We believe that the reduced virus rebound observed may be influenced by immune responses from T cells and antibodies induced by both ART and the vaccine. We appreciate your insight and agree that future studies should focus on investigating the activation effects of the vaccine under controlled conditions that simulate the absence of immune responses in primary animal cells. This will help us better understand the mechanisms involved and address your concerns more comprehensively.

      Reviewer #2 (Recommendations for the authors):

      The Authors have sufficiently addressed my comments. Below are a few minor changes that can help with clarity.

      Lines 126-127: This sentence should be changed. Perhaps, "these data suggests that .... Safety of... in PLWH might be tolerable, at least in vitro."

      Thanks for your suggestion. We have revised it accordingly. (Line 130).

      Lines 128-132: Would this not mean that reactivation is due to ICP0 gene? Have the authors tried to express ICP0-gene into J-Lat cells and see if that is the reason for reactivation? This seems somewhat incomplete. At the end of 132, please add ", in the presence of ICP0". Also a sentence describing this effect is warranted.

      Thank you for your insightful suggestion. Yes, both our data and previous literature supported that the ICP0 gene can play a significant role in the reactivation of HIV latency (Figure S4, Figure S5). Of note, we herein reported for the first time that ICP34.5 can act as an antagonistic factor for this reactivation of HIV latency by HSV-1. Thus, after the deletion of ICP34.5, the ability of HSV to reverse HIV latency was significantly enhanced. We have described this effect in the revised version accordingly. Additionally, we have added the phrase “in the presence of ICP0” to the results section (Lines 137) to clarify this point.

      MOSCA, J. D., BEDNARIK, D. P., RAJ, N. B., ROSEN, C. A., SODROSKI, J. G., HASELTINE, W. A., HAYWARD, G. S. & PITHA, P. M. 1987. Activation of human immunodeficiency virus by herpesvirus infection: identification of a region within the long terminal repeat that responds to a trans-acting factor encoded by herpes simplex virus 1. Proc Natl Acad Sci U S A 84:  7408.DOI: https://doi.org/10.1073/pnas.84.21.7408, PMID: 2823260

      NABEL, G. J., RICE, S. A., KNIPE, D. M. & BALTIMORE, D. 1988. Alternative mechanisms for activation of human immunodeficiency virus enhancer in T cells. Science 239:  1299.DOI: https://doi.org/10.1126/science.2830675, PMID: 2830675

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This paper introduces a new approach to modeling human behavioral responses using image-computable models. They create a model (VAM) that is a combination of a standard CNN coupled with a standard evidence accumulation model (EAM). The combined model is then trained directly on image-level data using human behavioral responses. This approach is original and can have wide applicability. However, many of the specific findings reported are less compelling.

      Strengths:

      (1) The manuscript presents an original approach to fitting an image-computable model to human behavioral data. This type of approach is sorely needed in the field.

      (2) The analyses are very technically sophisticated.

      (3) The behavioral data are large both in terms of sample size (N=75) and in terms of trials per subject.

      Weaknesses:

      Major

      (1) The manuscript appears to suggest that it is the first to combine CNNs with evidence accumulation models (EAMs). However, this was done in a 2022 preprint

      (https://www.biorxiv.org/content/10.1101/2022.08.23.505015v1) that introduced a network called RTNet. This preprint is cited here, but never really discussed. Further, the two unique features of the current approach discussed in lines 55-60 are both present to some extent in RTNet. Given the strong conceptual similarity in approach, it seems that a detailed discussion of similarities and differences (of which there are many) should feature in the Introduction.

      Thanks for pointing this out—we agree that the novel contributions of our model (the VAM) with respect to prior related models (including RTNet) should be clarified, and have revised the Introduction accordingly. We include the following clarifications in the Introduction:

      “The key feature of the VAM that distinguishes it from prior models is that the CNN and EAM parameters are jointly fitted to the RT, choice, and visual stimulus data from individual participants in a unified Bayesian framework. Thus, both the visual representations learned by the CNN and the EAM parameters are directly constrained by behavioral data. In contrast, prior models first optimize the CNN to perform the behavioral task, then separately fit a minimal set of high-level CNN parameters [RTNet, Rafiei et al., 2024] and/or the EAM parameters to behavioral data [Annis et al., 2021; Holmes et al., 2020; Trueblood et al., 2021]. As we will show, fitting the CNN with human data—rather than optimizing the model to perform a task—has significant consequences for the representations learned by the model.”

      E.g. in the case of RTNet, the variability of the Bayesian CNN weight distribution, the decision threshold, and the magnitude of the noise added to the images are adjusted to match the average human accuracy (separately for each task condition). RTNet is an interesting and useful model that we believe has complementary strengths to our own work.

      Since there are several other existing models in addition to the VAM and RTNet that use CNNs to generate RTs or RT proxies (by our count, at least six that we cite earlier in the Introduction), we felt it was inappropriate to preferentially include a detailed comparison of the VAM and RTNet beyond the passage quoted above.

      (2) In the approach here, a given stimulus is always processed in the same way through the core CNN to produce activations v_k. These v_k's are then corrupted by Gaussian noise to produce drift rates d_k, which can differ from trial to trial even for the same stimulus. In other words, the assumption built into VAM appears to be that the drift rate variability stems entirely from post-sensory (decisional) noise. In contrast, the typical interpretation of EAMs is that the variability in drift rates is sensory. This is also the assumption built into RTNet where the core CNN produces noisy evidence. Can the authors comment on the plausibility of VAM's assumption that the noise is post-sensory?

      In our view, the VAM is compatible with a model in which the drift rate variability for a given stimulus is due to sensory noise, since we do not specify the origin of the Gaussian noise added to the drift rates. As the reviewer notes, the CNN component of the VAM processes a given stimulus deterministically, yielding the mean drift rates. This does not preclude us from imagining an additional (unmodeled) sensory process that adds variability to the drift rates. The VAM simply represents this and other hypothetical sources of variability as additive Gaussian noise. We agree however that it is worthwhile to think about the origin of the drift rate variability, though it is not a focus of our work.

      (3) Figure 2 plots how well VAM explains different behavioral features. It would be very useful if the authors could also fit simple EAMs to the data to clarify which of these features are explainable by EAMs only and which are not.

      In our view, fitting simple EAMs to the data would not be especially informative and poses a number of challenges for the particular task we study (LIM) that are neatly avoided by using the VAM. In particular, as we show in Figure 2, the stimuli vary along several dimensions that all appear to influence behavior: horizontal position, vertical position, layout, target direction, and flanker direction. Since the VAM is stimulus-computable, fitting the VAM automatically discovers how all of these stimulus features influence behavior (via their effect on the drift rates outputted by the CNN). In contrast, fitting a simple EAM (e.g. the LBA model) necessitates choosing a particular parameterization that specifies the relationship between all of the stimulus features and the EAM model parameters. This raises a number of practical questions. For example, should we attempt to fit a separate EAM for each stimulus feature, or model all stimulus features simultaneously?

      Moreover, while we could in principle navigate these issues and fit simple EAMs to the data, we do not intend to claim that simple EAMs fail to explain the relationship between stimulus features and behavior as well as the VAM. Rather, the key strength of the VAM relative to simple EAMs is that it includes a detailed and biologically plausible model of human vision. The majority of the paper capitalizes on this strength by showing how behavioral effects of interest (namely congruency effects) can be explained in terms of the VAM’s visual representations.

      (4) VAM is tested in two different ways behaviorally. First, it is tested to what extent it captures individual differences (Figure 2B-E). Second, it is tested to what extent it captures average subject data (Figure 2F-J). It wasn't clear to me why for some metrics only individual differences are examined and for other metrics only average human data is examined. I think that it will be much more informative if separate figures examine average human data and individual difference data. I think that it's especially important to clarify whether VAM can capture individual differences for the quantities plotted in Figures 2F-J.

      We would like to clarify that Fig. 2J in fact already shows how well the VAM captures individual differences for the average subject data shown in Fig. 2H (stimulus layout) and Fig. 2I (stimulus position). For a given participant and stimulus feature, we calculated the Pearson's r between model/participant mean RTs across each stimulus feature value. Fig. 2J shows the distribution of these Pearson’s r values across all participants for stimulus layout and horizontal/vertical position.

      Fig. 2G also already shows how well the VAM captures individual differences in behavior. Specifically, this panel shows individual differences in mean RT attributable to differences in age. For Fig. 2F, which shows how the model drift rates differ on congruent vs. incongruent trials, there is no sensible way to compare the models to the participants at any level of analysis (since the participants do not have drift rates). 

      (5) The authors look inside VAM and perform many exploratory analyses. I found many of these difficult to follow since there was little guidance about why each analysis was conducted. This also made it difficult to assess the likelihood that any given result is robust and replicable. More importantly, it was unclear which results are hypothesized to depend on the VAM architecture and training, and which results would be expected in performance-optimized CNNs. The authors train and examine performance-optimized CNNs later, but it would be useful to compare those results to the VAM results immediately when each VAM result is first introduced.

      Thanks for pointing this out—we apologize for any confusion caused by our presentation of the CNN analyses. We have added in additional motivating statements, methodological clarifications, and relevant references to our Results, particularly for Figure 3 in which we first introduce the analyses of the CNN representations/activity. In general, each analysis is prefaced by a guiding question or specific rationale, e.g. “How do the models' visual representations enable target selectivity for stimuli that vary along several irrelevant dimensions?” We also provide numerous references in which these analysis techniques have been used to address similar questions in CNNs or the primate visual cortex.

      We chose to maintain the current organization of our results in which the comparison between the VAM and the task-optimized models are presented in a separate figure. We felt that including analyses of both the VAM and task-optimized models in the initial analyses of the CNN representations would be overwhelming for many readers. As the reviewer acknowledges, some readers may already find these results challenging to follow. 

      (6) The authors don't examine how the task-optimized models would produce RTs. They say in lines 371-2 that they "could not examine the RT congruency effect since the task-optimized models do not generate RTs." CNNs alone don't generate RTs, but RTs can easily be generated from them using the same EAM add-on that is part of VAM. Given that the CNNs are already trained, I can't see a reason why the authors can't train EAMs on top of the already trained CNNs and generate RTs, so these can provide a better comparison to VAM.

      We appreciate this suggestion, but we judge the suggestion to “train EAMs on top of the already trained CNNs and generate RTs” to be a significant expansion of the scope of the paper with multiple possible roads forward. In particular, one must specify how the outputs of the task-optimized CNN (logits for each possible response) relate to drift rates, and there is no widely-accepted or standard way to do this. Previously proposed methods include transforming representation distances in the last layer to drift rates (https://doi.org/10.1037/xlm0000968), fitting additional subject-specific parameters that map the logits to drift rates

      (https://doi.org/10.1007/s42113-019-00042-1), or using the softmax-scored model outputs as drift rates directly (https://doi.org/10.1038/s41562-024-01914-8), though in the latter case the RTs are not on the same scale as human data. In our view, evaluating these different methods is beyond the scope of this paper. An advantage of the VAM is that one does not have to fit two separate models (a CNN and a EAM) to generate RTs.

      Nonetheless, we agree that it would be informative to examine something like RTs in the task-optimized models. Our revised Results section now includes an analysis of the confidence of the task-optimized models’ decisions, which we use a proxy for RTs:   

      “Since the task-optimized models do not generate RTs, it is not possible to directly measure RT congruency effects in these models without making additional assumptions about how the CNN's classification decisions relate to RTs. However, as a coarse proxy for RT, we can examine the confidence of the CNN's decisions, defined as the softmax-scored logit (probability) of the most probable direction in the final CNN layer. This choice of RT proxy is motivated by some prior studies that have combined CNNs with EAMs [Annis et al., 2021; Holmes et al., 2020; Trueblood et al., 2021]. These studies explicitly or implicitly derive a measure of decision confidence from the activity of the last CNN layer. The confidence measure is then mapped to the EAM drift rates, such that greater decision confidence generally corresponds to higher drift rates (and therefore shorter RTs).

      We calculated the average confidence of each task-optimized CNN separately for congruent vs. incongruent trials. On average, the task-optimized models showed higher confidence on congruent vs. incongruent trials (W = 21.0, p < 1e-3, Wilcoxon signed-rank test; Cohen's d = 0.99; n = 75 models). These analyses therefore provide some evidence that task-optimized CNNs have the capacity to exhibit congruency effects, though an explicit comparison of the magnitude of these effects with human data requires additional modeling assumptions (e.g., fitting a separate EAM).”

      (7) The Discussion felt very long and mostly a summary of the Results. I also couldn't shake the feeling that it had many just-so stories related to the variety of findings reported. I think that the section should be condensed and the authors should be clearer about which explanations are speculations and which are air-tight arguments based on the data.

      We have shortened the Discussion modestly and we have added in some clarifying language to help clarify which arguments are more speculative vs. directly supported by our data.

      Specifically, we added in the phrase “we speculate that…” for two suggestions in the Discussion (paragraphs 3 and 5), and we ensured that any other more speculative suggestions contain such clarifying language. We have also added in subheadings in the Discussion to help readers navigate this section. 

      (8) In one of the control analyses, the authors train different VAMs on each RT quantile. I don't understand how it can be claimed that this approach can serve as a model of an individual's sensory processing. Which of the 5 sets of weights (5 VAMs) captures a given subject's visual processing? Are the authors saying that the visual system of a given subject changes based on the expected RT for a stimulus? I feel like I'm missing something about how the authors think about these results.

      We agree that these particular analyses may cause confusion and have removed them from our revised manuscript.

      Reviewer #2 (Public Review):

      In an image-computable model of speeded decision-making, the authors introduce and fit a combined CCN-EAM (a 'VAM') to flanker-task-like data. They show that the VAM can fit mean RTs and accuracies as well as the congruency effect that is present in the data, and subsequently analyze the VAM in terms of where in the network congruency effects arise.

      Overall, combining DNNs and EAMs appears to be a promising avenue to seriously model the visual system in decision-making tasks compared to the current practice in EAMs. Some variants have been proposed or used before (e.g., doi.org/10.1016/j.neuroimage.2017.12.078 , doi.org/10.1007/s42113-019-00042-1), but always in the context of using task-trained models, rather than models trained on behavioral data. However, I was surprised to read that the authors developed their model in the context of a conflict task, rather than a simpler perceptual decision-making task. Conflict effects in human behavior are particularly complex, and thereby, the authors set a high goal for themselves in terms of the to-be-explained human behavior. Unfortunately, the proposed VAM does not appear to provide a great account of conflict effects that are considered fundamental features of human behavior, like the shape of response time distributions, and specifically, delta plots (doi.org/10.1037/0096-1523.20.4.731). The authors argue that it is beyond the scope of the presented paper to analyze delta plots, but as these are central to studies of human conflict behavior, models that aim to explain conflict behavior will need to be able to fit and explain delta plots.

      Theories on conflict often suggest that negative/positive-trending delta plots arise through the relative timing of response activation related to relevant and irrelevant information.

      Accumulation for relevant and irrelevant information would, as a result, either start at different points in time or the rates vary over time. The current VAM, as a feedforward neural network model, does not appear to be able to capture such effects, and perhaps fundamentally not so: accumulation for each choice option is forced to start at the same time, and rates are a static output of the CNN.

      The proposed solution of fitting five separate VAMs (one for each of five RT quantiles) is not satisfactory: it does not explain how delta plots result from the model, for the same reason that fitting five evidence accumulation models (one per RT quantile) does not explain how response time distributions arise. If, for example, one would want to make a prediction about someone's response time and choice based on a given stimulus, one would first have to decide which of the five VAMs to use, which is circular. But more importantly, this way of fitting multiple models does not explain the latent mechanism that underlies the shape of the delta plots.

      As such, the extensive analyses on the VAM layers and the resulting conclusions that conflict effects arise due to changing representations across layers (e.g., "the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations") - while inspiring, they remain hard to weigh, as they are contingent on the assumption that the VAM can capture human behavior in the conflict task, which it struggles with. That said, the promise of combining CNNs and EAMs is clearly there. A way forward could be to either adjust the proposed model so that it can explain delta plots, which would potentially require temporal dynamics and time-varying evidence accumulation rates, or perhaps to start simpler and combine CCNs-EAMs that are able to fit more standard perceptual decision-making tasks without conflict effects.

      We thank the reviewer for their thoughtful comments on our work. However, we note that the

      VAM does in fact capture the positive-trending RT delta plot observed in the participant data (Fig. S4A), though the intercepts for models/participants differ somewhat. On the other hand, the conditional accuracy functions (Fig. S4B) reveal a more pronounced difference between model and participant behavior. As the reviewer points out, capturing these effects is likely to require a model that can produce time-varying drift rates, whereas our model produces a fixed drift rate for a given stimulus. We also agree that fitting a separate VAM to each RT quantile is not a satisfactory means of addressing this limitation and have removed these analyses from our revised manuscript.

      However, while we agree that accurately capturing these dynamic effects is a laudable goal, it is in our view also worthwhile to consider explanations for the mean behavioral effect (i.e. the accuracy congruency effect), which can occur independently of any consideration of dynamics. One of our main findings is that across-model variability in accuracy congruency effects is better attributed to variation in representation geometry (target/flanker subspace alignment) vs.

      variation in the degree of flanker suppression. This finding does not require any consideration of dynamics to be valid at the level of explanation we pursue (across-user variability in congruency effects), but also does not preclude additional dynamic processes that could give rise to more specific error patterns. Our revised discussion now includes a section where we summarize and elaborate on these ideas:

      “It is not difficult to imagine how the orthogonalization mechanism described above, which explains variability in accuracy congruency effects across individuals, could act in concert with other dynamic processes that explain variability in congruency effects within individuals (e.g., as a function of RT). In general, any process that dynamically gates the influence of irrelevant sensory information on behavioral outputs could accomplish this, for example ramping inhibition of incorrect response activation [https://doi.org/10.3389/fnhum.2010.00222], a shrinking attention spotlight [https://doi.org/10.1016/j.cogpsych.2011.08.001], or dynamics in neural population-level geometry [https://doi.org/10.1038/nn.3643]. To pursue these ideas, future work may aim to incorporate dynamics into the visual component and decision component of the VAM with recurrent CNNs [https://doi.org/10.48550/arXiv.1807.00053, https://doi.org/10.48550/arXiv.2306.11582] and the task-DyVA model [https://doi.org/10.1038/s41562-022-01510-8], respectively.”

      Reviewer #3 (Public Review):

      Summary:

      In this article, the authors combine a well-established choice-response time (RT) model (the Linear Ballistic Accumulator) with a CNN model of visual processing to model image-based decisions (referred to as the Visual Accumulator Model - VAM). While this is not the first effort to combine these modeling frameworks, it uses this combination of approaches uniquely.

      Specifically, the authors attempt to better understand the structure of human information representations by fitting this model to behavioral (choice-RT) data from a classic flanker task. This objective is made possible by using a very large (by psychological modeling standards) industry data set to jointly fit both components of this VAM model to individual-level data. Using this approach, they illustrate (among other results) (1) how the interaction between target and flanker representations influence the presence and strength of congruency effects, (2) how the structure of representations changes (distributed versus more localized) with depth in the CNN model component, and (3) how different model training paradigms change the nature of information representations. This work contributes to the ML literature by demonstrating the value of training models with richer behavioral data. It also contributes to cognitive science by demonstrating how ML approaches can be integrated into cognitive modeling. Finally, it contributes to the literature on conflict modeling by illustrating how information representations may lead to some of the classic effects observed in this area of research.

      Strengths:

      (1) The data set used for this analysis is unique and is made publicly available as part of this article. Specifically, they have access to data for 75 participants with >25,000 trials per participant. This scale of data/individual is unusual and is the foundation on which this research rests.

      (2) This is the first time, to my knowledge, that a model combining a CNN with a choice-RT model has been jointly fit to choice-RT data at the level of individual people. This type of model combination has been used before but in a more restricted context. This joint fitting, and in particular, learning a CNN through the choice-RT modeling framework, allows the authors to probe the structure of human information representations learned directly from behavioral data.

      (3) The analysis approaches used in this article are state-of-the-art. The training of these models is straightforward given the data available. The interesting part of this article (opinion of course) is the way in which they probe what CNN has learned once trained. I find their analysis of how distractor and target information interfere with each other particularly compelling as well as their demonstration that training on behavioral data changes the structure of information representations when compared to training models on standard task-optimized data.

      Weaknesses:

      (1) Just as the data in this article is a major strength, it is also a weakness. This type of modeling would be difficult, if not impossible to do with standard laboratory data. I don't know what the data floor would be, but collecting tens of thousands of decisions for a single person is impractical in most contexts. Thus this type of work may live in the realm of industry. I do want to re-iterate that the data for this study was made publicly available though!

      We suspect (but have not systematically tested) that the VAMs can be fitted with substantially less data. We use data augmentation techniques (various randomized image transformations) during training to improve the generalization capabilities of the VAMs, and these methods are likely to be particularly important when training on smaller datasets. One could consider increasing the amount of image data augmentation when working with smaller datasets, or pursuing other forms of data augmentation like resampling from estimated RT distributions (see https://doi.org/10.1038/s41562-022-01510-8 for an example of this). In general, we don’t think that prospective users of our approach should be discouraged if they have only a few hundred trials per subject (or less) - it’s worth trying!

      (2) While this article uses choice-RT data it doesn't fully leverage the richness of the RT data itself. As the authors point out, this modeling framework, the LBA component in particular, does not account for some of the more nuanced but well-established RT effects in this data. This is not a big concern given the already nice contributions of this article and it leads to an opportunity for ongoing investigation.

      We agree that fully capturing the more nuanced behavioral effects you mention (e.g. RT delta plots and conditional accuracy functions) is a worthwhile goal for future research—see our response to Reviewer #2 for a more detailed discussion. ----------

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The phrase in the Abstract "convolutional neural network models of visual processing and traditional EAMs are jointly fitted" made me initially believe that the two models were fitted independently. You may want to re-word to clarify.

      We think that the phrase “jointly fitted” already makes it clear that both the CNN and EAM parameters are estimated simultaneously, in agreement with how this term is usually used. But we have nonetheless appended some additional clarifying language to that sentence (“in a unified Bayesian framework”).

      (2) Lines 27-28: EAMs "are the most successful and widely-used computational models of decision-making." This is only true for the specific type of decision-making examined here, namely joint modeling of choice and response times. Signal detection theory is arguably more widely-used when response times are not modeled.

      Thanks for pointing this out - we have revised the referenced sentence accordingly.

      (3) Could the authors clarify what is plotted in Figure 2F?

      Fig. 2F shows the drift rates for the target, flanker, and “other” (non-target/non-flanker) accumulators averaged over trials and models for congruent vs. incongruent trials. In case this was a source of confusion, we do not show the value of the flanker drift rates on congruent trials because the flanker and target accumulators are identical (i.e. the flanker/congruent drift rates are equivalent to the target/congruent drift rates).

      (4) Lines 214-7: "The observation that single-unit information for target direction decreased between the fourth and final convolutional layers while population-level decoding remained high is especially noteworthy in that it implies a transition from representing target direction with specialized "target neurons" to a more distributed, ensemble-level code." Can the authors clarify why this is the only reasonable explanation for these results? It seems like many other explanations could be construed.

      We have added additional clarification to this section and now use more tentative language:

      “The observation that single-unit information for target direction decreased between the fourth and final convolutional layers indicates that the units become progressively less selective for particular target directions. Since population-level decoding remained high in these layers, this suggests a transition from representing target direction with specialized "target neurons" to a more distributed, ensemble-level code.”

      (5) Lines 372-376: "Thus, simply training the model to perform the task is not sufficient to reproduce a behavioral phenomenon widely-observed in conflict tasks. This challenges a core (but often implicit) assumption of the task-optimized training paradigm, namely that to do a task well, a training model will result in model representations that are similar to those employed by humans." While I agree with the general sentiment, I feel that its application here is strange. Unless I'm missing something, in the context of the preceding sentence, the authors seem to be saying that researchers in the field expect that CNNs can produce a behavioral phenomenon (RTs) that is completely outside of their design and training. I don't think that anyone actually expects that.

      We moved the discussion/analyses of RTs to the next paragraph. It should now be clear that this statement refers specifically to the absence of an accuracy congruency effect in the task-optimized models.

      (6) Lines 387-389: "As a result, the VAMs may learn richer representations of the stimuli, since a variety of stimulus features-layout, stimulus position, flanker direction-influence behavior (Figure 2)." That is certainly true of tasks like this one where an optimal model would only focus on a tiny part of the image, whereas humans are distracted by many features. I'm not sure that this distractibility is the same as "richer representations". When CNNs classify images based on the background, would the authors claim that they have richer representations than humans?

      We agree that “richer” may not be the best way to characterize these representations, and have changed it to “more complex”.

      (7) Is it possible that drift rate d_k for each response happens to be negative on a given trial? If so, how is the decision given on such trials (since presumably none of the accumulators will ever reach the boundary)?

      It is indeed possible for all of the drift rates to be negative, though we found that this occurred for a vanishingly small number of trials (mean ± s.e.m. percent trials/model: 0.080 ± 0.011%, n = 75 models), as reported in the Methods. These trials were excluded from analyses.

      (8)  Can the authors comment on how they chose the CNN architecture and whether they expect that different architectures will produce similar results?

      Before establishing the seven-layer CNN architecture used throughout the paper, we conducted some preliminary experiments using other architectures that differed primarily in the number of CNN layers. We found that models with significantly fewer than seven layers typically failed to reach human-level accuracy on the task while larger models achieved human-level accuracy but (unsurprisingly) took longer to train.

      Reviewer #3 (Recommendations For The Authors):

      - In the introduction to this paper (particularly the paragraph beginning in line 33), the authors note that EAMs have typically been used in simplified settings and that they do not provide a means to account for how people extract information from naturalistic stimuli. While I agree with this, the idea of connecting CNNs of visual processing with EAMs for a joint modeling framework has been done. I recommend looking at and referencing these two articles as well as adjusting the tenor of this part of an introduction to better reflect the current state of the literature. For full disclosure, I am one of the authors on these articles. https://link.springer.com/article/10.1007/s42113-019-00042-1 https://www.sciencedirect.com/science/article/abs/pii/S0010027721001323

      We agree—thanks for pointing this out. The revised Introduction now discusses prior related models in more detail (including those referenced above) and better clarifies the novel contributions of our model. We specifically highlight that a novel contribution of the VAM is that “the CNN and EAM parameters are jointly fitted to the RT, choice, and visual stimulus data from individual participants in a unified Bayesian framework.”

      - The statement in lines 56-58 implies that this is the first article to glue CNNs together with EAMs. I would edit this accordingly based on the prior comment here and references provided. I will note that the second feature of the approach in this paper is still novel and really nice, namely the fact that the CNN and the EAM are jointly fitted. In the aforementioned references, the CNN is trained on the image set, and individual level Bayesian estimation was only applied to the EAM. Thus, it may be useful to highlight the joint estimation aspect of this investigation as well as how the uniqueness of the data available makes it possible.

      Agreed—see above.

      - Figure 3c and associated text. I understand the MI analysis you are performing here, however it is difficult to interpret as it stands. In the figure, what does a MI of 0.1 mean?? Can you give some context to that scale? I do find the interpretation of the hunchback shape in lines 210-222 to be somewhat of a stretch. The discussion that precedes (lines 199-209) this is clear and convincing. Can this discussion be strengthened more? And more interpretability of Figure 3c would be helpful; entropic scales can be hard to interpret without some context or scale associated.

      The MI analyses in Fig. 3C (and also Figs. 4C and 6E) show normalized MI, in which the raw MI has been divided by the entropy of the stimulus feature distribution. This normalization facilitates comparing the MI for different stimulus features, which is relevant for Figs. 4C and 6E. The normalized MI has a possible range of [0, 1], where 1 indicates perfect correlation between the two variables and 0 indicates complete independence. We now note in the legend of these figures that the possible normalized MI range is [0, 1], which should help with interpreting these values. Our revised results section for Fig. 3C now also includes some additional remarks on our interpretation of the hunchback shape of the MI.

      - Lines 244-248 and the analyses in Figure 3 suggest a change in the behavior of the CNN around layer 4. This is just a musing, but what would happen if you just used a 4 layer CNN, or even a 3 layer? This is not just a methods question. Your analysis suggests a transition from localized to distributed information representation. Right now, the EAM only sees the output of the distributed representation. What if it saw the results the more local representations from early layers? Of course, a shallower network may just form the distributed representations earlier, but it would interesting if there were a way to tease out not just the presence of distributed vs local representations, but the utility of those to the EAM.

      Thanks for this interesting suggestion. We did do some preliminary experiments in models with fewer layers, though we only examined the outputs of these models and did not assess their representations. We found that models with 3–5 layers generally failed to achieve human-level accuracy on the task. In principle, one could relate this observation to the representations of these models as a means of assessing the relative utility of distributed/local representations. However, there are confounding factors that one would ideally control for in order to compare models with different numbers of layers in this fashion (namely, the number of parameters).

      - Section Line 359 (Task optimized models) - It would be helpful to clarify here what these task-optimized models are being trained to do. As I understand it, they are being trained to directly predict the target direction. But are you asking them to learn to predict the true target direction? Or are you training them to predict what each individual responds? I think it is the second (since you have 75 of these), but it's not clear. I looked at the methods and still couldn't get a clear description of this. Also, are you just stripping the LBA off of the end of the CNN and then essentially putting a softmax in its place? If so, it would be helpful to say so.

      The task-optimized models were actually trained to output the true target direction in each stimulus, rather than trained to match the decisions of the human participants. We trained 75 such models since we wanted to use exactly the same stimuli as were used to train each VAM. The task-optimized CNNs were identical to those used in the VAMs, except that the outputs of the last layer were converted to softmax-scored probabilities for each direction rather than drift rates. The Results and Methods section now included additional commentary that clarifies these points.

      - Line 373-376: This statement is pretty well established at this point in the similarity judgement literature. I recommend looking at and referencing https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.13226 https://www.nature.com/articles/s41562-020-00951-3 https://link.springer.com/article/10.1007/s42113-020-00073-z

      Thanks for pointing this out. For reference, the statement in question is “Thus, simply training the model to perform the task is not sufficient to reproduce a behavioral phenomenon widely-observed in conflict tasks. This challenges a core (but often implicit) assumption of the task-optimized training paradigm, namely that training a model to do a task well will result in model representations that are similar to those employed by humans.”

      We agree that the first and third reference you mention are relevant, and we now cite them along with some other relevant work. In our view, the second reference you mention is not particularly relevant (that paper introduces a new computational model for similarity judgements that is fit to human data, but does not comment on training models to perform tasks vs. fitting to human data).

      - Line 387-388: "VAMs may learn richer representations". This is a bit of a philosophical point, but I'll go ahead and mention it. The standard VAM does not necessarily learn "richer" feature representations. Rather, you are asking the VAM and task-optimized models to do different things. As a result, they learn different representations. "Better" or "richer" is in the eye of the beholder. In one view, you could view the VAM performance as sub-par since it exhibits strange artifacts (congruency effects) and the expansion of dimensionality in the VAM representations is merely a side-effect of poor performance. I'm not advocating this view, just playing devils advocate and suggesting a more nuanced discussion of the difference between the VAM and task-optimized models.

      We agree—this is a great point. We have changed this statement to read “the VAMs may learn more complex [rather than richer] representations of the stimuli”.

      - Lines 567-570: Here you discuss how the LBA backend of the VAM can't account for shrinking spotlight-like RT effects but that fitting models to different RT quantiles helps overcome this. I find this to be one of the weakest points of the paper (the whole process of fitting RT quantiles separately to begin with). This is just a limitation of the RT component of the model. This is a great paper but this is just a limitation inherent in the model. I don't see a need to qualify this limitation and think it would be better to just point out that this is a limitation of the LBA itself (be more clear that it is the LBA that is the limiting factor here) and that this leaves room for future research. From your last sentence of this paragraph, I agree that recurrent CNNs would be interesting. I will note that RNN choice-RT models are out there (though not with CNNs as part of the model).

      We agree and have revised this section of the Discussion accordingly (see our response to Reviewer #2 for more detail). We also removed the analyses of models trained on separate RT quantiles.

    1. Reviewer #1 (Public review):

      Summary:

      In a previous work, Prut and colleagues had shown that during reaching, high-frequency stimulation of the cerebellar outputs resulted in reduced reach velocity. Moreover, they showed that the stimulation produced reaches that deviated from a straight line, with the shoulder and elbow movements becoming less coordinated. In this report, they extend their previous work by the addition of modeling results that investigate the relationship between the kinematic changes and torques produced at the joints. The results show that the slowing is not due to reductions in interaction torques alone, as the reductions in velocity occur even for movements that are single joints. More interestingly, the experiment revealed evidence for the decomposition of the reaching movement, as well as an increase in the variance of the trajectory.

      Strengths:

      This is a rare experiment in a non-human primate that assessed the importance of cerebellar input to the motor cortex during reaching.

      Weaknesses:

      My major concerns are described below.

      If I understand the task design correctly, the monkeys did not need to stop their hand at the target. I think this design may be suboptimal for investigating the role of the cerebellum in control of reaching because a number of earlier works have found that the cerebellum's contributions are particularly significant as the movement ends, i.e., stopping at the target. For example, in mice, interposed nucleus neurons tend to be most active near the end of the reach that requires extension, and their activation produces flexion forces during the reach (Becker and Person 2019). Indeed, the inactivation of interposed neurons that project to the thalamus results in overshooting of reaching movements (Low et al. 2018). Recent work has also found that many Purkinje cells show a burst-pause pattern as the reach nears its endpoint, and stimulation of the mossy fibers tends to disrupt endpoint control (Calame et al. 2023). Thus, the fact that the current paper has no data regarding endpoint control of the reach is puzzling to me.

      Because stimulation continued after the cursor had crossed the target, it is interesting to ask whether this disruption had any effects on the movements that were task-irrelevant. The reason for asking this is because we have found that whereas during task-relevant eye or tongue movements the Purkinje cells are strongly modulated, the modulations are much more muted when similar movements are performed but are task-irrelevant (Pi et al., PNAS 2024; Hage et al. Biorxiv 2024). Thus, it is interesting to ask whether the effects of stimulation were global and affected all movements, or were the effects primarily concerned with the task-relevant movements.

      If the schematic in Figure 1 is accurate, it is difficult for me to see how any of the reaching movements can be termed single joint. In the paper, T1 is labeled as a single joint, and T2-T4 are labeled as dual-joint. The authors should provide data to justify this.

      Because at least part of this work was previously analyzed and published, information should be provided regarding which data are new.

    2. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In a previous work, Prut and colleagues had shown that during reaching, high-frequency stimulation of the cerebellar outputs resulted in reduced reach velocity. Moreover, they showed that the stimulation produced reaches that deviated from a straight line, with the shoulder and elbow movements becoming less coordinated. In this report, they extend their previous work by the addition of modeling results that investigate the relationship between the kinematic changes and torques produced at the joints. The results show that the slowing is not due to reductions in interaction torques alone, as the reductions in velocity occur even for movements that are single joints. More interestingly, the experiment revealed evidence for the decomposition of the reaching movement, as well as an increase in the variance of the trajectory.

      Strengths:

      This is a rare experiment in a non-human primate that assessed the importance of cerebellar input to the motor cortex during reaching.

      Weaknesses:

      My major concerns are described below.

      If I understand the task design correctly, the monkeys did not need to stop their hand at the target. I think this design may be suboptimal for investigating the role of the cerebellum in control of reaching because a number of earlier works have found that the cerebellum's contributions are particularly significant as the movement ends, i.e., stopping at the target. For example, in mice, interposed nucleus neurons tend to be most active near the end of the reach that requires extension, and their activation produces flexion forces during the reach (Becker and Person 2019). Indeed, the inactivation of interposed neurons that project to the thalamus results in overshooting of reaching movements (Low et al. 2018). Recent work has also found that many Purkinje cells show a burst-pause pattern as the reach nears its endpoint, and stimulation of the mossy fibers tends to disrupt endpoint control (Calame et al. 2023). Thus, the fact that the current paper has no data regarding endpoint control of the reach is puzzling to me.

      We appreciate the reviewer’s point that cerebellar contributions can be particularly critical near the endpoint of a reach. In our current task design, monkeys were indeed required to hold at the target briefly—100 ms for Monkeys S and P, and 150 ms for Monkeys C and M—before receiving a reward. However, given the size of the targets and the velocity of movements, it often happened that the monkey didn’t have to stop its movement to obtain a reward. Importantly, we relaxed the task’s requirements (by increasing target size and reducing temporal constraints) to allow monkeys to perform the task under cerebellar block conditions as we found that the strict criteria in these conditions yield a low success rate. This design is suboptimal for studying endpoint accuracy which, as we now appreciate, is an important aspect of cerebellar control. In our revision, we will clarify these aspects of the task design and acknowledge that it is sub-optimal for examining the role of cerebellum in end-point control. Future studies will explicitly address this point more carefully.

      Because stimulation continued after the cursor had crossed the target, it is interesting to ask whether this disruption had any effects on the movements that were task-irrelevant. The reason for asking this is because we have found that whereas during task-relevant eye or tongue movements the Purkinje cells are strongly modulated, the modulations are much more muted when similar movements are performed but are task-irrelevant (Pi et al., PNAS 2024; Hage et al. Biorxiv 2024). Thus, it is interesting to ask whether the effects of stimulation were global and affected all movements, or were the effects primarily concerned with the task-relevant movements.

      This is a very interesting suggestion. Although our main analysis focused on target-directed reaching movements, we have the data for the between-trial movements under continuous stimulation (e.g., return to center movements). In our revised supplementary material, we will examine the effect of cerebellar block on endpoint velocities in inter-trial movements versus task-related movements.

      If the schematic in Figure 1 is accurate, it is difficult for me to see how any of the reaching movements can be termed single joint. In the paper, T1 is labeled as a single joint, and T2-T4 are labeled as dual-joint. The authors should provide data to justify this.

      The is reviewer right and movements to all targets engages shoulder and elbow but the single joint participation varied in a target-specific manner. In the manuscript, we used the term “single-joint” to indicate a target direction in which one joint remains stationary, resulting in minimal coupling torque at the adjacent joint. Specifically, for Targets 1 and 5 in our experiments, the net torque (and thus acceleration) at the elbow was negligible, and hence the shoulder experienced correspondingly low coupling torque (as illustrated in Figure 3c of our manuscript). To avoid confusion, we will use the term ‘predominantly single-joint’ movements in our revised manuscript to indicate targets with low coupling torques. We will also include an additional figure in the revised supplementary material displaying the net torques at the shoulder and elbow, similar to Figures 2c and 3c. Our goal is to demonstrate that movements to targets 1 and 5 are characterized by predominantly one-joint engagement (i.e., the elbow is stationary with low net torque) and low coupling torques, rather than implying a purely isolated, single-joint motion.

      Because at least part of this work was previously analyzed and published, information should be provided regarding which data are new.

      We will include a clear statement in the Methods section specifying which components of the dataset and analyses are entirely new. While some of the same animals and stimulation protocol were presented in prior work, the inverse-dynamics modeling, analyses of progressive movement changes across trials under stimulation and invariance of motor noise to movement velocity are newly reported in this manuscript.

      Reviewer #2 (Public review):

      This manuscript asks an interesting and important question: what part of 'cerebellar' motor dysfunction is an acute control problem vs a compensatory strategy to the acute control issue? The authors use a cerebellar 'blockade' protocol, consisting of high-frequency stimuli applied to the cerebellar peduncle which is thought to interfere with outflow signals. This protocol was applied in monkeys performing center outreaching movements and has been published from this laboratory in several preceding studies. I found the take-home-message broadly convincing and clarifying - that cerebellar block reduces muscle activation acutely particularly in movements that involve multiple joints and therefore invoke interaction torques, and that movements progressively slow down to in effect 'compensate' for these acute tone deficits. The manuscript was generally well written, and the data was clear, convincing, and novel. My comments below highlight suggestions to improve clarity and sharpen some arguments.

      Primary comments:

      (1) Torque vs. tone: Is it known whether this type of cerebellar blockade is reducing muscle tone or inducing any type of acute co-contraction that could influence limb velocity through mechanisms different than 'atonia'? If so, the authors should discuss this information in the discussion section starting around line 336, and clarify that this motivates (if it does) the focus on 'torques' rather than muscle activation. Relatedly, besides the fact that there are joints involved, is there a reason there is so much emphasis on torque per se? If the muscle is deprived of sufficient drive, it would seem that it would be more straightforward to conceptualize the deficit as one of insufficient timed drive to a set of muscles than joint force. Some text better contextualizing the choices made here would be sufficient to address this concern. I found statements like those in the introduction "hand velocity was low initially, reflecting a primary muscle torque deficit" to be lacking in substance. Either that statement is self-evident or the alternative was not made clear. Finally, emphasize that it is a loss of self-generated torque at the shoulder that accounts for the velocity deficits. At times the phrasing makes it seem that there is a loss of some kind of passive torque.

      We appreciate the reviewer’s emphasis on distinguishing reduced muscle tone and altered co-contraction patterns as possible explanations for decreased limb velocity. Our focus on torques arises from previous studies suggesting that the core deficit in cerebellar ataxia is impaired prediction of coupling torques. This point will be added in the discussion section of our revised manuscript where we will explain why we prioritize muscle torques and how muscle-level activation collectively contributes to net joint torques. Also, we will underscore that the observed velocity deficits primarily reflect a reduction of self-generated torque at the shoulder (whether acute or adaptive), rather than any reduction in passive torques.

      (2) Please clarify some of the experimental metrics: Ln 94 RESULTS. The success rate is used as a primary behavioral readout, but what constitutes success is not clearly defined in the methods. In addition to providing a clear definition in the methods section, it would also be helpful for the authors to provide a brief list of criteria used to determine a 'successful' movement in the results section before the behavioral consequences of stimulation are described. In particular, the time and positional error requirements should be clear.

      Successful trials were trials in which monkeys didn’t leave the center position before the go signal and reached the peripheral target within a specific time criteria. These values varied in different monkeys. We will include detailed definitions of our success criteria in the revised methods section of our manuscript. Specifically, we will update our methods section to include (i) the timing criteria of each phase of the trials and (ii) the size of the peripheral targets indicating the tolerance for endpoint accuracy.

      (3) Based on the polar plot in Figure 1c, it seemed odd to consider Targets 1-4 outward and 5-8 inward movements, when 1 and 5 are side-to-side. Is there a rationale for this grouping or might results be cleaner by cleanly segregating outward (targets 2-4) and inward (targets 6-8) movements? Indeed, by Figure 3 where interaction torques are measured, this grouping would seem to align with the hypothesis much more cleanly since it is with T2,T3,and T4 where clear coupling torques deficits are seen with cerebellar block.

      We acknowledge the reviewer’s observation regarding Targets 1 and 5 being side-to-side rather than strictly “outward” or “inward.” In the first section of our results, we grouped the targets in this way to emphasize the notably stronger effect of the cerebellar block on targets involving shoulder flexion (‘outward’) as compared to those involving shoulder extension (‘inwards’). For subsequent analyses we focused on the effects of cerebellar block on outward targets where movements were single-joint (Target 1) vs. multi-joint (Targets 2-4). To clarify this aspect, in our revised manuscript we will explain the rationale for grouping T1–T4 as “outward” and T5–T8 as “inward,” including how we defined them.

      (4) I did not follow Figure 3d. Both the figure axis labels and the description in the main text were difficult to follow. Furthermore, the color code per animal made me question whether the linear regression across the entire dataset was valid, or would be better performed within animal, and the regressions summarized across animals. The authors should look again at this section and figure.

      We will revise the figure labels and legend to clarify how each axis is defined. Please note that pooling the data was done after confirming that data from each animal expressed a similar trend. Specifically, the correlation coefficients were all positive but statistically significant in 3 out of the 4 monkeys. Moreover, following the reviewers’ feedback, we also did a partial correlation analysis (which controls for the variability across monkeys) and found a significant correlation (r = 0.33, p < 0.001). These points will be described in the revised manuscript.

      (5) Line 206+ The rationale for examining movement decomposition with a cerebellar block is presented as testing the role of the cerebellum in timing. Yet it is not spelled out what movement decomposition and trajectory variability have to do with motor timing per se.

      The reviewer is right and the relations between timing, decomposition and variability need to be explicitly presented. In our revision, we will explain how decomposed movements may reflect impaired temporal coordination across multiple joints—a critical cerebellar function. We will also clarify how increased variability in joint coordination can result in increased trial-to-trial variability of trajectories.

      Reviewer #3 (Public review):

      Summary:

      In their manuscript, "Disentangling acute motor deficits and adaptive responses evoked by the loss of cerebellar output," Sinha and colleagues aim to identify distinct causes of motor impairments seen when perturbing cerebellar circuits. This goal is an important one, given the diversity of movement-related phenotypes in patients with cerebellar lesions or injuries, which are especially difficult to dissect given the chronic nature of the circuit damage. To address this goal, the authors use high-frequency stimulation (HFS) of the superior cerebellar peduncle in monkeys performing reaching movements. HFS provides an attractive approach for transiently disrupting cerebellar function previously published by this group. First, they found a reduction in hand velocities during reaching, which was more pronounced for outward versus inward movements. By modeling inverse dynamics, they find evidence that shoulder muscle torques are especially affected. Next, the authors examine the temporal evolution of movement phenotypes over successive blocks of HFS trials. Using this analysis, they find that in addition to the acute, specific effects on muscle torques in early HFS trials, there was an additional progressive reduction in velocity during later trials, which they interpret as an adaptive response to the inability to effectively compensate for interaction torques during cerebellar block. Finally, the authors examine movement decomposition and trajectory, finding that even when low-velocity reaches are matched to controls, HFS produces abnormally decomposed movements and higher than expected variability in trajectory.

      Strengths:

      Overall, this work provides important insight into how perturbation of cerebellar circuits can elicit diverse effects on movement across multiple timescales.

      The HFS approach provides temporal resolution and enables analysis that would be hard to perform in the context of chronic lesions or slow pharmacological interventions. Thus, this study describes an important advance over prior methods of circuit disruption, and their approach can be used as a framework for future studies that delve deeper into how additional aspects of sensorimotor control are disrupted (e.g., response to limb perturbations).

      In addition, the authors use well-designed behavioral approaches and analysis methods to distinguish immediate from longer-term adaptive effects of HFS on behavior. Moreover, inverse dynamics modeling provides important insight into how movements with different kinematics and muscle dynamics might be differentially disrupted by cerebellar perturbation.

      Weaknesses:

      The argument that there are acute and adaptive effects to perturbing cerebellar circuits is compelling, but there seems to be a lost opportunity to leverage the fast and reversible nature of the perturbations to further test this idea and strengthen the interpretation. Specifically, the authors could have bolstered this argument by looking at the effects of terminating HFS - one might hypothesize that the acute impacts on muscle torques would quickly return to baseline in the absence of HFS, whereas the longer-term adaptive component would persist in the form of aftereffects during the 'washout' period. As is, the reversible nature of the perturbation seems underutilized in testing the authors' ideas.

      We agree that our approach could more explicitly exploit the rapid reversibility of high-frequency stimulation (HFS) by examining post-stimulation ‘washout’ periods. However, for the present dataset, we ended the session after the set of cerebellar block trials. We plan to study the effect of cerebellar block on immediate post-block washout trials in the future.  

      The analysis showing that there is a gradual reduction in velocity during what the authors call an adaptive phase is convincing. That said, the argument is made that this is due to difficulty in compensating for interaction torques. Even if the inward targets (i.e., targets 6-8) do not show a deficit during the acute phase, these targets still have significant interaction torques (Figure 3c). Given the interpretation of the data as presented, it is not clear why disruption of movement during the adaptive phase would not be seen for these targets as well since they also have large interaction torques. Moreover, it is difficult to delve into this issue in more detail, as the analyses in Figures 4 and 5 omit the inward targets.

      The reviewer is right and movements to Targets 6–8 (inward) were seemingly unaffected despite also involving significant interaction torques. In fact, we have already attempted to address this issue in the discussion section of the version 1 of our manuscript. Specifically, we note that while outward targets (2–4) tend to involve higher coupling torque impulses on average, this alone does not fully explain the differential impact of cerebellar block, as illustrated by discrepancies at the individual target level (e.g., target 7 vs. target 1). We proposed two possible explanations: (1) a bias toward shoulder flexion in the effect of cerebellar block—consistent with earlier studies showing ipsilateral flexor activation or tone changes following stimulation or lesioning of the deep cerebellar nuclei; and (2) a posture-related facilitation of inward (shoulder extension) movements from the central starting position.

      The text in the Introduction and in the prior work developing the HFS approach overstates the selectivity of the perturbations. First, there is an emphasis on signals transmitted to the neocortex. As the authors state several times in the Discussion, there are many subcortical targets of the cerebellar nuclei as well, and thus it is difficult to disentangle target-specific behavioral effects using this approach. Second, the superior cerebellar peduncle contains both cerebellar outputs and inputs (e.g., spinocerebellar). Therefore, the selectivity in perturbing cerebellar output feels overstated. Readers would benefit from a more agnostic claim that HFS affects cerebellar communication with the rest of the nervous system, which would not affect the major findings of the study.

      The reviewer is right that the superior cerebellar peduncle carries both descending and ascending fibers, and that cerebellar nuclei project to subcortical as well as cortical targets. However, it is also important to note that in primates the cerebellar-thalamo-cortical (CTC) pathway greatly expanded (on the expanse of the cerbello-rubro-spinal tract) in mediating cerebellar control of voluntary movements (Horne and Butler, 1995). The cerebello-subcortical pathways lost its importance over the course of evolution (Nathan and Smith, 1982, Padel et al., 1981, ten Donkelaar, 1988). In our previous study we found that the ascending spinocerebellar axons which enter the cerebellum through the SCP are weakly task-related and the descending system is quite small (Cohen et al, 2017). However, we cannot rule out an effect of HFS mediated in part through other systems. In the revised introduction section, we will clarify this point and use more careful language about the scope of our stimulation, emphasizing that HFS disrupts cerebellar communication broadly, rather than solely the cerebello-thalamo-cortical pathway.

      The text implies that increased movement decomposition and variability must be due to noise. However, this assumption is not tested. It is possible that the impairments observed are caused by disrupted commands, independent of whether these command signals are noisy. In other words, commands could be low noise but still faulty.

      We recognize the reviewer’s concern about linking movement decomposition and trial-to-trial trajectory variability with motor noise. As presented in our discussion section, we interpret these motor abnormalities as a form of motor noise in the sense that they are generated by faulty motor commands. We draw our interpretation from the findings of previous research work which show that the cerebellum aids in the state estimation of the limb and subsequent generation of accurate feedforward commands. Therefore, disruption of the cerebellar output may lead to faulty motor commands resulting in the observed asynchronous joint activations (i.e., movement decomposition) and unpredictable trajectories (i.e., increased trial-to-trial variability). Both observed deficits resemble increased motor noise.

      Throughout the text, the use of the term 'feedforward control' seems unnecessary. To dig into the feedforward component of the deficit, the authors could quantify the trajectory errors only at the earliest time points (e.g., in Figure 5d), but even with this analysis, it is difficult to disentangle feedforward- and feedback-mediated effects when deficits are seen throughout the reach. While outside the scope of this study, it would be interesting to explore how feedback responses to limb perturbation are affected in control versus HFS conditions. However, as is, these questions are not explored, and the claim of impaired feedforward control feels overstated.

      We agree that to strictly focus on feedforward control, we could have examined the measured variables in the first 50-100 ms of the movement which has been shown to be unaffected by feedback responses (Pruszynski et al. 2008, Todorov and Jordan 2002, Pruszynski and Scott 2012, Crevecoeur et al. 2013). However, in our task the amplitude of movements made by our monkeys was small and therefore the response measures we used were too small in the first 50-100 ms for a robust estimation. Also, fixing a time window led to an unfair comparison between control and cerebellar block trials, in which velocity was significantly reduced and therefore movement time was longer. Therefore, we used the peak velocity, torque-impulse at the peak velocity and maximum deviation of the hand trajectory as response measures. We will acknowledge this point in the discussion section of our revised manuscript. We will also tone down references to feedforward control throughout the text of our revised manuscript as suggested by the reviewer.

      The terminology 'single-joint' movement is a bit confusing. At a minimum, it would be nice to show kinematics during different target reaches to demonstrate that certain targets are indeed single joint movements. More of an issue, however, is that it seems like these are not actually 'single-joint' movements. For example, Figure 2c shows that target 1 exhibits high elbow and shoulder torques, but in the text, T1 is described as a 'single-joint' reach (e.g. lines 155-156). The point that I think the authors are making is that these targets have low interaction torques. If that is the case, the terminology should be changed or clarified to avoid confusion.

      Indeed, as reviewer #1 also noted, movements to target 1 and 5 are not purely single-joint but rather have relatively low coupling torques. Our intention while using the term “single-joint” was to indicate a target direction in which one joint remains stationary, resulting in minimal coupling torque at the adjacent joint. Specifically, for Targets 1 and 5 in our experiments, the net torque (and thus acceleration) at the elbow was negligible, and hence the shoulder experienced correspondingly low coupling torque (as illustrated in Figure 3c of our manuscript). ). To avoid confusion, we will use the term ‘predominantly single-joint’ movements in our revised manuscript to indicate targets with low coupling torques. We will also include an additional figure in the revised supplementary material displaying the net torques at the shoulder and elbow, similar to Figures 2c and 3c. Our goal is to demonstrate that movements to targets 1 and 5 are characterized by predominantly one-joint engagement (i.e., the elbow is stationary with low net torque) and low coupling torques, rather than implying a purely isolated, single-joint motion.

      The labels in Figure 3d are confusing and could use more explanation in the figure legend.

      In Figure 3d, it is stated that data from all monkeys is pooled. However, if there is a systematic bias between animals, this could generate spurious correlations. Were correlations also calculated for each animal separately to confirm the same trend between velocity and coupling torques holds for each animal?

      We will revise the figure legend and main-text explanation for Figure 3d. Please note that pooling the data was done after confirming that data from each animal expressed a similar trend. Specifically, the correlation coefficients were positive but significant for 3 out of the 4 monkeys. Moreover, following the reviewers’ feedback, we also did a partial correlation analysis (which controls for the variability across monkeys) and found a significant correlation (r = 0.33, p < 0.001). These points will be described in the revised manuscript.

      In Table S1, it would be nice to see target-specific success rates. The data would suggest that targets with the highest interaction torques will have the largest reduction in success rates, especially during later HFS trials. Is this the case?

      We will provide a breakdown of the success rates as a function of targets. However, one should note that success/failure may depend on several factors beyond impaired limb dynamics. In a previous study (Nashef et al. 2019) we identified several causes of failure such as (i) not entering the central target in time, (ii) moving out too early from the peripheral target, (iii) Reaction time longer than permitted, or (iv) premature exit from the central target before permitted.

    1. Author response:

      eLife Assessment

      This valuable short paper is an ingenious use of clinical patient data to address an issue in imaging neuroscience. The authors clarify the role of face-selectivity in human fusiform gyrus by measuring both BOLD fMRI and depth electrode recordings in the same individuals; furthermore, by comparing responses in different brain regions in the two patients, they suggested that the suppression of blood oxygenation is associated with a decrease in local neural activity. While the methods are compelling and provide a rare dataset of potentially general importance, the presentation of the data in its current form is incomplete.

      We thank the Reviewing editor and Senior editor at eLife for their positive assessment of our paper. After reading the reviewers’ comments – to which we reply below - we agree that the presentation of the data could be completed. We provide additional presentation of data in the responses below and we will slightly modify Figure 2 of the paper. However, in keeping the short format of the paper, the revised version will have the same number of figures, which support the claims made in the paper.

      Reviewer #1 (Public review):

      Summary:

      Measurement of BOLD MR imaging has regularly found regions of the brain that show reliable suppression of BOLD responses during specific experimental testing conditions. These observations are to some degree unexplained, in comparison with more usual association between activation of the BOLD response and excitatory activation of the neurons (most tightly linked to synaptic activity) in the same brain location. This paper finds two patients whose brains were tested with both non-invasive functional MRI and with invasive insertion of electrodes, which allowed the direct recording of neuronal activity. The electrode insertions were made within the fusiform gyrus, which is known to process information about faces, in a clinical search for the sites of intractable epilepsy in each patient. The simple observation is that the electrode location in one patient showed activation of the BOLD response and activation of neuronal firing in response to face stimuli. This is the classical association. The other patient showed an informative and different pattern of responses. In this person, the electrode location showed a suppression of the BOLD response to face stimuli and, most interestingly, an associated suppression of neuronal activity at the electrode site.

      Strengths:

      Whilst these results are not by themselves definitive, they add an important piece of evidence to a long-standing discussion about the origins of the BOLD response. The observation of decreased neuronal activation associated with negative BOLD is interesting because, at various times, exactly the opposite association has been predicted. It has been previously argued that if synaptic mechanisms of neuronal inhibition are responsible for the suppression of neuronal firing, then it would be reasonable

      Weaknesses:

      The chief weakness of the paper is that the results may be unique in a slightly awkward way. The observation of positive BOLD and neuronal activation is made at one brain site in one patient, while the complementary observation of negative BOLD and neuronal suppression actually derives from the other patient. Showing both effects in both patients would make a much stronger paper.

      We thank reviewer #1 for their positive evaluation of our paper. Obviously, we agree with the reviewer that the paper would be much stronger if BOTH effects – spike increase and decrease – would be found in BOTH patients in their corresponding fMRI regions (lateral and medial fusiform gyrus) (also in the same hemisphere). Nevertheless, we clearly acknowledge this limitation in the (revised) version of the manuscript (p.8: Material and Methods section).

      In the current paper, one could think that P1 shows only increases to faces, and P2 would show only decreases (irrespective of the region). However, that is not the case since 11% of P1’s face-selective units are decreases (89% are increases) and 4% of P2’s face-selective units are increases. This has now been made clearer in the manuscript (p.5).

      As the reviewer is certainly aware, the number and position of the electrodes are based on strict clinical criteria, and we will probably never encounter a situation with two neighboring (macro-micro hybrid electrodes), one with microelectrodes ending up in the lateral MidFG, the other in the medial MidFG, in the same patient. If there is no clinical value for the patient, this cannot be done.

      The only thing we can do is to strengthen these results in the future by collecting data on additional patients with an electrode either in the lateral or the medial FG, together with fMRI. But these are the only two patients we have been able to record so far with electrodes falling unambiguously in such contrasted regions and with large (and comparable) measures.

      While we acknowledge that the results may be unique because of the use of 2 contrasted patients only (and this is why the paper is a short report), the data is compelling in these 2 cases, and we are confident that it will be replicated in larger cohorts in the future.

      Reviewer #2 (Public review):

      Summary:

      This is a short and straightforward paper describing BOLD fMRI and depth electrode measurements from two regions of the fusiform gyrus that show either higher or lower BOLD responses to faces vs. objects (which I will call face-positive and facenegative regions). In these regions, which were studied separately in two patients undergoing epilepsy surgery, spiking activity increased for faces relative to objects in the face-positive region and decreased for faces relative to objects in the face-negative region. Interestingly, about 30% of neurons in the face-negative region did not respond to objects and decreased their responses below baseline in response to faces (absolute suppression).

      Strengths:

      These patient data are valuable, with many recording sessions and neurons from human face-selective regions, and the methods used for comparing face and object responses in both fMRI and electrode recordings were robust and well-established. The finding of absolute suppression could clarify the nature of face selectivity in human fusiform gyrus since previous fMRI studies of the face-negative region could not distinguish whether face < object responses came from absolute suppression, or just relatively lower but still positive responses to faces vs. objects.

      Weaknesses:

      The authors claim that the results tell us about both 1) face-selectivity in the fusiform gyrus, and 2) the physiological basis of the BOLD signal. However, I would like to see more of the data that supports the first claim, and I am not sure the second claim is supported.

      (1) The authors report that ~30% of neurons showed absolute suppression, but those data are not shown separately from the neurons that only show relative reductions. It is difficult to evaluate the absolute suppression claim from the short assertion in the text alone (lines 105-106), although this is a critical claim in the paper.

      We thank reviewer #2 for their positive evaluation of our paper. We understand the reviewer’s point, and we partly agree. Where we respectfully disagree is that the finding of absolute suppression is critical for the claim of the paper: finding an identical contrast between the two regions in terms of RELATIVE increase/decrease of face-selective activity in fMRI and spiking activity is already novel and informative. Where we agree with the reviewer is that the absolute suppression could be more documented: it wasn’t, due to space constraints (brief report). We provide below an example of a neuron showing absolute suppression to faces. In the frequency domain, there is only a face-selective response (1.2 Hz and harmonics) but no significant response at 6 Hz (common general visual response). In the time-domain, relative to face onset, the response drops below baseline level. It means that this neuron has baseline (non-periodic) spontaneous spiking activity that is actively suppressed when a face appears.

      Author response image 1.

      (2) I am not sure how much light the results shed on the physiological basis of the BOLD signal. The authors write that the results reveal "that BOLD decreases can be due to relative, but also absolute, spike suppression in the human brain" (line 120). But I think to make this claim, you would need a region that exclusively had neurons showing absolute suppression, not a region with a mix of neurons, some showing absolute suppression and some showing relative suppression, as here. The responses of both groups of neurons contribute to the measured BOLD signal, so it seems impossible to tell from these data how absolute suppression per se drives the BOLD response.

      It is a fact that we find both kinds of responses in the same region.  We cannot tell with this technique if neurons showing relative vs. absolute suppression of responses are spatially segregated for instance (e.g., forming two separate sub-regions) or are intermingled. And we cannot tell from our data how absolute suppression per se drives the BOLD response. In our view, this does not diminish the interest and originality of the study, but the statement "that BOLD decreases can be due to relative, but also absolute, spike suppression in the human brain” will be rephrased in the revised manuscript, in the following way: "that BOLD decreases can be due to relative, or absolute (or a combination of both), spike suppression in the human brain”.

      Reviewer #3 (Public review):

      In this paper the authors conduct two experiments an fMRI experiment and intracranial recordings of neurons in two patients P1 and P2. In both experiments, they employ a SSVEP paradigm in which they show images at a fast rate (e.g. 6Hz) and then they show face images at a slower rate (e.g. 1.2Hz), where the rest of the images are a variety of object images. In the first patient, they record from neurons over a region in the mid fusiform gyrus that is face-selective and in the second patient, they record neurons from a region more medially that is not face selective (it responds more strongly to objects than faces). Results find similar selectivity between the electrophysiology data and the fMRI data in that the location which shows higher fMRI to faces also finds face-selective neurons and the location which finds preference to non faces also shows non face preferring neurons.

      Strengths:

      The data is important in that it shows that there is a relationship between category selectivity measured from electrophysiology data and category-selective from fMRI. The data is unique as it contains a lot of single and multiunit recordings (245 units) from the human fusiform gyrus - which the authors point out - is a humanoid specific gyrus.

      Weaknesses:

      My major concerns are two-fold:

      (i) There is a paucity of data; Thus, more information (results and methods) is warranted; and in particular there is no comparison between the fMRI data and the SEEG data.

      We thank reviewer #3 for their positive evaluation of our paper. If the reviewer means paucity of data presentation, we agree and we provide more presentation below, although the methods and results information appear as complete to us. The comparison between fMRI and SEEG is there, but can only be indirect (i.e., collected at different times and not related on a trial-by-trial basis for instance). In addition, our manuscript aims at providing a short empirical contribution to further our understanding of the relationship between neural responses and BOLD signal, not to provide a model of neurovascular coupling.

      (ii) One main claim of the paper is that there is evidence for suppressed responses to faces in the non-face selective region. That is, the reduction in activation to faces in the non-face selective region is interpreted as a suppression in the neural response and consequently the reduction in fMRI signal is interpreted as suppression. However, the SSVEP paradigm has no baseline (it alternates between faces and objects) and therefore it cannot distinguish between lower firing rate to faces vs suppression of response to faces.

      We understand the concern of the reviewer, but we respectfully disagree that our paradigm cannot distinguish between lower firing rate to faces vs. suppression of response to faces. Indeed, since the stimuli are presented periodically (6 Hz), we can objectively distinguish stimulus-related activity from spontaneous neuronal firing. The baseline corresponds to spikes that are non-periodic, i.e., unrelated to the (common face and object) stimulation. For a subset of neurons, even this non-periodic baseline activity is suppressed, above and beyond the suppression of the 6 Hz response illustrated on Figure 2. We mention it in the manuscript, but we agree that we do not present illustrations of such decrease in the time-domain for SU, which we did not consider as being necessary initially (please see below for such presentation).

      (1) Additional data: the paper has 2 figures: figure 1 which shows the experimental design and figure 2 which presents data, the latter shows one example neuron raster plot from each patient and group average neural data from each patient. In this reader's opinion this is insufficient data to support the conclusions of the paper. The paper will be more impactful if the researchers would report the data more comprehensively.

      We answer to more specific requests for additional evidence below, but the reviewer should be aware that this is a short report, which reaches the word limit. In our view, the group average neural data should be sufficient to support the conclusions, and the example neurons are there for illustration. And while we cannot provide the raster plots for a large number of neurons, the anonymized data will be made available upon publication of the final version of the paper.

      (a) There is no direct comparison between the fMRI data and the SEEG data, except for a comparison of the location of the electrodes relative to the statistical parametric map generated from a contrast (Fig 2a,d). It will be helpful to build a model linking between the neural responses to the voxel response in the same location - i.e., estimate from the electrophysiology data the fMRI data (e.g., Logothetis & Wandell, 2004).

      As mentioned above the comparison between fMRI and SEEG is indirect (i.e., collected at different times and not related on a trial-by-trial basis for instance) and would not allow to make such a model.

      (b) More comprehensive analyses of the SSVEP neural data: It will be helpful to show the results of the frequency analyses of the SSVEP data for all neurons to show that there are significant visual responses and significant face responses. It will be also useful to compare and quantify the magnitude of the face responses compared to the visual responses.

      The data has been analyzed comprehensively, but we would not be able to show all neurons with such significant visual responses and face-selective responses.

      (c) The neuron shown in E shows cyclical responses tied to the onset of the stimuli, is this the visual response?

      Correct, it’s the visual response at 6 Hz.

      If so, why is there an increase in the firing rate of the neuron before the face stimulus is shown in time 0?

      Because the stimulation is continuous. What is displayed at 0 is the onset of the face stimulus, with each face stimulus being preceded by 4 images of nonface objects.

      The neuron's data seems different than the average response across neurons; This raises a concern about interpreting the average response across neurons in panel F which seems different than the single neuron responses

      The reviewer is correct, and we apologize for the confusion. This is because the average data on panel F has been notch-filtered for the 6 Hz (and harmonic responses), as indicated in the methods (p.11):  ‘a FFT notch filter (filter width = 0.05 Hz) was then applied on the 70 s single or multi-units time-series to remove the general visual response at 6 Hz and two additional harmonics (i.e., 12 and 18 Hz)’.

      Here is the same data without the notch-filter (the 6Hz periodic response is clearly visible):

      Author response image 2.

      For sake of clarity, we prefer presenting the notch-filtered data in the paper, but the revised version will make it clear in the figure caption that the average data has been notch-filtered.

      (d) Related to (c) it would be useful to show raster plots of all neurons and quantify if the neural responses within a region are homogeneous or heterogeneous. This would add data relating the single neuron response to the population responses measured from fMRI. See also Nir 2009.

      We agree with the reviewer that this is interesting, but again we do not think that it is necessary for the point made in the present paper. Responses in these regions appear rather heterogenous, and we are currently working on a longer paper with additional SEEG data (other patients tested for shorter sessions) to define and quantify the face-selective neurons in the MidFusiform gyrus with this approach (without relating it to the fMRI contrast as reported here).

      (e) When reporting group average data (e.g., Fig 2C,F) it is necessary to show standard deviation of the response across neurons.

      We agree with the reviewer and have modified Figure 2 accordingly in the revised manuscript.

      (f) Is it possible to estimate the latency of the neural responses to face and object images from the phase data? If so, this will add important information on the timing of neural responses in the human fusiform gyrus to face and object images.

      The fast periodic paradigm to measure neural face-selectivity has been used in tens of studies since its original reports:

      - in EEG: Rossion et al., 2015: https://doi.org/10.1167/15.1.18

      - in SEEG: Jonas et al., 2016: https://doi.org/10.1073/pnas.1522033113

      In this paradigm, the face-selective response spreads to several harmonics (1.2 Hz, 2.4 Hz, 3.6 Hz, etc.) (which are summed for quantifying the total face-selective amplitude). This is illustrated below by the averaged single units’ SNR spectra across all recording sessions for both participants.

      Author response image 3.

      There is no unique phase-value, each harmonic being associated with a phase-value, so that the timing cannot be unambiguously extracted from phase values. Instead, the onset latency is computed directly from the time-domain responses, which is more straightforward and reliable than using the phase. Note that the present paper is not about the specific time-courses of the different types of neurons, which would require a more comprehensive report, but which is not necessary to support the point made in the present paper about the SEEG-fMRI sign relationship.

      g) Related to (e) In total the authors recorded data from 245 units (some single units and some multiunits) and they found that both in the face and nonface selective most of the recoded neurons exhibited face -selectivity, which this reader found confusing: They write “ Among all visually responsive neurons, we found a very high proportion of face-selective neurons (p < 0.05) in both activated and deactivated MidFG regions (P1: 98.1%; N = 51/52; P2: 86.6%; N = 110/127)’. Is the face selectivity in P1 an increase in response to faces and P2 a reduction in response to faces or in both it’s an increase in response to faces

      Face-selectivity is defined as a DIFFERENTIAL response to faces compared to objects, not necessarily a larger response to faces. So yes, face-selectivity in P1 is an increase in response to faces and P2 a reduction in response to faces.

      (1) Additional methods

      (a) it is unclear if the SSVEP analyses of neural responses were done on the spikes or the raw electrical signal. If the former, how is the SSVEP frequency analysis done on discrete data like action potentials?

      The FFT is applied directly on spike trains using Matlab’s discrete Fourier Transform function. This function is suitable to be applied to spike trains in the same way as to any sampled digital signal (here, the microwires signal was sampled at 30 kHz, see Methods).

      In complementary analyses, we also attempted to apply the FFT on spike trains that had been temporally smoothed by convolving them with a 20ms square window (Le Cam et al., 2023, cited in the paper ). This did not change the outcome of the frequency analyses in the frequency range we are interested in.

      (b) it is unclear why the onset time was shifted by 33ms; one can measure the phase of the response relative to the cycle onset and use that to estimate the delay between the onset of a stimulus and the onset of the response. Adding phase information will be useful.

      The onset time was shifted by 33ms because the stimuli are presented with a sinewave contrast modulation (i.e., at 0ms, the stimulus has 0% contrast). 100% contrast is reached at half a stimulation cycle, which is 83.33ms here, but a response is likely triggered before reaching 100% contrast. To estimate the delay between the start of the sinewave (0% contrast) and the triggering of a neural response, we tested 7 SEEG participants with the same images presented in FPVS sequences either as a sinewave contrast (black line) modulation or as a squarewave (i.e. abrupt) contrast modulation (red line).  The 33ms value is based on these LFP data obtained in response to such sinewave stimulation and squarewave stimulation of the same paradigm. This delay corresponds to 4 screen refresh frames (120 Hz refresh rate = 8.33ms by frame) and 35% of the full contrast, as illustrated below (please see also Retter, T. L., & Rossion, B. (2016). Uncovering the neural magnitude and spatio-temporal dynamics of natural image categorization in a fast visual stream. Neuropsychologia, 91, 9–28).

      Author response image 4.

      (2) Interpretation of suppression:

      The SSVEP paradigm alternates between 2 conditions: faces and objects and has no baseline; In other words, responses to faces are measured relative to the baseline response to objects so that any region that contains neurons that have a lower firing rate to faces than objects is bound to show a lower response in the SSVEP signal. Therefore, because the experiment does not have a true baseline (e.g. blank screen, with no visual stimulation) this experimental design cannot distinguish between lower firing rate to faces vs suppression of response to faces.

      The strongest evidence put forward for suppression is the response of non-visual neurons that was also reduced when patients looked at faces, but since these are non-visual neurons, it is unclear how to interpret the responses to faces.

      We understand this point, but how does the reviewer know that these are non-visual neurons? Because these neurons are located in the visual cortex, they are likely to be visual neurons that are not responsive to non-face objects. In any case, as the reviewer writes, we think it’s strong evidence for suppression.

      We thank all three reviewers for their positive evaluation of our paper and their constructive comments.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, the authors Eapen et al. investigated the peptide inhibitors of Cdc20. They applied a rational design approach, substituting residues found in the D-box consensus sequences to better align the peptides with the Cdc20-degron interface. In the process, the authors designed and tested a series of more potent binders, including ones that contain unnatural amino acids, and verified binding modes by elucidating the Cdc-20-peptide structures. The authors further showed that these peptides can engage with Cdc20 in the cellular context, and can inhibit APC/C<sup>Cdc20</sup> ubiquitination activity. Finally, the authors demonstrated that these peptides could be used as portable degron motifs that drive the degradation of a fused fluorescent protein.

      Strengths:

      This manuscript is clear and straightforward to follow. The investigation of different peptide variations was comprehensive and well-executed. This work provided the groundwork for the development of peptide drug modalities to inhibit degradation or apply peptides as portable motifs to achieve targeted degradation. Both of which are impactful.

      Weaknesses:

      A few minor comments:

      (1) In my opinion, more attention to the solubility issue needs to be discussed and/or tested. On page 10, what is the solubility of D2 before a modification was made? The authors mentioned that position 2 is likely solvent exposed, it is not immediately clear to me why the mutation made was from one hydrophobic residue to another. What was the level of improvement in solubility? Are there any affinity data associated with the peptide that differ with D2 only at position 2?

      The reviewer is correct that we have not done any detailed solubility characterisation; we refer only to observations rather than quantitative analysis. We wrote that we reverted from Leu to Ala due to solubility - we will clarify this statement to say that that we reverted to Ala, as it was the residue present in D1, for which we observed a measurable affinity by SPR and saw a concentration-dependent response in the thermal shift analysis. We do not have any peptides or affinity data that explore single-site mutations with the parental peptide of D2. D2 is included in the paper because of its link to the consensus D-box sequence and thus was the logical path to the investigations into positions 3 and 7 that come later in the manuscript.

      (2) I'm not entirely convinced that the D19 density not observed in the crystal structure was due to crystal packing. This peptide is peculiar as it also did not induce any thermal stabilization of Cdc20 in the cellular thermal shift assay. Perhaps the binding of this peptide could be investigated in more detail (i.e., NMR?) Or at least more explanation could be provided.

      This section will be clarified. The lack of observed density was likely due to the relatively low affinity of D19 and also to the lack of binding of the three C-terminal residues in the crystal, and consequently it has a further reduced affinity. The current wording in the manuscript puts greater emphasis on this second aspect being a D19-specific issue, even though it applies to all four soaked peptides. The extent of peptide-induced thermal stabilisations observed by TSA and CETSA is different, with the latter experiment consistently showing smaller shifts. This observation may be due to the more complex medium (cell lysate vs. purified protein) and/or different concentrations of the proteins in solution. In the CETSA, we over-expressed a HiBiT-tagged Cdc20, which is present in addition to any endogenously expressed Cdc20. Although we did not investigate it, the near identical D-box binding sites on Cdc20 and Cdh1 would suggest that there will be cross-specificity, which could further influence the CETSA experiments.

      Reviewer #2 (Public review):

      Summary:

      The authors took a well-characterised (partly by them), important E3 ligase, in the anaphase-promoting complex, and decided to design peptide inhibitors for it based on one of the known interacting motifs (called D-box) from its substrates. They incorporate unnatural amino acids to better occupy the interaction site, improve the binding affinity, and lay foundations for future therapeutics - maybe combining their findings with additional target sites.

      Strengths:

      The paper is mostly strengths - a logical progression of experiments, very well explained and carried out to a high standard. The authors use a carefully chosen variety of techniques (including X-ray crystallography, multiple binding analyses, and ubiquitination assays) to verify their findings - and they impressively achieve their goals by honing in on tight-binders.

      Weaknesses:

      Some things are not explained fully and it would be useful to have some clarification. Why did the authors decide to model their inhibitors on the D-box motif and not the other two SLiMs that they describe?

      For completeness, in addition to the D-box we did originally construct peptides based on the ABBA and KEN-box motifs, but they did not show any shift in melting temperature of cdc20 in the thermal shift assay whereas the D-box peptides did; consequently, we focused our efforts on the D-box peptides. Moreover, there is much evidence from the literature that points to the unique importance of the D-box motif in mediating productive interactions of substrates with the APC/C (i.e. those leading to polyubiquitination & degradation). One of the clearest examples is a study by Mark Hall’s lab (described in Qin et al. 2016), which tested the degradation of 15 substrates of yeast APC/C in strains carrying alleles of Cdh1 in which the docking sites for D-box, KEN or ABBA were mutated. They observed that whereas degradation of all 15 substrates depended on D-box binding, only a subset required the KEN binding site on Cdh1 and only one required the ABBA binding site. A more recent study from David Morgan’s lab (Hartooni et al. 2022) looking at binding affinities of different degron peptides concluded that KEN motif has very low affinity for Cdc20 and is unlikely to mediate degradation of APC/C-Cdc20 substrates. Engagement of substrate with the D-box receptor is therefore the most critical event mediating APC/C activity and the interaction that needs to be blocked for most effective inhibition of substrate degradation.

      What exactly do they mean when they say their 'observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast 'pseudo-substrate' inhibitor Acm1, acts to impede polyubiquitination of the bound protein'? It's an interesting thing to think about, and probably the paper they cite explains it more but I would like to know without having to find that other paper.

      Interesting results from a number of labs (Choi et al. 2008, Enquist-Newman et al. 2008, Burton et al. 2011, Qin et al. 2019) have shown that mutation of degron SLiMs in Acm1 that weaken interaction with the APC/C have the unexpected consequence of converting Acm1 from APC/C inhibitor to APC/C substrate. A necessary conclusion of these studies is that the outcome of degron binding (i.e. whether the binder functions as substrate or inhibitor) depends on factors other than D-box affinity and that D-box affinity can counteract them. One idea is that if a binder interacts too tightly, this removes some flexibility required for the polyubiquitination process. The most recent study on this question (Qin et al.2019) specifically pins the explanation for the inhibitory function of the high affinity D-box in Acm1 on its ‘D-box Extension’ (i.e. residues 8-12) preventing interaction with APC10. In our current study, the binding affinity of peptides is measured against Cdc20. In cellular assays however, the D-box must also engage APC10 for degradation to occur. It may be that the peptide binding most strongly to the D-box pocket on Cdc20 is less able to bind to APC10 and therefore less effective in triggering APC10-dependent steps in the polyubiquitination pathway. The important Hartooni et al. paper from David Morgan’s lab confirms that even though the binding of D-box residues to APC10 is very weak on its own, it can contribute 100X increase in affinity of a peptide by adding cooperativity to the interaction of D-box with co-activator.

      After further reading on this topic, we will modify the relevant piece of text from:

      “However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast ‘pseudo-substrate’ inhibitor Acm1, acts to impede polyubiquitination of the bound protein (Qin et al. 2019). Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. As shown in Qin et al., mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Qin et al. 2019). Overall, our results support the conclusions that all the D-box peptides engage productively with the APC/C and that the highest affinity interactors act as inhibitors rather than functional degrons of APC/C.”

      to:

      “However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with conclusions from other studies that affinity of degron binding does not necessarily correlate with efficiency of degradation. Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. A number of studies of a yeast ‘pseudo-substrate’ inhibitor Acm1, have shown that mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Choi et al. 2008, Enquist-Newman et al. 2008, Burton et al. 2011) through a mechanism that governs recruitment of APC10 (Qin et al. 2019). Our study does not consider the contribution of APC10 to binding of our peptides to APC/C<sup>Cdc20</sup> complex, but since there is strong cooperativity provided by this additional interaction (Hartooni et al. 2022) we propose this as the critical factor in determining the ability of the different peptides to mediate degradation of associated mNeon.”

      Re Figure 6 and the fact that we did look at peptide binding in cells, these experiments were done in unsynchronised cells, so most Cdc20 would not be bound to APC/C.

      Reviewer #3 (Public review):

      Summary:

      Eapen and coworkers use a rational design approach to generate new peptide-inspired ligands at the D-box interface of cdc20. These new peptides serve as new starting points for blocking APC/C in the context of cancer, as well as manipulating APC/C for targeted protein degradation therapeutic approaches.

      Strengths:

      The characterization of new peptide-like ligands is generally solid and multifaceted, including binding assays, thermal stability enhancement in vitro and in cells, X-ray crystallography, and degradation assays.

      Weaknesses:

      One important finding of the study is that the strongest binders did not correlate with the fastest degradation in a cellular assay, but explanations for this behavior were not supported experimentally. Some minor issues regarding experimental replicates and details were also noted.

      Interesting results from a number of labs (Choi et al. 2008, Enquist-Newman et al. 2008, Burton et al. 2011, Qin et al. 2019) have shown that mutation of degron SLiMs in Acm1 that weaken interaction with the APC/C have the unexpected consequence of converting Acm1 from APC/C inhibitor to APC/C substrate. A necessary conclusion of these studies is that the outcome of degron binding (i.e. whether the binder functions as substrate or inhibitor) depends on factors other than D-box affinity and that D-box affinity can counteract them. One idea is that if a binder interacts too tightly, this removes some flexibility required for the polyubiquitination process. The most recent study on this question (Qin et al.2019) specifically pins the explanation for the inhibitory function of the high affinity D-box in Acm1 on its ‘D-box Extension’ (i.e. residues 8-12) preventing interaction with APC10. In our current study, the binding affinity of peptides is measured against Cdc20. In cellular assays however, the D-box must also engage APC10 for degradation to occur. It may be that the peptide binding most strongly to the D-box pocket on Cdc20 is less able to bind to APC10 and therefore less effective in triggering APC10-dependent steps in the polyubiquitination pathway. The important Hartooni et al. paper from David Morgan’s lab confirms that even though the binding of D-box residues to APC10 is very weak on its own, it can contribute 100X increase in affinity of a peptide by adding cooperativity to the interaction of D-box with co-activator.

      After further reading on this topic, we will modify the relevant piece of text from:

      “However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast ‘pseudo-substrate’ inhibitor Acm1, acts to impede polyubiquitination of the bound protein (Qin et al. 2019). Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. As shown in Qin et al., mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Qin et al. 2019). Overall, our results support the conclusions that all the D-box peptides engage productively with the APC/C and that the highest affinity interactors act as inhibitors rather than functional degrons of APC/C.”

      to:

      “However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with conclusions from other studies that affinity of degron binding does not necessarily correlate with efficiency of degradation. Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. A number of studies of a yeast ‘pseudo-substrate’ inhibitor Acm1, have shown that mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Choi et al. 2008, Enquist-Newman et al. 2008, Burton et al. 2011) through a mechanism that governs recruitment of APC10 (Qin et al. 2019). Our study does not consider the contribution of APC10 to binding of our peptides to APC/C<sup>Cdc20</sup> complex, but since there is strong cooperativity provided by this additional interaction (Hartooni et al. 2022) we propose this as the critical factor in determining the ability of the different peptides to mediate degradation of associated mNeon.”

      Re Figure 6 and the fact that we did look at peptide binding in cells, these experiments were done in unsynchronised cells, so most Cdc20 would not be bound to APC/C.

    1. Now, it is clear that the decline of a language must ultimately have political and economic causes: it is not due simply to the bad influence of this or that individual writer. But an effect can become a cause, reinforcing the original cause and producing the same effect in an intensified form, and so on indefinitely. A man may take to drink because he feels himself to be a failure, and then fail all the more completely because he drinks. It is rather the same thing that is happening to the English language. It becomes ugly and inaccurate because our thoughts are foolish, but the slovenliness of our language makes it easier for us to have foolish thoughts. The point is that the process is reversible. Modern English, especially written English, is full of bad habits which spread by imitation and which can be avoided if one is willing to take the necessary trouble. If one gets rid of these habits one can think more clearly, and to think clearly is a necessary first step toward political regeneration: so that the fight against bad English is not frivolous and is not the exclusive concern of professional writers. I will come back to this presently, and I hope that by that time the meaning of what I have said here will have become clearer. Meanwhile, here are five specimens of the English language as it is now habitually written.

      I've noticed some people struggling to read any of this (I can't either). However, from my understanding the point is this:

      We don't really use English in a proper way because our own minds have also been "corrupted". This bad thoughts feed into the degredation of our language which feeds back into more bad thoughts.

      The way I summarized this is making me think Orwell might be right. -_-

    1. Latané and Darley’s original findings have been replicated in numerous studies. Increasing the number of bystanders inhibited helping behavior with many kinds of people, including children, college students, and future ministers (Darley & Batson, 1973; Latané & Nida, 1981; Plötner et al., 2015); in both small towns and large cities (Latané & Dabbs, 1975); in a variety of settings, such as psychology laboratories, city streets, and subway trains (Harrison & Wells, 1991; Latané & Darley, 1970; Piliavin & Piliavin, 1972); and with different kinds of emergencies, such as seizures, potential fires, fights, and accidents (Latané & Darley, 1968; Shotland & Straw, 1976; Staub, 1974), as well as with less-serious events such as having a flat tire (Hurley & Allen, 1974).

      One thing not mentioned in these discussions, though I imagine at least some of the researchers considered it: Fewer people helping when more bystanders are present may not be because everyone expects someone else to help. It may be because we as individuals do not like to stand out in a crowd, or act differently from others. Most of us are sensitive to what others will think of us. People inclined to help may be more self-secure and confident, or less concerned with public perceptions.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer 1

      Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

      We have not tested whether HAC1 mRNA is processed in S. cerevisiae. To address this question, we will perform RT-PCR to test it.

      In addition, as requested by the reviewers, we will further test the involvement of Ire1 in the HU/DIA-induced phenotype in S. pombe. For that, we will reassess our RNA-seq data and compare it with data from (Kimmig et al., 2012) (UPR activation in S. pombe). We will test the levels and splicing of mRNA of Bip1 upon HU/DIA treatments by RT-PCR and finally we will test the levels of Gas2p which has been described to decrease upon Ire1/UPR activation in S. pombe.

      We are confident in that the results of these experiments and the re-analysis of our RNA-Seq data will help us to infer the mechanisms that modulate the ER response to HU or DIA treatment.

      Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

      We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics.

      In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B).

      In addition, we plan to perform mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot.

      Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

      What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

      Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

      In this regard, the following information has been added to the manuscript (page 32-33, highlighted in blue):

      "We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype".

      Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

      DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, this hypothesis is supported by the appearance of cytoplasmic foci containing the guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding upon HU or DIA treatment (Figure 4I-J). We have already tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules. We have yet to include this data in the manuscript, but we plan to do that for the final version.

      To further explore the nature of the cytoplasmic aggregates induced by HU and DIA, we will test whether Hsp104-containing foci colocalize with guk1-9-GFP and/or Rho1.C17R-GFP foci.

      Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

      To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

      Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

      Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

      To fully rule out this possibility, we will test whether these Hsp104-aggregates colocalize with ER transmembrane proteins such as Rtn1 or Yop1, with Gma12-GFP that marks the Golgi apparatus and with the dye FM4-64 that stains endosomal-vacuole membranes.

      We have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of ypt1, vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

      To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

      To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

      On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

      Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

      Minor issues

      1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
      2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
      3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

      Reviewer 2

      Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

      We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

      We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

      Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

      This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

      We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

      These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 23.

      Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

      As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 22.

      We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 19).

      Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

      Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

      Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

      Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This is likely to decrease the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

      To further test this idea, we plan to carefully analyze the number, size and dynamics of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. We expect to find an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions.

      On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

      As this question has also been raised by reviewer 1, we have decided to further explore the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

      • We plan to test whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments.
      • We will test whether these foci are membrane bound.
      • We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
      • We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 30).

      The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

      We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers' concerns.

      Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

      To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

      Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

      Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

      We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress "protects" cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

      Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

      Minor points

      1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

      __ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

      Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

      __ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

      We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

      __ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

      We have included pertinent clarification in the new discussion.

      Reviewer 3

      Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

      Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

      With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we plan to reassess our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA. We also will test the levels of Bip1 and several UPR targets by RT-PCR and by western blot.

      Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

      We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

      In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (page 22).

      Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

      As the reviewer requested, we plan to test the effect of anisomycin (thapsigargin has been described to not work in yeast, as they lack a (SERCA)‐type Ca2+ pump (Strayle et al., 1999), which this drugs targets.

      Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We will further explore the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which will also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

      Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

      We plan on readdressing this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data, first by reevaluating our transcriptomic data and second by choosing Bip1 and some other of the differentially expressed genes in (Kimmig et al., 2012) (for example, Gas2, Pho1 or Yop1) and assessing by RT-PCR their mRNA levels in our experimental conditions. As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment.

      We are confident that the results of these experiments and the re-analysis of our RNA-Seq data will allow us to infer the mechanisms that modulate the ER response to HU or DIA treatment.

      Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

      We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

      "A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC){greater than or equal to}1 and an adjusted p-valueIn this regard, we plan to perform proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will also test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

      We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 31).

      Major issue #5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

      Minor concerns

      1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

      Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots, changing one would mean that, to avoid visual noise, we should change all. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

      2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

      We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

      3) Figures 1B through 1D do not indicate the HU concentration.

      We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.

      4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.

      Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.

      5) On page 8, they say "exposure to DIA" when they intend HU.

      This has been corrected in the manuscript.

      6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.

      We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.

      7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?

      20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.

      8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.

      Thanks to this comment, we realized the notation underneath Figure 1D (1E in the new version of the manuscript) could lead to misunderstandings, as the timings there were "random". We have now made a clarification for this panel to be clearer: the timings are normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with "40 minutes" does not mean N-Caps appear at that time point-quite the opposite, as most of them start to appear after >2 hours have passed in HU. We hope this can be better understood now.

      9) Figure S4 is missing the asterisk on the lower left cell.

      Fixed in the corresponding figure.

      10) How is roundness determined in Figure S4B?

      Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.

      The 'roundness' parameter in the 'Shape Descriptors' plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:

              Round=4 X [Area]/π X [Major axis]2
      

      where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.

      11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?

      Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.

      __12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __

      We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 30).

      The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.

      To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.

      13) Figure 4L is cited on page 28 when Figure 4K is intended.

      This has been corrected in the text, although new panels have been added and now it is 4N.

      • *
    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Review:

      Reviewer #1:

      (1) To support the finding that texture is not represented in a modular fashion, additional possibilities must be considered. These include (a) the effectiveness and specificity of the texture stimulus and control stimuli, (b) further analysis of possible structure in images that may have been missed, and (c) limitations of imaging resolution.

      Thank you for your comments. To address your concerns, we have conducted a new 3T fMRI experiment to demonstrate the effectiveness and specificity of our stimuli, performed further analyses to investigate possible structure of texture-selective activation, and discussed the limitations of imaging resolution.

      (a) To demonstrate the effectiveness and specificity of our stimuli, we conducted a new 3T fMRI experiment in five participants using an experimental design and texture families similar to those in Freeman (2013). Six texture stimuli in the 7T experiment were also included. To assess the effectiveness of each stimulus type, different texture families and their corresponding noise patterns were presented in separate blocks for 24 seconds, at a high presentation rate of 5 frames per second. In Figure S7, all texture families showed significantly stronger activation in V2 compared to their corresponding noise patterns, even for those that ‘appeared’ to have residual texture (e.g., the third texture family). These results demonstrate that our texture vs. noise stimuli were effective in producing texture-selective activations in area V2. Compared to the 7T results, the 3T data showed a notable increase in texture-selective activations in V2, likely due to increased stimulus presentation speed (1.25 vs. 5 frames/second). Future studies should use stimuli with faster presentation speed to validate our results in the 7T experiment.

      (b)Thank you for pointing out the possible structures of texture-selective activations in the peripheral visual field (Figure S1). In further analyses, we also found stronger texture selectivity in more peripheral visual fields (Figure 2D), and there were weak but significant correlations in the texture-noise activation patterns during split-half analysis (Author response image 2). Although this is not strong evidence for columnar organization of naturalistic textures, it suggests a possibility for modular organizations in the peripheral visual field.

      (c) Although our fMRI result at 1-mm isotropic resolution did not show strong evidence for modular processing of naturalistic texture in V2 stripe columns, this does not exclude the possibility that smaller modules exist beyond the current fMRI resolution. We have discussed this possibility in the revised manuscript.

      We hope this response clarifies our findings, and we have revised the conclusions in the manuscript accordingly.

      (2) More in-depth analysis of subject data is needed. The apparent structure in the texture images in peripheral fields of some subjects calls for more detailed analysis. e.g Relationship to eccentricity and the need for a 'modularity index' to quantify the degree of modularity. A possible relationship to eccentricity should also be considered.

      Based on your recommendations, we have performed further analysis and found interesting results regarding the modularity index in relation to eccentricity. As shown in Figure 2D, the texture-selectivity index increased as eccentricity. This may suggest a higher possibility of modular organization for texture representation in the peripheral compared to central visual fields. We have updated our results in Figure 2C, and discussed this possibility in the revised manuscript.

      (3) Given what is known as a modular organization in V4 and V3 (e.g. for color, orientation, curvature), did images reveal these organizations? If so, connectivity analysis would be improved based on such ROIs. This would further strengthen the hierarchical scheme.

      Following your recommendations, we have conducted further analysis to investigate the potential modular organizations in V4 and V3ab. In Figure S9 (Figure S9), vertices that are most responsive to color, disparity and texture were shown in a representative subject. Indeed, texture-selective patches can be found in both V4 and V3ab, along with the color- and disparity-selective patches. We agree with you that there should be pathway-specific connectivity among the same type of functional modules. In the informational connectivity analyses, we already used highly informative voxels by feature selection, which should mainly represent information from the modular organizations in these higher visual areas.

      Reviewer #2:

      (1) In lines 162-163, it is stated that no clear columnar organization exists for naturalistic texture processing in V2. In my opinion, this should be rephrased. As far as I understand, Figure 2B refers to the analysis used to support the conclusion. The left and middle bar plots only show a circular analysis since ROIs were based on the color and disparity contrast used to define thin and thick stripes. The interesting graph is the right plot, which shows no statistically significant overlap of texture processing with thin, thick, and pale stripe ROIs. It should be pointed out that this analysis does not dismiss a columnar organization per se but instead only supports the conclusion of no coincidence with the CO-stripe architecture.

      Thank you for your suggestions. Reviewer #1 also raised a similar concern. We agree that there may be a smaller functional module of textures in area V2 at a finer spatial scale than our fMRI resolution. We have rephrased our conclusions to be more precise.

      (2) In Figure 3, cortical depth-dependent analyses are presented for color, disparity, and texture processing. I acknowledge that the authors took care of venous effects by excluding outlier voxels. However, the GE-BOLD signal at high magnetic fields is still biased to extravascular contributions from around larger veins. Therefore, the highest color selectivity in superficial layers might also result from the bias to draining veins and might not be of neuronal origin. Furthermore, it is interesting that cortical profiles with the highest selectivity in superficial layers show overall higher selectivity across cortical depth. Could the missing increase toward the pial surface in other profiles result from the ROI definition or overall smaller signal changes (effect size) of selected voxels? At least, a more careful interpretation and discussion would be helpful for the reader.

      We agree with you that there will be residual venous effects even after removing voxels containing large veins. However, calculating the selectivity index largely removed the superficial bias (Figure 3). In the revised manuscript, we discussed the limitations of cortical depth-dependent analysis using GE-BOLD fMRI.

      In Line 397-403: “Due to the limitations of the T2*w GE-BOLD signal in its sensitivity to large draining veins (Fracasso et al., 2021; Parkes et al., 2005; Uludag & Havlicek, 2021), the original BOLD responses were strongly biased towards the superficial depth in our data (Figure S8). Compared to GE-BOLD, VASO-CBV and SE-BOLD fMRI techniques have higher spatial specificity but much lower sensitivity (Huber et al., 2019). As shown in a recent study (Qian et al., 2024), using differential BOLD responses in a continuous­­ stimulus design can significantly enhance the laminar specificity of the feature selectivity measures in our results (Figure 3).”

      It is unlikely that the strongest color selectivity index in the superficial depth is a result of stronger signal change or larger effect size in this condition. As shown by the original BOLD responses in Figure S8, all stimulus conditions produced robust activations that strongly biased to the superficial depth. High texture selectivity was also found in V4 and V3ab across cortical depth, which showed a flat laminar profile.

      (3) I was slightly surprised that no retinotopy data was acquired. The ROI definition in the manuscript was based on a retinotopy atlas plus manual stripe segmentation of single columns. Both steps have disadvantages because they neglect individual differences and are based on subjective assessment. A few points might be worth discussing: (1) In lines 467-468, the authors state that V2 was defined based on the extent of stripes. This classical definition of area V2 was questioned by a recent publication (Nasr et al., 2016, J Neurosci, 36, 1841-1857), which showed that stripes might extend into V3. Could this have been a problem in the present analysis, e.g., in the connectivity analysis? (2) The manual segmentation depends on the chosen threshold value, which is inevitably arbitrary. Which value was used?

      A previous study showed that the retinotopic atlas of early visual areas (V1-V3) aligned very well across participants on the standard surface after surface-based registration by the anatomical landmarks (Benson 2018). Thus, the group-averaged atlas should be accurate in defining the boundaries of early visual areas. To directly demonstrate the accuracy of this method, retinotopic data were acquired in five participants in a 3T fMRI experiment. A phase-encoded method was used to define the boundaries of early visual areas (black lines in Author response image 1), which were highly consistent with the Benson atlas.

      Although a few feature-selective stripes may extend into V3, these stripe patterns were mainly represented in V2. Thus, the signal contribution from V3 is likely to be small and should not affect the pattern of results. The activation map threshold for manual segmentation was abs(T)>2. We have clarified this in the revised methods.

      Author response image 1.

      Retinotopic ROIs defined by the Benson atlas (left) and the polar angle map (right) of the representative subject. Black lines denote the boundaries of early visual areas based on the retinotopic map from the subject.

      Benson, N. C., Jamison, K. W., Arcaro, M. J., Vu, A. T., Glasser, M. F., Coalson, T. S., Van Essen, D. C., Yacoub, E., Ugurbil, K., Winawer, J., & Kay, K. (2018). The Human Connectome Project 7 Tesla retinotopy dataset: Description and population receptive field analysis. J Vis, 18(13), 23. https://doi.org/10.1167/18.13.23

      (4) The use of 1-mm isotropic voxels is relatively coarse for cortical depth-dependent analyses, especially in the early visual cortex, which is highly convoluted and has a small cortical thickness. For example, most layer-fMRI studies use a voxel size of around isotropic 0.8 mm, which has half the voxel volume of 1 mm isotropic voxels. With increasing voxel volume, partial volume effects become more pronounced. For example, partial volume with CSF might confound the analysis by introducing pulsatility effects.

      We agree that a 1-mm isotropic voxel is much larger in volume than a 0.8-mm isotropic voxel, but the resolution along the cortical depth is not a big difference. In addition to our study, a previous study showed that fMRI at 1-mm isotropic resolution is capable of resolving cortical depth-dependent signals (Roefs et al., 2024; Shao et al., 2021). We have discussed these issues about fMRI resolution in the revised manuscript.

      In Line 403-408: “Compared to the submillimeter voxels, as used in most laminar fMRI studies, our fMRI resolution at 1-mm isotropic voxel may have a stronger partial volume effect in the cortical depth-dependent analysis. However, consistent with our results, previous studies have also shown that 7T fMRI at 1-mm isotropic resolution can resolve cortical depth-dependent signals in human visual cortex (Roefs et al., 2024; Shao et al., 2021).”

      Shao, X., Guo, F., Shou, Q., Wang, K., Jann, K., Yan, L., Toga, A. W., Zhang, P., & Wang, D. J. J. (2021). Laminar perfusion imaging with zoomed arterial spin labeling at 7 Tesla. NeuroImage, 245, 118724. https://doi.org/10.1016/j.neuroimage.2021.118724

      Roefs, E. C., Schellekens, W., Báez-Yáñez, M. G., Bhogal, A. A., Groen, I. I., van Osch, M. J., ... & Petridou, N. (2024). The Contribution of the Vascular Architecture and Cerebrovascular Reactivity to the BOLD signal Formation across Cortical Depth. Imaging Neuroscience, 2, 1–19.

      (5) The SVM analysis included a feature selection step stated in lines 531-533. Although this step is reasonable for the training of a machine learning classifier, it would be interesting to know if the authors think this step could have reintroduced some bias to draining vein contributions.

      We excluded vertices with extremely large signal change and their corresponding voxels in the gray matter when defining ROIs. The same number of voxels were selected from each cortical depth for the SVM analysis, thus there was no bias in the number of voxels from the superficial layers susceptible to large draining veins.

      Reviewer #3:

      The authors tend to overclaim their results.

      Re: Thank you for your comments. We added more control analyses to strengthen our findings, and gave more appropriate discussion of results.

      Recommendations for the authors:

      Reviewer #1:

      (1) Controls: There is a bit more complexity than is expressed in the introduction. The authors hypothesize that the emergence of computational features such as texture may be reflected in specialized columns. That is, if texture is generated in V2, there may be texture columns (perhaps in the pale stripes of V2); but if generated at a higher level, then no texture columns would be needed. This is a very interesting and fundamental hypothesis. While there may be merit to this hypothesis, the demonstration that color and disparity are modular but not texture falls short of making a compelling argument. At a minimum, the finding that texture is not organized in V2 requires additional controls. (a) To boost the texture signal, additional texture stimuli or a sequence of multiple texture stimuli per trial could be considered. (b) Unfortunately, the comparison noise pattern also seems to contain texture; perhaps a less textured control could be designed. (c) It also appears that some of the texture images in Supplementary Figure S1 contain possible structure, e.g. in more peripheral visual fields. (d) Is it possible that the current imaging resolution is not sufficient for revealing texture domains? (e) Note that 'texture' may be a property that defines surfaces and not contours. Thus, while texture may have orientation content, its function may be associated with the surface processing pathways. A control stimulus might contain oriented elements of a texture stimulus that do not elicit texture percept; such a control might activate pale and/or thick stripes (both of which contain orientation domains), while the texture percept stimulus may activate surface-related bands in V4.

      Thank you for your suggestions. They are extremely helpful in improving our manuscript. For the controls you mentioned in (a-d), we discussed them in the public review that we also attached below.

      (a) and (b): To demonstrate the effectiveness and specificity of our stimuli, we conducted a new 3T fMRI experiment in five participants using an experimental design and texture families similar to those in Freeman (2013). All texture stimuli in the 7T experiment were also included. To assess the effectiveness of each stimulus type, different texture families and their corresponding noise patterns were presented in separate blocks for 24 seconds, at a high presentation rate of 5 frames per second. In Figure S7, all texture families showed significantly stronger activation in V2 compared to their corresponding noise patterns, even for those that ‘appeared’ to have residual texture (e.g., the third texture family). These results suggest that our texture stimuli were effective in producing texture-selective activations in area V2 compared to the noise control. Compared to the 7T results, the 3T data showed a notable increase in texture-selective activations in V2, likely due to the increased stimulus presentation speed (1.25 vs. 5 frames/second). Weak texture activations might preclude the detection of columnar representations in the 7T experiment.

      (c) Thank you for pointing out the possible structures of texture-selective activations in the peripheral visual field (Figure S1). In further analyses, we also found stronger texture selectivity in more peripheral visual fields (Figure 2D), and there were weak but significant correlations in the texture-noise activation patterns during split-half analysis (Author response image 2). Although these are not strong evidence for columnar organization of naturalistic textures, it suggests a possibility for such organizations in the peripheral visual field.

      (d) Although our fMRI result at 1-mm isotropic resolution did not show strong evidence for modular processing of naturalistic texture in V2 stripe columns, this does not exclude the possibility that smaller modules exist beyond the current fMRI resolution. We have discussed these limitations in the revised manuscript.

      We fully agree with your explanation in (e). It fits our data very well. Both texture and control stimuli strongly activated the CO-stripes (Figure 2 and Figure 2D), while modular organizations for texture were found in V4 and V3ab (Figure S9). We have discussed this explanation in the revised manuscript.

      In Line 371-374: “Consistently, our pilot results also revealed modular organizations for textures in V4 and V3ab (Figure S9). These texture-selective organizations may be related to surface representations in these higher order visual areas (Wang et al., 2024).”

      (2) Overly simple description of FF, FB circuitry. The classic anatomical definition of feedforward is output from a 'lower' area, in most cases predominantly arising from superficial layers and projecting to middle layers of a 'higher area' (Felleman and Van Essen 1991). This description holds for V1-to-V2, V2-to-V3, and V2-to-V4. [Note there are also feedforward projections from central 5 degrees of V1-to-V4 (cf. Ungerleider) as well as V3-to-V4.] The definition of feedback can be more varied but is generally considered from cells in superficial and deep layers of 'higher' areas projecting to superficial and deep layers of 'lower' areas. Feedback inputs to V1 heavily innervate Layer 1 and superficial Layer 2, as well as the deep layers. Note that feedback connections from V2 to V1, similar to that from V1 to V2, are functionally specific, i.e. thin-to-blob and pale/thick-to interblob (Federer...Angelucci 2021, Hu...Roe 2022). Thus, current views are moving away from the dogma that feedback is diffuse. Recognition that feedback may be modular introduces new ideas about analysis.

      Thanks for your detailed recommendations. We have expanded the discussion of circuit models of functional connectivity in the introduction. Our model and experiments primarily aim to investigate how higher-level areas provide feedback to the V2 area. While we acknowledge that feedback may indeed be functionally specific, our methodology has some certain advantages: it ensures signal stability and avoids the double-dipping issue. Meanwhile, it also focuses on voxels with high feature selectivity, which may already be included in the modular organizations of early visual areas. In the functional connectivity analysis, we performed feature selection to use the most informative voxels. These voxels with high feature selectivity should already be included in the modular organizations of early visual areas. Identifying functionally specific feedback connections between modular areas will be an important and meaningful work for future research. We have added a discussion of this topic in the revised manuscript.

      In Line 136-138: “Only major connections were shown here. There are also other connections, such as V1 interblobs projecting to thick stripes (Federer et al., 2021; Hu & Roe, 2022; Sincich and Horton, 2005).”

      (3) Imaging superficial layers: Although removal of the top layer of cortical voxels (top 5% of voxels) is a common method for dealing with surface vascular artifact contribution to BOLD signal, it likely removes a portion of the Layer 1&2 feedback signals. Is this why the authors define feedback and deep layer to deep layer? If so, both superficial and deep-layer data in Figure 4 should be explicitly explained and discussed.

      Thank you for pointing this out. We would like to clarify the surface-based method removing vascular artifact. The vertices influenced by large pial veins were first defined on the cortical surface, and then voxels were removed from the entire columns corresponding to these vertices to avoid sampling bias along the cortical depth. Thus, there should be complete data from all cortical depths for the remaining columns. We defined the feedback connectivity from deep layers to deep layers because it represents strong feedback connections according to literature (Markov et al., 2013; Ullman, 1995) and also avoids confounding the feedforward signals from superficial layers.

      Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., Barone, P., Dehay, C., Knoblauch, K., & Kennedy, H. (2014). Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. The Journal of comparative neurology, 522(1), 225–259. https://doi.org/10.1002/cne.23458

      Ullman S. (1995). Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. Cerebral cortex, 5(1), 1–11. https://doi.org/10.1093/cercor/5.1.1

      (4) More detail on other subjects in Figure S1. Ten subjects conducted visual fixation and used a bite bar. Imaging data are illustrated in detail from one subject and the remaining subjects are depicted in graphs and in Supplemental Figure S1. Please provide arrowheads in each image to help guide the reader. Some kind of summary or index of modularity would also be helpful.

      Thanks for your suggestions. There are arrowheads in each image in our original manuscript and we have revised Figure S1 for better illustration. Additionally, we have added a table summarizing the number of stripes to provide a clearer overview.

      (5) How are ROIs in V3ab and V4 defined? V2 ROIs were defined (thin, thick, and pale stripe), but V3ab and V4 averaged across the whole area. Why not use the most activated "domains" from V3ab and V4? How does this influence connectivity analysis?

      Thank you for your question. We defined V4 and V3ab on the cortical surface using a retinotopic atlas (Benson 2018), which has been shown to be quite accurate in defining ROIs for the early visual areas. Since all ‘domains’ showed robust BOLD activation to our stimuli, we used voxels from the entire ROI in the depth-dependent analysis. In the functional connectivity analysis, we used the most informative voxels by feature selection, which should already be included in the feature domains.

      Minor:

      English language editing is needed.

      Thank you for your feedback. We have carefully revised the manuscript for clarity and readability.

      Line 31 "its" should be "their".

      Thank you. We have corrected "its" to "their".

      Replace 'representative subject' with 'subject'.

      We have replaced "representative subject" with "subject" in the manuscript.

      Replace 'naturalistic texture' with 'texture'.

      Thank you for your suggestion. The textures used in our experiment were generated based on the algorithm by Portilla and Simoncelli (2000), and the term "naturalistic texture" was used to be consistent with literature. The textures used in our study are different from traditional artificial textures, as they contain higher-order statistical dependencies. Following your recommendations, we have replaced ‘naturalistic texture’ with ‘texture’ in some places in the main text to improve readability.

      Typo: Line 126, Fig 2B should be 1B.

      Thank you. We have corrected "Fig 2B" to "Fig 1B" in Line 128.

      Fig. 2A: point out where are texture domains in anterior V2.

      The texture-selective activations in anterior V2 (corresponds to peripheral visual field) have been highlighted by arrowheads.

      Fig 2B, 3 legend: Round symbols are for each subject?

      Yes, the round symbols in Figures 2B represent data for individual participants. We have revised the legend for clarity.

      Fig. 3: Disparity and texture values do not look different across depth (except may the V2 texture values).

      While the difference in feature selectivity is small across cortical depths, they are highly consistent across participants. We have provided a figure showing the original BOLD responses in the revised manuscript (Figure S8 and Figure S8). Data from individual subjects were also available at Open Science Framework (OSF, https://doi.org/10.17605/OSF.IO/KSXT8 (‘rawBetaValues.mat’ in the data directory)).

      Line 57-59 The statement is not strictly accurate. V1 also has color, orientation, and motion representations.

      Thank you for your feedback. Our statement was intended to convey that M and P information from the geniculate input are transformed into representations of color, orientation, disparity, and motion in the primary visual cortex. We have clarified this point in the revised manuscript.

      In Line 58-60: “In the primary visual cortex (V1), the M and P information from the geniculate input are transformed into higher-level visual representations, such as motion, disparity, color, orientation, etc. (Tootell & Nasr, 2017).”

      Fig. 1B V1 interblobs also project to thick stripes (Sincich and Horton).

      Thank you for the additional information. We appreciate your input. Our figure is intended as a simplified schematic and does not fully represent all the connections. We have discussed this reference in the revised manuscript.

      In Line 136-138: “Only major connections were shown here. There are also other connections, such as V1 interblobs projecting to thick stripes (Federer et al., 2021; Hu & Roe, 2022; Sincich and Horton, 2005).”

      Line 207 "suggesting that both local and feedforward connections are involved in processing color information in area V2." Logic? English?

      Thank you for pointing this out. The superficial layers are involved in local intracortical processing by lateral connections and also send output to higher order visual areas along the feedforward pathway. Thus, the strongest color selectivity in the superficial depth of V2 supports that color information was processed in local neural circuits in area V2 and transmitted to higher order areas along the feedforward pathway. We have revised the manuscript for clarity.

      In Line 241-245: “According to the hierarchical model, the strongest color selectivity in the superficial cortical depth is consistent with the fact that color blobs locate in the superficial layers of V1 (Figure 1B, Felleman & Van Essen, 1991; Hubel & Livingstone, 1987; Nassi & Callaway, 2009). The strongest color selectivity in superficial V2 suggests that both local and feedforward connections are involved in processing color information (Figure 1C).”

      Line 254 "Laminar". Please use "cortical depth" or explicitly state that 'laminar' refers to superficial, middle, and deep as defined by cortical depth.

      Thank you for your suggestion. We have clarified the term "laminar" in the manuscript as referring to superficial, middle, and deep layers as defined by cortical depth.

      In Line 96-99: “To better understand the mesoscale functional organizations and neural circuits of information processing in area V2, the present study investigated laminar (or cortical depth-dependent) and columnar response profiles for color, disparity, and naturalistic texture in human V2 using 7T fMRI at 1-mm isotropic resolution.”

      Fig. S5 Please add a unit of isoluminance.

      Thank you for your suggestion. Supplementary Figure S10A and S10B illustrate the blue-matched luminance levels in RGB index. In our isoluminance experiment, blue was set as the reference color (RGB [0 0 255]) to measure the red and gray isoluminance.

      Line 448-449 To make this rationale clearer, refer to:

      Wang J, Nasr S, Roe AW, Polimeni JR. 2022. Critical factors in achieving fine‐scale functional MRI: Removing sources of inadvertent spatial smoothing. Human Brain Mapping. 43:3311-3331.

      Thank you for your suggestion. We have added this reference to better support the rationale of data analysis.

      Reviewer #2:

      (1) Line 126 should refer to Figure 1B.

      Thank you. We have corrected the reference in the revised manuscript as Figure 1B.

      (2) Even if only one naturalistic texture session was acquired per participant, it might be interesting to see the within-session repeatability by, e.g., splitting the texture runs into two halves.

      Thank you for your suggestion. We performed a split-half correlation analysis for participants who completed 10 runs in the naturalistic texture session. The result from one representative subject was shown in the figure below (for other participants, r = 0.38, 0.38, 0.24, and 0.23, respectively).

      Author response image 2.

      Split-half correlations for the texture-selective activation maps in a representative subject (S01) in V2.

      (3) Unfortunately, Figure S2 only shows the stripe ROIs but not V3ab or V4 ROIs. Including another figure that shows all ROIs in more detail would be interesting.

      Thank you for your suggestion. We have included a figure showing the ROIs for V4 and V3ab (the black dotted lines in Figure S9).

      (4) It would be helpful for the reader to have a more detailed discussion about methodological limitations, including the unspecificity of the GE-BOLD signal (Engel et al., 1997, Cereb Cortex, 7, 181-192; Parkes et al., 2005, MRM, 54, 1465-1472; Fracasso et al., 2021, Prog Neurobiol, 202, 102187) and the used voxel sizes.

      Thank you for your suggestion. We have added a more detailed discussion about the methodological limitations, including the unspecificity of the GE-BOLD signal and the voxel sizes used.

      In Line 397-408: “Due to the limitations of the T2*w GE-BOLD signal in its sensitivity to large draining veins (Fracasso et al., 2021; Parkes et al., 2005; Uludag & Havlicek, 2021), the original BOLD responses were strongly biased towards the superficial depth in our data (Figure S8). Compared to GE-BOLD, VASO-CBV and SE-BOLD fMRI techniques have higher spatial specificity but much lower sensitivity (Huber et al., 2019). As shown in a recent study (Qian et al., 2024), using differential BOLD responses in a continuous¬¬ stimulus design can significantly enhance the laminar specificity of the feature selectivity measures in our results (Figure 3). Compared to the submillimeter voxels, as used in most laminar fMRI studies, our fMRI resolution at 1-mm isotropic voxel may have a stronger partial volume effect in the cortical depth-dependent analysis. However, consistent with our results, previous studies have also shown that 7T fMRI at 1-mm isotropic resolution can resolve cortical depth-dependent signals in human visual cortex (Roefs et al., 2024; Shao et al., 2021).”

      (5) If I understand correctly, different numbers of runs/sessions were acquired for different subjects. It would be good to discuss if this could have impacted the results, e.g., different effect sizes could have biased the manual ROI definition.

      Thank you for your suggestion. Although there were differences in the number of runs/sessions acquired for different subjects, there were at least four runs of data for each experiment, which should be enough to examine the within-subject effect. We have discussed this point in the revised manuscript.

      In Line 481-484: “Although the number of runs were not equal across participants, there were at least four runs (twenty blocks for each stimulus condition) of data in each experiment, which should be sufficient to investigate within-subject effects.”

      (6) It would be good to add the software used for layer definition. Was it Laynii?

      We have provided more details in the revised methods.

      In Line 523-526: “An equi-volume method was used to calculate the relative cortical depth of each voxel to the white matter and pial surface (0: white matter surface, 1: pial surface, Supplementary Figure S11A), using mripy (https://github.com/herrlich10/mripy).”

      (7) It would be interesting to see (at least for one subject) the contrasts of color-selective thin stripes and disparity-selective thick stripes from single sessions to demonstrate the repeatability of measurements.

      Thank you for your suggestion. We have shown the test-retest reliability of the response pattern of color-selective thin stripes and disparity-selective thick stripes in a representative subject in Figure S5.

      (8) By any chance, do the authors also have resting-state data from the same subjects? It would be interesting to see the connectivity analysis between stripes and V3ab, V4 with resting-state data.

      Thank you for your suggestion. Unfortunately, we do not have resting-state data from the same subjects at this time. We agree with you that layer-specific connectivity analysis with resting-state data is very interesting and worth investigating in future studies.

      Reviewer #3:

      (1) For investigating information flow across areas, the authors rely on layer-specific informational connectivity analyses, which is an exciting approach. Covariation in decoding accuracy for a specific dependent variable between the superficial layers of a lower area and the middle layer of a higher area is taken as evidence for feedforward connectivity, whereas FB was defined as the connection between the two deep layers. Yet this method is not assumption-free. For example, the canonical idea (Figure 1C) of FF terminals exclusively arriving in layer 4 and FB terminals exclusively terminating in supra-or infragranular layers is not entirely correct. This is not even the case for area V1 - see for example Kathy Rockland's exquisite tractography studies, showing that even single axons with branches terminating in different layers. Also, feedback signals not only arrive in the deep layers of a lower area. Although these informational connectivity analyses can be suggestive of information flow, this reviewer doubts it can be considered as conclusive evidence. Therefore, the authors should drastically tone down their language in this respect, throughout the text. They present suggestive, not conclusive evidence. To obtain truly conclusive evidence, one likely has to perform laminar electrophysiological recordings simultaneously across multiple areas and infer the directionality of information flow using, for example, granger causality.

      Thank you for pointing out this important issue. In our response to a previous question (Reviewer #1, the 2nd comment), we have discussed other possible connections in addition to the canonical feedforward and feedback pathways. In the revised manuscript, the conclusion has been toned down to properly reflect our findings. However, we would also like to emphasize that our conclusion about laminar circuits was supported by converging lines of evidence. For example, in addition to the depth-dependent connectivity results, the role of feedback circuit in processing texture information was also supported by greater selectivity in V4 than V2, and the strongest deep layer selectivity in V2 (Figure 3C).

      (2) In the same realm, how reproducible are the information connectivity results? In the first part of the study, the authors performed a split-half analyses. This should be also done for Figure 4.

      Thank you for your suggestion. We have performed a split-half analysis for the informational connectivity results. As shown in Author response image 3, the results for the color experiment were robust and reproducible, while the disparity and texture connectivity results were less consistent between the two halves. The results from the second half (Author response image 3, below) are more consistent with the original findings (Figure 4). Overall, the pattern of results were qualitatively similar between the two halves. The inconsistency may be due to the fact that some participants had only four runs of data, which could make the split-half analysis less reliable.

      Author response image 3.

      Split-half analysis of informational connectivity.

      (3) Most of the other layer-specific claims (not the ones about the flow of information) are based on indices. It is unclear which ROIs contributed to these indices. Was it the entire extent of V1, V2, ...? Or only the visually-driven voxels within these areas? How exactly were the voxels selected? For V2, it would make sense to calculate the selectivity indices independently for the disparity and color-selective (putative) thick and (putative) thin stripe compartments, respectively. Adding voxels of non-selective compartments (e.g. putative thick stripe voxels for calculating the color-index; or adding putative thin-strip voxels for calculating the disparity index), will only add noise.

      In the revised manuscript, we have clarified that we selected the entire ROI in the depth-dependent analysis. Since our study does not have an independent functional localizer, using the entire ROI avoids the problem of double dipping. The processing of visual features is not confined solely to specific stripes. We have also provided a more comprehensive explanation of this issue in the discussion section.

      In Line 541-544: “For the cortical depth-dependent analyses in Figure 3, we used all voxels in the retinotopic ROI. Pooling all voxels in the ROI avoids the problem of double-dipping and also increases the signal-to-noise ratio of ROI-averaged BOLD responses.”

      (4) It is apparent from Figure 3, that the indices are largely (though not exclusively) driven by 2 subjects. Therefore, this reviewer wishes to see the raw data in addition to a table for calculating the color, disparity, and texture selectivity indices -along with the number of voxels that contributed to it.

      Thank you for your suggestion. We have provided a figure showing the original BOLD responses (Figure S8 and Figure S8). Data from individual subjects were also available at Open Science Framework (OSF, https://doi.org/10.17605/OSF.IO/KSXT8 (‘rawBetaValues.mat’ in the data directory)).

      Minor:

      (1) I typically find inferences about 'layer fMRI' vastly overstated. We all know that fMRI does not (yet) provide laminar-specific resolution, i.e., whereby meaningful differences in fMRI signals can be extracted from all 6 individual layers of neocortex, without partial volume effects, or without taking into account pre-and postsynaptic contributions of neurons to the fMRI signal (the cell bodies may very well lay in different layers than the dendritic trees etc.), or without taking into account the vascular anatomy, etc. The authors should use the term cortical depth-dependent fMRI throughout the text -as they do in the abstract and intro.

      Thank you for pointing out this important issue. We have now defined the meaning of layer or laminar as “cortical depth-dependent” in the introduction, to be consistent with the terminology in most published papers on this topic.

      (2) 1st sentence abstract: I disagree with this statement. The parallel streams in intermediate-level areas are probably equally well studied as the geniculostriate pathway -already starting with the seminal work of Hubel, Livingstone, and more recently by Angelucci and co-workers who looked in detail at the anatomical and functional interactions across sub-compartments of V1 and V2.

      Thank you for your feedback. In the revised manuscript, we have removed the term "much" from the first sentence of the abstract. Although there have been seminal studies of V2 sub-compartments in monkeys, only a few fMRI studies investigated this issue in humans.

      (3) The authors show inter-session correlations for color and disparity. This reviewer would like to see test-retest images since the explained variance is not terribly good. Also, show the correlation values for the inter-session texture beta values.

      Thank you for your suggestion. We have performed the test-retest reliability analysis of texture-selective patterns in the response to a previous question (Reviewer #2, the 2nd comment, Author response image 2).

      (4) The stripe definitions are threshold dependent. Please clarify whether the reported results are threshold-independent.

      Thank you for your question. To address your concern, we defined the stripe ROIs using different thresholds, and the results remained consistent. Specifically, we ranked the voxels in manually defined stripe ROIs by the color-disparity response. We then defined the lowest 10% as the thick stripe voxels, the highest 10% as thin stripe voxels, and the middle 10% as pale stripe voxels. Additionally, we adjusted the thresholds to 20% and 30% to define the three stripes (with 30% being the least strict threshold). Feature selectivities at different thresholds were shown in Figure S6 (from left to right: 10%, 20%, 30%). Notably, in all threshold conditions, there was no significant difference in texture selectivity across different stripes.

      (5) How were the visual areas defined?

      In the revised manuscript, we have provided a detailed description about methods.

      In Line 531-535: “ROIs were defined on the inflated cortical surface. Surface ROIs for V1, V2, V3ab, and V4 were defined based on the polar angle atlas from the 7T retinotopic dataset of Human Connectome Project (Benson et al., 2014, 2018). Moreover, the boundary of V2 was edited manually based on columnar patterns. All ROIs were constrained to regions where mean activation across all stimulus conditions exceeded 0.”

      (6) "According to the hierarchical model in Figure 1B and 1C, the strongest color selectivity in the superficial cortical depth is consistent with the fact that color blobs mainly locate in the superficial layers of V1, suggesting that both local and feedforward connections are involved in processing color information in area V2." But color-selective activation within V2 could be also consistent with feedback from other areas (some of which were not covered in the present experiments) -the more since most parts of the brain were not covered (i.e. a slab of 4 cm was covered)?

      Thank you for reminding us about this issue. We have discussed the possibility of feedback influence in explanation of the superficial bias of color selectivity in area V2.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Response to the Reviewer #1 (Public review):

      We greatly appreciate the reviewer’s high evaluation of our paper and helpful comments. As expected, we revealed that the CCL17/CCL22–CCR4 axes play an important role in guiding Tregs to the atherosclerotic aorta. Interestingly, we also demonstrated that these axes are critical for Treg-dependent regulation of proinflammatory T cell responses in lymphoid tissues and atherosclerotic aortas, which is a previously unrecognized role for CCR4 in regulating inflammatory immune responses. However, the role of the CCL17/CCL22–CCR4 axes in regulating inflammatory immune responses and atherosclerosis has not been fully elucidated and further investigation is needed.

      Response to the reviewer #2 (Public review):

      We greatly appreciate the reviewer’s high evaluation of our paper and helpful comments and suggestions. We isolated CD4<sup>+</sup>CD25<sup>+</sup> T cells and used them as Tregs in several experiments. As the reviewer pointed out, we realize that CD4<sup>+</sup>CD25<sup>+</sup> T cell population contains some activated effector T cells. However, in consideration of the high expression levels of the most reliable Treg marker Foxp3 in isolated CD4<sup>+</sup>CD25<sup>+</sup> T cells determined by flow cytometry, we believe that our method for separating Tregs would be acceptable.

      Regarding the role of Th17 cells in atherosclerosis, conflicting results have been reported. Therefore, it is unclear whether augmented Th17 cell immune responses contribute to accelerated atherosclerosis in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice.

      As the reviewer pointed out, it is important to consider the clinical relevance of our findings. We analyzed public database to determine if Ccr4 single nucleotide polymorphisms correlate with a higher incidence of atherosclerotic cardiovascular disease. However, no evidence supporting the clinical relevance of our findings was found.

      Response to the Reviewer #3 (Public review):

      We greatly appreciate the reviewer’s high evaluation of our paper and helpful comments and suggestions. In accordance with the reviewer’s suggestion, we described the detailed methods and carefully performed data analysis regarding flow cytometry, which would strengthen the conclusion of this study.

      We understood the importance of reviewer’s claim that CCR4 deficiency does not shift the Th1 cell/Treg balance toward Th1 cell responses in all lymphoid tissues. CCR4 deficiency promoted the accumulation of Th1 cells but did not affect the accumulation of Tregs in the atherosclerotic aorta, which led to the shift of the Th1 cell/Treg balance toward Th1 cell responses. The frequencies of both Tregs and Th1 cells in peripheral lymphoid tissues were increased by CCR4 deficiency, while these CCR4-deficient Tregs exhibited impaired suppressive function. Given this, we speculate that CCR4 deficiency may shift the Th1 cell/Treg balance toward Th1 cell responses in peripheral lymphoid tissues. However, it is difficult to clearly show this. We revised the manuscript accordingly.

      Although the reviewer pointed out the possibility that modulation of the Th1 cell/Th17 cell balance might be responsible for the changes in aortic inflammatory cells in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice, the role of Th17 cells in atherosclerosis remain controversial. However, we cannot completely exclude the possibility of the involvement of the Th17 response modulation in accelerated atherosclerosis in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice.

      As the limitation of this study, the phenotypic heterogeneity and dynamics of aortic leukocytes could not be revealed by flow cytometric analysis. Single-cell proteomic and transcriptomic approaches would provide additional important information on various aortic cells including immune cells and vascular cells.

      Reviewer #1 (Recommendations for the authors):

      Issue (1) Ideally, CCR4 could be deleted on Foxp3+ cells and some staining on double positive Rorg+Foxp3+ done. On the other side, a whole gene expression of infiltrated Foxp3 and effector could be also helpful. More challenging, it would be important to see whether those CCR4-specific Trges could or not regulate effector infiltrating cells.

      As the reviewer suggested, single-cell proteomic and transcriptomic approaches would be helpful to reveal the phenotypic heterogeneity and dynamics of aortic leukocytes including Tregs. Also, the use of conditional knockout mice would reveal the precise role of CCR4-expressing Tregs in regulating aortic immune cell infiltration and atherosclerosis.

      Reviewer #2 (Recommendations for the authors):

      Minor Suggestions:

      Issue (1) In supplementary Figure 1, CCR4 expression would be better represented by dot plots rather than histograms.

      We revised Supplementary Figure 1A through 1C.

      Issue (2) The reduction in CD103 expression shown in Figure 2E at 8 weeks should be discussed.

      In Figure 2E, we found that the expression of CD103 in peripheral LN Tregs was slightly lower in 8-week-old Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice than in age-matched Apoe<sup>-/-</sup> mice, while there was no difference in its expression levels between 18-week-old Apoe<sup>-/-</sup> and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice. In addition, there was no significant difference in the mRNA expression of this molecule in splenic Tregs between 8-week-old Apoe<sup>-/-</sup> and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice. Based on the minor effect of CCR4 deficiency on CD103 expression in Tregs, reduced CD103 expression in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice does not seem to be an important change.

      Issue (3) The increased expression of CD86 by DCs should be discussed.

      The upregulated CD86 expression on DCs in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice might be explained by the data on a Treg-DC coculture experiment showing the impaired cell–cell contacts between CCR4-deficient Tregs and DCs. On the other hand, the expression of another important costimulatory molecule CD80 on DCs was not altered in these mice, which is not consistent with the data on the above coculture experiment. The reason why only CD86 expression on DCs was upregulated in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice remains unclear.

      Issue (4) In Figures 5F-H, using larger dots would enhance visibility.

      We revised the graphs in Figure 5F-H.

      Issue (5) In Figure 5I, since the data is normalized, a one-sample t-test is more appropriate.

      In accordance with the reviewer’s suggestion, we reconsidered the data analysis. Because there was a dramatic difference in the absolute number of Kaede-expressing Tregs accumulated in the aorta among experiments, we were worried that the statistical analysis of the combined data from multiple experiments might draw a wrong conclusion. We have decided to show the representative data from 3 independent experiments in Figure 5I.

      Issue (6) On page 11, line 256, the text mentions IL4 and IL10 being detected by cytokine array; however, the figures do not show these cytokines.

      We are afraid that the reviewer might have misunderstood the data. The cytokine levels of IL-4 and IL-10 could not be detected by cytokine array analysis. Accordingly, we carefully revised the text in the manuscript.

      Issue (7). On page 14, lines 326-330, the text should be revised for clarity.

      We revised the text in the manuscript.

      Issue (8) Several data are marked as "not shown"; some of this information is relevant and should be included in the supplementary figures.

      We showed the data on CCL17 and CCL22 expression in peripheral LNs in Supplementary Figure 2.

      Major Suggestions:

      Issue (1) FoxP3 expression should be evaluated post-isolation of CD4<sup>+</sup>CD25<sup>+</sup> T cells, and FoxP3- CD4<sup>+</sup>CD25<sup>+</sup> T cells should be characterized. Tregs could be more effectively isolated using FoxP3eGFP mice.

      After isolation of CD4<sup>+</sup>CD25<sup>+</sup> T cells (the purity was >95%), we examined Foxp3 expression by flow cytometry and found that most of these cells express Foxp3 (Supplementary Figure 10). Therefore, CD4<sup>+</sup>CD25<sup>+</sup> T cells without Foxp3 expression, which are considered contaminated effector T cells, are minor cells and would not substantially affect the results. Nonetheless, the use of Foxp3-eGFP mice would enable us to isolate Tregs more accurately.

      Issue (2) In Figure 3, it would be interesting to evaluate whether there are RORgt+Tbet+ (IL17+IFNg+) cells. These cells would be pathogenic, whereas RORgt+CD73+ cells would be non-pathogenic.

      We analyzed CD4<sup>+</sup> T cells producing both IL-17 and IFN-γ in the peripheral lymphoid tissues of Apoe<sup>-/-</sup> and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice. We found that this cell population was quite rare and that there was no significant difference its proportion between the 2 groups, suggesting the possible minor contribution of this cell population to the atherosclerosis phenotype.

      Author response image 1.

      Issue (3) Different time points after adoptive cell transfer should be evaluated to confirm reduced migration to the atherosclerotic aorta.

      It would be interesting to evaluate Treg migration to the atherosclerotic aorta at different time points after Treg transfer. However, it seems difficult to accurately evaluate the migration of Tregs at later time points because they would proliferate in the aorta.

      Issue (4) The authors could evaluate whether Ccr4 SNPs correlate with an increased risk of atherosclerosis.

      As the reviewer pointed out, it is important to consider the clinical relevance of our findings. However, there is no evidence supporting that Ccr4 single nucleotide polymorphisms correlate with a higher incidence of atherosclerotic cardiovascular disease.

      Issue (5) The authors could evaluate if the transfer of Apoe<sup>-/-</sup> Tregs rescues early atherosclerosis development in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice.

      To confirm whether transfer of CCR4-intact Tregs rescues the development of early atherosclerotic lesions in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice, we injected Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice with saline or Tregs from Apoe<sup>-/-</sup> or Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice and analyzed the aortic root atherosclerotic lesions of recipient Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice. However, we found no significant difference in the aortic sinus plaque area among the 3 groups. We described this result in the results section and included the data in Supplementary Figure 8.

      Reviewer #3 (Recommendations for the authors):

      Analysis of TCD4<sup>+</sup> cell populations in different tissues:

      Issue (1) The description of flow cytometry analysis is incomplete and requires clarification. Please detail the use of controls to ensure correct analysis, including the following: i) cell viability; ii) staining controls to define positive and negative cells; iii) the gating strategy used to identify cell populations in each lymphoid tissue and aorta (please provide them as supplementary figures).

      As we thought that most of the prepared cells would be viable, we did not check their viability. Based on our previous work where various immune cells including Tregs, effector memory T cells, and helper T cell subsets were clearly detected, in this study we performed flow cytometric analysis of these immune cells without preparing negative controls stained with isotype control antibodies. The gating strategy of flow cytometric analysis of various immune cells in peripheral lymphoid tissues was reported in our previous report (J Am Heart Assoc 2024; 13: e031639). We provided the gating strategy of flow cytometric analysis of helper T cells and Tregs in the aorta in Supplementary Figure 9.

      Issue (2) The phenotype/differentiation markers used for analysing T CD4<sup>+</sup> cell subsets differ between lymphoid tissues and aortic lesions; might this influence results? If so, please comment on that.

      As the number of aortic T cells was quite few compared with that in peripheral lymphoid tissues, it seemed difficult to precisely detect aortic T cells including various helper T cell subsets and Tregs by intracellular cytokine staining. Therefore, we decided to analyze these cells by evaluating transcription factors specific for helper T cell subsets. The difference in the markers used for analyzing T cell subsets would not considerably influence the results.

      Issue (3) Considering my observations about the effect of CCR4 deficiency on the T CD4<sup>+</sup> differentiation profile in different tissues, I suggest comparing Th1/Treg and Th17/Treg ratios in all examined tissues. The modulation of the Th17/Th1 balance could shape inflammation.

      The Th1 cell/Treg balance is shifted toward Th1 cell responses in the atherosclerotic aorta of Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice, while this balance would not be altered in the peripheral lymphoid tissues. It remains unclear whether CCR4 deficiency affects the Th17 cell/Treg ratio. We do not think that it is important to investigate the effect of CCR4 deficiency on the balance of Th17 cell/Treg or Th17 cell/Th1 cell because the role of Th17 cell responses in atherosclerosis remains controversial.

      Issue (4) Cell numbers of recovered Treg from para-aortic lymphoid nodes and aortic tissues might not allow Treg functional assays. Analysis by flow cytometry of biomarkers of Treg activation state would be more informative than by quantifying mRNA expression levels. In particular, TGFβ analysis at the mRNA level does not provide much more information about the suppressive activity of Treg, and even at the protein level, the recognition of the active form of this cytokine is required. Analysis of PD1 (for exhausted cell phenotype) and Treg apoptosis along the stages of atherosclerosis could also yield useful information.

      We performed flow cytometric analysis of activation markers CTLA-4 and CD103, cell exhaustion marker PD1, and apoptosis in Tregs in the para-aortic LNs of Apoe<sup>-/-</sup> or Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice, and found no major differences in the expression levels of these molecules or the proportion of apoptotic cells between the 2 groups. We showed these data below.

      Author response image 2.

      Unfortunately, we failed to evaluate the activity of TGF-β in Tregs because an appropriate experimental method for precisely detecting its active form was unavailable.

      Issue (5) Regarding the result´s interpretation, I recommend being precise when concluding to avoid misunderstanding. A shift in the T CD4<sup>+</sup> response in lymphoid tissues might be interpreted as a modulation of the T cell differentiation process, which strongly depends on signals derived from DCs, which were not the focus of this study.

      There are two possible mechanisms for the altered CD4<sup>+</sup> T cell responses in peripheral lymphoid tissues, which include the modulation of their differentiation and proliferation processes. These processes are substantially regulated by DCs whose function could be favorably modulated by CCR4-expressing Tregs as described in the manuscript. Therefore, we think that the interactions between Tregs and DCs are crucial for shifting the CD4<sup>+</sup> T cell responses in peripheral lymphoid tissues, though it remains unclear which process plays a major role in regulating CD4<sup>+</sup> T cell polarization.

      Suppression studies:

      Issue (1) In vitro assays. According to the methodology suppression studies were performed using Treg collected from peripheral lymphoid nodes and spleen, but it is unclear whether these cells were analysed separately or as a pool (this was not clarified in the legend of Figure 5 either). Besides, be precise about which cells were used as antigen-presenting cells in the Treg suppression assay.

      In in vitro Treg suppression assay, we used Tregs purified from peripheral lymph nodes and spleen as a pool. We used splenocytes as antigen-presenting cells in Treg suppression assay. We revised the manuscript accordingly.

      Issue (2) Obtaining CD4<sup>+</sup>CD25<sup>+</sup> and CD4<sup>+</sup>CD25-. The control of the purity and viability of cell preparations from CCR4 deficient and CCR4 sufficient Apoe<sup>-/-</sup> mice should be included as a supplementary material; these purified cells were used in in vitro suppressive assays and in vivo cell transfer experiments, being relevant information to guarantee results. Since this control was performed by flow cytometry, I wonder whether Foxp3 levels were also checked.

      We included the data on the purity and viability of CD4<sup>+</sup>CD25<sup>+</sup> Tregs and CD4<sup>+</sup>CD25<sup>-</sup> T cells from Apoe<sup>-/-</sup> or Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice in Supplementary Figure 10. After the isolation of CD4<sup>+</sup>CD25<sup>+</sup> T cells, we examined Foxp3 expression by flow cytometry and found that most of these cells express Foxp3.

      Issue (3) For in vitro assays, IL-2, IL-10, and TGFβ measurement in culture supernatants could confirm and provide more information about Treg function.

      As both CD4<sup>+</sup>CD25<sup>+</sup> Tregs and CD4<sup>+</sup>CD25<sup>-</sup> T cells would produce various cytokines in in vitro Treg suppression assay, it is difficult to determine which cells mainly produce the above cytokines. Therefore, measurement of these cytokines would not provide more information about Treg function.

      Issue (4) It would be interesting to assess whether CCR4-mediated DC-Treg interaction is equally important to regulate Th1 than Th17 and Th2 activation; this likely requires using different settings to favour each activation profile.

      Based on our findings, we speculate that CCR4 may play an important role in regulating not only Th1 cell responses but also Th2 and Th17 cell responses by maintaining the interactions between Tregs and DCs. However, it may not be meaningful to investigate the effect of CCR4 deficiency on these T cell responses because the roles of Th2 and Th17 cell responses in atherosclerosis remain controversial.

      Issue (5) The authors showed that the presence of Treg decreased CD80 and CD86 surface levels in DCs in vitro, remarking a lower capacity of Treg derived from CCR4-deficient mice (Figure 5B). However, the fact that CD86 on splenic CD11c+MHC-II+ DCs in 8-week-old Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice was significantly higher than in Apoe<sup>-/-</sup> was underestimated (Supplementary Figure 4). This data needs reconsideration as it might indicate an in vivo more permissive activation state of DCs in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice than in Apoe<sup>-/-</sup> mice, explaining the augmented effector T cell response observed in these mice (Figure 2).

      Our finding of the upregulated CD86 expression on DCs in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice could be explained by the data on a Treg-DC coculture experiment showing the impaired ability of CCR4-deficient Tregs to downregulate CD80 and CD86 expression on DCs. As the reviewer pointed out, our data may indicate more permissive activation state of DCs and subsequent augmentation of effector T cell responses in Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice, which may be derived from impaired Treg suppressive function.

      Assays for chemokine levels and influence on T cell activation and traffic:

      Issue (1) Considering the findings described by Döring et al. (reference 24 in the paper), monitoring CCL22, CCL17, and CCL3 levels in the aorta and lymph nodes along atherosclerosis development would help in understanding when and how CCL17/CCL20-CCR4 might influence T cell activation and traffic. I wonder whether these chemokines were assayed by qPCR in lymphoid nodes and aorta from CCR4-deficient and sufficient Apoe<sup>-/-</sup> mice. The authors report that CCR8 (capable also of binding CCL17) was unaltered by CCR4 deficiency in splenic and para-aortic lymph nodes Treg from 8 and 18 weeks-old mice, respectively (Supplementary Figure 5 and 6), although a trend towards a high-level was observed for splenic Treg. It would be informative to evaluate CCR8 Treg levels along with atherosclerosis progress.

      As it is considered that the mRNA expression levels of chemokines do not necessarily reflect their protein expression levels, we did not analyze the mRNA expression of Ccl17 or Ccl22 by quantitative reverse transcription PCR. Instead of this, we evaluated the protein expression of CCL17 and CCL22 not only in the aorta but also in the peripheral lymph nodes of 18-week-old wild-type, Apoe<sup>-/-</sup>, and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice by immunohistochemistry. We found no marked differences in their expression levels in peripheral lymph nodes among these mice and included the data in Supplementary Figure 2.

      As we focused on the role of the CCL17/CCL22–CCR4 axes in atherosclerosis, we did not examine the expression of CCL3 that is not directly related to these axes. The evaluation of CCR8+ Treg proportion is beyond the scope of this study, though we are interested in the change of this population by CCR4 deficiency associated with atherosclerotic lesion development.

      Issue (2) According to IFNγ and IL-17 expressing TCD4<sup>+</sup> subclasses, Th1 and Th17 cell subset levels increase in the spleen (Figure 3B-D) and para-aortic lymphoid nodes (Figure 4E) in CCR4 absence. A comparison of the CCR4 dependence for the migration of Th17 and Th1 cell subsets to the aorta was not performed in this atherosclerosis model; this study could help to understand the mechanisms associated with the aortic inflammation development.

      To evaluate the migration of Th1 or Th17 cells in the aorta, we need to specifically isolate them from the peripheral lymphoid tissues of Apoe<sup>-/-</sup> or Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice and adoptively transfer them into recipient Apoe<sup>-/-</sup> mice. However, it is impossible to isolate alive Th1 or Th17 cells because specific cell surface markers that enable us to separate these cells are unavailable.

      Issue (3) The numbers of Kaede Treg cells detected in the aorta were extremely low in both Apoe<sup>-/-</sup> and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice (Figure 5I), opening results to question. Besides, the flow cytometry assay used for determining Kaede Treg cells in tissues was not well described. How were cell viability and formation of doublets examined to avoid artefacts? The gating strategy used to ensure a confident analysis of Kaede Tregs, particularly in the aorta, should be included as supplementary material.

      The extremely low number of Kaede-expressing Tregs migrated in the aorta of Apoe<sup>-/-</sup> and Ccr4<sup>-/-</sup>Apoe<sup>-/-</sup> mice may be derived from the small number of the transferred Tregs. As another explanation for this finding, Tregs may rarely migrate in the aorta under hypercholesterolemic conditions. We did not check the viability or doublets of Kaede-expressing Tregs because we thought that such experimental procedures would not considerably affect the results. We provided the gating strategy of flow cytometric analysis of Kaede-expressing Tregs in peripheral lymphoid tissues and aortas in Supplementary Figure 11.

      Other comments:

      Issue (1) As an alternative for statistical data analysis from independent experiments, two-way ANOVA with Tukey's post hoc (for data normally distributed) or the Mack Skillings exact test with Conover´s post hoc multiple comparison test (for a two-way layout in non-parametric conditions) could improve analysis.

      We performed statistical analysis in Figure 5A according to the reviewer’s suggestion.

      Issue (2) For future work, employing recombinant pseudo-receptor proteins capable of neutralizing chemokines (doi: 10.1016/j.jhep.2021.08.029) might help as an alternative to complete knockout mice.

      We thank the reviewer for giving us the information on an interesting approach as an alternative to CCR4-deficient mice.

    1. Reviewer #2 (Public review):

      Summary:

      This study aimed to investigate changes in neural responses over time after acute stress and their association with real-life stress. To this end, functional MRI data was collected from 3 tasks (Oddball, 2-back, Associative retrieval) early and late following stress and control conditions. Emotional ratings during a stressful week before an exam and a non-stressful week without an exam were used to index real-world stress. In total, data from 70 individuals were used for the analyses in the paper. Results showed increased oddball related activation early after stress whereas activation to the associative retrieval was reduced across early and late trials following stress compared with control. Brain activation during the oddball task after stress contrasted against control correlated with the index used to measure stress in the real-world. This is a very ambitious study and the findings that stress has opposite effects on the oddball and the associative retrieval tasks is new. However, I am not convinced that brain responses are correlated with real-world stress from the results presented in the paper. I also have several other concerns listed below.

      Strengths:

      The study uses a unique design based on hypothesis firmly grounded in theories of stress related brain function. Large amounts of data are collected for all of the 70 participants included in the analyses and the hypotheses tested using paired tests have strong statistical power. Data collection methods are sound aiming to reduce stress induced by being in the scanner environment for the first time and reducing variation in cortisol due to circadian rhythm.

      Weaknesses:

      An important argument in the paper is that neural responses associated with stress in the lab correspond to stress in real life. This conclusion is based on a single correlation analysis. This is weak evidence because the correlation is based on 70 individuals and may be driven by outliers. In fact, the correlation between the difference in stress-related SN activation (Stress-Control) and real life stress residual is likely to be driven by outliers. In fig 5b, there are 3 persons with SN values of around 2, which is twice as much as the fourth highest value. There is also 1 person with a Real life stress residual of -3 or -4, which is three to four times as much as the person with the second lowest value. These 4 outliers should be removed before calculating the correlation coefficient. Also, no power analysis is presented in the paper showing what effect size is needed for significant results given a sample size of 70.

      It is not clear why the activation maps from the tasks performed in the scanner are referred to as the SN, ECN, and DMN. They are discussed as if they were resting state networks. They are however not resting state networks because they are the results of contrasting two task conditions to each other and not the results from correlating BOLD time-series data from different regions within subjects. Even though masks corresponding to SN, ECN, and DMN are used to calculate means of all voxels, I think these contrasts should be referred to as the tasks that were used to evoke them. It becomes misleading to call them networks which usually refers to nodes and edges in fMRI studies. The first scan was a resting state scan, but these data are not presented in the paper.

      Introduction<br /> In the introduction it is said that there are genomically driven effects of cortisol 1 to 2 hours after stress. This is repeated in the discussion: "[the late stress phase] is thought to be dominated by genomically driven effects of glucocorticoids". (There is no reference to this statement however.) This idea, that gene expression should only be regulated by corticosteroids following stress seems unrealistic. The increase in cortisol was only around 60% from baseline in the current study which seems to be similar to other studies. This means that the baseline cortisol level is far from zero. Therefore, effects of cortisol on gene expression must occur all the time and be tightly regulated by circadian clocks. To propose that genomically driven effects of cortisol only exist 1 to 2 hours following stress is therefore too simplistic.

      In the last paragraph, it says that n=83. However, the final sample consists of 70 people. Correct this number.

      Methods<br /> The EMA data analysis is difficult to understand. Why are the residuals used instead of means for example? I could not understand how the residual values used in the analysis should be interpreted from the way this section was written. Therefore, I cannot judge whether the index is valid or reliable. Using mean values is more common than using residuals when investigating individual differences in stress responses. The use of residuals needs justification and clarification. The results from an analysis using mean values should also be reported.

      How was AUCi calculated? What software was used to calculate AUCi?

      How was the mediation analysis performed? The only information I found was: "We additionally ran separate models with an interaction term modelled for neural activity in the targeted ROI's to examine the relationship between task performance and neural responses, with random slopes and intercepts also modelled for ROI activity." This is not how mediation analyses are done conventionally. It is common to use structural equation modelling or a series of regression analyses. What is meant by separate models? Was a reduced model compared to a full model with an interaction term? In this case, this is not a mediation analysis. I think the term moderation is better to describe this analysis.

  6. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. When someone presents themselves as open and as sharing their vulnerabilities with us, it makes the connection feel authentic. We feel like they have entangled their wellbeing with ours by sharing their vulnerabilities with us. Think about how this works with celebrity personalities. Jennifer Lawrence became a favorite of many when she tripped at the Oscars [f2], and turned the moment into her persona as someone with a cool-girl, unpolished, unfiltered way about her. She came across as relatable and as sharing her vulnerabilities with us, which let many people feel that they had a closer, more authentic connection with her. Over time, that persona has come to be read differently, with some suggesting that this open-styled persona is in itself also a performance. Does this mean that her performance of vulnerability was inauthentic?

      When I read this paragraph, I think this phenomenon is very interesting. In particular, the example of Jennifer Lawrence makes me instantly understand what is expressed in topic sentence. When someone presents a very perfect feeling, maybe not many people like them because it gives others the impression that this person is very fake and unfamiliar. However, if some weaknesses or shortcomings are properly exposed, it may appear that the person is very friendly and sincere. I also looked up some relevant materials and found that we can make use of this psychological state to help us narrow the distance with others in the process of making friends.

    2. These needs may not always be as obvious in highly individualized societies, like Post-Enlightenment Europe and the United States. The possibility for self-reliance has been created in part by making certain things dependable and institutionalized. You can go get yourself food without feeling like you have to trust anyone because you can just go to the store (which has to adhere to corporate legal requirements) and buy food (the supply of which is made stable by complex networks of growing, manufacturing, and transportation, covered by the assurances of FDA-compliant labeling) from people who work there (and are subject to labor laws and HR regulations, which, if they are not followed, means the staff person does not get paid, so their wellbeing depends on them doing their job). The need to trust other people is obscured by the many institutions that we have created. Institutions have ways, sometimes, of getting around human whims and surprises. But at the end of the day, it is still hugely important to us that we feel clear about who can be trusted, and for what.

      This passage is a logically clear and vivid discussion, which successfully explains the deep human need for authenticity and its social significance. I think this example is very good. Everyone has the experience of going to the supermarket to buy things. This very daily example made me understand this argument at once and agree with it.

    3. These reactions make sense. Try to imagine the early days of human social life, before we started attaching our welfare to the land in terms of planting crops and building structures designed for permanence. Our nomadic forebears functioned in groups who coordinated in highly specialized ways to ensure the survival of the whole. Although such communities are often pictured as being prehistoric, primitive, and obsolete, we now know that such societies were and are highly sophisticated, often developing and depending on highly specified legal codes, some of which are still in use today in Bedouin communities in North Africa. Other nomadic groups, such as Roma people (which you may have heard derogatorily called ‘gypsies’), live within and around land-based nations and their various borders and laws. To ensure the survival of their ethnicity, cultures, and languages, they depend on being able to trust each other. The nations whose land we are living and studying on here also knew the importance of being able to know who can be trusted. These needs may not always be as obvious in highly individualized societies, like Post-Enlightenment Europe and the United States. The possibility for self-reliance has been created in part by making certain things dependable and institutionalized. You can go get yourself food without feeling like you have to trust anyone because you can just go to the store (which has to adhere to corporate legal requirements) and buy food (the supply of which is made stable by complex networks of growing, manufacturing, and transportation, covered by the assurances of FDA-compliant labeling) from people who work there (and are subject to labor laws and HR regulations, which, if they are not followed, means the staff person does not get paid, so their wellbeing depends on them doing their job). The need to trust other people is obscured by the many institutions that we have created. Institutions have ways, sometimes, of getting around human whims and surprises. But at the end of the day, it is still hugely important to us that we feel clear about who can be trusted, and for what.

      It’s interesting to think about how trust was such a fundamental part of survival in nomadic societies, and how that need for trust hasn’t gone away—it’s just evolved into more complex systems in modern life. I never really thought about it that way before, especially with things like buying food. Even though we don’t always feel like we need to trust the person behind the counter, so many systems rely on trust—like laws, regulations, and the people making sure everything runs smoothly. It makes me realize how much we depend on trust in ways we don’t always see.

    1. "We should make few friends for the sake of pleasure, since but little sweetness suffices to season life, just as little salt suffices for our meat."

      I found this quote/ analogy to be really beneficial as a way of making a point, but also interesting for what it might be extended to mean. I think this perfectly describes the previous sentence about a lack of mirth being better than too much merrymaking, since people would generally agree that plain food is more edible than way overly-salted food. But in either case, doing so once in a while is not going to kill you (nor will an occasional imbalance of mirth condemn a person). Perhaps more interestingly, this analogy made me wonder about the exceptions. For general eating, you may season meat lightly. But to preserve the meat due to certain seasons or circumstances, you might apply a lot of salt (like with jerky). Thus, how games would have been considered in very dark times like war, famine, disease, etc. On one hand, I could see a defense for perhaps even more merriment, given that these times would tax the soul further than usual and would thus need sufficient relaxation, as mentioned earlier by Aquinas' story of the tense bow. However, these times might also be deemed too inappropriate for games given the earlier mentioned possible objections of jokes being wrong for certain situations.

    1. However, in teacher research, the data collection effort is purposeful, deliberate, organized, and systematic. The information we gather from our data may serve as evidence that confirms our insights and validates our intuition.

      I chose this section to annotate because as a social media manager, I also have a purpose with my research and the data I receive will also confirm my intuition on what I felt I was looking for. I ultimately decided that I will do my research on social media in the sports world since I am in SRM. I haven't exactly decided where I will go with my question but I have some ideas. I have a feeling about what I think my results may look like, so whatever data i gather from games can help confirm if I was correct or not.

    1. However, most societies do not value creative thinking and so our skills in generating ideas rapidly atrophies, as we do not practice it, and instead actively learn to suppress it

      I think this point was pretty interesting. This reminds me of how in class, we talked about how when brainstorming ideas, we need to unfilter out our ideas and let them flow out, because if we filter out our ideas, we may lose out on interesting ideas that can contribute to the bigger picture of how we want the project to look like. Plus, taking a little bit and looking back at the idea later can also add interesting insights that make it useful as opposed to just saying it's a dumb idea and forgetting about it.

    1. One critique of human-centered design is that it narrowly focuses on people and their needs rather than a systems-level view of the activities that people engage in, and the multiple people and systems involved in those activities.

      I understand this point of view because this was my first thought when being introduced to the human centered design. I agree with this statement but maybe a little too much. What happens when we have to consider too many groups of people involved. A new design/solution may not be able to account for everybody without losing quality or function? How does one deal with that appropriately? I think as I start to design this question will be the most prominent.

    1. In fact, intercultural communication has the potential to enrich various aspects of our lives. In order to communicate well within various cultural contexts, it is important to keep an open mind and avoid making assumptions about others’ cultural identities. While you may be able to identify some aspects of the cultural context within a communication encounter, there may also be cultural influences that you can’t see. A competent communicator shouldn’t assume to know all the cultural contexts a person brings to an encounter, since not all cultural identities are visible. As with the other contexts, it requires skill to adapt to shifting contexts, and the best way to develop these skills is through practice and reflection.

      This is also one of the challenging parts especially if your culture differs with others. like in my country talking to someone older than you and making eye contact with them is disrespectful but here its different and like this annotation says at the end we just need to adapt skills through practice and reflection. i think this can take a while to do but the more you practice the better you succeed.

    1. Authors’ Response (31 October 2024)

      GENERAL ASSESSMENT

      Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

      We thank the reviewers for their time and effort in reviewing our manuscript. Based on their valuable comments and suggestions, we have made substantial revisions. The updated manuscript now includes two new experiments supporting that lysophospholipid-triggered channel activation promotes the release of signaling molecules critical for immune response and demonstrates that this novel class of agonist activates the inflammasome in human macrophages through endogenously expressed Panx1. To better highlight the significance of our findings, we have excluded the cryo-EM panel from this manuscript. We believe these changes address the main concerns raised by the reviewers and enhance the overall clarity and impact of our findings. Below, we provide a point-by-point response to each of the reviewers’ comments.

      RECOMMENDATIONS

      Essential revisions:

      1. The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the 2 key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.

      We appreciate and agree with the reviewers’ observation. Our initial manuscript may have been challenging to follow due to our use of both wild-type and GS-tagged versions of Panx1 from human and frog origins, combined with different fluorescence techniques across cell types. In this revision, we used only human wild-type Panx1 expressed in HEK293S GnTI<sup>-</sup> cells, except for activity-guided fractionation experiments, where we used GS-tagged Panx1 expressed in HEK293 cells (Fig. 1). For functional reconstitution studies, we employed YO-PRO-1 uptake assays, as optimizing the Venus-based assay was challenging. We have clarified these exceptions in the main text. We think these adjustments simplify the narrative and ensure an appropriate balance between main and supplemental figures.

      1. It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).

      We fully agree with the reviewer on the importance of ion selectivity experiments. However, comparing the properties of LPC-activated channels with those activated by membrane depolarization presented technical challenges, as LPC appears to stimulate Panx1 in synergy with voltage. Prolonged LPC exposure destabilizes patches, complicating G-V curve acquisition and kinetic analyses. While such experiments could provide mechanistic insights, we think they are beyond the scope of current study.

      1. Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.

      As mentioned above, we believe our revised version is more straightforward to digest. We have improved labeling and provided explanations where necessary to clarify the manuscript. While Panx1 expression levels are indeed higher in GnTI<sup>-</sup> than in HEK293 cells, we are uncertain whether the absence of detectable currents in HEK293 cells is solely due to expression levels. Some post-translational modifications that inhibit Panx1, such as lysine acetylation, may also impact activity. Future studies are needed to explore these mechanisms further.

      1. The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.

      We modified the Y-axis in Figure 2 to “Quench (%)” for clarity. The data reflects fluorescence reduction over time, starting from LPC addition, normalized to the maximal decrease observed after Triton-X100 addition (3 minutes), enabling consistent quenching value comparisons. Although the quenching value appears small, normalization against complete cell solubilization provides reproducible comparisons. We do not fully understand why CBX effects vary in Venus quenching experiments, but we speculate that its steroid-like pentacyclic structure may influence the lysophospholipid agonistic effects. As noted in prior studies (DOI: 10.1085/jgp.201511505; DOI: 10.7554/eLife.54670), CBX likely acts as an allosteric modulator rather than a simple pore blocker, potentially contributing to these variations.

      1. Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?

      Our recent study suggest that the electrostatic landscape along the permeation pathway may influence its ion selectivity (DOI: 10.1101/2024.06.13.598903). However, we have not yet fully elucidated how Panx1 permeates both anions and cations. Based on our findings, ion selectivity may vary with activation stimulus intensity and duration. Cation permeation through Panx1 is often demonstrated with YO-PRO-1, which measures uptake over minutes, unlike electrophysiological measurements conducted over milliseconds to seconds. We referenced two representative studies employing YO-PRO-1 to assess Panx1 activity. Whole-cell current measurements from a similar construct with an intracellular loop insertion indicate that our STREP-tagged construct likely retains functional capacity.

      1. In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.

      We used the ratio of LPC-induced current to the current measured at +110 mV to determine whether any of the mutants disproportionately affect LPC-induced current relative to depolarization-activated current. Since the mutants that did not respond to LPC also exhibited smaller voltage-stimulated currents than those that did respond, we reasoned that using this ratio would better capture the information the reviewer is suggesting to gauge. Showing the zero current level may be helpful if the goal was to compare basal currents, which in our experience vary significantly from patch to patch. However, since we are comparing LPC- and voltage-induced currents within the same patch, we believe that including basal current measurements would not add useful information to our study.

      Given that new experiments included to further highlight the significance of the discovery of Panx1 agonists, we opted to separate structure-based mechanistic studies from this manuscript and removed this experiment along with the docking and cryo-EM studies.

      1. The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.

      We agree with the reviewer and attempted 3D classification using multiple smaller masks including the suggested region. However, the maps remained poorly defined, and we were unable to confidently assign the NTD.

      1. The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."

      Both maps show that the narrowest constriction, formed by W74, has a diameter of approximately 9 Å. Previous steered molecular dynamics simulations suggest that ATP can permeate through such a constriction, implying an ion selection mechanism distinct from a simple steric barrier.

      1. The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?

      We appreciate the reviewer’s insight. We incubated LPC and nanodisc-reconstituted protein for 30 minutes, speculating that LPC approaches the pore similarly to other lipids in prior structures. In separate studies, we are optimizing conditions to capture more defined conformations.

      1. In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.

      We agree that the density corresponding the extended NTD into the cytoplasm is relatively weak. In our recent study, we compared two Panx1 structures with or without the mentioned C-terminal helix and found evidence suggesting the likelihood of NTD extension (DOI: 10.1101/2024.06.13.598903). Nevertheless, to prevent potential confusion, we have removed the cryo-EM panel from this manuscript.

      1. Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

      Thank you for the suggestion. We have removed this section to avoid potential confusion.

      Optional suggestions:

      1. The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.

      We have added a rationale in line 90, stating: “The soluble fractions were excluded from this study, as the most polar fraction induced strong channel activities in the absence of exogenously expressed pannexins.” Additionally, we have included a figure to support this rationale (Fig. S1A).

      1. The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.

      Unfortunately, in our hands, the prolonged application of lysolipids at concentrations necessary to achieve significant currents tends to destabilize the patch. This makes it challenging to obtain G-V curves or perform the previously mentioned kinetic analyses. We believe this destabilization may be due to lysolipids’ surfactant-like qualities, which can disrupt the giga seal. Additionally, prolonged exposure seems to cause channel desensitization, which could be another confounding factor.

      1. From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?

      Thank you for the comment, with which we agree. Our initial intention was to use the mutagenesis studies to experimentally support the docking study. Due to uncertainties associated with the presented cryo-EM maps, we have decided to remove this study from the current manuscript. We will consider the proposed experiments in a future study.

      1. Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.

      We believe the uncoordinated arrangement of NTDs is dynamic, allowing for potential variations in pore size during the activated conformation. Alternatively, NTD movement may be coupled with conformational changes in TM1 and the extracellular domain, which in turn could alter the electrostatic properties of the permeation pathway. We believe a functional study exploring this mechanism would be more appropriately presented as a separate study.

      1. Can the authors provide a positive control for these negative results presented in Fig S1B and C?

      The positive results are presented in Fig. 1D and E.

      1. Raw images in Fig S6 and Fig S7 should contain units of measurement.

      Thank you for pointing this out.

      1. It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.

      We attempted to superimpose the cryo-EM maps; however, visually highlighting the differences in figure format proved challenging. Higher-resolution maps would allow for model building, which would more effectively convey these distinctions.

      1. Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.

      Noted.

      1. A table for cryo-EM statistics should be included.

      Thanks, noted.

      1. n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.

      While we agree that transparency is essential, adding n-values to each graph would make some figures less clear and potentially harder to interpret in this case. We believe that the dot plots, n-value range, and statistical analysis provide adequate support for our claims.

      1. The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.

      Thank you for this comment. Although we intended to support the caspase activation mechanism in our introduction, we understand that the reviewer’s interpretation indicates a need for clarification. We hope the revised introduction removes any perception of dismissiveness.

      1. Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.

      For Fig. 4F, we used a distinct normalization method to account for substantial day-to-day variation in experiments involving body fluids. Notably, we did not apply this normalization to other experimental panels due to their considerably lower day-to-day variation.

      1. What was the rational for using the structure from ref 35 in the docking task?

      The docking task utilized the human orthologue with a flipped-up NTD. We believe that this flipped-up conformation is likely the active form that responds to lysolipids. As our functional experiments primarily use the human orthologue for biological relevance, this structure choice is consistent. Our docking data shows that LPC does not dock at this site when using a construct with the downward-flipped NTD.

      1. Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.

      Done.

      1. From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.

      We have added the molecular weight of each subtype in the figure legend.

      1. Please provide holding voltages and zero current levels in all figures presenting currents.

      We provided holding voltages. However, the zero current levels vary among the examples presented, making direct comparisons difficult. Since we are comparing currents with and without LPC, we believe that indicating zero current levels is unnecessary for this study.

      1. While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

      We are uncertain whether Panx3 is unaffected by lysophospholipids, as we have not observed activation of this subtype under any tested conditions.

      (This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

    2. Consolidated Peer Review Report (20 December 2023)

      GENERAL ASSESSMENT

      Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

      RECOMMENDATIONS

      Essential revisions:

      1. The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.
      2. It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).
      3. Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.
      4. The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.
      5. Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?
      6. In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.
      7. The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.
      8. The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."
      9. The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?
      10. In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.
      11. Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

      Optional suggestions:

      1. The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.
      2. The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.
      3. From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?
      4. Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.
      5. Can the authors provide a positive control for these negative results presented in Fig S1B and C?
      6. Raw images in Fig S6 and Fig S7 should contain units of measurement.
      7. It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.
      8. Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.
      9. A table for cryo-EM statistics should be included.
      10. n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.
      11. The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.
      12. Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.
      13. What was the rational for using the structure from ref 35 in the docking task?
      14. Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.
      15. From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.
      16. Please provide holding voltages and zero current levels in all figures presenting currents.
      17. While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

      REVIEWING TEAM

      Reviewed by:

      Jorge Contreras, Professor, University of California, Davis, USA: electrophysiology and ion channel mechanisms

      Wei Lü, Associate Professor, Department of Structural Biology, Van Andel Institute, USA: ion channel mechanisms, X-ray crystallography and cryo-EM

      Xiaofeng Tan, Research Fellow, NINDS, NIH, USA: structural biology (X-ray crystallography and cryo-electron microscopy) and ion channel mechanisms

      Kenton J. Swartz, Senior Investigator, NINDS, NIH, USA: ion channel structure and mechanisms, chemical biology and biophysics, electrophysiology and fluorescence spectroscopy

      Curated by:

      Kenton J. Swartz, Senior Investigator, NINDS, NIH, USA

      (This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Comments concerning minor and presentational issues have been omitted for brevity.)

    1. Reviewer #2 (Public review):

      Summary:

      This manuscript provides experimental evidence on circadian behavioural cycles in Antarctic krill. The krill were obtained directly from krill fishing vessels and the experiments were carried out on board using an advanced incubation device capable of recording activity levels over a number of days. A number of different experiments were carried out where krill were first exposed to simulated light:dark (L:D) regimes for some days followed by continuous darkness (DD). These were carried out on krill collected during late autumn and late summer. A further set of experiments was performed on krill across three different seasons (summer, autumn, winter), where incubations were all DD conditions. Activity was measured as the frequency by which an infrared beam close to the top of the incubation tube was broken over unit time. Results showed that patterns of increased and decreased activity that appeared synchronised to the LD cycle persisted during the DD period. This was interpreted as evidence of the operation of an internal (endogenous) clock. The amplitude of the behavioural cycles decreased with time in DD, which further suggests that this clock is relatively weak. The authors argued that the existence of a weak endogenous clock is an adaptation to life at high latitudes since allowing the clock to be modulated by external (exogenous) factors is an advantage when there is a high degree of seasonality. This hypothesis is further supported by seasonal DD experiments which showed that the periodicity of high and low activity levels differed between seasons.

      Strengths

      Although there has been a lot of field observations of various circadian type behaviour in Antarctic krill, relatively few experimental studies have been published considering this behaviour in terms of circadian patterns of activity. Krill are not a model organism and obtaining them and incubating them in suitable conditions are both difficult undertakings. Furthermore, there is a need to consider what their natural circadian rhythms are without the overinfluence of laboratory-induced artefacts. For this reason alone, the setup of the present study is ideal to consider this aspect of krill biology. Furthermore, the equipment developed for measuring levels of activity is well-designed and likely to minimise artefacts.

      Weaknesses

      I have little criticism of the rationale for carrying out this work, nor of the experimental design. Nevertheless, the manuscript would benefit from a clearer explanation of the experimental design, particularly aimed at readers not familiar with research into circadian rhythms. Furthermore, I have a more fundamental question about the relationship between levels of activity and DVM on which I will expand below. Finally, it was unclear how the observational results made here related to the molecular aspects considered in the Discussion.

      (1) Explanation of experimental design - I acknowledge that the format of this particular journal insists that the Results are the first section that follows the Introduction. This nevertheless presents a problem for the reader since many of the concepts and terms that would generally be in the Methods are yet to be explained to the reader. Hence, right from the start of the Results section, the reader is thrown into the detail of what happened during the LD-DD experiments without being fully aware of why this type of experiment was carried out in the first place. Even after reading the Methods, further explanation would have been helpful. Circadian cycle type research of this sort often entrains organisms to certain light cycles and then takes the light away to see if the cycle continues in complete darkness, but this critical piece of knowledge does not come until much later (e.g. lines 369-372) leaving the reader guessing until this point why the authors took the approach they did. I would suggest the following (1) that more effort is made in the Introduction to explain the exact LD/DD protocols adopted (2) that a schematic figure is placed early on in the manuscript where the protocol is explained including some logical flow charts of e.g. if behavioural cycle continues in DD then internal clock exists versus if cycle does not continue in DD, the exogenous cues dominate - followed by - major decrease in cyclic amplitude = weak clock versus minor decrease = strong clock and so on

      (2) Activity vs kinesis - in this study, we are shown data that (i) krill have a circadian cycle - incubation experiments; (ii) that krill swarms display DVM in this region - echosounder data (although see my later point). My question here is regarding the relationship between what is being measured by the incubation experiments and the in situ swarm behaviour observations. The incubation experiments are essentially measuring the propensity of krill to swim upwards since it logs the number of times an individual (or group) break a beam towards the top of the incubation tube. I argue that krill may be still highly active in the rest of the tube but just do not swim close to the surface, so this approach may not be a good measure of "activity". Otherwise, I suggest a more correct term of what is being measured is the level of "upward kinesis". As the authors themselves note, krill are negatively buoyant and must always be active to remain pelagic. What changes over the day-night cycle is whether they decide to expend that activity on swimming upwards, downwards or remaining at the same depth. Explaining the pattern as upward kinesis then also explains by swarms move upwards during the night. Just being more active at night may not necessarily result in them swimming upwards.

      (3) Molecular relevance - Although I am interested in molecular clock aspects behind these circadian rhythms, it was not made clear how the results of the present study allow any further insight into this. In lines 282 to 284, the findings of the study by Biscontin et al (2017) are discussed with regard to how TIM protein is degraded by light via the clock photreceptor CRYTOCHROME 1. This element of the Discussion would be a lot more relevant if the results of the present study were considered in terms of whether they supported or refuted this or any other molecular clock model. As it stands, this paragraph is purely background knowledge and a candidate for deletion in the interest of shortening the Discussion.

      Other aspects<br /> (i) 'Bimodal swimming' was used in the Abstract and later in the text without the term being fully explained. I could interpret it to mean a number of things so some explanation is required before the term is introduced.<br /> (ii) Midnight sinking - I was struck by Figure 2b with regards to the dip in activity after the initial ascent, as well as the rise in activity predawn. Cushing (1951) Biol Rev 26: 158-192 describes the different phases of a DVM common to a number of marine organisms observed in situ where there is a period of midnight sinking following the initial dusk ascent and a dawn rise prior to dawn descent. Tarling et al (2002) observe midnight sinking pattern in Calanus finmarchicus and consider whether it is a response to feeding satiation or predation avoidance (i.e. exogenous factors). Evidence from the present study indicates that midnight sinking (and potential dawn rise) behaviour could alternatively be under endogenous control to a greater or lesser degree. This is something that should certainly be mentioned in the Discussion, possibly in place of the molecular discussion element mentioned above - possibly adding to the paragraph Lines 303-319.

      (iii) Lines 200-207 - I struggled to follow this argument regarding Piccolin et al identifying a 12 h rhythm whereas the present study indicates a ~24 h rhythm. Is one contradicting the other - please make this clear.

      (iv) Although I agree that the hydroacoustic data should be included and is generally supportive of the results, I think that two further aspects should be made clear for context (a) whether there was any groundtruthing that the acoustic marks were indeed krill and not potentially some other group know to perform DVM such as myctophids (b) how representative were these patterns - I have a sense that they were heavily selected to show only ones with prominent DVM as opposed to other parts of the dataset where such a pattern was less clear - I am aware of a lot of krill research where DVM is not such a clear pattern and it is disingenuous to provide these patterns as the definitive way in which krill behaves. I ask this be made clear to the reader (note also that there is a suggestion of midnight sinking in Fig 5b on 28/2).

    2. Author response:

      Reviewer #1 (Public review):  

      Hüppe and colleagues had already developed an apparatus and an analytical approach to capture swimming activity rhythms in krill. In a previous manuscript they explained the system, and here they employ it to show a circadian clock, supplemented by exogenous light, produces an activity pattern consistent with "twilight" diel vertical migration (DVM; a peak at sunset, a midnight sink, and a peak in the latter half of the night). 

      They used light:dark (LD) followed by dark:dark (DD) photoperiods at two times of the year to confirm the circadian clock, coupled with DD experiments at four times of year to show rhythmicity occurs throughout the year along with DVM in the wild population. The individual activity data show variability in the rhythmic response, which is expected. However, their results showed rhythmicity was sustained in DD throughout the year, although the amplitude decayed quickly. The interpretation of a weak clock is reasonable, and they provide a convincing justification for the adaptive nature of such a clock in a species that has a wide distributional range and experiences various photic environments. These data also show that exogenous light increases the activity response and can explain the morning activity bouts, with the circadian clock explaining the evening and late-night bouts. This acknowledgement that vertical migration can be driven by multiple proximate mechanisms is important. 

      The work is rigorously done, and the interpretations are sound. I see no major weaknesses in the manuscript. Because a considerable amount of processing is required to extract and interpret the rhythmic signals (see Methods and previous AMAZE paper), it is informative to have the individual activity plots of krill as a gut check on the group data. 

      The manuscript will be useful to the field as it provides an elegant example of looking for biological rhythms in a marine planktonic organism and disentangling the exogenous response from the endogenous one. Furthermore, as high latitude environments change, understanding how important organisms like krill have the potential to respond will become increasingly important. This work provides a solid behavioral dataset to complement the earlier molecular data suggestive of a circadian clock in this species. 

      We appreciate the positive evaluation of our work by Reviewer 1, acknowledging our approach to record locomotor activity in krill as well as the importance of the findings in assessing krill’s potential to respond to environmental change in their habitat.  

      Reviewer #2 (Public review):  

      Summary: 

      This manuscript provides experimental evidence on circadian behavioural cycles in Antarctic krill. The krill were obtained directly from krill fishing vessels and the experiments were carried out on board using an advanced incubation device capable of recording activity levels over a number of days. A number of different experiments were carried out where krill were first exposed to simulated light:dark (L:D) regimes for some days followed by continuous darkness (DD). These were carried out on krill collected during late autumn and late summer. A further set of experiments was performed on krill across three different seasons (summer, autumn, winter), where incubations were all DD conditions. Activity was measured as the frequency by which an infrared beam close to the top of the incubation tube was broken over unit time. Results showed that patterns of increased and decreased activity that appeared synchronised to the LD cycle persisted during the DD period. This was interpreted as evidence of the operation of an internal (endogenous) clock. The amplitude of the behavioural cycles decreased with time in DD, which further suggests that this clock is relatively weak. The authors argued that the existence of a weak endogenous clock is an adaptation to life at high latitudes since allowing the clock to be modulated by external (exogenous) factors is an advantage when there is a high degree of seasonality. This hypothesis is further supported by seasonal DD experiments which showed that the periodicity of high and low activity levels differed between seasons. 

      Strengths 

      Although there has been a lot of field observations of various circadian type behaviour in Antarctic krill, relatively few experimental studies have been published considering this behaviour in terms of circadian patterns of activity. Krill are not a model organism and obtaining them and incubating them in suitable conditions are both difficult undertakings. Furthermore, there is a need to consider what their natural circadian rhythms are without the overinfluence of laboratory-induced artefacts. For this reason alone, the setup of the present study is ideal to consider this aspect of krill biology.

      Furthermore, the equipment developed for measuring levels of activity is well-designed and likely to minimise artefacts. 

      We would like to thank Reviewer 2 for their positive assessment of our approach to study the influence of the circadian clock on krill behavior. We are delighted, that Reviewer 2 found our mechanistic approach in understanding daily behavioral patterns of Antarctic krill using the AMAZE set-up convincing, and that the challenging circumstances of working with a polar, non-model species are acknowledged.

      Weaknesses 

      I have little criticism of the rationale for carrying out this work, nor of the experimental design. Nevertheless, the manuscript would benefit from a clearer explanation of the experimental design, particularly aimed at readers not familiar with research into circadian rhythms. Furthermore, I have a more fundamental question about the relationship between levels of activity and DVM on which I will expand below. Finally, it was unclear how the observational results made here related to the molecular aspects considered in the Discussion. 

      (1) Explanation of experimental design - I acknowledge that the format of this particular journal insists that the Results are the first section that follows the Introduction. This nevertheless presents a problem for the reader since many of the concepts and terms that would generally be in the Methods are yet to be explained to the reader. Hence, right from the start of the Results section, the reader is thrown into the detail of what happened during the LD-DD experiments without being fully aware of why this type of experiment was carried out in the first place. Even after reading the Methods, further explanation would have been helpful. Circadian cycle type research of this sort often entrains organisms to certain light cycles and then takes the light away to see if the cycle continues in complete darkness, but this critical piece of knowledge does not come until much later (e.g. lines 369372) leaving the reader guessing until this point why the authors took the approach they did. I would suggest the following (1) that more effort is made in the Introduction to explain the exact LD/DD protocols adopted (2) that a schematic figure is placed early on in the manuscript where the protocol is explained including some logical flow charts of e.g. if behavioural cycle continues in DD then internal clock exists versus if cycle does not continue in DD, the exogenous cues dominate - followed by - major decrease in cyclic amplitude = weak clock versus minor decrease = strong clock and so on 

      We would like to thank Reviewer 2 for pointing out that the experimental design and the rationale behind it are not becoming clear early in the manuscript, especially for people outside the field of chronobiology. We think that the suggestion to include a schematic figure early in the manuscript is excellent and we plan to implement this in a revised version of the manuscript.  

      (2) Activity vs kinesis - in this study, we are shown data that (i) krill have a circadian cycle - incubation experiments; (ii) that krill swarms display DVM in this region - echosounder data (although see my later point). My question here is regarding the relationship between what is being measured by the incubation experiments and the in situ swarm behaviour observations. The incubation experiments are essentially measuring the propensity of krill to swim upwards since it logs the number of times an individual (or group) break a beam towards the top of the incubation tube. I argue that krill may be still highly active in the rest of the tube but just do not swim close to the surface, so this approach may not be a good measure of "activity". Otherwise, I suggest a more correct term of what is being measured is the level of "upward kinesis". As the authors themselves note, krill are negatively buoyant and must always be active to remain pelagic. What changes over the day-night cycle is whether they decide to expend that activity on swimming upwards, downwards or remaining at the same depth. Explaining the pattern as upward kinesis then also explains by swarms move upwards during the night. Just being more active at night may not necessarily result in them swimming upwards. 

      We believe that there is a slight misunderstanding in the way that what we call “activity” is measured. The experimental columns are equipped with five detector modules, evenly distributed over the height of the column. In our analysis we count all beam breaks that are caused by upward movement, i.e. every time a detector module is triggered after a detector module at a lower position has been triggered, and not only when the top detector module is triggered. In this way, we record upward swimming movements throughout the column, and not only when the krill swims all the way to the top of the column. This still means that what we are measuring is swimming activity, caused by upward swimming. We use this measure, to deliberately separate increased swimming activity, from baseline activity (i.e. swimming which solely compensates for negative buoyancy) and inactivity (i.e. passive sinking). 

      A higher activity is thus at first interpreted as an increase in swimming activity, which in the field may result in upwards directed swimming but also could mean a horizontal increase in activity, for example representing increased foraging and feeding activity. This would explain the daily activity pattern observed under LD cycles (Fig. 2), which shows a general increase in activity during the dark phase. This nighttime increase could be used for both upward directed migration during sunset as well as horizontal directed swimming for feeding and foraging throughout the night.

      We will formulate the description of the activity metric more clearly in the revised version of the manuscript.

      (3) Molecular relevance - Although I am interested in molecular clock aspects behind these circadian rhythms, it was not made clear how the results of the present study allow any further insight into this. In lines 282 to 284, the findings of the study by Biscontin et al (2017) are discussed with regard to how TIM protein is degraded by light via the clock photreceptor CRYTOCHROME 1. This element of the Discussion would be a lot more relevant if the results of the present study were considered in terms of whether they supported or refuted this or any other molecular clock model. As it stands, this paragraph is purely background knowledge and a candidate for deletion in the interest of shortening the Discussion.  

      We agree that this part is not directly related to the data presented in the manuscript and will therefore omit this part in the revised version of the manuscript to keep the discussion concise and focused on the results. 

      Other aspects 

      (i) 'Bimodal swimming' was used in the Abstract and later in the text without the term being fully explained. I could interpret it to mean a number of things so some explanation is required before the term is introduced. 

      We thank the Reviewer for pointing this out and will provide an explanation for the term “bimodal swimming” in a revised version of the manuscript. 

      (ii) Midnight sinking - I was struck by Figure 2b with regards to the dip in activity after the initial ascent, as well as the rise in activity predawn. Cushing (1951) Biol Rev 26: 158-192 describes the different phases of a DVM common to a number of marine organisms observed in situ where there is a period of midnight sinking following the initial dusk ascent and a dawn rise prior to dawn descent. Tarling et al (2002) observe midnight sinking pattern in Calanus finmarchicus and consider whether it is a response to feeding satiation or predation avoidance (i.e. exogenous factors). Evidence from the present study indicates that midnight sinking (and potential dawn rise) behaviour could alternatively be under endogenous control to a greater or lesser degree. This is something that should certainly be mentioned in the Discussion, possibly in place of the molecular discussion element mentioned above - possibly adding to the paragraph Lines 303-319. 

      We would like to thank the Reviewer for pointing this out and agree that it would be interesting to add the idea of an endogenous control of midnight sinking to the discussion. We plan to implement this in a revised version of the manuscript. 

      (iii) Lines 200-207 - I struggled to follow this argument regarding Piccolin et al identifying a 12 h rhythm whereas the present study indicates a ~24 h rhythm. Is one contradicting the other - please make this clear. 

      In our study we found that the circadian clock drives a bimodal pattern of swimming activity in krill, meaning it controls two bouts of activity in a 24 h cycle. Piccolin et al. (2020) identified a swimming activity pattern of ~12 h (i.e. two peaks in 24 h) at the group level, which is in line with our findings at the individual level. We will revisit the mentioned section for more clarity in a revised version.   

      (iv) Although I agree that the hydroacoustic data should be included and is generally supportive of the results, I think that two further aspects should be made clear for context (a) whether there was any groundtruthing that the acoustic marks were indeed krill and not potentially some other group know to perform DVM such as myctophids (b) how representative were these patterns - I have a sense that they were heavily selected to show only ones with prominent DVM as opposed to other parts of the dataset where such a pattern was less clear - I am aware of a lot of krill research where DVM is not such a clear pattern and it is disingenuous to provide these patterns as the definitive way in which krill behaves. I ask this be made clear to the reader (note also that there is a suggestion of midnight sinking in Fig 5b on 28/2).  

      To clarify the mentioned points concerning the hydroacoustic data:

      a) As mentioned in the Methods section, only hydroacoustic data during active fishing was included in the analysis. E. superba occurs in large monospecific aggregations and the fishery is actively targeting E. superba and monitoring their catch and the proportion of non-target species continuously with cameras. Krill fishery bycatch rates are very low (0.1–0.3%, Krafft et al. 2018), and fishing operations would stop if non-target species were being caught in significant proportions at any time. Therefore, and supported by our own observations when we conducted the experiments, we argue that it is a valid assumption that the backscattering signal shown in Figure 5 is predominantly caused by E. superba. 

      b) We are aware of the fact that DVM patterns of Antarctic krill are highly variable and that normal DVM patterns do not need to be the rule (e.g. see our cited study on the plasticity of krill DVM by Bahlburg et al. 2023). The visualized data were not selected for their DVM pattern but represent the period directly preceding the sampling for behavioral experiments in four different seasons (namely S1-S4), including the day of sampling. These periods were chosen to assess the DVM behavior of krill swarms in the field in the days before and during the sampling for behavioral experiments. 

      We will include these aspects in the Methods section in a revised version of the manuscript in order to improve understanding.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors' research group had previously demonstrated the release of large multivesicular body-like structures by human colorectal cancer cells. This manuscript expands on their findings, revealing that this phenomenon is not exclusive to colorectal cancer cells but is also observed in various other cell types, including different cultured cell lines, as well as cells in the mouse kidney and liver. Furthermore, the authors argue that these large multivesicular body-like structures originate from intracellular amphisomes, which they term "amphiectosomes." These amphiectosomes release their intraluminal vesicles (ILVs) through a "torn-bag mechanism." Finally, the authors demonstrate that the ILVs of amphiectosomes are either LC3B positive or CD63 positive. This distinction implies that the ILVs either originate from amphisomes or multivesicular bodies, respectively.

      Strengths:

      The manuscript reports a potential origin of extracellular vesicle (EV) biogenesis. The reported observations are intriguing.

      Weaknesses:

      It is essential to note that the manuscript has issues with experimental designs and lacks consistency in the presented data. Here is a list of the major concerns:

      (1) The authors culture the cells in the presence of fetal bovine serum (FBS) in the culture medium. Given that FBS contains a substantial amount of EVs, this raises a significant issue, as it becomes challenging to differentiate between EVs derived from FBS and those released by the cells. This concern extends to all transmission electron microscopy (TEM) images (Figure 1, 2P-S, S5, Figure 4 P-U) and the quantification of EV numbers in Figure 3. The authors need to use an FBS-free cell culture medium.

      Although FBS indeed contains bovine EVs, however, the presence of very large multivesicular EVs (amphiectosomes) that our manuscript focuses on has never been observed and reported. For reported size distributions of EVs in FBS, please find a few relevant references below:

      PMID: 29410778, PMID: 33532042, PMID: 30940830 and PMID: 37298194

      All the above publications show that the number of lEVs > 350-500 nm is negligible in FBS. The average diameter of MV-lEVs (amphiectosomes) described in our manuscript is around 1.00-1.50 micrometer.

      Reviewer #1: These papers evaluated the effectiveness of various methods to eliminate EVs from FBS, emphasizing the challenges associated with the presence of EVs in FBS. They also caution against using FBS in EV studies due to these issues. However, I did not find a clear indication regarding the size distributions of EVs in FBS in these papers.

      Please provide accurate reference supporting the claim that 'lEVs > 350-500 nm are negligible in FBS.' The papers cited by the authors do not address this specific point.

      In the revised manuscript, we addressed the point that due to sterile filtering of FBS, it cannot contain large >0.22 µm EVs

      Our response to Reviewer #1 point 2. When we demonstrated the TEM of isolated EVs, we consistently used serum- free conditioned medium (Fig2 P-S, Fig2S5 J, O) as described previously (Németh et al 2021, PMID: 34665280).

      Reviewer #1: This is an important point that is not mentioned in the original main text, figure legend or method. Please address.

      We agree and we apologize for it. We added this information to the revised manuscript.

      Our response to Reviewer #1 point 3. Our TEM images show cells captured in the process of budding and scission of large multivesicular EVs excluding the possibility that these structures could have originated from FBS.

      Reviewer #1: These images may also depict the engulfment of EVs in FBS. Hence, it is crucial to utilize EV-free or EV-depleted FBS.

      As we mentioned earlier, we added the information to the revised manuscript that sterile filtering of the FBS presumably removed particles >0.22 µm EVs

      Our response to Reviewer #1 point 4. In addition, in our confocal analysis, we studied Palm-GFP positive, cell-line derived MV-lEVs. Importantly, in these experiments, FBS-derived EVs are non-fluorescent, therefore, the distinction between GFP positive MV-lEVs and FBS-derived EVs was evident.

      Reviewer #1: I agree that these fluorescent-labeled assays conclusively indicate that the MV-lEVs are originating from the cells. However, the images of concerns are the non- fluorescent-labeled images in (Figure 1, 2P-S, S5, Figure 4 P-U and Figure 3). The MV-lEVs may derive from both the cells and FBS.

      Please see above our response to points 1-3.

      Our response to Reviewer #1 point 5. In addition, culturing cells in FBS-free medium (serum starvation) significantly affects autophagy. Given that in our study, we focused on autophagy related amphiectosome secretion, we intentionally chose to use FBS supplemented medium.

      Reviewer #1 If this is a concern, the authors should use EV-depletive FBS.

      As we discussed above, sterile filtration of FBS removes particles >0.22 µm. In addition, based on our preliminary experiments, EV-depleted serum may effect cell physiology. 

      Our response to Reviewer #1 point 6. Even though the authors of this manuscript are not familiar with the technological details how FBS is processed before commercialization, it is reasonable to assume that the samples are subjected to sterile filtration (through a 0.22 micron filter) after which MV-lEVs cannot be present in the commercial FBS samples.

      Reviewer #1This is a fair comment that needs to be included in the manuscript.

      As you suggested, this comment is now included in the revised manuscript

      (2) The data presented in Figure 2 is not convincingly supportive of the authors' conclusion. The authors argue that "...CD81 was present in the plasma membrane-derived limiting membrane (Figures 2B, D, F), while CD63 was only found inside the MV-lEVs (Fig. 2A, C, E)." However, in Figure 2G, there is an observable CD63 signal in the limiting membrane (overlapping with the green signals), and in Figure 2J, CD81 also exhibits overlap with MV-IEVs.

      Both CD63 and CD81 are tetraspanins known to be present both in the membrane of sEVs and in the plasma membrane of cells (for references, please see Uniprot subcellular location maps: https://www.uniprot.org/uniprotkb/P08962/entry#subcellular_location https://www.uniprot.org/uniprotkb/P60033/entry#subcellular_location). However, according the feedback of the reviewer, for clarity, we will delete the implicated sentence from the text.

      Reviewer #1 Please also justify the statement questioned in (3) as these arguments are interconnected.

      We hope you find our above responses to your comment acceptable.

      (3) Following up on the previous concern, the authors argue that CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs, respectively (Figure 2-A-M). However, in lines 104-106, the authors conclude that "The simultaneous presence of CD63, CD81, TSG101, ALIX, and the autophagosome marker LC3B within the MV-lEVs..." This statement indicates that CD63 and CD81 co-localize to the MV-IEVs. The authors need to address this apparent discrepancy and provide an explanation.

      There must be a misunderstanding because we did not claim or implicate in the text that “CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs”. Here we studied co-localization of the above proteins in the case intraluminal vesicles (ILVs). In Fig 2. we did not show any analysis of limiting membrane co-localization.

      Reviewer #1 I have indicated that this statement is found in lines 104-106, where the authors argue, 'The simultaneous presence of CD63, CD81, TSG101, ALIX, and the autophagosome marker LC3B within the MV-lEVs...' If the authors acknowledge the inaccuracy of this statement, please provide a justification for this argument.

      For clarity, we modified the description of data shown in Fig2 in the revised manuscript.

      (4) The specificity of the antibodies used in Figure 2 should be validated through knockout or knockdown experiments. Several of the antibodies used in this figure detect multiple bands on western blots, raising doubts about their specificity. Verification through additional experimental approaches is essential to ensure the reliability and accuracy of all the immunostaining data in this manuscript.

      We will consider this suggestion during the revision of the manuscript.

      Reviewer #1:Please do so.

      We carefully considered the suggestion, but we realized that it was not feasible for us to perform gene silencing in the case of all our used antibodies before resubmission of our revised manuscript. However, we repeated the Western blot for mouse anti-CD81 (Invitrogen MAA5-13548) and replaced the previous Western blot by it in the revised manuscript (Fig.2-S4H)

      (5) In Figures 2P-R, the morphology of the MV-IEVs does not resemble those shown in Figures 1-A, H, and D, indicating a notable inconsistency in the data.

      EM images in Figure2 P-R show sEVs separated from serum-free conditioned media as opposed to MV-lEVs, which were in situ captured in fixed tissue cultures (Fig1). Therefore, the two EV populations necessarily have different size and structure. Furthermore, Fig. 1 shows images of ultrathin sections while in Figure 2P-R, we used a negative-positive contrasting of intact sEV-s without embedding and sectioning.

      (6) There are no loading controls provided for any of the western blot data.

      Not even the latest MISEV 2023 guidelines give recommendations for proper loading control for separated EVs in Western blot (MISEV 2023 , DOI: 10.1002/jev2.12404 PMID: 38326288). Here we applied our previously developed method (PMID: 37103858), which in our opinion, is the most reliable approach to be used for sEV Western blotting. For whole cell lysates, we used actin as loading control (Fig3-S2B).

      Reviewer #1: The blots referenced here (Fig2-S3; Fig2-S4B; Fig3-S2B) were conducted using total cell lysates, not EV extracts. Only one blot in Fig3-S2B includes an actin control. All remaining blots should incorporate actin controls for consistency.

      Fig2-S3 (corresponding to Fig2-S4 in the revised manuscript) only shows reactivity of the used antibodies. This Western blot is not intended to serve as a basis of any quantitative conclusions. Fig2-S4 (corresponding to Fig2-S5 in the revised manuscript) includes the actin control. Fig3-S2B shows the complete membrane, which was cut into 4 pieces, and the immune reactivity of different antibodies was tested. The actin band was included on the anti-LC3B blot. For clarity, we rephrased the figure legend.

      Additionally, for Figures 2-S4B, the authors should run the samples from lanes i-iii in a single gel.

      Please note that in Figure 2- S4B, we did run a single gel, and the blot was cut into 4 pieces, which were tested by anti-GFP, anti-RFP, anti-LC3A and anti-LC3B antibodies. Full Western blots are shown in Fig.3_S2 B, and lanes “1”, “2” and “3” correspond to “i”, “ii” and “iii” in Fig.2-S4, respectively.

      Reviewer #1: In the original Figure 2- S4B, the blots were sectioned into 12 pieces. If lanes "i," "ii," and "iii" were run on the same blot, the authors are advised to eliminate the grids between these lanes.

      Grids separating the lanes have been eliminated on Fig.2_S4 (now Fig.2_S5 in the revised manuscript).

      (7) In Figure 2-S4, is there co-localization observed between LC3RFP (LC3A?) with other MV-IFV markers? How about LC3B? Does LC3B co-localize with other MV-IFV markers?

      In Supplementary Figure 2-S4, we showed successful generation of HEK293T-PalmGFP-LC3RFP cell line. In this case we tested the cells, and not the released MV-lEVs. LC3A co-localized with the RFP signal as expected.

      Reviewer #1: Does LC3RFP colocalize with MV-IFV markers in HEK293T-PalmGFP-LC3RFP cell line? This experiment aims to clarify the conclusion made in lines 104-106, where the authors assert that 'The concurrent existence of CD63, CD81, TSG101, ALIX, and the autophagosome marker LC3B within the MV-lEVs...'

      In the case of PalmGFP-LC3RFP cells, LC3-RFP is overexpressed. Simultaneous assessment of this overexpressed protein with non-overexpressed, fluorescent antibod-detected molecules proved to be challenging because of spectral overlaps and inappropriate signal-noise ratios. Furthermore, in association with EVs, the number of antibody-detected molecules is substantially lower than in cells. Therefore, even though we tried, we could not successfully perform these experiments.

      (8) The TEM images presented in Figure 2-S5, specifically F, G, H, and I, do not closely resemble the images in Figure 2-S5 K, L, M, N, and O. Despite this dissimilarity, the authors argue that these images depict the same structures. The authors should provide an explanation for this observed discrepancy to ensure clarity and consistency in the interpretation of the presented data.

      As indicated in Material and Methods, Fig 2-S5 F, G, H and I are conventional TEM images fixed by 4% glutaraldehyde 1% OsO<sub>4</sub> 2h and embedded into Epon resin with a post contrasting of 3.75% uranyl acetate 10 min and 12 min lead citrate. Samples processed this way have very high structure preservation and better image quality, however, they are not suitable for immune detection. In contrast, Fig.2.-S5 K,L,M,N shows immunogold labelling of in situ fixed samples. In this case we used milder fixation (4% PFA, 0.1% glutaraldehyde, postfixed by 0.5% OsO<sub>4</sub> 30 min) and LR-White hydrophilic resin embedding. This special resin enables immunogold TEM analysis. The sections were exposed to H<sub>2</sub>O<sub>2</sub> and NaBH<sub>4</sub> to render the epitopes accessible in the resin. Because of the different applied techniques, the preservation of the structure is not the same. In the case of Fig.2 J, O, separated sEVs were visualised by negative-positive contrast and immunogold labelling as described previously (PMID: 37103858).

      Reviewer #1: Please include this justification in the revised version.

      We included this justification in the revised manuscript.

      (9) For Figures 3C and 3-S1, the authors should include the images used for EV quantification. Considering the concern regarding potential contamination introduced by FBS (concern 1), it is advisable for the authors to employ an independent method to identify EVs, thereby confirming the reliability of the data presented in these figures.

      In our revised manuscript, we will provide all the images used for EV quantification in Figure 3C. Given that Figures 3C and 3-S1 show MV-lEVs released by HEK293T-PlamGFP cells, the possible interference by FBS-derived non-fluorescent EVs can be excluded.

      Reviewer #1: Please provide all the images.

      Original LASX files are provided (DOI: 10.6019/S-BIAD1456 ).

      Reviewer #1: The images raising concerns regarding the contamination of EVs in FBS primarily consist of transmission electron microscopy (TEM) images, namely, Figure 1, 2P-S, S5, and Figure 4 P-U, along with the quantification of EV numbers in Figure 3. These concerns persist despite the use of fluorescent-labeled experiments. While fluorescent-labeled MV-lEVs are conclusively identified as originating from the cells, the MV-lEVs observed in Figure 1, 2P-S, S5, and Figure 4 P-U and Figure 3 may derive from both the cells and FBS.

      Large EVs (with diameter >800 nm) derived from FBS were not present in our experiments, as discussed above.

      (10) Do the amphiectosomes released from other cell types as well as cells in mouse kidneys or liver contain LC3B positive and CD63 positive ILVs?

      Based on our confocal microscopic analysis, in addition the HEK293T-PalmGFP cells, HT29 and HepG2 cells also release similar LC3B and CD63 positive MV-lEVs. Preliminary evidence shows MV-lEV secretion by additional cell types.

      The response of Reviewer #1: Please show these data in the revised manuscript. Moreover, do cells in mouse kidneys or liver contain LC3B positive and CD63 positive ILVs?

      We have added new confocal microscopic images to Fig2-S3 showing amphiectosomes released also by the H9c2 (ATCC) cardiomyoblast cell line. To preserve the ultrastructure of MV-lEVs in complex organs like kidney and liver, fixation with 4% glutaraldehyde with 1% OsO4 appears to be essential. This fixation does not allow for immune detection to assess LC3B and CD63 positive MV-lEVs in the ultrathin sections.

      Reviewer #2 (Public Review):

      Summary:

      The authors had previously identified that a colorectal cancer cell line generates small extracellular vesicles (sEVs) via a mechanism where a larger intracellular compartment containing these sEVs is secreted from the surface of the cell and then tears to release its contents. Previous studies have suggested that intraluminal vesicles (ILVs) inside endosomal multivesicular bodies and amphisomes can be secreted by the fusion of the compartment with the plasma membrane. The 'torn bag mechanism' considered in this manuscript is distinctly different because it involves initial budding off of a plasma membrane-enclosed compartment (called the amphiectosome in this manuscript, or MV-lEV). The authors successfully set out to investigate whether this mechanism is common to many cell types and to determine some of the subcellular processes involved.

      The strengths of the study are:

      (1) The high-quality imaging approaches used, seem to show good examples of the proposed mechanism.

      (2) They screen several cell lines for these structures, also search for similar structures in vivo, and show the tearing process by real-time imaging.

      (3) Regarding the intracellular mechanisms of ILV production, the authors also try to demonstrate the different stages of amphiectosome production and differently labelled ILVs using immuno-EM.

      Several of these techniques are technically challenging to do well, and so these are critical strengths of the manuscript.

      The weaknesses are:

      (1) Most of the analysis is undertaken with cell lines. In fact, all of the analysis involving the assessment of specific proteins associated with amphiectosomes and ILVs are performed in vitro, so it is unclear whether these processes are really mirrored in vivo. The images shown in vivo only demonstrate putative amphiectosomes in the circulation, which is perhaps surprising if they normally have a short half-life and would need to pass through an endothelium to reach the vessel lumen unless they were secreted by the endothelial cells themselves.

      Our previous results analyzing PFA-fixed, paraffin embedded sections of colorectal cancer patients provided direct evidence that MV-lEV secretion also occurs in humans in vivo (PMID: 31007874). Regarding your comment on the presence of amphiectosomes in the circulation despite their short half-lives, we would like to point out that Fig1.X shows a circulating lymphocyte which releases MV-lEV within the vessel lumen. Furthermore, in the revised manuscript, an additional Fig.1-S1 is provided. Here, we show the release of MV-lEVs both by an endothelial and a sub-endothelial cell (Fig.1-S1G). In addition, these images show the simultaneous presence of MV-lEVs and sEVs in the circulation (Fig.1-S1.A,C,D,H and I). The transmission electron micrographs of mouse kidney and liver sections provide additional evidence that the MV-lEVs are released by different types of cells, and the “torn bag release” also takes place in vivo (Fig.1.V).

      (2) The analysis of the intracellular formation of compartments involved in the secretion process (Figure 2-S5) relies on immuno-EM, which is generally less convincing than high-/super-resolution fluorescence microscopy because the immuno-labelling is inevitably very sporadic and patchy. High-quality EM is challenging for many labs (and seems to be done very well here), but high-/super-resolution fluorescence microscopy techniques are more commonly employed, and the study already shows that these techniques should be applicable to studying the intracellular trafficking processes.

      As you suggested, in the revised manuscript, we present additional super-resolution microscopy (STED) data. The intracellular formation of amphisomes, the fragmentation of LC3B-positive membranes and the formation of LC3B-positive ILVs were captured (Fig. 3B-F).

      (3) One aspect of the mechanism, which needs some consideration, is what happens to the amphisome membrane, once it has budded off inside the amphiectosome. In the fluorescence images, it seems to be disrupted, but presumably, this must happen after separation from the cell to avoid the release of ILVs inside the cell. There is an additional part of Figure 1 (Figure 1Y onwards), which does not seem to be discussed in the text (and should be), that alludes to amphiectosomes often having a double membrane.

      We agree with your comment regarding the amphisome membrane and we added a sentence to the Discussion of the revised manuscript. Fig1Y onwards is now discussed in the manuscript. In addition, we labelled the surface of living HEK293 cells with wheat germ agglutinin (WGA), which binds to sialic acid and N-acetyl-D-glucosamine. After removing the unbound WGA by washes, the cells were cultured for an additional 3 hours, and the release of amphiectosomes was studied. The budding amphiectosome had WGA positive membrane providing evidence that the external limiting membrane had a plasma membrane origin (Fig.3G)

      (4) The real-time analysis of the amphiectosome tearing mechanism seemed relatively slow to me (over three minutes), and if this has been observed multiple times, it would be helpful to know if this is typical or whether there is considerable variation.

      Thank you for this comment. In the revised manuscript, we highlight that the first released LC3 positive ILV was detected as early as within 40 sec.

      Overall, I think the authors have been successful in identifying amphiectosomes secreted from multiple cell lines and demonstrating that the ILVs inside them have at least two origins (autophagosome membrane and late endosomal multivesicular body) based on the markers that they carry. The analysis of intracellular compartments producing these structures is rather less convincing and it remains unclear what cells release these structures in vivo.

      I think there could be a significant impact on the EV field and consequently on our understanding of cell-cell signalling based on these findings. It will flag the importance of investigating the release of amphiectosomes in other studies, and although the authors do not discuss it, the molecular mechanisms involved in this type of 'ectosomal-style' release will be different from multivesicular compartment fusion to the plasma membrane and should be possible to be manipulated independently. Any experiments that demonstrate this would greatly strengthen the manuscript.

      We appreciate these comments of the reviewer. Experiments are on their way to elucidate the mechanism of the “ectosomal style” exosome release and will be the topic of our next publication.

      In general, the EV field has struggled to link up analysis of the subcellular biology of sEV secretion and the biochemical/physical analysis of the sEVs themselves, so from that perspective, the manuscript provides a novel angle on this problem.

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, the authors describe a novel mode of release of small extracellular vesicles. These small EVs are released via the rupture of the membrane of so-called amphiectosomes that resemble "morphologically" Multivesicular Bodies.

      These structures have been initially described by the authors as released by colorectal cancer cells (https://doi.org/10.1080/20013078.2019.1596668). In this manuscript, they provide experiments that allow us to generalize this process to other cells. In brief, amphiectosomes are likely released by ectocytosis of amphisomes that are formed by the fusion of multivesicular endosomes with autophagosomes. The authors propose that their model puts forward the hypothesis that LC3 positive vesicles are formed by "curling" of the autophagosomal membrane which then gives rise to an organelle where both CD63 and LC3 positive small EVs co-exist and would be released then by a budding mechanism at the cell surface that appears similar to the budding of microvesicles /ectosomes. Very correctly the authors make the distinction from migrasomes because these structures appear very similar in morphology.

      Strengths:

      The findings are interesting despite that it is unclear what would be the functional relevance of such a process and even how it could be induced. It points to a novel mode of release of extracellular vesicles.

      Weaknesses:

      This reviewer has comments and concerns concerning the interpretation of the data and the proposed model. In addition, in my opinion, some of the results in particular micrographs and immunoblots (even shown as supplementary data) are not of quality to support the conclusions.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Highlight MV-IEV, ILV and limiting membrane in Figure-1G, N, and U.

      Based on the suggestion, we revised Figure1

      (2) Figure 1-Y-AF are not mentioned in the text.

      In the revised manuscript, we discuss Figure 1Y-AF

      (3) The term "IEVs" in Figure 2-S2 is not defined.

      We modified the figure legend: we changed MV-lEV to amphiectosome

      (4) Need to quantify co-localization in Figure 2-S2.

      As suggested, we carried out the co-localisation analysis (Fig2-S2I), and Fig2-S2 was re-edited

      Reviewer #2 (Recommendations For The Authors):

      I have two recommendations for improving the manuscript through additional experiments:

      (1) I think the description of the intracellular processes taking place in order to form amphiectosomes would be much stronger if some super-resolution imaging could be undertaken. This should label the different compartments before and after fusion with specific markers that highlight the protein signature of the different limiting and ILV membranes much more clearly than immuno-EM. It will also help in characterising the double-membrane structure of amphiectosomes at the point of budding and reveal whether the patchy labelling of the inner membrane emerges after amphiectosome release (the schematic model currently suggests that it happens before).

      Thank you for your suggestion. STED microscopy was applied and results are shown in new Fig3 and the schematic model was modified accordingly.

      (2) The implications of the manuscript would be more wide-ranging if the authors could test genetic manipulations that are believed to block exosome or ectosome release, eg. Rab27a or Arrdc1 knockdown. This may allow them to determine whether MV-lEVs can be released independently of the classical exosome release mechanism because they use a different route to be released from the plasma membrane. This experiment is not essential, but I think it would start to address the core regulatory mechanisms involved, and if successful, would easily allow the authors to determine the ratio of CD63-positive sEVs being secreted via classical versus amphiectosome routes.

      The suggestion is very valuable for us and these studies are being performed in a separate project.

      I think there are several other ways in which the manuscript could be improved to better explain some of the approaches, findings and interpretation:

      (1) Include some explanation in the text of certain key tools, particularly:

      a. Palm-GFP and whether its expression might alter the properties of the plasma membrane since this is used in a lot of experiments and is the only marker that seems to uniformly label the outer membrane of amphiectosomes. One concern might be that its expression drives amphiectosome secretion.

      We found evidence for amphiectosome release also in the case of several different cells not expressing Palm-GFP. We believe, this excludes the possibility that Palm-GFP expression is the inducer of the amphiectosome release. Both by fluorescent and electron microscopy, the Palm-GFP non expressing cells showed very similar MV-lEVs. In addition, in the case of non-transduced HEK293 and fluorescent WGA-binding, we made similar observations.

      b. Lactadherin - does this label the amphiectosomes after their release or does the wash-off step mean that it only labels cells, which subsequently release amphiectosomes?

      Lactadherin labels the amphiectosomes after their release and fixation. Living cells cannot be labelled by lactadherin as PS is absent in the external plasma membrane layer of living cells. We used WGA on HEK293 cells to further support the plasma membrane origin of the external membrane of amphiectosomes.

      (2) Explain the EM and confocal imaging approaches more clearly. Most importantly, is a 3D reconstruction always involved to confirm that 'separated' amphiectosomes are not joined to cells in another Z-plane.

      Thank you for your suggestion. We have modified the manuscript accordingly

      (3) Presenting triple-labelled images with red, green and yellow channels does not allow individual labelling to be determined without single-channel images and even then, it is much more informative to use three distinguishable colours that make a different colour with overlap, eg. CMY? Fig.2_S2D and E do not display individual channels, so definitely need to be changed.

      In case of Fig.2_S2D, we now show the individual channels, the earlier E image has been removed. In case of the STED images, CMY colors had been used, as you suggested.

      (4) Please discuss in the text the data in Figure 1Y onwards concerning single/double membranes on MV-lEVs.

      In the revised manuscript, we discuss the question on single/double membranes and we refer to Figure 1Y-AF

      (5) On line 162, reword 'intraluminal TSPAN4 only' to 'one in which TSPAN4 is only intraluminal' to make it clear that other proteins are also marking the intraluminal region, not TSPAN4 only.

      We modified the text accordingly.

      (6) Points for further discussion and further conclusions:

      a. In vivo experiments - discuss the limitations of this part of the analysis - it seems that none of the amphiectosome markers have been analysed in this part of the study and the MV-lEVs are only in the circulation.

      b. Can the authors give any further indication of the levels of MV-lEVs relative to free sEVs from any of their studies?

      Using our current approach, it is not possible to determine the levels of MV-lEVs to free sEV. Without analyzing serial ultrathin sections, determination of the relative ratio of MV-lEVs and sEVs would depend on the actual section plane. In future projects, we will determine the ratio of LC3 positive and negative sEVs by single EV analysis techniques (such as SP-IRIS). In the revised manuscript, additional TEM images are included to provide evidence for the simultaneous presence of sEVs and MV-lEVs and MV-lEVs both inside and outside of the circulation.

      c. Please discuss the single versus double membrane issue (relating to experiments proposed above).

      We discuss this question in more details in the revised manuscript.

      d. Please point out that the release mechanism (plasma membrane budding) will involve different molecular mechanisms to establish exosome release, and this might provide a route to determine relative importance.

      We are currently running a systemic analysis of the release mechanism of amphiectosomes, and this will be the topic of a separate manuscript.

      Reviewer #3 (Recommendations For The Authors):

      * The model is not supported.

      * The data is not of quality.

      * The appropriate methods are not exploited.

      We are sorry, we cannot respond to these unsupported critiques.

    1. Unit 1 Socratic Seminar: Is Social Media More Beneficial or Negative to Society? Directions: Read and Annotate the readings by making comments in the document, or the margins if you’re doing it on paper on the 2 articles listed below. Use a physical highlighter or highlighter tool for quotes/ideas you want to explore more and talk about. TYPE YOUR ANSWERS IN BLUE IF DOING THIS DIGITALLY.<br /> Fill out the Summaries at the bottom of Fill out the 6 questions you will use during the Socratic to drive the conversation. Do this part LAST!

      Write out the 6 Questions you will use during the Socratic Seminar. (Do this LAST IN BLUE FONT) 1. How will social media evolve in the future 2.How is social media affecting our outside interactions with others 3. 4. 5. 6.

      Reading #1: Supporters Argue: Social Media Is Beneficial Overall 1a Supporters argue that social networking is a phenomenon that is beneficial overall and has changed the world for the better. Perhaps the greatest measure of social media's success, they contend, is the role it played in ousting undemocratic governments in Tunisia and Egypt. Journalist Peter Beaumont of the British newspaper the Guardian argued in 2011 that "a young woman or a young man with a smartphone" was the "defining" image of the Arab Spring. "The instantaneous nature of how social media communicate self-broadcast ideas, unlimited by publication deadlines and broadcast news slots, explains in part the speed at which these revolutions have unraveled, their almost viral spread across a region," he contended. "It explains, too, the often loose and non-hierarchical organization of the protest movements unconsciously modeled on the networks of the web." 2a Indeed, supporters argue that social media can be extremely useful in encouraging people who would not typically be politically motivated to engage in various issues or causes. While such statements are sometimes derided by critics as "hashtag activism" or "slacktivism," defenders insist that such actions really can make a difference. "What is commonly called slacktivism is not at all about 'slacking activists,'" Harvard University sociology professor Zeynep Tufekci wrote on her blog in 2012. "[R]ather it is about non-activists taking symbolic action—often in spheres traditionally engaged only by activists or professionals (governments, NGOs, international institutions.). Since these so-called 'slacktivists' were never activists to begin with, they are not in dereliction of their activist duties. On the contrary, they are acting, symbolically and in a small way, in a sphere that has traditionally been closed off to 'the masses' in any meaningful fashion." 3a Social media has many other benefits, advocates contend, including the potential to assist during times of catastrophe. During and after the terrorist attacks that rocked Paris, France, in November 2015, supporters note, people took to Facebook, Twitter, and other social media to communicate to loved ones that they were safe, or to offer refuge to people stranded in the city. "The attacks which ravaged the French capital yesterday showed how social media can also play a much more positive role," Forbes contributor Federico Guerrini wrote. "Facebook activated its Safety Check tool…to help people in areas affected by a disaster let their Facebook friends know they are safe. Twitter was also helpful: residents used the hashtag #porteouverte [open doors] to offer shelter to people stranded in the city." Advocates of social networking contend that sites like Facebook and Twitter have brought people closer together. "It has never been easier to make friends than it is right now, mainly thanks to social networking sites," writer Dave Parrack argued on the technology website MakeUseOf.com in 2012. "Just a few decades ago it was pretty tough to connect with people unless you were the overly outgoing type able to make conversation with anyone at a party. The rise of mobile phones helped change this, connecting people in a new way, but then social networks sprang up and the whole idea of friendship changed once more and forever." 4a Supporters maintain that social networking sites increasingly function as a refuge where people can relax with their friends and family. "This is where social media become a powerful social force in the modern sphere," Taso Lagos of the University of Washington wrote in the Seattle Times in 2012. "Because we live in a world of constant anxiety and stress about our lives, our careers, the planet and the fate of our families and friends, trusted sites like Facebook and Twitter are places we turn to relieve this tension and allow us to live and express our humanity." Social media, he argued, are "the community centers of the future." 5a Such sites provide many valuable benefits, defenders argue, including enhancing people's sense of self-worth. The act of taking and posting selfies, they contend, helps people exert control over their self-image and the way they are viewed. "The harshly judged practice of self-picture taking," Huffington Post contributor Molly Fosco wrote in March 2014, "while perhaps excessive or annoying at times, can actually be a really simple way to feel really good about yourself…. Although our selfies might be veiled in narcissism, self-obsession, or boastfulness I think that for many it's a genuine attempt to boost self-esteem. Seeing a close-up picture of your own face and willingly showing it to thousands of people with one click is a form of self-confidence that I don't think should be quickly dismissed." 6a Supporters of social media discount many of the fears typically raised by opponents, noting that it is common for new technology to stir criticism. In the late 19th century, they note, some observers predicted that the telephone would severely damage interpersonal relationships, just as detractors of social media do today. The telephone "was going to bring down our society," Megan Moreno of the University of Wisconsin in Madison told the New York Times in 2012. "Men would be calling women and making lascivious comments, and women would be so vulnerable, and we'd never have civilized conversations again." She added, "When a new technology comes out that is something so important, there is this initial alarmist reaction." Write out a 100-word summary of your thoughts/ideas/opinions of the strengths and weaknesses of the Beneficial Side. (TYPE IN BLUE FONT) Social media supporters argue that it is a good thing for the world and there is proof that it helps the movements like Arab Spring to go on smoothly and global peace talks to be the most constructive. It leads to a lot of people and even calling on them to fight for their cause. It is useful for giving real time help like social media platforms and info at the time of an emergency and also brings people who don't live close to each other, closer to each other. Social media can also lead to the development of good self esteem through many apps. These benefits of the media source have risks which include being heavily dependent on technology, getting wrong info, and the threat of getting into harmful sites with people despite how useful it can be sometimes .

      Reading #2: Opponents Argue: Social Media Is Not Beneficial Overall 1b Opponents of social networking argue that such sites are not beneficial overall and that they gradually erode many essential aspects of communication and socialization. "The shortcomings of social media would not bother me awfully if I did not suspect that Facebook friendship and Twitter chatter are displacing real rapport and real conversation," New York Times commentator Bill Keller argued in 2011. "The things we may be unlearning, tweet by tweet—complexity, acuity, patience, wisdom, intimacy—are things that matter." 2b Indeed, critics contend, the rise of social networking has coincided with a decline in the quality of conversation. "As we ramp up the volume and velocity of online connections, we start to expect faster answers," MIT psychology professor Sherry Turkle wrote in the New York Times in 2012. "To get these, we ask one another simpler questions; we dumb down our communications, even on the most important matters." 3b Opponents argue that social media can contribute to feelings of sadness and loneliness. A study by researchers at the University of Michigan in 2013, they note, found that college-aged users felt worse the more they used Facebook. Because people's Facebook personas are often curated to make their lives seem fun or perfect, critics argue, that browsing social media can contribute to feelings of inadequacy. "When you're on a site like Facebook, you get lots of posts about what people are doing," co-author John Jonides, a cognitive neuroscientist at the Department of Psychology at the University of Michigan, told National Public Radio in 2013. "That sets up a social comparison — you maybe feel your life is not as full and rich as those people you see on Facebook." 4b Social media, critics charge, can lead people to obsess about themselves and their self-image to the point where it can be harmful. People need to look deeper for self-worth, they contend, than achieving "likes" by posting selfies on social media. "[I]if you've just spent half an hour editing a photo by blurring around your eyes with one app, adding eyelashes with another, then changing the colors with a third," Teen Vogue contributor Tiffany Perry wrote in March 2016, "chances are you're giving too much merit to how others perceive you." 5b Other critics claim that the impact of social media on political phenomena like the Arab Spring has been overstated. New Yorker columnist Malcolm Gladwell noted in 2011 that many revolutions took place throughout history before the advent of social networking. "People with a grievance will always find ways to communicate with each other," he wrote. "How they choose to do it is less interesting, in the end, than why they were driven to do it in the first place." 6b Opponents also assert that promoting political or social causes on social media has little real impact other than to make the person making the post feel good about themselves. In 2013, for example, the United Nations Children's Emergency Fund (UNICEF), a U.N. organization that raises money to help and protect children throughout the world, ran an ad campaign with a slogan that read "Like us on Facebook, and we will vaccinate zero children against polio." The point of the campaign, UNICEF explained, was not to disparage "likes" but to encourage more active support, such as contributing money to buy vaccines. "Slacktivism's inherent laziness disqualifies it as a real agent of progress because it does not possess the enthusiasm necessary for change," contributor Elias Tavaras wrote for the Hill in January 2016. "How can a post on Facebook inspire necessary action, especially when sitting down on a comfy computer chair? Indeed, the passion one may feel disappears, with a simple scroll or is drowned out by the other slacktivist posts." 7b Critics charge that social media users are in danger of having their online personas co-opted by corporations eager to collect the information users share and employ it for marketing purposes. Robert Barry of the pop culture website The Quietus argues that social media is turning people into "branded products." "Online businesses which seem to be promising something for nothing—from social networking to file sharing—are really offering you, their audience, as a readymade and fully packaged item for purchase," he argued, "be that by the ghost of advertising's future, or the investor whose faith gives that ghost substance." Write out a 100-word summary of your thoughts/ideas/opinions of the strengths and weaknesses of the Against Side. (TYPE IN BLUE FONT)

    1. Why

      In the context of this poem, “why” does not seem to be a genuine question inviting explanation but rather a rhetorical one. If we read “why” this way, I think there are three main effects. One effect becomes obvious when considered in conjunction with the personified phrases that follow the word “why”: “fled the Ocean, “skipt the Mountains,” and “turned the Jordan.” Based on the rest of the poem, we already know that it was God who caused these phenomena. However, the “why” serves as a sort of rhetorical emphasis, forcing the recognition that it was God—no one or nothing else—who animated the inanimate. Secondly, given that God accomplishes this seemingly impossible feat, Milton’s use of “why” conjures a sense of awe. That is to say, by asking why an unmovable object moves, Milton forces his audience to confront that there is no rational explanation for these occurrences. Thus, the audience must instead indulge their awe, inspired by the inability to truly comprehend the extent and inner workings of God’s omnipotence. However, Milton’s questioning via the use of “why” may also seem somewhat foreboding when considered alongside the strength of these natural forces. In other words, why did these strong and dynamic entities flee? Knowing that the answer is God, the “why” almost seems to invite the realization that whatever caused these entities to skip or turn away must be fearfully powerful. These three readings of but a single word seem to summarize Milton’s portrayal of God in this poem: as a powerful and awesome—yet fear-inspiring—entity.

    1. 26. When Jesus was in Galilee at the beginning of his fourth year he was playing by the Jordan, and made seven pools. A boy spoilt them, and was struck dead. The parents complained. Joseph asked Mary to admonish Jesus. She begged him not to do such things, and he, not willing to grieve her, ‘smote the back side of the dead boy with his foot and bade him rise: which he did, and Jesus went on with his pools’. 27. He took clay from the pools and made twelve sparrows, on the sabbath. A Jew saw it and spoke to Joseph, who spoke to Jesus. Jesus clapped his hands and bade the sparrows fly away. All marvelled, and some went and told the chief priests and Pharisees. 28. The son of Annas the priest broke up the pools with a stick, and Jesus with a word withered him up. 29. Joseph was afraid and took Jesus home. On the way a boy ran against Jesus and got on his shoulder, meaning to hurt him. Jesus said, ‘May you not return whole from the way you go.’ He fell dead. Complaints of the parents, as in Thomas. Joseph to Jesus: ‘Why do you do such things? Many are now complaining against you and hate us on your account, and we suffer injuries through you.’ Jesus: ‘No son is wise whom his father has not taught according to the knowledge of this age, and the curse of his father hurts no man except those who do ill.’ All reviled Jesus to Joseph and he was afraid. ‘Then Jesus took the dead boy by the ear and held him up by it in the sight of all, and they saw Jesus speaking to him as a father to his son. And his spirit returned unto him and he lived again, and all marvelled.’ 30. Master Zacchaeus spoke reproachfully to Joseph; ‘You and Mary think more of your son than of the traditions of the elders.’ Joseph: ‘But who can teach him? if you can do so, we are very willing.’ Jesus overhearing said, ‘What you say is well for ordinary people: I have no earthly father. When I am lifted up from the earth I will make all mention of your descent to cease. I know when you were born and how long you have to live.’ All cried out in wonder, ‘We have never heard the like.’ Jesus: ‘Does this surprise you? I will tell you more. I have seen Abraham and spoken with him, and he has seen me.’ None could answer. Jesus: ‘I have been among you with the children, and you have not known me. I have spoken with you as with the wise and you have not understood my voice, for you are less than me, and of little faith.’ 31. Zacchaeus said, ‘Give him to me and I will take him to Levi who shall teach him letters.’ Levi bade him answer to Aleph: he was silent. Levi smote him with a rod of storax on the head. Jesus: ‘Why do you hit me? Know of a truth that he who is smitten teaches the smiter more than he is taught of him. For I can teach you the things that you yourself say. But all these who speak and hear are blind like sounding brass or a tinkling cymbal wherein is no perception of those things that are signified by their sound.’ Further he said to Zacchaeus, ‘Every letter from Aleph to Thau is discerned by the arrangement of it. First say what Thau is, and I will tell you what Aleph is.’ And again he said, ‘They who do not know Aleph, how can they tell Thau, hypocrites that they are? Say what Aleph is first and then will I believe you when you say Beth. He said to the master, ‘Let the master of the law say what the first letter is, or why it has many triangles [eight adjectives follow].’ Levi was stupefied and then began to lament, ‘Ought he to live on the earth? Nay, rather is he worthy to be hung on a great cross. He can put out fire and escape all torments by guile. I think he was born before the flood, before the deluge. What womb bare him? What mother gave him birth? What breasts suckled him? I fly before him’, etc., etc. Jesus smiled and said with command to all the children of Israel that stood and heard him, ‘Let the unfruitful bear fruit, and the blind see, and the lame walk straight, and the poor enjoy good things, and the dead revive, and every one return into a restored state, and abide in him who is the root of life and of everlasting sweetness.’ All were healed who had fallen into evil infirmities. No one thereafter dared to say aught to him or hear aught of him. 32. At Nazareth the boy Zeno fell from the upper storey and was raised. Joseph, Mary, and Jesus went thence to Jericho. 33. Jesus' pitcher was broken by a child, and he brought water in his cloak. 34. He took a little corn out of his mother's barn and sowed it. When reaped it made three measures, which he gave away. 35–6. [Translated below.] 37. A bed of six cubits was ordered of Joseph, and he told his lad to cut a beam of the right length, but he made it too short. Joseph was troubled. Jesus pulled it out to the right length. 38. He went to school the second time. ‘Say Alpha.’ Jesus: ‘Tell me first what Beta is, and I will tell you what Alpha is.’ The master smote him and died.

      Disconcerting childhood.

    1. To modern ears such language mocking and other Asian mocking may seem novel, but it is actually an old part of the white racist framing of Asian Americans. White English speakers on the West Coast developed this mocking in the mid- to late nineteenth century as their way of making fun of the English-Chinese speech of Chinese workers, as well as of racializing them. An early 1900s ragtime song goes, “Ching, Chong, Oh Mister Ching Chong, You are the king of Chinatown. Ching Chong, I love your sing-song.”2

      This makes me think about how racial stereotypes are a big part of American history. The "Ching Chong" slur against Asian Americans was a way to make them seem less than human. This shows that these harmful attitudes are still around today, and it makes it clear that we need to deal with them.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Therefore, their tool may be useful for stimulating multiple populations using a blue excitatory opsin in neuron A and their tool for red excitation of neuron B… Yet, there are no data presented that showcases their new tool for this purpose

      We agree with the reviewer that in this manuscript we have not experimentally shown the applicability of our system for dual optical stimulation. However, the suppression of blue-light excitation of ZipV/T-IvfChr-expressing neurons strongly suggests this can be used in experiments exciting populations of neurons similarly shown for BiPOLES. We don’t see a theoretical basis where this experiment cannot be done if sufficient cell targeting mechanisms (such as the use of cre-lox or retroAAV) is utilized. We have started several projects pursuing these utilities in the meantime.

      While they do show that red light = excitation and blue light = inhibition, they neither show 1) all-optical on/off modulation of the same cell; nor 2) high-frequency inhibition or excitation (max stim rate of 20hz, which is the same as the BiPOLES paper used for their LC stimulation paradigm; Vierock, as above, Figure 7a-d).

      Regarding point 1, we understand that the reviewer asks if we have optically excited (with red light) and inhibited (with blue light) the same neurons. If so, figure 4B1 (optical excitation of ZipT-IvfCh with red light) and figure 5A (optical inhibition of  ZipT-IvfCh with blue light) represent largely the same set of neurons.

      Regarding point 2, we respectfully disagree with the reviewer’s interpretation of Figure 7a-d) in Vierock et al. As we understand, in this part the authors apply a 20 Hz optical stimulation protocol to the LC neurons in vivo. However, there is no data showing that individual neurons do follow this stimulation protocol. To be clear, we are not saying that BiPOLES cannot drive 20 Hz APs. Very likely it can. It is based on ChrimsonR which is capable of doing so (Klapoetke et al., Figure 2). Although, in this manuscript we have not shown data for optical stimulation above 20Hz, our system is based on vfChrimson, which is known to drive AP of 100Hz and above (Mager et al., figure 2 and 3).  

      they must revise the manuscript to show that their approach is both 1) different in some way when compared to BiPOLES (it is my understanding that they did not do this, as per the supplementary alignment of the BiPOLES sequence and the sequence of the BiPOLES-like construct that they did test) and 2) that the properties that the investigators specifically tailored their construct to have confer some sort of experimental advantage when compared to the existing standard.

      In the latest version of the manuscript, we have compared our ZipV-IvfChr and the BiPOLES construct adapted with vfChrimson (Fig. 2 Suppl 1). The mean photocurrent amplitude of IvfChr in the ZipV-IvfChr construct is ~2.7 x higher than BiPOLES adapted with vfChrimson (14 randomly selected HEK293 cells in each group) (Fig. 2 Suppl 1B). We conducted this experiment in HEK293 cells to ensure accurate voltage-clamping and less biased cell selection. Even adjusting for the smaller photocurrent of vfChrimson vs ChrimsonR, this would still translate to ~1.6 x greater photocurrent with ZipV-IvfChr compared to the original BiPOLES utilizing ChrimsonR. We believe the increased efficiency of excitation is an important aspect of adapting vfChrimson for red-light excitation of neurons.

      Reviewer #2 (Public Review):

      (1) In the Introduction or Discussion, the authors could better motivate the need for a red-shifted actuator that lacks blue crosstalk, by giving some specific examples of how the tool could be productively used, e.g. pairing with another blue-shifted excitatory opsin in a different population, or pairing with a GFP-based fluorescent indicator, e.g. GCaMP. The motivation for the current tool is not obvious to non-experts.

      In the discussion, we now provided examples for potential use of the tool. For example, one of the key aspects that can be manipulated by the existing tool is the induction of spike-timing dependent plasticity with 2 wavelengths of light with blue light channelrhodopsin such as oChIEF is used to evoke presynaptic release and ZipT-IvfChr expressed in postsynaptic neuron. In this situation, the rapid termination of inhibitory response is critical so it does not interfere with the induction of LTP or LTD. Another experiment is the alternate control of projection neurons and interneurons in cortical areas, independent controls of neurons of direct and indirect pathways in the striatum to manipulate behavior.

      (2) Simultaneous excitation and inhibition are not the same as non-excitation. The authors mentioned shunting briefly. Another possible issue is changes in osmotic balance. Activation of a Na+ channel and a Cl- channel will lead to net import of NaCl into the cell, possibly changing osmotic pressure. Please discuss.

      We agree with the notion that osmotic, ionic and pH changes in small neuronal structure can be disruptive to the physiology and this is the reason we developed our approach where the fastest channelrhodopsins are used so we can minimize the channel opening time and the flux of ions through the channels when brief light illuminations are applied. Not only the flux of protons, sodium ions and calcium ions are minimized, the flux of chloride should be minimal as well (as the membrane potential should be close to the reversal potential of chloride reversal potential hence low ion flow). Hence our approach should be minimally disruptive compared to most other existing channelrhodopsin-based approaches when short or minimal light pulses were used in conjunction with our tools. This recommendation is included in the updated manuscript .

      (3) The authors showed that in ZipT-IvfChr, orange light drives excitation and blue light does not. But what about simultaneous blue and orange light? Can the blue light overwhelm the effect of the orange light? Since the stated goal is to open the blue part of the spectrum for other applications, one is now worried about "negative" crosstalk. Please discuss and, ideally, characterize this phenomenon.

      We now have performed this experiment. Simultaneous blue (470nm) and red light (635nm) stimulation does not produce AP (Fig .4 Suppl 1A)). This suggests the inhibitory effect of ACR is more efficient than the excitatory effects of IvfChr due to their higher conductance, this re-emphasizes the rapid termination of the ACR effects is critical for minimal disruption of physiological effects in such pairing strategy.

      (3.1) Does the use of the new tool require careful balancing of the expression levels of the ZipT and the IvfChr? Does it require careful balancing of blue and orange light intensities?

      As with any optogenetic tool, the users should validate the efficacy of the tool in their own system. Our tool solely relies on the balanced expression of the 2A system, the efficiency of the two opsins and their degradation of the time-span of expression. These aspects of the tool would be better addressed in future versions of the tools or improvement of the BiPOLES-type of tandem expression in subsequent versions. From the instrumentation side, the light intensity and differential penetration depth requires careful consideration. However, this holds true in most optogenetic and fluorescence imaging-based approaches as well. In the current update of the manuscript, we have included further discussion on these aspects as well.

      (3.2) Also, many opsins show complex and nonlinear responses to dual-wavelength illumination, so each component should be characterized individually under simultaneous blue + orange light.

      We now have performed this experiment (please see our comment to point 3)

      (3.3) I was expecting to see photocurrents at different holding potentials as a function of illumination wavelength for the coexpressed construct (i.e. to see at what wavelength it switches from being excitatory to inhibitory); and also to see I-V curves of the photocurrent at blue and orange wavelengths for the co-expressed constructs (i.e. to see the reversal potential under blue excitation). Overall, the patch clamp and spectroscopic characterization of the individual constructs was stronger than that of the combined constructs.

      We have added the IV curves for the co-expressed construct at different holding potentials for 470nm and 635nm wavelengths. This shows reverse potential for the two wavelengths that are intended for in vitro and in vivo applications. Performing a similar experiment for a variety of wavelengths would not be as valuable, in part, due to the enormous amount of data generated. As we have shown in the study, the response of any channelrhodopsins vary with different light duration and light intensities in addition to the wavelengths and holding potentials. The results for each recorded cell could include stimulation by different wavelengths, stimulation by different illumination intensities, stimulation with different light duration in addition to different holding potentials. Not only would the results be highly variable from cell-to-cell, there will be potentially hundreds or thousands of combinations to be tested per cell (e.g., 5 light intensities @1, 2.5 , 5 , 10 and 20 mW/mm>sup>2</sup>, 8 different wavelengths @ 450nm, 475nm, 500nm, 525nm, 550nm, 575nm, 600nm and 625nm, 7 light durations @ 1ms, 5ms, 10ms, 50ms, 100ms, 500ms and 1s, and , and 6 holding potentials @ -80mV, -70mV, -60mV, -40mV, -20mV and 0mV would result in 1680 stimulation conditions per recorded cell).Technically, the significant lowering of membrane resistance when both IvfChr and ZipACR variants are activated simultaneously would compromise the quality of voltage-clamping even in HEK293 cells with series resistance compensation. We have yet to see any other studies that had included such ambitious electrophysiology experiment for the channelrhodopsin characterization, likely due to the feasibility of such experiment.

      Reviewer #3 (Public Review):

      (1) The enhanced vf-Chrimson could potentially be a highlight of the manuscript, serving broader applications. Yet, gauging the overall improvements of ivf-Chrimson in comparison to other Chrimson variants remains intricate due to several reasons. First, photocurrents from ivf-Chrimson seem smaller than those from C-Chrimson (Supplemental Figure 3), and a direct comparison with standard vf-Chrimson is absent.

      We appreciate the reviewer’s positive view of our modified variant. We did not emphasize this particular modification as it was identical to our previous published modification and similar to that previously published by others (CsChrimson and C1Chrimson). In all these cases, improved membrane expression was consistently detected. We believe that expression data and our comparison of C-Chrimson and IvfChr is sufficient to justify the improved membrane expression and function.

      Second, while membrane expression of ivf-Chrimson appears enhanced in provided brightfield recordings, the quantitative analysis would necessitate confocal microscopy and a membrane marker (Supplemental Figure)

      We have now quantified the results with a membrane palmitoylated mCherry using confocal microscopy shown in Fig 2 Suppl1 A. We measured the Pearson Correlation Coefficient of the mCherry with EGFP or Citrine signal for the 6 constructs (vfChrimson, vfChrimson with trafficking sequence, vfChrimson with N-terminal signaling peptide from oChIEF (C-vfChrimson), vfChrimson with trafficking sequence and N-terminal signaling peptide from oChIEF (IvfChr), BiPOLES with EGFP or citrine and vfChrimson) and the results were identical and consistent with the prior results using epifluorescence microscopy.

      (2) Finally, other N-terminal modified Chrimson variants, like CsChrimson by Klapoetke et al. in 2014 and C1Chrimson by Oda et al. in 2018, have been generated. Comparing ivf-Chrimson to vf-CsChrimson or vf-C1Chrimson would be important to evaluate the benefits of the applied N-terminal modification.

      Our development of IvfChrimson is similar to the approach of vf-CsChrimson and identical to that of vf-C1Chrimson and we do not claim these modifications to be unique or superior. However, we have developed our design independently of these other studies and we have more extensive functional comparison and characterization data of our IvfChrimson variant than the other studies.

      (2.1) The action spectra of ZipACR suggest peak absorption of ZipACR WT and its mutant at 525 - 550 nm (Fig. 3). This is even further red-shifted than previously reported by Govorunova et al. Further action spectra recordings differ for all constructs between recordings initiated with blue or red light (Supplementary Fig. 5). This discrepancy is unexpected and should be discussed.

      We thank the reviewer for the comment, this was a mistake in the traces used for the figure. The example traces were the spectral response measured from the 400 nm to 650 nm instead of the 650 nm to 400 nm order shown in the spectral data. This has now been corrected.

      Additionally, the representative photocurrents of Zip(151V) in Fig. 3D1 do not align with the corresponding action spectrum in Fig. 3D2 as they show maximal photocurrents for 400 nm excitation.

      Please, see point above.

      (3) The authors introduce two different bicistronic expression cassettes-ZipT-IvfChR and ZipV-IvfChR-without providing clear guidelines on their conditions of use. Although the authors assert that ZipT is slower and further red-shifted than ZipV, the differences in the data for both ACR mutants are small and the benefits of the different final constructs should be explained.

      In our testing in neurons, ZipT has less ‘escaped’ spikes after the termination of the light pulses in the cells we have tested. However, this is dependent on the membrane properties such as capacitance and resistance of the cells. ZipV has a faster termination time and in some situations may be necessary due to its faster termination time and reduced disruption of physiological processes.

      We have now included this discussion in our updated manuscript.

      (4) The ZipT/V-IvfChRs are designed as bicistronic constructs; yet, disparities in membrane trafficking and protein degradation between the two channels could lead to divergences in blue and red light photoresponses. For future applicants, understanding the extent of expression ratio variations across cells using the presented expression cassettes could be of significance and should be discussed.

      We now have included this discussion in our responses above.

      Reviewer #1 (Recommendations For The Authors):

      (1) The Figure 1a mV cartoon traces for chloride are confusing. The chloride currents are depolarizing, not hyperpolarizing. As noted by the authors, these channels largely generate AP blockade through shunting inhibition (division), not hyperpolarization (subtraction).

      The figure has been corrected.

      (2) Figure 2A does not show where the light is applied. Why are some of the bars blue and some of them not filled?

      This has been corrected

      (3) Figure 2C1 does not show where the light is applied. There should be an inset to detail the blue-light-cessation-evoked AP. Also doesn't give the holding potential.

      The requested details are added.

      (4) Figure 2C2 inset is described as showing that "Light-induced currents with 470 nm illumination were initially outward but turned inward immediately following light offset." Is that correct? It looks to me like the current turns inward about half-way through the light pulse and then becomes even stronger after the light turns off. That is also consistent with the CC traces, which appear to show a transition toward depolarization during the light pulse before the AP initiation at light offset.

      Yes, the reviewer's observation is correct. There are blue light-induced outward and inward current peaks at the onset and offset of the light. Accordingly, we have modified the phrasing for Fig. 2C2.

      (5) Figure 3D1 shows that Zip(151V) has a peak current at 400nm, with a steady increase in current from red to blue, however, this is not the case in the summary data in 3D2. It's also not shown in Supplementary Figure 5B. What's going on?

      We apologize for the prior version of the figure associated with the first submission. The example traces from 400nm -> 650 nm were incorrectly included in the figure whereas the 650nm -> 400 nm example traces should be included. This has been corrected.

      (6) Figure 3D1 has no time scale.

      It is now been included

      (7) Figure 3E1 should read "Transduced" and not "Transfected"

      This has been corrected.

      (8) IvfChr fidelity drops off dramatically at 20hz...down to 50% efficiency of generating APs. This is described in the legend as "high frequency". Maybe the cart came before the horse in this figure...as it looks like in panel C that using less light power density improves fidelity in the dual opsin configuration with red light stimulation...why not use that power for the characterization? Did you try any higher frequencies? Or longer pulse widths? This is an important characterization to inform further use of the tool. This shortcoming isn't a cell-intrinsic limitation, as the 470nm stim with IVfChr was 100% successful at both 10hz and 20hz.

      It is known that red but not blue light pulses induce desensitization (optical fatigue) in red-shifted ChR variants. Indeed, one can reinstate the response to red light, by giving violet-blue light pulses (Fig 4. Suppl 2). We think this is the reason that the 470nm stimulation was more effective in inducing AP in cells expressing IvfChR. Higher light intensities induce greater desensitization, but are preferred for faster opening of channels and depolarization of neurons. This can explain why, in some situations, lower light intensities were more effective in producing APs when pulse trains were used. We have recordings from cells firing APs at 40Hz (not included). All these cells had high expression levels of the opsin.   

      (9) Figure 4D: why use 100ms pulse width? How do you know that this isn't causing depol block? Or some of the nefarious concerns that are raised in the discussion, such as "...disrupt[ion of] normal neuronal physiology and signal processing that occurs in millisecond time scale"?

      We used 100ms pulse duration to follow the published protocol that this experiment is based on (Lin et al., 2013, Nature Neuroscience). 

      (10) Figure 4E-bottom: What is the blue peak at light onset? Is the tool driving early activation before silencing?

      There seems to be an early, sharp and brief activation by blue light. We don’t know the definite cause of this, but we speculate this is driven by blue-light activation of ZipACR and not the IvfChr portion of the construct. The reason is that such a sharp rise is absent when only IvfChr is expressed (Fig. 4E, upper panel). Soma-targeted motif tethered to channelrhodopsins is known to result in preferential expression of channels close to soma but does not exclude the expression of channelrhodopsin in axonal and dendritic compartments, especially when animals are allow to recover for long period of time after viral injection. We believe that ZipACR at axonal terminals where the chloride concentration is high can still cause blue-light evoked depolarization and transmitter release. We observed this phenomenon in two mice in their first trial. The data for individual trials for each mouse are included in a supplementary table.

      (11) Figure 4G: Earlier in this same figure (B2, C), 470nm light was more effective at stimulating IvfChr than 635nm light. Is it unexpected that 638nm light would in this in vivo context be more effective at driving IvfChr responses than 450 nm light (at least as reflected by the AUC measurements)? Does this reflect fiber placement and light penetration/scattering?

      The spectral peaks of Chrimson-based variants including vfChrimson are all centered around 600 nm, and at 635 / 638 nm light, the amplitudes of photo-response decline, the channel onset slows, and the channels suffer greater desensitization. In isolated preparations where the light penetration is similar between 635 / 638 nm and 470 nm, 470 nm responses can outperform 635 / 638 nm responses due to its lack of desensitization and higher consistency in its response. This is also a strong reason that we have developed our current approach. In in vivo preparation shown in Fig. 4D-G, the much higher tissue penetration of 638nm light due to reduced absorption and reduced scattering can offset the performance of IvfChr to 450 nm light.  

      (12) In the methods, it is noted that different viral batches appear to generate different levels of neuronal toxicity. If that is the case, how did you differentiate between true differences between constructs vs. differential cell health effects?

      For figure 4D-F (whisker movement), we determined virus toxicity using NeuN staining. In slice recordings, we used the electrophysiological property of the neurons to assess their health. For this manuscript, we had one batch of virus that produced toxicity. We did not include any data from this batch.

      Reviewer #2 (Recommendations For The Authors):

      ● Define AUC on first use.

      It is now defined.

      ● Figure 3C2: Please explain how the photocurrents were normalized. As presented, it looks like under strong orange light, the ZipACR has higher photocurrent than the ivfChr.

      This is due to the fact vfChrimson and other Chrimson-based variants do not fully recover in the dark after 590 nm stimulation. We tested IvfChrimson with both reconditioning light pulse of 405 nm and without 405 nm and we can consistently reach a greater ‘maximal’ response from the same cell after 405 nm reconditioning (see Fig. 4 Suppl 2). We therefore normalize the response to the maximal recorded response of the cell often achieved with 10 or 20 mW/mm<sup>2</sup> 590 nm stimulation after 405 nm reconditioning. We understand this can be confusing and have now replaced the light-intensity response in Fig. 3C2 with the one with 405 nm reconditioning which is easier to interpret for the readers.

      ● P. 3: "As expected, blue light pulses induce transient membrane suppression..." Unclear what "suppression" means. Shunting? Hyperpolarization?

      We rephrased this to “As expected, blue light pulses transiently suppress APs…”

      ● P. 3: "illumination at 470 nm and 590 nm wavelengths led to similar amounts of courtship song (110.1 {plus minus} 12.8 and 78.5 {plus minus} 11.6,n = 16-17, respectively)". What are the units of "courtship song"?

      The unit for courtship song is the number of pulses per 10 seconds. This has been clarified in the figure.

      ● P. 5: The quantification of photocurrent in terms of pA/pF/A.U. is non-standard. I understand the impetus to normalize by expression to give something proportional to per-molecule conductance, but a user cares about overall photocurrent. Please also give the real photocurrents, either pA or pA/pF.

      We have provided the real photocurrent in pA or pA/pF where scientifically appropriate. To avoid selection and experimenter’s bias in our data, we did not set criteria for data elimination for cells with specific fluorescence intensity or photocurrent amplitude. Some resulting response can range from vary up to 20 folds from the same construct in many experiments. We do not believe that averaging absolute photocurrent amplitude would be justified due to the imbalance of weighing in the results. We do acknowledge that not selecting or eliminating data points would introduce higher noise in recordings with smaller responses but this is preferable over the selection or experimenter bias that is likely to be introduced otherwise.

      ● Please quote illumination intensities wherever possible.

      ● P. 7: why was the red light crosstalk into Zip(151T) tested at 635 nm instead of 590 nm? Isn't the relevant parameter 590 nm, since that will be used for the excitatory opsin?

      In all our characterizations of the constructs using slice electrophysiology recordings, we used 635nm instead of 590nm. The reason is that compared to 590nm wavelength, at 635nm the photocurrent for Zip(151T) and Zip(151V) is significantly reduced (Fig. 3D1,D2).

      ● P. 10: "we examined the power at which responses to 470 nm and 635 nm lights induce APs in neurons expressing ZipT-IvfChr, ZipV-IvfChr, or IvfChr", but the preceding sentence says you didn't test the ZipT-IvfChr. This is confusing, please clarify.

      The previous paragraph refers to the photocurrent recordings in HEK293 cells where our fast LED based illumination system is limited to 590 nm light, whereas the subsequent paragraph refers to the brain slice neuronal recordings. We have now emphasized the difference of the experiments in the rewrite.

      ● Fig. 4B1, top: Why don't the blue traces return to the same baseline after the stimulus epochs?

      We observed this shift in baseline (~4mV more depolarized) in cells expressing IvfChR (or vfChR) only with blue light stimulation. This was observed in the neurons recorded in the CA1 as well (data not shown). There was no such a change following red light stimulation (Fig. 4B1). Therefore, this should not affect the applicability of our construct. The original paper introducing vfChR did not test the responses of their constructs to blue light. There could be another photocycle state that is activated stronger by 470nm than 590nm and it has a slow off-rate, but this is only a speculation from our side. It must be noted we did not observe such a phenomenon in cells expressing ChrimsonR (Fig. 1 Suppl 1C).

      ● Fig. S3B, right: The two colors are barely distinguishable on the graph. Consider more distinct colors and/or different symbols.

      It has been changed accordingly.

      ● P. 15: "However, we do not recommend the use of orange light pulses, as we observed a significant photocurrent in this wavelength." Not clear what this is referring to. Which construct? Under which circumstances shouldn't one use orange light pulses? Where's the data showing this?

      This is referring to Fig. 3D1,D2 and Figure 4 suppl Fig. 2 which show a normalized ~40-50% photocurrent at 590nm. Now in the text, the reference figures for the data are added.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Audio et al. measured cerebral blood volume (CBV) across cortical areas and layers using high-resolution MRI with contrast agents in non-human primates. While the non-invasive CBV MRI methodology is often used to enhance fMRI sensitivity in NHPs, its application for baseline CBV measurement is rare due to the complexities of susceptibility contrast mechanisms. The authors determined the number of large vessels and the areal and laminar variations of CBV in NHP and compared those with various other metrics.

      Strengths:

      Non-invasive mapping of relative cerebral blood volume is novel for non-human primates. A key finding was the observation of variations in CBV across regions; primary sensory cortices had high CBV, whereas other higher areas had low CBV. The measured CBV values correlated with previously reported neuronal and receptor densities.

      Weaknesses:

      A weakness of this manuscript is that the quantification of CBV with postprocessing approaches to remove susceptibility effects from pial and penetrating vessels, as well as orientation dependency, is not fully validated, especially on a laminar scale. Further specific comments follow.

      We suspect that the comment regarding the lack of validation on laminar level stems from an error made by the corresponding author in the original bioRxiv submission (v1, May 17th https://www.biorxiv.org/content/10.1101/2024.05.16.594068v1?versioned=true), where Figure 3 which contains laminar validation was lost during pdf conversion. After submitting to E-Life, this mistake was quickly identified, and a corrected manuscript was re-uploaded to the bioRxiv (v2, June 5th, https://doi.org/10.1101/2024.05.16.594068). Although we informed the eLife staff about the update, it appears that the revised manuscript may not have reached reviewer #1 in time. We sincerely apologize for any confusion or inconvenience this may have caused.

      (1) Baseline CBV indices were determined using contrast agent-enhanced MRI (deltaR2*). Although this approach is suitable for areal comparisons, its application on a laminar scale has not been validated in the literature or in this study. By comparing with histological vascular information of V1, the authors attempted to validate their approach. However, the generalization of their method is questionable. The main issue is whether the large vessel contribution is minimized by processing approaches properly in various cortical areas (such as clusters 1-3 in Figure 5). It would be beneficial to compare deltaR2* with deltaR2 induced by contrast agents in a few selected slices, as deltaR2 is supposed to be sensitive to microvessels, not macrovessels. Please discuss this issue.

      The requested validation is presented in Figure 3F, which compares our deltaR2* measurements with previously invasive estimates of large vessel, capillary and cytochrome oxidase (CO) levels in V1 (Weber et al., 2008; doi.org/10.1093/cercor/bhm259). Our deltaR2* values show a stronger correspondence with microvascularity and CO levels than large vessels. Moreover, Figure 3D illustrates relative differences between V1 and V2, which closely align with the relative vascular volume differences reported by Zheng et al., 1991. It is important to note that Weber and colleagues averaged across V2-V5 due to similar vascularity across these areas. In our material, we also observed similar vascularity in these areas, though V5 (e.g., MT) has slightly denser vascularity, in agreement with reports of CO staining.

      Additionally, we report similar GM/WM vascular density, and high vascular density in primary sensory areas. Unfortunately, available ground-truth data on vascularity does not provide further (general) validation data for laminar vasculature in macaques (such as those in cluster 1-3; Fig. 5). That said, we have provided substantial evidence linking whole-brain vascular measures with variations in neuron (for data distribution, see Supp. Fig. 6F) and receptor densities, which we believe provides strong support for our approach.

      We would like to clarify that the authors do not assert that gradient-echo MRI is exclusively sensitive to microvessels and not macrovessels. This is not stated anywhere in the manuscript. If any sentence appears misleading, please let us know, and we will consider revising it. It is well-established that large vessels contribute to ΔR2* (Ogawa et al., 1993; Boxerman et al., 1995), and this is clearly stated in the manuscript (introduction, methods, results and discussion) and demonstrated in Figures 2A, B, and Supp. Figs. 2, 3, and 4. The primary concern, as the reviewer also noted, is whether we have sufficiently minimized the contribution of large vessels in our parcellated data analysis.

      At the parcellated level, we used the median value to avoid skewness in the data distribution, which primarily arises from large vessels, as regions near these vessels exhibit higher ΔR2*. The skewness of ΔR2* is also visible in Figure 1F, G. While this approach mitigates this large-small vessel issue, it does not entirely resolve it, as a slight linear increase toward the cortical surface remains (in all parcels). This is likely due to our inability to delineate all penetrating vessels as shown in Figure 2E and because contrast agents cumulatively accumulate toward superficial layers where blood originates and returns to the pial surface. To mitigate this issue, we detrended across layers the parcellated profiles, obtaining results similar to the ground-truth measures of vascularity in V1-V5 and CO histology in V1.

      (2) High-resolution MRI with a critical sampling frequency estimated from previous studies (Weber 2008, Zheng 1991) was performed to separate penetrating vessels, which is considered one of the major advancements in this study. However, this approach is still insufficient to accurately identify the number of vessels due to the blooming effects of susceptibility and insufficient spatial resolution. There was no detailed description of the detection criteria. More importantly, the number of observable penetrating vessels is dependent on imaging parameters and the dose of the contrast agent. If imaging slices were obtained in parallel to the cortex with higher in-plane resolution, it would likely improve the detection of penetrating vessels. Using higher-field MRI would further enhance the detection of penetrating vessels. Therefore, the reported value is only applicable to the experimental and processing conditions used in this study. Detailed selection criteria should be mentioned, and all potential pitfalls should be discussed.

      We believe that Figure 2 represents a significant conceptual and data analysis advancement in the field of vascular imaging. To the best of our knowledge, this is the first MRI study attempting to assess vessel density across cortical layers and compare the number of vessels to the known ground-truth. While we do not claim to have achieved a perfect solution (as shown in Figure 2), we offer a robust challenge to the imaging community by introducing this novel benchmarking approach. Our hope is that this conceptual framework will inspire the MR imaging community to tackle this challenge.

      Regarding imaging parameters, TE did not have much effect on our results, with a slight effect observed in the superficial layers due to the presence of large pial vessels (blooming effect; Fig. 2C). This also suggests that similar results could be achieved by changing the contrast agent dose, though there are, of course, CNR requirements and limitations at either end of the spectrum.

      We completely agree with the reviewer that spatial resolution is critical in resolving the arterio-venous networks, and we have dedicated significant attention to this topic in the introduction, results and discussion sections. We also agree with the reviewer that if imaging slices were obtained in parallel to the cortex with higher in-plane resolution, it would improve the detection of vessels. However, while this approach is ideal for counting vessels in a single plane and isolated region of cortex, it is less suited to the surface mapping of vessels, which is the focus of our study.

      Regarding the exclusion of vessels, based on visual comparison of vessels in volume space, Frangi-filter detection of vessels in volume space, and surface detection of vessels, we found no evidence to develop additional exclusion criteria (Supp. Fig. 3). On the contrary, we identified a number of false negatives in both the surface maps and volume maps. Notable exceptions to this rule seemed to occur at premotor areas F2 and F3 (Matelli et al., 1984; Patterns of cytochrome oxidase activity in the frontal agranular cortex of the macaque monkey). In these regions, we observed peculiar “pockets” of signal drop-out in equivolumetric layers 4-5. It is unclear what these signal-voids represent but it is interesting to note that these cortical areas F1-F5 were originally delineated by distinct CO+ positive large cells (Matelli et al., 1984).

      (3) Attempts to obtain pial vascular structures were made (Figure 2). As mentioned in this manuscript, the blooming effect of susceptibility contrasts is problematic. In the MRI community, T1-based Gd contrast agents have been used for mapping large vasculature, which is a better approach for obtaining pial vascular structures. Alternatively, computer tomography with a blood contrast agent can be used for mapping blood vasculature noninvasively. This issue should be discussed.

      We agree with the reviewer that T1-based contrast agents may offer more precise direct localization of large vessels in pial vasculature. However, the primary focus of our study was not on visualizing pial vascular structures, but rather on measuring vascular volume across cortical layers. For this purpose, we opted to use ferumoxytol, which provides superior T2*-contrast and about ten times longer plasma half-life compared to gadolinium. While we anticipated artifacts from the pial network, we developed a novel method to indirectly map these long-distance susceptibility artifacts arising from large vessels onto the cortical surface (Fig. 2A). If the goal would be to specifically visualize pial vessels, we applaud the high-resolution TOF angiography developed for direct vessel visualization (Bollman et al., 2022; https://doi.org/10.7554/eLife.71186)

      Changes in text:

      “4.1 Methodological considerations - vessel density informed MRI

      While the pial vessels can be directly visualized using high-resolution time-of-flight MRI (Bollmann et al., 2022), and computed tomography (Starosolski et al., 2015), imaging of the dense vascularity within the large and highly convoluted primate gray matter presents other formidable challenges. Here, we used a combination of ferumoxytol contrast agent and cortical layer resolution 3D gradient-echo MRI to map cerebrovascular architecture in macaque monkeys. These methods allowed us to indirectly delineate large vessels and indirectly estimate translaminar variations in cortical microvasculature.”

      (4) Since baseline R2* is related to baseline R2, vascular volume, iron content, and susceptibility gradients, it is difficult to correlate it with physiological parameters. Baseline R2* is also sensitive to imaging parameters; higher spatial resolution tends to result in lower R2* values (closer to the R2 value). Therefore, baseline R2* findings need to be emphasized.

      We agree with the reviewer's comment on the complexity of correlating baseline R2* with vasculature, given its sensitivity to multiple factors such as venous oxygenation, iron content, and imaging parameters such as image resolution. While our study focuses on vascular measurements, one could also highlight iron’s role in brain energy metabolism. Deoxygenated blood affects R2*, iron in oligodendrocytes supports myelination and neuronal signaling, and iron’s role in cytochrome c oxidase during electron transport impacts mitochondrial energy production. These metabolic factors collectively affect baseline R2* and link it to vasculature. Though quantitative susceptibility mapping (QSM) could help differentiate these different factors, it is beyond the scope of this study.

      (5) CBV-weighted deltaR2* is correlated with various other metrics (cytoarchitectural parcellation, myelin/receptor density, cortical thickness, CO, cell-type specificity, etc.). While testing the correlation between deltaR2* and these other metrics may be acceptable as an exploratory analysis, it is challenging for readers to discern a causal relationship between them. A critical question is whether CBV-weighted deltaR2* can provide insights into other metrics in diseased or abnormal brain states. If this is the case, then high-resolution deltaR2* will be useful. Please comment on this possibility.

      We agree with the reviewer that correlation deltaR2* with other metrics, such as myelin and cortical thickness, receptors and interneuron types, remains exploratory. Establishing causal relationships requires advanced multivariate analysis across cortical layers, but mapping histological stains to cortical layers is still under development. While this exploratory approach is promising, the ability to apply these insights to diseased or abnormal brain states is not yet clear. Layer-specific analysis of vasculature and function in disease is a future goal, and ongoing work aims to expand this line of inquiry. For now, while high-resolution deltaR2* may indeed offer diagnostic potential, we prefer to refrain from overstating its clinical utility at this stage. We agree that multimodal studies integrating neuroanatomy, function, and vascular metrics will be valuable for deeper insights into brain abnormalities.

      Changes in text:

      “4.3 The vascular network architecture is intricately connected to the neuroanatomical organization within cerebral cortex

      …To comprehensively understand the factors contributing to the vascular organization of the brain, experimental disentanglement through multivariate analysis of laminar cell types and receptor densities is needed (Hayashi et al., 2021, Froudist-Walsh et al., 2023).”

      (6) There is no discussion about the deltaR2* difference across subcortical areas (Figure 1). This finding is intriguing and warrants a thorough discussion in the context of the cortical findings.

      We thank the reviewer for this comment. We have expanded discussion on subcortical structures:

      Section 4.3, 1st paragraph:

      “In the cerebral cortex, neurons account for a significant portion (≈80-90%) of energy demand, with most of this energy allocated to signaling (≈80%) and maintaining membrane resting potentials (≈20%) (Attwell and Laughlin, 2001; Howarth et al., 2012). Since firing frequency is modulatory and the neural networks utilize distributed coding, the maintenance of resting-state membrane potential determines the minimal energy budget and the lower-limit for cerebral perfusion. Based on neuronal variability and energy dedicated to maintaining surface potential, this suggest an approximate (4 × 20% ≈) 80% variation in CBF and a resultant 25% variation in CBV across the cortex, in line with Grubbs' law (CBV = 0.80 × CBF0.38) (Grubb et al., 1974). In the cerebellar cortex, neuron density is higher, and the resting potentials are thought to account for more than 50% of energy usage (Howarth et al., 2012), aligning with its higher vascular volume compared to the cerebral cortex (Fig. 1F). However, this is a simplified estimation, and a more comprehensive assessment would need to account for consider an aggregate of biophysical factors such as…”

      Section 4.3, 4th paragraph:

      “When viewed in terms of information flow, CBV appear to decrease along the canonical circuit pathway (e.g., L4→L2/3→L5) in the primary visual cortex (Douglas and Martin, 2007) and as one ascends the hierarchy (e.g., V1→V2→V3&4→MT→7A) from primary sensory areas (Fig. 3F, Supp. Fig. 8) (Felleman and Van Essen et al., 1991, Markov et al., 2014). A similar pattern is observed in the auditory hierarchy, where the inferior colliculus, an early processing hub, exhibits the highest vascular volume, followed by a gradual reduction along cortical auditory ‘where’ and ‘what’ pathways (Fig. 1F, Fig. 3B).”

      (7) Figure 3 is missing. Several statements in the manuscript require statistics (e.g., bimodality in Figure 2D, Figure 3F).

      We apologize to the reviewer for the absence of Figure 3 in the initial submission.

      As for statistical testing of bimodality, we respectfully disagree and feel that this would not add much value to the manuscript. We think a descriptive, rather than rigorous, approach is sufficient in this context.

      Reviewer #2 (Public review):

      Summary:

      This manuscript presents a new approach for non-invasive, MRI-based measurements of cerebral blood volume (CBV). Here, the authors use ferumoxytol, a high-contrast agent, and apply specific sequences to infer CBV. The authors then move to statistically compare measured regional CBV with the known distribution of different types of neurons, markers of metabolic load, and others. While the presented methodology captures an estimated 30% of the vasculature, the authors corroborated previous findings regarding the lack of vascular compartmentalization around functional neuronal units in the primary visual cortex.

      Strengths:

      Non-invasive methodology geared to map vascular properties in vivo.

      Implementation of a highly sensitive approach for measuring blood volume.

      Ability to map vascular structural and functional vascular metrics to other types of published data.

      Weaknesses:

      The key issue here is the underlying assumption about the appropriate spatial sampling frequency needed to capture the architecture of the brain vasculature. Namely, ~7 penetrating vessels / mm2 as derived from Weber et al 2008 (Cer Cor). The cited work begins by characterizing the spacing of penetrating arteries and ascending veins using a vascular cast of 7 monkeys (Macaca mulatta, same as in the current paper). The ~7 penetrating vessels / mm2 are computed by dividing the total number of identified vessels by the area imaged. The problem here is that all measurements were made in a "non-volumetric" manner and only in V1. Extrapolating from here to the entire brain seems like an over-assumption, particularly given the region-dependent heterogeneity that the current paper reports.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      - For broader readership, it would be beneficial to provide a guide on how to interpret baseline R2* versus ΔR2*.

      The text was edited as follows:

      “…For quantitative assessment, R<sub>2</sub>* values were estimated from multi-echo gradient-echo images acquired both before and after the administration of ferumoxytol contrast agent (Table 1). Subsequently, the baseline R<sub>2</sub>* and ΔR<sub>2</sub>*, an indirect proxy measure of CBV (Boxerman et al., 1995), volume maps for each subject were mapped onto the twelve native equivolumetric layers (ELs) (Fig. 1C). Each vertex was then corrected for normal of the cortex relative to B<sub>0</sub> direction (Supp. Fig. 1). Surface maps for each subject were registered onto a Mac25Rhesus average surface using cortical curvature landmarks and then averaged across the subjects (Fig. 1D, E). Around cortical midthickness, the distribution of R<sub>2</sub>*, an aggregate measure for ferritin-bound iron, myelin content and venous oxygenation levels (Langkammer et al., 2012), resembled the spatial pattern of ΔR<sub>2</sub>* vascular volume. However, across cortical layers, these measures exhibited reversed patterns: R<sub>2</sub>* increased toward the white matter surface, whereas ΔR<sub>2</sub> decreased (Fig. 1E, G).”

      - The legends in Figure 1 describe green/cyan arrows, which are not visible in the figure itself.

      We thank the reviewer for noting this discrepancy. The reference to green/cyan arrows was removed from the Figure 1 legend.

      - There are typos in Section 3.3: "(Figure 4A, E)" and "(cluster 3; Figure 3)" should be corrected to Figure 5.

      We thank the reviewer for noting this error. The references to the Figures were corrected.

      Reviewer #2 (Recommendations for the authors):

      The work is elegantly presented and very easy to follow. The figures and the data presented there are compelling and well-organized. I have enjoyed reading the paper, despite my disagreement with the validity of the methodology presented.

      Validation against MRA methods (high resolution needed here, Bolan et al 2006, cited also by the authors). Certainly, that work used a much higher magnetic field. This could be done through collaboration if such a magnet is not available. In my humble opinion, the current arguments provided in the paper as validation fall short in convincing future readers. Other TOF approaches might be better suited (in combination with line scanning or single plane sequences) for the 3T used in this work.

      We appreciate the reviewer’s suggestion regarding time-of-flight (TOF) angiography at ultra-high magnetic fields, such as 9.4T for improved visualization of fast-flowing blood in arterial vessels, as elegantly demonstrated in Bolan et al., 2006. However, our focus was on mapping vasculature across cortical layers and TOF is not optimal for imaging slow capillary blood inflow. To enhance CNR also at capillary level, we used ferumoxytol-contrast agent to create quantitative CBV-weighted cortical layer maps (Boxerman et al., 1995).

      We are open to collaborative opportunities to revisit this work using ultra-high magnetic field strengths and more detailed neuroanatomical ground-truth measures. However, the recommended line scanning or single-plane sequences, at least on first impression, seem inadequate for whole-brain coverage and cortical surface mapping.

      Some of the methodology can be made more accessible to non-MRI readers. For example, a more elaborate explanation of R2* and ΔR2 could benefit future readers.

      Elaborated as requested (see above reply).

      A more detailed discussion of the limitations of the methodology could also be beneficial here. Explain the potential implications of under-sampling denser vascular areas (i.e. with potentially more than 7 penetrating vessels per mm2).

      V1, with its highest neuronal density, likely also has the highest feeding/draining vessel density. Based on this, we hypothesized that a 0.23 mm isotropic image resolution would sufficiently capture cortical arterio-venous networks, but we did not achieve the expected detection of 7 penetrating vessels per mm<sup>2</sup>. Consequently, we refrained from quantifying vessel density in other areas, albeit we did report the total vessel count.

      This under-sampling likely biases our ΔR2* estimates, skewing them toward larger vessels. To address this, we used median parcel values to avoid over-representing large vessels (the long-tail in ΔR2 parcels data distribution represents large vessels) and corrected for the cortical surface bias where blood originates from and returns to the pial network. These steps helped mitigate large vessel bias as described in the methods, results and discussion (see also our response to Reviewer #1, question #1).

      To improve clarity for readers, we further clarified:

      Methods:

      “The effect of blood accumulation in large feeding arteries and draining veins toward in the superficial layers was estimated using linear model and regressed out from the parcellated ΔR<sub>2</sub>* maps.”

      Results:

      “To mitigate bias resulting from undersampling the large-caliber vessels (Fig. 2A, B), median parcel values were obtained and M132 parcellated ΔR2* profiles were then detrended across ELs in each subject and then averaged.”

      Discussion:

      “This methodology, however, has known limitations. First, gradient-echo imaging is more sensitized toward large pial vessels running along the cortical surface and large penetrating vessels, which could differentially bias the estimation of Δ R<sub>2</sub>* across cortical layers (Fig. 2A, 2B) (Boxermann et al., 1995; Zhao et al., 2006). Additionally, vessel orientation relative to the B<sub>0</sub> direction introduce strong layer-specific biases in quantitative ΔR<sub>2</sub>* measurements (Supp. Fig. 1C) (Ogawa et al., 1993; Viessmann et al., 2019; Lauwers et al., 2008). To address these concerns, we conducted necessary corrections for B<sub>0</sub>-orientation, obtained parcel median values and regressed linear-trend thereby mitigating the effect of undersampling large-caliber vessels across ELs (Fig. 2C, Supp. Fig. 1).” 

      Please note, we are currently unable to create BALSA links to the figures due to maintenance issues at the data repository. As a result, we have opted to remove the links: