Author Response

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

Reviewer #1, in both the public review and recommendations to authors, raises the important question of generalizability of the new technique to other brain areas, to analysis with sorters other than Kilosort, and in the absence of reference data. Specifically, how can experimenters working in brain areas other than visual cortex understand if the tracking is functioning, and set the parameters in the tracking pipeline.

We agree that generalizability of the tracking procedure is a serious issue, especially with respect to other brain areas with varying degrees of measured waveform preservation over time. As the number of potential recording conditions is combinatorial to experimentally test, we instead address these issues in the manuscript by providing a general prescription for interpreting the distribution of vertical distances of matched pairs that can be used for data from any recording using any spike-sorter (Methods section 4.2, Supplement section 8.4, figure S9, paragraphs 7-10 of the Discussion section). This extension of the method allows users to estimate the matching success in the context of their own data, even in the absence of reference data. To address the concern of overfitting, we have also added discussion covering adjustment of the two parameters in the procedure (the relative weight of waveform distance vs. physical distance, and the threshold for accepting matches as real) to the Discussion section.

Reviewer #2 suggested clarification of the following points in the public review. We answer those here and have also clarified these points in the main text where appropriate.

(1) What is the purpose of testing the drift correction with imposed drift (Figure 2, page 6 in the original manuscript), and how the value was chosen?

To test the ability of EMD to detect substantial drift, we need examples that resemble experimental data, including error in fit unit positions and units with no correct matches. We chose to create these examples by taking waveform and position sets from real data with modest drift, and adding a fixed shift to one dataset. The value of 12 um in the figure is arbitrary, simply an example in the range of real drift. These tests allow us to demonstrate the success of EMD for detection of drift in real data.

(2) How is performance affected by using a different weighting of the 2 measures (physical distance and waveform distance) in the EMD?

Recovery rate (number of reference units successfully matched in EMD) vs weighting of the waveform distance is shown in Supplement section 8.10. Recovery rate increases with low values of waveform weighting, leveling off at a value of 1500. We selected that inflection point for the analysis in this paper, to avoid coincidental matching of physically distant units with similar waveforms.

(3) Should the intervals measured in the survival plot in Figure 5 be identical for the three different classes of tracked neurons?

The plot includes all chains of tracked neurons, which can start on arbitrary days in the set of all recordings (see the definition of chains in section 2.4). As a result, the gaps between days, which determine where there is a point on the plot, can be different for the three sets of neurons (reference, putative, and mixed). We have added a comment to the Figure 5 caption to ensure this is clear.

(4) Would other metrics of the similarity of visual responses work better?

The similarity metric we use was adopted from the original paper using this data (reference 7). We chose to use the same metric both to take advantage of the original authors’ expertise about the data and allow for reasonable comparison of the new technique to theirs. It is correct that this similarity metric alone does not allow for unique matching (see Discussion and Supplement section 8.2). However, the agreement of EMD with reference pairs determined from the combination of position and visual response similarity is very high, suggesting there are few incorrect reference pairs. Any incorrect reference pairs cause an underestimate of the tracking accuracy.

(5) Add a definition of ROC.

Added this definition to the text.

Reviewer #1 Recommendation to authors:

The main text needs proofreading.

We agree that the manuscript needed more thorough proofreading, and we have made corrections of typos and minor language errors throughout.

Additional comment from the authors:

Since the posting of this manuscript, another method for tracking neurons has been introduced:

Enny H. van Beest, Célian Bimbard, Julie M. J. Fabre, Flóra Takács, Philip Coen, Anna Lebedeva, Kenneth Harris, Matteo Carandini, Tracking neurons across days with high-density probes, bioRxiv 2023.10.12.562040; doi: https://doi.org/10.1101/2023.10.12.562040

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

Strengths:

The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

Weaknesses:

The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

We agree with the reviewer in their assessment that more independent stem cell clones and an equal gender balance would be preferable. We will mention these considerations as limitations of our study and encourage a larger-scale follow-up.

Regarding the estimated copy numbers, we would like to highlight that they have been extensively in the field, with direct validation of the differences in copy numbers with orthogonal methods like FACS2-4,7,10. Furthermore, the original paper directly compared the copy numbers estimated using the “proteomic ruler” to spike-in protein epitope signature tags and found remarkable concordance. This was performed with a much older generation mass spectrometer with reduced peptide coverage, and the author predicted that higher coverage would increase the quantitative performance.

Reviewer #2 (Public Review):

Summary:

Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine.

A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs.

This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

We thank the reviewer for the positive assessment.

Strengths:

The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

Weaknesses:

While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

We agree with the Reviewer that our study does not provide a mechanistic reason for the quantitative differences between the two cell types. However, we will include an expanded section in the discussion where we discuss the potential causes.<br /> We also agree studying hiPSCs reprogrammed from different cell types, such as blood lymphocytes, would be of great interest and will include a section about this within the discussion to encourage further research into the area.

Reviewer #3 (Public Review):

Summary:

In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

Strengths:

The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

Weaknesses:

It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

We respectfully disagree with the reviewer on this point. Our data shows that hESCs and hiPSCs show significant differences in protein mass and cell size, validated by the EZQ assay and FACS, while having no significant differences in their cell cycle profiles. Thus increased size and protein content would require higher growth rates to sustain the increased mass, which is what we show.

The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.<br /> We apologise for this omission; the details will be included in the revised version of the document.

Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

We agree with the reviewer on this. The hiPSC lines were generated by the HipSci consortium in the Wellcome Sanger Centre as described in the flagship HipSci paper13. We cite the flagship paper which specifies in great detail the reprogramming protocols and quality control measures, including looking at copy number variations13. However, we agree that we did not make this information easily accessible for readers. We also believe it is relevant to also explicitly include this information on our manuscript instead of expecting readers to look at the flagship paper. These details will be added to the revised version.

The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

We agree with the reviewer on this. FACS is indeed much more quantitative and a better method to study heterogeneity. However, we did not have protocols to study these markers using FACS.

TEM analysis should ideally be quantified.

We agree with the reviewer that it would be nice to have a quantitative measure.

All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

We agree with the reviewer completely. These points will be remediated in the revised version of the manuscript.

Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

We will include these details within the updated manuscript.

The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

We agree with the reviewer and will add a clear sentence in the discussion making this point explicitly.

Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

Regarding H1 histones, our study of the literature as well as interaction with chromatin and histone experts both within our institute and externally have not shed light into what the differences could imply. We think this is an interesting result that merits further study, but we don’t have a clear hypothesis on the consequences.

In summary, we thank the reviewers for their comments and will prepare a revised version that addresses their suggestions.

Author Response

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

eLife assessment

This study uses a multi-pronged empirical and theoretical approach to advance our understanding of how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats. The work makes an important contribution and parts of the data and analyses are solid, although several of the main claims are only partially supported or overstated and require additional support.

Public Reviews:

We thank the Editor and both Reviewers for their time and for their constructive evaluation of our manuscript. We worked to address each comment and suggestion offered by the Reviewers in our revision—please see our point-by-point responses below.

Reviewer #1 (Public Review):

Summary:

In this highly ambitious paper, Breen and Deffner used a multi-pronged approach to generate novel insights on how differences between male and female birds in their learning strategies might relate to patterns of invasion and spread into new geographic and urban areas.

The empirical results, drawn from data available in online archives, showed that while males and females are similar in their initial efficiency of learning a standard color-food association (e.g., color X = food; color Y = no food) scenario when the associations are switched (now, color Y = food, X= no food), males are more efficient than females at adjusting to the new situation (i.e., faster at 'reversal learning'). Clearly, if animals live in an unstable world, where associations between cues (e.g., color) and what is good versus bad might change unpredictably, it is important to be good at reversal learning. In these grackles, males tend to disperse into new areas before females. It is thus fascinating that males appear to be better than females at reversal learning. Importantly, to gain a better understanding of underlying learning mechanisms, the authors use a Bayesian learning model to assess the relative role of two mechanisms (each governed by a single parameter) that might contribute to differences in learning. They find that what they term 'risk sensitive' learning is the key to explaining the differences in reversal learning. Males tend to exhibit higher risk sensitivity which explains their faster reversal learning. The authors then tested the validity of their empirical results by running agent-based simulations where 10,000 computersimulated 'birds' were asked to make feeding choices using the learning parameters estimated from real birds. Perhaps not surprisingly, the computer birds exhibited learning patterns that were strikingly similar to the real birds. Finally, the authors ran evolutionary algorithms that simulate evolution by natural selection where the key traits that can evolve are the two learning parameters. They find that under conditions that might be common in urban environments, high-risk sensitivity is indeed favored.

Strengths:

The paper addresses a critically important issue in the modern world. Clearly, some organisms (some species, some individuals) are adjusting well and thriving in the modern, human-altered world, while others are doing poorly. Understanding how organisms cope with human-induced environmental change, and why some are particularly good at adjusting to change is thus an important question.

The comparison of male versus female reversal learning across three populations that differ in years since they were first invaded by grackles is one of few, perhaps the first in any species, to address this important issue experimentally.

Using a combination of experimental results, statistical simulations, and evolutionary modeling is a powerful method for elucidating novel insights.

Thank you—we are delighted to receive this positive feedback, especially regarding the inferential power of our analytical approach.

Weaknesses:

The match between the broader conceptual background involving range expansion, urbanization, and sex-biased dispersal and learning, and the actual comparison of three urban populations along a range expansion gradient was somewhat confusing. The fact that three populations were compared along a range expansion gradient implies an expectation that they might differ because they are at very different points in a range expansion. Indeed, the predicted differences between males and females are largely couched in terms of population differences based on their 'location' along the rangeexpansion gradient. However, the fact that they are all urban areas suggests that one might not expect the populations to differ. In addition, the evolutionary model suggests that all animals, male or female, living in urban environments (that the authors suggest are stable but unpredictable) should exhibit high-risk sensitivity. Given that all grackles, male and female, in all populations, are both living in urban environments and likely come from an urban background, should males and females differ in their learning behavior? Clarification would be useful.

Thank you for highlighting a gap in clarity in our conceptual framework. To answer the Reviewer’s question—yes, even with this shared urban ‘history’, it seems plausible that males and females could differ in their learning. For example, irrespective of population membership, such sex differences could come about via differential reliance on learning strategies mediated by an interaction between grackles’ polygynous mating system and malebiased dispersal system, as we discuss in L254–265 (now L295–306). Population membership might, in turn, differentially moderate the magnitude of any such sex-effect since an edge population, even though urban, could still pose novel challenges—for example, by requiring grackles to learn novel daily temporal foraging patterns such as when and where garbage is collected (grackles appear to track this food resource: Rodrigo et al. 2021 [DOI: 10.1101/2021.06.14.448443]). We now introduce this important conceptual information— please see L89–96.

Reinforcement learning mechanisms:

Although the authors' title, abstract, and conclusions emphasize the importance of variation in 'risk sensitivity', most readers in this field will very possibly misunderstand what this means biologically. Both the authors' use of the term 'risk sensitivity' and their statistical methods for measuring this concept have potential problems.

Please see our below responses concerning our risk-sensitivity term.

First, most behavioral ecologists think of risk as predation risk which is not considered in this paper. Secondarily, some might think of risk as uncertainty. Here, as discussed in more detail below, the 'risk sensitivity' parameter basically influences how strongly an option's attractiveness affects the animal's choice of that option. They say that this is in line with foraging theory (Stephens and Krebs 2019) where sensitivity means seeking higher expected payoffs based on prior experience. To me, this sounds like 'reward sensitivity', but not what most think of as 'risk sensitivity'. This problem can be easily fixed by changing the name of the term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. We further apologise for not clearly explaining how our lambda parameter estimates such risk-sensitive foraging. To do so here, we need to consider our Bayesian reinforcement learning model in full. This model uses observed choice-behaviour during reinforcement learning to infer our phi (information-updating) and lambda (risksensitivity) learning parameters. Thus, payoffs incurred through choice simultaneously influence estimation of each learning parameter—that is, in a sense, they are both sensitive to rewards. But phi and lambda differentially direct any reward sensitivity back on choicebehaviour due to their distinct definitions. Glossing over the mathematics, for phi, stronger reward sensitivity (bigger phi values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For lambda, stronger reward sensitivity (bigger lambda values) means stronger internal determinism about seeking the non-risk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’. We hope this information, which we have incorporated into our revised manuscript (please see L153–161), clarifies the rationale and mechanics of our reinforcement learning model, and why lamba measures risk-sensitivity.

In addition, however, the parameter does not measure sensitivity to rewards per se - rewards are not in equation 2. As noted above, instead, equation 2 addresses the sensitivity of choice to the attraction score which can be sensitive to rewards, though in complex ways depending on the updating parameter. Second, equations 1 and 2 involve one specific assumption about how sensitivity to rewards vs. to attraction influences the probability of choosing an option. In essence, the authors split the translation from rewards to behavioral choices into 2 steps. Step 1 is how strongly rewards influence an option's attractiveness and step 2 is how strongly attractiveness influences the actual choice to use that option. The equation for step 1 is linear whereas the equation for step 2 has an exponential component. Whether a relationship is linear or exponential can clearly have a major effect on how parameter values influence outcomes. Is there a justification for the form of these equations? The analyses suggest that the exponential component provides a better explanation than the linear component for the difference between males and females in the sequence of choices made by birds, but translating that to the concepts of information updating versus reward sensitivity is unclear. As noted above, the authors' equation for reward sensitivity does not actually include rewards explicitly, but instead only responds to rewards if the rewards influence attraction scores. The more strongly recent rewards drive an update of attraction scores, the more strongly they also influence food choices. While this is intuitively reasonable, I am skeptical about the authors' biological/cognitive conclusions that are couched in terms of words (updating rate and risk sensitivity) that readers will likely interpret as concepts that, in my view, do not actually concur with what the models and analyses address.

To answer the Reviewer’s question—yes, these equations are very much standard and the canonical way of analysing individual reinforcement learning (see: Ch. 15.2 in Computational Modeling of Cognition and Behavior by Farrell & Lewandowsky 2018 [DOI: 10.1017/CBO9781316272503]; McElreath et al. 2008 [DOI: 10.1098/rstb/2008/0131]; Reinforcement Learning by Sutton & Barto 2018). To provide a “justification for the form of these equations'', equation 1 describes a convex combination of previous values and recent payoffs. Latent values are updated as a linear combination of both factors, there is no simple linear mapping between payoffs and behaviour as suggested by the reviewer. Equation 2 describes the standard softmax link function. It converts a vector of real numbers (here latent values) into a simplex vector (i.e., a vector summing to 1) which represents the probabilities of different outcomes. Similar to the logit link in logistic regression, the softmax simply maps the model space of latent values onto the outcome space of choice probabilities which enter the categorial likelihood distribution. We can appreciate how we did not make this clear in our manuscript by not highlighting the standard nature of our analytical approach—we now do so in our revised manuscript (please see L148–149). As far as what our reinforcement learning model measures, and how it relates cognition and behaviour, please see our previous response.

To emphasize, while the authors imply that their analyses separate the updating rate from 'risk sensitivity', both the 'updating parameter' and the 'risk sensitivity' parameter influence both the strength of updating and the sensitivity to reward payoffs in the sense of altering the tendency to prefer an option based on recent experience with payoffs. As noted in the previous paragraph, the main difference between the two parameters is whether they relate to behaviour linearly versus with an exponential component.

Please see our two earlier responses on the mechanics of our reinforcement learning model.

Overall, while the statistical analyses based on equations (1) and (2) seem to have identified something interesting about two steps underlying learning patterns, to maximize the valuable conceptual impact that these analyses have for the field, more thinking is required to better understand the biological meaning of how these two parameters relate to observed behaviours, and the 'risk sensitivity' parameter needs to be re-named.

Please see our earlier response to these suggestions.

Agent-based simulations:

The authors estimated two learning parameters based on the behaviour of real birds, and then ran simulations to see whether computer 'birds' that base their choices on those learning parameters return behaviours that, on average, mirror the behaviour of the real birds. This exercise is clearly circular. In old-style, statistical terms, I suppose this means that the R-square of the statistical model is good. A more insightful use of the simulations would be to identify situations where the simulation does not do as well in mirroring behaviour that it is designed to mirror.

Based on the Reviewer’s summary of agent-based forward simulation, we can see we did a poor job explaining the inferential value of this method—we apologise. Agent-based forward simulations are posterior predictions, and they provide insight into the implied model dynamics and overall usefulness of our reinforcement learning model. R-squared calculations are retrodictive, and they say nothing about the causal dynamics of a model. Specifically, agent-based forward simulation allows us to ask—what would a ‘new’ grackle ‘do’, given our reinforcement learning model parameter estimates? It is important to ask this question because, in parameterising our model, we may have overlooked a critical contributing mechanism to grackles’ reinforcement learning. Such an omission is invisible in the raw parameter estimates; it is only betrayed by the parameters in actu. Agent-based forward simulation is ‘designed’ to facilitate this call to action—not to mirror behavioural results. The simulation has no apriori ‘opinion’ about computer ‘birds’ behavioural outcomes; rather, it simply assigns these agents random phi and lambda draws (whilst maintaining their correlation structure), and tracks their reinforcement learning. The exercise only appears circular if no critical contributing mechanism(s) went overlooked—in this case computer ‘birds’ should behave similar to real birds. A disparate mapping between computer ‘birds’ and real birds, however, would mean more work is needed with respect to model parameterisation that captures the causal, mechanistic dynamics behind real birds’ reinforcement learning (for an example of this happening in the human reinforcement learning literature, see Deffner et al. 2020 [DOI: 10.1098/rsos.200734]). In sum, agent-based forward simulation does not access goodness-of-fit—we assessed the fit of our model apriori in our preregistration (https://osf.io/v3wxb)—but it does assess whether one did a comprehensive job of uncovering the mechanistic basis of target behaviour(s). We have worked to make the above points on the method and the insight afforded by agent-based forward simulation explicitly clear in our revision—please see L192–207 and L534–537.

Reviewer #2 (Public Review):

Summary:

The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find moderate support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as those observed in the Grackle data. In the third part, the authors present an evolutionary model, in which they show that learning strategies as observed in male Grackles do evolve in what they regard as conditions present in urban environments.

Strengths:

The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show moderately better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency, especially regarding data analysis.

Thank you—we are pleased to receive this positive evaluation, particularly concerning our efforts to improve scientific transparency via our study’s preregistration (https://osf.io/v3wxb).

Weaknesses:

One major weakness of this manuscript is the fact that the authors are working with quite low sample sizes when we look at the different populations of edge (11 males & 8 females), middle (4 males & 4 females), and core (17 males & 5 females) expansion range. Although I think that when all populations are pooled together, the sample size is sufficient to answer the questions regarding sex differences in learning performance and which learning processes might be used by grackles but insufficient when taking the different populations into account.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results (it might; so might muchneeded population replicates—see L310), but our Bayesian models still allow us to learn a lot from our current data. We now explain this in our revised manuscript—please see L452–457.

Another weakness of this manuscript is that it does not set up the background well in the introduction. Firstly, are grackles urban dwellers in their natural range and expand by colonising urban habitats because they are adapted to it? The introduction also fails to mention why urban habitats are special and why we expect them to be more challenging for animals to inhabit. If we consider that one of their main questions is related to how learning processes might help individuals deal with a challenging urban habitat, then this should be properly introduced.

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question. We have worked towards flushing out how urban-imposed challenges faced by grackles, such as the wildlife management efforts introduced in L64–65 (now L85–86), may apply to animals inhabiting urban environments more broadly; for example, we now include an entire paragraph in our Introduction detailing how urban environments may be characterised differently to nonurban environments, and thus why they are perhaps more challenging for animals to inhabit— please see L56–71.

Also, the authors provide a single example of how learning can differ between populations from more urban and more natural habitats. The authors also label the urban dwellers as the invaders, which might be the case for grackles but is not necessarily true for other species, such as the Indian rock agama in the example which are native to the area of study. Also, the authors need to be aware that only male lizards were tested in this study. I suggest being a bit more clear about what has been found across different studies looking at: (1) differences across individuals from invasive and native populations of invasive species and (2) differences across individuals from natural and urban populations.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49), and we are careful to call attention to the fact that these data cover both resident urban and non-urban species as well as urban invasive species (please see L49–50). We also revised our labelling of the lizard species (please see L44). We are aware only male lizards were tested but this information is not relevant to substantiating our use of this study; that is, to highlight that learning can differ between urbandwelling and non-urban counterparts. We hope the changes we did make to our manuscript satisfy the Reviewer’s general suggestion to add biological clarity.

Finally, the introduction is very much written with regard to the interaction between learning and dispersal, i.e. the 'invasion front' theme. The authors lay out four predictions, the most important of which is No. 4: "Such sex-mediated differences in learning to be more pronounced in grackles living at the edge, rather than the intermediate and/or core region of their range." The authors, however, never return to this prediction, at least not in a transparent way that clearly pronounces this pattern not being found. The model looking at the evolution of risk-sensitive learning in urban environments is based on the assumption that urban and natural environments "differ along two key ecological axes: environmental stability 𝑢 (How often does optimal behaviour change?) and environmental stochasticity 𝑠 (How often does optimal behaviour fail to pay off?). Urban environments are generally characterised as both stable (lower 𝑢) and stochastic (higher 𝑠)". Even though it is generally assumed that urban environments differ from natural environments the authors' assumption is just one way of looking at the differences which have generally not been confirmed and are highly debated. Additionally, it is not clear how this result relates to the rest of the paper: The three populations are distinguished according to their relation to the invasion front, not with respect to a gradient of urbanization, and further do not show a meaningful difference in learning behaviour possibly due to low sample sizes as mentioned above.

Thank you for highlighting a gap in our reporting clarity. We now take care to transparently report our null result regarding our fourth prediction; more specifically, that we did not detect credible population-level differences in grackles’ learning (please see L130). Regarding our evolutionary model, we agree with the Reviewer that this analysis is only one way of looking at the interaction between learning phenotype and apparent urban environmental characteristics. Indeed, in L282–288 (now L325–329) we state: “Admittedly, our evolutionary model is not a complete representation of urban ecology dynamics. Relevant factors—e.g., spatial dynamics and realistic life histories—are missed out. These omissions are tactical ones. Our evolutionary model solely focuses on the response of reinforcement learning parameters to two core urban-like (or not) environmental statistics, providing a baseline for future study to build on”. But we can see now that ‘core’ is too strong a word, and instead ‘supposed’, ‘purported’ or ‘theorised’ would be more accurate—we have revised our wording throughout our manuscript to say as much (please see, for example, L24; L56; L328). We also further highlight the preliminary nature of our evolutionary model, in terms of allowing a narrow but useful first-look at urban eco-evolutionary dynamics—please see L228–232. Finally, we now detail the theorised characteristics of urban environments in our Introduction (rather than in our Results; please see L56–71), and we hope that by doing so, how our evolutionary results relate to the rest of our paper is now better set up and clear.

In conclusion, the manuscript was well written and for the most part easy to follow. The format of eLife having the results before the methods makes it a bit harder to follow because the reader is not fully aware of the methods at the time the results are presented. It would, therefore, be important to more clearly delineate the different parts and purposes. Is this article about the interaction between urban invasion, dispersal, and learning? Or about the correct identification of learning mechanisms? Or about how learning mechanisms evolve in urban and natural environments? Maybe this article can harbor all three, but the borders need to be clear. The authors need to be transparent about what has and especially what has not been found, and be careful to not overstate their case.

Thank you, we are pleased to read that the Reviewer found our manuscript to be generally digestible. We have worked to add further clarity, and to tempter our tone (please see our above and below responses).

Reviewer #1 (Recommendations For The Authors):

Several of the results are based on CIs that overlap zero. Tone these down somewhat.

We apologise for overstating our results, which we have worked to tone down in our revision. For instance, in L185–186 we now differentiate between estimates that did or did not overlap zero (please also see our response to Reviewer 2 on this tonal change). We note we do not report confidence intervals (i.e., the range of values expected to contain the true estimate if one redoes the study/analysis many times). Rather, we report 89% highest posterior density intervals (i.e., the most likely values of our parameters over this range). We have added this definition in L459, to improve clarity.

The literature review suggesting that urban environments are more unpredictable is not convincing. Yes, they have more noise and light pollution and more cars and planes, but does this actually relate to the unpredictability of getting a food reward when you choose an option that usually yields rewards?

To answer the Reviewer’s question—yes. But we can see that by not including empirical examples from the literature, we did a poor job of arguing such links. In L43–49 we now give three empirical examples; more specifically, we state: “[...] experimental data show the more variable are traffic noise and pedestrian presence, the more negative are such human-driven effects on birds' sleep (Grunst et al., 2021), mating (Blickley et al., 2012), and foraging behaviour (Fernández-Juricic, 2000).” We note we now detail such apparently stable but stochastic urban environmental characteristics in our Introduction rather than our Results section, to hopefully improve the clarity of our manuscript (please see L56–71). We further note that we cite three literature reviews—not one—suggesting urban environments are stable in certain characteristics and more unpredictable in others (please see L59–60). Finally, we appreciate such characterisation is not certain, and so in our revision we have qualified all writing about this potential dynamic with words such as “apparent”, “supposed”, “theorised”, “hypothesised” etc.

It would be interesting to see if other individual traits besides sex affect their learning/reversal learning ability and/or their learning parameters. Do you have data on age, size, condition, or personality? Or, the habitat where they were captured?

We do not have these data. But we agree with the Reviewer that examining the potential influence of such covariates on grackles’ reinforcement learning would be interesting in future study, especially habitat characteristics (please see L306–309).

For most levels of environmental noise, there appears to be an intermediate maximum for the relationship between environmental stability and the risk sensitivity parameter. What does this mean?

There is indeed an intermediate maximum for certain values of environmental stochasticity (although the differences are rather small). The most plausible reason for this is that for very stable environments, simulated birds essentially always “know” the rewarded solution and never need to “relearn” behaviour. In this case, differences in latent values will tend to be large (because they consistently get rewarded for the same option), and different lambda values (in the upper range) will produce the same choice behaviour, which results in very weak selection. While in very unstable environments, optimal choice behaviour should be more exploratory, allowing learners to track frequently-changing environments. We now note this pattern in L240–248.

Reviewer #2 (Recommendations For The Authors):

L2: I'd encourage the authors to reconsider the term "risk-sensitive learning", at least in the title. It's not apparent to me how 'risk' relates to the investigated foraging behaviour. Elsewhere, risk-reward sensitivity is used which may be a better term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. In explaining our reinforcement model, we also now detail how risk relates to foraging behaviour. Specifically, in L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies why lamba measures risk-sensitivity, and why we continue to use this term.

L1-3: The title is a bit misleading with regard to the empirical data. From the data, all that can be said is that male grackles relearn faster than females. Any difference between populations actually runs the other way, with the core population exhibiting a larger difference between males and females than the mid and edge populations.

It is customary for a manuscript title to describe the full scope of the study. In our study, we have empirical data, cognitive modelling, and evolutionary simulations of the background theory all together. And together these analytical approaches show: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; and (3) risk-sensitive learning should be favoured in statistical environments characterised by purported urban dynamics. So, we do not feel our title “Leading an urban invasion: risk-sensitive learning is a winning strategy” is misleading with regard to our empirical data; it just doesn’t summarise only our empirical data. Finally, as we now state in L312–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences.

L13: "Assayed", is that correctly put, given that the authors did not collect the data?

Merrian-Webster defines assay as “to analyse” or “examination or determination as to characteristics”, and so to answer the Reviewer’s question—yes, we feel this is correctly put. We note we explicitly introduce in L102–103 that we did not collect the data, and we have an explicit “Data provenance” section in our methods (please see L342–347).

L42-46: The authors provide a single example of how learning can differ between populations from more urban and more natural habitats. I would like to point out that many of these studies do not directly confirm that the ability in question has indeed led to the success of the species tested (e.g. show fitness consequences). Then the authors could combine these insights to form a solid prediction for the grackles. As of now, this looks like cherry-picking supportive literature without considering negative results.

Here are some references that might be helpful in identifying relevant literature to cite:

Szabo, B., Damas-Moreira, I., & Whiting, M. J. (2020). Can cognitive ability give invasive species the means to succeed? A review of the evidence. Frontiers in Ecology and Evolution, 8, 187.

Griffin AS, Tebbich S, Bugnyar T, 2017. Animal cognition in a human-dominated world. Anim Cogn 20(1):1-6.

Kark, S., Iwaniuk, A., Schalimtzek, A., & Banker, E. (2007). Living in the city: Can anyone become an "urban exploiter"? Journal of Biogeography, 34(4), 638-651.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49). We are aware that direct evidence of fitness consequences is entirely lacking in the scientific literature on cognition and successful urban invasion; hence why such data is not present in our paper. But we now explicitly point out a role for likely fitness-affecting anthropogenic disturbances on sleep, mate, and foraging behaviour on animals inhabiting urban environments (please see L63–68). We hope these new data bolster our predictions for our grackles. Finally, the Reviewer paints a (in our view) inaccurate picture of our use of available literature. Nevertheless, to address their comment, we now highlight a recent meta-analysis advocating for further research to confirm apparent ‘positive’ trends between animal ‘smarts’ and successful ‘city living’ (please see L43).

L64: Is their niche historically urban, or have they recently moved into urban areas?

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question.

L66-67: This is an important point that is however altogether missing from the discussion.

We thank the Reviewer for highlighting a gap in our discussion regarding populationlevel differences in grackles’ reinforcement learning. In L310–312 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L68-71: The paper focuses on cognitive ability. The whole paragraph sets up the prediction of why male grackles should be better learners due to their dispersal behaviour. This example, however, focuses on aggression, not cognition. Here is a study showing differences in learning in male and female mynas that might be better suited:

Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS, 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn 20(1):65-74.

We thank the Reviewer for suggesting this paper. We feel it is better suited to substantiating our point in the Discussion about reversal learning not being indicative of cognitive ability—please see L276–277.

L73: Generally, I suggest not writing "for the first time" as this is not a valid argument for why a study should be conducted. Furthermore, except for replication studies, most studies investigate questions that are novel and have not been investigated before.

The Reviewer makes a fair point—we have removed this statement.

L80-81: Here again, this is left undiscussed later on.

By ‘this’ we assume the Reviewer is referring to our hypothesis, which is that sex differences in dispersal are related to sex differences in learning in an urban invader— grackles. At the beginning of our Discussion, we state how we found support for this hypothesis (please see L250–261); and in our ‘Ideas and speculation’ section, we discuss how these hypothesis-supporting data fit into the literature more broadly (please see L294–331). We feel this is therefore sufficiently discussed.

L77-81: This sentence is very long and therefore hard to read. I suggest trying to split it into at least 2 separate sentences which would improve readability.

Per the Reviewer’s useful suggestion, we have split this sentence into two separate sentences—please see L97–115.

L83: Please explain choice-option switches. I am not aware of what that is and it should be explained at first mention.

We apologise for this operational oversight. We now include a working definition of speed and choice-option switches at first mention. Specifically, in L107–108 we state: “[...] we expect male and female grackles to differ across at least two reinforcement learning behaviours: speed (trials to criterion) and choice-option switches (times alternating between available stimuli)”.

L83-87: Again, a very long sentence. Please split.

We thank the Reviewer for their suggestion. In this case we feel it is important to not change our sentence structure because we want our prediction statements to match between our manuscript and our preregistration.

L96-97: Important to not overstate this. It merely demonstrates the potential of the proposed (not detected) mechanism to generate the observed data.

As in any empirical analysis, our drawn conclusions depend on causal assumptions about the mechanisms generating behaviour (Pearl, J. (2009). Causality). Therefore, we “detected” specific learning mechanisms assuming a certain generative model, namely reinforcement learning. As there is overwhelming evidence for the widespread importance of value-based decision making and Rescorla-Wagner updating rules across numerous different animals (Sutton & Barto (2018) Reinforcement Learning), we would argue that this assumed model is highly plausible in our case. Still, we changed the text to “inferred” instead of “detected” learning mechanisms to account for this concern—please see L123–124.

L99: "urban-like settings" again a bit confusing. The authors talk about invasion fronts, but now also about an urbanisation gradient. Is the main difference between the size and the date of establishment, or is there additionally a gradient in urbanisation to be considered?

We now include a paragraph in our Introduction detailing apparent urban environmental characteristics (please see 56–71), and we now refer to this dynamic specifically when we define urban-like settings (please see L126–127). To answer the Reviewer’s question—we consider both differences. Specifically, we consider the time since population establishment in our paper (with respect to our behavioural and mechanistic modelling), as well as how statistical environments that vary in how similar they are to apparently characteristically urban-like environments, might favour particular learning phenotypes (with respect to our evolutionary modelling). We hope the edits to our Introduction as a whole now make both of the aims clear.

L11-112: Above the authors talk about a comparable number of switches (10.5/15=0.7), and here of fewer number of switches (25/35=0.71), even though the magnitude of the difference is almost identical and actually runs the other way. The authors are probably misled by their conservative priors, which makes the difference appear greater in the second case than in the first. Using flat priors would avoid this particular issue.

Mathematically, the number of trials-to-finish and the number of choice-optionswitches are both a Poisson distributed outcome with rate λ (we note lambda here is not our risk-sensitivity parameter; just standard notation). As such, our Poisson models infer the rate of these outcomes by sex and phase—not the ratio of these outcomes by sex and phase. So comparing the magnitude of divided medians of choice-option-switches between the sexes by phase is not a meaningful metric with respect to the distribution of our data, as the Reviewer does above. For perspective, 1 vs. 2 switches provides much less information about the difference in rates of a Poisson distribution than 50 vs 100 (for the former, no difference would be inferred; for the latter, it would), but both exhibit a 1:2 ratio. To hopefully prevent any such further confusion, and to focus on the fact that our Poisson models estimate the expected value i.e., the mean, we now report and graph (please see Fig. 2) mean and not median trialsto-finish and total-switch-counts. Finally, we can see that our use of the word “conservative” to describe our weakly informative priors is confusing, because conservative could mean either strong priors with respect to expected effect size (not our parameterisation) or weak priors with respect to such assumptions (our parameterisation). To address this lack of clarity, we now state that we use “weakly informative priors” in L457–458.

L126: It is not clear what risk sensitivity means in the context of these experiments.

Thank you for pointing out our lack of clarity. In L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies what risk sensitivity means and measures, with respect to our behavioural experiments.

L128-129: I find this statement too strong. A plethora of other mechanisms could produce similar patterns, and you cannot exclude these by way of your method. All you can show is whether the mechanism is capable of producing broadly similar outcomes as observed

In describing the inferential value of our reinforcement learning model, we now qualify that the insight provided is of course conditional on the model, which is tonally accurate. Please see L161.

L144: As I have already mentioned above, here is the first time we hear about unpredictability related to urban environments. I suggest clearly explaining in the introduction how urban and natural environments are assumed to be different which leads to animals needing different cognitive abilities to survive in them which should explain why some species thrive and some species die out in urbanised habitats.

Thank you for this suggestion. We now include a paragraph in our Introduction detailing as much—please see L56–71.

L162: "almost entirely above zero" again, this is worded too strongly.

In reporting our lambda across-population 89% HPDI contrasts in L185–186, we now state: “[...] across-population contrasts that lie mostly above zero in initial learning, and entirely above zero in reversal learning”. Our previous wording stated: ““[...] across-population contrasts that lie almost entirely above zero”. The Reviewer was correct to point out that this previous wording was too strong if we considered the contrasts together, as, indeed, we find the range of the contrast in initial learning does minimally overlap zero (L: -0.77; U: 5.61), while the range of the contrast in reversal learning does not (L: 0.14; U: 4.26). This rephrasing is thus tonally accurate.

L178-179: I think it should be said instead that the model accounts well for the observed data.

We have rephrased in line with the Reviewer’s suggestion, now stating in L217–218 that “Such quantitative replication confirms our reinforcement learning model results sufficiently explain our behavioural sex-difference data.”

L188-190: I am not convinced this is a general pattern. It is quite a bold claim that I don't find to be supported by the citations. Why should biotic and abiotic factors differ in how they affect behavioural outcomes? Also, events in urban environments such as weekend/weekday could lead to highly regular optimal behaviour changes.

Please see our response to Reviewer 1 on this point. We note we now touch on such regular events in L94–96.

L209-211: The first sentence is misleading. The authors have found that males and females differ in 'risk sensitivity', that their learning model can fit the data rather well, and that under certain, not necessarily realistic assumptions, the male learning type is favoured by natural selection in urban environments. A difference between core, middle, and edge habitats however is barely found, and in fact seems to run the other way than expected.

In our study, we found: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; (3) we are sufficiently certain risk-sensitive learning generates our sex-difference data, as our agentbased forward simulations replicate our behavioural results (not because our model ‘fits’ the data, but because we inferred meaningful mechanistic differences—see our response to Reviewer 1 on this point); and (4) under theorised dynamics of urban environments, natural selection should favour risk-sensitive learning. We therefore do not feel it is misleading to say that we mapped a full pathway from behaviour to mechanisms through to selection and adaptation. Again, as we now state in L311–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences. And we note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale—please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324.

L216: "indeed explain" again worded too strongly.

We have tempered our wording. Specifically, we now state in L218: “sufficiently explain”. This wording is tonally accurate with respect to the inferential value of agent-based forward simulations—please see L192–207 on this point.

L234: "reward-payoff sensitivity" might be a better term than risk-sensitivity?

Please see our earlier response to this suggestion. We note we have changed this text to state “risk-sensitive learning” rather than “reward-payoff sensitivity”, to hopefully prevent the reader from concluding only our lambda term is sensitive to rewards—a point we now include in L153–154.

L234-237: I think these points may be valuable, but come too much out of the blue. Many readers will not have a detailed knowledge of the experimental assays. It therefore also does not become clear how they measure the wrong thing, what this study does to demonstrate this, or whether a better alternative is presented herein. It almost seems like this should be a separate paper by itself.

We apologise for this lack of context. We now explicitly state in L275 that we are discussing reversal learning assays, to give all readers this knowledge. In doing so, we hope the logic of our argument is now clear: reversal learning assays do not measure behavioural flexibility, whatever that even is. The Reviewer’s suggestion of a separate paper focused on what reversal learning assays actually measure, in terms of mechanism(s), is an interesting one, and we would welcome this discussion. But any such paper should build on the points we make here.

L270-288: Somewhere here the authors have to explain how they have not found differences between populations, or that in so far as they found them, they run against the originally stated hypothesis.

We thank the Reviewer for these suggestions. In L310—313 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L284: should be "missing" not "missed out"

We have made this change.

L290-291: It is unclear what "robust interactive links" were found. A pattern of sexbiased learning was found, which can potentially be attributed to evolutionary pressures in urban environments. An interaction e.g. between learning, dispersal, and sex can only be tentatively suggested (no differences between populations). Also "fully replicable" is a bit misleading. The analysis may be replicable, but the more relevant question of whether the findings are replicable we cannot presently answer.

We apologise for our lack of clarity. By “robust” we mean “across population”, which we now state in L333. We again note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale— please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324. Finally, the Reviewer makes a good point about our analyses but not our findings being replicable. In L334 we now make this distinction by stating “analytically replicable”.

L306-315: I think you have a bit of a sample size issue not so much when populations are pooled but when separated. This might also factor in the fact that you do not really find differences across the populations in your analysis. When we look at the results presented in Figure 2 (and table d), we can see a trend towards males having better risk sensitivity in core (HPDI above 0) and middle populations (HPDI barely crossing 0) but the difference is very small. Especially the results on females are based on the performance of only 8 and 4 females respectively. I suggest making this clear in the manuscript.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results; it might; so might muchneeded population replicates—see L310. But our Bayesian models still allow us to learn a lot from our current data, and, at present, we infer no meaningful population-level behavioural or mechanistic differences in grackles’ behaviour. To make clear the inferential sufficiency of our analytical approach, we now include some of the above points in our Statistical analyses section in L452–457. Finally, we caution against speculating on any between-population variation, as we now highlight in L311—313 of our Discussion.

Figure 2: I think the authors should rethink their usage of colour in this graph. It is not colour-blind friendly or well-readable when printed in black and white.

We used the yellow (hex code: #fde725) and green (hex code: #5ec962) colours from the viridis package. As outlined in the viridis package vignette (https://cran.rproject.org/web/packages/viridis/index.html), this colour package is “designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing”.

Figure 3B: Could the authors turn around the x-axis and the colour code? It would be easier to read this way.

We appreciate that aesthetic preferences may vary. In this case, we prefer to have the numbers on the x-axis run the standard way i.e., from small to large. We note we did remove the word ‘Key’ from this Figure, in line with the Reviewer’s point about these characteristics not being totally certain.

I also had a look at the preregistration. I do think that there are parts in the preregistration that would be worth adding to the manuscript:

L36-40: This is much easier to read here than in the manuscript.

We changed this text generally in the Introduction in our revision, so we hope the Reviewer will again find this easier to read.

L49-56: This is important information that I would also like to see in the manuscript.

We no longer have confidence in these findings, as our cleaning of only one part of these data revealed considerable experimenter oversight (see ‘Learning criterion’).

L176: Why did you remove the random effect study site from the model? It is not part of the model in the manuscript anymore.

The population variable is part of the RL_Comp_Full.stan model that we used in our manuscript to assess population differences in grackles’ reinforcement learning, the estimates from which we report in Table C and D (please note we never coded this variable as “study cite”). But rather than being specified as a random effect, in our RL_Comp_Full.stan model we index phi and lambda by population as a predictor variable, to explicitly model population-level effects. Please see our code:

https://github.com/alexisbreen/Sex-differences-in-grackles- learning/blob/main/Models/Reinforcement%20learning/RL_Comp_Full.stan

L190-228: I am wondering if the model validation should also be part of the manuscript as well, rather than just being in the preregistration?

We are not sure how the files were presented to the Reviewer for review, but our study preregistration, which includes our model validation, should be part of our manuscript as a supplementary file.

Author Response

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

Reviewer #1 (Public Review):

(1.1) This work introduces a new method of imaging the reaction forces generated by small crawling organisms and applies this method to understanding locomotion of Drosophila larva, an important model organism. The force and displacement data generated by this method are a qualitative improvement on what was previously available for studying the larva, improving simultaneously the spatial, temporal, and force resolution, in many cases by an order of magnitude. The resulting images and movies are quite impressive.

We thank the reviewer for their recognition of the achievements our work presents and for their feedback with regard to what they consider our most important findings and the points raised in their review. We will address these points individually below.

(1.2) As it shows the novel application of recent technological innovations, the work would benefit from more detail in the explanation of the new technologies, of the rationales underlying the choice of technology and certain idiosyncratic experimental details, and of the limitations of the various techniques. In the methods, the authors need to be sure to provide sufficient detail that the work can be understood and replicated. The description of the results and the theory of motion developed here focus only on forces generated when the larva pushes against the substrate and ignores the equally strong adhesive forces pulling the larva onto the substrate.

As the reviewer correctly points out, our present work adapts a recently developed set of methods (namely, ERISM and WARP) for use with small soft-bodied animals. The foundational methods have been described in detail in previous publications (refs, 23 and 26). However, upon reflection, we agree that more information can be provided to ensure our work is more accessible and reproducible. We also agree that some additional clarifying information on our approach could be helpful. We have addressed this in the following ways:

(1) We have included a detailed Key Resources table in the methods section to allow for maximum transparency on equipment and reagent sourcing. This can now be found on Pages 16-19.

(2) We have modified the ‘Freely behaving animals force imaging’ section of the Materials and Methods section to include more detailed information on practical aspects of conducting experiments. These changes can be found on page 23-24 (lines 566–567, 571-577).

(3) We have re-ordered the Materials and Methods section, such that microcavity fabrication and microcavity characterisation occur prior to the description of ERISM and WARP experiments - this change should hopefully aid replication. Details regarding the application of a silicone well to the surface of microcavities have also been added (lines 472-474).

(4) We have added additional text in the Introduction and Results (Pages 3-4 and 7, lines 56-86, and 152-153) to explain our rationale for using ERISM/WARP and additional text in the discussion that discusses the potential role(s) of adhesive forces in larval locomotion (Page 12, lines 301307).

(1.3) The substrate applies upward, downward, and horizontal forces on the larva, but only upward and downward forces are measured, and only upward forces are considered in the discussions of "Ground Reactive Forces." An apparent weakness of the WARP technique for the study of locomotion is that it only measures forces perpendicular to the substrate surface ("vertical forces" in Meek et al.), while locomotion requires the generation of forces parallel to the substrate ("horizontal forces"). It should be clarified that only vertical forces are studied and that no direct information is provided about the forces that actually move the larva forward (or about the forces which impede this motion and are also generated by the substrate). Along with this clarification, it would be helpful to include a discussion of other techniques, especially micropillar arrays and traction force microscopy, that directly measure horizontal forces and of why these techniques are inappropriate for the motions studied here.

We attempted to provide a streamlined Introduction in our initial submission and then compared ERISM/WARP to other methods in our discussion. We are happy to provide a brief overview of substrate force measurement methods in the introduction to help set the stage for readers. The Introduction section of our revised manuscript now contains the following comparison of different mechanobiological imaging techniques on pages 3-4 lines 56-86:

‘However, in the field of cellular mechanobiology, many new force measuring techniques have been developed which allow measurement of comparatively small forces from soft structures exhibiting low inertia (15–17) often with relatively high spatial-resolution. Early methods such as atomic force microscopy required the use of laser-entrained silicon probes to make contact with a cell of interest (15). This approach is problematic for studying animal behaviour due to the risk of the laser and probe influencing behaviour. Subsequently, techniques have been developed which allow indirect measurement of substrate interactions. One such approach is Traction Force Microscopy (TFM) in which the displacement of fluorescent markers suspended in a material with known mechanical properties relative to a zero-force reference allows for indirect measurement of horizontally aligned traction forces (17–19). This technique allows for probe-free measurement of forces, but the need to obtain a precise zero-force reference would make time-lapse measurements on behaving animals challenging; further, depending on the version used, it has insufficient temporal resolution for the measurement of forces produced by many behaving animals, despite recent improvements (20). A second approach revolves around the use of micropillar arrays; in this technique, horizontally-aligned traction forces are measured by observing the deflection of pillars made of an elastic material with known mechanical properties. This approach can be limited in spatial resolution and introduces a non-physiological substrate that may influence animal behavior (21,22).

Recently we have introduced a technique named Elastic Resonator Interference Stress Microscopy (ERISM) which allows for the optical mapping of vertically aligned GRFs in the pico and nanonewton ranges with micrometre spatial resolution by monitoring local changes in optical resonances of soft and deformable microcavities. This technique allows reference-free mapping of substrate deformations and calculation of vertically directed GRFs; it has been used to study a range of questions related to exertion of cellular forces (23–25). Until recently, this technique was limited by its low temporal resolution (~10s), making it unsuitable for recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26). Given ERISM/WARP allows for probe-free measurement of vertical ground reaction forces with high spatial and temporal resolution, it becomes an attractive method for animal-scale mechanobiology.’

(1.4) The larvae studied are about 1 mm long and 0.1 mm in cross-section. Their volumes are therefore on order 0.01 microliter, their masses about 0.01 mg, and their weights in the range of 0.1 micronewton. This contrasts with the force reported for a single protpodium of 1 - 7 micronewtons. This is not to say that the force measurements are incorrect. Larvae crawl easily on an inverted surface, showing gravitational forces are smaller than other forces binding the larva to the substrate. The forces measured in this work are also of the same magnitude as the horizontal forces reported by Khare et al. (ref 32) using micropillar arrays.

I suspect that the forces adhering the larva to the substrate are due to the surface tension of a water layer. This would be consistent with the ring of upward stress around the perimeter of the larva visible in S4D, E and in video SV3. The authors remark that upward deflection of the substrate may be due to the Poisson's ratio of the elastomer, but the calibration figure S5 shows that these upward deflections and forces are much smaller than the applied downward force. In any case, there must be a downward force on the larva to balance the measured upward forces and this force must be due to interaction with the substrate. It should be verified that the sum of downward minus upward forces on the gel equals the larva's weight (given the weight is neglible compared to the forces involved, this implies that the upward and downward forces should sum to 0).

We have carefully calculated the forces exerted by protopodia and are confident in the accuracy of our measurements as reported. We further agree with the reviewer’s suggestion that gravitational forces can be largely neglected.

As the reviewer points out, one would expect forces due to upward and downward deflections to cancel when considering the entire system. However, we see indications that the counteracting / balancing force often acts over a much larger area than the acting force, e.g. a sharp indentation by a protopodium might be counteracted by an upward deflection over a 10-20 fold larger radius and hence 100 to 400-fold larger area, thereby reducing the absolute value of the upward deflection at any given pixel surrounding the indentation. This in turn increases error in determining the integrated upward deformation, making it difficult to perform an absolute comparison of acting and counteracting force. Further, recording the entire counteracting force induced deformation would require acquiring data with a prohibitively large field of view.

We agree that in some situations, water surface tension may be adhering animals to the substrate. Importantly, this is a challenge that the animal faces outside the lab in its natural environment of moist rotting fruit and yeast. The intricate force patterns seen in our study in the presence of water surface tension are therefore ecologically relevant. In other situations (e.g. preparing for pupation), larvae are able to stick to dry surfaces, suggesting that other adhesive forces such as mucoid adhesion can also come into play in certain behavioural contexts. A full characterization of the effects of water tension and mucoid adhesion are beyond the scope of this study. However, we have now added a sentence on pages 8 and 12 commenting on these other biomechanical forces at play:

‘We also observed that the animals travel surrounded by a relatively large water droplet (lines 189-190).’

‘We observed that larvae travel surrounded by moisture from a water droplet, which produces a relatively large upwardly directed force in a ring around the animal. The surface tension produced by such a water droplet likely serves a role in adhering the animal to the substrate. However, during forward waves, we found that protopodia detached completely during SwP, suggesting this surface tensionrelated adhesion force can be easily overcome by the behaving animal. (lines 301-307) .’

(1.5) Much of the discussion and the model imply that the sites where the larva exerts downward force on the gel are the sites where horizontal propulsion is generated. This assumption should be justified. Can the authors rule out that the larva 'pulls' itself forward using surface tension instead of 'pushing' itself forward using protopodia?

Determining the exact ‘sites’ where horizontal propulsion is generated is challenging. In our conceptual model, movement is not initiated by protopodia per se, but rather by a constellation of muscle contractions, which act upon the hydrostatic skeleton, which in turn causes visceral pistoning that heaves larvae forward. This is based on previous findings in Ref 31. While there are indeed downward protopodial ‘vaulting’ forces prior to initiation of swing, we propose that the main function of protopodia is not to push the larvae forward, but rather to provide anchoring to counteract opposing forces generated by muscles. We agree that water surface tension could also be sculpting biomechanical interactions; however, a full characterization of how water surface tension shapes larval locomotion is beyond the scope of this study.

Since we have observed larvae move over dry terrain (e.g. glass) without an encasing water bubble, we do not believe that an encasing water bubble is strictly required for locomotion. We have also seen no obvious locomotion related modulations in the pulling forces created by water bubbles encasing larva, which would be expected if animals were somehow using water tension to pull themselves forward. Overall, the most likely explanation is that larvae use a mixture of biomechanical tactics to suit the moment in a given environment. This represents a challenge but also an opportunity for future research.

We have now added additional text in the ‘Functional subdivisions within protopodia’ subsection to discuss these nuances (page 14, lines 382-387):

‘This increased force transmitted into the substrate is unexpected as the forces generated for the initiation of movement should arise from the contraction of the somatic muscles. We propose that the contraction of the musculature responsible for sequestration acts to move haemolymph into the protopodia thus exerting an increased pressure onto the substrate while the contact area decreases as a consequence of the initiation of sequestration.’

and (page 15, lines 398-399):

‘Water surface films appear to facilitate larval locomotion in general but the biomechanical mechanisms by which they do this remain unclear.’

(1.6) More detail should be provided about the methods, their limitations, and the rationale behind certain experimental choices.

We thank the reviewer for this comment. As this significantly overlaps with a point raised earlier, we kindly direct them to our answer to comment #1.2 above.

(1.7) Three techniques are introduced here to study how a crawling larva interacts with the substrate: standard brightfield microscopy of a larva crawling in an agarose capillary, ERISM imaging of an immobilized larva, and WARP imaging of a crawling larva. The authors should make clear why each technique was chosen for a particular study - e.g. could the measurements using brightfield microscopy also be accomplished using WARP? They should also clarify how these techniques relate to and possibly improve on existing techniques for measuring forces organisms exert on a substrate, particularly micropillar arrays and Traction Force Microscopy.

Indeed, each of the three methods used has a specific merit. The brightfield microscopy was selected to track features on the animal’s body and to provide a basic control for the later measurements. However, this technique cannot directly measure the substrate interaction, it only allows inferences to be made from tracked features at the substrate interface. ERISM provides high resolution maps of the indentation induced by the larva; it is also extensively validated for mapping cell forces and the data analysis is robust against defects on the substrate (refs 23, 24 and 25). However, as we explain in the manuscript, ERISM lacks the temporal resolution needed to monitor mechanical activity of behaving larva. Its use was therefore limited to the study of anaesthetised animals. For mapping forces exerted by behaving larva, we used WARP which is a further development of ERISM that offers higher frame rates but at the cost of requiring more extensive calibration (Supplementary Figure S4). The streamlined introduction of the different methods in our original manuscript originates from our attempt to be as concise as possible. However, as state in response to comment #1.2, we agree that additional explanation and discussion will be helpful for readers and that it will helpful to briefly refer to other methods for force mapping. We have now added references to a variety of techniques in the Introduction (Page 3-4, lines 56-86) as stated in a prior response.

(1.8) As written, "(ERISM) (19) and a variant, Wavelength Alternating Resonance Pressure microscopy (WARP) (20) enable optical mapping of GRFs in the nanonewton range with micrometre and millisecond precision..." (lines 53-55) may generate confusion. ERISM as described in this work has a much lower temporal resolution (requires the animal to be still for 5 seconds - lines 474-5); In this work, WARP does not appear to have nanonewton precision (judging by noise on calibration figures) and it is not clear that it has millisecond precision (the camera used and its frame rate should be specified in the methods).

Previous studies have demonstrated the capabilities and limitations of ERISM and WARP. Upon reflection, we agree that our wording here could be more precise. To clarify our claim, we now separate the statements on ERISM and WARP in the introduction as follows (page 4, lines 78-83):

“Until recently, this technique was limited by its low temporal resolution (~10s) making it unsuitable for use in recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26)”

While WARP can achieve comparable force resolution as ERISM when used in a cellular context (c.f. Ref 26), we agree that for the present study, the resolution was in the 10s of nanonewton range, due to the need to use stiffer substrates and larger fields of view.

The camera used in our work was specified in the appropriate subsection of the Materials and Methods (“All WARP and ERISM images were acquired using an Andor Zyla 4.2 sCMOS camera (Andor Technology, Belfast, UK)”). We apologise that the exact frame rate used in our current work was not mentioned in our original manuscript; this has now been added to the ‘Freely behaving animals force imaging’ section of the Materials and Methods (page 23, lines 574-577).

(1.9) It would be helpful to have a discussion of the limits of the techniques presented and tradeoffs that might be involved in overcoming them. For instance, what is the field of view of the WARP microscope, and could it be increased by choosing a lower power objective? What would be required to allow WARP microscopy to measure horizontal forces? Can a crawling larva be imaged over many strides by recentering it in the field of view, or are there only particular regions of the elastomer where a measurement may be made?

We agree with the reviewer that some discussion of the limitations of our technique will allow readers to have a more informed appreciation of what we are capable of measuring using WARP. However, as this is the first work to ever demonstrate such measurements, the limitations and tradeoffs cannot all be known with certainty at the present stage.

To answer your individual questions:

(1) There is a trade-off between numerical aperture and the ability to resolve individual interference fringes. Since our approach to calculate displacement from reflection maps relies upon counting of individual fringe transitions, going to a lower powered objective risks having these fringes blend and thus the identification of the individual transitions becoming impossible. The minimum numerical aperture of the objective will therefore generally depend on the steepness of indentations produced by the animals; the steeper an indentation, the closer the neighbouring fringes and thus the higher the required magnification to resolve them.

(2) From WARP and ERISM data, one can make inferences about horizontal forces, as is described in detail in our earlier publications about ERISM (ref, 23). However, quantitation of horizontal forces at sufficient temporal resolution to allow the investigation of behaving Drosophila larva is currently not possible.

(3) Many strides can indeed be imaged using our technique, however, this comes with additional technical challenges. Whether or not the animal itself can be recentred is an ongoing challenge. We have found that the animals are amenable to recentring themselves within the field of view if chasing an attractive odorant. However, manual recentering using a paintbrush risks destroying the top surface of the soft elastic resonator and recentering the microscope stage would require real-time object tracking which has been outside the scope of this original work, given the other challenging requirements on hardware and optics for obtaining high quality force maps.

To provide more information on limitations of our technique, we have added the following text into the discussion (pages 13-14, lines 356-370).

‘Despite the substantial advances they have provided, the use of WARP and ERISM also brings challenges and has several technical limitations. For example, fabrication of resonators is much more challenging than preparation of the agarose substrates conventionally used for studying locomotion of Drosophila. This problem is compounded by the fragility of the devices owing to the fragility of the thin gold top mirror. This becomes problematic when placing animals onto the microcavities, as often the area local to the initial placement of the animal is damaged by the paintbrush used to move the animals. Further, as a result of the combining of the two wavelengths, the effective framerate of the resultant displacement and stress maps is equal to half of the recorded framerate of the interference maps. To be able to monitor fast movements, recording at very high framerates is therefore necessary which, depending on hardware, might require imaging at reduced image size, but this in turn reduces the number of peristaltic waves that can be recorded before the animal escapes the field of view. A further limitation is that WARP and ERISM are sensitive mainly to forces in the vertical direction; this is complementary to TFM, which is sensitive to forces in horizontal directions. Using WARP in conjunction with high speed TFM (possibly using the tuneable elastomers presented here) could provide a fully integrated picture of underlying vertical and horizontal traction forces during larval locomotion.’ And further on page 13, lines 337-341:

‘More detailed characterisation of this behaviour remains a challenge owing to the changing position of the mouth hooks. Due to their rigid structure and the relatively large forces produced in planting, mouth hooks produce substrate interaction patterns which our technique struggles to map accurately due to overlapping interference fringes ambiguating the fringe transitions.’

We trust that the above discussion and our modifications to our manuscript resulting from these will address the reviewer’s concerns.

Reviewer #2 (Public Review):

(2.1) With a much higher spatiotemporal resolution of ground dynamics than any previous study, the authors uncover new "rules" of locomotory motor sequences during peristalsis and turning behaviors. These new motor sequences will interest the broad neuroscience community that is interested in the mechanisms of locomotion in this highly tractable model. The authors uncover new and intricate patterns of denticle movements and planting that seem to solve the problem of net motion under conditions of force-balance. Simply put, the denticulated "feet" or tail of the Drosophila larva are able to form transient and dynamic anchors that allow other movements to occur.

We thank the reviewer for their feedback and the information regarding which of our results is likely to resonate most impactfully with readers from a biological background.

The biology and dynamics are well-described. The physics is elementary and becomes distracting when occasionally overblown. For example, one doesn't need to invoke Newton's third law, per se, to understand why anchors are needed so that peristalsis can generate forward displacements. This is intuitively obvious.

We are sorry to hear that the reviewer found some of the physics details distracting. To address this concern, we have simplified some of the language while still attempting to keep the core arguments intact. For context and analogy, we still believe that including a brief reference to the laws of motion is helpful for some readers to explain some of our results and highlight their general implications, especially with regard to anchoring against reaction forces.

One of our objectives is to make this article accessible and interesting for biologists and physicists at all levels. We feel it is important to reach out to both communities and try to be inclusive as possible in our writing. Newton’s 3rd law is clearly relevant for our study and it is a common point of reference for anyone with a highschool education, and so we feel it is appropriate to mention it as a way to help readers across disciplines understand the biophysical challenges faced by the animals we study.

(2.2) Another distracting allusion to "physics" is correlating deformation areas with displaced volume, finding that "volume is a consequence of mass in a 2nd order polynomial relationship". I have no idea what this "physics" means or what relevance this relationship has to the biology of locomotion.

Upon reflection, we agree that this language may be overly complex and distracts from what is, at its core, a simple, but important principle governing how Drosophila larvae interact with their substrates. The point we are trying to make is that our data show that forces exerted by an animal are proportional in a non-linear way to contact area. This suggests that to increase force exerted on the substrate, an animal must increase contact area. We do not observe contact area remaining constant while force increases, or vice versa. To make this result more clear, we have made several changes in our revised manuscript. Figure 5B no longer shows the relationship between the protopodial contact area and the displaced volume of the elastic resonator, but instead now shows the protopodial contact area and recorded force transmitted into the substrate. This then shows that in order to increase force transmitted into the substrate, these animals must increase their contact area. We have made changes to the figure legend of Figure 5 and the statements in the Results section accordingly (Page 9, lines 220-222).

2.3 The ERISM and WARP methods are state-of-the-art, but aside from generally estimating force magnitudes, the detailed force maps are not used. The most important new information is the highly accurate and detailed maps of displacement itself, not their estimates of applied force using finite element calculations. In fact, comparing displacements to stress maps, they are pretty similar (e.g., Fig 4), suggesting that all experiments are performed in a largely linear regime. It should also be noted that the stress maps are assumed to be normal stresses (perpendicular to the plane), not the horizontal stresses that are the ones that actually balance forces in the plane of animal locomotion.

We largely agree with the statement made by the reviewer here. However, we have found that in many contexts, audiences appreciate having the absolute number of the forces and stresses involved reported. Therefore, where possible, we have used stress maps, rather than displacement maps. We also observe that while stress and displacement maps show similar patterns, features sometimes appear sharper in the stress map, which is a result of the finite element algorithm being able to attribute a broad indentation to a somewhat more localised downward force. We have thus opted to keep to original stress maps. We have been more explicit about WARP and ERISM being more tuned to recording vertically directed forces throughout the revised manuscript (lines 75, 78, 86, 162, 301, 305, 336).

We have also modified our Discussion section to encourage further investigation of our proposed model using a technique more tuned to horizontal stresses (pages 12-13, lines 324-328):

‘However, WARP microscopy is best suited to measurements of forces in the vertical direction, and though we can make inferences such as this as they are a consequence of fundamental laws of physics, we present this conclusion as a testable prediction which could be confirmed using a force measurement technique more tuned to horizontally directed forces relative to the substrate.’

(2.4) But none of this matters. The real achievements are the new locomotory dynamics uncovered with these amazing displacement measurements. I'm only asking the authors to be precise and down-to-earth about the nature of their measurements.

We thank the reviewer for their perceptiveness in finding that though the forces are interesting, the interactions themselves are the most noteworthy result here. We trust that with the changes made in our revised manuscript, the description is now more “down-to-earth”, more concise where appropriate, and accurate as to which results are particularly important and novel.

(2.5) It would be good to highlight the strength of the paper -- the discovery of new locomotion dynamics with high-resolution microscopy -- by describing it in simple qualitative language. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles.

We thank the reviewer for this recommendation. We agree that including a more explicit statement of some of our findings, especially with regards to these new posterior tripod structures and the whole-abdomen preparatory anchoring prior to head sweeps, would make the paper more impactful. As a result, we have modified the discussion section to include a statement for each new result and have also amended our abstract as a result (lines 407-416):

“Here we have provided new insights into the behaviour of Drosophila larval locomotion. We have provided new quantitative details regarding the GRFs produced by locomoting larvae with high spatiotemporal resolution. This mapping allowed the first detailed observations of how these animals mitigate friction at the substrate interface and thus provide new rules by which locomotion is achieved. Further, we have ascribed new locomotor function to appendages not previously implicated in locomotion in the form of tripod papillae, providing a new working hypothesis of how these animals initiate movement. These new principles underlying the locomotion outlined here may serve as useful biomechanical constraints as called for by the wider modelling community (39).”

(2.6) As far as I know, these anchoring behaviors are new. It is intuitively obvious that anchoring has to occur, but this paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

We agree with the reviewer on this. We think it is best to let our colleagues reflect on our findings and then decide how best to include them in future models.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please be sure to describe in a figure caption or in the methods the details of the optical setup, especially the focal lengths of all the lenses, including the objective, and part numbers of the LEDs and filters. It would be helpful to have a figure in the main paper explaining the principles of ERISM/WARP microscopy along with the calibration measurements and computational pipeline (this would mainly combine elements already in the supplement). Such a figure should also include details of the setup that are alluded to in the methods but not fully explained (for instance, a "silicone well" is referred to in the methods but never described). The calibration of elastomer stiffness that now appears in the main text could be made a supplementary figure, unless there is some new art in the fabrication of the elastomers that should be highlighted as an advance in the main text.

We appreciate the importance of explaining our methods to readers.

In response to the public comments, we have added further details in our methods section to clarify practical aspects and ensure that readers will be able to reproduce our work.

In Supplemental Figure 2, we show the full optical light path for ERISM and WARP along with named components. In addition, the principles of ERISM and WARP microscopy have already been extensively described in previous publications (See Refs 23-26). In light of this, we feel that the best approach in this paper is to direct readers to those publications.

We feel that it is appropriate to present the calibration of elastomer stiffness in the main text because this is indeed a new innovation that is not just about making the elastomers but making force sensors based on these different materials. This is really important because it shows how researchers can tune the stiffness of an ERISM/WARP elastomer to match the type of tissue or organism under study. This is really the key technical advance that enables whole animal biomechanics across a range of animal sizes, so we think it is appropriate to keep it in the main text.

We want to make sure that we do not oversell this point, and we feel that we make it sufficiently clear in the main text of our manuscript that making elastomer based force sensors of appropriate stiffness is important, when we state

“First, we developed optical microcavities with mechanical stiffnesses in the range found in hydrogel substrates commonly used for studying Drosophila larval behaviour, i.e. Young’s modulus (E) of 10-30kPa (36–38).” (p. 5, ll. 124) and later

“Here we used Drosophila larvae as a test case, but our methods now allow elastic optical resonators to be tuned to a wide range of animal sizes and thus create new possibilities for studying principles of neuro-biomechanics across an array of animals.” (p. 12, ll. 337)

I would appreciate a description of the "why" behind some experimental choices, as understanding the motivation would be helpful for other researchers looking to adopt these techniques.

We have now added additional text in the introduction and discussion that explains the rationale behind our experimental choices. in more detail. Please see our response to Reviewer 1’s public comments on the same point.

(1) The WARP and ERISM experiments were conducted on a collagen coated gold surface rather than agarose. Why? EG does agarose not adhere to the gold, or would its thickness interfere with the measurement?

The gold layer is applied above the elastomer and the collagen on top of the gold layer makes the gold a more natural biological surface for the animals. Agarose is unsuitable as an elastomer because it would dry during the vacuum based deposition of the gold. It is also unsuitable as a surface coating on top of the gold as the coating on the gold needs to very thin to preserve the spatial and mechanical resolution of our sensors. Further, processing of agarose generally requires temperatures of 60°C and higher which we find can damage the elastomer / gold films.

(2) The ERISM measurements are made on a cold anesthetized animal right as it starts to wake up (visible mouth-hooks movement), which presents some difficulty. Why not start imaging while the animal is still completely immobile? Or why not use a dead larva?

This approach allowed us to get measurements of forces exerted by denticles that are physiologically and biomechanically accurate. In dead or fully anesthetized animals, one cannot be sure that the forces exerted by denticles and denticle bands are representative of the forces exerted by an animal with active hydrostatic control.

(3) In the ERISM setup the monochromator is spatially filtered by focusing through pinhole, while in the WARP setup, the LEDs are not.

Yes that’s correct. The LED light sources used in WARP have better spatial homogeneity than the tungsten filament used in ERISM and so a pinhole is not required in WARP.

(4) SV4 shows the interference image of a turning larva (presumably from one illumination wavelength) rather than a reconstruction of the displacement or stresses. Why?

We felt that in this particular case the interference images provided a clearer representation of the behavioural sequence, showing both the small indentations generated by individual denticles and the larger indentations of the animal overall.

Lines 49-50 "a lack of methods with sufficient spatiotemporal resolution for measuring GRFs in freely behaving animals has limited progress." This needs a discussion of what sufficient spatial and temporal resolutions would be and how existing methods fall short of these goals.

We have now rewritten the introduction to include an overview of other alternative approaches and of what we see as the requirements here. See our response to the public comments.

Figure caption 1B (line 789) refers to "concave areas of naked cuticle (black line) which generally do not interact with the substrate" While I think this might be supported by later WARP images, it's not clear how the technique of figure 1 measures interaction, which could e.g. be mediated by surface tension of a transparent fluid.

The technique of Figure 1 provides qualitative information which as the reviewer points out is validated by WARP measurements later.

Lines 184-189 "However, unexpectedly, we observed an additional force on the substrate when protopodia leave the substrate (SI) and when they are replanted (ST). To investigate whether this force was due to an active behaviour or due to shifting body mass, we plotted integrated displacement (i.e. displaced volume) against the contact area for each protopodium, combining data from multiple forwards waves (Figure 5B). Area is correlated with displaced volume for most time points, indicating that volume is a consequence of mass in a 2nd order polynomial relationship." I couldn't follow this argument at all.

We have now reworded this section and explained our rationale. Also see our response to a similar critique in Reviewer 2’s public comments.

Generally the authors might reconsider their use of acronyms. e.g. (244-246) "SI latencies were much more strongly correlated with wave duration across most segments than ST latencies. SIs scale with SwP and this could be mediated by proprioceptor activity in the periphery" is made more difficult to parse by the abbreviations.

As we need to refer to these terms multiple times throughout the manuscript, we feel the use of acronyms is appropriate here.

The video captions are inadequate. Please expand on them to explain clearly what is shown, and also describe in the methods how the data were acquired and processed. For instance, it seems that in SV3 a motion correction algorithm is applied so that the larva appears stationary even as it crawls forward. I think "fourier filtered" means that the images were processed with a spatial high pass filter - this should be explained and the parameters noted.

We have revisited the video captions provided in the supplementary information document and conclude that these contain the important information. The mode of acquisition are described in the methods, e.g. Video 1 and 2 see section in Methods on “Denticle band kinematic imaging” and Videos 3 and 4 see section in Methods on WARP. Supplementary Video 3 does not make use of motion correction; indeed, one can see the larvae moving upwards/forwards in the field of view. We apologize for not explaining the Fourier filtering process for Video 3. We have now modified the video caption to read as follows:

Video SV3. WARP imaging during forwards peristalses.

Video showing high frame rate displacement maps produced by a freely behaving Drosophila larva. Displacement maps were Fourier filtered to make denticulated cuticle more readily visible and projected in 3D to show the effects of substrate interaction. Details of the Fourier filtering procedure were described elsewhere [Kronenberg et al, Nat Cell Biol 19, 864–872 (2017)].

What were the reflectances of the bottom (10 nm Au/Cr) and top (15nm Au) metal layers at the wavelengths used? I imagine the bottom layer should be less than 38%, the top layer higher, and the product of the square of the bottom transmission and the top reflectance coefficients equal to the bottom reflectance (to make the two paths of the interferometer contribute equal intensity), but none of this is stated.

The reflectance of the gold mirrors was studied in detail in prior work on ERISM. See Kronenberg et al, Nat Cell Biol 19, 864–872 (2017). We therefore refrained from adding a complete optical characterization of the ERISM sensors again here. In brief, we found that a reflectance >13% at each Au mirror is required for reliable ERISM measurements.

The description of the gold coated elastomer as a microcavity is confusing to me. Does the light really make multiple round trips between the plates before returning to the detector? The loss of light on each round trip would depend on the reflectance and parallelism of the top and bottom mirrors. From the WARP calculation it's appears that there is only one round trip - a pi/2 phase shift results from the calculation for one round trip: 2pi*2nL 5nm/(630nm)^2, with n = 1.4 and L = 8 microns - if there were two round trips, the phase shift would be pi etc. Would this better be described as a mostly common path interferometer?

The physics of our devices is best described within the framework of thin film interference and (weak) microcavity optics. Indeed, light can make multiple roundtrips, though it gets attenuated with each reflection. The complete calculation of the multiple roundtrips is only required to obtain quantitative information on the amount of light that is reflected. The spectral position of minima in reflectance can also be obtained from assuming one roundtrip which is what is done in the description of the WARP calculations.

Figure 2 e,f: the line fits appear to be dominated by the data points at 2 s. If these are removed, do the fits change? To support the argument that 2e shows a correlation and 2f does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

If we remove the data points at 2 s, then R^2’s for swing initiation latencies change as follows: A2: 0.35 to 0.005; A4: 0.78 to 0.31; A6: 0.61 to 0.01. The data in 2e,f are the averages from 3 waves in each animal and so the data points at 2 s are not simply the result of single ‘rogue’ waves but rather averages of several trials. Further, if all individual waves are plotted, we can see that the overall trends are still visible.

We don’t think it is appropriate to remove the data at 2 s from our analysis, but we take the point regarding statements about presence or absence of correlation in a formal sense. We have therefore changed the wording in the description of 2e,f to refer simply to the fact that wave duration can ‘largely determine' latencies in some instances, but is less able to in other instances, as is suggested by the R^2 (coefficient of determination) data. In discussion, we have also adjusted our wording.

Figure 4 - please provide in the main figure or as a supplement the full images (i.e. not cropped to the assumed shape of the larva)

We do not feel that it is necessary or helpful to provide the full images given that the focus of the analysis is on dynamics of protopodia movements.

Figure 5e top: single data points around wave duration 0.6s appear to dominate fit lines. Does removing these points alter the fits? To support the argument that 5e top shows a correlation and 5e bottom does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

In Figure 5e, we are showing all waves analysed across animals. If we remove the datapoints at 0.6 s, A2 R^2 changes from 0.24 to 0.05, A4 R^2 changes from 0.48 to 0.11, A6 R^2 changes from 0.69 to 0.34; however we don’t feel it is appropriate to remove these data from our analysis. We take the point about needing to be cautious about making claims about correlation versus no correlation and have now reworded description of these results along same lines as Figure 4.

It appears from the methods (467-489) that animals were kept wet for warp imaging but not for ERISM imaging. Please confirm or explain further the presence or absence of a water layer in these two sets of measurements, as this could affect the adhesion forces.

In each case, the animals were transferred onto experimental substrates with a moistened paintbrush. We have added text explicitly stating this in the methods section.

Kim et al. Nature Methods 2017 (10.1038/nmeth.4429) describes recording two images separated by less than 60 microseconds using a scientific CMOS camera with a frame rate of 200 Hz. This is accomplished by triggering a pulsed LED once at the end of one frame's capture window and then a second time at the beginning of the next frame's window (see Supplementary Figure 10). I'm not sure if this trick is widely known, but it's worth considering if the authors are running into a problem with movement between the two wavelength exposures in their WARP setup.

Thank you for this tip. We will take this under consideration for future work.

Is the setup compatible with optogenetics? (EG is the red light dim enough that it wouldn't activate CsChrimson, or could a longer wavelength led be used for interferometry?) If so, activation of mooncrawler descending neuron (MDN) could be used to study backward crawling (or thermogenetic activation of MDN), e.g. to contrast the sites and order of "anchoring" between the two directions of crawling.

The set-up is potentially compatible with optogenetics. We are in the process of exploring this in current ongoing work.

Reviewer #2 (Recommendations For The Authors):

Simplify/reduce the commentary about force measurements, and highlight the clear, qualitative descriptions of the novel locomotion patterns that they have observed. The microscopy and movements seem to matter more than the ground force estimations.

We have addressed these issues in our responses to Reviewer 2’s public comments.

Author Response

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

We thank the reviewers for their valuable feedback which has improved this work greatly from its original form, and are elated to have such glowing reviews of the revised work published alongside the revised preprint. Reviewer 3 raises some final salient points, which deserve a brief address here.

Teeth: We thank the reviewer for clarifying their points. We do make the assumption that the ecological parameter space of toothed and beaked organisms will be comparable. Both are governed by the same set of physical principles and have the jaw bone as the most likely point of failure (teeth are harder than bone, and keratinous rhamphothecae are malleable and can be regrown with relative ease when deformed). Differences in stress/strain distribution between toothed and beaked organisms will occur but are already accounted for in our methods as we model both the teeth and rhamphotheca and will observe these different effects. We have added an explicit statement of this hypothesis to the Methods section of the manuscript.

Cranial kinesis: In our opinion, it is a safe assumption that the lower jaws of extant birds and enantiornithines are comparable. We do not see why the acquisition of kinesis in the upper jaw would generally affect the functional role of or constraints on the lower jaw. One possibility we discussed is that a quickly-moving kinetic premaxilla could let the lower jaw move a shorter distance during effective prey capture and lower the selection for speed (i.e. allow jaw-closing MA to remain higher). While we have added this possibility to our call for the investigation of cranial kinesis, we consider it too speculative to begin altering interpretations of fossil taxa. All raw measurement data remains available so that, if evidence is found for cranial kinesis having predictable effects on our measured parameters, future researchers can re-analyse our data and update any ecological predictions accordingly.

Organization: To our knowledge eLife format incorporates what one would think of as a Conclusions section into the Discussion. Our Discussion section currently contains 18 subheadings which should guide a reader to any specific topic of interest. The Discussion also progresses from a more narrow to broad focus which we and several colleagues find intuitive.

We thank all three reviewers once again for their feedback that has improved this work and their kind words throughout the process.

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

We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

• Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

• Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

• Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

• Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

• Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

• Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

• Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

References

Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The current work by Kulich et al. examines the dynamic relocalization of NGR1 (LAZY2) a member of the LAZY protein family which is key for auxin redistribution during gravitropic responses. After gravistimulation of the triple mutant ngr123 (lazy234), the PIN3 activating kinase D6PK is not polarized in the columella cells.

Strengths:

The authors show a thorough characterization of NGR1 relocalization dynamics after gravistimulation.

Weaknesses:

Genetically the relocalization of D6PK depends on the LAZY protein family, but some essential details are missing in this study. On the one hand, NGR1-GFP does not associate with the BFA compartments and maintains its association with the PM and amyloplasts. On the other hand, D6PK relies on GNOM, via vesicle trafficking sensitive to BFA, suggesting that D6PK follows a different relocalization route than NGR1 which is BFA-insensitive. Based on these observations, D6PK relocalization requires the LAZY proteins, but D6PK and NGR1 relocalize through independent routes. How can this be interpreted or reconciled?

Response: Since we demonstrated that D6PK does not relocalize in the absence of NGR proteins, we conclude that NGR1 acts upstream of D6PK. The molecular mechanism driving this interaction is not fully understood; however, it is evident that NGR1 triggers the mobilization of D6PK. Despite previous investigations into D6PK mobility, the underlying mechanisms remain elusive. Notably, despite its sensitivity to BFA, D6PK does not localize to BFA bodies and does not undergo conventional endocytosis (https://doi.org/10.1016/j.devcel.2014.05.006). We fully acknowledge the importance and interest in gaining a better understanding of these processes, and it will be a focal point of our future research.

Two other works (now published) provide valuable and fundamental findings related to the mechanism examined in the current manuscript and display complementary and similar results to the ones shown in the current manuscript. Given the similarities in the examined mechanisms, these preprints should be referenced, recognized, and discussed in the manuscript under review. It is assumed that the three projects were independently developed, but the results of these previous works should be addressed and taken into account at least during the discussion and when drawing any conclusions. This does not mean that this work is less relevant. On the contrary, some of the observations that seem to be redundant are more solid, and firm conclusions can now be drawn from them.

Response: We have included and discussed these works in the revised discussion

Reviewer #2 (Public Review):

Summary:

This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

The current manuscript complements and extends Nishimura et al., Science, 2023. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180-degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and an ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths:

This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well-written and accessible to a general readership. The figures are clear and of high quality, and the methods are sufficiently explained for reproduction of the experiments.

Weaknesses:

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends.

Response: We added this information to the figure legends

The title claims a bit more than what is actually shown in the manuscript: While auxin response reporter alterations are monitored, "rapid redirection of auxin fluxes" are not really directly addressed and, while D6PK can activate PIN proteins in other contexts, it is not explicitly shown in the manuscript that PIN3 is a target in the context of columella cells in vivo. A title such as "Rapid redirection of D6 protein kinase during Arabidopsis root gravitropism relies on plasma membrane translocation of NGR proteins" would reflect the results better.

Response: We modified the title to Rapid translocation of NGR proteins driving polarization of PIN-activating D6 protein kinase during root gravitropism

Fig. 4: The point that D6PK is transcytosed cannot be made here based on the data of these authors. They should have used a photoswitchable version of NGR1 to show that the same molecules observed at the upper PM are translocated to the lower PM. Nishimura and colleagues actually did that for NGR4. However, this is a lot of work and maybe for NGR1 that fusion would have too low fluorescence intensity (as it was the case for NGR3). So, I think a rewording would be sufficient such as NGR-dependent reorientation of D6PK plasma membrane localization" as this does not say, from where it comes to the lower PM. Theoretically, the signal could also be amyloplast-derived or newly synthesized (or just folded) NGR1-GFP.

Response: We fully agree and rephrased the text using translocation instead of transcytosis

The authors make a model in which D6PK AGCVIII kinase-dependent on NGRs activates PIN3 to drive auxin fluxes. However, alterations in auxin responses are observed prior to PIN3 reorientation. They should explain this discrepancy better and clearly describe that this is a working hypothesis for the future rather than explicitly proven, yet.

Reviewer #3 (Public Review):

The mechanism controlling plant gravity sensing has fascinated researchers for centuries. It has been clear for at least the past decade that starch-filled plastids (termed statoliths) in specialised gravity-sensing columella cells sense changes in root orientation, triggering an asymmetric auxin gradient that alters root growth direction. Nevertheless, exactly how statolith movement triggers PIN auxin efflux carrier activation and auxin gradient formation has remained unclear until very recently. A series of new papers (in Science and Cell) and this manuscript report how LAZY proteins (also referred to as NEGATIVE GRAVITROPIC 50 RESPONSE OF ROOTS; NGR) play a pivotal role in regulating root gravitropism. In terms of their overall significance, their collective findings provide seminal insights into the very earliest steps for how plant roots sense gravity which are arguably the most important papers about root gravitropism in the past decade.

In the current manuscript, Kulich et al initially report (through creating a functional NGR1-GFP reporter) that "NGR1-GFP displayed a highly specific columella expression, which was most prominent at the PM and the statolith periphery." Is NGR1-GFP expressed in shoot tissues? If yes, is it in starch sheath (the gravity-sensing equivalent of root columella cells)? The authors also note "NGR1-GFP signal from the PM was not evenly distributed, but rather polarized to the lower side of the columella cells in the vicinity of the sedimented statoliths (Fig. 1A)." and (when overexpressing NGR-GFP) "chloroplasts in the vicinity of the PM strongly correlated with NGR1 accumulating at the PM nearby, similar to the scenario in columella" suggesting that NGR1 does not require additional tissue-specific factors (i.e. trafficking proteins or lipids) to assist in its intracellular movement from plastid to PM.

Response: Yes, NGR1, also called LAZY2 is expressed in the inner hypocotyl tissues, according to https://doi.org/10.1104/pp.17.00942. Unfortunately, we saw very little signal with our NGR-GFP construct, possibly due to NGR1-GFP weak signal and/or NGR1 being expressed only exclusively in the inner tissues.

Next, the authors study the spatiotemporal dynamics of NGR1-GFP re-localisation with other early gravitropic signals and/or components Calcium, auxin, and PIN3. The temporal data presented in Figure 1 illustrates how the GCaMP calcium reporter (in panel E) revealed "the first signaling event in the root gravitropic bending is the statolith removal from the top membrane, rather than its arrival at the bottom" It appeared that the auxin DII-VENUS reporter was also changing rapidly (panel G) - was this detectable BEFORE statolith re-sedimentation?

Response: In our data (Figure 1G), we observe that the increase in signal at the top side begins prior to starch sedimentation, in contrast to the bottom side, where the decrease starts only after starch grains land on the bottom membrane. While this observation aligns with our hypothesis and other data, we refrained from commenting on it due to the small differences between the first 2-3 timepoints, which are obscured by noise. This phenomenon arises because the DII response relies on protein degradation and is relatively slow. Hence, for rapid tracking of the auxin response, we utilized auxin-induced calcium as a proxy, with NPA treatment serving as a negative control.

Please can the authors explain their NPA result in Fig 1E? Why would treatment with the auxin transport inhibitor NPA block Ca signalling (unless the latter was dependent on the former)?

Response: Auxin induces rapid calcium transients (e.g., http://dx.doi.org/10.1016/j.cub.2015.10.025). Consequently, when auxin reaches the bottom elongation zone approximately 5-6 minutes after rotation, we observe an increased GCaMP signal at this location. Notably, when we inhibit PIN function using NPA, the GCaMP signal persists, but the difference between the top and bottom diminishes. This validates that the calcium transients at the bottom side can be interpreted as monitoring increase in auxin accumulation as a result of auxin transport.

They go on to note "This initial auxin asymmetry is mediated by PIN-dependent auxin transport, despite visible polarization of PIN3 can be detected only later" which suggests that PIN activity was being modified prior to PIN polarisation.

In contrast to other proteins involved in gravity response like RLDs and PINs, NGR1 localization and gravity-induced polarization does not undergo BFA-sensitive endocytic recycling by ARF-GEF GNOM. This makes sense given NGR1 is initially targeted to plastids, THEN the PM. Does NGR1 contain a cleavable plastid targeting signal? The authors go on to elegantly demonstrate that NGR1 PM targeting relies on palmitoylation through imaging and mutagenesis-based transgenic ngr rescue assays.

Response: Yes, there is weakly conserved plastid targeting signal on NGR1. Although we also started researching in this direction, we quickly realized, that two other groups showed very comprehensive data regarding NGR plastid localization.

Finally, the authors demonstrate that gravitropic-induced auxin gradient formation is initially dependent on PIN3 auxin efflux activation (prior to PIN3 re-localisation). This early PIN3 activation process is dependent on NGR1 re-targeting D6PK (a PIN3 activating kinase). This elegant molecular mechanism integrates all the regulatory components described in the paper into a comprehensive root gravity sensing model.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Line 83: This construct fully rescued the agravitropic bending phenotype of the ngr1/2/3 triple mutant (see further).

What does it mean the see further in this context?

Response: It is a reference to the second part of the manuscript (Fig. 3, Supplementary Fig S3, Fig S4), where we extensively address the complementation with wild type and point mutated versions of NGR. There we show that the construct we are using is functional. This does not prove, but strongly imply that the GFP signal we obtain is relevant. We updated the text to point this out.

Line 101: Timing of events during the gravitropic response

When describing the equipment employed and the rotation applied to the samples, "the vertical stage microscope and minimized the time required for rotating the sample. 180{degree sign} rotation..."

The authors mentioned a travel time of 5 minutes first and later of 15 minutes for the relocalization of NGR1. Are these two different experiments? Were there two different rotation angles or degrees applied? Could the authors please rephrase this part of the description to answer these questions and help the reader understand how the assay performed?

Response: We added this explanation to the text.

Figure 1 E, F, and G.

Could the authors please provide pictures and/or videos for the PIN3 localization dynamics, intracellular calcium transients, and auxin reporter DII-Venus? In other words, show the complementing images for Figure 1E, 1F, and 1G as the authors did for Figure 2D where authors presented the pictures and the corresponding quantification plots.

Response: We wanted to avoid overcrowding the figure, but we would also love to show the videos. Therefore, we did additional supplementary movie 3, where we put all the additional observations.

Line 194: This implies the existence of posttranslational modifications such as S-acylation to associate with PM.

Why is this specific modification suggested/examined and no other modification? What is the criteria to select this kind of modification? Based on what premises? Could the authors elaborate on that? Could the authors please include references?

Response: Thank you for this comment. We of course first checked the prediction tools which have shown very strongly conserved S-acylation side. We now clarified this in the text and added other modifications as an example. Later on, we rule out myristoylation (that happens on the glycins) and prenylation (it happens only at the C-terminus CAAX box).

Line 255: NGR1 PM localization is synergistically mediated by polybasic regions and a palmitoylation site

Similarly to the previous commentary, How and why are these regions examined/analyzed? Likewise, why is the palmitoylation site selected? Please provide some background, criteria, and references.

Response: Here, we clearly state that the prediction of the palmitoylation site is made based on the GPS lipid prediction tool.

As for the polybasic region, these can be seen upon manual inspection of the primary protein sequence. We simply looked at the protein and saw it there. We rephrased the text so that it is more clear.

Reviewer #2 (Recommendations For The Authors):

Please, proofread the manuscript for style and minor language errors.

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends. Where it has been performed it is not given what "n" number of roots, cells, or plasma membranes were analyzed NGR1-GFP and no information is given whether the data is derived from a representative experiment or several or pooled data from several experiments. This certainly requires revision in Fig. 1D-G, Fig. 2B-D, Fig. S2 B,E, Fig. 3B,D, F-H, Fig. S.3 B,D, Fig. S. 4 ,E-H, Fig. 4 D.

Response: Thank you, we added this information to the figure legends.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors. The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment, and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stressresponsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard-to-fully interpret but well-documented series of experiments the authors note that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be affected via YBR283C.

Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNAformyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Thank you for your thorough and insightful review of our manuscript. We value your positive feedback and recognition of the strengths in our study. Your constructive comments have been carefully considered, leading to the inclusion of RMD9, identified as 'Required for Meiotic Nuclear Division,' and the addition of the relevant reference (PMID: 12586695) in the revised manuscript. This information has been incorporated into the second paragraph of the "The YBR238C paralogue RMD9 deletion decreases the lifespan of cells" results section.

Furthermore, we appreciate the reviewer's suggestion to include the 2007 Genetics paper on translation initiation in Saccharomyces cerevisiae mitochondria (PMID: 17194786). This citation has been integrated into our revised manuscript.

We believe that these revisions significantly strengthen the manuscript and address the concerns raised by Reviewer #1. We thank the reviewer for their time and valuable input.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. Curiously, despite these selection criteria, the authors only use CLS as a proxy for cellular aging throughout their study and do not explore the effects of ybr238c deletion on RLS. This does not diminish their conclusions, but given the importance of this phenotype in their selection criteria, it is surprising that the authors did not choose to test both types of aging throughout their study.

Nonetheless, the authors demonstrate that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild-type cells, consistent with "accelerated aging" as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. However, the authors then link the increased CLS in Δybr238c yeast to mitochondrial function using only a handful of assays that do not directly test mitochondrial function. These include total cellular ATP levels, levels of reactive oxygen species, and the transcript levels of select nuclear-encoded mitochondrial genes. Yeast is well established to generate ATP through non-mitochondrial pathways such as glycolysis in fermentive conditions. While it is possible that the ATP levels assayed in the manuscript were tested in stationary phase, which would more likely reflect "mitochondrial function," the methods nor the figure legends contain these details, which are critical for the interpretation of these data. Similarly, ROS can be generated through non-mitochondrial pathways, and the transcription of nuclear-encoded mitochondrial genes is an indirect measure of mitochondrial function at best. Thus, the authors' proposed connection of ybr238c to mitochondrial function is correlative and should be substantiated with assays that more closely align with organellar function, such as respirometry or assaying the activity of oxidiative phosphorylation complexes. Finally, the authors attempt to tie the phenotypes of mitochondrial dysfunction caused by the deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the presentation of the data, such as reporting ATP levels as relative percentages or failing to perform appropriate statistical comparisons between conditions in which the authors derive conclusions, renders the data difficult to interpret. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but its influence on mitochondrial activity and ties to TORC1 signaling remain speculative.

We would like to express our gratitude to Reviewer #2 for the thoughtful feedback on our manuscript. We have carefully considered your comments and have made comprehensive revisions to address the concerns raised.

We appreciate the suggestion to investigate the role of YBR238C in replicative lifespan (RLS). However, we want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. Given the existing body of literature on this aspect, we chose not to duplicate these efforts in our study.

Instead, we focused our efforts on validating the role of YBR238C in chronological lifespan (CLS) phenotype, a finding reported in only one genome-wide study (reference 38). To enhance the comprehensiveness of our study, we performed analyses on different phenotypes, including mitochondria activity and oxidative stress, under both logarithmic-phase (condition for RLS) and stationary phase (condition for CLS). We now clearly indicate the logarithmic-phase/stationary phase conditions in the figure legends of the manuscript, specifying whether the conditions are relevant to RLS or CLS. Additional results of the new experiments have been included in the revised manuscript as supplementary figures (S3E-S3I).

To address concerns about the indirect nature of our mitochondrial function assays, we have performed relative mitochondria content (S3F), quantification of ROS levels from fermentative to stationary phase conditions (S3G), and assessment in respiratory glycerol medium (S3H), which provides a more direct insight into mitochondrial biology. Additionally, we have investigated the resistance of ybr238c∆ cells to H2O2 toxicity and found them to be more resistant compared to wild-type cells.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #2, and we hope these modifications address the concerns raised effectively.

Reviewer #3 (Public Review):

Summary: The study by Alfatah et al. presented a role for YBR238C in mediating lifespan through improved mitochondrial function in a TOR1-dependent metabolic pathway. The authors used a dataset comparison approach to identify genes positively modulating yeast chronological (CLS) and Replicative (RLS) lifespan when deleted, and their expression is reduced under Rapamycin treatment condition. This approach revealed an unknown, mitochondria-localized yeast gene YBR238C, and through mechanistic studies, they identified its paralogous gene RMD9 regulating lifespan in an antagonistic effect.

Strengths:

Findings have valuable implications for understanding the YBR238C-mediated, mitochondrial-dependent yeast lifespan regulation, and the interplay between two paralogous genes in the regulation of mitochondrial function represents an inserting case for gene evolution.

Weaknesses:

Overall, the implication/findings of this study are restricted only to the yeast model since these two genes do not have any homology in higher eukaryotes. The primary methods must be carefully designed by considering two different metabolic states: respiration-associated with CLS and fermentation-associated with RLS in a single comparative approach. Yeast CLS and RLS are two completely different processes. It is already known that most gene-regulating CLS is not associated with RLS or vice versa. The method section is poorly written and missing important information. The experimental approaches are poorly designed, and variability across the datasets (e.g., media condition "YPD," "SC" etc.) and their experimental conditions are not well described/considered; thus, presented data are not conclusive, which decreases the overall rigor of the study.

We sincerely appreciate your thorough review of our manuscript and your insightful comments. We acknowledge the limitation of our study being yeast-specific due to the absence of homologous genes in higher eukaryotes. However, we would like to highlight the significance of our findings in revealing a feedback loop between mitochondrial function and TORC1 signaling (TORC1-Mitochondria-TORC1 or TOMITO signaling process) in cellular lifespan regulation.

Our interpretation of the experimental results is grounded in recent literature. Two studies (references 62 and 63) support our findings by demonstrating TORC1 activation after mitochondrial electron transport chain dysfunction and the delay in brain pathology progression upon TORC1 inhibition, respectively. These studies, discussed in our manuscript, reinforce the relevance of our work in a broader biological context.

We recognize the importance of carefully designing our primary methods to account for the different metabolic states associated with cellular processes, such as respiration in cellular lifespan (CLS) and fermentation in replicative lifespan (RLS). We want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. To avoid duplicating these efforts, we have chosen not to reiterate these findings in our study. However, we have clarified the logarithmic-phase/stationary phase conditions in the figure legends, specifying their metabolic states relevance to RLS or CLS. Additionally, we have included new supplementary figures (S3E-S3I) to provide further details on the new experiments conducted.

We appreciate your feedback regarding the clarity and completeness of our method section. In the revised manuscript, we have invested additional effort to enhance the clarity of the method section, providing a more detailed account of the experimental procedures, including the missing information you identified.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #3, and we hope these modifications address the concerns raised effectively.

Reviewer #1 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) "TORC1 positively regulates aging, and its inhibition increases lifespan in various eukaryotic organisms including yeast and mammalian 13,26,27,29,30." Here I would suggest replacing "mammalian" with "mammals".

We have amended the sentence as recommended.

(2) "Next, we experimentally tested whether the transcriptome longevity signatures are associated with enhanced mitochondrial metabolism, whether the cellular energy level has gone up and cellular stress responses are induced with a switch to oxidative metabolism 47,48." Here I would replace "transcriptome longevity signatures is" with "transcriptome longevity signatures are".

We have amended the sentence as recommended.

(3) "Thus, HAP4-independent mechanism does exist through which YBR238C also affects cellular aging (Figure 3I)." I would replace "Thus, HAP4-independent" with "Thus, a HAP4-independent".

We have amended the sentence as recommended.

(4) "We examined other mitochondrial dysfunctional conditions to confirm that suppressive effect of rapamycin is not only specific to YBR238C-OE." I would change "that suppressive effect" to "that the suppressive effect".

We have amended the sentence as recommended.

(5) "Understanding the mechanism of aging will also require to understand the role of many genes of yet unknown function as YBR238C at the beginning of this work." I would switch "require to understand" to "require understanding".

We have amended the sentence as recommended.

(6) "The gene lists that modulate cellular lifespan in aging model organism yeast Saccharomyces cerevisiae were extracted from database SGD 22 and GenAge 23 (as of 8th November 2022)" "yeast" should not be italicized.

Corrected.

(7) Figure 1, panels C and D, ybr238c should be italicized.

Corrected.

(8) Figure 2B, top left-most (oxidative phosphorylation) network. I might consider repositioning some labels to make them more readable if possible.

Thank you for your feedback. The figure labels in Figure 2B are default from Metascape analysis, so repositioning isn't feasible. However, we have indicated in the figure legends that the full set of genes for functional enrichment analysis and the MCODE complex is available in Additional File 3.

(9) Figure 4E, rmd9, pet100, and cox6 should be italicized.

Corrected.

(10) Figure 5C, rmd9 and rmd9 ybr238c should be italicized. Corrected.

Reviewer #2 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) The presentation of data as heatmaps (Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, 6K) obfuscates the quantitative nature of the data. These data would be much stronger if presented as bar graphs with appropriate statistical analysis. If the authors prefer the visual of the heat map, there should be some statistical analysis performed to accompany these figures. This is particularly important for Figure 3D, in which the authors state "We found that HAP4 deletion significantly decrease the ETC complex I-V genes' expression" (bottom of page 8). As no statistical analyses were performed, the authors should refrain from using such language as it is unsupported by the data as analyzed.

Thank you for your insightful comments and suggestions regarding the presentation of our data. We appreciate the attention you have given to Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, and 6K.

In response to your feedback, we have carefully re-evaluated our approach. Considering the large volume of data associated with our lifespan analysis at different time points, we initially chose to visualize it using heatmaps to comprehensively capture the complexity of the results. However, we have now incorporated quantification information into the heatmaps.

For Figure 3D, which addresses the impact of HAP4 deletion on the expression of ETC complex I-V genes, we have replaced the heatmap with a bar graph. This modification allows for a clearer representation of the quantitative nature of the data. Moreover, we have conducted thorough statistical analyses comparing data between ybr238c∆ and ybr238c∆ hap4∆ to support the statements made in the text. The results of these analyses are now included in the revised figure. Moreover, we also replaced the Figure 6K heatmap with a bar graph.

We believe that these changes enhance the interpretability and robustness of our findings. We are grateful for your guidance, and we are confident that these adjustments will strengthen the overall quality of our manuscript.

(2) The presentation of ATP data, given its importance in supporting the core conclusions of this manuscript, is poor. The conditions under which yeast was collected are not reported, making these data impossible to interpret; total cellular ATP levels would be significantly altered and influenced by separate pathways in fermentive versus stationary phases. Minimally, the authors should describe the conditions of yeast growth (e.g., age, culture media) in which these measurements were made. The presentation of relative ATP percentages is problematic, particularly with measurements that deviate so far from wild-type ATP levels in conditions such as those in Figure 6A, in which the authors report that rapamycin induces a 1200% increase in cellular ATP. Previous papers have established that ATP levels in yeast hover around 4 mM and are stable through the cell cycle and across nutrient conditions (PMID: 30858198, 35438635). Given this, the reported ATP levels would be expected to be near 48 mM, which is strongly outside of the typically accepted values of 1-10 mM for this metabolite. Without understanding the contexts in which these measurements are made, as well as the absolute values for these measurements (which would be easily achievable through the use of a standard curve of ATP), these data are uninterpretable. Furthermore, it seems unlikely that yeast would be able to accommodate shifts of ATP levels that span an order of magnitude without dire cellular consequences, particularly during rapamycin treatment.

We appreciate the valuable feedback from the reviewer regarding the importance of providing detailed information on yeast growth conditions for interpreting ATP data. In response to this suggestion, we have enhanced the figure legends associated with the relevant figures to include a comprehensive description of the yeast growth conditions. This now specifies the age of the culture, culture media composition, and other pertinent parameters.

In addressing the concern raised about the rapamycin-induced ATP increase, we have carefully re-examined our experimental procedures. We performed additional experiments and confirmed the consistency of our findings in logarithmic-treated cultures. The results remain in alignment with our initial observations, reinforcing the reliability and reproducibility of our data.

(3) As stated above, the inference of mitochondrial function from cellular ATP levels, cellular ROS levels, and gene expression of a handful of nuclear-encoded genes is not sound. The authors should include further experimentation as evidence of mitochondrial functionality, such as respirometry or metabolic flux experiments.

Thank you for your constructive feedback on our manuscript. We appreciate your careful consideration of our work. In response to your concerns regarding the indirect nature of our mitochondrial function assays, we have implemented the following changes: We have incorporated additional assays to provide a more direct insight into mitochondrial biology. Specifically, we performed relative mitochondria content analysis (S3F) and quantified ROS levels under fermentative to stationary phase conditions (S3G). These assays offer a more direct and comprehensive assessment of mitochondrial function. Furthermore, we conducted experiments in respiratory glycerol medium (S3H) to complement our previous findings.

To further support our claims, we investigated the resistance of ybr238c∆ cells to H2O2 toxicity. Our results demonstrate that these cells exhibit increased resistance compared to wild-type cells. This additional evidence strengthens the link between mitochondrial function and cellular response to oxidative stress.

We believe these adjustments address your concerns and significantly enhance the robustness of our study. We hope you find these modifications satisfactory. We are grateful for your valuable input, which has undoubtedly improved the clarity and reliability of our findings.

(4) Multiple gene expression analyses are performed on n=2 measurements, and this should be bolstered by further replicates. Many bar graphs do not have accompanying statistics; these should be added. Some statistical tests are performed across inappropriate comparisons, such as Figure 3G, in which expression levels of mitochondrial genes in both deletion and overexpression strains should be compared to a wild-type control rather than to each other.

Thank you for your thorough review and constructive feedback on our manuscript. We appreciate your careful examination of our work. In response to your comments, we have made the following revisions to address your concerns: The multiple gene expression analysis in our study focused specifically on ETC genes. It is important to note that ETC genes themselves represent multiple replicates within the ybr238c deletion and overexpression cells, as illustrated in Figures 4D, 4G, and 6B.

We acknowledge and appreciate your observation regarding Figure 3G. To address this concern, we have revised the statistical comparisons. The expression levels of mitochondrial genes in the overexpression strain are now appropriately compared to a wild-type control. This correction has been applied in the figure that correctly corresponds to text in the manuscript.

(5) Figure 2B is uninterpretable as it stands, as most gene symbols are obscured.

We appreciate the reviewer's attention to Figure 2B and the feedback provided. Regarding the gene labels in Figure 2B, we would like to clarify that these labels are default outputs from the Metascape analysis, and unfortunately, repositioning them within the current figure layout isn't feasible without compromising the integrity of the information.

However, we have taken the reviewer's concern seriously and have made efforts to address the interpretability issue. To provide readers with access to the full set of genes for functional enrichment analysis and the MCODE complex, we have included this information in Additional File 3. The figure legends have been updated accordingly to guide readers to refer to Additional File 3 for a more detailed examination of the gene symbols and their annotations.

We hope that this solution addresses the concern raised by the reviewer.

(6) The conclusions to be drawn from Figure 3A are not clear, and this figure is cited only once in the text along with two other figures (page 8).

Thank you for your valuable feedback. We have carefully considered your comments and made revisions to improve the clarity of the conclusions drawn from Figure 3A.

(7) Figure 6K reports a range of 100-200% cell survival - how does a cell have 200% survival? Isn't survival binary (i.e., you survive or you are dead)? Perhaps this is meant to be relative to another condition; this should be more clearly stated in the figure, or the axis should be normalized to a maximum of 100% survival.

Thank you for your guidance and valuable feedback. Based on your recommendation, we have made significant changes to Figure 6K in the revised manuscript. Specifically, we replaced the heatmap with a bar graph to enhance clarity. Additionally, we would like to highlight that cell survival of combined treated cells is measured relative to the control treatment, which is considered 100% survival. This aims to provide a more accurate and comprehensible representation of the data. We believe these modifications contribute to a clearer presentation of our findings.

(8) The authors state that "TORC1 inhibition in yeast and human cells with mitochondrial dysfunction suppresses their accelerated aging." No studies of aging were done in human cells; survival in response to mitochondrial toxins does not reveal aging phenotypes. To state such is a substantial overstatement and should be amended to perhaps "cellular survival" rather than directly linked to aging.

We appreciate the careful review of our manuscript and the constructive feedback provided by the reviewer. In response to the concern raised regarding the statement about TORC1 inhibition and accelerated aging in human cells, we have revised the relevant passage as follows: "In turn, TORC1 inhibition in yeast and human cells with mitochondrial dysfunction enhances their cellular survival." We believe that this modification accurately reflects the outcomes of our experiments and addresses the concern raised by the reviewer. We would like to express our gratitude for the valuable feedback, which has contributed to the improvement of our manuscript. Thank you for your thoughtful consideration.

Reviewer #3 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

The authors should have attempted to fully characterize the RLS and CLS phenotype of strains lacking the YBR238C and RMD9 gene, the single most important gene identified in this study. Before further characterization, its association with aging must be tested to replicate findings from the literature. Although Figure 3 shows partially characterized CLS in SC medium, different media conditions could be tested, and the full spectrum of CLS lifespan curves should be represented. RLS phenotypes of these cells were not analyzed throughout the study.

We appreciate the suggestion to investigate the role of YBR238C in both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). However, it's essential to note that the involvement of YBR238C in the RLS phenotype has been previously documented in four studies (references 7, 39, 40, and 41). Considering the established literature on this matter, we chose not to duplicate these efforts in our study.

Our primary focus was on confirming the role of YBR238C in the chronological lifespan (CLS) phenotype, as indicated by a genome-wide study (reference 43). Accordingly, we also conducted an analysis of the role of RMD9 in CLS. The methods and figure legends explicitly state that CLS experiments for prototrophic CEN.PK113-7D strains were conducted in synthetic defined (SD) medium containing 6.7 g/L yeast nitrogen base with ammonium sulfate without amino acids and 2% glucose. For auxotrophic BY4743 strains, SD medium was supplemented with histidine (40 mg/L), leucine (160 mg/L), and uracil (40 mg/L).

It is important to clarify that SC medium was not used for CLS analysis. Instead, we employed SD medium, recommended for CLS analysis (reference 15; PMID: 22768836). The CLS experiments were conducted using three different methods, providing a comprehensive representation of the entire CLS lifespan (Figures 1C, 1D, 1E, and 1F).

While we did not present the Replicative Lifespan (RLS) phenotype explicitly, we performed experiments such as mitochondrial activity and ROS production under both CLS and RLS conditions. These additional analyses contribute valuable insights into the broader implications of YBR238C and RMD9 on cellular function.

We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We hope these explanations address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

In addition, authors include RNAseq data from Rapamycin-treated cells to identify differentially expressed genes. Notably, genes with decreased expression were used to compare KO strains' lifespan phenotype. Additional RNAseq analyses were performed on individual KO cells. The methodology section needs to be better written with information on which media and metabolic state that these cells are collected after treatment with rapamycin. If the cells are collected during logarithmic growth, the data can be compared with RLS aging gene sets only. A separate experiment has to be performed on stationary cells (respiratory) to collect RNAseq data after rapamycin treatment, then can be compared to the CLS aging gene set.

Thank you for your insightful comments and considerations regarding our methodology for obtaining Rapamycin response genes (RRGs). We appreciate the opportunity to address your concerns and provide further clarification on our experimental approach.

As mentioned in our manuscript, we obtained RRGs by treating logarithmic cells with 50 nM Rapamycin for 1 hour, and the details have been included in supplementary Figure S1C legends. Our primary objective was to compare these RRGs with agingassociated genes that modulate both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). We acknowledge the significance of this comparison and believe that our approach, treating logarithmic cells, is suitable for achieving this goal.

It is important to note that the use of a higher concentration of Rapamycin for treatment renders the cells less efficient in terms of growth, resulting in a very low optical density (OD) at 72 hours, as illustrated in Figure 6H. Unfortunately, due to this limitation in growth efficiency, obtaining Rapamycin response genes at the stationary phase was not feasible in our experimental setup.

As the experimental conditions vary among the reports and the gene expression signature significantly changes under different metabolic conditions, the media condition that samples are collected for RNAseq analyses should match the media condition that the lifespans of those KO strains are tested. However, more information needs to be detailed on these methodologies. For example, the transcriptomic signature of the YBR238C KO strain should be done under both fermentative and respiratory conditions to understand the true gene expression signature associated with CLS and RLS. Throughout the manuscript, these two metabolic conditions and associated lifespan types (CLS vs. RLS) are not differentiated and treated as the same, probably causing the biggest confounding effect that resulted in the identification of a single yeast-specific gene.

We obtained the transcriptomic signature of the YBR238C KO strain from logarithmic phase cultures. This consistency was maintained to align with the Rapamycin Response Genes (RRGs) obtained from logarithmic cells treated with rapamycin. Detailed methodology and metabolic status information is provided in the method section and relevant figure legends.

To broaden the scope of our study, we conducted analyses on various phenotypes, including mitochondrial activity and oxidative stress, under both logarithmic phase (relevant to Replicative Lifespan, RLS) and stationary phase (relevant to Chronological Lifespan, CLS). We have now explicitly indicated the logarithmic phase/stationary phase conditions in the figure legends of the manuscript, specifying their relevance to RLS or CLS.

Results from these additional experiments have been incorporated into the revised manuscript as supplementary figures (S3E-S3I). We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We trust that these explanations effectively address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

YBR238C gene KO effect on mitochondrial function missing comprehensive characterization. Whether the improved mito function caused by increased mtDNA copy number and/or increased mitochondrial number could be easily tested by analyzing normalizing RNAseq reads from mtDNA genes to reads from nucDNA genes. Data could be further combined with western blot specific to mito membrane proteins to analyze mito copy number.

Thank you for your insightful comments and suggestions. Following your recommendation, we conducted an assessment of relative mitochondrial content (see Figure S3F) and observed significantly higher mtDNA content in the ybr238c∆ compared to the wild type (see Figure S3F). Additionally, we have incorporated the methodology for mitochondrial DNA copy number analysis in the methods section.

The two paralogous gene interaction is an interesting observation. However, in yeast, it is known that deletion of one of the paralogous genes causes copy number amplification of the certain chromosome that the other paralogous gene is located, causing aneuploid chromosome. Many of the observed phenotypes can be associated with increased chromosome copy number and should be carefully tested. However, the authors did not consider this important point. Simply, using RNA seq data normalized read/per chromosome could be plotted to analyze the karyotype of YBR238C and RMD9 KO cells.

We appreciate your thoughtful consideration of our work and the suggestion to investigate chromosome copy number variations. While we did not directly test the chromosome copy, we want to highlight that our study extensively explores the impact of YBR238C on cellular lifespan through an RMD9-dependent mechanism (Figure 5). Deletion of YBR238C increases, whereas overexpression of YBR238C decreases the expression of its paralog, RMD9 (Figure 5F). Furthermore, this phenotype is associated with the lifespan of YBR238C-deleted and overexpressed cells. In our study, we have thoroughly investigated this aspect.

Author Response

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

We appreciate the care and the detail shown by the Reviewers. Their comments have made our article more focused and more accessible to a general audience.

We would like to begin with a comment about the last sentence of the “eLife assessment”. The evolution of metamorphosis in insects was a major triumph in animal evolution that subsequently impacted almost every aspect of plant and animal evolution in the terrestrial and freshwater aquatic biospheres. Unlike the metamorphoses of most other groups, whose evolutions are lost in time, insect evolution arose relatively recently (~400 mya) and insect orders have branched off at various points in this evolution and have persisted to modern times. Although these “relic” groups also have undergone millions of years of evolution and specialization, they still provide us with windows into how this progression may have come about. The study of these groups provides a unique opportunity to explore the mechanisms that underlie major life history shifts and should be of interest to anyone interested in evolution – not just entomologists.

Reviewer #1 (Public Review):

Summary:

This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:

• JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.

• JH redirects ecdysone-induced molting to direct formation of a more mature cuticle

• Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects

• The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

We appreciate the comments about the strengths and weaknesses of the paper. To deal with the weaknesses, we have condensed some of the Results to make them less cumbersome and the Discussion has been completely revised, keeping a sharp focus on the actions of JH in Thermobia embryos and how these actions relate to the status quo functions of JH in insects with metamorphosis. As part of the revision of the Discussion, we have replaced Figures 10 and 11.

Reviewer #1 (Recommendations For The Authors):

In keeping with my public review, this paper is very strong and I have very few suggestions for improvement. They are:

(1) Thermobia are extant insects and are not ancestral insects. It is likely that they retain features found in an insect ancestor. However, these insects have been evolving for a very long time, and for any one feature, many changes may have occurred, both gain and loss of gene function and morphology. Further, even for morphological features present in an extant species that are the same as an ancestor, genetic pathways regulating this feature may have changed over time (see for examples papers from the Haag and Pick labs). Although I realize this is a small, possibly almost semantic point, I feel it is important to be precise here. For example, in the title, "before" is speculative as there could have been a different role in the ancestor with the role in embryogenesis arising in lineages leading to Thermobia; similarly in the abstract, "this ancestral role of JH' is an overstatement since we cannot actually measure the ancestral role.

Since the title has already been cited in a Perspectives review, we decided to keep the title as is.

(2) I don't understand the results in Met and myo in Fig. 3B. Perhaps include them in the explanation of Fig.3 and not after the description of Fig. 4 and explain them in more detail (or perhaps not include them at all?). I don't really understand the statistical analysis of these panels either.

We have revised the figure legends to explain the statistics.

(3) Another point regarding language - talking about the embryo being "able" to go through a developmental stage implies decision-making. I would suggest dropping that wording (e.g, in the description of Fig. 5C). Similarly, in explaining Fig. 6B, it would be more correct to say "JH treatment no longer inhibited" than as written "could no longer inhibit" (implying 'no matter how hard it tried, it still couldn't do it')

We have removed the “can’t” wording. Figure 6 has been revised

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

The unique and intriguing aspect of juvenile hormone is its status quo action in the control of metamorphosis. Our reason for dealing with an insect group that branched off from the line of insects that eventually evolved metamorphosis, was to gain insight into the ancestral functions of this hormone. Our data from Thermobia as well as that from grasshoppers and crickets indicate that the developmental actions of JH were originally confined to embryogenesis where it promoted the terminal differentiation of the embryo. Its actions in promoting differentiation also included suppressing morphogenesis. This latter function was not pronounced during embryogenesis because JH only appeared after morphogenesis was essentially completed. However, it was a preadaptation that proved useful in more derived insects that delayed aspects of morphogenesis into the postembryonic realm. JH was then used postembryonically to inhibit morphogenesis until late in juvenile growth when JH disappears, and this inhibition is released.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

We disagree with the reviewer. An analogous situation would be in exploring gene function in which both gain-of-function and loss-of-function experiments often provide complementary insights into how a gene functions. We see JH effects only when its receptor, Met, is present and JH can induce its main effector protein, Kr-h1. The latter gives us confidence that we are looking at bona fide JH effects. We have also kept in mind, though, that the nature of the responding tissues is changing through time. Nevertheless, we see a consistent pattern of responses in the embryo and these can be related to its postembryonic effects in metamorphic insects.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

We have tried to dial back the speculation.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Our Figure 12 should put this into perspective.

Reviewer #2 (Recommendations For The Authors):

Main points

(1) Please, reduce the level of overinterpretation of ectopic treatment experiments with JHm, since the resulting observations represent effects, but not necessarily functions of JH.

We have revised this section to indicate that the “effects” of ectopic treatments provide insights into the function of JH. Using a genetic analogy, both “loss-of-function” and “gain-of-function” experiments provide insights into a given gene. (see response to Public Comments)

(2) Especially in the sections "JH and the progressive nature of embryonic molts" and "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories", and the entire "Discussion", please keep the level of speculation within reasonable limits, avoiding especially the inference of conclusions on the basis of speculation, itself based on previous speculation.

We have toned down some of the speculation and provided reasons why it is worth suggesting.

(3) Please revisit the argued roles of myoglianin in the story, in light of its effects as an inhibitor of JH production, repressing the expression of JHAMT, as has been reliably demonstrated in hemimetabolan species (DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R).

Our appreciation to the reviewer. We are more explicit about the relationship between JH and myo.

Minor points

(4) Please keep the consistency of the scientific binomial nomenclature for the species mentioned. For example, read "Manduca sexta" (in italics) at the first mention, and then "M. sexta" (in italics) in successive mentions (instead of reading "Manduca" on page 17, and then "Manduca sexta" on page 18, for example). The same for "Drosophila" ("Drosophila melanogaster" first, and then "D. melanogaster"), "Thermobia" ("Thermobia domestica" first, and then "T. domestica"), etc. In the figure legends, I recommend using the complete name: Thermobia domestica, in the main heading.

Where there is no possibility of confusion, we intend to use Thermobia, rather than T. domestica, etc. We think that it is easier for a non-specialist to read and it is commonly done in endocrine papers.

(5) There is no purpose in evolution and biological processes. Thus, I suggest avoiding expressions that have a teleological aftertaste. For example (capitals are mine), on p. 3 "appears to have been extended into postembryonic life where it acts TO antagonize morphogenic and allow the maintenance of a juvenile state".

We have tried to avoid teleological wording.

(6) The title "The embryonic role of juvenile hormone in the firebrat, Thermobia domestica, reveals its function before its involvement in metamorphosis" contains a redundancy ("role" and "function"), and an apparent obviousness ("before its involvement in metamorphosis"). I suggest a more straightforward title. Something like "Juvenile hormone plays developmental functions in the embryo of the firebrat Thermobia domestica, which predate its status quo action in metamorphosis".

As noted above, we are retaining the title since it has already been cited.

(7) Page 2. "The transition from larva to adult then occurred through a transitional stage, the pupa, thereby providing the three-part life history diagnostic of the "complete metamorphosis" exhibited by holometabolous insects (reviews: Jindra, 2019; Truman & Riddiford, 2002, 2019)". I suggest adding the reference ISBN: 9780128130209 9 7 8 - 0 - 1 2 - 8 1 3 0 2 0 - 9, as the most comprehensive and recent review on complete metamorphosis.

Done

(8) Page 3. "These severe developmental effects suggest that the developmental role of JH in insects was initially CONFINED to the embryonic domain" (capitals are mine). This appears contradictory with the observations of Watson, 1967, on the relationships between the apparition of scales and JH, mentioned shortly before by the authors.

This is explained in the Discussion. Although JH can suppress scale appearance in the J4 stage, we have not been able to show that scales appearance is caused by changes in the juvenile JH titer.

(9) Page 4. "we measured JH III levels during Thermobia embryogenesis at daily intervals starting at 5 d AEL". Why not before, like in the case of ecdysteroids? The authors might perhaps argue that the levels of Kr-h1 expression are consistently low from the very beginning, according to Fernandez-Nicolas et al, 2022 (reference cited later in the manuscript).

(10) Page 4. "Ecdysteroid titers through embryogenesis and the early juvenile instars were measured using the enzyme immunoassay method (Porcheron et al., 1989) that is optimized for detecting 20-hydroxyecdysone (20E)". The antibody generated by Porcheron (and now sold by Cayman) recognizes ecdysone and 20-hydroxyecdysone alike. But that's not relevant here. I would refer to "ecdysteroids" when mentioning measurements. Also in figure 2B (and "juvenile hormone III" without the formula, in Panel A, for harmonization). And I would not expand on specifications, like those at the beginning of page 5, or towards the end of page

We thank the reviewer for this important correction.

(12) ("the fact that we detected only a slight rise in ecdysteroids at this time (Fig 2B) is likely due to the assay that we used being designed to detect 20E rather than ecdysone").

Omitted.

(11) Page 5. "Low levels of Kr-h1 transcripts were present at 12 hr after egg deposition, but then were not detected until about 6 d AEL when JH-III first appeared". There is a very precise Kr-h1 pattern in Fernandez-Nicolas et al. 2023 (reference mentioned later in the manuscript).

(12) Page 5. "notably myoglianin (myo), have become prominent as agents that promote the competence and execution of metamorphosis in holometabolous and hemimetabolous insects (He et al., 2020; Awasaki et al., 2011)". See my note 3 above.

The myoglianin issue has been revised.

(13) Page 5. "a drug that suppresses JH production". Rather, "a drug that destroys the JH producing tissues". Why the way, do the authors know when the CA are formed in T. domestica embryo development?

We prefer to keep our original wording. There have been some cases in which precocene has blocked JH production but did not kill the CA cells. We do not have observations that show that 7EP kills the CA cells in Thermobia embryos.

(14) Page 5. "subsequent treatment with a JHm". I would say here that the JHm is pyriproxyfen, not on page 6 or page 7. Thus, to be consistent, after the first mention of "pyriproxyfen (JHm)" on page 5, I'd consistently use the abbreviation "JHm".

(15) Page 9. "Limb loss in such embryos was often STOCHASTIC, i.e., in a given embryo some limbs were completely lost while others were maintained in a reduced state" (capitals are mine). The meaning of "stochastic" is random, involving a random variable; it is a concept usually associated to probability theory and related fields. I suggest using the less specialized word "variable", since to ascertain that the values are really stochastic would require specific mathematical approaches.

We are still using stochastic because the loss is random.

(16) Page 10. "9E). Indeed, the JH treatment redirects the molt to be more like that to the J2 stage, rather than to the E2 (= J1) stage". Probably too assertive given the evidence available (see my points 1 and 2 above).

We do not see a problem with our conclusion. In response to the JHm treatment, the embryo produced a smooth, rather than a “pebbly” cuticle, failed to make the J1-specific egg tooth, and attempted to make cuticular lenses (a J2 feature). This ability of premature JH exposure to cause embryos to “skip” a stage is also seen in locusts (Truman & Riddiford, 1999) and crickets (Erezyilmaz et al., 2004). The JHm treatment resulted in the production of smooth cuticle, lack of a hatching tooth, and an attempt to make cuticular lenses.

(17) Page 11. "early JHM treatment", read "early JHm treatment".

Corrected

(18) Page 11. "likely. A target of JH, and likely Kr-h1, in Thermobia is myoglianin...". Please see my notes 1, 2, and especially 3, above.

This has been revised

(19) Page 13. "the locust, Locusta americana (Aboulafia-Baginshy et al.,1984)". Please read "the locust, Locusta migratoria (Aboulafia-Baginshy et al.,1984)".

Corrected

(20) Page 13 "Acheta domesticus" three times. The correct name now is "Acheta domestica", after harmonizing the declension of the specific name with the generic one. See additionally my note 4 above.

Acheta domesticus has been used in hundreds (thousands?) of papers since it was originally named by Linnaeus. We will continue to use it.

(21) Page 15, "(also called the vermiform larva (Bernays, 1971) redirects embryonic development to form an embryo with proportions, cuticular pigmentation, cuticular sculpturing and bristles characteristic of a nymph, while pronymph modifications, such as the cuticular surface sculpturing (Bernays, 1971)". The reference "Bernays, 1971" is indeed "Bergot et al., 1971".

There was a mistake in the references. The Bernays reference was omitted from the revised Discussion

(22) Page 16. "Since JH also induces Kr-h1 in embryos of many insects, including Thermobia". I'm not sure that this has been studied in many insects. In any case, any reference would be useful.

(23) Page 17. "Tribolium casteneum". Please read "Tribolium castaneum".

Changed

(24) Page 17. "...results in a permanent larva that continues to molt well after it has surpassed its critical weight (He et al., 2019)". The paper of He et al., 2019 is preceded by two key papers that previously demonstrate (and in hemimetabolan insects) that myoglianin is a determining factor in the preparation for metamorphosis: DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R). See my note 3 above.

Corrected in revision

(25) Page 18. "These persisting embryonic primordia join the wing primordia in delaying their morphogenesis into postembryonic life". This reader does not understand this sentence.

Made clearer in the revision.

(26) Page 18. "is first possible in the commercial silkworm (Daimon et al., 2015)". Please mention the scientific Latin name of the species, Bombyx mori.

(27) Page 19. "The functioning of farnesol derivatives in growth versus differentiation control extends deep into the eukaryotes.../... this capacity was eventually exploited by the insects to provide the hormonal system that regulates their metamorphosis". This information appears quite out of place.

We have retained this point.

(28) Page 21. Heading "Hormones". I suggest using the heading "Bioactive compounds", as neither pyriproxyfen nor 7-ethoxyprecocene are hormones.

Done

(29) Page 29, legend of figure 1. "Photomicrographs" is somewhat redundant. The technical word is "micrographs". "Thermobia domestica" appears in the explanation of panel C, but this is not necessary, as the name appears in the main heading of the legend.

Done

(30) Page 30, legend of figure 2. Panel B, see my comment 10 above. Why embryonic age is expressed in % embryo development in panel C (and in days in panels A and B)?

All have been converted to days AEL

(31) Page 35, legend of figure 5. "Photomicrograph" see my note 28 above.

Done

(32) Page 40, figure 10. In panel A, the indication of the properties of JH is misleading. The arrow going to promoting differentiation and maturation is OK, but the repression sign that indicates suppression of morphogenetic growth and cell determination seems to suggest that JH has retroactive effects. In panel B, I suggest to label "Flies" instead of "Higher Diptera", which is an old-fashioned term. In any case, see my general comments 1 and 2, above, about speculation.

Figure has been completely revised

(33) Figure 11. See my general comments 1 and 2, above, about speculation.

Figure has been revised

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.

The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.

The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

We agree completely and should be a feature of future work.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

We have tried to be less all-encompassing in the Discussion. The strongest comparisons can be made between ametabolous and hemimetabolous insects and we have focused most of the Discussion on the role of JH in that transition. We still include some discussion of holometabolous insects because the ancestral embryonic functions of JH may be somehow related to the unusual reappearance of JH in the prepupal period. We have reduced this discussion to only a few sentences.

Reviewer #3 (Recommendations For The Authors):

(1) The overall manuscript is very long (especially the discussion), and the main messages of the manuscript get lost in some of the details. I would suggest that the authors move some of the results to the supplementary material (e.g. it might be possible to put a lot of the detail of Thermobia embryogenesis into the supplementary text if the authors feel it is appropriate). The discussion contains a lot of speculation and I suggest the authors make this more concise. One example: At the moment there is a large section on the modification in JH function during the evolution of holo and hemi-metabolous life history strategies. There are some interesting ideas in this section and the authors do a good job of integrating their findings with the literature - but I would encourage the authors to limit the bulk of their discussion to the specific things that their results demonstrate. E.g. The first half of p17 contains too much detail, and the focus should be on the relationship with Thermobia (as at the bottom of p17).

Section has been revised and is more focused

(2) I would also suggest a thorough proofread of the manuscript, I have highlighted some of the errors/points of confusion that I found in the list below - but this list is unlikely to be exhaustive . We appreciate catching the errors. Hopefully the final version is better proofed.

(3) It might be me, but I found the wording in the second half of the abstract a bit confusing. Particularly the statement about the redeployment of morphogen systems - could this be stated more clearly?

Abstract has been revised.

(4) Introduction

a. "powered flight" rather than 'power flight'

Done

b. 'brought about a hemimetabolous lifecycle' implies causality which hasn't been shown and directionality to evolution - suggest 'facilitated the evolution of a hemi...". Similar comment for 'subsequent step to complete metamorphosis'.

c. Bottom of p2 - unclear whether you are referring to hemi- holo- or both

d. Suggest removing sentence beginning "besides its effects..." as the relevance of the role of JH in caste isn't clear.

Kept sentence but removed initial clause

e. State that Thermoia is a Zygentoma.

Done

f. Throughout - full species names on first usage only, T. domestica on subsequent usages.

We will continue to use genus names for the reason given above.

Gene names e.g. kr-h1 in italics.

g. 'antagonise morphogens"? rather than 'antagonise morphoentic'.

Done

(5) Results

a. Unclear why drawings are provided rather than embryonic images in Fig. 1A

We think that the points can be made better with diagrams.

b. Top of p4, is 'slot' the correct word?

Corrected

c. Unclear why the measurements of JHIII weren't measured before 5 days AEL, especially given that many of the manipulative experiments are at earlier time points than this. I appreciate that, based on kr-h1, levels that JHIII is also likely to be low.

d. Reference for the late embryonic peak of 20E being responsible for the J2 cuticle?

Clarified that this is an assumption

e. Clarify "some endocrine related transcripts" why were these ones in particular picked? Kr-h1 is a good transcriptional proxy for JH and Met is the JH-receptor, why myoglianin and not some of the other transcriptional proxies of neuroendocrine signalling?

Hopefully, the choice is clearer.

f. Fig 2C rather than % embryo development for the gene expression data please represent this in days (to be consistent with your other figures).

It is now consistent with other parts of figure.

g. In Fig. 3 the authors do t-tests, because there are three groups there needs to be some correction for multiple testing (e.g. Bonferroni) can the authors add this to the relevant methods section?

We think that pair-wise comparisons are appropriate.

h. Fig. 3 legend: you note that you treat stage 2 juveniles with 7EP - I couldn't tell what AEL this corresponded to.

This is after hatching so AEL does not apply.

i. Top of p7 'deformities' rather than 'derangements'?

Done

j. Regarding the dosage effects of embryonic abnormalities - it would be good to include these in the supp material, as it convinces the reader that the effects you have seen aren't just due to toxicity.

It is not clear what the objection is.

k. Bottom of p7 'problematic' not 'problematical'

Done

l. P8 Why are the clusters of Its important? - provide a bit more interpretation for the reader here.

This is clear in the revised version.

m. P9 Why is the modulation of transcription of kr-h1, met, and myo important in this context

Explained

n. P9 'fig. 7F'? there is no Fig. 5F

Thanks for catching the typo.

o. Fig. 7B add to the legend which treatment the dark and light points correspond to.

We think it is obvious from the labeling on Fig 7B.

(6) Discussion:

a. What do we know about how terminal differentiation is controlled in non-insect arthropods? Most of the discussion is focused on insects (which makes sense as JH is an insect-specific molecule), but if the authors are arguing the ancestral role of JH it would be useful to know how their findings relate to non-insect arthropods.

We have not been able to find any information about systemic signals being involved in non-insect arthropods.

b. There is no Fig. 5E (are they referring to 7E?)

Yes, it should have been Fig. 7E.

c. Is myoglianin a direct target of JH in other species?

Other reports are in postembryonic stages and show that myoglianin suppresses JH production. Our paper is the first examination in embryos and we find that the opposite is true – i.e., that JH treatment suppresses myoglianin production. We suspect that these two signaling systems are mutually inhibitory. It would be interesting to see whether treatment of a post-critical weight larva with JH (which would induce a supernumerary larval molt) would also suppress myoglianin production (as we see in Thermobia embryos).

d. P12 What is the evidence that JH interacts with the first 20E peak to alter the embryonic cuticle?

We are not sure what the issue is. The experimental fact is that treatment with JH before the E1 ecdysteroid peak causes the production of an altered E1 cuticle. We are faced with the question of why is this molt sensitive to JH when the latter will not appear until 3 or 4 days later? A possible answer is that the ecdysone response pathway has a component that has inherent JH sensitivity. The mosquito data suggest that Taiman provides another link between JH and ecdysone action

e. Top of p13 - this paragraph can be cut down substantially. Although this is evidence that JH can alter ecdysteriods - it is in a species that is 400 my derived from the target species. Is it likely to be the exact same mechanism? I would encourage the authors to distil and retain the most important points.

This paragraph has been shortened and focused.

f. Bottom of p13 - what does this study add to this knowledge?

The response of Thermobia embryos to JH treatment is qualitatively the same as seen in other short germband embryos. This similarity supports the assumption that the same responses would have been seen in their last common ancestor.

g. P19 the last paragraph in the conclusions is really peripherally relevant to the paper and is a bit of a stretch, I would encourage the authors to leave this section out.

We agree that it is a stretch. JH and its precursor MF are the only sesquiterpene hormones. How did they come about to acquire this function? We think it is worth pointing out the farnesol metabolites have been associated with promoting differentiation in various eukaryotes. An ancient feature of these molecules in promoting (maintaining?) differentiation may have been exploited by the insects to develop a unique class of hormones. It is worth putting the idea out to be considered.

h. P19 "conclusions" rather than 'concluding speculations'.

Changed as suggested.

Methods:

It is standard practice to include at least two genes as reference genes for RT-qPCR analysis (https://doi.org/10.1186/gb-2002-3-7-research0034, https://doi.org/10.1373/clinchem.2008.112797) If there are large-scale differences in the tissues being compared (e.g. as there are here during development) then more than two reference genes may be required and a reference gene study (such as https://doi.org/10.3390%2Fgenes12010021) is appropriate. Have the authors confirmed that rp49 is stably expressed during the stages of Thermobia development that they assay here?

We have explained our choice in the Methods.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

Strengths:

The main strength and novelty of the proposed approach lies in the idea of combining stateof-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with recent developments in the field of protein language models (the ESM2 model was used). The authors show that features extracted from high-dimensional, information-rich ESM2 sequence embeddings can be suitable for efficient use with the aforementioned tools.

The reduced features take the form of amino acid occurrence probability matrices estimated from ESM2 masked-token predictions, or structural descriptors predicted by a modified variant of the ESM2 model. However, we believe that these should not be called "embeddings" or "representations". This is because they don't come directly from any layer of these networks, but rather from their final predictions.

We agree that there is some room for discussion about whether the amino acid probabilities returned by pre-trained ESM-2 and the 3Di sequences returned by ESM-2 3B 3Di can be properly referred to as “embeddings”. The term “embedding” doesn’t have a formal definition, other than some kind of alternative vector representation of the input data which, preferably, makes the input data more suitable for some downstream task. In that simple sense of the word “embedding”, amino acid probabilities and 3Di sequences output by our models are, indeed, types of embeddings. We posed the question on Twitter (https://twitter.com/TrichomeDoctor/status/1715051012162220340) and nobody responded, so we are left to conclude that the community is largely ambivalent about the precise definition of “embedding”.

We’ve added language in our introduction to make it more clear that this is our working definition of an “embedding”, and why that definition can apply to profile HMMs and 3Di sequences.

The benchmarks presented suggest that the approach improves sensitivity even at very low sequence identities <20%. The method is also expected to be faster because it does not require the computation of multiple sequence alignments (MSAs) for profile calculation or structure prediction.

Weaknesses:

The benchmarking of the method is very limited and lacks comparison with other methods. Without additional benchmarks, it is impossible to say whether the proposed approach really allows remote homology detection and how much improvement the discussed method brings over tools that are currently considered state-of-the-art.

We thank the reviewer for the comment. To address the question, we’ve expanded the results by adding a new benchmark and added a new figure, Figure 4. In this new content, we use the SCOPe40 benchmark, originally proposed in the Foldseek paper (van Kempen et al., 2023), to compare our best method, ESM-2 3B 3Di coupled to Foldseek, with several other recent methods. We find our method to be competitive with the other methods.

We are hesitant to claim that any of our proposed methods are state-of-the-art because of the lack of a widely accepted standard benchmark for remote homology detection, and because of the rapid pace of advancement of the field in recent years, with many groups finding innovative uses of pLMs and other neural-network models for protein annotation and homology detection.

Reviewer #2 (Public Review):

Summary:

The authors present a number of exploratory applications of current protein representations for remote homology search. They first fine-tune a language model to predict structural alphabets from sequence and demonstrate using these predicted structural alphabets for fast remote homology search both on their own and by building HMM profiles from them. They also demonstrate the use of residue-level language model amino acid predicted probabilities to build HMM profiles. These three implementations are compared to traditional profile-based remote homology search.

Strengths:

• Predicting structural alphabets from a sequence is novel and valuable, with another approach (ProstT5) also released in the same time frame further demonstrating its application for the remote homology search task.

• Using these new representations in established and battle-tested workflows such as MMSeqs, HMMER, and HHBlits is a great way to allow researchers to have access to the state-of-the-art methods for their task.

• Given the exponential growth of data in a number of protein resources, approaches that allow for the preparation of searchable datasets and enable fast search is of high relevance.

Weaknesses:

• The authors fine-tuned ESM-2 3B to predict 3Di sequences and presented the fine-tuned model ESM-2 3B 3Di with a claimed accuracy of 64% compared to a test set of 3Di sequences derived from AlphaFold2 predicted structures. However, the description of this test set is missing, and I would expect repeating some of the benchmarking efforts described in the Foldseek manuscript as this accuracy value is hard to interpret on its own.

The preparation of training and test sets are described in the methods under the heading “Fine tuning ESM-2 3B to convert amino acid sequences into 3Di sequences”. Furthermore, there is code in our github repository to reproduce the splits, and the entire model training process: https://github.com/seanrjohnson/esmologs#train-esm-2-3b-3di-starting-from-the-esm-2-3bpre-trained-weights

We didn’t include the training/validation/test splits in the Zenodo repository because they are very large: train 33,924,764; validation 1,884,709; test 1,884,710 sequences, times 2 because there are both amino acid and 3Di sequences. It comes out to about 30 Gb total, and is easily rebuilt from the same sources we built it from.

We’ve added the following sentence to the main text to clarify:

“Training and test sets were derived from a random split of the Foldseek AlphaFold2 UniProt50 dataset (Jumper et al., 2021; van Kempen et al., 2023; Varadi et al., 2022), a reducedredundancy subset of the UniProt AlphaFold2 structures (see Methods for details).”

To address the concern about comparing to Foldseek using the same benchmark, we’ve expanded the results section and added a new figure, Figure 4 using the SCOPe40 benchmark originally presented in the Foldseek paper, and subsequently in the ProstT5 paper to compare Foldseek with ESM-2 3B 3Di to Foldseek with ProstT5, AlphaFold2, and experimental structures.

• Given the availability of predicted structure data in AFDB, I would expect to see a comparison between the searches of predicted 3Di sequences and the "true" 3Di sequences derived from these predicted structures. This comparison would substantiate the innovation claimed in the manuscript, demonstrating the potential of conducting new searches solely based on sequence data on a structural database.

See response above. We’ve now benchmarked against both ProstT5 and AF2.

• The profile HMMs built from predicted 3Di appear to perform sub-optimally, and those from the ESM-2 3B predicted probabilities also don't seem to improve traditional HMM results significantly. The HHBlits results depicted in lines 5 and 6 in the figure are not discussed at all, and a comparison with traditional HHBlits is missing. With these results and presentation, the advantages of pLM profile-based searches are not clear, and more justification over traditional methods is needed.

We thank the reviewer for pointing out the lack of clarity in the discussion of lines 5 and 6.

We’ve re-written that section of the discussion, and reformatted Figure 3 to enhance clarity.

We agree, a comparison to traditional HHBlits could be interesting, but we don’t expect to see stronger performance from the pLM-predicted profiles than from traditional HHBlits, just as we don’t see stronger performance from pLM-hmmscan or pLM-Foldseek than from the traditional variants. We think that the advantages of pLM based amino acid hmm searches are primarily speed. There are many variables that can influence speed of generating an MSA and HMM profile, but in general we expect that it will be much slower than generating an HMM profile from a pLM.

We don’t know why making profiles of 3Di sequences doesn’t improve search sensitivity, we just think it’s an interesting result that is worth presenting to the community. Perhaps someone can figure out how to make it work better.

• Figure 3 and its associated text are hard to follow due to the abundance of colors and abbreviations used. One figure attempting to explain multiple distinct points adds to the confusion. Suggestion: Splitting the figure into two panels comparing (A) Foldseek-derived searches (lines 7-10) and (B) language-model derived searches (line 3-6) to traditional methods could enhance clarity. Different scatter markers could also help follow the plots more easily.

We thank the reviewer for this helpful comment. We’ve reformatted Figure 3 as suggested, and we think it is much easier to read now.

• The justification for using Foldseek without amino acids (3Di-only mode) is not clear. Its utility should be described, or it should be omitted for clarity.

To us, the use of 3Di-only mode is of great theoretical interest. From our perspective, this is one of our most significant results. Previous methods, such as pLM-BLAST and related methods, have made use of very large positional embeddings to achieve sensitive remote homology search. We show that with the right embedding, you don’t need very many bits per position to get dramatically improved search sensitivity from Smith-Waterman, compared to amino acid searches. We also doubt that predicted 3Di sequences are the optimal small encoding for remote homology detection. This result and observation opens up an exciting avenue for future research in developing small, learned positional embeddings that are optimal for remote homology detection and amenable to SIMD-optimized pre-filtering and Smith-Waterman alignment steps.

We’ve expanded the discussion, explaining why we are excited about this result.

• Figure 2 is not described, unclear what to read from it.

It's just showing that ESM-2-derived amino acid probabilities closely resemble amino acid frequencies in MSAs. We think it gives readers some visual intuition about why predicted profile HMMs perform as well as they do. We’ve added some additional explanation of it in the text.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The paper would mainly benefit from a more comprehensive benchmark:

We suggest that the authors extend the benchmark by including the reference methods (HHpred and Foldseek) run with their original representations, i.e., MSAs obtained with 2-3 iterations of hhblits (for HHpred) and experimental or predicted structures (for Foldseek). HHpred profile-profile comparisons and Foldseek structure-structure comparisons would be important reference points for assessing the applicability of the proposed approach in distant homology detection. It is also essential to compare the method with other emerging tools such as EBA (DOI: 10.1101/2022.12.13.520313), pLM-BLAST (DOI: 10.1101/2022.11.24.517862), DEDAL (DOI: 10.1038/s41592-022-01700-2), etc.

We also suggest using an evolutionary-oriented database for the benchmark, such as ECOD or CATH (these databases classify protein domains with known structures, which is important in the context of including Foldseek in the benchmark). We ran a cursory benchmark using the ECOD database and generated HH-suite .hhm files (using the single_seq_to_hmm.py and hhsearch_multiple.py scripts). Precision and recall appear to be significantly lower compared to "vanilla" hhsearch runs with MSA-derived profiles. It would also be interesting to see benchmarks for speed and alignment quality.

The pLM-based methods for homology detection are an emerging field, and it would be important to evaluate them in the context of distinguishing between homology and analogy. In particular, the predicted Foldseek representations may be more likely to capture structural similarity than homology. This could be investigated, for example, using the ECOD classification (do structurally similar proteins from different homology groups produce significant matches?) and/or resources such as MALISAM that catalog examples of analogy.

We’ve added the SCOPe40 benchmark, which we think at least partially addresses these comments, adding a comparison to pLM-BLAST, ProstT5, and AF2 followed by Foldseek. The question of Analogy vs homology is an interesting one. It could be argued that the SCOPe40 benchmark addresses this in the difference between Superfamily (distant homology) and Fold (analogy, or very distant homology).

Our focus is on remote homology detection applications rather than alignment quality, so we don’t benchmark alignment quality, although we agree that those benchmarks would be interesting.

Page 2, lines 60-67. This paragraph would benefit from additional citations and explanations to support the superiority of the proposed approach. The fact that flattened embeddings are not suitable for annotating multidomain proteins seems obvious. Also, the claim that "current search implementations are slow compared to other methods" should be supported (tools such as EBA or pLM-BLAST have been shown to be faster than standard MSA-based methods). Also, as we mentioned in the main review, we believe that the generated pseudo-profiles and fine-tuned ESM2 predictions should not be called "smaller positional embeddings".

Discriminating subdomains was a major limitation of the influential and widely-cited PfamN paper (Bileschi et al., 2022), we’ve added a citation to that paper in that paragraph for readers interested in diving deeper.

To address the question of speed, we’ve included data preparation and search benchmarks as part of our presentation of the SCOPe40 benchmark.

Finally, we were not sure why exactly every 7th residue is masked in a single forward pass. Traditionally, pseudo-log likelihoods are generated by masking every single token and predicting probabilities from logits given the full context - e.g. https://arxiv.org/pdf/1910.14659.pdf. Since this procedure is crucial in the next steps of the pipeline, it would be important to either experiment with this hyperparameter or explain the logic used to choose the mask spacing.

We’ve added discussion of the masking distance to the Methods section.

Reviewer #2 (Recommendations For The Authors):

• While the code and data for the benchmark are available, the generation of searchable databases using the methods described for a popular resource such as Pfam, AFDB, SCOP/CATH which can be used by the community would greatly boost the impact of this work.

3Di sequences predicted by ESM-2 3B 3Di can easily be used as queries against any Foldseek database, such as PDB, AFDB, etc. We’ve added Figure 4E to demonstrate this possibility, and added some related discussion.

• Minor: In line 114, the text should likely read "compare lines 7 and 8" instead of "compare lines 6 and 7."

We’ve clarified the discussion of Figure 3.

Author Response

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

We thank the editors and reviewers for their tremendously helpful comments. We outline below changes we have made to the manuscript in response to each point. These include new analyses and a substantial rewrite to address the concerns about lack of clarity.

We believe the revisions strengthen the evidence for our conclusion that grid fields can be either anchored to or independent from a task reference frame, and that anchoring is selectively associated with successful path integration-dependent behaviour. Our additional analyses of non-grid cells indicate that while some are coherent with the grid population, many are not, suggesting cell populations within the MEC may implement grid-dependent and grid-independent computations in parallel.

We hope the reviewers will agree that our novel experimental strategy complements and avoids limitations of perturbation-based approaches, and by providing evidence to dissociate the two major hypotheses for whether and when grid cells contribute to behaviour our results are likely to have a substantial impact on the field.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behaviour… The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behaviour in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

Thankyou for the overview and constructive comments.

(1) While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.

(1.1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.

The lack of information about the trial organisation was a substantial oversight in our preparation of the first version of the manuscript. Session make up can not account for effects on grid stability and its relationship to behavioural outcome but this was not made at all clear.

In all sessions trial types were varied in a fixed repeating sequence. Therefore, continuous blocks of trials on which grid firing is anchored (or independent from) the track can not be explained by the mouse experiencing a particular trial type. We have revised the manuscript to make this clearer, e.g. p 5, ‘These switches could not be explained by variation between trials in the availability of cues or rewards, as these were interleaved in blocks that repeated throughout a session (see Methods), whereas periods in which grid cell activity was in a given mode extended across the repeating blocks (e.g. Figures 3D,E, 4A, 5E,F).’ and methods p 12, ‘Trials were delivered in repeating blocks throughout a recording session…’

(1.2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behaviour and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.

As we note above, because trial types were interleaved in a fixed sequence, experience of a particular trial type can not account for switching between task-anchored and taskindependent firing modes. This was insufficiently clear in the first version of the manuscript.

We varied the proportions of trials of a particular type between sessions with the aim of maximising the number of non-beaconed and probe trials. This was necessary because we find that if we introduce too high a proportion of these trials early in training then mice appear to ‘lose interest’ in the task and their performance drops off. We therefore used an approach in which we increased the proportions of non-beaconed and probe trials over training days as mice became familiar with the task. This is now described in the methods (p 12).

Because the decision for when to vary the proportion of trial types was based on the previous day’s performance, the experimental design was not optimised for addressing the reviewer’s question about dissociating experimental from natural differences in mice. To provide some initial insight we have analysed the relationship between task anchored coding and proportion of beaconed trials in a session (Figure 3, Figure Supplement 7). While on average there is a higher proportion of trials in which grid fields are task-anchored in sessions with more beaconed trials, this effect is small and most of the variance is independent from the proportion of beaconed trials.

(1.3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?

Thankyou for flagging this. This reflected an inconsistency arising from the way we detected stops that we have now corrected. Briefly, the temporal resolution of the processed location data against which the stop detection threshold was applied was insufficiently high. As a result, stops in the non-beaconed group were picked up, as they tended to be longer because mice remained still to consume rewards, whereas some stops in the probe group were missed because they were relatively short. We have corrected this by repeating the analyses on raw position data at the highest temporal resolution available. This analysis is now clearly described in the Methods (see p13 “A stop was registered in Blender3D if the speed of the mouse dropped below 4.7 cm/s. Speed was calculated on a rolling basis from the previous 100 ms at a rate of 60 Hz.”).

(1.4) It is not shown how the behaviours (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviours led to the different encoding schemes.

Because trial types were interleaved and repeated with a period less than the length of typical trial sequences during which grid cell activity remained either task-anchored or taskindependent, differences between trial types are unlikely to explain use of the different coding schemes. Hopefully, this is clarified by the comments above.

To further describe the relationship between behavioural outcomes, trial types and grid anchoring, we now also show running speed as a function of location for each combination of trial types and trial outcomes (Figure 6, Figure Supplement 1). This illustrates and replicates our previous findings (Tennant et al. 2018) that running speed profiles are similar for a given trial outcome regardless of trial type (Figure 6, Figure Supplement 1A), and further further shows that the behavioural profile for a given trial outcome and trial-type does not differ when grid cells are in task-anchored and task-independent modes (Figure 6, Figure Supplement 1B). This further argues against the possibility that difference in behaviours leads to the different encoding schemes.

(2) Regarding the behaviour and activity encoding on a trial-by-trial basis, did the behavioural change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behaviour, or the other way around.

This is a good question but our experimental design lacks sufficient statistical power to address the timing of mode switches within a trial. This is because mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioural outcome is variable (increasing the required n for adequate power).

(3) The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviours.

Switching between position encoding schemes appears to be rare within our dataset and unlikely to contribute to variation in behaviour. In most sessions we did not observe switching between grid phases / position encodings (e.g. Figures 2A-B, 3B-E, 4A, 5C-D, F). In one session we found switching between different phases when grid cells were taskanchored. Because the grid period was unchanged, the spatial periodograms remained similar. We report this example in the revised manuscript (Figure 5E).

(4) The percentage of neurons categorised in each coding scheme was similar between nongrid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.

We very much appreciate this suggestion. We note first that while the proportion of taskanchored grid and non-grid cells is similar, task-independent periodic firing of non-grid cells is much rarer than for grid cells (Figure 2E), suggesting a dissociation between the populations. To further address the question we have included additional analyses of nongrid cells (Figure 3, Figure Supplement 5). This shows that while some non-grid cells have anchoring that switches coherently with simultaneously recorded grid cells, others do not. Figures 4 and 5 now show examples of non-grid cell activity recorded simultaneously with grid cells.

Together, our data suggest that the MEC implements multiple coding schemes: one that is associated with the grid network and includes some non-grid cells; and one (or more) that can be independent from the grid network. This dissociation adds to the insights into MEC function that are provided by our study and is now highlighted in the abstract and discussion.

(5) In Figure 2 there are several cell examples that are categorised as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.

We have now included examples in Figure 2A-C where the grid mode is stable throughout a session. As the view of activity at a session level is important, we have not updated Figure 2F, but have clarified the terminology to now clearly refer to classification at either season or trial levels. In addition, we have repeated the analyses shown in Figure 2F but after grouping cells according to whether their firing has a single mode on >85% of the trials (Figure 3 Figure Supplement 4). This analysis supports similar conclusions to those of Figure 2F.

(6) The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

We very much appreciate the feedback and have substantially rewritten the manuscript. We have paid particular attention to explaining key concepts in the introduction and have carefully checked the figures. We welcome further feedback on whether this is now clearer.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behaviour performance on a virtual task… This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviours. More studies in which grid cell activity can be associated with navigational abilities are needed.

Thank you for the supportive comments and highlighting the importance of the question.

The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behaviour. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioural performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

Again, thank you for the supportive comments. We recognise the previous version of the manuscript did not sufficiently clarify the motivation for our approach, or the benefits of capitalising on behavioural variable variability as a complementary strategy to perturbation approaches. We now make this clearer in the revised introduction (p 2, paragraphs 2 and 3).

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

We appreciate the ambiguity here was confusing. We have re-named the groups to ‘taskanchored’, corresponding to when grid cells encode position on the track (as well as distance as the reviewer correctly points out), and ‘task-independent’, corresponding to the group we previously referred to as distance encoding.

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioural performance on non-beaconed trials requires the animal to anchor its spatial behaviour to VR cues.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behaviour'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behaviour is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text.

Thank you for the comments. We recognise now that the previous text was insufficiently clear. We have modified the introduction to clarify the value of an approach that takes advantage of behavioural variability. Importantly, this approach is complementary to perturbation strategies we and others have used previously. In particular it addresses critical limitations of perturbation strategies which can be confounded by off-target effects and possible adaptation, both of which are extremely difficult to fully rule out. We hope that with this additional clarification it is now clear that as for any important question multiple and complementary testing strategies are required to make progres, and second, that our study makes a new and important contribution by introducing a novel experimental approach and by following this up with careful analyses that clearly distinguish competing hypotheses.

However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioural performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

We’re unclear here about what the reviewer means by ‘more straightforward’ as clear relationships between activity of single grid cells and populations of grid cells are well established (Gardner et al., 2021; Waaga et al., 2021; Yoon et al., 2013).

To give a clearer indication of the corresponding population level representations, as mentioned in response to Reviewer #1, we now include additional data showing many simultaneously recorded neurons, and analyses of non-grid as well as grid cells (Figures 4, 5, Figure 5 Figure Supplement 2).

To reconcile results with our previous study of MEC inactivation we have paid additional attention to the roles of non-grid cells (following suggestions by Reviewer #1). We show that while some non-grid cells show transitions between task-anchored and task-independent firing that are coherent with the grid population, many others have more stable firing that is independent of grid representations. This is consistent with the idea that the MEC supports localised behaviour in the cued and uncued versions of the task (Tennant et al., 2018), and suggests that while grid cells preferentially contribute when cues are absent, non-grid cells could also support the cued version. We make this additional implication clear in the revised abstract and discussion.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorrelograms. It would be interesting to compare with a different method based on the similarities between raw maps.

While our main analyses use a periodogram-based method to identify when grid cells are / are not anchored to the task environment, we validate these analyses by examination of the rate maps in each condition (Figures 2-4). For example, when grid cells are task-anchored, according to the periodogram analysis, the rate maps clearly show spatially aligned peaks, whereas when grid cells are not anchored the peaks in their rate maps are not aligned (Figure 2A vs 2B; Figure 3B-E; Figure 4C). We provide further validation by showing that spatial information (in the track reference frame) is substantially higher when grid cell activity is task-anchored vs task-independent (Figures 2F, 3G, 4F and Figure 3 Figure Supplement 4).

To further address this point we have carried out additional complementary analyses in which we identify task anchored vs task independent modes using a template matching method applied to the raw rate maps (Figure 6, Figure Supplement 2). These analyses support similar conclusions to our periodogram-based analyses.

Beyond this minor point, cell categorization is performed using all trial types.

Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information.

We recognise that the description of the task design was insufficiently clear but are unsure why ‘it is difficult to extrapolate general information’. Before addressing this point, we should first be clear that mice are not ‘forced’ to adopt any particular strategy. Rather, on uncued trials a path integration strategy is the most efficient way to solve the task. However, mice could instead use a less efficient strategy, for example by stopping at short intervals they still obtain rewards. Detailed behavioural analyses indicate that such random stopping strategies are used by naive mice, while with training mice learn to use spatial stopping strategies (Tennant et al. 2018).

In terms of ‘extracting general information’ from the task, the following findings lead to general predictions: 1) Grid cells can exist in either task-anchored or task-independent periodic firing modes; 2) These modes can be stable across a session, but often modeswitching occurs within a session; 3) While some non-grid cells show task-independent periodic firing, this is much less common than for grid cells, which suggests a model in which many non-grid MEC neurons operate independently from the grid network; 4) When a marker cue is available mice locate a reward equally well when grid cells are in taskanchored versus task-independent modes, which argues against theories in which grid cells are a key part of a general system for localisation; 5) When markers cues are absent taskanchored grid firing is associated with successful reward localisation, which corroborates a key prediction of theories in which grid cells contribute to path integration.

In revising the manuscript we have attempted to improve the writing to make these advances clearer, and have clarified methodological details that made interpretation more challenging than it should have been. For example, as noted in our response to Reviewer #1, we have included additional details to clarify the organisation of trials and relationships between trials, behavioural outcomes and neural codes observed.

On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'. I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward).

Again, this point perhaps reflects a lack of clarity on our part in writing the manuscript. When grid cells are anchored to the track reference frame (now called ‘tasked anchored’, previously ‘position encoding’), then the location of the rate peaks in grid firing is reliable from trial to trial. This is the case whether or not the trial is cued. When grid cells are independent of the track reference frame (now called ‘task independent’, previously ‘distance encoding’), then the location of the firing rate peaks vary from trial to trial. In the latter case, position can not be read out directly from trial to trial.

In principle, in the task-independent mode track position could be calculated by storing the grid network configuration at the start of the track, which would differ on each trial, and then implementing a mechanism to readout relative distance as mice move along the track. However, if mice do use this computation we would expect them to do so equally well on cued and uncued trials. By contrast, our results clearly show a dissociation between trial types in the relationship between grid firing and behavioural outcome. We highlight and discuss this possibility in the revised manuscript (p 10, ‘Alternatively, mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials.

We have revised the manuscript to clarify our goal of distinguishing major hypotheses for the roles of grid cells in behaviour (Introduction, ‘On the one hand, theoretical arguments that grid cell populations can generate high capacity codes imply that they could in principle contribute to all spatial behaviours (Fiete et al., 2008; Mathis et al., 2012; Sreenivasan and Fiete, 2011). On the other hand, if the behavioural importance of grid cells follows from their hypothesised ability to generate position representations by integrating self-motion signals (McNaughton et al., 2006), then their behavioural roles may be restricted to tasks that involve path integration strategies.’

By showing that performance on cued trials is similar regardless of whether grid cells are task-anchored or not, we provide strong evidence against the idea that grid firing is in general necessary for location-based behaviours. By showing that task anchoring is associated with successful localisation when cues are absent we corroborate a key prediction of hypothesised roles for grid cells in path integration-dependent behaviour. Therefore, we substantially reduce the space of behaviours to which grid cells might contribute. Importantly, this space is much larger for the MEC, which is required for cued and uncued versions of the task. We have revised the introduction and discussion to make these points clearer.

While we believe our results add a key piece of evidence to the puzzle of when and where grid cells contribute to behaviour, we agree that further work will be required to develop and test more refined hypotheses. Alternative models also remain plausible, for example perhaps the behaviourally relevant computations are implemented elsewhere in the brain with grid anchoring to the track as an indirect consequence. Nevertheless, explanations of this kind are more difficult to reconcile with evidence that inactivation of stellate cells in the MEC impairs learning of the task, and other manipulations that modify grid firing impair performance on similar tasks. We now discuss these possibilities (discussion p 10, ‘mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

It could be more informative to push forward the correlative analysis by looking at whether behavioural performance can be predicted by the coding scheme on a trial-by-trial basis.

The previous version of the manuscript showed these analyses (now in Figure 6). Thus, task anchored grid firing predicts more successful performance on uncued trials at the session level (Figure 6A-B) and at the trial level (Figure 6C-D).

Reviewer #1 (Recommendations For The Authors):

(1) The author particularly mentioned that the 1D tracks are different from the "cue-rich environments that are typically used to study grid cells". It is not clear what conclusions would hold for a cue-rich environment or a track, which may require relatively less path integration compared to the cue-sparse environment. This point should be discussed.

This is an important point that we did not pay sufficient attention to in the previous version of the manuscript. Our finding of successful localisation in the cued environment when grid cells are not task anchored implies that grid anchoring is not required to solve cued tasks. The implication here is that cue rich environments may then not be the most suitable for investigation of grid roles in behaviour as non-grid mechanisms may suffice, although this does not rule out the possibility that anchored grid codes may play important roles in learning about cue rich environments. We now address this point in the discussion (p 10, ‘An implication of this result is that cue rich tracks often used to investigate grid activity patterns may not engage behaviours that require anchored grid firing.’).

(2) It would be good to see the statistics for the number of different cells (stable position or distance encoding, and unstable cells) identified per mouse/session and the number of grid cells per session.

These are now added to Supplemental Data 2 and will also be accessible through code and datasets that we will make available alongside the version of record.

(3) Figure 2F: any explanation about why AG cells had high spatial information?

Previously the calculation used bits per spike and as aperiodic cells have low firing rates the spatial information was high. We have replaced this with bits per second, which provides a more intuitive measure and no longer implies high spatial information. We have amended this in the methods (p 15, ‘Spatial information was calculated in bits per second…’).

(4) The following methods sections should provide additional details:

(4.1) Details of the training protocol are largely left to reference papers. The reference papers give a general outline of the training protocol, but the details are not completely comparable given the single experiment performed on these mice. More details should be given on training stages and experience at the time of the experiment.

The task is more clearly described in the introduction (p 3), and additional details of the training protocol are now provided in the methods (p 12-13).

(4.2) The methods reference mean speed across sessions, but it is not clear where this was used.

This was very poor wording. We have now changed this to ‘For each session the mean speed was calculated for each trial outcome’.

(4.3) The calculation of the spatial autocorrelogram on a per-trial basis should be more explicitly stated. Is it the average of each 10 cm increment with the centre trial?

We have added additional information to the methods (p 16-18).

(4.4) 1D field detection is not sufficiently explained in Figure 1/S2. This information should also appear in the methods section.

This is now clarified on page 16 in section ‘Analysis of neural activity and behaviour during the location memory task’.

(5) The data in Figure 4A and B only shows speed vs. location for one example mouse. The combined per mouse or per session data should also be shown.

This is now shown in Figure 5A and Figure 5, Figure Supplemental 2

(6) Figure 5 is somewhat confusing. Why are A/B by session and C/D by trial? The methods imply that A/B are originally averaged by cell, but that duplicate cells in the same session are excluded because behaviour versus session type is identical. This method should be valid if all grid cells within a session are all "stable". This is likely given the synchrony of code-switching between grid cells, but not all co-active grid cells behaved identically.

It is understandable that C/D are performed by trial, but it should be made clear that it is not a comparable analysis to A/B. It is unclear what N refers to in C. The figure says by trial, but the legend says the error bar is by cell. If data is calculated by trial and then averaged by cell, this should be more clearly stated.

In Figure 6A/B (previously Figure 5A/B) we focus our analysis on sessions in which the mode of grid firing, either task-anchored or task-independent, was relatively stable on a trialto-trial basis (see Figure 3F for definitions). This enables us to then compare behaviour averaged across each session, with sessions categorised as task-anchored and task independent. This analysis has the advantage that it focuses on large blocks of time (whole sessions) in which the mode of grid firing is unambiguous, but the disadvantage is that it excludes many sessions in which grid firing switches between task-anchored and taskindependent modes.

Figure 6C/D (previously Figure 5C/D) addresses this limitation by carrying out similar analyses with behaviour sorted into task-anchored versus task-independent groups at the level of trials. A potential limitation for this analysis is that grid firing is somewhat variable on a trial-by-trial basis and so some trials may be mis-classified. We don’t expect this to lead to systematic bias, but it may make the data more noisy. Nevertheless, these analyses are important to include as they allow assessment of whether conclusions from 6A/B hold when all sessions are considered.

We have added additional clarification of the rationale for these analyses to the main text (p7-8, ‘’We addressed this by using additional trial-level comparisons’). We have also added clarification in the methods section for categorisation of task-anchored versus taskindependent trials when multiple grid cells were recorded simultaneously (p 17, ‘When assigning a common classification across a group of cells recorded simultaneously...’) and an explanation for the N in the figure legend. We also clarify that the analyses use a nested random effects design to account for dependencies at the levels of sessions and mice (methods, p 20, ‘Random effects had a nested structure to account for animals and sessions…’) .

(7) Panels E and F of Figure 5 are not explained in the main text.

This is now corrected (see p8, ‘Additional analyses…’).

(8) Figure 5: Since stable grid cells and all grid cells are shown, it will be better to show unstable cells, which can be compared with grid cells.

Given that the rationale for differences between Figure 6A/B and C/D (previously Figure 5AD) were not previously clear, the reason for focussing on stable grid cells here was likely also not clear (see point 6 above). We don’t show unstable grid cells in Figure 6A-B as the behaviour averaged at the level of a session would be a mix of trials when they are taskanchored and when they are task-independent. Therefore, the analysis would not test predictions about the relationship between task-anchored vs task-independent modes and behaviour. We hope this is now clear in the manuscript given the revisions introduced to address point 6 above.

(9) The methods describing the statistics for these experiments are also confusing. The methods section should be written more clearly, and it should be made clear in the text or figure legend whether this data is the "original" data or is processed in relation to the model, such as excluding duplicate grid cells within a session. The figure legend should also state that a GLMM was used to calculate the statistics.

We have revised the methods section with the goal of improving clarity, adding detail and removing ambiguity. This includes updates of the methods for the GLMM analysis, which are referred to within the Figure 6 legend. A clear definition of a stable session is now also added to the Figure 6 legend.

Reviewer #2 (Recommendations For The Authors):

When grid fields are anchored to the virtual world (position mode), there is probably small trialto-trial variability in the firing location of the firing fields. Is this trial-to-trial variability related to the variability in the stop location? This would provide a more direct link between path integration in grid cell networks and behaviour that depends on path integration.

When attempting to address this we find that the firing of individual grid cells is too variable to allow sufficiently precise decoding of their fields at a single trial level. This is expected given the Poisson statistics of spike generation and previous evaluations of grid coding (e.g. (Stemmler et al., 2015)).

The conclusion of the abstract is: "Our results suggest that positional anchoring of grid firing enhances the performance of tasks that require path integration." This statement is slightly confusing. The task requires 1) anchoring the behaviour to the visual cues presented at the start of the trial and 2) path integration from thereon to identify the rewarded location. The performance is higher when grid cells anchor to the visual cues presented at the start of the trial. What the results show is that the anchoring of grid firing fields to visual landmarks enhances the performance of tasks that require path integration from visual landmarks (i.e. grid cells being anchored to the reference frame that is behaviorally relevant).

To try to more clearly explain the logic and conclusion we have rewritten the abstract, including the final sentence.

Similar comment for the title of Figure 5: "Positional grid coding is not required for cued spatial localisation but promotes path integration-dependent localisation." Positional coding means that grid cells are anchored to the behaviorally relevant reference frame.

To address the lack of clarity we have modified the little of Figure 6 (previously Figure 5) to read ‘Anchoring of grid firing to the task reference frame promotes localisation by path integration but is not required for cued localisation’.

In Figure 1, there is a wide range of beaconed (40-80%) and non-beaconed (10-60%) trials given. It is not 100% clear whether these refer to the percentage of trials of a given type within the recording sessions. Was the proportion of non-beaconed trials manipulated? If so, was the likelihood of position and distance coding changing according to the percentage of nonbeaconed trials?

The ranges given refer to proportions across different behavioural sessions. Within any given behavioural session the proportion was constant. We now make this clear in the figure legend and in the results and methods sections.

We did not manipulate proportions of trial types during a session. Manipulations betweens sessions were carried out with the goal of maximising the numbers of uncued trials that the mice would carry out (see response to public comments above). While the effect of trial-type at the session level is not relevant to the hypotheses we aim to test here, we have included an additional analysis of the relationship between task anchoring and the proportions of trial types in a session (Figure 3, Figure Supplement 7)(also discussed above). As disentangling the effects of learning and motivation will be complex and likely require new experimental designs we have not drawn strong conclusions or pursued the analysis further..

I was not convinced that the labels "position" and "distance" were appropriate for the two grid cell firing modes. My understanding is that the "position" code also requires the grid cell network to estimate distance. It seems that the main difference between the "position" and "distance" modes is that when in the "position" mode, the activity on the torus is reset to a constant toroidal location when the animal reaches a clearly identifiable location on the virtual track. In the "distance" mode, this resetting does not take place.

As previously mentioned, we agree these terms weren’t the best and have since relabelled these as “task-anchored” and “task-independent”.

There are a few sections in the manuscript that implicitly suggest that a causal link between grid cell activity and behaviour was demonstrated. For instance: "It has been challenging to directly test whether and when grid cells contribute to behaviour.": The assumption here is that the manuscript overcomes this challenge, but the study is correlative.

We have modified the wording to be clear that we are introducing new tests of predictions made by hypotheses about causal relationships between grid coding and behaviour (introduction, p 1-2). We also clarify that our results argue against the hypothesis that grid cells provide a general coded for behaviour, but corroborate predictions of hypotheses in which they are specifically important for path integration (discussion, p 10).

We have modified the title abstract and main text to try to treat claims about causality with care. We now more thoroughly introduce and contrast the approach we report here with previous experiments that use perturbations (introduction, p2). While it is tempting to make stronger claims for causality with these approaches, there are also logical limitations with perturbation-based approaches, for example the challenges of fully excluding off target effects and adaptation. We now explain how these strategies are complementary. Our view is that both strategies will be required to develop strong arguments for whether and when grid cells contribute to behaviour. From this perspective, it is encouraging that our conclusions are in agreement with what are probably the most specific perturbations of grid cells reported to date (Gil et al. 2017), while perturbations that more generally affect MEC function appear to impair cued and path integration-dependent behaviours (Tennant et al. 2018). We now discuss these points more clearly (introduction, p 2).

I am slightly confused by the references to the panels in Figure 4.

"In some sessions, localization of the reward occurred almost exclusively when grid cells were anchored to position and not when they encoded distance (Figure 4C). Figure 4C only shows position coding.

"In other sessions, animals localised the reward when grid firing was anchored to position or distance, but overall performance was improved on positional trials (Figure 4D-E)." The reference should probably point to Figure 4E-F or just to 4E.

"In a few sessions, we observed spatial stopping behaviour comparable to cued trials, even when grid firing almost exclusively encoded distance rather than position (Figure 4F)." From Figure 4F, it seems that the performance on non-beaconed trials is better during "position" coding.

We have now updated Figure 5 (Figure 4 in the original manuscript) and references to the Figure in the text. Now Figure 5 shows the activity of cells recorded in stable and unstable task-anchored and task-independent sessions (see Figure 5C-F).

Minor issues:

Is this correct: (Figure 4A and Figure 4, Figure Supplement 1).

This has been corrected.

Figure 4B: There could be an additional label for position and distance.

Figure 4B from the original manuscript has now been removed.

Figure 4C-F. The panels on the right side should be explained in the Figure Legend.

Legends for Figure 5C-F (previously Figure 4C-F) have now been updated.

Reviewer #3 (Recommendations For The Authors):

Specific questions :

(1) Position coding reflects a coding scheme in which fields are spaced by a fixed distance; previous studies have shown that a virtual track grid map is a slice of the 2D classic grid. In that case, the fields are still anchored to the track but would produce a completely different map. Did the authors check whether it is the case at least for some cells? If not, what could explain such a major difference?

Το avoid confusion we now use the term ‘task-anchored’ rather than ‘position coding’ (see comments above). We should further clarify that our conclusions rest on whether or not the grid fields are anchored to the track. Task anchored firing does not require that grid fields maintain their spacing from 2D environments, only that fields are at the same track position on each trial. Thus, whether the spacing of the fields corresponds to a slice through a 2D grid makes no difference to the hypotheses we test here.

We agree that the relationship between 1D and 2D field organisation could be an interesting future direction, for example anchoring could involve resetting the grid phase while maintaining a stable period, or it could be achieved through local distortions in the grid period. However, since these outcomes would not help distinguish the hypotheses we test here we have not included analyses to address them.

(2) Previous studies have highlighted the role of grid cells in goal coding. Here there is an explicit reward in a particular area. Are there any grid modifications around this area? This question is not addressed in this study.

Again, we note that the hypotheses we test here relate to the firing mode of grid cells - taskanchored or task-independent - and interpretation of our results is independent from the specific pattern of grid fields on the track. This question nevertheless leads to an interesting prediction that if grid fields cluster in the goal area then this clustering should be apparent in the task-anchored but not the task-independent firing mode.

We test this by considering the average distribution of firing fields across all grid cells in each firing mode (Reviewer Figure 1). We find that when grid firing is task-anchored there is a clear peak around the reward zone, which is consistent with previous work by Butler et al. and Boccara et al. Consistent with our other prediction, this peak is reduced when grid cells are in the task-independent mode.

Author response image 1.

Plot shows the grid field distribution during stable grid cell session (> 85 % task-anchored or task-independent) (A) or during task-anchored and task-independent trials (B). Shaded regions in A and B represent standard error of the mean measured across sessions and epochs respectively.

(3) The behavioural procedure during recording is not fully explained. Do trial types alternate within the same session by blocks? How many trials are within a block? Is there any relation between trial alternation and the switch in the coding scheme observed in a large subset of the grid cells?

We agree this wasn’t sufficiently clear in the previous version of the manuscript. Trial types were interleaved in a fixed order within each session. We have updated the results and methods sections to provide details (see responses above).

(4) From the examples in Figure 2 it seems that firing fields tend to shift toward the start position. Is it the case in all cells? Could this reflect some reorganisation at the network level with cells signalling the starting as time progresses?

This is inconsistent between cells. To make this variability clear we have included additional examples of spiking profiles from different grid cells (Figure 2 - 5). Because quantification of the phenomena would not, so far as we can tell, help distinguish our core hypotheses we have not included further analyses here.

(5) Are grid cells with different coding properties recorded in different parts of the MEC? Are there any differences between these cell categories in the 2D map?

The recordings we made are from the dorsal region of the MEC (stated at the start of the results section). We don’t have data to speak to other parts of the MEC.

Minor:

There are very few grid cell examples that repeat in the different figures. I would suggest showing more examples both in the main text and supplementary material.

We have now provided multiple additional examples in Figures 2, 4 and 5. Grid cell examples repeat in the main figures twice, in both cases only when showing additional examples are shown from the same recording session (Figure 2A example #1 with Figure 5C, Figure 3E with Figure 4A). Further similar repeats are found in the supplemental figures (Figure 3D with Figure 5, Figure Supplement 2A, Figure 3C with Figure 5, Figure Supplement 2F).

Fig1 A-B shows the predictions in a 1D track based on distance or position coding. The A inset represents the modification of field distribution from a 2D arena to a 1D track, as performed in this study. The inset B is misleading since it represents the modifications expected from a circular track to a 1D track as in Jacob et al 2019, that is not what the authors studied. It would be better to present either the predictions based on the present study or the prediction based on previous studies. In that case, they should mention the possibility that the 1D map is a slice of the 2D map.

The goal of Figure 1A-B is to illustrate predictions (right) based on conclusions from previous studies (left). Figure 1A shows predicted 1D track firing given anchoring to the environment typically observed in grid cell studies in 2D arenas. Figure 1B shows predicted 1D track firing given the firing shifting firing patterns observed by Jacob et al. in a circular 2D track. To improve clarity, we have modified the legend to make clear that the schematics to the right are predictions given the previous evidence summarised to the left. As we outline above, the critical prediction relates to whether the representations anchor to the track. Whether the 1D representation is a perfect slice isn’t relevant to the hypotheses tested and so isn’t included in the schematic (see comments above).

Author Response

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

eLife assessment

This study is valuable as it sheds light on the pivotal role played by alterations in glycan metabolism within chondrocytes in the onset of cartilage degeneration and early onset of osteoarthritis (OA) through the process of hypertrophic differentiation of chondrocytes, giving insights into the identification of nascent markers for early-stage OA. Although the methods, data, and analyses broadly support the claims, the data shown by the authors are incomplete because the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs has not been fully elucidated. The authors' deductions stand to gain further credence through undertaking additional experiments aimed at analyzing the mechanisms underlying the changes in glycometabolism in cartilage, such as the meticulous identification of the target glycan molecules bearing core fucose and analysis of endochondral ossification in cartilage-specific Fut8 KO mice.

We wish to express our strong appreciation to the Reviewer for his or her insightful comments on our paper. We feel the comments have helped us significantly improve the paper. In particular, we wish to acknowledge the Reviewer’s highly valuable comments on the effect of Fut8 on endochondral ossification.

Reviewer #1 (Public Review): :<br /> Summary:

This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:

This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

We thank reviewer #1 for their interest in our work and their overall positive report.

Weaknesses:

More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

To understand the mechanisms of cartilage degeneration induced by changes in glycometabolism, we attempted additional experiments using rescue experiments with external administration of TGF-β. We had shown that the addition of mannosidase to an organ culture system of normal wild-type mouse cartilage increased TGF-β gene expression from 6 hours (Fig. 3E) and that TGF-β expression was even suppressed in chondrocytes from Fut8 cKO mice (Fig. 4D). In addition to these results, an early OA model in which mannosidase is added to the cartilage was used to test the effect of exogenous TGF-β. As a result, under TGF-β treated conditions, no degenerative changes occurred when high-mannose type N-glycans were trimmed, and proteoglycan leakage during the recovery period was significantly reduced. This was considered to be a very useful finding and it was decided to include the experimental results in Figure 4F, rather than making them supplement data.

Reviewer #2 (Public Review):

Summary:

This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:

This paper is interesting in the clinical field, but unfortunately, the data is mostly descriptive and does not have a significant impact on the scientific community in general.

We thank reviewer #2 for their interest in our work and their overall positive report. In response to your comment about our attempts to show that glycan changes occur at the precursor stage of cartilage substrate degeneration and that this glycosylation is also what triggers substrate degeneration, we would like to add that reversing cartilage substrate degeneration is a very ambitious challenge. We are currently in the preparatory stages of characterizing the appropriate glycan-substrate relationships to 'rescue' cartilage tissue from degeneration, and we hope to use this approach to provide information on the pre-developmental stages of OA.

Weaknesses:

If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.

As the increase in NO was observed in the organ culture system and cartilage is a tissue without vascular invasion, we thought that the involvement of immune cells could be excluded. On the other hand, our research group has reported that chondrocytes themselves have inflammatory circuits (Ota et al., Arthritis Rheum. 2019. DOI:10.1002/art.41182), but as we did not find increased expression of NF-κB, an indicator of inflammatory amplifier activation, we concluded that inflammation was not involved in this study.

It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alpha-mannosidase administration increases core fucose.

The report by Toegel et al. that the synthesis of complex-type N-glycans (Man2a1, Mgat2) is predicted in human OA chondrocytes along with the expression of Fut8 also led to the expectation that administration of α-mannosidase would increase core fucose. However, there was no conclusive evidence that administration of α-mannosidase increased core fucose; in 1987, Vignon et al performed an enzyme assay on experimental OA cartilage (rabbit ACLT model) and showed that mannosidase was very high in operated joints and that its activity increased and decreased with the severity of fibrosis in the cartilage. The results suggest that glycoprotein hexose degradation is an early transient event in the enzymatic process of cartilage destruction. These findings led to the conception of a novel 'pre-OA model' in which mannosidase is added to the joint. The present study is valuable in its demonstration that glycometabolism is a driver of degeneration.

(see manuscript REF. 25, 9)

Toegel et al., Arthritis Res. Ther. 2013. DOI:10.1186/ar4330

Vignon et al., Clin Rheumatol. 1987. DOI:10.1007/BF02201026

Reviewer #3 (Public Review):

Summary:

In the manuscript "Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis", the authors investigate the glycan structural changes in the context of pre-OA conditions. By mainly conducting animal experiments and glycomic analysis, this study clarified the molecular mechanism of N-glycan core fucosylation and Fut8 expression in the extracellular matrix resilience and unrecoverable cartilage degeneration. Lastly, a comprehensive glycan analysis of human OA cartilage verified the hypothesis.

Strengths:

Generally, this manuscript is well structured with rigorous logic and clear language. This study is valuable and important in the early diagnosis of OA patients in the clinic, which is a great challenge nowadays.

We thank reviewer #3 for their interest in our work and their mainly positive report. This is precisely the purpose of our study, as we are primarily interested in the detection of conditions prior to the onset of OA.

Weaknesses:

I recommend minor revisions:

(1) I would suggest the authors prepare an illustrative scheme for the whole study, to explain the complex mechanism and also to summarize the results.

We would like to thank the reviewer for this comment and have created a new Figure 7 for the overall study scheme.

We included the following statement in the opening discussion part:

"The objective of this work was to provide novel and translational insights into pathogenesis of OA associated with changes in glycan structure. A graphical abstract summarizing our findings is shown in Fig. 7." (line199-201, p9)

(2) Including but not limited to Figures 2A-C, Figures 3A and C, Figure 4B, and Figures 5A and D. The texts in the above images are too small to read, I would suggest the authors remake these images.

The font size of the figures has been reviewed and revised throughout.

(3) The paper is generally readable, but the language could be polished a bit. Several writing errors should be realized during the careful check.

Thanks to your suggestion, I have noticed several writing errors. In addition, we have had the manuscript rewritten by an experienced scientific editor, who has improved the grammar and stylistic expression of the paper.

(4) As several species and OA models were conducted in this study, it would be better if the authors could note the reason behind their choice for it.

The authors agree with the reviewer's argument that since several species and OA models were performed in this study, it would be better to note the reason for their choice.

We first attempted to inject mannosidase into rabbits, matching the animal species to a previous paper showing that N-glycans are altered prior to degeneration of the cartilage matrix. Next, we checked whether similar changes occur in mouse cartilage after mannosidase treatment, assuming that we would verify this in genetically engineered mice. We then used the integrated glycome in human cartilage to see if the corefucosylation phenomenon detected was conserved across species.

For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA.

We included the following statements in each Methods part:

"We injected mannosidase into rabbit knee joints in accordance with a previous paper showing that N-type glycans are altered prior to cartilage matrix degeneration." (line289-290, p12)

"Organ culture experiments in mice were established to study the effects of mannosidase on articular cartilage without immunoreaction and in anticipation of later candidate gene research using transgenic mice." (line326-328, p14)

"To determine whether the glycosylation detected is conserved across species, we analyzed the total glycome in human cartilage." (line407-408, p17)

We included the following statements in the Discussion part:

"For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA." (line254-257, p11)

Reviewer #1 (Recommendations For The Authors):

(1) The cited literature states that core fucosylation by FUT8 has a chondroprotective effect via the TGF-β pathway and that the loss of these chondroprotective effects in Fut8 led to cartilage degeneration, but these need to be proven by experiment.

We agree that corefucosylation and the TGF-β signaling pathway are important lines of investigation. We have now acknowledged this and added in the revised manuscript that additional experiments have shown that TGF-β restores the protective effects of Fut8 cKO cartilage by external administration.

We included the following statements in the Results part:

"To evaluate whether TGF-β1 decreases cartilage degeneration after mannosidase stimulation, TGF-β1 was exogenously added to Col2-Fut8−/− cartilage in the presence of α-mannosidase stimulation for 24 h. The samples treated with TGF-β1 leaked significantly less PG following mannosidase stimulation compared to samples not treated with TGF-β1 (Fig. 4F)." (line143-147, p6-7)

We included the following statements in the Discussion part:

"Here, the exogenous addition of TGF-β1 rescued them from cartilage degeneration." (line274-275, p12)

(2) There are skeletal differences in cartilage-specific Fut8 KO mice compared to WT, and the effect of Fut8 on endochondral ossification should also be analyzed.

We agree that Fut8 is associated with various endochondral ossification processes (for example by the TGF-β signaling pathway). Moreover, we would like to thank the reviewer for the proposed experiment.

The growth curve was normal at birth, with differences beginning around weaning (~3 w for mice). Therefore, we evaluated the epiphyseal line of 4-week-old mice stained with toluidine, type 10 collagen, and proliferating cell nuclear antigen. This is similar to the epiphyseal growth plate phenotype of Smad3ex8/ex8 mice by Yang et al. and is consistent with the finding that Smad3 deficiency does not affect chondrogenesis during developmental stages, but the hypertrophic zone is increased in 3-4 week-old Smad3 KO mice. Chondrocytes in Fut8 cKO mice were suppressed of Tgf-β expression (Fig. 4D), suggesting that inhibition of TGF-β signaling, which is suppressive for late hypertrophic chondrocyte differentiation, led to the increased height of the hypertrophic zone.

The results suggested that the growth plate of Fut8 cKO mice had an enlarged hypertrophic layer and decreased primary trabecular bone. Because these results have important implications for the content of the paper, we have included the staining results in Figure 5 and added a graph quantitatively assessing the extent of the hypertrophic zone as supplementary Figure S6.

We included the following statement in the Results part:

"To assess the role of FUT8 in endochondral ossification, we performed an epiphyseal plate analysis of 4-week-old Col2-Fut8−/− mice. This uncovered a significant enlargement of the zone of hypertrophic chondrocytes in the growth plates of the long bones of Col2-Fut8−/− mice compared to controls (Fig. 5C, S6 Figure)." (line154-158, p7)

We included the following statement in the Discussion part:

"The high-mannose/corefucosylation relationship estimated function to maintain formed cartilage. In endochondral ossification, the Fut8 cKO growth plate had an enlarged hypertrophic zone and reduced primary spongiosa because it is involved in the next process of cartilage replacement into bone rather than the process of cartilage formation." (line214-217, p9)

Literature mentioned above (not included in manuscript):

Yang X, et al. TGF-beta/Smad3 signals repress chondrocyte hypertrophic differentiation and are required for maintaining articular cartilage. J Cell Biol. 2001;153(1):35–46.

(3) The DMM model analysis is performed with n=5 for each group. Please consider if the sample size is sufficient.

In the literature, the sample sizes for DMM models have varied in previous studies (Doyran et al., n=5; Liao et al., n=6-7; Ouhaddi et al., n=8). Therefore, we performed a preliminary test of the DMM in WT and Flox mice with n=3 each and a power analysis with the outcome set to the OARSI score at 8 weeks. This resulted in n=4. The sample size for this study was increased to n=5 to account for attrition. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately.

Literature mentioned above (not included in manuscript):

(1) Doyran B, Tong W, Li Q, Jia H, Zhang X, Chen C, et al. Nanoindentation modulus of murine cartilage: a sensitive indicator of the initiation and progression of post-traumatic osteoarthritis. Osteoarthr Cartil. 2017;25(1):108–17.

(2) Liao L, Zhang S, Gu J, Takarada T, Yoneda Y, Huang J, et al. Deletion of Runx2 in Articular Chondrocytes Decelerates the Progression of DMM-Induced Osteoarthritis in Adult Mice. Sci Rep. 2017 24;7(1):2371.

(3) Ouhaddi Y, Nebbaki SS, Habouri L, Afif H, Lussier B, Kapoor M, et al. Exacerbation of Aging-Associated and Instability-Induced Murine Osteoarthritis With Deletion of D Prostanoid Receptor 1, a Prostaglandin D2 Receptor. Arthritis Rheum. 2017;69(9):1784–95.

Reviewer #2 (Recommendations For The Authors):

This paper is suitable for publication in clinical Journals related to osteoarthritis and cartilage.

Identification of core fucosylated glycans from chondrocytes is essential for this type of paper.

We mentioned that we had identified similar corefucosylated glycans in isolated mouse chondrocytes from the cartilage (line117-118, p5), but we have now also added the following to the subtitle of the Results section to avoid any potential confusion: "Corefucosylated N-glycan was formed in resilient cartilage and its isolated chondrocyte" (line109, p5)

Thank you again for your comments on our paper. We trust that the revised manuscript is suitable for publication.

Author Response

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

eLife assessment

This manuscript describes fundamental single-molecule correlative force and fluorescence microscopy experiments to visualize the 1D diffusion dynamics and long-range nucleosome sliding activity of the yeast chromatin remodelers, RSC and ISW2. Compelling evidence shows that both remodelers exhibit 1D diffusion on bare DNA but utilize different mechanisms, with RSC primarily hopping and ISW2 mainly sliding on DNA. These results will be of interest to researchers working on chromatin remodeling.

Reviewer #1 (Public Review):

Single-molecule visualization of chromatin remodelers on long chromatin templates-a long sought-after goal-is still in its infancy. This work describes the behaviors of two remodelers RSC and ISW2, from SWI/SNF and ISWI families respectively, with well-conducted experiments and rigorous quantitative analysis, thus representing a significant advance in the field of chromatin biology and biophysics. Overall, the conclusions are supported by the data and the manuscript is clearly written. However, there are a few occasions where the strength of the conclusion suffers from low statistics. Some of the statements are too strong given the evidence presented.

We thank the reviewer for the thorough and considerate review of our manuscript. We have increased the statistics when possible and have toned down the conclusions wherever further experimentation to improve statistics could not be done expeditiously.

Specific Comments:

(1) It is confusing what is the difference between the "non-diffusive" behavior of the remodeler upon nucleosome encounter and the nucleosome-translocating behavior in the presence of ATP. For example, in Figure 3F, readers can see a bit of nucleosome translocation in the first segment. Is the lower half-life of "non-diffusive" ISW2 with ATP on a nucleosome array because it is spending more time translocating nucleosomes? The solid and dashed green lines in Figure 3F and 3G are not explained. It is also not explained why Figure 3H and 3I are fit by double exponentials.

We thank the reviewer for calling upon us to clarify these points. In both the case of translocation and stable non-translocating colocalization, the chromatin remodeler is marked as “non-diffusive” because the molecule is not moving quickly enough to be detected by our rolling-window (20 frames considered) diffusion coefficient analysis. We have updated the text to point out the translocation that is occurring in the panels indicated and noted that this type of motion is not detected by our automated analysis. Thus, translocation events were manually segmented for analysis from kymographs; a note of this was added to the results section (Results section # 1; Paragraph # 2).

To address the question of whether the half-life of “non-diffusive” ISW2 with ATP on the nucleosome array is because of increased time spent in translocation, we have computed the percentage of “non-diffusive” time spent translocating in the presence of ATP for both remodelers; for ISW2, 14% of “non-diffusive” times are translocation whereas for RSC, 28% of “non-diffusive” times are translocation. Given that these percentages are not negligible, the reviewer helped identify an important parameter that better describes the effects of ATP hydrolysis on nucleosome binding for ISW2. In addition, we computed and compared the half-life of translocation times for both remodelers to the “non-diffusive” times and found that RSC translocates with a half-life of 20 s (similar to the half-life of “non-diffusion”) whereas ISW2 translocates with a half-life of 17 s (longer than the half-life of “non-diffusion”). We believe that this new information improves understanding of the role of ATP hydrolysis in turning over ISW2-nucleosome binding interactions, which result in the shorter “non-diffusive” lifetime as well as the shorter and more rarely observed ISW2 translocation events. We have updated the text to include these observations and our interpretation (Results section # 3; Paragraph # 3). As was already included in the text (Results section # 3; Final Paragraph), we speculate that this behavior may be due to a hydrolysis-dependent turnover of the ISW2-nucleosome bound state and refer the reader to Tim Richmond’s 2004 EMBO paper titled “Reaction cycle of the yeast Isw2 chromatin remodeling complex” in which bulk experiments show that ATP hydrolysis affects ISW2-nucleosome bound lifetimes.

We thank the reviewer for also pointing out where details were missing from the figure legend and results section regarding Figure 3. We have added a description of the dashed and solid lines to the figure legend (Figure 3; Legend). We have also described why Figures 3H and I are fit to double exponentials to the results section (Results section # 3; Paragraph # 2).

(2) What is the fraction of 1D vs. 3D nucleosome encountered by the remodelers? This is an important parameter to compare between RSC and ISW2.

We thank the reviewer for raising this point. We agree that this is an important parameter to compare between RSC and ISW2; knowledge of this parameter would enable quantitative predictions to be made from our data regarding target localization efficiency increases owed to 1D scanning for each remodeler. We regretfully could not quantify this due to technical limitations of our measurements. A note about this limitation along with an explanation for why we were unable to quantify this parameter have been added to the main text (Results section # 3; end of Paragraph # 1).

(3) A major conclusion stated repeatedly in the manuscript is that nucleosome translocation by a remodeler is terminated by a downstream nucleosome. But this is based on a total of 4 events. The problem of dye photobleaching was mentioned, which is a bit surprising considering that the green excitation was already pulsed. The authors should try to get more events by lowering the laser power or toning down the conclusion that translocation termination is prominently due to blockage by a downstream nucleosome. Quantifying the translocation distances before termination, in addition to the durations (Figure 4G and 4H), would also be helpful.

We thank the reviewer for these observations and feedback. We agree that only 4 observations of direct visualization of remodeler translocation termination by a downstream nucleosome is a small n-value, and have chosen to omit presentation of these rare events in the manuscript.

(4) The claim on nucleosome translocation directionality is also based on a small number of events, particularly for RSC. 6/9 is hardly over 50% if one considers the Poisson counting error (RSC was also found to switch directions.) If the authors would like to make a firm statement to support the "push-pull" model, they should obtain more events.

We thank the reviewer for this critique and agree with the reviewer’s concern. In addition to adding data from two additional experimental replicates of RSC nucleosome translocation (which had the smaller n-value), we have also re-evaluated all events containing translocation for additional evidence in support or against the “push-pull” model. Previously we were only considering events where 1D diffusion on DNA leads immediately to translocation. Now we add the following categories to the count: (1) events where translocation terminates with the remodeler dissociating from the nucleosome and performing a 1D diffusive search, (2) events where 1D diffusion on DNA leads to association with a nucleosome and after a paused colocalization we observe translocation, and (3) the inverse scenario of (2) (see schematics in Figure 5 – figure supplement 1). These new results, detailed below, are now included in place of the older results in (Results Section # 5; Paragraph # 2). Furthermore, we toned down our argument and clarified that a larger n-value would be needed to be definitive, especially since we observe RSC switching directions, as the reviewer points out.

By aggregating in new RSC data and using only events where 1D diffusion leads immediately to translocation, we observe 10/12 events in support of the “push” model. If we include these other categories in addition to aggregating the previous data with the new data, a total of 20/25 events are in support of the “push” model. For RSC, the breakdown in the other categories was as follows: (1) 7/10 events, (2) 1/1 events with a paused time of 5 seconds, and (3) 2/2 events with a paused time of 36 and 50 seconds.

For ISW2, we had previously reported 12/13 events where 1D search lead immediately to translocation. After combing through the data a second time, we decided to omit two events which were less clear; Now we report 10/11 events in support of the “pull” model from this initial category. If we include these other categories in addition to the original, a total of 19/21 events are in support of the “pull” model. For ISW2, the breakdown in the other categories was as follows: (1) 4/4 events, (2) 4/4 events with pause times of 44, 27, 29, and 8 seconds, (3) 1/2 events with paused times of 5 and 19 seconds.

(5) At 5 pN of tether tension, the outer wrap of nucleosomes is destabilized, which could impact nucleosome translocation dynamics. Additionally, a low buffer flow was kept on during data acquisition, which could bias remodeler diffusion behavior. The authors should rule out or at a minimum discuss these possibilities.

We thank the reviewer for raising the important point regarding outer wrap destabilization of the nucleosome occurring at 5pN of tension. We have added an additional section to the discussion that reviews the literature on tension effects on nucleosome stability as well as what is currently known of the effects of tension on remodeler translocation on DNA (Discussion Paragraph # 3). While we cannot exclude the possibility that the 5pN of tension used in this study is a causative factor of the observed fast speed or high processivity nucleosome translocation that we report, we believe that with the modifications made to the text to emphasize to the reader of these possibilities, the reader can draw informed conclusions on the significance of our findings. The topic of force effects on remodeling outcomes is an interesting subject for the future.

We apologize that the experimental details on buffer flow used during imaging was unclear in our initial submission; we do not have buffer flowing during imaging, rather the buffer containing protein is flowed over the DNA at low pressure just prior to imaging. The flow is completely stopped before the DNA or nucleosome array is stretched to 5pN of tension for imaging (See Methods section: Single Molecule Tracking and Analysis).

Reviewer 1 (Recommendations For The Authors):

(1) The figure panels could be better arranged to focus on the main messages of the paper.

(i) Figure 3C-E should go to a supplemental figure.

We thank the reviewer for this helpful suggestion. As recommended, we moved Figure 3C to the supplemental figure as this panel did not pertain to the main message of the paper.

(ii) Figure 4 could be split into two figures, one characterizing processive nucleosome translocation (4C, D, G, H, I, J, K, and relevant panels in S4), and the other showing the differential directionality of each remodeler (4E, F, L, and relevant panels in S4).

We thank the reviewer for their suggestions that help better organize our presentation of the data. As the reviewer suggests, we split figure 4 into two figures: figure 4 which now focuses on translocation characterization and figure 5 which now focuses on the differential directionality of each remodeler.

(iii) The nucleotide condition should be clearly indicated in the figures or legends. For example, it is unclear if the data in Figure 2 were generated with or without ATP.

We thank the reviewer for taking note of this. We have added clear indications of the nucleotide condition to figures where this is relevant, including in Figure 2 as indicated.

(iv) There are many cartoon panels, and some are redundant (e.g., Figure 1A and 1B, Figure 3A and 3B).

We thank the reviewer for bringing up this point. We agree that some cartoons are redundant. We have eliminated Figure panel 1B and Figure panel 3A of the original figures from the new figures.

(2) The last paragraph of the Results section should be moved to Discussion. This paper did not directly address the effects of RSC/ISW2 on NDR length.

We thank the reviewer for this suggestion. We agree and have moved the last paragraph of the Results section to the Discussion..

(3) There are some typos in the text. For example, "Of the two main types of 1D diffusion, hopping and sliding" is not a complete sentence.

We thank the reviewer for catching this typo and bringing our attention to others. Upon a more careful proofreading of the text and figures we have caught and amended this and other typos.

(4) What are the green lines in Figure S1F?

We thank the reviewer for asking this question. The green lines were meant emphasize how the percentage of traces in the majority high diffusion category increases for RSC but not for ISW2 in response to increases in the KCl concentration. Since this was confusing, we removed these green lines.

Reviewer # 2 (Public Review):

Summary:

The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion, and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest to the scientific community, and will undoubtedly spark exciting future research.

Strengths:

The mechanism of target search for chromatin-interacting protein machines is a 'hot' topic, and this manuscript provides extremely important and timely new information about how RSC and ISW2 find the nucleosomes they slide. Intriguingly, although both remodelers analyzed in this study can diffuse along DNA, the diffusion mechanisms are substantially different, with extremely interesting mechanistic implications.

The strong directional preference in nucleosome sliding by ISW2 dictated by the direction it approaches the nucleosomes from during 1D sliding on DNA is a very intriguing result with interesting implications for the regulation of nucleosome organization around promoters. It will be of great interest to the scientific community and will undoubtedly inspire future research.

Relatively little is known about nucleosome sliding at longer ranges (>100bp), and this manuscript provides a unique view into such sliding and also establishes a versatile methodology for future studies.

Weaknesses:

All measurements were conducted at 5pN tension, which induces unwrapping of the outer DNA gyre from nucleosomes. This could potentially represent a limitation for experiments involving nucleosomes, since partial nucleosome unwrapping could affect the behavior of remodelers, especially their sliding of nucleosomes.

We thank the reviewer for succinctly summarizing the strengths and weaknesses of our study. We have changed the Discussion to better review the literature on the effects of 5pN of tension on nucleosome wrapping and have more clearly presented the limitations of our studying owing to our conducting measurements at 5pN of tension. In doing so, we have tried to emphasize the strengths of our study identified by the reviewer and better inform the reader of the weaknesses.

Reviewer #2 (Recommendations For The Authors):

Although not required, nucleosome sliding data under lower tensions (e.g., <=2pN) could be a valuable addition to the manuscript. Indeed, to my knowledge, there is no data on force-dependent rates of nucleosome sliding, so a conclusive demonstration of changes in remodeling rate with tension would be an exciting new result and might be discussed in the context of a potential tension in chromatin. If such experiments cannot readily be added, the authors could alternatively discuss this potential limitation in more detail.

We thank the reviewer for this suggestion. We agree that adding data at lower tensions (<= 2pN) would have been valuable. Due to time constraints, this will be the subject for the future. We agree that knowledge of the effects of tension would be especially interesting in light of the possibility that tension on chromatin in cells may be affecting remodeler function. We have added a discussion of this potential significance of future work to the discussion (Discussion Section; Paragraph # 3). We have also elaborated on the potential limitation of only conducting measurements at 5pN to the discussion (Discussion Section; Paragraph # 3), as the reviewer recommends.

The quantitative implications of the proposed mechanism for targeting ISW2 and RSC towards +1 and -1 nucleosomes are highly interesting. To further strengthen the mechanistic implications, the authors could consider quantitatively analyzing how the observed 1D diffusion would affect the probabilities of binding to +1 and -1 versus to other nucleosomes.

We thank the reviewer for their thoughtful suggestion. While we would have liked to present a final quantitative model that integrates the experimental parameters on 1D diffusion that we present in this study with the parameters extracted from live cell single particle tracking studies, there are key parameters for model building that are missing from our study, due to technical limitations. Namely, we were not able to quantify the fraction of 1D vs 3D nucleosome encounters by remodelers, because the majority of the protein that we image has been bound before the start of imaging; very few proteins bind the nucleosome arrays after the start of imaging as the protein concentration in the imaging chamber is very low. This makes observing binding directly to a nucleosome a very rare event, especially due to the sparse density of nucleosomes (~10) on the array (~50,000 kb).

The low-diffusion state is intriguing - could the authors speculate about the nature of this state?

We thank the reviewer for the question. We had added some speculation about the nature of the low-diffusion state to the results section (Results Section # 1; Paragraph 2). One thought that we have is that this may be due to more stable interactions made between remodelers and free DNA when they become trapped in a conformation state that binds more tightly to DNA. Conformational changes may result in different scanning speeds for chromatin remodelers; e.g. SWR1 was shown to scan DNA quicker when bound to ATP (Carcamo, C. et al. eLife 2022). Another possibility is that certain sequences due to their intrinsic curvature, for instance, or their AT-content may trap the remodeler which may make more contacts with the DNA at these sites.

Minor points:

Information on the labeling efficiencies for the remodelers would be helpful.

We thank the reviewer for pointing this out. We assessed labeling saturation by running gels of remodeler labeling with increasing molar ratios of dye to protein and did not observe increased labeling efficiency above the molar ratio used for proteins imaged in our study (see added Figure 1 – figure supplement 1, panel A). From this, we assessed that we have high protein labeling efficiency. We could not assess the labeling efficiency using the standard absorbance method as the extinction coefficient for JFX650 was measured with 1% v/v TFA (PMCID: PMC8154212) which is not compatible for use in assessing our protein labeling efficiency in an aqueous buffer.

How were the experimental conditions adjusted for two-color diffusion experiments in order to optimize the probability of observing two remodeler molecules with different labels at the same time.

We thank the reviewer for this clarifying question. To image both remodelers on the same DNA, we combined the remodelers using the same concentrations that produced single molecule densities when the remodelers were imaged separately. We have clarified this point in the Methods section: “Bimolecular Remodeler-Remodeler Imaging and Interaction Analysis”.

The authors should check the figures for consistency of labeling and provide definitions for abbreviations used in them (e.g. CDF and PDF).

We thank the reviewer for catching inconsistencies in labeling in our figures. We have updated the figures such that there is consistent labeling throughout. We have also provided definitions for abbreviations such as Cumulative Distribution Function (CDF) and Probability Distribution Function (PDF) in the figure legends where applicable.

In the section "Remodeler-remodeler collisions during 1D search" (4th line from the end) reference to Fig3D seems to be out of place.

We thank the reviewer for catching this typo. We have reworded this section such that each figure panel can be discussed sequentially, eliminating this out of place reference to Fig 3D.

Author Response

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

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further improve the manuscript. Following the suggestions of the reviewers we have run a number of new simulations including mutations of the PIP binding residues and with an elastic network allowing more mobility of the linker. Together these excellent ideas have allowed us to strengthen the conclusions of the study. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Public Review):

Summary:

Here, the authors were attempting to use molecular simulation or probe the nature of how lipids, especially PIP lipids, bind to a medically-important ion channel. In particular, they look at how this binding impact the function of the channel.

Strengths:

The study is very well written and composed. The techniques are used appropriately, with plenty of sampling and analysis. The findings are compelling and provide clear insights into the biology of the system.

Weaknesses:

A few of the analyses are hard to understand/follow, and rely on "in house" scripts. This is particularly the case for the lipid binding events, which can be difficult to compute accurately. Additionally, a lack of experimental validation, or coupling to existing experimental data, limits the study.

Our analysis scripts have now been made publicly accessible as a Jupyter notebook on Github https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project

It is my view that the authors have achieved their aims, and their findings are compelling and believable. Their findings should have impacts on how researchers understand the functioning of the Nav1.4 channel, as well as on the study of other ion channels and how they interact with membrane lipids.

Reviewer #2 (Public Review):

Summary:

Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete and lacks the expected confirmatory studies which are relevant to such proposals.

Strengths:

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation.

Weaknesses:

However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Following the reviewers suggestion we have conducted new simulations demonstrating that there are many fewer protein-PIP interactions after mutating the positive residues as shown in the new Supplementary Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However, as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewer’s suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7, PIP binding to VSDI-III can impair activation of the channel.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

Done as described above

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it.

Done as described above

Minor points:

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP bound the channel is expected to recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Public Review):

Summary:

This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

Strengths:

Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipidprotein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

Weaknesses:

Although the set of current simulations and analysis supports the conclusions drawn nicely, there are some limitations imposed by the authors on the course-grained simulations. If those were not imposed, it would have allowed for an even richer set and more thorough exploration of the protein-lipid interactions. The Martini 2 force field indeed cannot change secondary structure but if run with a properly tuned elastic network instead of backbone restraints, the change in protein configuration can be sampled and/or some adaptation of the protein to the specific protein environment can be observed. Additionally, with the 4to1 heavy atoms to a bead mapping some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored.

We thank the reviewer for their excellent suggestions and have run new simulations with an elastic network instead of backbone restraints which have generated new insights. Indeed, as shown in the new panel Fig 4E, the new data allows us to demonstrate that the presence of PIP in the proposed binding site stabilises binding of the DIII-DIV linker to the inactivation receptor site, strengthening the conclusions of the paper.

We thank the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 line 16 to reflect this. We have not run additional CG simulations with each of these parameters but use the all-atom simulations to examine the interactions of phosphates at specific positions.

In our atomistic simulations, we backmapped both PI(4,5)P2 and PI(4)P in the binding site to study their specific interactions. We chose to focus on PI(4,5)P2 given its physiological significance. However, we agree that differences in binding with PI(3,4)P2 would be interesting and warrants future investigation. We also note that the newer Martini3 forcefield would be useful in further work to differentiate between PIP subspecies interactions.

Detailed Comments

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further strengthen the manuscript. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Recommendations For The Authors):

I don't have many suggestions for the manuscript, just a few text edits. Of course, experimental analysis would bolster the claims made in the text, but I don't believe that this is necessary, given the quality of the data.

I understand the focus on the PIP lipids, but it's a shame that the high binding likelihood of glycosphingolipid isn't considered or analysed in any way. This is an especially interesting lipid from the point-of-view of raftlike membrane domains. Given the potential role of raft-like domains in sodium channel function, I feel this would be worth a paragraph or two in the discussion.

We thank the reviewer for bringing our attention to this interesting point. Glycolipids accumulate around Nav1.4 in our complex membrane simulations, however, given reports that carbohydrates tend to interact too strongly in the Martini2.2 forcefield (Grünewald et al. 2022, Schmalhorst et al. 2017) and there are no specific residues on Nav1.4 that interact preferentially with glycolipid species, we chose not to focus on this. However, we have noted that interactions with other lipids deserve further attention in our revised discussion.

The analyses have been run using Martini 2. I don't suggest the authors repeat using the Martini 3 force field, but some mention of this in the discussion would be good.

We have added the following statement to the discussion: “Our coarse grain simulations were carried out using the Martini2.2 forcefield, for which lipid parameters for many plasma membrane lipids have been developed. We expect that future investigations of lipid-protein interactions will benefit from use of the newer, refined Martini 3 forcefield (Souza et al. 2021) as parameters become available for more lipid types.

This might just be an oversight, but no mention is made of an elastic network applied to the backbone beads.

Lack of a network has been known to cause the protein to collapse, so if this is missing, I'd like to see an RMSD to show that the protein dynamics are not compromised.

While no elastic network was used in our original CG simulations, weak protein backbone restraints (10 kJ mol-1 nm-2) used in our simulations allowed us to maintain the structure while allowing some protein movement. However, following the suggestion of reviewer 3, we conducted additional simulations with an elastic instead of backbone restraints as described in the results on page 9 line 30-37 (and in Fig 4E) of the revised manuscript.

Minor

•In Fig 3B, are these lipids binding to the channel at the same time? And therefore do the authors see cooperativity?

The Fig 3B caption has been amended in the revised manuscript to read “Representative snapshots from the five longest binding events from different replicates, showing the three different PIP species (PIP1 in blue, PIP2 in purple and PIP3 in pink) binding to VSD-IV and the DIII-IV linker.” We cannot comment on PIP cooperativity based on these simulations shown in Fig 3, due to the artificially high concentrations used here; however, in model complex membrane simulations we see co-binding of PIPs at the binding site. This is likely due to PIP’s ability to accumulate together and the high density of positively charged residues in the region, attracting and supporting multiple PIP bindings.

•What charges were used for the atomistic PIP lipids? Does this match the CG lipids?

We used the CHARMM-GUI PIP parameters for the atomistic simulations. SAPI24 (PIP2) has a headgroup charge of –4e which is one less negative charge than the CG PIP2; whereas SAPI14 (PIP1) has a charge of –3e which is the same as the CG PIP1. We have explicitly included this charge information in the updated Methods of the manuscript (on page 15-16).

•Line 259-260: "we performed embedded three structures"

Corrected in the revised manuscript.

•Line 272: "us" should be "µs"

Corrected in the revised manuscript.

•Line 434: kJ/mol should probably also have 'nm-2' included

Corrected in the revised manuscript.

•What charge state titratable residues were set to, and were pKa analyses done to decide this?

Charge states were assigned to default values at neutral pH. We appreciate that future studies could examine this more carefully using constant pH simulations or similar.

•It's stated that anisotropic scaling is used the AT sims - is this correct? If so, is there a reason this was chosen over semi-isotropic scaling?

Anisotropic scaling was used for the atomistic simulations allowing all box dimensions to change independently.

•I would recommend in-house analysis scripts are made available on GitHub or similar, just so the details can be seen.

Per the reviewer’s request, the Jupyter notebooks used for analysis has been made available on GitHub (https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project ).<br /> -One coarse grained notebook:

• Lipid DE

• Contact occupancy + outlier plots

• Binding duration plots

• Minimum distance plots

• Number of ARG/LYS plots

• PIP Occupancy, binding duration, gating charge residues

• One atomistic notebook:

• RMSD, RMSF and distance between IFM and its binding pocket (using MDAnalysis)

• Atomistic PIP headgroup interaction analyses and plots (using ProLIF)

As a final note, I am NOT saying this needs to be done for the current study, but I recommend the authors try the PyLipID package (https://github.com/wlsong/PyLipID) if they haven't yet, as it might be useful for similar projects they run in the future (i.e. for binding site identification, accurate binding kinetics calculations, lipid pose generation etc.).

We thank the reviewer for this suggestion and will keep this in mind for future projects.

Reviewer #2 (Recommendations For The Authors):

Lin Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete lacks the expected confirmatory studies which are relevant to such proposals.

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation. However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it. Minor points:

Following the reviewer’s suggestion we have conducted new simulations demonstrating that there are notably fewer protein-PIP interactions after performing charge neutralizing and charge reversal mutations to the positive residues as shown in the new Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewers suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7 PIP binding to VSDI-III can impair activation of the channel.

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP present the channel will recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Recommendations For The Authors):

As mentioned in the public review, overall, I am impressed with the manuscript and do think the conclusions are supported. There are, however, quite a few mistakes, mostly minor (listed below). Additionally, I do have a few questions and several extensions that could be done and I mention a few but fully realize many of those could be outside of the scope of the current manuscript.

We greatly appreciate the time taken by Reviewer 3 to carefully review our manuscript and provide detailed comments. We believe their suggestions have helped to improve our manuscript.

First comments are in general about the PIP subtype.

• In the paper you claim:

L196, "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding"

L367, "it does not distinguish between phosphate positions within each charge state (e.g. PI(3,4)P2 vs PI(4,5)P2)."

This is not true the PIP2 most commonly used in Martini 2 is from dx.doi.org/10.1021/ct3009655 and is a PI(3,4)P2 subtype. Also other extensions and alternative parameters exist for PIPs in Martini 2 e.g. http://cgmartini.nl/index.php/tools2/other-tools - Martini lipid .itp generator has all three main variants of both PIP1 and PIP2.

As described in the response to the public review we are grateful for the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 to reflect this, and clarified the parameters chosen in the methods section (page 16 line 2-3). We have not run additional CG simulations with each of these parameters in the current work but use the all-atom simulations to examine the interactions of phosphates at specific positions.

• One detail that is missing in the manuscript is some mention of the charge state of the PIPs e.g. Fig.1D does not specify and Fig.4D PIP2 looks like -2 on position 5 and -1 on position 4. Which I think fits the used SAPI24, please specify. Also, what if you use SAPI25 with the flipped charges would that significantly alter the results?

The charge state of PIP2 is -2e on the 5’ phosphate and -1e on the 4’ phosphate, using the SAPI24 CHARMM lipid parameters. We have ensured that this charge information is stated clearly in the revised manuscript in the methods section on page 16 (line 21). We considered looking at SAPI25, however we expected that it would behave quite similarly, given that the PIP headgroup can adopt slightly different poses and orientations within the binding site across replicates and does fluctuate over simulations (Fig S8). We have noted this in the revised discussion on page 14 line 15-17.

• I was very intrigued and puzzled by the lower binding of PIP3 vs PIP2 in the Martini simulations. Could it be that PIP3 has a harder time fully entering the binding site, or maybe just sampling? i.e. and its lower number of binding events is a sampling issue.

We agree with the reviewer that PIP3 is less able to access the binding site than PIP2, likely because of its larger size. This might also be why we see PIP1 binding at the location via a more buried route (since it has the smallest headgroup size). However, PIP1 does not have enough negative charge to keep it in the binding site. It seems to be a Goldilocks-like situation where PIP2 has the optimal size and charge to allow access and stable binding at the site. We also see that when PIP3 enters the binding site it leaves before the end of the simulations. While it is hard to prove statistical significance given the number of binding and dissociation events even with the high and equal concentrations of all three PIP species in the enriched PIP membrane CG simulations, the data strongly suggests preferential binding of PIP2 over PIP3.

Also the same L196 sentence as above "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding". The later part is also wrong, there are no conformational changes due to the restraints on the protein backbone, from methods "backbone beads were weakly restrained to their starting coordinates using a force constant of 10 kJ mol−1nm−2". Martini in general might have a hard time with some conformational changes and definitely cannot sample changes in secondary structure, but conformational changes can, and have on many occasions, been successfully sampled (even full ion channel opening and closing).

On a similar note, in L179 you mention "owing to the flexibility of the linker." Hose does this fit with simulation with position restraints on all backbone atoms?

We applied fairly weak restraints to the backbone only – therefore we still observe some flexibility in the highly flexible loop portion of the linker, where sidechains are able to flip between membrane-facing and cytosol-facing orientations.

However, after reading the comments from the reviewer we have run additional simulations with an elastic network rather than backbone restraints on the DIII-DIV linker which have given further insight. As seen in Fig 4E and described in the results paragraph on page 9 line 30-37 of the revised manuscript, we can see that the presence of PIP does stabilise the linker in its receptor site. To accentuate this effect, we also ran simulation of the ‘IQM’ mutant known to have a less stable fast inactivated state due to weaker binding to the receptor. Without backbone restraints we can see partial dissociation of the DIII-DIV linker from the receptor that is partially rescued by the presence of PIP.

I know the paper focuses on PIPs, also very nicely in Fig.2B and Fig. S1-2 the lipid enrichment is shown for other lipids, but why show all lipid classes except cholesterol? And, for the left-hand panels in Fig. S1-2 those really should be leaflet specific - as both the membrane and protein are asymmetric.

The depletion/enrichment of Cholesterol is shown in Fig 2B and as are the Lipid Z-Density maps and contact occupancy structures a (in row 5 of Fig S2, labeled as CL in yellow). The Z-density maps are meant to provide an overall summary of lipid distribution. The contact occupancy structures showing the transverse views and intracellular/ extracellular views provide a better indication of the occupancy across the different leaflets.

In L237 for the comparison of Cav2.2 and Kv7.1 bound to PI(4,5)P2 structures: They do agree well with the PIP1 simulations but not as much for the main PIP2 binding site. If you look in the CG simulations, is there another (not the main) PIP2 binding site at that same location (which might also be stable in AA simulations)?

In some replicates of the CG simulations, we identify stable PIP1 binding via the other orientation (i.e. the one that overlaps with the Cav2.2 and Kv7.1 structures). Since we did not directly observe any PIP2 binding events from the other orientation, we did not run any backmapped atomistic simulations with PIP2 at this position. However, the binding site residues that the PIP1/2 headgroup binds to are the same regardless of which side PIP1/2 approaches from. We would expect that PIP2 bound from the alterative position is also stable.

Two references I want to put for consideration to the authors, for potential inclusion if the authors find their inclusion would strengthen the manuscript. This one gives a good demonstration of using the same PM mixture to define lipid protein fingerprints with Martini:

https://pubs.acs.org/doi/10.1021/acscentsci.8b00143.

And this one https://pubmed.ncbi.nlm.nih.gov/33836525/ shows how Nav1.4 function could also be affected by general changes in bilayer properties (in addition to the specific lipid interactions explored here).

We thank the reviewer for bringing to our attention these two relevant references that will help to respectively substantiate the use Martini to study membrane protein-lipid interactions, as well as, why Nav channels are interesting to study in the context of their membrane environment (and also the potential implications with drugs that can bind from within the membrane). We have added these citations to the introduction and discussion.

Minor comments and fixes:

L2, Title: A binding site for phosphoinositide modulation of voltage-gated sodium channels described by multiscale simulations

The title reads very strangely to me, should it be "A binding site for phosphoinositide" ; "modulation". We thank the reviewer for this comment - title has been updated to: A binding site for phosphoinositides described by multiscale simulations explains their modulation of voltage gated sodium channels.

L25, Abstract, "The phosphoinositide PI(4,5)P2 decreases Nav1.4 activity by increasing the difficulty of channel opening, accelerating fast activation and slowing recovery from fast inactivation." Assuming this is referring to results from Gada et al JGP, 2023 should this not be "accelerating fast inactivation"?

Corrected in the revised manuscript.

L71 maybe good to write the longer version of IFM on first use e.g. Ile-Phe-Met (IFM), as to not mistake it for some random three letter acronym.

Corrected in the revised manuscript.

L109, Fig.2. Maybe change the upper and lower leaflet to intracellular and cytoplasmic leaflets (or outer / inner). In D "(D) Distribution of PIP binding occupancies (left)" something missing can I assume, for/over all lipids exposed residues. Also, for D I am a little confused how occupancy is defined as the total occupancy per residue dose not add up to 100.

The figure has been updated with intracellular and cytoplasmic leaflet labels. The binding occupancy distribution boxplot shows binding occupancies for all lipid exposed residues. In our analysis, we define contact occupancy as the proportion of simulation time in which a lipid type is within 0.7 nm of a given residue. It is possible for more than one lipid to be within this cut in any given frame – that is, both a PIP and PE can be simultaneously bound.

L160 "occurring the identified site" in the

Corrected in the revised manuscript.

L170 "PIP3 (headgroup charge: -7e) has interacts similarly to PIP1," - remove has Corrected in the revised manuscript.

L194, "reducing system size" the size does not change, I am assuming you want to say reducing the number of particles?

Corrected in the revised manuscript.

L252, Fig.6 "(B) Occupancy of all PIPs (PIP1, PIP2, PIP3) at binding site residues in the three systems" A little confusing, initially was expecting 3x3 data points per residue, maybe change to, Combined occupancy of all PIPs...

Corrected in the revised manuscript.

L253, Fig.6 D, I don't really have a good suggestion for improvement here, so this is just a FYI that this panel was very confusing for me and took some time to figure out what is shown.

We have added to the caption of Fig. 6D to try to clarify this panel.

L257, Fig.6 (F) not in bold

Corrected in the revised manuscript.

L259 "PIP binding, we performed embedded three structures of Nav1.7" something missing?

Corrected in the revised manuscript.

L272, "In triplicate 50 us coarse-grained simulations" us instead of (micro_greek)s

Corrected in the revised manuscript.

L272, that paragraph how long/many simulations only reported for the inactivated Nav1.7 system not the Nav1.7-NavPas chimera, which I am assuming is the same?

Corrected in the revised manuscript.

L297, "marked by both shortened inactivation times", can I assume this is: shortened times to inactivation (i.e. to get inactivated not times in the inactivated states)?

Corrected in the revised manuscript.

L331, "are conserved in Nav1.1-1.9 (Fig. 5D)," Fig.5C Corrected in the revised manuscript.

L353, "channel opening []" [] maybe a missing reference?

Thank you for pointing out this oversight - Goldschen-Ohm et al. has been cited here.

L394, "The composition of the complex mammalian membrane is as reported in Ingólfsson, et al. (38)." Ref 38 is the "Computational lipidomics of the neuronal plasma membrane" which indeed uses the 63 component PM but the original reference for the average 63 lipid mixture PM is dx.doi.org/10.1021/ja507832e.

Corrected in the revised manuscript.

L404, "Additionally, a model Nav1.7 with all four VSDs in the deactivated state using Modeller (40)." Something missing, e.g. was also built and simulated for ...

Corrected in the revised manuscript.

Table S1 "Disease information", I am guessing this should be Disease information; mechanism? Of the x5 entries two have mechanism, one has "; unknown significance ", one has "; unknown" maybe clarify in title and make same if unknown.

Corrected in the revised manuscript.

Table S1 and S2 have different styles.

The tables have been amended to have the same style.

Fig. S3 "for all 12 lipid types in the mammalian membrane " there are many more lipid types in a typical PM (hundreds) and 63 in the PM mixture simulated here, so maybe write: 12 lipid classes?

Corrected in the revised manuscript.

Fig.S6 PIP headgroup, can I assume that is for the bound PIP only, please specify.

Only a single PIP at the identified binding site was backmapped into all cases of atomistic simulations. We have now clarified this point in the methods, results and the FigS6 caption.

Writing of PI(4,5)P2 and PI(4)P1 most of the time use 1 and 2 as subscripts but not always (at least not in SI), also the same with Nav vs Na_v (v subscript) and even NAV (in Table S1).

Subscripts have been implemented in the updated Supplementary Information (as well as within various figures and throughout the manuscript).

Author Response

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

eLife assessment

This landmark study sheds light on a long-standing puzzle of Protein kinase A activation in Trypanosoma. Extensive experimental work provides compelling evidence for the conclusions of the manuscript. It represents a significant advancement in our understanding of the molecular mechanism of Cyclic Nucleotide Binding domains and will be of interest to researchers with interest in kinases and mechanistic studies.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Cyclic Nucleotide Binding (CNB) domains are pervasive structural components involved in signaling pathways across eukaryotes and prokaryotes. Despite their similar structures, CNB domains exhibit distinct ligand-sensing capabilities. The manuscript offers a thorough and convincing investigation that clarifies numerous puzzling aspects of nucleotide binding in Trypanosoma.

Strengths:

One of the strengths of this study is its multifaceted methodology, which includes a range of techniques including crystallography, ITC (Isothermal Titration Calorimetry), fluorimetry, CD (Circular Dichroism) spectroscopy, mass spectrometry, and computational analysis. This interdisciplinary approach not only enhances the depth of the investigation but also offers a robust cross-validation of the results.

Weaknesses:

None noticed.

Reviewer #2 (Public Review):

Summary:

This manuscript clearly shows that Trypanosoma PKA is controlled by nucleoside analogues rather than cyclic nucleotides, which are the primary allosteric effectors of human PKA and PKG. The authors demonstrate that the inosine, guanosine, and adenosine nucleosides bind with high affinity and activate PKA in the tropical pathogens T. brucei, T. cruzi and Leishmania. The underlying determinants of nucleoside binding and selectivity are dissected by solving the crystal structure of T. cruzi PKAR(200-503) and T. brucei PKAR(199-499) bound to inosine at 1.4 Å and 2.1 Å resolution and through comparative mutational analyses. Of particular interest is the identification of a minimal subset of 2-3 residues that controls nucleoside vs. cyclic nucleotide specificity.

Strengths:

The significance of this study lies not only in the structure-activity relationships revealed for important targets in several parasite pathogens but also in the understanding of CNB's evolutionary role.

Weaknesses:

The main missing piece is the model for activation of the kinetoplastid PKA which remains speculative in the absence of a structure for the trypanosomatid PKA holoenzyme complex. However, this appears to be beyond the scope of this manuscript, which is already quite dense.

We fully agree that insight into the activation mechanism and its possible deviation from the mammalian paradigm requires a holoenzyme structure revealing the details of R-C interaction. We have attempted Cryo-EM from LEXSY-produced holoenzyme, yet upscaling the purification procedures described in this manuscript have repeatedly failed in spite of numerous protocol changes and optimizations. Much more work is required to achieve this.

Reviewer #2 (Recommendations For The Authors):

Some minor points to consider for enhancing the impact of this interesting manuscript:

(1) The nucleoside affinities measured are mainly for the regulatory subunits unbound to the kinase domain. How would nucleoside affinities change when the regulatory subunits are bound to the kinase domain, which is presumably the case under resting conditions? An estimation of this change in affinity is important because it more closely relates to the variations in cellular nucleoside concentrations needed for activation.

This is an important question and we have given an indirect answer in the manuscript, but not very explicit. The EC50 values for kinase activation of the purified holoenzyme complexes are very similar or almost identical to the kD values measured by ITC with free regulatory subunits. By inference, the binding kD for the holoenzyme and for the free R-subunit cannot be very different. In addition, we have recently determined the EC50 for PKA activation in vivo in trypanosomes using a bioluminescence complementation reporter assay. The values fit perfectly to the values obtained with purified holoenzyme (Wu et al. in preparation). A sentence in Results (lines 201-203) has been added.

(2) The authors should point out that a major implication of nucleoside vs. cyclic nucleotide activation is in terms of signal termination. If phosphodiesterases (PDEs) are responsible for cAMP/cGMP signal termination, what terminates nucleoside-dependent signaling? Although the answer to this question may not be known at this stage, it is important to highlight this critical implication of the authors' study.

The mechanism of signal termination is indeed unknown so far. We speculate that some enzymes of the purine salvage pathways are differentially localized in subcellular compartments and thereby able to establish microdomains that enable nucleoside signaling. In addition, PKA subunit phosphorylations/dephosphorylations and/or protein turnover may also regulate signal termination. As an example, free PKAC1 is rapidly degraded upon depletion of the PKAR subunit by RNAi. We have now mentioned signal termination in Discussion and have revised the last part of Discussion (lines 567-602). A possible approach to monitor compartmentalized signaling would be using the FluoSTEPs technology (Tenner et al., Sci. Adv. 2021; 7: eabe4091), but adapting this to the trypanosome system will not be a short-term task.

Author Response

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

We highly thank the editor and reviewers for their time and insightful comments and suggestions. We have made revisions by performing additional experiments and analysis, and clarified the items based on the suggestions.

Reviewer #1 (Public Review):

Summary of Author's Objectives:

The authors aimed to explore JMJD6's role in MYC-driven neuroblastoma, particularly in the interplay between pre-mRNA splicing and cancer metabolism, and to investigate the potential for targeting this pathway.

Strengths:

(1) The study employs a diverse range of experimental techniques, including molecular biology assays, next-generation sequencing, interactome profiling, and metabolic analysis. Moreover, the authors specifically focused on gained chromosome 17q in neuroblastoma, in combination with analyzing cancer dependency genes screened with Crispr/Cas9 library, analyzing the association of gene expression with prognosis of neuroblastoma patients with large clinical cohort. This comprehensive approach strengthens the credibility of the findings. The identification of the link between JMJD6-mediated premRNA splicing and metabolic reprogramming in MYC-driven cancer cells is innovative.

(2) The authors effectively integrate data from multiple sources, such as gene expression analysis, RNA splicing analysis, JMJD6 interactome assay, and metabolic profiling. This holistic approach provides a more complete understanding of JMJD6's role.

(3) The identification of JMJD6 as a potential therapeutic target and its correlation with the response to indisulam have significant clinical implications, addressing an unmet need in cancer treatment.

Weaknesses:

(1) The manuscript contains complex technical details and terminology that may pose challenges for readers without a deep background in molecular biology and cancer research. Providing simplified explanations or additional context would enhance accessibility.

We have provided simplified explanations for some terminology.

(2) It would be beneficial to explore whether treatment with JMJD6 inhibitors, both in vitro and in vivo, can effectively target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells.

Unfortunately, there is no potent and selective JMJD6 inhibitors available.

Reviewer #3 (Public Review):

Summary:

Jablonowski and colleagues studied key characteristics of MYC-driven cancers: dysregulated pre-mRNA splicing and altered metabolism. This is an important field of study as it remains largely unclear as to how these processes are coordinated in response to malignant transformation and how they are exploitable for future treatments. In the present study, the authors attempt to show that Jumonji Domain Containing 6, Arginine Demethylase And Lysine Hydroxylase (JMJD6) plays a central role in connecting pre-mRNA splicing and metabolism in MYC-driven neuroblastoma. JMJD6 collaborates with the MYC protein in driving cellular transformation by physically interacting with RNA-binding proteins involved in pre-mRNA splicing and protein regulation. In cell line experiments, JMJD6 affected the alternative splicing of two forms of glutaminase (GLS), an essential enzyme in the glutaminolysis process within the central carbon metabolism of neuroblastoma cells. Additionally, the study provides in vitro (and in silico) evidence for JMJD6 being associated with the anti-proliferation effects of a compound called indisulam, which degrades the splicing factor RBM39, known to interact with JMJD6.

Overall, the findings presented by Jabolonowski et al. begin to illuminate a cancer-promoting metabolic, and potentially, a protein synthesis suppression program that may be linked to alternative pre-mRNA splicing through the action of JMJD6 - downstream of MYC. This discovery can provide further evidence for considering JMJD6 as a potential therapeutic target for the treatment of MYC-driven cancers.

Strengths:

Alternative Splicing Induced by JMJD6 Knockdown: the study presents evidence for the role of JMJD6 in alternative splicing in neuroblastoma cells. Specifically, the RNA immunoprecipitation experiments demonstrated a significant shid from the GAC to the KGA GLS isoform upon JMJD6 knockdown. Moreover, a significant correlation between JMJD6 levels and GAC/KGA isoform expression was identified in two distinct neuroblastoma cohorts. This suggests a causative link between JMJD6 activity and isoform prevalence.

Physical Interaction of JMJD6 in Neuroblastoma Cells: The paper provides preliminary insight into the physical interactome of JMJD6 in neuroblastoma cells. This offers a potential mechanistic avenue for the observed effects on metabolism and protein synthesis and could be exploited for a deeper investigation into the exact nature, and implications of neuroblastoma-specific JMJD6 protein-protein interactions.

Weaknesses:

There are several areas that would benefit from improvements with regard to the current data supporting the claims of the paper (i.e., the conclusion presented in Figure 8).

Neuroblastoma Modelling Strategy: The study heavily relies on cell lines without incorporating patient derived cells/biomaterials. Using databases to fill gaps in the experimental design can only fortify the observations to a certain extent. A critical oversight is the absence of non-cancerous control cells in many figures, and the rationale for selecting specific cell lines for assays/approaches remains somewhat unclear. A foundational control for such experiments should involve the non-transformed neural crest cell line, which the authors have readily available. Are the observed splicing and metabolic effects of JMJD6 specific to neuroblastoma? Is there a neuroblastoma-specific JMJD6 interactome? Is MYC function essential?

In Vivo Modelling: The inclusion of a genetic mouse model combined with an inducible JMJD6 knockdown, would enhance the study by allowing examination of JMJD6's role during both tumor initiation and growth in vivo. For instance, the TH-MYCN mice overexpressing MYCN in neural crest cells, could be a promising choice.

Dependence on Colony Formation Assay: The study leans on 2D and semi-quantitative colony formation assays to assess malignant growth. To validate the link between the mechanistic insights discussed (e.g., reduced protein synthesis) and JMJD6-mediated malignant growth as a potential therapeutic target, evidence from in vivo or representative 3D models would be crucial.

Data Presentation and Rigor: The presented data is predominantly qualitative and necessitates quantification. For instance, Western blots should be quantified. The RNAseq, metabolism, and pulldown data should be transparently and numerically presented. The figure legends seem elusive and their lack of transparency (oden with regards to biological repeats, error bars, cell line used etc.) is concerning. Adequate citation and identification of all data sources, including online resources, are imperative. The manuscript would also benefit from a more rigorous depiction and quantification of RNA interference of both stable and transient knockdowns with quantitative validation at mRNA and protein levels.

Novelty Concerns: The emphasis on JMJD6 as a novel neuroblastoma target is contingent on the new mechanistic revelations about the JMJD6-centered link between splicing, metabolism, and protein synthesis. Given that JMJD6 has been previously linked to neuroblastoma biology, the rationale (particularly in Figure 1) for concentrating on JMJD6 may stem more from bias rather than data-driven reasoning.

Depth of Mechanistic Investigation: Current evidence lacks depth in key areas such as JMJD6-RNA binding. A more thorough approach would involve pinpointing specific JMJD6 binding sites on endogenous RNAs using techniques such as cross-linking and immunoprecipitation, paired with complementary proximity-based methodologies. Regarding the presented metabolism data, diving deeper into metabolic flux via isotope labeling experiments could shed light on dynamic processes like TCA and glutaminolysis. As it stands, the 'pathway cartoon' in Figure 6d appears overly qualitative.

Response: We agree with this reviewer that more in-depth studies are needed to understand the biological functions of JMJD6 in neuroblastoma. We have included one paragraph “limitation of the study” to point out that additional work needs to be done to address the comments from this reviewer.

We have also added details in figure legend to increase rigor.

Reviewer #1 (Recommendations For The Authors):

In this study, Jablonowski and colleagues identify the link between JMJD6-mediated pre-mRNA splicing and metabolic reprogramming in cancer cells, with implications for therapeutic response to splicing inhibitors. I have reviewed your manuscript and found it quite promising. However, there are some specific points that require further clarification and additional experiments. Please consider the following comments:

Major concerns:

(1) Regarding Figure 1d and e: to enhance the robustness of your findings, it would be beneficial to include additional datasets, such as the Kocak-649 dataset. It is important to narrow down the analysis to high-risk patient groups when examining survival rates, specifically to investigate whether the elevated expression of the 114 gene signature correlates with poor survival within this subgroup. Additionally, please consider conducting a more detailed breakdown of the subsets depicted in Fig. 1b to explore the association between their expression levels and patient survival rates.

Response: We have included the Kocak-649 datasets as Supplemental Figure 1. We have further analyzed the 114 gene signature in low-risk and high-risk patients, respectively, as Supplemental Figure 2.

(2) Fig. 2b: Similar to the previous comment, it would strengthen your findings to include survival rate analysis in more datasets, particularly in high-risk patient groups.

Response: We have further analyzed the association of JMJD6 with survival in low-risk and high-risk patients, respectively, as Supplemental Figure 3. Regardless of the risk factors, high expression of JMJD6 was associated with a poor outcome.

(3) In reference to Fig. S1D, please clarify the time point under investigation. It looks like siRNAs were utilized in this study. Ensure consistency between the siRNA # mentioned in the methods section and what is presented in Fig. S1d.

Response: We have clarified the time point under investigation in Fig. S1D (now as Fig. S4D). We have corrected the siRNA# on the method section.

Additionally, it would be beneficial to include data on knockdown efficacy and consider incorporating western blot results, similar to those presented in Fig. 2c.

Response: These experiments were performed as shown in Figure 4C. We assumed the knockdown efficiency was comparable.

Furthermore, I recommend analyzing the RNA-seq data from JMJD6-depleted BE(2)C cells to identify any alterations in the expression of neuronal differentiation signature genes, with the aim of exploring potential associations with changes in cell morphology showed in Fig. S1D.

Response: We have analyzed the data and indeed like this reviewer expected, we do see the upregulation of neuronal differentiation pathways. We have included the data as Fig. S7B.

(4) Fig. 4g: Confirm whether the data is related to GAC, and if so, where is the data for KGA?

Response: We apologize for this. KGA data was missed when we assembled the figure. We have added back as Figure 4H.

(5) In relation to Fig. 4, I suggest conducting experiments to individually silence GAC and KGA, if feasible (for instance, by targeting their 3'-UTRs). This would allow for a more in-depth investigation into whether GAC and KGA play essential roles in NB cell proliferation.

Response: As this reviewer suggested, we have performed the experiments to knock down GAC and KGA in BE2C cells, and we found that both isoforms seemed to be important for cell survival. We have included the data as Figure 5G-I. Additionally, we have also performed RNA-seq to understand the differential functions of GAC and KGA in neuroblastoma cells when they were overexpressed separately. We have included the data as Figure 5E,F, and Supplemental Figure 9.

(6) Fig. 5c: Could this protein synthesis reduction be attributed to an artificial overexpression of JMJD6? It would be interesting to investigate whether the genetic silencing of JMJD6 has an impact on total protein synthesis.

Response: This is a great question but could be very challenging to have a definitive answer. Since cells are not happy with knockdown of JMJD6, we may have a secondary effect resulting from activation of cell death. While we have successfully generated single cell JMJD6 CRISPR KO clones, the cells are not happy either. In the future, we may generate dTAG knockin cell line which will allow us to induce an acute protein degradation, and then we can assess if JMJD6 loss will consequently impact total protein synthesis.

(7) Fig. S7: the authors have shown that knocking down of JMJD6 in NB cells reduced cell proliferation (Fig. 2c-e). Please clarify how you obtained sufficient cells ader CRISPR knockout of JMJD6 clones and whether the cells remained healthy. It would be helpful to provide cell images.

Response: We harvested cells at different time points in Fig 2C-E, and we have added the information in Figure legends. Cells were not happy ader JMJD6 KD or KO. We therefore harvest cells for Western blot at an early time point while stained cells for survival effect at a late time point.

(8) Fig. 7f: Address the paradox where JMJD-knockdown cells grow slower (Fig. 2c-e), but these JMJD-KO4E5 cells grow at a similar rate compared to SKNAS-WT in the DMSO treatment group. Clarify whether this aligns with the results observed with shRNA results shown in Fig. 2c-e.

Response: The JMJD6 KO cells grew much slower than the wild-type cells. In these experiments, we intentionally seeded a lot more cells for JMJD6 KO clone so that we can have a comparable comparison for the cells with DMSO treatment.

Minor concerns:

(1) Fig. 2c: Please specify the time point for Fig. 2c to provide a clearer context for readers.

We have added the information.

(2) In Line 204, it is stated that 'Supplementary Table 3,' which describes the 'Correlation of JMJD6 KO and its co-dependency genes,' can actually be found in 'Supplementary Table 4.' Please clarify this discrepancy.

We apologize for this. We probably accidentally uploaded the duplicates. We have uploaded the new table in our revision.

(3) Line 207: The order of figures should be clarified. Fig. 3c should be mentioned before Fig. 3b in the text.

Yes, we did.

(4) In Line 216, it is mentioned that 'Supplementary Table 4,' which describes 'Differentially expressed genes by JMJD6 KD,' can actually be found in 'Supplementary Table 3.' Please provide clarification for this discrepancy.

We have corrected this.

(5) Line 244-247: Please provide clarification of this section to ensure readers can fully understand your point.

We have rephrased the sentence.

(6) Line 1048: Confirm whether Fig. 2c represents siRNA or shRNA, as the label in the graph does not match the figure legends.

Sorry for this. We have corrected.

(7) Line 1161: Provide clarification regarding the use of Image J from k, and in Line 1162, specify the source of Image J from l.

We apologized for the confusion of our description. We meant “Image J” sodware. We have corrected in Figure legend.

Reviewer #2 (Recommendations For The Authors):

Suggestions to authors:

Line 39 - suggest introducing JMJD6.

Response: We have added the full name of JMJD6.

Line 47 - suggest slightly rephrasing 'metabolic program that is coupled with...'.

We have made a slight change by changing “coupled” to “associate”.

Line 85 - please delete/replace 'exceptional'; proofread for inadequate use of ambiguous wording.

We have changed it as “significant”.

Line 141 - please concisely define 'high risk'.

We have defined it with a citation (line 142-146).

Line 143 - please concisely define 'event free'.

We have defined the event free and overall survival precisely (line 149, 150).

Line 153 - provide an adequate citation for 'cBioportal'.

We have added the citation (line166).

Line 161 - please state the utilized cell lines.

We have referenced to Materials and Methods (line 175).

Line 166 - please note that 'morphological changes' of a cell do not suffice to determine 'stemness', please rephrase.

We agreed and changed it to “regulate cellular differentiation” (line 181).

Line 182 - provide a quantifiable measure for color change and or remove observation from the narrative.

We have removed “indicative of acidic pH change” (line 198).

Line 185 - the statement commencing with 'It is believed...' requires referencing.

We have added references (line 200).

Line 187 - please provide an adequate citation for the 'JoMa1' neural crest-derived cells (J. Maurer and colleagues?).

We have added the reference (line 201).

Line 203 - please provide an adequate citation for 'DepMap'.

There is no citation specifically for DepMap and that’s why we can only provide the DepMap link.

Line 234 - please provide an adequate citation for 'two algorithms'.

We have provided the reference (line 265).

Line 265 - please provide a rationale for the choice of the three tested cell lines.

We have added definition by saying C-MYC overexpressed SKNAS, BE2C and SIMA with MYCN amplification (line 302, 303).

Line 279 - suggest rephrasing 'gaining more ATPs'.

We have removed these words as we do not have direct evidence to show ATP production (line 320).

Line 342 - suggest rephrasing 'are in the only gene signature'.

We have rephrased by saying “lysine demethylase (HDM) genes, including JMJD6, are present in the most significantly enriched gene signature in indisulam-sensitive cells” (line 416-416).

Line 424 - please state the source or all cell lines (commercial provider?).

We have added the source of cell lines.

Lines 438 to 442 - are STR and mycoplasma profiling data adequately presented in the manuscript?

We routinely test STR and mycoplasma for all cell lines cultured in hood in our Department every month.

Lines 520 onwards - is the JMJD6 knockout generation data (e.g., cell viability upon knockout) adequately presented in the manuscript? Why does the study depend on transient transfection of siRNAs for obtaining mechanistic results?

We created stable JMJD6 KO clones by selecting single cell with complete knockout. Cells are not happy ader KO. siRNA knockdown is a method for relatively acute depletion of JMJD6, which is easy and fast, and may be more reliable to assess the direct effect of JMJD6.

Figures: please provide adequate axis-labeling for all graphs (e.g., FIg2 b, and e).

We have added the axis labeling.

Discussion line 370 - what is meant by 'too harsh' - please use unambiguous phrasing to highlight limitations.

We have changed to “stringent”.

Please provide a study limitation paragraph.

We have added one limitation paragraph.

Limitation of the study

Our study focused on the understanding of JMJD6 function in neuroblastoma cell lines. In the future, we will consolidate our study by expanding our models to patient-derived xenograds, organoids, and neuroblastoma genetic models, in comparison with non-cancerous cells. Although we have identified a conserved interactome of JMJD6 in neuroblastoma cells, it remains to be determined whether it is neuroblastoma-specific and essential to MYC-driven cancers. The genome-wide RNA binding by JMJD6 in cancer cells and normal cells coupled with isotope labeling to dissect the metabolic effect of JMJD6 will enhance our understanding of the biological functions of JMJD6, awaiting future studies. Inability to target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells by pharmacologic inhibition of JMJD6 is another limitation, due to lack of selective and potent JMJD6 inhibitors.

Additional editing and proof-reading of the manuscript's narrative, figures, legends, and methods is highly recommended.

We have gone through the whole MS to have proof-reading.

Author Response

We are grateful for the reviewers' appreciation of our work and for their constructive feedback. We will address their comments through a revised version of the manuscript.

Reviewer #1 (Public Review):

This study by Paoli et al. used a resonant scanning multiphoton microscope to examine olfactory representation in the projection neurons (PNs) of the honeybee with improved temporal resolution. PNs were classified into 9 groups based on their response patterns. Authors found that excitatory repose in the PNs precedes the inhibitory responses for ~40ms, and ~50% of PN responses contain inhibitory components. They built the neural circuit model of the mushroom body (MB) with evolutionally conserved features such as sparse representation, global inhibition, and a plasticity rule. This MB model fed with the experimental data could reproduce a number of phenomena observed in experiments using bees and other insects, including dynamical representations of odor onset and offset by different populations of Kenyon cells, prolonged representations of after-smell, different levels of odor- specificity for early/delay conditioning, and shift of behavioral timing in delay conditioning. The trace conditioning was not modeled and tested experimentally. Also, the experimental result itself is largely confirmatory to preceding studies using other organisms. Nonetheless, the experimental data and the model provide a solid basis for future studies.

We thank the reviewer for summarizing the value of our study and recognizing its generality and significance. As suggested, in a revised version of the manuscript, we will discuss the implication of our approach for the context of trace conditioning. The model we presented hinges on the learning-induced plasticity of KC-to-MBON synapses recruited during the learning window (i.e., the simulated US arrival). In the case of trace conditioning, the model predicts that the time of the behavioral response time should match the expected US arrival. Contrary to this prediction, preliminary analyses on empirical measurements of PER latency upon trace conditioning indicate this is not the case. In a revised version of the manuscript, we will discuss the differences between the predictions of the model and the experimental observations in a trace conditioning paradigm.

Reviewer #2 (Public Review):

The study presented by Paoli et al. explores temporal aspects of neuronal encoding of odors and their perception, using bees as a general model for insects. The neuronal encoding of the presence of an odor is not a static representation; rather, its neuronal representation is partly encoded by the temporal order in which parallel olfactory pathways participate and are combined. This aspect is not novel, and its relevance in odor encoding and recognition has been discussed for more than the past 20 years.

The temporal richness of the olfactory code and its significance have traditionally been driven by results obtained based on electrophysiological methods with temporal resolution, allowing the identification and timing of the action potentials in the different populations of neurons whose combination encodes the identity of an odor. On the other hand, optophysiological methods that enable spatial resolution and cell identification in odor coding lack the temporal resolution to appreciate the intricacies of olfactory code dynamics.

(1) In this context, the main merit of Paoli et al.'s work is achieving an optical recording that allows for spatial registration of olfactory codes with greater temporal detail than the classical method and, at the same time, with greater sensitivity to measure inhibitions as part of the olfactory code.

The work clearly demonstrates how the onset and offset of odor stimulation triggers a dynamic code at the level of the first interneurons of the olfactory system that changes at every moment as a natural consequence of the local inhibitory interactions within the first olfactory neuropil, the antennal lobe. This gives rise to the interesting theory that each combination of activated neurons along this temporal sequence corresponds to the perception of a different odor. The extent to which the corresponding postsynaptic layers integrate this temporal information to drive the perception of an odor, or whether this sequence is, in a sense, a journey through different perceptions, is challenging to address experimentally.

In their work, the authors propose a computational approach and olfactory learning experiments in bees to address these questions and evaluate whether the sequence of combinations drives a sequence of different perceptions. In my view, it is a highly inspiring piece of work that still leaves several questions unanswered.

We thank the reviewer for considering that our work has an inspiring nature. Below we have tried to answer the questions raised by the following comments, and we will include part of these answers in the revised version of our manuscript.

(2) In my opinion, the detailed temporal profile of the response of projection neurons and their respective probabilities of occurrence provide valuable information for understanding odor coding at the level of neurons transferring information from the antennal lobes to the mushroom bodies. An analysis of these probabilities in each animal, rather than in the population of animals that were measured, would aid in better comprehending the encoding function of such temporal profiles. Being able to identify the involved glomeruli and understanding the extent to which the sequence of patterns and inhibitions is conserved for each odor across different animals, as it is well known for the initial excitatory burst of activity observed in previous studies without the fine temporal detail, would also be highly significant.

We thank the reviewer for recognizing the relevance of the findings in understanding the logic of olfactory coding. We agree about the importance of establishing if the different glomerular response profiles are evenly distributed across individuals or have individual biases. In the revised version of the manuscript, we will provide data on the distribution of response profiles for each animal and for different olfactory stimuli. Also, we fully agree on the importance of assessing to what extent such response profiles - largely determined by the local network of AL interneurons - are glomerulus-specific and conserved across individuals.

In my view, the computational approach serves as a useful tool to inspire future experiments; however, it appears somewhat simplistic in tackling the complexity of the subject. One question that I believe the researchers do not address is to what extent the inhibitions recorded in the projection neurons are integrated by the Kenyon cells and are functional for generating odor-specific patterns at that level.

The model we proposed represents, indeed, a simplification of olfactory signal processing throughout the honey bee olfactory circuit. Still, it shows that simple but realistic rules can be sufficient to grasp some fundamental aspects of olfactory coding. However, we agree with the reviewer and believe that such a minimalistic model can provide a basis for designing future experiments in which complexity can be increased by adding relevant features, such as the learning-induced plasticity of PN-to-KC synapses or the divergence of multiple PNs from the same glomerulus to different KCs

Concerning the reviewer's question on the involvement of inhibitory inputs in generating odor-specific patterns at the level of the KCs, the short answer is yes, they contribute to the summed input of a target KC, thus to the odor representation. In designing the model, we considered that a given glomerulus provides maximal input at maximal excitation and minimal input (=0 input) at maximal inhibition. For this reason, an inhibited glomerulus contributes less (to KC action potential probability) than a glomerulus showing baseline activity. This, in turn, contributes less than an excited glomerulus. From the modeling point of view, normalizing the signal between 0 and 1 (i.e., setting minimal inhibition to 0 and maximal excitation to 1) would yield a similar result as with the current approach, where values range from -25% to +30% F/F. We implement the model's description to clarify this point.

Lastly, the behavioral result indicating a difference in conditioned response latency after early or delayed learning protocol is interesting. However, it does not align with the expected time for the neuronal representation that was theoretically rewarded in the delayed protocol. This final result does not support the authors' interpretation regarding the existence of a smell and an after-smell as separate percepts that can serve as conditioned stimuli.

Considering that our odor stimulus lasted 5 seconds, glomerular activity is highly variable at odor onset (i.e., within the first 1s) because of short excitatory response profiles and the delayed and slower onset of inhibitory responses. After the initial phase, the neural representation of the stimulus becomes more stable. Consequently, a neural signature learned in the case of delay conditioning, i.e., with the US appearing towards the end of the olfactory stimulation (t = 4 - 5s), may present itself much earlier (t = 1.5s), triggering a behavioral response that largely anticipates the expected US arrival time.

In the model, we observe an early decrease in action potential probability even in the case of delay conditioning. This occurs because the synapses recruited during the last second of olfactory stimulation (within the learning window during which CS and US overlap) become inactive. Because odorant-induced activity recruits highly overlapping synaptic populations between 1.5 and 5 s from the onset, a learning-induced inactivation of part of these synapses will result in a reduced action-potential probability in the modeled MBON. Importantly, this event will not be governed by time but by the appearance of the learned synaptic configuration.

We will add a new section to the revised version of the manuscript to clarify this concept and perform further analyses to characterize the contribution of different response types to the modeled response latency.

Author Response

Reviewer #1 (Public Review):

Strengths:

• The paper is clearly written, and all the conclusions stem from a set of 3 principles: circular topology, rotational symmetry, and noise minimization. The derivations are sound and such rigor by itself is commendable.

• The authors provide a compelling argument on why evolution might have picked an eight-column circuit for path-integration, which is a great example of how theory can inform our thinking about the organization of neural systems for a specific purpose.

• The authors provide a self-consistency argument on how cosine-like activity supports cosine-like connectivity with a simple Hebbian rule. However, their framework doesn't answer the question of how this system integrates angular velocity with the correct gain in the absence of allothetic cues to produce a heading estimate (more on that on point 3 below).

Weaknesses:

• The authors make simplifying assumptions to arrive at the cosine activity/cosine connectivity circuit. Among those are the linear activation function, and cosine driving activity u. The authors provide justification for the linearization in methods 3.1, however, this ignores the well-established fact that bump amplitude is modulated by angular velocity in the fly head direction system (Turner-Evans et al 2017). In such a case, nonlinearities in the activation function cannot be ignored and would introduce harmonics in the activity.

We thank the reviewer for pointing out this omission. We added a paragraph at the end of section 4.1 clarifying that transient non-linearity, for instance when the circuit is actively receiving external input, is compatible with our work because we only need linearity in the line attractor, but not outside (lines 407-419).

“In more intuitive terms, the neurons have a saturating nonlinear activation function where they modulate their gain based on the total activity in the network. If the activity in the network is above the desired level, r, the gain is reduced and the activity decreases, and when the activity of the network is less than desired level, both the gain and the activity increase. Note that in this scenario transient deviations from the line attractor, which would induce nonlinear behaviour in the circuit dynamics, are tolerable. External inputs, u(t), could transiently modify the shape of the activity, producing activity shapes deviating from what the linear model can accommodate. For example, the shape of the bump attractor could be modified through nonlinearities while the insect attains high angular velocity (Turner-Evans et al., 2017).

Such nonlinear dynamics do not conflict with the theory developed here, which only requires linearity when the activity is projected onto the circular line attractor. In our framework, the linearity of integration at the circular line attractor is not a computational assumption, but rather it emerges from the principle of symmetry.”

Furthermore, even though activity has been reported to be cosine-like, in fact in the fruit fly it takes the form of a somewhat concentrated activity bump (~80-100 degrees, Seelig & Jayaraman 2015; Turner-Evans et al 2017), and one has to take into account the smoothing effect of calcium dynamics too which might make the bump appear more cosine-like. So in general, it would be nice to see how the conclusions extend if the driving activity is more square-like, which would also introduce further harmonics.

We added a cautionary comment on the sinusoidal activity (lines 222-226).

“We note, however, that data from the fruit fly shows a more concentrated activity bump than what would be expected from a perfect sinusoidal profile (Seelig and Jayaraman, 2015; Turner-Evans et al., 2017), and that calcium imaging (which was used to measure the activity) can introduce biases in the activity measurements (Siegle et al., 2021; Huang et al., 2021). Thus the sinusoidal activity we model is an approximation of the true biological process rather than a perfect description.”

Overall, it would be interesting to see whether, despite the harmonics introduced by these two factors interacting in the learning rule, Oja's rule can still pick up the "base" frequency and produce sinusoidal weights (as mentioned in methods 3.8). At this point, the examples shown in Figure 5 (tabula rasa and slightly perturbed weights) are quite simple. Such a demonstration would greatly enhance the generality of the results.

We also extended the self-consistency framework from Oja’s rule to the non-linear case, and found that while Oja’s rule with non-linear neurons would not give pure harmonics, the secondary harmonics will remain small. We added a sentence explaining this in the main text (section 2.4, lines 309-312) and a methods section to develop the self-consistency framework for the case of non-linear activations (section 4.7.2).

“For neurons with a nonlinear activation function, secondary harmonics would emerge, but would remain small under mild assumptions, as shown in Section 4.7.2. Oja’s rule will still cause the weights to converge to approximately sinusoidal connectivity.”

• The match of the theoretical prediction of cosine-like connectivity profiles with the connectivity data is somewhat lacking. In the locust the fit is almost perfect, however, the low net path count combined with the lack of knowledge about synaptic strengths makes this a motivating example in my opinion. In the fruit fly, the fit is not as good, and the function-fitting comparison (Methods Figure 6) is not as convincing. First, some function choices clearly are not a good fit (f1+2, f2). Second, the profile seems to be better fit by a Gaussian or other localized function, however the extra parameter of the Gaussian results in the worst AIC and AICc. To better get at the question of whether the shape of the connectivity profile matches a cosine or a Gaussian, the authors could try for example to fix the width of the Gaussian (e.g. to the variance of the best-fit cosine, which seems to match the data very well even though it wasn't itself fit), and then fit the two other parameters to the data. In that case, no AIC or AICc is needed. And then do the same for a circular distribution, e.g. von Mises.

We also included the fit with von Mises and Gaussian with the width parameters fixed to match the cosine as the reviewer suggested. We found that even though these two distributions fit the data better, the difference is very small (2%), probably due to the high variability of the fruit fly connectome data. We also changed the wording and state that the theory is compatible with experimental data.

In the Methods 4.6 (lines 568-585), we wrote

“As a complementary approach to evaluate the shape of the distribution, we first fit the Gaussian and von Mises distributions to the best fit f = 1 curve. We then freeze the width parameters of the distributions (σ_g for the Gaussian and κ_v for the von Mises) and only optimise the amplitude and vertical offset parameters (β and γ) to fit the data. This approach limits the number of free parameters for the Gaussian and von Mises distributions to two, to match the sinusoid. The results are shown in Methods Fig. 6 and Table 5. Both the fixed-width Gaussian and von Mises distributions are a slightly better fit to the data than the sinusoid, but the differences between the three curves are very small.

In simplifying the fruit fly connectome data, we assumed all synapses of different types were of equal weight, as no data to the contrary were available. Different synapse types having different strengths could introduce nonlinear distortions between our net synaptic path count and the true synaptic strength, which could in turn make the data a better or worse fit for a sinusoidal compared to a Gaussian profile. As such, we don’t consider the only 2% relative differences between the f = 1 sinusoid and fixed-width Gaussian and von Mises distributions to be conclusive.

Overall, we find that the cosine weights that emerge from our derivations are a very close match for the locust, but less precise for the fly, where other functions fit slightly better. Given the limitations in using the currently available data to provide an exact estimate of synaptic strength (for the locust), and due to the high variability of the synaptic count (for the fruit fly), we consider that our theory is compatible with the observed data.”

In addition, the theoretical prediction of cosine-like connectivity is not clearly stated in the abstract, introduction, or discussion. As a prediction, I believe it should be center forward, as it might be revisited again in the future in lieu of e.g. new experimental data.

We added the explicit prediction in the abstract and the introduction (lines 52-53).

• I find the authors' claim that Oja's rule suffices to learn the insect head direction circuit (l. 273-5) somewhat misleading/vague. The authors seem to not be learning angular integration here at all. First, it is unclear to me what is the form of u(t). Is it the desired activity in the network at time t given angular velocity? This is different than modelling a population of PEN neurons jointly tuned to head direction and angular velocity, and learning weights so as to integrate angular velocity with the correct gain (Vafidis et al 2022). The learning rule here establishes a self-consistency between sinusoidal weights and activity, however, it does not learn the weights from PEN to EPG neurons so as to perform angular integration. Similar simple Hebbian rules have been used before to learn angular integration (Stringer et al 2002), however, they failed to learn the correct gain. Therefore, the authors should limit the statement that their simpler learning rule is enough to learn the circuit (l. 273-5), making sure to outline differences with the current literature (Vafidis et al 2022).

We agree and we clarified that we focus only on the self-sustained activity condition. We appended the following text to the first and last paragraphs of section 2.4.

For the first (lines 279-284): “Our approach follows from previous research which has shown that simple Hebbian learning rules can lead to the emergence of circular line attractors in large neural populations (Stringer et al., 2002), and that a head direction circuit can emerge from a predictive rule (Vafidis et al., 2022). In contrast to this work, we focus only on the self-sustaining nature of the heading integration circuit in insects and show that our proposed sinusoidal connectivity profile can emerge naturally.”

For the last (lines 317-321): “However, this learning rule only applies to the weights that ensure stable, self-sustaining activity in the network. The network connectivity responsible for correctly integrating angular velocity inputs (given by the PEN to EPG connections in the fly) might require more elements than a purely Hebbian rule (Stringer et al., 2002), such as the addition of a predictive component (Vafidis et al., 2022).”

Author Response

Public reviews:

Reviewer 1:

Weaknesses:

While I generally agree with the author's interpretations, the idea of Saccorhytida as a divergent, simplified off-shot is slightly contradictory with a probably non-vermiform ecdysozoan ancestor. The author's analyses do not discard the possibility of a vermiform ecdysozoan ancestor (importantly, Supplementary Table 4 does not reconstruct that character),

Reply: Thanks for the comments. Saccorhytids are only known from the early Cambrian and their unique morphology has no equivalent among any extinct or extant ecdysozoan groups. This prompted us to consider them as a possible dead-end evolutionary off-shot. The nature of the last common ancestor of ecdysozoan (i.e. a vermiform or non-vermiform animal with capacities to renew its cuticle by molting) remains hypothetical. At present, palaeontological data do not allow us to resolve this question. The animal in Fig. 4b at the base of the tree is supposed to represent an ancestral soft-bodied form with no cuticle from which ecdysozoan evolved via major innovations (cuticular secretion and ecdysis). Its shape is hypothetical as indicated by a question mark. Our evolutionary model is clearly intended to be tested by further studies and hopefully new fossil discoveries.

and outgroup comparison with Spiralia (and even Deuterostomia for Protostomia as a whole) indicates that a more or less anteroposteriorly elongated (i.e., vermiform) body is likely common and ancestral to all major bilaterian groups, including Ecdysozoa. Indeed, Figure 4b depicts the potential ancestor as a "worm". The authors argue that the simplification of Saccorhytida from a vermiform ancestor is unlikely "because it would involve considerable anatomical transformations such as the loss of vermiform organization, introvert, and pharynx in addition to that of the digestive system". However, their data support the introvert as a specialisation of Scalidophora (Figure 4a and Supplementary Table 4), and a pharyngeal structure cannot be ruled out in Saccorhytida. Likewise, loss of an anus is not uncommon in Bilateria. Moreover, this can easily become a semantics discussion (to what extent can an animal be defined as "vermiform"? Where is the limit?).

Reply: We agree with you that “vermiform” is an ill-defined term that should be avoided. “Elongated” might be a better term to designate the elongation of the body along the antero-posterior axis. Changes have been made in the text to solve this semantic problem. Priapulid worms or annelids are examples of extremely elongated, tubular animals. In saccorhytids, the antero-posterior elongation is present (as it is in the vast majority of bilaterians) but extremely reduced, Saccorhytus and Beretella having a sac-like or beret-shape, respectively. That such forms may have derived from elongated, tubular ancestors (e.g. comparable with scalidophoran worms) would require major anatomical transformations that have no equivalent among modern animals. We agree that further speculation about the nature of these transformations is unnecessary and should be deleted simply because the nature of these ancestors is purely hypothetical. We also agree that the loss of anus and the extreme simplification of the digestive system is common among extant bilaterians. The single opening seen in Saccorhytus and possibly Beretella may result from a comparable simplification process. In Figure 4b, the hypothetical pre-ecdysozoan animal is slightly elongated (antero-posterior axis and polarity) but in no way comparable with a very elongated and cylindrical ecdysozoan worm (e.g. extant or extinct priapulid).

Therefore, I suggest to leave the evolutionary scenario more open. Supporting Saccorhytida as a true group at the early steps of Ecdysozoa evolution is important and demonstrates that animal body plans are more plastic than previously appreciated. However, with the current data, it is unlikely that Saccorhytida represents the ancestral state for Ecdysozoa (as the authors admit), and a vermiform nature is not ruled out (and even likely) in this animal group. Suggesting that the ancestral Ecdysozoan might have been small and meiobenthic is perhaps more interesting and supported by the current data (phylogeny and outgroup comparison with Spiralia).

Reply: We agree the evolutionary scenario should be more open, especially the evolutionary process that gave rise to Saccorhytida. Again, we know nothing about the morphology of the ancestral ecdysozoan (typically the degree of body elongation, whether it had a differentiated introvert or not, whether it had a through gut or not). Simplification appears as one possible option, but which assumes that the ancestral ecdysozoan was an elongated animal with a through gut. Changes will be made in Fig.4A accordingly. Alternatively, the ancestral ecdysozoan might have been small and meiobenthic.

Reviewer 2:

Weaknesses:

The preservations of the specimens, in particular on the putative ventral side, are not good, and the interpretation of the anatomical features needs to be tested with additional specimens in the future. The monophyly of Cycloneuralia (Nematoida + Scalidophora) was not necessarily well-supported by cladistic analyses, and the evolutionary scenario (Figure 4) also needs to be tested in future works.

Reply: Yes, we agree that our MS is the first report on an enigmatic ecdysozoan. Whereas the dorsal side of the animal is well documented (sclerites), uncertainties remain concerning its ventral anatomy (typically the mouth location and shape). Additional better-preserved specimens will hopefully provide the missing information. Concerning Cycloneuralia, their monophyly is generally better supported by analyses based on morphological characters than in molecular phylogenies. I

Reviewer 3:

Weaknesses: I, as a paleontology non-expert, experienced several difficulties in reading the manuscript. This should be taken into consideration when assuming a wide range of readers including non-experts.

Reply: We have ensured that the text is comprehensible to biologists. Our main results are summarized in relatively simple diagrams (e.g. Fig. 4). We are aware that technical descriptive terms may appear obscure to non-specialists. However, we think that our text-figures help the reader to understand the morphology of these ancient animals.

Author Response

eLife assessment

This study presents a useful comparison of the dynamic properties of two RNA-binding domains. The data collection and analysis are solid, making excellent use of a suite of NMR methods. However, evidence to support the proposed model linking dynamic behavior to RNA recognition and binding by the tandem domains remains incomplete. The work will be of interest to biophysicists working on RNA-binding proteins.

Response: We thank eLife for taking the time and effort to review our manuscript. Evidence from the literature and our study shows a great deal of parity between the dynamic behavior of dsRBDs and its dsRNA-recognition and -binding, which helped us culminate in proposing a fair model. As mentioned in the manuscript, we have been working on the suggested experiments to further support our proposed model.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In the manuscript entitled "Differential conformational dynamics in two type-A RNA-binding domains drive the double-stranded RNA recognition and binding," Chugh and co-workers utilize a suite of NMR relaxation methods to probe the dynamic landscape of the TAR RNA binding protein (TRBP) double-stranded RNA-binding domain 2 (dsRBD2) and compare these to their previously published results on TRBP dsRBD1. The authors show that, unlike dsRBD1, dsRBD2 is a rigid protein with minimal ps-ns or us-ms time scale dynamics in the absence of RNA. They then show that dsRBD2 binds to canonical A-form dsRNA with a higher affinity compared to dsRBD1 and does so without much alteration in protein dynamics. Using their previously published data, the authors propose a model whereby dsRBD2 recognizes dsRNA first and brings dsRBD1 into proximity to search for RNA bulge and internal loop structures.

Response: We thank the Reviewer for sending us an encouraging review. We have combined the findings reported in the literature with new ones, that led us to propose the dsRNA-binding model by tandem A-form dsRBDs.

We propose that dsRBD1 can first recognize a variety of sequential and structurally different dsRNAs. dsRBD2 assists the interaction with a higher affinity, thus fortifying the interaction between TRBP and a possible substrate. This may enable the other associated proteins like Dicer and Ago2 to perform critical biological functions.

However, the following statements made in the comment above are factually incorrect.

(1) They then show that dsRBD2 binds to canonical A-form dsRNA with a higher affinity compared to dsRBD1 and does so without much alteration in protein dynamics.

However, we have explicitly shown the perturbation in dsRBD2 dynamics upon RNA binding.

(2) Using their previously published data, the authors propose a model whereby dsRBD2 recognizes dsRNA first and brings dsRBD1 into proximity to search for RNA bulge and internal loop structures.

Our previously published data suggests that dsRBD1, owing to its high conformational dynamics in solution, is able to recognize a variety of structurally and sequentially different dsRNA (PMID: 35134335). dsRBDs preferably bind to the double-stranded region (minor-major-minor-groove) of an A-form RNA (PMID: 24801449; PMID: 27332119) and do not search for bulge and internal loop structures as a part of the binding event. Even though dsRBDs preferably bind to the double-stranded region, they can still accommodate perturbation in the A-form helix due to mismatch and bulges with decreased binding affinity (PMID 25608000). However, it is a matter of future research to identify how much of a deviation from the A-form structure can be accommodated by the dsRBDs. The diffusion event observed in the literature (PMID: 23251028) also does not show any direct implication to search for bulge and internal loop structures.

Strengths:

The authors expertly use a variety of NMR techniques to probe protein motions over six orders of magnitude in time. Other NMR titration experiments and ITC data support the RNA-binding model.

Weaknesses:

The data collection and analysis are sound. The only weakness in the manuscript is the lack of context with the much broader field of RNA-binding proteins. For example, many studies have shown that RNA recognition motif (RRM) domains have similar dynamic characteristics when binding diverse RNA substrates. Furthermore, there was no discussion about the entropy of binding derived from ITC. It might be interesting to compare with dynamics from NMR.

Response: We understand the reviewer’s point that this study is focused on a dsRNA-binding mechanism rather than addressing the much broader field of RNA-binding. There are multiple challenges in finding a single mechanism that works for all RNA-binding proteins. For instance, RRM is a single-stranded RNA binding domain that is able to read out the substrate base sequence. RRM behaves entirely differently than the dsRBD in terms of sequence specificity. Besides, several other RNA-binding domains like the KH-domain, Puf domains, Zinc finger domains, etc., showcase a unique RNA-binding behavior. Thus, it would be really difficult to draw a single rule of thumb for RNA-recognition behavior for all these diverse domains.

Thank you for pointing out the entropy of binding from ITC. We shall include the discussion about the entropy of binding in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

Proteins that bind to double-stranded RNA regulate various cellular processes, including gene expression and viral recognition. Such proteins often contain multiple double-stranded RNA-binding domains (dsRBDs) that play an important role in target search and recognition. In this work, Chug and colleagues have characterized the backbone dynamics of one of the dsRBDs of a protein called TRBP2, which carries two tandem dsRBDs. Using solution NMR spectroscopy, the authors characterize the backbone motions of dsRBD2 in the absence and presence of dsRNA and compare these with their previously published results on dsRBD1. The authors show that dsRBD2 is comparatively more rigid than dsRBD1 and claim that these differences in backbone motions are important for target recognition.

Strengths:

The strengths of this study are multiple solution NMR measurements to characterize the backbone motions of dsRBD2. These include 15N-R1, R2, and HetNOE experiments in the absence and presence of RNA and the analysis of these data using an extended-model-free approach; HARD-15N-experiments and their analysis to characterize the kex. The authors also report differences in binding affinities of dsRBD1 and dsRBD2 using ITC and have performed MD simulations to probe the differential flexibility of these two domains.

Weaknesses:

While it may be true that dsRBD2 is more rigid than dsRBD1, the manuscript lacks conclusive and decisive proof that such changes in backbone dynamics are responsible for target search and recognition and the diffusion of TRBP2 along the RNA molecule. To conclusively prove the central claim of this manuscript, the authors could have considered a larger construct that carries both RBDs. With such a construct, authors can probe the characteristics of these two tandem domains (e.g., semi-independent tumbling) and their interactions with the RNA. Additionally, mutational experiments may be carried out where specific residues are altered to change the conformational dynamics of these two domains. The corresponding changes in interactions with RNA will provide additional evidence for the model presented in Figure 8 of the manuscript. Finally, there are inconsistencies in the reported data between different figures and tables.

Response: We thank the reviewer for the comprehensive and insightful review. A larger construct carrying both RBDs was not used because of the multiple challenges pertaining to dynamics study by NMR spectroscopy (intrinsic R2 rates of the dsRBD1-dsRBD2 construct would be high, resulting in broadened peaks) as per our previous experience (PMID: 35134335). There would be additional dynamics in that construct coming from domain-domain relative motions, difficult to deconvolute the dynamics information. Further, the dsRNA needed to bind to this construct will be longer, thereby causing further line broadening in NMR.

Coming to mutational studies, careful designing of domain mutants remains as a challenge because the conformational dynamics in both the domains are distributed all through the backbone rather than only in the RNA-binding residues. The mutational studies would need an exhaustive number of mutations in protein as well as RNA to draw a parallel between the binding and dynamics. Having said that, we are working on making such mutations in the protein (at several locations to freeze the dynamics site-specifically) and the RNA (to change the shape of the dsRNA) to systematically study this mechanism, which will be out of scope of this manuscript.

The reviewer has rightly pointed out some subtle superficial differences. These superficial differences are present because of the context in which we are describing the data. For example, in Figure S4 we are talking about the average relaxation rates and nOe values for only the common residues we were able to analyze between two magnetic field strengths 600 and 800 MHz. Whereas in Figure 6, we are comparing the averages of the core dsRBD residues at 600 MHz, in presence and absence of D12RNA. The differences however are minute falling well within the error range.

Author Response

eLife assessment

The manuscript explores the ways in which the genetic code evolves, specifically how stop codons are reassigned to become sense codons. The authors present phylogenetic data showing that mutations at position 67 of the termination factor are present in organisms that nevertheless use the UGA codon as a stop codon, thereby questioning the importance of this position in the reassignment of stop codons. Alternative models on the role of eRF1 would reflect a more balanced view of the data. Overall, the data are solid and these findings will be valuable to the genomic/evolution fields.

Public Reviews:

Reviewer #1 (Public Review):

The issue:

The ciliates are a zoo of genetic codes, where there have been many reassignments of stop codons, sometimes with conditional meanings which include retention of termination function, and thus > 1 meaning. Thus ciliate coding provides a hotspot for the study of genetic code reassignments.

The particular issue here is the suggestion that translation of a stop (UGA) in Blastocritihidia has been attributed to a joint change in the protein release factor that reads UGA's and also breaking a base pair at the top of the anticodon stem of tRNATrp (Nature 613, 751, 2023).

The work:

However, Swart, et al have looked into this suggestion, and find that the recently suggested mechanism is overly complicated.

The broken pairing at the top of the anticodon stem of tRNATrp indeed accompanies the reading of UGA as Trp as previously suggested. It changes the codon translated even though the anticodon remains CCA, complementary to UGG. A compelling point is that this misreading matches previous mutational studies of E coli tRNA's, in which breaking the same base pair in a mutant tRNATrp suppressor tRNA stimulated the same kind of miscoding.

This is a fair characterization, and we would also note the additional positive aspect: that we observed there is consistency in the presence of 4 bp tRNA-Trp anticodon stems in those ciliates which translate UGA as tryptophan, and generally 5 bp anticodon stems in those that do not (including Euplotes with UGA=Cys).

But the amino acid change in release factor eRF1, the protein that catalyzes termination of protein biosynthesis at UGA is broadly distributed. There are about 9 organisms where this mutation can be compared with the meaning of UGA, and the changes are not highly correlated with a change in the meaning of the codon. Therefore, because UGA can be translated as Trp with or without the eRF1 mutation, Swart et al suggest that the tRNA anticodon stem change is the principal cause of the coding change.

We do think multiple lines of evidence support the shorter tRNA anticodon stem promoting UGA translation, but also think other changes in the translation system may be important. For instance, structural studies suggest interaction of ribosomal RNA with extended stop codons (particularly the base downstream of the triplet) during translation termination (Brown et al. 2015, Nature). As we noted, previous studies have sought to correlate individual eRF1 substitutions with genetic code changes, but the proposed correlations have invariably disappeared once new tranches of eRF1 sequences and alternative genetic codes for different species became available. This is why we concluded that there needs to be more focus on obtaining and understanding molecular structures during translation termination, particularly in the organisms with alternative codes.

The review:

Swart et al have a good argument. I would only add that eRF1 participation is not ruled out, because finding that UGA encodes Trp does not distinguish between encoding Trp 90% of the time and encoding it 99% of the time. The release factor could still play a measurable quantitative role, but the major inference here seems convincing.

We agree that eRF1 may participate and compete with the tRNA, but we question the hypothesis that the particular amino acid position/substitution proposed by Kachale et al. 2023 is the key. There is experimental evidence in the form of Ribo-seq for the ciliate Condylostoma magnum (A67), which does appear to efficiently translate UGA sense codons (Swart et al. 2016, Figure S3: https://doi.org/10.1016/j.cell.2016.06.020): we observed no dip in ribosome footprints downstream of these codons, as there would be in the case of classical translational readthrough in standard genetic code organisms (which is usually relatively inefficient - certainly well below 50% of upstream translation from our reading of the literature). Ribo-seq also supports efficient termination at those Condylostoma UGA codons that are stops.

Of course, the entire translation system may have evolved to be as efficient as what we currently observe, and it is not unreasonable to consider that it may have been less efficient in the past. However, not so inefficient that the error rate incurred would have been strongly deleterious. Importantly also, we believe the role of multiple eRF1 paralogs in translation termination in the ciliates really needs to be investigated, given that translation is inherently probabilistic with any of these proteins potentially being incorporated into the ribosome.

Reviewer #2 (Public Review):

The manuscript raises interesting observations about the potential evolution of release factors and tRNA to readdress the meaning of stop codons. The manuscript is divided into two parts: The first consists of revealing that the presence of a trp tRNA with an AS of 5bp in Condylostoma magnum is probably linked to contamination in the databases by sequences from bacteria. This is an interesting point which seems to be well supported by the data provided. It highlights the difficulty of identifying active tRNA genes from poorly annotated or incompletely assembled genomes.

We will consider adding subheadings in revising the manuscript to make the structure more explicit, as it really has three parts to it, with the third largely in the supplement. The “good” was that there is a range of support for the 4 bp AS stem, with new evidence we supplied from ciliates and older studies with E. coli tRNAs. The “bad” is that scrutiny of eRF1 sequences, with the addition of ones we provided, contradicts the hypothesis by Kachale et al. that a S67A/G substitution is necessary for genetic code evolution in Blastocrithidia and certain ciliates. The “ugly” is that a tRNA shown in a main figure in Kachale et al. 2023, and which was investigated in a number of subsequent experiments, is almost certainly a bacterial contaminant.

Proper scrutiny of the bacterial tRNA should have led to its immediate recognition and rejection, as one of us did years ago in searches of tRNAs in a preliminary Condylostoma genome assembly (only predicted 4 bp AS tRNA secondary structures were shown in Swart et al. 2016, Fig S4B and C). Evidence for the bacterial nature of this tRNA was placed in the supplement of the present manuscript, as the meat of the critique was the consideration of the evidence for and against its good and bad aspects. The bacterial tRNA secondary structure has been removed from the main figure by Kachale et al. 2023, and downstream experiments based on synthetic constructs for this tRNA have also been revised (https://www.nature.com/articles/s41586-024-07065-0).

Much of the rest of the supplement served to correct multiple errors in genetic codes in public sequence databases that led to additional errors and difficulties in interpreting the eRF1 substitutions in Kachale et al. 2023. It is important that these codes get corrected. If not they create multiple headaches for users besides those investigating genetic codes, as we found out in communications with authors and a colleague of Kachale et al. 2023 (in particular, leading to thousands of missing genes in the macronuclear genome of the standard code ciliate Stentor coeruleus that were removed in automated GenBank processing due to incorrectly having an alternative genetic code specified).

Recently the NCBI Genetic Codes curators reinstated a genetic code incorrectly attributed to the ciliate Blepharisma (“Blepharisma nuclear genetic code”) (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi#SG15), despite us requesting a reasonable fix years ago. This would be very confusing for those that are not in the know. We have explained this confusion in our supplement too. Thus we also hope that this paper will aid in communication with the genetic code database curators and in correcting such issues.

The second part criticises the fact that a mutation at position S67 of eRF1 is required to allow the UGA codon to be reassigned as a sense codon. As supporting evidence, they provide a phylogenetic study of the eRF1 factor showing that there are numerous ciliates in which this position is mutated, whereas the organism shows no trace of the reassignment of the UGA codon into a sense codon. While this criticism seems valid at first glance, it suffers from the lack of information on the level of translation of UGA codons in the organisms considered.

Firstly, we not only showed that there are organisms with the S67 substitution but no UGA reassignment, but also provided evidence for the converse: organisms with a UGA=Trp reassignment but without the S67 substitution (both ciliates and a non-ciliate). So, two related lines of substitutions were not consistent with the eRF1 substitution hypothesis proposed.

Secondly, we disagree that there is a “lack of information about UGA translation in the organisms considered”. Evolution has already supplied information as to whether UGA codons are translated at an appreciable level in the organisms of interest, in the form of codon frequencies within their protein-coding sequences and those ending them. If UGA was translated at appreciable levels, it would be found at a corresponding frequency in coding sequences. In genomes with thousands of genes, if not predicted as amino acids, they likely primarily serve as stops. Low levels of potential readthrough of actual stops would not change the arguments. With the exception of selenocysteine translation (which is restricted to a limited number of genes by the condition of requiring a specific mRNA secondary structure) there is no expectation of meaningful levels of UGA translation when this codon is missing from the bulk of coding sequences (CDSs).

This is well illustrated by the heterotrichs, a clade of ciliates that use a variety of genetic codes. In heterotrichs that use the standard code, UGA is virtually absent from coding sequences, only appearing at the 3’ end of transcripts in the predicted stop codon and 3’-UTR (Seah et al. 2022, Figure 5). This contrasts notably with other genera like Blepharisma where appreciable levels of UGA codons occur throughout coding sequences, upstream of the predicted UAA and UAG stops (Seah et al. 2022, Figure 5: https://www.biorxiv.org/content/biorxiv/early/2022/07/12/2022.04.12.488043/F5.large.jpg). The difference in the UGA, UAG and UAA codon frequencies in 3’ UTRs compared to the upstream frequencies in CDSs of standard genetic code heterotrichs is stark. Frequencies of all three codons are elevated in the 3’ UTRs of all heterotrich ciliates, irrespective of their genetic codes (Seah et al. 2022, Figure 5), according with these codons not being deleterious in this region and strongly selected against upstream, within CDSs.

The reviewer raises the possibility that UGA may appear to be a stop codon but still have biologically significant translational readthrough. We think that this is unlikely in the heterotrich ciliate species discussed here, which have extremely short (median 21-26 bp) and AU-rich 3’-UTRs compared to yeast and animals (Seah et al. 2022). Therefore, in heterotrichs where UGA is predicted to be a stop, translational readthrough would lead to extensions of only a few amino acids and be relatively inconsequential, as there are plenty of secondary UAA, UAG and UGA codons downstream of the typical stop.

If one were to consistently pursue the reviewer’s line of argumentation, one would also have to argue against the very reasoning used in Kachale et al. 2023 about all the stop codon predictions/reassignments in protists for which experiments were not conducted in S. cerevisiae or other translation systems, as well as decades of prior work using sequence conservation in multiple sequence alignments to infer alternative genetic codes.

Furthermore, experimental information for UGA translation levels is available for the ciliate Condylostoma magnum, predominantly in the form of Ribo-seq (Swart et al. 2016). Similarly to Condylostoma’s UAA and UAG codons, Ribo-seq shows that the UGA codons are generally either efficiently translated when present in the bodies of CDSs or terminate translation as actual stops close to mRNA 3’ termini/poly(A) tails (Swart et al. 2016). Thus, irrespective of the presence of the hypothesized eRF1 substitution there is an example of relatively discrete reading of UGA codons in ciliates as either stops or amino acids. This contrasts with Kachale et al 2023’s experiments in yeast with yeast eRF1 S67G or Blastocrithida eRF1 which also has glycine at the equivalent position that appear to lead to modest readthrough. In addition, efficient reading of codons in either of two ways also occurs in the ciliate genus Euplotes in which “stop” codons can either serve as frameshift sites during translation within coding sequences or be actual stops when they are close to 3’ mRNA termini (Lobanov et al. 2017), as verified by Ribo-seq and protein mass spectrometry.

It has been clearly shown that S67G or S67A mutations allow a strong increase in the reading of UGA codons by tRNAs, so this point is not in doubt. However, this has been demonstrated in model organisms, and we now need to determine whether other changes in the translational apparatus could accompany this mutation by modifying its impact on the UGA codon. This is a point partly raised at the end of the manuscript.

There is no doubt that S67G or S67A mutations lead to increased translational readthrough, but this is restricted to experiments with or in baker’s yeast or other standard genetic code surrogate model organisms. Experiments introducing eRF1 sequences from alternative genetic code eukaryotes into translation systems of such standard genetic code eukaryotes are not compelling because the rest of the associated translation system has also evolved tremendously. As far as we are aware, no in vivo experiments with ciliate eRF1s have been conducted to determine if position 67 or other substitutions have any effect. These considerations are critical given the vast evolutionary distances between yeasts, Blastocrithidia, the ciliates and Amoebophrya sp. ex Karlodinium veneficum. On the other hand, the evolutionary information presented contradicts the importance of this substitution in the Amoebophyra species and ciliates. We will consider how to incorporate these ideas in the revised version of the manuscript.

Indeed, it is quite possible that in these organisms the UGA codon is both used to complete translation and is subject to a high level of readthrough. Actually, in the presence of a mutation at position 67 (or elsewhere), the reading of the UGA can be tolerated under specific stress conditions (nutrient deficiency, oxidative stress, etc.), so the presence of this mutation could allow translational control of the expression of certain genes.

As explained a couple replies above, it is not constructive to invoke the additional complexity of conditional translation or any other kinds of factors that lead to enhanced readthrough, because the translation of UGA sense codons in the ciliate Condylostoma, where we have supporting experimental evidence, does not resemble translational readthrough. These codons occur in constitutively expressed single-copy genes, like a tryptophan tRNA synthetase and an eRF1 protein (Swart et al. 2016), not ones that might be expected to be conditionally translated.

On the other hand, it seems obvious to me that there are other ways of reading through a stop codon without mutating eRF1 at position S67. So the absence of a mutation at this position is not really indicative of a level of reading of the UGA codon.

It may seem obvious to the reviewer, but that is neither what Kachale et al. originally proposed nor what we questioned. Kachale et al. hypothesized that mutation of S67 to A or G is necessary for UGA=Trp translation, but we provided evidence that it is not: multiple organisms with S67 or C67 that translate UGA as tryptophan. Kachale et al. also originally suggested that the S67 to A/G substitution is also necessary in Condylostoma for UGA translation as tryptophan by weakening its recognition of this codon as a stop (from their abstract: “Virtually the same strategy has been adopted by the ciliate Condylostoma magnum.”). However, as we have stated, Condylostoma (A67) is both able to efficiently terminate at UGA stop codons and to efficiently translate (other) UGA sense codons, which does not fit this hypothesis.

Before writing such a strong assertion as that found on page 3, experiments should be carried out. The authors should therefore moderate their assertion.

Experiments should be carried out in the organisms in which stop codon reassignments have readily occurred and their close relatives that have not, not distantly related ones where they rarely, if ever, occur, like yeasts. We made this point in the conclusion. There is too much emphasis on models for investigation of genetic code evolution via stop codon reassignments in questionable models and too little investigation in the really good ones, particularly the ciliates. This clade has genera that are amenable to molecular experiments including Paramecium, Tetrahymena and Oxytricha. We plan to add some text about these considerations in revision.

To make a definitive conclusion, we would need to be able to measure the level of termination and readthrough in these organisms. So, from my point of view, all the arguments seem rather weak.

We reiterate: there is experimental information about translation and termination in two ciliate species worth considering, including one that translates UGA codons depending on their context. If one chooses to ignore the evolutionary information presented, this not only ignores all prior approaches to infer genetic codes, but also the fact that there is experimental verification and other lines of evidence supporting these approaches.

Moreover, the authors themselves indicate that the conjunction between a Trp tRNA that is efficient at reading the UGA codon and an eRF1 factor that is not efficient at recognising this stop codon could be the key to reassignment.

This does not convey well what we wrote, since the main consideration was overall eRF1 structure, rather than individual amino acid substitutions. Here are the key sentences:

“Instead, in a transitional evolutionary phase, codons may be interpreted in two ways, with potential eRF1-tRNA competition. With time, beneficial mutations or modifications in either the tRNA or eRF1 (or other components of translation) that reduce competition may be selected.

Instead of focusing on individual eRF1 substitutions, we propose future investigations should more generally explore the structure of non-standard genetic code eRF1’s captured in translation termination in the context of their own ribosomes.”

Author Response

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

eLife assessment

This study presents a valuable finding on the distinct subpopulation of adipocytes during brown-to-white conversion in perirenal adipose tissue (PRAT) at different ages. The evidence supporting the claims of the authors is convincing, although specific lineage tracing of this subpopulation of cells and mechanistic studies would expand the work. The work will be of interest to scientists working on adipose and kidney biology.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors performed single nucleus RNA-seq for perirenal adipose tissue (PRAT) at different ages. They concluded a distinct subpopulation of adipocytes arises through brown-to-white conversion and can convert to a thermogenic phenotype upon cold exposure.

Strengths:

PRAT adipose tissue has been reported as an adipose tissue that undergoes browning. This study confirms that brown-to-white and white-to-beige conversions also exist in PRAT, as previously reported in the subcutaneous adipose tissue.

Response: We thank the reviewer for summarizing the strengths of our manuscript. However, we would like to clarify two points here. First, PRAT has been reported as a visceral adipose depot that contains brown adipocytes and a process of continuous replacement of brown adipocytes by white adipocytes has been previously suggested based on histological assessment. There is no evidence that PRAT undergoes browning, unless cold exposure is involved. Second, unlike the brown-to-white conversion, white-to-beige conversion in PRAT was not observed under normal conditions. The adipocyte population that arises from brown-to-white conversion (mPRAT-ad2) can respond to cold and restore their UCP1 expression. However, the adipocytes that arise from the mPRAT-ad2 subpopulation after cold exposure have a distinct transcriptome to that of cold-induced beige adipocyte in iWAT (Figure S7K) and are more related to iBAT brown adipocytes (Figure 6E). Therefore, it is more of a white-to-brown conversion in PRAT upon cold exposure rather than white-to-beige conversion and the underlying mechanism is likely different from the white-to-beige conversion in the subcutaneous adipose tissue.

Weaknesses:

(1) There is overall a disconnection between single nucleus RNA-seq data and the lineage chasing data. No specific markers of this population have been validated by staining.

Response: We are not sure what “this population” refers to. We assume that it is the Ucp1-&Cidea+ mPRAT-ad2 adipocyte subpopulation. If so, we did not identify specific markers for these adipocytes as shown in Figure 1H and statements in the Discussion section. mPRAT-ad2 is negative for Ucp1 and Cyp2e1, which are markers for mPRAT-ad1 and mPRAT-ad3&4, respectively. To visualize the mPRAT-ad2 adipocytes on tissue sections, we collected pvPRAT and puPRAT of Ucp1CreERT2;Ai14 mice one day after tamoxifen injection and stained with CYP2E1 antibody and BODIPY. The Tomato-&CYP2E1-&BODIPY+ cells represent the mPRAT-ad2 adipocytes. Based on such strategy, we revealed a significantly higher percentage of mPRAT-ad2 cells in puPRAT than pvPRAT (presented as Figure S3E in the revised manuscript).

(2) It would be nice to provide more evidence to support the conclusion shown in lines 243 to 245 "These results indicated that new BAs induced by cold exposure were mainly derived from UCP1- adipocytes rather than de novo ASPC differentiation in puPRAT". Pdgfra-negative progenitor cells may also contribute to these new beige adipocytes.

Response: We stained pvPRAT and puPRAT of the PdgfraCre;Ai14 mice with the adipocyte marker Plin1 and observed a 100% overlap between the tdTomato signal and the Plin1 staining, after examining a total of 832 and 628 adipocytes in pvPRAT and puPRAT of two animals (Figure S4). Plin1 stains all adipocytes, while the endogenous tdTomato labels both the adipocytes and blood vessels. This result suggests that all adipocytes in mPRAT are derived from Pdgfra-expressing cells, which is in line with a previous study that integrated several single-cell RNA sequencing data sets and showed that Pdgfra is expressed by virtually all ASPCs (Ferrero et al., 2020).

Also, we would like to point out that the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT than the beige adipocytes of iWAT (Figure 6E and S7K).

Ferrero, R., Rainer, P., and Deplancke, B. (2020). Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell Heterogeneity. Trends Cell Biol 30, 937-950.

(3) The UCP1Cre-ERT2; Ai14 system should be validated by showing Tomato and UCP1 co-staining right after the Tamoxifen treatment.

Response: We collected pvPRAT and puPRAT of 1- and 6-month-old Ucp1CreERT2;Ai14 mice one day after the last tamoxifen injection and stained with UCP1 antibody to check the overlap between the Tomato and UCP1signal. All Tomato+ cells were UCP1+, indicating 100% specificity of the Ucp1CreERT2; and the labelling efficiency was over 93% at both time points for both regions (Figure S3C-D).

Reviewer #2 (Public Review):

Summary:

In the present manuscript, Zhang et al utilize single-nuclei RNA-Seq to investigate the heterogeneity of perirenal adipose tissue. The perirenal depot is interesting because it contains both brown and white adipocytes, a subset of which undergo functional "whitening" during early development. While adipocyte thermogenic transdifferentiation has been previously reported, there remain many unanswered questions regarding this phenomenon and the mechanisms by which it is regulated.

Strengths:

The combination of UCP1-lineage tracing with the single nuclei analysis allowed the authors to identify four populations of adipocytes with differing thermogenic potential, including a "whitened" adipocyte (mPRAT-ad2) that retains the capacity to rapidly revert to a brown phenotype upon cold exposure. They also identify two populations of white adipocytes that do not undergo browning with acute cold exposure.

Anatomically distinct adipose depots display interesting functional differences, and this work contributes to our understanding of one of the few brown depots present in humans.

Weaknesses:

The most interesting aspect of this work is the identification of a highly plastic mature adipocyte population with the capacity to switch between a white and brown phenotype. The authors attempt to identify the transcriptional signature of this ad2 subpopulation, however, the limited sequencing depth of single nuclei somewhat lessens the impact of these findings. Furthermore, the lack of any form of mechanistic investigation into the regulation of mPRAT whitening limits the utility of this manuscript. However, the combination of well-executed lineage tracing with comprehensive cross-depot single-nuclei presented in this manuscript could still serve as a useful reference for the field.

Response: The sequencing depth of our data is comparable, if not better than previously published snRNA-seq studies on adipose tissue (Burl et al., 2022; Sarvari et al., 2021; Sun et al., 2020). Therefore, the depth of our data has reached the limit of the 3’ sequencing methods. Unfortunately, due to size limitation of the adipocytes, it is challenging to sort them for Smart-seq. We suspect that lack of specific markers for mPRAT-ad2 is partly due to its intermediate and plastic phenotype. Regarding the mechanistic regulation of mPRAT whitening, we believe that it is more suitable to leave such investigations for a separate follow-up and more in-depth study.

Burl, R.B., Rondini, E.A., Wei, H., Pique-Regi, R., and Granneman, J.G. (2022). Deconstructing cold-induced brown adipocyte neogenesis in mice. Elife 11. 10.7554/eLife.80167.

Sarvari, A.K., Van Hauwaert, E.L., Markussen, L.K., Gammelmark, E., Marcher, A.B., Ebbesen, M.F., Nielsen, R., Brewer, J.R., Madsen, J.G.S., and Mandrup, S. (2021). Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution. Cell Metab 33, 437-453 e435. 10.1016/j.cmet.2020.12.004.

Sun, W., Dong, H., Balaz, M., Slyper, M., Drokhlyansky, E., Colleluori, G., Giordano, A., Kovanicova, Z., Stefanicka, P., Balazova, L., et al. (2020). snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis. Nature 587, 98-102. 10.1038/s41586-020-2856-x.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) There is overall a disconnection between single nucleus RNA-seq data and the lineage chasing data. No specific markers of this population have been validated by staining.

(2) It would be nice to provide more evidence to support the conclusion shown in lines 243 to 245: "These results indicated that new BAs induced by cold exposure were mainly derived from UCP1- adipocytes rather than de novo ASPC differentiation in puPRAT". Pdgfra-negative progenitor cells may also contribute to these new beige adipocytes.

(3) The UCP1Cre-ERT2; Ai14 system should be validated by showing Tomato and UCP1 co-staining right after the Tamoxifen treatment.

Please see above for the responses.

Reviewer #2 (Recommendations For The Authors):

• Without specific lineage tracing it is not possible to conclude that the mPRAT-ad2 population converted to beige with CE. The authors should change this wording from "likely" to "possible".

Response: We have changed the word “likely” to “possible” in the text. Also, we would like to point out that the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT than the beige adipocytes of iWAT (Figure 6E and S7K).

• The sentence "precursor cells may be less sensitive to environmental temperature and have a limited contribution to mature adipocyte phenotypes through de novo adipogenesis after cold exposure." and others like it should be changed to indicate the acute timeframe of this experiment. It has been shown that the precursors make a more significant contribution to de novo beige adipogenesis with chronic cold exposure.

Response: We have modified the sentence as follows: “precursor cells may be less sensitive to acute environmental temperature drop and have a limited contribution to mature adipocyte phenotypes through de novo adipogenesis after cold exposure”. As mentioned above, the cold-induced adipocytes in mPRAT resemble more to the brown adipocytes of iBAT and therefore may have a different mechanism to the de novo beige adipogenesis with chronic cold exposure.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors have addressed the specific comments made upon the initial submission. In particular, they have now provided an explanation, why their GSDM tree looks different than previously published trees. The authors have also followed my initial suggestion to consider the highly-conserved residue following the cleavage site in bird GSDMA forms. Some of the more general weaknesses remain, since they cannot easily be addressed. I agree with the suggestions made by reviewer #2 to further improve the manuscript.

We thank the reviewer for their insight which we think has improved our manuscript. We have additionally made the changes requested by this reviewer and reviewer #2 in the next section.

Reviewer #2 (Recommendations For The Authors):

The authors responded sincerely to our reviewers' questions in the revised manuscript and I sufficiently understand. After re-reading it, however, I found two issues that need to be revised, so please consider doing them.

(1) New sentences (Page 5, lines 209-212) that the authors have added are better written in the subsection, "Bird GSDMA is activated .." after some modification. Because there is an undeniable sense of suddenness in present position.

We agree with this evaluation and have moved these sentences to a more natural position in the following section.

(2) Regarding the chromosomal location of the GSDMA gene, the authors describe that the genes of mammals, birds, and reptiles localize the same genetic locus, but no data are presented. To support their claim, it should also be presented as a supplementary figure.

We agree with this evaluation and have generated Figure 1 – Supplemental 4 to show the synteny of the GSDMA locus from humans to GSDMEc in sharks.

Author Response

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

Response to Reviewing Editor:

Comment: Bladder dysfunction following spinal cord injury (SCI) represents a severe and disabling complication and we lack effective therapies. Following evidence that AMPA receptors play a key role in bladder function the authors show convincingly that AMPA allosteric activators can ameliorate many of the subacute defects in bladder and sphincter function following SCI, including prolonged voiding intervals and high bladder pressure thresholds for voiding. These valuable results in rodents may help in the development of these agents as therapeutics for humans with SCI-induced bladder dysfunction.

Response: We thank the reviewing editor for their assessment of this manuscript and positive comments. We also appreciate the opportunity to revise this manuscript for publication in eLife. We have addressed the excellent comments of the three reviewers. We have included detailed response-to-reviewer comments below to address each specific point. Based on the reviewers’ critiques, we feel our re-working of the manuscript has made for a greatly improved study.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Spinal cord injury (SCI) causes immediate and prolonged bladder dysfunction, for which there are poor treatments. Following up on evidence that AMPA glutamatergic receptors play a key role in bladder function, the authors induced spinal cord injury and its attendant bladder dysfunction and examined the effects of graded doses of allosteric AMPA receptor activators (ampakines). They show that ampakines ameliorate several prominent derangements in bladder function resulting from SCI, improving voiding intervals and pressure thresholds for voiding and sphincter function.

Strengths:

Well-performed studies on a relevant model system. The authors induced SCI reproducibly and showed that they had achieved their model. The drugs revealed clear and striking effects. Notably, in some mice that had such bad SCI that they could not void, the drug appeared to restore voiding function.

Weaknesses:

The studies are well conducted, but it would be helpful to include information on the kinetics of the drugs used, their half-life, and how long they are present in rats after administration. What blood levels of the drugs are achieved after infusion? How do these compare with blood levels achieved when these drugs are used in humans?

Response: We thank Reviewer #1 for the positive comments and their helpful critique. We address each of the specific comments below (in the “Recommendations for the Authors” section of this Response to Reviewer Comments document), and have made changes to the manuscript based on these excellent points.

Reviewer #2 (Public Review):

Summary:

In this study, Rana and colleagues present interesting findings demonstrating the potential beneficial effects of AMPA receptor modulators with ampakines in the context of the neurogenic bladder following acute spinal cord injury. Neurogenic bladder dysfunction is characterized by urinary retention and/or incontinence, with limited treatments available. Based on recent observations showing that ampakines improved respiratory function in rats with SCI, the authors explored the use of ampakine CX1739 on bladder and external urethral sphincter (EUS) function and coordination early after mid-thoracic contusion injury. Using continuous flow cystometry and EUS myography the authors showed that ampakine treatment led to decreased peak pressures, threshold pressure, intercontraction interval, and voided volume in SCI rats versus vehicle-treated controls. Although CX1739 did not alter EUS EMG burst duration, treatment did lead to EUS EMG bursting at lower bladder pressure compared to baseline. In a subset of rats that did not show regular cystometric voiding, CX1739 treatment diminished non-voiding contractions and improved coordinated EUS EMG bursting. Based on these findings the authors conclude that ampakines may have utility in recovery of bladder function following SCI.

Strengths:

The experimental design is thoughtful and rigorous, providing an evaluation of both the bladder and external urethral sphincter function in the absence and presence of ampakine treatment. The data in support of a role for CX1789 treatment in the context of the neurogenic bladder are presented clearly, and the conclusions are adequately supported by the findings.

Weaknesses:

Since CX1789 was administered in the context of cystometry and urethral sphincter EMG, a brief discussion of how ampakines could be used in a therapeutic context in humans would help to understand the translational significance of the work. The study lacks information on the half-life of CX1789 and how might this impact the implementation of CX1789 for clinical use. In addition, the study was limited to female rats. Lastly, given the male bias of traumatic SCI in humans, a brief discussion of this limitation is warranted.

Response: We thank Reviewer #2 for their positive comments and their helpful critique. We address each of the specific comments below (in the “Recommendations for the Authors” section of this Response to Reviewer Comments document). We have also made changes to the manuscript based on the three excellent discussion points brought up by the reviewer.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Rana and colleagues examined the effect of a "low impact" ampakine, an AMPA receptor allosteric modulator, on the voiding function of rats subjected to midline T9 spinal cord contusion injury. Previous studies have shown that the micturition reflex fully depends on AMPA glutaminergic signaling, and, that the glutaminergic circuits are reorganized after spinal cord injury. In chronic paraplegic rats, other circuits (no glutaminergic) become engaged in the spinal reflex mechanism controlling micturition. The authors employed continuous flow cystometry and external urethral sphincter electromyography to assess bladder function and bladder-urethral sphincter coordination in naïve rats (control) and rats subjected to spinal cord injury (SCI). In the acute phase after SCI, rats exhibit larger voids with lower frequency than naïve rats. This study shows that CX1739 improves, in a dose-dependent manner, bladder function in rats with SCI. The interval between voids and the voided volume was reduced in rats with SCI when compared to controls. In summary, this is an interesting study that describes a potential treatment for patients with SCI.

Strengths:

The findings described in this manuscript are significant because neurogenic bladder predisposes patients with SCI to urinary tract infections, hydronephrosis, and kidney failure. The manuscript is clearly written. The study is technically outstanding, and the conclusions are well justified by the data.

Weaknesses:

The study was conducted 5 days after spinal cord contusion when the bladder is underactive. In rats with chronic SCI, the bladder is overactive. Therefore, the therapeutic approach described here is expected to be effective only in the underactive bladder phase of SCI. The mechanism and site of action of CX1739 is not defined.

Response: We thank Reviewer #3 for the positive comments and their helpful critique. We address each of the specific comments below (in the “Recommendations for the Authors” section of this Response to Reviewer Comments document), and have made changes to the manuscript based on the excellent point mentioned in the weakness section.

Comment: Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

Response: We have addressed all comments of both reviewers. We detail our responses in this Response to Reviewer Comments document and have made the associated modifications to the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

Comment: These are well-performed studies.

Response: We thank the reviewer for their positive comment.

Comment: It would be useful to know the blood levels of the drug that are achieved by the infusions, and how long the drugs remain after infusion. Is the 45-minute interval between doses appropriate for the drug's kinetics?

Response: While blood levels of ampakine were not tested in this study, pharmacokinetic parameters for CX1739 in Sprague Dawley rats have previously been determined following an intravenous administration of CX1739. The mean plasma half-life of CX1739 was 1.25 ± 0.03 hrs, with a Tmax of 30 minutes (information provided through personal communication with RespireRx). Although the 45 minutes interval between doses would not be within the time frame of post administration clearance of the first CX1739 dose from the system, the plasma levels would be considerably lower by 45 mins post administration. A limitation of terminal cystometry preparations is the duration you can maintain a single animal, and this was also included in our rationale for dosing every 45 mins. In our experience longer recordings can increase variability. A 45 min window allowed for the anesthetized procedure to remain under ~6 hours. Further, in our studies investigating the impact of ampakines in rats following an SCI, acute impacts of intravenous ampakine administration were observed for up to 30 minutes. (Rana et al., 2021) Along with the half-life and data from the respiratory system informed our decision here. We have added this rationale to the methods section and in part to the discussion section (Page 11, 2930).

Comment: Since a major plus of these studies is their potential applicability to humans with SCI, it would be helpful to know whether the drug levels achieved here resemble those that were achieved in human trials to date.

Response: Since blood/plasma levels were not tested in the current study, we cannot comment on the comparison of blood plasma levels achieved in human trials. However, we have expanded upon this point in the discussion section (page 29-30).

Comment: The authors could also provide us with a bit more description of the different classes of ampakines, and why they chose the one they used.

Response: Thank you for this suggestion. We would like to highlight a section in our discussion (Page 28-29) where we have an in-depth description of the two classes of ampakines in the discussion and the rationale for selecting the low-impact CX1739 drug.

Comment: Lastly, the first reference is cited twice in the bibliography.

Response: The duplicate reference has been removed.

Reviewer #2 (Recommendations For The Authors):

Comment: Overall, the findings support the potential for ampakine administration in the setting of neurogenic bladder dysfunction following SCI. The manuscript was well written, the experimental design was rigorous, the data were of excellent quality, and the conclusions were adequately supported by the findings. Weaknesses are considered minor and can be addressed mostly by clarification as noted below.

Response: We thank the reviewer for their positive comments.

Comment: Since CX1789 was provided in the context of cystometry and EUS EMG, a brief discussion of how ampakines could be used in a therapeutic context in humans would help to understand the translational significance of the work.

Response: Thank you for this important comment to include a discussion about translational significance of CX1739. We have included a discussion (Page 34) about the translational significance of this work in the discussion section of the last paragraph.

Comment: No information is provided on the half-life of CX1789 and how might this impact the implementation of CX1789 for clinical use. The inclusion of this information would help the reader to appreciate the potential for and limitations of clinical implementation.

Response: Although pharmacokinetic analyses were not conducted as part of this study, we have included details of CX1739 plasma pharmacokinetics examined in Sprague-Dawley rats (Page 11, 29-30). This information has been provided through personal communication with RespireRx.

Comment: The study was limited to female rats. Would the authors anticipate different efficacy of CX1789 in male rats? A comment on the choice of animal sex and implications for interpretation of the findings would strengthen the discussion and potential clinical implementation given the male bias of traumatic SCI in humans.

Response: Thank you for your important comment. In this study, females were chosen primarily due to the fact they have better recovery outcomes from spinal cord injury. During initial preliminary data gathering, we used both male and female rats and found that the male rats often did not recover cytometric voiding at this time point. So we chose to continue only with the female rats in this current study. It is well established that female rats have better urogenic recovery from SCI effects, perhaps due to the easier postoperative care. It is critical that we complete future studies in both male and female rats, however, we will have to change our experimental paradigm (time after injury, and or severity of injury) to make comparisons between SCI and intact male rats. We have now included this important topic of our sex selection in the methods section (Page 6) of the manuscript and have also expanded this point in the discussion section (page 30).

Reviewer #3 (Recommendations For The Authors):

Comment: The impact of ampakine treatment on EUS EMG activity is not obvious from the data presented in Fig. 5C-F. I do see in the magnified area of the SCI rat tracing some clear EUS activity with 15 mg/kg of CX1739. However, statistically, there is not a significant improvement in bladder-urethral sphincter coordination in rats treated with ampakine. Authors should discuss how or why ampakine treatment improves bladder function without affecting bladder-urethral sphincter coordination. The background noise of the EUS EMG in Fig. 5B changes dramatically between conditions. Are these tracings from the same experiment? If yes, please explain why the background noise changes during the course of the experiment. Was this change in background noise observed only in SCI rats?

Response: Thank you for such an interesting comment. Although our data analysis shows no statistically significant difference in the duration or amplitude of EUS EMG bursting when comparing vehicle to ampakine treatment. However, we did see a difference in the threshold at which bursting occurred (Fig 5C-F). Rats that lost complete coordination (Figure 6) due to injury, ampakines provide further confirmation about producing EUS EMS bursting and coordinated voiding.

Therefore, these results suggest that ampakines have some positive modulatory effects on EUS EMG bursting events. Overall, we did not see any significant differences of the background noise of EUS EMG between conditions during experiments both in spinal intact and SCI. The background noise of the EUS EMG in Fig. 5B decreases after baseline and HPCD due to changes in experimental conditions (needed to use slightly more urethane due to showing up of animal’s consciousness). We would also like to confirm that these tracings are from the same experiment. Accordingly, we have made further clarifications in the manuscript.

Comment: Tables 1 and 2 show the same data as figures 3 and 4. I suggest removing the tables. In addition, table 2 includes letters (A, B, C, D) to indicate statistical significance. However, no indication of the meaning of these letters is provided. What does "levels not connected by same letter are significantly different" mean? Please clarify. I suggest including the statistical comparisons in Fig. 4

Response: While we did consider adding statistical bars in the graphs themselves, the number of comparisons being conducted reduced the readability of the graphs. Thus, we would like preserve the current format of the table and provide the readers with all statistical comparisons being made. The statement “levels not connected by the same letter are significantly different” indicates that only treatment groups for an outcome that do not have an overlapping letter, such as baseline (A) and HPCD (A) values for threshold pressures are different from the 5 mg/kg (B,C,D), 10 mg/kg (C,D) and 15 mg/kg (D) group in the SCI rats. Further, threshold pressures in the 5 mg/kg, 10 mg/Kg and 15 mg/kg groups are not significantly different from each other. These results have also been described in detail in the results section. Lastly, we acknowledge the redundancy of data presented in Tables 1 and 2. These two tables have been moved to the supplemental section.

Comment: A study by Yoshiyama and colleagues previously showed that the AMPA antagonists LY215490 completely abolished the reflex bladder contractions and EMG activity of the EUS muscle during a continuous filling in naïve rats (JPET 1997). Surprisingly, CX1739, a low-impact AMPA receptor activator, does not affect bladder contractions or EMG activity in naïve rats. Authors should discuss the reason for this discrepancy.

Response: Thank you for this comment. We believe the different pharmacokinetics of the drugs can explain these effects. We have included this critical point in the discussion (page 31-32).

Comment: The conclusion that CX1739 is acting on sensory pathways is highly speculative and needs additional support. The functional status of the afferent pathways is uncertain following SCI. Please revise.

Response: Thank you for this comment. We agree, in retrospect, that this speculative comment is an overassumption, and we have removed it from the discussion. We have modified the discussion to remove focus from the sensory nervous system and, more generally, discuss the location of AMPA receptors in the voiding neurocircuitry (page 31).

Comment: Figure 3. It's difficult to see the asterisks that indicate statistical significance. Please use a line or a bigger symbol to indicate statistical differences between groups.

Response: Thank you for the suggestion we have modified the figure to make the asterisks bigger and added a line.

Comment: Data for peak pressure should be included in Figures 3 and 4.

Response: Thank you for pointing out one of the important parameters of cystometry which is peak pressure. As we did not see significant changes in bladder peak contraction pressure between spinal intact and SCI rats, we prefer not to show a graph of peak pressure (in Fig 3) to highlight other parameters that showed significant injury effects, such as baseline pressure, ICI, threshold, and voided volume. However, peak pressure reduced similarly both in spinal intact and SCI rats, suggesting that ampakine has some treatment effects on peak pressure that we prefer to include in Fig 4. We modified our results section and have included a description on peak pressures in the result section.

Comment: The peak pressure was reduced in both naïve and SCI rats treated with ampakine. Therefore, the peak pressure is not one of the parameters that improves by ampakine in SCI rats.

Response: Yes, we agree that peak pressures between spinal intact and SCI rats were comparable. Some treatment effects of ampakine on peak pressure were observed both between spinal intact and SCI rats. We have amended the manuscript to make this clearer.

Comment: The reference from Yoshiyama et al (1999) is duplicated.

Response: Thank you for catching this error. The references have been combined in the revised version.

Comment: Page 15, the authors state that "Coordinated bladder contractions and associated EUS EMG activity were readily demonstrated in all 7 naïve animals". In other sections, they referred to 8 naïve rats. What is the actual number of naïve rats?

Response: Thanks for pointing out this error. The actual number of naïve rats is 8. We have rectified this error.

Author Response

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

We thank the Editors and the Reviewers for their comments on the importance of our work “showing a new role of caveolin-1 as an individual protein instead of the main molecular component of caveolae” in contributing to membrane bending rigidity and for constructive and thoughtful remarks that have allowed us to improve the manuscript.

Indeed, we here establish the contributing role of caveolin-1 to membrane mechanics by a molecular mechanism that needs to be further addressed. To that respect, we thank the reviewers for suggesting avenues to improve the presentation and discussion of our hypotheses based on results of theoretical model and independent biophysical measurements of membrane mechanics in tube pulling from plasma membrane spheres, which concur to support the key role of caveolin-1 in building membrane bending rigidity.

To fulfill the recommendations of the reviewers we have modified the manuscript, as discussed below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Because of the role of membrane tension in the process, and that caveloae regulate membrane tension, the authors looked at the formation of TEMs in cells depleted of Caveolin1 and Cavin1 (PTRF): They found a higher propensity to form TEMs, spontaneously (a rare event) and after toxin treatment, in both Caveolin 1 and Cavin 1. They show that in both siRNA-Caveolin1 and siRNA-Cavin1 cells, the cytoplasm is thinner. They show that in siCaveolin1 only, the dynamics of opening are different, with notably much larger TEMs. From the dynamic model of opening, they predict that this should be due to a lower bending rigidity of the membrane. They measure the bending rigidity from Cell-generated Giant liposomes and find that the bending rigidity is reduced by approx. 50%.

Strengths:

They also nicely show that caveolin1 KO mice are more susceptible to death from infections with pathogens that create TEMs.

Overall, the paper is well-conducted and nicely written. There are however a few details that should be addressed.

See below modifications brought to the manuscript in response to the Reviewer’s remarks.

Reviewer #2 (Public Review):

Summary:

The manuscript by Morel et al. aims to identify some potential mechano-regulators of transendothelial cell macro-aperture (TEM). Guided by the recognized role of caveolar invaginations in buffering the membrane tension of cells, the authors focused on caveolin-1 and associated regulator PTRF. They report a comprehensive in vitro work based on siRNA knockdown and optical imaging approach complemented with an in vivo work on mice, a biophysical assay allowing measurement of the mechanical properties of membranes, and a theoretical analysis inspired by soft matter physics.

Strengths:

The authors should be complimented for this multi-faceted and rigorous work. The accumulation of pieces of evidence collected from each type of approach makes the conclusion drawn by the authors very convincing, regarding the new role of cavolin-1 as an individual protein instead of the main molecular component of caveolae. On a personal note, I was very impressed by the quality of STORM images (Fig. 2) which are very illuminating and useful, in particular for validating some hypotheses of the theoretical analysis.

Weaknesses:

While this work pins down the key role of caveolin-1, its mechanism remains to be further investigated. The hypotheses proposed by the authors in the discussions about the link between caveolin and lipids/cholesterol are very plausible though challenging. Even though we may feel slightly frustrated by the absence of data in this direction, the quality and merit of this paper remain.

We thank the reviewer for mentioning the merit of our work which lays the foundations for more molecular mechanistic work on a possible role of lipids/cholesterol in the building of membrane bending rigidity by caveolin-1 and which is currently carried out by some of the authors, and which shows that the question is indeed challenging as indicated by the reviewer. This is now stated in the results section, as suggested (Page 12) :

"To test these predictions, we have treated cells with methyl-beta-cyclodextrin to deplete cholesterol from the plasma membrane and reduce its bending rigidity (47); unfortunately, this treatment affected the cell morphology, which precluded further analysis."

The analogy with dewetting processes drawn to derive the theoretical model is very attractive. However, although part of the model has already been published several times by the same group of authors, the definition of the effective membrane rigidity of a plasma membrane including the underlying actin cortex, was very vague and confusing.

We thank the reviewer for mentioning the importance of defining the terms “membrane bending rigidity” as well as “effective membrane bending rigidity” that is now used and defined in the material and method section in the Physical modelling description (see considerations below), while for the sake of simplicity we use the term “membrane bending rigidity” in the main text, which is now defined in the introduction section : “membrane bending rigidity, i.e. the energy required to locally bend the membrane surface”.

Indeed, in a liposome, a rigorous derivation leads to a relationship between the membrane tension and the variation of the projected area, which are related by the bending rigidity: this relationship is known as the Helfrich’s law. This statistical physics approach is only rigorously valid for a liposome, whereas its application to a cell is questionable due to the presence of cytoskeletal forces acting on the membrane. Nevertheless, application of the Helfrich’s law to cell membranes may be granted on short time scales, before active cell tension regulation takes place (Sens P and Plastino J, 2015 J Phys Condens Matter), especially in cases where cytoskeletal forces play a modest role, such as red blood cells (Helfrich W 1973 Z Naturforsch C). The fact that the cytoskeletal structure and actomyosin contraction are significantly disrupted upon cell intoxication-driven inhibition of the small GTPase RhoA, as shown here for the first time by STORM analysis, supports the applicability of Helfrich’s law to describe TEM opening. Because of the presence of proteins, carbohydrates, and the adhesion of the remaining actin meshwork after toxin treatment, we expect the Helfrich relationship to somewhat differ from the case of a pure lipidic membrane. We account for these effects via an “effective bending rigidity”, a term used in the detailed discussion of the model hypotheses, which corresponds to an effective value describing the relationship between membrane tension and projected area variation in our cells.

The following discussion has been extended and improved in the Physical modeling part of the materials & methods section (Pages 23-24): “κ is the effective bending rigidity of the cell membrane, which quantifies the energy required to bend the membrane. (…). While rigorously derived for a pure lipid membrane, we assumed that Helfrich’s law is applicable to describe the relationship between the effective membrane tension acting on TEMs and the observed projected surface in our cells. We expect Helfrich’s law to be applicable on short time scales, before active cell tension regulation takes place (73), especially in cases where cytoskeletal forces play a modest role, such as for red blood cells (74) or for the highly disrupted cytoskeletal structure of our intoxicated cells. Thus, the parameter κ in Eq. 2 is an effective bending rigidity, whose value may somewhat differ from that of a pure lipid membrane to account for the role played by protein inclusions and the mechanical contribution of the remaining cytoskeletal elements after cell treatment with the toxin”

Here, for the first time, thanks to the STORM analysis, the authors show that HUVECs intoxicated by ExoC3 exhibit a loose and defective cortex with a significantly increased mesh size. This argues in favor of the validity of Helfrich formalism in this context. Nonetheless, there remains a puzzle. Experimentally, several TEMs are visible within one cell. Theoretically, the authors consider a simultaneous opening of several pores and treat them in an additive manner. However, when one pore opens, the tension relaxes and should prevent the opening of subsequent pores. Yet, experimentally, as seen from the beautiful supplementary videos, several pores open one after the other. This would suggest that the tension is not homogeneous within an intoxicated cell or that equilibration times are long. One possibility is that some undegraded actin pieces of the actin cortex may form a barrier that somehow isolates one TEM from a neighboring one.

As pointed by the Reviewer, we expect that membrane tension is neither a purely global nor a purely local parameter. Opening of a TEM will relax membrane tension over a certain distance, not over the whole cell. Moreover, once the TEM closes back, membrane tension will increase again. This spatial and temporal localization of membrane tension relaxation explains that the opening of a first TEM does not preclude the opening of a second one or enlargement of the TEM when the actin cable is cut by laser ablation (20). On the other hand, membrane tension is not a purely local property. Indeed, we observe that when two TEMs enlarge next to each other, their shape becomes anisotropic, as their enlargement is mutually hampered in the region separating them. We account for this interaction by treating TEM membrane relaxation in an additive fashion. We emphasize that this simplified description is used to predict maximum TEM size, corresponding to the time at which TEM interaction is strongest. As the reviewer points out, it would be more questionable to use this additive treatment to predict the likelihood of nucleation of a new TEM, which is not done here.

Accordingly, the Physical modelling part in the materiel and methods has been modified into: “Eq. 2 treats the effect of several simultaneous TEMs in an additive manner. This approximation is used here to predict TEM size, because at maximum opening of simultaneous TEMs their respective membrane relaxation is felt by each other, as it can be inferred from the shape that neighboring TEMs adopt in experiments. This additive treatment would appear less appropriate to describe the likelihood of nucleating a second TEM in the presence of a first one (a calculation that is not performed here), since membrane relaxation by a TEM may not be felt at membrane regions distant from it.”

Could the authors look back at their STORM data and check whether intoxicated cells do not exhibit a bimodal population of mesh sizes and possibly provide a mapping of mesh size at the scale of a cell?

To address the question raised by the Reviewer we decided to plot the whole distribution of mesh sizes in addition to the average value per cell. We did not observe a bimodal distribution but rather a very heterogeneous distribution of mesh size going up to a few microns square in all conditions of siRNA treatments. Moreover, we did not observe a specific pattern in the distribution of mesh size at the scale of the cell, with very large mesh sizes being surrounded by small ones. We also did not observe any specific pattern for the localization of TEM opening, as described in the paper, making the correlation between mesh size and TEM opening difficult.

This following sentence has been added in the results section (Pages 8-9): “Indeed, we observed in cells treated with ExoC3 no specific cellular pattern or bimodal distribution of mesh size between the different siRNA conditions but a rather very heterogeneous distribution of mesh size values that could reach a few square microns in all conditions. ”

In particular, it is quite striking that while bending rigidity of the lipid membrane is expected to set the maximal size of the aperture, most TEMs are well delimited with actin rings before closing. Is it because the surrounding loose actin is pushed back by the rim of the aperture? Could the authors better explain why they do not consider actin as a player in TEM opening?

Actin ring assembly and stiffening is indeed a player in TEM opening, that was investigated in the work by Stefani et al., 2017 Nat comm. Interference of actin ring assembly and stiffening is included in our differential equation describing TEM opening dynamics (second term on the left-hand side of Eq. 3). In some cases, actin ring assembly is the dominant player, such as in TEM opening after laser ablation (ex novo TEM opening/widening). In contrast, here we investigate de novo TEM opening, for which we expect that bending rigidity can be estimated without accounting for actin assembly, as we previously reported (19). Such a bending rigidity estimate (Eq. 5) is obtained by considering two different time scales: the time scale of membrane tension relaxation, governed by bending rigidity, and the time scale of cable assembly, governed by actin dynamics. We expect the first time scale to be shorter, and thus the maximum size of de novo TEMs to be mainly constrained by membrane tension relaxation. Two paragraphs related to the discussion of the different time scales have been added to 1) the discussion section, and 2) to the physical modelling part discussed in the materiel and methods section of the revised manuscript (see below).

The following paragraph has been added in the discussion (Pages 14-15): “Our study shows that membrane rigidity sets the maximal size of TEM aperture, although an actin ring appears before TEM closure (20). Actin ring assembly and stiffening is indeed a player in TEM opening, and it is included in our differential equation describing TEM opening dynamics (Eq. 3). In some configurations, actin ring assembly is the dominant player, such as in TEM opening after laser ablation (ex novo TEM opening), as we previously reported (20). In contrast, here we investigate de novo TEM opening, for which we expect that bending rigidity can be estimated without accounting for actin assembly (19). Such a bending rigidity estimate (Eq. 5) is obtained by considering two different time scales: the time scale of membrane tension relaxation, governed by bending rigidity, and the time scale of cable assembly, governed by actin dynamics. We expect the first-time scale to be shorter, and thus the maximum size of de novo TEMs to be mainly constrained by membrane tension relaxation. However, we cannot rule out that the formation of an actin cable around the TEM before it reaches its maximum size may limit the correct estimation of the bending rigidity.”

The following paragraph has been added in the physical modelling part of the materiel and methods section (Pages 24-25) “A limitation of our theoretical description arises from the use of spatially uniform changes in parameter values to describe differences between experimental conditions, thus assuming spatially uniform effects. However, we cannot exclude the existence of non-uniform effects, such as changes in the size and organization of the remaining actin mesh, which could set local, non-uniform barriers to TEM enlargement in a manner not accounted for by our model.” And “We note that the estimate of κ provided by Eq. 5 is independent of α and thus of actin cable assembly. This simplification arises from membrane tension relaxing over a shorter time scale than actin assembly. Thus, we expect the maximum size of de novo TEMs to be mainly constrained by membrane tension relaxation (19), unlike ex novo TEM enlargement upon laser ablation, for which the dynamics of actin cable assembly control TEM opening (20)”

Instead of delegating to the discussion the possible link between caveolin and lipids as a mechanism for the enhanced bending rigidity provided by caveolin-1, it could be of interest for the readership to insert the attempted (and failed) experiments in the result section. For instance, did the authors try treatment with methyl-beta-cyclodextrin that extracts cholesterol (and disrupts caveolar and clathrin pits) but supposedly keeps the majority of the pool of individual caveolins at the membrane?

As recommended by the reviewer we have added the following sentence (Page 12): “We have treated cells with methyl-beta-cyclodextrin to deplete cholesterol from the plasma membrane and reduce its bending rigidity (47); unfortunately, this treatment affected the cell morphology, which precluded further analysis”

Tether pulling experiments on Plasma membrane spheres (PMS) are real tours de force and the results are quite convincing: a clear difference in bending rigidity is observed in controlled and caveolin knock-out PMS. However, one recurrent concern in these tether-pulling experiments is to be sure that the membrane pulled in the tether has the same composition as the one in the PMS body. The presence of the highly curved neck may impede or slow down membrane proteins from reaching the tether by convective or diffusive motion.

We thank the Reviewer for mentioning the dedicated work accomplished with tether pulling experiments on PMS and for pointing the obtention of convincing results that align well with the hypotheses drawn from the theoretical model thereby allowing us to propose a direct or indirect role of caveolin-1 in the building of membrane rigidity. As pointed out by the reviewer, a concern with tube pulling experiments is related to the dynamics of equilibration of membrane composition between the nanotube and the rest of the membrane. In our experiments, we have waited about 30 seconds after tube pulling and after changing membrane tension. We have checked that after this time, the force remained constant, implying that we have performed experiments of tube pulling from PMS in technical conditions of equilibrium that ensure that lipids and membrane proteins had enough time to reach the tether by convective or diffusive motion.

The revised version of the manuscript now includes the following sentence and a representative example of force vs time plot (Page 12): “We waited about 30 seconds after tube pulling and changing membrane tension and checked that we reached a steady state (Fig. S5), where lipids and membrane proteins had enough time to equilibrate.”

Could the authors propose an experiment to demonstrate that caveolin-1 proteins are not restricted to the body of the PMS and can access to the nanometric tether?

In principle, this could be further checked using cells expressing GFP-caveolin-1 to generate PMS as done in Sinha et al., 2011 and by analyzing a steady protein signal in the tube. This would confirm the equilibration, provided that caveolin-1 is recruited in the nanotube due to mechanical reasons that are now discussed in the discussion section (Pages 13-14) : “Our tube pulling experiments can be discussed along 2 lines. Indeed, since caveolin-1 is inserted in the cytosolic leaflet of the plasma membrane, when a nanotube is pulled towards the exterior of the PMS, we can expect 2 situations depending on the ability of caveolin-1 to deform membranes, which remains to be addressed (24). i) If Cav1 does not bend membranes, it could be recruited in the nanotube at a density similar to the PMS and our force measurement would reflect the bending rigidity of the PMS membrane. Cav1 could then stiffen membrane either as a stiff inclusion at high density or/and by affecting lipid composition. ii) If Cav1 bends the membrane, it is expected from caveolae geometry that the curvature in the tube would favor Cav1 exclusion. The force would then reflect the bending rigidity of the membrane depleted of Cav1, which should be the same in both types of experiments (WT and Cav1-depleted conditions) if the lipid composition remains unchanged upon Cav1 depletion. Note that the presence of a very reduced concentration of Cav1 as compared to the plasma membrane has been reported in tunneling nanotubes (TNT) connecting two neighboring cells (51). These TNTs have typical diameters of similar scale than diameters of tubes pulled from PMS. At this stage, we cannot decipher between both properties for Cav1. Considering a direct mechanical role of Cav1, previous studies showed that inclusion of integral proteins in membranes had no impact on bending rigidity, as shown in the bacteriorhodopsin experiment (52), or even decreased membrane rigidity as reported for the Ca2+-ATPase SERCA (53). Previous simulations have also confirmed the softening effect of protein inclusions (54). Nevertheless, our observations could be explained by a high density of stiff inclusions in the plasma membrane (>>10%), which is generally not achievable with the reconstituted membranes. Considering an impact on lipid composition, it is well established that caveolae are enriched with cholesterol, sphingomyelin, and glycosphingolipids, including gangliosides (55,56), which are known to rigidify membranes (57,47). Thus, caveolin-1 might contribute to the enrichment of the plasma membrane with these lipid species. We did not establish experimental conditions allowing us to deplete cholesterol without compromising the shape of HUVECs, which prevented a proper analysis of TEM dynamics. Moreover, a previous attempt to increase TEMs width by softening the membrane through the incorporation of poly-unsaturated acyl chains into phospholipids failed, likely due to homeostatic adaptation of the membrane’s mechanical properties (18). Further studies are now required to establish whether and how caveolin-1 oligomers control membrane mechanical parameters through modulation of lipids organization or content. Caveolin-1 expression may also contribute to plasma membrane stiffening by interacting with membrane-associated components of the cortical cytoskeletal or by structuring ordered lipid domains. Nevertheless, it has been reported that the Young’s modulus of the cell cortex dramatically decreases in ExoC3-treated cells (17) suggesting a small additional contribution of caveolin-1 depletion to membrane softening. This is supported by 2D STORM data showing a dramatic reorganization of actin cytoskeleton in ExoC3-treated cells into a loose F-actin meshwork that is not significantly exacerbated by caveolin-1 depletion. Altogether, our results suggest that the presence of Cav1 stiffens plasma membranes, and that the exact origin of this effect must be further investigated.”

Author recommendations

Reviewer #1 (Recommendations For The Authors):

Suggestions for improvements:

(1) Depletion of both Cavin1 and Caveolin1 increases the density of TEMs. Membrane tension is a critical parameter of the initiation phase of TEMs, its nucleation, and initial enlargement. From the TEM dynamics, the authors should be able to measure membrane tension. The expectation is that in both Caveolin1 and Cavin1 depleted cells, tension is higher (because there is no caveolae), explaining why there are more TEMs.

While we cannot directly measure membrane tension, we can estimate membrane tension variations using our theoretical modeling. As reported in the article, we predict that depleting Caveolin-1 leads to a significant 2-fold increase of membrane tension, which can explain the concomitant increase in the nucleation of TEMs, as the reviewer points out. In contrast, the model predicts no significant increase of membrane tension upon Cavin-1/PTRF depletion, whereas TEM nucleation also increases significantly (but less than upon Caveolin-1 depletion). Altogether, we can explain these results by considering that membrane tension is an important player in TEM nucleation, but not the only one. Notably, we expect cell height to be another important player, as it sets an energy barrier for the basal and apical membranes to meet each other and fuse. Indeed, we report that membrane height is reduced upon depletion Cavin-1, thus explaining the observed increase in TEM nucleation. The importance of reducing cell thickness to increase the TEM opening likelihood is best supported by previous data showing that pushing forces applied on the apical membrane induced the opening of TEMs (Ng et al., 2017 MBoC).

An improved discussion of the parameters controlling TEM nucleation has been included in the discussion of the revised manuscript, as follow (Page 15): “Our study points to underlying mechanisms by which caveolae regulate the frequency of TEM nucleation. Nucleation of TEMs requires the apposition of the basal and apical cell membranes, which is hindered by the intermembrane distance, set by the cell height. Meeting of the two membranes may create an initial precursor tunnel, which needs to be sufficiently big to enlarge into an observable TEM, instead of simply closing back. The size of the minimal precursor tunnel required to give rise to a TEM increases with membrane bending rigidity and decreases with membrane tension (19). Silencing cavin-1 or caveolin-1 both lead to a decrease in cell height, thus favoring the likelihood of precursor tunnel nucleation. While silencing cavin-1 has no significant impact on either membrane tension or bending rigidity, silencing caveolin results in both an increase of membrane tension and a decrease of bending rigidity, which results in a decrease in the required minimal radius of the precursor tunnel, thus further favoring TEM nucleation. Overall, our results offer a consistent picture of the physical mechanisms by which caveolae modulate TEM nucleation.”

(2) In Figure 2B, the authors state that there is no significant difference in the actin mesh size while I see a clear higher average value and distribution in siCAV1+. This seems to correlate with the differences in TEM maximal sizes. How can the authors completely exclude that the actin organisation is not in part responsible for the larger TEMs observed in siCAV1 cells?

In our theoretical modeling of TEM opening dynamics, all differences between conditions are described by changes in what we consider as “effective” parameter values. Thus, changes in actin organization may induce a change in the "effective bending rigidity" parameter controlling membrane tension relaxation. A limitation of such a description is that all changes are assumed to be spatially uniform. However, it is possible that changes in actin mesh size and organization set local barriers to TEM enlargement in a way that would not be appropriately described by our model. While our current modeling appears to provide a consistent interpretation of our observations, we cannot completely exclude the existence of such local effects.

This limitation of our current interpretation is now mentioned in the following paragraph, which has been added in the physical modelling part of the materiel and methods section (Page 24) : “A limitation of our theoretical description arises from the use of spatially uniform changes in parameter values to describe differences between experimental conditions, thus assuming spatially uniform effects. However, we cannot exclude the existence of non-uniform effects, such as changes in the size and organization of the remaining actin mesh, which could set local, non-uniform barriers to TEM enlargement in a manner not accounted for by our model.”

(3) It would be nice to see the results of Table 1 (in particular the thickness of cells) in a Bar plot.

The experimental values of cell volumes and areas are reported in bar plots of Fig. 3C and 3D. In contrast, we chose not to depict values of cell eight in bar plots considering that these values were calculated from mean values of cell areas and volumes reported in Fig. 3C and 3D, i.e. rough division of volumes over areas, with error propagation. Since the volume and areas are not performed on the same set of cells, it is not possible to divide the repeats one by one and to provide cell numbers, which are key parameters to perform statistical tests.

(4) There are two reasons why Caveolin1 could change the bending rigidity. First, because it makes the membrane stiffer, or because the presence of caveolin1 (that binds to cholesterol) in the plasma membrane changes the lipid composition. It would be nice if the authors could provide some lipidomics analysis to see if there is a lipid change in siCAV1 cells.

We thank the reviewer for pointing the importance of clarifying the hypotheses regarding a direct or indirect role of caveolin-1 in membrane bending rigidity which might be related to changes in membrane lipid composition especially cholesterol and sphingomyelin. We have modified the discussion section to integrate this point. The lipidomic approach is certainly interesting to address the question of the role of caveolin-1 in building membrane bending rigidity. Indeed, some of the authors have addressed the specific questions related to Cav-1 spontaneous curvature and its effect on the lipid composition of the plasma membrane in two separate manuscripts (in preparation). They represent comprehensive studies by themselves that will provide mechanistic insights on how caveolin-1 builds membrane bending rigidity and as follow up of the present manuscript which reports the importance of the regulation of membrane rigidity in cell biology and during infectious processes.

Reviewer #2 (Recommendations For The Authors):

The paper is nicely written and the results are convincing. The three main comments and questions from the Public Review do not necessarily call for new experiments. However, clarifications are required. This work can be very useful. Better not to leave any difficulty or weakly justified hypothesis under the carpet.

To fulfill with the reviewer comments, we have improved the discussion regarding the hypothesizes which can be drawn about of a direct versus indirect mechanistic role of caveolin-1 in the regulation of effective membrane bending rigidity and which might be related to changes in membrane lipid composition or via regulation of the cytoskeleton, which we cannot exclude.

• Minor correction: in the abstract: replace "the enhanced nucleation" with "the enhanced occurrence of nucleation events".

The abstract has been changed accordingly : “The enhanced occurrence of TEM nucleation events correlates with a reduction of cell height, …”

Author Response

The authors' responses to the public reviews can be found here

The following is the authors’ response to the most recent recommendations.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I appreciate the effort that the authors have put into this revised version of the manuscript. Before going into details, I would suggest that, in the future, the authors include enough information in their response to allow reviewers to follow the changes made. Not simply "Fixed", but instead "we have modified the description of these results and now state on lines XXX to XXX (revised text)".

We greatly apologize, we certainly did not wish to cause more work for the reviewer to find the necessary changes. We will list the line number and our changes in the following response.

The authors' response to my comments was confined to the minor points, with no attention to more important questions regarding speculations about mechanism which were (and still are) presented as factual conclusions. I do not consider the responses adequate.

We responded to each of your comments and where we disagree, we have explained in detail.

With respect to the meaning of "above" and "below" in the context of an intracellular organelle, I think that referring to up and down in a figure is fine, provided that the cytoplasmic and luminal sides are indicated in that figure. I think that labeling to that effect in each figure would be immensely helpful for the reader.

We agree with this point and have updated all the figures to include these labels.

The statement on lines 333-335 about non-competitive inhibition is a bit naïve. The only thing ruled out by this type of inhibition is that substrate and TBZ binding do not share the same binding process, in which case they would compete. It doesn't show that TBZ gets to its binding site from the lumen or from the bilayer, or by any other process that isn't shared with substrate. It also doesn't rule out kinetic effects, such as slow inhibitor dissociation, that result in non-competitive kinetics. Please rewrite this sentence to indicate that one explanation of the non-competitive nature of TBZ inhibition would be that TBZ diffuses into the vesicle and binds from the lumen. It's not the only explanation.

We have changed this sentence lines 334-336 to be more speculative and not include any statement about non-competitive inhibition. Please see, “Studies have proposed that TBZ first enters VMAT2 from the lumenal side, binding to a lumenal-open conformation.”

The revised version integrates the MD simulations into a plausible mechanism for luminal release of substrate. A key element in this mechanism is the protonation of D33, E312 and D399, which allows substrate to leave following water entry into the binding site. The acidic interior of synaptic vesicles should facilitate such protonation, but the fate of those protons needs to be considered. Are any of them predicted to dissociate prior to the return to a cytoplasm-facing conformation? If so, are all 3 released in that conformation? Postulating protonation events at one point in the reaction cycle requires some accounting for those protons - or at least recognition of the problem of reconciling their binding with the known stoichiometry of VMAT.

We completely agree with this point and while we cannot account for all protons with a single structure and simulation of neurotransmitter release, some discussion of the fate of the protons is warranted. We have included a highly speculative statement in the discussion on this point, see lines 462-465, “Given the known transport stoichiometry of two protons per neurotransmitter, we speculate that two protons may dissociate back into the lumen, perhaps driven by the formation of salt bridges between D33 and K138 or R189 and E312 for example in an cytosol-facing state.”

Reviewer #3 (Recommendations For The Authors):

On page 13, line 238, the statement "The protonation states of titratable residues D33, E312, D399, D426, K138 and R189, which are in close proximity to TBZ, also impact its binding stability (Table 4)" is misleading. Table 4 only shows that D426 is charged and what the pKa values are. This should be rephrased to separate out which residues are in close proximity from what is known about how their protonation states affect TBZ stability.

We agree with this statement and have rephrased this on line 290-294 on page 13 to read, “Several titratable residues, including D33, E312, D399, D426, K138, and R189, line the central cavity of VMAT2 and impact TBZ binding stability (Table 4). We found that maintaining an overall neutral charge within the TBZ binding pocket, as observed in system TBZ_1, most effectively preserves the TBZ-bound occluded state of VMAT2. Residues R189 and E312 in particular are within close proximity of TBZ and participate directly in binding.” We note that given the acidic pH of the vesicle lumen (5.5), it is likely all four residues may be protonated to a significant degree in this state.

Typos:

• luminal is another name for the drug generically known as phenobarbital, lumenal means in the lumen. (This typo seems to have crept into the published literature now too).

Thank you for pointing this out. Indeed, we had considered carefully whether to use ‘lumenal’ or ‘luminal’ in our revised text. In fact, both are used interchangeably throughout the scientific literature and luminal is the more commonly used term. Please also see: https://www.merriam-webster.com/medical/luminal we do agree that there may be confusion because ‘Luminal’ is a trademark of phenobarbital. Therefore, we have changed the text to read ‘lumenal’ throughout.

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

Reviewer #1 (Recommendations For The Authors):

I congratulate the authors on this study, which I enjoyed reading. Overall, the study reports a novel and exciting new structure for a member of the SLC18 family of vesicular monoamine transporters. Associated MD, binding and transport assays provide support for the hypothesis and firm up the modelled pose for the TBZ drug. The main strengths of the study largely sit with the structure, which, as the authors say, provides additional and essential insights above those available from AF2. The structures also reveal several potentially interesting observations concerning the mechanism of gating and proton-driven transport. The main weakness lies in the limited mutational data and studies into the role of pH in regulating ligand binding. As detailed below, my main comment would be to spend a little extra time expanding the mutational data (perhaps already done during the review?) to enable more evidence-based conclusions to be drawn.

We thank reviewer #1 for their helpful comments and suggestions. We agree that mutational analysis specifically of neurotransmitter transport would strengthen the mechanistic conclusions of the work. We also agree with reviewer #1 and #3 that the role of pH and the protonation state of charged residues was a weakness in the first version of the manuscript. Therefore, we have expanded our mutational and computational data as detailed below and we believe that this has further solidified our findings.

Specific comments & suggestions:

It is an interesting strategy to fuse the mVenus and anti-GFP nanobody to the N-/C-termini. The authors should also include in SI Fig. 1 a full model for the features observed in these maps and deposit this in the PDB.

Great point, we have made a main text panel describing the construct. Figure S1 includes a full description of the construct. The reviewer will note that the PDB entry contains the entire amino acid sequence of the construct and while the GFP and GFP-Nb cannot be well modeled into the density, we have included all of the relevant information for the reader.

Difficult to make out the ligand in Fig. 2b, I would suggest changing the color of the carbon atoms.

Fixed.

It is difficult to make out the side chains in ED Fig. 5d.

This is now its own supplemental figure and is presented larger.

ED Figures are called out of order in the manuscript. For example, in line 143 ED Fig.6 is called before ED Fig. 5d (line 152), and then ED 5d is called before ED 5a. This makes it rather confusing to follow the description, analysis, and data when reading the paper. Although there are other examples. I would suggest trying to order the figure callouts to flow with the narrative of the study.

Agreed. Fixed.

It wasn't clear to me what the result was produced by just imaging the ligand-free chimaera protein. It would be useful to say whether this resulted in low-resolution maps and whether the presence of the TBZ compound was essential for high-resolution structure determination.

The ligand is likely required for structure determination. We have not, however, made such a statement largely because we have yet to determine an apo reconstruction.

The role of E127 and W318 on EL1 in gating the luminal side of the transporter is very intriguing. As the authors suggest, this may represent an atypical gating mechanism for the MFS (line 182). I did wonder if the authors had considered providing more insight into this potentially novel mechanism. Additional experiments would be further mutations of W318 to F, Y, V, and I to see if they can identify a non-dead variant that could be analysed kinetically. They may have more luck with variants of E127, as they suggest this stabilises W318. If these side chains are important for gating and transport regulation, one might expect to see interesting effects on the transport kinetics.

This is a fantastic suggestion. We have done this, and we think that the reviewer will find the results to be quite interesting. Some VMAT2 sequences have an R or an H at position 318 while VPAT has an F at the equivalent position. We have made these mutants including the E127A mutant and analyzed them using TBZ binding and transport experiments. Interestingly the W318R, H, and F mutants preserve activity in varying degrees with the R mutant closely resembling wild type. W318A has no transport activity. Only the W318F mutant retains some TBZ binding. The E127A mutant also has little transport activity but nearly wild type like TBZ binding which we believe suggests a role for this residue also in stabilizing W318.

The authors identify an interesting polar network, which is described in detail and shown in Fig. 2d. However, the authors present no experimental data to shed further mechanistic insight into how these side chains contribute to monoamine transport or ligand binding. Additional experiments that would be helpful here might include repeating the binding and competition assays shown in Fig. 1c under different pH conditions for the WT and different mutations of this polar network. At present, this section of the manuscript is very descriptive without providing much novel insight into the mechanism of VMAT transport. I did wonder whether a similar analysis of pH effects on DTBZ binding might also provide insight into the role of E312 and the role of protons in the mechanism.

Thank you, we have addressed this point in several different ways. The first is that many of these residues have already been characterized in several earlier studies, see refs 31, 32, and 42 and we have incorporated this into our discussion where appropriate. With respect to E312, the reviewers’ comments are again very appropriate. We have addressed this using computational experiments exploring the protonation status of E312 and other residues as well as TBZ. Our simulations and Propka calculations clearly show that E312 must be protonated and TBZ must be deprotonated to maintain TBZ binding. We have also extended these computational studies toward understanding the protonation status of residues which orchestrate dopamine binding and release.

The authors then describe the binding pose for TBZ. This section also provides some biochemical characterisation of the binding site, in the form of the binding assay introduced in Fig. 1. However, the insights are again somewhat reduced as the mutants were chosen to show reduced binding. Could the authors return to this assay and try more conservative mutations of the key side chains to illuminate more detail? For example, does an R189K mutant still show binding but not transport? Similarly, what properties does an E312D have? The authors speculate that K138 might play a role in coupling ligand binding/transport to the protonation, possibly through an interaction with D426 and D33 (line 236). Given the presence of D33 in the polar network described previously, I was left wondering how this might occur. I feel that some of the experiments with pH and conservative mutants might shed some light on this important aspect. Please label the data points in Fig. 3d.

Indeed, alanine mutants at these positions while valuable do not provide the level of detailed insight into mechanism that we also would have liked to obtain. Thus, we have made more conservative and targeted mutants like the R189K mutant and various mutants at N34 for example and tested them in both transport and binding assays. We have also made a mutant at K138 and found that it is not transport competent or able to bind TBZ to a significant degree. With respect to labels and color codes, we have made the color codes consistent between the bar graphs and the curves. We have also labeled the data points in the figure legends.

The manuscript currently doesn't present a hypothesis for how TBZ induces the 'dead-end' complex compared to physiological ligands. Does the MD shed any light on this aspect of the study? If the authors place the physiological ligand in the same location as the TBZ and run the simulation for 500ns, what do they observe? 100ns is also a very short time window. I appreciate the comment about N34 in line 303, but is this really the answer? It would be very interesting to provide more evidence on this important aspect of VMAT pharmacology.

MD with a natural ligand (dopamine) provides substantial insight into why TBZ is a dead-end complex. Since water cannot penetrate into the binding site in the TBZ bound complex, this does not allow for substantial luminal release. In contrast, simulations conducted in the presence of DA bound to the occluded VMAT2 show the propensity of that structure to accommodate an influx of water molecules that promote the release of DA to the lumen. The new results are illustrated in Figure 5 (main text) as well as supplemental figure 8 panels d-h. The new simulations further emphasized the importance of the protonation state of acidic residues near the substrate-binding pocket.

Reviewer #2 (Recommendations For The Authors):

Line 68, "both sides of the membrane" -> "alternately to either side of the membrane".

Fixed. Thanks.

Transmembrane proteins in intracellular organelles present unique issues of nomenclature. I suggest the authors refer to cytoplasmic and luminal faces of the protein (not intracellular or extracellular (line 124)) and adhere to these names to avoid confusion. This creates problems for loops called IL and EL, but they could be defined on first use.

We agree with this point and had initially gone with the conventional definitions used in the literature. We have now changed this throughout the text to be luminal and cytosolic.

Lines 135-6, are these residue numbers correct? The pdb file lists 126 as Asp and 333 as Ala.

Thank you. This is fixed.

ED Fig. 6 is not clear. A higher-resolution figure is needed.

We have updated this figure and hope that the reviewer will find it to be much clearer.

Lines 158-9, Is there any data to support effects on dynamics or folding? If not, please indicate that this is speculation.

Fixed.

Line 174, Should "I315" be "L315"?

Fixed.

Line 179, Please indicate what is meant by "inner" and "below" (also lines 183 and 258).

We have added Figure calls here where needed.

Line 192, S197 is listed as part of polar network 1, but not discussed further. Is it actually involved, or just in the neighborhood?

It is part of the network, but we did not discuss in further detail because we do not have data indicating its precise function and thus have left this as a description.

Line 199, E312, and N388 are fairly distant from each other. Do you want to clarify why they represent a network?

While they are not within hydrogen bonding distance, we nevertheless include them as part of the same network because they may come into closer proximity in a different conformational state.

Line 206, Protonation of all 3? VMAT2 doesn't transport 3 protons per cycle. Please clarify.

We believe that these residues may be protonated, but they may not necessarily all be involved in proton transport.

Line 219, Do you mean the aspartate unique to DAT, NET, and SERT? This is Gly in all the amino acid transporters in the NSS family. Please be specific.

Fixed. Thank you.

Line 224, "mutation of E312 to Q" or "mutation of Glu312 to Gln".

Fixed. Thank you.

Fig. 3d, Normally, one would expect full saturation curves for each mutant. How can a reader distinguish between low affinity or a decrease in the number of binding sites? Would full binding curves be prohibitive for the mutants because of the cost or availability of the ligand? These points should be addressed. A couple of the curves are not visible. Would an expanded scale inset show them more clearly? Also, would it be possible to include chemical structures for all ligands discussed?

Many if not most of these mutants bind TBZ with such low affinity that it is not possible to measure a full saturation curve either because of ligand availability (radioactive ligand concentration is only in µM) or due to technical issues with being able to measure such low affinity binding. We have changed the presentation of the curves and have split the gating and binding site mutants into their own figures. We feel this improves the readability of these curves. We have also included a table with the respective Kd values determined for each of the mutants where possible.

Line 235, The distances are long for a direct interaction between K138 and the TBZ methoxy groups. The unusual distances should be mentioned if an interaction is being proposed.

We do not think that K138 is directly involved in TBZ binding, however this was written in a confusing way and has been now changed.

Line 243, Please give a quantitative estimate of the affinity difference. "modestly" is vague.

It is an approximately 2-fold difference. Fixed in the text.

Line 248, 150 nM is, at best, a Kd, not an affinity.

Agreed, this is changed.

Reviewer #3 (Recommendations For The Authors):

The (3 x ~100ns-long) molecular dynamics simulations provided suggest some instability of the pose identified by cryo-EM. While it is not unreasonable that ligands shift around and adopt multiple conformations within a single binding site (in a reversible manner), the present results do raise questions about the assumptions made when starting the simulations, in particular (1) the protonation states of charged residues in the TBZ binding sites; (2) the parameters used for tetrabenazine; (3) the conformations of acidic side chains that are notoriously difficult to resolve in cryoEM maps; and (4) any contributions of the truncated regions truncated in the simulated structure, namely the cysteine cross-linked loop and the terminal domains. The authors should examine and/or discuss these contributions before attributing mechanistic insights into the newly observed binding orientation.

In order to estimate the effects of protonation states on TBZ binding, we now added three new systems with altered protonation on TBZ and binding pocket lining residues (see Table 3 in the revised vision); and for each system, we performed multiple MD runs to address the question and concerns raised by reviewer.

Regarding the protonation states: Propka3.0 was used to determine the protonation states, finding that E312 and D399 should be protonated. If I am not mistaken, this version of ProPka cannot account for non-protein ligands (https://github.com/jensengroup/propka). Given their proximity to the binding site, these protonation states will be critical factors for the stability of the simulations. The authors could test their assumption by repeating the calculations with Propka 3.1 or higher, to establish sensitivity to the ligand. Beyond this, showing the resultant hydrogen bond networks will help to reassure the reader that the dynamics in the lumenal gates do not arise from an artifact.

We thank the reviewer for suggestion of using higher version of Propka. We used the most recent Propka3.5 and carried out protonation calculations in the presence and absence of TBZ. The new calculations are presented in Table 4 and SI Figure 8c of the revised version.

It should be possible to assess whether waters penetrate the ligand binding site during the simulations if that is of concern.

We now added the number of waters within the ligand binding pockets for all MD simulations we performed, which are presented in Table 3 and Table 5 of the revised version.

Finally, I didn't fully understand the conclusion based on the simulations and the "binding affinity" calculations: do they imply that the pose identified in the EM map is not stable? What is the value of the binding affinity histogram?

We apologize for this confusion. For each MD snapshot, we calculated TBZ binding affinity using PRODIGY-LIG (Vangone et al., Bioinformatics 2019), which is a contact-based tool for computing ligand binding affinity. The binding affinity histogram shown in the original submission was the histogram of those binding affinities calculated for MD snapshots. In the revision, we replaced binding affinity histogram by time evolution of binding affinity changes (SI Fig 6c in the revision). The simulations confirmed that the pose identified in the EM map is stable, with a flattened binding affinity of -9.4 ± 0.3 kcal/mol in all three runs.

Recommendations regarding writing/presentation:

The authors use active tense terminology in attributing forces to elements of structure (cinching, packing tightly, locking). While appealing and commonplace in structural biology, this style frequently overstates the understanding obtained from a static structure and can give a rather misleading picture, so I encourage rephrasing.

We appreciate this point; the use of these words is not meant to overstate or provide a misleading picture but rather to aid the reader in mechanistic understanding of the proposed processes.

I would also recommend replacing the terms "above" and "below" for identifying aspects of the structure; the protein's location in the vesicular membrane makes these terms particularly difficult to follow.

These terms refer specifically to the Figures themselves which we have always oriented with the luminal side at the top of the page and the cytosolic on the bottom. We have indicated in Figure 1 the orientation of VMAT2. The Figures are the point of reference which we refer to, and the ‘above’ and ‘below’ terms have been used to assist the reader to make the manuscript easier for a more casual or non-expert reader to follow.

Minor corrections:

• the legend in Figure 2 lacks details, e.g. how many simulation frames are shown, how were the electrostatic maps calculated?

We revised Figure 2 and moved simulation frames to SI figure 6e. A total of 503 simulation frames are shown.

• how were the TBZ RMSDs calculated? using all atoms or just the non-hydrogen atoms?

For TBZ RMSDs, we used non-hydrogen atoms. This information is presented in the Methods section.

MD simulation snapshots and input files can be provided via zenodo or another website.

We will upload snapshots and input files to Zenodo upon acceptance of the manuscript.

Reviewing editor specific points:

Specific points

L.97: Remove "readily available"

Fixed.

L.99: The authors are not measuring competition binding. It is well known that reserpine and substrates inhibit TBZ binding only at concentrations 100 times higher than their respective KD and KM values. It is, therefore, surprising that the authors use this isotherm and refrain from commenting on the significance of the finding. Moreover, the presentation of results as "Normalized Counts" does not provide any information about the fraction of VMAT molecules binding the ligand. At least, the authors should provide the specific activity of the ligand, and the number of moles bound per mole of protein should be calculated.

The point was not to infer any details about the conformations that TBZ and reserpine bind but merely to point out that both constructs have a similar behavior with respect to their Ki for reserpine. We have added a sentence to say that reserpine binding stabilizes cytoplasmic-open so the reader is aware of the significance of this competition experiment.

L.102: The characterization of serotonin transport activity needs to be more satisfactory. The Km in rVMAT2 is 100-200 nM, so why are the experiments done at 1 and 10 micromolar? Is the Km of this construct very different? The results provided (counts per minute at the steady state) need to give more information.

The Km of human VMAT2 varies somewhat according to the source but has generally been reported to be between 0.6 to 1.4 µM for serotonin according to these references.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019297/ https://www.cell.com/cell/pdf/0092-8674(92)90425-C.pdf https://www.pnas.org/doi/abs/10.1073/pnas.93.10.5166

Fig 1B could be more informative. I suggest adding a cartoon model with TMs labeled, similar to ED Fig6a.

This panel is to aid the reader in accessing the overall map quality and thus we do not wish to add additional labels/fits which would distract from that point. Instead, we have added overall views of the model in Figs 2,3.

L.179: The authors claim that the inner gate is located "below" (whatever this could mean) the TBZ ligand. In L.214, they claim that TBZ adopts a pose.....just "below" the location of the luminal gating residues. Please clarify and use appropriate terminology.

This refers to the position of these residues in the Figures themselves. We have added figure calls where appropriate here.

Fig. 4: The cartoon could be more informative.

We have added more information to the mechanism cartoon which is now Figure 6. This incorporates some of our new data and we believe it will be more informative.

L. 213: The paragraph describes residues involved in TBZ binding. Mutagenesis is used to validate the structural information. However, the results (ED fig. 5B) must be corrected for protein expression levels. In the Methods section, the authors state (L.444), "Mutants were evaluated similarly from cell lysates of transfected cells." Without normalization of protein expression levels, the results are meaningless even if they agree with predictions.

In fact, we have normalized the concentrations of protein in our binding experiments. This was noted in the methods section. And to account for these differences, experiments were conducted using 2.5 nM of VMAT2 protein as assessed by FSEC.

L.220: The referral to ED Fig.7 is not appropriate here. The figure shows docking-predicted poses of dopamine and serotonin.

Figure call has been changed.

L.226: The referral to Fig. 3b needs to be corrected. The figure shows TBZ and not the neurotransmitter.

This has been corrected.

L. 337: "The neurotransmitter substrate is bound at the central site." What do the authors mean in this cartoon? Do they have evidence for this? Tetrabenazine is not a substrate.

This cartoon drawing is meant to illustrate the elements of structure. Similar drawings are presented throughout the literature such as here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940252/ Figure 3 and here: https://pubs.acs.org/doi/10.1021/acs.chemrev.0c00983 Figure 2.

The same compound is mentioned with different names: 3H-dihydrotetrabenazine and 3H-labeled DTBZ.

Fixed.

ED fig 1d is illegible.

The high-resolution figure is completely legible. We will provide this to the journal upon publication.

Figure 2d: A side view would be more visual.

We have updated this figure and believe that it is much easier to understand now.

L. 179: The inner gate is located 'below' the TBZ ligand

Please see above response, this refers to the figures themselves. The figures are our point of reference.

L. 213-215: Tetrabenazine binding site just 'below' the location of the luminal gating residues.

See above.

Throughout the paper, results are given as cpm or counts. The reader can only estimate the magnitude of the binding/transport by knowing the specific activity of the radiolabel. I recommend switching to nano/picomoles or supplying enough information to understand what the given cpm values could mean.

Binding experiments were done using scintillation proximity assays and therefore converting the CPMs to values in pmol of bound ligand is simply not possible. For the transport experiments (now Fig 1d) the point was to show that the wild type was similar in activity to the chimera. In our new transport experiments we have presented for the mutants, many experiments were combined together and therefore, we have normalized the counts to the relative activity of wild type VMAT2.

Author Response

Reviewer #1 (Public Review):

Summary:

This manuscript introduces an exciting way to measure SARS-CoV-2 aerosolized shedding using a disposable exhaled breath condensate collection device (EBCD). The paper draws the conclusion that the contagious shedding of the virus via aerosol route persists at a high level 8 days after symptoms.

Strengths:

The methodology is potentially of high importance and the paper is clearly written. The study design is clever. If aerosolized viral load kinetics truly differed from those of nasal swabs, then this would be a very important finding.

Thank you for your encouraging remarks. We agree that a comparison between aerosolized viral load and nasal swabs would strengthen our findings, and we have collected new specimens which will enable this comparison: In each session we collected both nasal swabs and exhaled breath samples, and we are in the process of analyzing these data. These data will be included in our revised manuscript.

Weaknesses:

The study conclusions are not entirely supported by the data for several reasons:

(1) Most data points in the study are relatively late during infection when viral loads from other compartments (nasal and oral swabs) are typically much lower than peak viral loads which often occur in the pre-symptomatic or early symptomatic phase of infection. Moreover, the generation time for SARS-CoV-2 has been estimated to be 3-4 days on average meaning that most infections occur before or very early during symptoms. Therefore, the available epidemiologic data does not support 12 days of infection (day 8 symptoms) as important for most transmissions. Therefore, many of the measurement timepoints in this study may not be relevant for transmission.

Thank you for your comment. Notably, our new data set includes a small number of specimens that were collected prior to the start of symptoms, and so we may be able to partially address this concern with those data. That said, we agree that a limitation of our study is that we were unable to collect specimens prior to symptom onset, and that this pre-symptomatic period represents a fruitful area for future work. However, significant questions do remain open regarding transmission dynamics of SARS-CoV-2, including the extent of transmission after symptom onset, and therefore, despite this limitation of our data, we feel that our method may contribute to further understanding of those dynamics. However, we will include a more prominent discussion of this limitation in the revised manuscript.

(2) Fig 1A would be more powerful as a correlation plot between viral load from nasal samples (x-axis) and aerosol (y-axis). One would expect at least a rough correlation (as has been seen between viral loads in oral and nasal samples) and deviations from this correlation would provide crucial information about how and when aerosol shedding is discordant from nasal samples (ie early vs late time points, low versus high viral loads< etc...). It is too strong to state correspondence is 100% when viral load is only measured in one compartment and nasal swabs are reduced to the oversimplified "positive or negative".

Thank you for this suggestion, we agree that the figure would be more powerful as a correlation plot between viral load from nasal samples and aerosol. Unfortunately, at the time these samples were collected, the ER at Northwestern Hospital was diagnosing SARS-CoV-2 patients using the Abbott ID NOW rapid diagnostic platform, which, despite being a PCR-based system, does not provide quantitative information about viral loads, and instead provides a binary positive/negative result. Since we were looking for a direct comparison between the clinical diagnostic test and our test, we considered the binary aspect of our data (detected/undetected), and found 100% correspondence, meaning that when the clinical test detected SARS-CoV-2, our test did too. We have collected additional data which includes quantitative PCR values from nasal swabs collected at the same time as breath samples and we will include these data in the format you suggest, once analyzed, in our revised manuscript.

(3) Results are reported in RNA copies which is fine but particle-forming units (pfu, or quantitative culture) are likely a more accurate surrogate of infectivity. It is quite possible that all of these samples would have been negative for pfu given that the ratio of RNA: pfu is often >1000 (though also dynamic over time during infection). This could be another indicator that most samples in the study were collected too late during infection to represent contagious time points.

We agree that culturing exhaled breath samples would be an important addition to our understanding of the transmission dynamics of SARS-CoV-2 and we consider this to be an important next step for our method. Because we did not perform culturing of our breath samples in this study, we avoided making claims about infectivity of our samples in this manuscript, and instead speculate about the future utility of our method in understanding transmission dynamics, once an appropriate surrogate of infectivity is performed. We will make sure this is clearer in the revised manuscript. That said, other groups have successfully cultured breath samples with corresponding CT values in a range that are well within the range we found in our study, and sufficient for transmission (for example, Alsved et al, 2023, CT range ~33-38). These studies support the idea that a significant portion of the viral RNA measured in our samples may come from viable virus. Therefore, quantifying the ratio of viable to nonviable virus in our samples is an important next step. We appreciate this comment, and we will add a clearer discussion of this point to the revised manuscript.

(4) Individual kinetic curves should be shown for participants with more than three time points to demonstrate whether there are clear kinetic trends within individuals that would help further validate this approach. The inclusion of single samples from individuals is less informative.

We will add individual kinetic curves to the revised manuscript.

(5) The S-shaped model in 2A is somewhat misleading as it is fit to means but there is tremendous variability within the data. Therefore the 8-day threshold should be listed clearly as a mean but not a rule for all individuals. The statement that viral RNA copies do not decrease until 8 days from symptom onset is unlikely to be true for all infected people and can't be made based on the available data in this study given that many people contributed only one datapoint.

We will clarify the language in the manuscript and make limitations of the 8-day interpretation clearer.

(6) The incubation period for SARS-CoV-2 is highly variable. Therefore duration of symptoms is a rather poor correlate of the duration of infection. This further diminishes the interpretive value of positive samples from individuals who were only sampled once.

We will add a discussion of this point to the revised manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Lane and colleagues measured the abundance of SARS-CoV-2 on breath in 60 outpatients after the development of COVID-19 symptoms using a novel breath collection apparatus. They found that, overall, viral abundance remains high for approximately eight days following the development of symptoms, after which viral abundance on breath drops to a low level that may persist for approximately 20 days or more. They did not identify significant differences in viral shedding on breath by vaccination status or viral variant. They also noted substantial variation in the degree and duration of shedding across individuals.

Strengths:

The primary strengths of this study are (1) the focus on breath, rather than the more traditional nasal/oropharyngeal swabs, and (2) the fact that the data were collected at multiple time points for each infection. This allows the authors to characterize not only mean viral abundance across individuals but also how that abundance changes over time, allowing for a better understanding of the potential duration of infectiousness of SARS-CoV-2.

Weaknesses:

The sample size is moderate (60) and focuses only on outpatients. While these are minor weaknesses (as the authors note, the majority of SARS-CoV-2 transmission likely occurs among those with symptoms below the threshold of hospitalization), it would nevertheless be useful to have a fuller understanding of variation in viral shedding across clinical groups.

We agree this would be very interesting and feel our method, which is straightforward to perform in clinical settings, lends itself to future studies across clinical groups. We have added discussion of this to the discussion section of the manuscript.

Furthermore, the study lacks information on viral shedding prior to the development of symptoms, which may be a critical period for transmission. Since the samples were collected at home by study participants using a novel apparatus, it is difficult to assess the degree to which actual variation in viral abundance, user variability, and/or measurement variation is inherent to the apparatus.

This is a great point, which we will discuss in our revised manuscript.

Author Response

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

Reviewer #1 (Recommendations For the Authors):

(1) While not absolutely necessary - it would be nice to see at least at the in-situ level what happens to the handful of other HC-important transcription factors in the Rbm24 KO (IKZF2, Barlh1, RFX) as the authors did look at Insm1.

Reply: Thanks for your suggested experiments. We agree that knowing whether the genes that are known to be involved in cell survival regulation are changed will provide insights into the mechanisms underlying cell death of Rbm24-/- HCs. Our data showed that Ikzf2 seemed to be upregulated when in the Rbm24-/- HCs, relative to Rbm24+/+ HCs at P5. We also tested Barlh1 and RFX, but we did not obtain confident data to present. Nonetheless, following the reviewer’s logic, we further tested Gata3, another gene involved in HC survival, and found that Gata3 was down-regulated in Rbm24 -/- HCs, compared to Rbm24+/+ HCs. Please refer to the text on lines 12-22 on page 12 and lines 1-10 on page 13, and Figure 3-figure supplement 1.

(2) Major comments: The nomenclature for mouse gene vs. mouse protein needs to be addressed throughout the manuscript. The nomenclature when referring to a mouse gene: gene symbols are italicized, with only the first letter in upper-case (e.g. Rbm24).

The nomenclature when referring to a mouse protein: Protein symbols are not italicized, and all letters are in upper-case (e.g. RBM24).

Reply: Thanks for pointing it out. In the entire manuscript, we have followed the reviewer’s comments to list gene and protein.

(3) Supplemental Figure 2D: Individual data points should be displayed on the bar graph via dots. SEM is not appropriate for this graph as SEM precision with only 3 samples is low. Furthermore, readers are more interested in knowing the variability within samples and not proximity of mean to the population mean, therefore standard deviation (SD) should be used instead.

Reply: We have edited the Figure 1-figure supplement 2D, as suggested. The Figure 1figure supplement 2 legend was updated, too. Please refer to line 21-22 on page 32.

(4) Red/Green should be avoided, especially when both are on the same image (merged immunofluorescence images that are found throughout the manuscript). I highly recommend changing to a color-blind friendly color scheme (such as cyan/green/magenta, cyan/magenta/yellow, etc.) for inclusivity.

Reply: Thanks for pointing it out. We have changed the red to magenta in all our Figures and figure supplements.

(5) Minor comments: As CRISPR-stop is a major method used throughout the paper, a brief explanation is needed for readers to understand what this methodology entails and why it was used. Something along the lines of," The CRISPR-stop technique allows for the introduction of early stop codons without the induction of DNA damage via Cas9 which can cause deleterious effects".

Reply: We have further elaborated how CRISPR-stop works and its advantages. Please refer to lines 8-13 on page 5.

(6) Page 5; line 5 - "Phenotypes occur earlier..." Grammar

Reply: The grammar error was corrected. Please refer to line 4, page 5.

(7) Page 5; line 5 - "Given Pou4f3 is the upstream regulator..." Not proven, rephrase

Reply: We have rephrased this sentence. Please refer to lines 5-6 on page 5.

(8) Supplemental 1A: Fine, Proof of knockout, I wouldn't mention INSM1 being "irregular"

Reply: We have rephrased this sentence. Please refer to lines 2-3 on page 6.

(9) Page 5; line21 - "Alignment of Insm1+ OHCs was not as regular..." Not a good description

Reply: We have rephrased this sentence. Please refer to lines 2-3 on page 6.

(10) Page 6; line11 - "Rbm24 was completely absent.." Redundancy with line 9

Reply: Thanks for pointing it out, and we have removed the redundant sentence.

(11) Page 7 - HA tag should be indicated originally as: Hemaglutinin (HA)

Reply: We have switched “HA” to “Hemaglutinin (HA)”. Please refer to line 15, page 7.

(12) Page 9, line 11- "Determine if autonomous/noncell autonomous." Disagree, cells still clustered in supplemental fig 4.

Reply: We have removed this sentence.

Reviewer #2 (Recommendations For The Authors):

The writing of the manuscript is adequate, but it would certainly be improved by professional editing.

Reply: Thanks for the reviewer’s encouraging comments. The revised version of our manuscript has been edited by an English native speaker.

Author Response

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

eLife assessment

The manuscript from Richter et al. is a very thorough anatomical description of the external sensory organs in Drosophila larvae. It represents an important tool for investigating the relationship between the structure and function of sensory organs. Using improved electron microscopy analysis and digital modeling, the authors provide compelling evidence offering the basis for molecular and functional studies to decipher the sensory strategies of larvae to navigate through their environment.

Public Reviews:

Summary

This is a very meticulous and precise anatomical description of the external sensory organs (sensillia) in Drosophila larvae. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Improved serial electron microscopy and digital modeling are used to the fullest to provide a definitive and clear picture of the sensory organs, the sensillia, and adjacent ganglia, providing an integral and accurate map, which is dearly needed in the field. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments and construct a complete structural and neuronal map of the external larval sensilla.

Strengths

It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely use it as a reference paper. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. It will allow for investigating sensory processing in depth. Serial electron microscopy and digital modeling are used to the fullest to provide a comprehensive, definitive, and clear picture of the sensory organs. The discussion places the anatomical data into a functional and developmental frame. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

Weaknesses

There are no apparent weaknesses, although it is not a complete novel anatomical study. It revisits many data that already existed, adding new information. However, the repetitiveness of some data and prior studies may be avoided for easy readability.

We would like to thank the reviewers for their respective reviews. The detailed comments and efforts have helped us to improve our manuscript. In the following, we have listed the comments one by one and provide the respective information on how we addressed the concerns.

Recommendations for the authors:

We have tried to address every single comment as far as possible. In order to structure our response a little better, we have listed the relevant page number and the original comments once again. Directly following this you will find our response and a description of what we have changed in the manuscript.

REVIEWER #1 (Recommendations For The Authors):

I have a few comments that will help the reader navigate this long and detailed paper.

REVIEWER 1.1. page 4

The final section of "the Structural organization of Drosophila larvae" needs some reorganization.

Specifically:

"The DO and the TO are prominently located on the tip of the head lobes" Can the authors rewrite the sentence in a way that it is clear that there is one DO and one VO on each side of the head? Check at the beginning of each section, please. There is a mention about hemi-segments but it is still confusing.

Done – replaced with “The largest sense organs of Drosophila larvae are arranged in pairs on the right and left side of the head.”

REVIEWER 1.2. page 5

"The sequence of sensilla is always similar for and different between T1, T2-T3, and A1-A7" This sentence is not clear, please break it into two sentences.

Done – replaced with: “We noticed varying arrangements for T1, T2-T3, and A1-A7, with a consistent sequence of sensilla in each configuration.”

REVIEWER 1.3. figures page 4

Double hair can't be found in Figure 1B or C (is it h3, h4?) - please clarify.

Done - changed to double hair organ in page 11, included double hair sketch in legend in figure 1B. We changed the name of the structure to double hair organ, to clarify that this is a compound sensillum consisting of two individual sensilla.

REVIEWER 1.4. page 5

The authors go back and forth in their descriptions of the different sensory organs. Knob sensilla and then papilla sensilla are discussed and then a few lines later a further description is done. Please unify the description of each separately.

Done – we restructured the whole section.

REVIEWER 1.5. figures page 6

"We found three hair sensilla on T1-T3, and "two" on A1-A7" - in the figure there seem to be "four" on A1-A7.

Done – we included the two hair sensilla of the double hair organ

REVIEWER 1.6. figures page 6

DORSAL ORGAN:

Can the authors explain the colour map meaning in Figure 2A? It is explained in 2C but the image already has colours. Add your sentence "Color code in A applies to all micrographs in this Figure".

Done – we added a sentence to explain that the color code in A applies to the whole figure.

REVIEWER 1.7. page 6

Page 10: which comprises seven olfactory sensilla "composing" three dendrites each: replace this with"with". At the end, we want to think 7 X 3= 21 ORNs.

Done – replaced.

REVIEWER 1.8. page 9

CHORDOTONAL ORGANS:

"We find these these DO associated ChO (doChO).. .". Please remove one "these"

Done – removed.

REVIEWER 1.9. page 8

Is the DO associated ChO part of the dorsal ganglion???? It does not look like it. Could you clarify?

Done – we added a sentence that clarifies that the ChO neuron is not iside the DOG.

REVIEWER 1.10. page 9 VENTRAL ORGAN: A figures page 12

Please add to the Figure 8 legend the description of 8c' and 8c'?

Done – added description in figure legend.

B page 9

8H, what are the *, arrows? Please clarify - it is hard to interpret the figure.

Done – we added parentheses in the figure legend that state which structures the asterisks and arrows indicate.

C page 9

"Three of them are innervated by a single neuron () and one by two neurons () (Figure 8F-I). Please add which are innervated by 1 (VO1, VO2-VO4) and which by 2 (VO3).

Done – we added parentheses that clarify which sensilla are innervated by 1 or 2 neurons.

REVIEWER 1.11. page 9

Can you add something (or speculate) about the difference in sensory processing of the different types of sensilla?

Done – new sentence in discussion:

‘Their different size and microtubule organization likely correlate with processing of different stimulus intesities applied to the mechanotransduction apparatus (Bechstedt et al. 2010).’

REVIEWER 1.12. figures page 16

PAPILLA AND HAIR SENSILLA:

FIGURE 10a, please add the name of each sensillum from p1, p2, px py, etc... (if not we have to go back to figure 1 when you describe specific ps.)

Thanks for the comment, it really makes it a lot easier for the reader.

REVIEWER 1.13. figures page 18 Figure 11, can you add the name of each hair, please?

Done – updated figure.

REVIEWER 1.14. figures pages 16, 18, 20

In Figures 10, 11, and 12 you clearly draw an area on the internal side that I assume is what you call the "electron-dense sheath". It is wider in papilla sensilla than in hair sensilla, most likely due to the difference in stimuli sensed that you explain in detail in the discussion. Can you say in the figure what this "internal" thing is? Can you add this difference to your list "Apart from the difference in outer appearance and structure of the tubular body"?

This is the basal septum, but it is not certain that it is wider in the papillae sensillae, at least we could not observe this in our data sets. The impression could have been created by different scales in the 3D reconstructions and a perspective view. Therefore, we do not want to list this as a difference here, as we are not sure.

However, we have now specified the socket septum in the figure legends and in Figures 10A, 11A and 12A.

REVIEWER 1.15. page 11

KNOB SENSILLA:

Page 25;" Knob sensilla have been described under "vaious" names such as": add various.

Done

REVIEWER 1.16. page 12

"reveals that the three hair and the two papilla sensilla are associated with a single dendrite." Can you write that "reveals THAT EACH OF the three hair and the two papilla sensilla" if not it seems that there is only one dendrite.

Done

REVIEWER 1.17. figures page 25 TERMINAL SENSORY CONES:

Please name the t1-t7 cones in Figure 15A.

Done – we updated the figure.

REVIEWER 1.18. page 13

The spiracle sense organ deserves a new paragraph. As does the papilla sensillum of the anal plate.

Done – we added subtitles before the prargraphs.

Discussion:

REVIEWER 1.19. page 15

Page 38: "v'entral" correct typo

PAGE 15

Done – we have updated the nomenclature  ventral 1 (v), ventral 2 (v’) and ventral 3 (v’’)

REVIEWER #2 (Recommendations For The Authors):

I have only a few comments:

REVIEWER 2.1. page 5

p.5, right column, middle: the use of trichoid, campaniform, and basiconical (sensilla) in previous works were based on even older papers and reviews that attempted to link EM architecture to function (e.g., KEIL, T. A. & STEINBRECHT, R. A. (1986). Mechanosensitive and olfactory sensilla of insects. In Insect Ultrastructure, vol. 2. (ed. R. C. King & H. Akai), pp. 477-516. New York/London: Plenum Press). Trichoid sensilla can be mechano-sensitive, olfactory, or gustatory; trichoid simply refers to the shape (hair). The same applies to basiconical sensilla. The use of "campaniform", which Ghysen et al called "papilla sensilla", was the only really problematic case, because these (Drosophila larval) sensilla did not really resemble closely the classical campaniform sensilla (e.g., adult haltere). The only reason we called them campaniform is because they were not more similar to any other type of (previously named) sensillum.

Thank you for the explanation. The nomenclature of structures is generally always a complex topic with often different approaches and principles. We are aware of this and have therefore tried to be as careful as possible. We were not sure from this comment whether you were suggesting to change the text or whether you wanted to explain how these names were assigned to the sensilla in the past. However, we hope that the current version is in line with your understanding, but could of course make changes if necessary (see also comments of reviewer 1).

REVIEWER 2.2. page 9

p.21, Labial Organ: the ventral lip is the labium; the dorsal one is the labrum.

Done – replaced labrum with labium.

REVIEWER 2.3. page 9

p.20/21, Ventral organ and labial organ: here, the projection of the axons could be mentioned as an ordering principle. In the previous literature, for larva and embryo, a labial organ (lbo) was described that most likely corresponds to the labial organ presented here. This (previously mentioned) lbo characteristically projects along the labial nerve to the labial segment (hence the name). It fasciculates with axons of another sensory complex, also generated by the labial segment, namely the ventral pharyngeal sensory organ (VPS). Does the labial organ described here share this axonal path?

Yes, it has the same axonal pathway and is the same organ as the lbo. We have tried to standardise the nomenclature for all important external head organs (DO, TO, VO, LO) and have therefore used abbreviations with two letters. However, to avoid confusion, we have now added that the LO was also called lbo in the past.

For the ventral organ, the segmental origin (to my knowledge) was never clarified. The axons of the ventral organ project along the maxillary nerve (which carries axons of the terminal=maxillary organ). This nerve, closely before entering the VNC, splits into a main branch to the maxillary segment (TO axons) and a thinner branch that appears to target the mandibular segment. This branch could contain the axons of the ventral organ (as described previously and in this paper). Could the authors confirm this axonal projection of the VO?

In this work, we did not focus on the axonal projections into the SEZ. This is also not a simple and fast process, as in the entire larval dataset, the large head nerves unfortunately exhibit a highly variable quality of representation. Therefore, the reconstruction of nerves and individual neurons within it is often challenging and very time-consuming. The research question is, of course, very intriguing, and one could also attempt to match each sensory neuron of the periphery with the existing map of the brain connectome. However, this is a project in itself, exceeding the scope of this work, and is therefore more feasible as a subsequent project.

REVIEWER #3 (Recommendations For The Authors):

Minor suggestions that the authors might consider:

REVIEWER 3.1. figures all

Recheck the scale bar in figures and figure legends. Missing in a few places.

Done – we replaced or added some (missing) scale bars in figures and figure legends (see annotated figure document).

REVIEWER 3.2. figures page 4

The color schematic in Figure 1 can be improved for readability.

Done – we changed the color schematic, especially for the head region to improve readability.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. The authors find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

Strengths & Weaknesses:

Various analyses nicely support the authors' claims. Accordingly, I have only one significant comment and several minor comments that focus on wordsmithing - e.g., clarifying the interpretation of statistical results and requesting additional citations to support claims in the introduction.

We are pleased the reviewer found the analyses to support the claims. We have addressed comments related to clarifying interpretations as suggested in the Recommendations to the Authors. For example, we have added discussion and clarification on the other parameters that could affect the strength of ERC correlations.

Reviewer #2 (Public Review):

Summary:

The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction types that all fall under the umbrella explanation of 'co-functional interactions'.

Strengths:

Very smart experimental design that leverages individual protein domains for increased resolution.

Weaknesses:

Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

We appreciate the reviewer’s acknowledgement of the experimental design. We have addressed the nuance of the results by changing the title and clarifying other statements throughout the manuscript as suggested in the reviewer’s recommendations. We have also addressed reviewer comments asking for further explanation on using Fisher transformations when normalizing the Pearson correlations for branch counts.

Reviewer #3 (Public Review):

Summary:

The paper makes a convincing argument that physical interactions of proteins do not cause substantial evolutionary co-variation.

Strengths:

The presented analyses are reasonable and look correct and the conclusions make sense.

Weaknesses:

The overall problem of the analysis is that nobody who has followed the literature on evolutionary rate variation over the last 20 years would think that physical interactions are a major cause of evolutionary rate variation. First, there have been probably hundreds of studies showing that gene expression level is the primary driver of evolutionary rate variation (see, for example, [1]). The present study doesn't mention this once. People can argue the causes or the strength of the effect, but entirely ignoring this body of literature is a serious lack of scholarship. Second, interacting proteins will likely be co-expressed, so the obvious null hypothesis would be to ask whether their observed rates are higher or lower than expected given their respective gene expression levels. Third, protein-protein interfaces exert a relatively weak selection pressure so I wouldn't expect them to play much role in the overall evolutionary rate of a protein.

We thank the reviewer for their comments and suggestions. A point to immediately clarify is that the methods studied in this manuscript deal with rate variation of individual proteins over time, and if that variation correlates with that of another protein.. The numerous studies the reviewer refers to deal with explaining the differences in average rate between proteins. These are different sources of variation. It has not, to our knowledge, been shown that variation in the expression level of a single protein over time is responsible for its variation in evolutionary rate over time, let alone to a degree that allows its variation to correlate with that of a functionally related protein. That question interests us, but it is not the focus of this study.

In our study, we sought to test for a contribution of physical interaction to the correlation of evolutionary rate changes as they vary over time, i.e. between branches. We made many changes to clarify this distinction in our revisions.

We agree that the manuscript would be more clear to define the forces proposed to lead to difference in rate in general, which includes expression levels. We had generally considered expression level as one of the many potential non-physical forces, but failed to make that explicit and instead focused on selection pressure. In our revision we describe expression level as another potential driver of evolutionary rate variation over time. References to previous literature have been made in the introduction. We also added a more explicit explanation of the rate covariation over time that we are measuring in contrast with the association between expression level and rate differences between proteins that was studied in previous literature.

On point 3, the authors seem confused though, as they claim a co-evolving interface would evolve faster than the rest of the protein (Figure 1, caption). Instead, the observation is they evolve slower (see, for example, [2]). This makes sense: A binding interface adds additional constraint that reduces the rate at which mutations accumulate. However, the effect is rather weak.

The values in Fig 1B are a measure of correlation, specifically a Fisher transformed correlation coefficient. They are not evolutionary rates, so they are not reflecting faster or slower evolution, rather more or less covariation of evolutionary rates over time. We are not predicting that physically interacting interfaces evolve faster than the rest of the protein, but rather that if physical interaction drives covariation in evolutionary rates over time, their correlation would be stronger between pairs of physically interacting domains. In response, we have used clearer language in the figure caption and reorganized labels in Figure 1B to clearly show that the values are correlations. Revised Figure 1 Legend:

“Overview of experimental schema and hypotheses. Proteins that share functional/physical relationships have similar relative rates of evolution across the phylogeny, as shown in (A) with SMC5 and SMC6. The color scale along the bottom indicates the relative evolutionary rate (RER) of the specific protein for that species compared to the genome-wide average. A higher (red) RER indicates that the protein is evolving at a faster rate than the genome average for that branch. Conversely, a lower (blue) RER indicates that protein is evolving at a slower rate than the genome average. The ERC (right) is a Pearson correlation of the RERs for each shared branch of the gene pair. (B) Suppose the correlation in relative evolutionary rates between two proteins is due to compensatory coevolution and physical interactions. In that case, the correlation of their rates (ie. ERC value) would be higher for just the amino acids in the physically interacting domain. (C) Outline of experimental design. Created with Biorender.com”

All in all, I'm fine with the analysis the authors perform, and I think the conclusions make sense, but the authors have to put some serious effort into reading the relevant literature and then reassess whether they are actually asking a meaningful question and, if so, whether they're doing the best analysis they could do or whether alternative hypotheses or analyses would make more sense.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523088/

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854464/

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments

(1) Numerous parameters influence ECR calculation. The authors note that their use of a large dataset of budding yeast provides sufficient statistical power to calculate ECR. I agree with that. However, a discussion of other parameters needs to be improved, especially when comparing the present study to others like Kann et al., Hakes et al., and Jothi et al.. For example, what is the evolutionary breadth and depth used in the Kann, Hakes, Jothi and other studies? How does that compare to the present study? Budding yeast evolve rapidly with gene presence/absence polymorphisms observed in genes otherwise considered universally conserved. Is there any reason to expect different results in a younger, slower-evolving clade such as mammals? There is potential to acknowledge and discuss other parameters that may influence ECR, such as codon optimization and gene/complex "essentiality," among others.

More discussion of these parameters is a good idea. We have added the number and phylogeny of species used in the previous studies in the discussion paragraph starting with “Previous studies attributed varying degrees of evolutionary rate covariation signal to physical interactions between proteins.” We also like the idea of studying the effect of younger and more slowly evolving clades as opposed to the contrary, but currently we lack the required number of datasets to do this.

We have also added more discussion and clarification of potential non-physical forces leading to ERC correlations in the introduction.

Minor comments

(1) It would be good to add a citation to the second sentence of the first paragraph, which reads, "It has been observed that some genes have rates that covary with those of other genes and that they tend to be functionally related."

Added citation to Clark et al. 2012

(2) In the last sentence of the first paragraph of the introduction, ERC is discussed in the context of only amino acid divergence, however, there is no reason that DNA sequences can't be used, especially if ERC is being calculated among species that are less ancient than, for example, Saccharomycotina yeasts. Thus, it may be more accurate to suggest that ERC measures how correlated branch-specific rates of sequence divergence are with those of another gene.

Nice suggestion to generalize. We have made this change.

(3) ERC was not calculated in reference #2. For the sentence "Protein pairs that have high ERC values (i.e., high rate covariation) are often found to participate in shared cellular functions, such as in a metabolic pathway2 or meiosis3 or being in a protein complex together," I think more appropriate citations (including inspiring work by the corresponding author) would be

a) Coevolution of Interacting Fertilization Proteins (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000570)

b) Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007720)

c) An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function (https://www.science.org/doi/10.1126/sciadv.abn0105)

d) PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data (https://academic.oup.com/bioinformatics/article/37/16/2325/6131675)

Thank you for pointing out these works. We agree that there are more appropriate citations and we have referenced your suggested b-d.

(4) The dataset of 343 yeast species also includes outgroup taxa. Therefore, indicating that 332 species are Saccharomycotina yeast and 11 are closely related outgroup taxa may be more accurate.

Thank you for the suggestion, the following sentence has been added, citing the Shen et. al 2018 paper that the dataset was derived from:

“To investigate the discrepancy between contributions to ERC signal from co-function and physical interaction, we used a dataset of 343 evolutionarily distant yeast species. 332 of the species are Saccharomycotina with 11 closely related outgroup species providing as much evolutionary divergence as humans to roundworms3”

(5) Are there statistics/figures to support the claim that "Almost all complexes and pathways had mean ERC values significantly greater than a null distribution consisting of random protein pairs"?

This is shown in supplementary figure 1. A reference to this figure was added as well as quantification within the text.

(6)Similar to the previous comment, can quantitative values be added to the statement "While protein complexes appear to have higher mean ERC scores than the pathways..."?

The median of the mean ERC scores for protein complexes is 5.366 while the median for the mean ERC score in pathways is 4.597. This quantification has been included in the text: “While protein complexes have higher mean ERC scores (median 5.366) than the pathways (median 4.597), the members of a given complex are also co-functional, making interpretation of the relative contribution of physical interactions to the average ERC score difficult”

These quantifications are were also added to the figure caption for figure 2A

(7) A semantic point: In the sentence "The lack of significance in the global permutation test shows that the...", I recommend saying that the analysis suggests, not shows, because there is potential for a type II error.

Good suggestion, we have made this change.

(8) The authors suggest that shared evolutionary pressures, "and hence shared levels of constraint," drive signatures of coevolution. The manuscript does not delve into selection measures (e.g., dN/dS). Perhaps it would be more representative to remove any implication of selection.

We have added better language to clarify that discussion of selection is purely a hypothesis and that selection is not probed in our analyses.

“Previous work finds evidence that relaxation of selective constraint can lead to drastic rate variation and hence covariation6. Rather, the greater and consistent contribution comes from non-physical interaction drivers that could include variation in essentiality, expression level, codon adaptation, and network connectivity. These non-physical forces would be under shared selective pressures and hence shared levels of constraint, the result of which was elevated ERC between non-interacting proteins, as visible in our study of genetic pathways that do not physically interact (Figure 2).”

Reviewer #2 (Recommendations For The Authors):

Major comments:

-Title: In my opinion, the title of the manuscript is a somewhat misleading summary of the results of this paper. In the majority of the analyses in this paper, physical interactions do account for a significantly outsized portion of the ERC signature. The current title downplays the consistent (although sometimes small effect-sized) result that physically interacting domains do show higher ERC than non-physically interacting domains by every statistical measure employed in this paper to compare physical vs non-physical interactions. The authors' interpretation of their results within the manuscript body is that the effect of physical interactions is an inconsistent, weak, and non-generalizable driver of ERC. I generally agree with the authors' interpretations, but the nuance of these interpretations is lost in the title of the paper. I would suggest rewording the title to try to capture the nuance or at least be subjectively accurate. For example, stating that "...physical interactions are not the sole driving force.." is inarguably accurate based on these results.

As an alternative title, I would suggest focusing on an important takeaway from the paper: ERC is a reliable predictor of co-functional interactions but not necessarily physical interactions. I agree with the statement that "there is not a strong enough signal to confidently call an interaction physical or not and would be of little value to an experimentalist wanting to infer interacting domains" and I think that a title that emphasizes this idea would be more accurate and impactful.

Great suggestion. We agree that the current title is downplaying the minutiae of the method and the signal we capture with it, we have used your suggested title.

There are an outsized number of complexes that had ROC-AUCs greater than 0.5 which is why we performed the permutation tests to determine how significant each of the individual ROC-AUCs were given the differing number of protein/domain pairs in each complex. Between the statistical methods used only 3 of the 17 complexes ranked physical interactions significantly higher than non-physically interacting domains in every analysis. Even among the 3 that were statistically significant some of the physically interacting domains still fell among the bottom portion of the ERC scores for that complex (Figure 5: MCM and CUL8 complexes) This is why we concluded that physical interactions are not the sole driving force of the signal captured by ERC.

-Abstract: related to my preceding comment, the word "negligible" in the abstract is misleading. If physical interactions were truly entirely negligible, the comparisons of physically interacting vs non-physically interacting domains would yield 0.5. Instead, these comparisons always yielded results greater than 0.5. Consider rewording.

Thank you for the suggestion this phrasing has been changed to “Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC”

We agree that “negligible” may be too strong of a word, however, the comparisons do not always yield results greater than 0.5.

5 of the 17 complexes do not reach the 0.5 threshold for the initial ROC analysis and even among those that do, only 4 had significantly high ROC-AUCs. You are correct that the signal is not completely negligible which is why we continued by determining if the physical interaction was driving high ERC only within proteins (Figure 5)

-Figure 3: I think there may be an error in the domain labeling in Figure 3. The comparison between OKP1_2 and AME1_3 is the highest ERC value in the matrix. From the complex structure, it appears that OKP1_2 and AME1_3 are two helix domains that appear to physically interact. However, in the ERC matrix, they are not shaded to indicate they are a physical interaction pair. Please double-check that the interacting domains are properly annotated, since mis-annotation would have a large impact on the interpretation of this figure with respect to the overall question the paper addresses.

Thank you for catching this - fixed.

Minor comments:

-Methods: "The full ERC pipeline can be found at (Github)." Provide github URL here? Thanks for the catch, fixed

-Discussion: "Evidence for physical coevolution however was tempered by a global permutation test, which did not reach significance, indicating that this inference is sensitive to approach and further underlines the relatively weak contribution of physical coevolution." The word "relatively" may not be a good choice of words. In comparison to what? As is, the phrasing could be interpreted as implying "in comparison to non-physical interactions". This would not be accurate, because the results show that in general, physical interactions are a stronger contributor to ERC (consistent trend but varied significance, depending on methodology) than non-physical interactions.

Thank you for your help with clarification. The word relatively was removed.

However, we do not agree that in general physical interactions are a stronger contributor to ERC than non-physical interactions (such as gene expression, codon adaptation, etc.). In all of our statistical tests a maximum of 5 of the 17 complexes ranked physical interactions significantly higher than non-physical interactions. While the ROC-AUC is greater than 0.5 for 12 of the 17 complexes only 4 of those were significant.

-I have not seen Fisher-transformed correlation coefficients used in the context of ERC. I understand that it's helpful in normalizing the results so that they are comparable between ERC comparisons with differing numbers of overlapping branches (i.e. points on a linear correlation plot). A reference of where the authors got this idea or a little more verbiage to describe the rationale would be helpful. On a related note, I would expect that using linear correlation p-value instead of R-squared would account for differences in overlapping branches, eliminating the need to apply fisher-transformation. It would be helpful for the authors to outline their rationale for using a correlation coefficient rather than a P-value.

We agree that this method could be made clearer. We made a methodological choice to use Fisher transformation over linear correlation p-value. Both methods should achieve the same end result by taking the number of branches into consideration. We have added additional explanation to the results section “Both protein pathways and complexes have elevated ERC”:

“ERC was calculated for all pairs of the 12,552 genes. For each pair the correlation is Fisher transformed to normalize for the number of shared branches that contribute to the correlation. This normalization is necessary to reduce false positives that have high correlation solely due to a small number of data points. This normalization also allows for direct comparison of ERC between gene pairs that have differing numbers of branches contributing to the score.”

We also added additional explanation in the methods section including the formula used to calculate the Fisher transformation

-Did the authors use Pearson or Spearman correlation coefficient?

Pearson. We clarified this in the methods section, “Calculating evolutionary rate covariation” : “Evolutionary rate covariation is calculated by correlating relative evolutionary rates (RERs) between two gene trees using a Pearson correlation.”

-Did the authors explore ERC between domains within a single protein? Do domains within a protein exhibit ERC? I would expect that they do. If they do, this could likely be attributed to linkage/genetic hitchhiking, representing a new angle/factor beyond physical interaction that could lead to ERC. This is just an idea for a future analysis, not necessarily a request within the scope of the present paper.

We did calculate the ERC between domains of a single protein but did not include them in the analysis since they didn’t address the specific question we posed. As expected they are highly correlated, and past unpublished studies in the lab do find a very weak, but detectable genome-wide, signature of rate covariation between neighboring colinear genes on a chromosome. That signal was however so weak as to be eclipsed by true functional relationships, when present.

Reviewer #3 (Recommendations For The Authors):

Please read the literature and revise accordingly.

We understand the confusion surrounding previous literature on the relationship between expression levels and evolutionary rates when comparing between different proteins. Those studies clearly showed how expression level is highly predictive of a given protein’s average evolutionary rate. However, we are studying the change in evolutionary rate over branches for single proteins. This is inherently different because we’re following rate fluctuations in the same protein over time. To our knowledge it has not yet been shown that expression level commonly varies enough over time to produce large rate variations over time in the same protein, and if it is responsible for the correlations of rate we observe between co-functional proteins. It is however reasonable to expect that what governs between-protein differences in rate could also contribute to between-branch differences (over time for a single protein). In fact, our earlier study approached this (Clark et al. Genome Research 2012). We expect expression level could influence rate over time and lump its effect together with general non-physical forces, such as selection pressures. We recognize we could do better in defining more of the non-physical forces and the past literature. We added the following section to the introduction and many other clarifying statements throughout the manuscript:

“For the purposes of this study, the forces that contribute to correlated evolutionary rates are grouped into two bins, physical and non-physical. The physical force is coevolution occurring at physical interaction interfaces. Non-physical forces include gene co-expression, codon adaptation, selective pressures, and gene essentiality. There is a well accepted negative relationship between gene expression and rate of protein evolution where genes that are highly expressed generally have slower rates of evolution14,15. However, Cope et al.16 found that there is a weak relationship between both gene expression and the number of interactions a protein has with the coevolution of expression level. Conversely, they found a strong relationship between proteins that physically interact and the coevolution of gene expression. These findings illuminate the difference between the strong relationship of gene expression level on the average evolutionary rate of a protein and the weak contribution of gene expression level to correlated evolutionary rates of proteins across branches. The finding that physically interacting proteins have strong expression level coevolution brings to question how much coevolution of physically interacting proteins contributes to overall covariation in protein evolutionary rates.”

Author Response

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

eLife assessment

This study presents valuable findings that examine both how Down syndrome (DS)-related physiological, behavioral, and phenotypic traits track across time, as well as how chronic treatment with green tea extracts 25 enriched in epigallocatechin-3-gallate (GTE-EGCG), administered in drinking water spanning prenatal through 5 months of age, impacts these measures in wild-type and Ts65Dn mice. However, the strength of the evidence is incomplete, due to high variability across measures, perhaps attributable to a failure to include sex as a factor for measures known to be sexually dimorphic. This study is of interest to scientists interested in Down Syndrome and its' treatment, as well as scientists who study disorders that impact multiple organ systems.

Public Reviews:

Using Ts65Dn - the most commonly used mouse model of Down syndrome (DS) - the goal of this study is two-pronged: 1) to conduct a thorough assessment of DS-related genotypic, physiological, behavioral, and phenotypic measures in a longitudinal manner; and 2) to measure the effects of chronic GTE-EGCG on these measures in the Ts65Dn mouse model. Corroborating results from several previous studies on Ts65Dn mice, findings of this study show confirm the Ts65Dn mouse model exhibits the suite of traits associated with DS. The findings also suggest that the mouse model might have experienced drift, given the milder phenotypes than those reported by earlier studies. Results of the GTE-EGCG treatment do not support its therapeutic use and instead show that the treatment exacerbated certain DS-related phenotypes.

Strengths:

The authors performed a rigorous assessment of treatment and examined treatment and genotypic alterations at multiple time points during growth and aging. Detailed analysis shows differences in genotype during aging as well as genotype with treatment. This study is solid in the overarching methodological approach (with the exception of RNAseq, described below). The biggest strength of the study is its approach and dataset, which corroborate results from a multitude of past studies on Ts65Dn mice, albeit on adult specimens. It would be beneficial for the dataset to be made available to other researchers using a public data repository.

We deeply appreciate the reviewers' positive feedback. Their acknowledgment of the solid methodological approach and the rigorous assessment of genotypic and treatment effects over various developmental stages resonates with our motivation. Their suggestion to make the dataset available in a public data repository for other researchers is well-taken. We are committed to data sharing and we are creating a dedicated platform to facilitate the accessibility of our research data to the scientific community. Given its size and complexity, we currently hold the dataset available upon reasonable request to the corresponding authors.

Weaknesses:

There are several primary weaknesses, described below:

Sex was not considered in the analyses.

The number of experimental animals of each sex are not clearly represented in the paper, but are buried in supplemental tables, and the Ns for the RNAseq are unclear. No analyses were done to examine sex differences in male/female DS or WT animals with or without treatment. Body measurements will greatly vary by sex, but this was not taken into consideration during assessments. As such, there is a high amount of variability within each cohort measured for body assessments (tibia, body weight, skeletal development etc.). Supplemental table 14 had the list of each animal, but not collated by sex, genotype or treatment, making it difficult to assess the strength of each measurement.

Our study primarily concentrated on providing a holistic understanding of the impact of trisomy and GTE-EGCG treatment on Down syndrome, and was not explicitly designed to investigate sexual dimorphism. However, instead of reporting on only one sex and thereby obviating sex as a source of variation, as in previously published studies, we decided to include both male and female mice within the study design to represent a more realistic portrayal of the nature of Down syndrome in a heterogeneous population. By encompassing both sexes, we aim to better capture the variability in Down syndrome.

As we do acknowledge the significance of sex bias in scientific research, we considered performing post-hoc analyses to test the effect of sexual dimorphism, but found that our dataset was underpowered to obtain reliable results, since our experiments were not a priori designed to investigate this question and sample sizes for each sex by separate were not large enough. Nevertheless, considering the reviewer’s comment, we have taken specific steps to improve the representation of sex-related information and to enhance the clarity of our manuscript.

First, we have redesigned all figures using empty and full symbols to distinguish male from female mice within each analysis, providing readers with an immediate sense of the sex distribution in each experimental group. Moreover, we have modified Supplementary Table 1 to offer a comprehensive breakdown of the number of male and female mice for each test, along with their respective genotypes and treatment groups. This table aims to make the sample size and sex distribution within our study as transparent as possible for our readers. While we acknowledge that our study lacked the statistical power to perform a detailed sex-based analysis, the visual representation of sex in our data shows which systems are mainly affected by sexual dysmorphism. This evidence can guide future investigations directly designed to investigate sexual effects in certain systems or structures.

Key results are not clearly depicted in the main figures

Rigorous assessment of each figure and the clarity of the figure to convey the results of the analysis needs to be performed. Many of the figures do not clearly represent the findings, with authors heavily relying on supplemental figures to present details to explain results. Figure legends do not adequately describe figures; rather, they are limited to describing how the analysis is performed. For example, LDA plots in Figure 4 do not clearly convey the results of metabolite analysis.

Overall, the amount of data presented here is overwhelming, making it difficult to interpret the findings. Some assessments that do not add to the overall paper need to be removed. Clarifying the text, figures and trimming the supplement to represent the data in a manner that is easily understood will improve the readability of the paper. For example, perhaps measures which are not strongly impacted by genotype could be moved to the supplement, because they are not directly relevant to the question of whether GTE-EGCG reverses the impact of trisomy on the measures.

As rightly pointed out by the reviewers, the vast amount of data generated by our holistic and longitudinal approach is one of the primary strengths, but also an important challenge in our study. Our dataset encompasses a comprehensive assessment of the effects of treatment and genotypic alterations at multiple time points during growth and aging. This multi-dimensional evaluation is pivotal to our research, and relegating data to supplementary material would restrict access to this holistic understanding. Our aim is to provide readers with a complete view of the complex interactions we have explored, and retaining this data in the main text is essential to uphold the integrity of our work.

Indeed, we specifically chose to submit or manuscript to eLife because this journal allows to access supplemental information directly from the text and figures in the main manuscript and best aligned with our approach to data presentation. The eLife format permits us to offer readers a quick and informative overview of all the data within the main figures employing multivariate techniques such as Linear Discriminant Analysis or Principal Component Analysis. Subsequently, we supply more detailed analyses in the supplementary figures for readers who wish to delve deeper into specific aspects. Furthermore, while certain figures may be categorized as supplementary, for us it is crucial, and we would like to emphasize, that every result is comprehensively described in the main text.

Acknowledging the concerns raised about the density of our paper and the potential challenges in interpreting the findings, we have conducted a thorough review of the text and figure legends. We have made revisions with the goal to enhance clarity and readability. We have made dedicated efforts to ensure that readers can readily grasp the significance of our results and appreciate the intricacies of our findings. We firmly believe that with these revisions, our chosen approach is the most effective means of presenting the richness of our data and maintaining the integrity of our findings.

Lack of clarity in the behavioral analyses

Behavioral assessments are not clearly written in the methods. For example, for the novel object recognition task, it isn't clear how preference was calculated. Is this simply the percent of time spent with the novel object, or is this a relative measure (novel:familiar ratio)? This matters because if it is simply the percent of time, the relevant measure is to compare each group to 50% (the absence of a preference). The key measures for each test need to be readily distinguished from the control measures.

There are also many dependent behavioral measures. For example, speed and distance are directly related to each other, but these are typically reported as control measures to help interpret the key measure, which is the anxiety-like behavior. Similarly, some behavioral tests were used to represent multiple behavioral dimensions, such as anxiety and arousal. In general, the measurements of arousal seem atypical (speed and distance are typically reported as control measures, not measures of arousal). Similarly, measures of latency during training would not typically be used as a measure of long-term memory but instead reported as a control measure to show learning occurred. LDA analysis requires independence of the measures, as well as normality. It does not appear that all of the measures fed into this analysis would have met these assumptions, but the methods also do not clearly describe which measures were actually used in the LDA.

We agree with the reviewers’ concerns about the clarity of our behavioral analyses and we have thus added information to the methods section to clarify the procedures. Specifically, for SPSN, social approach was recorded as time spent close to STR1, and a preference ratio was calculated as Pref= 100 Time close to STR1/(Time close to STR1 + Time close to Empty). Social recognition memory was scored as preference towards STR2 and calculated as Pref =100 (time close to STR2) / (Time close to STR1 + Time close to STR2). For NOR, preference for novel object was calculated as Pref=100* Time novel object / (Time familiar object + novel object).

With regards to the different variables reported for the behavioral protocols, we agree that some measures, such as path length and speed can be used as control measures. For example, in an open field test, path length is an important control measure to assess whether an animal is engaged in the task. However, if an animal is actively moving, the amount of distance covered can but does not have to correlate with the amount of time that a mouse spends in the center of the open field. Using the measure of distance covered as a measure for general arousal and time spent in the center as a measure for anxiety related behavior allows a more nuanced evaluation of animal behavior. For instance, two animals spending similar amounts of time in the center may exhibit differences in the distance they cover. In this scenario, we would argue that anxiety related behavior (defined as exploring the center of an open field) would not reflect well a behavioral difference between the two animals, while the aspect of arousal clearly is a differencing factor.

Regarding the PA task and the use of latency during training, we agree that typically latency during training can be used as control measure to show that learning occurred. However, our study involved testing animals at two distinct time points. Contextual fear conditioning creates very robust memory traces that can persist for weeks or even months, and therefore the starting premise is very different when repeating the test. Initially, the animals were experimentally naïve and had not yet experienced a foot shock, leading to a rapid entry into the dark box. However, after experiencing the first CS-US presentation, a robust and persistent contextual fear memory trace is formed. Therefore, the latency observed in the second training phase of the PA reflects in essence long-term contextual fear memory, that is robustly displayed in WT animals but less in treated WT and TS animals. We have included this clarification in the methods and results sections.

Finally, we want to thank the reviewer for noticing the error in the LDAs, as the analysis was indeed performed including dependent variables for some systems. We have re-evaluated the LDAs for the behavioral tests and tibia microarchitecture tests, excluding dependent variables. As a result, the text and significance levels have been adjusted accordingly. To enhance transparency and clarity, we have included Supplementary Table S21, which precisely outlines the variables included in each LDA.

Unclear value of RNAseq

RNAseq was performed in cerebellum, a relatively spared region in DS pathology at an early time point in disease. Further, the expression of 125 genes triplicated in DS was shown in a PCA plot to highly overlap with WT, indicating that there are minimal differences in gene expression in these genes. If these genes are not critical for cerebellar function, perhaps this could account for the lack of differences between WT and Ts65Dn mice. If the authors are interested in performing RNAseq, it would have made more sense to perform this in hippocampus (to compare with metabolites) and to perform more stringent bioinformatic analysis than assessment by PCA of a limited subset of genes. Supplementary Table S14, which shows the differentially expressed genes, appears to be missing from the manuscript and cannot be evaluated. Additionally, the methods of the RNAseq are not sufficiently described and lack critical details. For example, what was the normalization performed, and which groups were compared to identify differentially expressed genes? It would also be worthwhile to describe how animals were identified for RNAseq-were those animals representative of their groups across other measures?

We acknowledge the reviewers' comments on the RNAseq analysis and would like to provide additional insights into our rationale and choices for this analysis. The primary aim of our RNAseq analysis was to offer supplementary evidence in support of the broader context of our paper. Rather than focusing on specific genes, our aim was to assess potential alterations in transcription within genes triplicated in the mouse model and explore differentially expressed genes across the entire genome. Therefore, we conducted a global analysis of the triplicated genes using a PCA and analyzed the differentially expressed genes across the entire genome as shown in Supplementary Table S14. The table was originally included as a separate Excel file but apparently it was not received by the reviewers. We have contacted the eLife editorial to ensure its inclusion in the current version. Furthermore, we have modified the text to clarify that both the triplicated genes and the entire genome were analyzed.

Regarding the use of cerebellum instead of hippocampus, we agree with the reviewers that the hippocampus is a major tissue of interest in the study of Down Syndrome since it mostly relates to cognition. Trisomic patients, however, also display other typical features such as for example a delay in the acquisition of motor skills. Here we decided to focus on the cerebellum as it is primarily associated to the locomotor system but also plays a role in other cognitive functions such as language processing and memory. Furthermore, at the time of the RNAseq analysis, the mice were 8 months old, equivalent to the adult human stage, and previous studies have shown transcriptomic alterations in this tissue and mouse model (Olmos-Serrano et al., 2016; Saran et al., 2003).

The lack of observable differences between WT and Ts65Dn mice in our PCA analysis may be attributed to several factors as discussed in our article. First, the high variability within each group, inherent to the complexity of DS, may obscure inter-group differences. Additionally, the subtlety of gene expression differences between WT and trisomic mice in the set of triplicated genes, as suggested by other transcriptomics studies on DS (Aït Yahya-Graison et al., 2007; Lyle et al., 2004; Olmos-Serrano et al., 2016; Saran et al., 2003), may contribute to the limited distinctions observed. Furthermore, regarding treatment effects, the timing of the RNAseq analysis should be considered since it was conducted at the endpoint, three months after treatment cessation. This temporal aspect could imply that the effects of the drug are not persistent, and a molecular memory might not be formed and maintained.

Nevertheless, we appreciate the reviewers' constructive comments and acknowledge the potential for more stringent bioinformatic analyses. While our intention was to provide an initial, global perspective, we are eager to support further investigations that delve deeper into the complexities of DS-related molecular mechanisms. Consequently, the dataset is available for other researchers to explore more specific questions upon request.

Finally, we have updated the methods section of the article to offer more detailed information on RNAseq processing and analysis. We have also clarified that all the surviving mice were included in the analysis.

Recommendations for the authors:

(1) Please add power calculations for each of the assessments.

We would like to clarify that we had already conducted power calculations as part of the initial planning and design phase of our study. After data acquisition and analysis, we have utilized appropriate statistical methods to interpret the results based on the data we have collected. Given that we had conducted a priori power calculations prior to data collection and that our analysis is based on the acquired data, we do not see the added value in including post hoc power calculations. Our primary focus has been on performing the correct statistical analyses to accurately interpret the results and draw meaningful conclusions.

(2) Introduction has some excessive references for each statement, which are not necessary. For instance: lines 67-73 are only references for 1 statement and lines 74-76 are references for a 2nd statement in the same sentence.

We have removed redundant references.

(3) Introduction: Lines 136-146 Gene names need to be spelled out, not just the IDs. Were these studies done in human or mouse models of DS?

We have spelled out the names of the genes.

(4) Why was brain volume and brain structure size normalized to body weight, not clearly explained?

The choice to normalize brain volume and brain structure size to body weight was a deliberate decision made to address potential confounding factors in our study. In the case of trisomic (TS) mice, they are generally smaller in size compared to their wild-type (WT) counterparts. The same may hold true for sex-related size differences. Without normalization, assessing brain volume and structure size could be misleading, as it might reflect the differences in overall body size rather than providing insights into the specific aspects of brain structure that we aimed to investigate. We have clarified this in the methods section.

(5) In cognitive tests, some of the WT data represented in Figure 3 does not match supplemental findings. Again power calculations may indicate a higher number of WT mice are needed to clarify this discrepancy.

We appreciate the reviewers' observation regarding the disparities between the data presented in Figure 3 and the supplemental figures. We would like to clarify that these variations are a result of the distinct analytical approaches employed in the two sets of data.

In Figure 3 and all main figures, the data were analyzed using multivariate tests, which consider multiple variables simultaneously and are particularly suited for investigating the collective impact of multiple factors. Conversely, the results shown in the supplementary figures were derived from univariate tests, which focus on individual variables and are well-suited for addressing specific questions related to each variable in isolation. The discrepancies between the data in the main figures and the supplementary figures can be attributed to the differences in the analytical methods chosen.

As for the suggestion of conducting power calculations to address the observed differences, we believe that the differences in data are inherent to the distinct analytical strategies and the specific research questions each analysis intended to answer. Power calculations may not be the most suitable approach in this context, as they pertain to sample size planning for hypothesis testing and may not reconcile the inherent dissimilarity between multivariate and univariate analyses.

Aït Yahya-Graison, E., Aubert, J., Dauphinot, L., Rivals, I., Prieur, M., Golfier, G., . . . Potier, M. C. (2007). Classification of human chromosome 21 gene-expression variations in Down syndrome: impact on disease phenotypes. Am J Hum Genet, 81(3), 475-491. https://doi.org/10.1086/520000

Lyle, R., Gehrig, C., Neergaard-Henrichsen, C., Deutsch, S., & Antonarakis, S. E. (2004). Gene expression from the aneuploid chromosome in a trisomy mouse model of down syndrome. Genome Res, 14(7), 1268-1274. https://doi.org/10.1101/gr.2090904

Olmos-Serrano, J. L., Kang, H. J., Tyler, W. A., Silbereis, J. C., Cheng, F., Zhu, Y., . . . Sestan, N. (2016). Down Syndrome Developmental Brain Transcriptome Reveals Defective Oligodendrocyte Differentiation and Myelination. Neuron, 89(6), 1208-1222. https://doi.org/10.1016/j.neuron.2016.01.042

Saran, N. G., Pletcher, M. T., Natale, J. E., Cheng, Y., & Reeves, R. H. (2003). Global disruption of the cerebellar transcriptome in a Down syndrome mouse model. Hum Mol Genet, 12(16), 2013-2019. https://doi.org/10.1093/hmg/ddg217

Author Response

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

Public Reviews:

Reviewer #1:

Summary:

In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

Strengths:

(1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

(2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

Weaknesses:

The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

(1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

We are very grateful that Reviewer #1 proposed the possibility that MTA and MTB form a receptor-ligand complex in which one acting as the ligand and the other as the receptor. We considered this hypothesis when asking how dose MTRC function, too. However, our current results do not support this idea. For instance, if MTA were a ligand and MTB a receptor, we would expect a mating signal upon treatment with MTAxc protein, but not with MTBxc. Contrary to this expectation, our experiments revealed that both MTAxc and MTBxc exhibit very similar effects (Figure 5, green and blue), and their combined treatment produces a stronger effect (Figure 5, teal). This suggests a mixed function for both proteins. (We incorporated this discussion into the revised version [line 120-121, 240-244].) It is pity that our current knowledge does not provide a detailed molecular mechanism for this intricate system. We are actively investigating the protein structures of MTA, MTB, and the entire MTRC, hoping to gain deeper insights into the molecular functions of MTA and MTB.

Additionally, we also realized that the expression we used in the previous version, “simple receptor-ligand model”, is not clearly defined. As Reviewer #1 pointed out, in this section, we examined whether the individual proteins of MTA and MTB act as a couple of receptor and ligand. We think this is the simplest possibility as a null hypothesis for Tetrahymena mating-type recognition. We have clarified it in the revised version (line 90-91, 104-106). According to this section, we proposed that MTA and MTB may form a complex that serves as a recognizer (functioning as both ligand and receptor) (line 117-118).

(2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

Several factors could contribute to the inability of MTA/Bxc to induce costimulation. It is highly likely that additional components are necessary, given that MTA/B form a protein complex with other proteins. Moreover, the expression of MTA/Bxc in insect cells, compared with Tetrahymena, might result in differences in post-translational modifications. Additionally, there are variations in protein conditions; on the Tetrahymena membrane, these proteins are arranged regularly and concentrated in a small area, while MTA/Bxc is randomly dispersed in the medium. The former condition could be more efficient. If there is a threshold required to stimulate a costimulation marker, MTA/Bxc may fail to meet this requirement. Much more studies are needed to fully answer this question. We acknowledged this limitation in the revised version (line 244-248).

Reviewer #2:

This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

Thanks for the valuable comments and suggestions. We are currently in the process of constructing deletion strains for these genes. As of now, we have successfully obtained ΔMRC1-3 and MRC4-6 knockdown strains. Our preliminary observations indicate that ΔMRC1-3 strains are unable to undergo mating. However, we prefer not to include these results in the current manuscript, as we believe that more comprehensive studies are still needed.

Reviewer #3:

The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

We express our gratitude to Reviewer #3 for proposing the occupation model and have incorporated this possibility into the manuscript. We believe it is possible that occupation may serve as the molecular mechanism through which self-recognition negatively regulates mating. If there is a physical interaction between mating-type proteins of the same type, but this interaction blocks the recognition machinery rather than initiating mating, it can be considered a form of self-recognition. This aligns with the observation that strains expressing MTA/B6 and MTB2 mate normally with WT cells of all mating types except for VI and II (line 203-204). A concise discussion on this topic is included in the manuscript (line 288-293, 659-661). We are actively investigating the downstream aspects of mating-type recognition, and we hope to provide further insights into this question soon.

The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

We are very grateful that Reviewer #3 provide this insightful view and relevant papers. In response to the feedback, we removed the sentences regarding “multiple mating types greatly complicate the process” in the revised version. Instead, we have introduced a discussion section comparing the mating systems of yeasts and Tetrahymena (line 279-286).

The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013.

In T. thermophila, the MIC (diploid) contains several mating-type gene pairs (mtGP, i.e., MTA and MTB) organized in a tandem array at the mat locus on each chromosome. In sexual reproduction, the new MAC of the progeny develops from the fertilized MIC through a series of genome editing events, and its ploidy increases to ~90 by endoreduplication. During this process, mtGP loss occurs, resulting in only one mtGP remaining on the MAC chromosome. The mating-type specificity of mtGPs on each chromosome within one nucleus becomes relatively pure through intranuclear coordination. After multiple assortments (possibly caused by MAC amitosis during cell fission), only mtGPs of one mating-type specificity exist in each cell, determining the cell’s mating type.

It is pity that the exact mechanisms involved in this complicated process remain a black box. The loss of mating-type gene pairs is hypothesized to involve a series of homologous recombination events, but this has not been completely proven. Furthermore, there is no clear understanding of how intranuclear coordination and assortment are achieved. While we have made observations confirming these events, a breakthrough in understanding the molecular mechanism is yet to be achieved.

We included more information in the revised version (line 672-683). Given the complexity of these unusual processes, we recommend an excellent review by Prof. Eduardo Orias (PMID: 28715961), which offers detailed explanations of the process and related concepts (line 685-686).

Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

Sorry that we didn’t realize the term “contact” is not precise enough. In Tetrahymena, physical contact is indeed necessary, but it can refer to temporary interactions. Unlike yeast, Tetrahymena cells exhibit rapid movement, swimming randomly in the medium. Occasionally, two cells may come into contact, but they quickly separate instead of sticking together. Even newly formed loose pairs often become separated. As a result, one cell can come into contact with numerous others and stimulate them. We have clarified this aspect in the revised version (line 50-51, 57).

Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

We have clarified the expression in the revised version (line 20, 38, 40, 45).

Recommendations for the authors:

Reviewer #1:

I really enjoyed reading this manuscript and I think a few tweaks in the writing/data presentation could greatly improve the experience for the reader:

(1) The information about your previous work in identifying downstream proteins CDK19, CYC9, and CIP1 (lines 170-173) could be directly presented in the introduction.

We have moved this information in the introduction in the revised version (line 74-77).

(2) For a reader who is not familiar with Tetrahymena, a few more details on how reporter and knock-out lines are generated would be beneficial.

We introduced the knock-out method in Figure 2 – figure supplement 1B, HA-tag method in Figure 3A, and MTB2-eGFP construction method in Figure 4E. In addition, we introduced how co-stimulation markers observed in Materials and Methods (line 404-410)

(3) Figures 5 and 6: clarify the types of pairing and treatments that were done directly in the figure (eg. adding additional labels). As of now, it is necessary to go through the text and legend to try and understand in detail what was done.

Cell types and treatments were directly introduced in the revised figure (Figure 5 and 6).

(4) The logical transition in lines 136-142 is hard to follow.

We rewrote this paragraph in the revised version (lines 143-156). Additionally, we added a figure to illustrate the theoretical mating-type recognition model between WT cells and ΔCDK19, ΔCYC9 cells, MTAxc, MTBxc proteins, and ΔMTA, ΔMTB cells (Figure 2 – figure supplement 1D-G).

(5) Lines 191-196: the fact that cells expressing multiple mating types can self goes against an active self-rejection system - if this is the case there should be self-rejection among all expressed mating types. Unless non-self recognition is an active process and self-recognition is simply the absence of non-self recognition. The authors briefly mention this in lines 263-265, but it would be interesting to expand and clarify this.

We appreciate that Reviewer #1 notice the interesting selfing phenotype of the MTB2-eGFP (MTVI background) strain. We further discussed it in the revised manuscript (line 298-306).

(6) The authors briefly mention the possibility of different mating types using different recognition mechanisms (lines 255-260), based on the big differences in the size of the mating-specific region of MT proteins. Following this and the weakness nr. 2, I think it would be pertinent to gather and present more information on the properties and structures of the mating-type specific regions of MT proteins. Simple in silico analysis of motifs, structure, etc. could help clarify the role of these regions. It seems more parsimonious that MT proteins would have variable mating type specific regions that account for the recognition of the different mating types, and conserved cytoplasmic functions that could trigger a single downstream signaling cascade. It would be interesting to know the authors' opinion on this.

We are very grateful for this suggestion. Actually, we are currently working on determining the 3D structure of MTRC. The Alphafold2 prediction indicates that the MT-specific region is comprised of seven global β-sheets, resembling the structure of immunoglobulins (Ig). Our most recent cryo-EM results have revealed a ~15Å structure, aligning well with the prediction. However, the main challenge lies in the low expression levels, both in Tetrahymena and insect/mammal cells. We anticipate obtaining more detailed results soon. Therefore, we prefer to present the MT recognition model with robust experimental evidence in the future, and didn’t discuss too much on this aspect in the current manuscript.

(7) Adding a figure including a proposed model, as well as expanding the discussion on the points presented as "weaknesses" would help clarify the ideas/hypothesis on how the mating recognition works. I think this would really elevate the paper and help highlight the results.

We added a figure to introduce the model and the weaknesses in the revised version (Figure 7, line 656-665).

(8) Line 202-203: It is far-fetched to infer subcellular localization based on the data presented here, couterstaining with other dyes and antibodies specific to certain cell components, as well as negative control images, are required.

Thanks for the suggestion. We attempted to stain cell components using various dyes and antibodies. Unfortunately, we found that cell surface and cilia (especially oral cilia) is very easy to give a false positive signal. We think this issue seriously affects the credibility of the results. It may seem like splitting hairs, but we are trying to be precise.

Meanwhile, we still believe the mating-type proteins localizes to cell surface because MTA-HA is identified in the isolated cell surface proteins.

Regarding negative control, as shown in Fig. 4G, where a MTB2-eGFP cell is pairing with a WT cell, no GFP signal is observed in the WT cell.

(9) Lines 131: clarify the sentence - expression of Con-A receptors requires both MTA and MTB (MTA to receive the signal).

We modified the sentence in the revised version (line 139-140).

Reviewer #2:

Minor points.

(1) Line 194-196. Why are these cells able to self?

These cells able to self may because the MTRC contain heterotypic mating-type proteins (MTA6 and MTB2), which activate mating when they interact with another heterotypic MTRC (line 207-208).

(2) Line 232. What do the authors mean by the term synergistic effect here? Definition and statistics?

Sorry about the confusion. The synergistic effect refers to the effect of MTAxc and MTBxc become stronger when using together. We clarified it in the revised version (line 232).

(3) For Figure 4 panel D, are there antibodies that are available as a control for cilia? If so, then blotting this membrane would show that cilia-associated proteins are in the cilia preparation, which is a standard control for sub-cellular fractionation.

Thanks for the suggestion. Unfortunately, we didn’t find a suitable cilia-specific antibody yet. Instead, we employed MS analysis to confirm the presence of cilia proteins in this sample (line 195-196, Figure 4–Source data 1). We also observed the sample under the microscope, which directly revealed the presence of cilia (Figure 4C).

(4) At least one reference cited in the text was not present in the reference list. The authors should go through the references cited to ensure that all have made it into the reference list.

We have checked all the references.

Some minor edits:

(1) MTA and MTB are presented in both roman and italics (e.g. line 209) in the manuscript. Maybe all should be in italics? Or is this a distinction between the gene and the protein?

The italics word (MTA) refers to gene, and non-italics word (MTA) refers to protein.

(2) Line 251. Change "achieving" to "achieve".

We have corrected this word (line 266).

Reviewer #3:

Line 101. It would help to explain this expectation earlier in this paragraph.

We explained the expectation in the revised version (line 92-97, 104-106).

Line 109. How is a co-receptor different from the MTRC complex?

We have rewritten the relevant sentences to enhance clarity (line 116-119). The molecular function of the MTRC complex could involve acting as a co-receptor or recognizer (functioning as both ligand and receptor). Based on the results presented in this section, we propose that MTA and MTB may function as a complex, but the confirmation of this hypothesis (MTRC) is provided in a later section. Therefore, we did not use the term “MTRC” here. These sentences briefly discuss the molecular function of this complex and explain why MTRC does not appear to function as a co-receptor.

Line 251: which "dual approach" is referred to?

Dual approach is referred to both self and non-self recognition. We explained it in the revised version (line 265-266).

Line 258: what "different mechanisms" do the authors have in mind? Why would a different mechanism be expected? The different sizes could have evolved for (coevolutionary?) selection on the same mechanism.

Sorry about the confusion. We clarified it in the revised version (line 269-278).

What we intended to express is that we are uncertain whether the mating-type recognition model we discovered in T. thermophila is applicable to all Tetrahymena species due to significant differences in the length of the mating-type-specific region. We believe it is important to highlight this distinction to avoid potential misinterpretations in future studies involving other Tetrahymena species. At the same time, we look forward to future research that may provide insights into this question.

Fig 2 C&D. Is it correct that these figures show the strains only after 'preincubation'? This is not apparent from the caption of the text. Additionally, the order of the images is very confusing. Write in the figures (so not just in the caption) what the sub-script means.

These panels are re-organized in the revised version (Fig. 2C&D). There are three kinds of pictures: “not incubated”, “WT pre-incubated by mutant” and “mutant pre-incubated by WT”.

The methods used to generate Figure 5 are not clearly described. I understand that the obtained xc proteins were added to the cells, and then washed, after which a test was performed mixing WT-VI and WT-VII cells. Were both cells treated? Or only one of the strains? The explanation for the reused washing medium is not clear and the method is not indicated.

Both cells are treated. More details are provided in the revised manuscript (line 230-231, 633-634, 637-639, Fig. 5). To prepare the starvation medium containing mating-essential factors, cells were starved in fresh starvation medium for ~16 hours. Subsequently, cells were removed by three rounds of centrifugation (1000 g, 3 min) (line 330-332).

In general, the figures are difficult to understand without repeated inquiries in the captions. Give more information in the figures themselves.

More information is introduced in the figure (Fig. 2C, Fig. 3B, Fig. 4A, B, D, Fig. 5 and Fig. 6).

Author Response

Reviewer #1 (Public Review):

Summary:

This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:

The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

We thank the reviewer for sharing interest in the research problem and for recognizing the strengths of our work.

Weaknesses:

(1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

We agree with the reviewer that one primary concern is to properly evaluate the effectiveness of the proposed method. However, as we move toward complex pathways, knowledge of the “true” steady-state goal sets is often unknown which is where the use of machine learning methods as the one we propose are particularly interesting (but challenging to evaluate).

For simple models whose true steady-state distribution can be derived numerically and/or analytically, it is very likely that their exploration will be much simpler and this is not where a lot of improvement over random search may be found, which explains our focus on more complex models. While we agree that it is still interesting to evaluate exploration methods on these simple models for checking their behavior, it is not clear how to scale this analysis to the targeted more complex systems.

For systems whose true steady state distribution cannot be derived analytically or numerically, we believe that random search is a pertinent baseline as it is commonly used in the literature to discover the attractors/trajectories of a biological network. For instance, Venkatachalapathy et al. [1] initialize stochastic simulations at multiple randomly sampled starting conditions (which is called a kinetic Monte Carlo-based method) to capture the steady states of a biological system. Similarly, Donzé et al. [29] use a Monte Carlo approach to compute the reachable set of a biological network «when the number of parameters is large and their uncertain range is not negligible».

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal....", mean considering that interventions 'Sets the initial state...' as explained in Table 2?

We thank the reviewer for asking for clarification, as indeed the IMGEP methodology originates from developmental robotics scenarios which generally focus on the problem of robotic sequential decision-making, therefore assuming state action trajectories as presented in Forestier et al. [65]. However, in both cases, note that the IMGEP is responsible for sampling parameters which then govern the exploration of the dynamical system. In Forestier et al. [65], the IMGEP also only sets one vector at the start (denoted θ∈Θ) which was specifying parameters of a movement (like the initial state of the GRN), which was then actually produced with dynamic motion primitives which are dynamical system equations similar to GRN ODEs, so the two systems are mathematically equivalent. More generally, while in our case the “intervention” of the IMGEP (denoted i ∈I) only controls the initial state of the GRN, future work could consider more advanced sequential interventions simply by setting parameters of an action policy π_i at the start which could be called during the GRN’s trajectory to sample control actions π_i (a_(t+1) 〖|s〗_(t0:t+1),a_t) where s_t would be the state of the GRN. In practice this would also require setting only one vector at the start, so it would remain the same exploration algorithm and only the space of parameters would change, which illustrates the generality of the approach.

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

The purpose of Figure 2 is to illustrate an example of GRN trajectory in transcriptional space, and to illustrate what “interventions” and “perturbations” can be in that context. To that end we have used the fixed initial conditions provided in the BIOMD0000000647, replicating Figure 5 of Cho et al. [56]. While we are not sure of what the reviewer means with “typical” scale of this phase space, we would like to point reviewer toward Figure 8 which shows examples of certain paths that indeed reach further point in the same phase space (up to ~10μM in RKIPP_RP levels and ~300μM in ERK levels). However, while the paths displayed in Figure 8 are possible (and were discovered with the IMGEP), note that they may be “rarer” to occur naturally in the sense that a large portion of the tested initial conditions with random search tend to converge toward smaller (ERK, RKIPP_RP) steady-state values similar to the ones displayed in Figure 2.

(4) Table 2:

a) Where is 'effective intervention' used in the method?

b) in my opinion 'controllability', 'trainability', and 'versatility' are different terms. If their correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing.

a) We thank the reviewer for pointing out that “effective intervention” is not explicitly used in the method. The idea here is that as we are exploring a complex dynamical system (here the GRN), some of the sampled interventions will be particularly effective at revealing novel unseen outcomes whereas others will fail to produce a qualitative change to the distribution of discovered outcomes. What we show in this paper, for instance in Figure 3a and Figure 4, is that the IMGEP method is particularly sample-efficient in finding those “effective interventions”, at least more than a random exploration. However we agree that the term “effective intervention” is ambiguous (does not say effective in what) and propose to replace it with “salient intervention” in the revised version.

b) We thank the reviewer for highlighting some confusing terms in our chosen vocabulary, and we will try to clarify those terms in the revised version. We agree that controllability/trainability and versatility are not exactly equivalent concepts, as controllability/trainability typically refers to the amount to which a system is externally controllable/trainable whereas versatility typically refers to the inherent adaptability or diversity of behaviors that a system can exhibit in response to inputs or conditions. However, they are both measuring the extent of states that can be reached by the system under a distribution of stimuli/conditions, whether natural conditions or engineered ones, which is why we believe that their correspondence is relevant.

I don't see how this table generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

We propose to replace “generalize” with “investigate” in the revised version.

Reviewer #2 (Public Review):

Summary:

Etcheverry et al. present two computational frameworks for exploring the functional capabilities of gene regulatory networks (GRNs). The first is a framework based on intrinsically-motivated exploration, here used to reveal the set of steady states achievable by a given gene regulatory network as a function of initial conditions. The second is a behaviorist framework, here used to assess the robustness of steady states to dynamical perturbations experienced along typical trajectories to those steady states. In Figs. 1-5, the authors convincingly show how these frameworks can explore and quantify the diversity of behaviors that can be displayed by GRNs. In Figs. 6-9, the authors present applications of their framework to the analysis and control of GRNs, but the support presented for their case studies is often incomplete.

Strengths:

Overall, the paper presents an important development for exploring and understanding GRNs/dynamical systems broadly, with solid evidence supporting the first half of their paper in a narratively clear way.

The behaviorist point of view for robustness is potentially of interest to a broad community, and to my knowledge introduces novel considerations for defining robustness in the GRN context.

We thank the reviewer for recognizing the strengths and novelty of the proposed experimental framework for exploring and understanding GRNs, and complex dynamical systems more generally. We agree that the results presented in the section “Possible Reuses of the Behavioral Catalog and Framework” (Fig 6-9) can be seen as incomplete along certain aspects, which we tried to make as explicit as possible throughout the paper, and why we explicitly state that these are “preliminary experiments”. Despite the discussed limitations, we believe that these experiments are still very useful to illustrate the variety of potential use-cases in which the community could benefit from such computational methods and experimental framework, and build on for future work.

Some specific weaknesses, mostly concerning incomplete analyses in the second half of the paper:

(1) The analysis presented in Fig. 6 is exciting but preliminary. Are there other appropriate methods for constructing energy landscapes from dynamical trajectories in gene regulatory networks? How do the results in this particular case study compare to other GRNs studied in the paper?

We are not aware of other methods than the one proposed by Venkatachalapathy et al. [1] for constructing an energy landscape given an input set of recorded dynamical trajectories, although it might indeed be the case. We want to emphasize that any of such methods would anyway depend on the input set of trajectories, and should therefore benefit from a set that is more representative of the diversity of behaviors that can be achieved by the GRN, which is why we believe the results presented in Figure 6 are interesting. As the IMGEP was able to find a higher diversity of reachable goal states (and corresponding trajectories) for many of the studied GRNs, we believe that similar effects should be observable when constructing the energy landscapes for these GRN models, with the discovery of additional or wider “valleys” of reachable steady states. We could indeed add other case studies in the supplementary to support the argument for the revised version.

Additionally, it is unclear whether the analysis presented in Fig. 6C is appropriate. In particular, if the pseudopotential landscapes are constructed from statistics of visited states along trajectories to the steady state, then the trajectories derived from dynamical perturbations do not only reflect the underlying pseudo-landscape of the GRN. Instead, they also include contributions from the perturbations themselves.

We agree that the landscape displayed Fig. 6C integrates contributions from the perturbations on the GRN’s behavior, and that it can shape the landscape in various ways, for instance affecting the paths that are accessible, the shape/depth of certain valleys, etc. But we believe that qualitatively or quantitatively analyzing the effect of these perturbations on the landscape is precisely what is interesting here: it might help 1) understand how a system respond to a range of perturbations and to visualize which behaviors are robust to those perturbations, 2) design better strategies for manipulating those systems to produce certain behaviors

(2) In Fig. 7, I'm not sure how much is possible to take away from the results as given here, as they depend sensitively on the cohort of 432 (GRN, Z) pairs used. The comparison against random networks is well-motivated. However, as the authors note, comparison between organismal categories is more difficult due to low sample size; for instance, the "plant" and "slime mold" categories each only have 1 associated GRN. Additionally, the "n/a" category is difficult to interpret.

We acknowledge that this part is speculative as stated in the paper: “the surveyed database is relatively small with respect to the wealth of available models and biological pathways, so we can hardly claim that these results represent the true distribution of competencies across these organism categories”. However, when further data is available, the same methodology can be reused and we believe that the resulting statistical analyses could be very informative to compare organismal (or other) categories.

(3) In Fig. 8, it is unclear whether the behavioral catalog generated is important to the intervention design problem of moving a system from one attractor basin to another. The authors note that evolutionary searches or SGD could also be used to solve the problem. Is the analysis somehow enabled by the behavioral catalog in a way that is complementary to those methods? If not, comparison against those methods (or others e.g. optimal control) would strengthen the paper.

We thank the reviewer for asking to clarify this point, which might not be clearly explained in the paper. Here the behavioral catalog is indeed used in a complementary way to the optimization method, by identifying a representative set of reachable attractors which are then used to define the optimization problem. For instance here, thanks to the catalog, we 1) were able to identify a “disease” region and several possible reachable states in that region and 2) use several of these states as starting points of our optimization problem, where we want to find a single intervention that can successfully and robustly reset all those points, as illustrated in Figure 8. Please note that given this problem formulation, a simple random search was used as an optimization strategy. When we mention more advanced techniques such as EA or SGD, it is to say that they might be more efficient optimizers than random search. However, we agree that in many cases optimizing directly will not work if starting from random or bad initial guess, and this even with EA or SGD. In that case the discovered behavioral catalog can be useful to better initialize this local search and make it more efficient/useful, akin to what is done in Figure 9.

(4) The analysis presented in Fig. 9 also is preliminary. The authors note that there exist many algorithms for choosing/identifying the parameter values of a dynamical system that give rise to a desired time-series. It would be a stronger result to compare their approach to more sophisticated methods, as opposed to random search and SGD. Other options from the recent literature include Bayesian techniques, sparse nonlinear regression techniques (e.g. SINDy), and evolutionary searches. The authors note that some methods require fine-tuning in order to be successful, but even so, it would be good to know the degree of fine-tuning which is necessary compared to their method.

We agree that the analysis presented in Figure 9 is preliminary, and thank the reviewer for the suggestion. We would first like to refer to other papers from the ML literature that have more thoroughly analyzed this issue, such as Colas et al. [74] and Pugh et al. [34], and shown the interest of diversity-driven strategies as promising alternatives. Additionally, as suggested by the reviewer, we added an additional comparison to the CMA-ES algorithm in order to complete our analysis. CMA-ES is an evolutionary algorithm which is self-adaptive in the optimization steps and that is known to be better suited than SGD to escape local minimas when the number of parameters is not too high (here we only have 15 parameters). However, our results showed that while CMA-ES explores more the solution space at the beginning of optimization than SGD does, it also ultimately converges into a local minima similarly to SGD. The best solution converges toward a constant signal (of the target b) but fails to maintain the target oscillations, similar to the solutions discovered by gradient descent. We tried this for a few hyperparameters (init mean and std) but always found similar results. We report the novel results at https://developmentalsystems.org/curious-exploration-of-grn-competencies/tuto2.html (bottom cell, Figure 4). We suggest including the updated figure and caption in the revised version.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

This is significant work, and you should certainly make the best case you can on the weaknesses discussed.

We thank reviewer for this positive comment on the significance of our work. The referee indicates as weaknesses (i) that the force involving the bent or straight αI-helix is not readily apparent, (ii) the residue types were not varied in the helix mutations, and (iii) that the chemical shift perturbations are indirect observations.

We think we have tried to address a large part of these questions by being very careful in our analysis and by the discussion in the manuscript. The following remarks may help to clarify this further:

(i) The force emanating from the helix is e.g. visualized in the PC2 loadings in Figure 6E of the PCA carried on all observed SH3-SH2-KD resonances for all apo forms of the helix mutants. The SH2 residues identified by these loadings are in direct vicinity to the αI-helix. The respective PC2 scores correlate to 98% with the vmax of the catalytic reaction and to 94 % with the PC1 scores found for imatinib-induced opening. Importantly, the structure of the KD with the straight αI-helix indicates that mostly residues F516, Q517, S520, and I521 would clash with the SH2 domain in a closed core (Figure 6F). Thus, the expected clashes are in direct vicinity of the SH2 residues identified by the PC2 loadings as correlated to vmax and imatinib-induced opening. These data are completely orthogonal and show that most of the force is coming from residues F516, Q517, S520, and I521 in the αI-αI’ turn.

(ii) We agree that we mainly used truncations of the αI-helix to study its involvement in activation. Point (i) makes it clear that a larger part of the αI-helix effects is caused by steric clashes of the residues in the αI-αI’ turn. In the latter region, we don’t expect strong amino acid type-specific effects besides excluded volume. Due to expression problems, we could not vary the helix length between residues 519 and 534. However, in this region we introduced the amino acid type mutation E528K. The latter showed a clear specific effect. Further amino acid type-specific effects may be possible in this region. However, we expect that the identified electrostatic E528-R479 interaction is one of the most important interactions in this region.

(iii) We agree that chemical shift changes of individual resonances are often hard to interpret. However, we want to stress that our conclusions are all drawn from principal component analyses, which in all cases had as input well over 100 if not over 200 1H-15H resonances. The first two principal components of these analyses are robust averages over many residues, which reveal general correlated structural trends.

We assume that chemical shift deposition etc will be pursued.

We are currently depositing a larger collection of our Abl data to the “Biological Magnetic Resonance Data Bank (BMRB)”, which includes the NMR chemical shift data of the present work. A ‘collection’ will be a new feature of the BMRB, and we are in discussion with their staff. We will provide the accession codes as soon as possible (probably within the next month) to be included into the final version of the manuscript. We have amended the Data Availability Section accordingly.

Reviewer #2 (Recommendations For The Authors):

1) The overall discussion of the implications of the described allostery on kinase activation is provided through lenses of imatinib binding, which is used as an experimental trigger to disassemble the autoinhibited core. Can the authors elaborate in the Discussion on what event would play this role in the kinase catalytic cycle, communicating to helix I? Would dissociation of the myristate from the active site be hypothesized to be the first step in kinase activation? While I understand that certainty may be challenging to attain, it would be good to introduce some ideas into the Discussion.

We appreciate the reviewer’s suggestions for the discussion and added the following text to the Conclusion section:

"We have used here imatinib binding to the ATP-pocket as an experimental tool to disassemble the Abl regulatory core. Our previous analysis (Sonti et al., 2018) of the high-resolution Abl transition-state structure (Levinson et al., 2006) indicated that due to the extremely tight packing of the catalytic pocket, binding and release of the ATP and tyrosine peptide substrates is only possible if the P-loop and thereby the N-lobe move towards the SH3 domain by about 1–2 Å. This motion is of similar size and direction as the motion of the N-lobe observed in complexes with imatinib and other type II inhibitors (Sonti et al., 2018). From this we concluded that substrate binding opens the Abl core in a similar way as imatinib. The present NMR and activity data now clearly establish the essential role of the αI-helix both in the imatinib- and substrate-induced opening of the core, thereby further corroborating the similarity of both disassembly processes.

Notably, the used regulatory core construct Abl83-534 lacks the myristoylated N-cap. Although we have previously demonstrated that the latter construct is predominantly assembled (Skora et al., 2013), the addition of the myristoyl moiety is expected to further stabilize the assembled conformation in a similar way as asciminib.

Considering this mechanism, dissociation of myristoyl from the native Abl 1b core may be a first step during activation. However, it should be kept in mind that the Abl 1a isoform lacks the N-terminal myristoylation, and it is presently unclear whether other moieties bind to the myristoyl pocket of Abl 1a during cellular processes."

2) Can the authors comment more on the differentiation between assembled conformations induced by type I inhibitor binding vs apo forms (or AMP-PNP and allosteric inhibitor) reported in Figure 3B? The differences are clearly identified by PCA but not sufficiently discussed.

As indicated in the text, we think two structural effects are intermingled within PC2. Due to this admixture, it is hard to draw strong conclusions and we don’t want to expand on this too much. We have slightly modified the respective paragraph (p.7) as follows):

"As the affected residues react differently to perturbations by type I inhibitors and truncation of the αI’-helix (Figure 3A, right), we attribute this behavior to two effects intermixed into the PC2 detection: (i) a minor rearrangement of the SH3/KD N-lobe interface caused by filling of the ATP pocket with type I inhibitors, which in contrast to the stronger N-lobe motion induced by type II inhibitors does not yet lead to core disassembly and (ii) a small rearrangement of the SH2/KD C-lobe interface caused by shortening and mutations of the αI-helix."

3) The allosteric connection between active site inhibitor binding and the myristate/allosteric inhibitor binding has been observed in the past and noted before, in papers such as Zhang et al, Nature 2010. While the authors reference this paper, they do not acknowledge its specific findings or engage in a broader discussion of how their conclusions relate to this work.

We have modified the beginning of the Conclusion section:

"The allosteric connection between Abl ATP site and myristate site inhibitor binding has been noted before, albeit specific settings such as construct boundaries and the control of phosphorylation vary in published experiments. Positive and negative binding cooperativity of certain ATP-pocket and allosteric inhibitors has been observed in cellular assays and in vitro (Kim et al., 2023; Zhang et al., 2010). Furthermore, hydrogen exchange mass spectrometry has indicated changes around the unliganded ATP pocket upon binding of the allosteric inhibitor GNF-5 (Zhang et al., 2010). Here, we present a detailed high-resolution explanation of these allosteric effects via a mechanical connection between the kinase domain N- and C-lobes that is mediated by the regulatory SH2 and SH3 domains and involves the αI helix as a crucial element.

Specifically, we have established a firm correlation between the kinase activity of the Abl regulatory core, the imatinib (type II inhibitor)-induced disassembly of the core, which is caused by a force FKD–N,SH3 between the KD N-lobe and the SH3 domain, and a force FαI,SH2 exerted by the αI-helix towards the SH2 domain. The FαI,SH2 force is mainly caused by a clash of the αI-αI’ loop with the SH2 domain. Both the FKD–N,SH3 and FαI,SH2 force act on the KD/SH2SH3 interface and may lead to the disassembly of the core, which is in a delicate equilibrium between assembled and disassembled forms. As disassembly is required for kinase activity, the modulation of both forces constitutes a very sensitive regulation mechanism. Allosteric inhibitors such as asciminib and also myristoyl, the natural allosteric pocket binder, pull the αI-αI’ loop away from the SH2 interface, and thereby reduce the FαI,SH2 force and activity. Notably, all observations described here were obtained under nonphosphorylated conditions, as phosphorylation will lead to additional strong activating effects."

4) Figure 6 could do a better job of providing an illustration of steric clashes.

We have revised Figure 6, panel F, in order to better illustrate the steric clashes, and modified the legend accordingly.

5) There is a typo in line 5 from the top on page 11 (dash missing from "83534" superscript).

Thank you. This was fixed.

Author Response

Many thanks for handling our manuscript (eLife-RP-RA-2023-93968) entitled "Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain", by García-Cuesta et al. We are delighted to hear your willingness to consider our manuscript following appropriate revision. We have carefully read the referees' commentaries and have organized new experiments to address their specific queries.

Reviewer #1 (Public Review)

The computational methodology is going to be carefully reviewed. In particular to justify the software and techniques used in this manuscript. We will also describe the method for identifying the pocket on the CXCR4 structure as well as the workflow used to explain the transition from docking evaluation to MD analyses. Additionally, we will conduct experiments to enhance the results and address the specific feedback provided, ultimately improving the overall reliability.

Reviewer #2 (Public Review)

Although the paper was initiated by titrating the compounds in migration experiments, we are going to add new kinetics and titration of concentrations in these experiments. In addition, we are going to change the way in which we present the data from the singlemolecule tracking experiments. We will add a representative video of each experimental condition, and include some of the mean square displacement curves to support our data on the analysis of the diffusion coefficient (D1-4) to give a more conclusive view of receptor clustering. Regarding the tumorigenesis experiments we will include the individual data points and we will try to perform kinetics with distinct concentrations of the drug.

Author Response

Reviewer #1 (Public Review):

This manuscript from Kavanjoo et al examines the role of macrophages within the fetal liver beyond erythrocyte maturation. Using single-cell sequencing, high-resolution imaging, and inducible genetic deletion of yolk-sac (YS) derived macrophages, the authors demonstrate that heterogeneous fetal liver macrophages regulate erythrocyte enucleation, interact physically with fetal HSCs, and may regulate neutrophil accumulation in the fetal liver. The data as presented do not strongly support the authors’ conclusion that fetal macrophages in the liver regulate the HSC niche or granulopoiesis from HSCs.

Fetal-derived resident tissue macrophages are increasingly implicated in regulation of adult tissue function and homeostasis, but considerably less is known regarding the function of fetal macrophages during development. Macrophages in the fetal liver have been shown to form erythroblastic islands, where they regulate erythrocyte maturation. Here, the authors performed single-cell sequencing on fetal liver macrophages (Cd11b-lo) to gain insight into heterogeneity and utilized previously published pre-Mac signatures from the YS to focus on YS-derived macrophages. These clusters were then further cross-referenced with surface protein expression as determined by multidimensional flow cytometry to hone in on a very specific subset of three groups of F4/80hi macrophages defined by multiple surface markers. Fate-mapping with three models (Tnfrsf11a-Cre - YS pMAC derived; Ms4a3Cre - FL monocyte derived; CXCR4-Cre-ERT2 - definitive HSC derived) revealed that three major subsets are all derived from YS pMACs.

We thank the reviewer for the comments and have addressed all points below. If certain points were mentioned twice, we responded at the position where the point was raised the first time.

However, the relative frequencies of these specific populations are not shown, and because the single sequencing analysis goes through so many iterations of re-clustering that initiates by focusing specifically on pMAC signatures, this result is not surprising.

Probing gene expression within each of the three clusters revealed ligand expression suggesting cell-cell interactions, and cross-referencing with a fetal LT-HSC gene expression dataset revealed potential receptor-ligand interactions. Microscopic investigation of physical interactions between specific macrophage subsets and HSCs was not particularly convincing. In Figure 3C, for example, Cluster C is very difficult to visualize. It would again be helpful to know what the ratios are within the FL for each cluster. Data in Figure 3F are not well represented by Data in Figure 3E.

We showed frequencies after CODEX in the original manuscript (Fig. S3A, now Figure 4 - figure supplement 1A) since isolation of cells often induces an artifact, and relative frequencies after scRNA-seq experiments never represent the actual cell numbers present in situ. However, also the CODEX analysis has its weakness, especially in dense tissues, as the automated gating method may not catch every macrophage due to its star-shaped structure. Thus, we have now included the absolute numbers of macrophage subpopulations in Figure 7C. We have tried to improve the visualization of the clusters in Figure 3C (now Figure 4C) by zooming into a specific region. The Voronoi diagram is a powerful method that allows for an overall spatial visualization of cell distribution in large tissue pieces. In the high-resolution PDF that we provide, zooming into the PDF file should allow the reader to see each cluster in great detail.

To improve the data of macrophage-HSC interaction we have performed 3D reconstructions and quantified the distance of CD150+ and Iba1+ cells in 3D (new Figure 3C-E) as the thin cryosectioning used for CODEX is not suitable to reconstruct these interactions properly (see also lines 328-331). Thus, Figure 3E was not able and also not meant to represent data shown in Figure 3F (now Figure 4E and 4F). Figure 3E is just meant to show examples of all clusters sitting in proximity to CD150+ HSCs.

Furthermore, deletion of YS pMAC-derived macrophages the Tnfrsf11a-Cre X Spi1fl/fl resulted in broad macrophage depletion - although the authors did not demonstrate this using the carefully refined phenotypes they had defined earlier in the manuscript. Nonetheless, the authors demonstrate that macrophage depletion did affect erythroid enucleation, as expected, and the authors also showed some effect of macrophage deletion on LT-HSC gene expression by bulk transcription analysis. These effects were relatively small, however, and this was clear in the absence of effects on hematopoiesis in vivo or HSC proliferation ex vivo. To further investigate the effects of macrophage deletion on downstream hematopoieisis, the authors re-assessed the myeloid compartment following macrophage deletion, and identified and specifically focused on an observed increase in neutrophils in response to macrophage depletion. Based on this increase, they tested HSC differentiation using a colony-forming assay, which shows a slight increase in GM colonies that is also reflective of a slight but insignificant increase in total colony forming capability. The authors concluded that loss of fetal macrophages causes a reprogramming of HSCs to the granulocytic lineage. However, the colony-forming assay and subtle differences in gene expression are not sufficient to conclude that fetal HSCs have been reprogrammed towards granulocytic lineage by macrophage deletion.

We thank the reviewer for this comment and have improved the manuscript accordingly: We have performed the colony-forming assay again with n=5 embryos per genotype that were harvested on the same day, which resulted in a similar phenotype as before, with the differences of GM colonies now being significant. Further, we quantified the depletion of all macrophage subpopulations in the Tnfrsf11a-Cre X Spi1fl/fl model (Fig. 7C). To strengthen the point that the transient lack of macrophages when HSCs arrive in the fetal liver leads to their reprograming, we included flow cytometry data from E16.5 and E18.5 where we still see an increase of neutrophils in the fetal liver, despite the fact that macrophages are repopulating the empty niche (Fig. 7E, F). To show that this is a cell-intrinsic effect, we have performed adoptive transfer experiments supporting our claim that loss of macrophages reprograms HSCs toward the granulocytic lineage (Fig. 7H, I)

Overall, there are some interesting pieces of data in this manuscript, including the classification of new subsets of macrophages in the liver, their fate-mapping to the YS, and gene expression analysis. However, the data as presented do not strongly support a role for these particular macrophage subsets in regulating HSCs or fetal hematopoiesis within the fetal liver niche. Although there may be specific subsets of fetal liver macrophages that more closely physically interact with HSCs, deletion of what appeared to be a vast majority of macrophages in the FL did not appear to affect cellularity of hematopoietic stem and progenitor cells in vivo, and was not shown to convincingly affect HSC function. The mechanism by which macrophage deletion affected granulopoiesis could be independent from HSCs, and would be interesting to further explore.

We hope that with new set of experiments we were able to convince the reviewer of the importance of macrophages in the HSC niche.

Reviewer #2 (Public Review):

Using a single-cell omics approach combined with spatial proteomics and genetic fate mapping, Kayvanjoo et al found that fetal liver (FL) macrophages cluster into distinct yolk sac-derived subpopulations and that some of the HSCs in FL preferentially associate with one of the identified macrophage subpopulations. FLs lacking macrophages show a delay in erythropoiesis. The authors also try to identify a role of macrophages for HSCs function in FL, and claim that macrophages affect myeloid differentiation of HSCs. Experimental support for the function of macrophages on HSCs remains weak. Taken together, their data provide a precise map of FL macrophage subpopulations, which is novel and will serve the field well.

We thank the reviewer for the positive assessment. We have now strengthened the data regarding the impact of granulopoiesis by performing additional CFU assays and adoptive transfers.

Author Response

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

Public Reviews:

We thank the reviewers for their insightful comments on our manuscript. We have addressed the reviewers’ comments below and in the revised manuscript.

Reviewer #1:

Comment #1: The authors found differences in the initial spike doublet of action potentials between cortical neurons in experimental and control conditions (Figure 2e). The action potential firing frequency of the first two APs (instant firing frequency) of recorded neurons shall be quantified to investigate whether there are statistical differences between the action potential firing frequency in cortical neurons in different experimental groups versus control conditions.

Response: As suggested by the reviewer, we have quantified the first interspike interval (ISI; time between the 1st and 2nd action potential). The data is included in Fig. 2h as well as in Fig. S3e and Table 1. The Results and Methods have also been updated accordingly.

Comment #2: The mTORS12215Y induced the largest changes in Ih current amplitudes in cortical neurons compared with other experimental conditions. Whether the HCN4 channel expression is regulated by mTOR pathway activation, or could there be possible interactions between the HCN channel and mTORS12215Y mutant protein?

Response: Our previous findings using the RhebS16H mutation support the idea that increased expression of HCN4 channels is regulated by mTOR pathway activation. This is evidenced by its sensitivity to rapamycin (a mTOR inhibitor) and expression of constitutively active 4E-BP1 (a translational repressor downstream of mTORC1). Since mTORS2215Y directly hyperactivates mTORC1 and there are no known interactions between HCN channels and mTORS2215Y, our data strongly suggests that abnormal HCN4 channel expression occurs via mTORC1 hyperactivation in this condition. We have revised our Discussion to make this point clearer.

Comment #3: A comparison of the electrophysiological characteristics of cortical neurons in different experimental conditions in the present study and pathological neurons in human FCD reported in previous literature could be interesting. Inducing pathological gene mutations or knocking out key genes in mTOR pathway in the rodent cortex - which approach could better model human FCD?

Response: We agree with the reviewer and have added a new paragraph in the Discussion to compare our electrophysiology results to those of previous studies done on human FCDII and TSC cytomegalic neurons. With regards to the reviewer’s question about which of the two approaches in the rodent cortex – inducing pathological gene mutations or knocking out key genes in the mTOR pathway – would better model human FCD, our study emphasizes the importance of considering gene-specific mechanisms in FCDII. Thus, modeling the genetically distinct FCDIIs will require using gene-specific manipulations. We have revised our Discussion to include this point. With that said, for some phenotypes that are generalized across FCDII independent of the mTOR pathway genes, using pathogenic mutations of mTOR activators or knockout of negative mTOR regulators would likely both be appropriate models. Of note, as discussed in the manuscript, there are also technical factors to be considered when choosing to use a pathogenic gene mutation versus knocking out a gene (the latter which would depend on the half-life of the proteins).

Reviewer #2:

Comment #1: The authors postulate that all the findings are dependent on mTORC1-related effects but don't assess whether some of the differences could be due to effects on mTORC2 signaling. mTORC2 is an important and poorly understood alternative isoform of mTOR (due to rictor binding) that has effects on distinct cell signaling pathways and in particular actin polymerization. This doesn't diminish the effects of the current analysis of mTORC1 but could explain genotypic differences in each variable. A few prior studies have assessed the role of mTORC2 in epileptogenesis and cortical malformations (Chen et al., 2019).

Response: We agree with the reviewer and have revised our Discussion to include the possibility of mTORC2 contribution to the gene-specific phenotypic differences.

Comment #2: The slice recordings were performed in the usual recording aCSF buffer conditions but there is no assessment of the role of amino acids or nutrients in the bath. While it is clear that valuable and viable acute slice recordings can be made in aCSF, the role of the mTOR pathway is to modulate cell growth in response to nutrient conditions. Thus, one variable that could be manipulated and assessed currently in this study is the levels of amino acids i.e., leucine and arginine added to the bath since DEPDC5 and TSC1 are responsive to ambient amino acid levels.

Response: We thank the reviewer for this great suggestion, and we intend to pursue this as part of another study.

Comment #3: The analysis concedes that the role of somatic mutations in cortical malformations may depend not only on genotypic effects but also on allelic load and cellular subtype affected by the mutation. Thus, it would be interesting to see if electroporation either at E14 or E16, thereby affecting a distinct pool of progenitors, would mitigate or accentuate differences between mTOR pathway genes.

Response: We agree with the reviewer. This is a crucial experiment that we hope to perform in the future. We have also added a paragraph in our Discussion to address this important point.

Comment #4: Treatment with rapamycin and zatebradine in each condition would have added to the strength of the findings to determine the mTOR-dependence and reversibility of HCN4 effects.

Response: We previously demonstrated the mTORC1 dependence of HCN4 expression in the RhebS16H condition using rapamycin and expression of constitutively active 4E-BP1. 4E-BP1 is a translational repressor downstream of mTORC1. In the 4E-BP1 study, we used a conditional system to express 4EBP1F113A (mutation that resists inactivation by mTORC1) in adolescent mice while RhebS16H (and thus mTORC1 activation) was expressed embryonically. 4E-BP1F113A expression suppressed Ih current and HCN4 expression, suggesting that aberrant HCN4 expression can be reversed by decreasing mTORC1regulated translation. Based on these data and the findings that rapamycin suppressed abnormal HCN4 expression, we postulate that increased HCN4 expression in the different gene conditions examined in the present study occurs via the mTORC1 pathway. However, we agree with the reviewer that treating each of the conditions with rapamycin would provide direct evidence of their mTORC1 dependence. Additionally, treating each condition with the HCN channel blocker zatebradine would also add strength to the findings. We have added a comment in the Discussion to acknowledge this point.

Reviewer #1 (Recommendations For The Authors):

Comment #1: The authors found increased frequency or amplitudes of spontaneous postsynaptic currents in different experimental cohorts. These data may not be sufficient to conclude increased synaptic excitability, because there are no pharmacological experiments to verify whether the recorded inward currents are excitatory or inhibitory postsynaptic currents. An alternative approach could be analyzing the decay time of spontaneous postsynaptic currents, the excitatory postsynaptic currents had relatively faster decay time compared with inhibitory postsynaptic currents.

Response: Thank you for the comment. We apologize for the lack of clarity and have added the following text in the Results to clarify: “To separate sEPSCs from spontaneous inhibitory postsynaptic currents (sIPSCs), we used an intracellular solution rich in K-gluconate to impose a low intracellular Cl- concentration and recorded at a holding potential of -70 mV, which is near the Cl- reversal potential. The 90%-10% decay time of the measured synaptic currents ranged between 4-8 ms in all conditions (mean ± SD: control: 4.9 ± 1.6; RhebY35L: 5.2 ± 1.4; mTORS2215Y: 7.4 ± 1.4; control: 6.8 ± 0.7; Depdc5KO: 7.4 ± 1.0; PtenKO: 8.1 ± 0.9; Tsc1KO: 7.4 ± 0.9), consistent with the expected decay time for sEPSCs and shorter than the decay time for sIPSCs (Kroon et al, 2019). The recorded synaptic currents were therefore considered to be sEPSCs.”

Comment #2: There are typos of Depdc5 in the text and figure legends.

Response: Thank you for noticing this error. We have corrected the typos in the manuscript.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Zhu and colleagues aimed to clarify the importance of isoform diversity of PCDHg in establishing cortical synapse specificity. The authors optimized 5' single-cell sequencing to detect cPCDHg isoforms and showed that the pyramidal cells express distinct combinations of PCDHg isoforms. Then, the authors conducted patch-clamp recordings from cortical neurons whose PCDHg diversity was disrupted. In the elegant experiment in Figure 3, the authors demonstrated that the neurons expressing the same sets of cPCDHg isoforms are less likely to form synapses with each other, suggesting that identical cPCDHg isoforms may have a repulsive effect on synapse formation. Importantly, this phenomenon was dependent on the similarity of the isoforms present in neurons but not on the amount of proteins expressed.

One of the major concerns in an earlier version was whether PCDHg isoforms, which are expressed at a much lower level than C-type isoforms, have true physiological significance. The authors conducted additional experiments to address this point by using PCDHg cKO and provided convincing data supporting their conclusion. The results from PCDHg C4 overexpression, showing no impact on synaptic connectivity, further clarified the importance of isoforms. I have no further concerns, however, I would like to point out that the evidence for the necessity of the PCDHg isoform is still lacking because most experiments were done by overexpression. It would be helpful for the readers if the authors could add this point to the discussion.

Thank you for the positive feedback on our work. We have now incorporated a discussion of the limitations associated with overexpression.

Reviewer #2 (Public Review):

This short manuscript by Zhu et al. describes an investigation into the role of gamma protocadherins in synaptic connectivity in the mouse cerebral cortex. First, the authors conduct a single-cell RNA-seq survey of postnatal day 11 mouse cortical neurons, using an adapted 10X Genomics method to capture the 5' sequences that are necessary to identify individual gamma protocadherin isoforms (all 22 transcripts share the same three 3' "constant" exons, so standard 3'biased methods can't distinguish them). This method adaptation is an advance for examining individual gamma transcripts, and it is helpful to publish the method, the characterization of which is improved in this revised manuscript. The results largely confirm what was known from other approaches, which is that a few of the 19 A and B subtype gamma protocadherins are expressed in an apparently stochastic and combinatorial fashion in each cortical neuron, while the 3 C subtype genes are expressed ubiquitously. Second, using elegant paired electrophysiological recordings, the authors show that in gamma protocadherin cortical slices, the likelihood of two neurons on layers 2/3 being synaptically connected is increased. That suggests that gamma protocadherins generally inhibit synaptic connectivity in the cortex; again, this has been reported previously using morphological assays, but it is important to see it confirmed here with physiology. Finally, the authors use an impressive sequential in utero electroporation method to provide evidence that the degree of isoform matching between two neurons negatively regulates their reciprocal synaptic connectivity. These are difficult experiments to do, and while some caveats remain, the main result is consistent. Strengths include the impressive methodology and improved demonstration of the previously-reported finding that gamma protocadherins work via homophilic matching to put a brake on synapse formation in the cortex. Weaknesses include the writing, which even in the revision fails to completely put the new results in context with prior work, which together has largely shown similar results; a still-incomplete characterization of a new alpha protocadherin KO mouse (a minor point but it should still be addressed); and a lack of demonstration of protein levels in electroporated brains. Because of the unique organization and expression pattern of the gamma protocadherins, it is unlikely that these results will be directly applicable to the broader understanding of the role of cell adhesion molecules in synapse development. However, the methodology, which is now better described, should be applicable more broadly and the improved demonstration of the role of gamma protocadherin's negative role in cortical synaptogenesis is helpful.

Thank you for the positive comments on our work. We have taken your suggestion into account and expanded our discussion to contextualize our research within the broader field of PCDH. Additionally, we have included more data to further illustrate the decrease in αPCDH expression in Pcdha conditional knockout mice. Your feedback has been invaluable in enhancing our manuscript.

Reviewer #3 (Public Review):

In this study, Zhu and authors investigate the expression and function of the clustered Protocadherins (cPcdhs) in synaptic connectivity in the mouse cortex. The cPcdhs encode a large family of cadherin-related transmembrane molecules hypothesized to regulate synaptic specificity through combinatorial expression and homophilic binding between neurons expressing matching cPcdh isoforms. But the evidence for combinatorial expression has been limited to a few cell types, and causal functions between cPcdh diversity and wiring specificity have been difficult to test experimentally. This study addresses two important but technically challenging questions in the mouse cortex: 1) Do single neurons in the cortex express different cPcdh isoform combinations? and 2) Does Pcdh isoform diversity or particular combinations among pyramidal neurons influence their connectivity patterns? Focusing on the Pcdh-gamma subcluster of 22 isoforms, the group performed 5'end-directed single-cell RNA sequencing from dissociated postnatal (P11) cortex. To address the functional role of Pcdhg diversity in cortical connectivity, they asked whether the Pcdhgs and isoform matching influence the likelihood of synaptic pairing between 2 nearby pyramidal neurons. They performed simultaneous whole-cell recordings of 6 pyramidal neurons in cortical slices, and measured paired connections by evoked monosynaptic responses. In these experiments, they measured synaptic connectivity between pyramidal neurons lacking the Pcdhgs, or overexpressing dissimilar or matching sets of Pcdhg isoforms introduced by electroporation of plasmids encoding Pcdhg cDNAs.

Overall, the study applies elegant methods that demonstrate that single cortical neurons express different combinations of Pcdh-gamma isoforms, including the upper layer Pyramidal cells that are assayed in paired recordings. The electrophysiology data demonstrate that nearby Pyramidal neurons lacking the entire Pcdhg cluster are more likely to be synaptically connected compared to the control neurons, and that overexpression of matching isoforms between pairs decreases the likelihood to be synaptically connected. These are important and compelling findings that advance the idea that the Pcdhgs are important for cortical synaptic connectivity, and that the repertoire of isoforms expressed by neurons influence their connectivity patterns potentially through a self/nonself discrimination mechanism. However, the findings are limited to probability in connectivity and do they do not support the authors' conclusions that Pcdhg isoforms regulate synaptic specificity, 'by preventing synapse formation with specific cells' or to 'unwanted partners'. Characterizations of the cellular basis of these defects are needed to determine whether they are secondary to other roles in cell positioning, axon/dendrite branching and synaptic pruning, and overall synaptic formation. Claims that Pcdh-alpha and Pcdhg C-type isoforms are not functionally required are premature, due to limitations of the experiments. Moreover, claims that 'similarity level of γPCDH isoforms between neurons regulate the synaptic formation' are not supported due to weak statistical analyses presented in Fig4. The overstatements should be corrected. There was also missed opportunity to clearly discuss these results in the context of other published work, including recent publications focused on the cortex.

Thank you for your feedback on the strengths and weaknesses of our work. In terms of the cellular basis of affected synaptic connectivity caused by γ-PCDH isoforms, we have compared the probability of connectivity for neuronal pairs with similar range of distance. Our findings indicate that the manipulation primarily affects pairs within the 50-150 micrometer range, suggesting that cell positioning might be a critical factor for the impact of γ-PCDH on synapse formation. However, we acknowledge that we couldn't definitively determine whether the negative effect on synaptic connectivity stems directly from impaired synapse formation or indirectly from synaptic pruning or the influence of PCDHγ on axon/dendritic branching. We've added these limitations to our discussion to provide a more comprehensive view of our research. Furthermore, we've adjusted our statements to better reflect the significance of our findings. Your feedback has been instrumental in improving the clarity and depth of our manuscript.

Strengths:

• The 5' end sequencing with a Pcdhg-amplified library is a technical feat and addresses the pitfall of conventional scRNA-Seq methods due to the identical 3'sequences shared by all Pcdhg isoform and the low abundance of the variable exons. New figures with annotated cell types confirm that several pyramidal and inhibitory cortical subpopulations were captured.

Statistical assessment of co-occurrence of isoform expression within clusters is also a strength.

• By establishing the combinatorial expression of Pcdhgs by maturing pyramidal cells, the study further substantiates the 'single neuron combinatorial code for cPcdhs' model. Although combinatorial expression is not universal (ie. serotonergic neurons), there was limited evidence. The findings that individual pyramidal neurons express ~1-3 variable Pcdhg transcripts plus the Ctype transcripts aligns with single RT-PCR studies of single Purkinje cells (Esumi et al 2005; Toyoda et al 2014). They differ from the findings by Lv et al 2022, where C-type expression was lower among pyramidal neurons. OSNs also do not substantially express C-type isoforms (Mountoufaris et al 2017; Kiefer et al 2023). Differences, and the advantages of the 5'end -directed sequencing (vs. SmartSeq) could be raised in the discussion.

• Simultaneous whole-cell recordings and pairwise comparisons of pyramidal neurons is a technically outstanding approach. They assess the effects of Pcdhg OE isoform on the probability of paired connections.

• The connectivity assay between nearby pairs proved to be sensitive to quantify differences in probability in Pcdhg-cKO and overexpression mutants. The comparisons of connectivity across vertical vs lateral arrangement are also strengths. Overexpressing identical Pcdhg isoform (whether 1 or 6) reduces the probability of connectivity, but there are caveats to the interpretations (see below).

Weaknesses:

n earby pairs but are not sufficient evidence for synapse specificity. The cPcdhs play multiple roles in neurite arborization, synaptic density, and cell positioning. Kostadinov 2015 also showed that starburst cells lacking the Pcdhgs maintained increased % connectivity at maturity, suggesting a lack of refinement in the absence of Pcdhgs. The known roles raise questions on how these manipulations might have primary effects in these processes and then subsequently impact the probability of connectivity. Investigations of morphological aspects of pyramidal development would strengthen the study and potentially refine the findings. The authors should more clearly relate their findings to the body of cPcdh studies in the discussion.

Previous studies revealed the adverse effects of γ-PCDHs on dendritic spines, demonstrating that their absence results in increased dendritic spines density, while overexpression leads to a reduction. In our study, we consistently observed that γ-PCDHs exert a negative influence on synaptic connectivity. This consistency strengthens the overall body of evidence in support of the role of γ-PCDHs in synaptic connectivity and dendritic spine regulation. While we have previously mentioned this point in our discussion to highlight the concordance between our findings and prior research, your input is greatly appreciated in reinforcing the scientific context of our work.

• Pcdhg cKO-dependent effects on connectivity occur between closely spaced soma (50-100um - Figure 2E), highlighting the importance of spatial arrangement to connectivity (also noted by Tarusawa 2016). Was distance considered for the overexpression (OE) assays, and did the authors note changes in cell distribution which might diminish the connectivity? Recent work by Lv et al 2022 reported that manipulating Pcdhgs influences the dispersion of clonally-related pyramidal neurons, which also impacts the likelihood of connections. Overexpression of Pcdhgc3 increased cell dispersion and decreased the rate of connectivity between pairs. Though these papers are mentioned, they should be discussed in more detail and related to this work.

Our data indicated that variable γ-PCDH isoforms primarily influence synaptic connectivity in neuronal pairs within the 50-150 micrometer range. Notably, as the distance between neurons increases, we observed a corresponding reduction in synaptic connectivity, as illustrated in Figure 2E. We have also included additional discussion regarding potential variances among different C-type isoforms.

• Though the authors added suggested citations and improved the contextualization of the study, several statements do not accurately represent the cited literature. It is at the expense of crystalizing the novelty and importance of this present work. For instance, Garrett et al 2012 PMID: 22542181 was the first to describe roles for Pcdhgs in cortical pyramidal cells and dendrite arborization, and that pyramidal cell migration and survival are intact. Line 52 cited Wang et al 2002, but this was limited to gross inspection. Garrett et al is the correct citation for: 'The absence of γ-PCDH does not cause general abnormality in the development of the cerebral cortex, such as cell differentiation, migration, and survival (Wang et al., 2002).' Second, single cell cPcdh diversity is introduced very generally, as though all neuron types are expected to show combinatorial variable expression with ubiquitous C-Type expression. But those initial studies were limited to Purkinje cells (Esumi 2005 and Toyoda 2014). Profiling of serotonergic neurons and OSN reveals different patterns (citations needed for Chen 2017 PMID: 28450636; Mountofaris et al PMID: 2845063; Canzio 2023 PMID: 37347873), raising the idea that cPcdh diversity and ubiquitous Ctype expression is not universal. Thus, the authors missed the opportunity to emphasize the gap regarding cPcdh diversity in the cortex.

We would like to extend our gratitude to the reviewer for pointing out the citation related to the roles of γ-PCDHs in the neocortex. After a thorough review of both papers, Wang et al., 2002 and Garrett et al., 2012, we concur that it would be more appropriate to cite both of these papers here. Your suggestion to underscore the diverse expression patterns of γPCDHs in neocortical neurons is well-received, and we have integrated this aspect of our findings with previous observations into a new paragraph within the discussion section. Your insights have greatly enriched the depth of our paper, and we genuinely appreciate your contribution.

• They have not shown rigorously and statistically that the rate of connectivity changes with% isoform matching. In Figure 4D, comparisons of % isoform matching in OE assays show a single statistical comparison between the control and 100% groups, but not between the 0%, 11% and 33% groups. Is there a significant difference between the other groups? Significant differences are claimed in the results section, but statistical tests are not provided. The regression analysis in 4E suggests a correlation between % isoform similarity and connectivity probability, but this is not sound as it is based on a mere 4 data points from 4D. The authors previously explained that they cannot evaluate the variance in these recordings as they must pool data together. However, there should be some treatment of variability, especially given the low baseline rate of connectivity. Or at the very least, they should acknowledge the limitations that prevent them from assessing this relationship. Claims in lines 230+ are not supported: ' Overall, our findings demonstrate a negative correlation between the probability of forming synaptic connections and the similarity level of γPCDH isoforms expressed in neuron pairs (Fig. 4E)".

We employed a bootstrap method to estimate the potential variance in the analysis presented in Fig. 4E. It's important to note that due to methodological limitations, a comprehensive assessment of variance based solely on recordings from a single animal is challenging. As such, we have adjusted our claims to be more aligned with our observations.

• Figure 4 provides connectivity probability, but this result might be affected by overall synapse density. Did connection probability change with directionality (e.g between red to green cells, or green to red cells).

As suggested by the reviewer, we have conducted an analysis to assess the directionality of connections under different conditions. This analysis involved comparing the directionalities of connections following the overexpression of six variable isoforms, as depicted in Fig. 3E. Upon examining 33 connected OE-Ctrl pairs following the electroporation of these 6 isoforms, we observed 3 pairs with bidirectional connections, 19 pairs with connections from OE to Ctrl, and 11 with connections from Ctrl to OE. To assess the statistical significance of these observations, we applied a Chi-square test. The results from this analysis indicated that there was no significant difference in the directionality of connections. These findings offer further support for the idea that overexpressing multiple γ-PCDH isoforms within a single neuron might not be sufficient to alter its connections with other neurons.

• Generally, the statistical approaches were not sufficiently described in the methods nor in the figure legends, making it difficult to assess the findings. They do not report on how they calculated FDR for connectivity data, when this is typically used for larger multivariate datasets.

We employed the False Discovery Rate (FDR) correction, specifically the BenjaminiHochberg method, to determine which values remained statistically significant. This method is widely accepted and involves inputting all the p-values and the total number, 'n.' Additional details about this correction are now provided in the Method section for clarity.

• The possibility that the OE effects are driven by total Pcdhg levels, rather isoform matching, should be examined. As shown by qRT-PCR in Fig. 3, expression of individual isoforms can vary. It is reasonable that protein levels cannot be measured by IHC, although epitope tags could be considered as C-terminal tagging of cPcdhs preserves the function in mice (see Lefebvre 2008). Quantification of constant Pcdhg RNA levels by qRT-PCR or sc-RT-PCR would directly address the potential caveat that OE levels vary with isoform combinations.

Through a series of multiple whole-cell recordings, we examined neuronal pairs within the 0% group, where both neurons exhibited overexpression of different combinations of γPCDH isoforms. What we discovered is that the connectivity level within pairs of neurons where both neurons overexpressed γ-PCDH isoforms, pairs with only one neuron overexpressing these isoforms, and pairs with two control neurons (lacking overexpression) was remarkably similar. However, as we incrementally raised the similarity level between the recorded neurons by increasing the overlap in the combinatorial expression of γ-PCDH isoforms, we observed a gradual decrease in the connectivity probability between these neurons. Notably, the connectivity probability reached its minimum when the recorded cells had the exact same combinatorial expression of γ-PCDH isoforms at the 100% similarity level. These findings suggest that the similarity level between neurons, rather than the absolute expression level of γ-PCDH isoforms, plays a critical role in affecting synapse formation.

-A caveat for the relative plasmid expression quantifications in Figure 3-S1 is that IHC was used to amplify the RFP-tagged isoform, and thus does not likely preserve the relationship between quantities and detection.

We attempted to enhance the mNeongreen signal, known for its exceptional signal-tonoise ratio, by utilizing the 32f6-100 antibody from Chromotek. However, our observations did not reveal any additional cells through immunostaining compared to the images obtained solely based on the mNeongreen signal. This indicates that the application of the available antibody did not yield a significant improvement in cell detection.<br /> It's important to emphasize that if the RFP signal is overvalued, it would result in an increase in both the "red only" and "red in total" categories. However, it's worth noting that the "red only" category is more sensitive to the outcome than the "red in total" category. Therefore, an overvaluation of the RFP signal would lead to an underestimation of the total estimated plasmid content in electroporated neurons. Consequently, this would result in a lower estimate for the proportion of co-expression cells rather than a higher estimate. We have updated the calculation method in the "Estimating the numbers of overexpressed γPCDH isoform" section to reflect these considerations.

• Figure 1 didn't change in response to reviews to improve clarity. New panels relating to the scRNASeq analyses were added to supplementary data but many are central and should be included in Figure 1 (ie. S1-Fig6D). In the Results, the authors state that neuronal subpopulations generally show a combinatorial expression of some variable RNA isoforms and near ubiquitous C-type expression. But they only show data for the Layer 2/3 neuron-specific cluster in S1-Fig-6D, and so it is not clear if this pattern applies to other clusters. Fig. S1-5 show a low number of expressed isoforms per cell, but specific descriptions on whether these include C-type isoforms would be helpful. Figure 1F showing isoform profile in all neurons is not particularly meaningful. There is a lot of interest in neuron-type specific differences in cPcdh diversity, and the authors could highlight their data from S1-5 accordingly.

In addition to the layer 2/3 cluster, we observed a diverse combinatorial expression of various variable γ-PCDH isoforms alongside nearly ubiquitous C-type expression in all other clusters of cells. We have now explicitly mentioned this observation in the main text. To underscore this point further, we have included a new figure, Fig. 1-S6, which provides information on the similarity analysis for all other clusters. It's important to note that the data in previous Fig. S1-5 (now renumbered as S1-7) were solely related to "variable" isoforms. We apologize for any confusion and have made this clarification by including it in the title of the figure.

• The concept of co-occurrence and results should be explained within the results section, to more clearly relate this concept to data and interpretations. Explanations are now found in the methods, but this did not improve the clarity of this otherwise very interesting aspect of the study.

Thanks for your suggestion. We have incorporated some of the explanations from the methods section into the main text t, mainly for the concept of “co-occurence”.

• The claim that C-type Pcdhgs do not functionally influence connectivity is premature. Tests were limited to PcdhgC4, which has unique properties compared to the other 2 C-type isoforms (Garrett et al 2019 PMID: 31877124; Mancia et al PMID: 36778455). The text should be corrected to limit the conclusion to PcdhgC4, and not generally to C-type. The authors should test PcdhgC3 and PcdhgC5 isoforms.

We have changed the claim for PcdhgC4, but not generally for C-type to better reflect our observation.

• The group generated a novel conditional Pcdh-alpha mouse allele using CRISPR methods, and state that there were no changes in synaptic connectivity in these Pcdh-alpha mutants. But this claim is premature. The Southern blots validate the targeting of the allele. But further validations are required to establish that this floxed allele can be efficiently recombined, disrupting Pcdha protein levels and function. Pcdha alleles have been validated by western blots and by demonstration of the prominent serotonergic axonal phenotype of Pcdha-KO (ie. Chen 2017 PMID: 28450636; IngEsteves 2018 PMID: 29439167).

We have obtained a new set of qRT-PCR data that confirms the decreased expression of α-PCDH in Pcdha CKO mice. These data have been integrated into Figure 2-S2D.

• The Discussion would be strengthened by a deeper discussion of the findings to other cPcdh roles and studies, and of the limitations of the study. The idea that the Pcdhgs are influencing the rate of connectivity through a repulsion mechanism or synaptic formation (ie through negative interactions with synaptic organizers such as Nlgn - Molumby 2018, Steffen 2022) could be presented in a model, and supported by other literature.

I would like to express my sincere appreciation to the reviewer for their invaluable comments and suggestions, which have led to extended discussions within our work. We have incorporated these suggestions into our paper to establish stronger connections between our observations and prior research findings.

Reviewer #1 (Recommendations For The Authors):

1) In Figure S6, the authors measured Euclidean distance from the single cell data to take account of the isoform expression levels in explaining diversity. However, it is hard to interpret the data without any control. The authors could measure the same value from a shuffled /randomized dataset for comparison (similarly to Fig 1F).

We understand the reviewer's concern about the significance of the Euclidean distance analysis without an appropriate control. The inclusion of the Euclidean distance metric was initially a response to suggestions from other reviewers who recommended incorporating diverse methods for analyzing expression patterns among neurons.

In response to your valuable feedback, we have taken measures to address these concerns. We have introduced shuffled data for comparison, thus enhancing the meaningful context for interpreting the results derived from the Euclidean distance analysis.

2) The authors need to clarify which cortical regions were used for electrophysiological experiments.

Apologies for any confusion. To clarify, all recordings were conducted on neurons located in layer 2/3 of the neocortex without further discrimination. We have reinstated this information in both the main text and the methods section to ensure its clarity.

Reviewer #2 (Recommendations For The Authors):

There are still some issues that must be addressed.

1) The references to gamma protocadherin repulsion are not correct in context. A repulsive role of homophilic interaction has been inferred from certain knockout phenotypes in a subset of neurons (not in cortical neurons). However, repulsion has never been shown to follow gamma protocadherin engagement. The authors present no new evidence that their results are attributable to cellular repulsion at nascent synaptic contacts. The mechanism is unknown. The references to repulsion to explain their results should make it clear that this is one possible explanation, but it is not shown. Also some references in the text are not correct. For example, line 63/64: the results of Molumby and Steffen are not involving homophilic adhesion or repulsion, but rather a cis interaction with neuroligins. Those papers should not be discussed as involving repulsion as in the reference to Lefebvre 2012. Also line 268/269 "Together with previous findings (Molumby,,,Tarusawa), our observations solidify repulsion effect of g-PCDH on synapse formation. . .". This is not the case. Neither Molumby nor Tarusawa demonstrated any such repulsion.

Thank you to the reviewer for pointing out the errors in our citations. We have made the necessary corrections to the citations and have also refined the descriptions of our observations to improve clarity and accuracy.

2) The discussion of the results when C4 is overexpressed must also be greatly toned down. C4 is a strange C-type protein--it cannot get to the cell surface alone but relies on other cPCDHs for this, and its primary role is in preventing cell death. It is odd that the authors used this isoform to represent C-types. They should have used C3, which two recent papers showed have specific roles at some synapses (Meltzer et al 2023, Ginty lab) and in dendrite branching (Steffen et al 2023, Weiner lab) , or C5. It is entirely possible that just C4 has no role in synaptic matching--but C3 and C5 might. They should not conclude that the C-types have no such role and only A and B types do. That must be toned down (e.g., line 198/199, line 281).

We acknowledge that using C4 to represent all three C-types (C3, C4, and C5) is not accurate. We have now modified the statement in the main text to rectify this.

3) For the citation of Pcdhg flox/flox mice (line 126), Prasad et al., Development, 2008, Weiner lab, should also be cited as it fully characterized that line that was also used in Lefebvre et al 2008. They were co-published.

Thank you for highlighting the missing citation, and we have now included it in the relevant section.

4) the Pcdh alpha KO Mouse characterization is still insufficient. The authors must show that alpha expression is gone following introduction of Cre, either by RT-PCR using alpha constant domain primers, or an alpha antibody on Western. blot. The southern and off-target sequencing do not confirm that all alpha gene expression is gone.

Thank you for your feedback. We have conducted the qRT-PCR analysis as per your suggestion. The results clearly indicate a substantial reduction in α-PCDH expression within the neocortex of Pcdha cKO mice. We have thoughtfully incorporated this data into the manuscript, and it is visually represented in the new panel of Figure 2-S2D. Your valuable input has contributed to enhancing the quality of our work, and we sincerely appreciate the opportunity to address this important aspect.

5) I do not understand something in Figure 4-S1A. Why with 0% matching is synaptic connectivity so low? This is not the same as in Figure 3E. This has to be explained because it does suggest that overexpression of ANY isoforms can inhibit synapse formation, which is consistent with Molumby 2017, even though this paper says it is not just the levels but the isoform specificity.

The panel of Fig.4-S1A illustrates the connection rate between neurons with the same color (icons in upper left), representing cells that express the same combination of γ-PCDHs (100% of similarity). The X-axis (0%, 11%, 33%, and 100%) reflects the similarity level between the 2 populations of cells (GFP and RFP).

6) There are still issues with the English grammar in the paper. It is not too bad in the main text but someone should re-edit it. However, the figure legends are indeed much worse and truly must be edited professionally before they are acceptable.

We apologize for our English writings in the paper. We have now polished most part of the manuscript, especially the parts for figure legends.

Reviewer #3 (Recommendations For The Authors):

• This study has many strengths and innovative findings. Most comments above included suggestions to strengthen the paper. The overall message that Pcdhgs influence the rate of synaptic connectivity between nearby cells is compelling. How this Pcdhg-isoform-dependent process could influence synaptic specificity can be explored in a model in the discussion. But this study did not test a role in 'synaptic specificity'; this term should be removed from the title and line 81 in the intro.

Thank you for your invaluable comments aimed at improving our paper. Regarding the title, we believe that "synaptic connectivity" might be a more suitable choice than "synaptic specificity." However, we're open to considering other alternatives as well.

• The manuscript and overall quality of the science will be improved by removing those sections that are not adequately investigated (ie.Pcdh-a cKO; PcdhgC4 is assessed but findings can't be extended to other C-type isoforms) and by outlining limitations of the study.

We have modified the related claim mentioned in the main text.

• The studies negatively correlating between isoform matching and connectivity are not robust. Additional approaches are needed if the authors want to make this claim.

In Figure 4E, we have implemented a bootstrapping method. Bootstrapping is a statistical technique falling under the broader category of resampling methods. It involves random sampling from the observed data with replacement, enabling the calculation of standard errors, confidence intervals, and supporting hypothesis testing.

• Statistical approaches should be described in methods, figure legends.

More information about statistical approaches has been added in the figure legends.

• The discussion should elaborate on the limitations of the study, and relate to other studies, including Lv et al 2022.

We have added more discussion to relate our observations to previous findings.

Author Response

Note to the editor and reviewers.

All the authors would like to thank the editorial team and the two anonymous reviewers for their efforts and thoughtfulness in assessing our manuscript. We very much appreciate it and we all believe that the manuscript has been much improved in addressing the comments and suggestions made.

General considerations on the revised manuscript

We have applied extensive modifications to the manuscript with our main goal being the improvement of clarity. The Introduction has been changed mainly to introduce precisely our terminology and we have stuck to it in the rest of the manuscript. The Results section has been divided up into more defined sections. The discussion has been extensively re-written to improve clarity, following the suggestion of the reviewers. Main figures 1 and 4 have been modified with clearer schematics. Supplementary figures and legends have been modified and several supplementary schematic figures have been added to clearly present our interpretations for various data. We have added a Supplementary Discussion where the most detailed technical parts of our discussion are presented to avoid unnecessarily weighing down the main discussion, where our main conclusions are outlined. We have presented our mass photometry mixing experiment in a new supplementary figure, with detailed explanation. We have also expanded our discussion of in vivo and general relevance of our study.

Response to manuscript evaluation

Our manuscript has been evaluated as a valuable study and presenting solid experimental evidence. We appreciate the recognition of our work.

Two weaknesses were identified by reviewers: 1) our experiments do not completely exclude the possibility of an alternative nucleophile. This relates to the evaluation of our experimental evidence. 2) Our study does not address the in vivo relevance of the interface swapping phenomenon, which relate to the value of the study for the community.

Response to the evaluation of experimental evidence (Weakness #1):

We argued in the original manuscript that we have excluded completely the presence of an alternative nucleophile. This conclusion is based on a series of experiments which were presented in the originally submitted manuscript. These experiments are not discussed by the reviewers in relation to this main conclusion and therefore we suggest that they have not been properly evaluated. We believe our conclusion to be appropriately supported by these data (see our response to reviewer #1). In addition, the criticism of our gel-filtration data by reviewer #2 was based on a misinterpretation of Supplementary figure 1 b. We accept of course that the way the data was presented could be misleading and we assume responsibility for this. We have attempted to correct this by changing the main text and the figures legends and annotation. In conclusion, we believe that the evaluation of experimental evidence as presented in the revised manuscript could be upgraded to “convincing”.

Response to our study general relevance evaluation (weakness #2):

We agree with both reviewers about the in vivo relevance of our observation being an important question, not addressed so far. Indeed, the value of our study would be greatly increased by in vivo data and be of interest to a wider audience. However, we would like to argue that our study would interest a wider audience than initially stated for the following reasons: 1) Our study is the first evidence of interface swapping in vitro and will constitute a base to investigate this phenomenon both in vivo and in vitro. It will therefore interest a wide audience due to the potential involvement of interface swapping in a wide range of processes, such as recombination, evolution, and drug targeting (see also below). 2) DNA cleavage is the central mode of action of antibiotics targeting bacterial type II topoisomerases (i.e. topoisomerases “poisons”). This already established target is one of the few having produced new scaffolds and too few new antibacterial are in production to fulfill medical needs. The role of interface stability is also emerging as a modulator of the efficiency of topoisomerase poisons. See for instance (Germe, Voros et al. 2018, Bandak, Blower et al. 2023). By shedding light on interface dynamics, our study will be of interest to scientist interested in the development of these drugs. In addition, the heterodimer system can potentially produce detailed mechanistic information (Gubaev, Weidlich et al. 2016, Hartmann, Gubaev et al. 2017, Stelljes, Weidlich et al. 2018) not only on gyrase but also on other, dimeric type II topoisomerases or even other dimeric enzyme in general. We have amended the manuscript to make these points clearer. Therefore, we believe that the evaluation of the revised manuscript’s relevance could be upgraded to “important”.

Point-by-point response to the reviewer

Reviewer #1 (Public Review):

Germe and colleagues have investigated the mode of action of bacterial DNA gyrase, a tetrameric GyrA2GyrB2 complex that catalyses ATP-dependent DNA supercoiling. The accepted mechanism is that the enzyme passes a DNA segment through a reversible double-stranded DNA break formed by two catalytic Tyr residues-one from each GyrA subunit. The present study sought to understand an intriguing earlier observation that gyrase with a single catalytic tyrosine that cleaves a single strand of DNA, nonetheless has DNA supercoiling activity, a finding that led to the suggestion that gyrase acts via a nicking closing mechanism. Germe et al used bacterial co-expression to make the wild-type and mutant heterodimeric BA(fused). A complexes with only one catalytic tyrosine. Whether the Tyr mutation was on the A side or BA fusion side, both complexes plus GyrB reconstituted fluoroquinolone-stabilized double-stranded DNA cleavage and DNA supercoiling. This indicates that the preparations of these complexes sustain double strand DNA passage. Of possible explanations, contamination of heterodimeric complexes or GyrB with GyrA dimers was ruled out by the meticulous prior analysis of the proteins on native Page gels, by analytical gel filtration and by mass photometry. Involvement of an alternative nucleophile on the Tyr-mutated protein was ruled unlikely by mutagenesis studies focused on the catalytic ArgTyrThr triad of residues. Instead, results of the present study favour a third explanation wherein double-strand DNA breakage arises as a consequence of subunit (or interface/domain) exchange. The authors showed that although subunits in the GyrA dimer were thought to be tightly associated, addition of GyrB to heterodimers with one catalytic tyrosine stimulates rapid DNA-dependent subunit or interface exchange to generate complexes with two catalytic tyrosines capable of double-stranded DNA breakage. Subunit exchange between complexes is facilitated by DNA bending and wrapping by gyrase, by the ability of both GyrA and GyrB to form higher order aggregates and by dense packing of gyrase complexes on DNA. By addressing a puzzling paradox, this study provides support for the accepted double strand break (strand passage) mechanism of gyrase and opens new insights on subunit exchange that may have biological significance in promoting DNA recombination and genome evolution.

The conclusions of the work are mostly well supported by the experimental data.

Strengths:

The study examines a fundamental biological question, namely the mechanism of DNA gyrase, an essential and ubiquitous enzyme in bacteria, and the target of fluoroquinolone antimicrobial agents.

The experiments have been carefully done and the analysis of their outcomes is comprehensive, thoughtful and considered.

The work uses an array of complementary techniques to characterize preparations of GyrA, GyrB and various gyrase complexes. In this regard, mass photometry seems particularly useful. Analysis reveals that purified GyrA and GyrB can each form multimeric complexes and highlights the complexities involved in investigating the gyrase system.

The various possible explanations for the double-strand DNA breakage by gyrase heterodimers with a single catalytic tyrosine are considered and addressed by appropriate experiments.

The study highlights the potential biological importance of interactions between gyrase complexes through domain-or subunit-exchange

We thank the reviewer for their support, effort, and comments. The above is a great summary.

Weaknesses:

The mutagenesis experiments described do not fully eliminate the perhaps unlikely participation of an alternative nucleophile.

We agree that the mutagenesis experiment on its own does not fully eliminate the possibility of an alternative nucleophile. The number of residues mutated is limited, and therefore it is possible we have missed a putative alternative nucleophile.

However, we have other data and experiments supporting the conclusion that no alternative nucleophile exists. Therefore, we want to stress that our conclusion that no such alternative exist is based on these extra data. These data and experiments are not discussed by either reviewer despite being present in the original manuscript. This puzzled us and we have modified the manuscript and the figures in the hope that they, and their significance, would not be missed.

Briefly:

1) We have performed cleavage-based labeling of the nucleophile responsible for cleavage. This experiment is depicted in Figure 4. The nucleophilic activity of the residue involved results in covalent link between the polypeptide (that includes the residue) and radiolabeled DNA. Therefore, a polypeptide that includes an active nucleophile will be radiolabeled and visible, whereas a polypeptide that is missing an active nucleophile will remain unlabeled and invisible. We can distinguish the BA and the A polypeptide from their size. In the case of the BA.A complex both the BA polypetide and the A polypetide are radiolabeled and therefore both have an active nucleophile. In the case of the BAF.A complex, the unmutated A polypeptide is labeled, meaning that a nucleophile is still active. In contrast, the BAF polypeptide shows no detectable labeling. This result means that removing the hydroxyl group from the catalytic tyrosine abolishes any protein-DNA covalent link, suggesting that no other nucleophile from the BA polypetidic chain can substitute for the catalytic tyrosine hydroxyl group. This experiment excludes the possibility of an alternative nucleophile coming from the polypeptidic chain of either GyrA or GyrB. This experiment, described in figure 4, is not discussed by the reviewer. This experiment is similar in principle to early experiments identifying catalytic tyrosine in topoisomerases. See for instance, (Shuman, Kane et al. 1989).

2) The experiment above does not exclude a nucleophile coming from the solvent. To exclude this possibility, we have used T5 exonuclease (which needs a free 5’ DNA end to digest) and ExoIII (which need a free 3’ DNA end to digest). We have shown the reconstituted cleavage is not sensitive to T5 and sensitive to ExoIII. This shows that the 5’ end of the cleaved sites are protected by a bulky polypeptide impairing T5 activity, which is active in our reaction as shown by the digestion of a control DNA fragment. This experiment shows that the reconstituted cleavage is very unlikely to come from a small nucleotide potentially provided by the solvent. This experiment is described in the main text and the results are shown in supplementary figure 5. It is not mentioned by either reviewer.

3) Finally, we would like to emphasize our experiment comparing the BAF.A59 to BALLL.A59. The BALLL.A59 complex displays increased cleavage compared to BAF.A59. If this increased cleavage was due to an alternative nucleophile on the BALLL side, we would expect an accompanying increase in supercoiling activity since the BALLL.A59 possesses one CTD, which is sufficient for supercoiling. The fact that no increased supercoiling activity is observed strongly suggests subunit exchange reconstituting an A59 dimer, inactive for supercoiling but active for cleavage. We believe this somewhat complex observation to be quite significant and we have attempted to clarify the manuscript and discuss its full significance in several places.

Reviewer #1 (Recommendations For The Authors):

An interesting paper on DNA gyrase that explains a puzzling paradox in terms of the double-strand break mechanism.

Major points

1) The authors consider several mechanisms that could potentially explain their data. On page 15, the authors present the evidence against the nicking closing mechanism proposed by Gubaev et al. Throughout the manuscript, they indicate where their experimental results agree with this earlier work but should also indicate and account for differences. For example, Gubaev et al describe cross linking experiments that they claim rule out subunit exchange. These aspects should be clearly explained.

Thank you for the suggestion. We have re-written the discussion to address this point. We are extensively discussing experiments from (Gubaev, Weidlich et al. 2016), and offer our interpretation of apparently conflicting results. We suggest that their experiments are basically consistent with our data when correctly interpreted. To keep the main manuscript clear, we have added a supplementary discussion where experiments from (Gubaev, Weidlich et al. 2016) are discussed further in relation to our data.

2) Page 9. The experiments done to rule out the perhaps unlikely alternative nucleophile hypothesis relate to the possible role of the Arg and Threonine of the RYT triad. These residues are close to the DNA and therefore are prime candidates and attractive targets for mutagenesis. However, strictly speaking, the mutant enzyme data presented do not rule all possibilities. For example, Serine is often the nucleophile used by resolvases to effect DNA recombination via subunit exchange. The ideal experiment to rule out/rule in other nucleophiles would be to identify the residue(s) that become attached to DNA in the cleavage reaction.

Please see above. We have effectively ruled an alternative nucleophile with our cleavage-based labeling experiment and others that were present and discussed in the original manuscript but were missed. We have modified the manuscript and figures in order to make this point clearer than before.

3) p17. The readout for subunit exchange used by the authors is double-stranded DNA cleavage. Attempts to directly detect the formation of the DNA cleaving complexes GyrA2B2 and (GyrBA)2 (arising from subunit exchange between heterodimers) by mass photometry were not successful. Perhaps FRET would have been another approach to try as it could also detect interface and domain interchanges.

Directly detecting interface exchange directly by proximity experiment would be extremely useful. FRET would have to be done in the BAF.A + GyrB configuration where the amount of interface exchange is important. Now, we do not have the tools to do that and developing them would be outside the scope of the study. We propose cross linking experiment to be done in the future. We argue that the manuscript is convincing without these for now. This will be addressed in the future. This point, and other possible future experiments are now discussed in the discussion section.

4) The underlying canvas of this paper is the strand passage mechanism of gyrase. It would seem appropriate to include the papers first proposing it - Brown P.O and Cozzarelli N.R. (1979) and Mizuuchi K et al (1980).

We very much agree. These papers have now been added in the introduction as appropriate, highlighting the relationship between double-strand cleavage and the strand-passage mechanism.

5) Figure 1. The quality of the insets is poor. It is difficult to pick out the key catalytic residues and their disposition vis-a-vis DNA.

We agree, Figure 1 has been re-done and the schematic theme has been harmonized throughout the whole manuscript. We very much hope that clarity has improved. Thank you for the suggestion.

6) The experimental work is a very detailed analysis of a specific feature of engineered gyrase heterodimers. Making the work accessible to the general reader will be important. Using shorter paragraphs each with a specific theme might help. In particular, the second paragraph of the Results on p7, the section on p9 and bottom of p11, p13 and the first paragraph of the Discussion on p14 are each a page or more long. A shorter manuscript that avoids overinterpretation of the smaller details would also help.

We agree. We have now split long paragraphs into individual sections, with titles, in the Results. This structure is recapitulated at the beginning of the discussion, and we have split the discussion into shorter paragraphs, each with a unique point being made.

7) The impact of the Gubaev et al (2016) paper for the field in general, and as the catalyst for the present work should be better documented. Mention of this earlier paper and its significance at the beginning of the Abstract and elsewhere e.g in the Introduction might also help with a more logical organization of the current findings and result in a shorter paper (which would be easier to read).

We have added a reference to (Gubaev, Weidlich et al. 2016) in the abstract and have expanded our introduction

Minor points

1) Legends for Figs 2 and 6; Supplementary Figs 1 and 8. The designation of subfigures as a, b, c, d , e etc appears to be incorrect. Check throughout and in the text.

The manuscript has been checked for such errors.

2) Figure 2, and first paragraph p8. Peaks in Fig 2c should be labelled to facilitate discussion on p8.

Agreed, this has been done.

3) Supplementary Fig 4 and elsewhere in the manuscript. A variety of notations are used to denote phenylalanine mutants e.g. AsubscriptF, AsuperscriptF and AF. Check and use one format throughout.

Done

4) Figures showing gels include the label '+EtBr, +cipro'. This is somewhat confusing because EtBr was contained in the gel (not the samples) whereas cipro was included in the reaction. Modify or describe in the legend..

We have re-written the figure legend.

5) Supplementary Fig 4b describes a small effect on the ratio of linear to nicked DNA for the triple LLL mutant. Is this significant? How many times was the measurement made?

This has been addressed in the original manuscript in the supplementary data. In term of quantification, the experiment has been done 3 times for each prep, with the same GyrB prep and concentration. The standard error is displayed on the figure. This result is very reproducible and have been reproduced more than 3 times. No LLL cleavage assay showed more single-strand than double-strand cleavage. For the phenylalanine mutant, no cleavage assay showed more double-strand than single-strand cleavage.

6) Supplementary Fig 5 legend. Should 'L' read 'size markers' (and give their sizes)?

Yes indeed, we have modified the figure to clarify.

7) p11 line 5. Is this statement correct?

Yes, it is correct. Although we hope we are on the same line. When the Tyrosine is mutated on one side only of the heterodimer, both single- and double-strand cleavage are protected from T5 exonuclease digestion.

8) 12 last line should read...and supercoiling activity (not shown)..were

Thank you, done.

There are a number of typos throughout the text, for example:

Page 3 line..Difficult to conclude...what?

Page 3 para 3...Lopez....and Blazquez

We have corrected these typos and checked the whole manuscript.

Reviewer #2 (Public Review):

DNA gyrase is an essential enzyme in bacteria that regulates DNA topology and has the unique property to introduce negative supercoils into DNA. This enzyme contains 2 subunits GyrA and GyrB, which forms an A2B2 heterotetramer that associates with DNA and hydrolyzes ATP. The molecular structure of the A2B2 assembly is composed of 3 dimeric interfaces, called gates, which allow the cleavage and transport of DNA double stranded molecules through the gates, in order to perform DNA topology simplification. The article by Germe et al. questions the existence and possible mechanism for subunit exchange in the bacterial DNA gyrase complex.

The complexes are purified as a dimer of GyrA and a fusion of GyrB and GyrA (GyrBA), encoded by different plasmids, to allow the introduction of targeted mutations on one side only of the complex. The conclusion drawn by the authors is that subunit exchange does happen, favored by DNA binding and wrapping. They propose that the accumulation of gyrase in higher-order oligomers can favor rapid subunit exchange between two active gyrase complexes brought into proximity.

The authors are also debating the conclusions of a previous article by Gubaev, Weidlich et al 2016 (https://doi.org/10.1093/nar/gkw740). Gubaev et al. originally used this strategy of complex reconstitution to propose a nicking-closing mechanism for the introduction of negative supercoils by DNA gyrase, an alternative mechanism that precludes DNA strand passage, previously established in the field. Germe et al. incriminate in this earlier study the potential subunit swapping of the recombinant protein with the endogenous enzyme, that would be responsible for the detected negative supercoiling activity.

Accordingly, the authors also conclude that they cannot completely exclude the presence of endogenous subunits in their samples as well.

Strengths

The mix of gyrase subunits is plausible, this mechanism has been suggested by Ideka et al, 2004 and also for the human Top2 isoforms with the formation of Top2a/Top2b hybrids being identified in HeLa cells (doi: 10.1073/pnas.93.16.8288).

Germe et al have used extensive and solid biochemical experiments, together with thorough experimental controls, involving :

• the purification of gyrase subunits including mutants with domain deletion, subunit fusion or point mutations.

• DNA relaxation, cleavage and supercoiling assays

• biophysical characterization in solution (size exclusion chromatography, mass photometry, mass spectrometry)

Together the combination of experimental approaches provides solid evidence for subunit swapping in gyrase in vitro, despite the technical limitations of standard biochemistry applied to such a complex macromolecule.

We thank the reviewer for their supportive and considered comments.

Weaknesses

The conclusions of this study could be strengthened by in vivo data to identify subunit swapping in the bacteria, as proposed by Ideka et al, 2004. Indeed, if shown in vivo, together with this biochemical evidence, this mechanism could have a substantial impact on our understanding of bacterial physiology and resistance to drugs.

Thank you for this comment. Indeed, whether this interface exchange can happen in vivo and lead to recombination is a very important question. However, we believe that this is outside the scope of this study simply because of the amount of work one can fit into one paper. Proving that interface exchange can happen in vitro has already necessitated a number of non-trivial experiments and likewise investigating interface exchange in vivo will require a careful, long-term study (see our reply to reviewer #2 comment, who also raised this point). We can’t address it with one additional experiment with the tools we have. However, we very much hope to do it in the future.

Reviewer #2 (Recommendations For The Authors):

Specific questions and comments for the authors:

1) Complex identification during purification

The statement line 236-237 that "Our heterodimer preparation showed a single-peak on a gel-filtration column, distinct from the GyrA dimer peak" is not entirely clear. In Fig supp 1 b, how can the authors conclude from the superose 6 that GyrBA is separated from the GyrA dimer? Since they seem close in size 160/180kDa, they are unlikely to be well separated in a superose 6 gel filtration column. The SDS-PAGE seems to show both species in the same fractions #15-17 therefore it would not be possible to distinguish GyrBA. A from A2.

There appears to be some confusion about what Supp Fig. 1b shows. First, in all our gel filtration conditions both GyrBA and GyrA can’t exist as monomers at a significant concentration. Therefore, we can never observe the GyrBA monomer on a gel filtration column. Supp Fig. 1b shows the gel filtration profile of the BA.A heterodimer only. This is the output of the last, polishing step in the reaction. We analyze these results using SDS-PAGE. Therefore, the BA.A heterodimer will be denatured and separated into 2 polypeptides: GyrBA and GyrA, which migrates according to their size in an SDS-PAGE and forms two bands. These two bands do not represent two separate species in solution. They represent the separation of one species only, the BA.A heterodimer into its two, denatured, subunits: GyrA and GyrBA. We do not conclude from Supp Fig. 1 as a whole that GyrBA and the GyrA dimer are well separated, and this is not stated in the manuscript. We conclude that the BA.A dimer is fairly well separated from the GyrA dimer. They have significant different size (~260 kDa and ~180 kDa respectively) and form different peaks on a gel filtration column. The BA.A heterodimer has a GyrA subunit and therefore will shows a GyrA band on an SDS-PAGE, like the GyrA dimers but the two are obviously distinct in their quaternary structure. We are hoping that our new schematics and re-write of some of the results and figure legends will clarify this.

Panel 6 shows a different elution volume for the 2 species BA.A and A2 on an analytical S200 column, which appears better at separating the complexes in this size range.

Did the authors consider using a S200 column instead of superose 6 for the sample preparation, to optimize the separation of GyrBA. A from A2?

This is not a necessarily true statement (see above). We have not run the GyrA dimer on a Superose 6 column. The analysis was done on an s200 because extensive data for the GyrA dimer was already available with this, already calibrated column. We do not expect the Superose 6 to be worse in this size range. In fact, it might even be better. The Superose 6 profile in Supp. Fig. 1b shows BA.A only and no GyrA dimer. We have clarified the annotations in the figure to make this clearer.

Regarding the analytical gel filtration experiment, there is however an overlap in the elution volume in the analytical column, therefore how can the authors ensure there is no excess free A2 complex in the GyrBA. A sample?

Indeed, there is an overlap, but we argue that it is overstated. The important part of the overlap is where the maximum height of the GyrA peak is positioned compared to the BA.A trace, not where the traces intersect. This overlap is minimal. If a contaminating GyrA peak was hidden in the BA.A peak, it would have to be at least 10 times less intense than the BA.A peak. Since BA.A and GyrA dimer have roughly the same extinction coefficient, this means that a contamination would detectable at 10 % or even less. Our mass photometry further excludes such contamination.

Alternatively, the addition of a larger (cleavable) tag at the C-terminal end of the BA construct (therefore not disturbing dimer association) could allow to better distinguish the 2 populations already at the size exclusion step.

This is true and could allow cleaner purification. There are also other ways to achieve cleaner purification, like adding a secondary tag. However, like we argue in the manuscript, our contaminations are already minimal. It is questionable what benefits could be gained in changing the protocol. We also argue that the tandem tag method does not completely exclude contamination (Supplementary Discussion) and therefore we are not sure if this would be worth the time and expenditure.

2) GyrA and GyrB Oligomers:

In the mass photometry experiment, the authors explain that the low concentration of the proteins promotes dissociation of GyrA dimers, hence the detection of GyrA monomers instead of GyrA dimers, which are also detected in the GyrBA.A sample.

However, it cannot be concluded that the GyrA dimer is not formed in the condition of the gel filtration chromatography, at higher concentration.

In our mass photometry experiment, The BA.A sample is not as diluted as the GyrA dimer and much closer to our experimental condition. Since we have calculated the dissociation constant, we can calculate the expected level of dissociation (or reassociation). The level of dissociation is minimal in these conditions. If some dissociation is expected from the BA.A heterodimers, a very low amount of GyrBA monomer should also be present and yet they are not observed. We presume that it is because mass photometry is much more sensitive to GyrA (see our mixing mass photometry experiment that we have added). If the GyrA would reassociate at higher concentration, it would do so either with itself (forming a GyrA dimer) or with the GyrBA monomer, reforming the heterodimer. Assuming both GyrA dimer and heterodimer have the same dissociation constant, roughly one third of the GyrA monomer would reassociate with themselves. Assuming even complete reassociation of the GyrA dimer, this would leave only GyrA dimer accounting for 2% of the prep.

Another interpretation would be to assume that GyrBA monomers are not present at all and that GyrA monomer are reassociating only with themselves. This is not valid because of the following thermodynamic reason:

Since the profile for the GyrA dimer are collected at equilibrium, we should expect a ratio between GyrA monomer and dimers that follow the dissociation constant. In other words, if the GyrA monomer were in equilibrium with GyrA dimer we should expect a much higher dimer concentration already as the GyrA monomers are not as dilute. We do not observe a GyrA dimer peak in the BA.A profile, even though we can detect a low amount of GyrA dimer mixed with BA.A. Therefore, we conclude that the observed GyrA monomer must be in equilibrium with another dimerization partner, which is most probably the GyrBA monomer (see above). Therefore, only a minimal amount of GyrA dimer is expected to be formed at higher concentration by direct reassociation. This could probably increase if we let this solution-based exchange carry on for a long time at dissociation equilibrium. We have actually shown that this solution-based exchange is very slow and take several days because of the low dissociation at equilibrium.

The mass spectrometry analysis in Fig 2 confirms the presence of (monomeric) GyrA in the sample, despite different experimental conditions.

The concentration of heterodimer in the mass spectrometry experiment is actually higher than in the mass photometry experiment. This shows that self-reassociation of the GyrA monomer as suggested above is undetectable with mass spectrometry at higher concentration.

We considered that the “GyrA monomer” peak could be a contaminating GyrB monomer, which is ~90 kDa, which would explain the lack of reassociation. However, the mass spectrometry peak shows precisely the expected molecular weight of GyrA so we interpret this peak as arising from very limited dissociation of the BA.A heterodimer. The reassociation is limited at high concentration due simply to the fact that the difference in concentration between the mass photometry and our other experimental conditions is not that high. The GyrA dimer had to be diluted 400 times to see significant dissociation and yet even at this very low concentration the dissociation is far from complete.

Our general conclusions on the couple of point above is that we cannot completely exclude the presence of GyrA dimers being present, although they are undetectable in our working conditions either by mass photometry (lower concentration), Mass spectrometry (higher concentration) and even gel filtration (even higher concentration, see above). For the mass photometry, we have established that our detection threshold for a contamination is very low (see our mixing experiment).

Figure 2A: the authors state in the introduction that GyrB is a monomer in solution and then explain that the upper bands in the native gel are multimer of GyrB. Could the authors comment and provide the size exclusion profile of the Gyr B purification?

We have expanded our discussion of this. However, we have not been successful in collecting a gel filtration profile for GyrB. This is likely due to excessive oligomerization at the concentration we are using for gel filtration. We suggest that our mass photometry and Blue-Native PAGE experiment shows clearly that GyrB can be detected as a monomer in solution at the appropriate dilution. However, GyrB tends to oligomerize in a regular fashion (Consider especially Supp Fig. 8a), which suggest that it could align heterodimers on DNA in a linear, regular orientation. We have added a discussion of this.

Together the relevance of the oligomeric state of purified GyrA or GyrB should be clarified, relative to their role in subunit swapping.

We have added explanation in our discussion, while also trying to not be too speculative. Basically, we believe that GyrB oligomerization is likely to be involved. It is difficult to conclude for GyrA since no experiment has allowed us to test it. Therefore, the role of GyrA oligomerization, if any, is unclear. The GyrA tetramer is very prominent though and forms very easily. GyrB on the contrary forms longer oligomers more readily than GyrA and we surmise that this would help interface exchange. However, the structure of these GyrA and GyrB oligomers is not clear, which make it difficult to go beyond speculation on this. It would be a very interesting experiment if we were able to suppress GyrB oligomerization whilst conserving its ability to promote strand-passage and cleavage. Same goes for GyrA. Unfortunately, we are unable to do that at this time.

4) Subunit exchange

Line 320: the concept of subunit exchange in this context should be clearly explained. If one understands correctly, the authors mean that the BAF polypeptide, part of the BAF.A complex, could be replaced by a combination of B+A therefore forming a fully functional WT A2B2 gyrase complex.

Thank you for the suggestion. We have harmonized and clearly defined our terminology for interface swapping and subunit exchange in the introduction and attempted to be much more rigorous when referring to it.

A great effort has been done in this study to explain all the pros and cons of the experimental design but the length of the explanations may prevent readers outside of the field to fully appreciate the conclusions. This article would benefit from the addition of a few schematics to summarize the working hypothesis.

Thanks for the suggestion. We have added a series of schematics to illustrate our interpretation for each construct. As mentioned above the terminology has been more rigorously defined and updated throughout the manuscript.

5) Presence of endogenous GyrA

Line 419-425: it is quite difficult to follow the explanations regarding the possible contamination of the sample by endogenous GyrA.

Maybe these points should rather be addressed in the discussion, when debating the conclusions of Gubaev et al.

We agree. We have re-organized the Discussion doing just that. We added a Supplementary Discussion in which we further discuss the contamination problem in relation to (Gubaev, Weidlich et al. 2016).

Production of the subunits in another (non bacterial) expression system or a cell free system may prevent the association of endogenous protein.

Absolutely. We are planning on addressing this in the future, using the yeast expression system.

6) Mechanism for subunit swapping

Lines 588-595: As described by the authors the BA fusion shows decreased activity when compared with the WT probably due to limited conformational flexibility in absence of an additional linker sequence between the fused subunits.

The affinity of BA for A may possibly be reduced compared to the free A2B2 complex, due to a relative stiffness of the fusion upon full association with a free B subunit, as rightfully pointed by the authors.

If subunit exchange do happen in vitro, at least in the conditions of this study, the authors could assess the affinity of BA for A, when compared to the association of free B and A subunits

Experiments using analytical ultracentrifugation or surface plasmon resonance (SPR) may allow to determine the relative affinity of the BA +(A+B) compared to the A2B2 complex. This could be done also for the BALLL mutant and association with A59.

It would be extremely useful to measure the affinity of BA for A. However, this is difficult because of the high affinity of the interface. To measure a dissociation constant, one has to be able to measure the concentration of the monomer and the dimer at equilibrium. Because of this, the complex must be diluted enough to see any dissociation, making detection difficult. In practice, this also means that we cannot purify monomeric versions of these subunits. We therefore can’t perform “on-rate” study on an SPR surface, which would require flowing monomers on its partner subunit tethered to the SPR surface. However, we could perform “off-rate” studies, but the dissociation time is likely to be very long, making the measurement difficult. We have not tried it though, and it could turn out to be informative. An analysis of antibodies off-rate done in the past could provide a guideline for us to perform this experiment. Analytical ultracentrifugation is an excellent technique and could in theory provide information. In practice however it would be still necessary to dilute the complex enough to obtain significant dissociation at equilibrium, making detection difficult. As far as we are aware, analytical ultracentrifugation rely on UV absorbance for protein detection and therefore we probably would not detect our material at the necessary dilution. We are however open-minded about technique with very sensitive detection methods that could be used.

9) In vivo relevance

The study does not conclude on the subunits exchange in vivo, which have been suggested by earlier studies by Ikeda et al. To elaborate further on the relevance of such mechanism in the bacteria, experiments involving the fluorescent labeling of endogenous / exogenous mutant subunits may be required to provide further information on this phenomenon.

We completely agree that the in vivo relevance of such phenomena is the central question. Addressing this directly is not trivial though. Expressing both BA and A in vivo will results in random partnering and lead to a mix of dimers: A2 (1/4), BA2(1/4) and BA.A (1/2), assuming equal interface affinity. Therefore, to see subunit exchange in the same way as in vitro, one would have to get rid of the BA2 and A2 dimer together, or the BA.A dimer only. Our initial strategy to do that would be to engineer a specific dimer as being uniquely targeted for degradation. This could allow us to “get rid” of for instance the BA.A dimer. Subsequently, we would turn off the degradation and translation together and observe the rate of subunit exchange. This is not trivial though and would be the subject of a further study.

10) Figure 3: I guess the "intact" label refers to the supercoiled DNA (SC) ? It also appears as "uncleaved" in supp Figure 6. The same label for this topoisomer should be used throughout.

Thank you for pointing that out. It has now been corrected.

Bandak, A. F., T. R. Blower, K. C. Nitiss, R. Gupta, A. Y. Lau, R. Guha, J. L. Nitiss and J. M. Berger (2023). "Naturally mutagenic sequence diversity in a human type II topoisomerase." Proceedings of the National Academy of Sciences 120(28).

Germe, T., J. Voros, F. Jeannot, T. Taillier, R. A. Stavenger, E. Bacque, A. Maxwell and B. D. Bax (2018). "A new class of antibacterials, the imidazopyrazinones, reveal structural transitions involved in DNA gyrase poisoning and mechanisms of resistance." Nucleic Acids Res.

Gubaev, A., D. Weidlich and D. Klostermeier (2016). "DNA gyrase with a single catalytic tyrosine can catalyze DNA supercoiling by a nicking-closing mechanism." Nucleic Acids Res 44(21): 10354-10366.

Hartmann, S., A. Gubaev and D. Klostermeier (2017). "Binding and Hydrolysis of a Single ATP Is Sufficient for N-Gate Closure and DNA Supercoiling by Gyrase." J Mol Biol 429(23): 3717-3729. Shuman, S., E. M. Kane and S. G. Morham (1989). "Mapping the active-site tyrosine of vaccinia virus DNA topoisomerase I." Proc Natl Acad Sci U S A 86(24): 9793-9797.

Stelljes, J. T., D. Weidlich, A. Gubaev and D. Klostermeier (2018). "Gyrase containing a single C-terminal domain catalyzes negative supercoiling of DNA by decreasing the linking number in steps of two." Nucleic Acids Res.

Author Response

Reviewer #3 (Public Review):

Strengths:

NanoPDLIM2, nanotechnologies that efficiently deliver lentivirus overcomes resistance to chemotherapy and anti-PD-1 immunotherapy. This is a new strategy for enhancing the efficiency of immune checkpoint inhibitors.

This finding is important from a clinical translation perspective, but I have several minor concerns.

Weaknesses:

1) Please describe the mechanism of increased MHC class I and PD-L1 by PDLIM2.

Our previous studies showed that PDLIM2 induces MHC-I induction through decreasing STAT3 whereas it is dispensable for PD-L1 expression (Sun et al, 2019, PMID: 31757943). In line with the studies, PD-L1 is induced by chemotherapeutic drugs, but not by NanoPDLIM2 (Figure 6A). Together with the roles of PDLIM2 in repressing RelA-dependent MDR1 induction by chemotherapy and in preventing expression of cell survival and proliferation genes by targeting both RelA and STAT3 (Sun et al, 2019, PMID: 31757943), further providing the mechanistic basis for the combination and synergistic effect of nanoPDLIM2, anti-PD-1 and chemo drugs. The improvement has now been further incorporated.

2) Please describe the mechanism of decreased MDR1, nuclear RelA and STAT3 by PDLIM2.

Our previous studies demonstrated that PDLIM2 reduces MDR1 expression by degrading nuclear RelA (Sun et al, 2019, PMID: 31757943).

3) Please determine whether PDLIM2 expression directly impacts immune cells (function and number)?

As shown in Figure 5, NanoPDLIM2 increased the number and activation of tumor infiltrating lymphocytes (TILs); and in prior study, PDLIM2 knockout repressed the numbers of TILs and inhibited the activation of CD4+ and CD8+ T cells, while its re-expression in lung tumors led to T cell activation (Sun et al. 2019, PMID: 31757943). On the other hand, selective deletion of PDLIM2 in immune cells and in particular myeloid cells repressed the numbers and activation of TILs (Li et al, 2021, PMID: 33539325; PMCID: PMC8021114). Thus, PDLIM2 may impact immune cells both directly and indirectly, particularly when nanoparticles can deliver PDLIM2 into both tumor cells and tumor-associated immune cells (despite PDLIM2 is delivered into much fewer immune cells compared to tumor cells).

4) What is the efficiency of PDLIM2 delivery? Does delivery efficiency determine anti-tumor effect?

As shown in the manuscript, the dose of PDLIM2 used already shows high delivery (20-30 copies per tumor cell in Figure 3B) and therapeutic efficacy in the mouse model of refractory lung cancer and particularly when being combined with anti-PD-1 and chemo drugs. It is of interest to test different doses in the model for the best delivery and efficacy, which is actively being pursued in the lab.

5) Authors used a non-immunogenic tumor model. Can you demonstrate the combination effect with PDLIM2 in immunogenic lung cancer models to determine whether the combination of PDLIM2 with anti-PD-1 Ab confers a synergistic effect without chemotherapy?

Yes, it is of interest to demonstrate the combination of PDLIM2 and anti-PD-1 in immunogenic lung cancer models with chemotherapy although a synergy is highly expected. The greatest challenge in the lung cancer field is the low response of non-immunogenic tumor, which is the focus of the current manuscript.

6) On page 11, % change can make one over-interpret data.

The % change has been removed from the manuscript.

7) In Figure 5, what is the difference between 5A and 5D?

Figure 5A shows the increase of TILs by nanoPDLIM2 in animals that did not receive PD-1 blockade immunotherapy, Figure 5D shows the increase of TILs by nanoPDLIM2 in animals received PD-1 blockade immunotherapy.

8) It is unclear whether PDLIM2 confers an additive or a synergistic effect with anti-PD-1/chemo.

PDLIM2 nanotherapy confers a synergistic effect with chemotherapy on increasing apoptosis in tumors (Figure 4B) and tumor reduction (Figure 4A and 6E, left panel, tumor number), confers a synergistic effect with antiPD-1 on increasing CD4+ and CD8+ TILs (Figure 5A and 5D), and apoptosis in tumors (Figure 5F), and an additive effect on tumor reduction (Figure 5C and 6E), and confers a synergistic effect with chemotherapy plus anti-PD-1 on increasing CD4+ and CD8+ TILs (Figure 5A and 6F) and tumor reduction (Figure 6E, left panel, tumor number).

9) Have the authors tested any toxicity in normal lungs?

Same to tumor lungs, no obvious toxicity has been observed in normal lungs.

Reviewer #1 (Recommendations For The Authors):

The paper is clear and well-written, although some minor edits are needed. For example, the title could be changed to reflect both human and mouse studies in the manuscript for more general readers. Moreover, 'lung cancer' should be used instead of 'lung cancers'. The manuscript could be further improved by validating their findings in a different model and particularly the syngeneic model of metastatic lung cancer for a better overall survival time by the new combination therapy, given the fact that clinical trial studies usually start in patients with metastatic tumors. But this is optional because the therapeutic effect on primary lung cancer is already significant.

Thanks for the correction and wonderful suggestions. The “lung cancers” were replaced with “lung cancer”, and the title was changed to “Improving PD-1 blockade plus chemotherapy for complete remission of lung cancer by nanoPDLIM2”.

Reviewer #2 (Recommendations For The Authors):

1) What is the rationale for i.v. injection of nanoparticles containing PDLIM2 plasmid? Intranasal administration of nanoparticles may potentially target nanoPDLIM2 specifically to the lungs. Another potential option is intranasal infection of mice with adenovirus expressing PDLIM2.

The rationale for i.v. injection of nanoPDLIM2 is that iv injected nanoPDLIM2 first reach into the lung and more importantly tumor tissues as well as the convenience and high efficacy of mouse i.v. injection, particularly when multiple injections are needed. Mice are much less stressful compared to other intranasal or even intratracheal injection. Adenovirus can be used only once, because it will initiate ant-viral immune response in mice.

2) The authors examine PDLIM2 expression in lung tumors 1 week after i.v. administration of nanoparticles (Fig. 3A). Do all tumor cells express PDLIM2 after nanoPDLIM2 treatment? How long does PDLIM2 persist in the tumors? The kinetics of PDLIM2 expression may be informative to help interpret the results from the various combination treatments given to the mice. Multiple rounds of nanoPDLIM2 treatment could potentially improve the efficacy of the treatment.

For all the sections examined (n=6), PDLIM2 was re-expressed in most but not all lung cancer cells at 1-week of the i.v administration. Accordingly, nanoPDLIM2 was injected weekly. We are examining if PDLIM2 reexpression can last longer. We are also testing the best dose with the best efficacy.

3) Does the plasmid DNA from nanoparticles trigger an innate immune response in the lung that contributes to anti-tumor responses?

In line with previous studies showing no effect on immune responses (Bonnet et al. 2008. PMID: 18709489), the dose used in current study does not significantly affect immune cells in the lung, suggesting no obvious effect of nanoparticles with empty plasmid on innate immune response.

4) In Fig. 4, does the combination of nanoPDLIM2 and chemotherapy diminish STAT3 nuclear staining?

NanoPDLIM2 alone decreased nuclear STAT 3 in tumor cells (Figure 2C), it also diminished nuclear STAT3 in tumor cells with the combination of chemotherapy.

Author Response

We thank you for your careful review of our manuscript and helpful comments and suggestions. We have carefully considered each point and have addressed them by adding changes to the manuscript and figures. The text below detailed our responses and edits.

Reviewer #1 (Public Review):

Summary:

Liao et al leveraged two powerful genomics techniques-CUT&RUN and RNA sequencing-to identify genomic regions bound by and activated or inactivated by SMAD1, SMAD5, and the progesterone receptor during endometrial stromal cell decidualization.

Strengths:

The authors utilized powerful next generation sequencing and identified important transcriptional mechanisms of SMAD1/5 and PGR during decidualization in vivo.

Weaknesses:

Overall, the manuscript and study are well structured and provide critical mechanistic updates on the roles of SMAD1/5 in decidualization and preparation of the maternal endometrium for pregnancy. Please consider the following to improve the manuscript:

• Figure 4: A and C show bar graphs, not histograms. Please alter this phrasing.

Figure legends were adjusted as suggested.

• What post hoc test was performed on qPCR analyses? (Figure 6). It is evident that any assumptions of equal variance need to be negated due to the wide dispersion in experimental response invalidating the assumptions of a one-way ANOVA.

Yes, a Tukey’s post hoc test was performed on the qPCR analyses. To address the reviewer’s question regarding equal variance, normality of the dataset was examined by D’agostino & Pearson test in GraphPad Prism. The data demonstrated a normal distribution pattern, thus justifying the one-way ANOVA test.

• Figure 6: what data points are plotted? Are these technical replicates from individual wells or qPCR technical replicates?

The dataset represents three technical and three biological data points.

• Figure 6: Consider changing graph colors to increase visibility of error bars and data points.

Thank you for this suggestion. The colors of the error bars in Figure 6 have been changed to increase visibility. Additionally, different shapes have been utilized to distinguish between different groups.

• Figure 6 legend: no histograms are shown in this figure. Refer to all gene names utilizing proper nomenclature and conventions (gene names should be italicized).

The legend was adjusted as suggested with the correct nomenclature implemented.

• qPCR analyses: qPCR normalization should be done to at least two internal control genes, preferably three according to the MIQE guidelines (PMID: 19246619).

As suggested, we have performed additional qPCR analysis with normalization done to three internal controls.

• Supplement figure 2: graphs are bar graphs, not histograms.

The legends have been changed as suggested.

Reviewer #2 (Public Review):

Summary:

Liao and colleagues generated tagged SMAD1 and SMAD5 mouse models and identified genome occupancy of these two factors in the uterus of these mice using the CUT&RUN assay. The authors used integrative bioinformatic approaches to identify putative SMAD1/5 direct downstream target genes and to catalog the SMAD1/5 and PGR genome co-localization pattern. The role of SMAD1/5 on stromal decidualization was assayed in vitro on primary human endometrial stromal cells. The new mouse models offer opportunities to further dissect SMAD1 and SMAD5 functions without the limitation from SMAD antibodies, which is significant. The CUT&RUN data further support the usefulness of these mouse models for this purpose.

Strengths:

The strength of this study is the novelty of new mouse models and the valuable cistromic data derived from these mice.

Weaknesses:

The weakness of the present version of the manuscript includes the self-limited data analysis approaches such as the proximal promoter based bioinformatic filter and a missed opportunity to investigate the role of SMAD1/5 on determining the genome occupancy of major uterine transcription regulators.

Thank you for the comments. We addressed the limitation of the promoter-based analysis in the discussion and pointed out the possibility of analyzing additional genomics features (Lines 548551). Based on the suggestions, we also included an analysis in which we compared SMAD1/5 binding activities in this study to known major uterine transcription regulators’ binding activities (namely, SOX17 and NR2F2) using published ChIP-seq data in the mouse uterus. Results from this analysis are discussed in Lines 426-436. Content from the adjusted manuscript is copied below.

Lines 548-551:

“From pathway enrichment analysis, we demonstrate that genes with SMAD1/5 and PR bound at the promoter regions are enriched for key pathways in directing the decidualization process, such as WNT and relaxin signaling pathways. Future studies can benefit from analyzing binding events beyond the promoter regions.”

Lines 426-436:

“To further evaluate the key roles of SMAD1/5 as major uterine transcription regulators, we cross-compared the genomic binding sites of SMAD1/5 with known key transcription factors, namely aforementioned SOX17 (Supplement Figure 1E), as well as NR2F2 (Supplement Figure 1F), an essential regulator of hormonal response, using our CUT&RUN data sets and published mouse uterine SOX17 and NR2F2 ChIP-seq data sets (GSE118328, GSE232583). Among the annotated genes, 5402 genes are shared between SMAD1/5 and SOX17, and 1922 genes are shared between SMAD1/5 and NR2F2. Such observations indicate a potential co-regulatory mechanism between SMAD1/5 and other key uterine transcription factors in maintaining appropriate uterine functions. Overall, our analyses demonstrate that the transcriptional activity of SMAD1, SMAD5, and PR coordinate the expression of key genes required for endometrial receptivity and decidualization.”

Reviewer #3 (Public Review):

Summary:

As SMAD1/5 activities have previously been indistinguishable, these studies provide a new mouse model to finally understand unique downstream activation of SMAD1/5 target genes, a model useful for many scientific fields. Using CUT&RUN analyses with gene overlap comparisons and signaling pathway analyses, specific targets for SMAD1 versus SMAD5 were compared, identified, and interpreted. These data validate previous findings showing strong evidence that SMADs directly govern critical genes required for endometrial receptivity and decidualization, including cell adhesion and vascular development. Further, SMAD targets were overlapped with progesterone receptor binding sites to identify regions of potential synergistic regulation of implantation. The authors report strong correlations between progesterone receptor and SMAD1/5 direct targets to cooperatively promote embryo implantation. Finally, the authors validated SMAD1/5 gene regulation in primary human endometrial stromal cells. These studies provide a data-rich survey of SMAD family transcription, defining its role as a governor of early pregnancy.

Strengths:

This manuscript provides a valuable survey of SMAD1/5 direct transcriptional events at the time of receptivity. As embryo implantation is controlled by extensive epithelial to stromal molecular crosstalk and hormonal regulation in space and time, the authors state a strong, descriptive narrative defining how SMAD1/5 plays a central role at the site of this molecular orchestration. The implementation of cutting-edge techniques and models and simple comparative analyses provide a straightforward, yet elegant manuscript.

Although the progesterone receptor exists as a major regulator of early pregnancy, the authors have demonstrated clear evidence that progesterone receptor with SMAD1/5 work in concert to molecularly regulate targets such as Sox17, Id2, Tgfbr2, Runx1, Foxo1 and more at embryo implantation. Additionally, the authors pinpoint other critical transcription factor motifs that work with SMADs and the progesterone receptor to promote early pregnancy transcriptional paradigms.

Weaknesses:

Although a wonderful new tool to ascertain SMAD1 versus SMAD5 downstream signaling, the importance of these factors in governing early pregnancy is not novel. Furthermore, functional validation studies are needed to confirm interactions at promoter regions. Addtionally, the authors presume that all overlapped genes are shared between progesterone receptor and SMAD1/5, yet some peak representations do not overlap. Although, transcriptional activation can occur at the same time, they may not occur in the same complex. Thus, further confirmation of these transcriptional events is warranted.

Thank you for the review; we appreciate these valuable comments. Although we used an overlap approach to investigate the gene regulatory networks between SMAD1/5 and PR at the gene level, we functionally validated the regulatory effect in an in vitro decidualization model using a qPCR approach. We acknowledge that gene activations may not occur at the exact same complex, but functional validation screenings at the promoter level are beyond the scope of the study. However, we added the discussion about the possibility of proposed investigations in Lines 553-558. Our current dataset and validation studies support our conclusions with robust evidence. Content from Lines 553-558 is copied below.

Lines 553-558: “In this study, we determined the overlapped transcriptional control between SMAD1/5 and PR at the gene level, and functionally validated the regulatory effect at the transcript level in a human stromal cell decidualization model. While we observe a subset of peak representations that do not overlap at the base pair level in the promoter regions, future functional screenings at the promoter level, such as luciferase reporter assays to assess transcriptional co-activation by SMAD1/5 and PR, will advance this study.”

• Since whole murine uterus was used for these studies, the specific functions of SMAD1/5 in the stroma versus the epithelium (versus the myometrium) remain unknown. Specific roles for SMAD1/5 in the uterine stroma and epithelial compartments still need to be examined. Also, further work is needed to delineate binding and transcriptional activation of SMAD1/5 and the progesterone receptor in stromal versus epithelial uterine compartments.

Thank you for the comments. Indeed, our study was performed in the whole mouse uterus, which includes stroma, epithelium and myometrium. Our previous data shows that nuclear SMAD1/5 are localized to both the stroma and epithelium in the decidua zone during the decidualization process at 4.5 dpc (PMID:34099644). Published in vivo studies also demonstrate the essential role of SMAD1/5 in the uterine epithelium and stroma compartments, respectively (PMIDs:35383354/27335065/17967875). Although we believe the binding/transcriptional activation of SMAD1/5 and PR occurs in both compartments based on the mouse phenotypic data, opportunities for further compartment-specific analysis were granted and discussion regarding such investigations was added (Lines 501-513). Content from Lines 501-513 is copied below.

Lines 501-513:

“Published studies have shown that nuclear SMAD1/5 localize to the stroma and epithelium during the decidualization process at 4.5dpc during the window of implantation. Conditional deletion of SMAD1/5 exclusively in the uterine epithelium using lactoferrin-icre (Ltf-icre) results in severe subfertility due to impaired implantation and decidual development. Conditional deletion of SMAD1/5/4 exclusively in the cells from mesenchymal lineage (including uterine stroma) using anti-Mullerian hormone type 2 receptor cre (Amhr2-cre) results in infertility with defective decidualization. Given the essential roles of SMAD1/5 in both stroma and epithelium identified by previous studies, we believe that transcriptional co-regulation by SMAD1/5 and PR reported here using the whole uterus validates a relationship between SMAD1/5 and PR in both the stromal and epithelial compartments. However, it does not rule out the potential coregulation of SMAD1/5 and PR in the myometrium, immune cells, and/or endothelium, given that whole uterus was used. The specific transcriptional evaluations of SMAD1/5 in the stroma versus the epithelium would require future single-cell sequencing (i.e., digital cytometry) and/or spatial transcriptomic analysis.”

• There are asynchronous gene responses in the SMAD1/5 ablated mouse model compared to the siRNA-treated human endometrial stromal cells. These differences can be confounding, and more clarity is required in understanding the meaning of these differences and as they relate to the entire SMAD transcriptome.

Thank you for the comments. From the mouse models with SMAD1/5 conditional deletions, we observed phenotypic defects at 4.5 dpc, which is the beginning of decidualization in the mouse. Our study used human endometrial stromal cells as a model to validate our findings functionally, aiming to mimic the specific time point during decidualization. Differences between the two models may arise from the strategy used to perturb SMAD1/5; in the mouse, a complete knockout of SMAD1/5 was used, resulting in failed decidualization, while the human endometrial stromal cells used an siRNA knockdown approach, which decreased the potential for decidualization. As such, this information needs to be considered when evaluating genome-wide effects on the transcriptome. We added a discussion of this point to Lines 564-572. Content from Lines 564-572 is copied below.

Lines 564-572:

“Since mice only undergo decidualization upon embryo implantation whilst human stromal cells undergo cyclic decidualization in each menstrual cycle in response to rising levels of progesterone, asynchronous gene responses may occur in comparison between mouse models and human cells. However, cellular transformation during decidualization is conserved between mice and humans, which makes findings in the mouse models a valuable and transferable resource to be evaluated in human tissues. Accordingly, our functional validation studies were performed using human endometrial stromal cells induced to decidualize in vitro for four days, which models the early phases of decidualization. Additional transcriptomic studies of the SMAD1/5 perturbations in human endometrial stromal cells will be of great resource in understanding the entire SMAD1/5 regulomes in humans.”

Reviewer #1 (Recommendations For The Authors):

• Minor grammatical errors requiring attention such as inserting punctuation at the end of sentences and including figure legends prior to the end of sentence punctuation.

Thanks for the comments. Additional proofreading was conducted for the revision.

Reviewer #2 (Recommendations For The Authors):

1) Between SMAD1 and SMAD5, does losing one SMAD affect the other SMAD's genome occupancy?

Thanks for the comments. Based on the mouse phenotypic data that conditional deletion of SMAD1 in the uterus does not affect female fertility, while conditional deletion of SMAD5 leads to subfertility, and conditional deletion of both SMAD1 and SMAD5 leads to complete infertility. We believe losing one SMAD will affect the other SMAD's genome occupancy. This point is discussed in Lines 514-517, with contents copied below.

Lines 514-517: “Although our studies herein confirm that SMAD1 and SMAD5 proteins have distinct transcriptional regulatory activities, our previous studies demonstrated that while SMAD5 can functionally replace SMAD1, SMAD1 cannot replace SMAD5 in the uterus. How this epistatic relationship is established in a tissue-specific manner still needs to be determined by further biochemical investigations.”

2) In light of SMAD1/5 and PGR co-occupied cis-acting elements and coregulating uterine transcriptome, does loss of SMAD1/5 alter the PGR and ESR1 genome occupancy?

Thanks for the comments. In the SMAD1/5 double conditional knockout mice, we observe the hyposensitivity towards progesterone and unopposed estrogen responses. We hypothesize that loss of SMAD1/5 alters PR genome occupancy and subsequently ER genome occupancy is altered as a secondary effect. To functionally address this question, genomic profiling studies need to be performed in the SMAD1/5 knockout mice, and, ideally, also performed in the PR knockout mice. However, such large-scale studies are beyond the scope of the current study and will not affect our conclusions under physiological conditions. We did include additional discussion regarding this comment in Lines 551-553, with the contents copied below.

Lines 551-553: “Profiling the PR genome occupancy in the SMAD1/5 deficient mice would provide an interesting perspective to reevaluate the major regulatory roles of SMAD1/5 in mediating uterine transcriptomes.”

3) In terms of investigating the impact of SAMD1/5 on cell type composition, perhaps the digital cytometry approach (e.g., PMID: 31061481) could provide unbiased inferences.

Thank you for the comments. We included expression analysis of a subset of SMAD1/5 direct target genes over different uterine compartments (Figure 4E). We also added the discussion of the opportunities for further compartment-specific analysis, including but not limited to the digital cytometry approach in Lines 506-513, with the contents copied below.

Line 506-513:

“Given the essential roles of SMAD1/5 in both stroma and epithelium identified by previous studies, we believe that the transcriptional co-regulatory roles of SMAD1/5 and PR reported here using the whole uterus validates a relationship between SMAD1/5 and PR in both the stromal and epithelial compartments. However, it does not rule out potential co-regulatory roles of SMAD1/5 and PR in the myometrium, immune cells, and/or endothelium, given that whole uterus was used. The specific transcriptional evaluations of SMAD1/5 in the stroma versus the epithelium would require future single-cell sequencing (i.e., digital cytometry) and/or spatial transcriptomic analysis.”

4) The limitation of focusing on the promoter occupied SMADs should be discussed.

Additional discussion of the limitation of focusing on the promoter regions was added in Lines 548-551, with contents copied below.

Lines 548-551:

“From pathway enrichment analysis, we demonstrate that genes with SMAD1/5 and PR bound at the promoter regions are enriched for key pathways in directing the decidualization process, such as WNT and relaxin signaling pathways. Future studies can benefit from analyzing binding events beyond the promoter regions.”

5) Methods: The reagent and the condition for PGR CUT&RUN is missing.

Information added in Line 153.

1. Line 260: Please clarify the statement of "suggesting the transcriptional of PR depends on BMP/SMAD1/5 signaling".

Thanks for the suggestion. The sentence was rephrased to (Lines 258-261) “Our previous studies revealed that conditional ablation of SMAD1 and SMAD5 in the uterus decreased P4 response during the peri-implantation period, suggesting that the transcriptional activities of PR depend on BMP/SMAD1/5 signaling.”

7) Line 280-289: This statement belongs to the discussion section.

The statement was moved as suggested.

8) Figure 4E is not cited in the result section.

Figure 4E was cited in the results section in the revised version. (Line 386)

9) Figures 3C, 3D, 3E, 3F, 5B and 5D: please include the full lists in the supplemental data so that labs with limited bioinformatic capabilities could use these findings to facilitate scientific discovery.

Data regarding the aforementioned figures were included in Supplement Tables 3-8 and Supplement Files 1-2.

10) Figure 2B and Figure 5A: the heatmaps without further grouping on common and distinct genome occupancy among assayed factors provided minimum useful information. Please reconsider the presentation format in order to deliver more meaningful results.

Figure 2B and Figure 5A were replotted with clustering using the k-means algorithm. Methods and legends were updated accordingly.

Reviewer #3 (Recommendations For The Authors):

To delineate specific roles for SMAD1/5 in the uterine stroma and epithelial compartments, methods such as single cell sequencing or spatial transcriptomic analysis may be warranted.

The manuscript now includes the discussion of future opportunities in investigating the roles of SMAD1/5 in different uterine compartments using single-cell sequencing and/or spatial transcriptomic analysis (Lines 498-513), with contents copied below.

Lines 498-513:

“Our studies also examined the role of SMAD1/5 in mediating progesterone responses at the genomic and transcription levels. Similarly, our analysis was based on data sets generated from the whole mouse uterus, which contains multiple compartments of the uterine structures, including but not limited to epithelium and stroma. Published studies have shown that nuclear SMAD1/5 localize to the stroma and epithelium during the decidualization process at 4.5 dpc, during the window of implantation. Conditional deletion of SMAD1/5 exclusively in the uterine epithelium using lactoferrin-icre (Ltf-icre) results in severe subfertility due to impaired implantation and decidual development. Conditional deletion of SMAD1/5/4 exclusively in the cells from mesenchymal lineage (including uterine stroma) using anti-Mullerian hormone type 2 receptor cre (Amhr2-cre) results in infertility with defective decidualization. Given the essential roles of SMAD1/5 in both stroma and epithelium identified by previous studies, we believe that the transcriptional co-regulatory roles of SMAD1/5 and PR reported here using the whole uterus validates a relationship between SMAD1/5 and PR in both the stromal and epithelial compartments. However, it does not rule out potential co-regulatory roles of SMAD1/5 and PR in the myometrium, immune cells, and/or endothelium, given that whole uterus was used. The specific transcriptional evaluations of SMAD1/5 in the stroma versus the epithelium would require future single-cell sequencing (i.e., digital cytometry) and/or spatial transcriptomic analysis.”

Author Response

We would like to thank the editor and the reviewers for their constructive comments and the chance to revise the manuscript. The suggestions have allowed us to improve our manuscript. We have been able to fulfil all reviewer comments and added new statistical analyses to examine associations for subsets of data. Whilst suggested by a reviewer, we did not perform large-scale experiments to confirm the viability of low sporozoite densities at different time-points post salivary gland colonization. For these assays there are currently no satisfactory in vitro models for sporozoites harvested from single mosquitoes and setting up and validating such experiments could be a PhD project in itself. We do consider this suggestion very relevant but beyond the scope of the current work.

Relevantly, during the time the manuscript was under review at eLife, we have been able to examine the multiplicity of infection in our field experiments. This was, as written in the original manuscript, a key reason to also perform experiments in the field where there is a greater diversity of parasite lines. We have successfully performed AMA-1 amplicon deep sequencing on infected mosquito salivary glands and infected skins. Although this does not change the key messages of the manuscript and is secondary to our main hypothesis, we do consider it a relevant addition since we were able to demonstrate that for some infected mosquitoes from the Burkina Faso study, multiple clones were expelled by mosquitoes during probing on a single piece of artificial skin. We have added a short paragraph to our revised manuscript and updated the acknowledgement section to include the supporting researcher who conducted those experiments.

Reviewer #1 (Public Review):

Summary: There is a long-believed dogma in the malaria field; a mosquito infected with a single oocyst is equally infectious to humans as another mosquito with many oocysts. This belief has been used for goal setting (and modelling) of malaria transmission-blocking interventions. While recent studies using rodent malaria suggest that the dogma may not be true, there was no such study with human P. falciparum parasites. In this study, the numbers of oocysts and sporozoite in the mosquitoes and the number of expelled sporozoites into artificial skin from the infected mosquito was quantified individually. There was a significant correlation between sporozoite burden in the mosquitoes and expelled sporozoites. In addition, this study showed that highly infected mosquitoes expelled sporozoites sooner.

Strengths:

• The study was conducted using two different parasite-mosquito combinations; one was lab-adapted parasites with Anopheles stephensi and the other was parasites, which were circulated in infected patients, with An. coluzzii. Both combinations showed statistically significant correlations between sporozoite burden in mosquitoes and the number of expelled sporozoites.

• Usually, this type of study has been done in group bases (e.g., count oocysts and sporozoites at different time points using different mosquitoes from the same group). However, this study determined the numbers in individual bases after multiple optimization and validation of the approach. This individual approach significantly increases the power of correlation analysis.

Weaknesses:

• In a natural setting, most mosquitoes have less than 5 oocysts. Thus, the conclusion is more convincing if the authors perform additional analysis for the key correlations (Fig 3C and 4D) excluding mosquitoes with very high total sporozoite load (e.g., more than 5-oocyst equivalent load).

In the revised manuscript, we have also performed our analysis including only the subset of mosquitoes with low oocyst burden. In our Burkina Faso experiments, where we could not control oocyst density, 48% (15/31) of skins were from mosquitoes with <5 oocyst sheets. Whilst low oocyst densities were thus not very uncommon, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities.

• As written as the second limitation of the study, this study did not investigate whether all expelled sporozoites were equally infectious. For example, Day 9 expelled sporozoites may be less infectious than Day 11 sporozoites, or expelled sporozoites from high-burden mosquitoes may be less infectious because they experience low nutrient conditions in a mosquito. Ideally, it is nice to test the infectivity by ex vivo assays, such as hepatocyte invasion assay, and gliding assay at least for salivary sporozoites. But are there any preceding studies where the infectivity of sporozoites from different conditions was evaluated? Citing such studies would strengthen the argument.

We appreciate this thought and can see the value of these experiments. We are not aware of any studies that examined sporozoite viability in relation to the day of salivary gland colonization or sporozoite density.

One previous study assessed the NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (https://doi.org/10.1111/cmi.12745), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

• Since correlation analyses are the main points of this paper, it is important to show 95% CI of Spearman rank coefficient (not only p-value). By doing so, readers will understand the strengths/weaknesses of the correlations. The p-value only shows whether the observed correlation is significantly different from no correlation or not. In other words, if there are many data points, the p-value could be very small even if the correlation is weak.

We appreciate this comment and agree that this is indeed insightful. We have added the 95% confidence intervals to all figure legends and main text. We also provide them below.

Fig 3b: 95% CI: 0.74, 0.85

Fig 3c: 95% CI: 0.17, 0.50

Fig 4c: 95% CI: 0.80, 0.95

Fig 4d: 95% CI: 0.52, 0.82

Supp Fig 5a: 95% CI: 0.74, 0.85

Supp Fig 5b: 95% CI: 0.73, 0.93

Supp Fig 6: 95% CI: 0.11, 0.48

Supp Fig 7: 95% CI: -0.12, 0.16

Reviewer #2 (Public Review):

Summary: The malaria parasite Plasmodium develops into oocysts and sporozoites inside Anopheles mosquitoes, in a process called sporogony. Sporozoites invade the insect salivary glands in order to be transmitted during a blood meal. An important question regarding malaria transmission is whether all mosquitoes harbouring Plasmodium parasites are equally infectious. In this paper, the authors investigated the progression of P. falciparum sporozoite development in Anopheles mosquitoes, using a sensitive qPCR method to quantify sporozoites and an artificial skin system to probe for parasite expelling. They assessed the association between oocyst burden, salivary gland infection intensity, and sporozoites expelled.

The data show that higher sporozoite loads are associated with earlier colonization of salivary glands and a higher prevalence of sporozoite-positive salivary glands and that higher salivary gland sporozoite burdens are associated with higher numbers of expelled sporozoites. Intriguingly, there is no clear association between salivary gland burdens and the prevalence of expelling, suggesting that most infections reach a sufficient threshold to allow parasite expelling during a mosquito bite. This important observation suggests that low-density gametocyte carriers, although less likely to infect mosquitoes, could nevertheless contribute to malaria transmission.

Strengths: The paper is well written and the work is well conducted. The authors used two experimental models, one using cultured P. falciparum gametocytes and An. stephensi mosquitoes, and the other one using natural gametocyte infections in a field setup with An. coluzzii mosquitoes. Both studies gave similar results, reinforcing the validity of the observations. Parasite quantification relies on a robust and sensitive qPCR method, and parasite expelling was assessed using an innovative experimental setup based on artificial skin.

Weaknesses: There is no clear association between the prevalence of sporozoite expelling and the parasite burden. However, high total sporozoite burdens are associated with earlier and more efficient colonization of the salivary glands, and higher salivary gland burdens are associated with higher numbers of expelled sporozoites. While these observations suggest that highly infected mosquitoes could transmit/expel parasites earlier, this is not directly addressed in the study. In addition, whether all expelled sporozoites are equally infectious is unknown. The central question, i.e. whether all infected mosquitoes are equally infectious, therefore remains open.

We agree that the manuscript provides important steps forward in our understanding of what makes an infectious mosquito but does not conclusively demonstrate that highly infected mosquitoes are more likely to initiate a secondary infection. We consider this to be beyond the scope of the current work although the current work lays the foundation for these important future studies. For human Plasmodium infections the most satisfactory answer on the infectiousness of low versus high infected mosquitoes comes from controlled human infection models. In response to reviewer comments, we have extended our Discussion section to highlight this importance. To accommodate the (very fair) reviewer comments, we have avoided any phrasings that suggest that our findings demonstrate differences in transmission.

Reviewer #3 (Public Review):

Summary: This study uses a state-of-the-art artificial skin assay to determine the quantity of P. falciparum sporozoites expelled during feeding using mosquito infection (by standardised membrane feeding assay SMFA) using both cultured gametocytes and natural infection. Sporozoite densities in salivary glands and expelled into the skin are quantified using a well-validated molecular assay. These studies show clear positive correlations between mosquito infection levels (as determined by oocyst numbers), sporozoite numbers in salivary glands, and sporozoites expelled during feeding. This indicates potentially significant heterogeneity in infectiousness between mosquitoes with different infection loads and thus challenges the often-made assumption that all infected mosquitoes are equally infectious.

Strengths: Very rigorously designed studies using very well validated, state-of-the-art methods for studying malaria infections in the mosquito and quantifying load of expelled sporozoites. This resulted in very high-quality data that was well-analyzed and presented. Both sources of gametocytes (cultures vs. natural infection) show consistent results further strengthening the quality of the results obtained.

Weaknesses: As is generally the case when using SMFAs, the mosquito infections levels are often relatively high compared to wild-caught mosquitoes (e.g. Bombard et al 2020 IJP: median 3-4 ), and the strength of the observed correlations between oocyst sheet and salivary gland sporozoite load even more so between salivary gland sporozoite load and expelled sporozoite number may be dominated by results from mosquitoes with infection levels rarely observed in wild-caught mosquitoes. This could result in an overestimation of the importance of these well-observed positive relationships under natural transmission conditions. The results obtained from these excellently designed and executed studies very well supported their conclusion - with a slight caveat regarding their application to natural transmission scenarios

For efficiency and financial reasons, we have worked with an approach to enhance mosquito infection rates. If we had worked with gametocytes at physiological concentrations and a small number of donors, we probably have had considerably lower mosquito infection rates. Whilst this would indeed result in lower infection burdens in the sparse infected mosquitoes, addressing the reviewer concern, it would have made the experiments highly inefficient and expensive. The skin mimic was initially provided free of charge when the matrix was close to the expiry date but for the experiments in Burkina Faso we had to purchase the product at market value. Whilst we consider the biological question sufficiently important to justify this investment – and think our findings prove us right – it remained important to avoid using skins for uninfected mosquitoes. Since oocyst prevalence and density are strongly correlated (doi: 10.1016/j.ijpara.2012.09.002; doi: 10.7554/eLife.34463), a low oocyst density in natural infections typically coincides with a high proportion of negative mosquitoes.

Of note, our approach did result in the inclusion of 15 skins from infected mosquitoes with 1-4 oocysts. This number may be modest but we did include observations from this low oocyst range which is, we agree, highly important for better understanding malaria epidemiology.

This work very convincingly highlights the potential for significant heterogeneity in the infectiousness between individual P. falciparum-infected mosquitoes. Such heterogeneity needs to be further investigated and if again confirmed taken into account both when modelling malaria transmission and when evaluating the importance of low-density infections in sustaining malaria transmission.

Reviewer #4 (Public Review):

Summary: The study compares the number of sporozoites expelled by mosquitoes with different Plasmodium infection burden. To my knowledge this is the first report comparing the number of expelled P. falciparum sporozoites and their relation to oocyst burden (intact and ruptured) and residual sporozoites in salivary glands. The study provides important evidence on malaria transmission biology although conclusions cannot be drawn on direct impact on transmission.

Strengths: Although there is some evidence from malaria challenge studies that the burden of sporozoites injected into a host is directly correlated with the likelihood of infection, this has been done using experimental infection models which administer sporozoites intravenously. It is unclear whether the same correlation occurs with natural infections and what the actual threshold for infection may be. Host immunity and other host related factors also play a critical role in transmission and need to be taken into consideration; these have not been mentioned by the authors. This is of particular importance as host immunity is decreasing with reduction in transmission intensity.

Weaknesses: The natural infections reported in the study were not natural as the authors described. Gametocyte enrichment was done to attain high oocyst infection numbers. Studying natural infections would have been better without the enrichment step. The infected mosquitoes have much larger infection burden than what occurs in the wild.

Nevertheless, the findings support the same results as in the experiments conducted in the Netherlands and therefore are of interest. I suggest the authors change the wording. Rather than calling these "natural" infections, they could be called, for example, "experimental infections with wild parasite strains".

We have addressed these concerns and, in the process, also changed our manuscript title. The following sentences have been changed:

“It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and natural infections in Burkina Faso”.

Now reads: “It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and experimental infections with naturally circulating parasite strains in Burkina Faso”. 226-228 “Experimental infections with naturally circulating parasite strains show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

Has now replaced the original phrasing: “Natural infected mosquitoes by gametocyte carriers in Burkina Faso show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

I do not believe the study results generate sufficient evidence to conclude that lower infection burden in mosquitoes is likely to result in changes to transmission potential in the field. In study limitations section, the authors say "In addition, our quantification of sporozoite inoculum size is informative for comparisons between groups of high and low-infected mosquitoes but does not provide conclusive evidence on the likelihood of achieving secondary infections. Given striking differences in sporozoite burden between different Plasmodium species - low sporozoite densities appear considerably more common in mosquitoes infected with P. yoelii and P. berghei the association between sporozoite inoculum and the likelihood of achieving secondary infections may be best examined in controlled human infection studies. However, in the abstract conclusion the authors state "Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites and may need to be considered in estimations of transmission potential." Kindly consider ending the sentence at "expelled sporozoites." Future studies on CHMI can be recommended as a conclusion if authors feel fit.

We agree that we need to be very cautious with conclusions on the impact of our findings for the infectious reservoir. We have rephrased parts of our abstract and have updated the Discussion section following the reviewer suggestions. We agree with the reviewer that CHMI studies are recommended and have expanded the Discussion section to make this clearer. The sentence in the abstract now ends as:

"Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites. Future work is required to determine the direct implications of these findings for transmission potential."

Reviewer #1 (Recommendations For The Authors):

• Prevalence data shown in Fig 2A and Table S1 are different. For example, >50K at Day 11, Fig 2A shows ~85% prevalence, but Table S1 says 100%. If the prevalence in Table S1 shows a proportion of observations with positive expelled sporozoites (instead of a proportion of positive mosquitoes shown in Fig 2A), then the prevalence for <1K at Day 11 cannot be 6.7% (either 0 or 20% as there were a total of 5 observations). So in either case, it is not clear why the numbers shown in Fig 2A and Table S1 are different.

Figure 2A and Table S2 are estimated prevalence and odds ratios from an additive logistic regression model (i.e. excluding the interaction between day and sporozoite categories). Table S1 includes this interaction when estimating prevalence and odds ratios and as we can see some categories in the interaction were extremely small resulting in blown up confidence intervals especially in day 11. So Table S1 and Fig 2A are the results from two different models. Whilst our results are thus correct, we can understand the confusion and have added a sentence to explain the model used in the figure/table legends.

Figure. 2 Extrinsic Incubation Period in high versus low infected mosquitoes. A. Total sporozoites (SPZ) per mosquito in body plus salivary glands (x-axis) were binned by infection load <1k; 1k-10k; 10k-50k; >50k and plotted against the proportion of mosquitoes (%) that were sporozoite positive (y-axis) as estimated from an additive logistic regression model with factors day and SPZ categories. Supplementary Table S1. The extrinsic incubation period of P. falciparum in An. stephensi estimated by quantification of sporozoites on day 9, 10, 11 by qPCR. Based on infection intensity mosquitoes were binned into four categories (<1k, 1k-10k, 10k-50k, >50) that was assessed by combining sporozoite densities in the mosquito body and salivary gland. Prevalences and odds ratios were estimated from a logistic regression model with factors day, SPZ category and their interaction.

There are 3 typos in the paper. Please fix them.

Line 464; ...were counted using a using an incident....

Line 473; Supplementary Figure 7 should be Fig S8.

Line 508: ...between days 9 and 10 using a (t=-2.0467)....

We appreciate the rigour in reviewing our text and have corrected all typos.

Reviewer #2 (Recommendations For The Authors):

High infection burdens may result in earlier expelling capacity in mosquitoes, which would reflect more accurately the EIP. The fact that earlier colonization of SG and correlation between SG burden and numbers expelled suggest it could be the case, but it would be interesting to directly measure the prevalence of expelling over time to directly assess the effect of the sporozoite burden (not just at day 15 but before). This could reveal how the parasite burden in mosquitoes is a determinant of transmission.

We appreciate this suggestion and will consider this for future experiments. It adds another variable that is highly relevant but will also complicate comparisons where sporozoite expelling is related to both time since infectious blood meal and salivary gland sporozoite density (that is also dependent on time since infectious bloodmeal). Moreover, we then consider it important to measure this over the entire duration of sporozoite expelling, including late time-points post infectious bloodmeal. This may form part of a follow-up study.

Another question is whether all sporozoites (among expelled parasites) are equally infective, i.e. susceptible to induce secondary infection. If not, this could reconcile the data of this study and previous results in the rodent model where high burdens were associated with an increased probability to transmit.

As also indicated above, we are aware of a single study that assessed NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (ref), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

The authors evaluated oocyst rupture at day 18, i.e. 3 days after feeding experiments (performed at day 15). Did they check in control experiments that the prevalence of rupture oocysts does not vary between day 15 and day 18?

We did not do this and consider it very unlikely that there is a noticeable increase in the number of ruptured oocysts between days 15 and 18. We observe that salivary gland invasion plateaus around day 12 and the provision of a second bloodmeal that is known to accelerate oocyst maturation and rupture (doi: 10.1371/journal.ppat.1009131) makes it even less likely that a relevant fraction of oocysts ruptures very late. Perhaps most compellingly, the time of oocyst rupture will depend on nutrient availability and rupture could thus occur later for oocysts from a heavily infected gut compared to oocysts from mosquitoes with a low infection burden. We observe a very strong association between salivary gland sporozoite density (day 15) and oocyst density (assessed at day 18) without any evidence for change in the number of sporozoites per oocyst for different oocyst densities. In our revised manuscript we have also assessed correlations for different ranges of oocyst intensities and see highly consistent correlation coefficients and find no evidence for a change in ‘slope’. If oocyst rupture would regularly happen between days 15 and 18 and this late rupture would be more common in heavily infected mosquitoes, we would expect this to affect the associations presented in figures 3B and 4C This is not the case.

The authors report higher sporozoite numbers per oocyst and a higher proportion of SG invasion as compared to previous studies (30-50% rather than 20%). How do they explain these differences? Is it due to the detection method and/or second blood meal? Or parasite species?

We were also intrigued by these findings in light of existing literature. To address potential discrepancies, it is indeed possible that the 2nd bloodmeal made a difference. In addition, NF54 is known to be a highly efficient parasite in terms of gametocyte formation and transmission. And there are marked differences in these performances between NF54 isolates and definitely between NF54 and its clone 3D7 that is regularly used. We also used a molecular assay to detect and quantify sporozoites but consider it less likely that this is a major factor in terms of explaining SG invasion since sporozoite densities were typically within the range that would be detected by microscopy. We can only hypothesize that the 2nd bloodmeal may have contributed to these findings and acknowledge this in the revised Discussion section.

The median numbers of expelled sporozoites seem to be higher in the natural gametocyte infection experiments as compared to the cultures. Is it due to the mosquito species (An. coluzzii versus An. stephensi?).

The added value of our field experiments, a more relevant mosquito species and more relevant parasite isolates, is also a weakness in terms of understanding possible differences between in vitro experiments and field experiments with naturally circulating parasite strains. We only conclude that our in vitro experiments do not over-estimate sporozoite expelling by using a highly receptive mosquito source and artificially high gametocyte densities. We have clarified this in the revised Discussion.

39% of sporozoite-positive mosquitoes failed to expel, irrespective of infection densities. Could the authors discuss possible explanations for this observation?

In paragraph 304-307 we now write that:” This finding broadly aligns with an earlier study of Medica and Sinnis that reported that 22% of P. yoelii infected mosquitoes failed to expel sporozoites. For highly infected mosquitoes, this inefficient expelling has been related to a decrease of apyrase in the mosquito saliva”.

In Figure 3, it would be interesting to zoom in the 0-1k window, below the apparent threshold for successful expelling.

We have generated correlation estimates for different ranges of oocyst and sporozoite densities and added these in Supplementary Table 5. We agree that this helps the reader to appreciate the contribution of different ranges of parasite burden to the observed associations.

In Fig S8. Did they observe intact oocysts with fixed samples? These could be shown as well in the figure.

We have incorporated this comment. An intact oocyst from fixed samples was now added to Fig S10.

Minor points

-line 119: LOD and LOQ could be defined here.

We agree that this should have been defined. We changed line 119 to explain LOD and LOQ to: …“the limit of detection (LOD) and limit of quantification (LOQ)”….

• line 126: the title does not reflect the content of this paragraph.

We have changed the title: “Immunolabeling allows quantification of ruptured oocysts ”into: A comparative analysis of oocyst densities using mercurochrome staining and anti-CSP immunostaining.

-line 269: infectivity is not appropriate. The data show colonization of SG.

Line 269: infectivity has been changed with colonization of salivary glands.

There seems to be a problem with Fig S6. The graph seems to be the same as Fig 3C. Please check whether the graph and legends are correct.

Supplementary Figure 6 shows the sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites while Fig 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1qPCR without any threshold density. We have explained this in more detail in the revised supplemental figure where we now state

“Of note, this figure differs from Figure 3C in the main text in the following manner. This figure presents sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites to conclude sporozoite positivity while Figure 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1 qPCR without any threshold density and thus includes all observations with a qPCR signal”

Reviewer #3 (Recommendations For The Authors):

Congratulations to the authors for the really excellently designed and rigorously conducted studies.

My main concern is in regards to the relatively high oocyst numbers in their experimental mosquitoes (from both sources of gametocytes) compared to what has been reported from wild-caught mosquitoes in previous studies in Burkina Faso.

We have addressed this concern above. For completeness, we include the main points here again. We enriched gametocytes for efficiency reasons, experiments on gametocytes at physiological concentrations would have resulted in a lower oocyst density (and thus more ‘natural’ although a minority of individuals achieves very high oocyst densities in all studies that included a broad range of oocyst densities (e.g. doi: 10.1016/j.exppara.2014.12.010; doi: 10.1016/S1473-3099(18)30044-6). Of note, we did include 15 skins from low oocyst densities (1-4 oocysts). Whilst low oocyst densities were thus not very uncommon in our sample set, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities. In the revised manuscript we have also performed our analysis including only the subset of mosquitoes with low oocyst burden.

The best way to address this would be to do comparable artificial skin-feeding experiments on such wild-caught mosquitoes, but I appreciate that this is very difficult to do.

This would indeed by difficult to do. Mostly because infection status can only be examined post-hoc and it is likely that >95% of mosquitoes are sporozoite negative at the moment experiments are conducted (in many settings this will even be >99%). Importantly, also in wild-caught mosquitoes very high oocyst burdens are observed in a small but relevant subset of mosquitoes (doi: 10.1016/j.ijpara.2020.05.012).

Instead, I would suggest the authors conduct addition analysis of their data using different cut-offs for maximum oocyst numbers (e.g. <5, <10, <20) to determine if these correlations hold across the entire range of observed oocyst sheets and salivary gland sporozoite load.

We have provided these calculations for the proposed range of oocyst numbers. In addition, we also provided them for a range of sporozoite densities. These findings are now provided in

Entire range of observed oocyst sheets and salivary gland sporozoite load. A minor point on the regression lines in Figures 3 & 4: both variables in these plots have inherent variation (measurement & natural), but regression techniques such as reduced major exit regression (MAR) that allow error in both x and y variables may be preferable to a standard lines regression. Also, as it is implausible that mosquitoes with zero sporozoite in salivary glands expel several hundred sporozoites at feeding, the regression should probably also be constrained to pass through the 0,0 point.

Since the main priority of the analyses is the correlation, and not the fit of the regression line – which is only for indication, and also because of the availability of software, we did not change the type of regression. We have however added a disclaimer to the legend, and we have also forced the intercept to 0 – which does indeed better reflect the biological association. Additionally we added 95% confidence intervals to all Spearman’s correlation coefficients in the legends.

Author Response

Reviewer #1 (Public Review):

The authors propose a hypothesis for ovarian carcinogenesis based on epidemiological data, and more specifically they suggest that the latter relates to ascending genital tract "infection" or "dysbiosis", the resulting fallopian tube inflammation ultimately predisposing to ovarian cancer.

While this hypothesis would ideally be addressed in a longitudinal set-up with repeated female genital tract sampling, such an approach is obviously hard to realize. Rather, the authors present this hypothesis as a rationale for a cross-sectional study involving 81 patients with ovarian cancer (most with the most common subtype of high grade serous ovarian carcinoma, though other subtypes were also included), as well as 106 control patients with various non-infectious conditions including endometriosis and benign ovarian cysts. In all patients was there a comprehensive microbiome sampling of ovarian surface/fallopian tube, cervix and peritoneal cavity as well sampling of a number of potential sources of contamination, including surgery sites, ambient environment, consumables used in the DNA extraction and sequencing pipeline, etc. In line with the hypothesis presented at the outset, species with a threshold of at least 100 reads in both at least one cervical and at least one fallopian tube sample, while absent from environmental swabs, were considered relevant to the postulated pathway.

Remarkably, fallopian tube microbiota in ovarian cancer patients tended to cluster more closely to those retrieved from the paracolic gutter, than fallopian tube microbiota in non-cancer controls, which showed more relative similarity to vaginal/genital tract microbiota.

Although not really addressed by the authors, there also seem to be quite a few differences, at least in terms of abundance, in cervical microbiota between ovarian cancer patients and controls as well, which is an interesting finding, even when accounting for differences in age distribution between ovarian cancer patients and included control patients.

Overall, very few data are available thus far on the upper genital tract/fallopian tube microbiome, while also invariably controversial, as it has proven extremely difficult to obtain pelvic samples in a valid, "sterile" manner, i.e. without affecting a resident low-biomass microbiome to be analyzed. The authors took a number of measures to counter so, and in this respect, this is likely the largest and most valid study on the subject, even though biases and contamination can never be completely excluded in this context.

As such, I believe the strength of this study and paper primarily relates to the rigour of the methodology, thereby giving us a valuable insight in the presumed fallopian tube/ovarian surface microbiome, which may definitely serve as an impetus and a reference to future translational ovarian cancer research, or ovarian microbiome research for that matter.

I believe that the authors should acknowledge in more detail, that the data obtained from their cross-sectional study, valid as these are, do not provide any direct support to the hypothesis - albeit also plausible - set forth, a discussion that I somehow missed to a certain extent. It is important to realize in this and related contexts that neoplasia may well induce microbiome alterations through a variety of mechanisms, hence microbiome alterations not per se being causative. Conclusions should therefore be more reserved. Along the same lines, potential biases introduced through the selection of control patients (some detail here would be insightful) also deserves some discussion, as it is not known, whether other conditions such as benign ovarian cysts or endometriosis have some relationship with the human microbiome, be it causative or 'reversely causative', see for instance very recent work in Science Translational Medicine.

We appreciate the reviewer’s detailed review and thoughtful comments. We have added the following sentences in the Discussion to address the reviewer’s concern: “Due to the cross-sectional nature of the study, we have limited ability to link specific bacteria to ovarian carcinogenesis, as we would need to demonstrate that exposure to bacteria precedes the cancer. However, identifying associations between FT microbiota and OC is a critical first step. Further investigations, especially backed by in vitro studies, are needed to test our initial hypotheses.”

Reviewer #2 (Public Review):

The authors aimed to investigate the microbiota present in the fallopian tubes (FT) and its potential association with ovarian cancer (OC). They collected swabs intraoperatively from the FT and other surgical sites as controls to profile the FT microbiota and assess its relationship with OC.

They observed a clear shift in the FT microbiota of OC patients compared to non-cancer patients. Specifically, the FT of OC patients had more types of bacteria typically found in the gastrointestinal tract and the mouth. In contrast, vaginal bacterial species were more prevalent in non-cancer patients. Serous carcinoma, the most common OC subtype, showed a higher prevalence of almost all FT bacterial species compared to other OC subtypes.

The strengths of the study include its large sample size, rigorous collection methods, and use of controls to identify the possible contaminants. Additionally, the study employed advanced sequencing techniques for microbiota analysis. However, there are some weaknesses to consider. The study relied on swabs collected intraoperatively, which may not fully represent the microbiota in the FT during normal physiological conditions. The study also did not establish causality between the identified bacteria and OC but rather demonstrated an association. Regardless, the findings are important and these questions need to be addressed by future studies. A few additions in data representation and analysis are instead recommended.

Overall, the authors achieved their aims of identifying the FT microbiota and assessing its relationship with OC. The results support the conclusion that there is a clear shift in the FT microbiota in OC patients, paving the way for further investigations into the role of these bacteria in the pathogenesis of ovarian cancer.

The identification of specific bacterial species associated with OC could contribute to the development of novel diagnostic and therapeutic approaches. The study design and the data generated here can be valuable to the research community studying the microbiota and its impact on cancer development. However, further research is needed to validate these findings and elucidate the underlying mechanisms linking the FT microbiota shift and OC.

We appreciate the reviewer’s detailed review and positive comments.

Reviewer #3 (Public Review):

The findings of Bo Yu and colleagues titled "Identification of fallopian tube microbiota and its association with ovarian cancer: a prospective study of intraoperative swab collections from 187 patients" describes the identification of the fallopian tube microbiome and relationship with ovarian cancer. The studies are highly rigorous obtaining specimens from the fallopian tube, ovarian surfaces, paracolic gutter of patients of known or suspected ovarian cancer or benign tumor patients. The investigators took great care to ensure there was no or limited contamination including test the surgical suite air, as the test locations are from low abundance microbiota. The findings provide evidence that the microbiota in the fallopian tube, especially in ovarian cancer has similarities to gut microbial communities. This is a potentially novel observation.

The studies investigate the microbiome of >1000 swabs from 81 ovarian cancer and 106 non-cancer patients. The sites collected are low biomass microbiota making the study particularly challenging. The studies provide descriptive evidence that the ovarian cancer fallopian tube microbiota contain species that are similar to the gut microbiota. In contrast the fallopian tube microbiota of non-cancer patients that exhibit more similarity to the uterine/cervical microbiota. This may be a relevant observation but is highly descriptive with limited insights on the functional relevance.

The data indicate the presence of low biomass FT microbiota. The findings support the existence of FT microbiota in ovarian cancer that appears to be related to gut microbial species. While interesting, there is no insights on how and why these microbial species are found in the FT. The studies only identify the species but there is no transcriptomic analysis to provide an indication on whether the bacteria are activating DNA damage pathways. This is an interesting observation that requires more insights to address how these bacteria reach the fallopian tube and a related question is whether these bacteria are found in the peritoneum.

An additional concern is whether these data can be used to develop biomarkers of disease and early detection of disease. can the investigators detect the ovarian cancer FT microbiota in cervical/vaginal secretions? That may yield more significant insights for the field.

We appreciate the reviewer’s detailed review and thoughtful comments. We have added the following sentences in the Discussion to acknowledge the reviewer’s concern: “Due to the cross-sectional nature of the study, we have limited ability to link specific bacteria to ovarian carcinogenesis, as we would need to demonstrate that exposure to bacteria precedes the cancer. However, identifying associations between FT microbiota and OC is a critical first step. Further investigations, especially backed by in vitro studies, are needed to test our initial hypotheses.”

Reviewer #1 (Recommendations For The Authors):

I have no additional comments here.

Reviewer #2 (Recommendations For The Authors):

The data analysis and data representation could be improved by the following points:

1. To compare the microbiota and assess the overall microbiota structure difference between the cancer vs non cancer cohort alpha- and beta-diversity of the microbial communities can be conducted.

2. A differential abundance analysis could also be conducted to assess the differences at the genera and taxa level between the cancer vs non cancer cohorts.

3. The analysis suggested above can also be conducted in the serous vs non serous cancer cohorts.

4. In Figure 4 and 5 it would be more intuitive to show the predominant niche of each bacterium by color coding

We appreciate these helpful suggestions from the reviewer. We have added Figure 2B to address the diversity as well as the differences between cancer versus non-cancer cohorts. We have added in the Results section the description of our findings in Figure 2B. We have added color coding to Figure 4 and 5 as the reviewer suggested.

Reviewer #3 (Recommendations For The Authors):

These studies are interesting but are very descriptive with no obvious approaches for understanding the mechanisms of FT microbiota in ovarian cancer. The identification of these bacteria is not sufficient to draw implications on their impact on ovarian cancer development or progression. This needs to be addressed.

We agree with the reviewer and have added the following sentences in the Discussion to acknowledge the reviewer’s concern: “Due to the cross-sectional nature of the study, we have limited ability to link specific bacteria to ovarian carcinogenesis, as we would need to demonstrate that exposure to bacteria precedes the cancer. However, identifying associations between FT microbiota and OC is a critical first step. Further investigations, especially backed by in vitro studies, are needed to test our initial hypotheses.”

Author Response

Responses to public reviews

Reviewer 1

We thank the reviewer for the valuable and constructive comments and are pleased that the re-viewer finds our study timely and our behavioral results clear.

1) The RSA basically asks on the lowest level, whether neural activation patterns (as measured by EEG) are more similar between linked events compared to non-linked events. At least this is the first question that should be asked. However, on page 11 the authors state: "We ex-amined insight-induced effects on neural representations for linked events [...]". Hence, the critical analysis reported in the manuscript fully ignores the non-linked events and their neu-ral activation patterns. However, the non-linked events are a critical control. If the reported effects do not differ between linked and non-linked events, there is no way to claim that the effects are due to experimental manipulation - neither imagination nor observation. Hence, instead of immediately reporting on group differences (sham vs. control) in a two-way in-teraction (pre vs. post X imagination vs. observation), the authors should check (and re-port) first, whether the critical experimental manipulation had any effect on the similarity of neural activation patterns in the first place.

We completely agree that the non-link items are a critical control. Therefore, we had reported not only the results for linked but also for non-linked events on page 15, lines 336-350. We clarified this important point now on page 12 lines 283-286:

“Subsequently, we examined insight-induced effects on neural representations for linked (vs. non-linked) events by comparing the change from pre- to post-insight (post-pre) and the difference between imagination and observation (imagination - observation) between cTBS and sham groups using an independent cluster-based permutation t-test.”

Moreover, to directly compare linked and non-linked events we performed a four-way in-teraction including link vs. non-link. This analysis yielded a significant four-way interaction, showing that the interaction of time (pre vs. post), mode of insight (imagination vs. obser-vation) and cTBS differed for linked vs. non-linked items. We then report the follow-up analyses, separately for linked and non-linked events. Please see pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

2) Overall, the focus on the targeted three-way interaction is poorly motivated. Also, a func-tional interpretation is largely missing.

In order to better explain our motivation for the three-way interaction, we em-phasized in the introduction the importance of disentangling potential differences due to the mode of insight, given the known role of the angular gyrus in imagination on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked event.”

As discussed on page 21 (starting from line 478; see also the intro on page 4), we expected that the angular gyrus would be particularly implicated in imagination-based insight, given its known role in imagination (e.g.: Thakral et al., 2017). Moreover, given the angular gyrus’s strong connectivity with other regions, the results observed may not be driven by this re-gion alone but also by interconnected regions, such as the hippocampus. We clarified these important points at the very end of the discussion on pages 23-24, lines 543-560:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014).”

3) "Interestingly, we observed a different pattern of insight-related representational pattern changes for non-linked events." It is not sufficient to demonstrate that a given effect is pre-sent in one condition (linked events) but not the other (non-linked events). To claim that there are actually different patterns, the authors would need to compare the critical condi-tions directly (Nieuwenhuis et al., 2011).

We completely agree and now compared the two conditions directly. Specifical-ly, we now report the significant four-way interaction, including the factor link vs. non-link, before delving into separate analyses for linked and non-linked events on pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

4) "This analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B)." (p. 11). The authors report results with specificity for certain topographical locations. However, this is in stark contrast to the fact that the authors derived time X time RSA maps.

We did derive time × time similarity maps for each electrode within each partic-ipant, which allowed us to find a cluster consisting of specific electrodes. We apologize for not making this aspect clear enough and have, therefore, modified the respective part of our methods section on page 38, lines 951-952:

“In total, this analysis produced eight Representational Dissimilarity Matrices (RDMs) for each electrode and each participant.”

5) "These theta power values were then combined to create representational feature vectors, which consisted of the power values for four frequencies (4-7 Hz) × 41 time points (0-2 sec-onds) × 64 electrodes. We then calculated Pearson's correlations to compare the power pat-terns across theta frequency between the time points of linked events (A with B), as well as between the time points of non-linked events (A with X) for the pre- and the post-phase separately, separately for stories linked via imagination and via observation. To ensure un-biased results, we took precautions not to correlate the same combination of stories twice, which prevented potential inflation of the data. To facilitate statistical comparisons, we ap-plied a Fisher z-transform to the Pearson's rho values at each time point. This yielded a global measure of similarity on each electrode site. We, thus, obtained time × time similarity maps for the linked events (A and B) and the non-linked events (A and X) in the pre- and post-phases, separately for the insight gained through imagination and observation." (p. 34+35).

If RSA values were calculated at each time point and electrode, the Pearson correlations would have been computed effectively between four samples only, which is by far not enough to derive reliable estimates (Schönbrodt & Perugini, 2013). The problem is aggra-vated by the fact that due to the time and frequency smoothing inherent in the time-frequency decomposition of the EEG data, nearby power values across neighboring theta frequencies are highly similar to start with. (e.g., Schönauer et al., 2017; Sommer et al., 2022).

Alternative approaches would be to run the correlations across time for each electrode (re-sulting in the elimination of the time dimension) or to run the correlations at each time point across electrodes (resulting in the elimination of topographic specificity).

At least, the authors should show raw RSA maps for linked and non-linked events in the pre- and post-phases separately for the insight gained through imagination and observa-tion in each group, to allow for assessing the suitability of the input data (in the supple-ments?) before progressing to reporting the results of three-way interactions.

Although we do see the reviewer’s point, we think that an RSA specific to the theta range yielding electrode specific time × time similarity maps must be run this way, otherwise, as you pointed out, one or the other dimension is compromised. Running an RSA across time for each electrode will lead to computing a similarity measure between the events without information on when these stimuli become more or less similar, thereby ig-noring the temporal dynamics crucial to EEG data and not taking advantage of the high temporal resolution. Conversely, conducting an RSA across electrodes might result in an overall similarity measure per participant, disregarding the spatial distribution and potential variations among electrodes. Although EEG has limited spatial resolution, different elec-trodes can capture differences that may aid in understanding neural processing. However, as suggested by the reviewer, we included the raw RSA maps for linked and non-linked events separately for pre- and post-phases, imagination and observation and link and non-link in the supplement and refer to these data in the results section on pages 12-13, lines 293-295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Reviewer 2

We thank the reviewer for the very helpful and constructive comments and appreciate that the reviewer finds our study relevant to all areas of cognitive research.

1) While the observed memory reconfiguration/changes are attributed to the angular gyrus in this study, it remains unclear whether these effects are solely a result of the AG's role in re-configuration processes or to what extent the hippocampus might also mediate these memory effects (e.g., Tambini et al., 2018; Hermiller et al., 2019).

We agree that, in addition to the critical role of the angular gyrus, there may be an involvement of the hippocampus. We point now explicitly to the modulatory capacities of angular gyrus stimulation on the hippocampus. Please see page 4, lines 81-88:

“One promising candidate that may contribute to insight-driven memory reconfiguration is the angular gyrus. The angular gyrus has extensive structural and functional connections to many other brain regions (Petit et al., 2023), including the hippocampus (Coughlan et al., 2023; Uddin et al., 2010). Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014). Furthermore, the angular gyrus has been implicated in a myriad of cognitive func-tions, including mental arithmetic, visuospatial processing, inhibitory control, and theory-of-mind (Cattaneo et al., 2009; Grabner et al., 2009; Lewis et al., 2019; Schurz et al., 2014).”

We further added a new paragraph to the discussion pointing at the possibility that not solely the angular gyrus but another brain region, such as the hippocampus, may have me-diated the changes observed in our study on pages 23-24, lines 546-562:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

2) Another weakness in this manuscript is the use of different groups of participants for the key TMS intervention, along with underspecified or incomplete hypotheses/predictions.

In our view, the chosen between-subjects design is to be preferred over a crossover design for several reasons. First, our choice aimed to eliminate potential se-quence effects that may have adversely affected performance in the narrative-insight task (NIT). Second, this approach ensured consistency in expectations regarding the story links while also mitigating potential differences induced by fatigue. Additionally, we accounted for the potential advantage of a within-subject design – the stimulation of the same brain – by utilizing neuro-navigated TMS for targeting the stimulation coordinate. Finally, it is im-portant to note that we measured the event representations pre- and post-insight and that also the mode of insight was manipulated within-subject. Thus, our design did include a within-subject component and we are convinced that the chosen paradigm balances the different strengths and weaknesses of within-subject and between-subjects designs in the best possible manner. We specified our rationale for choosing a between-subjects ap-proach in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Moreover, to provide a comprehensive portrayal of the two groups, we incorporated de-scriptions concerning trait and state variables alongside age and motor thresholds and in-cluded t-test comparisons between these variables on page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-404:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

For greater precision in outlining our hypotheses, we specified these at the end of the in-troduction on pages 4-55, lines 107-118:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures.”

3) Furthermore, in some instances, the types of analyses used do not appear to be suitable for addressing the questions posed by the current study, and there is limited explanation pro-vided for the choice of analyses and questionnaires.

We addressed this concern by inserting a new section “control variables” in the methods explaining our rationale for employing the different questionnaires as control var-iables on pages 40-41, lines 1003-1019:

“Control variables In order to ensure that the observed effects were solely attributable to the TMS manipula-tion and not influenced by other factors, we comprehensively evaluated several trait and state variables. To account for potential variations in anxiety levels that could impact our re-sults, we specifically measured state and trait anxiety using STAI-S and STAI-T (Laux et al., 1981), thus minimizing the potential confounding effects of anxiety on our findings (Char-pentier et al., 2021). Additionally, we evaluated participants’ chronic stress levels using the TICS (Schulz & Schlotz, 1999) to exclude any group variations that might explain the effect on memory, cosidering the well-established impact of stress on memory (Sandi & Pinelo-Nava, 2007; Schwabe et al., 2012). Moreover, we assessed participants’ depressive symp-toms employing the BDI (Hautzinger et al., 2006), to guarantee group comparability on this clinical measure. We further assessed fundamental personality dimensions using the BFI-2 (Danner et al., 2016) to exclude any potential group discrepancies that could account for dif-ferences observed. Lastly, we assessed participants’ imaginative capacities using the FFIS (Zabelina & Condon, 2019), to ensure uniformity across groups regarding this central varia-ble, considering the significant role of imagination in relation to the cTBS-targeted angular gyrus (Thakral et al., 2017).”

We further specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-85:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

Reviewer 3

We thank the reviewer for the constructive and very helpful comments. We are pleased that the reviewer considered our experimental design to be strong and our behavioral results to be striking.

1) My major criticism relates to the main claim of the paper regarding causality between the angular gyrus and the authors' behavior of interest. Specifically, I am not convinced by the evidence that the effects of stimulation noted in the paper are attributable specifically to the angular gyrus, and not other regions/networks.

While our results showed specific changes after cTBS over the angular gyrus, demonstrating a causal involvement of the angular gyrus in these effects, we completely agree that this does not rule out an involvement of additional areas. In particular, there is evidence suggesting that cTBS over parietal regions, such as the angular gyrus, could poten-tially influence hippocampal functioning. We address this issue now in a new paragraph that we have added to the discussion, on pages 23-24, lines 546-564:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations.”

Responses to reviewer recommendations

Reviewer 1

1) On page 26, the authors write: "[...] different video events (A, B, and X) were recalled from day one [...]". I may have missed this point, but I had the impression that the task was con-ducted within one day.

Indeed, this study was conducted within a single day. We rephrased the respec-tive statement accordingly. Please see page 7, lines 149-153:

“To test this hypothesis and the causal role of the angular gyrus in insight-related memory reconfigurations, we combined the life-like video-based narrative-insight task (NIT) with representational similarity analysis of EEG data and (double-blind) neuro-navigated TMS over the left angular gyrus in a comprehensive investigation within a single day.”

We further included this information in the methods section on page 27, lines 634-635:

“In total, the experiment took about 4.5 hours per participant and was completed within a single day. ”

Reviewer 2

1) There is a substantial disconnection between the introduction and the methods/results sec-tion. One reason is that there is not sufficient detail regarding the hypotheses/predictions and the specific types of analyses chosen to test these hypotheses/predictions. Additionally, it is not explained what comparisons and outcomes would be informative/expected. This should be made clear. Second and related to the above, the rationale for conducting certain types of analyses (correlation, coherence, see below) sometimes is not specified.

To address this concern, we elaborated on our hypotheses incorporating specif-ic predictions for the free recall, given its higher variability than the other memory measures, and for imagination vs. observation at the end of the introduction on pages 4-5, lines 107-122:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures. Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connec-tivity analysis building upon prior findings concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational ap-proach to relate observations from these two domains.”

Moreover, we made our rationale for the employed analyses more explicit and specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

2) The authors suggest that besides Branzi et al. (2021), this is one of the first studies showing that memory update is linked to the AG. I suggest having a look at work from Tambini, Nee, & D'Esposito, 2018, JoCN, and other papers from Joel Voss' group that target a similar re-gion of AG/Inferior parietal cortex. Many studies, using multiple TMS protocols, have now shown this brain region is causally involved in episodic and associative memory encoding.

As mentioned above, further consideration of this literature is important as it delves into the region's hippocampal connectivity (and other network properties), and how that mediates the memory effects. Indeed because of the nature of the methods employed in this study, we do not know if the memory-related behavioural effects are due to TMS-changes induced at the AG's versus the hippocampal' s level, or both. How do the current findings square with the existing TMS effects from this region? Can the connectivity profile of the target re-gion highlighted by previous studies provide further insight into how the current behaviour-al effect arises? Some comments on this could be added to the discussion.

We completely agree that the other studies showing enhanced associative memory after TMS to parietal regions need to be addressed. Therefore, we updated the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Moreover, we included a section specifically addressing the possibility that the effects ob-served may pertain to having modulated other regions via the targeted region and updated the discussion on pages 23-24, lines 543-562:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) Another comment I have regards the results observed for the observation vs imagination insight conditions. The authors mention that the 'changes in representational similarity for the observation condition should be interpreted with caution, as these seemingly opposite changes appeared to be at least in part driven by group differences already in the pre-phase before participants gained insight.' I wonder what these group differences are and whether the authors have any hypothesis about what factors determined them.

We could only speculate about the basis of the observed pre-insight phase dif-ferences. However, we provide now the raw RSA data as supplemental material to make the pattern of the (raw) RSA findings in the pre- and post-insight phases more transparent. We refer the interested reader to this material on pages 12-13, lines 293 to 295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Furthermore, the age of participants is not reported separately for the two groups (cTBS to AG vs Sham), I think. This should be reported including a t-test showing that the two groups have the same age.

We agree and report now explicitly that groups did not significantly differ in rel-evant control variables including age. Please see page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-412:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

The fact this study is not a within-subject design makes difficult the interpretation of the results and this should be recognised as an important limitation of the study.

As outlined above, a within-subject design would in our view come with several disadvantages, such as significant sequence/carry-over effects. Moreover, the neural rep-resentation change was measured in a pre-post design, enabling us to measure the insight-driven neural reconfiguration at the individual level.

We clarify our rationale for the between-subjects factor TMS in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Furthermore, we included our rationale for choosing a between-subjects approach for the crucial TMS manipulation in the methods section on page 25, lines 601-604:

“We implemented a mixed-design including the within-subject factors link (linked vs. non-linked events), session (pre- vs. post-link), and mode (imagination vs. observation) as well as the between-subjects factor group (cTBS to the angular gyrus vs. sham) to mitigate the risk of carry-over effects and sequence biases of the crucial cTBS manipulation.”

4) The angular gyrus is a heterogeneous region with multiple graded subregions. The one tar-geted in the present study is the ventral AG which has strong connections with the episodic-hippocampal memory system. I was wondering if this might explain why the AG TMS ef-fects on representational changes have been observed for events linked via imagination but not direct observation. Perhaps the stimulation of a more 'visual' AG subregion (see Hum-phreys et al., 2020, Cerebral Cortex) would have resulted in a different (opposite) pattern of results. It would be good to add some comments on this in the discussion.

We appreciate this interesting perspective offered regarding the potential out-comes of our study, particularly in relation to the activation of a more ventral sub region of the angular gyrus. We incorporated this idea into our discussion, alongside considerations regarding the potential effects of a more dorsal angular gyrus stimulation on observation-based linking. However, caution is warranted recognizing the inherent limitations posed by the precision of TMS manipulations, which is further underscored by our electric field simu-lations, utilizing a 10 mm radius. We included this section in the discussion on pages 23-24, lines 546-569:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations. Yet, while acknowledging the functional heterogeneity within the angular gy-rus (Humphreys et al., 2020), pinpointing specific sub regions via TMS remains challenging due to its limited focal precision at the millimeter level (Deng et al., 2013; Thielscher & Kammer, 2004), as reinforced by our electric field simulations utilizing a 10 mm radius. Hence, drawing definitive conclusions regarding distinct angular gyrus sub regions requires future research employing rigorous checks to assess the focality of their stimulation.”

5) Regarding the methods section, I have the following specific queries. It is unclear what is the purpose of the coherence and correlation analyses (pages 35, 36). Could the authors pro-vide further clarification on this? These analyses seem not to be mentioned anywhere in the introduction. This should be clarified briefly in the introduction and then in the methods sec-tion. The same for the questionnaires (anxiety, stress, etc): It is unclear the reason for col-lecting this type of data. This should be clarified in the introduction as well.

We agree, and have updated the introduction as follows on page 5, lines 118-122:

“Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connectivity analysis building upon findings from the RSA anal-yses concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational approach to relate observations from these two domains.”

We additionally provided an explanation for including these questionnaires in the introduc-tion on page 5, lines 126-129:

“To control for any group differences beyond the TMS manipulation, we gathered various control variables through questionnaires, including trait- and state-anxiety, depressive symptoms, chronic stress levels, personality dimensions, and imaginative capacities.”

Moreover, we elaborated on the underlying rationale guiding our chosen analytical ap-proaches. Therefore, we specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Furthermore, we added an explanation on why we opted for the RSA approach in the methods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

6) The preregistration webpage is in German. This is not ideal as it means that the information is available only to German speakers.

This webpage can easily be switched to English by changing the settings in the top right corner:

To address this issue, we included a description of how to set the webpage to English in the methods section on page 25, lines 581-582:

“For translation to English, please adjust the page settings located in the top right corner.”

7) Page 18. 'NIT' and 'MAT' - avoid abbreviations when possible.

We included the full name for the narrative-insight task (NIT) on page 7, line 151, line 153, and line 165, page 8 lines 177-178 and line 187, page 19 on line 427, page 26 on line 615, line 629 and line 632, page 27, line 653, page 30, lines 730-731, page 31, line 754, page 35, line 870, line 873, and page 36 and line 885.

We further included the full name for the multi-arrangements task (MAT) on page 19, lines 428-429.

8) Line 21....we further observed DECREASED...should be replaced with INCREASED, if I am not wrong.

We checked the sentence again and it looks correct to us, since it describes the change for observation-based insight, not imagination-based insight. We clarified that this finding pertains to observation-based linking by modifying the sentence on page 23, lines 525-528, as follows:

“Following cTBS to the angular gyrus, we further observed decreased pattern similarity for non-linked events in the observation-based condition, resembling the pattern change ob-served in the sham group for linked events, which may highlight the role of the angular gy-rus in representational separation during observation-based linking”

Reviewer 3

1) The major claim of the paper is that the angular gyrus is causally involved in insight-driven memory reconfiguration. To the authors' credit, they localized stimulation to the angular gyrus using an anatomical scan, the strength of the estimated electromagnetic field in the angular gyrus correlated with their behavioral results, and there were also brain-behavior correlations involving sensors located in the parietal lobe. However, the minimum evidence needed to claim causality is 1) evidence of a behavioral change (which the authors found) and 2) evidence of target engagement in the angular gyrus. It is also important to show brain-behavior correlations between target engagement and behavior. Although the au-thors stimulated the angular gyrus, that does not mean that rTMS specifically affected this region or that the behavioral results can be attributed to rTMS effects on the angular gyrus. As the authors point out, the angular gyrus has dense connections with other regions such as the hippocampus. In fact, several studies have shown that angular gyrus (or near AG) stimulation affects the hippocampal network (Wang et al., 2014, Science; Freedberg et al. 2019, eNeuro; Thakral et al., 2020, PNAS). EEG also has a poor spatial resolution, so even though the results were attributable to parieto-temporal sensors, this is not sufficient evi-dence to claim that the angular gyrus was modulated. Source localization would be re-quired to reconstruct the signal specifically from the AG. Thus, with the manuscript written as is, the authors can claim that "cTBS to the angular gyrus modulates insight-driven memory reconfiguration," but the current claim is not sufficiently substantiated.

While acknowledging the potential role of the angular gyrus in driving the ob-served changes, we recognize that the available evidence may not be sufficient. Conse-quently, we have introduced several modifications within our manuscript to address this concern.

In the revised Introduction, we now explicitly address the possibility of a stimulation of the hippocampus via the angular gyrus on page 4, lines 84-85:

“Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014).”

Additionally, we included relevant evidence demonstrating previous instances of targeted stimulation of the angular gyrus, which led to alterations in hippocampal connectivity and associative memory. These insights have been included in the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Next, we have integrated crucial modifications essential for establishing a conclusive infer-ence of causality in our study. Moreover, we now explore the potential mediation of the effects observed from angular gyrus stimulation through other brain regions, like the hip-pocampus. In addition, we have highlighted prior work where such stimulation coincided with alterations in associative memory. For the updated discussion section, please see pag-es 23-24, lines 538-562:

“Although our study provided evidence suggesting a causal role of the angular gyrus in in-sight-driven memory reconfigurations – highlighted by behavioral changes after cTBS to the angular gyrus, neural changes in left parietal regions, and relevant brain-behavior associa-tions – it is important to acknowledge the limitations imposed by the spatial resolution of EEG. Consequently, the precise source of the observed signal changes in the parietal re-gions remains uncertain, potentially tempering the definitive nature of these findings. Fur-thermore, the differential impact of cTBS to the angular gyrus on neural reconfigurations between events linked via imagination and those linked via observation may be attributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). An-other intriguing aspect to consider is that the stimulated site was situated in the more ven-tral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

We further replaced terms that imply inhibition of the angular gyrus with a more operation-ally descriptive phrase:

“cTBS to the angular gyrus”

2) The authors frequently claim that cTBS is "inhibitory stimulation" and that inhibition of the angular gyrus caused their effects. There is a common misconception within the cognitive neuroscience literature that stimulation is either "inhibitory" or "excitatory," but there is no such thing as either. The effects of rTMS are dependent on many physiological, state, and trait-specific variables and the location of stimulation. For example, while cTBS does repro-ducibly inhibit behavior supported by the motor cortex (Wilkinson et al., 2010, Cortex; Rosenthal et al., 2009, J Neurosci), cTBS of the posterior parietal cortex reproducibly en-hances hippocampal network functional connectivity and episodic memory (Hermiller et al., 2019, Hippocampus; Hermiller et al., 2020, J Neurosci). The authors reference the Huang et al. (2005) paper as evidence of its inhibitory effects but work in this paper is not sufficient to broadly categorize cTBS as inhibitory. First, Huang et al. stimulated the motor cortex and measured the effects on corticospinal excitability, which is significantly different from what the current authors are measuring. Furthermore, this oft-cited study only included 9 sub-jects. Other studies have found that the effects of theta-burst are significantly more varia-ble when more subjects are used. For example, intermittent theta-burst, which is assumed to be excitatory based on the Huang paper, was found to produce unreliable excitatory ef-fects when more subjects were examined (Lopez-Alonso, 2014, Brain Stimulation). Thus, the a priori assumption that stimulation would be inhibitory is weak and cTBS should not be dis-cussed as "inhibitory."

We agree and included now a statement in the methods section that explicitly states that cTBS effects may be region-specific on page 33, lines 817-819:

“Nonetheless, the effects of cTBS appear to vary based on the targeted region, with cTBS to parietal regions demonstrating the capability to enhance hippocampal connectivity (Hermiller et al., 2019, 2020).”

We further substituted all terminology suggestive of an inhibitory effect with the phrase:

“cTBS to the angular gyrus”.

However, it is important to note, that while other studies (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014) found increased hippocampal connectivity after rTMS to a parie-tal region as well as enhanced associative memory, we observed impaired memory for the linked events. We included this clarification in the discussion on page 24, lines 558-562:

“In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) The hypothesis at the end of the introduction did not strike me as entirely clear. From this hypothesis, it seems that the authors are just comparing the differences in memory and re-configuration during imagination-based insight links. However, the authors also include ob-servation-based links and a non-linking condition, which seem ancillary to the main hy-pothesis. Thus, I am confused about why these extra factors were included and exactly what statistical results would confirm the authors' hypothesis.

We agree, and have clarified our hypotheses on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to reduce the increase in neural similarity for linked events and in-crease of neural dissimilarity for non-linked events.”

4) Many of the distributions throughout the paper do not look normal. Was normality checked? Are non-parametric stats warranted?

We evaluated and reported the normality assumption in our behavioral anal-yses. Despite the non-normal distribution of our data, we chose to utilize linear-mixed models due to their robust performance even in case of deviations from normal distribu-tions. This update in our methods section can be found on page 36, lines 890-896:

“After outlier correction, we identified non-normality in our data using a Shapiro-Wilk test (narrative-insight task: W = 0.92, p < 0.001; multi-arrangements task: W = 0.94, p < 0.001; forced-choice recognition: W = 0.50, p < 0.001; free recall details: W = 0.85, p < 0.001; free recall naming of linking events: W = 0.94, p < 0.001). However, we mitigated this by employ-ing linear-mixed models (LMMs), recognized for their robustness even with non-normally distributed data (Schielzeth et al., 2020).”

We recalculated the correlational analysis between the RSA data and the behavioral recall of linking events by using the Spearman method on page 13, lines 306-308:

“Furthermore, to address a deviation from the normality assumption, the correlational analysis was repeated using the Spearman method, which indicated an even stronger cor-relation (r(59) = 0.32, p = 0.012).”

We further recalculated the correlation between the change in coherence for linked events and the recall of details for events linked via imagination on page 16, lines 376-378:

“Please note that for addressing a deviation from the normality assumption, the correla-tional analysis was repeated using the Spearman method, which yielded a significant corre-lation of similar strength (r(59) = 0.31, p = 0.015).”

Our EEG analyses , including RSA and coherence analyses, utilized a cluster-based permuta-tion test (Fieldtrip; Oostenveld et al., 2011). These tests do not assume a normal distribu-tion by utilizing empirical sampling for statistical inference. This approach ensures robust-ness without constraints imposed by specific distributional assumptions. Subsequent t-tests, stemming from significant clusters identified in the initial non-parametric analyses, were extensions of the robust non-parametric approach and did not require additional normality testing.

5) Can the authors include more detail about the sham coil? Was it subthreshold? Did the EMF cross the skull?

The sham coil, also obtained from MAG & More GmbH, München, Germany, provided a similar sensory experience; however, the company did not specify any field strength (n.a.) as this coil was purposefully designed to prevent the induction of an elec-tromagnetic field (EMF) capable of penetrating the skull, thereby ensuring it had no impact on the brain. We clarified on this point in the methods section on pages 31-32, lines 772-778:

“Two identically looking but different 70 mm figure-of-eight-shaped coils were used de-pending on the TMS condition: The PMD70-pCool coil (MAG & More GmbH, München, Germany) with a 2T maximum field strength was used for cTBS, while the PMD70-pCool-SHAM coil (MAG & More GmbH, München, Germany), with minimal magnetic field strength, was employed for sham, providing a similar sensory experience, with stimulation pulses being scattered over the scalp and not penetrating the skull.”

6) There are differences between exclusion criteria in pre-registration and report. For example, BMI is an exclusion factor in the report, but not in the pre-registration. Can the authors provide a reason for this deviation?

This discrepancy is due to (partial) participant recruitment from previous fMRI studies conducted in our lab that involved a stress induction protocol (as a structural MRI image was needed for the ‘neuronavigated’ TMS). Owing to the distinct cortisol stress reac-tivity observed in individuals with varying body mass indices (BMIs), participants with a BMI below 19 or above 26 kg/m² were excluded from these studies. To maintain consistency within our sample, only participants meeting these criteria were included. We elaborated on this point in the methods section on page 25, lines 586-592:

“Participants were screened using a standardized interview for exclusion criteria that com-prised a history of neurological and psychiatric disease, medication use and substance abuse, cardiovascular, thyroid, or renal disease, evidence of COVID-19 infection or expo-sure, and any contraindications to MRI examination or TMS. Additionally, participants with a body mass index (BMI) below 19 or above 26 kg/m² were excluded. This decision stemmed from recruiting some participants from prior studies that incorporated stress induction pro-tocols, which imposed this specific criterion (Herhaus & Petrowski, 2018; Schmalbach et al., 2020).”

7) Were impedances monitored and minimized during EEG?

Yes, they were monitored. We clarified this point in the methods section on page 34, lines 845-847:

“We maintained impedances within a range of ± 20 μV using the common mode sense (CMS) and driven right leg (DRL) electrodes, serving as active reference and ground, re-spectively”

8) I think there may be a typo related to the Thakral coordinates. I believe Thakral used MNI coordinates -48,-64, 30, whereas the authors stated they used -48,-67,30. Is this a mistake?

Upon reevaluation of our study coordinates, we identified a slight deviation in our stimulation coordinates compared to those reported by Thakral et al. (2017; +3mm on the y-axis). This variance resulted from the required MNI to Talairach (TAL) transformations necessary for utilizing the neuronavigation software Powermag View! (MAG & More GmbH, München, Germany). Notably, this deviation was consistent across all participants in our study. While TMS is more precise than tDCS, its focality is not as fine-grained down to the millimeter level. Despite this, our electric field simulations, adopting a 10mm radius, ef-fectively encompassed the original coordinates specified by Thakral et al. (2017). This radius ensured coverage over the intended target area, mitigating the impact of this minor devia-tion on the overall study outcomes. We updated the methods section accordingly on page 33, lines 800-806:

“Based on the individual T1 MR images, we created 3D reconstructions of the participants' heads, allowing us to precisely locate the left angular gyrus coordinate (MNI: -48, -67, 30), initially derived from previous work (Thakral et al., 2017), for TMS stimulation. Despite a mi-nor deviation in coordinates due to necessary MNI to Talairach transformations for soft-ware compatibility (Powermag View! by MAG & More GmbH, München, Germany), our methodology ensured precise localization of the angular gyrus target area.”

9) How was the tail of the coil positioned during stimulation? Was it individualized so that the lobes of the coil are perpendicular to the nearest gyrus, as is commonly done?

The coil handle always pointed upwards to maintain optimal positioning with the coil holder. We followed the positioning procedure in the neuronavigation software Powermag View!, which did not indicate any positioning of the coil handle but specified the position and angle of the coil itself. To incorporate this aspect, we updated the legend of figure 2 on page 11, lines 260-261:

“Please note that in the study, the coil handle was oriented upwards; however, in this illus-tration, it has been intentionally depicted as pointing downwards for better visibility pur-poses.”

We further updated the method section on page 33, lines 723-824:

“The coil was positioned tangentially on the head and mechanically fixed in a coil holder, with its handle pointing upwards to maintain its position”

Author Response

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

Reviewer #1:

This work describes a new and powerful approach to a central question in ecology: what are the relative contributions of resource utilisation vs interactions between individuals in the shaping of an ecosystem? This approach relies on a very original quantitative experimental set-up whose power lies in its simplicity, allowing an exceptional level of control over ecological parameters and of measurement accuracy.

In this experimental system, the shared resource corresponds to 10^12 copies of a fixed single-stranded target DNA molecule to which 10^15 random single-stranded DNA molecules (the individuals populating the ecosystem) can bind. The binding process is cycled, with a 1000x-PCR amplification step between successive binding steps. The composition of the population is monitored via high-throughput DNA sequencing. Sequence data analysis describes the change in population diversity over cycles. The results are interpreted using estimated binding interactions of individuals with the target resource, as well as estimated binding interactions between individuals and also self-interactions (that can all be directly predicted as they correspond to DNA-DNA interactions). A simple model provides a framework to account for ecosystem dynamics over cycles. Finally, the trajectory of some individuals with high frequency in late cycles is traced back to the earliest cycles at which they are detected by sequencing. Their propensities to bind the resource, to form hairpins, or to form homodimers suggest how different interaction modes shape the composition of the population over cycles.

The authors report a shift from selection for binding to the resource to interactions between individuals and self-interactions over the course of cycles as the main drivers of their ecosystem. The outcome of the experiment is far from trivial as the individual resource binding energy initially determines the relative enrichment of individuals, and then seems to saturate. The richness of the population dynamics observed with this simple system is thus comparable to that found in some natural ecosystems. The findings obtained with this new approach will likely guide the exploration of natural ecosystems in which parameters and observables are much less accessible.

My review focuses mainly on the experimental aspects of this work given my own expertise. The introduction exposes very convincingly the scientific context of this work, justifying the need for such an approach to address questions pertaining to ecology. The manuscript describes very clearly and rigorously the experimental setup. The main strengths of this work are (i) the outstanding originality of the experimental approach and (ii) its simplicity. With this setup, central questions in ecology can be addressed in a quantitative manner, including the possibility of running trajectories in parallel to generalize the findings, as reported here. Technical aspects have been carefully implemented, from the design of random individuals bearing flanking regions for PCR amplification, binding selection and (low error) amplification protocols, and sequencing read-out whose depth is sufficient to capture the relevant dynamics.<br /> :<br /> We thank the reviewer for summarizing our work and the main findings in a very clear and effective manner.

One missing aspect in the data analysis is the quantification of the effect of PCR amplification steps in shaping the ecosystem (to be modeled if significant). In addition, as it stands the current work does not fully harness the power of the approach. For instance, with this setup, one can tune the relative contributions of binding selection vs amplification for instance (to disentangle forces that shape the ecosystem). One can also run cycles with new DNA individuals, designed with arbitrarily chosen resource binding vs self-binding, that are predicted to dominate depending on chosen ecological parameters. I have three main recommendations to the authors:

1) PCR amplification steps (and not only binding selection steps) should be taken into account when interpreting the outcome of experiments.

2) More generally, a systematic analysis of the possible modes of propagation of a DNA molecule from one cycle to the next, including those considered as experimental noise, would help with interpreting the results.

3) Testing experimentally the predictions from the analysis and the modelling of results would strengthen the case for this approach.

Despite its conceptual simplicity, our approach has indeed a few experimental handles that enable exploring a relevant variety of conditions much beyond those described in this paper, of which we are very aware. These involve selection vs. amplification or set the stage to explore competition, parasitism or cooperation among specific species, as the reviewer points out, but also introduce mutations and explore the kinetics of evolution in static or dynamic environments. Ongoing experiments are considering some of these conditions. We modified the text to mention more explicitly these possibilities, which are now mentioned in p11 lines 376-378 and lines 416-417. The three points raised by the reviewer helped us to further improve and clarify strengths and limitations of our work, as detailed below.

Regarding the first point, here are my suggestions :

• Run one cycle of just amplification vs 'binding + amplification', or simply increase the number of PCR cycles (and subsample the product) to check whether it impacts the population composition, in particular for sequences with predictions derived from the current analysis.

The point raised by the reviewer is indeed very relevant and not discussed in our manuscript. Prompted by the reviewer’s comment, we performed two new experiments to distinguish resource-binding selection from PCR amplification effects.

First, we performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

This indicates that the drift due to non-specific binding + PCR amplification is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.

This results are now cited in p14 lines 468-470, and described in Appendix 1, Experimental controls.

Second, we tested the effect of PCR amplification on the selection process. We exploited the fact that we have aliquots for each generation of our evolution experiment, which we sampled and saved after PCR and before sequencing. We thus chose a specific generation - specifically generation 9 from Oligo 1 experiments - and performed another PCR round we proceeded directly to sequencing with no beadsselection step. We then compared the ensemble of oligos obtained in this way, which we named Oligo 1 “cycle 9 replica”, with both the original Oligo1 cycle 9, and with Oligo1 cycle 10.

We sampled 20 times 4 x 10^5 sequences from the cycle 9 dataset, from cycle 9 replica and from cycle 10 with a bootstrap approach. To compare the three systems we extracted the fraction of the population of each covered by the 10 most abundant individuals. The results are shown in Figure 2 - Figure Supplement 4. In the figure caption further details on the analysis can be found. The similarity between cycle 9 and cycle 9 replica and the marked difference between cycle 9 replica and cycle 10

indicates that the relevant part of the selection is indeed performed by the resourcebinding mechanism, while drifts induced by PCR play a secondary role.

As a further check, we compared the specific sequences across the 20 samples in cycle 9 and cycle 9 replica datasets and found that the 10 most abundant sequences are almost always the same. In particular, the first 8/9 are always the same, possibly shuffled.

These new pieces of evidence are now cited in p14 lines 483-484 and described in Appendix 1, Experimental controls.

• Sequencing read-out includes the same PCR protocol as the one used for amplification steps, so read-out potentially has an effect on the composition of the ecosystem. Again, varying the number of PCR cycles is a direct way to test this.

The PCR amplification involved in the read-out might have a minor effect on the sequencing outcome but not on the composition of the ecosystem. In fact, the sample that undergoes sequencing is taken from the pool at each cycle, and not inserted back into it. Thus, it does not participate in the following selection steps. This is specified in the text at p3 line 104

• Could self-interactions (hairpins of homodimers) benefit individuals during amplification steps? The role of self-interactions during binding selection steps could also be tested directly over one cycle (again varying the relative weight of the binding vs amplification to disentangle both).

Our choice of conditions for PCR amplification were thought to minimize effects of this type. PCR amplification is carried out at 68 C, a temperature at which, given the level of self and mutual complementarity in the sequences analyzed in the text, hairpins or homodimers should be melted and thus have no effect. This is specified in the text at p. 14 lines 479-480 However, if an effect is present, it gives a disadvantage (rather than an advantage) to self-interacting individuals. For the amplification step we used Q5® Hot Start HighFidelity DNA Polymerase, which does not possess strand displacement activity. Therefore, in theory, if during amplification the polymerase encounters a double strand portion, it stops and synthesizes only a truncated product, which will be then lost during the purification step. In other words, sequences with secondary and/or tertiary structures are less likely to be amplified during the polymerization step. As a consequence, a DNAi that is characterized by this kind of structures, will be negatively selected even in the case of optimal binding to the resource, and will be underrepresented in the pool.

About the second point:

• Regarding the effect of sampling (sequencing read-out), PCR amplification errors: explicitly check the consistency of observations with the expected outcome, in the methods section (right now these aspects are only briefly mentioned in the main text), which would highlight again the level of control and accuracy of the system.

Hoping to have well interpreted the request, we performed a technical replicate sequencing Oligo 1 cycle 9 again and analyzed the sequences that have at least 100 reads (corresponding to 27.42% of the total reads). We find that among the 800 DNA species that have at least 100 reads, 93.6% are found in both replicates. All the nonoverlapping sequences have very low abundance, close to 100.

Moreover, we compare the population size of each DNA species between the two replicas, after having equalized the database sizes. The results are now cited in p14 lines 509-510, In Appendix 1, Experimental Controls and shown in Figure 2-figure supplement 3, where we plot the ratio of the number of reads in the two replicates for each sequence as a function of the number of reads in one. We found an average of 0.965 with a standard deviation of 0.119. High fluctuations are found in the most rare species, as expected.

We think this evaluation indeed strengthens the solidity of our results.

• I have a small concern about target resource accessibility: is there any spacer between the ssDNA and the bead? The methods section does not mention any, and I would expect such a proximity between the target DNA and the bead to yield steric repulsion that impedes interactions with random DNA individuals.

Yes, there is a 12-carbon spacer between the bead and the resource, which was inserted exactly to make the resource more accessible. This information is now available in Table 1 of Supplementary Information detailing the sequences used in the experiment. However, as now described in the text (p8 lines 284-286), we observe that the interaction with the resource is always shifted to the 3', the terminal furthest from the bead, indicating some residual issue of accessibility to the resource sections closest to the bead.

• Regardless of the existence of a spacer, binding of random DNA molecules to beads instead of the target DNA constitutes a potential source of noise (described for now as '1-x' in the IBEE model), which can be probed by swapping targets, selecting without target etc.

This issue is addressed by the test with bare beads described above, in which we found little effects, corresponding to small 1−𝑥 value.

• Is there any recombination potentially occurring during amplification steps? This could be tested with a set of known molecules amplified over 24 amplification steps in a row (no binding step).

It is possible for recombination to occur during the amplification steps. In Appendix 2, the section "By-Product Formation from PCR Amplification", discusses PCR byproducts as aberrant forms of amplification, such as recombination events. We adopted several strategies to limit by-product formation, such as: i) use of “blockers” characterized by a phosphate group at 3’ end (thus inhibiting their usage during the amplification and allowing a better control of the reaction conditions over the PCR cycles), ii) a high annealing temperature (to limit the possibility of a spurious primer annealing to the random region), iii) fewer PCR cycles, iv) a high primer concentration, v) a very short elongation step (all these strategies have been implemented to avoid a possible mispriming event between different DNAi, and the formation of concatemers). However, the formation of by-products is a problem inherent to the technique: in fact, it is a known issue for classical SELEX technology (Tolle et al. 2014), mainly due to the random region within the DNAi. Q5® Hot Start High-Fidelity DNA Polymerase (New England Biolabs, Ipswich, MA, USA) has an error rate of <0.44 x 10-6/base.

In classic SELEX technology, the average number of selection cycles is 10. This limitation is partly due to the increase in PCR by-products. As we can see from Figure 2 Supplementary Figure 1, the percentage of PCR by-products is less than 20% at cycle 12, and then increases dramatically in the following cycles. We are performing a series of experiments with known and limited sequences to verify and better understand the phenomenon for future applications of the SEDES platform. On this issue we decided not to modify the manuscript since we think it is already well discussed in Appendix 1.

And the third point:

• Perform one cycle (or a few cycles) with random DNA individuals, the most frequent individuals at the end of the current experiment, newly designed individuals with higher binding affinity to the target than currently dominating individuals, newly designed individuals with higher propensity to form hairpins or to form homodimers. Such experimental testing of predictions from the data analysis/modeling, typical of a physics approach, would illustrate the level of understanding one can reach with a simple yet powerful experimental setup.

We perfectly agree that the approach we propose and the set of results we obtained call for further investigations that could strengthen analysis and modeling. The final aim we envisage is the understanding, within this simplified approach, of key evolutionary factors such as fitness. Indeed, becoming able to write an explicit fitness function would be a significant new contribution to the understanding of evolutionary processes, even within the limited settings of the ADSE approach, as discussed in the conclusions of the manuscript.

However, undergoing such an analysis is a long and expensive job, which we have started and will be completed in a not immediate future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population and discuss in a future contribution the behavior of smaller designed sets of competing, collaborating or parasitic individuals.

Looking ahead, additional stages of investigations will also include mutations - to investigate the kinetics of speciation, and, in an even further stage, the interplay between evolution kinetics and dynamical mutation of resources.

I have a few smaller points:

• It would be very useful to provide the expected dynamic range of binding free energies (in terms of DeltaG and omega): what is the maximum binding free energy for the perfect complement?

The NUPACK-computed binding free energy of a 20 basis-long oligomer complementary to the resource (𝜔=20) is -24.36 Kcal/mol for Oligo1 and -23.08 Kcal/mol for oligo 2. This is the best answer we can offer to the reviewer’s request, since the maximum binding free energy of DNAi individuals (much longer than the target strand) would include contributions from the unpaired bases. Indeed, the values give above are approached by the left tail of the distribution of Fig. 3a, which however includes DNAi self-energies.<br /> The perfect complement binding free energy is now cited in the text as a reference for the dynamical range of DeltaG (p4 lines 151-152).

• How is the number of captured DNA molecules quantified? Is 10^12 measured, estimated, or hypothesized?

The number of sequences was calculated from data obtained from 260 nm absorbance quantification. We have now added this information in the Methods, Selection Phase” section.

Reviewer #2:

Summary:

In this manuscript, the authors introduced ADSE, a SELEX-based protocol to explore the mechanism of emergency of species. They used DNA hybridization (to the bait pool, "resources") as the driving force for selection and quantitatively investigated the factors that may contribute to the survival during generation evolution (progress of SELEX cycle), revealing that besides individual-resource binding, the inter- and intra-individual interactions were also important features along with mutualism and parasitism.

Strengths:

The design of using pure biochemical affinity assay to study eco-evolution is interesting, providing an important viewpoint to partly explain the molecular mechanism of evolution.

Weaknesses:

Though the evidence of the study is somewhat convincing, some aspects still need to be improved, mostly technical issues.

Major:

1) There are a few technical issues that the authors should clarify in the manuscript to make the analysis more transparent:

1.1) To my understanding, it is difficult to guarantee the even distribution of different species (individuals) in the initial individual pool. Even though the authors have shown in Fig. 2a that the top 10 sequences take up ~ 0% in the pool, it remains unclear how abundant these top and bottom representative sequences are, given the huge number of the pool (10E15). Can the author show the absolute number of these sequences in different quantiles? Please show both Oligo sets.<br /> : First, we thank the reviewer for both positive and critical comments that have guided us in reformulating or clarifying some messages of our work.

As for this specific point: 10E15 is a small number compared to 4^50 = 10E30, the number of possible sequences of length 30. Thus, we don’t expect more than one individual per sequence in the initial pool. However, sequencing requires a preparation amplification, which may lead to detecting a few sequences with more than one individual.

Specifically, in the initial pool of Oligo 1, the most abundant individual (of sequence GAACTAAAGGGGCGGTGTCCACTTGCCTGTAGTGGTTATCAGTCCGGTTG)has 3 copies. The 0.7% of the sequences has 2 copies, while the vast majority of strings (99.3% on a sample of about 1.5 x 10E6 sequenced DNAi) is present in one copy only. A similar situation holds for Oligo 2, with 4 DNAi present in 3 copies and the 0.8% of the sequences (in a pool of 2 x 10E6 DNAi) in 2 copies.

It is worth noticing that none of the 10 most abundant species in the last cycle is present in the sample. Indeed, the fraction of the pool which is sequenced is removed from the population that undergoes evolution (as now specified in p2, line 104). We specified in the text (p2, lines 69-70, p3 lines 94-96) the fact that in the initial pool no sequence is expected to be present in more than one individual.

1.2) The author claimed that they used two different oligo sets (Oligo1 and Oligo2) in this study. It is unclear which data was used in the presentation. How reproducible are they? Similar to this concern, how reproducible if the same oligo set was used to repeat the experiment?

The oligo used in the main text was declared in Methods, Replica section. It is now declared also in the main text (p3 lines 106-108 and in the captions of Figure 2, Figure 3 and Figure 4). Reproducibility is addressed in: Figure 2-figure supplement 5; Figure 2-figure supplement 6; Appendix 2: Results of the experimental replica.

It should also be noted that two starting pools of random 50mers are necessarily disjoint sets for the same reason discussed in the previous answer: the probability of common sequences in two 10E15 selections from a 10E30 is negligibly small. Thus, it is expected that each time a new evolution experiment is started, different dominant sequences are found. However, the statistical properties of the DNAi pool during the evolution process of Oligo1 and Oligo2 are similar as discussed in Appendix 2 of the paper.

1.3) PCR and illumina sequencing itself introduced selection bias. How would the analysis eliminate them? The authors only discussed the errors created during PCR cycles (page 3, lines 115-122). However the PCR itself would prefer to amplify some sequences over the others (e.g. with high GC content). Similarly, the illumina sequencing would be difficult to sequence the low complexity sequences. How would this be circumvented?

Yes, both PCR and Illumina sequencing have some known biases in the amplification process (e.g. sequencing of homopolymers or amplification of GC-rich sequences) that are intrinsic to the used techniques. Regarding PCR, we implemented a thermal protocol optimized for our chosen experimental setup, characterized by very short denaturation, annealing and amplification steps performed at high temperatures. Regarding Illumina sequencing, we can’t rule out a bias against specific sequences (e.g, homopolymers), which however should not be captured during the selection step, due to the design of the resource. Also, the libraries subjected to sequencing are characterized by a low complexity: according to the experimental design, the first and last 25 nucleotides are the same for all DNAi, the only differences being in the central 50 nt-long sequence. It is known that a low complexity library might encounter problems during sequencing due to the design of Illumina instruments: nucleotide diversity, especially in the first sequencing cycles, is critical for cluster filtering, optimal run performance and high-quality data generation. To overcome this limitation, the obtained libraries were run together with more complex and diverse library preparations: the ADSE sequences were about 1-2% of the total reads per run, corresponding to only a few million reads.

This discussion is now in Appendix 1, Intrinsic limitations of the molecular approach.

1.4) Some DNA sequences would bind to the beads instead of the resource sequence coated on them. Should the author run the experiment using bead alone as a control?<br /> : We performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

This indicates that the drift due to non-specific binding (+ PCR amplification) is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.<br /> This part is now discussed in Appendix 1, Experimental controls.

2) It would be interesting to study the impact of environmental factors, for example, changing pH, salt concentration, and detergent. Would these factors accelerate/decelerate the evolution?

We agree that the approach we propose and the set of results we obtained call for further investigations. However, performing these additional experiments, which would require a minimum of 6 generations each, is a long and expensive job, which we have started and will not be completed in the near future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population in the selected conditions and leave the exploration of new conditions, potentially opening new evolutionary scenarios, to a future contribution. In fact, our aim was to show that through our platform we can indeed observe fundamental elements of evolution in a non-biological system, which, in the set of chosen parameters, we do.

3) The concentration of individual oligo is apparently one of the most important factors in determining the interactions. In later cycles, some oligos become dominant, namely with extremely higher concentrations compared to their concentration in earlier cycles. This would definitely affect its interaction with resources, or self-interaction, or interaction with other oligos in the pool. However, the authors failed to discuss this factor, which may explain the exponential enrichment in later cycles.

We agree with the reviewer that this is an important point, but we disagree that we have not discussed it. We introduce the topic at the end of the “Null Model and Eco-evolutionary Algorithm”, where we comment on the change of the gamma parameter by saying that there must be a shift in the evolution process, first dominated by the interactions with the resources, and in later stages by some other factors (lines 227230) that we then discuss in “Self and mutual DNAi interactions are evolutionary drivers”. In this latter chapter and in the following, we indeed discussed the effects of mutual and self interactions between DNAi.

Indeed, a key point in our paper is the change in the gamma parameter necessary to match the IBEE model to experiments, as it is now more openly stated (p5 lines 217218 where we also mention figure 2-supplement 8 which clearly shows the necessity of a variable gamma). The two regimes enlightened by the gamma value must reflect a change in the competition for the resources and interactions among species. In the first generations, where the diversity of species is large (there are few strings for each species) and binding to the resources generally very week (small <omega>), the affinity with the resource is the main driving force (fast growth of <omega>), while mutual interactions remain too random to favor any species in particular. In the later cycles instead, when <omega> becomes large enough to provide a significant stability to the resource-binding of the majority of species, the dominating species compete more intensively on the basis of their structure and capacity of self-defense, parasitism and mutualism, a condition in which evolution affects more modifications in sequences than in <omega>.

Certainly, our understanding of this shift is based on statistical behavior and it is inferential, based on the study of specific DNAi described in the last part of the manuscript. For a better molecular model, more experiments with selected DNAi competing, cooperating or being parasitic would be necessary, with the final aim of defining a predictive fitness function. Alas, this requires months of further investigation. :

4) The author observed the different behaviors of medium 𝜔 in early and late cycles, referring to Fig 2h. Using the IBEE model, they found out it is the change of gamma. However, the authors did not further discuss the molecular mechanism. It could be very interesting to understand the evolutionary change of these individuals.

This comment might be related to the previous one. It is true that our discussion and understanding of the whole process is statistical, and misses a molecular model to predict the value of gamma.

However, the specific behavior that the reviewer asks about (those in Fig. 2h) is not related to the change in gamma. Even if gamma remains as in the first part of the evolution (gamma = 3), the species with overlap between 6 and 10 would first grow in number and later decrease. Indeed, during the first cycles they have an advantage with respect to the majority of species with lower maximum overlap, a condition that favors their amplification. However, in the second stage of the evolution dominant species with a larger affinity emerge and outcompete the individuals of this class. We added a sentence in the text to clarify this point (p7 lines 227-229).

5) In Figure 2f, some high w become quite missing. Should the authors give some interpretation? It is not observed in cycle 12 though (panel e).

Such an effect is just due to under-sampling. In a pool of 10^n oligomers, any sequence with a given 𝜔 with P(omega) < 10E-n will have a vanishing probability to appear in that sample.<br /> At cycle 12 the overall number of sequenced strands is larger than at cycle 24, due to the growing presence of PCR by-products. Thus, the right tail of the cyan distribution at the last cycle is sampled with less accuracy than at cycle 12. We have added a sentence in the revised manuscript (p5 lines 177-178) to clarify this point.

6) It would be interesting to further explore if another type of selection resource is used, for example protein that binds to particular sequences, i.e. transcription factors. Previous studies have used a large amount of sequence-specific transcription factors to run SELELX. Since the data have existed there, why not explore?

This is an interesting suggestion: can we use data from “ordinary” SELEX favoring specific sequences to explore sequence evolution? Two limitations make us a bit skeptical on this path: first, the consensus sequences of DNA-binding proteins are rather short and typically target dsDNA rather than ssDNA; second, the free energy of interaction is known only for the consensus sequence but not for sequences with all possible mutations with respect to the consensus sequence, making very hard to develop any molecular understanding of the process.

Minor:

1) There is no figure legend or in-text citation of Figure 2b.

2) Please correct "⁃C" with "{degree sign}C" in lines 470, 471, 472, 477 et al.

3) Typos and grammar issues should be corrected. Examples are shown below (but not limited to these only):

• mixed use of past and present tense.

• Line 152, "basis" should be "bases".

• Line 277, "a impediment" should be "an impediment"

• Line 278, "a major deadly threats" should be "major deadly threats"<br /> :<br /> We are sorry for the mistakes, and we have corrected them. Many thanks to the reviewer!

Author Response

Reviewer #1 (Public Review):

The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain. Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies. The authors could have also better integrated the RNAseq findings into mechanistic experiments, including testing whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain.

While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain.

We agree with the reviewer regarding the importance of examining sex differences on stimulation-evoked BBB changes. To address this issue we have: (1) clarified in the methods section that the human study involved both males and females; (2) added a section to the discussion highlighting the male bias as a key limitation of our animal experiments; and (3) stated that future work should examine whether stimulation-evoked BBB changes differ between makes and females.

Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies.

We are grateful for this comment and agree with the reviewer that the potential effects of anesthesia should be discussed. We have added the following discussion paragraph:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018).”

Reviewer #2 (Public Review):

Summary:

This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the bloodbrain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium, and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood-brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity, and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a breakdown in the blood-brain barrier following activation of brain areas through a motor task.

Strengths:

This study is novel and the results are potentially important as they demonstrate an unexpected breakdown of the blood-brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity.

The strengths of the study are:

1) The use of an in vivo model with multiple methods to investigate the blood-brain barrier response to a forelimb stimulation.

2) The determination of a potential functional role for the observed leakage of the blood-brain barrier from both a genetic and electrophysiological viewpoint.

3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.

4) Preliminary experiments demonstrating a similar observation of activity-dependent breakdown of the blood-brain barrier in humans.

Weaknesses:

There are both conceptual and experimental weaknesses.

1) The stimulation is in an animal anesthetized with ketamine, which can affect critical receptors (ie NMDA receptors) in synaptic plasticity.

We agree that the potential effects of anesthesia should be considered. The Discussion was revised to address this point: “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018)”

2) The stimulation protocol is prolonged and it would be helpful to know if briefer stimulations have the same effect or if longer stimulations have a greater effect ie does the leakage give a "readout" of the stimulation intensity/length.

Thank you for this important comment. We are also very curious about the potential relationship between stimulation magnitude/duration and subsequent leakage and have added the following statement to the discussion:

“Future studies should also explore the effects of stimulation magnitude/duration on BBB modulation, as well as the stimulation threshold between physiological and pathological increase in BBB permeability.”

Our current findings indicate that a one-minute stimulation does not affect vascular permeability or SEP and we aim to test additional stimulation paradigms in future studies.

3) For some of the experiments (see below), the numbers of animals are low and the statistical tests used may not be the most appropriate, making the results less clear cut.

We appreciate this comment and have revised the statistical analysis of Figure 1J,K. We now use a nested t-test to test for differences between rats (as opposed to sections). The differences remain significant (EB, p=0.0296; Alexa, p=0.0229). The text was modified accordingly.

4) The experimental paradigms are not entirely clear, especially the length of time of drug application and the authors seem to try to detect enhancement of a blocked SEP.

Thank you for pointing this out. Figures 2&3 were revised for clarification and a ‘Drug Application’ subsection was added to the methods section.

5) It is not clear how long the enhancement lasts. There is a remark that it lasts longer than 5 hours but there is no presentation of data to support this.

Thank you for this comment. As the length of experiments differed between animals, the exact length could not be specifically stated. To clarify this point, we revised the text to indicate that LTP was recorded until the end of each experiment (between 1.5-5 hours, depending on the condition the animal was in). We also added a panel to figure 2 (Figure 2d) with exemplary data showing potentiation 60, 90, and 120 min post stimulation.

6) The spatial and temporal specificity of this effect is unclear (other than hemispheric in rats) and even less clear in humans.

Our animal experiments (using both in vivo imaging and histological analysis) showed no evidence of BBB modulation outside the cortical somatosensory area corresponding to the limbs. We looked at the entirety of the coronal section of the brain and found enhancement solely in the somatosensory area corresponding to limb. The right side of panels h and i in Figure 1 show an x20 magnification of the section, focusing on the enhanced area. The whole section was not shown, as no fluorescence was found outside the magnified area. Moreover, our quantification showed that the enhancement was specific to the contralateral and not ipsilateral somatosensory cortex (Figure 1 j-k).

We agree that temporal specificity needs to be further explored, and we have now stated that in the discussion: “Future studies are needed to explore the BBB modulating effects of additional stimulation protocols – with varying durations, frequencies, and magnitudes. Such studies may also elucidate the temporal and ultrastructural characteristics that may differentiate between physiological and pathological BBB modulation.”

We also agree that larger studies are needed to better understand the specificity of the observed effect in humans, and to account for potential inter-human variability in vascular integrity and brain function due to different schedules, diets, exercise habits, etc.

8) The experimenters rightly use separate controls for most of the experiments but this is not always the case, also raising the possibility that the application of drugs was not done randomly or interleaved, but possibly performed in blocks of animals, which can also affect results.

Thank you for pointing out this lack of clarity. We have now highlighted that drug application was done randomly.

9) Methyl-beta-cyclodextrin clears cholesterol so the effect on albumin transport is not specific, it could be mediating its effect through some other pathway.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10uM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is observed at 5mM mβCD and not at 2.5mM/5mM (Koudinov & Koudinova, 2001). This point was added to the discussion.

10) Since the breakdown of the blood-brain barrier can be inhibited by a TGF-beta inhibitor, then this implies that TGFbeta is necessary for the breakdown of the blood-brain barrier. This does not sit well with the hypothesis that TGF-beta activation depends upon blood-brain barrier leakage.

Thank you for pointing out this lack of clarity. We have added a discussion paragraph that clarifies our hypothesis: “As mentioned above, albumin is a known activator of TGF-β signaling, and TGF-β has a well-established role in neuroplasticity. Interestingly, emerging evidence suggests that TGF-β also increases cross-BBB transcytosis (Betterton et al., 2022; Kaplan et al., 2020; McMillin et al., 2015; Schumacher et al., 2023). Hence, we propose the following two-part hypothesis for the TGF-β/BBB-mediated synaptic potentiation observed in our experiments: (1) prolonged stimulation triggers TGF-β signaling and increased caveolae-mediated transcytosis of albumin; and (2) extravasated albumin induces further TGF-β signaling, leading to synaptogenesis and additional cross-BBB transport – in a self-reinforcing positive feedback loop. Future research is needed to examine the validity of this hypothesis.

Reviewer #3 (Public Review):

Summary:

This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood-brain barrier (BBB) was opened by prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity-related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggested that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB.

Strengths:

The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves the opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data.

Weaknesses:

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

The conclusions are not compelling however because of a lack of explanation of methods and quantification.

Thank you for this comment. In the revised paper, we expanded the Methods section to better describe the procedures and approaches we used for data analysis.

It also is not clear whether the prolonged stimulation in the rat was normal conditions.

We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1) In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2) Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3) The levels of SEP potentiation we observed are similar to those reported in:

a) Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b) Humans following a 15 min task (McGregor et al., 2016).

This important point is now presented in the discussion.

Reviewer #1 (Recommendations For The Authors):

The discussion would benefit from additional discussion of the potential impacts of sex and anesthesia in their findings.

We agree with the reviewer and have added the following paragraph to the discussion:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can potentially alter neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketaminexylazine anesthesia, which unlike other anesthetics, was shown to maintain robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018). Another limitation of our animal study is the potentially non-specific effect of mβCD – an agent that disrupts caveola transport but may also lead to cholesterol depletion (Keller & Simons, 1998). To mitigate this issue, we used a very low mβCD concentration (10uM), orders of magnitude below the concentration reported to deplete cholesterol (Koudinov et al). Lastly, our animal study is limited by the inclusion of solely male rats. While our findings in humans did not point to sex-related differences in stimulation-evoked BBB modulation, larger animals and human studies are needed to examine this question.”

The figure text is quite small.

Thank you for pointing this out, we revised all figures and increased font size for clarity.

Including pharmacological concentrations within the figure legends would improve the readability of the manuscript.

Thank you for this suggestion, the figure legends were modified accordingly.

In methods for immunoassays the 5 groups could be more clear by stating that there are 3 timepoints for stimulation experiments. There is a typo in this section where the 24-hour post is stated twice in the same sentence.

Thank you for pointing this out, the text was modified accordingly.

Reviewer #2 (Recommendations For The Authors):

1) In Figure 1, J and K seem to indicate that in these experiments the statisitics were done per slice and not per animal. This is not a reasonable approach, a repeat measure ANOVA or averaging for each animal are more appropriate statistical approaches.

We thank the reviewer for pointing this out. The statistical analysis for Figure 1j,k was modified. We now use a nested ttest to test for differences between rats and not sections. The differences are still significant (EB, p=0.0296; Alexa, p=0.0229). The manuscript was modified accordingly.

2) In Figure 2, the protocol does not seem to give much idea about time course. There was a stimulation test for 1 minute before and then 1 minute after the 30-minute stimulation train. How was potentiation assessed for the next 5 hours and where are the data?

Potentiation was assessed by repeating 1min test stim every 30 min for the duration of the experiment, we added a panel to show late potentiation, see response above.

3) In Figure 2, there is a notable lack of controls eg the effect of sham stimulation and application of saline. These are important as the drift of response magnitude can be a problem in long experiments.

We did test for the potential presence of response drift, by examining whether SEPs of non-stimulated animals change over time (at baseline, 30 or 60 minutes of recording; n=6). No statistical differences were found. Our analysis focused on using each animal as its own control (i.e., comparing baseline SEP to SEP post albumin perfusion), because SEP studies highlight the importance of comparing each animal to its own baseline, due to the large inter-animal variability (All et al., 2010; Mégevand et al., 2009; Zandieh et al., 2003).

4) Figure 3 a is not clear – were the drugs applied throughout?

Thank you for pointing this out. We have revised Figure 3 a to show that the drugs were applied for 50 min before the stimulation.

5) In Figure 3 panel d is repeated in panel j. This needs correcting

Thank you. This mistake was fixed.

6) In LTP-type experiments usually the antagonist is applied during the stimulation and then washed out. This avoids the problem in this figure in which CNQX effectively blocks transmission and so it is not possible to detect any enhancement if it were there. Eg in panel e, CNQX block transmission, and then the assessment is performed when the AMPA receptors are blocked after 30 minutes of stimulation. If receptors are blocked no enhancement will be detectable. Moreover, surely the question is the ratio of the effect of 30-minute stimulation on the SEP in the presence of CNQX and so the statistics should be done on the fold change in the SEP following 30-minute stimulation in the presence of CNQX.

Thank you. The protocol might have been misrepresented in the original figure. We modified Fig 3a to clarify that the antagonists were indeed washed out upon stimulation start to make sure the receptors are not blocked during the test stimulation following the 30 min stimulation. In addition, we tested for the difference in fold change between 30 min stim, and 30 min stimulation following antagonists wash-in (Fig 3f and Fig S2a).

7) Interesting in Figure f, stimulation, albumin, and AP5 all seem to have the same enhancement of the SEP. Is the lack of effect of 30-minute stimulation in the presence of AP5, a ceiling effect ie AP5 has enhanced the SEP, and no further enhancement from stimulation is possible.

This is a very interesting point that will require further research.

8) SJN seems to block neurotransmission. What is the mechanism? The same analysis as for CNQX should be performed ie what is the fold change not compared to baseline but in the presence of SJN.

Our quantification showed that SJN did not significantly reduce the SEP max amplitude, and we therefore did not include this graph in the figure.

9) Please acknowledge that the effect of mbetaCD is non-specific. There is a large literature on the effects of cholesterol depletion on LTP.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10µM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is only observed at a concentration of 5mM (Koudinov & Koudinova, 2001). This point is now discussed under the discussion paragraph describing the study’s limitations.

10) k&l seem to have used the same control in which case they should not be analysed separately (they are all part of the same experiment).

We agree with the reviewer and have revised the figure accordingly.

11) The difference in gene expression in Figure 4 would be more convincing if it could be prevented by for example a TGFbeta inhibitor.

We agree and acknowledge the impact such experiments could provide. We plan to incorporate these experiments into our future studies.

12) Figure 5 seems to indicate bilateral and widespread BBB modulation arguing that this may be a non-specific effect. Panel g should look at other neocortical regions eg occipital cortex.

We agree and thank the reviewer for this comment. We revised the figure to include other cortical areas, such as the frontal and occipital cortices (Figure 5g)

Minor comments

1) Paired data eg in Fig 2D are better represented by pairing the dots usually with a line.

2) Please correct the %fold baseline in axes in graphs which show % change for baseline.

3) Figure 4 is not correctly referred to in the text.

We agree with all the points raised by the reviewer and revised the figures and text accordingly.

Reviewer #3 (Recommendations For The Authors):

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

Major concerns:

Methods need more explanation. Rationales need more justification. Examples are provided below.

Throughout many sections of the paper, sample sizes and stats are often missing. For stats, please provide p-values and other information (tcrit, U statistic, F, etc.)

Thank you, we added the relevant information where it was missing throughout the manuscript.

For transcriptomics, they might have found changes in BBB-related genes if they assayed vessels but they assayed the cortex.

We agree with the reviewer that this would be a very interesting future direction. The present study could not include this kind of analysis due to lack of access to vasculature isolation methods or single-cell RNA seq.

What were the inclusion/exclusion criteria for the subjects?

Thank you for pointing out this lack of clarity. The methods section (under ‘Magnetic Resonance Imaging’ – ‘Participants’) was expanded to include the following:

“Male and female healthy individuals, aged 18-35, with no known neurological or psychiatric disorders were recruited to undergo MRI scanning while performing a motor task (n=6; 3 males and 3 females). MRI scans of 10 sex- and age- matched individuals (with no known neurological or psychiatric disorders) who did not perform the task were used as control data (n=10; 5 males and 5 females.

Were they age and sex-matched?

They were, indeed, age and sex-matched. This was now clarified in the relevant Methods section.

Were there other factors that could have influenced the results?

Certainly. Human subjects are difficult to control for due to different schedules, diets, exercise habits, and other factors that may impact vascular integrity and brain function. Larger multimodal studies are needed to better understand the observed phenomenon.

Fig. 1. Images are very dim. Text here and in other figures is often too small to see. Some parts of the figures are not explained.

Our apologies. Figures and legends were revised accordingly.

Fig 2a, f. I don't see much difference here- do the authors think there was?

We agree that the difference may not be visually obvious. The quantification of trace parameters (amplitude and area under curve) does, however, reveal a significant SEP difference in response to both stimulation (panels X and y) and albumin (panels z and q).

Fig 3 d and j seem the same.

We thank the reviewer for noticing. This was a copy mistake that was now rectified.

Lesser concerns and examples of text that need explana9on:

Introduction

Insulin-like growth factor is transported. From where to where?

The text was edited to clarify that this was cross-BBB influx of insulin-like growth factor-I.

RMT that underlies the transport of plasma proteins was induced by physiological or non-physiological stimulation.

This was shown without stimulation, in normal physiology of young and aged healthy mice. The text was edited to clarify this point.

What was the circadian modulation that was shown to implicate BBB in brain function?

The text was edited for clarity.

Results

When the word stimulation is used please be specific if whiskers are moved by an experimenter, an electrode is used to apply current, etc.

We have now moved the ‘Stimulation protocol’ section closer to beginning of the Methods and emphasized that we administered electrical stimulation to the forepaw or hindlimb using subdermal needle electrodes.

Please explain how the authors are convinced they localized the vascular response.

The vascular response was localized via: (1) visual detection of arterioles that dilated in response to stimulation (due to functional hyperemia / neurovascular coupling) [figure 1 d]; and (2) quantitative mapping of increased hemoglobin concentration (Bouchard et al., 2009) [Figure 1 b]. This is now mentioned in the methods (under ‘In vivo imaging’) and results (under the ‘Stimulation increases BBB permeability’).

"30 min of limb stimulation" means what exactly? 6 Hz 2mA for 30 min?

Thank you. The text was revised for clarity (Methods under ‘Stimulation protocol’):

“The left forelimb or hind limb of the rat was stimulated using Isolated Scmulator device (AD Instruments) attached with two subdermal needle electrodes (0.1 ms square pulses, 2-3 mA) at 6 Hz frequency. Test stimulation consisted of 360 pulses (60 s) and delivered before (as baseline) and after long-duration stimulation (30 min, referred throughout the text as ‘stimulation’). In control and albumin rats, only short-duration stimulations were performed. Under sham stimulation, electrodes were placed without delivering current.”

Histology that was performed to confirm extravasation needs clarification because if tissue was removed from the brain, and fixed in order to do histology, what is outside the vessels would seem likely to wash away.

Thank you for pointing out the need to clarify this point. The Histology description in the Methods section was revised in the following manner:

“Albumin extravasacon was confirmed histologically in separate cohorts of rats that were anesthetized and stimulated without craniotomy surgery. Assessment of albumin extravasacon was performed using a well-established approach that involves peripheral injection of either labeled-albumin (bovine serum albumin conjugated to Alexa Flour 488, Alexa488-Alb) or albumin-labeling dye (Evans blue, EB – a dye that binds to endogenous albumin and forms a fluorescent complex), followed by histological analysis of brain tissue (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Obermeier et al., 2013; Veksler et al., 2020). Since extravasated albumin is taken up by astrocytes (Ivens et al., 2007; Obermeier et al., 2013), it can be visualized in the brain neuropil after brain removal and fixation (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Veksler et al., 2020). Five rats were injected with Alexa488-Alb (1.7 mg/ml) and five with EB (2%, 20 mg/ml, n=5). The injections were administered via the tail vein. Following injection, rats were transcardially perfused with…”

It is not clear why there was extravasacon contralateral but not ipsilateral if there are cortical-cortical connections.

Interpersonally, we also did not observe ipsilateral SEP in response to limb stimulation, with evidence of SEP and BBB permeability only in the contralateral sensorimotor region. This finding is consistent with electrophysiological and fMRI studies showing that peripheral stimulation results in predominantly contralateral potentials (Allison et al., 2000; Goff et al., 1962).

After injection of Evans blue or Alexa-Alb, how was it shown that there was extravasacon?

Extravasalon in cortical sections was visualized using a fluorescent microscope (Figure 1 h-i). Since extravasated albumin is taken up by astrocytes, fluorescent imaging can be used for visualizing and quantifying labeled albumin (Ahishali & Kaya, 2020; Ivens et al., 2007; Knowland et al., 2014). Here is the relevant methods excerpt:

“Coronal sections (40-μm thick) were obtained using a freezing microtome (Leica Biosystems) and imaged for dye extravasacon using a fluorescence microscope (Axioskop 2; Zeiss) equipped with a CCD digital camera (AxioCam MRc 5; Zeiss).”

How is a sham control not stimulated - what is the sham procedure?

In the sham stimulation protocol electrodes were placed, but current was not delivered. A section titled ‘Stimulation protocol’ was added to the methods to clarify this point.

What was the method for photothrombosis-induced ischemia?

The procedure for photothrombosis-induced ischemia is described under the Methods section ‘Immunoassays’ – ‘Enzyme-linked immunosorbent assay (ELISA) for albumin extravasalon’:

“Rats were anesthetilzed and underwent … photothrombosis stroke (PT) as previously described (Lippmann et al., 2017; Schoknecht et al., 2014). Briefly, Rose Bengal was administered intravenously (20 mg/kg) and a halogen light beam was directed for 15 min onto the intact exposed skull over the right somatosensory cortex.”

Fig 1d. All parts of d are not explained.

Thank you for pointing this out. In the revised manuscript, the panels of this figure were slightly reordered, and we made sure all panels are explained in the legend.

e. Is the LFP a seizure? How physiological is this- it does not seem very physiological.

Thank you for your comment. We believe that this activity is not a seizure because it lacks the typical slow activity that corresponds to the “depolarizalon shir” observed during seizures (Ivens et al., 2007; Milikovsky et al., 2019; Zelig et al., 2022).

f. Permeability index needs explanation. How was the area chosen for each rat? Randomly? Was it the same across rats?

We have now revised the Methods section to provide a clearer description of the permeability index calculation and the choice of the imaging area:

“Across all experiments, acquired images were the same size (512 × 512 pixel, ~1x1 mm), centered above the responding arteriole. Images were analyzed offline using MATLAB as described (Vazana et al., 2016). Briefly, image registration and segmentation were performed to produce a binary image, separating blood vessels from extravascular regions. For each extravascular pixel, a time curve of signal intensity over time was constructed. To determine whether an extravascular pixel had tracer accumulation over time (due to BBB permeability), the pixel’s intensity curve was divided by that of the responding artery (i.e., the arterial input function, AIF, representing tracer input). This ratio was termed the BBB permeability index (PI), and extravascular pixels with PI > 1 were identified as pixels with tracer accumulation due to BBB permeability.”

g. For Evans blue and Alexa-Alb was the sample size rats or sections?

Thank you for this question. We revised the statistical analysis for Figure 1j,k to appropriately asses the differences between rats. We used a nested t-test to test for differences between rats (and not sections). The differences remained significant (EB, p=0.0296; Alexa, p=0.0229) and the text was modified accordingly.

h, i, j need more contrast and/or brightness to appreciate the images. Arrows would help. The text is too small to read.

Thank you. This issue was addressed in the revised paper.

To induce potentiation, 6 Hz 2 mA stimuli were used for 30 min. Please justify this as physiological.

Thank you for the comment. We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1. In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2. Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3. The levels of SEP potentiation we observed are similar to those reported in:

a. Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b. Humans following a 15 min task (McGregor et al., 2016).

We have revised the Discussion of the paper to clarify this important point.

The test stimulus to evoke somatosensory evoked potentials was 1 min. Was this 6 Hz 2 mA for 1 min? Please justify.

Yes. We chose these parameters as these ranges were shown to induce the largest changes in blood flow (with laserdoppler flowmetry) and summated SEP (Ngai et al., 1999), corresponding with our findings. We also show that these stimulation parameters do not induce changes in BBB permeability nor synaptic potentiation, therefore served as test control.

How long after the 30 min was the test stimulus triggered- immediately? 30 sec afterwards?

The test stimulus was applied 5 min afterwards to allow for BBB imaging protocol (now explained in the Methods section).

How were amplitude and AUC measured? Baseline to peak? For AUC is it the sum of the upward and downward deflections comprising the LFP?

Yes, and yes. This is now clarified in the ‘Analysis of electrophysiological recordings’ section in the Methods.

How was the same site in the somatosensory cortex recorded for each animal?<br /> Potentiation was said to last >5 hrs. How often was it measured? Was potentiation the same for the amplitude and the AUC?

The location of the cranial window over the somatosensory cortex was the same in all rats. The location of the specific responding arteriole may change between animals, but the recording electrode was places around the responding arteriole in the same approaching angle and depth for all animals.

As the length of experiments differed between animals, the exact length could not be specifically stated. We therefore revised the text to clarify that LTP was recorded until the end of each experiment (depending on the animal condition, between 1.5-5 hours) and added a panel to figure 2 (Figure 2f) with exemplary data showing potentiation 120 min (2hr) post stimulation.

Why was 25% of the serum level of albumin selected- does the brain ever get exposed to that much? Was albumin dissolved in aCSF or was aCSF chosen as a control for another reason?

Yes, albumin was dissolved in aCSF and the solution was allowed to diffuse through the brain. The relatively high concentration of albumin was chosen to account for factors that lower its effective tissue concentration:

1. The low diffusion rate of albumin (Tao & Nicholson, 1996).

2. The likelihood of albumin to encounter a degradation site or a cross-BBB efflux transporter (Tao & Nicholson, 1996; Zhang & Pardridge, 2001).

Figure 2.

a. Please show baseline, the stimulus, and aftier the stimulus.

Please point out when there was stimulacon.

What is the inset at the top?

The inset on top is the example trace of the stimulus waveform, the legend of the figure was modified for clarity.

b. Please show when the stimulus artifact occurred. The end of the 1-minute test stimulus period is fine. Why are the SEPs different morphologies? It suggests the different locations in the cortex were recorded.

What is shown is the averaged SEP response over 1min test stimulus, each SEP is time locked to each stimulus. Regarding SEP waveform, it does indeed show different morphology between animals, as sometimes different arterioles respond to the stimulation, and we localize the recording to the responding vessel in each rat. However, in each rat the recording is only from one location. Once the electrode was positioned near the responding arteriole it was not moved.

d, e. What are the stats?

h, i. Add stats. Are all comparisons Wilcoxon? Please provide p values.

The comparisons were performed with the Wilcoxon test. We now state that and provide the exact p values.

j. What was selected from the baseline and what was selected during Albumin and how long of a record was selected?

What program was used to create the spectrogram?

What is meant by changes at frequencies above 200 Hz, the frequencies of HFOs?

The Method section (under ‘Electrophysiology – Data acquisition and analyses’) has been revised for clarification. Spectrogram was created with MATLAB and graphed with Prism. For analysis, we selected a 10 min recorded segment before starting albumin perfusion, and 10 min after terminating albumin perfusion.

When the cortial window was exposed to drugs, what were concentrations used that were selective for their receptor? How long was the exposure?

Was the vehicle tested?

We have revised the Methods section (under ‘Animal preparation and surgical procedures - Drug application’) to clarify the duration and concentration used and justification. All blockers were exposed for 50 min. The vehicle was an artificial cerebrospinal fluid solution (aCSF).

For PSD-95, what was the area of the cortex that was tested?

Were animals acutely euthanized and the brain dissected, frozen, etc?

We have revised the Methods section (under ‘Immunoassays’) for clarity.

What is mbetaCD?

The full term was added to the results section. It is also mentioned in the Methods.

Is SJN specific at the concentration that was chosen? Did it inhibit the SEP?

In the concentration used in our experiments, SJN is a selective TGF-β type I receptor ALK5 inhibitor (see (Gellibert et al., 2004)).

Fig. 3b. It looks like CNQX increased the width of the vessels quite a bit. Please explain.

For AP5, very large vessels were imaged, making it hard to compare to the other data.

The vascular dilation in response to the stimulation under CNQX was similar to that seen under “normal” conditions (i.e. aCSF). As for AP5, in some experiments the responding arteriole was in close proximity to a large venule that cannot be avoidable while imaging. For quantification we always measured arterioles within the same diameter range.

e. Sometimes CNQX did not block the response after 30 min stimulation. Why?

CNQX is washed out before the 30 min stimulation starts, so it is not expected to block the response to stimulation. However, in some cases the response to stimulation was lower in amplitude, likely due to residual CNQX that did not wash out completely.

Regarding DEGs, on the top of p 10 what are the percentages of?

In this analysis we tested in each hemisphere how many genes expressed differentially between 1 and 24 hours post stimulation (either up- or down- regulated). The results were presented as the percentages of differentially expressed genes in each hemisphere (13.2% contralateral, and 7.3% ipsilateral). The text was rephrased for clarity.

Please add a ref for the use of the JSD metric methods and support for its use as the appropriate method. Other methods need explanation/references.

References were added to the text to clarify. The Jensen-Shannon Divergence metric is commonly used to calculate the statistical pairwise distance among two distributions (Sudmant et al., 2015). From comparing a few different distance metric calculations including JSD, our results were similar irrespective of the distance metric applied. Therefore, we demonstrate the variability between paired samples of stimulated and non-stimulated cortex of each animal at two time points following stimulation (24 h vs. 1 h) using JSD.

What synaptic plasticity genes were selected for assay and what were not?

What does "largely unaffected" mean? Some of the genes may change a small amount but have big functional effects.

The selected genes of interest were taken from a large list compiled from previous publications (see (Cacheaux et al., 2009; Kim et al., 2017)) and are well documented in gene ontology databases and tools (e.g., Metascape, (Zhou et al., 2019)).

We agree that the term ‘largely unaffected’ is suboptimal, and we rephrased this section of the results to indicate that “No significant differences were found in BBB or inflammation related genes between the hemispheres”. We also agree that a small number of genes can have big functional effects. Future studies are needed to better understand the genes underlying the observed BBB modulation.

Please note that Slc and ABCs are not only involved in the BBB.

Thank you. We modified the text to no longer specify that these are BBB-specific transporters.

Please explain the choice of the stress ball squeeze task, and DCE.

DCE is a well-established method for BBB imaging in living humans, and it is cited throughout the manuscript. The ball squeeze task was chosen as it is presumed to involve primarily sensory motor areas, without high-level processing (Halder et al., 2005). This is now stated in the discussion.

What is Gd-DOTA?

Gd-DOTA is a gadolinium-based contrast agent (gadoterate meglumine, AKA Dotarem). Text was revised for clarity. Please see the Methods section under ‘Magnetic Resonance Imaging’ - ‘Data Acquisition’.

What does a higher percentage of activated regions mean- how was activacon defined and how were regions counted?

Higher percentage of activated regions refers to regions in which voxels showed significant BOLD changes due to the motor task preformed. The statistical approaches and analyses are detailed in the Methods section under ‘Magnetic Resonance Imaging - Preprocessing of functional data, and fMRI Localizer Motor Task’.

Figure. 4

Was stimulation 1 min or 30 min.?

30 min, Text has been revised for clarity.

What is the Wald test and how were p values adjusted-please add to the Stats section.

The Methods section under ‘Statistical analysis’ was revised to clarify this point.

Is there a reason why p values are sometimes circles and otherwise triangles?

The legend was revised to explain that ”Circles represent genes with no significant differences between 1 and 24 h poststimulation. Upward and downward triangles indicate significantly up- and down- regulated genes, respectively.”

How can a p-value be zero? Please explain abbreviations.

The p-value is very low (~10-10) and therefore appears to be zero due to the scale of the y-axis.

Fig. 5b.

There are unexplained abbreviations.

The x on the ball and hand is not clear relative to the black ball and hand.

Thank you for noticing. We revised the figure for clarity.

c. What was the method used to make an activator map and what is meant by localizer task?

The explanation of the “fMRI Localizer Motor Task” section in the methods was revised for added clarity.

f. What is the measurement "% area" that indicates " BBB modulation"?

Is it in f, the BBB permeable vessels (%)? f. Please explain: "Heatmap of BBB modulated voxels percentage in motor/sensory-related areas of task vs. controls."

The %area measurement indicates the percentage of voxels within a specific brain region that have a leaky BBB. See Methods.

Is Task - the control?

Yes.

Supplemental Fig. 2.

Why is AUC measured, not amplitude?

The amplitude, and now also the AUC are shown in Figure 3.

b. There is no comparison to baseline. The arrowhead points to the start of stimulation but there is no arrowhead marking the end.

In the revised paper we added a grey shade over the stimulation period to better visualize the difference to baseline. In this panel we wanted to show that NMDA receptor antagonist did not block the SEP, while AMPA receptor antagonist did.

c. In the blot there are two bands for PSD95- which is the one that is PSD95? There is no increase in PSD95 uncl 24 hrs but in the graph in d there is. In the blot, there is a strong expression of PSD95 ipsilateral compared to contralateral in the sham-why?

What is the percent change fold?

The PSD-95 is the top and larger band. The lower band was disregarded in the analysis. The example we show may not fully reflect the group statistics presented in panel d. Upon quantification of 8 animals, PSD-95 is significantly higher 30 min and 24 hours post stimulation in the contralateral hemisphere. No significant changes were found in sham animals. The % change fold refers to the AUC change compared to baseline. This panel was now incorporated in Figure 3 (panel h), and the title was corrected to “|AUC|, % change from baseline”.

Supplemental Fig. 4.

a. If ipsilateral and contralateral showed many changes why do the authors think the effects were only contralateral?

Our gene analysis was designed to complement our in vivo and histological findings, by assessing the magnitude of change in differentially expressed genes (DEGs). This analysis showed that: (1) the hemisphere contralateral to the stimulus has significantly more DEGs than the ipsilateral hemisphere; and (2) the DEGs were related to synaptic plasticity and TGF-b signaling. These findings strengthen the hypothesis raised by our in vivo and histological experiments.

Supplemental Fig. 5 includes many processes not in the results. Examples include dorsal cuneate and VPL, dynamin, Kir, mGluR, etc. The top right has numbers that are not mentioned. If the drawings are from other papers they should be cited.

The drawings of Figure 5 are original and were not published before. This hypothesis figure points to mechanisms that may drive the phenomena described in the paper. The legend of the figure was revised to include references to mechanisms that were not tested in this study.

Papers referenced in this letter:

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Author Response

Reviewer #1 (Public Review)

Midbrain dopamine neurons have attracted attention as a part of the brain's reward system. A different line of research, on the other hand, has shown that these neurons are also involved in higher cognitive functions such as short-term memory. However, these neurons are thought not to encode short-term memory itself because they just exhibit a phasic response in short-term memory tasks, which cannot seem to maintain information during the memory period. To understand the role of dopamine neurons in short-term memory, the present study investigated the electrophysiological property of these neurons in rodents performing a T-maze version of a short-term memory task, in which a visual cue indicated which arm (left or right) of the T-maze was associated with a reward. The animal needed to maintain this information while they were located between the cue presentation position and the selection position of the T-maze. The authors found that the activity of some dopamine neurons changed depending on the information while the animals were located in the memory position. This dopamine neuron modulation was unable to explain the motivation or motor component of the task. The authors concluded that this modulation reflected the information stored as short-term memory.

I was simply surprised by their finding because these dopamine neurons are similar to neurons in the prefrontal cortex that store memory information with sustained activity. Dopamine neurons are an evolutionally conserved structure, which is seen even in insects, whereas the prefrontal cortex is developed mainly in the primate. I feel that their findings are novel and would attract much attention from readers in the field. But the authors need to conduct additional analyses to consolidate their conclusion.

We thank reviewer #1 for the positive assessment and for the valuable and constructive comments on our manuscript.

Reviewer #1 (Recommendations to The Authors)

(1) The authors found the dopamine neuron modulation that reflected the memory information during the delay period. Here the dopamine neuron activity was aligned by the position, not by time, in which the animals needed to maintain the information. Usually, the activity was aligned by time, and many studies found that dopamine neurons exhibited a short duration burst in response to rewards and behaviorally relevant stimuli including visual cues presented in short-term memory tasks. For comparison, I (and probably other readers) want to see the time-aligned dopamine neuron modulation that reflected the memory information. Did the modulation still exist? Did it have a long duration? The authors just showed the time-aligned "population" activity that exhibited no memory-dependent modulation.

We agree that the point raised by the reviewer is important. To address this question, we added a new paragraph to the Methods section titled “Methodological considerations” (in line 793 of the revised manuscript), where we explain the caveats of using time alignment in the T-maze task study. We also created a new sup figure 5 to clarify our argument. As the figure shows, we did not observe major differences in the firing rates when they were arranged by position or time. More importantly, we did not detect brief bursts of activity in response to the visual cue which could reflect an RPE signaling scheme. Our interpretation is that in the T-maze task, DA neurons encode “miniature” RPE signals between successive states in the T-maze, which are hard to detect, especially when neurons receive a continuous sensory input during trials.

(2) Several studies have reported that dopamine neurons at different locations encode distinct signals even within the VTA or SNr. Were the locations of dopamine neurons maintaining the memory information different from those of other dopamine neurons?

We thank the reviewer’s comment. Indeed, there is evidence from recent studies demonstrating that DA neurons form functional and anatomical clusters in the VTA and SN. Following the reviewer’s advice, we report the anatomical structure of memory and non-memory-specific neurons in the revised manuscript. You can read these results in the paragraph “Anatomical organization of trajectory-specific neurons.” in the “Results” section (in line 383 of the revised manuscript) and in the new sup figure 11. We only observed a clear functional-anatomical segregation in GABA neurons, but not in DA neurons. But we should note that the absence of segregation in the DA neurons could be accounted for by the fact that we recorded mostly from the lateral VTA, therefore we do not have any numbers from the medial VTA.

(3a) Did the dopamine neurons maintaining the memory information respond to reward?

We believe that we have already provided the data that can partially answer this question by correlating the firing rate difference between the reward and memory delay sections. This result was described in the “Neuronal activities in delay and reward are unrelated.” paragraph and in Figure 6. Moreover, motivated by the reviewer’s question, we also performed additional analysis, which is included in the revised manuscript. Briefly, we clustered significant responses between the memory delay and reward sections (Category 1: Left-signif, R-signif or No-signif / Category 2: Memory delay or Reward). We discovered that only a very small number of neurons showed the same significant trajectory preference in the memory delay and reward sections (i.e., significant preference for left trials in the memory delay and significant preference for the left reward). In fact, more significant neurons showed a preference for opposite trajectories (i.e. significant preference for left trials in memory delay and a significant preference for right rewards). A description of the new results is included in the “Neuronal activities in delay and reward are unrelated.” paragraph (in line 349 of the revised manuscript) and in the new supplementary Figure 11.

(3b) Did they encode reward prediction error? The relationship between the present data and the conventional theory may be valuable.

We understand that the readers of this study will come up with the question of how memory-specific activities are related to RPE signaling. However, the T-maze task we used in this research was designed for studying working memory and was not adequate to extract information about the RPE signaling of DA neurons.

RPE signaling is mainly studied in Pavlovian conditioning. These are low-dimensional tasks with usually four (4) states (state1: ITI, state2: trial start, state3: stimulus presentation, state4: reward delivery). Evidence of RPE signaling is extracted from the firing activity of states 3 and 4 (which is theorized to be related to the difference in the values for states 3 and 4).

However, in the T-maze task, the number of states is hard to define and practically countless. In these conditions, it has been suggested that numerous small RPEs are signaled while the mice navigate the maze; Thus, they are very difficult to detect. To our knowledge, only Kim et al 2020, Cell, vol183, pg1600, managed to detect the RPE signaling activity of DA neurons while mice were teleported in a virtual corridor.

Another confounding factor in extracting RPE signals in the T-maze task is that the environment is high-dimensional and DA neurons are multitasking. Therefore, it is likely that RPE signaling could be masked by other parallel encoding schemes.

We have added these descriptions in the “Methodological considerations” (in line 793 of the revised manuscript).

(4) Did the dopamine neurons maintaining the memory information (left or right) prefer a contralateral direction like neurons in the motor cortex?

We thank the reviewer for this comment. Indeed, the majority of the memory-specific DA neurons showed a preference for the contralateral direction. We report this result in the legend of the new sup fig 10 (in line 1668 of the revised manuscript).

(5) As shown in Table S2, the proportion of GABA neurons maintaining the memory information (left or right during delay) was much larger than that of dopamine neurons. It seems to be strange because the main output neurons in the VTA are dopaminergic. What is the role of these GABA neurons?

We thank the reviewer for pointing this out. The present study shows that in both populations a sizeable portion of neurons show memory-specific encoding activities. However, the percentage of memory-encoding GABA neurons is more than twice as large as in the DA neurons. Moreover, we show that GABA neurons are functionally and anatomically segregated.

From this evidence, one could raise the hypothesis that the GABA neurons have a primary role and that the activity of DA neurons is a collateral phenomenon, triggered in a sequence of events within the VTA network. To characterize the (1) role and (2) importance of GABA neurons in memory-guided behavior, one should first identify the afferent and efferent projections of these cells in great detail. Unfortunately, we do not provide anatomical evidence.

So far, with the electrophysiological data we have collected (unit and field recordings), we can address an alternative hypothesis. It has been reported earlier (but we have also observed) that the VTA circuit engages in behaviorally related network oscillations which range from 0.4Hz up to 100Hz. Converging evidence from different brain regions, in vitro preparations but also in vivo recordings agree that local networks of inhibitory neurons are crucial for the generation, maintenance, and spectral control of network oscillations. Ongoing analysis, which we hope will lead to a publication, is looking for the behavioral correlates of network oscillations on the T-maze task, as well as the correlation of single-unit firing activity to the field oscillations. We expect to detect a higher field-unit coherence in GABA neurons, which could explain their stronger engagement in memory-specific encoding activity.

The potential role of GABA neurons in network oscillations is discussed in the revised manuscript in a newly added paragraph in line 564.

Reviewer #2 (Public Review)

The authors phototag DA and GABA neurons in the VTA in mice performing a t-maze task, and report choice-specific responses in the delay period of a memory-guided task, more so than in a variant task w/o a memory component. Overall, I found the results convincing. While showing responses that are choice selective in DA neurons is not entirely novel (e.g. Morris et al NN 2006, Parker et al NN 2016), the fact that this feature is stronger when there is a memory requirement is an interesting and novel observation.

I found the plots in 3B misleading because it looks like the main result is the sequential firing of DA neurons during the Tmaze. However, many of the neurons aren't significant by their permutation test. Often people either only plot the neurons that are significant, or plot with cross-validation (ie sort by half of the trials, and plot the other half).

Relatedly, the cross-task comparisons of sequences (Fig, 4,5) are hampered by the fact that they sort in one task, then plot in the other, which will make the sequences look less robust even if they were equally strong. What happens if they swap which task's sequences they use to order the neurons? I do realize they also show statistical comparisons of modulated units across tasks, which is helpful.

We thank reviewer #2 for the valuable and constructive comments on our manuscript. If, as the reviewer commented, the rate differences between left and right trajectories were only the result we want to claim, there may be a way to show only those whose left and right are significant. However, the sequential activity is also one of the points we wanted to display. We did not emphasize this result because it has already been shown by Engelhard et al. 2019. However, after reading the reviewer's comments, we decided to add a few lines in the "Results" (in lines 205 - 215 of the revised manuscript) and "Discussion" (in line 453 of the revised manuscript) describing the sequential activity of the VTA circuit. In those lines, we explained that DA activity is position-specific (resulting in sequential activity) and that a fraction of them also have left-right specificity.

Overall, the introduction was scholarly and did a good job covering a vast literature. But the explanation of t-maze data towards the end of the introduction was confusing. In Line 87, I would not say "in the same task" but "in a similar task" because there are many differences between the tasks in question.

We thank the reviewer for pointing out this mistake. In the revised manuscript, we replaced “in the same task” with “in a similar task” (in line 85 of the revised manuscript).

And not clear what is meant by "by averaging neuronal population activities, none of these computational schemes would have been revealed. " There was trial averaging, at least in Harvey et al. I thought the main result of that paper related to coding schemes was that neural activity was sequential, not persistent. I think it would help the paper to say that clearly.

We admit that this sentence leaves room for misunderstanding. We were mainly referring to DA studies using microdialysis or fiber photometry techniques. We decided to delete this sentence in the revised manuscript.

Also, I'm not aware it was shown that choice selectivity diminishes when the memory demand of the task is removed - please clarify if that is true in both referenced papers.

The reviewer’s remark is correct. None of these reports show explicitly that memory-specific activities are diminished without the memory component. Therefore, we deleted this sentence in the revised manuscript.

If so, an interpretation of this present data could be found in Lee et al biorxiv 2022, which presents a computational model that implies that the heterogeneity in the VTA DA system is a reflection of the heterogeneity found in upstream regions (the state representation), based on the idea that different subsets of DA neurons calculate prediction errors with respect to different subsets of the state representation.

We thank the reviewer for sharing this interpretation. We agree that this theory would support our results. In the revised manuscript we briefly discuss the Lee et al. report (in line 460 of the revised manuscript).

I am surprised only 28% of DA neurons responded to the reward - the reward is not completely certain in this task. This seems lower than other papers in mice (even Pavlovian conditioning, when the reward is entirely certain). It would be helpful if the authors comment on how this number compares to other papers.

In Pavlovian conditioning, neuronal responses to rewards are compared to a relatively quiet period of firing activity (usually the inter-trial interval epoch). As the reviewer pointed out, in the present study, the number of DA neurons responding to reward is smaller compared to the earlier studies. We hypothesize that this is due to our comparison method. We compared the post-reward response to an epoch when the animal was running along the side arms and the majority of neurons were highly active, instead of comparing it to a quiescent baseline epoch.

Reviewer #2 (Recommendations to The Authors)

Can you clarify what disparity you are referring to here? "Disparities between this 438 and our study in the proportions of modulated neurons could be attributed to the 439 different recording techniques applied as well as the maze regions of interest; for 440 example, Engelhard et al. analyzed neuronal firing activities in the visual-cue period 441 (Engelhard et al., 2019), whereas we focused on memory delay.". Is it the fact that Engelhard et al did not report choice-selective activity? They did report cue-side-selective activity, with some neurons responsive to cues on one side, and other neurons responsive to cues on the other side. Because there are more cues on the left when the mouse turns left, these neurons do indeed have choice-selective responses.

We thank the reviewer for this comment. We agree that we need to clarify further our argument. As the reviewer pointed out, Engelhard et al identified choice-specific DA neurons. However, they reported the encoding properties of DA neurons only in the visual-cue period and the reward period. Remarkably, although the task has a memory delay, they did not report the neuronal firing activities for this delay period. Instead, in the present study we dedicated most of our analysis to characterizing the firing properties of VTA neurons in the delay period.

Also, in response to your comment, we edited the paragraph where we describe the disparities between our study and Engelhard et al (in line 466 in the revised manuscript).

I don't think this sentence of intro is needed since it doesn't really contain new info: "Therefore, we looked for hints 116 of memory-related encoding activities in single DA and GABA neurons by 117 characterizing their firing preference for opposite behavioral choices.".

We agree with the reviewer. Therefore, we deleted this sentence in the revised manuscript.

I didn't understand this line of discussion: "Our evidence does not question the validity of this computational model, since we do not provide evidence of how the selective preference for one response over the other translates into the release site.".

The gating theory is based on experimental evidence of neuronal firing activities of DA neurons but also takes into consideration (to a lesser degree) the pre- and post-synaptic processes at the DA release sites (inverted U-shape of D1R activity). We thought that the reader may come to the conclusion that we question the validity of the gating theory. But this is not our intention, especially when we do not provide important evidence such as (1) the projection sites of DA and GABA neurons and (2) the sequence of events that take place at the synaptic triads following the DA and GABA release.

After reading your comment we came to the conclusion that this sentence should be omitted because it is not within the scope of this study to question the validity of the gating theory. Instead, we dedicated a few lines of text to explaining which components of the gating theory (“update”, “maintenance & manipulation” and “motor preparation”) could be attributed to the trajectory-specific activities in the memory delay of the T-maze task. (section “Activities of midbrain DA neurons in short-term memory” in line 417 of the revised manuscript).

In 1B, please illustrate when the light pulses are on & off?

Following the reviewer’s instruction, we added colored bars on top of the raster plots in Figure 1B, indicating the light induction conditions.

In legend for 6C, please clarify it's a correlation between the difference in R and L choice activity across the epochs (if my understanding is correct).

The reviewer’s understanding is correct. We took this advice into consideration to further clarify the methods of analysis that led to the plot in Figure 6C (in line 1246 in the revised manuscript).

Author Response

eLife assessment

The important work by Aballay et al. significantly advances our understanding of how G protein-coupled receptors (GPCRs) regulate immunity and pathogen avoidance. The authors provide convincing evidence for the GPCR NPR-15 to mediate immunity by altering the activity of several key transcription factors. This work will be of broad interest to immunologists.

The authors express their sincere appreciation to Timothy Behrens (Senior Editor), the Reviewing Editor, and the original reviewers for their considerate and favorable assessment of our manuscript.

Reviewer #1 (Public Review):

Summary:

Otarigho et al. presented a convincing study revealing that in C. elegans, the neuropeptide Y receptor GPCR/NPR-15 mediates both molecular and behavioral immune responses to pathogen attack. Previously, three npr genes were found to be involved in worm defense. In this study, the authors screened mutants in the remaining npr genes against P. aeruginosa-mediated killing and found that npr-15 loss-of-function improved worm survival. npr-15 mutants also exhibited enhanced resistance to other pathogenic bacteria but displayed significantly reduced avoidance to S. aureus, independent of aerotaxis, pathogen intake and defecation. The enhanced resistance in npr-15 mutant worms was attributed to upregulation of immune and neuropeptide genes, many of which were controlled by the transcription factors ELT-2 and HLH-30. The authors found that NPR-15 regulates avoidance behavior via the TRPM gene, GON-2, which has a known role in modulating avoidance behavior through the intestine. The authors further showed that both NPR-15-dependent immune and behavioral responses to pathogen attack were mediated by the NPR-15-expressing neurons ASJ. Overall, the authors discovered that the NPR-15/ASJ neural circuit may regulate distinct defense mechanisms against pathogens under different circumstances. This study provides novel and useful information to researchers in the fields of neuroimmunology and C. elegans research.

The authors are grateful for the thoughtful and insightful comments on our manuscript. Your feedback has been instrumental in refining our work, and we appreciate the time and expertise you have invested in evaluating our study.

Strengths:

1) This study uncovered specific molecules and neuronal cells that regulate both molecular immune defense and behavior defense against pathogen attack and indicate that the same neural circuit may regulate distinct defense mechanisms under different circumstances. This discovery is significant because it not only reveals regulatory mechanisms of different defense strategies but also suggests how C. elegans utilize its limited neural resources to accomplish complex regulatory tasks.

The authors express gratitude to the reviewer for recognizing that the present study revealed specific molecules and neuronal cells involved in regulating both molecular immune defense and behavioral defense against pathogen attacks. Additionally, the acknowledgment that the same neural circuit may oversee distinct defense mechanisms under different circumstances is appreciated.

2) The conclusions in this study are supported by solid evidence, which are often derived from multiple approaches and/or experiments. Multiple pathogenic bacteria were tested to examine the effect of NPR-15 loss-of-function on immunity; the impacts of pharyngeal pumping and defecation on bacterial accumulation were ruled out when evaluating defense; RNA-seq and qPCR were used to measure gene expression; gene inactivation was done in multiple strains to assess gene function.

The authors thank the reviewer for appreciating that this study is supported by solid evidence.

3) Gene differential expression, gene ontology, and pathway analyses were performed to demonstrate that NPR-15 controls immunity by regulating immune pathways.

The authors thank the reviewer for appreciating the Gene differential expression, gene ontology, and pathway analyses performed in the study.

4) Elegant approaches were employed to examine avoidance behavior (partial lawn, full lawn, and lawn occupancy) and the involvement of neurons in regulating immunity and avoidance (the use of a diverse array of mutant strains).

The author thanks the reviewer for appreciating the approaches used in this study.

5) Statistical analyses were appropriate and adequate.

The authors thank the reviewer for appreciating the Statistical analyses used in this study.

Reviewer #2 (Public Review):

Summary:

The authors are studying the behavioral response to pathogen exposure. They and others have previously describe the role that the G-protein coupled receptors in the nervous system plays in detecting pathogens, and initiating behavioral patterns (e.g. avoidance/learned avoidance) that minimize contact. The authors study this problem in C. elegans, which is amenable to genetic and cellular manipulations and allow the authors to define cellular and signaling mechanisms. This paper extends the original idea to now implicate signaling and transcriptional pathways within a particular neuron (ASJ) and the gut in mediating avoidance behaviour.

Strengths:

The work is rigorous and elegant and the data are convincing. The authors make superb use of mutant strains in C. elegans, as well tissue specific gene inactivation and expression and genetic methods of cell ablation. to demonstrate how a gene, NPR15 controls behavioral changes in pathogen infection. The results suggest that ASJ neurons and the gut mediate such effects. I expect the paper will constitute an important contribution to our understanding of how the nervous system coordinates immune and behavioral responses to infection.

The authors sincerely thank the reviewer for the thoughtful and positive review of our manuscript. We greatly appreciate the time and effort you dedicated to evaluating our work, and we are pleased that you find our study to be a rigorous and elegant contribution to the understanding of behavioral responses to pathogen exposure.

Reviewer #1 (Recommendations For The Authors):

The authors have adequately addressed my concerns and questions. I have no more comments or recommendations for the authors.