5,827 Matching Annotations
  1. May 2024
    1. Author response:

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

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major recommendations

      (1) In lines 42-44 (abstract), the authors state that "ASARs function as essential RNA scaffolds for the assembly of hnRNP complexes that help maintain the structural integrity of each mammalian chromosome". Similar conclusions are restated in lines 138-140. Based on the data presented, it is evident that ASARs localization on chromatin is dependent on hnRNPs. However, there is insufficient evidence to conclude that ASARs cause the assembly of hnRNP complexes or that these hnRNP complexes are directly responsible for the regulation of chromosome replication. Please revise your claims.

      We have modified the text as follows: “Our results further demonstrate the role that ASARs play during the temporal order of genome-wide replication, and we propose that ASARs function as essential RNA scaffolds for the assembly of hnRNP complexes that help maintain the structural integrity of each mammalian chromosome.”

      (2) In the analysis in Figure 1C- F, it is unclear why XIST is used as a comparison to ASAR6-141. A more meaningful control would be to show that hnRNPs preferentially bind ASAR6-141 relative to all expressed transcripts. Also, some panels are missing the y-axis label.

      We have genetically validated 8 different ASAR genes for their role in controlling chromosome-wide replication timing. The only other gene known to control chromosome-wide replication timing is XIST, which also encodes a chromosome-associated lncRNA. Our analysis of publicly available eCLIP data (and previous literature on XIST-binding proteins) showed substantial overlap between RBPs that associate with ASARs and XIST. Hence, we anticipated that at least some RBP knockdowns would affect both lncRNAs, despite their contrasting functions. In addition, we routinely use XIST RNA as a positive control in RNA FISH assays, as the XIST RNA FISH protocol represents a robust and well validated chromosomal RNA FISH procedure.

      y-axis labels have been added to Figure 1.

      (3) In Figure 2K&L, it would be beneficial to quantify and normalize the BrdU incorporation, as ectopic integration of the sense 7kb region appears to result in overall higher BrdU incorporation in all chromosomes, not just chromosome 5.

      There are two main aspects of the BrdU incorporation assay that we use: 1) The BrdU incorporation banding pattern on each chromosome is unique to that chromosome, and the banding pattern is also representative of the time during S phase when the BrdU incorporation occurred, i.e. we detect a different banding pattern if BrdU is incorporated in early S phase versus late S phase. 2) The amount of BrdU incorporation can be used to measure the synchrony between chromosome homologs, but only within the same cell. Thus, we generate a ratio of BrdU incorporation in chromosome homologs in individual cells, then compare the ratio of incorporation into each chromosome pair in multiple cells (see Figure 2B-E). The overall BrdU incorporation into the chromosomes of different cells is quite variable; however, the banding pattern and ratio of BrdU incorporation in chromosome homologs in individual cells is comparable, unless we have disrupted or ectopically integrated an ASAR. Given the variability in overall BrdU incorporation detected between different cells in the population this is not a useful readout for measuring synchronous versus asynchronous replication between chromosome homologs.

      (4) hnRNP protein can regulate multiple aspects of RNA processing other than chromatin retention. Hence, it would be beneficial to rule out an alternative hypothesis as to what the hnRNP knockdowns do to ASAR6-131? For example, assessing changes in RNA levels or splicing upon knockdown of hnRNPs using qPCR?

      We agree that direct roles for any of the hnRNP/RBPs that are critical for ASAR RNA localization and replication timing have not been established. However, our findings combined with the observation that cells depleted of HNRNPU show reduced origin licensing in G1, and show reduced origin activation frequency during S phase (PMID: 34888666), supports a role for HNRNPU, either directly or indirectly, in DNA replication. Furthermore, we also found that depletion of the DNA replication fork remodeler HLTF or the deubiquitinase UCHL5 also results in mis-localization of ASAR RNAs, and results in asynchronous replication of every autosome pair, indicating that ASAR RNA mis-localization and asynchronous replication are not simply a phenotype associated with hnRNP depletions. A full mechanistic understanding of the role that ASAR RNAs play in combination with this relatively large and diverse set of hnRNP/RBPs will require a better understanding of the direct roles that each protein, and any higher order complexes that contain these proteins, play in regulating DNA synthesis, splicing, transcription, chromatin structure and/or ASAR RNA localization.

      (5) Both the disruption and ectopic expression of the 7kb region result in delayed chromosome replication. Would one not expect there to be opposing effects on replication timing? Please discuss.

      One puzzling set of observations is that loss of function mutations and gain of function mutations of ASAR genes result in a similar delayed replication timing and delayed mitotic condensation phenotype. We have detected delayed replication timing in human cells following genetic knockouts (loss of function) of eight different ASAR genes located on 5 different autosomes. We have also detected delayed replication timing on mouse chromosomes expressing transgenes (gain of function) from three different ASAR genes (ASAR6, ASAR6-141, and ASAR15). The ASAR transgenes ranged in size from an ~180kb BAC, to an ~3kb PCR product. One possible explanation for these observations is that ectopic integration of ASAR transgenes function in a dominant negative manner by interfering with the endogenous “ASARs” on the integrated chromosomes. Consistent with this possibility is that we recently identified ASAR candidate genes on every human autosome (PMC9588035). Our favored model is that expression of ASAR transgenes integrated into mouse chromosomes disrupts the function of endogenous ASARs by "out-competing" them for shared RBPs. We also point out that a similar ectopic integration assay, using Xist transgenes, has been an informative assay for characterization of Xist functions, including the ability to delay replication timing and induce gene silencing on autosomes (reviewed in PMID:19898525). One intriguing observation (yet largely ignored by the X inactivation field) is that deletion of the Xist gene on either the active or inactive X chromosomes in somatic cells results in delayed replication timing of the X chromosomes (PMC1667074; PMC1456779). Thus, both loss of function and gain of function mutations of Xist result in a similar delayed replication timing phenotype. Given these parallels between Xist and ASAR gene mutation phenotypes we were curious to test the consequences of ASAR gain of function on the inactive X chromosome. In this manuscript, we integrated the ~7kb ASAR6-141 transgene into the inactive X chromosome, and detected a delayed replication timing phenotype on the integrated X chromosome. We also detected an association between Xist and ASAR RNAs using RNA FISH in interphase cells (Figure 4A and 4B), which supports the observations that ASAR RNAs and XIST RNA are bound by a partially overlapping set of hnRNP/RBPs (Figure 1D-F), and is consistent with the model that ASAR transgenes disrupt function by competition for shared RBPs. Dissecting the roles that the hnRNP/RBPs that interact with both ASAR and XIST RNAs will undoubtably give important insights into both XIST and ASAR function, and how these poorly understood chromosomal phenotypes are generated.

      Minor recommendations

      (1) In Figure 1G, it would be informative to show where the LINE-1 element within ASAR6-141 is located to get a sense of what hnRNP proteins bind to it.

      There are numerous LINE-1 elements within the ASAR6-141 gene. The ~7kb RBPD does not contain LINE-1 sequences. Therefore, we did not detect significant hnRNP/RBP eCLIP peaks within LINE-1 sequences.

      (2) The rationale for ectopic integration of the 7kb region into the inactive X-chromosome is unclear. Is there something unique about the replication of the inactive X or were you interested in seeing whether the 7kb region could escape X-inactivation?

      Given the parallels between Xist and ASAR gene mutation phenotypes, i.e. loss of function and gain of function result in delayed replication timing (see above), we were curious to test the consequences of ASAR gene gain of function on the inactive X chromosome. One possibility was reversal of X inactivation and a shift to earlier replication timing. However, we detected delayed replication timing on the inactive X, and an enhanced XIST RNA FISH signal that overlapped with the ASAR RNA. This speaks to the comment of Reviewer 2 questioning: "Is it possible that integration might alter Xist expression confounding this interpretation? ". The enhanced XIST RNA FISH signal suggests that the delayed replication of the inactive X is not due to reduced expression of XIST RNA.

    1. Author response:

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

      eLife assessment

      In this potentially useful study, the authors attempt to use comparative meta-analysis to advance our understanding of life history evolution. Unfortunately, both the meta-analysis and the theoretical model is inadequate and proper statistical and mechanistic descriptions of the simulations are lacking. Specifically, the interpretation overlooks the effect of well-characterised complexities in the relationship between clutch size and fitness in birds.

      Public Reviews:

      We would like to thank the reviewers for their helpful comments, which have been considered carefully and have been valuable in progressing our manuscript. The following bullet points summarise the key points and our responses, though our detailed responses to specific comments can be found below:<br /> - Two reviewers commented that our data was not made available. Our data was provided upon submission and during the review process, however was not made accessible to the reviewers. Our data and code are available at https://doi.org/10.5061/dryad.q83bk3jnk.

      - The reviewers have highlighted that some of our methodology was unclear and we have added all the requested detail to ensure our methods can be easily understood.

      - The reviewers highlight the importance of our conclusions, but also suggest some interpretations might be missing and/or are incomplete. To make clear how we objectively interpreted our data and the wider consequences for life-history theory we provide a decision tree (Figure 5). This figure makes clear where we think the boundaries are in our interpretation and how multiple lines of evidence converge to the same conclusions.

      Reviewer #1 (Public Review):

      This paper falls in a long tradition of studies on the costs of reproduction in birds and its contribution to understanding individual variation in life histories. Unfortunately, the meta-analyses only confirm what we know already, and the simulations based on the outcome of the meta-analysis have shortcomings that prevent the inferences on optimal clutch size, in contrast to the claims made in the paper.

      There was no information that I could find on the effect sizes used in the meta-analyses other than a figure listing the species included. In fact, there is more information on studies that were not included. This made it impossible to evaluate the data-set. This is a serious omission, because it is not uncommon for there to be serious errors in meta-analysis data sets. Moreover, in the long run the main contribution of a meta-analysis is to build a data set that can be included in further studies.

      It is disappointing that two referees comment on data availability, as we supplied a link to our full dataset and the code we used in Dryad with our submitted manuscript. We were also asked to supply our data during the review process and we again supplied a link to our dataset and code, along with a folder containing the data and code itself. We received confirmation that the reviewers had been given our data and code. We support open science and it was our intention that our dataset should be fully available to reviewers and readers. Our data and code are at https://doi.org/10.5061/dryad.q83bk3jnk.

      The main finding of the meta-analysis of the brood size manipulation studies is that the survival costs of enlarging brood size are modest, as previously reported by Santos & Nakagawa on what I suspect to be mostly the same data set.

      We disagree that the main finding of our paper is the small survival cost of manipulated brood size. The major finding of the paper, in our opinion, is that the effect sizes for experimental and observational studies are in opposite directions, therefore providing the first quantitative evidence to support the influential theoretical framework put forward by van Noordwijk and de Jong (1986), that individuals differ in their optimal clutch size and are constrained to reproducing at this level due to a trade-off with survival. We further show that while the manipulation experiments have been widely accepted to be informative, they are not in fact an effective test of whether within-species variation in clutch size is the result of a trade-off between reproduction and survival.

      The comment that we are reporting the same finding as Santos & Nakagawa (2012) is a misrepresentation of both that study and our own. Santos & Nakagawa found an effect of parental effort on survival only in males who had their clutch size increased – but no effect for males who had their clutch size reduced and no survival effect on females for either increasing or reducing parental effort. However, we found an overall reduction in survival for birds who had brood sizes manipulated to be larger than their original brood (for both sexes and mixed sex studies combined). In our supplementary information, we demonstrate that the overall survival effect of a change in reproductive effort is close to zero for males, negative (though non-significant) for females and significantly negative for mixed sexes (which are not included in the Santos & Nakagawa study). Please also note that the Santos & Nakagawa study was conducted over 10 years ago. This means we added additional data (L364-365). Furthermore, meta-analyses are an evolving practice and we also corrected and improved on the overall analysis approach (e.g. L358-359 and L 393-397, and see detailed SI).

      The paper does a very poor job of critically discussing whether we should take this at face value or whether instead there may be short-comings in the general experimental approach. A major reason why survival cost estimates are barely significantly different from zero may well be that parents do not fully adjust their parental effort to the manipulated brood size, either because of time/energy constraints, because it is too costly and therefore not optimal, or because parents do not register increased offspring needs. Whatever the reason, as a consequence, there is usually a strong effect of brood size manipulation on offspring growth and thereby presumably their fitness prospects. In the simulations (Fig.4), the consequences of the survival costs of reproduction for optimal clutch size were investigated without considering brood size manipulation effects on the offspring. Effects on offspring are briefly acknowledged in the discussion, but otherwise ignored. Assuming that the survival costs of reproduction are indeed difficult to discern because the offspring bear the brunt of the increase in brood size, a simulation that ignores the latter effect is unlikely to yield any insight in optimal clutch size. It is not clear therefore what we learn from these calculations.

      The reviewer’s comment is somewhat of a paradox. We take the best studied example of the trade-off between reproductive effort and parental survival – a key theme in life history and the biology of ageing – and subject this to a meta-analysis. The reviewer suggests we should interpret our finding as if there must be something wrong with the method or studies we included, rather than considering that the original hypothesis could be false or inflated in importance. We do not consider questioning the premise of the data over questioning a favoured hypothesis to necessarily be the best scientific approach here. In many places in our manuscript, we question and address, at length, the underlying data and their interpretation (L116-117, L165-167, 202-204 and L277-282). Moreover, we make it clear that we focus on the trade-off between current reproductive effort and subsequent parental survival, while being aware that other trade-offs could counter-balance or explain our findings (discussed on L208-210 & L301-316). Note that it is also problematic, when you do not find the expected response, to search for an alternative that has not been measured. In the case here, of potential trade-offs, there are endless possibilities of where a trade-off might operate between traits. We purposefully focus on the one well-studied and most commonly invoked trade-off. We clearly acknowledge, though, that when all possible trade-offs are taken into account a trade-off on the fitness level can occur and cite two famous studies (Daan et al., 1990 and Verhulst & Tinbergen 1991) that have shown just that (L314-316).

      So whilst we agree with the reviewer that the offspring may incur costs themselves, rather than costs being incurred by the parents, the aim of our study was to test for a general trend across species in the survival costs of reproductive effort. It is unrealistic to suggest that incorporating offspring growth into our simulations would add insight, as a change in offspring number rarely affects all offspring in the nest equally and there can even be quite stark differences; for example, this will be most evident in species that produce sacrificial offspring. This effect will be further confounded by catch-up growth, for example, and so it is likely that increased sibling competition from added chicks alters offspring growth trajectories, rather than absolute growth as the reviewer suggests. There are mixed results in the literature on the effect of altering clutch size on offspring survival, with an increased clutch size through manipulation often increasing the number of recruits from a nest.

      What we do appreciate from the reviewer’s comment is that the interpretation of our findings is complex. Even though our in-text explanation includes the caveats the reviewer refers to, and are discussed at length, their inter-relationships are hard to appreciate from a text format. To improve this presentation and for ease of the reader, we have added a decision tree (Figure 5) which represents the logical flow from the hypothesis being tested through to what overall conclusion can be drawn from our results. We believe this clarifies what conclusions can be drawn from our results. We emphasise again that the theory that trade-offs between reproductive effort and parental survival being the major driver of variation in offspring production was not supported though is the one that practitioners in the field would be most likely to invoke, and our result is important for this reason.

      There are other reasons why brood size manipulations may not reveal the costs of reproduction animals would incur when opting for a larger brood size than they produced spontaneously themselves. Firstly, the manipulations do not affect the effort incurred in laying eggs (which also biases your comparison with natural variation in clutch size). Secondly, the studies by Boonekamp et al on Jackdaws found that while there was no effect of brood size manipulation on parental survival after one year of manipulation, there was a strong effect when the same individuals were manipulated in the same direction in multiple years. This could be taken to mean that costs are not immediate but delayed, explaining why single year manipulations generally show little effect on survival. It would also mean that most estimates of the fitness costs of manipulated brood size are not fit for purpose, because typically restricted to survival over a single year.

      First, our results did show a survival cost of reproduction for brood manipulations (L107-123, Figure 1, Table 1). Note, however, that much theory is built on the immediate costs of reproduction and, as such, these costs are likely overinterpreted, meaning that our overall interpretation still holds, i.e. “parental survival trade-off is not the major determinative trade-off in life history within-species” (Figure 5).

      We agree with the reviewer that lifetime manipulations could be even more informative than single-year manipulations. Unfortunately, there are currently too few studies available to be able to draw generalisable conclusions across species for lifetime manipulations. This is, however, the reason we used lifetime change in clutch size in our fitness projections, which the reviewer seems to have missed – please see methods line 466-468, where we explicitly state that this is lifetime enlargement. Of course, such interpretations do not include an accumulation of costs that is greater than the annual cost, but currently there is no clear evidence that such an assumption is valid. Such a conclusion can also not be drawn from the study on jackdaws by Boonekamp et al (2014) as the treatments were life-long and, therefore, cannot separate annual from accrued (multiplicative) costs that are more than the sum of the annual costs incurred. Note that we have now included specific discussion of this study in response to the reviewer (L265-269).

      Details of how the analyses were carried out were opaque in places, but as I understood the analysis of the brood size manipulation studies, manipulation was coded as a covariate, with negative values for brood size reductions and positive values for brood size enlargements (and then variably scaled or not to control brood or clutch size). This approach implicitly assumes that the trade-off between current brood size (manipulation) and parental survival is linear, which contrasts with the general expectation that this trade-off is not linear. This assumption reduces the value of the analysis, and contrasts with the approach of Santos & Nakagawa.

      We thank the reviewer for highlighting a lack of clarity in places in our methods. We have added additional detail to the methodology section (see “Study sourcing & inclusion criteria” and “Extracting effect sizes”) in our revised manuscript. Note, that our data and code was not shared with the reviewers despite us supplying this upon submission and again during the review process, which would have explained a lot more of the detail required.

      For clarity in our response, each effect size was extracted by performing a logistic regression with survival as a binary response variable and clutch size was the absolute value of offspring in the nest (i.e., for a bird that laid a clutch size of 5 but was manipulated to have -1 egg, we used a clutch size value of 4). The clutch size was also standardised and, separately, expressed as a proportion of the species’ mean.

      We disagree that our approach reduces the value of our analysis. First, our approach allows a direct comparison between experimental and observational studies, which is the novelty of our study. Our approach does differ from Santos & Nakagawa but we disagree that it contrasts. Our approach allows us to take into consideration the severity of the change in clutch size, which Santos & Nakagawa do not. Therefore, we do not agree that our approach is worse at accounting for non-linearity of trade-offs than the approach used by Santos & Nakagawa. Arguably, the approach by Santos & Nakagawa is worse, as they dichotomise effort as increased or decreased, factorise their output and thereby inflate their number of outcomes, of which only 1 cell of 4 categories is significant (for males and females, increased and decreased brood size). The proof is in the pudding as well, as our results clearly demonstrate that the magnitude of the manipulation is a key factor driving the results, i.e. one offspring for a seabird is a larger proportion of care (and fitness) than one offspring for a passerine. Such insights were not achieved by Santos & Nakagawa’s method and, again, did not allow a direct quantitative comparison between quality (correlational) and experimental (brood size manipulation, i.e. “trade-off”) effects, which forms a central part of our argumentation (Figure 5). 

      Our analysis, alongside a plethora of other ecological studies, does assume that the response to our predictor variable is linear. However, it is common knowledge that there are very few (if any) truly linear relationships. We use linear relationships because they serve a good approximation of the trend and provide a more rigorous test for an underlying relationship than would fitting nonlinear models. For many datasets the range of added chicks required to estimate a non-linear relationship was not available. The question also remains of what the shape of such a non-linear relationship should be and is hard to determine a priori. There is also a real risk when fitting non-linear terms that they are spurious and overinterpreted, as they often present a better fit (denoting one df is not sufficient especially when slopes vary). We have added this detail to our discussion.

      The observational study selection is not complete and apparently no attempt was made to make it complete. This is a missed opportunity - it would be interesting to learn more about interspecific variation in the association between natural variation in clutch size and parental survival.

      We clearly state in our manuscript that we deliberately tailored the selection of studies to match the manipulation studies (L367-369). We paired species extracted for observational studies with those extracted in experimental studies to facilitate a direct comparison between observational and experimental studies, and to ensure that the respective datasets were comparable. The reviewer’s focus in this review seems to be solely on the experimental dataset. This comment dismisses the equally important observational component of our analysis and thereby fails to acknowledge one of the key questions being addressed in this study. Note that in our revised version we have edited the phylogenetic tree to indicate for which species we have both types of information, which highlights our approach to selecting observational data (Figure 3).

      Reviewer #2 (Public Review):

      I have read with great interest the manuscript entitled "The optimal clutch size revisited: separating individual quality from the costs of reproduction" by LA Winder and colleagues. The paper consists in a meta-analysis comparing survival rates from studies providing clutch sizes of species that are unmanipulated and from studies where the clutch sizes are manipulated, in order to better understand the effects of differences in individual quality and of the costs of reproduction. I find the idea of the manuscript very interesting. However, I am not sure the methodology used allows to reach the conclusions provided by the authors (mainly that there is no cost of reproduction, and that the entire variation in clutch size among individuals of a population is driven by "individual quality").

      We would like to highlight that we do not conclude that there is no cost of reproduction. Please see lines 336–339, where we state that our lack of evidence for trade-offs driving within-species variation in clutch size does not necessarily mean the costs of reproduction are non-existent. We conclude that individuals are constrained to their optima by the survival cost of reproduction. It is also an over-statement of our conclusion to say that we believe that variation in clutch size is only driven by quality. Our results show that unmanipulated birds that have larger clutch sizes also lived longer, and we suggest that this is evidence that some individuals are “better” than others, but we do not say, nor imply, that no other factors affect variation in clutch size. We have added Figure 5 to our manuscript to help the reader better understand what questions we can answer with our study and what conclusions we can draw from our results.

      I write that I am not sure, because in its current form, the manuscript does not contain a single equation, making it impossible to assess. It would need at least a set of mathematical descriptions for the statistical analysis and for the mechanistic model that the authors infer from it.

      We appreciate this comment, and have explained our methods in terms that are accessible to a wider audience. Note, however, that our meta-analysis is standard and based on logistic regression and standard meta-analytic practices. We have added the model formula to the model output tables.

      For the simulation, we simply simulated the resulting effects. We of course supplied our code for this along with our manuscript (https://doi.org/10.5061/dryad.q83bk3jnk), though as we mentioned above, we believe this was not shared with the reviewers despite us making this available for the review process. We therefore understand why the reviewer feels the simulations were not explained thoroughly. We have revised our methods section and added details which we believe make our methodology more clear without needing to consult the supplemental material. However, we have also added the equations used in the process of calculating our simulated data to the Supplementary Information for readers who wish to have this information in equation form.

      The texts mixes concepts of individual vs population statistics, of within individual vs among-individuals measures, of allocation trade-offs and fitness trade-offs, etc ....which means it would also require a glossary of the definitions the authors use for these various terms, in order to be evaluated.

      We would like to thank the reviewer for highlighting this lack of clarity in our text. Throughout the manuscript we have refined our terminology and indicated where we are referring to the individual level or the population level. The inclusion of our new Figure 5 (decision tree) should also help in this context, as it is clear on which level we base our interpretation and conclusions on.

      This problem is emphasised by the following sentence to be found in the discussion "The effect of birds having naturally larger clutches was significantly opposite to the result of increasing clutch size through brood manipulation". The "effect" is defined as the survival rate (see Fig 1). While it is relatively easy to intuitively understand what the "effect" is for the unmanipulated studies: the sensitivity of survival to clutch size at the population level, this should be mentioned and detailed in a formula. Moreover, the concept of effect size is not at all obvious for the manipulated ones (effect of the manipulation? or survival rate whatever the manipulation (then how could it measure a trade-off ?)? at the population level? at the individual level ?) despite a whole appendix dedicated to it. This absolutely needs to be described properly in the manuscript.

      Thank you for identifying this sentence for which the writing was ambiguous, our apologies. We have now rewritten this and included additional explanation. L282-290: ‘The effect on parental annual survival of having naturally larger clutches was significantly opposite to the result of increasing clutch size through brood manipulation, and quantitatively similar. Parents with naturally larger clutches are thus expected to live longer and this counterbalances the “cost of reproduction” when their brood size is experimentally manipulated. It is, therefore, possible that quality effects mask trade-offs. Furthermore, it could be possible that individuals that lay larger clutches have smaller costs of reproduction, i.e. would respond less in terms of annual survival to a brood size manipulation, but with our current dataset we cannot address this hypothesis (Figure 5).’

      We would also like to thank the reviewer for bringing to our attention the lack of clarity about the details of our methodology. We have added details to our methodology (see “Extracting effect sizes” section) to address this (see highlighted sections). For clarity, the effect size for both manipulated and unmanipulated nests was survival, given the brood size raised. We performed a logistic regression with survival as a binary response variable (i.e., number of individuals that survived and number of individuals that died after each breeding season), and clutch size was the absolute value of offspring in the nest (i.e., for a bird that laid a clutch size of 5 but was manipulated to have -1 egg, we used a clutch size value of 4). This allows for direct comparison of the effect size (survival given clutch size raised) between manipulated and unmanipulated birds.

      Despite the lack of information about the underlying mechanistic model tested and the statistical model used, my impression is still that the interpretation in the introduction and discussion is not granted by the outputs of the figures and tables. Let's use a model similar to that of (van Noordwijk and de Jong, 1986): imagine that the mechanism at the population level is

      a.c_(i,q)+b.s_(i,q)=E_q

      Where c_(i,q) are s_(i,q) are respectively the clutch size for individual i which is of quality q, and E_q is the level of "energy" that an individual of quality q has available during the given time-step (and a and b are constants turning the clutch size and survival rate into energy cost of reproduction and energy cost of survival, and there are both quite "high" so that an extra egg (c_(i,q) is increased by 1) at the current time-step, decreases s_(i,q) markedly (E_q is independent of the number of eggs produced), that is, we have strong individual costs of reproduction). Imagine now that the variance of c_(i,q) (when the population is not manipulated) among individuals of the same quality group, is very small (and therefore the variance of s_(i,q) is very small also) and that the expectation of both are proportional to E_q. Then, in the unmanipulated population, the variance in clutch size is mainly due to the variance in quality. And therefore, the larger the clutch size c_(i,q) the higher E_q, and the higher the survival s_(i,q).

      In the manipulated populations however, because of the large a and b, an artificial increase in clutch size, for a given E_q, will lead to a lower survival s_(i,q). And the "effect size" at the population level may vary according to a,b and the variances mentioned above. In other words, the costs of reproduction may be strong, but be hidden by the data, when there is variance in quality; however there are actually strong costs of reproduction (so strong actually that they are deterministic and that the probability to survive is a direct function of the number of eggs produced)

      We would like to thank the reviewer for these comments. We have added detail to our methodology section so our models and rationale are more clear. Please note that our simulations only take the experimental effect of brood size on parental survival into account. Our model does not incorporate quality effects. The reviewer is right that the relationship between quality and the effects exposed by manipulating brood size can take many forms and this is a very interesting topic, but not one we aimed to tackle in our manuscript. In terms of quality we make two points: (1) overall quality effects connecting reproduction and parental survival are present, (2) these effects are opposite in direction to the effects when reproduction is manipulated and similar in magnitude. We do not go further than that in interpreting our results. The reviewer is correct, however, that we do suggest and repeat suggestions by others that quality can also mask the trade-off in some individuals or circumstances (L74-76, L95-98 & L286-289), but we do not quantify this, as it is dependent on the unknown relationship between quality and the response to the manipulation. A focussed set of experiments in that context would be interesting and there are some data that could get at this, i.e. the relationship between produced clutch size and the relative effect of the manipulation (now included L287-290). Such information is, however, not available for all studies and, although we explored the possibility of analysing this, currently this is not possible with adequate confidence and there is the possible complexity of non-linear effects. We have added this rationale in our revision (L259-265).

      Moreover, it seems to me that the costs of reproduction are a concept closely related to generation time. Looking beyond the individual allocative (and other individual components of the trade-off) cost of reproduction and towards a populational negative relationship between survival and reproduction, we have to consider the intra-population slow fast continuum (some types of individuals survive more and reproduce less (are slower) than other (which are faster)). This continuum is associated with a metric: the generation time. Some individuals will produce more eggs and survive less in a given time-period because this time-period corresponds to a higher ratio of their generation time (Gaillard and Yoccoz, 2003; Gaillard et al., 2005). It seems therefore important to me, to control for generation time and in general to account for the time-step used for each population studied when analysing costs of reproduction. The data used in this manuscript is not just clutch size and survival rates, but clutch size per year (or another time step) and annual (or other) survival rates.

      The reviewer is right that this is interesting. There is a longstanding unexplained difference in temperate (seasonal) and tropical reproductive strategies. Most of our data come from seasonal breeders, however. Although there is some variation in second brooding and such, these species mostly only produce one brood. We do agree that a wider consideration here is relevant, but we are not trying to explain all of life history in our paper. It is clearly the case that other factors will operate and the opportunity for trade-offs will vary among species according to their respective life histories. However, our study focuses on the two most fundamental components of fitness – longevity and reproduction – to test a major hypothesis in the field, and we uncover new relationships that contrast with previous influential studies and cast doubt on previous conclusions. We question the assumed trade-off between reproduction and annual survival. We show that quality is important and that the effect we find in experimental studies is so small that it can only explain between-species patterns but is unlikely to be the selective force that constrains reproduction within species. We do agree that there is a lot more work that can be done in this area. We hope we are contributing to the field, by questioning this central trade-off. We have incorporated some of the reviewers suggestions in the revision (L309-315). We have added Figure 5 to make clear where we are able to reach solid conclusions and the evidence on which these are based as clearly as possible in an easily accessible format.

      Finally, it is important to relate any study of the costs of reproduction in a context of individual heterogeneity (in quality for instance), to the general problem of the detection of effects of individual differences on survival (see, e.g., Fay et al., 2021). Without an understanding of the very particular statistical behaviour of survival, associated to an event that by definition occurs only once per life history trajectory (by contrast to many other traits, even demographic, where the corresponding event (production of eggs for reproduction, for example) can be measured several times for a given individual during its life history trajectory).

      Thank you for raising this point. The reviewer is right that heterogeneity can dampen or augment selection. Note that by estimating the effect of quality here we give an example of how heterogeneity can possibly do exactly this. We thank the reviewer for raising that we should possibly link this to wider effects of heterogeneity and we have added to our discussion of how our results play into the importance of accounting for among-individual heterogeneity (L252-256).

      References:

      Fay, R. et al. (2021) 'Quantifying fixed individual heterogeneity in demographic parameters: Performance of correlated random effects for Bernoulli variables', Methods in Ecology and Evolution, 2021(August), pp. 1-14. doi: 10.1111/2041-210x.13728.

      Gaillard, J.-M. et al. (2005) 'Generation time: a reliable metric to measure life-history variation among mammalian populations.', The American naturalist, 166(1), pp. 119-123; discussion 124-128. doi: 10.1086/430330.

      Gaillard, J.-M. and Yoccoz, N. G. (2003) 'Temporal Variation in Survival of Mammals: a Case of Environmental Canalization?', Ecology, 84(12), pp. 3294-3306. doi: 10.1890/02-0409.

      van Noordwijk, A. J. and de Jong, G. (1986) 'Acquisition and Allocation of Resources: Their Influence on Variation in Life History Tactics', American Naturalist, p. 137. doi: 10.1086/284547.

      Reviewer #3 (Public Review):

      The authors present here a comparative meta-analysis analysis designed to detect evidence for a reproduction/ survival trade-off, central to expectations from life history theory. They present variation in clutch size within species as an observation in conflict with expectations of optimisation of clutch size and suggest that this may be accounted for from weak selection on clutch size. The results of their analyses support this explanation - they found little evidence of a reproduction - survival trade-off across birds. They extrapolated from this result to show in a mathematical model that the fitness consequences of enlarged clutch sizes would only be expected to have a significant effect on fitness in extreme cases, outside of normal species' clutch size ranges. Given the centrality of the reproduction-survival trade-off, the authors suggest that this result should encourage us to take a more cautious approach to applying concepts the trade-off in life history theory and optimisation in behavioural ecology more generally. While many of the findings are interesting, I don't think the argument for a major re-think of life history theory and the role of trade-offs in fitness maximisation is justified.

      The interest of the paper, for me, comes from highlighting the complexities of the link between clutch size and fitness, and the challenges facing biologists who want to detect evidence for life history trade-offs. Their results highlight apparently contradictory results from observational and experimental studies on the reproduction-survival trade-off and show that species with smaller clutch sizes are under stronger selection to limit clutch size.

      Unfortunately, the authors interpret the failure to detect a life history trade-off as evidence that there isn't one. The construction of a mathematical model based on this interpretation serves to give this possible conclusion perhaps more weight than is merited on the basis of the results, of this necessarily quite simple, meta-analysis. There are several potential complicating factors that could explain the lack of detection of a trade-off in these studies, which are mentioned and dismissed as unimportant (lines 248-250) without any helpful, rigorous discussion. I list below just a selection of complexities which perhaps deserve more careful consideration by the authors to help readers understand the implications of their results:

      We would like to thank the reviewer for their thoughtful response and summary of the findings that we also agree are central to our study. The reviewer also highlights areas where our manuscript could benefit from a deeper consideration and we have added detail accordingly to our revised discussion.

      We would like to highlight that we do not interpret the failure to detect a trade-off as evidence that there is not one. First, and importantly, we do find a trade-off but show this is only incurred when individuals produce a clutch beyond their optimal level. Second, we also state on lines 322-326 that the lack of evidence to support trade-offs being strong enough to drive variation in clutch size does not necessarily mean there are no costs of reproduction.

      The statement that we have constructed a mathematical model based on the interpretation that we have not found a trade-off is, again, factually incorrect. We ran these simulations because the opposite is true – we did find a trade-off. There is a significant effect of clutch size when manipulated on annual parental survival. We benefit from our unique analysis allowing for a quantitative fitness estimate from the effect size on annual survival (as this is expressed on a per-egg basis). This allowed us to ask whether this quantitative effect size can alone explain why reproduction is constrained, and we evaluate this using simulations. From these simulations we find that this effect size is too small to explain the constraint, so something else must be going on, and we do spend a considerable amount of text discussing the possible explanations (L202-215). Note that the possibly most parsimonious conclusion here is that costs of reproduction are not there, or simply small, so we also give that explanation some thought (L221-224 and L315-331).

      We are disappointed by the suggestion that we have dismissed complicating factors that could prevent detection of a trade-off, as this was not our intention. We were aiming to highlight that what we have demonstrated to be an apparent trade-off can be explained through other mechanisms, and that the trade-off between clutch size and survival is not as strong in driving within-species variation in clutch size as previously assumed. We have added further discussion to our revised manuscript to make this clear and give readers a better understanding of the complexity of factors associated with life-history theory, including the addition of a decision tree (Figure 5).

      • Reproductive output is optimised for lifetime reproductive success and so the consequences of being pushed off the optimum for one breeding attempt are not necessarily detectable in survival but in future reproductive success (and, therefore, lifetime reproductive success).

      We agree this is a valid point, which is mentioned in our manuscript in terms of alternative stages where the costs of reproduction might be manifested (L316-320). We would also like to highlight that , in our simulations, the change in clutch size (and subsequent survival cost) was assumed for the lifetime of the individual, for this very reason.

      • The analyses include some species that hatch broods simultaneously and some that hatch sequentially (although this information is not explicitly provided (see below)). This is potentially relevant because species which have been favoured by selection to set up a size asymmetry among their broods often don't even try to raise their whole broods but only feed the biggest chicks until they are sated; any added chicks face a high probability of starvation. The first point this observation raises is that the expectation of more chicks= more cost, doesn't hold for all species. The second more general point is that the very existence of the sequential hatching strategy to produce size asymmetry in a brood is very difficult to explain if you reject the notion of a trade-off.

      We agree with the reviewer that the costs of reproduction can be absorbed by the offspring themselves, and may not be equal across offspring (we also highlight this at L317-318 in the manuscript). However, we disagree that for some species the addition of more chicks does not equate to an increase in cost, though we do accept this might be less for some species. This is, however, difficult to incorporate into a sensible model as the impacts will vary among species and some species do also exhibit catch-up growth. So, without a priori knowledge on this, we kept our model simple to test whether the effect on parental survival (often assumed to be a strong cost) can explain the constraint on reproductive effort, and we conclude that it does not.

      We would also like to make clear that we are not rejecting the notion of a trade-off. Our study shows evidence that a trade-off between survival and reproductive effort probably does not drive within-species variation in clutch size. We do explicitly say this throughout our manuscript, and also provide suggestions of other areas where a trade-off may exist (L317-320). The point of our study is not whether trade-offs exist or not, it is whether there is a generalisable across-species trend for a trade-off between reproductive effort and survival – the most fundamental trade-off in our field but for which there is a lack of conclusive evidence within species. We believe the addition of Figure 5 to our reviewed manuscript also makes this more evident.

      • For your standard, pair-breeding passerine, there is an expectation that costs of raising chicks will increase linearly with clutch size. Each chick requires X feeding visits to reach the required fledge weight. But this is not the case for species which lay precocious chicks which are relatively independent and able to feed themselves straight after hatching - so again the relationship of care and survival is unlikely to be detectable by looking at the effect of clutch size but again, it doesn't mean there isn't a trade-off between breeding and survival.

      Precocial birds still provide a level of parental care, such as protection from predators. Though we agree that the level of parental care in provisioning food (and in some cases in all parental care given) is lower in precocial than altricial birds, this would only make our reported effect size for manipulated birds to be an underestimate. Again, we would like to draw the reviewer’s attention to the fact we did detect a trade-off in manipulated birds and we do not suggest that trade-offs do not exist. The argument the reviewer suggests here does not hold for unmanipulated birds, as we found that birds that naturally lay larger clutch sizes have higher survival.

      • The costs of raising a brood to adulthood for your standard pair-breeding passerine is bound to be extreme, simply by dint of the energy expenditure required. In fact, it was shown that the basal metabolic rate of breeding passerines was at the very edge of what is physiologically possible, the human equivalent being cycling the Tour de France (Nagy et al. 1990). If birds are at the very edge of what is physiologically possible, is it likely that clutch size is under weak selection?

      If birds are at the very edge of what is physiologically possible, then indeed it would necessarily follow that if they increase the resource allocated in one area then expenditure in another area must be reduced. In many studies, however, the overall brood mass is increased when chicks are added and cared for in an experimental setting, suggesting that birds are not operating at their limit all the time. Our simulations show that if individuals increase their clutch size, the survival cost of reproduction counterbalances the fitness gained by increasing clutch size and so there is no overall fitness gain to producing more offspring. Therefore, selection on clutch size is constrained to the within-species level. We do not say in our manuscript that clutch size is under weak selection – we only ask why variation in clutch size is maintained if selection always favours high-producing birds.

      • Variation in clutch size is presented by the authors as inconsistent with the assumption that birds are under selection to lay the Lack clutch. Of course, this is absurd and makes me think that I have misunderstood the authors' intended point here. At any rate, the paper would benefit from more clarity about how variable clutch size has to be before it becomes a problem for optimality in the authors' view (lines 84-85; line 246). See Perrins (1965) for an exquisite example of how beautifully great tits optimise clutch size on average, despite laying between 5-12 eggs.

      We thank the reviewer for highlighting that our manuscript may be misleading in places, however, we are unsure which part of our conclusions the author is referring to here. The question we pose is “Why don’t all birds produce a clutch size at the population optimum?”, and is central to the decades-long field of life-history theory. Why is variation maintained? As the reviewer outlines, there is extensive variability, with some birds laying half of what other birds lay.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Title: while the costs of reproduction are possibly important in shaping optimal clutch size, it is not clear what you can about it given that you do not consider clutch / brood size effects on fitness prospects of the offspring.

      We have expanded on our discussion of how some costs may be absorbed by the offspring themselves. However, a change in offspring number rarely affects all offspring in the nest equally and there can even be quite stark differences; for example this will be most evident in species that produce sacrificial offspring. This effect will be further confounded by catch-up growth. There are mixed results in the literature on the effect of altering clutch size on offspring survival, with an increased clutch size through manipulation often increasing the number of recruits from a nest. We have focussed on the relationship between reproductive effort and survival because it is given the most weight in the field in terms of driving intra-specific variation in clutch size. We have altered our title to show we focus on the survival costs specifically: “The optimal clutch size revisited: separating individual quality from the parental survival costs of reproduction”.

      (2) L.11-12: I agree that this is true for birds, but this is phrased more generally here. Are you sure that that is justified?

      The trade-off between survival and reproductive effort has largely been tested experimentally through brood manipulations in birds as this provides a good system in which to test the costs and benefits of increasing parental effort. The work in this area has provided theory beyond just passerine birds, which are the most commonly manipulated group, to across-taxa theories. We are unaware of any study/studies that provide evidence that the reproduction/survival trade-off is generalisable across multiple species in any taxa. As such, we do believe this sentence is justified. An example is the lack of a consistent negative genetic correlation in populations of fruitflies, for example, that has also been hailed as a lack-of-cost paradigm. Furthermore, some mutants that live longer do so without a cost on reproduction.

      (3) L.13-14: Not sure what you mean with this sentence - too much info lacking.

      We have added some detail to this sentence.

      (4) L.14: it is slightly awkward to say 'parental investment and survival' because it is the survival effect that is usually referred to as the 'investment'. Perhaps what you want to say is 'parental effort and survival'?

      We have replaced “parental investment” with “reproductive effort”

      (5) L.15: you can omit 'caused'. Compared to control treatment or to reduced broods? Why not mention effects or lack thereof of brood reduction? And it would be good to also mention here whether effects were similar in the sexes.

      Please see our methodology where we state that we use clutch size as a continuous variable (we do not compare to control or reduced but include the absolute value of offspring in a logistic regression). The effects of a brood reduction are drawn from the same regression and so are opposite. Though we appreciate the detail here is lacking to fully comprehend our study, we would like to highlight this is the abstract and details are provided in the main text.

      (6) L. 15: I am not sure why you write 'however', as the finding that experimental and natural variation have opposite effects is in complete agreement with what is generally reported in the literature and will therefore surprise no one that is aware of the literature.

      We use “however” to highlight the change in direction of the effect size from the results in the previous sentence. We also believe that ours ise the first study that provides a quantitative estimate of this effect and that previous work is largely theoretical. The reviewer states that this is what is generally reported but it is not reported in all cases, as some relationships between reproductive effort and survival are negative (for the quality measurement, in correlational space, see Figure 1).

      (7) L.16: saying 'opposite to the effect of phenotypic quality' seems difficult to justify, as clutch size cannot be equated with phenotypic quality. Perhaps simply say 'natural variation in clutch size'? If that is what you are referring to.

      Please note we are referring to effect sizes here –- that is, the survival effect of a change in clutch size. By phenotypic quality we are referring to the fact that we find higher parental survival when natural clutch sizes are higher. It is not the case that we refer to quality only as having a higher clutch size. This is explicitly stated in the sentence you refer to. We have changed “effect” to “effect size” to highlight this further.

      (8) L.18: why do you refer to 'parental care' here? Brood size is not equivalent to parental care.

      Brood size manipulations are used to manipulate parental care. The effect on parental survival is expected to be incurred because of the increase in parental care. We have changed “parental care” to “reproductive effort” to reduce the number of terms we use in our manuscript.

      (9) L.18-19: suggest to tone down this claim, as this is no more than a meta-analytic confirmation of a view that is (in my view) generally accepted in the field. That does not mean it is not useful, just that it does not constitute any new insight.

      We are unaware of any other study which provides generalisable across-species evidence for opposite effects of quality and costs of reproduction. The work in this area is also largely theoretical and is yet to be supported experimemtally, especially in a quantitative fashion. It is surprising to us that the reviewer considers there to be general acceptance in a field, rather than being influenced by rigorous testing of hypotheses, made possible by meta-analysis, the current gold standard in our field.

      (10) L.21: what does 'parental effort' mean here? You seem to use brood size, parental care, parental effort, and parental investment interchangeably but these are different concepts. Daan et al (1990, Behaviour), which you already cite, provide a useful graph separating these concepts. Please adjust this throughout the manuscript, i.e. replace 'reproductive effort' with wording that reflect the actual variable you use.

      We have not used the phrase “parental effort” in this sentence. We agree these are different concepts but in this context are intertwined. For example, brood size is used to manipulate parental care as a result of increased parental effort. We do agree the manuscript would benefit from keeping terminology consistent throughout the manuscript and have adjusted this throughout.

      (11) L.23: perhaps add 'in birds' somewhere in this sentence? Some reference to the assumptions underlying this inference would also be useful. Two major assumptions being that birds adjusted their effort to the manipulation as they would have done had they opted for a larger brood size themselves, and that the costs of laying and incubating extra eggs can be ignored. And then there is the effect that laying extra eggs will usually delay the hatch date, which in many species reduces reproductive success.

      Though our study does exclusively use birds, birds have been used to test the survival/reproduction trade-off because they present a convenient system in which to experimentally test this. The conclusions from these studies have a broader application than in birds alone. We believe that although these details are important, they are not appropriate in the abstract of our paper.

      (12) L.26: how is this an explanation? It just repeats the finding.

      We intend to refer to all interpretations from all results presented in our manuscript. We have made this more clear by adjusting our writing.

      (13) L.27: I do not see this point. And 'reproductive output' is yet another concept, that can be linked to the other concepts in the abstract in different ways, making it rather opaque.

      We have changed “reproductive output” to “reproductive effort”.

      (14) L.33: here you are jumping from 'resources' to 'energetically' - it is not clear that energy is the only or main limiting resource, so why narrow this down to energy?

      We do not say energy is the only or main limiting resource. We simply highlight that reproduction is energetically demanding and so, intuitively, a trade-off with a highly energetically demanding process would be the focal place to observe a trade off. We have, though, replaced “energetically” with “resource”.

      (15) L.35-36: this is new to me - I am not aware of any such claims, and effects on the residual reproductive value could also arise through effects on future reproduction. The authors you cite did not work on birds, or (in their own study systems) presented results that as far as I remember warrant such a general statement.

      The trade-off between reproduction and survival is seminal to the disposable soma theory, proposed by Kirkwood. Though Kirkwood’s work was largely not focussed on birds, it had fundamental implications for the field of evolutionary ecology because of the generalisable nature of his proposed framework. In particular, it has had wide-reaching influence on how the biology of aging is interpreted. The readership of the journal here is broad, and our results have implications for that field too. The work of Kirkwood (many of the papers on this topic have over 2000 citations each) has been perhaps overly influential in many areas, so a link to how that work should be interpreted is highly relevant. If the reviewer is interested in this topic the following papers by one of the co-authors and others could be of interest, some of which we could not cite in the main manuscript due to space considerations:

      https://www.science.org/doi/pdf/10.1126/sciadv.aay3047

      https://agingcelljournal.org/Archive/Volume3/stochasticity_explains_non_genetic_inheritance_of_lifespan/

      https://pubmed.ncbi.nlm.nih.gov/21558242/

      https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2435.13444

      https://www.nature.com/articles/362305a0

      https://www.cell.com/trends/ecology-evolution/fulltext/S0169-5347(12)00147-4

      https://www.cell.com/cell/pdf/S0092-8674(15)01488-9.pdf

      https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-018-0562-z

      (16) L.42: this could be preceded with mentioning the limitations of observational data.

      We have added detail as to why brood manipulations are a good test for trade-offs and so this is now inherently implied.

      (17) L.42-43: why?

      We have added detail to this sentence.

      (18) L.45: do any of the references cited here really support this statement? I am certain that several do not - in these this statement is an assumption rather than something that is demonstrated. It may be useful to look at Kate Lessell's review on this that appeared in Etologia, I think in the 1990's. Mind however that 'reproductive effort' is operationally poorly defined for reproducing birds - provisioning rate is not necessarily a good measure of effort in so far as there are fitness costs.

      We have updated the references to support the sentence.

      (19) L.47: Given that you make this statement with respect to brood size manipulations in birds, it seems to me that the paper by Santos & Nakagawa is the only paper you should cite here. Given that you go on to analyze the same data it deserves to be discussed in more detail, for example to clarify what you aim to add to their analysis. What warrants repeating their analysis?

      Please first note that our dataset includes Santos & Nakagawa and additional studies, so it is not accurate to say we analyse the same data. Furthermore, we believe our study has implications beyond birds alone and so believe it is appropriate to cite the papers that do support our statement. We have added details to the methods to explicitly state what data is gathered from Santos & Nakagawa (it is only used to find the appropriate literature and data was re-extracted and re-analysed in a more appropriate way) and, separately, how we gathered the observational studies (see L352-381).

      (20) L.48: There are more possible explanations to this, which deserve to be discussed. For example, brood size manipulations may not have been that effective in manipulating reproductive effort - for example, effects on energy expenditure tend to be not terribly convincing. Secondly, the manipulations do not affect the effort incurred in laying eggs (which also biases your comparison with natural variation in clutch size). Thirdly, the studies by Boonekamp et al on Jackdaws found that while there was no effect of brood size manipulation on parental survival after one year of manipulation, there was a strong effect when the same individuals were manipulated in the same direction in multiple years. This could be taken to mean that costs are not immediate but delayed, explaining why single year manipulations generally show little effect on survival. It would also mean that most estimates of the fitness costs of manipulated brood size are not fit for purpose, because typically restricted to survival over a single year.

      Please see our response to this comment in the public reviews.

      Out of interest and because the reviewer mentioned “energy expenditure” specifically: There are studies that show convincing effects of brood size manipulation on parental energy expenditure. We do agree that there are also studies that show ceilings in expenditure. We therefore disagree that they “tend to be not terribly convincing”. Just a few examples:

      https://academic.oup.com/beheco/article/10/5/598/222025 (Figure 2)

      https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2435.12321 (Figure 1)

      https://besjournals.onlinelibrary.wiley.com/doi/full/10.1046/j.1365-2656.2000.00395.x (but ceiling at enlarged brood).

      (21) L.48, "or, alternatively, that individuals may differ in quality": how do you see that happening when brood size is manipulated, and hence 'quality' of different experimental categories can be assumed to be approximately equal? This point does apply to observational studies, so I assume that that is what you had in mind, but that distinction should be clear (also on line 54).

      We have made it more clear that we determine if there are quality effects separate to the costs of reproduction found using brood manipulation studies.

      (22) L.50: Drent & Daan, in their seminal paper on "The prudent parent" (1980, Ardea) were among the earliest to make this point and deserve to be cited here.

      We have added this citation

      (23) L.51, "relative importance": relative to what? Please be more specific.

      We have adjusted this sentence.

      (24) L.54: Vedder & Bouwhuis (2018, Oikos) go some way towards this point and should be explicitly mentioned with reference to the role of 'quality' effects on the association between reproductive output and survival.

      We have added this reference.

      (25) L.55: can you be more specific on what you want to do exactly? What you write here could be interpreted differently.

      We have added an explicit aim after this sentence to be more clear.

      (26) L.57: Here also a more specific wording would be useful. What does it mean exactly when you say you will distinguish between 'quality' and 'costs'?

      We have added detail to this sentence.

      (27) L.62: it should be clearer from the introduction that this is already well known, which will indirectly emphasize what you are adding to what we know already.

      We would argue this is not well known and has only been theorised but not shown empirically, as we do here.

      (28) L.62: you equate clutch size with 'quality' here - that needs to be spelled out.

      We refer to quality as the positive effect size of survival for a given clutch size, not clutch size alone. We appreciate this is not clear in this sentence and have reworded.

      (29) L.64: this looks like a serious misunderstanding to me, but in any case, these inferences should perhaps be left to the discussion (this also applies to later parts of this paragraph), when you have hopefully convinced readers of the claims you make on lines 62-63.

      We are unsure of what the reviewer is referring to as a misunderstanding. We have chosen this format for the introduction to highlight our results. If this is a problem for the editors we will change as required.

      (30) L.66: quantitative comparison of what?

      Comparison of species. We have changed the wording of this sentence

      (31) L.67-69: this should be in the methods.

      We have used a modern format which highlights our result. We are happy to change the format should the editors wish us to.

      (32) L.74-88: suggest to (re)move this entire paragraph, presenting inferences in such an uncritical manner before presenting the evidence is inappropriate in my view. I have therefore refrained from commenting on this paragraph.

      We have chosen a modern format which highlights our result. We are happy to change the format should the editors wish us to.

      (33) L.271, "must detail variation in the number of raised young": it is not sufficiently clear what this means - what does 'detail' mean in this context? And what does 'number of raised young' mean? The number hatched or raised to fledging?

      We have now made this clear.

      (34) L271, "must detail variation in the number of raised young": looking at table S4, it seems that on the basis of this criterion also brood size manipulation studies where details on the number of young manipulated were missing are excluded. I see little justification for this - surely these manipulations can for example be coded as for example having the average manipulation size in the meta-analysis data set, thereby contributing to tests of manipulation effects, but not to variation within the manipulation groups?

      We have done in part what the reviewer describes. We are specifically interested in the manipulation size, so we required this to compare effect sizes across species and categories, a key advance of our study and outlined in many places in our manuscript. Note, however, that we only need comparative differences, and have used clutch size metrics more generally to obtain a mean clutch size for a species, as well as SD where required. Please also note that our supplement details exactly why studies were excluded from our analysis, as is the preferred practice in a meta-analysis.

      (35) L.271, "referred to as clutch size": the point of this simplification is not clear to me why it is clearly confusing - why not refer to 'brood size' instead?

      Brood size and clutch size can be used interchangeably here because, in the observational studies, the individuals vary in the number of eggs produced, whereas for brood manipulations this obviously happens after hatching and brood is perhaps a more appropriate term, but we wanted to simplify the terminology used. However, we use clutch size throughout as the aim of our study is to determine why individuals differ in the number of offspring they produce, and so clutch size is the most appropriate term for that.

      (36) L.280: according to the specified inclusion criteria (lines 271/272) these studies should already be in the data set, so what does this mean exactly?

      Selection criteria refers to whether a given study should be kept for analysis or not. It does not refer to how studies were found. Please see lines 361-378 for details on how we found studies (additional details are also in the Supplementary Methods).

      (37) L.281: the use of 'quality' here is misleading - natural variation in clutch or brood size will have multiple causes, variation in phenotypic quality of the individuals and their environment (territories) is only one of the causes. Why not simply refer to what you are actually investigating: natural and experimental variation in brood size.

      We disagree, our study aims to separate quality effects from the costs of reproduction and we use observational studies to test for quality differences, though we make no inference about the mechanisms. We do not imply that the environment causes differences in quality, but that to directly compare observation and experimental groups, they should contain similar species. So, to be clear again, quality refers to the positive covariation of clutch size with survival. We feel that we explain this clearly in our study’s rationale and have also improved our writing in several sections on this to avoid any confusion (see responses to earlier comments by the three reviewers).

      (38) L.283, "in most cases": please be exact and say in xx out xx cases.

      We have added the number of studies for each category here.

      (39) L.283-285: presumably readers can see this directly in a table with the extracted data?

      Our data and code can be accessed with the following link: https://doi.org/10.5061/dryad.q83bk3jnk. We believe the data are too large to include as a table in the main text and are not essential in understanding the paper. Though we do believe all readers should have access to this information if they wish and so is publicly available.

      (40) L.293: there does not seem to be a table that lists the included studies and effect sizes. It is not uncommon to find major errors in such tables when one is familiar with the literature, and absence of this information impedes a complete assessment of the manuscript.

      We supplied a link to our full dataset and the code we used in Dryad with our submitted manuscript. We were also asked to supply our data during the review process and we again supplied a link to our dataset and code, along with a folder containing the data and code itself. We received confirmation that the reviewers had been given our data and code. We support open science and it was our intention that our dataset should be fully available to reviewers and readers. We believe the data are too large to include as a table in the main text and are not essential in understanding the paper. Our data and code are at https://doi.org/10.5061/dryad.q83bk3jnk.

      (41) L.293: from how many species?

      We have added this detail.

      (42) L.296, "longevity": this is a tricky concept, not usually reported in the studies you used, so please describe in detail what data you used.

      We have removed longevity as we did not use this data in our current version of the manuscript.

      (43) L. 298: again: where can I see this information?

      Our data and code can be accessed with the following link: https://doi.org/10.5061/dryad.q83bk3jnk. We did supply this information when we submitted our manuscript and again during the review process but we believe this was not passed onto the reviewers.

      (44) L. 304, "we used raw data": I assume that for the majority of papers the raw data were not available, so please explain how you dealt with this. Or perhaps this applies to a selection of the studies only? Perhaps the experimental studies?

      By raw data, we mean the absolute value of offspring in the nest. We have changed the wording of this sentence and added detail about whether the absolute value of offspring was not present for brood manipulation studies (L393-397).

      (45) L.304: When I remember correctly, Santos and Nakagawa examined effects of reducing and enlarging brood size separately, which is of importance because trade-off curves are unlikely to be linear and whether they are or not has major effects on the optimization process. But perhaps you tackled this in another way? I will read on.....

      You are correct that Santos & Nakagawa compared brood increases and reductions to control separately. Note that this only partially accounts non-linearity and it does not take into account the severity of the change in brood size. By using a logistic regression of absolute clutch size, as we have done, we are able to directly compare brood manipulations with experimental studies. Please see Supplementary Methods lines 11-12, where we have added additional detail as to why our approach is beneficial in this analysis.

      (46) L.319: what are you referring to exactly with "for each clutch size transformation"?

      We refer to the raw, standardised and proportional clutch size transformations. We have added detail here to be more clear.

      (47) L.319: is there a cost of survival? Perhaps you mean 'survival cost'? This would be appropriate for the experimental data, but not for the observational data, where the survival variation may be causally unrelated to the brood size variation, even if there is a correlation.

      We have changed “cost of survival” to “effect of parental survival”. We only intend to imply causality for the experimental studies. For observational studies we do not suggest that increasing clutch size is causal for increasing survival, only correlative (and hence we use the phrase “quality”).

      (48) L.320: please replace "parental effort" with something like 'experimental change in brood size'.

      We have changed “parental effort” to “reproductive effort”

      (49) L.321: due to failure of one or more eggs to hatch, and mortality very early in life, before brood sizes are manipulated, it is not likely that say an enlargement of brood size by 1 chick can be equated to the mean clutch size +1 egg / check. For example, in the Wytham great tit study, as re-analysed by Richard Pettifor, a 'brood size manipulation' of unmanipulated birds is approximately -1, being the number of eggs / chicks lost between laying and the time of brood size manipulation. Would this affect your comparisons?

      Though we agree these are important factors in determining what a clutch/brood size actually is for a given individual/pair, as this can vary from egg laying to fledging. We do not believe that accounting for this (if it was possible to do so) would significantly affect our conclusions, as observational studies are comparable in the fact that these birds would also likely see early life mortality of their offspring. It is also possibly the case that parents already factor in this loss, and so a brood manipulation still changes the parental care effort an individual has to incur.

      (50) L.332: instead of "adjusted" perhaps say 'mean centred'?

      We have implemented this suggestion.

      (51) L.345: this statement surprised me, but is difficult to verify because I could not locate a list of the included studies. However, to my best knowledge, most studies reporting brood size manipulation effects on parental survival had this as their main focus, in contrast to your statement.

      Our data and code can be accessed with the following link: https://doi.org/10.5061/dryad.q83bk3jnk. We did supply this information when we submitted our manuscript and again during the review process but we believe this was not passed onto the reviewers by the journal, although supplied by us on several occasions. We regret that the reviewer was impeded by this unfortunate communication failure, but we did our best to make the data available to the reviewers during the initial review process.

      (52) L.361-362: this seems a realistic approach from an evolutionary perspective, but we know from the jackdaw study by Boonekamp that the survival effect of brood size manipulation in a single year is very different from the survival effect of manipulating as in your model, i.e. every year of an individual's life the same manipulation. For very short-lived species this possibly does not make much difference, but for somewhat longer-lived species this could perhaps strongly affect your results. This should be discussed, and perhaps also explored in your simulations?

      Note that the Boonekamp study does not separate whether the survival effects are additive or

      multiplicative. As such, we do not know whether the survival effects for a single year manipulation are just small and hard to detect, or whether the survival effects are multiplicative. Our simulations assumed that the brood enlargement occurred every year throughout their lives. We have added some text to the discussion on the point you raise.

      (53) L.360: what is "lifetime reproductive fitness"? Is this different from just "fitness"?

      We have changed “lifetime reproductive fitness” to “lifetime reproductive output”.

      (54) L.363: when you are interested in optimal clutch size, why not also explore effects of reducing clutch size?

      As we find that a reduction in clutch size leads to a reduction in survival (for experimental studies), we already know that these individuals would have a reduced fitness return compared to reproducing at their normal level, and so we would not learn anything from adding this into our simulations. The interest in using clutch size enlargements is to find out why an individual does not produce more offspring than it does, and the answer is that it would not have a fitness benefit (unless its clutch size and survival rate combination is out of the bounds of that observable in the wild).

      (55) Fig.1 - using 'parental effort' in the y-axis label is misleading, suggest to replace with e.g. "clutch or brood size". Using "clutch size" in the title is another issue, as the experimental studies typically changed the number of young rather than the number of eggs.

      We have updated the figure axes to say “clutch size” rather than “parental effort”. Please see response to comment 35 where we explain our use of the term “clutch size” throughout this manuscript.

      (56) L.93 - 108: I appreciate the analysis in Table 1, in particular the fact that you present different ways of expressing the manipulation. However, in addition, I would like to see the results of an analysis treating the manipulations as factor, i.e. without considering the scale of the manipulation. This serves two purposes. Firstly, I believe it is in the interest of the field that you include a detailed comparison with the results of Santos & Nakagawa's analysis of what I expect to be largely the same data (manipulation studies only - for this purpose I would also like to see a comparison of effect size between the sexes). Secondly, there are (at least) two levels of meta-analysis, namely quantifying an overall effect size, and testing variables that potentially explain variation in effect size. You are here sort of combining the two levels of analysis, but including the first level also would give much more insight in the data set.

      Our main intention here was to improve on how the same hypothesis was approached by Santos & Nakagawa. We did this by improving our analysis (on a by “egg” basis) and by adding additional studies (i.e. more data). In this process mistakes are corrected (as we re-extracted all data, and did not copy anything across from their dataset – which was used simply to ensure we found the same papers); more recent data were also added, including studies missed by Santos & Nakagawa. This means that the comparison with Santos & Nakagawa becomes somewhat irrelevant, apart from maybe technical reasons, i.e. pointing out mistakes or limitations in certain approaches. We would not be able to pinpoint these problems clearly without considering the whole dataset, yet Santos & Nakagawa only had a small subset of the data that were available to us. In short, meta-analysis is an iterative process and similar questions are inevitably analysed multiple times and updated. This follows basic meta-analytic concepts and Cochrane principles. Except where there is a huge flaw in a prior dataset or approach (like we sometimes found and highlighted in our own work, e.g. Simons, Koch, Verhulst 2013, Aging Cell), in itself a comparison of the kind the reviewer suggests distracts from the biology. With the dataset being made available others can make these comparisons, if required. On the sex difference, we provide a comparison of effect sizes separated between both sexes and mixed sex in Table S2 and Figure S1.

      (57) L.93 - 108: a thing that does not become clear from this section is whether experimentally reducing brood size affects parental survival similarly (in absolute terms) as enlarging brood size. Whether these effects are symmetric is biologically important, for example because of its effect on clutch size optimization. In the text you are specific about the effects of increasing brood size, but the effect you find could in theory be due entirely to brood size reduction.

      We have added detail to make it clear that a brood reduction is simply the opposite trend. We use linear relationships because they serve a good approximation of the trend and provide a more rigorous test for an underlying relationship than would fitting nonlinear models. For many datasets there is not a range of chicks added for which a non-linear relationship could be estimated. The question also remains of what the shape of this non-linear relationship should be and is hard to determine a priori.

      We have added some discussion on this to our manuscript (L278-282), in response to an earlier comment.

      (58) L.103-107: this is perhaps better deferred to the discussion, because other potential explanations should also be considered. For example, there have been studies suggesting that small birds were provisioning their brood full time already, and hence had no scope to increase provisioning effort when brood size was experimentally increased.

      We agree this is a discussion point but we believe it also provides an important context for why we ran our simulations, and so we believe this is best kept brief but in place. We agree the example you give is relevant but believe this argument is already contained in this section. See line 121-123 “...suggesting that costs to survival were only observed when a species was pushed beyond its natural limits”.

      (59) L.103-107: this discussion sort of assumes that the results in Table 1 differ between the different ways that the clutch/brood size variation is expressed. Is there any statistical support for this assumption?

      We are unsure of what the reviewer means here exactly. Note that in each of the clutch size transformations, experimental and observational effect sizes are significantly opposite. For the proportional clutch size transformation, experimental and observation studies are both separately significantly different from 0.

      (60) L.104: at this point, I would like to have better insight into the data set. Specifically, a scatter plot showing the manipulation magnitude (raw) plotted against control brood size would be useful.

      Our data and code can be accessed with the following link: https://doi.org/10.5061/dryad.q83bk3jnk. We did supply this information when we submitted our manuscript and again during the review process but we believe this was not passed onto the reviewers by the journal.

      Thank you for this suggestion: this is a useful suggestion also to illustrate how manipulations are relatively stronger for species with smaller clutches, in line with our interpretation of the result presented in Figure 2. We have added Figure S1 which shows the strength of manipulation compared to the species average.

      (61) L. 107: this seems a bold statement - surely you can test directly whether effect size becomes disproportionally stronger when manipulations are outside the natural range, for example by including this characterization as a factor in the models in Table 1.

      It is hard to define exactly what the natural range is here, so it is not easy to factorise objectively, which is why we chose not to do this. However, it is clear that for species with small clutches the manipulation itself is often outside the natural range. Thank you for your suggestion to include a figure for this as it is clear manipulations are stronger in species with smaller clutches. We attribute this to species being forced outside their natural range. We consider our wording makes it clear that this is our interpretation of our findings and we therefore do not think this is a bold statement, especially as it fits with how we interpret our later simulations.

      (62) Fig.3, legend: the term 'node support' does not mean much to me, please explain.

      Node support is a value given in phylogenetic trees to dictate the confidence of a branch. In this case, values are given as a percentage and so can translate to how many times out of 100 the estimate of the phylogeny gives the same branching. Our values are low, as we have relatively few species in our meta-analysis.

      (63) Fig.3: it would be informative when you indicate in this figure whether the species contributed to the experimental or the observational data set or both.

      We have added into Fig 3 whether the species was observational, experimental or both.

      (64) L.139: the p-value refers to the interaction between species clutch size and treatment (observational vs. experimental), but it appears that no evidence is presented for the correlation being significant in either observational or experimental studies.

      We agree that our reporting of the effect size could be misinterpreted and have added detail here. The statistic provided describes the slopes are significantly different between observational and experimental, implying there are differences between the slopes of small and large clutch-laying species.

      (65) L.140: I am wondering to what extent these correlations, which are potentially interesting, are driven by the fact that species average clutch size was also used when expressing the manipulation effect. In other words, to what extent is the estimate on the Y-axis independent from the clutch size on the X-axis? Showing that the result is the same when using survival effect sizes per manipulation category would considerably improve confidence in this finding.

      We are unsure what the reviewer means by “per manipulation category”. Please also note that we have used a logistic regression to calculate our effect sizes of survival, given a unit increase in reproductive effort. So, for example, if a population contained birds that lay 2,3 or 4 eggs, provided that the number of birds which survived and died in each category did not change, if we changed the number of eggs raised to 10,11 or 12, respectively, then our effect size would be the same. In this way, our effect sizes are independent of the species’ average clutch size.

      (66) L.145: when I remember correctly, Santos & Nakagawa considered brood size reduction and enlargement separately. Can this explain the contrasting result? Please discuss.

      You are correct, in that Santos & Nakagawa compared reductions and enlargements to controls separately. However, we found some mistakes in the data extracted by Santos & Nakagawa that we believe explain the differences in our results for sex-specific effect sizes. We do not feel that highlighting these mistakes in the main text is fair, useful or scientifically relevant, as our approach is to improve the test of the hypothesis.

      (67) L.158-159: looking at table S2 it seems to me you have a whole range of estimates. In any case, there is something to be said for taking the estimates for females because it is my impression (and experience) that clutch size variation in most species is a sex-linked trait, in that clutch size tends to be repeatable among females but not among males.

      We agree that, in many cases, the female is the one that ultimately decides on the number of chicks produced. We did also consider using female effect sizes only, however, we decided against this for the following reasons: (1) many of the species used in our meta-analysis exhibit biparental care, as is the case for many seabirds, and so using females only would bias our results towards species with lower male investment; in our case this would bias the results towards passerine species. (2) it has also been shown that, as females in some species are operating at their maximum of parental care investment, it is the males who are able to adjust their workload to care for extra offspring. (3) we are ultimately looking at how many offspring the breeding adults should produce, given the effort it costs to raise them, and so even if the female chooses a clutch size completely independently of the male, it is still the effort of both parents combined that determines whether the parents gain an overall fitness benefit from laying extra eggs. (4) some studies did not clearly specify male or female parental survival and we would not want to reduce our dataset further.

      (68) L.158-168: please explain how you incorporated brood size effects on the fitness prospects of offspring, given that it is a very robust finding of brood size manipulation studies that this affects offspring growth and survival.

      We would argue this is near-on impossible to incorporate into our simulations. It is unrealistic to suggest that incorporating offspring growth into our simulations would add insight, as a change in offspring number rarely affects all offspring in the nest equally and there can even be quite stark differences; for example, this will be most evident in species that produce sacrificial offspring. This effect will be further confounded by catch-up growth, for example, and so it is likely that increased sibling competition from added chicks alters offspring growth trajectories, rather than absolute growth as the reviewer suggests. There are mixed results in the literature on the effect of altering clutch size on offspring survival, with an increased clutch size through manipulation often increasing the number of recruits from a nest. It would be interesting, however, to explore this further using estimates from the literature, but this is beyond our current scope, and would in our initial intuition not be very accurate. It would be interesting to explore how big the effect on offspring should be to constrain effect size strongly. Such work would be more theoretical. The point of our simple fitness projections here is to aid interpretation of the quantitative effect size we estimated.

      (69) L.163: while I can understand that you select the estimate of -0.05 for computational reasons, it has enormous confidence intervals that also include zero. This seems problematic to me. However, in the simulations, you also examined the results of selecting -0.15, which is close to the lower end of the 95% C.I., which seems worth mentioning here already.

      Thank you for this suggestion. Yes, indeed, our range was chosen based on the CI, and we have now made this explicit in the manuscript.

      (70) L.210: defined in this way, in my world this is not what is generally taken to be a selection differential. Is what you show not simply scaled lifetime reproductive success?

      As far as we are aware, a selection differential is the relative change between a given group and the population mean, which is what we have done here. We appreciate this is a slightly unusual context in which to place this, but it is more logical to consider the individuals who produce more offspring as carrying a potential mutation for higher productivity. However, we believe that “selection differential” is the best terminology for the statistic we present. We also detail in our methodology how we calculate this. We have adjusted this sentence to be more explicit about what we mean by selection differential.

      (71) L.177-180: is this not so because these parameter values are closest to the data you based your estimates on, which yielded a low estimate and hence you see that here also?

      We are unsure of what exactly the reviewer means here. The effect sizes for our exemplar species were predicted from each combination of clutch size and survival rate. Note that we used a range of effect sizes, higher than that estimated in our meta-analysis, to explore a large parameter space and that these same conclusions still hold.

      (72) L.191-194: these statements are problematic, because based on the assumption that an increase in brood size does not impact the fitness prospects of the offspring, and we know this assumption to be false.

      Though we appreciate that some cost is often absorbed by the offspring themselves, we are unaware of any evidence that these costs are substantial and large enough to drive within-species variation in reproductive effort, though for some specific species this may be the case. However, in terms of explaining a generalisable, across-species trend, the fitness costs incurred by a reduction in offspring quality are unlikely to be significantly larger than the survival costs to reproduce. We also find it highly unlikely the cost to fitness incurred by a reduction in offspring quality is large enough to counter-balance the effect of parental quality that we find in our observational studies. We do also discuss other costs in our discussion.

      (73) L.205: here and in other places it would be useful to be more explicit on whether in your discussion you are referring to observational or experimental variation.

      We have added this detail to our manuscript. Do note that many of our conclusions are drawn by the combination of results of experimental and observational studies. We believe the addition of Figure 5 makes this more clear to the reader.

      (74) L.225: this may be true (at least, when we overlook the misuse of the word 'quality' here), but I would expect some nuance here to reflect that there is no surprise at all in this result as this pattern is generally recognized in the literature and has been the (empirical) basis for the often-repeated explanation of why experiments are required to demonstrate trade-offs. On a more quantitative level, it is worth mentioning the paper of Vedder & Bouwhuis (2017, Oikos) that essentially shows the same thing, i.e. a positive association between reproductive output and parental survival.

      We have added some discussion on this point, including adding the citation mentioned. However, we would like to highlight that our results demonstrate that brood manipulations are not necessarily a good test of trade-offs, as they fail to recognise that individuals differ in their underlying quality. Though we agree that this result should not necessarily be a surprising one, we have also not found it to be the case that differences in individual quality are accepted as the reason that intra-specific clutch size is maintained – in fact, we find that it is most commonly argued that when costs of reproduction are not identifiedit is concluded that the costs must be elsewhere – yet we cannot find conclusive evidence that the costs of reproduction (wherever they lie) are driving intra-specific variation in reproductive effort. Furthermore, some studies in our dataset have reported negative correlations between reproductive effort and survival (see observational studies, Figure 1).

      (75) L.225-226: perhaps present this definition when you first use the term.

      We have added more detail to where we first use and define this term to improve clarity (L57-58).

      (76) L.227-228, "currently unknown": this statement surprised me, given that there is a plethora of studies showing within-population variation in clutch size to depend on environmental conditions, in particular the rate at which food can be gathered.

      We mean to question that if an individual is “high quality”, why is it not selected for? We have rephrased, to improve clarity.

      (77) L.231: this seems no more than a special case of the environmental effect you mention above.

      We think this is a relevant special case, as it constitutes within-individual variation in reproduction that is mistaken for between-individual variation. This is a common problem in our field, that we feel needs adressing. We only have between-individual variation here in our study on quality, and by highlighting this we show that there might not be any variation between individuals, but this could come about fully (doubtful) or partly (perhaps likely) due to terminal effects.

      (78) L235-236: but apparently depending on how experimental and natural variation was expressed? Please specify here.

      We are not sure what results the reviewer is referring to here, as we found the same effect (smaller clutch laying species are more severely affected by a change in clutch size) for both clutch size expressed as raw clutch size and standardised clutch size.

      (79) L.237: the concept of 'limits' is not very productive here, and it conflicts with the optimality approach you apply elsewhere. What you are saying here can also be interpreted as there being a non-linear relationship between brood size manipulation and parental survival, but you do not actually test for that. A way to do this would be to treat brood size reduction and enlargement separately. Trade-off curves are not generally expected to be linear, so this would also make more sense biologically than your current approach.

      We have replaced “limits” with “optima”. We believe our current approach of treating clutch size as a continuous variable, regardless of manipulation direction, is the best approach, as it allows us to directly compare with observational studies and between species that use different manipulations (now nicely illustrated by the reviewer’s suggested Figure S1). Also note that transforming clutch size to a proportion of the mean allows us to account for the severity in change in clutch size. We also do not believe that treating reductions and enlargements separately accounts for non-linearity, as either we are separating this into two linear relationships (one for enlargements and one for reductions) or we compare all enlargements/reductions to the control, as in Santos & Nakagawa 2012, which does not take into account the severity of the increase, which we would argue is worse for accounting for non-linearity. Furthermore, in the cases where the manipulation involved one offspring only, we also cannot account for non-linearity.

      (80) L.239: assuming birds are on average able to optimize their clutch size, one could argue that any manipulation, large or small, on average forces birds to raise a number of offspring that deviates from their natural optimum. At this point, it would be interesting to discuss in some detail studies with manipulation designs that included different levels of brood size reduction/enlargement.

      We agree with the reviewer that any manipulation is changing an individual’sclutch size away from its own individual optima, which we have argued also means brood manipulations are not necessarily a good test of whether a trade-off occurs in the wild (naturally), as there could be interactions with quality – we have now edited to explicitly state this (L299-300).

      (81) L.242-244: when you choose to maintain this statement, please add something along the lines of "assuming there is no trade-off between number and quality of offspring".

      As explained above, though we agree that the offspring may incur some of the cost themselves, we are not aware of any evidence suggesting this trade-off is also large enough to drive intra-specific variation in clutch size across species. Furthermore, in the context here, the trade-off between number and quality of offspring would not change our conclusion – that the fitness benefit of raising more offspring is offset by the cost on survival. We have added detail on the costs incurred by offspring earlier in our discussion (L309-315). The addition of Figure 5 should help interpret these data.

      (82) L.253: instead of reference 30 the paper by Tinbergen et al in Behaviour (1990) seems more appropriate.

      We believe our current citation is relevant here but we have also added the Tinbergen et al (1990) citation.

      (83) L.253-254: such trade-offs may perfectly explain variation in reproductive effort within species if we were able to estimate cost-benefit relations for individuals. In fact, reference 29 goes some way to achieve this, by explaining seasonal variation in reproductive effort.

      We are unaware of any quantitative evidence that any combination of trade-offs explains intra-specific variation in reproductive effort, especially as a general across-species trend.

      (84) L.255: how does one demonstrate "between species life-history trade-offs"? The 'trade-off' between reproductive rate and survival we observe between species is not necessarily causal, and hence may not really be a trade-off but due to other factors - demonstrating causality requires some form of experimental manipulation.

      Between-species trade-offs are well established in the field, stemming from GC Williams’ seminal paper in 1966, and for example in r/K selection theory. It is possible to move from these correlations to testing for causation, and this is happening currently by introducing transgenes (genes from other species) that promote longevity into shorter-lived species (e.g., naked-mole rat genes into mice). As yet it is unclear what the effects on reproduction are.

      (85) L.256: it is quite a big claim that this is a novel suggestion. In fact, it is a general finding in evolutionary theory that fitness landscapes tend to be rather flat at equilibrium.

      It is important to note here that we simulate the effect size found, and hence this is the novel suggestion, that because the resulting fitness landscape is relatively flat there is no directional selection observed. We did not intend to suggest our interpretation of flat fitness landscapes is novel. We have changed the phrasing of this sentence to avoid misinterpretation.

      (86) L.259: why bring up physiological 'costs' here, given that you focus on fitness costs? Do you perhaps mean fitness costs instead of physiological costs? Furthermore, here and in the remainder of this paragraph it would be useful to be more specific on whether you are considering natural or experimental variation.

      The cost of survival is a physiological cost incurred by the reduction of self-maintenance as a result of lower resource allocation. This is one arm of fitness; we feel it would be confusing here to talk about costs to fitness, as we do not assess costs to future reproduction (which formed the large part of the critique offered by the reviewer). We would like to highlight that the aim of this manuscript was to separate costs of reproduction from the effects of quality, and this is why we have observational and experimental studies in one analysis, rather than separately. Our conclusion that we have found no evidence that the survival cost to reproduce drives within-species variation in clutch size comes both from the positive correlation found in the observational studies and our negligible fitness return estimates in our simulations. We therefore, do not believe it is helpful to separate observational and experimental conclusions throughout our manuscript, as the point is that they are inherently linked. We hope that with the addition of Figure 5 that this is more clear.

      (87) L.262: The finding that naturally more productive individuals tend to also survive better one could say is by definition explained by variation in 'quality', how else would you define quality?

      We agree, and hence we believe quality is a good term to describe individuals who perform highly in two different traits. Note that we also say the lack of evidence that trade-offs drive intra-specific variation in clutch size also potentially suggests an alternative theory, including intra-specific variation driven by differences in individual quality.

      Supplementary information

      (88) Table S1: please provide details on how the treatment was coded - this information is needed to derive the estimates of the clutch size effect for the treatments separately.

      We have added this detail.

      (89) Table S2: please report the number of effect sizes included in each of these models.

      We have added this detail.

      (90) Table S4: references are not given. Mentioning species here would be useful. For example, Ashcroft (1979) studied puffins, which lay a single egg, making me wonder what is meant when mentioning "No clutch or brood size given" as the reason for exclusion. A few more words to explain why specific studies were excluded would be useful. For example, what does "Clutch size groups too large" mean? It surprises me that studies are excluded because "No standard deviation reported for survival" - as the exact distribution is known when sample size and proportion of survivors is known.

      We have updated this table for more clarity.

      (91) Fig.S1: please plot different panels with the same scale (separately for observational and experimental studies). You could add the individual data points to these plots - or at least indicate the sample size for the different categories (female, male, mixed).

      We have scaled all panels to have the same y axis and added sample sizes to the figure legend.

      (92) Fig.S3: please provide separate plots for experimental and observational studies, as it seems entirely plausible that the risk of publication bias is larger for observational studies - in particular those that did not also include a brood size manipulation. At the same time, one can wonder what a potential publication bias among observational studies would represent, given that apparently you did not attempt to collect all studies that reported the relevant information.

      We have coloured the points for experimental and observational studies. Note that a study is an independent effect size and, therefore, does not indicate whether multiple data (i.e., both experimental and observational studies) came from the same paper. As we detail in the paper and above in our reviewer responses, we searched for observational studies from species used in the experimental studies to allow direct comparison between observational and experimental datasets.

      Reviewer #2 (Recommendations For The Authors):

      I strongly recommend improving the theoretical component of the analysis by providing a solid theoretical framework before, from it, drawing conclusions.

      This, at a minimum, requires a statistical model and most importantly a mechanistic model describing the assumed relationships.

      We thank the reviewer for highlighting that our aims and methodology are unclear in places. We have added detail to our model and simulation descriptions and have improved the description of our rationale. We also feel the failure of the journal to provide code and data to the reviewers has not helped their appreciation of our methodology and use of data.

      Because the field uses the same wording for different concepts and different wording for the same concept, a glossary is also necessary.

      We thank the reviewer for raising this issue. During the revision of this manuscript, we have simplified our terminology or given a definition, and we believe this is sufficient for readers to understand our terminology.

      Reviewer #3 (Recommendations For The Authors):

      • The files containing information of data extracted from each study were not available so it has not been possible to check how any of the points raised above apply to the species included in the study. The ms should include this file on the Supp. Info as is standard good practice for a comparative analysis.

      We supplied a link to our full dataset and the code we used in Dryad with our submitted manuscript. We were also asked to supply our data during the review process and we again supplied a link to our dataset and code, along with a folder containing the data and code itself. We received confirmation that the reviewers had been given our data and code. We support open science and it was our intention that our dataset should be fully available to reviewers and readers. We believe the data is too large to include as a table in the main text and is not essential in understanding the paper. Our data and code are at https://doi.org/10.5061/dryad.q83bk3jnk.

      • For clarity, refer to 'the effect size of clutch size on survival" rather than simply "effect size". Figures 1 and 2 require cross-referencing with the main text to understand the y-axis.

      We have added detail to the figure legend to increase the interpretability of the figures.

      • Silhouettes in Figure 3 (or photos) would help readers without ornithological expertise to understand the taxonomic range of the species included in the analyses.

      We have added silhouettes into Figure 3.

      • Throughout the discussion: superscripts shouldn't be treated as words in a sentence so please add authors' names where appropriate.

      We have added author names and dates where required.

    1. Author response:

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

      eLife assessment

      This valuable paper presents a new protocol for quantifying tRNA aminoacylation levels by deep sequencing. The improved methods for discrimination of aminoacyl-tRNAs from non-acylated tRNAs, more efficient splint-assisted ligation to modify the tRNAs' ends for the following RT-PCR reaction, and the use of an error-tolerating mapping algorithm to map the tRNA sequencing reads provide new tools for anyone interested in tRNA concentrations and functional states in different cells and organisms. The results and conclusions are solid with well-designed tests to optimize the protocol under different conditions.

      Public Reviews:

      We thank both reviewers for suggestions, feedback and improvements. We address these pointwise below.

      Reviewer #1 (Public Review):

      Summary:

      The manuscript of Davidsen and Sullivan describes an improved tRNA-seq protocol to determine aminoacyl-tRNA levels. The improvements include: (i) optimizing the Whitfeld or oxidation reaction to select aminoacyl-tRNAs from oxidation-sensitive non-acylated tRNAs; (ii) using a splint-assisted ligation to modify the tRNAs' ends for the following RT-PCR reaction; (iii) using an error-tolerating mapping algorithm to map the tRNA sequencing reads that contain mismatches at modified nucleotides.

      Strengths:

      The two steps, the oxidation, and the splint-assisted ligation are yield-diminishing steps, thus the protocol of Davidsen and Sullivan is an important improvement of the current protocols to enhance the quantification of aminocyl-tRNAs.

      Weaknesses:

      The oxidation and the selection of aminoacyl-tRNA is the first step in all protocols. Thereafter they differ on whether blunt ligation, hairpin (DM-tRNA-seq, YAMAT-seq, QuantM-seq, mim tRNA-seq, LOTTE tRNA-seq), or splint ligation is used and finally what detection method is applied (i-tRAP, tRNA microarrays). What is the correlation to those alternative approaches (e.g. i-tRAP (PMID 36283829), tRNA microarrays (PMID: 31263264) etc.)? What is the correlation with other approaches with which this improved protocol shares some steps (DM-tRNA-seq, mim-tRNA-seq)?

      We appreciate the fair assessment and fully agree that our work would benefit from a large comparison between all known tRNA-seq methods. We did directly compare many elements of our method to those of other methods (e.g. ligation efficiency and barcode bias); however, as noted by the reviewer we did not perform a direct end-to-end comparison with all other methods. An ideal comparison would require running several different sample conditions and technical replicates through our protocol and repeating the process across a half dozen or so other methods as they are described. Unfortunately, this approach is unlikely to be feasible since each method uses different oligos, reagents and kits, and all would have to be acquired at substantial cost. Some methods also rely on other detection methods such as microarrays, qPCR, or Illumina sequencing, which would also make this goal all the more onerous. There are also different pipelines for data processing that, in some instances, make the final results hard to compare. In short, this would be a monumental and expensive task to do comprehensively. We also worry that, even if these experiments were conducted such that some variables were concluded to be superior, they could still be challengeable based on perceived or actual protocol differences from the prior art. In summary, we think that an overall comparison with each method would be ideal, but practical concerns limit us to optimizing and comparing the variables that we found to be most prone to introducing bias in the results.

      For methods that measure tRNA expression levels (DM-tRNA-seq, YAMAT-seq, QuantM-seq, mim-tRNA-seq, LOTTE tRNA-seq etc.) there are some fundamental problems regarding absolute quantification using NGS that preclude simple comparisons. These problems are well known in the field of microRNA (Fuchs et al. (2012) [PMID: 25942392]) and arise due to several factors introduced during processing steps such as purification, ligation, reverse transcription and amplification. With the lack a “true” quantitation benchmark it would be difficult to make quantitative claims from each.  Therefore, in our own work we benchmark tRNA expression levels for sample-to-sample reproducibility (i.e. precision) as further explained in the response to reviewer #2.

      For comparison to methods that measure tRNA charge we did have an opportunity to compare our results with those of another study. To this end, we have added a figure comparing the baseline charge found using our method and the one used in Evans et al. (Revised manuscript Figure 2—figure supplement 9). This comparison finds broadly similar results for tRNA charge, including similar trends for a subset of Glu, Ser and Pro codons that are notable for their lowered basal tRNA charge.

      Reviewer #2 (Public Review):

      Davidsen and Sullivan present an improved method for quantifying tRNA aminoacylation levels by deep sequencing. By combining recent advances in tRNA sequencing with lysine-based chemistry that is more gentle on RNA, splint oligo-based adapter ligation, and full alignment of tRNA reads, they generate an interesting new protocol. The lab protocol is complemented by a software tool that is openly available on Github. Many of the points highlighted in this protocol are not new but have been used in recent protocols such as Behrens et al. (2021) or McGlincy and Ingolia (2017). Nevertheless, a strength of this study is that the authors carefully test different conditions to optimize their protocol using a set of well-designed controls.

      The conclusions of the manuscript appear to be well supported by the data presented. However, there are a few points that need to be clarified.

      We appreciate the acknowledgement of the strength of our aminoacylation controls and agree that our method is relying on many aspects of the mentioned prior work.  

      (1) One point that remains unsatisfactory is a better benchmarking against the state of the art. It is currently impossible to estimate how much the results of this new protocol differ from alternative methods and in particular from Behrens et al. (2021). Here it will be helpful to perform experiments with samples similar to those used in the mim-tRNAseq study and not with H1299 cells.

      We fully agree that more rigorous benchmarking would be desirable. As also noted in the response to reviewer #1, a full end-to-end comparison of methods would be ideal but would be onerous and expensive in practice, so we focused on optimizing the steps we found to be most prone to introducing bias in the data.

      We agree that Behrens et al., (2021) has substantial methodological overlap with our work and was instrumental in our efforts; however, the focus of their manuscript was largely on quantification of tRNA abundance and modifications, rather than the tRNA charge. In fact, tRNA charge was only determined for yeast in that study. Quantifying the abundance of short RNAs using NGS is very difficult (Fuchs et al. (2012) [PMID: 25942392]) and will likely require the use of a mixture of tRNAs as spike-in references for normalization (Bissels et al. (2009) [PMID: 19861428]). In the case of Behrens et al. (2021), they did not use a spike-in tRNA reference, but instead correlated gene copy number with their measured tRNA abundance. They also compare to Northern blotting for two tRNA transcripts, showing a directionally similar result; however, no quantitative claims can be made measurement accuracy. Until a good method of normalizing tRNA quantification is found, we believe that sample-to-sample reproducibility (i.e. precision) is the most useful objective to optimize because this will allow detection of differential expression. Towards that end, we quantified the precision of our method (Figure 4 and its two supplementary figures) with associated statistics, which can be used to estimate the number of samples required to detect significance during differential expression analysis. For tRNA charge, quantification is easier, which is why we present statistics on both accuracy and precision. In this case we can better compare results across methods, and so we have added a comparison of our results to the charge quantification from Evans et al. (2017) (Figure 2—figure supplement 9).

      (2) While the protocol aims to implement an improved method for quantification of tRNA aminoacylation, it can also be used for tRNA quantification and analysis of tRNA modifications. It will increase the impact of this study if the authors benchmark the outcomes of their protocol with other tRNA sequencing protocols with samples similar to these papers, which will be important for certain research teams that are unlikely to implement two different tRNA sequencing methods. Are there any possible adaptations that would allow the analysis of tRNA fragments?

      The first part of this comment regarding comparison of methods is addressed in response to in the prior reviewer comment and in the response to reviewer 1. In the specific case of tRNA modifications, the issue is similar to abundance quantification in that a “true” reference of modified tRNA is likely necessary for proper quantification, alongside testing of each method simultaneously.

      Regarding tRNA fragments, our method is not suitable for this use case. This is because our adapter ligation step depends on an intact tRNA structure with either CCA or CC overhang on the 3’-end and thus we almost exclusively get reads with CCA/CC ends and no reads from fragments. This specificity is good for increasing charge quantification accuracy but not good for the methods versatility. For a more versatile method we recommend Watkins et al. (2022) [PMID: 35513407].

      (3) Like Behrens et al. (2021), Davidsen and Sullivan use TGIRT-III RT for their analyses. The enzyme is not currently available in a form suitable for tRNA-seq. It would be very helpful to test different new RT enzymes that are commercially available. The example of Maxima RT - Figure 2 Supp 6 - shows significantly lower performance than the presented TGIRT-III RT data. In lines 296-298, the authors mention improvements to the protocol by using ornithine. Why are these improvements not included?

      We share similar concerns that the TGIRT-III enzyme is no longer commercially available. It became unavailable while we were preparing this manuscript, reflected by the fact that almost all our figures are made using this enzyme. Others have discovered this too and Lucas et al. (2023) [PMID: 37024678] tested several RT polymerases using TapeStation as a readout for readthrough. As they reported that Maxima has good performance, we decided to test it on a full run with replicates. The results are outlined in Figure 2—figure supplement 6 and for resubmission we have added a table to the appendix that compares the alignment statistics. Unfortunately, the readthrough of the Maxima polymerase on cytoplasmic tRNAs is not as high as for TGIRT-III; however, interestingly it seems to have better performance for mitochondrial tRNAs (Figure 2 – Figure Supplement 6). Regardless, in the initial paper submission we failed to evaluate whether this readthrough difference affected charge measurements. We have now fixed this by adding Figure 2—figure supplement 7, which shows that there are no differences in charge measurements TGIRT-III vs. Maxima. Not surprisingly, there are substantial differences between polymerases when looking at relative tRNA abundance (which affirms the discussion above related to the difficulty of tRNA abundance quantification); however, the high sample-to-sample reproducibility remains intact with either polymerase. An exhaustive search for better polymerases is warranted but falls outside the scope of our work.

      Regarding the improvements suggested by us, using ornithine as a cleavage catalyst instead of lysine, we first learned about this possibility later and thus only want to make readers aware that other options exist. We have clarified the paragraph to make this clearer.

      (4) A technical concern: The samples are purified multiple times using a specific RNA purification kit. Did the authors test different methods to purify the RNA and does this influence the result of the method?

      In the past, we have relied exclusively on alcohol precipitation but during the development of this protocol we found it easier and more reproducible to use column-based purification when possible. However, as we have not made a direct comparison this remains anecdotal evidence. Nonetheless, to minimize any possible bias of column-based purification you will notice that we use columns with binding capacity 5x higher than the highest amount of RNA/DNA added to the column.

      (5) The study would benefit from an explicit step-by-step protocol, including the choice of adapters that are shown to work best in the protocol.

      This is a great point! We have included tables with all the oligos used (Supplementary file 1), a detailed step-by-step protocol with pictures of anticipated gel results (Supplementary file 2) and an overview of the RNA/DNA manipulations to make it clear where adapter sequences are located (Supplementary file 3). For the data processing we provide a comprehensive example in the Github repository. All this was included in our first submission of this manuscript (as well as on bioRxiv), but we suspect this was not readily accessible to the reviewers. We will make sure that these documents are going to be available through eLife and have emphasized their existence in the main text of the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      To stratify this improvement a comparison to the most common methods should be made. For example, how do the results with the improved protocol with i-tRAP (PMID 36283829), tRNA microarrays (PMID: 31263264), or with the approaches the improved protocol shares with some other tRNA-seq approaches (DM-tRNA-seq, mim-tRNA-seq)?

      Once again, we thank the reviewer for the good recommendations. The points about direct comparisons were discussed above.

      Reviewer #2 (Recommendations For The Authors):

      These are all great points; we address them below.

      Minor points:

      - Please use chemical conventions, e.g. for mcm5s2U and NaIO4 with superscript or subscript.

      Fixed.

      - Figure 2F: Glu GAA is only 82% charged; can this be due to mcm5s2U (Figure 3 supp 2) leading to a misalignment? What happens to Ser-NNN? Why is mitochondrial tRNA so much less charged?

      Regarding the Glu-GAA charge at baseline, we do not think this is an artifact of the mcm5s2U modification as it would then also be expected for Gln-CAA and Lys-AAA. The same occurs in the charge data in Evans et al. (2017) and they use a very different alignment strategy. Lastly, the charge titration and half-life experiments show no evidence of inaccuracy/bias for Glu-GAA.

      But the question remains – why is the charge of Glu-GAA so low? At this point our best guess is speculative. It may have something to do with the strong enrichment of Glu-GAA codons in the A site found by ribosome profiling on mouse embryonic stem cells (Ingolia et al. (2011) [PMID: 22056041]).

      - Spell out "clvg" or "dphs" in the figure legend of Figure 2 and others. Similar for other abbreviations in figures. They are not always explained in the legends.

      Fixed.

      - Figure 3 supp 2: Please use U instead of T in the anticodons. The labels are a bit confusing. Please clearly align to the tick (also for Figure 3C).

      Fixed.

      - Line 220-223. Which RT enzyme was used for Figure 3 supp 2? Does it make a difference?

      TGIRT-III was used. Only Figure 2—figure supplement 6 and Figure 2—figure supplement 7 (added for resubmission) show data with the Maxima polymerase. To address the second part of the question we have added a comparison between TGIRT-III and Maxima for mcm5s2U modification detection (Figure 3—figure supplement 3). Interestingly, there is a polymerase specific signature for mcm5s2U modifications; however, more work would be required to determine which polymerase is best suited for detection of this and other modifications.

      - Figure 4 supp 1 and Figure 4 supp 2 change order.

      Fixed.

      Typos:

      - Figure 1 and Figure 1-figure supplement 1: In the periodate the "-" is in a small box (at least in my PDF viewer). Can this box be removed?

      - Line 175: duplicated verb.

      - Line 348: "moved".

      Thanks for catching these. They have now been fixed.

    1. Author response:

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

      Reviewer #1 (Public Review):

      (1) Measurement of secreted amylase could be seen as direct evidence of sweating, however, how to determine the causal relationship between climbing behavior and sweating? Friction force may also be reduced when there is too much fingertip moisture.

      As the reviewer notes, measurement of secreted amylase can provide direct evidence of sweating, and we performed an iodine and starch reaction. Upon observing the involvement of TRPV4 in mouse foot pad perspiration, we then considered which type of behavioral analysis would be suitable to evaluate this perspiration. We agree with the reviewer’s point that friction force in the climbing test may be reduced by excessive sweating. However, we did not observe severe sweating in the absence of acetylcholine treatment. Accordingly, we interpreted that the increase in the climbing test failure rate for TRPV4KO mice could reflect the reduced friction force associated with the lack of TRPV4 activity.

      (2) For the human skin immunostaining, did the author use the same TRPV4 antibody as used in the mouse staining? Did they validate the specificity of the antibody for the human TRPV4 channel? 

      We used different antibodies for human and mouse samples. Since commercially available anti-TRPV4 antibodies do not work well with mouse samples, we generated our own anti-TRPV4 antibody and validated its specificity.

      (3) In lines 116-117, the authors tried to determine "the functional interaction of TRPV4 and ANO1 is involved in temperature-dependent sweating", however, they only used the TRPV4 ko mice and did not show any evidence supporting the relationship between TRPV4 and ANO1. 

      As the reviewer pointed out, based on the data presented in the original submission we cannot conclude that an interaction between TRPV4 and ANO1 is involved in perspiration. However, we think that the data for TRPV4KO mice presented in Figure 3 of the original version does indicate that TRPV4 is involved in perspiration. The finding that menthol and its related compounds, which inhibit the function of both TRPV4 and ANO1 (see our publication in Scientific Reports 7: 43132, 2017), blocked perspiration in both wild-type and TRPV4KO mice (original Figure 3C, D) indicates involvement of either TRPV4 or ANO1 in perspiration. In the revised version, we present results for additional iodine and starch reaction experiments using Ani9, a potent and specific ANO1 inhibitor. Ani9 drastically inhibited perspiration from mouse food pads both at 25 °C and 35 °C. Based on these collective results, we concluded that both TRPV4 and ANO1, likely acting as a complex, are involved in perspiration. We present the new data with Ani9 in the revised Figure 3E, F.

      (4) Figure 3-4 is quite confusing. At 25˚C, no sweating difference was observed between TRPV4 and wt mice (Fig 3A-3D), suggesting both Ach-induced sweating and basal sweating are TRPV4-independent at 25˚C, however, the climbing test was done at 26-27 ˚C and the data showed a climbing deficit in TRPV4 ko mice. How to interpret the data is unclear. 

      Thank you for raising this point. In the iodine and starch reaction experiment, we observed no significant reduction in perspiration in the absence of acetylcholine at 25 °C, which is the same condition as in the climbing test, whereas we detected less perspiration for TRPV4KO mice. In a trial using additional mice, we detected significantly less perspiration under control conditions without acetylcholine at 25 °C, which is consistent with the results of the climbing test. We have added this new data to the revised Figure 3A, B.

      (5) Were there any gender differences associated with sweating in mice? In Figure 3, the mouse number for behavior tests should be at least 5. 

      The TRPV4KO mice reproduced poorly and we were unable to obtain sufficient numbers of male and female mice to determine whether there were gender differences in sweating. However, according to the reviewer’s suggestion, and as mentioned above, we increased the number of experiments to obtain the results shown in the revised Figure 3. We did not a observe a significant difference in sweating with the larger sample size, which supports our conclusions.

      (6) 8- to 21-week-old mice were used in the immunostaining, the time span is too long. 

      Given the difficulty in obtaining sufficient numbers of TRPV4KO mice, we used a somewhat wider age distribution to obtain samples for immunostaining. However, we did not observe age-dependent differences in immunostaining. We reference this point in the revised manuscript.

      (7) The authors used homozygous TRPV4 ko mice for all experiments. What are control mice? Are they littermates of the TRPV4 ko mice? 

      We did not use littermates for our in vivo experiments because the TRPV4KO mice reproduced poorly and the litter sizes were small. However, we did backcross the KO mice to the commercially available wild-type mice more than ten times. As such, we expect that the wild-type and TRPV4KO mice will have similar genetic backgrounds. In addition, we have published multiple studies that have successfully used this method, which we think supports the reliability of our results for experiments involving mice.

      Reviewer #2 (Public Review):

      (1) The coexpression data needs additional controls. In the TRPV4 KO mice, there appears to be staining with the TRPV4 Ab in TRPV4 KO mice below the epidermis. This pattern appears similar to that of the location of the secretory coils of the sweat glands (Fig 1A). Is the co-staining the authors note later in Figure 1 also seen in TRPV4 KOs? This control should be shown, since the KO staining is not convincing that the Ab doesn't have off-target binding. 

      We thank the reviewer for raising these concerns about immunostaining. As the reviewer notes, in the low power image the signals appeared to be weak and punctate signals were present in the basal region of glandular cells. Although we did not identify immunohistochemical conditions that produced no signal, tissue sections from WT mice stained with anti-TRPV4 antibody showed conspicuous apical signals for the glandular cells facing lumen. Meanwhile, TRPV4KO tissues showed no signals at the apical region of the glandular cells, where the TRPV4-ANO1 interaction is expected to occur. We confirmed no trace signals in the TRPV4KO tissues in the immunoblotting.

      (2) Are there any other markers besides CGRP for dark cells in mice to support the conclusion that mouse secretory cells have clear cell and dark cell properties? 

      We did not stain with other dark cell markers. Based on previous studies describing the differences between clear and dark cells in mouse eccrine glands, we think that dark and clear cells cannot be clearly discriminated, as we described in lines 93-96 of the Results. We identified secretory cells using CK8 and dark cells with CGRP, a marker of dark cells in human eccrine glands (Zancanaro et al. 1999 J Anat). Our result showed that CGRP immunostaining could not discriminate between clear and dark cells, which is consistent with a previous report showing that mouse secretory cells were assumed to be undifferentiated and primitive based on electron microscopic observation (Kurosumi et al. 1970 Arch Histol Jap).

      (3) The authors utilize menthol (as a cooling stimulus) in several experiments. In the discussion, they interpret the effect of menthol as potentially disrupting TRPV4-ANO1 interactions independent of TRPM8. Yet, the role of TRPM8, such as in TRPM8 KO mice, is not evaluated in this study.

      We performed the iodine and starch reaction experiments with TRPM8KO mice. In the TRPM8KO mice, the sweat spots did not differ from those seen for WT mice (p=0.63, t-test), and there was also a significant reduction in sweating with menthol treatment following acetylcholine stimulation that was similar to that seen for WT mice. These results would rule out the involvement of TRPM8 in a menthol-induced reduction in sweating. We have included this data in the revised Figure 3D.

      (4) Along those lines, the authors suggest that menthol inhibits eccrine function, which might lead to a cooling sensation. But isn't the cooling sensation of sweating from evaporative cooling? In which case, inhibiting eccrine function may actually impair cooling sensations.

      Menthol has a non-specific effect that activates TRPM8, TRPV3 and TRPA1, and inhibits TRPV1, TRPV4 and ANO1. Therefore, we did not carry out a climbing test with menthol in part because menthol-dependent TRPA1 activation decreased the propensity of the mice to climb. As the reviewer notes, TRPM8 activation following topical application of menthol may cause a cooling sensation elicited in sensory neurons beneath the skin. However, the comfortable cooling sensation could also be caused in part by decreased sweating. The relationship between a comfortable cooling sensation and less perspiration following menthol application may be difficult to determine, and we have mentioned this in the updated Discussion.

      (5) The climbing assay is interesting and compelling. The authors note performing this under certain temperature and humidity conditions. Presumably, there is an optimal level of skin moisture, where skin that is too dry has less traction, but skin that is too wet may also have less traction. It would bolster this section of the study to perform this assay under hot conditions (perhaps TRPV4 KO mice, with impaired perspiration, would outperform WT mice with too much sweating?), or with pharmacologic intervention using TRPV4 agonists or antagonists to more rigorously evaluate whether this model correlates to TRPV4 function in the setting of different levels of perspiration.

      We thank the reviewer for this suggestion. Upon detecting the involvement of TRPV4/ANO1 interaction in perspiration, we considered different behavioral analyses that can be performed to demonstrate whether the TRPV4/ANO1 interactions are involved in perspiration. As the reviewer suggested, there should be an optimal level of sweating. Therefore, we first set the room temperature at 26-27 ˚C and humidity at 35-50%. To our knowledge, this is the first demonstration of temperature-dependent sweating of mouse foot pads. In humans, palm sweating is often referred to as psychotic sweating that is known to be regulated by sympathetic nerve activity. Here we tested whether foot pad sweating might be related to friction force wherein sufficient amounts of sweating could increase the friction force and in turn increase the success rate for the climbing test using a vinyl-covered slippery slope that was selected based on several trials to determine the optimal surface material and slope angles. As the reviewer suggests, the success rates could be affected by multiple factors, and hot temperatures likely induce more sweating that could increase the success rates in the climbing test. We will need to carry out additional experiments that are beyond the scope of this study to examine these temperature-dependent effects. Generally, sweating is regulated by sympathetic nerve activity that occurs in response to increased brain neuron excitation. However, here we raise for the first time the possibility that sweating might be regulated by local temperature sensation mediated through TRPV4 that may be effective for fine-tuning of perspiration activity. We have updated the Discussion to reference this possibility.

      (6) There are other studies (PMID 33085914, PMID 31216445) that have examined the role of TRPV4 in regulating perspiration. The presence of TRPV4 in eccrine glands is not a novel finding. Moreover, these studies noted that TRPV4 was not critical in regulating sweating in human subjects. These prior studies are in contradiction to the mouse data and the correlation to human anhidrotic skin in the present study. Neither of these studies is cited or discussed by the authors, but they should be. 

      We thank the reviewer for referencing these other studies concerning the possible involvement of TRPV4 in perspiration in humans. These studies focused on the vasodilating effects of TRPV4 and drew the conclusion that TRPV4 is not involved in sweating in humans, which is in contrast to our data for mice and humans. Multiple factors could explain the apparent difference between the two studies. For example, the parameters they examined differed from ours in that we assessed patients with AIGA, whereas the previous studies involved healthy volunteers. We have updated the Discussion to note the difference in the results of our and previous studies.   

      Reviewer #3 (Public Review):

      (1) Figure 2: The calcium imaging-based approach shows average traces from 6 cells per genotype, but it was unclear if all acinar cells tested with this technique demonstrated TRPV4-mediated calcium influx, or if only a subset was presented.

      “n = 6” does not indicate the number of cells, but rather 6 independent experiments that each had over 20 ROIs of sweat glands. We have clarified this point in the updated figure legend.

      (2) Figure 4: The climbing behavioral test shows a significant reduction in climbing success rate in TRPV4-deficient mice. The authors ascribe this to a lack of hind paw 'traction' due to deficiencies in hind paw perspiration, but important controls and evidence that could rule out other potential confounds were not provided or cited. 

      As noted in our response to Comment 5 made by Reviewer #2, we spent considerable time identifying optimal conditions that would delineate success rates in the climbing experiments. We are confident that TRPV4KO mice had significantly lower success rates than WT mice, but there are various factors that could affect the experimental outcomes. We reference these factors in the updated Discussion.

      (3) In general, the results support the authors' claims that TRPV4 activity is a necessary component of sweat gland secretion, which may have important implications for controlling perspiration as well as secretion from other glands where TRPV4 may be expressed. 

      As described above, the results we obtained in the climbing test can be affected by various factors. However, based on the consistency of the results obtained for the climbing test and the iodine and starch reaction assay, we think that our interpretation is correct. In terms of the involvement of TRPV4/ANO1 interactions in fluid secretion, we previously reported that the TRPV4/ANO1 complex is involved in cerebrospinal fluid secretion in the mouse choroid plexus (FASEB J. 2014) and in saliva and tear secretion in mouse salivary and lacrimal glands (FASEB J. 2018). Together, these findings suggest that this mechanism is common to water efflux from exocrine glands.

      Reviewer #1 (Recommendations For The Authors):

      (1) An exocrine gland-specific trpv4 knockout mouse should be used, as TRPV4 is also expressed by muscles, global knockout TRPV4 may affect the TRPV4-dependent muscle strength and reduce the climbing ability in mice. 

      As the reviewer suggests, use of mice with TRPV4 knockout specific to exocrine glands would be preferable to mice having global TRPV4 knockout given that TRPV4 is expressed in multiple tissues. We agree with this suggestion, but we do not currently have such mice in hand. However, as mentioned above, we have reported the involvement of theTRPV4/ANO1 interaction in cerebrospinal fluid secretion from the choroid plexus in mice (FASEB J. 28: 2238-2248, 2014), as well as saliva and tear secretion in mouse salivary and lacrimal glands (FASEB J. 32: 1841-1854, 2018.), suggesting that the TRPV4/ANO1 interaction could be widely involved in exocrine gland functions that involve water movement. We have updated the Discussion to reference this point.  

      (2) The authors showed Calcium imaging data that Menthol inhibits TRPV4-dependent calcium influx. However, it is well known that menthol induces the sensation of cooling by activating TRPM8. More evidence, including patch clamp recordings, should be done to verify the inhibition effects of menthol on TRPV4 and ANO1. Moreover, Fig 3E-3F could only suggest that menthol-induced cooling sensation may affect sweating but not the inhibition effect of menthol on TRPV4 and ANO1 channels. 

      We agree that more evidence including patch-clamp recordings can verify the inhibitory effects of menthol on TRPV4 and ANO1. We did not include such experiments here since we previously showed that menthol and related agents indeed inhibit TRPV4- and ANO1-mediated currents (Sci. Rep. 7: 43132, 2017). We now cite this paper in the revised version.

      (3) Excepting the climbing test, are there any other better models to asses the sweating-related behaviors? 

      When we detected the involvement of TRPV4/ANO1 interactions in perspiration, we considered different types of behavioral analyses that could be used to demonstrate TRPV4/ANO1-dependent perspiration. We think that the climbing experiment is the best test, particularly since foot pads are one of the few regions on mice that is not covered by fur and thus amenable to evaluation of perspiration using an iodine and starch test.

      Reviewer #2 (Recommendations For The Authors):

      (1) I was confused by a section in the introduction on lines 59-60: How does Cl- efflux lead to the formation of a physical complex in cells with high intracellular Cl-? What is the physical complex? This seems like several disparate concepts combined together, which need to be clarified.

      We apologize for the incomplete descriptions of several of our previous works. We have amended the Introduction section in the revised manuscript.

      Reviewer #3 (Recommendations For The Authors):

      (1) TRPV4 is expressed by multiple other cell types in the skin (keratinocytes, macrophages etc.) which may have an impact on peripheral sensory function. Is there evidence that TRPV4-deficient animals have relatively normal sensory acuity and/or proprioception? Such evidence would lend more credibility to the reported findings in the climbing test. 

      As the reviewer points out, TRPV4 is expressed by multiple other cell types in the skin. To date we have found that TRPV4KO mice show no differences in sensory functions compared to WT mice. Whether TRPV4 is involved in proprioception is unclear, based on both our own observation and those that appear in the literature, although TRPV4 is clearly activated by mechanical stimuli. We previously compared the mechanical sensitivity of TRPV4 and Piezo1 in bladder epithelial cells, and found that Piezo 1 shows much higher sensitivity relative to TRPV4 (J. Biol. Chem. 289: 16565-16575, 2014), which is consistent with the involvement of Piezo1, rather than TRPV4, in proprioception. Although TRPV4 is reported to be expressed in sensory neurons, we did not detect TRPV4-mediated responses in isolated rat and mouse DRG neurons, suggesting that TRPV4-positive sensory neurons are relatively rare.

      (2) The methods section refers to loading entire sweat glands with Fura-2 dye for calcium imaging, but the figure legend refers to sweat gland acinar cells. Resolving this ambiguity would help readers to interpret the data. 

      We apologize for this error and have made an appropriate correction in the revised manuscript.

      (3) Alternatively, could acute intraplantar injection of a TRPV4 antagonist (e.g. GSK205) in wild-type mice phenocopy the TRPV4-knockout mouse deficits, or could normal climbing behavior be restored in the TRPV4 knockout by adding artificial perspiration to their hindpaws?

      We thank the reviewer for raising this interesting possibility and suggesting use of TRPV4 agonists or antagonists in the climbing tests. We agree that results of such an experiment would support the involvement of TRPV4 in sweating. We tried to do such experiments using injection of TRPV4 regulators into mouse hindpaws. However, the injections themselves appeared to impact climbing ability, perhaps in part due to painful sensations associated with the injection. Similarly, menthol injection appeared to reduce climbing activity, likely through pain sensations associated with TRPA1 activation. As such, we did not pursue these experiments.

    1. Author Response:

      We sincerely value the insightful and constructive feedback provided by the reviewers, which has been instrumental in identifying areas of our manuscript that required further clarification or amendment. Below are our responses detailing each comment.

      Reviewer 1:

      (1) One major issue arises in Figure 4, the recording of VLPO Ca2+ activity. In Lines 211-215, they stated that they injected AAV2/9-DBH-GCaMP6m into the VLPO, while activating LC NE neurons. As they claimed in line 157, DBH is a specific promoter for NE neurons. This implies an attempt to label NE neurons in the VLPO, which is problematic because NE neurons are not present in the VLPO. This raises concerns about their viral infection strategy since Ca activity was observed in their photometry recording. This means that DBH promoter could randomly label some non-NE neurons. Is DBH promoter widely used? The authors should list references. Additionally, they should quantify the labeling efficiency of both DBH and TH-cre throughout the paper.

      (1) In Figure 5, we found that the VLPO received the noradrenergic projection from LC, indicating the recorded Ca2+ activity may come from the axon fibers corresponding to the projection. Similarly, Gunaydin et al. (2014) demonstrated that fiber photometry can be used to selectively record from neuronal projection.

      (2) Located in the inner membrane of noradrenergic and adrenergic neurons, DBH (Dopamine-beta-hydroxylase) is an enzyme that catalyzes the conversion of dopamine to norepinephrine, and therefore plays an important role in noradrenergic neurotransmission. DBH is a marker of noradrenergic neurons. Zhou et al. (2020) clarified the probe specifically labeled noradrenergic neurons by immunolabeling for DBH. Recently, DBH promoter have been used in several studies (e.g., Han et al., 2024; Lian et al., 2023). The DBH-Cre mice are widely used to specifically labeled noradrenergic neurons (e.g., Li et al., 2023; Breton-Provencher et al., 2022; Liu et al., 2024). As reviewer said, it is difficult to distinguish the role of NE or DA neurons when using the TH promoter in VLPO. Therefore, we used DBH promoter with more specific labeling. LC is the main noradrenergic nucleus of the central nervous system. In our study, we injected rAAV-DBH-GCaMP6m-WPRE (Figure 2 and 8) and rAAV-DBH-EGFP-S'miR-30a-shRNA GABAA receptor)-3’-miR30a-WPRES (Figure 9) into the LC. The results showed that DBH promoter could specifically label noradrenergic neurons in the LC, while non-specific markers outside the LC were almost absent. As suggested, we will quantify the labeling efficiency of both DBH and TH-cre throughout the revised manuscript. This updated figure will provide a more rigorous analysis.

      (2) A similar issue arises with chemogenetic activation in Fig. 5 L-R, the authors used TH-cre and DIO-Gq virus to label VLPO neurons. Were they labelling VLPO NE or DA neurons for recording? The authors have to clarify this.

      As previously addressed in response to Comment #1, we acknowledge that it is difficult to distinguish the role of NE or DA neurons when using the TH promoter in VLPO. In the revised manuscript, we are considering conducting more restricted AAV injections into the VLPO to verify terminal expressions in the LC.

      (3) Another related question pertains to the specificity of LC NE downstream neurons in the VLPO. For example, do they preferentially modulate GABAergic or glutamatergic neurons?

      As suggested, we will supplement the multi-label ISH of LC NE downstream neurons in the VLPO to reveal the types of neurons they modulate.  

      (4) In Figure 1A-D, in the measurement of the dosage-dependent effect of Mida in LORR, were they only performed one batch of testing? If more than one batch of mice were used, error bar should be presented in 1B. Also, the rationale of testing TH expression levels after Mid is not clear. Is TH expression level change related to NE activation specifically? If so, they should cite references.

      (1) As recommended, we will supplement error bar in the revised manuscript.

      (2) As reviewer suggested, the use of TH as a marker of NE activation is controversial, so in the revised manuscript, we will directly determine central norepinephrine content.

      (5) Regarding the photometry recording of LC NE neurons during the entire process of midazolam injection in Fig. 2 and Fig. 4, it is unclear what time=0 stands for. If I understand correctly, the authors were comparing spontaneous activity during the four phases. Additionally, they only show traces lasting for 20s in Fig. 2F and Fig. 4L. How did the authors select data for analysis, and what criteria were used? The authors should also quantify the average Ca2+ activity and Ca2+ transient frequency during each stage instead of only quantifying Ca2+ peaks. In line 919, the legend for Figure 2D, they stated that it is the signal at the BLA; were they also recorded from the BLA?

      (1) In this study, we used optical fiber calcium signal recording, which is a fluorescence imaging based on changes in calcium. The fluorescence signal is usually divided into different segments according to the behavior, and the corresponding segments are orderly according to the specific behavior event as the time=0. The mean calcium fluorescence signal in the time window 1.5s or 1s before the event behavior is taken as the baseline fluorescence intensity (F0), and the difference between the fluorescence intensity of the occurrence of the behavior and the baseline fluorescence intensity is divided by the difference between the baseline fluorescence intensity and the offset value. That is, the value ΔF/F0 represents the change of calcium fluorescence intensity when the event occurs. The results of the analysis are commonly represented by two kinds of graphs, namely heat map and event-related peri-event plot (e.g., Cheng et al., 2022; Gan-Or et al., 2023; Wei et al., 2018). In Fig. 2, the time points for awake, midazolam injection, LORR and RORR in mice were respectively selected as time=0, while in Fig. 4, RORR in mice was selected as time=0. The selected traces lasting for 20s was based on the length of a complete Ca2+ signal. We will explain the Ca2+ recording experiment more specifically in the revised manuscript.

      (2) To the BLA, we sincerely apologize for our carelessness, the signal we recorded were from the LC rather than the BLA. We will carefully check and correct similar problems in the revised manuscript.

      Reviewer 2:

      In figure legends, abbreviations in figure should be supplemented as much as possible. For example, "LORR" in Figure 1.

      As suggested, we will supplement abbreviations in figure as much as possible in the revised manuscript.

      References

      Gunaydin LA, Grosenick L, Finkelstein JC, et al. Natural neural projection dynamics underlying social behavior. Cell. 2014;157(7):1535-1551. doi:10.1016/j.cell.2014.05.017

      Zhou N, Huo F, Yue Y, Yin C. Specific Fluorescent Probe Based on "Protect-Deprotect" To Visualize the Norepinephrine Signaling Pathway and Drug Intervention Tracers. J Am Chem Soc. 2020;142(41):17751-17755. doi:10.1021/jacs.0c08956

      Han S, Jiang B, Ren J, et al. Impaired Lactate Release in Dorsal CA1 Astrocytes Contributed to Nociceptive Sensitization and Comorbid Memory Deficits in Rodents. Anesthesiology. 2024;140(3):538-557. doi:10.1097/ALN.0000000000004756

      Lian X, Xu Q, Wang Y, et al. Noradrenergic pathway from the locus coeruleus to heart is implicated in modulating SUDEP. iScience. 2023;26(4):106284. Published 2023 Feb 27. doi:10.1016/j.isci.2023.106284

      Li C, Sun T, Zhang Y, et al. A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice. Neuron. 2023;111(17):2727-2741.e7. doi:10.1016/j.neuron.2023.05.023

      Breton-Provencher V, Drummond GT, Feng J, Li Y, Sur M. Spatiotemporal dynamics of noradrenaline during learned behaviour. Nature. 2022;606(7915):732-738. doi:10.1038/s41586-022-04782-2

      Liu Q, Luo X, Liang Z, et al. Coordination between circadian neural circuit and intracellular molecular clock ensures rhythmic activation of adult neural stem cells. Proc Natl Acad Sci U S A. 2024;121(8):e2318030121. doi:10.1073/pnas.2318030121

      Cheng J, Ma X, Li C, et al. Diet-induced inflammation in the anterior paraventricular thalamus induces compulsive sucrose-seeking. Nat Neurosci. 2022;25(8):1009-1013. doi:10.1038/s41593-022-01129-y

      Gan-Or B, London M. Cortical circuits modulate mouse social vocalizations. Sci Adv. 2023;9(39):eade6992. doi:10.1126/sciadv.ade6992

      Wei YC, Wang SR, Jiao ZL, et al. Medial preoptic area in mice is capable of mediating sexually dimorphic behaviors regardless of gender. Nat Commun. 2018;9(1):279. Published 2018 Jan 18. doi:10.1038/s41467-017-02648-0

    1. Author response:

      Reviewer 1:

      A limit of the paper is that the biological mechanisms by which intracellular mechanics is modulated (e.g. among cell types) remains unexplored and only briefly discussed. Yet this limit is greatly offset by the rigor of the approach.

      We thank the reviewer for the valuable feedback. The question regarding the biological mechanisms responsible for the different mechanical properties is, indeed, a highly important and interesting issue. In line with the reviewer, we consider this so important that it requires an extra, dedicated research focus, which is far beyond the scope of this article. By introducing the concept of the mechanical fingerprint, we provide in this work the framework to systematically investigate biological mechanisms but also the functional relevance of the intracellular mechanical properties in future studies. In the revised manuscript, we’ll elaborate on the discussion.

      Reviewer 2:

      The most difficult part of the method is the part with actin polymerization inhibition with cytochalasin B. The data shows that viscoelastic parameters as well as active energy parameters are unaffected by cytochalasin B. It is reasonable to expect that elasticity will reduce and fluidity will increase upon application of such a drug. The stiffness-reducing effect was observed only when CB was used with nocodazole most likely because of phagocytosis of the bead, which is governed by microtubule. The use of other actin-depolymerizing drugs such as latrunculin A would be needed to test actin’s role in mechanical fingerprints. If actin’s role is only explained by accompanying microtubule inhibition, it is not a convenient system to directly test the mechano-adaptation process.

      We thank the reviewer for the time and the instructive feedback. Our finding that actin depolymerization has no effect on the intracellular mechanics may appear unfamiliar, as many rheological studies performed on the cell’s cortex highlight the importance of actin on the mechanical properties of the whole cell. However, as the actin network is reported to be very sparse away from the cortex it is not impossible that the mechanical properties may be dominated by other structures in the cytoplasm. Indeed, our findings are consisted with other studies that see no strong effect of actin depolymerization on the interphase intracellular mechanics (e.g. https://doi.org/10.1016/j.bpj.2023.04.011 or https://doi.org/10.1038/s41567-021-01368-z). Still, we fully agree with the reviewers that this is an important point. In a revised version we aim to investigate the effect of other actin-depolymerizing drugs and will try to perform immunostaining to visualize and further illuminate the potential compensation mechanism between actin and MT.

      Depolymerization of MT with nocodazole did not reduce the solid-like property A. Adding discussion and comparison with other papers in the literature using nocodazole will be helpful in understanding why.

      Again, we agree with the reviewer and propose to further study this point by performing additional immunostainings and by elaborating on the discussion, also including the results of other studies.

      Reviewer 3:

      The importance of the mechanical fingerprint is diluted due to some missing controls needed for biological relevance.

      We thank the reviewer for his valuable time and feedback. This comment is in line with the point already raised by reviewer 1 and highlights the important question of how the intracellular mechanical properties are related to the actual cell function. We fully agree with the reviewers that at this point we can only report on differences, but cannot claim a biological function that is depending on the fingerprint. Although we think the alignment between function and the mechanical fingerprints allows the hypothesis that the biological system is tuning its mechanical properties for a specific function, we do not want to make any claim in this direction at the current state of our research. Hence, to answer these intriguing questions, carefully designed control experiments are required, as pointed out by the reviewer. However, this direction is not the scope of this manuscript. Here, we establish the tools we’ll use in future studies to address these highly relevant questions. Therefore, we propose to discuss these important future directions in a revised manuscript.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Kroll et al. conduct an in-depth behavioral analysis of F0 knockouts of 4 genes associated with late-onset Alzheimer's Disease (AD), together with 3 genes associated with early- onset AD. Kroll and colleagues developed a web application (ZOLTAR) to compare sleep-associated traits between genetic mutants with those obtained from a panel of small molecules to promote the identification of affected pathways and potential therapeutic interventions. The authors make a set of potentially important findings vis-à-vis the relationship between AD-associated genes and sleep. First, they find that loss-of-function in late-onset AD genes universally results in nighttime sleep loss, consistent with the well-supported hypothesis that sleep disruption contributes to Alzheimer's-related pathologies. psen-1, an early-onset associated AD gene, which the authors find is principally responsible for the generation of AB40 and AB42 in zebrafish, also shows a slight increase in activity at night and slight decreases in nighttime sleep. Conversely, psen-2 mutations increase daytime sleep, while appa/appb mutations have no impact on sleep. Finally, using ZOLTAR, the authors identify serotonin receptor activity as potentially disrupted in sorl1 mutants, while betamethasone is identified as a potential therapeutic to promote reversal of psen2 knockout-associated phenotypes.

      This is a highly innovative and thorough study, yet a handful of key questions remain. First, are nighttime sleep loss phenotypes observed in all knockouts for late-onset AD genes in the larval zebrafish a valid proxy for AD risk?

      We cannot say, but it is an interesting question. We selected the four late-onset Alzheimer’s risk genes (APOE, CD2AP, CLU, SORL1) based on human genetics data and brain expression in zebrafish larvae, not based on their likelihood to modify sleep behaviour, which we could have tried by searching for overlaps with GWAS of sleep phenotypes, for example. Consequently, we find it remarkable that all four of these genes caused a night-time sleep phenotype when mutated. We also find it reassuring that knockout of appa/appb and psen2 did not cause a night-time sleep phenotype, which largely excludes the possibility that the phenotype is a technical artefact (e.g. caused by the F0 knockout method) or a property of every gene expressed in the larval brain.

      Having said that, it could still be a coincidence, rather than a special property of genes associated with late-onset AD. In addition to testing additional late-onset Alzheimer’s risk genes, the ideal way to answer this question would be to test in parallel a random set of genes expressed in the brain at this stage of development. From this random set, one could estimate the proportion of genes that cause a night-time sleep phenotype when mutated. One could then use that information to test whether late-onset Alzheimer’s risk genes are indeed enriched for genes that cause a night-time sleep phenotype when mutated.

      For those mutants that cause nighttime sleep disturbances, do these phenotypes share a common underlying pathway? e.g. Do 5-HT reuptake inhibitors promote sleep across all 4 late-onset genes in addition to psen1? Can 5-HT reuptake inhibitors reverse other AD-related pathologies in zebrafish? Can compounds be identified that have a common behavioral fingerprint across all or multiple AD risk genes? Do these modify sleep phenotypes?

      To attempt to answer these questions, we used ZOLTAR to generate predictions for all the knockout behavioural fingerprints presented in the study, in the same way as for sorl1 in Fig. 5 and Fig. 5–suppl. 1. Here are the indications, targets, and KEGG pathways which are shared by the largest number of knockouts:

      – Four indications are shared by 4/7 knockouts: “mydriasis” (dilated pupils, significant for psen1, apoea/apoeb, cd2ap, clu); “fragile X syndrome” (psen1, apoea/apoeb, cd2ap, sorl1), “insomnia” (psen2, apoea/apoeb, cd2ap, sorl1); “malignant essential hypertension” (appa/appb, psen1, apoea/apoeb, cd2ap).

      – Two targets are shared by 5/7 knockouts: “glycogen synthase kinase−3 alpha” (psen1, apoeab, cd2ap, clu, sorl1) and “neuronal acetylcholine receptor beta−2” (appa/appb, psen1, apoeab, cd2ap, clu).

      – Two KEGG pathways are shared by 5/7 knockouts: “cholinergic synapse” (psen1, apoea/apoeb, cd2ap, clu, sorl1) and “nitrogen metabolism” (appa/appb, psen1, psen2, cd2ap, clu).

      As reminder, we hypothesised that loss of Sorl1 affected serotonin signalling based on the following annotations being significant: indication “depression”, target “serotonin transporter”, and KEGG pathway “serotonergic synapse”. All three are also significant for psen2 knockouts, but none others. ZOLTAR therefore does not predict serotonin signalling to be a major theme common to all mutants with a night-time sleep loss phenotype.

      While perhaps not surprising, we find reassuring that insomnia appears in the indications shared by the largest number of knockouts. apoea/apoeb, cd2ap, sorl1 also happen to be the knockouts with the largest loss in night-time sleep.

      Particularly interesting is cholinergic signalling appearing in the most common targets and KEGG pathways. Acetylcholine signalling is a major theme in research on Alzheimer’s disease. For example, the first four drugs ever approved by the FDA to treat Alzheimer’s disease were acetylcholinesterase inhibitors, which increase acetylcholine signalling by preventing its breakdown by acetylcholinesterase. These drugs are generally considered only to treat symptoms and not modify disease course, but this view has been called into question (Munoz-Torrero, 2008; Relkin, 2007). If, as ZOLTAR suggests, mutations in several Alzheimer’s risk genes affect cholinergic signalling early in development, this would point to a potential causal role of cholinergic disruption in Alzheimer’s disease.

      We see that literature also exists on the involvement of glycogen synthase kinase-3 in AD (Lauretti et al., 2020). We plan to explore further these predictions in a future study.

      Finally, the web- based platform presented could be expanded to facilitate comparison of other behavioral phenotypes, including stimulus-evoked behaviors.

      Yes, absolutely. The behavioural dataset we used (Rihel et al., 2010) did not measure other stimuli than day/night light transitions, but the “SauronX” platform and dataset (Myers-Turnbull et al., 2022) seems particularly well suited for this. To provide some context, we and collaborators have occasionally used the dataset by Rihel et al. (2010) to generate hypotheses or find candidate drugs that reverse a behavioural phenotype measured in the sleep/wake assay (Ashlin et al., 2018; Hoffman et al., 2016). The present work was the occasion to enable a wider and more intuitive use of this dataset through the ZOLTAR app, which has already proven successful. Future versions of ZOLTAR will seek to incorporate larger drug datasets using more types of measurements.

      Finally, the authors propose but do not test the hypothesis that sorl1 might regulate localization/surface expression of 5-HT2 receptors. This could provide exciting / more convincing mechanistic support for the assertion that serotonin signaling is disrupted upon loss of AD-associated genes.

      5-HT receptor type 4a is another candidate as it was shown to interact with sorting nexin 27, a subunit of retromer (Joubert et al., 2004). We see that antibodies against human 5-HT receptor type 2 and 4a exist; whether they would work in zebrafish remains to be tested, and in our experience, the availability of antibodies suitable for immunohistochemistry in the zebrafish is a serious experimental roadblock.

      Despite these important considerations, this study provides a valuable platform for high-throughput analysis of sleep phenotypes and correlation with small-molecule-induced sleep phenotypes.

      Strengths:

      - Provides a useful platform for comparison of sleep phenotypes across genotypes/drug manipulations.

      - Presents convincing evidence that nighttime sleep is disrupted in mutants for multiple late-onset AD-related genes.

      - Provides potential mechanistic insights for how AD-related genes might impact sleep and identifies a few drugs that modify their identified phenotypes

      Weaknesses:

      - Exploration of potential mechanisms for serotonin disruption in sorl1 mutants is limited.

      - The pipeline developed can only be used to examine sleep-related / spontaneous movement phenotypes and stimulus-evoked behaviors are not examined.

      - Comparisons between mutants/exploration of commonly affected pathways are limited.

      Thank you for these excellent suggestions, please see our answers above.

      Reviewer #2 (Public Review):

      Summary:

      This work delineates the larval zebrafish behavioral phenotypes caused by the F0 knockout of several important genes that increase the risk for Alzheimer's disease. Using behavioral pharmacology, comparing the behavioral fingerprint of previously assayed molecules to the newly generated knockout data, compounds were discovered that impacted larval movement in ways that suggest interaction with or recovery of disrupted mechanisms.

      Strengths:

      This is a well-written manuscript that uses newly developed analysis methods to present the findings in a clear, high-quality way. The addition of an extensive behavioral analysis pipeline is of value to the field of zebrafish neuroscience and will be particularly helpful for researchers who prefer the R programming language. Even the behavioral profiling of these AD risk genes, regardless of the pharmacology aspect, is an important contribution. The recovery of most behavioral parameters in the psen2 knockout with betamethasone, predicted by comparing fingerprints, is an exciting demonstration of the approach. The hypotheses generated by this work are important stepping stones to future studies uncovering the molecular basis of the proposed gene-drug interactions and discovering novel therapeutics to treat AD or co-occurring conditions such as sleep disturbance.

      Weaknesses:

      - The overarching concept of the work is that comparing behavioral fingerprints can align genes and molecules with similarly disrupted molecular pathways. While the recovery of the psen2 phenotypes by one molecule with the opposite phenotype is interesting, as are previous studies that show similar behaviorally-based recoveries, the underlying assumption that normalizing the larval movement normalizes the mechanism still lacks substantial support. There are many ways that a reduction in movement bouts could be returned to baseline that are unrelated to the root cause of the genetically driven phenotype. An ideal experiment would be to thoroughly characterize a mutant, such as by identifying a missing population of neurons, and use this approach to find a small molecule that rescues both behavior and the cellular phenotype. If the connection to serotonin in the sorl1 was more complete, for example, the overarching idea would be more compelling.

      Thank you for this cogent criticism.

      On the first point, we were careful not to claim that betamethasone normalises the molecular/cellular mechanism that causes the psen2 behavioural phenotype. Having said that, yes, to a certain extent that would be the hope of the approach. As you say, every compound which normalises the behavioural fingerprint will not normalise the underlying mechanism, but the opposite seems true: every compound that normalises the underlying mechanism should also normalise the behavioural fingerprint. We think this logic makes the “behaviour-first” approach innovative and interesting. The logic is to discover compounds that normalise the behavioural phenotype first, only subsequently test whether they also normalise the molecular mechanism, akin to testing first whether a drug resolves the symptoms before testing whether it actually modifies disease course. While in practice testing thousands of drugs in sufficient sample sizes and replicates on a mutant line is challenging, the dataset queried through ZOLTAR provides a potential shortcut by shortlisting in silico compounds that have the opposite effect on behaviour.

      You mention a “reduction in movement bouts” but note here that the number of behavioural parameters tested is key to our argument. To take the two extremes, say the only behavioural parameter we measured in psen2 knockout larvae was time active during the day, then, yes, any stimulant used at the right concentration could probably normalise the phenotype. In this situation, claiming that the stimulant is likely to also normalise the underlying mechanism, or even that it is a genuine “phenotypic rescue”, would not be convincing. Conversely, say we were measuring thousands of behavioural parameters under various stimuli, such as swimming speed, position in the well, bout usage, tail movements, and eye angles, it seems almost impossible for a compound to rescue most parameters without also normalising the underlying mechanism. The present approach is somewhere in-between: ZOLTAR uses six behavioural parameters for prediction (e.g. Fig 6a), but all 17 parameters calculated by FramebyFrame can be used to assess rescue during a subsequent experiment (Fig. 6c). For both, splitting each parameter in day and night increases the resolution of the approach, which partly answers your criticism. For example, betamethasone rescued the day-time hypoactivity without causing night-time hyperactivity, so we are not making the “straw man argument” explained above of using any broad stimulant to rescue the hypoactivity phenotype.

      Furthermore, for diseases where the behavioural defect is the primary concern, such as autism or bipolar disorder, perhaps this behaviour-first approach is all that is needed, and whether or not the compound precisely rescues the underlying mechanism is somewhat secondary. The use of lithium to prevent manic episodes in bipolar disorder is a good example. It was initially tested because mania was thought to be caused by excess uric acid and lithium can dissolve uric acid (Mitchell and Hadzi-Pavlovic, 2000). The theory is now discredited, but lithium continues to be used without a precise understanding of its mode of action. In this example, behavioural rescue alone, with tolerable secondary effects, is sufficient to be beneficial to patients, and whether it modulates the correct causal pathway is secondary.

      On the second point, we agree that testing first ZOLTAR on a mutant for which we have a fairly good understanding of the mechanism causing the behavioural phenotype could have been a productive approach. Note, however, that examples already exist in the literature. First, Hoffman et al. (2016) found that drugs generating behavioural fingerprints that positively correlate with the cntnap2a/cntnap2b double knockout fingerprint are enriched with NMDA and GABA receptor antagonists. In experiments analogous to our citalopram treatment (Fig. 5c,d), cntnap2a/cntnap2b knockout larvae were found to be overly sensitive to the NMDA receptor antagonist MK-801 and the GABAA receptor antagonist pentylenetetrazol (PTZ). Among other drugs tested, zolpidem, a GABAA receptor agonist, caused opposite effects on wild-type and cntnap2a/cntnap2b knockout larvae. Knockout larvae also had fewer GABAergic neurons in the forebrain. Second, Ashlin et al. (2018) found that the fingerprint of pitpnc1a knockout larvae clustered with anti-inflammatory compounds. Flumethasone, an anti-inflammatory corticosteroid, caused a lower increase in activity when added to knockout larvae compared to wild-type larvae. While these studies did not use precisely the same analysis that ZOLTAR runs, they used the same rationale and behavioural dataset to make these predictions (Rihel et al., 2010), which shows that approaches like ZOLTAR can point to causal processes.

      Related to your next point, we may reduce the discussion on sorl1 and serotonin and add some of the present arguments instead, depending on the results from  testing a second SSRI (see next point).

      - The behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram is based on a small number of animals. The KO Euclidean distance measure is also more spread out than for the other datasets, and it looks like only five or so fish are driving the group difference. It also appears as though the numbers were also from two injection series. While there is nothing obviously wrong with the data, I would feel more comfortable if such a strong statement of a result from a relatively subtle phenotype were backed up by a higher N or a stable line. It is not impossible that the observed difference is an experimental fluke. If something obvious had emerged through the HCR, that would have also supported the conclusions. As it stands, if no more experiments are done to bolster the claim, the confidence in the strength of the link to serotonin should be reduced (possibly putting the entire section in the supplement and modifying the discussion). The discussion section about serotonin and AD is interesting, but I think that it is excessive without additional evidence.

      We mostly agree with this criticism. One could interpret the larger spread of the data for sorl1 larvae treated with 10 µM citalopram as evidence that the knockout larvae do indeed react differently to the drug at this dose. However, the result indeed does not survive removing the top 5 (p = 0.87) or top 3 (p = 0.18) sorl1 larvae.

      Given that the HCR did not reveal anything striking, we agree with you that too much of our argument relies on this result being robust. As you and reviewer #3 suggest, we plan on repeating this experiment with a different serotonin reuptake inhibitor (SSRI). If the other SSRI also shows a differential effect, this should strengthen the claim that ZOLTAR correctly predicted serotonin signalling as being affected by the loss of Sorl1, even if we did not discover the molecular mechanism.

      - The authors suggest two hypotheses for the behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram. While the first is tested, and found to not be supported, the second is not tested at all ("Ruling out the first hypothesis, sorl1 knockouts may react excessively to a given spike in serotonin." and "Second, sorl1 knockouts may be overly sensitive to serotonin itself because post-synaptic neurons have higher levels of serotonin receptors."). Assuming that the finding is robust, there are probably other reasons why the mutants could have a different sensitivity to this molecule. However, if this particular one is going to be mentioned, it is surprising that it was not tested alongside the first hypothesis. This work could proceed without a complete explanation, but additional discussion of the possibilities would be helpful or why the second hypothesis was not tested.

      There are no strong scientific reasons why this hypothesis was not tested. The lead author (F Kroll) moved to a different lab and country so the project was finalised at that time. We do not plan on testing this hypothesis at this stage. However, we will adapt the wording to make it clear this is one possible alternative hypothesis which could be tested in the future, rather than the only alternative.

      - The authors claim that "all four genes produced a fairly consistent phenotype at night". While it is interesting that this result arose in the different lines, the second clutch for some genes did not replicate as well as others. I think the findings are compelling, regardless, but the sometimes missing replicability should be discussed. I wonder if the F0 strategy adds noise to the results and if clean null lines would yield stronger phenotypes. Please discuss this possibility, or others, in regard to the variability in some phenotypes.

      For the first part of this point, please see below our answer to Reviewer #3, point (2) c.

      Regarding the F0 strategy potentially adding variability, it is an interesting question which we tested in a larger dataset of behavioural recordings from F0 and stable knockouts for the same genes (unpublished). In summary, the F0 knockout method does not increase clutch-to-clutch or larva-to-larva variability in the assay. F0 knockout experiments found many more significant parameters and larger effect sizes than stable knockout experiments, but this difference could largely be explained by the larger sample sizes of F0 knockout experiments. In fact, larger sample sizes within individual clutches appears to be a major advantage of the F0 knockout approach over in-cross of heterozygous knockout animals as it increases sensitivity of the assay without causing substantial variability. We plan to report in more details on this analysis in a separate paper as we think it would dilute the focus of the present work.

      - In this work, the knockout of appa/appb is included. While APP is a well-known risk gene, there is no clear justification for making a knockout model. It is well known that the upregulation of app is the driver of Alzheimer's, not downregulation. The authors even indicate an expectation that it could be similar to the other knockouts ("Moreover, the behavioural phenotypes of appa/appb and psen1 knockout larvae had little overlap while they presumably both resulted in the loss of Aβ." and "Comparing with early-onset genes, psen1 knockouts had similar night-time phenotypes, but loss of psen2 or appa/appb had no effect on night-time sleep."). There is no reason to expect similarity between appa/appb and psen1/2. I understand that the app knockouts could unveil interesting early neurodevelopmental roles, but the manuscript needs to be clarified that any findings could be the opposite of expectation in AD.

      On “there is no reason to expect similarity […]”, we disagree. Knockout of appa/appb and knockout psen1 will both result in loss of Aβ (appa/appb encode Aβ and psen1 cleaves Appa/Appb to release Aβ, cf. Fig. 3e). Consequently, a phenotype caused by the loss of Aβ, or possibly other Appa/Appb cleavage products, should logically be found in both appa/appb and psen1 knockouts.

      On “it is well known that the upregulation of APP is the driver of Alzheimer’s, not downregulation”; we of course agree. Among others, the examples of Down syndrome, APP duplication (Sleegers et al., 2006), or mouse models overexpressing human APP show definitely that overexpression of APP is sufficient to cause AD. Having said that, we would not be so quick in dismissing APP knockout as potentially relevant to understanding of Alzheimer’s disease. Loss of soluble Aβ due to aggregation could contribute to pathology (Espay et al., 2023). Without getting too much into this intricate debate, links between levels of Aβ and risk of disease are often counter-intuitive too. For example, out of 138 PSEN1 mutations screened in vitro, 104 reduced total Aβ production and 11 even seemingly abolished the production of both Aβ40 and Aβ42 (Sun et al., 2017). In short, loss of soluble Aβ occurs in both AD and in our appa/appb knockout larvae, but the ideal approach would be to study zebrafish larvae with an in-frame deletion in the Aβ sequence within appa/appb.

      We will adapt the language to address your point. We would not want to imply, for example, that the absence of a night-time sleep phenotype for appa/appb is contradictory to the body of literature showing links between Aβ and sleep, including in zebrafish (Özcan et al., 2020). As you say, our experiment tested loss of App, including Aβ, while the literature typically reports on overexpression of APP, as in APP/PSEN1-overexpressing mice (Jagirdar et al., 2021).

      Reviewer #3 (Public Review):

      In this manuscript by Kroll and colleagues, the authors describe combining behavioral pharmacology with sleep profiling to predict disease and potential treatment pathways at play in AD. AD is used here as a case study, but the approaches detailed can be used for other genetic screens related to normal or pathological states for which sleep/arousal is relevant. The data are for the most part convincing, although generally the phenotypes are relatively small and there are no major new mechanistic insights. Nonetheless, the approaches are certainly of broad interest and the data are comprehensive and detailed.

      A notable weakness is the introduction, which overly generalizes numerous concepts and fails to provide the necessary background to set the stage for the data.

      Major points

      (1) The authors should spend more time explaining what they see as the meaning of the large number of behavioral parameters assayed and specifically what they tell readers about the biology of the animal. Many are hard to understand--e.g. a "slope" parameter.

      We agree that some parameters do not tell something intuitive about the biology of the animal. It would be easy to speculate. For example, the “activity slope” parameter may indicate how quickly the animal becomes tired over the course of the day. On the other hand, fractal dimension describes the “roughness/smoothness” of the larva’s activity trace (Fig. 2–suppl. 1a); but it is not obvious how to translate this into information about the physiology of the animal. We do not see this as an issue though. While some parameters do provide intuitive information about the animal’s behaviour (e.g. sleep duration or sunset startle as a measure of startle response), the benefit of having a large number of behavioural parameters is to compare behavioural fingerprints and assess rescue of the behavioural phenotype by small molecules (Fig. 6c). For this purpose, the more parameters the better. The “MoSeq” approach from Wiltschko et al., 2020 is a good example from literature that inspired our own Fig. 6c. While some of the “behavioural syllables” may be intuitive (e.g. running or grooming), it is probably pointless to try to explain the ‘meaning’ of the “small left turn in place with head motion” syllable (Wiltschko et al., 2020). Nonetheless, this syllable was useful to assess whether a drug specifically treats the behavioural phenotype under study without causing too many side effects. Unfortunately, ZOLTAR has to reduce the FramebyFrame fingerprint (17 parameters) to just six parameters to compare it to the behavioural dataset from Rihel et al., 2010, but here, more parameters would almost certainly translate into better predictions too, regardless of their intuitiveness.

      It is true however that we do not give much information on how some of the less intuitive parameters, such as activity slope or fractal dimension, are calculated or what they describe about the dataset (e.g. roughness/smoothness for fractal dimension). We will improve this in our revised version.

      (2) Because in the end the authors did not screen that many lines, it would increase confidence in the phenotypes to provide more validation of KO specificity. Some suggestions include:

      a. The authors cite a psen1 and psen2 germline mutant lines. Can these be tested in the FramebyFrame R analysis? Do they phenocopy F0 KO larvae?

      We unfortunately do not have those lines. We investigated the availability of importing a psen2 knockout line from abroad, but the process of shipping live animals is becoming more and more cost and time prohibitive. However, we observed the same pigmentation phenotype for psen2 knockouts as reported by Jiang et al., 2018, which is at least a partial confirmation of phenocopying a loss of function stable mutant. 

      b. psen2KO is one of the larger centerpieces of the paper. The authors should present more compelling evidence that animals are truly functionally null. Without this, how do we interpret their phenotypes?

      We disagree that there should be significant doubt about these mutants being truly functionally null,  given the high mutation rate and presence of the expected pigmentation phenotype (Jiang et al., 2018, Fig. 3f and Fig. 3–suppl. 2). The psen2 F0 knockouts were virtually 100% mutated at three exons across the gene (mutation rates were locus 1: 100 ± 0%; locus 2: 99.99 ± 0.06%; locus 3: 99.85 ± 0.24%). Additionally, two of the three mutated exons had particularly high rates of frameshift mutations (locus 1: 97 ± 5%; locus 2: 88 ± 17% frameshift mutation rate). It is virtually impossible that a functional protein is translated given this burden of frameshift mutations. Phenotypically, in addition to the pigmentation defect, double psen1/psen2 F0 knockout larvae had curved tails, the same phenotype as caused by a high dose of the γ-secretase inhibitor DAPT (Yang et al., 2008). These double F0 knockouts were lethal, while knockout of psen1 or psen2 alone did not cause obvious morphological defects. Evidently, most larvae must have been psen2 null mutants in this experiment, otherwise functional Psen2 would have prevented early lethality.

      Translation of zebrafish psen2 can start at downstream start codons if the first exon has a frameshift mutation, generating a seemingly functional Psen2 missing the N-terminus (Jiang et al., 2020). Zebrafish homozygous for this early frameshift mutation had normal pigmentation, showing it is a reliable marker of Psen2 function even when it is mutated. This mechanism is not a concern here as the alternative start codons are still upstream of two of the three mutated exons (the alternative start codons discovered by Jiang et al., 2020 are in exon 2 and 3, but we targeted exon 3, exon 4, and exon 6).

      We understand that the zebrafish community may be cautious about F0 phenotyping compared to stably generated mutants. As mentioned to Reviewer 2, we are planning to assemble a paper that expressly examines F0s vs. stable mutants to allay some of these concerns. We would also suggest that our current manuscript, which combines CRISPR-F0 rapid screening with in silico pharmacological predictions, ultimately represents a first step in characterizing the functions of genes.

      c. Related to the above, for cd2AP and sorl1 KO, some of the effect sizes seem to be driven by one clutch and not the other. In other words, great clutch-to-clutch variability. Should the authors increase the number of clutches assayed?

      Correct, there is great clutch-to-clutch variability in this behavioural assay. This is not specific to our experiments. Even within the same strain, wild-type larvae from different clutches (i.e. non-siblings) behave differently (Joo et al., 2021). This is why it is essential to compare behavioural phenotypes within individual clutches (i.e., from a single pair of parents, one male and one female), as we explain in Methods (section Behavioural video-tracking) and in the documentation of the FramebyFrame package. We often see two different experimental designs in literature: comparing non-sibling wild-type and mutant larvae, or pooling different clutches which include all genotypes (e.g., pooling multiple clutches from heterozygous in-crosses or pooling wild-type clutches before injecting them). The first experimental design causes false positive findings, as the clutch-to-clutch variability we and others (Joo et al., 2021) observe gets interpreted as a behavioural phenotype. The second experimental design should not cause false positives but will decrease the sensitivity of the assay by increasing the spread within genotypes. In both cases, the clutch-to-clutch variability is hidden, either by interpreting it as a phenotype (first case) or by adding it to animal-to-animal variability (second case). Our experimental design is technically more challenging as it requires obtaining large clutches from unique pairs of parents. However, this approach is better as it clearly separates the different sources of variability (clutch-to-clutch or animal-to-animal). As for every experiment, yes, a larger number of replicates would be better, but we do not plan to assay additional clutches at this time. Our work heavily focuses on the sorl1 and psen2 knockout behavioural phenotypes. The key aspects of these phenotypes were effectively tested in four clutches as sorl1 were also tested in the citalopram experiment (Fig. 5), and psen2 was also tested in the small molecule rescue experiment (Fig. 6 and Fig. 6–suppl. 1). In the citalopram experiment, one H2O-treated sorl1 knockout clutch (n = 10) replicates fairly well the baseline recordings in Fig. 4–suppl. 5, the other does not but had especially low sample size (n = 6).

      We also plan to test another SSRI on sorl1 knockouts, so this point will be addressed.

      (3) The authors make the point that most of the AD risk genes are expressed in fish during development. Is there public data to comment on whether the genes of interest are expressed in mature/old fish as well? Just because the genes are expressed early does not at all mean that early- life dysfunction is related to future AD (though this could be the case, of course). Genes with exclusive developmental expression would be strong candidates for such an early-life role, however. I presume the case is made because sleep studies are mainly done in juvenile fish, but I think it is really a pretty minor point and such a strong claim does not even need to be made.

      This is a fair criticism but we do not make this claim, at least not from expression. The reviewer is probably referring to the following quote:

      “[…] most of these were expressed in the brain of 5–6-dpf zebrafish larvae, suggesting they play a role in early brain development or function,”

      which does not mention future risk of Alzheimer’s disease. We do suggest that these genes have a function in development. After all, every gene that plays a role in brain development must be expressed during development, so this wording seems reasonable. As noted, the primary goal was to check that the genes we selected were indeed expressed in zebrafish larvae before performing knockout experiments. Our discussion does raise the hypothesis that mutations in Alzheimer’s risk genes impact brain development and sleep early in life, but this argument primarily relies on our observation that knockout of late-onset Alzheimer’s risk genes causes sleep phenotypes in 7-day old zebrafish larvae and from previous work showing brain structural differences in infants and children at high genetic risk of Alzheimer’s disease (Dean et al., 2014; Quiroz et al., 2015), not solely on gene expression early in life.

      (4) A common quandary with defining sleep behaviorally is how to rectify sleep and activity changes that influence one another. With psen2 KOs, the authors describe reduced activity and increased sleep during the day. But how do we know if the reduced activity drives increased behavioral quiescence that is incorrectly defined as sleep? In instances where sleep is increased but activity during periods during wake are normal or elevated, this is not an issue. But here, the animals might very well be unhealthy, and less active, so naturally they stop moving more for prolonged periods, but the main conclusion is not sleep per se. This is an area where more experiments should be added if the authors do not wish to change/temper the conclusions they draw. Are psen2 KOs responsive to startling stimuli like controls when awake? Do they respond normally when quiescent? Great care must be taken in all models using inactivity as a proxy for sleep, and it can harm the field when there is no acknowledgment that overall health/activity changes could be a confound. Particularly worrisome is the betamethasone data in Figure 6, where activity and sleep are once again coordinately modified by the drug.

      This is a fair criticism. We agree it is a concern, especially in the case of psen2 as we claim that day-time sleep is increased while zebrafish are diurnal. We do not rely heavily on the day-time inactivity being sleep (the ZOLTAR predictions or the small molecule rescue do not change whether the parameter is called sleep or inactivity), but  our choice of labelling may be misleading. We will try to test this claim by plotting the distribution of the inactive period durations. If psen2 knockout larvae indeed sleep more during the day compared to controls, we might predict that inactive periods longer than 1 minute to increase disproportionately compared to the increase in shorter inactive periods.

      To address, “are psen2 KO responsive to startling stimuli like controls when awake/when quiescent”, we can try to look at the behaviour of psen2 knockout larvae that were awake (i.e., moved in the preceding one minute) or ‘asleep’ (i.e., did not move in the preceding one minute) at the light transitions and count the proportion of psen2 knockout or control larvae which displayed a startle response. If most psen2 knockouts react to the light transition, it should at least exclude the concern that they are very unhealthy, as the reviewer suggests. This criticism seems challenging to definitely address experimentally though. A possible approach could be to use a closed-loop system which, after one minute of inactivity, triggers a stimulus which is sufficient to startle an awake larva but not an asleep larva. If psen2 knockout larvae indeed sleep more during the day, the stimulus should usually not be sufficient to startle them. Note, how to calibrate this stimulus is also not straightforward. We do not plan to test this, but our analysis of the light transitions may provide a decent proxy.

      (5) The conclusions for the serotonin section are overstated. Behavioural pharmacology purports to predict a signaling pathway disrupted with sorl1 KO. But is it not just possible that the drug acts in parallel to the true disrupted pathway in these fish? There is no direct evidence for serotonin dysfunction - that conclusion is based on response to the drug. Moreover, it is just 1 drug - is the same phenotype present with another SSRI? Likewise, language should be toned down in the discussion, as this hypothesis is not "confirmed" by the results (consider "supported"). The lack of measured serotonin differences further raises concern that this is not the true pathway. This is another major point that deserves further experimental evidence, because without it, the entire approach (behavioral pharm screen) seems more shaky as a way to identify mechanisms. There are any number of testable hypotheses to pursue such as a) Using transient transgenesis to visualize 5HT neuron morphology (is development perturbed: cell number, neurite morphology, synapse formation); b) Using transgenic Ca reporters to assay 5HT neuron activity.

      Regarding the comment, “is it not just possible that the drug acts in parallel to the true disrupted pathway”, we think no, assuming we understand correctly your question. Key to our argument is the fact that sorl1 knockout larvae react differently to the drug than control larvae. As an example, take night-time sleep bout length, which was not affected by knockout of sorl1 (Fig. 4–suppl. 5). For the sake of the argument, say only dopamine signalling (the “true disrupted pathway”) was affected in sorl1 knockouts but that serotonin signalling was intact. Assuming that citalopram specifically alters serotonin signalling, then treatment should cause the same increase in sleep bout length in both knockouts and controls as serotonin signalling is intact in both. This is not what we see, however. Citalopram caused a greater increase in sleep bout length in sorl1 knockouts than in scrambled-injected larvae. In other words, the effect is non-additive, in the sense that citalopram did not add the same number of Z-scores to sorl1 knockouts or controls. We think this shows that serotonin signalling is somehow different in sorl1 knockouts. Nonetheless, we would concede that the experiment does not necessarily says much about the importance of the serotonin disruption caused by loss of Sorl1. It could be, for example, that the most salient consequence of loss of Sorl1 is cholinergic disruption (see reply to Reviewer #1 above) and that serotonin signalling is a minor theme.

      Furthermore, we agree with you and Reviewer #2 that the conclusions are overly confident. We will repeat this experiment with another SSRI as you suggest. Your suggestions to further test the serotonin system in the sorl1 knockouts are excellent as well, however we do not plan to pursue them at this stage.

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    1. Author response:

      We thank the reviewers for their efforts. They have pointed out several shortcomings and made very helpful suggestions. Below, we shortly address the weak points that the reviewers brought up and outline what improvements we intend to make for the revised paper in response.

      Reviewer #1:

      The interpretation of CNN results, especially the number of layers in the final model and its relationship with the processing of visual words in the human brain, needs to be further strengthened.

      The results of our experimentation with the number of layers and the number of units in each layer can be found in the supplementary information. In the revised version, we will bring some of these results into the main text and discuss them more thoroughly.

      Reviewer #2:

      As has been shown over many decades, many potential computational algorithms, with varied model architectures, can perform the task of text recognition from an image. However, there is no evidence presented here that this particular algorithm has comparable performance to human behavior (i.e. similar accuracy with a comparable pattern of mistakes). This is a fundamental prerequisite before attempting to meaningfully correlate these layer activations to human neural activations. Therefore, it is unlikely that correlating these derived layer weights to neural activity provides meaningful novel insights into neural computation beyond what is seen using traditional experimental methods.

      We very much agree with the reviewer that a qualitative analysis of whether the model can explain experimental effects needs to happen before a quantitative analysis, such as evaluating model-brain correlation scores. In fact, this is one of the key points we wished to make.

      This starts with the observation that "traditional" models of reading (=those that do not rely on deep learning) cannot explain some very basic human behavioral results, such as humans being able to recognize a word regardless of exact letter shape, size, and (up to a point) rotation. This is not so much a failure on the part of traditional models as it is a difference in focus. There are models of vision that focus on these low-level things, currently dominated by deep learning, but these are rarely evaluated in the context of reading, which has its own literature and well-known experimental effects. We believe the current version of the manuscript makes insufficiently clear what the goals of our modeling effort are exactly, which is something we will attempt to correct in the revision.

      Since our model only covers the first phase of reading, with a special focus on letter shape detection, we sought to compare it with neuroimaging data that can provide "snapshots" of the state of the brain during these early phases, rather than comparing it with behavioral results that occur at the very end. However, we very much make this comparison in the spirit hinted at by the reviewer. The different MEG components have a distinct "behavior" to them in the way they respond to different experimental conditions (Figure 2), and the model needs to replicate this behavior (Figure 4). Only then do we move on to a quantitative analysis.

      One example of a substantial discrepancy between this model and neural activations is that, while incorporating frequency weighting into the training data is shown to slightly increase neural correlation with the model, Figure 7 shows that no layer of the model appears directly sensitive to word frequency. This is in stark contrast to the strong neural sensitivity to word frequency seen in EEG (e.g. Dambacher et al 2006 Brain Research), fMRI (e.g. Kronbichler et al 2004 NeuroImage), MEG (e.g. Huizeling et al 2021 Neurobio. Lang.), and intracranial (e.g. Woolnough et al 2022 J. Neurosci.) recordings. Figure 7 also demonstrates that the late stages of the model show a strong negative correlation with font size, whereas later stages of neural visual word processing are typically insensitive to differences in visual features, instead showing sensitivity to lexical factors.

      We are glad the reviewer brought up the topic of frequency balancing, as it is a good example of the importance of the qualitative analysis. As the reviewer points out, frequency balancing during training only had a moderate impact on correlation scores and from that point of view does not seem impactful. However, when we look at the qualitative evaluation, we see that with a large vocabulary, a model without frequency balancing fails to properly distinguish between consonant strings and (pseudo)words (Figure 4, 5th row). Hence, from the point of view of being able to reproduce experimental effects, frequency balancing had a large impact. It is true that the model, even with frequency balancing, only captures letter- and bigram-frequency effects and not word-frequency effects, as we know the N400 is sensitive to. This could mean that N400 word-frequency effects are driven by mechanics that our current model lacks, such as top-down effects from systems further up the processing pipeline.

      We agree with the reviewer that the late-stage sensitivity of the model to font size must be seen as a flaw. Of course, we say as much when we discuss this result in the paper. Important context for this flaw is that the main aim of the model is to reproduce the experimental effects of Vartiainen et al. (2011), which does not include manipulation of word length. The experimental contrasts in Figure 7 are meant to explore a bit beyond the boundaries of that particular study, but were never considered "failure points". When presenting a model, it's important to show its limitations too.

      Another example of the mismatch between this model and the visual cortex is the lack of feedback connections in the model. Within the visual cortex, there are extensive feedback connections, with later processing stages providing recursive feedback to earlier stages. This is especially evident in reading, where feedback from lexical-level processes feeds back to letter-level processes (e.g. Heilbron et al 2020 Nature Comms.). This feedback is especially relevant for the reading of words in noisy conditions, as tested in the current manuscript, as lexical knowledge enhances letter representation in the visual cortex (the word superiority effect). This results in neural activity in multiple cortical areas varying over time, changing selectivity within a region at different measured time points (e.g. Woolnough et al 2021 Nature Human Behav.), which in the current study is simplified down to three discrete time windows, each attributed to different spatial locations.

      In this study, we make a start in showing how deep learning techniques could be beneficial to enhance models of reading by showing how even a simple CNN, after a few enhancements, can account for several experimental MEG effects that we see in reading tasks, but are outside the focus of traditional models of reading. We never intended to claim that our model offers a complete view of all the processes involved. This is why we have dedicated a section in the Discussion to the various ways in which our simple CNN is incomplete as a model of reading. In this section we hint at the usage of recurrent connections, but the reviewer does an excellent job of highlighting the importance of top-down connections even in models focusing on early visual processes, which we are very happy to include in this section.

      The presented model needs substantial further development to be able to replicate, both behaviorally and neurally, many of the well-characterized phenomena seen in human behavior and neural recordings that are fundamental hallmarks of human visual word processing. Until that point, it is unclear what novel contributions can be gleaned from correlating low-dimensional model weights from these computational models with human neural data.

      The CNN model we present in this study is a small piece in a bigger effort to employ deep learning techniques to further enhance already existing models of reading. For our revision, we plan to expand on the question of where to go from here and outline our vision on how these techniques could help us better model the phenomena the reviewer speaks of. We agree with the reviewer that there is a long way to go, and we are excited to be a part of it.

      Reviewer #3:

      The paper is rather qualitative in nature. In particular, the authors show that some resemblance exists between the behavior of some layers and some parts of the brain, but it is hard to quantitively understand how strong the resemblances are in each layer, and the exact impact of experimental settings such as the frequency balancing (which seems to only have a very moderate effect according to Figure 5).

      The large focus on a qualitative evaluation of the model is intentional. The ability of the model to reproduce experimental effects (Figure 4) is a pre-requisite for any subsequent qualitative metrics (such as correlation) to be valid. The introduction of frequency balancing is a good example of this. As the reviewer points out, frequency balancing during training has only a moderate impact on correlation scores and from that point of view does not seem impactful. However, when we look at the qualitative evaluation, we see that with a large vocabulary, a model without frequency balancing fails to properly distinguish between consonant strings and (pseudo)words (Figure 4, 5th row). Hence, from the point of view of being able to reproduce experimental effects, frequency balancing has a large impact.

      That said, the reviewer is right to highlight the value of quantitative analysis. An important limitation of the "traditional" models of reading that do not employ deep learning is that they operate in unrealistically simplified environments (e.g. input as predefined line segments, words of a fixed length), which makes a quantitative comparison with brain data problematic. The main benefit that deep learning brings may very well be the increase in scale that makes more direct comparisons with brain data possible. In our revision we will attempt to capitalize on this benefit more. The reviewer has provided some helpful suggestions for doing so in their recommendations.

      The experiments only consider a rather outdated vision model (VGG).

      VGG was designed to use a minimal number of operations (convolution-and-pooling, fully-connected linear steps, ReLU activations, and batch normalization) and rely mostly on scale to solve the classification task. This makes VGG a good place to start our explorations and see how far a basic CNN can take us in terms of explaining experimental MEG effects in visual word recognition. However, we agree with the reviewer that it is easy to envision more advanced models that could potentially explain more. For our revision, we plan to expand on the question of where to go from here and outline our vision on what types of models would be worth investigating and how one may go about doing that in a way that provides insights beyond higher correlation values.

    1. Author response:

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

      eLife assessment

      This structural and biochemical study of the mouse homolog of acidic mammalian chitinase (AMCase) enhances our understanding of the pH-dependent activity and catalytic properties of mouse AMCase and sheds light on its adaptation to different physiological pH environments. The methods and analysis of data are solid, providing several lines of evidence to support a development of mechanistic hypotheses. While the findings and interpretation will be valuable to those studying AMCase in mice, the broader significance, including extension of the results to other species including human, remain unclear.

      Public Reviews:

      Reviewer #1 (Public Review):

      General comments:

      This paper investigates the pH-specific enzymatic activity of mouse acidic mammalian chitinase (AMCase) and aims to elucidate its function's underlying mechanisms. The authors employ a comprehensive approach, including hydrolysis assays, X-ray crystallography, theoretical calculations of pKa values, and molecular dynamics simulations to observe the behavior of mouse AMCase and explore the structural features influencing its pH-dependent activity.

      The study's key findings include determining kinetic parameters (Kcat and Km) under a broad range of pH conditions, spanning from strong acid to neutral. The results reveal pH-dependent changes in enzymatic activity, suggesting that mouse AMCase employs different mechanisms for protonation of the catalytic glutamic acid residue and the neighboring two aspartic acids at the catalytic motif under distinct pH conditions.

      The novelty of this research lies in the observation of structural rearrangements and the identification of pH-dependent mechanisms in mouse AMCase, offering a unique perspective on its enzymatic activity compared to other enzymes. By investigating the distinct protonation mechanisms and their relationship to pH, the authors reveal the adaptive nature of mouse AMCase, highlighting its ability to adjust its catalytic behavior in response to varying pH conditions. These insights contribute to our understanding of the pH-specific enzymatic activity of mouse AMCase and provide valuable information about its adaptation to different physiological conditions.

      Overall, the study enhances our understanding of the pH-dependent activity and catalytic properties of mouse AMCase and sheds light on its adaptation to different physiological pH environments.

      Reviewer #2 (Public Review):

      Summary:

      In this study of the mouse homolog of acidic mammalian chitinase, the overall goal is to provide a mechanistic explanation for the unusual observation of two pH optima for the enzyme. The study includes biochemical assays to establish kinetic parameters at different solution pH, structural studies of enzyme/substrate complexes, and theoretical analysis of amino acid side chain pKas and molecular dynamics.

      Strengths:

      The biochemical assays are rigorous and nicely complemented by the structural and computational analysis. The mechanistic proposal that results from the study is well rationalized by the observations in the study.

      Weaknesses:

      The overall significance of the work could be made more clear. Additional details could be provided about the limitations of prior biochemical studies of mAMC that warranted the kinetic analysis. The mouse enzyme seems unique in terms of its behavior at high and low pH, so it remains unclear how the work will enhance broader understanding of this enzyme class. It was also not clear can the findings be used for therapeutic purposes, as detailed in the abstract, if the human enzyme works differently.

      We have edited the paper to address these concerns

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      (1) Regarding the pH profiles of mouse AMCase, previous studies have reported its activity at pH 2.0 and within the pH range of 3-7. In this paper, the authors conducted kinetic measurements and showed that pH 6.5 is optimal for kcat/Km. The authors emphasize the significance of mouse AMCase's activity in the neutral region, particularly at pH 6.5, for understanding its physiological relevance in humans. To provide a comprehensive overview, it would be valuable for the authors to summarize the findings from previous and current studies, discuss their implications for future pulmonary therapy in humans, and cite relevant literature. Additionally, the authors should highlight their research's specific contributions and novel findings, such as the determination of kinetic parameters (Kcat and Km) under different pH conditions. Emphasizing why previous studies may have required these observations and underscoring the importance of the present findings in addressing those knowledge gaps will help readers understand the significance of the study and its impact on the field of enzymology.

      We thank the reviewer for this comment. In keeping with the knowledge gaps addressed directly by this paper, we have not augmented the discussion of future pulmonary therapy in humans. We have summarized the present findings at the end of the introduction as follows:

      “We measured the mAMCase hydrolysis of chitin, which revealed significant activity increase under more acidic conditions compared to neutral or basic conditions. To understand the relationship between catalytic residue protonation state and pH-dependent enzyme activity, we calculated the theoretical pKa of the active site residues and performed molecular dynamics (MD) simulations of mAMCase at various pHs. We also directly observed conformational and chemical features of mAMCase between pH 4.74 to 5.60 by solving X-ray crystal structures of mAMCase in complex with oligomeric GlcNAcn across this range.”

      (2) Regarding the implications of the pKa values and Asp138 orientation for the pH optima, it would be valuable for the authors to discuss the variations in optimal activity by pH among GH-18 chitinases and investigate the underlying factors contributing to these differences. In particular, exploring the role of Asp138 orientation in chitotriosidase, another mammalian chitinase, would provide important insights. Chitotriosidase is known to be inactive at pH 2.0, and it would be interesting to investigate whether the observed orientation of Asp138 towards Glu140 in mouse AMCase for pH 2.0 activity is lacking in chitotriosidase.

      There are similar rotations of the two acidic residues in the literature on Chit1. The variety of crystal pH conditions and the lack of a straightforward mechanism for pKa shifts in AMCase make it difficult to draw a comparison to why Chit1 is inactive at low pH, but this is an interesting area for future study. See a more full discussion in: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760363/

      Furthermore, considering the lower activity of human AMCase at pH 2.0, it would be worthwhile to examine whether the Asp138 orientation towards Glu140, as observed in mouse AMCase, is also absent in human AMCase. Exploring this aspect will help determine if the orientation of Asp138 plays a critical role in pH-dependent activity in human AMCase.

      The situation for hAMCase is similar to Chit1 as the rotations observed here for mAMCase are also present. It is not the whether Asp138 can rotate, but rather the relevant energetic penalties as we discuss in the manuscript.

      (3) In a previous study by Okawa et al.(Loss and gain of human acidic mammalian chitinase activity by nonsynonymous SNPs. Mol Biol Evol 33, 3183-3193, 2016), it was reported that specific amino acid substitutions (N45D, D47N, and R61M) encoded by nonsynonymous single nucleotide polymorphisms (nsSNPs) in the N-terminal region of human AMCase had distinct effects on its chitinolytic activity. Introducing these three residues (N45D, D47N, and R61M) could activate human AMCase. This activation significantly shifted the optimal pH from 4-5 to 2.0.

      Considering the significant impact of these amino acid substitutions on the pH-dependent activity of human AMCase, the authors should discuss this point in the manuscript's discussion section. Incorporating the findings and relating them to the current study's observations on pH optima and Asp138 orientation can provide a comprehensive understanding of the factors influencing pH-dependent activity in AMCase.

      We added a citation and dicuss how the mutations identified by this study could potentially shift the pKa of key catalytic residues:

      “Okawa et al identified how primate AMCase lost activity by integration of specific, potentially pKa-shifting, mutations relative to the mouse counterpart42b.”

      (4) To further strengthen the discussion, the authors could explore the ancestral insectivorous nature of placental mammals and the differences in chitinase activity between herbivorous and omnivorous species. Incorporating these aspects would add depth and relevance to the overall discussion of AMCase. AMCase is an enzyme known for its role in digesting insect chitin in the stomachs of various insectivorous and omnivorous animals, including bats, mice, chickens, pigs, pangolins, common marmosets, and crab-eating monkeys 1-7. However, in certain animals, such as dogs (carnivores) and cattle (herbivores), AMCase expression and activity are significantly low, leading to impaired chitin digestion 8. These observations suggest a connection between dietary habits and the expression and activity of the AMCase gene, ultimately influencing chitin digestibility across different animal species 8.

      (1) Strobelet al. (2013). Insectivorous bats digest chitin in the stomach using acidic mammalian chitinase. PloS one 8, e72770.

      (2) Ohno et al. (2016). Acidic mammalian chitinase is a proteases-resistant glycosidase in mouse digestive system. Sci Rep 6, 37756.

      (3) Tabata et al. (2017). Gastric and intestinal proteases resistance of chicken acidic chitinase nominates chitin-containing organisms for alternative whole edible diets for poultry. Sci Rep 7, 6662.

      (4) Tabata et al. (2017). Protease resistance of porcine acidic mammalian chitinase under gastrointestinal conditions implies that chitin-containing organisms can be sustainable dietary resources. Sci Rep 7, 12963.

      (5) Ma et al. (2018). Acidic mammalian chitinase gene is highly expressed in the special oxyntic glands of Manis javanica. FEBS Open Bio 8, 1247-1255.

      (6) Tabata et al. (2019). High expression of acidic chitinase and chitin digestibility in the stomach of common marmoset (Callithrix jacchus), an insectivorous nonhuman primate. Sci. Rep. 9. 159.

      (7) Uehara et al. (2021). Robust chitinolytic activity of crab-eating monkey (Macaca fascicularis) acidic chitinase under a broad pH and temperature range. Sci. Rep. 11, 15470.

      (8) Tabata et al. (2018). Chitin digestibility is dependent on feeding behaviors, which determine acidic chitinase mRNA levels in mammalian and poultry stomachs. Sci Rep 8, 1461.

      This overall point is covered by our brief discussion on diet differences:

      “However, hAMCase is likely too destabilized at low pH to observe an increase in _k_cat. hAMCase may be under less pressure to maintain high activity at low pH due to humans’ noninsect-based diet, which contains less chitin compared to other mammals with primarily insect-based diets42. “

      (5) It is important for the authors to clearly state the limitations of their simulations and emphasize the need for experimental validation or additional supporting evidence. This will provide transparency and enable readers to understand the boundaries of the study's findings. A comprehensive discussion of limitations would contribute to a more robust interpretation of the results.

      We added a sentence to the discussion:

      “Our simulations have important limitations that could be overcome by quantum mechanical simulations that allow for changes in protonation state and improved consideration of polarizability.”

      Minor comments:

      (1) Regarding the naming of AMCase, it is important to accurately describe it based on its acidic isoelectric point rather than its enzymatic activity under acidic conditions based on the original paper (Reference #14 (Boot, R. G. et al. Identification of a novel acidic mammalian chitinase distinct from chitotriosidase. J. Biol. Chem. 276, 6770-6778 (2001)).

      We have made this modification

      (2) In the introduction, providing more context regarding the terminology of acidic mammalian chitinase (AMCase) would be beneficial. While AMCase was initially discovered in mice and humans, subsequent research has revealed its presence in various vertebrates, including birds, fish, and other species. Therefore, it would be appropriate to include the alternative enzyme name, Chia (chitinase, acidic), in the introduction to reflect its broader distribution across different organisms. This clarification would enhance the readers' understanding of the enzyme's taxonomy and facilitate further exploration of its functional significance in diverse biological systems.

      We have made this modification

      (3) The authors mention that AMCase is active in tissues with neutral pHs, such as the lung. However, it is important to consider that the pH in the lung is lower, around 5, due to the presence of dissolved CO2 that forms carbonic acid. The lung microenvironment is known to vary, and specific regions or conditions within the lung may have slightly different pH levels. By addressing the pH conditions in the lungs and their relationship to AMCase's activity, the authors can enhance our understanding of the enzyme's function within its physiological context. A thorough discussion of the specific pH conditions in the lung and their implications for AMCase's activity would provide valuable insights into the enzyme's role in lung pathophysiology.

      To keep the focus on the insights we have made, we have elected not to expand this discussion.

      (4) It would be helpful for the authors to provide more information about the substrate or products of AMCase. The basic X-ray crystal structures used in this study are GlcNAc2 or GlcNAc3, known products of AMCase. Including details about the specific ligands involved in the enzymatic reactions would enhance the understanding of the study's focus.

      We are unclear about what this means - and since it is a minor comment, we have elected not to change the discussion of substrates here.

      (5) The authors should critically evaluate the inclusion of the term "chitin-binding" in the Abstract and Introduction. Suppose substantial evidence or discussion regarding the specific chitin-binding properties of the enzyme or its relevance to the immune response needs to be included. In that case, removing or modifying that statement might be appropriate.

      We are unclear about what this means - and since it is a minor comment, we have elected not to change the discussion of “chitin-binding” here.

      (6) The authors developed an endpoint assay to measure the activity of mouse AMCase across a broad pH range, allowing for direct measurement of kinetic parameters. The authors should provide a more detailed description of the methods used, including any specific modifications made to the previous assay, to ensure reproducibility and facilitate further research in the field. It is important to clearly show the novelty of their endpoint assay compared to previous methods employed in other reports. The authors should also explain how their modified endpoint assay differs from existing assays and highlight its advancements or improvements. This will help readers understand the unique features and contributions of the assay in the context of previous methods.

      We have included a detailed method description and figures already. See also our previous paper by Barad which includes other, related, assays.

      (7) The authors suggest that mouse AMCase may be subject to product inhibition, potentially due to its transglycosylation activity, which can affect the Michaelis-Menten model predictions at high substrate concentrations. However, the reviewer needed help understanding the specific impact of transglycosylation on the kinetic parameters. It would be helpful for the authors to provide a more appropriate and detailed explanation, clarifying how transglycosylation activity influences the kinetic behavior of AMCase and its implications for the observed results.

      The experiments to conclusively demonstrate this are beyond our current capabilities.

      (8) In the Abstract, the authors state, "We also solved high resolution crystal structures of mAMCase in complex with chitin, where we identified extensive conformational ligand heterogeneity." This reviewer suggests replacing "chitin" with "oligomeric GlcNAcn" throughout the text, specifically about biochemical experiments. It is important to accurately describe the experimental conditions and ligands used in the study.

      We have made these changes throughout the manuscript

      (9) In the introduction, the authors mention "a polymer of β(1-4)-linked N-acetyl-D-glucosamine (GlcNAc)". In this case, the letter "N" should be italicized to conform to the proper notation for the monosaccharide abbreviation.

      corrected (and hopefully would have been done so by the copy editor!)

      (10) In the introduction, the authors state, "In the absence of AMCase, chitin accumulates in the airways, leading to epithelial stress, chronic activation of type 2 immunity, and age-related pulmonary fibrosis5,6". It is recommended to clarify that "AMCase" refers to "acidic mammalian chitinase (AMCase)" in this context, as it is the first mention of the enzyme in the introduction.

      We moved that section so that it flows better and is introduced with the full name.

      (11) In the introduction, the authors state, "Mitigating the negative effects of high chitin levels is particularly important for mammalian lung and gastrointestinal health." This reviewer requests further clarification on the connection between chitin and gastrointestinal health. Please provide an explanation or reference to support this statement.

      We have modified this sentence to:

      “Chitin levels can be potentially important for mammalian lung and gastrointestinal health.”

      (12) In the introduction, the authors mention that "Acidic Mammalian Chitinase (AMCase) was originally discovered in the stomach and named for its high enzymatic activity under acidic conditions." It is recommended to include Reference #14 (Boot et al. J. Biol. Chem. 276, 6770-6778, 2001) as it provides the first report on mouse and human AMCase, contributing to the understanding of the enzyme.

      However, it is worth noting that while this paragraph primarily focuses on human tissues, Reference #14 primarily discusses mouse AMCase but also reports on human AMCase. Additionally, References #8 and #9 mainly discuss mouse AMCase. This creates confusion in the description of human and mouse AMCase within the paragraph.

      Considering that this paper aims to focus on the unique features of mouse AMCase, it is suggested that the authors provide a more specific and balanced description of both human and mouse AMCase throughout the main text..

      We have clarified the origin of the name AMCase and the results distinguish the two orthologs in the text with h or mAMCase.

      (13) Figure 1A in the Introduction section has been previously presented in several papers. The authors should consider moving this figure to the Results section and present an alternative figure based on their experimental results to enhance the novelty and impact of the study.

      We have considered this option, but prefer the original placement.

      (14) In the Results section, the authors mentioned, "Prior studies have focused on relative mAMCase activity at different pH18,20, limiting the ability to define its enzymological properties precisely and quantitatively across conditions of interest." It would be beneficial for the authors to include reference #14, the first report showing the pH profile of mouse AMCase, to support their statement.

      We have added this reference

      (15) Regarding the statement, "To overcome the pH-dependent fluorescent properties of 4MU-chitobioside, we reverted the assay into an endpoint assay, which allowed us to measure substrate breakdown across different pH (Supplemental Figure 1A)", the authors should provide a more detailed description of the improvements made to measure AMCase activity. Additionally, it would be helpful to include a thorough explanation of the figure legend for Supplementary Figure 1A to provide clarity to readers.

      We have included a detailed method description and figures already. See also our previous paper by Barad which includes other, related, assays.

      (16) Figure 1B shows that the authors used the AMCase catalytic domain. It would benefit the authors to explain the rationale behind this choice in the figure legend or the main text.

      This point is addressed in the text:

      “Previous structural studies on AMCase have focused on interactions between inhibitors like methylallosamidin and the catalytic domain of the protein.”

      (17) For Figures 1C-E, it is recommended that the authors include error bars in their results to represent the variability or uncertainty of the data. In Figure 1E, the authors should clarify the units of the Y-axis (e.g., sec-1 µM-1). Additionally, in Figure 1F, the authors should explain how the catalytic acidity is shown.

      We have added error bars and axis labels. Figure 1F is conceptual, so we are leaving it as is.

      (18) The authors stated, "These observations raise the possibility that mAMCase, unlike other AMCase homologs, may have evolved an unusual mechanism to accommodate multiple physiological conditions." It would be helpful for the authors to compare and discuss the pH-dependent AMCase activity of mouse AMCase with other AMCase homologs to support this statement.

      That is an excellent idea for future comparative studies, but beyond the scope of what we are examining in this paper.

      (19) The authors should explain Supplemental Figures 1B and C in the Results or Methods sections to provide context for these figures.

      We are unclear about what this means - and since it is a minor comment, we have elected not to change these sections.

      (20) Supplemental Figure 3 is missing any description. It would be important for the authors to include a mention of this figure in the main text before Supplemental Figure 4 to guide the readers.

      The full legend is in there now and the reference to Supplemental 4 was mislabeled.

      (21) For Supplemental Figure 4, the authors should explain the shape of the symbol used in the figure. Additionally, they should explain "apo" and "holoenzyme" in the context of this figure.

      Unclear what a shape means in this context - perhaps the confusion arises because these are violin plots showing distributions.

      (22) Table 1 requires a more detailed explanation of its contents. Additionally, Tables 2 and 3 need to be included. The authors should include these missing tables in the revised version and explain their contents appropriately.

      Table 1 is the standard crystallographic table - there isn’t much more detailed explanation that can be offered. Tables 2 and 3 were not transferred properly by BioRxiv but were included in the review packet as requested a day after submission.

      (23) In Figure 4, it would be beneficial to enlarge Panels A-C to improve the ease of comprehension for readers. Additionally, it is recommended to use D136, D138, and E140 instead of D1, D2, and E to label the respective parts. The authors should also explain the meaning of the symbol used in the figure.

      Since it is a minor comment, we have elected not to change these figures.

      (24) In Figure 5, it would be beneficial to enlarge Panels A-C to improve the ease of comprehension for readers.

      Since it is a minor comment, we have elected not to change these figures.

      (25) Similarly, in Figure 6, all panels should be enlarged to enhance the ease of comprehension for readers.

      Since it is a minor comment, we have elected not to change these figures.

      Reviewer #2 (Recommendations For The Authors):

      In general, I did not identify many detailed or technical concerns with the work. A few items for the authors to consider are listed below.

      (1) The interpretation of the crystallographic datasets seems complicated by the heterogeneity in the substrate component. It might be nice to see more critical analysis of the approach here. Are there other explanations or possible models that were considered? Do other structures of chitinases or other polysaccharide hydrolases exhibit the same phenomenon?

      We have tried in writing it to provide a very critical approach to this and it is quite likely that other structures contain unmodeled density containing similar heterogeneity (but it is just unmodeled).

      (2) It would be ideal to include more experimental validation of the proposed mechanism. Much of the manuscript includes theoretical validations (pKa estimation, dynamics, etc) - but it would be optimal to make an enzyme variant or do an experiment with a substrate analog.

      Yes - we agree that follow on experiments are needed to fully test the mechanism and that those will be the subject of future work.

      (3) For an uninitiated reviewer, I think the major issue with this study is that the broader significance of the work and how it fits into the context of other work on these enzymes is not clear. It would be helpful to be more specific about what we know of mechanism from work on other enzymes to help the reader understand the motivation for this study.

      We have added w few additional references, guided by reviewer 1 comments, that should help in this respect.

    1. Author response:

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

      Public Reviews: 

      Reviewer #1 (Public Review): 

      In this manuscript by Wu et al., the authors present the high-resolution cryoEM structures of the WT Kv1.2 voltage-gated potassium channel. Along with this structure, the authors have solved several structures of mutants or experimental conditions relevant to the slow inactivation process that these channels undergo and which is not yet completely understood. 

      One of the main findings is the determination of the structure of a mutant (W366F) that is thought to correspond to the slow inactivated state. These experiments confirm results in similar mutants in different channels from Kv1.2 that indicate that inactivation is associated with an enlarged selectivity filter. 

      Another interesting structure is the complex of Kv1.2 with the pore-blocking toxin Dendrotoxin 1. The results show that the mechanism of the block is different from similar toxins, in which a lysine residue penetrates the pore deep enough to empty most external potassium binding sites. 

      The quality of the structural data presented in this manuscript is very high and allows for the unambiguous assignment of side chains. The conclusions are supported by the data. This is an important contribution that should further our understanding of voltagedependent potassium channel gating. Specific comments are appended below. 

      (1) In the mains text's reference to Figure 2d residues W18' and S22' are mentioned but are not labeled in the insets. 

      Now labeled in Fig. 2D

      (2) On page 8 there is a discussion of how the two remaining K+ ions in binding sites S3 and S4 prevent permeation K+ in molecular dynamics. However, in Shaker, inactivated W434F channels can sporadically allow K+ permeation with normal single-channel conductance but very reduced open times and open probability at not very high voltages. 

      Addressed in the Discussion, lines 480-490.

      (3) The structures of WT in the absence of K+ show a narrower selectivity filter, however, Figure 4 does not convey this finding. In fact, the structure in Figure 4B is constructed at such an angle that it looks as if the carbonyl distances are increased, perhaps this should be fixed. Also, it is not clear how the distances between carbonyls given in the text on page 12 are measured. Is it between adjacent or kitty-corner subunits? 

      We decided to remove mention of carbonyl distances, because at our resolutions the atoms are not resolved.

      (4) It would be really interesting to know the authors' opinions on the driving forces behind slow inactivation. For example, potassium flux seems to be necessary for channels to inactivate, which might indicate a local conformational change is the trigger for the main twisting events proposed here. 

      We cite Sauer et al. (2011) for the idea that the intact selectivity filter is a strained conformation, and its relaxation yields the wide vestibule seen in NaK2K and Kv channels.  Lines 434-439.

      Reviewer #2 (Public Review): 

      There are four Kv1.2 channel structures reported: the open state, the C-type inactivated state, a dendrotoxin-bound state, and a structure in Na+. 

      A high-resolution crystal structure of the open state for a chimeric Kv1.2 channel was reported in 2007 and there is no new information provided by the cryoEM structure reported in this study. 

      The cryo-EM structure of the C-type inactivated state of the Kv1.2 channel was determined for a channel with the W to F substitution in the pore helix. A cryo-EM structure of the Shaker channel and a crystal structure of a chimeric Kv1.2 channel with an equivalent W to F mutation were reported in 2022. Cryo-EM structures of the C-type inactivated Kv1.3 channel are also available. All these previous structures have provided a relatively consistent structural view of the C-type inactivated state and there is no significant new information that is provided by the structure reported in this study. 

      A structure of the Kv1.2 channel blocked by dendrotoxin is reported. A crystal structure of charybdotoxin and the chimeric Kv1.2 channel was reported in 2013. Density for dendrotoxin could not be clearly resolved due to symmetry issues and so the definitive information from the structure is that dendrotoxin binds, similarly to charybdotoxin, at the mouth of the pore. A potential new finding is that there is a deeper penetration of the blocking Lys residue in dendrotoxin compared to charybdotoxin. It will however be necessary to use approaches to break the symmetry and resolve the electron density for the dendrotoxin molecule to support this claim and to make this structure significant.  

      We have now succeeded in breaking the symmetry and present in Fig. 3 a C1 structure of the toxin-channel complex. In the improved map we now see that our previous conclusion was wrong: the penetration of Lys5 cannot be much deeper than that seen in CTx and ShK structures. However for some reason the pattern of ion-site occupancies in the blocked state is different in this structure than in the others. Fig. 3, Fig. 4E; text lines 559-568.

      The final structure reported is the structure of the Kv1.2 channel in K+ free conditions and with Na+ present. The structure of the KcsA channel by the MacKinnon group in 2001 showed a constricted filter and since then it has been falsely assumed by the K channel community that the lowering of K concentration leads to a construction of the selectivity filter. There have been structural studies on the MthK and the NaK2K channels showing a lack of constriction in the selectivity filter in the absence of K+. These results have been generally ignored and the misconception of filter constriction/collapse in the absence of K+ still persists. The structure of the Kv1.2 channel in Na+ provided a clear example that loss of K+ does not necessarily lead to filter constriction. 

      We are grateful to the reviewer for pointing out this serious omission. We now cite other work including from the Y. Jiang and C. Nichols labs showing examples of outer pore expansion and destabilization. Page p. 4, lines 90-104; lines 421-439.

      The structure in Na+ is significant while the other structures are either merely reproductions of previous reports or are not resolved well enough to make any substantial claims. 

      We now state more clearly the confirmatory nature of our Kv1.2 open structure (lines 71-74) and the similarities of the inactivated-channel structures (lines 193196).

      Reviewer #3 (Public Review): 

      Wu et al. present cryo-EM structures of the potassium channel Kv1.2 in open, C-type inactivated, toxin-blocked and presumably sodium-bound states at 3.2 Å, 2.5 Å, 2.8 Å, and 2.9 Å. The work builds on a large body of structural work on Kv1.2 and related voltage-gated potassium channels. The manuscript presents a large quantity of structural work on the Kv1.2 channel, and the authors should be commended on the breadth of the studies. The structural studies seem well-executed (this is hard to fully evaluate because the current manuscript is missing a data collection and refinement statistics table). The findings are mostly confirmatory, but they do add to the body of work on this and related channels. Notably, the authors present structures of DTXbound Kv1.2 and of Kv1.2 in a low concentration of potassium (with presumably sodium ions bound within the selectivity filter). These two structures add new information, but the studies seem somewhat underdeveloped - they would be strengthened by accompanying functional studies and further structural analyses. Overall, the manuscript is well-written and a nice addition to the field. 

      The data collection and refinement table has been added (Fig. 4 supplement 3.)

      We agree and regret the lack of functional studies. We have not been able to carry them out because work in our laboratory is winding down and the lab soon will be closing.

      Recommendations for the authors: 

      Reviewer #2 (Recommendations For The Authors): 

      (1) It is not obvious from the data shown how well the side chain positions in the inactivated state are defined by the electron density. These figures should be redone. Maybe the use of stereo would be useful. This will be particularly useful for the reader to decide if the small changes in, for example, the positioning of the carbonyl oxygens are believable. 

      Figure 2 – figure supplement 4 shows the stereo views.

      (2) The authors note the changes observed (though small) in the VSD which were not observed in other structures. The relevance of this observation is not described. Do these changes arise due to the different environments of detergents versus nanodisc etc. in the different structures?

      We’ve now inserted a note about variety of environments and how this might be a cause of the difference: lines 280-285.  

      Are there changes in the pore-VSD interface in the inactivated and the open channel structures and if yes, then do mutations at these residues affect inactivation?

      There is surprisingly little movement at the S4-S5 interface residues identified by Bassetto et al. (2022) as having effects on inactivation. Lines 262-267.

      (3) For the structures in Na+, it is important to provide analytical data showing the biochemical behavior of the channel. This is also true for the wild type and the W to F mutant channel. Size exclusion profiles should be included. 

      The SEC profile (noisy, but showing a clear peak) of the channel in Na+ is now shown in Fig. 4 supplement 1. Low expression of the W366F mutant produced even worse SEC results, but we include a representative micrograph of W366F in Na+ to show the monodispersed protein prep. In Figure 5 – figure supplement 1.

      Reviewer #3 (Recommendations For The Authors): 

      Portions of text from the manuscript are indicated by quotations. 

      Introduction: "One goal of the current study was to examine the structure of the native Kv1.2 channel." 

      Comment, minor points: The authors refer to the Kv1.2 construct used for the structural studies as "native Kv1.2". I found this somewhat confusing because the word "native" suggests derived from a native source. The phrasing above also gives the impression that the structure by Wu et al is the first structure of Kv1.2. The Kv1.2 construct is essentially identical to the one used by Long et al in 2005 to determine the initial structure of Kv1.2 (PDB 2A79). The authors discuss a subsequent paddle-chimera Kv1.2-2.1 structure from 2007 (PDB 2R9R) in the introduction, but it would be prudent to mention the 2005 one of Kv1.2 as well. The open structure determined by Wu et al. is an improvement on the 2A79 structure in that the 2A79 structure was modeled as a poly-alanine model within the voltage sensor domain. Nevertheless, the Kv1.2-2.1 structure (2R9R) is highly similar to the 2A79 structure of Kv1.2. The 2007 structure indicated that Kv1.2-2.1 recapitulates structural features of Kv1.2. It is therefore not surprising that the open structure presented here is highly similar to that of both PDB 2A79 (Kv1.2) and PDB 2R9R (Kv1.2-2.1).  

      We failed to point out the high quality of the original Long et al. 2005 structure and its comparisons with the chimeric structure in Long et al. 2007. We now have tried to correct this: lines 70-74.

      Comment: The cryo-EM analyses suggest that a large percentage (most?) of the particles are missing the beta subunit. This should be commented on somewhere.      

      Now noted on lines 120-132, we pooled particles with and without beta subunits. 

      Regarding ions in the selectivity filter, one-dimensional plots of the density would strengthen the analysis.

      Now included in Fig. 4.

      Also, one should mention caveats associated with identifying ions in cryo-EM maps and the added difficulty/uncertainty when the density is located along a symmetry axis (C4 axis, due to the possible build-up of noise). C1 reconstructions, showing density within the filter, if possible, would strengthen the analyses.

      You are correct. However local resolution is highest in the selectivity filter region. So I think that since the CTF-based filtering is constant over all the structure I think the SNR will be good on axis. 

      Comment: The section on channel inactivation could be simplified by stating that the structure is highly similar to W17'F structures of other Kv channels. (And then discussing possible differences).  

      We now note, “overall conformational difference is identical…” p. 7, lines 193-196.

      "Salt bridges involving the S4 Arg and Lys residues are shifted slightly (Figure 2-figure supplement 3A-D). Arg300 (R3) is in close proximity to Glu226 on the S2 helix for the open channel, while R3 is closer to Glu183 in the S2 helix. The Glu226 side chain adopts a visible interaction with R4 in the inactivated state." 

      Comment: The density for these acidic amino acids seems weak, especially in the inactivated state. It seems like a stretch to make much of their possible conformational changes. 

      We’ve included stereo pairs in Fig. 2 – figure supplement 4.

      "By adding 100 nM α-DTx to detergent solubilized Kv1.2 protein we obtained a cryo-EM structure at 2.8 Å resolution of the complex." 

      Comment: 100 nm. might be lower than the Kv concentration. The current methods are ambiguous on the concentration of Kv channel used for the DTx sample. From the methods, it seems possible that 100 nM DTX is a sub-stoichiometric amount relative to the channel. Regardless, the cryo-EM data seems to suggest that a large percentage of particles do not have DTx bound. This surely complicates the interpretation of density within the filter (which has partly been ascribed to a lysine side chain from DTx).

      The reviewer correctly points a potentially serious problem. It turns out that the 100nM figure we quoted was incorrect, and the actual concentration of toxin, >400 nM, was substantially greater than the protein concentration. This is confirmed by the small fraction (<1%) of 3D class particles that do not show the toxin density (lines 303-306).

      Comment: The methods on atomic structure building/refinement (Protein model building, refinement, and structural analysis) are sparse. A table is needed showing data collection and refinement statistics for each of the structures. This data should also provide average B factors for the ions in the filter. An example can be found in PMID 36224384. 

      Data collection and statistics are now in Fig. 4 – figure supplement 3.

      "In the selectivity filter of the toxin-bound channel (Figure 3E) a continuous density is seen to extend downward from the external site IS0 through to the boundary between IS1 and IS2. This density is well modeled by an extended Lys side chain from the bound toxin, with the terminal amine coordinated by the carbonyls of G27”.

      Comment: While there seems to be extra density in site IS0 from the figures, the density ascribed to lysine in the filter doesn't seem that distinct from those of ions in the open structure. 1-dimensional density plots and some degree of caution may be prudent. Could there, for example, be a mixture of toxin-bound and free channels in the dataset?

      Could the lysine penetrate to different depths? If the toxin binds with nM affinity, why are any channels missing the toxin? Have the authors modeled an atomic structure of the entire toxin bound to the channel to evaluate how plausible the proposed binding of the lysine is? Can the toxin be docked onto Kv1.2 with the deep positioning of the lysine and not clash with the extracellular surface of Kv1.2? 

      We also were concerned about these issues. We have been able to obtain a C1 reconstruction of the toxin-channel complex. In building the atomic model we found that indeed the Lys5 side chain could not penetrate as far as we had thought, and appears to be coordinated by the first carbonyl pair. Fig. 3; text lines 331-332. 

      "Toxin binding shrinks the distances between opposing carbonyl oxygens in the selectivity filter, forming a narrower tunnel into which the Lys side chain fits (Figure 3F). The second and fourth carbonyl oxygen distances are substantially reduced from 4.7 Å and 4.6 Å in an open state to 3.7 Å and 3.9 Å, respectively (Figure 4E). In a superposition of Kv1.2 open-state and α-DTX-bound P-loop structures, there is also an upward shift of the first three carbonyl groups by 0.7~1.0 Å (Figure 4F). " 

      Comment: I suspect the authors intend to refer to Figure 3F rather than 4. I would be cautious here. The refined positions of the carbonyl oxygens are almost certainly affected by the presence or absence of ions in the atomic model during refinement. The density and the resolution of the map may not be able to distinguish small changes to the positions of the carbonyl oxygens (and these differences/uncertainties are compounded by the C4 symmetry). 

      "On the other hand, the terminal amine of lysine in α-DTX is deeply wedged at the second set of carbonyls, narrowing both IS1 and IS2 while displacing ions from the sites (Figure 3-figure supplement 2A). CTX does not cause narrowing of the selectivity filter or displacements of the carbonyls (Figure 3-figure supplement 2B). "

      Comment: Again, caution would be prudent here.  

      We are very grateful to the reviewer for pointing out these problems. We have removed these statements that are weakly supported at our resolution level.

      "Shaker channels are able to conduct Na+ in the absence of K+ (Melishchuk et al., 1998)." 

      Comment: How about the Kv1.2 channel? Is Kv1.2 able to conduct Na+ in the absence of K+ ? This would certainly be relevant for interpreting the conformation of the filter and the density ascribed to Na+ for the structure in sodium.  

      We agree wholeheartedly, but unfortunately we are no longer capable of doing the measurements as our lab will soon close.

      "Ion densities are seen in the IS1, IS3, and IS4 ion binding sites, but the selectivity filter shows a general narrowing as would be expected for binding of sodium ions. The second, third, and fourth carbonyl oxygen distances are reduced from 4.7 Å, 4.7 Å, and 4.6 Å in the open state to 4.4 Å, 3.9 Å, and 4.5 Å, respectively. The rest of the channel structure is very little perturbed. " 

      Comment: The density for IS4 seems weak. To me, it looks like IS1 and IS3 are occupied, whereas IS2 and IS4 are much weaker. 1-dimensional density plots would be helpful. I would suggest caution in commenting too strongly on the "general narrowing" since the resolution of the maps, the local density, and the atomic structure refinement would be consistent with coordinate errors of 0.5 Å or more - and would be compounded (~ doubled) by measuring between symmetry-related atoms.  

      We present 1D plots in Fig. 4E. We no longer comment on “narrowing”

      "Finally, the snake toxin a-Dendrotoxin (DTx) studied here is seen to block Kv1.2 by insertion of a lysine residue into the pore." 

      Comment: Discussion (and references) should be given regarding what was known prior to this study on the mode of inhibition by DTx. 

      Discussion and references now added, lines 287-301.

      "On the other hand, a lengthy molecular-dynamics simulation of deactivation in the Kv1.2-2.1..." 

      Comment: I don't think mentioning this personal communication adds to the manuscript. 

      Actually the original “personal communication” reference was there because the situation is complicated. The movie S3 accompanying the Jensen et al. paper shows deactivation and dewetting of the channel during a 250 us simulation. In the movie there are ions visible in the selectivity filter for the first 50 us, but after that the SF appears empty. Puzzled by this we contacted Dr. Jensen who explained that the movie was in error, ions remain in the SF throughout the entire 250 us. We now cite Jensen (2012) along with the personal communication.

      "The difference between the open and inactivated Kv1.2 structures, like the difference in Kv1.2-2.1 (Reddi et al., 2022) and Shaker (Tan et al., 2022) can be imagined as resulting from a two-step process." 

      Comment: Confusing phrasing because the authors mean to compare their structure to inactivated structures of Kv1.2-2.1 and shaker. 

      Fixed, lines 220-222.

      "Molecular dynamics simulations by Tan et al. based on the Shaker-W17'F structure show that IS3 and IS4 are simultaneously occupied by K+ ions in the inactivated state." 

      Comment: I think that the word "show" is too strong. Perhaps "suggest" 

      The MD result seems to us to be unequivocal, that most of the time the two sites are occupied by ions.

      References are needed for the following statements:  

      -  "as well as the charge-transfer center phenylalanine"

      Now citing Tao et al. 2010, line 156.

      - "total gating charge movement in Shaker channels is larger, about 13 elementary charges per channel" 

      Now citing the review by Islas, 2015 (line 166-169).

      "The selectivity filter of potassium channels consists of an array of four copies of the extended loop (the P-loop) formed by a highly conserved sequence, in this case, TTVGYGD. Two residues anchor the outer half of the selectivity filter and are particularly important in inactivation mechanisms (Figure 2B, right panels). Normally, the tyrosine Y28' (Y377 in Kv1.2) is constrained by hydrogen bonds to residues in the pore helix and helix S6 and is key to the conformation of the selectivity filter. The final aspartate of the P-loop, D30' (D379 in Kv1.2) is normally located near the extracellular surface and has a side chain that also participates in H-bonds with W17' (W366 in Kv1.2) on the pore helix." 

      Citations added (Pless 2013, Sauer 2011) lines 211-214.

      - "During normal conduction, ion binding sites in the selectivity filter are usually occupied by K+ and water molecules in alternation." 

      Added Morais-Cabral et al. 2001, p. 17, lines 463-465.

    1. Author response:

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

      eLife assessment

      The authors present evidence suggesting that MDA5 can substitute as a sensor for triphosphate RNA in a species that naturally lacks RIG-I. The key findings are potentially important for our understanding of the evolution of innate immune responses, but the evidence is incomplete, as additional biochemical and functional experiments are needed to unambiguously assign MDA5 as a bona fide sensor of triphosphate RNA in this model. This also leaves the title as overstating its case.

      We would like to thank the editorial team for these positive comments on our manuscript and the constructive suggestions to improve our manuscript. According to the suggestions and valuable comments of the referees, we have added substantial amounts of new data and analysis to substantiate our claims, and the manuscript, including the title, has been carefully revised to better reflect our conclusions. We are now happy to send you our revised manuscript, we hope the modified manuscript addresses your and the reviewers’ concerns satisfactorily and is suitable for publication in eLife now.

      Reviewer #1 (Public Review):

      This study offers valuable insights into host-virus interactions, emphasizing the adaptability of the immune system. Readers should recognize the significance of MDA5 in potentially replacing RIG-I and the adversarial strategy employed by 5'ppp-RNA SCRV in degrading MDA5 mediated by m6A modification in different species, further indicating that m6A is a conservational process in the antiviral immune response.

      However, caution is warranted in extrapolating these findings universally, given the dynamic nature of host-virus dynamics. The study provides a snapshot into the complexity of these interactions, but further research is needed to validate and extend these insights, considering potential variations across viral species and environmental contexts.

      We concur with the viewpoint that virus-host coevolution complicates the derivation of universal conclusions. To address this challenge, incorporated additional experiments and data based on the suggestions of the reviewers. These experiments were carried out across diverse models, including two distinct vertebrate species (M. miiuy and G. gallus), two different viruses (SCRV and VSV), and the synthesis of corresponding 5’ppp-RNA probes. We believe that these supplementary data bolster the evidence supporting the immune replacement role of MDA5 in the recognition of 5'ppp-RNA in RIG-I deficient species (Figure 1C-1E, Figure 2O and 2P, Figure 4). Moreover, we have duly incorporated references in both the introduction and discussion sections to further support our conclusion that MDA5 in T. belangeri, a mammal lacking RIG-I, possesses the ability to detect RNA viruses posed as RIG-I agonists (doi: 10.1073/pnas.1604939113). Lastly, meticulous revisions have been undertaken in the manuscript, including adjustments to the title, to ensure harmonization with our research outcomes.

      Reviewer#2 (Public Review):

      This manuscript by Geng et al. aims to demonstrate that MDA5 compensates for the loss of RIG-I in certain species, such as teleost fish miiuy croaker. The authors use siniperca cheats rhabdovirus (SCRV) and poly(I:C) to demonstrate that these RNA ligands induce an IFN response in an MDA5-dependent manner in M. miiuy derived cells. Furthermore, they show that MDA5 requires its RD domain to directly bind to SCRV RNA and to induce an IFN response. They use in vitro synthesized RNA with a 5'triphosphate (or lacking a 5'triphosphate as a control) to demonstrate that MDA5 can directly bind to 5'-triphosphorylated RNA. The second part of the paper is devoted to m6A modification of MDA5 transcripts by SCRV as an immune evasion strategy. The authors demonstrate that the modification of MDA5 with m6A is increased upon infection and that this causes increased decay of MDA5 and consequently a decreased IFN response.

      The key message of this paper, i.e. MDA5 can sense 5'-triphosphorylated RNA and thereby compensate for the loss of RIG-I, is novel and interesting, yet there is insufficient evidence provided to prove this hypothesis. Most importantly, it is crucial to test the capacity of in vitro synthesized 5'-triphosphorylated RNA to induce an IFN response in MDA5-sufficient and -deficient cells. In addition, a number of important controls are missing, as detailed below.

      To further support the notion that MDA5 is capable of detecting 5'ppp-RNA in species lacking RIG-I, we conducted additional experiments. Initially, we isolated the RNA from SCRV and VSV viruses. Subsequently, we synthesized 5'ppp-RNA probes that corresponded to the genome termini of SCRV and VSV in vitro. Then, these RNAs were treated with Calf intestinal phosphatase (CIAP) to generate dephosphorylated derivatives. Next, we separately tested the activation ability of various RNAs on IRF3 dimer and IFN response in MKC (M. miiuy kidney cell line) and DF-1 (G. gallus fibroblast cell line) cells, and determined that the immune activation ability of SCRV/VSV viruses depends on their triphosphate structure (Figure 1C-1E, Figure 4C and 4J). In addition, the knockdown of MDA5 inhibited the immune response mediated by SCRV RNA (Figure 2P and 2Q). Finally, we incorporated essential experimental controls (Figure 4B and 4I). We think that the inclusion of these supplementary experimental data significantly enhances the credibility and further substantiates our hypothesis.

      The authors describe an interaction between MDA5 and STING which, if true, is very interesting. However, the functional implications of this interaction are not further investigated in the manuscript. Is STING required to relay signaling downstream of MDA5?

      To better explore the role of STING in MDA5 signal transduction, we constructed a STING expression plasmid and synthesized specific siRNA targeting STING. Next, we found that co-expression of STING and MDA5 significantly enhance MDA5-mediated IFN-1 response during SCRV virus infection (Figure 2N). Conversely, silencing of STING expression restored the MDA5-mediated IFN-1 response (Figure 2O). These findings provide important evidence for the critical involvement of STING in the immune signaling cascade mediated by MDA5 in response to 5'ppp-RNA viruses.

      The second part of the paper is quite distinct from the first part. The fact that MDA5 is an interferon-stimulated gene is not mentioned and complicates the analyses (i.e. is there truly more m6A modification of MDA5 on a per molecule basis, or is there simply more total MDA5 and therefore more total m6A modification of MDA5).

      For the experimental data analysis in Figure 5E and 5F, we first compared the m6A-IP group to the input group, and then normalized the control group (IgG group of 5E and Mock group of 5F) to a value of “1”. Given the observed variability in MDA5 expression levels within the input group of Mock and SCRV virus-infected cells, our analysis represents the actual m6A content of each MDA5 molecule. To enhance clarity, we have updated the label on the Y-axis in Figure 5E and 5F.

      Finally, it should be pointed out that several figures require additional labels, markings, or information in the figure itself or in the accompanying legend to increase the overall clarity of the manuscript. There are frequently details missing from figures that make them difficult to interpret and not self-explanatory. These details are sometimes not even found in the legend, only in the materials and methods section. The manuscript also requires extensive language editing by the editorial team or the authors.

      We acknowledge the valuable feedback from the reviewer and have made significant improvements to our manuscript based on the recommendations provided in the "Recommendation for the authors" section. Furthermore, we have conducted a thorough review of the entire article, resulting in substantial enhancements to the format, clarity, and overall readability of our manuscript.

      Reviewer#3 (Public Review):

      Summary: In this manuscript, the authors investigated the interaction between the pattern recognition receptor MDA5 and 5'ppp-RNA in a teleost fish called Miiuy croaker. They claimed that MDA5 can replace RIG-I in sensing 5'ppp-RNA of Siniperca cheats rhabdovirus (SCRV) in the absence of RIG-I in Miiuy croaker. The recognition of MDA5 to 5'ppp-RNA was also observed in the chicken (Gallus gallus), a bird species that lacks RIG-I. Additionally, they reported that the function of MDA5 can be impaired through m6A-mediated methylation and degradation of MDA5 mRNA by the METTL3/14-YTHDF2/3 regulatory network in Miiuy croaker under SCRV infection. This impairment weakens the innate antiviral immunity of fish and promotes the immune evasion of SCRV.

      Strengths:<br /> These findings provide insights into the adaptation and functional diversity of innate antiviral activity in vertebrates.

      Weaknesses:<br /> However, there are some major and minor concerns that need to be further addressed. Addressing these concerns will help the authors improve the quality of their manuscript.One significant issue with the manuscript is that the authors claim to be investigating the role of MDA5 as a substitute for RIG-I in recognizing 5'ppp-RNA, but their study extends beyond this specific scenario. Based on my understanding, it appears that sections 2.2, 2.3, 2.5, 2.6, and 2.7 do not strictly adhere to this particular scenario. Instead, these sections tend to investigate the functional involvement of Miiuy croaker MDA5 in the innate immune response to viral infection. Furthermore, the majority of the data is focused on Miiuy croaker MDA5, with only a limited and insufficient study on chicken MDA5. Consequently, the authors cannot make broad claims that their research represents events in all RIG-I deficient species, considering the limited scope of the species studied.

      We agree with the reviewer's perspective that functional analysis of MDA5 in M. miiuy may not adequately represent all species lacking RIG-I. To address this concern, we have incorporated additional experimental data utilizing different model systems, including two different vertebrate species (M. miiuy and G. gallus), two distinct viruses (SCRV and VSV), and the synthesis of two corresponding 5’ppp-RNA probes. While the functional characterization of G. gallus MDA5 remains relatively limited compared to M. miiuy, our current experimental findings provide support for two key observations. Firstly, the triphosphate structure of the VSV virus is pivotal in activating the innate immune response in G. gallus against the virus (Figure 1D and 4J). Secondly, G. gallus MDA5 can recognize 5’ppp-RNA (Figure 4I, 4K and 4L). Consequently, although we cannot definitively establish the immune surrogate function of MDA5 in all RIG-I-deficient species, our research data further substantiates this hypothesis. Moreover, we have adopted a more cautious attitude in summarizing our experimental conclusions, thereby enhancing the rigor of our manuscript language.

      The current title of the article does not align well with its actual content. It is recommended that the focus of the research be redirected to the recognition function and molecular mechanism of MDA5 in the absence of RIG-I concerning 5'ppp-RNA. This can be achieved through bolstering experimental analysis in the fields of biochemistry and molecular biology, as well as enhancing theoretical research on the molecular evolution of MDA5. It is advisable to decrease or eliminate content related to m6A modification.

      Following the reviewer's recommendations, we have revised the title to emphasize that our main research focus is a teleost fish devoid of RIG-I. Furthermore, we have conducted additional molecular experiments to further elucidate the 5'ppp-RNA recognition function of MDA5 in RIG-I-deficient species. In an attempt to analyze the potential molecular evolution of MDA5 resulting from RIG-I deficiency, we collected MDA5 coding sequences from diverse vertebrates. However, due to multiple independent loss events of RIG-I in fish, fish with or without RIG-I genes in the phylogenetic tree cannot be effectively clustered separately, making it extremely difficult to perform this aspect of analysis. Consequently, we have regrettably opted to forgo the molecular evolution analysis of MDA5.

      Our article topic is to reveal an antagonistic phenomenon between fish receptor and RNA viruses. The MDA5 of RIG-I-lost fish has evolved the ability to recognize 5’ppp-RNA virus and mediate IFN response to resist SCRV infection. Conversely, the m6A methylation mechanism endows the SCRV virus with a means to weaken the immune capacity of MDA5. Therefore, we believe that the latter part is an important part of the arms race between the virus and its host, and should be retained.

      Additionally, the main body of the writing contains several aspects that lack rigor and tend to exaggerate, necessitating significant improvement.

      We appreciate the reviewer’s comment and have improved the manuscript addressing the points raised in the “Recommendation for the authors”. We have added corresponding experiments to strengthen the verification of the conclusions, and in addition, we are more cautious in summarizing the language of the conclusions.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The evidential foundation within the Result 1 section appears somewhat tenuous.

      Firstly, the author derives conclusions regarding the phenomenon of RIG-I loss in lower vertebrates by referencing external literature and conducting bioinformatics analyses. It is pertinent to inquire whether the author considered fortifying these findings through additional WB/PCR experiments, particularly for evaluating RIG-I expression levels across diverse vertebrates, encompassing both lower and higher orders.

      Firstly, the species we analyzed are mostly model species with excellent genomic sequence information in the database. Secondly, the RIG-I protein sequences (at least some domain sequences) are relatively conserved in vertebrates. Therefore, the credibility of evaluating the existence of RIG-I in these species through homology comparison is high. Therefore, we do not intend to conduct additional PCR/WB experiments to confirm this.

      Additionally, following the identification of RIG-I loss, the author postulates MDA5 as a substitute of RIG-I, grounding this speculation in the analysis of MDA5 and LGP2 protein structures. It is imperative to address whether the author could enhance the manuscript by supplying expression data for MDA5 and LGP2 across different vertebrates and elucidating further why MDA5 is posited as the compensatory mechanism for RIGI loss.

      Like MDA5, LGP2 is also an interferon-stimulating gene, so they both likely exhibit high sensitivity to viral infections. Therefore, we think that comparing the expression data of these two genes is difficult to evaluate their function. In mammals, the regulatory mechanisms of LGP2 to RIG-I and MDA5 were complicated and ambiguous. To evaluate the potential function of LGP2 in M. miiuy, we further constructed LGP2 plasmid and synthesized siRNA targeting LGP2. Then, our results indicate that mmiLGP2 can enhance the antiviral immune response mediated by mmiMDA5 (Figure 1H and 1I), further indicating the regulatory role of mmiLGP2 in RLR signaling, rather than acting as a compensatory receptor for RIG-I.

      Also, is it conceivable that other receptors contribute to this compensatory effect in lower vertebrates?

      5’ triphosphate short blunt-end double-strand RNA is the ligand of RIG-I as contained in the panhandle of negative-strand viral genomes. We mainly focus on the immune recognition and compensatory effects of other receptors on RIG-I loss, and MDA5, as the protein with the most similar structure, first attracted our attention. In addition, IFIT proteins have been reported to recognize triphosphate single-stranded RNA (doi: 10.1038/nature11783). However, we used SCRV and VSV RNA as viral models, both of which have negative stranded genomes and meet the ligand standards of RIG-I, rather than IFIT. Therefore, we excluded the IFIT protein from our research scope.

      (2) The article exclusively employs a singular type of 5'PPP-RNA virus and one specific lower vertebrate species, thereby potentially compromising the robustness of the assertion that this phenomenon is prevalent in lower vertebrates. To bolster this claim, could the author consider incorporating data from an alternative 5'PPP-RNA virus and a different lower vertebrate species?

      To address this concern, we have incorporated additional experimental data utilizing different model systems, including two different vertebrate species (M. miiuy and G. gallus) and two distinct viruses (SCRV and VSV). While the functional characterization of G. gallus MDA5 remains relatively limited compared to M. miiuy, our current experimental findings provide support for two key observations. Firstly, the triphosphate structure of the VSV virus is pivotal in activating the innate immune response in G. gallus against the virus (Figure 1D and 4J). Secondly, G. gallus MDA5 can recognize 5’ppp-RNA (Figure 4I, 4K and 4L). Consequently, these experimental results further confirmed the conservatism of this immune compensation mechanism.

      (3) A nuanced consideration of the statement in Result 5 is warranted. Examination of the results under SCRV infection conditions suggests dynamic fluctuations in MDA5 expression levels, challenging the veracity of the statement implying "increased expression", which contradicts the proposed working model of this article.

      Because MDA5 acts as a receptor and plays a recognition immune role in the early stages of virus infection, the expression of MDA5 in the early stage of SCRV infection rapidly increases. In the later stage of infection, the expression of MDA5 may gradually decrease again due to the negative feedback mechanism in the host body to prevent excessive inflammation. However, compared to the uninfected group, the expression of MDA5 was significantly increased in the SCRV-infected group, so we believe that the term "increased expression" is not a problem. In addition, the m6A mechanism can weaken the function of MDA5, but it still cannot prevent the overall increase of MDA5 expression, which is not contradictory to the working model in this article.

      Additionally, the alterations in m6A levels in miiuy croaker under SCRV infection conditions warrant clarification. Could the author employ m6A dot blotting to supplement the findings related to total m6A levels?

      Our previous studies (doi: 10.4049/jimmunol.2200618) have suggested that the total m6A level is increased after SCRV infection in miiuy croaker. We cited this conclusion in the discussion of our manuscript.

      (4) It would be beneficial if the editors could assist the author in enhancing the language of the manuscript.

      We have carefully checked the full article and modified it with Grammarly tools, and we believe that the grammar, format, and readability of our articles have been greatly improved.

      Reviewer #2 (Recommendations For The Authors):

      Figure 1

      (1) Figure 1B - some clarification needs to be added about this figure in the text. It is unclear what the main point is that the authors would like to convey.

      What we want to emphasize is that some species with RIG-I, such as zebrafish, have also experienced RIG-I loss events, but have undergone whole genome replication events before the loss, thus preserving a copy of RIG-I. This indicates that loss events of RIG-I are very common in vertebrates and do not occur randomly. We have elaborated on this point in the results and discussion.

      (2) Figure 1C - is not very informative other than showing Mm MDA5 and LGP2 side-by-side. It would be more useful to show a comparison of human RIG-I/MDA5 alongside Mm and Gg MDA5. Are there any conserved/shared key residues between hRIG-I/hMDA5 versus mmMDA5?

      Homologous proteins are often known to adopt the same or similar structure and function. We have added human RIG-I domain information to this figure (Figure 1F). By comparing the domain information of human RIG-I with M. miiuy MDA5 and LGP2, M. miiuy MDA5 has a similar structure to human RIG-I, making it most likely to compensate for the missing RIG-I. While M. miiuy LGP2 lacks the CARD domain, which is crucial for signal transduction, so we will shift our focus to M. miiuy MDA5. In addition, we collected protein sequences of MDA5 and RIG-I from various vertebrates to identify key residues evolved in recognizing 5'ppp-RNA by M. miiuy MDA5. However, unfortunately, no potential residues were found during the comparison process.

      Figure 2

      (1) Figure 2B - It would be important to demonstrate MDA5-Flag expression by immunoblot and compare MDA5-Flag overexpression to endogenous MDA5 expression using the anti-MDA5 antibody from panel 2A. If IF is used, more cells need to be visible in the field.

      After transfecting the MDA5 plasmid into MKC, endogenous MDA5 expression was detected using MDA5 antibodies. The results showed a significant increase in MDA5 protein levels, indicating that MDA5 antibodies can specifically recognize MDA5 protein. In addition, we retained the original immunofluorescence images to better demonstrate the subcellular localization of MDA5.

      (2) Figure 2C - The 1:1 stoichiometry of MDA5:MAVS (in the absence of any stimulus) is quite surprising. How does the interaction between MDA5 and MAVS change upon stimulation with an RNA ligand (SCRV, poly(I:C))?

      We do not believe that the actual stoichiometry between MDA5 and MAVS is what you described as 1:1. In fact, the proportion of proteins in the complex depends on many factors in the experimental results with Co-IP. Firstly, the MDA5 plasmid in this study has a 3 × Flag tag, while the MAVS only has a 1x Myc tag, which makes the antibody more sensitive for detecting MDA5-Flag. In addition, the Co-IP results are also affected by multiple factors such as the type of antibody and the number of recoveries, making it difficult to estimate the actual ratio of MDA5 to MAVS. Based on the above reasons and the fact that the detection of the interaction strength between MDA5 and MAVS after infection seems to be off-topic, we did not continue to explore this point.

      (3) Figure 2D - The interaction between MDA5 and STING is a very interesting finding but is not elaborated on in the paper (even though the interaction between MDA5 and STING is mentioned in the abstract). The manuscript would be strengthened if the interaction between MDA5 and STING is further investigated. For example, does the IFN response that is reported in panels 2E to 2H require the presence of STING? Does mmMDA5 signal via STING in response to a DNA ligand?

      We appreciate the referee's suggestion to study the mutual influence between MDA5 and STING. We found that co-expression of STING and MDA5 can enhance MDA5-mediated IFN-1 response during SCRV virus infection, while knocking down STING can restore MDA5-mediated IFN-1 (Figure 2N and 2O). This indicates that STING plays an important signaling role in the immune response of MDA5 to RNA viruses. We understand the importance of cGAS/STING pathways in identifying exogenous DNA, so exploring the MDA5 pathway for DNA ligand recognition is an interesting and meaningful perspective. But this seems to be detached from the theme of our article, so we didn't continue to explore this point.

      (4) Figures 2F and 2H - the authors demonstrate that SCRV induces a type I IFN response in an MDA5-dependent manner. While SCRV is a single-stranded negative-sense RNA virus that contains 5'ppp-RNA, it cannot be excluded that MDA5 is activated here in response to a double-stranded RNA intermediate of viral origin or even a host-derived RNA whose expression or modification is altered during infection. To demonstrate in an unambiguous manner that MDA5 senses 5'ppp-RNA, it is crucial to use the in vitro synthesized 5'ppp-RNA (and its dephosphorylated derivative as a control) from Fig. 4 in these experiments.

      We transfected 5 'ppp SCRV and 5' ppp VSV (and their dephosphorylated derivatives) synthesized in vitro into MKC cells and DF-1 cells, respectively. The results showed that 5’ppp-RNAs significantly promoted the formation of IRF3 dimers, while their dephosphorylated derivatives did not (Figure 4C and 4J). In addition, we extracted virus RNA from the SCRV and VSV viruses and dephosphorylated them with Calf intestinal phosphatase (CIAP). These RNAs were transfected into MKC and DF-1 cells and found that the immune response mediated by virus RNAs was much higher than the dephosphorylated form (Figure 1C-1E). The above results indicate that the immune response activated by SCRV and VSV is indeed dependent on their triphosphate structure. Finally, the IRF3 dimer and IFN induction activated by SCRV RNA can be inhibited by si-MDA5 (Figure 2P and 2Q), further demonstrating the involvement of MDA5 in the immune response mediated by 5’ppp-RNA ligands.

      (5) In mice and humans, MDA5 is known to collaborate with LGP2 to jointly induce an IFN response. Does M.miiuy express LGP2? If so, it would be informative to include a siRNA targeting LGP2 in the experiments in panel F. In mammals, LGP2 potentiates the response via MDA5 while it may inhibit RIG-I activation.

      M.miiuy express LGP2. We constructed an LGP2 plasmid and synthesized si-LGP2 to investigate the impact of LGP2 on MDA5-mediated immune processes (Figure 1G-1I). The results showed that LGP2 can enhance the IFN response mediated by MDA5 during SCRV virus infection, similar to that in mammals.

      (6) Minor comment - Is the poly(I:C) used in this figure high or low molecular weight poly(I:C)? HMW poly(I:C) preferentially stimulates MDA5, while LMW poly(I:C) preferentially stimulates RIG-I.

      We used poly(I:C)-HMW as a positive control for activating MDA5. We have modified the relevant information in Figure 2 and its legend.

      Figure 3

      (1) Figure 3F/G - The normalization in this Figure is difficult to interpret. It would be better to split Figure 3G into 4 separate graphs and include the mock-infected cells alongside the infected samples (as done in Figure 2).

      To better demonstrate the function of the RD domain of MDA5 in M. miiuy, we have changed the experimental plan, as shown in figure 3F. We detected the induction of antiviral factors by overexpression of MDA5 and MDA5-△RD under poly (I:C)-HMW stimulation. This can indicate that the RD domain of MDA5 has a conserved function in the recognition of poly(I:C)-HMW in M. miiuy, and can serve as a positive control for the recognition of SCRV virus by the RD domain.

      Figure 4

      (1) Figure 4B - A number of important controls are missing. Was the immunoprecipitation of RNA successful? This could be shown by running a fraction of the immunoprecipitated material on an RNA gel and/or by showing that the input RNA was depleted after IP. In addition, a control IP (Streptavidin beads without biotinylated RNA) is missing to ensure that MDA5 does not stick non-specifically to the Streptavidin resin.

      We appreciate the referee's suggestions. We rerun this experiment and added a non-biomarker RNA IP control group, and the results showed that MDA5 did not adsorb non-specific onto the beads (Figure 4B). In addition, based on the referee's suggestion, we tested the consumption of RNA before and after immunoprecipitation, and the results showed that biotin-labeled RNA, rather than non-biotin-labeled RNA, could be adsorbed by beads, indicating the success of RNA precipitation. However, we think that this is not necessary for the final presentation of the experimental results, so we did not show this in the figure.

      (2) Figure 4B - It is unclear why there is such a large molecular weight difference between endogenous MDA5 and MDA5-Flag (110 kDa versus 130/140 kDa). Why is there less MDA5-Flag retrieved than endogenous MDA5?

      After careful analysis, we believe that the significant difference in molecular weight between endogenous MDA5 and MDA5 Flag may be due to three reasons. Firstly, MDA5 flag has a 3× Flag tag. Secondly, as shown in the primer table, we constructed MDA5 between the NotI and XbaI cleavage sites in the pcDNA3.1 vector, which are located at the posterior position in the vector. This means that the Flag tag has a certain distance from the starting codon of MDA5, and these sequences on the vector can also be translated and increase the molecular weight of the exogenous MDA5 protein. Finally, in order to facilitate the amplification of the primers, the F-terminal primers of MDA5 contain a small portion of the 3'UTR sequence (excluding the stop codon). These above reasons may have led to significant differences in molecular weight. In addition, in order to supplement important experimental controls, we have conducted a new RNA pull-down experiment as shown in Figure 4B.

      (3) Minor point: Figure 4B - please clarify in the figure whether RNA or protein is immunoprecipitated and via which tags.

      We have conducted a new RNA pull-down experiment as shown in Fig 4B, and we have clearly labeled the relevant information in the figure.

      (4) Figure 4E - the fraction of MDA5 that binds 5'ppp-RNA seems incredibly minor. And why is this experiment done using 5'OH-RNA as a competitor, rather than simply incubating MDA5 and 5'OH-RNA together and demonstrating that these do not form a complex?

      The proportion of MDA5 combined with 5’ppp-RNA is influenced by many conditions, including the concentration and purity of the probe and purified protein. In addition, the dosage ratio between the RNA probe and MDA5 protein in the EMSA experiment can also have a significant impact on the results. Therefore, it is not possible to accurately determine the actual binding force between MDA5 and RNA. In the EMSA experimental program, both cold probes (5’ppp-RNA) and mutated cold probes (5’OH-RNA and 5’pppGG-RNA) are crucial for demonstrating the specific binding between MDA5 and 5’ppp-RNA, as they can exclude false positive errors caused by factors such as the presence of biotin in the purified MDA5 protein itself.

      (5) Figure 4B/4C/4F - These experiments would be strengthened by including an MDA5 mutant that cannot bind to RNA. These mutants are well-described in mammals. If these residues are conserved, it is straightforward to generate this mutant.

      As shown in Figure 3, the MDA5 of M. miiuy has an RD domain that can recognize the SCRV virus. We constructed MDA5-△RD mutant plasmids with 6x His-tags and purified them for EMSA experiments (Figure 4E). The experimental results further indicate that MDA5, rather than MDA5-△RD, can bind to 5’ppp-SCRV (Figure 4G). This further confirms the crucial role of the RD domain in recognizing the 5'ppp-RNA virus.

      (6) Minor point: Figure 4E: please clarify in which lanes MDA5 has been added.

      Thank you for the referee's suggestion. We have synthesized new 5'ppp-RNA probes (5’ppp-SCRV and their dephosphate derivatives) and rerun this experiment, and relevant information has been added in the Figure (Figure 4F).

      Figure 5

      (1) Figure 5C - As MDA5 is an interferon-stimulated gene (as shown in panel G/H/I)) the increased MDA5 expression could simply explain the increase in the amount of m6A-MDA5 that is immunoprecipitated after infection. Could this figure be improved by doing a fold change between input vs m6A-IP OR uninfected vs SCRV-infected conditions? This would reveal whether the modification of MDA5 with m6A is really increased after infection.

      As shown in Figure 5F below, our data indicates that the proportion of m6A-modified MDA5 does indeed increase after SCRV infection, rather than solely due to the increased expression of MDA5 itself.

      (2) Figure. 5E/F - The y-axis is unclear: relative MDA5 m6A levels. Relative to what? Input? Mock infected?

      For experiments in Figure 5E/F, we first compared the m6A-IP group with the input group, and then normalized the control group (IgG group of 5E and Mock group of 5F) to “1”. We have replaced the Y-axis name with a clearer one (Figure 5E and 5F).

      (3) General comment - It is not mentioned in the text that MDA5 is an interferon-stimulated gene. This would account for the increase in expression (qPCR) after viral infection or poly(I:C) transfection, hence there is no novelty in this finding. In addition, the authors suggest that MDA5 increases at the protein level (by immunoblot) but the increase on these blots is not convincing (figure 5H/5I).

      We understand that the increase in expression of MDA5 as an interferon-stimulated gene after viral infection is a common phenomenon. We present this to further validate the m6A sequencing transcriptome data, and to demonstrate that although m6A modification interferes with MDA5 expression during viral infection, it cannot prevent the increase of mRNA level of MDA5. In addition, we rerun the experiment and the results showed that the expression of MDA5 protein can indeed be specifically activated by the SCRV virus and poly(I:C)-HMW.

      Figure 6

      (1) Figure 6E - What was the MOI of the virus used in this experiment? It is not mentioned in the figure legend.

      MOI=5, we have added this point in the figure legend.

      Figure 7

      (1) Figure 7J - This graphic is somewhat misleading and should be altered to better reflect the conclusions that are drawn in the manuscript. The graphic suggests that MAVS and STING interact, but this is not demonstrated in the paper. In addition, the paper does not demonstrate whether MAVS or STING (or both) are needed downstream of MDA5 to relay signalling. Finally, please draw an arrow from type I IFNs to increased expression of MDA5 to illustrate that MDA5 is an ISG.

      Thank you for the referee's suggestion. We have revised the images to more accurately match the conclusions of the manuscript (Figure 7J). Firstly, we have separated the STING protein from the MAVS protein. Secondly, arrows have been used to indicate that MDA5 is an IFN-stimulated gene. Finally, as we have added relevant experiments to demonstrate the importance of MITA protein in the signaling process of MDA5-activated IFN response. In addition, the function of MAVS binding to MDA5 protein and promoting its signal transduction is very conserved, and there is a good research background even in fish with RIG-I deficiency (10.1016/j.dci.2021.104235). Therefore, in Figure 7J, we still chose to bind MAVS to MDA5 protein and use it as a downstream signal transducer of MDA5.

      Discussion<br /> (1) There is very little discussion about METTL and YTHDF proteins in the discussion despite the fact that the last 2 figures are entirely devoted to these proteins.

      Based on the referee's suggestion, we have added relevant content about METTL and YTHDF proteins in the discussion. In addition, the basic mechanism and function of METTL and YTHDF proteins were briefly described in the introduction.

      Reviewer #3 (Recommendations For The Authors):

      Please refer to the specific suggestions and recommendations. They include proposals for experimental additions, improved methodologies, and suggestions to resolve writing-related concerns.

      Major concerns

      (1) I suggest changing the article title to "Functional Replacement of RIG-I with MDA5 in Fish Miiuy Croaker", or a similar title, to make it more focused and closely aligned with the content of the article.

      Following the reviewer's recommendations, we have revised the title to emphasize our primary research subject is a teleost fish that lacks RIG-I. In addition, we have changed “5’ppp-RNA” to “5’ppp-RNA virus” to emphasize the interaction between the virus and the receptor. We believe that the revised title is more in line with the content of the article.

      (2) Due to the inherent limitations in genome sequencing, assembly, and annotation for the Miiuy croaker, comprehensive annotation of immune-related genes remains incomplete. To address this critical gap, it is recommended that authors establish experimental protocols, such as Fluorescence In Situ Hybridization (FISH), to confirm the absence of RIG-I in the Miiuy croaker. They should simultaneously employ MDA5 probes as a positive control for validation purposes.

      The miiuy croaker has good genomic information at the chromosomal level (doi: 10.1016/j.aaf.2021.06.001). In addition, studies have shown that RIG-I is absent in the orders of Perciformes (doi: 10.1016/j.fsirep.2021.100012), while miiuy croaker belongs to the order Perciformes, so it does indeed lose the RIG-I gene. Therefore, we do not intend to use FISH technology to prove this.

      (3) Similarly, it is recommended that the authors first provide evidence of the presence of 5'ppp at the 5' terminus of the genome RNA of SCRV, as demonstrated in the study by Goubau et al. (doi: 10.1038/nature13590, Supplementary figure 1). This evidence is crucial before drawing conclusions about the compensatory role of MDA5 in recognizing 5'ppp RNA viruses, using SCRV as the viral model.

      As suggested by the referee, we extracted SCRV RNA from SCRV virus particles and assessed the 5’-phosphate-dependence of stimulation by SCRV RNA. Calf intestinal phosphatase (CIAP) treatment substantially reduced the stimulatory activity of SCRV RNA in MKC cells of M. miiuy (Figure 1C and 1E). In addition, similar results were obtained by transfecting VSV-RNA isolated from VSV virus into DF-1 cells of G. gallus (Figure 1D). The above evidences confirm the presence of triphosphate molecular features between SCRV and VSV viruses, and indicating that birds and fish lacking RIG-I have other receptors that can recognize 5’ppp-RNA.

      (4) The 62-nucleotide (nt) 5'ppp-RNA utilized in this study was obtained from Vesicular Stomatitis Virus (VSV). In order to provide direct evidence, it is necessary to include a 62-nt 5'ppp-RNA that is directly derived from SCRV itself.

      We adopted this suggestion and synthesized a 67-nucleotide 5’ppp-SCRV RNA probe. We found that 5’ppp-SCRV activates dimerization of IRF3 and binds to MDA5 of M. miiuy in a 5’-triphosphate-dependent manner (Figure 4A-4F).

      (5) Given that RNAs with uncapped diphosphate (PP) groups at the 5′ end also activate RIG-I, similar to RNAs with 5′-PPP moieties, and the 5′-terminal nucleotide must remain unmethylated at its 2′-O position to allow RNA recognition by RIG-I, it is necessary for the authors to conduct additional experiments to supplement and validate these two distinguishing features of RIG-I in RNA recognition. This will provide more reliable evidence for the replacement of RIG-I by MDA5 in RNA recognition.

      Thank you for the reviewer's professional suggestions. We understand that exploring the combination of 5’pp-RNA and 2′-O-methylated RNA with MDA5 can further demonstrate the alternative function of MDA5. But we think that the use of 5’ppp-RNA and their dephosphorylation derivatives can fully demonstrate that the MDA5 of M. miiuy and G. gallus have evolved to recognize 5’triphosphate structure like human RIG-I. Therefore, we do not intend to conduct any additional experiments

      (6) In section 2.3, the authors assert that Miiuy croaker recognizes SCRV through its RD domain. This claim is supported by their data showing that cells overexpressed with the MDA5 ΔRD mutant lost the ability to inhibit SCRV replication. As a result, the authors draw the conclusion that "these findings provide evidence that MDA5 may recognize 5'-triphosphate-dependent RNA (5'ppp-RNA) through its RD domain." However, to strengthen their argument, the authors should first demonstrate that during SCRV infection, MDA5-mediated antiviral immune response is indeed initiated by recognizing the 5'ppp part of the SCRV RNA, rather than the double-strand part (which can exist in ssRNA virus) of the viral RNA, as this is naturally a ligand for MDA5. Additionally, the authors should treat the isolated SCRV RNA with CIP to remove the phosphate group and examine the binding of MDA5 with SCRV RNA before and after treatment. They should also transfect CIP-treated or untreated SCRV RNA into MDA5 knockdown and wild-type MKC cells to investigate the induction of antiviral signaling and levels of viral replication. Finally, the authors should verify the binding ability of the mutants with isolated SCRV RNA, with or without CIP treatment, to determine which domain of MDA5 is responsible for SCRV 5'ppp-RNA recognition.

      We understand the reviewer's concern that MDA5 may be identified by binding to dsRNA in the SCRV virus. Based on the reviewer's suggestion, we extracted SCRV RNA and obtained its dephosphorylated RNA using Calf intestinal phosphatase (CIAP). Next, we transfected them into MDA5-knockdown and wild-type MKC cells, and detected the dimerization of IRF3 and IFN reaction. The results indicate that SCRV RNA does indeed activate immunity in a triphosphate-dependent manner, and knockdown of MDA5 prevents immune activation of SCRV RNA (Figure 1C and 1E, Figure 2P and 2Q). Finally, we synthesized a 5'ppp-SCRV RNA probe and demonstrated that MDA5 binds to 5'ppp-SCRV through the RD domain (Figure 4E-4G). We believe that these results can better demonstrate that MDA5 recognizes 5’ppp-RNA through its RD domain and addresses the concerns of the reviewers.

      (7) Similarly, merely presenting Co-IP data demonstrating the interaction between Miiuy croaker MDA5 and STING in overexpressed EPC cells does not justify the claim that "in vertebrates lacking RIG-I, MDA5 can utilize STING to facilitate signal transduction in the antiviral response". This is because interactions observed through overexpression may not accurately reflect the events occurring during viral infection or their actual antiviral functions. To provide more robust evidence, it is essential to conduct functional experiments after STING knockout (or at least knockdown). Furthermore, it is important to note that Miiuy Croaker alone cannot adequately represent all "vertebrates lacking RIG-I".

      We found that co-expression of STING and MDA5 can enhance MDA5-mediated IFN-1 response during SCRV virus infection, while knocking down STING can restore MDA5-mediated IFN-1 response (Figure 2N and 2O). This indicates that STING plays an important signaling role in the immune response of MDA5 to RNA viruses. In addition, loss of RIG-I is a common phenomenon in vertebrates, and STING of birds such as chickens (doi: 10.4049/jimmunol.1500638) and mammalian tree shrews (doi: 10.1073/pnas.1604939113) can also bind to MDA5, indicating that STING can indeed play a crucial role in MDA5 signaling in species with RIG-I deficiency. We have added this section to our discussion and elaborated on our observations in more cautious language.

      (8) In the manuscript, a series of experiments were conducted using an antibody (Beyotime Cat# AF7164) against endogenous MDA5. The corresponding immunogen for this MDA5 antibody is a recombinant fusion protein containing amino acids 1-205 of human IFIH1/MDA5 (NP_071451.2). However, the amino acid sequences of IFIH1/MDA5 differ substantially between humans and Miiuy croaker, which could introduce errors in the results. Therefore, it is essential to employ antibodies specifically designed for targeting Miiuy croaker's own MDA5 in the experiments.

      As shown in Figure 2B, endogenous MDA5 antibodies can detect the MDA5 portion that is forcibly overexpressed by plasmids, suggesting that the MDA5 antibody can indeed specifically recognize the MDA5 protein of M. miiuy.

      (9) It is recommended to investigate the phosphorylation of IRF3 in order to confirm the downstream signaling pathway during viral infection when MDA5 is knocked down or overexpressed.

      Due to the lack of available phosphorylation antibodies for fish IRF3, we used IRF3 dimer experiments to detect downstream signaling (Figure 1C and 1D, Figure 2P, Figure 4C and 4J).

      (10) The use of poly I:C as a mimic for dsRNA to investigate MDA5's recognition of 5'ppp-RNA in hosts lacking RIG-I, as well as the examination of the regulatory role of MDA5 m6A methylation upon activation by 5'ppp-RNA, may be inappropriate. Poly I:C does not possess 5'ppp, and while it has been identified as a ligand for MDA5 in various studies, MDA5 cannot serve as a substitute for RIG-I in recognizing poly (I:C). Therefore, the authors should utilize 5'ppp-dsRNA as the mimic and include the corresponding 5'ppp-dsRNA control without a 5'triphosphate as the negative control (both available from InvivoGen). This approach will specifically elucidate the mechanisms involved when MDA5 functions similarly to RIG-I in the recognition of 5'ppp-RNA.

      In our study, we used poly(I:C)-HMW, a known dsRNA mimetic that can be preferentially recognized by MDA5 rather than RIG-I, as a positive control for activating MDA5. What we want to demonstrate is that, like poly(I:C)-HMW (positive control), SCRV can also promote MDA5-mediated IFN immunity, further indicating the important role of MDA5 in 5’ppp-RNA virus invasion. We have clearly labeled the type of poly(I:C) in the figures and legends to avoid misunderstandings for readers.

      (11) In Figure 2, Figure 3, and Figure 6, the appearance of virus plaques is not readily apparent, and it is necessary to replace these images with clearer photographs. It appears that MKC or MPC cells are not appropriate for conducting plaque assays. To accurately assess viral proliferation, the authors should measure key indicators throughout the process, such as the production of positive-strand RNAs (+RNAs), replication intermediates (RF), and transcription of subgenomic RNAs. This approach is preferable to solely measuring the M and G protein genes from the virus genome as positive results can still be observed in contaminated cells.

      As pointed out by the reviewer, we also think that the virus plaque images in Figure 2K and Figure 3D are not clear enough, so we have replaced them with new clear images (Figure 2J and Figure 3D). But we think that other images can clearly display the proliferation of the SCRV virus, so we did not replace them. In addition, the primers we currently use do measure +RNA, so the replication level of the SCRV virus can be accurately evaluated without being affected by virus contamination. Because the regions where the two pairs of primers are located belong to the SCRV-M and SCRV-G protein genes, we label them as SCRV-M and SCRV-G to distinguish between the two pairs of genes. To avoid reader misunderstanding, we have modified the Y-axis label in the figures (Figure 2I and 2K, Figure 3E, Figure 6E and 6O).

      (12) There is a substantial disparity in the molecular size of M. miiuy MDA5 between endogenous and exogenously expressed proteins, as shown in Figure 2A and 2C-D. Please provide clarification.

      Please refer to the response to Reviewer 2's question regarding Figure 4B above.

      (13) The manuscript incorporates the evolutionary perspective, but lacks specific evolutionary analysis. Thus, it is essential to include relevant analysis to comprehend the evolutionary dynamics and positive selection on MDA5 and LGP2 in the absence of RIG-I in Miiuy croaker. This can be achieved through theoretical calculations using appropriate algorithms, such as the branch models and branch-site models based on the maximum-likelihood method implemented in the phylogenetic analysis by maximum likelihood (PAML) package.

      In fact, we have analyzed the molecular evolution of MDA5 and LGP2. Unfortunately, even when analyzing only the MDA5/LGP2 CDS sequences in fish, we found that the topologies of gene trees of MDA5/LGP2 were largely consistent with the species tree. Thus, species with or without RIG-I in the gene trees cannot effectively separate clusters, making it extremely difficult to analyze the molecular evolution of MDA5/LGP2 caused by RIG-I deficiency. Consequently, we gave up this aspect of analysis.

      (14) If the narrative regarding m6A methylation goes beyond the activation of MDA5 through recognition of 5'ppp-RNA and represents a regulatory mechanism for all MDA5 activation events, it is not relevant to the theme of "An arms race under RIG-I loss: 5'ppp-RNA and its alternative recognition receptor MDA5." Therefore, all investigations in this paper should focus solely on events when MDA5 recognizes 5'ppp-RNA. Any data associated with the broader regulatory mechanisms and m6A methylation of MDA5 should be excluded from this manuscript and instead be included in a separate study dedicated to exploring this specific topic.

      Our theme aims to showcase RNA viruses, rather than an interaction between 5'ppp-RNA and host virus receptors, which our current topic cannot accurately express. Therefore, we made two main changes: firstly, we limited the study species to M. miiuy, although some studies on the functional substitution of MDA5 for RIG-I involved birds. Secondly, change “5’ppp-RNA” to “5’ppp-RNA virus”. We believe that the revised title is more in line with our current research contents.

      (15) The running title appears to be hastily done.

      We modified it to “MDA5 recognizes 5’ppp-RNA virus in species lacking RIG-I”.

      (16) There are many descriptions that are not strongly related to the main theme of the article in the introduction section, making it lengthy and fragmented. Please focus on the research background of RIG-I and MDA5, including their structures, functions, and regulatory mechanisms, as well as the research progress on the compensatory effect of MDA5 in the absence of RIG-I and its evolutionary adaptation mechanism in other species.

      Based on the suggestions of the reviewers, we have removed some of the less relevant content in the introduction and added research progress on the compensatory effect of MDA5 in the evolutionary adaptation mechanism of tree shrews in the absence of RIG-I.

      (17) Lines 149-156 in the "Results" section include content that resembles an "Introduction" It is important to avoid duplicating information in the results section. Therefore, the authors are encouraged to revise this paragraph to ensure conciseness in the article.

      We have streamlined this section to enhance the article's conciseness and clarity.

      (18) In the "Results" section, at line 177, the authors assert, "As depicted in Figure 1F-1H," which should be corrected to Figure 2F-2H. Furthermore, the y-axis of the two figures on the right-hand side of Figure 2H represents the ISG15 genes. At line 182, "as demonstrated in Figure 1I-1L," should be revised as "as illustrated in Figure 2I-2L". The authors demonstrated a lack of attention to detail.

      Thank you to the reviewer for pointing out our errors, and we have made the necessary corrections.

      (19) In lines 197-198, the authors stated that "MDA5-ΔRD showed an inability to interact with SCRV." However, Figure 3D did not reveal any significant difference, thus it is advisable to repeat this experiment at least once.

      We have replaced this virus spot image with a new one (Figure 3D).

      (20) In lines 200-201 of the "2.3 RD domain is required for MDA5 to recognize SCRV" section, the authors report that the expression of antiviral genes was induced by the overexpression of both MDA5 and MDA5-ΔRD, even in the absence of infection (Figure 3F). Why does the expression of antiviral genes increase in the absence of viral RNA stimulation? Please provide a reasonable explanation.

      In the absence of viral infection, overexpression of viral receptor proteins may still transmit erroneous signaling, affecting the body's immunity. We speculate that due to the preservation of the CARD domain by MDA5 and MDA5-ΔRD, they can still induce the expression of antiviral factors without ligands, although this induction effect is much smaller than that of viral infection. However, in order to better demonstrate the function of the RD domain of MDA5 in M. miiuy, we have changed the experimental plan, as shown in the figure 3F. We detected the induction of antiviral factors by overexpression of MDA5 and MDA5-△RD under poly (I:C)-HMW stimulation. This can indicate that the RD domain has a conserved function in the recognition of poly(I:C)-HMW in M. miiuy, and can serve as a positive control for the recognition of SCRV virus invasion by the RD domain of MDA5.

      (21) Please provide the GeneBank accession number of M. miiuy MDA5.

      The GeneBank accession number of M. miiuy MDA5 was added in the section 4.5 plasmids construction.

      (22) The content of lines 228-233 in the "Results" section bears resemblance to that of the "Introduction." To ensure the avoidance of information duplication, it is recommended to remove this paragraph from the results section.

      This section has been streamlined.

      (23) The bands of mmiMDA5 in the 5'ppp-RNA and dsRNA lanes in Figure 4B are weak and almost unobservable. Please replace them with clear images.

      We have rerun this experiment and replaced the images (Figure 4B).

      (24) In Figure 5G and at line 253, there are only results presented for the SCRV infection group, while no results are shown for the control group. This raises the question of why the control group results are missing. It is necessary to provide a reasonable explanation or correction for this issue.

      The "0 h" infection time point of the SCRV virus is the control group, and we have replaced it with a more intuitive image (Figure 5G).

      (25) In Figure 7C, it would be necessary to include the western blot result of YTHDF protein expression in order to verify the efficiency of YTHDF siRNA.

      In fact, we have attempted to detect the endogenous expression of YTHDF protein using available commercial antibodies. Unfortunately, only the YTHDF2 antibody can specifically recognize the endogenous protein expression of YTHDF2 in M. miiuy. In addition, the knockdown effect of si-YTHDF2 has been validated by YTHDF2 antibody (doi: 10.4049/jimmunol.2200618).

      (26) In line 422 of the "4.3 Cell culture and treatment" section, the paragraph raises a question regarding the nature of Miiuy croaker kidney cells (MKCs) and spleen cells (MPCs) - whether they are cell lines or freshly isolated cells (or primary cultures) derived from kidney and spleen tissues. If these cells are indeed cell lines, it is requested to provide detailed information about the sources and properties of the cells (such as whether they are epithelial cells or other mixed cell types) and the generations of propagation. Alternatively, if the cells were freshly isolated or primary cultures obtained from fish, the method for cell isolation should be provided. The source and stability of cells are extremely important for ensuring the repeatability and reliability of experimental outcomes.

      M. miiuy kidney cells (MKCs) and spleen cells (MPCs) are cell lines derived from the kidney and spleen tissues of M. miiuy, with passages ranging from 20 to 40 times. These details have been incorporated into section 4.3.

      (27) There are many inaccurate descriptions in the text, which employ concepts that are too broad. These descriptions need to be narrowed down to specific species or objects. Here are a few examples, along with the necessary revisions. Other similar instances should also be revised accordingly. For instance, in line 119, "fish MDA5" should be changed to "Miiuy croaker MDA5." Similarly, in line 166, "fish MDA5-mediated signaling pathway" should be changed to "Miiuy croaker MDA5-mediated signaling pathway." In line 174, "fish MDA5" should be revised to "Miiuy croaker MDA5." Additionally, in line 185, "antiviral responses of teleost" should be changed to "antiviral responses of Miiuy croaker." In line 197, "interact with SCRV" should be revised to "interact with 5'ppp-RNA of SCRV." In line 337, "loss of RIG-I in the vertebrate" should be modified to "loss of RIG-I in Miiuy croaker and chicken." Similarly, in line 338, "MDA5 of fish" should be changed to "MDA5 of Miiuy croaker." Lastly, in line 348, "RIG-I deficient vertebrates" should be revised to "RIG-I deficient Miichthys miiuy and Gallus gallus."

      Thank you for the reviewer's suggestions. We have made revisions to these inaccurate descriptions and reviewed the entire manuscript to address similar statements with broad concepts.

      (28) Finally, it should be noted that a similar discovery has already been reported in tree shrews (Ling Xu, et al., Proc Natl Acad Sci., 2016, 113(39):10950-10955). This article shares similarities with that research report, therefore it is necessary to discuss in detail the relationship between the two in the discussion and compare and analyze the evolutionary patterns of MDA5 from it.

      Based on the reviewer's suggestions, we have compared the similarities and differences between these two reports during the discussion and analyzed the evolutionary dynamics of MDA5 in these vertebrates lacking RIG-I.

      Minor concerns:

      Thank you to the reviewer for their meticulous examination to our manuscript, we have made revisions to the following suggestions.

      (1) At line 120, the sentence "SCRV(one 5'ppp-RNA virus)" should have a space between "SCRV" and "(one 5'ppp-RNA virus)". Please make this correction.

      Corrected.

      (2) At lines 147-148, the sentence "However, the downstream gene of TOPORSa is missing a RIG-I" is not accurate and needs modification.

      We have modified this sentence.

      (3) At line 184, "findings indicate" should be corrected to "findings indicated".

      Corrected.

      (4) At line 189, "a 5'ppp-RNA virus" should be deleted and the text seems redundant.

      Deleted.

      (5) At line 198, "replication. (Figure 3C-3E)", please remove the punctuation between "replication" and "(Figure 3C-3E)".

      Corrected.

      (6) At line 416 in "Materials and methods" section, "4.2 Sample and challenge" should be corrected to "4.2 Fish and challenge".

      Corrected.

      (7) At line 419, the authors state that "The experimental procedure for SCRV infection was performed as described", please briefly describe the SCRV infection method and the infectious dose.

      Based on the reviewer's suggestions, we have added relevant descriptions of SCRV infection in section 4.2.

      (8) There are several formatting issues in the "Materials and Methods" section. For instance, in line 424, there is no space between the number and letter in "100 μg/ml" and "26 ℃" should be corrected to "26℃". Additionally, in line 430, "Cells" should be corrected to "cells".

      Corrected.

      (9) At line 446, "50 ng/ul" and "100 mU/ul" should be corrected to "50 ng/μl" and "100 mU/μl".

      Corrected.

      (10) At line 459, "primers 1)" should be corrected to "primers".

      Corrected.

      (11) At lines 461-464, the description "For protein purification, MDA5 plasmids with 6× His tag was constructed based on pcDNA3" seems to be no direct logical connection between protein purification and the plasmid construction. Please make the necessary corrections.

      Corrected.

      (12) At line 548, "cytoplasmic" should be corrected to "Cytoplasmic".

      Corrected.

      (13) At line 549, "5× 107" should be corrected to "5 × 107".

      Corrected.

      (14) At line 557, "MgCl2" should be corrected to "MgCl2".

      Corrected.

      (15) At line 558, "6 %" should be corrected to "6%".

      Corrected.

      (16) At line 565, "50μg" should be corrected to "50 μg".

      Corrected.

      (17) At line 571, "300{plus minus}50 bp." should be corrected to "300 {plus minus} 50 bp."

      Corrected.

      (18) At lines 592-593, the sentence "After several incubations, the m6A level was quantified colorimetrically at a wavelength of 450 nm" does not read smoothly, please improve it.

      Revised.

      (19) At line 786, "MDA5 recognize" should be corrected to "MDA5 recognized".

      Corrected.

      (20) At lines 788 and 798, "Pulldown" should be corrected to "Pull-down".

      Corrected.

      (21) At lines 790 and 796, "bluestaining" should be corrected to "blue staining".

      Deleted.

      (22) At line 825, "SCRV and infection" should be corrected to "SCRV infection".

      Corrected.

      (23) At lines 826-827, "SCRV (H) and poly(I:C) (I) infection" should be corrected to "SCRV infection (H) and poly(I:C) stimulation (I)".

      Corrected.

    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      This work shows, based on basic laboratory investigations of invitro-grown bacteria as well as human bone samples, that conventional bacterial culture can substantially underrepresent the quantity of bacteria in infected tissues. This has often been mentioned in the literature, however, relatively limited data has been provided to date. This manuscript compares culture to a digital droplet PCR approach, which consistently showed greater levels of bacteria across the experiments (and for two different strains).

      Strengths:

      Consistency of findings across in vitro experiments and clinical biopsies. There are real-world clinical implications for the findings of this study.

      Weaknesses:

      No major weaknesses. Only three human samples were analyzed, although the results are compelling.

      We only put in three examples of clinical diagnosis to showcase the application of this method particularly to osteomyelitis. For further validation, larger cohort studies are required, which are currently underway.

      Reviewer #2 (Public Review):

      In this study, the authors address discrepancies in determining the local bacterial burden in osteomyelitis between that determined by culture and enumeration by DNA-directed assay. Discrepancies between culture and other means of bacterial enumeration are long established and highlighted by Staley and Konopka's classic, "The great plate count anomaly" (1985). Here, the authors first present data demonstrating the emergence of discrepancies between CFU counts and genome copy numbers detected by PCR in S. aureus strains infecting osteocyte-like cells. They go on to demonstrate PCR evidence that S. aureus can be detected in bone samples from sites meeting a widely accepted clinicopathological definition of osteomyelitis. They conclude their approach offers advantages in quantifying intracellular bacterial load in their in vitro "co-culture" system.

      The publication related to “The great plate count anomaly (1985)” has been added to revised version as new reference #2.

      Weaknesses

      - My main concern here is the significance of these results outside the model osteocyte system used by this group. Although they carefully avoid over-interpreting their results, there is a strong undercurrent suggesting their approach could enhance aetiologic diagnosis in osteomyelitis and that enumeration of the infecting pathogen might have clinical value. In the first place, molecular diagnostics such as 16S rDNA-directed PCR are well established in identifying pathogens that don't grow. Secondly, it is hard to see how enumeration could have value beyond in vitro and animal model studies since serial samples will rarely be available from clinical cases.

      Indeed, we initiated this study for the purpose of trying to improve the diagnostic outcomes for osteomyelitis, in particular that associated with prosthetic joint infection (PJI) but also all other forms, as the current gold-standard diagnostic approaches for this type of infection, either bacterial culture or whole genome sequencing, are very time consuming and costly, and yet are not necessarily accurate. Our method has the benefits (not limited to) of achieving absolute quantification of bacterial load in a shortened time period (in the order of hours) in clinical bone specimens from infected patients. Many of the identified bacterial species in patients were not able to be diagnosed by standard bacterial culturing. Moreover, one of the problematic features of treating bone infection is that repetitive surgeries are usually needed, particularly in PJI, hence, serial clinical bone specimens from the same patient are in fact often available. Therefore, our method of being able to quantify bacterial load offers the advantage of monitoring the infected status throughout the treatment journey. In this study, we chose the tuf gene as the targeting sequence to amplify the bacterial signal instead of the well-established 16S PCR for the reason that tuf provides much better sequence discrimination between bacterial species. Therefore, the short PCR amplicon of just 271 bp used in our study, is able to give us a highly accurate taxonomic readout. By this approach, we again shorten the time required for diagnosis. In the last paragraph of the Discussion in the revised manuscript, extra text, a figure demonstrating the strong sequence diversity in tuf (Supplementary Figure 2) and an additional reference have been added to address the Reviewer’s concerns.

      - I have further concerns regarding the interpretation of the combined bacterial and host cell-directed PCRs against the CFU results. Significance is attached to the relatively sustained genome counts against CFU declines. On the one hand, it must be clearly recognised that the detection of bacterial genomes does not equate to viable bacterial cells with the potential for further replication or production of pathogenic factors. Of equal importance is the potential contribution of extracellular DNA from lysed bacteria and host cells to these results. The authors must clarify what steps, if any, they have taken to eliminate such contributions for both bacteria and host cells. Even the treatment with lysotaphin may have coated their osteocyte cultures with bacterial DNA, contributing downstream to the ddPCR results presented.

      We agree that concerns around the interpretation of any molecular readout need to be taken into account. We have yet to find a method that can definitively identify bacterial viability in a clinical setting in the absence of culture. However, PJI and osteomyelitis in general is characterised by a high percentage of culture-negative infection cases, calling for such molecular approaches. Commercially available, so called “live/dead” bacterial PCR reagents exist that act as PCR signal inhibitors by penetrating the cell wall of compromised cells to prevent the PCR signal being generated from those cells. In our experience, while these can provide a certain level of added scrutiny in an experimental setting, they are not definitive because the reaction is often incomplete in an idealised situation and also the reagent may cancel signal from viable bacteria growing under conditions of stress, such as during antimicrobial treatment and host-derived stress imparted in intracellular or intra-tissue environments. Indeed, such stresses are likely contributors to clinical non-culturability. Whole genome sequencing would provide more certainty of bacterial viability to demonstrate genomic intactness but as we discuss herein, this a lengthy and costly process, and one which may prove difficult from host tissue with a low pathogen load. It should be noted that the significance of any diagnostic readout, including from culture, WGS or our method reported here would need to be interpreted by the treating clinical team. We would argue that a rapid, practical molecular diagnostic method in the absence or even presence of culture would provide treating clinicians with an improved rationale for tailoring antimicrobial treatments. 

      Strengths

      - On the positive side, the authors provide clear evidence for the value of the direct buffer extraction system they used as well as confirming the utility of ddPCR for quantification. In addition, the successful application of MinION technology to sequence the EF-Tu amplicons from clinical samples is of interest.

      - Moreover, the phenomenology of the infection studies indicating greater DNA than CFU persistence and differences between the strains and the different MOI inoculations are interesting and well-described, although I have concerns regarding interpretation.

    1. Author response:

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

      We are thankful for the comments and suggestions from the Editor and Reviewers about our manuscript submitted to the eLife Journal. We have addressed all the comments, and we think these modifications will help bring clarity to our message and be helpful to your readership. Here we include an outline of the corrections performed, as well as a detailed response to each of the reviewer’s comments.

      As per the Editor and Reviewers suggestions, outline of corrections:

      ·        The title of the manuscript has been changed to reflect a more conservative conclusion.

      ·        Changes in the main manuscript text were made to enhance clarity, including the use genetic terminology and naming.

      ·        Specific responses to some comments from the reviewers are included in this document. We combined some comments that would be better addressed together.

      Accompanied to this letter is an updated version of our manuscript with the track changes feature enabled. Again, we are thankful of the comments and suggestions we received, and we hope this revised version of our manuscript will be accompanied by an updated assessment and public reviews and a final eLife Version of Record.

      Response to the public review and minor recommendations.

      From Reviewer #1:

      The major inference of the work is that SIV infection of gorillas drove the observed diversity in gorilla CD4. This is supported by the majority of SNPs being localized to the CD4 D1, which directly interacts with the envelope, and the demonstrated functional consequences of that diversity for viral entry. However, SIVgor (to the best of my knowledge) only infects Western lowland gorillas (Gorilla gorilla gorilla), and one Gorilla gorilla diehli and three Gorilla beringei graueri individuals were included in the haplotype and allele frequency analyses. The presence of these haplotypes or the presence of similar allele frequencies in Eastern lowland and mountain gorillas would impact this conclusion. It would be helpful for the authors to clarify this point.

      From Reviewer #1 (minor comment):

      Which subspecies of gorilla are the nsSNPs coming from? Gorilla gorilla diehli [n =1]; Gorilla beringei graueri [n = 3]) are not extant reservoirs of SIV and to my knowledge are not thought to have been, and so it's important to point out where the diversity is coming from if the authors are asserting that SIVgor drove this population-level diversity in gorilla CD4.

      We initially included genomic data from all the gorilla individuals available to maximize sensitivity to identify allelic variants. Although evidence points to eastern gorillas not being currently infected with SIV, our results show that all allelic variants identified have differential susceptibility to the HIV-1 and SIVcpz strains tested. The allelic variants we identified with this genomic data set match the variants identified by Russell et al (doi.org/10.1073/pnas.2025914118), including the ones found in eastern gorillas, and recapitulate that those variants have differential susceptibility to lentiviral entry, similar to the variants of western populations. Whether eastern gorillas have been exposed to lentiviruses in the past remains unknown.

      From Reviewer #1:

      The authors appear to use a somewhat atypical approach to assess intra-population selection to compensate for relatively small numbers of NHP sequences (Fig. 6). However, they do not cite precedence for the robustness of the approach or the practice of grouping sequences from multiple species for the endemic vs other comparison. They also state in the methods that some genes encoded in the locus were removed from the analysis "because they have previously been shown to directly interact with a viral protein." This seems to undercut the analysis and prevents alternative explanations for the observed diversity in CD4 (e.g., passenger mutations from selection at a neighboring locus).

      Given the nature of our samples, to detect any influence of natural selection acting on CD4, we chose to compare patterns of molecular evolution of CD4 to its neighboring loci. Comparisons of molecular evolution signatures across genomic regions are the basis of methods to detect positive selection (e.g., Sabeti DOI: 10.1038/nature01140). For our comparison, the neighboring loci represent our neutral standard for the genomic region CD4 resides. Our rationale is that demographic and neutral influences on the number and frequency of polymorphic sites in a region would equally affect all loci in a genomic region. Because these neighboring loci are our neutral benchmark, we excluded before analysis other genes in this genomic region that interact with viruses. The logic is that these loci may be evolving under the influence of positive selection and would decrease the power of our comparison. None of the excluded loci are direct neighbors to CD4. This, and given that the CD4 genomic region in humans is of average recombination rate, dampens the possibility that what we are observing at CD4 is due to selection acting at a neighboring locus. In addition, the classic population genetic method to detect positive selection, the McDonald-Kreitman test (McDonald DOI: 10.1038/351652a0), was originally presented combining polymorphism data across species. We assume that any effect on levels of diversity created by combining variability between species would equally affect all loci included in the study, not just CD4.

      From Reviewer #1:

      Data in Figure 5 is graphed as % infected cells instead of virus titer (TDU/mL). It's unclear why this is the case, and prevents a comparison to data in Figure 2 and Figure 4.

      From Reviewer #1 (minor comment):

      Figure 5: the data presentation is now shown as % infected cells instead of viral titer. This makes it difficult to compare data from Figure 5 to other figures. Can the authors please either justify this change, display data consistently or provide matched data displays as a Supplemental Figure?

      For the experiments presented in figures 2 and 4 we used different volumes of infecting pseudoviruses, which allowed us to identify the linear range of infection. Then, based on the number of cells plated per experimental replicate, we calculated a virus titer. In follow-up experiments (Fig. 5), we used fixed volumes of virus that would infect ~10-20% of control (wild-type; wt) CD4-expressing cells. Comparisons were then made between wt and mutated CD4s, and these data are best presented in their raw forms as percent cells infected.  Although this change in method prevents direct comparison between the figures, we focused on the differences observed between the experimental conditions per experimental panel.

      From Reviewer #1:

      The lack of pseudotyping with SIVgor envelope is a surprising omission from this study, that would help to contextualize the findings.

      From Reviewer #2 (minor comment):

      The inclusion of HIV-1 but not SIVgor strains in Figures 2D/E is somewhat conspicuous since chimpanzee alleles certainly differ in susceptibility to SIVcpz (and SIVgor) strains per Russell et al. 2021. The authors should either test some SIVgor infections, cite published data on at least extant human/chimpanzee/gorilla CD4 susceptibility to SIVgor, or address why they did not include it.

      We agree the data of host susceptibility to SIVgor strains would have been an interesting question to explore. However, we opted to focus on the transmission of SIVcpz strains into gorilla populations for this study. It is worth mentioning that we have cloned SIVgor envelope genes from some strains into our expression system, but we were unable to recover infectious pseudoviruses using an HIV-1DEnv-GFP backbone. This suggests that HIV-1 may be incompatible with incorporating SIVgor Env into virus particles. Recently, Russell et al (DOI: 10.1073/pnas.2025914118) managed to generate SIVgor Env pseudotyped virions using a different backbone (SIVcpzDEnv-GFP) that was unavailable to us at the time of this study.

      From Reviewer #1:

      Similarly, building gorilla CD4 haplotype SNPs onto the hominin ancestor (as opposed to extant human CD4) may provide additional insights that are meaningful toward understanding the evolutionary trajectory of gorilla CD4.

      We decided to use the extant human CD4 as a backbone to test the effects on the individual amino acid variants found in the allelic diversity of the gorilla population since the human protein is highly susceptible to all the HIV-1 and SIV strains tested, and the expected phenotype is a loss-of-function. Since the D1 of the human and ancestral sequences for CD4 are almost identical (except for a change that is fixed in gorillas), and they showed similar levels of susceptibility to lentivirus entry, we expect that the phenotypes found would be the same if the gorilla SNPs were built into the ancestral CD4 backbone.

      From Reviewer #2:

      To bolster the argument that lentiviruses are indeed the causative driver of this diversification, which seems likely from a logical perspective but is difficult to prove, Warren et al. pursue two novel lines of evidence. First, the authors reconstruct ancestral CD4 genes that predate lentiviral infection of hominid populations. They then demonstrate that resistance to lentiviral infection is a derived trait in chimpanzees and gorillas, which have been co-evolving with endemic lentiviruses, but not in humans, which only recently acquired HIV. Nevertheless, the derived resistance could be stochastic or due to drift. This argument would be strengthened by demonstrating that bonobo and orangutan CD4, which also do not have endemic lentiviruses, resemble the ancestral and human susceptibility to great-ape-infecting lentiviruses.

      From Reviewer #2 (minor comment):

      The data presented in Figure 2, showing that chimp and gorilla (but not human) CD4 resistance to lentiviral infection is a derived trait, is very intriguing for suggesting that endemic lentiviruses are the causative driver of CD4 evolution. Nevertheless, this could be stochastic or due to genetic drift. Given the later emphasis on several other non-endemically infected species, the authors should at the very least include the sequences for bonobo and orangutan CD4 in the presented alignment (Fig 2B). Ideally, they would also test these orthologs to demonstrate that they are not resistant to lentiviruses infecting great apes (SIVcpz / HIV-1 / SIVgor). If they have also derived resistance, this would suggest a possible other evolutionary driver or genetic drift.

      Based on our analysis on polymorphic sites using available data from populations of apes, we strongly believe the accumulation of resistant polymorphisms in CD4 did not arise in a stochastic manner. The frequency and accumulation of these changes strongly correlate with the function of CD4 as a receptor for lentivirus entry. We agree that experimentally testing the CD4 protein from bonobo and orangutan would strengthen our conclusions; however, based on our genomic analyses, we decided to focus on the species that would present a higher level of variability of susceptibility to the lentivirus tested, namely gorillas and chimpanzees.

      From Reviewer #2:

      Warren et al. provide a population genetic argument that only endemically infected primates exhibit diversifying selection, again arguing for endemic lentiviruses being the evolutionary driver. The authors compare SNP occurrence in CD4 to neighboring genes, demonstrating that non-synonymous SNP frequency is only elevated in endemically infected species. Moreover, these amino-acid-coding changes are significantly concentrated in the CD4 domain that binds the lentiviral envelope. This is a creative analysis to overcome the problem of very small sample sizes, with very few great ape individuals sequenced. The additional small number of species compared (2-3 in each group) also limits the power of the analysis; the authors could consider expanding their analysis to Old World Monkey species that do or do not have endemic lentiviruses, as well as great apes.

      The scope of this project was to evaluate the differential phenotype of the accumulated polymorphisms found in the ape branch of the primates. Although evaluating the accumulation of polymorphisms in a broader range of primates would generate interesting observations, this would likely require increasing the total number of primate species to include sampling along the speciation tree, many of which lack population level data.

      From Reviewer #1 (minor comment):

      Ancestral reconstruction methods and associated data tables should be included to indicate statistical support for assigned codons. A comment on ambiguity at relevant positions is needed. Similarly, given the polymorphic nature of gorilla and chimpanzee CD4, how confident are the authors in their ancestral reconstructions based on a single representative genome per species? Does this change when you include the broader panel of gorilla sequences? Is the ancestral reconstruction robust to other methods besides PAML?

      We used the PAML software package to reconstruct the ancestral hominin and hominid sequence of CD4 because it is a standard and well recognized method for this purpose. For this analysis, we used the set of primate sequences selected for positive selection analyses (see methods), namely the longest isoform sequences for each of the available species that best aligned with human CD4. We feel that the best way to perform to the ancestral state reconstruction was to use only these curated sequences instead of the population level sequences, removing potential biases introduced by having different numbers of variants per species. 

      From Reviewer #1 (minor comment):

      Page 10: "It seems that allele 2, which doesn't have this glycan, would be at a fitness disadvantage. In support of this, allele 2 is one of the least frequent alleles in the gorilla population that we surveyed (Figure 3B)." - this inference depends on the gorilla species that encode allele 2 and allele frequencies. There are statistical tests to address this inference.

      Population genetic statistics that test for skews in sample allele frequencies are not appropriate here due to the nature of the samples in this study. However, the reviewer is correct that our inference in allele frequency is dependent on the gorilla species that we find this allele in. Allele 2 is found in the Gorilla beringei graueri subspecies of gorilla included in this study.  We only have data for three individuals (six alleles) from this subspecies compared to 51 individual (102 alleles) from Gorilla gorilla gorilla. As such, genetic subdivision between the gorilla subspecies could also produce the low frequency of allele 2 observed in our sample.

      From Reviewer #1 (minor comment):

      Page 11: "These results imply that the resistance to SIVcpz found in gorilla individuals is not dependent on single amino acids, but rather the cumulative effect of multiple SNPs." Would it be more relevant (or relevant in other ways) to test this statement by putting those mutations into the hominid ancestor? Testing individual residues in the context of human CD4 may be subject to epistasis or several other factors.

      We agree that constructing multiple of the resistant SNPs in the susceptible human background would have strengthened our hypothesis, as all these amino acid changes are associated with increased resistance to at least one of the lentiviruses tested. However, the number of CD4 variants to test would increase significantly and we feel that this approach was out of the scope of this manuscript.

      From Reviewer #1 (minor comment):

      Figure 6: If you perform this analysis on chimpanzee CD4 alone do you get the same result? Just gorillas? If you remove eastern/mountain gorillas? The very small numbers of non-human non-SIV-reservoir great apes may preclude a strong conclusion.

      We agree that our study is limited by the small number of available sequences from individuals of the studied species. If we remove a whole species or subspecies the statistical power would be greatly reduced. Removing all chimpanzees or gorillas (or a subspecies) would still show that only each of those species accumulate SNPs in the D1 region of CD4, although with less statistical significance.

      From Reviewer #2 (minor comment):

      Related to Figure 2: It would strengthen the argument that resistance is a derived trait if the authors mapped the causative mutations from gorilla CD4 onto the ancestral hominin CD4. However, this experiment is not particularly critical, merely a suggestion.

      We appreciate this suggestion. We decided to use the human CD4 backbone as it is widely susceptible to lentiviral entry. The hominid and hominin ancestral sequences are almost identical to the human sequence in domain 1, except for a fixed mutation shared with the gorilla CD4. We expect that the SNPs observed in the gorilla population would also reduce susceptibility to lentivirus entry in the ancestral CD4 reconstructions.

      From Reviewer #2 (minor comment):

      Related to Figure 3B: It is difficult to make much of the allele frequency for 8 alleles in 32 individuals. Can the authors collate this with allele frequency for the referenced 100 individuals from Russell et al. 2021, to give a better sense of population frequency? This may allow the authors to better correlate allele frequency with SIVcpz resistance patterns in Figure 4, strengthening their argument that more resistant alleles should be over-represented in the population.

      At the time of our analysis the data from Russell (DOI: 10.1073/pnas.2025914118) was not available to collate or compare. When that data became available, we immediately compared the existence of the alleles found and confirmed that the ones we found were also detected in the samples used in that study.

      From Reviewer #2 (minor comment):

      Related to Figure 6: As written, several methodological details should be clarified. How were human genomes selected to limit the sample size to 50?

      We selected a total of 50 human individuals in order to size-match the sample size of the largest group in Fig 6B (chimpanzee, n=50). We randomly selected 10 individuals for each of the 5 superpopulations [Africans (AFR), Admixed Americans (AMR), East Asians (EAS), Europeans (EUR) and South Asians (SAS)] defined by the 1000 Genome Project.

      From Reviewer #2 (minor comment):

      Related to Figure 6: What comparison is being reported for the Mann-Whitney U test (CD4 vs. which gene)? Are the means shown in A an average of 2 (endemic) or 3 (non-endemic) species - if so, the authors should show the individual data points to give a clearer depiction of the data spread. In addition, it is not clear that a statistical test with sample sizes of 2 is meaningful, since Mann Whitney typically assumes n > 5. To strengthen this statistical argument, it may be necessary to include additional species that have (a) multiple genomes (or at least this locus) sequenced, and (b) have or lack lentiviral sequences. This may necessitate expanding the analysis to include Old World Monkeys (e.g. Rhesus Macaque Genome Project).

      In the Figure 6 we use the Mann-Whitney U test to compare variation between CD4 and the neighboring loci. The average and SEM are for two endemic and four non-endemic species (two orangutan datasets are from two distinct species vs the gorilla subspecies). It is true our sample size is small for any statistical testing. For the Mann-Whitney U-test it is generally preferred to have n > 5 in each group. So, we do run into problems with the endemically infected comparisons as we only have two data points (chimpanzee and gorilla) for the CD4 group. For the uninfected species, CD4 has four data points.

      From Reviewer #1 (minor comment):

      Page 6. "This suggests that the ancestral versions of CD4 in apes were susceptible to primate lentivirus entry" - The data show that tested virus pseudotyped with SIV/HIV envs can engage ancestral CD4 in the context of a canine cell line expressing human CCR5, but not necessarily that this interaction was sufficient for the process of entry per se, especially in the context of a gorilla (or hominid) cell. Some additional context would be useful for a broad readership.

      From Reviewer #1 (minor comment):

      Page 6: "but that selective pressures exerted by SIVs in the chimpanzee and gorilla lineages have led to the retention of mutations that confer resistance to primate lentivirus infection. This has not happened in humans where selective pressure by HIV-1 is too new" - this cannot be concluded from the data in Figure 1. It would be more appropriate as a Discussion point.

      From Reviewer #1 (minor comment):

      Page 14: "Natural tolerance is often required before a virus can establish itself long term in a host reservoir, and thus understanding it is key to understanding virus reservoirs in nature" - please provide a reference. This is one among several theories of long-term host-virus evolution dynamics/outcomes, and further discussion may benefit the broad readership of eLife.

      From Reviewer #1 (minor comment):

      Page 15: "There is a surprising outcome of virus-driven host evolution in that the divergence and diversity of these host genes ultimately comes at a detriment to the very viruses that drove this evolution." - it is not clear to this reviewer why this is surprising.

      From Reviewer #2 (minor comment):

      Related to Figure 5A: The authors suggest that the gorilla glycosylation site provides resistance to SIVcpz, based on TAN1.910, but in fact the glycosylated allele is no more resistant than the un-glycosylated allele to most SIVcpz strains (in Figure 4). The authors should acknowledge this more clearly in the text.

      From Reviewer #2 (minor comment):

      The title of this article (that infection "has driven selection") is somewhat overstated - though it seems very likely that lentiviruses are driving CD4 diversification, this is difficult to prove. The arguments presented here rely on very few data points: modern chimp and gorilla compared to ancestral CD4, and a population genetic analysis relying on 2 or 3 species with 10-50 individuals each. The authors should either bolster these arguments (see the above suggestions) and/or soften the claim in the title.

      Modifications to the main text of the manuscript have been made to enhance clarity on the subjects stated above.

    1. Author response:

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

      We provide below a point-by-point reply to the Reviewers, and hope that our new manuscript will now meet the Reviewers’ concerns and the requirements for publication in eLife. 

      In summary, we have performed a new set of mouse humanization experiments using a new cohort of 4 additional HLA-DRB1*15-typed MS patients as donors, all presenting with highly active disease and under treatment with natalizumab. The new experiments aim to strengthen and further extend the findings of the original paper that HLA restriction rather than disease status plays an important role in the development of CNS inflammation. Additionally, we performed EAE using a revised protocol using lower amounts of peptide antigens to reduce the possibility of immune tolerance. Indeed, our original observations were further enriched with the finding that immunization increases infiltration of the CNS by human CD4 T cells, a finding consistent with EAE pathology, and that these human CD4 T cells co-localize with human CD8 T cells in the brain lesions. Further, we provide more detailed information concerning the EBV infection status of the PBMC donors used for humanization and find some first indications of relationships between the B cell engraftment in humanized mice, EBV status  of the donors and the development of brain lesions that might stimulate further investigation in future studies.   

      Point-by-point reply to reviewers:

      Reviewer 1:

      We thank Reviewer 1 for their valuable comments, and for their support of the overall approach as a model system. We have addressed the comments by providing additional requested information, as well as performing a EAE with a revised protocol, as suggested. We believe the new results significantly upgrades the information gained from this study.

      (1) Throughout their paper, the authors never quantify the difference in CD4 vs CD8 T cell infiltration into the CNS. While repeatedly claiming that there are fewer CD4 T cells present than CD8 T cells within the CNS, this data is not included. Further, spinal cord numbers of CD4 and CD8 are not provided in lieu of CD3 T cell characterization.

      Reply: We have now included quantitative data for the differences in CD4 vs CD8 T cells in the brain and spinal cord of non-immunized and EAE immunized mice. Thus, in brain (Fig. 2E) and spinal cord (Fig. 3D) of non-immunized mice, and brain (Fig. 4D, E, L) and spinal cord (Fig. 5D) of immunized mice we show data for numbers of hCD8 and hCD4 T cells, and ratios of CD4 to CD8 in at borders and parenchyma. Notably, using a revised EAE protocol in the second set of experiments, we observed a marked increase in hCD4 T cell infiltration at the CNS borders and parenchyma, an observation consistent with successful EAE immunization.

      B cells don't make up any significant component of the cells transferred from HLA-DR15 donors. While the cells transferred from the HLA-DR13 donor are composed of a considerable number of B cells, the mice that received these cells didn't develop any signs of neurologic disease.

      In the second experiment using new DR15 MS donors, we observed significant B cell engraftment also in several groups of DR15 MS mice. With the additional groups of mice, we were able to see a relationship between B cell engraftment in DR13 and DR15 MS mice with indicators of recent or ongoing reactivation of EBV. This is an interesting preliminary observation that might be tested in future larger studies. 

      (2) Incomplete exploration of potential experimental autoimmune encephalomyelitis (EAE) modeling. Comparison of the susceptibility of B2m-NOG mice to EAE dependent on various peptide doses would be highly informative. Given that the number of hCD45+ in the periphery of NOG mice decreases following this immunization it would be prudent for the authors to determine if such a high peptide dose is truly ideal for EAE development in this mouse model.

      Reply: We thank the reviewer for this critical comment. In the second group of experiments (DR15 MS2-5), we revised the EAE protocol to use lower amounts of peptides in a single immunization, thereby greatly reducing the exposure of human T cells to antigen and risk of tolerance/anergy. This resulted in (i), by-pass of the reduction in proportions of peripheral hCD45 cells following immunization in the peripheral blood (Fig. 1A), and (ii), increased numbers of hCD4 T cells and hCD4/hCD8 T cell ratios at the borders and infiltrating the parenchyma of brain (Fig. 4D,E) and spinal cord (Fig. 5D). 

      (3) The degree of myelin injury is not presented. The statement is repeatedly made that "demyelination was not observed in the brain or spinal cord" but no quantification of myelin staining is shown.  

      Reply: The reviewer refers to a pivotal feature (and limitation) of this particular humanized model. Despite significant T cell infiltration of white and grey matter regions of brain and spinal cord, there is no detectable demyelination. This has also been reported by in independent study using a similar humanized system (Zayoud et al., 2013). We have supplemented the figures with photomicrographs showing the presence of unperturbed myelin in the corpus callosum white T cell lesions (Fig. 4F, inset stained with Luxol fast blue), and a confocal micrograph in the same region double-immunostained for hCD45 immune cells and MBP (Fig. 4G). 

      Minor points:

      Method of quantification (e.g. cells per brain slice in figures 2E; 4E) is not very quantitative and should be justified or more appropriately updated to be more rigorous in methodology.

      Reply: In the new figures, we have changed the method of quantification of brain parenchyma infiltrating cells from per brain slice, to cells per tissue area mm2 (Fig. 2D, Fig. 4D).

      Fig. 4 data should be shown from un-immunized DR15 MS and DR15 HI mice.

      Reply: We now include the quantitative data from un-immunized mice compared to immunized mice in all groups (Fig. 4 C-E). 

      Reviewer 2:

      We thank Reviewer 2 for their very pertinent comments and overall for highlighting the importance of humanized mice as an approach for further understanding the pathobiology of MS. We also thank this reviewer for their positive comments concerning the study design, specifically the use of fresh PBMC isolated from HLADRB1-typed MS individuals and healthy control. The reviewer highlights 4 major weaknesses of the study that we have tried to address in order to increase the value of the study.

      (i) Lack of sufficient sample size (n=1 in each group) to make any conclusion.

      Reply: We have increased the sample size for the DR15 MS group from n=1 to n=5 by generating new humanized mice using PBMC freshly isolated from additional MS donors, all HLA-DRB1*5 with active RRMS and under treatment with natalizumab. Here we were able to maximize on our excellent collaboration with neurologists at the neighboring University Hospital, which runs a large organized MS outpatient clinic, with HLADRB1-typed MS individuals that are closely monitored over the course of their disease and therapy. In this way, we were able to address the engraftment success of human immune cells and variability in CNS lesion development across mice generated from 5 different DR15 MS patients. We also monitored markers for EBV activation status in all the patients used for mouse humanization in this study. 

      (ii) Lack of phenotype in mice.

      Reply: As already described in the results and address in the discussion, the B2m-NOG immunodeficient mouse strain used here is a state-of-the-art experimental tool for humanization studies, but unfortunately fails to support engraftment by human monocytes. We and previous groups (Zayoud et al., 2013) show that CNS lesions in humanized mice contain high numbers of hCD4 and CD8 T cells, accompanied by locally activated murine microglia and astrocytes, but lack human monocytes. The humanized mice contain large proportions of immature mouse CD11b+Ly6Chi monocytes in the periphery (Suppl. Table 4) but these cells are not recruited into the CNS in non-immunized or immunized humanized mice, potentially due to incompatible chemokine signals across mouse/human. The absence of human monocyte engraftment in this model is the most likely reason that lesions do not demyelinate and this limitation of the currently available host mouse strains is one that needs to be addressed before full modelling of CNS demyelination by human immune cells can be achieved.

      (iii) No disease phenotype even in humanized mice immunized for disease using standard disease induction protocol employed in an animal model of MS.

      Reply: As described above, following the suggestion of reviewer 1 (point 2) we revised the EAE protocol to use lower amounts of peptides given as a single immunization. This resulted in increased numbers of hCD4 T cells and the hCD4/hCD8 T cell ratios at the borders and infiltrating the parenchyma of brain ((Fig. 1E, Fig. 2D) and spinal cord (Fig. 5D), all indicative of a successful EAE immunization. Although immunized mice showed lesions with mixed populations of hCD4 and hCD8 T cells, demyelination and therefore clinical symptoms were again not observed. As outlined in (ii) above, successful human monocyte engraftment would be fundamental for the development of demyelination and clinical symptoms in PBMC humanized mice, and new immunodeficient animal strains should be developed to achieve this.  

      (iv) Mechanistic data on why CD8 T cells are more enriched than CD4+ T cells.

      Reply: The question of why hCD8 T cells are more enriched in the CNS than hCD4 cells is answered at least in part by the results from our new EAE experiments, which clearly show that immunization increases CNS infiltration by hCD4 T cells versus hCD8 T cells. In general, EAE protocols are designed to activate antigen-specific CD4 T cells and this is verified in the CNS of immunized humanized mice, where hCD4 T cells infiltrate to join hCD8T cells in lesion areas. The predilection of hCD8 T cells for CNS is obvious in non-immunized humanized mice, especially in the parenchyma (see Fig. 2E) and MS patients, while hCD4 infiltration becomes important after EAE immunization. The humanized model system might therefore represent a unique tool for studying mechanisms underlying preferential hCD8 T cell involvement in MS neuroinflammaton, a system that is not accurately modelled in current EAE models. As this reviewer correctly points out, this is very important point as postmortem MS patients’ brains have more CD8 T cells than CD4 T cells.

    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      In this study, the authors provide a new computational platform called Vermouth to automate topology generation, a crucial step that any biomolecular simulation starts with. Given a wide arrange of chemical structures that need to be simulated, varying qualities of structural models as inputs obtained from various sources, and diverse force fields and molecular dynamics engines employed for simulations, automation of this fundamental step is challenging, especially for complex systems and in case that there is a need to conduct high-throughput simulations in the application of computer-aided drug design (CADD). To overcome this challenge, the authors develop a programming library composed of components that carry out various types of fundamental functionalities that are commonly encountered in topological generation. These components are intended to be general for any type of molecules and not to depend on any specific force field and MD engines. To demonstrate the applicability of this library, the authors employ those components to re-assemble a pipeline called Martinize2 used in topology generation for simulations with a widely used coarse-grained model (CG) MARTINI. This pipeline can fully recapitulate the functionality of its original version Martinize but exhibit greatly enhanced generality, as confirmed by the ability of the pipeline to faithfully generate topologies for two high-complexity benchmarking sets of proteins.

      Strengths:

      The main strength of this work is the use of concepts and algorithms associated with induced subgraph in graph theory to automate several key but non-trivial steps of topology generation such as the identification of monomer residue units (MRU), the repair of input structures with missing atoms, the mapping of topologies between different resolutions, and the generation of parameters needed for describing interactions between MRUs.

      Weaknesses:

      Although the Vermouth library appears promising as a general tool for topology generation, there is insufficient information in the current manuscript and a lack of documentation that may allow users to easily apply this library. More detailed explanation of various classes such as Processor, Molecule, Mapping, ForceField etc. that are mentioned is still needed, including inputs, output and associated operations of these classes. Some simple demonstration of application of these classes would be of great help to users. The formats of internal databases used to describe reference structures and force fields may also need to be clarified. This is particularly important when the Vermouth needs to be adapted for other AA/CG force fields and other MD engines.

      We thank the reviewer for pointing out the strengths of the presented work and agree that one of the current limitations is the lack of documentation about the library. In the revision, we point more clearly to the documentation page of the Vermouth library, which contains more detailed information on the various processors. The format of the internal databases has also been added to the documentation page. Providing a simple demonstration of applications of these classes is a great suggestion, however, we believe that it is more convenient to provide those in the form of code examples in the documentation or for instance jupyter notebooks rather than in the paper itself.  

      The successful automation of the Vermouth relies on the reference structures that need to be pre-determined. In case of the study of 43 small ligands, the reference structures and corresponding mapping to MARTINIcompatible representations for all these ligands have been already defined in the M3 force field and added into the Vermouth library. However, the authors need to comment on the scenario where significantly more ligands need to be considered and other force fields need to be used as CG representations with a lack of reference structures and mapping schemes.

      We acknowledge that vermouth/martinize2 is not capable of automatically generating Martini mappings or parameters on the fly for unknown structures that are not part of the database. However, this capability is not the purpose of the program, which is rather to distribute and manage existing parameters. Unlike atomistic force fields, which frequently have automated topology builders, Martini parameters are usually obtained for a set of specific molecules at a time and benchmarked accordingly. As more parameters are obtained by researchers, they can be added to the vermouth library via the GitHub interface in a controlled manner. This process allows the database to grow and in our opinion will quickly grow beyond the currently implemented parameters. Furthermore, the API of Vermouth is set up in a way that it can easily interface with automated topology builders which are currently being developed. Hence this limitation in our view does not diminish the applicability of vermouth to high-throughput applications with many ligands. The framework is existing and works, now only more parameters have to be added.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript by Kroon, Grunewald, Marrink and coworkers present the development of Vermouth library for coarse grain assignment and parameterization and an updated version of python script, the Martinize2 program, to build Martini coarse grained (CG) models, primarily for protein systems.

      Strengths:

      In contrast to many mature and widely used tools to build all-atom (AA) models, there are few well-accepted programs for CG model constructions and parameterization. The research reported in this manuscript is among the ongoing efforts to build such tools for Martini CG modeling, with a clear goal of high-throughput simulations of complex biomolecular systems and, ultimately, whole-cell simulations. Thus, this manuscript targets a practical problem in computational biophysics. The authors see such an effort to unify operations like CG mapping, parameterization, etc. as a vital step from the software engineering perspective.

      Weaknesses:

      However, the manuscript in this shape is unclear in the scientific novelty and appears incremental upon existing methods and tools. The only "validation" (more like an example application) is to create Martini models with two protein structure sets (I-TASSER and AlphaFold). The success rate in building the models was only 73%, while the significant failure is due to incomplete AA coordinates. This suggests a dependence on the input AA models, which makes the results less attractive for high-throughput applications (for example, preparation/creation of the AA models can become the bottleneck). There seems to be an improvement in considering the protonation state and chemical modification, but convincing validation is still needed. Besides, limitations in the existing Martini models remain (like the restricted dynamics due to the elastic network, the electrostatic interactions or polarizability).

      We thank the reviewer for pointing out the strengths of the presented work, but respectfully disagree with the criticism that the presented work is only incremental upon existing methods and tools. All MD simulations of structured proteins regardless of the force field or resolution rely on a decent initial structure to produce valid results. Therefore, failure upon detection of malformed protein input structures is an essential feature for any high-throughput pipeline working with proteins, especially considering the computational cost of MD simulations. We note that programs such as the first version of Martinize generate reasonable-looking input parameters that lead to unphysical simulations and wasted CPU hours.

      The alpha-fold database for which we surveyed 200,000 structures only contained 7 problematic structures, which means that the success rate was 99% for this database. This example simply shows that users potentially have to add the step of fixing atomistic protein input structures, if they seek to run a high-throughput pipeline.

      But at least they can be assured that martinize2 will make sure to check that no issues persist.

      Furthermore, we note that the manuscript does not aim to validate or improve the existing Martini (protein) models. All example cases presented in the paper are subject to the limitations of the protein models for the reason that martinize2 is only the program to generate those parameters. Future improvements in the protein model, which are currently underway, will immediately be available through the program to the broader community.  

      Reviewer #3 (Public Review):

      Summary:

      The manuscript Kroon et al. described two algorithms, which when combined achieve high throughput automation of "martinizing" protein structures with selected protonation states and post-translational modifications.

      Strengths:

      A large scale protein simulation was attempted, showing strong evidence that authors' algorithms work smoothly.

      The authors described the algorithms in detail and shared the open-source code under Apache 2.0 license on GitHub. This allows both reproducibility of extended usefulness within the field. These algorithms are potentially impactful if the authors can address some of the issues listed below.

      We thank the reviewer for pointing out the strengths.  

      Weaknesses:

      One major caveat of the manuscript is that the authors claim their algorithms aim to "process any type of molecule or polymer, be it linear, cyclic, branched, or dendrimeric, and mixtures thereof" and "enable researchers to prepare simulation input files for arbitrary (bio)polymers". However, the examples provided by the manuscript only support one type of biopolymer, i.e. proteins. Despite the authors' recommendation of using polyply along with martinize2/vermouth, no concrete evidence has been provided to support the authors' claim. Therefore, the manuscript must be modified to either remove these claims or include new evidence.

      We acknowledge that the current manuscript is largely protein-centric. To some extent this results from the legacy of martinize version 1, which was also only used for proteins. However, to show that martinize2 also works for cyclic as well as branched molecules we implemented two additional test cases and updated formerly Figure 6 and now Figure 7. Crown ether is used as an example of a cyclic molecule whereas a small branched polyethylene molecule is a test case for branching. Needless to say both molecules are neither proteins nor biomolecules. 

      Method descriptions on Martinize2 and graph algorithms in SI should be core content of the manuscript. I argue that Figure S1 and Figure S2 are more important than Figure 3 (protonation state). I recommend the authors can make a workflow chart combining Figure S1 and S2 to explain Martinize2 and graph algorithms in main text.

      The reviewer's critique is fair. Given the already rather large manuscript, we tried to strike a balance between describing benchmark test cases, some practical usage information (e.g. the Histidine modification), and the algorithmic library side of the program. In particular, we chose to add the figure on protonation state, because how to deal with protonation states—in particular, Histidines—was amongst the top three raised issues by users on our GitHub page. Due to this large community interest, we consider the figure equally important. However, we moved Figure S1 from the Supporting Information into the manuscript and annotated the already mentioned text with the corresponding panels to more clearly illustrate the underlying procedure. 

      In Figure 3 (protonation state), the figure itself and the captions are ambiguous about whether at the end the residue is simply renamed from HIS to HIP, or if hydrogen is removed from HIP to recover HIS.

      Using either of the two routes yields the same parameters in the end, which are for the protonated Histidine. In the second route, the extra hydrogen on Histidine is detected as an additional atom and therefore a different logic flow is triggered. Atoms are never removed, but only compounded to a base block plus modification atoms. We adjusted the figure caption to point this out more clearly.  

      In "Incorporating a Ligand small-molecule Database", the authors are calling for a community effort to build a small-molecule database. Some guidance on when the current database/algorithm combination does or does not work will help the community in contributing.

      Any small molecule not part of the database will not work. However, martinize2 will quickly identify if there are missing components of the system and alert the users. At that point, the users can decide to make their files, guided by the new documentation pages. 

      A speed comparison is needed to compare Martinize2 and Martinize.

      We respectfully disagree that a speed comparison is needed. We already alerted in the manuscript discussion that martinize2 is slower, since it does more checks, is more general, and does not only implement a single protein model.

    1. Author response:

      We would like to thank the reviewers for their constructive feedback. We have thoroughly considered their concerns and comments and we aim to include some additional results in an updated version of this manuscript. In addition, we would like to address some of the comments, with which we respectfully disagree. Below is our point-by-point reply.

      Reviewer 1:

      Summary:

      This paper is focused on the role of Cadherin Flamingo (Fmi) - also called Starry night (stan) - in cell competition in developing Drosophila tissues. A primary genetic tool is monitoring tissue overgrowths caused by making clones in the eye disc that express activated Ras (RasV12) and that are depleted for the polarity gene scribble (scrib). The main system that they use is ey-flp, which makes continuous clones in the developing eye-antennal disc beginning at the earliest stages of disc development. It should be noted that RasV12, scrib-i (or lgl-i) clones only lead to tumors/overgrowths when generated by continuous clones, which presumably creates a privileged environment that insulates them from competition. Discrete (hs-flp) RasV12, lgl-i clones are in fact out-competed (PMID: 20679206), which is something to bear in mind. 

      We think it is unlikely that the outcome of RasV12, scrib (or lgl) competition depends on discrete vs. continuous clones or on creation of a privileged environment. As shown in the same reference mentioned by the reviewer, the outcome of RasV12, scrib (or lgl) tumors greatly depends on the clone being able to grow to a certain size. The authors show instances of discrete clones where larger RasV12, lgl clones outcompete the surrounding tissue and eliminate WT cells by apoptosis, whereas smaller clones behave more like losers. It is not clear what aspect of the environment determines the ability of some clones to grow larger than others, but in neither case are the clones prevented from competition. Other studies show that in mammalian cells, RasV12, scrib clones are capable of outcompeting the surrounding tissue, such as in Kohashi et al (2021), where cells carrying both mutations actively eliminate their neighbors.

      The authors show that clonal loss of Fmi by an allele or by RNAi in the RasV12, scrib-i tumors suppresses their growth in both the eye disc (continuous clones) and wing disc (discrete clones). The authors attributed this result to less killing of WT neighbors when Myc over-expressing clones lacking Fmi, but another interpretation (that Fmi regulates clonal growth) is equally as plausible with the current results.

      See point (1) for a discussion on this.

      Next, the authors show that scrib-RNAi clones that are normally out-competed by WT cells prior to adult stages are present in higher numbers when WT cells are depleted for Fmi. They then examine death in RasV12, scrib-i ey-FLP clones, or in discrete hs-FLP UAS-Myc clones. They state that they see death in WT cells neighboring RasV12, scrib-i clones in the eye disc (Figures 4A-C). Next, they write that RasV12, scrib-I cells become losers (i.e., have apoptosis markers) when Fmi is removed. Neither of these results are quantified and thus are not compelling. They state that a similar result is observed for Myc over-expression clones that lack Fmi, but the image was not compelling, the results are not quantified and the controls are missing (Myc over-expressing clones alone and Fmi clones alone).

      We assayed apoptosis in UAS-Myc clones in eye discs but neglected to include the results in Figure 4. We will include them in the updated manuscript. Regarding Fmi clones alone, we direct the reviewer’s attention to Fig. 2 Supplement 1 where we showed that fminull clones cause no competition. Dcp-1 staining showed low levels of apoptosis unrelated to the fminull clones or twin-spots, and we will comment on this in the revised manuscript.

      Regarding the quantification of apoptosis, we did not provide a quantification, in part because we observe a very clear visual difference between groups (Fig. 4A-K), and in part because it is challenging to come up with a rigorous quantification method. For example, how far from a winner clone can an apoptotic cell be and still be considered responsive to the clone? For UAS-Myc winner clones, we observe a modest amount of cell death both inside and outside the clones, consistent with prior observations. For fminull UAS-Myc clones, we observe vastly more cell death within the fminull UAS-Myc clones and modest death in nearby wildtype cells, and consequently a much higher ratio of cell death inside vs outside the clone. Because of the somewhat arbitrary nature of quantification, and the dramatic difference, we initially chose not to provide a quantification. However, given the request, we chose an arbitrary distance from the clone boundary in which to consider dying cells and counted the numbers for each condition. We view this as a very soft quantification, but will report it in a way that captures the phenomenon in the revised manuscript.

      They then want to test whether Myc over-expressing clones have more proliferation. They show an image of a wing disc that has many small Myc overexpressing clones with and without Fmi. The pHH3 results support their conclusion that Myc overexpressing clones have more pHH3, but I have reservations about the many clones in these panels (Figures 5L-N).

      As the reviewer’s reservations are not specified, we have no specific response.

      They show that the cell competition roles of Fmi are not shared by another PCP component and are not due to the Cadherin domain of Fmi. The authors appear to interpret their results as Fmi is required for winner status. Overall, some of these results are potentially interesting and at least partially supported by the data, but others are not supported by the data.

      Strengths: 

      Fmi has been studied for its role in planar cell polarity, and its potential role in competition is interesting.

      Weaknesses:

      (1) In the Myc over-expression experiments, the increased size of the Myc clones could be because they divide faster (but don't outcompete WT neighbors). If the authors want to conclude that the bigger size of the Myc clones is due to out-competition of WT neighbors, they should measure cell death across many discs of with these clones. They should also assess if reducing apoptosis (like using one copy of the H99 deficiency that removes hid, rpr, and grim) suppresses winner clone size. If cell death is not addressed experimentally and quantified rigorously, then their results could be explained by faster division of Myc over-expressing clones (and not death of neighbors). This could also apply to the RasV12, scrib-i results.

      Indeed, Myc clones have been shown to divide faster than WT neighbors, but that is not the only reason clones are bigger. As shown in (de la Cova et al, 2004), Myc-overexpressing cells induce apoptosis in WT neighbors, and blocking this apoptosis results in larger wings due to increased presence of WT cells. Also, (Moreno and Basler, 2004) showed that Myc-overexpressing clones cause a reduction in WT clone size, as WT twin spots adjacent to 4xMyc clones are significantly smaller than WT twin spots adjacent to WT clones. In the same work, they show complete elimination of WT clones generated in a tub-Myc background. Since then, multiple papers have shown these same results. It is well established then that increased cell proliferation transforms Myc clones into supercompetitors and that in the absence of cell competition, Myc-overexpressing discs produce instead wings larger than usual.

      In (de la Cova et al, 2004) the authors already showed that blocking apoptosis with H99 hinders competition and causes wings with Myc clones to be larger than those where apoptosis wasn’t blocked. As these results are well established from prior literature, there is no need to repeat them here.

      (2) This same comment about Fmi affecting clone growth should be considered in the scrib RNAi clones in Figure 3.

      In later stages, scrib RNAi clones in the eye are eliminated by WT cells. While scrib RNAi clones are not substantially smaller in third instar when competing against fmi cells (Fig 3M), by adulthood we see that WT clones lacking Fmi have failed to remove scrib clones, unlike WT clones that have completely eliminated the scrib RNAi clones by this time. We therefore disagree that the only effect of Fmi could be related to rate of cell division.

      (3) I don't understand why the quantifications of clone areas in Figures 2D, 2H, 6D are log values. The simple ratio of GFP/RFP should be shown. Additionally, in some of the samples (e.g., fmiE59 >> Myc, only 5 discs and fmiE59 vs >Myc only 4 discs are quantified but other samples have more than 10 discs). I suggest that the authors increase the number of discs that they count in each genotype to at least 20 and then standardize this number.

      Log(ratio) values are easier to interpret than a linear scale. If represented linearly, 1 means equal ratios of A and B, while 2A/B is 2 and A/2B is 0.5. And the higher the ratio difference between A and B, the starker this effect becomes, making a linear scale deceiving to the eye, especially when decreased ratios are shown. Using log(ratios), a value of 0 means equal ratios, and increased and decreased ratios deviate equally from 0.

      Statistically, either analyzing a standardized number of discs for all conditions or a variable number not determined beforehand has no effect on the p-value, as long as the variable n number is not manipulated by p-hacking techniques, such as increasing the n of samples until a significant p-value has been obtained. While some of our groups have lower numbers, all statistical analyses were performed after all samples were collected. For all results obtained by cell counts, all samples had a minimum of 10 discs due to the inherent though modest variability of our automated cell counts, and we analyzed all the discs that we obtained from a given experiment, never “cherry-picking” examples. For the sake of transparency, all our graphs show individual values in addition to the distributions so that the reader knows the n values at a glance.

      (5) Figure 4 - shows examples of cell death. Cas3 is written on the figure but Dcp-1 is written in the results. Which antibody was used? The authors need to quantify these results. They also need to show that the death of cells is part of the phenotype, like an H99 deficiency, etc (see above).

      Thank you for flagging this error. We used cleaved Dcp-1 staining to detect cell death, not Cas3 (Drice in Drosophila). We will update all panels replacing Cas3 by Dcp-1.

      As described above, cell death is a well established consequence of myc overexpression induced cell death and we feel there is no need to repeat that result. To what extent loss of Fmi induces excess cell death or reduces proliferation in “would-be” winners, and to what extent it reduces “would-be” winners’ ability to eliminate competitors are interesting mechanistic questions that are beyond the scope of the current manuscript.

      (6) It is well established that clones overexpressing Myc have increased cell death. The authors should consider this when interpreting their results.

      We are aware that Myc-overexpressing clones have increased cell death, but it has also been demonstrated that despite that fact, they behave as winners and eliminate WT neighboring cells. And as mentioned in comment (1), WT clones generated in a 3x and 4x Myc background are eliminated and removed from the tissue, and blocking cell death increases the size of WT “losers” clones adjacent to Myc overexpressing clones.

      (7) A better characterization of discrete Fmi clones would also be helpful. I suggest inducing hs-flp clones in the eye or wing disc and then determining clone size vs twin spot size and also examining cell death etc. If such experiments have already been done and published, the authors should include a description of such work in the preprint.

      We have already analyzed the size of discrete Fmi clones and showed that they did not cause any competition, with fmi-null clones having the same size as WT clones in both eye and wing discs. We direct the reviewer’s attention to Figure 2 Supplement 1.

      (8) We need more information about the expression pattern of Fmi. Is it expressed in all cells in imaginal discs? Are there any patterns of expression during larval and pupal development?

      Fmi is equally expressed by all cells in all imaginal discs in Drosophila larva and pupa. We will include this information in the updated manuscript.

      (9) Overall, the paper is written for specialists who work in cell competition and is fairly difficult to follow, and I suggest re-writing the results to make it accessible to a broader audience.

      We have endeavored to both provide an accessible narrative and also describe in sufficient detail the data from multiple models of competition and complex genetic systems. We hope that most readers will be able, at a minimum, to follow our interpretations and the key takeaways, while those wishing to examine the nuts and bolts of the argument will find what they need presented as simply as possible.

      Reviewer 2:

      Summary:

      In this manuscript, Bosch et al. reveal Flamingo (Fmi), a planar cell polarity (PCP) protein, is essential for maintaining 'winner' cells in cell competition, using Drosophila imaginal epithelia as a model. They argue that tumor growth induced by scrib-RNAi and RasV12 competition is slowed by Fmi depletion. This effect is unique to Fmi, not seen with other PCP proteins. Additional cell competition models are applied to further confirm Fmi's role in 'winner' cells. The authors also show that Fmi's role in cell competition is separate from its function in PCP formation.

      We would like to thank the reviewer for their thoughtful and positive review.

      Strengths:

      (1) The identification of Fmi as a potential regulator of cell competition under various conditions is interesting.

      (2) The authors demonstrate that the involvement of Fmi in cell competition is distinct from its role in planar cell polarity (PCP) development.

      Weaknesses:

      (1) The authors provide a superficial description of the related phenotypes, lacking a comprehensive mechanistic understanding. Induction of apoptosis and JNK activation are general outcomes, but it is important to determine how they are specifically induced in Fmi-depleted clones. The authors should take advantage of the power of fly genetics and conduct a series of genetic epistasis analyses.

      We appreciate that this manuscript does not address the mechanism by which Fmi participates in cell competition. Our intent here is to demonstrate that Fmi is a key contributor to competition. We indeed aim to delve into mechanism, are currently directing our efforts to exploring how Fmi regulates competition, but the size of the project and required experiments are outside of the scope of this manuscript. We feel that our current findings are sufficiently valuable to merit sharing while we continue to investigate the mechanism linking Fmi to competition.

      (2) The depletion of Fmi may not have had a significant impact on cell competition; instead, it is more likely to have solely facilitated the induction of apoptosis.

      We respectfully disagree for several reasons. First, loss of Fmi is specific to winners; loss of Fmi has no effect on its own or in losers when confronting winners in competition. And in the Ras V12 tumor model, loss of Fmi did not perturb whole eye tumors – it only impaired tumor growth when tumors were confronted with competitors. We agree that induction of apoptosis is affected, but so too is proliferation, and only when in winners in competition.

      (3) To make a solid conclusion for Figure 1, the authors should investigate whether complete removal of Fmi by a mutant allele affects tumor growth induced by expressing RasV12 and scrib RNAi throughout the eye.

      We agree with the reviewer that this is a worthwhile experiment, given that RNAi has its limitations. However, as fmi is homozygous lethal at the embryo stage, one cannot create whole disc tumors mutant for fmi. As an approximation to this condition, we have introduced the GMR-Hid, cell-lethal combination to eliminate non-tumor tissue in the eye disc. Following elimination of non-tumor cells, there remains essentially a whole disc harboring fminull tumor. Indeed, this shows that whole fminull tumors overgrow similar to control tumors, confirming that the lack of Fmi only affects clonal tumors. We will provide those results in the updated manuscript.

      (4) The authors should test whether the expression level of Fmi (both mRNA and protein) changes during tumorigenesis and cell competition.

      This is an intriguing point that we would like to validate. We are currently performing immunostaining for Fmi in clones to confirm whether its levels change during competition. We will provide these results in the updated manuscript.

      Reviewer 3:

      Summary: <br /> In this manuscript, Bosch and colleagues describe an unexpected function of Flamingo, a core component of the planar cell polarity pathway, in cell competition in the Drosophila wing and eye disc. While Flamingo depletion has no impact on tumour growth (upon induction of Ras and depletion of Scribble throughout the eye disc), and no impact when depleted in WT cells, it specifically tunes down winner clone expansion in various genetic contexts, including the overexpression of Myc, the combination of Scribble depletion with activation of Ras in clones or the early clonal depletion of Scribble in eye disc. Flamingo depletion reduces the proliferation rate and increases the rate of apoptosis in the winner clones, hence reducing their competitiveness up to forcing their full elimination (hence becoming now "loser"). This function of Flamingo in cell competition is specific to Flamingo as it cannot be recapitulated with other components of the PCP pathway, and does not rely on the interaction of Flamingo in trans, nor on the presence of its cadherin domain. Thus, this function is likely to rely on a non-canonical function of Flamingo which may rely on downstream GPCR signaling.

      This unexpected function of Flamingo is by itself very interesting. In the framework of cell competition, these results are also important as they describe, to my knowledge, one of the only genetic conditions that specifically affect the winner cells without any impact when depleted in the loser cells. Moreover, Flamingo does not just suppress the competitive advantage of winner clones, but even turns them into putative losers. This specificity, while not clearly understood at this stage, opens a lot of exciting mechanistic questions, but also a very interesting long-term avenue for therapeutic purposes as targeting Flamingo should then affect very specifically the putative winner/oncogenic clones without any impact in WT cells.

      The data and the demonstration are very clean and compelling, with all the appropriate controls, proper quantification, and backed-up by observations in various tissues and genetic backgrounds. I don't see any weakness in the demonstration and all the points raised and claimed by the authors are all very well substantiated by the data. As such, I don't have any suggestions to reinforce the demonstration.

      While not necessary for the demonstration, documenting the subcellular localisation and levels of Flamingo in these different competition scenarios may have been relevant and provided some hints on the putative mechanism (specifically by comparing its localisation in winner and loser cells). 

      Also, on a more interpretative note, the absence of the impact of Flamingo depletion on JNK activation does not exclude some interesting genetic interactions. JNK output can be very contextual (for instance depending on Hippo pathway status), and it would be interesting in the future to check if Flamingo depletion could somehow alter the effect of JNK in the winner cells and promote downstream activation of apoptosis (which might normally be suppressed). It would be interesting to check if Flamingo depletion could have an impact in other contexts involving JNK activation or upon mild activation of JNK in clones.

      We would like to thank the reviewer for their thorough and positive review.

      Strengths: 

      - A clean and compelling demonstration of the function of Flamingo in winner cells during cell competition.

      - One of the rare genetic conditions that affects very specifically winner cells without any impact on losers, and then can completely switch the outcome of competition (which opens an interesting therapeutic perspective in the long term)

      Weaknesses: 

      - The mechanistic understanding obviously remains quite limited at this stage especially since the signaling does not go through the PCP pathway.

      Reviewer 2 made the same comment in their weakness (1), and we refer to that response. In future work, we are excited to better understand the pathways linking Fmi and competition.

    1. Author response:

      Reviewer #1 (Public Review):

      We thank Reviewer #1 for the professional evaluation and raising important points. We will address those comments in the updated manuscript and especially improve the discussion in respect to the two points of concern.

      (1) How can GlnA1 activity further be stimulated with further increasing 2-OG after the dodecamer is already fully assembled at 5 mM 2-OG.

      We assume a two-step requirement for 2-OG, the dodecameric assembly and the priming of the active sites. The assembly step is based on cooperative effects of 2-OG and does not require the presence of 2-OG in all 2-OG-binding pockets: 2-OG-binding to one binding pocket also causes a domino effect of conformational changes in the adjacent 2-OG-unbound subunit, as also described for Methanothermococcus thermolithotrophicus GS in Müller et al. 2023. Due to the introduction of these conformational changes, the dodecameric form becomes more favourable even without all 2-OG binding sites being occupied. With higher 2-OG concentrations present (> 5mM), the activity increased further until finally all 2-OG-binding pockets were occupied, resulting in the priming of all active sites (all subunits) and thereby reaching the maximal activity.

      (2) The contradictory results with previously published data on the structure of M. mazei by Schumacher et al. 2023.

      We certainly agree that it is confusing that Schumacher et al. 2023 obtained a dodecameric structure without the addition of 2-OG, which we claim to be essential for the dodecameric form. 2-OG is a cellular metabolite that is naturally present in E. coli, the heterologous expression host both groups used. Since our main question focused on analysing the 2-OG effect on GS, we have performed thorough dialysis of the purified protein to remove all 2-OG before performing MP experiments. In the absence of 2-OG we never observed significant enzyme activity and always detected a fast disassembly after incubation on ice. We thus assume that a dodecamer without 2-OG in Schuhmacher et al. 2023 is an inactive oligomer of a once 2-OG-bound form, stabilized e.g. by the presence of 5 mM MgCl2.

      The GlnA1-GlnK1-structure (crystallography) by Schumacher et al. 2023 is in stark contrast to our findings that GlnK1 and GlnA1 do not interact as shown by mass photometry with purified proteins. A possible reason for this discrepancy might be that at the high protein concentrations used in the crystallization assay, complexes are formed based on hydrophobic or ionic protein interactions, which would not form under physiological concentrations.

      Reviewer #2 (Public Review):

      We thank Reviewer #2 for the detailed assessment and valuable input. We will address those comments in the updated manuscript and clarify the message.

      (1) The discrepancy of the dodecamer formation (max. at 5 mM 2-OG) and the enzyme activity (max. at 12.5 mM 2-OG).

      We assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site. See also Reviewer #1 R.1). We assume this is the reason why the activity of dodecameric GlnA1 can be further enhanced by increased 2-OG concentration until all catalytic sites are primed.

      (2) The lack of the structure of a 2-OG and ATP-bound GlnA1.

      Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (3) The observed GlnA1-filaments are an interesting finding.

      We certainly agree with the referee on that point, that the stacked polymers are potentially induced by 2-OG or ions. However, it is out of the main focus of this manuscript to further explore those filaments. Nevertheless, this observation could serve as an interesting starting point for future experiments.

      Reviewer #3 (Public Review):

      We thank Reviewer #3 for the expert evaluation and inspiring criticism.

      (1) Encouragement to examine ligand-bound states of GlnK1.

      We agree and plan to perform the suggested experiments exploring the conditions under which GlnA1 and GlnK1 might interact. We will perform the MP experiments in the presence of ATP. In GlnA1 activity test assays when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (2) The exact role of 2-OG could have been dissected much better.

      We agree on that point and will improve the clarity of the manuscript. See also Reviewer #1 R.1.

      (3) The lack of studies on dimers.

      This is actually an interesting point, which we did not consider during writing the manuscript. Now, re-analysing all our MP data in this respect, GlnA1 is likely a dimer as smallest species. Consequently, we will add more supplementary data which supports this observation and change the text accordingly.

      (4) Previous studies und structures did not show the 2-OG.

      We assume that for other structures, no additional 2-OG was added, and the groups did not specifically analyse for this metabolite either. All methanoarchaea perform methanogenesis and contain the oxidative part of the TCA cycle exclusively for the generation of glutamate (anabolism) but not a closed TCA cycle enabling them to use internal 2-OG concentration as internal signal for nitrogen availability. In the case of bacterial GS from organisms with a closed TCA cycle used for energy metabolism (oxidation of acetyl CoA) like e.g. E. coli, the formation of an active dodecameric GS form underlies another mechanism independent of 2-OG. In case of the recent M. mazei GS structures published by Schumacher et al. 2023, the dodecameric structure is probably a result from the heterologous expression and purification from E. coli. (See also Reviewer #1 R.2). One example of methanoarchaeal glutamine synthetases that do in fact contain the 2-OG in the structure, is Müller et al. 2023.

    1. Author response:

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

      Reviewer #1 (public review and recommendations for the authors):

      Major points:

      (1) The identification of RAMP4 is a pivotal discovery in this paper. The sophisticated AlphaFold prediction, de novo model building of RAMP4's RBD domain, and sequence analyses provide strong evidence supporting the inclusion of RAMP4 in the ribosome-translocon complex structure.

      However, it is crucial to ensure the presence of RAMP4 in the purified sample. Particularly, a validation step such as western blotting for RAMP4 in the purified samples would strengthen the assertion that the ribosome-translocon complex indeed contains RAMP4. This is especially important given the purification steps involving stringent membrane solubilization and affinity column pull-down.

      As suggested, we have added Western blots showing that RAMP4 is retained at secretory translocons (and not multipass translocons) after solubilisation, affinity purification, and recovery of ribosome-translocon complexes (Fig. 3F). This data supports both our assignment of RAMP4 in ribosome-translocon complexes, and also the structure-based proposition that its occupancy is mutually exclusive with the multipass translocon (in particular, the PAT complex).  

      (2) Despite the comprehensive analyses conducted by the authors, it is challenging to accept the assertion that the extra density observed in TRAP class 1 corresponds to calnexin. The additional density in TRAP class 1 appears to be less well-resolved, and the evidence for assigning it as calnexin is insufficient. The extra density there can be any proteins that bind to TRAP. It is recommended that the authors examine the density on the ER lumen side. An investigation into whether calnexin's N-globular domain and P-domain are present in the ER lumen in TRAP class 1 would provide a clearer understanding.

      We agree that the Calnexin assignment is less confident than the other assignments in this manuscript, and that further support would be ideal. We have exhaustively searched our maps for any unexplained density connected with the putative Calnexin TMD, and have found none. This is consistent with Calnexin's lumenal domain being flexibly linked to its TMD, and thus would not be resolved in a ribosome-aligned reconstruction.

      Our assignment of this TMD to Calnexin was based on existing biochemical data (referenced in the paper) favouring this as the best working hypothesis by far: Calnexin is TRAP’s only abundant co-purifying factor, and their interaction is sensitive to point mutations in the Calnexin TMD. Recognising that this is not conclusive, we have ensured that the text and figures consistently describe this assignment as provisional or putative.

      (3) In the section titled 'TRAP competes and cooperates with different translocon subunits,' the authors present a compelling explanation for why TRAP delta defects can lead to congenital disorders of glycosylation. To enhance this explanation, it would be valuable if the authors could provide additional analyses based on mutations mentioned in the references. Specifically, examining whether these mutations align with the TRAP delta-OSTA structure models would strengthen the link between TRAP delta defects and the observed congenital disorders of glycosylation.

      We agree that mapping disease-causing point mutants to the TRAP delta structure could be potentially informative. Unfortunately, the referenced TRAP delta disease mutants act by simply impairing TRAP delta expression, and thus admit no such fine-grained analyses. However, sequence conservation is our next best guide to mutant function. We note in the text that the contact site charges on TRAP delta and RPN2 are conserved, and that the closest-juxtaposed interaction pair (K117 on TRAPδ and D386 on RPN2) is also the most conserved.

      Here are some minor points:

      (1) In the introduction, when the EMC, PAT, and BOS complexes were initially mentioned, it would be beneficial for the authors to provide more context or cite relevant references. This additional information will aid readers in better understanding these complexes, ensuring a smoother comprehension of their significance in the context of the study.

      The Introduction has been edited to provide more context with relevant references. 

      (2) In Figure 7, it would be valuable for the authors to include details on how they sampled the sequence alignments. 

      To clarify this methodological point, we have revised the Figure 7 caption to include these sentences: “The logo plots in panels A and D represent an HMM generated by jackHMMER upon convergence after querying UniProtKB’s metazoan sequences with the human TRAPα sequence. Only signal above background is shown, as rendered by Skylign.org.”

      Reviewer #2 (public review and recommendations for the authors):

      Strengths:

      The manuscript contains numerous novel new structural analyses and their potential functional implications. While all findings are exciting, the highlight is the discovery of RAMP4/SERP1 near the Sec61 lateral gate. Overall, the strength is the thorough and extensive structural analysis of the different high-resolution RTC classes as well as the expert bioinformatic evolutionary analysis.

      Weaknesses:

      A minor downside of the manuscript is the sheer volume of analyses and mechanistic hypotheses, which makes it sometimes difficult to follow. The authors might consider offloading some analyses based on weaker evidence to the supplement to maximize impact.

      We agree that the manuscript is long, but we have retained what we feel are the most important findings in the main text because the supplement is often undiscoverable via literature searches. Indeed, we chose eLife for its flexibility regarding article length and suitability for extended and detailed analyses. 

      Major:

      - Figure S1 does not capture the fact that a PAT-free subset of particles is analyzed. The PAT classification step should be added.

      We apologise for having caused some confusion on this point: we do not show a PAT classification step because there was none. Instead we reanalysed the whole dataset with a focus on Sec61 and TRAP. The very little PAT present (9% of particles, per Smalinskaitė et al. 2022) appeared as a very weak density in some of the closed-Sec and weak-TRAP classes.

      - The assignment of calnexin appears highly speculative. As the authors acknowledge the EM density is clearly of insufficient resolution for identification, and also AF2 does not render orthogonal support for the interpretation. The binding to TRAPg also does not explain complex formation in lower eukaryotes that do not have TRAPg. The authors may consider moving the calnexin assignment and interpretation to the supplement as it appears highly speculative. In any case, it should not be referred to as a hypothesis and not a structure.

      We agree that the Calnexin assignment is less confident than the other assignments in this manuscript, and that further support would be ideal. Our assignment of this TMD to Calnexin was based on existing biochemical data (referenced in the paper) favouring this as the best working hypothesis by far: Calnexin is TRAP’s only abundant co-purifying factor, and their interaction is sensitive to point mutations in the Calnexin TMD. Recognising that this is not conclusive, we have ensured that the text and figures consistently describe this assignment as provisional or putative.

      - P. 8: "This extensive competition explains why prior studies found TRAP in only 40% of MPT complexes, but at high occupancy at all other RTCs29". The interpretation is at odds with a recent re-analysis of the same dataset (preprint: Gemmer et al 2023, https://doi.org/10.1101/2023.11.28.569136), which finds TRAP occupancy to negatively correlate with PAT, not BOS.

      The reviewer is correct that the Gemmer study demonstrates a negative correlation between PAT and TRAP occupancy, but it does not, as the reviewer claims, argue against a negative correlation between BOS and TRAP. In fact it agrees that Sec61•BOS•PAT complex would clash with TRAP, and that therefore “BOS could trigger release of TRAP from the multipass translocon.” Thus, there is no conflict between the two studies. The revised text in this passage now cites the Gemmer et al. preprint and clarifies that TRAP is partially displaced by competition with BOS, but retained at the translocon via its ribosome-binding domain.  

      - P. 7/8: the authors suggest that TRAPd may be important for OSTA recruitment and hence TRAPd deletion may cause glycosylation defects in patients by failure to recruit OSTA. However, cryo-ET studies (Pfeffer et al, Nat. Comms 2017) showed that OSTA still binds in patient-derived microsomes (and the OSTA-TRAPd interaction). The author should discuss their model in the light of these data.

      As explained in the text, our hypothesis predicts that TRAPδ is more important for OSTA’s recruitment to the RTC than for its RTC affinity: “OSTA’s attraction to TRAPδ is weak compared to its binding to the ribosome, but TRAPδ may nonetheless help recruit OSTA, since TRAPδ would attract OSTA from most possible angles of approach, whereas OSTA’s ribosome contacts are stereospecific.” Therefore the fact that Pfeffer et al. 2017 found OSTA at some TRAPδ-negative RTCs is not surprising. For confirmation we would look for TRAPδ-dependent glycosylation sites in fast-folding domains or otherwise kinetically sensitive loci, and indeed TRAP-dependence screens return complex profiles that could be consistent with such a mechanism (Phoomak et al. 2021).

      - Some confidence measure for the assignment of SERP1/RAMP4 should be provided adding support for the claim "The resolution of the RBD density was sufficient for de novo modelling". Indeed, the N-terminal ribosome-bound segment appears well resolved and programs like Modelangelo or FindMySequence should provide a confidence measure for the assignment of the density to SERP1. The TM part appears less well resolved, but the connectivity to the Nterminus may justify the assignment, which should be elaborated on.

      Although we appreciate the value of tools like Modelangelo or FindMySequence, and would have used them if we were resting our assignment of RAMP4 on its RBD alone, we feel that such analyses would be superfluous here. They would quantify only the buildability of RAMP4’s

      RBD, whereas the real question of RAMP4’s assignability is independently supported by AlphaFold’s confirmation of RAMP4’s TMD as the Sec61-binding density, and further biochemical data provided or cited in the paper.

      - P. 3: "Because PAT complex recruitment and MPT assembly are just beginning, ..." the implicit kinetic model seems to be that the MPT subcomplexes assemble on ribosome and Sec61. What is the evidence for this model and later recruitment of PAT (as opposed to GEL, BOS, and PAT binding pre-assembled)?

      The work of Sundaram et al. (PMID 36261522) established that PAT, GEL and BOS do not coassociate appreciably in the absence of the ribosome-Sec61 complex. This is consistent with the structural data in Smalinskaite et al. (PMID 36261528), which shows that PAT, GEL, and BOS each contact the ribosome (and Sec61 in the case of PAT and BOS), but have few if any specific contacts among themselves. Finally, data in both of these studies show that recruitment of each complex to the RNC is not lost when any of them is missing, arguing that each is capable of independent recruitment to ribosome-Sec61 complexes. 

      - p. 4: the meaning of the sentence "Stabilising interactions with this widely conserved motif may help Sec61 respond to its diverse substrates with a consistent open state." is not entirely clear. Published single-particle cryo-EM structures of RTC appear to have resulted in various degrees of openness.

      Here we were referring not to RTC structures in general, but to substrate-engaged RTCs in particular.  The two substrate-engaged RTC structures under discussion in this paragraph are nearly identical (Figure 2c) despite large differences in substrate sequence (RhoTM2 vs preprolactin’s SP). We were surprised to find that this engaged structure creates noncovalent bonds between the Sec61 N-half and the ribosome. This bonding would tend to stabilise this particular engaged structure, and this stabilisation helps explain why the newly observed TMengaged structure is so similar to the previously observed SP-engaged structure. Without this stabilising N-half interaction, one might instead expect to see more variability, such as the reviewer suggests.

      - A recent analysis of heimdallarchaea already hypothesized TRAP in these organisms and should be cited: Eme et al, Nature 618:992-999 (2023). The novel findings of this manuscript compared to Eme et al should be discussed.

      We thank the reviewer for bringing this relevant contemporaneous work to our attention. Reviewing the putative TRAP homologs identified by Eme et al, we find that most do not in fact appear to be TRAP homologs at all, judged by the measures used in our work (reciprocal HHpred queries against the human proteome and predicted structural similarity). This is not surprising since Eme et al. relied on low-threshold sequence similarity searches rather than structural measures. To acknowledge this work, we have added a sentence as follows (italics): “To test whether these candidates are also similar to TRAPαβγ in sequence, we used them to perform reciprocal HHpred queries of the human proteome, and in each case the corresponding human TRAP protein was the top hit (E = 0.031 for TRAPα, 9.4×10-14 for TRAP β, and 110 for

      TRAPγ). A contemporaneous study has also claimed to find TRAP homologs in

      Heimdallarchaeota (Eme et al. 2023), although some caution is warranted in these assignments because they do not seem to share predicted structural similarity to TRAP subunits and do not find human homologs in reciprocal HHpred queries.”

      - Given that the authors expand the evolutionary analysis of TRAP to archaea it would be helpful if sampling for RAMP4 were consistent (i.e., is TRAP present in the early eukaryotes that do not feature RAMP4? Is RAMP4 absent from heimdallarchaea?).

      As stated in the text, RAMP4’s absence from early-branching eukaryotic taxa indicates that it was also absent from their archaeal ancestors. We did of course run such queries for completeness and indeed find no archaeal RAMP4. TRAP, for its part, is generally present in early-branching eukaryotic taxa, as stated in the text, and this necessarily includes those from which RAMP4 is absent.

      - The authors may consider discussing (Gemmer et al 2023, https://doi.org/10.1101/2023.11.28.569136), which comes to similar conclusions for NEMO integration into the MPT.

      We thank the reviewer for bringing this relevant work to our attention. We have added the following sentence to the section on NOMO: “Contemporaneous work has arrived at a similar model for PLD10-12 but did not model PLD1 (Gemmer et al. 2023).”

      - The abundance approximation of RAMP4 in the native translocon by OccuPy should probably be taken with a grain of salt. The '80%' mentioned in the conclusion may stick around and could eventually turn out to be closer to 100%.

      It is certainly possible that the occupancy of RAMP4 is higher than OccuPy estimates.

      Unfortunately no available method can provide occupancy estimates with confidence intervals. The Western blots we have added to the revised manuscript are consistent with high occupancy, but cannot discriminate between 80 or 100%.

      Minor

      - p. 5: The following sentence is incomplete: "Together, these factors explain why RAMP4's occupancy in prior cryo-EM maps was low enough to be overlooked, although in hindsight seems to be visible in several7,68,69"

      Thank you for catching this typo. We have revised the sentence as follows: “Together, these factors explain why RAMP4's occupancy in prior cryo-EM maps was low enough to be overlooked, although in hindsight it is visible in several of them.”

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Weaknesses:

      The authors demonstrate that ASGR1 is degraded in response to RSPO2RA-antibody treatment through both the proteasomal and the lysosomal pathway, suggesting that this is due to the RSPO2RA-mediated recruitment of ZNRF3/RNF43, which have E3 ubiquitin ligase activity. The paper doesn't show, however, if ASGR1 is indeed ubiquitinated.

      We thank the reviewer for this comment. We have now conducted ASGR1 ubiquitination assays by immunoprecipitation (IP) of ubiquitin in the membrane protein extract, and immunoblotting (IB) ASGR1 after treating HepG2 cells with our SWEETS molecules or controls. The new data demonstrated ubiquitination of ASGR1 with SWEETS treatment (new Fig. S3A and S3B). Additionally, we blocked the potential ubiquitination of ASGR1 by mutating the two lysine residues in the cytoplasmic domain and compared the ASGR1 degradation after SWEETS treatment. The new data show that removing the potential ubiquitylation Lys sites prevented ASGR1 degradation post SWEETS treatment (new Fig. S3C). These new results provide direct evidence that ASGR1 is ubiquitinated to undergo lysosome or proteasome degradation.

      The authors conclude that the RSPO2A-Ab fusions can act as a targeted protein degredation platform, because they can degrade ASGR. While I agree with this statement, I would argue that the goal of these Abs would not be to degrade ASGR per se. The argumentation is a bit confusing here. This holds for both the results and the discussion section: The authors focus on the dual role of their agents, i.e. on promoting both WNT signaling AND on degrading ASGR1. They might want to reconsider how they present their data (e.g. it may be interesting to target ASGR1, but one would presumably then like to do this without also increasing WNT responsiveness?).

      We thank the reviewer for this comment. As the reviewer states, the initial goal of the RSPO2RA-ab fusions was to generate tissue-specific RSPO mimetics that focus on elimination of E3. As an unintended consequence, we observed enhanced elimination of ASGR as well. While this was unintended, the results did provide POC that when an E3 ligase is brought into proximity of another protein, ubiquitination and degradation of this protein may occur. Additionally, our results highlight that one needs to be careful in fully assessing the impact of bispecific molecules on the intended target as well as unintended targets to understand the potential side effects of such bispecific molecules. We have revised the manuscript to make this more clear, both in the Results and Discussion sections.

      Lines 326-331: The authors use a lot of abbreviations for all of the different protein targeting technologies, but since they are hinting at specific mechanisms, it would be better to actually describe the biological activity of LYTAC versus AbTAC/PROTAB/REULR so non-experts can follow.

      We thank the reviewer for this suggestion. We have added more details in the Discussion to highlight the different mechanisms of the various systems described.

      Can the authors comment on how 8M24 and 8G8 compare to 4F3? The latter seems a bit more specific (ie. lower background activity in the absence of ASGR1 in 5C)? Are there any differences/advances between 8M24 and 8G8 over 4F3? This remains unclear.

      These three antibodies bind different regions/epitopes on ASGR. 8M24 and 8G8 bind non-overlapping epitopes on the carbohydrate recognition domain (CRD), while 4F3 binds the stalk region outside of the CRD. This information is in the Results section of the manuscript. We do not believe that the difference in the ASGR binding epitopes contributes to the slight differences in the background activity. The slight differences may be due to differences in the conformation of the antibodies resulting from the differences in their primary sequences, and these differences may not be significant. We have now repeated the experiments in Fig. 5C and 5D to address the reviewer’s next comment on the axis. These new data (new Fig. 5C and 5D) show less background differences between the molecules.

      Can the authors ensure that the axes are labelled/numbered similarly for Fig 5B-D? This will make it easier to compare 5C and 5D.

      We thank the reviewer for this suggestion. The y-axes in Fig. 5B–D now have the same scale and number format. For Figs. 5C and 5D, we focus on the potency increases of the SWEETS molecules post ASGR1 overexpression.

      Reviewer #2 (Public Review):

      Weaknesses:

      The authors show crystal structures for binding of these antibodies to ASGR1/2, and hypothesize about why specificity is mediated through specific residues. They do not test these hypotheses.

      We thank the reviewer for this comment. We did not further test the residue contributions to binding and specificity as this is not the main focus of the current manuscript. We have revised the section and tuned down the claims for specificity.

      The authors demonstrate in hepatocyte cell lines that these function as mimetics, and that they do not function in HEK cells, which do not express ASGR1. They do not perform an exhaustive screen of all non-hepatocyte cells, nor do they test these molecules in vivo.

      We agree with the reviewer. For the 4F3-based SWEETS molecule, additional in vitro and in vivo specificity characterized were performed and described in Zhang et al., Sci Rep, 2020. Since 8M24 is human specific and 8G8 only weakly interacts with mouse receptors, in vivo experiments in mouse were not performed. While we did not extensively test the 8M24- and 8G8-based SWEETS on additional cell lines or in vivo, we do believe the data presented strongly support the hepatocyte-specific effects of these molecules.

      Surprisingly, these molecules also induced loss of ASGR1, which the authors hypothesize is due to ubiquitination and degradation, initiated by the E3 ligases recruited to ASGR1. They demonstrate that inhibition of either the proteasome or lysosome abrogates this effect and that it is dependent on E1 ubiquitin ligases. They do not demonstrate direct ubiquitination of ASGR1 by ZNRF3/RNF43.

      We thank the reviewer for this comment. We have now conducted ASGR1 ubiquitination assays by immunoprecipitation (IP) of ubiquitin in the membrane protein extract, and immunoblotting (IB) ASGR1 after treating HepG2 cells with our SWEETS molecules or controls. The new data demonstrate ubiquitination of ASGR1 with SWEETS treatment (new Figs. S3A and S3B). Additionally, we blocked the potential ubiquitination of ASGR1 by mutating the two lysine residues in the cytoplasmic domain and compared the ASGR1 degradation after SWEETS treatment. The new data show that removing the potential ubiquitylation Lys sites prevented ASGR1 degradation post SWEETS treatment (new Fig. S3C). These new results provide direct evidence that ASGR1 is ubiquitinated to undergo lysosome or proteasome degradation.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      There are multiple instances where articles (i.e. the use of "the") are missing.

      We thank the reviewer for this comment. Following the suggestion, the manuscript has gone through a detailed review by an editorial service, and these and other grammatical errors have been corrected.

      Reviewer #2 (Recommendations For The Authors):

      The best I can think of is to inject these into Wnt reporter mice (or maybe humanized mice) and see if the liver lights up while other tissues do not.

      We thank the reviewer for this suggestion. The liver specificity was demonstrated in vivo in our earlier publication (SciRep, 10:13951, 2020) with the 4F3-RSPO2RA molecule. Unfortunately, as the results in this manuscript show, the new ASGR binders 8M24 and 8G8 either do not bind or only weakly interact with mouse receptors. Therefore, the in vivo experiments were not performed here.

      You could also consider addressing some of the statements in the manuscript that are currently hypothetical experimentally.

      We thank the reviewer for this comment. We did not further test the residues’ contribution to binding and specificity as this is not the main focus of the current manuscript. We have revised the section and tuned down the claims for specificity.

      It would be easier to compare the graphs in 5B-D if all Y-axes were the same scale, with the same scientific notation.

      We thank the reviewer for this suggestion. The y-axes in Fig. 5B-D now have the same scale and number format. For Figs. 5C and 5D, we focus on the potency increases of the SWEETS molecules post ASGR1 overexpression.

      Some of the western blots in Figure 6 do not have antibody/target labels, making them harder to interpret.

      All the Western blots antibody/target labels are on the right side of the blots for each panel, we have now made the text bold and thus easier to identify.

      Figure 6 and Supplementary Figure 2 are the same I think.

      Figure 6 and Supplementary Figure 2 show the same experimental set-up performed on two different cell lines, Fig. 6 is on Huh7 cells and Supplementary Fig. 2 is on HepG2 cells. The results from these two cell lines are quite consistent, making their appearance very similar.

    1. Author response:

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

      eLife assessment

      This is a valuable study that develops a new model of the way muscle responds to perturbations, synthesizing models of how it responds to small and large perturbations, both of which are used to predict how muscles function for stability but also how they can be injured, and which tend to be predicted poorly by classic Hill-type models. The evidence presented to support the model is solid, since it outperforms Hill-type models in a variety of conditions. Although the combination of phenomenological and mechanistic aspects of the model may sometimes make it challenging to interpret the output, the work will be of interest to those developing realistic models of the stability and control of movement in humans or other animals.

      Reviewer #1 (Public Review):

      Muscle models are important tools in the fields of biomechanics and physiology. Muscle models serve a wide variety of functions, including validating existing theories, testing new hypotheses, and predicting forces produced by humans and animals in health and disease. This paper attempts to provide an alternative to Hill-type muscle models that includes contributions of titin to force enhancement over multiple time scales. Due to the significant limitations of Hill-type models, alternative models are needed and therefore the work is important and timely.

      The effort to include a role for titin in muscle models is a major strength of the methods and results. The results clearly demonstrate the weaknesses of Hill models and the advantages of incorporating titin into theoretical treatments of muscle mechanics. Another strength is to address muscle mechanics over a large range of time scales.

      The authors succeed in demonstrating the need to incorporate titin in muscle models, and further show that the model accurately predicts in situ force of cat soleus (Kirsch et al. 1994; Herzog & Leonard, 2002) and rabbit posts myofibrils (Leonard et al. 2010). However, it remains unclear whether the model will be practical for use with data from different muscles or preparations. Several ad hoc modifications were described in the paper, and the degree to which the model requires parameter optimization for different muscles, preparations and experiment types remains unclear.

      I think the authors should state how many parameters require fitting to the data vs the total number of model parameters. It would also be interesting for the authors to discuss challenges associated with modeling ex vivo and in vivo data sets, due to differences in means of stimulation vs. model inputs.

      (1) I think the authors should state how many parameters require fitting to the data vs the total number of model parameters.

      The total number of model parameters are listed in Table 1. Each parameter has, in addition, references listed for the source of data (if one exists) along with how the data were used (’C’ calculate, ’F’ fit, ’E’ estimated, or ’S’ for scaled) for the specific simulations that appear in this paper. While this is a daunting number of parameters, only a few of these parameters must be updated when modeling a new musculotendon.

      Similar to a Hill-type muscle model, at least 5 parameters are needed to fit the VEXAT model to a specific musculotendon: maximum isometric force (fiso), optimal contractile element (CE) length, pennation angle, maximum shortening velocity, and tendon slack length. However, similar to a Hill model, it is only possible to use this minimal set of parameters by making use of default values for the remaining set of parameters. The defaults we have used have been extracted from mammalian muscle (see Table 1) and may not be appropriate for modeling muscle tissue that differs widely in terms of the ratio of fast/slow twitch fibers, titin isoform, temperature, and scale.

      Even when these defaults are appropriate, variation is the rule for biological data rather than the exception. It will always be the case that the best fit can only be obtained by fitting more of the model’s parameters to additional data. Standard measurements of the active force-length relation, passive forcelength relation, and force-velocity relations are quite helpful to improve the accuracy of the model to a specific muscle. It is challenging to improve the fit of the model’s cross-bridge (XE) and titin models because the data required are so rare. The experiments of Kirsch et al., Prado et al, and Trombitas et´ al. are unique to our knowledge. However, if more data become available, it is relatively straight forward to update the model’s parameters using the methods described in Appendix B or the code that appears online (https://github.com/mjhmilla/Millard2023VexatMuscle).

      We have modified the manuscript to make it clear that, in some circumstances, the burden of parameter identification for the VEXAT model can be as low as a Hill model:

      - Section 3: last two sentences of the 2nd paragraph, found at: Page 10, column 2, lines 1-12 of MillardFranklinHerzog v3.pdf and 05 MillardFranklinHerzog v2 v3 diff.pdf

      - Table 1: last two sentences of the caption, found at: Page 11 of MillardFranklinHerzog v3.pdf and 05 MillardFranklinHerzog v2 v3 diff.pdf

      (2) It would also be interesting for the authors to discuss challenges associated with modeling ex vivo and in vivo data sets, due to differences in means of stimulation vs. model inputs.

      All of the experiments simulated in this work are in-situ or ex-vivo. So far the main challenges of simulating any experiment have been quite consistent across both in-situ and ex-vivo datasets: there are insufficient data to fit most model parameters to a specific specimen and, instead, defaults from the literature must be used. In an ideal case, a specimen would have roughly ten extra trials collected so that the maximum isometric force, optimal fiber length, active force-length relation, passive force-length relation (upto ≈ 0_._6_f_oM), and the force-velocity relations could be identified from measurements rather than relying on literature values. Since most lab specimens are viable for a small number of trials (with the exception of cat soleus), we don’t expect this situation to change in future.

      However, if data are available the fitting process is pretty straight forward for either in-situ or ex-vivo data: use a standard numerical method (for example non-linear least squares, or the bisection method) to adjust the model parameters to reduce the errors between simulation and experiment. The main difficulty, as described in the previous paragraph, is the availability of data to fit as many parameters as possible for a specific specimen. As such, the fitting process really varies from experiment to experiment and depends mainly on the richness of measurements taken from a specific specimen, and from the literature in general.

      Working from in-vivo data presents an entirely different set of challenges. When working with human data, for example, it’s just not possible to directly measure muscle force with tendon buckles, and so it is never completely clear how force is distributed across the many muscles that typically actuate a joint. Further, there is also uncertainty in the boundary condition of the muscle because optical motion capture markers will move with respect to the skeleton. Video fluoroscopy offers a method of improving the accuracy of measured boundary conditions, though only for a few labs due to its great expense. A final boundary condition remains impossible to measure in any case: the geometry and forces that act at the boundaries as muscle wraps over other muscles and bones. Fitting to in-vivo data are very difficult.

      While this is an interesting topic, it is tangent to our already lengthy manuscript. Since these reviews are public, we’ll leave it to the motivated reader to find this text here.

      Reviewer #2 (Public Review):

      This model of skeletal muscle includes springs and dampers which aim to capture the effect of crossbridge and titin stiffness during the stretch of active muscle. While both crossbridge and titin stiffness have previously been incorporated, in some form, into models, this model is the first to simultaneously include both. The authors suggest that this will allow for the prediction of muscle force in response to short-, mid- and long-range stretches. All these types of stretch are likely to be experienced by muscle during in vivo perturbations, and are known to elicit different muscle responses. Hence, it is valuable to have a single model which can predict muscle force under all these physiologically relevant conditions. In addition, this model dramatically simplifies sarcomere structure to enable this muscle model to be used in multi-muscle simulations of whole-body movement.

      In order to test this model, its force predictions are compared to 3 sets of experimental data which focus on short-, mid- and long-range perturbations, and to the predictions of a Hill-type muscle model. The choice of data sets is excellent and provide a robust test of the model’s ability to predict forces over a range of length perturbations. However, I find the comparison to a Hill-type muscle model to be somewhat limiting. It is well established that Hill-type models do not have any mechanism by which they can predict the effect of active muscle stretch. Hence, that the model proposed here represents an improvement over such a model is not a surprise. Many other models, some of which are also simple enough to be incorporated into whole-body simulations, have incorporated mechanistic elements which allow for the prediction of force responses to muscle stretch. And it is not clear from the results presented here that this model would outperform such models.

      The paper begins by outlining the phenomenological vs mechanistic approaches taken to muscle modelling, historically. It appears, although is not directly specified, that this model combines these approaches. A somewhat mechanistic model of the response of the crossbridges and titin to active stretch is combined with a phenomenological implementation of force-length and force-velocity relationships. This combination of approaches may be useful improving the accuracy of predictions of muscle models and whole-body simulations, which is certainly a worthy goal. However, it also may limit the insight that can be gained. For example, it does not seem that this model could reflect any effect of active titin properties on muscle shortening. In addition, it is not clear to me, either physiologically or in the model, what drives the shift from the high stiffness in short-range perturbations to the somewhat lower stiffness in mid-range perturbations.

      (1) It is well established that Hill-type models do not have any mechanism by which they can predict the effect of active muscle stretch.

      While many muscle physiologists are aware of the limitations of the Hill model, these limitations are not so well known among computational biomechanists. There are at least two reasons for this gap: there are few comprehensive evaluations of Hill models against several experiments, and some of the differences are quite nuanced. For example, active lengthening experiments can be replicated reasonably well using a Hill model if the lengthening is done on the ascending limb of the force length curve. Clearly the story is quite different on the descending limb as shown in Figure 9. Similarly, as Figure 8 shows, by choosing the right combination of tendon model and perturbation bandwidth it is possible to get reasonably accurate responses from the Hill model to stochastic length changes. Yet when a wide variety of perturbation bandwidths, magnitudes, and tendon models are tested it is clear that the Hill model cannot, in general, replicate the response of muscle to stochastic perturbations. For these reasons we think many of the Hill model’s drawbacks have not been clearly understood by computational biomechanists for many years now.

      (2) Many other models, some of which are also simple enough to be incorporated into whole-body simulations, have incorporated mechanistic elements which allow for the prediction of force responses to muscle stretch. And it is not clear from the results presented here that this model would outperform such models.

      We agree that it will be valuable to benchmark other models in the literature using the same set of experiments. Hopefully we, or perhaps others, will have the good fortune to secure research funding to continue this benchmarking work. This will, however, be quite challenging: few muscle models are accompanied by a professional-quality open-source implementation. Without such an implementation it is often impossible to reproduce published results let alone provide a fair and objective evaluation of a model.

      (3) For example, it does not seem that this model could reflect any effect of active titin properties on muscle shortening.

      The titin model described in the paper will provide an enhancement of force during a stretch-shortening cycle. This certainly would be an interesting next experiment to simulate in a future paper.

      (4) In addition, it is not clear to me, either physiologically or in the model, what drives the shift from the high stiffness in short-range perturbations to the somewhat lower stiffness in mid-range perturbations.

      We can only respond to what drives the frequency dependent stiffness in the model, though we’re quite interested in what happens physiologically. Hopefully that there are some new experiments done to examine this phenomena in the future. In the case of the model, the reasons are pretty straight forward: the formulation of Eqn. 16 is responsible for this shift.

      Equation 16 has been formulated so that the acceleration of the attachment point of the XE is driven by the force difference between the XE and a reference Hill model (numerator of the first term in Eqn. 16) which is then low pass filtered (denominator of the first term in Eqn. 16). Due to this formulation the attachment point moves less when the numerator is small, or when the differences in the numerator change rapidly and effectively become filtered out. When the attachment point moves less, more of the CE’s force output is determined by variations in the length of the XE and its stiffness.

      On the other hand, the attachment point will move when the numerator of the first term in Eqn. 16 is large, or when those differences are not short lived. When the attachment point moves to reduce the strain in the XE, the force produced by the XE’s spring-damper is reduced. As a result, the CE’s force output is less influenced by variations of the length of the XE and its stiffness.

      Reviewer #2 (Recommendations for the Authors):

      I find the clarity of the manuscript to be much improved following revision. While I still find the combination of phenomenological and mechanistic approaches to be a little limiting with regards to our understanding of muscle contraction, the revised description of small length changes makes the interpretation much less confusing.

      Similarly, while I agree that Hill-type models are widely used their limitations have been addressed extensively and are very well established. Hence, moving forward I think it would be much more valuable to start to compare these newer models to one another rather than just showing an improvement over a Hill model under (very biologically important) conditions which that model has no capacity to predict forces.

      (1) While I still find the combination of phenomenological and mechanistic approaches to be a little limiting with regards to our understanding of muscle contraction ...

      We have had to abstract some of the details of reality to have a model that can be used to simulate hundreds of muscles. In contrast, FiberSim produced by Kenneth Campbell’s group uses much less abstraction and might be of greater interest to you. FiberSim’s models include individual cross-bridges, titin molecules, and an explicit representation of the spatial geometry of a sarcomere. While this model is a great tool for testing muscle physiology questions through simulation, it is computationally expensive to use this model to simulate hundreds of muscles simultaneously.

      Kosta S, Colli D, Ye Q, Campbell KS. FiberSim: A flexible open-source model of myofilament-level contraction. Biophysical journal. 2022 Jan 18;121(2):175-82.https://campbell-muscle-lab.github.io/FiberSim/

      (2) Similarly, while I agree that Hill-type models are widely used their limitations have been addressed extensively and are very well established.

      Please see our response 1 to Reviewer # 1.

      (3) Hence, moving forward I think it would be much more valuable to start to compare these newer models to one another rather than just showing an improvement over a Hill model under (very biologically important) conditions which that model has no capacity to predict forces.

      Please see our response to 2 to Reviewer #1.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary: 

      In the paper by Choi et al., the authors aimed to develop base editing strategies to convert CAG repeats to CAA repeats in the huntingtin gene (HTT), which causes Huntington's disease (HD). They hypothesized that this conversion would delay disease onset by shortening the uninterrupted CAG repeat. Using HEK-293T cells as a model, the researchers employed cytosine base editors and guide RNAs (gRNAs) to efficiently convert CAG to CAA at various sites within the CAG repeat. No significant indels, off-target edits, transcriptome alterations, or changes in HTT protein levels were detected. Interestingly, somatic CAG repeat expansion was completely abolished in HD knock-in mice carrying CAA-interrupted repeats. 

      Correction of factual errors

      We analyzed HEK293 cells, not "HEK-293T".

      Strengths: 

      This study represents the first proof-of-concept exploration of the cytosine base editing technique as a potential treatment for HD and other repeat expansion disorders with similar mechanisms. 

      Weaknesses: 

      Given that HD is a neurodegenerative disorder, it is crucial to determine the efficiency of the base editing strategies tested in this manuscript and their feasibility in relevant cells affected by HD and the brain, which needed to be improved in this manuscript. 

      We appreciate the reviewer's constructive recommendations. Our genetic investigation focused on understanding observations in HD patients to develop genetic-based treatment strategies and test their feasibility. We agree with the reviewer regarding the importance of data from relevant cell types. Unfortunately, the levels of CAG-to-CAA conversion in the patient-derived neurons were modest, as described in our manuscript (approximately 2%). In addition, AAV did not produce detectable conversions in the brain of HD knock-in mice (data not shown), which was somewhat expected from the literature (PMID: 31937940). We believe some technical hurdles can be overcome by developing efficient delivery methods. Nonetheless, it will be an important follow-up study to perform preclinical studies employing optimized base editing strategies and efficient brain delivery methods to fully demonstrate the therapeutic potential of BE strategies. 

      Reviewer #2 (Public Review):

      Summary: 

      In a proof-of-concept study with the aspiration of developing a treatment to delay HD onset, Choi et al. design and test an A>G DNA base editing strategy to exploit the recently established inverse relationship between the number of uninterrupted CAG repeats in polyglutamine repeat expansions and the age-of-onset of Huntington's Disease (HD). Most of the study is devoted to optimizing a base editing strategy typified by BE4max and gRNA2. The base editing is performed in human HEK293 cells engineered with a 51 CAG canonical repeat and in HD knock-in mice harboring 105+ CAG repeats. 

      Correction of factual errors

      We tested base editing strategies aimed at C > T conversion, not A > G DNA base editing. In addition to HEK293 and knock-in mice, we tested base editing strategies in patient-derived iPSC and neurons.

      Weaknesses: 

      Genotypic data on DNA editing are not portrayed in a clear manner consistent with the study's goal, namely reducing the number of uninterrupted CAG repeats by a clinically relevant amount according to the authors' least square approximated mean age-at-onset. No phenotypic data are presented to show that editing performed in either model would lead to reduced hallmarks of HD onset. 

      More evidence is needed to support the central claims and therapeutic potential needs to be more adequate. 

      Our strategies for converting CAG to CAA in model systems resulted in quantitative DNA modification in a population of cells. Consequently, individual cells may carry different genotypes, some harboring CAA and others CAG at the same genomic location. Therefore, using a standard genotype format for DNA to present base editing outcomes may not be ideal. Instead, we presented the resulting genotype data in a quantitative fashion to provide the percentage of conversion at each site. This approach allows for an intuitive interpretation of both the extent of repeat length reduction and the proportion of such modifications.

      Currently, genetically precise HD mouse models with robust motor and behavioral phenotypes are unavailable. While some HD mouse models, such as the BAC and YAC models, feature pronounced behavioral phenotypes, they consist of interrupted CAG repeat sequences, making them unsuitable for base conversion studies due to their inherently short uninterrupted repeats. Although genetically precise HD knockin mouse models exist, they do not manifest motor symptom-like phenotypes. Given that CAG repeat expansion is the primary driver of the disease and knock-in mice recapitulate such phenomenon, our genetic investigation focused on assessing the effects of base conversion on CAG repeat instability in knock-in mice. However, as emphasized by the reviewer, subsequent preclinical studies to evaluate the therapeutic efficacy of CAG-to-CAA conversion strategies using mouse models harboring uninterrupted adult-onset CAG repeats and robust HD-like phenotypes remain crucial.

      Reviewer #3 (Public Review):

      Summary: 

      In human patients with Huntington's disease (HD), caused by a CAG repeat expansion mutation, the number of uninterrupted CAG repeats at the genomic level influences age-at-onset of clinical signs independent of the number of polyglutamine repeats at the protein level. In most patients, the CAG repeat terminates with a CAACAG doublet. However, CAG repeat variants exist that either do not have that doublet or have two doublets. These variants consequently differ in their number of uninterrupted CAG repeats, while the number of glutamine repeats is the same as both CAA and CAG codes for glutamine. The authors first confirm that a shorter uninterrupted CAG repeat number in human HD patients is associated with developing the first clinical signs of HD later. They predict that introducing a further CAA-CAG doublet will result in years of delay of clinical onset. Based on this observation, the authors tested the hypothesis that turning CAG to CAA within a CAG repeat sequence using base editing techniques will benefit HD biology. They show that, indeed, in HD cell models (HEK293 cells expressing 16/17 CAG repeats; a single human stem cell line carrying a CAG repeat expansion in the fully penetrant range with 42 CAG repeats), their base editing strategies do induce the desired CAG-CAA conversion. The efficiency of conversion differed depending on the strategy used. In stem cells, delivery posed a problem, so to test allele specificity, the authors then used a HEK 293 cell line with 51 CAG repeats on the expanded allele. Conversion occurred in both alleles with huntingtin protein and mRNA levels; transcriptomics data was unchanged. In knock-in mice carrying 110 CAG repeats, however, base editing did not work as well for different, mainly technical, reasons. 

      Correction of factual errors

      "HD cell models (HEK293 cells expressing 16/17 CAG repeats" is an incorrect description. It should be "HD cell models (HEK293 cells expressing 51/17 CAG repeats".

      Strengths: 

      The authors use state-of-the-art methods and carefully and thoroughly designed experiments. The data support the conclusions drawn. This work is a very valuable translation from the insight gained from large GWAS studies into HD pathogenesis. It rightly emphasises the potential this has as a causal treatment in HD, while the authors also acknowledge important limitations. 

      Weaknesses: 

      They could dedicate a little more to discussing several of the mentioned challenges. The reader will better understand where base editing is in HD currently and what needs to be done before it can be considered a treatment option. For instance, 

      - It is important to clarify what can be gained by examining again the relationship between uninterrupted CAG repeat length and age-at-onset. Could the authors clarify why they do this and what it adds to their already published GWAS findings? What is the n of datasets? 

      Published HD GWAS (PMID: 31398342) compared the onset age of duplicated interruption and loss of interruption to that of canonical repeats to determine whether uninterrupted CAG repeat or polyglutamine determines age at onset. However, GWAS findings did not quantify the magnitude of the unexplained remaining variance in age at onset in duplicated interruption and loss of interruption. Our study further investigated to gain insights into the amount of additional impact of duplicated interruption to estimate the maximum clinical benefits of base editing strategies for CAG-to-CAA conversion. Since the purpose of this genetic analysis is described in the result section already, we added the following sentence in the introduction section to bring up what is unknown. 

      "Still, age at onset of loss of interruption and duplicated interruption was not fully accounted for by uninterrupted CAG repeat, suggesting additional effects of non-canonical repeats."

      We added sample size for the least square approximation analysis in the text and corresponding figure legend. Sample sizes for molecular and animal experiments can be found in the corresponding figure legend.

      - What do they think an ideal conversion rate would be, and how that could be achieved? 

      It is a very important question. However, speculating the ideal conversion levels is out of the scope of this genetic investigation. A series of preclinical studies using relevant models may generate data that may shed light on the conversion rate levels that are required to produce meaningful clinical benefits. In the discussion section, we added the following sentence. 

      "Currently, the ideal levels of CAG-to-CAA conversion that produce significant clinical benefits are unknown. A series of preclinical studies using relevant model systems may generate data that may shed light on the optimal conversion rate levels that are required to produce significant clinical benefits."

      - Is there a dose-effect relationship for base editing, and would it be realistic to achieve the ideal conversion rate in target cells, given the difficulties described by the authors in differentiated neurons from stem cells? 

      We observed a clear dose-response relationship between the amount of BE reagents and the levels of conversion in non-neuronal cells. Unfortunately, the conversion rate was low in neuronal cells, potentially due to limited delivery, as speculated in the result section. As described in the discussion sections, we predict that efficient delivery methods will be crucial to produce significant CAG-to-CAA conversion to achieve therapeutic benefits.

      - The liver is a good tool for in-vivo experiments examining repeat instability in mouse models. However, the authors could comment on why they did not examine the brain.

      We focused on liver instability because of 1) the expectation that delivery/targeting efficiency is significantly lower in the brain (PMID: 31937940) and 2) shared underlying mechanisms between the brain and liver (described in the result section). The following sentence was added in the method section to provide a rationale for liver analysis. 

      "Since significantly lower delivery/targeting efficiency was expected in the brain 34, we focused on analyzing liver instability."

      - Is there a limit to judging the effects of base editing on somatic instability with longer repeats, given the difficulties in measuring long CAG repeat expansions? 

      Determining the levels of base conversion using sequencing technologies gets harder as repeats become longer. Fragment analysis can overcome such technical difficulty if conversion efficiency is high. As pointed out, the repeat expansion measure is also challenging because amplification is biased toward shorter alleles. However, if repeat sizes are relatively similar, the levels of repeat expansion as a function of base conversion can be determined relatively precisely without a significant bias by a standard fragment analysis approach. 

      - Given the methodological challenges for assessing HTT fragments, are there other ways to measure the downstream effects of base editing rather than extrapolate what it will likely be?

      Our CAG-to-CAA conversion strategies are not expected to directly generate fragments of huntingtin DNA, RNA, or protein. In contrast, immediate downstream effects of CAG-to-CAA conversion include sequence changes (DNA and RNA) and alteration of repeat instability, which are presented in the manuscript. If repeat instability is associated with HTT exon 1A fragment, base conversion strategies may indirectly alter the levels of such putative toxic species, which remains to be determined.  

      - Sequencing errors could mask low-level, but biologically still relevant, off-target effects (such as gRNAdependent and gRNA-independent DNA, Off-targets, RNA off-targets, bystander editing). How likely is that? 

      We agree with the reviewer that increased editing efficiency is expected to increase the levels of off-target editing. However, the field is actively developing base editors with minimal off-target effect (PMID: 35941130), which will increase the safety aspects of this technology for clinical use. We added the following sentence.  "In addition, developing base editors with high level on-target gene specificity and minimal off-target effects is a critical aspect to address 100."

      - How worried are the authors about immune responses following base editing? How could this be assessed? 

      We added the following sentence in the discussion section as the reviewer raised an important safety issue.  

      "Thorough assessments of immune responses against base editing strategies (e.g., development of antibody, B cell, and T cell-specific immune responses) and subsequent modification (e.g., immunosilencing) 101 will be critical to address immune response-associated safety issues of BE strategies."

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The following points could be considered to improve the overall quality of the manuscript: 

      (1) The authors mentioned that the reason for checking repeat instability in the nonneuronal cells was due to the availability of specific types of AAV; there are other subtypes of AAVs available to infect neurons and iPSCs. 

      Our pilot experiments testing several AAV serotypes in patient-derived iPSC and HD knock-in mice showed that only AAV9 converted CAG to CAA at detectable levels in the liver, not in the brain or neurons. We also speculate that difficulties in targeting the CAG repeat region due to GC-rich sequence contributed to low conversion efficiency. Therefore, subsequent optimization of base editor and delivery may improve BE strategies for HD, permitting robust conversion at the challenging locus. 

      (2) Despite its bold nature, minimal data in the manuscript demonstrate that this gene editing strategy is disease-modifying.

      Resources required to demonstrate the therapeutic benefits of CAG-to-CAA conversion strategies are not fully available. Especially, relevant HD mouse models that carry uninterrupted adult onset CAG repeat and that permit measuring the levels of disease-modifying are lacking, as described in our response to the second reviewer. Given that CAG repeat expansion is the primary driver of the disease, this genetic investigation focused on determining the impacts of base editing strategies on CAG repeat expansion. Still, as indicated by the reviewer, follow-up preclinical studies to evaluate the levels of disease-modifying of CAG-to-CAA conversion strategies using relevant mouse models represent important next steps.

      (3) Off-target analysis at the DNA level was limited to "predicted" off-target sites. What about possible translocations that can result from co-nicking on different chromosomes, as a large number of potential targets exist? 

      Among gRNAs we tested, we focused on gRNAs 1 and 2, which predicted small numbers of off-target. Therefore, our off-target analysis at the DNA level was focused on validating those predicted off-targets. As pointed out, thoroughly evaluating off-target effects will be necessary when candidate BE strategies take the next steps for therapeutic development.

      Genomic translocation caused by double-strand breaks can produce negative consequences, such as cancer. Importantly, although paired nicks efficiently induced translocations, translocations were not detected when a single nick was introduced on each chromosome (PMID: 25201414). Therefore, it is predicted that BE strategies using nickase confers little risk of translocation.

      (4) For in vivo work, somatic repeat expansion was analyzed only in peripheral tissue samples. Since the main affected cellular population in HD is the brain, the outcome of this treatment on a disease-relevant organ still needs to be determined. 

      Challenges in delivery to the brain made us determine instability in the liver since many mechanistic components of somatic CAG repeat instability are shared between the liver and striatum, as rationalized in the manuscript. However, we agree with the reviewer regarding the importance of determining the effects of base conversion on brain instability. We added the following sentence in the method section to provide a rationale. "Since significantly lower delivery/targeting efficiency was expected in brain 34, we focused on analyzing liver instability."

      Reviewer #2 (Recommendations For The Authors):

      Throughout the manuscript, the authors apologize for techniques that do not work when workarounds seem readily apparent to an expert in the field. In its current form, the manuscript reads verbose, speculative, apologetic, and preliminary. 

      Drug development programs that are supported by human genetics data show increased success rates in clinical trials (PMID: 26121088, 31827124, 31830040). This is why this genetic study focused on 1) investigating observations in HD subjects and 2) subsequently developing treatment strategies that are supported by patient genetics. As the first illustration of base editing in HD, the main scope of our manuscript is to justify the genetic rationale of CAG-to-CAA conversion and demonstrate the feasibility of therapeutic strategies rooted in patient genetics. As our study was not aimed at entirely demonstrating the clinical benefits of base editing strategies in HD, some of our data were based on tools and approaches that were not fully optimal. We agree with the reviewer that it will be an important next step to employ optimized approaches to evaluate the efficacy of base editing strategies in model systems. Nevertheless, our novel base conversion strategies derived from HD patient genetics represent a significant advancement as they may contribute to developing effective treatments for this devastating disorder. 

      Reviewer#3 (Recommendations For The Authors):

      It would make for an easier read if abbreviations were kept to a minimum. 

      As recommended, we decreased the use of abbreviations. The following has been spelled out throughout the manuscript: CR (canonical repeat), LI (loss of interruption), DI (duplicated interruption), and CBE (cytosine base editor). Other abbreviations with infrequent usage (e.g., ABE, SS, QC) were also spelled out in the text.

    1. Author response:

      Reviewer #1: 

      Summary:

      In this study, the authors used a multi-alternative decision task and a multidimensional signal-detection model to gain further insight into the cause of perceptual impairments during the attentional blink. The model-based analyses of behavioural and EEG data show that such perceptual failures can be unpacked into distinct deficits in visual detection and discrimination, with visual detection being linked to the amplitude of late ERP components (N2P and P3) and discrimination being linked to the coherence of fronto-parietal brain activity.

      Strengths:

      The main strength of this paper lies in the fact that it presents a novel perspective on the cause of perceptual failures during the attentional blink. The multidimensional signaldetection modelling approach is explained clearly, and the results of the study show that this approach offers a powerful method to unpack behavioural and EEG data into distinct processes of detection and discrimination.

      Weaknesses:

      (1.1) While the model-based analyses are compelling, the paper also features some analyses that seem misguided, or, at least, insufficiently motivated and explained. Specifically, in the introduction, the authors raise the suggestion that the attentional blink could be due to a reduction in sensitivity or a response bias. The suggestion that a response bias could play a role seems misguided, as any response bias would be expected to be constant across lags, while the attentional blink effect is only observed at short lags. Thus, it is difficult to understand why the authors would think that a response bias could explain the attentional blink.

      A deficit in T2 identification accuracy could arise from either sensitivity or criterion effects; the criterion effect may manifest as a choice bias. For example, in short T1-T2 lag trials, when T2 closely follows T1, participants may adopt a more conservative choice criterion for reporting the presence of T2. Moreover, criterion effects need not be uniform across lags: A participant could infer the T1-T2 lag interval based on various factors, including trial length, thereby permitting them to adjust their choice criterion variably across different lags. We will provide a more detailed illustration of this claim in the revision.

      (1.2) A second point of concern regards the way in which the measures for detection and discrimination accuracy were computed. If I understand the paper correctly, a correct detection was defined as either correctly identifying T2 (i.e., reporting CW or CCW if T2 was CW or CCW, respectively, see Figure 2B), or correctly reporting T2's absence (a correct rejection). Here, it seems that one should also count a misidentification (i.e., incorrect choice of CW or CCW when T2 was present) as a correct detection, because participants apparently did detect T2, but failed to judge/remember its orientation properly in case of a misidentification. Conversely, the manner in which discrimination performance is computed also raises questions. Here, the authors appear to compute accuracy as the average proportion of T2-present trials on which participants selected the correct response option for T2, thus including trials in which participants missed T2 entirely. Thus, a failure to detect T2 is now counted as a failure to discriminate T2. Wouldn't a more proper measure of discrimination accuracy be to compute the proportion of correct discriminations for trials in which participants detected T2?

      Detection and discrimination accuracies were computed with precisely the same procedure, and under the same conditions, as described by the Reviewer (underlined text, above). We regret our poor description; we will improve upon it in the revised manuscript.

      (1.3) My last point of critique is that the paper offers little if any guidance on how the inferred distinction between detection and discrimination can be linked to existing theories of the attentional blink. The discussion mostly focuses on comparisons to previous EEG studies, but it would be interesting to know how the authors connect their findings to extant, mechanistic accounts of the attentional blink. A key question here is whether the finding of dissociable processes of detection and discrimination would also hold with more meaningful stimuli in an identification task (e.g., the canonical AB task of identifying two letters shown amongst digits). There is evidence to suggest that meaningful stimuli are categorized just as quickly as they are detected (Grill-Spector & Kanwisher, 2005; Grill-Spector K, Kanwisher N. Visual recognition: as soon as you know it is there, you know what it is. Psychol Sci. 2005 Feb;16(2):152-60. doi: 10.1111/j.0956-7976.2005.00796.x. PMID: 15686582.). Does that mean that the observed distinction between detection and discrimination would only apply to tasks in which the targets consist of otherwise meaningless visual elements, such as lines of different orientations?

      Our results are consistent with previous literature suggested by the Reviewer. Specifically, we do not claim that detection and discrimination are sequential processes; in fact, we modeled them as concurrent computations (Figs. 3A-B). Yet, our results suggest that these processes possess distinct neural bases. We have discussed this idea briefly in the Discussion section (e.g., “Yet, we found no evidence for these two computations being sequential…”). We will discuss this further in the revised manuscript in the context of previous literature.

      Reviewer #2:

      Summary:

      The authors had two aims: First, to decompose the attentional blink (AB) deficit into the two components of signal detection theory; sensitivity and bias. Second, the authors aimed to assess the two subcomponents of sensitivity; detection and discrimination. They observed that the AB is only expressed in sensitivity. Furthermore, detection and discrimination were doubly dissociated. Detection modulated N2p and P3 ERP amplitude, but not frontoparietal beta-band coherence, whereas this pattern was reversed for discrimination.

      Strengths:

      The experiment is elegantly designed, and the data - both behavioral and electrophysiological - are aptly analyzed. The outcomes, in particular the dissociation between detection and discrimination blinks, are consistently and clearly supported by the results. The discussion of the results is also appropriately balanced.

      Weaknesses:

      (2.1) The lack of an effect of stimulus contrast does not seem very surprising from what we know of the nature of AB already. Low-level perceptual factors are not thought to cause AB. This is fine, as there are also other, novel findings reported, but perhaps the authors could bolster the importance of these (null) findings by referring to AB-specific papers, if there are indeed any, that would have predicted different outcomes in this regard.

      While there is consensus that the low-level perceptual factors are not affected by the attentional blink, other studies may suggest evidence to the contrary (e.g., Chua et al, Percept. Psychophys., 2005). We will highlight the significance of our findings in the context of such conflicting evidence in literature, in the revised manuscript.

      (2.2) On an analytical note, the ERP analysis could be finetuned a little more. The task design does not allow measurement of the N2pc or N400 components, which are also relevant to the AB, but the N1 component could additionally be analyzed. In doing so, I would furthermore recommend selecting more lateral electrode sites for both the N1, as well as the P1. Both P1 and N1 are likely not maximal near the midline, where the authors currently focused their P1 analysis.

      We will incorporate these additional analyses in the revised manuscript.

      (2.3) Impact & Context:

      The results of this study will likely influence how we think about selective attention in the context of the AB phenomenon. However, I think its impact could be further improved by extending its theoretical framing. In particular, there has been some recent work on the nature of the AB deficit, showing that it can be discrete (all-or-none) and gradual (Sy et al., 2021; Karabay et al., 2022, both in JEP: General). These different faces of target awareness in the AB may be linked directly to the detection and discrimination subcomponents that are analyzed in the present paper. I would encourage the authors to discuss this potential link and comment on the bearing of the present work on these behavioural findings.

      Thank you. We will discuss our findings in the context of these recent studies.

      Reviewer #3:

      Summary:

      In the present study, the authors aimed to achieve a better understanding of the mechanisms underlying the attentional blink, that is, a deficit in processing the second of two target stimuli when they appear in rapid succession. Specifically, they used a concurrent detection and identification task in- and outside of the attentional blink and decoupled effects of perceptual sensitivity and response bias using a novel signal detection model. They conclude that the attentional blink selectively impairs perceptual sensitivity but not response bias, and link established EEG markers of the attentional blink to deficits in stimulus detection (N2p, P3) and discrimination (fronto-parietal high-beta coherence), respectively. Taken together, their study suggests distinct mechanisms mediating detection and discrimination deficits in the attentional blink.

      Strengths:

      Major strengths of the present study include its innovative approach to investigating the mechanisms underlying the attentional blink, an elegant, carefully calibrated experimental paradigm, a novel signal detection model, and multifaceted data analyses using state-of-theart model comparisons and robust statistical tests. The study appears to have been carefully conducted and the overall conclusions seem warranted given the results. In my opinion, the manuscript is a valuable contribution to the current literature on the attentional blink. Moreover, the novel paradigm and signal detection model are likely to stimulate future research.

      Weaknesses:

      Weaknesses of the present manuscript mainly concern the negligence of some relevant literature, unclear hypotheses, potentially data-driven analyses, relatively low statistical power, potential flaws in the EEG methods, and the absence of a discussion of limitations. In the following, I will list some major and minor concerns in detail.

      Major points

      (3.1) Hypotheses:

      I appreciate the multifaceted, in-depth analysis of the given dataset including its high amount of different statistical tests. However, neither the Introduction nor the Methods contain specific statistical hypotheses. Moreover, many of the tests (e.g., correlations) rely on selected results of previous tests. It is unclear how many of the tests were planned a priori, how many more were performed, and how exactly corrections for multiple tests were implemented. Thus, I find it difficult to assess the robustness of the results.

      As outlined in the Introduction, we hypothesized that neural computations associated with target detection would be characterized by regional neuronal markers (e.g., parietal or occipital ERPs), whereas computations linked to feature discrimination may involve neural coordination across multiple brain regions (e.g. fronto-parietal coherence). We planned and conducted our statistical tests based on this hypothesis. All multiple comparison corrections (e.g., Bonferroni-Holm correction, see Methods) were performed separately for each class of analyses. We will clarify these hypotheses and provide further details in the revised manuscript.

      (3.2) Power:

      Some important null findings may result from the rather small sample sizes of N = 24 for behavioral and N = 18 for ERP analyses. For example, the correlation between detection and discrimination d' deficits across participants (r=0.39, p=0.059) (p. 12, l. 263) and the attentional blink effect on the P1 component (p=0.050, no test statistic) (p. 14, 301) could each have been significant with one more participant. In my opinion, such results should not be interpreted as evidence for the absence of effects.

      We agree and will revise the manuscript accordingly. We will also report Bayes factor (BF) values, where relevant, to further evaluate these claims.

      (3.3) Neural basis of the attentional blink:

      The introduction (e.g., p. 4, l. 56-76) and discussion (e.g., p. 19, 427-447) do not incorporate the insights from the highly relevant recent review by Zivony & Lamy (2022), which is only cited once (p. 19, l. 428). Moreover, the sections do not mention some relevant ERP studies of the attentional blink (e.g., Batterink et al., 2012; Craston et al., 2009; Dell'Acqua et al., 2015; Dellert et al., 2022; Eiserbeck et al., 2022; Meijs et al., 2018).

      We will motivate and discuss our study in the context of these previous studies. 

      (3.4) Detection versus discrimination:

      Concerning the neural basis of detection versus discrimination (e.g., p. 6, l. 98-110; p. 18, l. 399-412), relevant existing literature (e.g., Broadbent & Broadbent, 1987; Hillis & Brainard, 2007; Koivisto et al., 2017; Straube & Fahle, 2011; Wiens et al., 2023) is not included.

      Thank you for these suggestions. We will include these important studies in our discussion.

      (3.5) Pooling of lags and lags 1 sparing:

      I wonder why the authors chose to include 5 different lags when they later pooled early (100, 300 ms) and late (700, 900 ms) lags, and whether this pooling is justified. This is important because T2 at lag 1 (100 ms) is typically "spared" (high accuracy) while T2 at lag 3 (300 ms) shows the maximum AB (for reviews, see, e.g., Dux & Marois, 2009; Martens & Wyble, 2010). Interestingly, this sparing was not observed here (p. 43, Figure 2). Nevertheless, considering the literature and the research questions at hand, it is questionable whether lag 1 and 3 should be pooled.

      Lag-1 sparing is not always observed in attentional blink studies; there are notable exceptions that do not report such sparing (Hommel et al., Q. J. Exp. Psychol., 2005; Livesay et al., Attention, Percept. Psychophys., 2011). Our statistical tests revealed no significant difference in accuracies between short lag (100 and 300 ms) trials or between long lag (700 and 900 ms) trials but did reveal significant differences between the short and long lag trials (ANOVA, followed by post-hoc tests). To simplify the presentation of the findings, we pooled together the short lag (100 and 300 ms) and, separately, the long lag (700 and 900 ms) trials. We will present these analyses, and clarify the motivation for pooling in the revised manuscript. 

      (3.6) Discrimination in the attentional blink

      Concerning the claims that previous attentional blink studies conflated detection and discrimination (p. 6, l. 111-114; p. 18, l. 416), there is a recent ERP study (Dellert et al., 2022) in which participants did not perform a discrimination task for the T2 stimuli. Moreover, since the relevance of all stimuli except T1 was uncertain in this study, irrelevant distractors could not be filtered out (cf. p. 19, l. 437). Under these conditions, the attentional blink was still associated with reduced negativities in the N2 range (cf. p. 19, l. 427-437) but not with a reduced P3 (cf. p. 19, l 439-447).

      We will address the difference between our findings and those of Dellert et al (2022) in the revised manuscript.

      (3.7) General EEG methods:

      While most of the description of the EEG preprocessing and analysis (p. 31/32) is appropriate, it also lacks some important information (see, e.g., Keil et al., 2014). For example, it does not include the length of the segments, the type and proportion of artifacts rejected, the number of trials used for averaging in each condition, specific hypotheses, and the test statistics (in addition to p-values).

      We regret the oversight. We will include these details in the revised Methods.

      (3.8) EEG filters:

      P. 31, l. 728: "The data were (...) bandpass filtered between 0.5 to 18 Hz (...). Next, a bandstop filter from 9-11 Hz was applied to remove the 10 Hz oscillations evoked by the RSVP presentation." These filter settings do not follow common recommendations and could potentially induce filter distortions (e.g., Luck, 2014; Zhang et al., 2024). For example, the 0.5 high-pass filter could distort the slow P3 wave. Mostly, I am concerned about the bandstop filter. Since the authors commendably corrected for RSVP-evoked responses by subtracting T2-absent from T2-present ERPs (p. 31, l. 746), I wonder why the additional filter was necessary, and whether it might have removed relevant peaks in the ERPs of interest.

      Thank you for this suggestion. We will repeat this analysis by removing these additional filters.

      (3.9) Coherence analysis:

      P. 33, l. 786: "For subsequent, partial correlation analyses of coherence with behavioral metrics and neural distances (...), we focused on a 300 ms time period (0-300 ms following T2 onset) and high-beta frequency band (20-30 Hz) identified by the cluster-based permutation test (Fig. 5A-C)." I wonder whether there were any a priori criteria for the definition and selection of such successive analyses. Given the many factors (frequency bands, hemispheres) in the analyses and the particular shape of the cluster (p. 49, Fig 5C), this focus seems largely data-driven. It remains unclear how many such tests were performed and whether the results (e.g., the resulting weak correlation of r = 0.22 in one frequency band and one hemisphere in one part of a complexly shaped cluster; p. 15, l. 327) can be considered robust.

      Please see responses to comments #3.1 and #3.2 (above). In addition to reporting further details regarding statistical tests and multiple comparisons corrections, we will compute and report Bayes factors to quantify the strength of the evidence for correlations, as appropriate.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary: 

      The current manuscript provides an extensive in vivo analysis of two guidance pathways identifying multiple mechanisms that shape the bifurcation of DRG axons when forming the dorsal funiculus in the DREZ. 

      Strengths: 

      Multiple mouse mutant lines were used, together with complementary techniques; the results are very clear and compelling. 

      The findings are very significant and clearly move forward our understanding of the regulation of axonal development at the DREZ. 

      Weaknesses: 

      No major weaknesses were found. As it is I have no recommendations that would increase the clarity or quality of the manuscript. 

      Reviewer #2 (Public Review):

      Summary: 

      In this manuscript, the authors conduct a detailed analysis of the molecular cues that control the guidance of bifurcated dorsal root ganglion axons in a key region of the spinal cord called the dorsal funiculus. This is a specific case of axon guidance that occurs in a precise way. The authors knew that Slit was important but many axons still target correctly in Slit knockouts, suggesting a role for other guidance factors. Netrin1 is also expressed in this region, so they looked at netrin mutants. The authors found axons outside the DREZ in the Ntn1 mutants, and they show by single-neuron genetic labeling that many of these come from DRG neurons. Quantified axonal tracing studies in Slit1/2, Ntn1, or triple mutant embryos support the idea that Slit and Ntr1 have distinct functions in guidance and that the effect of their loss is additive. Interestingly none of these knockouts affect bifurcation itself but rather the guidance of one or both of the bifurcated axon terminals. Knockout of the Slit receptors (Robo1/2) or the Netrin 1 receptor (DCC) in embryos causes similar guidance defects to loss of the ligands, providing additional confirmation of the requirement for both guidance pathways. 

      Strengths: 

      This study expands understanding of the role of the axon guidance factors Ntr1/DCC and Slit/Robo in a specific axon guidance decision. The strength of the study is the careful axonal labeling and quantification, which allows the authors to establish precise consequences of the loss of each guidance factor or receptor. 

      Weaknesses: 

      There are some places in the text where the discussion of these data is compared with other studies and models, but additional details would help clarify the arguments. 

      The details were added to the first section of Discussion in the revision to address this weakness.  Also see the response to the recommendations below.

      Reviewer #3 (Public Review):

      Summary: 

      In this paper, Curran et al investigate the role of Ntn, Slit1, and Slit 2 in the axon patterning of DRG neurons. The paper uses mouse genetics to perturb each guidance molecule and its corresponding receptor. Cre-based approaches and immunostaining of DRG neurons are used to assess the phenotypes. Overall, the study uses the strength of mouse genetics and imaging to reveal new genetic modifiers of DRG axons. The conclusions of the experiments match the presented results. The paper is an important contribution to the field, as evidence that dorsal funiculus formation is impacted by Ntn and Slit signaling. However, there are some potential areas of the manuscript that should be edited to better match the results with the conclusions of the work. 

      Strengths: 

      The manuscript uses the advantage of mouse genetics to investigate the axon patterning of DRG neurons. The work does a great job of assessing individual phenotypes in single and double mutants. This reveals an intriguing cooperative and independent function of Ntn, Slit1, and Slit2 in DRG axon patterning. The sophisticated triple mutant analysis is lauded and provides important insight. 

      Weaknesses: 

      Overall, the manuscript is sound in technique and analysis. However, the majority of the manuscript is about the dorsal funiculus and not the bifurcation of the axons, as the title would make a reader believe. Further, the manuscript would provide a more scholarly discussion of the current knowledge of DRG axon patterning and how their work fits into that knowledge. 

      We revised the title as suggested.  Additional discussion of DRG axon growth at the DREZ is added to the last section of the Discussion in the revision.  Also see the response to the recommendations below.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Given the reasons stated above, I have no specific recommendations for the authors. 

      There is a typo in the Abstract (... mice with triple deletion of Ntn1, Slit2, and Slit2....). 

      Corrected in the revision.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors twice repeated that their data on DRG guidance defects in the Ntn1 mutants differ from studies previously published in references 19 and 26. However it is unclear to me, without having read those other studies, what is actually different between this study and those, and why there would be differences between the results from two groups. If the authors think this is an important point to make they need to more clearly say what the other group saw and offer an explanation of why the data may be different. 

      We added detailed comparison of the defects from different studies to the first section of the Discussion and suggested multiple roles of Ntn1 in controlling sensory axon growth at the DREZ in the revision.

      (2) In the final section of the discussion it says, "The guidance regulation of DRG axon bifurcation by Slit and Ntn1 may be similar to but overshadowed by their function in midline guidance [43]." The meaning of this sentence was unclear to me. I had been thinking that since there are total knockout embryos (not conditional) there could be patterning effects that happen before the DRG branching that influence the formation of the DREZ. Is this what the authors mean to say here? How can the authors show that the guidance factors they have knocked out are actually functioning in the DRG neurons? 

      We agree with the reviewer that the first sentence is vague, so we edited the paragraph and included the discussion of the regulation of DRG axons at the DREZ, which was the main theme of this last section.  In addition, we agree with the reviewer’s suggestion of the possible indirect role of Ntn1 on DRG axons via the control of interneuron migration.  This possibility was included in the last paragraph of the Discussion.

      (3) In several of the figures (3T, 5I, 5J) there are distance measurements that are presumably averages of multiple axons in 3 or 4 embryos because 3-4 points are shown per graph. However, the figure and methods do not say how many axons were measured per embryo and I could not find if it says these numbers are averages. Clarifying the details of these panels would be useful. 

      The n is the number of animals analyzed and is now added to the figure legends.  From each animal, multiple sections (2-4) were analyzed for various parameters in Fig. 3 and 5.  This information was added to the Method section of the revision.

      Reviewer #3 (Recommendations For The Authors):

      Overall the data matches the conclusions in the paper. However, to this reviewer, the title suggests that Ntn and Slit will have defects in bifurcation. This is not the presented phenotype. I recommend the authors change the title to better reflect the findings of the work. 

      We edited the title of the revised manuscript to reflect the control of growth direction in the context of bifurcation.  

      The introduction of the work clearly outlines what is known about DREZ formation in mice but could extend its discussion to other systems like chick and zebrafish (Jaeda Coutinho-Budd et al. 2008, Wang and Scott 2000, Golding et al 1997, Nichols and Smith 2019, Kikel-Coury et al 2021). These studies are particularly important given that pioneer events, including bifurcation, can be visualized. Acknowledging the contribution of other model systems to the understanding of DRG axon patterning is important to improve the scholarly discussion of the paper. 

      We added more detailed discussion of the current knowledge of DRG axon growth at the DREZ from several relevant studies of the rodent and zebrafish models in the last section of Discussion.

      In the data presented, the authors see defects in the axon patterning of DRG neurons and conclude it is a defect in the dorsal funiculus formation. Another interpretation is that a subset of axons cannot invade the spinal cord boundary properly. This phenotype was observed in zebrafish with timelapse imaging (Kikel-Coury et al 2021). It may not be necessary to specifically test the axons' ability to enter the spinal cord in this paper, but the possibility that this could drive the presented phenotypes should be more clearly stated in the results. Entry is not thoroughly addressed in this paper and would need to be confirmed by labeling the edge of the spinal cord with a second reporter. No entry would obviously impact axon targeting. However, delayed entry could place the axon in a navigation environment that is atypical, causing it to navigate aberrantly and present as a funiculus phenotype. 

      We thank the reviewer for raising this very interesting point.  In our present view, dorsal funiculus formation is related to DRG axon patterning, which involves growth, guidance, and bifurcation of the incoming afferents at the dorsal spinal cord.  We believe that these events are highly coordinated by various environmental cues to generate the DREZ and the dorsal funiculus.  The defects we observed could result from the disruption of such coordination that leads to misregulation of DRG axon entry at the dorsal spinal cord, as suggested by the reviewer.  We propose that further analysis by time-lapse imaging as done in zebrafish would provide better understanding of such coordination.  This discussion was included in the last section of Discussion. 

      The authors should clarify that their approach does not knock out molecules in a cell-specific way. This would specifically impact the interpretation of the Dcc phenotypes. It is possible that UNC-40/DCC is guiding cells that are not labeled. The non-autonomous role of UNC-40/DCC should be clearly stated as a possibility. 

      This discussion was added to the last paragraph of the Discussion section.

    1. Author response:

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

      We are thankful to all reviewers and to you for your careful analysis of our work and for the feedback you all provided. The reviews were fundamentally positive with very minor modifications suggested, which we have addressed in this new version as follows.

      (1) We changed Figure 1 to include a high resolution image of the 3D structure of the low affinity complex between the RBD and the GM1 tetrasaccharide (GM1os), see panel d. We predicted this structure through extensive sampling through MD simulations as part of earlier work aimed at guiding the resolution of a crystal structure. Due to insurmountable difficulties in the crystallization of such complex the work was only published as an extended abstract(Garozzo, Nicotra, and Sonnino 2022). Following one of the reviewer’s suggestions we added all the details on the computational approach we used as Supplementary Material.

      (2) We added the comment and corresponding references to the Discussion section in relation to earlier work flagged by one of the Reviewers (Rochman et al. 2022) “Further to this, our results show that taking into consideration the effects on _N-_glycosylation on protein structural stability and dynamics in the context of specific protein sequences may be key to understanding epistatic interactions among RBD residues, which would be otherwise very difficult, where not impossible, to decipher.”

      References

      Garozzo, Domenico, Francesco Nicotra, and Sandro Sonnino. 2022. “‘Glycans and Glycosylation in SARS-COV2 Infection’ Session at the XVII Advanced School in Carbohydrate Chemistry, Italian Chemical Society. July 4th -7th 2021, Pontignano (Si), Italy.” Glycoconjugate Journal 39 (3): 327–34.

      Rochman, Nash D., Guilhem Faure, Yuri I. Wolf, Peter L. Freddolino, Feng Zhang, and Eugene V. Koonin. 2022. “Epistasis at the SARS-CoV-2 Receptor-Binding Domain Interface and the Propitiously Boring Implications for Vaccine Escape.” MBio 13 (2): e0013522.

    1. Author response:

      eLife assessment

      This study presents potentially valuable insights into the role of climbing fibers in cerebellar learning. The main claim is that climbing fiber activity is necessary for optokinetic reflex adaptation, but is dispensable for its long-term consolidation. There is evidence to support the first part of this claim, though it requires a clearer demonstration of the penetrance and selectivity of the manipulation. However, support for the latter part of the claim is incomplete owing to methodological concerns, including unclear efficacy of longer-duration climbing fiber activity suppression.

      We sincerely appreciate the thoughtful feedback provided by the reviewer regarding our study on the role of climbing fibers in cerebellar learning. Each point raised has been carefully considered, and we are committed to addressing them comprehensively. We acknowledge the importance of addressing methodological concerns, particularly regarding the efficacy of long-term suppression of CF activity, as well as ensuring clarity regarding penetrance and selectivity of our manipulation. To this end, we have outlined plans for substantial revisions to the manuscript to adequately address these issues.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The study by Seo et al highlights knowledge gaps regarding the role of cerebellar complex spike (CS) activity during different phases of learning related to optokinetic reflex (OKR) in mice. The novelty of the approach is twofold: first, specifically perturbing the activity of climbing fibers (CFs) in the flocculus (as opposed to disrupting communication between the inferior olive (IO) and its cerebellar targets globally); and second, examining whether disruption of the CS activity during the putative "consolidation phase" following training affects OKR performance.

      The first part of the results provides adequate evidence supporting the notion that optogenetic disruption of normal CF-Purkinje neuron (PN) signaling results in the degradation of OKR performance. As no effects are seen in OKR performance in animals subjected to optogenetic irradiation during the memory consolidation or retrieval phases, the authors conclude that CF function is not essential beyond memory acquisition. However, the manuscript does not provide a sufficiently solid demonstration that their long-term activity manipulation of CF activity is effective, thus undermining the confidence of the conclusions.

      Strengths:

      The main strength of the work is the aim to examine the specific involvement of the CF activity in the flocculus during distinct phases of learning. This is a challenging goal, due to the technical challenges related to the anatomical location of the flocculus as well as the IO. These obstacles are counterbalanced by the use of a well-established and easy-to-analyse behavioral model (OKR), that can lead to fundamental insights regarding the long-term cerebellar learning process.

      Weaknesses:

      The impact of the work is diminshed by several methodological shortcomings.

      Most importantly, the key finding that prolonged optogenetic inhibition of CFs (for 30 min to 6 hours after the training period) must be complemented by the demonstration that the manipulation maintains its efficacy. In its current form, the authors only show inhibition by short-term optogenetic irradiation in the context of electrical-stimulation-evoked CSs in an ex vivo preparation. As the inhibitory effect of even the eNpHR3.0 is greatly diminished during seconds-long stimulations (especially when using the yellow laser as is done in this work (see Zhang, Chuanqiang, et al. "Optimized photo-stimulation of halorhodopsin for long-term neuronal inhibition." BMC biology 17.1 (2019): 1-17. ), we remain skeptical of the extent of inhibition during the long manipulations. In short, without a demonstration of effective inhibition throughout the putative consolidation phase (for example by showing a significant decrease in CS frequency throughout the irradiation period), the main claim of the manuscript of phase-specific involvement of CF activity in OKR learning can not be considered to be based on evidence.

      Second, the choice of viral targeting strategy leaves gaps in the argument for CF-specific mechanisms. CaMKII promoters are not selective for the IO neurons, and even the most precise viral injections always lead to the transfection of neurons in the surrounding brainstem, many of which project to the cerebellar cortex in the form of mossy fibers (MF). Figure 1Bii shows sparsely-labelled CFs in the flocculus, but possibly also MFs. While obtaining homogenous and strong labeling in all floccular CFs might be impossible, at the very least the authors should demonstrate that their optogenetic manipulation does not affect simple spiking in PNs.

      Finally, while the paper explicitly focuses on the effects of CF-evoked complex spikes in the PNs and not, for example, on those mediated by molecular layer interneurons or via direct interaction of the CF with vestibular nuclear neurons, it would be best if these other dimensions of CF involvement in cerebellar learning were candidly discussed.

      We appreciate the thorough review and recognize both the strengths and weaknesses highlighted.

      We concur with the reviewer’s assessment of the novelty of our approach, particularly in specifically perturbing the activity of CF in the flocculus and examining the effects during different phases of learning. Also the usage of OKR behavior paradigm adds strength to our study by providing a well-established model for investigating cerebellar learning processes.

      Regarding concerns about the efficacy of long-term optogenetic inhibition and the specificity of viral targeting, we are committed to addressing these issues through additional experiments. Specifically, we aim to demonstrate sustained inhibition of CF transmission by verifying the maintenance of inhibition throughout the putative consolidation phase. This may involve monitoring CF activity during the irradiation period in vivo. Furthermore, we plan to provide further characterization of viral targeting to ensure specificity of our approach.  

      Additionally, we recognize the importance of discussing alternative mechanisms of CF involvement in cerebellar learning. Hence, we will expand the manuscript to provide more comprehensive discussion of these dimensions of CF function to provide a clearer understanding of the broader implications of our findings.

      Reviewer #2 (Public Review):

      Summary:

      The authors aimed to explore the role of climbing fibers (CFs) in cerebellar learning, with a focus on optokinetic reflex (OKR) adaptation. Their goal was to understand how CF activity influences memory acquisition, memory consolidation, and memory retrieval by optogenetically suppressing CF inputs at various stages of the learning process.

      Strengths:

      The study addresses a significant question in the cerebellar field by focusing on the specific role of CFs in adaptive learning. The authors use optogenetic tools to manipulate CF activity. This provides a direct method to test the causal relationship between CF activity and learning outcomes.

      Weaknesses:

      Despite shedding light on the potential role of CFs in cerebellar learning, the study is hampered by significant methodological issues that question the validity of its conclusions. The absence of detailed evidence on the effectiveness of CF suppression and concerns over tissue damage from optogenetic stimulation weakens the argument that CFs are not essential for memory consolidation. These challenges make it difficult to confirm whether the study's objectives were fully met or if the findings conclusively support the authors' claims. The research commendably attempts to unravel the temporal involvement of CFs in learning but also underscores the difficulties in pinpointing specific neural mechanisms that underlie the phases of learning. Addressing these methodological issues, investigating other signals that might instruct consolidation, and understanding CFs' broader impact on various learning behaviors are crucial steps for future studies.

      We appreciate the reviewer’s recognition of the significance of our study in addressing the fundamental question of the role of CF in adaptive learning within the cerebellar field. The use of optogenetic tools indeed provides a direct means to investigate the causal relationship between CF activity and learning outcomes.

      To address concerns regarding the effectiveness of CF suppression during consolidation, we plan to conduct further in-vivo recordings. These will demonstrate how reliably CF transmission can be suppressed through optogenetic manipulation over an extended period.

      In response to the concern about potential tissue damage from laser stimulation, we believe that our optogenetic manipulation was not strong enough to induce significant heat-induced tissue damage in the flocculus. According to Cardin et al. (2010), light applied through an optic fiber may cause critical damage if the intensity exceeds 100 mW, which is eight times stronger than the intensity we used in our OKR experiment. Furthermore, if there had been tissue damage from chronic laser stimulation, we would expect to see impaired long-term memory reflected in abnormal gain retrieval results tested the following day. However, as shown in Figures 2 and 3, there were no significant abnormalities in consolidation percentages even after the optogenetic manipulation.

      Finally, we appreciate the reviewer’s recognition of the challenges involved in pinpointing specific neural mechanisms. We plan to expand the discussion to address these complexities and outline future research directions.

    1. Author Response:

      eLife assessment

      We thank the Editors for identifying qualified reviewers. We agree that the “evidence supporting this claim (that ‘many breast cancer mutations are mildly deleterious’) is incomplete”. Much more detail is needed to state this decisively and we do not claim completeness here. As far as validation, we carried out synthetic testing of the models as suggested by Reviewer #1 and the results seem good.

      Reviewer #1:

      We thank the Reviewer for a very thorough examination of not only the current paper but also our previous paper. We agree that the illustration material can be overwhelming and we plan to use the Reviewer’s advice in that matter. In addition, we originally put some textbook material in the Appendix, and arguably some of it may be considered superfluous.

      Most of the references the Reviewer provides are known to us, although it is likely we should cite and discuss more. All of the above will be included in the revision we are planning.

      The Reviewer is certainly correct that population growth and spatial effects play a major role in cancer. However, the effects of constraining environment are quite strong and the reality lies somewhere between the Moran and branching process models; exactly what we attempt to clarify. As for spatial effects, most tumors extracted in clinic are dissected in bulk and sub-sampling is rare, so the spatial information is rarely accessible.

      The subsequent point of importance concerns the weak specificity of the site frequency spectra (SFS) with respect to the underlying genetic and demographic forces. This cannot be denied. However, we just meant to state that our SFS are consistent with a model involving slightly deleterious passengers.

      Regarding the validation of the estimation procedures which is a point well-taken, we carried out synthetic testing of the models as suggested by Reviewer #1 and the results seem good. This will be discussed in full in the revision.

      In our view, the most important remark is the one concerning scaling of the models. The Reviewer is certainly correct that 100 stem cells are insufficient to drive a realistic tumor. However, what we had in mind but not explained sufficiently, is that a sample of 100 cells corresponds to average-depth coverage in bulk sequencing. Therefore, the strict interpretation is that the model mirrors what is observed in the sample. A more accurate approach would be to up-scale the model and then sample 100 cells from it. The Moran-type model can be up-scaled using diffusion approximation, and we hope to include these computations in the revision. The associated criticism concerning tumor growth seems less relevant, since we experimented with less or more stringent constraints in our models.

      Reviewer #2:

      We thank Reviewer #2 for studying our paper and some very positive comments. Among others, the Reviewer underscores the fact that the Moran-type model generates SFS concordant with the data (with all necessary reservations). The Reviewer concurs with us that conditioning on non-extinction is not very common in the literature, while it should be.

      Similarly as the Reviewer, we are somewhat puzzled by the differences in behavior between models A and B. Model B seems more parsimonious, but Model A looks more similar to the critical or slightly supercritical branching process. We will work to clarify these observations.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors set out to develop genetic tools that can specifically and comprehensively label Axo-Axonic Cells (AACs), also known as Chandelier cells. These AACs possess unique morphological and connectivity features, making them an ideal subject for studying various aspects of cell types across different experimental methods. To achieve both specificity and comprehensiveness in AAC labeling, the authors employ an intersectional strategy that combines lineage origin and molecular markers. This approach successfully targets AACs across the mouse brain and reveals their widespread distribution in various brain structures beyond the previously known regions. Additionally, the authors utilize rabies transneuronal labeling to provide a comprehensive overview of AACs, their variations, and input sources throughout the brain. This experimental approach offers a powerful model system for investigating the role of AACs in circuit development and function across diverse brain regions.

      Strengths:

      Genetic Tools and Specificity: The authors' genetic tools show qualitative evidence of specificity for AACs, opening new avenues for targeted research on these cells. The use of intersectional strategies enhances the precision of AAC labeling.

      Widespread Distribution: The study significantly broadens our understanding of AAC distribution, revealing their presence in brain regions beyond what was previously documented. This expanded knowledge is a valuable contribution to the field.

      Transneuronal Labeling: The inclusion of rabies transneuronal labeling provides a comprehensive view of AACs, their variations, and input sources, allowing for a more holistic understanding of their role in neural circuits.

      Weaknesses:

      Quantitative Analysis: While the claim of specificity appears qualitatively convincing, the manuscript could be improved with more quantitative analysis.

      We are glad that the reviewers appreciated our multimodal and brain-wide characterizations of the AAC population. We include many qualitative AAC examples and would like to highlight the quantitative nature of our whole brain cell body and cartridge analyses, made possible by transgenic targeting and our serial two-photon tomography imaging platform (STP). In addition to providing this brain wide AAC atlas, we also propose AACs as perhaps one of the best case examples for a bona fide cell type, which may inspire further in-depth anatomical and functional studies of AACs, and efforts to capture other ground truth cell types.

      Comprehensiveness Claim: The assertion of comprehensiveness, implying labeling "almost all" AACs in all brain regions, is challenging to substantiate conclusively. Acknowledging the limitations of proving complete comprehensiveness and discussing them in the discussion section would be more appropriate than asserting it in the results section.

      We thank the reviewer for this suggestion and have revised the results and discussion sections accordingly. The issue of how to access comprehensiveness in AAC labeling is a fair and important point, as dense brain-wide AAC labeling has not been achieved and assessed before. Previous studies had used less efficient and specific methods for capturing AACs, primarily in select areas of cortex, hippocampus, and amygdala. These AAC populations are recapitulated by our genetic strategies with higher density and specificity. It does not seem that we have missed any previously-reported AAC populations; in fact, we discovered multiple previously unreported populations. Another evidence supporting our “comprehensive” labeling of AACs is that two independent Unc5b and Pthlh transgenic strategies showed very similar AAC distribution patterns (Fig. 1 Suppl. 3). However, we recognize that probably the only way to fully assess “completeness” of labeling may be to compare with anatomical ground truth, such as by dense EM reconstruction of all AACs across the brain volume. This is currently not technically possible but may become feasible in the future. 

      Local Inputs: While the manuscript focuses on inter-areal inputs to AACs, it would benefit from exploring local inputs as well. Identifying the local neurons that target AACs and analyzing their patterns could provide valuable insights into AAC function within specific brain regions.

      This is a good suggestion. However, our serial two-photon tomography imaging platform does not have the capability for reliably preserving tissue sections for immunohistochemical processing afterward. Additionally, though our starter AAV injections were limited to 100-150nL, there were far too many input cells labelled at the injection side to resolve individual input cells and correlate with their synaptic partners (e.g. a rabies-labelled pyramidal cell within the injection site may still project to starter cell few hundred microns away). Thus, our rabies input mapping was best suited for characterizing long-range inputs and was the focus here. For studying local inputs to AACs, future studies could combine very dilute starter AAV injections with multi-marker characterization of cell types by immunohistochemistry or FISH.  

      Discussion Focus: The discussion section should delve deeper into the biological implications of the findings, moving beyond technical significance. Exploring similarities and differences in input patterns between AACs and other cell types, and linking them to the locations of starter cells or specific connectivity patterns in the brain, would enrich the discussion. For instance, investigating whether input patterns can be predicted based on the locations of starter cells or connectivity specificity could provide valuable insights.

      We thank the reviewer for this suggestion. We have expanded the discussion to include more on the relevance and implications of our input mapping results to different starter populations of AACs.

      Reviewer #2 (Public Review):

      Summary:

      The goals of this study were to develop a genetic approach that would specifically and comprehensively target axo-axonic cells (AACs) throughout the brain and then to describe the patterns and characteristics of the targeted AACs in multiple, selected brain regions. The investigators have been successful in providing the most complete description of the regional distribution of putative (pAACs) throughout the brain to date. The supporting evidence is convincing, even though incomplete in some brain regions. The findings should serve as a guide for more detailed studies of AACs within each brain region and lead to new insights into the connectivity and functional organization of this important group of GABAergic interneurons.

      Strengths:

      The study has numerous strengths. A major strength is the development of a unique intersectional genetic strategy that uses cell lineage (Nkx2.1) and molecular (Unc5b or Pthlh) markers to identify axo-axonic AACs specifically and, apparently, nearly completely throughout the mouse brain. While AACs have been described previously in the cerebral cortex, hippocampus, and amygdala, there has been no specific genetic marker that selectively identifies all AACs in these regions.

      The current genetic strategy has labeled pAACs in a large number of additional brain regions, including the claustrum-insular complex, extended amygdala, and several olfactory centers. In general, the findings provide support for the specificity of the methods for targeting AACs, and include some examples of labeling near markers of axon initial segments. However, the Investigators are careful to refer to labeled neurons as "putative AACs" as they have not been fully characterized and their identity verified.

      The descriptions and numerous low-magnification images of the brain provide a roadmap for subsequent, detailed studies of AACs in numerous brain regions. The overview and summaries of the findings in the Abstract, Introduction, and Discussion are particularly clear and helpful in placing the extensive regional descriptions of AACs in context.

      Weaknesses:

      One weakness of the study is the lack of an illustration of the high-resolution cell labeling that can be achieved with the methods, including labeling of numerous rows of axon terminals in contact with axon initial segments. The initial images of the brain-wide distribution of putative AACs are necessarily presented at low magnification. Although the authors indicate that the cells have "highly characteristic AAC labeling patterns throughout the neocortex, hippocampus and BLA", these morphological details cannot be visualized by the reader at the current magnification, even when the images are enlarged on the computer screen. Some of the details become evident in later Figures, but an initial illustration of single cell labeling with confocal microscopy, or tracing of their characteristic axonal arbors, would support the specificity of the labeling in the low magnification images.

      We thank the reviewer for the suggestion. We have now added high-resolution images showing the colocalization of AAC axon boutons (cartridges) along AnkG positive postsynaptic axon initial segments in Fig. 2 Suppl. 1, Figure 1 panels a, d, e, and Fig. 4 panels b, c. These images unequivocally demonstrate AAC identity and specificity.

      Table 1 indicates that the AAC identity of the cells has been validated in many brain regions but not in all. The methods used for validation have not been described and should be included for completeness. The authors are careful to acknowledge that labeled cells in some regions have not been validated and refer to such cells as pAACs.

      Validation was defined by colocalization of RFP-labelled AAC cartridges and AnkryinG or Phospho-IκBα-labelled axon initial segments, imaged by confocal microscopy. We provide high-magnification examples throughout figures 2-6 and supplements. We have also tried to clarify this better in the methods section entitled “Immunohistochemistry.” Putative AAC (pAACs) refers to populations in which relatively few single cell examples of AACs exhibiting co-localized cartridges were found, largely due to the sparsity of the low tamoxifen dosage used (see response above).

      The intersectional genetic methods included the use of the lineage marker Nkx2.1 with either Unc5b or Pthlh as the molecular marker. As described, the mice with intersectional targeting of Nkx2.1 and Unc5b appear to show the most specific brain-wide labeling for AACs, and the majority of the descriptions are from these mice. The targeting with Nkx2.1 and Pthlh is less convincing. The title for Figure 1 Supplemental Figure 3 suggests a similar AAC distribution in the Pthlh;Nkx2.1 mouse compared to the Unc5b;Nkx2.1 mouse. However, the descriptions of the individual panels suggest a number of inconsistencies and non-AAC labeling. The heavy labeling in the caudate and cells in layer 4 is particularly problematic. Based on the data presented, it appears that heavy labeling achieved in these mice could not be relied on for specific labeling of all AACs, although specific labeling could be achieved under some conditions, such as following tamoxifen administration at select ages.

      The reviewer is correct about Pthlh being less specific for AACs than Unc5b when crossed to a constitutive Nkx2.1 recombinase driver line. Pthlh/Nkx2.1 intersection labeled a set of layer 4 cells in somatosensory cortex and dense cells in striatum, which are clearly not AACs. But these are the only main difference compared to Unc5b/Nkx2.1 intersection. As the reviewer points out, it is only when Pthlh is crossed to an inducible Nkx2.1-CreER line and induced embryonically with tamoxifen that there is more specific AAC labeling (at least in cortex). We included this data as well as the intersection with VIP-Cre in case either of these are useful to researchers studying fate-mapping of AACs or bipolar cell interneurons. We have also revised the title of Fig. 1 Suppl. 3 to better convey this.

      The methods described for dense labeling and single-cell labeling are described briefly in the methods. Some discussion of the development of the methods would be useful, including how it was determined that methods for heavy labeling identified AACs specifically and completely.

      We have added a description on the development of these to the methods section entitled “Animals.”

      Reviewer #3 (Public Review):

      Summary:

      Raudales et al. aimed at providing an insight into the brain-wide distribution and synaptic connectivity of bona fide GABAergic inhibitory interneuron subtypes focusing on the axo-axonic cell (AAC), one of the most distinctive interneuron subtypes, which innervates the axon initial segments of glutamatergic projection neurons. They establish intersectional genetic strategies that enable them to specifically and comprehensively capture AACs based on their lineage (Nkx2.1) and marker expression (Unc5b, Pthlh). They find that AACs are deployed across essentially all the pallium-derived brain structures as well as the anterior olfactory nucleus, taenia tecta, and lateral septum. They show that AACs in distinct areas and layers of the neocortex as well as different subregions of the hippocampal formation display unique soma and synaptic density and morphological variations. Rabies virus-based retrograde monosynaptic input tracing reveals that AACs in the neocortex, the hippocampus, and the basolateral amygdala receive synaptic inputs from common as well as specific brain regions and supports the utility of this novel genetic approach. This study elucidates brain-wide neuroanatomical features and morphological variations of AACs with solid techniques and analysis. Their novel AAC-targeting strategies will facilitate the study of their development and function in different brain regions. The conclusions in this paper are well supported by the data. However, there are a few comments to strengthen this study.

      (1) The definition of putative AAC (pAAC) is unclear and Table 1 may not be accurate. Although the authors find synaptic cartridges of RFP-labeled cells in the claustro-insular complex and the dorsal endopiriform nuclei, they still consider these cells as pAACs (not validated). The authors claim that without examining the presence of synaptic cartridges, RFP-labeled cells in the hypothalamus and the bed nuclei of the stria terminalis (BNST) are pAACs while those in the L4 of the somatosensory cortex in Pthlh;Nkx2.1;Ai65 mice are non-AACs. In Table 1, the BNST is supposed to contain AACs (validated), but in the text, the authors claim that RFP-labeled cells in the BNST are pAACs. Could the authors clarify how AACs, pAACs, and non-AACs are defined?

      We thank the reviewer for their interest and comments on our work. Please see our response to reviewer 2 for clarification on putative pAACs. Additionally, we have clarified in the methods under “Immunohistochemistry” how we defined AACs, pAAC, and non-AACs. For BNST we did not positively identify more than a few exhibiting overlap with AnkryinG/IκBα, so we currently leave them as pAACs—Table 1 has been corrected to reflect this.

      (2) The intersectional strategies presented in this study could also specifically capture developing AACs. If so, how early are AACs labeled in the brain? It would also be nice if the authors could add a simple schematic like Fig. 1a showing the time course of Pthlh expression.

      We thank the reviewer for suggesting the application of our method in studying AAC development. As the onset of Unc5b is in early postnatal time, tamoxifen induction of Unc5b-CreER in early postnatal days can enable studies of AAC neurite and synapse development, maturation, and plasticity. Similarly, Pthlh expression in the brain is relatively low/absent at P4 and present at P14 and later timepoints. Pthlh-Flp;Nkx2.1-Cre intersection can be used to study postnatal AAC development and plasticity.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      While the claim of specificity appears qualitatively convincing, additional quantitative analysis would make the authors' claim much stronger. For example in Figure 4 (f-h), where the authors show an overlap of AAC axons with AnkG labeling, there also appears to be a region of AAC axon lacking adjacent AnkG labeling. The author could quantify the fraction of cartridges that overlap with AnkG labeling in different brain regions, potentially stringing their claim that pAACs are AACs as well as providing important documentation of the diversity or homogeneity of compartment targeting across the brain.

      As mentioned previously, we only performed AnkG co-labeling analysis on low-dose tamoxifen/sparsely labelled samples in which we could readily differentiate individual cells. This was performed on samples with the Ai65 cytoplasmic reporter—for validation purposes we could positively identify co-labelled cartridges, but it would be more difficult to accurately identify any cartridges not co-labeled (since the entire axon was labelled with RFP). For precisely identifying and mapping AAC cartridge locations we found the intersectional synaptophysin-EGFP reporter (Fig. 2k-n) to be a more precise method for specifically labeling the “cartridge” segment of AAC axons. However, we did not try AnkG staining on samples from this reporter line, as they were set aside for STP imaging.

      Regarding the claim of comprehensiveness, labeling "almost all" AACs in all brain regions is a high standard and challenging to demonstrate conclusively. The study already significantly expands our understanding of AAC distribution, and the authors might consider discussing the limitations of proving complete comprehensiveness in the discussion rather than claiming it in the results section.

      We again thank the reviewer for this critique. As mentioned above, we have revised the results and discussion sections to better convey this point across.

      Furthermore, the manuscript connectivity section primarily focuses on inter-areal inputs to AACs, but it could benefit from exploring local inputs as well. By identifying the local neurons that target AACs, the authors could ask if there is any general property or rule of the local projections to AACs across the brain, or at least within the cortex. Moreover, a clear indication of the injection site would be helpful, particularly in Figure 7, where there seems to be some discrepancy between the histograms and fluorescent images regarding local projections. The histograms of Figure 7, seem to indicate that the local projection to AACs is a small fraction of all the presynaptic neurons, however, the fluorescent image for the SSp seems to suggest otherwise with many fluorescent cells in the injected area.

      We thank the reviewer for these comments. Regarding the local inputs in the rabies tracing datasets, it is a limitation (as mentioned above) of our STP platform’s inability to preserve tissue for immunohistochemistry labeling as well as our relatively dense starter cell labeling. Instead, our focus here was on long-range inputs (i.e. outside the ipsilateral ARA area of injection), which was simply not known for these AAC populations. We have revised the Figure 7 legend and added a description in the methods section to more clearly indicate that we only included long-range input projections in the Figure 7 histograms.

      In the discussion, the authors should delve more into the biological implications of their findings rather than solely emphasizing the technical significance. They could explore the similarities and differences in input patterns between AACs and other cell types, potentially linking them to the locations of their starter cells or specific connectivity patterns in the brain. For example, the authors could check if the input patterns could be predicted from the projections to the layers where their starter cells are located (either from an Atlas like the Allen Connectivity Atlas, or from retrograde rabies injections in the same locations). Can the differences between the input patterns to PVC and AAC be predicted for their location versus some specificity of connections?

      Thank you for the extensive comment. We address this point above, and have revised our discussion accordingly.

      Reviewer #2 (Recommendations For The Authors):

      The Figure legends vary in completeness and quality.

      (1) The legend for Figure 1 is very informative, and section e-g serves as a useful guide, as the legend includes the names of the brain regions related to the abbreviations and also indicates the specific panels that show the identified structures. Because of the large number of structures and the number of panels in each Figure, it would be ideal to follow the same pattern in the remaining figures.

      (2) Several edits are needed in the legend for Figure 1 Supplement Figure 1. The descriptions of a-f could be improved by providing general terms to describe the brain regions associated with the latter list of abbreviations (as has been done with the identification of the cerebral cortex, hippocampus, and olfactory centers and their related panels). One suggestion would be to write out insula, claustrum, and endopiriform prior to listing the abbreviations (AI, CLA, EP) (b-c) and adding amygdaloid complex and extended amygdala before the abbreviations (COA, BLA, MeA) (d-f) and (BST) (d).

      We thank the reviewer, as the suggestion of further expanding the abbreviations is a good one. As such, we have revised/reorganized the anatomical abbreviations in the figure legends for Figure 1 Supplement Figures 1, 2, and 3.

      Descriptions for Panels g-j require editing to link the appropriate panels and the descriptions. Panels for BSTpr appear to be g-h (rather than f-g) and i,j (rather than h-i.

      We have fixed this typo in the legend for Figure 1 Supplement Figure 1.

      Descriptions for Panels k-n could be edited to include abbreviations for the identified brain regions. For example, include the abbreviation ARHP after arcuate nuclei and indicate panels m-n (rather than j-l); include PVP after paraventricular and indicate panel n (rather than m); include DMPH after dorsomedial nuclei and indicate k-m (rather than j-l).

      Thank you for the suggestion. We have expanded the abbreviations in Figure 1 Supplement 1 accordingly.

      Reviewer #3 (Recommendations For The Authors):

      (1) Please clarify if tdTomato, EGFP (from helper AAVs), and RFP (from rabies virus) are native signals or IHC signals in legends.

      We have added the descriptors “native” or “stained” to all figure legends containing fluorescent images.

      (2) Fig. 4b and c: Please add insets of high-magnification images showing AAC boutons along AnkG-labeled AISs.

      We have added these insets to Fig. 4b and c.

      (3) Fig. 7S1: It appears that d and e are reversed. Judging from the positions of starter cells, d is for PV-Cre? Please make sure. It is also better to draw the laminar border in d and e.

      The original genotype labels are correct for Fig. 7S1 d and e. We have added the laminar borders as suggested.

      (4) Fig. 9b: Just for consistency, please label with the name of the helper AAV.

      Added.

      (5) Line 617: intragranular>>>infragranular?

      Corrected, thank you.

      (6) It may be unclear to some readers if the images in the figures are from confocal or STP. The authors may want to clarify that all images in the figures are generated by confocal microscopy in the method section.

      We have clarified this better in the methods section, “Microcopy and image analysis.”

      (7) The authors should clarify that STP was used to map input cells to the brain in the result section.

      We have added this description in the results section.

    1. Author response:

      We thank the reviewers and editors for their review and assessment of our manuscript and comprehensive feedback. The manuscript will be revised to address all the reviewers’ comments. Specifically, to address the comment of Reviewer 1 and the editor regarding the lack of quantitative comparison between the classical and fractal cycle approaches and identification of the source of the discrepancies between classical and fractal cycles, we plan to perform and report the following analyses and comparisons:

      (1) Intra-method reliability

      a) Classical cycles. An additional scorer will independently define onsets and offsets of all classical sleep cycles for all datasets and mark sleep cycles with skipped REM sleep. Likewise, we will perform automatic sleep cycle detection. We will add a new Supplementary table showing the averaged cycle durations obtained by the two scorers and automatic algorithm as well as the inter-scorer rate agreement and update the Supplemental Excel file with corresponding information for each cycle for each participant for each dataset.

      b) Fractal cycles. We will correlate the durations of fractal cycles calculated using the parameters defined in the Main text with those calculated using different parameters, namely, the longer and shorter smoothing window lengths, higher and lower minimum peak prominence. Likewise, we will correlate the durations of fractal cycles calculated using frontal vs other available electrodes.

      (2) Origin of method differences

      In the current version of our Manuscript, we describe a few possible sources of discrepancies between classical and fractal cycle durations and numbers. Following the suggestion of one of the reviewers, in the revised Manuscript, we will quantify the sources of discrepancies between the two methods in order to identify the “criteria for recordings in which fractal cycles will produce similar results to the classical method”. Specifically, we will calculate the correlation between the difference in classical vs fractal sleep cycle durations on one side, and either the amplitudes of fractal descend/ascend, relative durations of cycles with skipped REM sleep and wake after sleep onset, or peak flatness on the other side.    

      In addition, we will include a new figure, illustrating the goodness of fit of the data as assessed by the IRASA method. Likewise, we will update Supplementary File 1 (that shows classical and fractal sleep cycles for each participant) with marks that highlight the onsets and offsets of sleep cycles as well as the cycles with skipped REM sleep.

    1. Author response:

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

      eLife assessment 

      This study explores the role of one the most abundant circRNAs, circHIPK3, in bladder cancer cells, providing convincing data that circHIPK3 depletion affects thousands of genes and that those downregulated (including STAT3) share an 11-mer motif with circHIPK3, corresponding to a binding site for IGF2BP2. The experiments demonstrate that circHIPK3 can compete with the downregulated mRNAs targets for IGF2BP2 binding and that IGF2BP2 depletion antagonizes the effect of circHIPK3 depletion by upregulating the genes containing the 11mer motif. These valuable findings contribute to the growing recognition of the complexity of cancer signaling regulation and highlight the intricate interplay between circRNAs and protein-coding genes in tumorigenesis. 

      Public Reviews: 

      Reviewer #1 (Public Review): 

      In this work the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3. They demonstrate that circHIPK3 interacts with an RNA binding protein (IGF2BP2), sequestering it away from its target mRNAs. This interaction is shown to regulates the expression of hundreds of genes that share a specific sequence motif (11-mer motif) in their untranslated regions (3'-UTR), identical to one present in circHIPK3 where IGF2BP2 binds. The study further focuses on the specific case of STAT3 gene, whose mRNA product is found to be downregulated upon circHIPK3 depletion. This suggests that circHIPK3 sequesters IGF2BP2, preventing it from binding to and destabilizing STAT3 mRNA. The study presents evidence supporting this mechanism and discusses its potential role in tumor cell progression. These findings contribute to the growing complexity of understanding cancer regulation and highlight the intricate interplay between circRNAs and protein-coding genes in tumorigenesis.

      Strengths:

      The authors show mechanistic insight into a proposed novel "sponging" function of

      circHIPK3 which is not mediated by sequestering miRNAs but rather a specific RNA binding protein (IGF2BP2). They address the stoichiometry of the molecules involved in the interaction, which is a critical aspect that is frequently overlooked in this type of studies. They provide both genome-wide analysis and a specific case (STAT3) which is relevant for cancer progression. Overall, the authors have significantly improved their manuscript in their revised version.

      Weaknesses:

      While the authors have performed northern blots to measure circRNA levels, an estimation of the circRNA overexpression efficiency, namely the circular-to-linear expression ratio, would be desired. The seemingly contradictory effects of circHIPK3 and STAT3 depletion in cancer progression, are now addressed by the authors in their revised manuscript, incorporating potential reasons that might explain such complexity.

      We have now included a full version of the northern blot, where no discernible linear precursor can be detected, supporting efficient circHIPK3 WT and circHIPK3 MUT production (please see the detailed description in the specific comments below). We agree that the observations about STAT3 homeostasis and cancer progression, is not a straightforward extrapolation as discussed. 

      Reviewer #2 (Public Review):

      Summary: 

      The authors have diligently addressed most of the points raised during the review process (except the important point of "additional in vitro experiments [...] needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype" for which no additional experiments were performed), resulting in an improvement in the study. The data are now described with clarity and conciseness, enhancing the overall quality of the manuscript. 

      Strengths: 

      New, well-defined molecular mechanism of circRNAs involvement in bladder cancer. 

      Weaknesses: 

      Lack of solid translational significance data. 

      The focus of this study has been to disclose molecular mechanisms of action by circHIPK3, with implications for cancer. We agree that further studies are needed to fully understand the impact of circHIPK3 in bladder cancer.  

      Reviewer #3 (Public Review):

      In Okholm et al., the authors evaluate the functional impact of circHIPK3 in bladder cancer cells. By knocking down circHIPK3 and performing an RNA-seq analysis, the authors found thousands of deregulated genes which look unaffected by miRNAs sponging function and that are, instead, enriched for a 11-mer motif. Further investigations showed that the 11mer motif is shared with the circHIPK3 and able to bind the IGF2BP2 protein. The authors validated the binding of IGF2BP2 and demonstrated that IGF2BP2 KD antagonizes the effect of circHIPK3 KD and leads to the upregulation of genes containing the 11-mer. Among the genes affected by circHIPK3 KD and IGF2BP2 KD, resulting in downregulation and upregulation respectively, the authors found the STAT3 gene, which also consistently has concomitant upregulation of one of its targets TP53. The authors propose a mechanism of competition between circHIPK3 and IGF2BP2 triggered by IGF2BP2 nucleation, potentially via phase separation. 

      Strengths: 

      Although the number of circRNAs continues to grow, this field lacks many instances of detailed molecular investigations. The presented work critically addresses some of the major piaalls in the field of circRNAs, and there has been a careful analysis of aspects frequently poorly investigated. Experiments involving use of time-point knockdown followed by RNAseq, investigation of miRNA-sponge function of circHIPK3, identification of 11-mer motif, identification and validation of IGF2BP2, and the analysis of copy number ratio between circHIPK3 and IGF2BP2 in assessing the potential ceRNA mode of action are thorough and convincing. 

      Weaknesses: 

      It is unclear why the authors used certain bladder cancer cells versus non-bladder cells in some experiments. The efficacy of certain experiments (specifically rescue experiments) and some control conditions is still questionable. Overall, the presented study adds some further knowledge in describing circHIPK3 function, its capability to regulate some downstream genes, and its interaction and competition for IGF2BP2. 

      We have provided a discussion and argumentation of how certain bladder cancer cells (and non-bladder cancer cells) have been used in this study in our previous rebuttal letter and also clarified this further in the materials and methods section in the first revision. Regarding control conditions for experiments, we believe we have included all necessary controls and explanations for these in the revised version (please see the detailed description in the specific comments below). 

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major points about revised manuscript

      (1) In Supplementary Figure S5H, the membrane may have been trimmed too closely to the circRNA band, potentially resulting in the absence of the linear RNA band. Could the authors provide a full image of the membrane that includes the loading points? Having access to the complete image would allow for a more comprehensive evaluation of the results, including the presence or absence of expected linear and circular RNA bands.

      I have taken the liberty to move this “major point” from the public review section, as I believe it would be too detailed for this section. We have included the full section of the northern blot, according to the reviewers recommendations. 

      As described in the previous rebuttal letter our northern blots suffered from heavy background signal arising from the rRNA bands, which was the reason for cuttng the northern blot in the previous version of Supplementary figure S5H. We have now shown the entire blot as suggested by the reviewer, so that the reader can more clearly inspect any potential linear precursor band. We previously stated that we could not assess the circular-to-linear ratio due to background signal, since a potential linear HIPK3 precursor RNA could be masked by the rRNA signal. However, the theoretical size of a linear precursor is ~2.9 kb – a region where we do not detect any distinct bands (just above the 18S band), making a rather efficient circularization very likely. In support of this claim, we are using the Laccase2 vector described in Kramer et al, 2015 (Genes dev), which is proven to produce high levels of circHIPK3 compared to negligable amounts of linear precursor (although in a different cell line). We have also included a 5.8S rRNA probe to control for loading and RNase R activity (can also be ascertained by the disappearence of 18S/28S bands). Since we do not have the option to use another probe (limited by the BSJ-specific probe) and it is not practical to deplete for rRNA from 20 µg samples of total RNA, prior to running the northern blot, we find that this data sufficiently proves that our vector constructs produce a decent amount of RNase R-resistant circHIPK3, with no visible/discernible linear precursor.    

      Minor points about revised manuscript

      (1) In Supplementary Figure S3B, the authors offer no explanation as to why genes that become upregulated upon circHIPK3 knockdown generally contain more circHIPK3-RBP binding sites other than for IGF2BP2. A clarification would be of help.

      Again, this issue has been addressed in the previous rebuttal letter. Our response is repeated below:

      We do not have any evidence to explain this observation. One possibility is that other RBPs elicit mRNA-stabilizing effects on average, whereas abundant IGF2BP2 (~ 120.000200.000 copies per cell) now able to bind more target mRNAs and elicit destabilization. This remains highly speculative though.

      (2) In Supplementary Figure S3D, the authors' claim that the 11-mer motif is found more bound to IGF2BP2 than for other circHIPK3-RBPs should referred to the corresponding dataset/reference.

      Again, this issue has been addressed in the previous rebuttal letter. Our response is repeated below:

      This information is stated in the figure legend (K562) and we have now included it in the main text as well: “We evaluated how often binding sites of circHIPK3-RBPs overlap the 11-mer motif and found that this is more often the case for IGF2BP2 binding sites than binding sites of the other circHIPK3-RBPs when scrutinizing K562 datasets (Supplementary Figure S3D)”.

      (3) In the rescue experiment where both circHIPK3 and IGF2BP2 are downregulated, using the term "normalization" to mean reestablishing normal levels of gene expression can lead to confusion with the concept of normalization as it is commonly understood in the context of data processing (i.e. the mathematical process of adjusting data to account for various factors that might affect measurements). I would recommend the authors to use a term that more specifically describes the biological process they are referring to, such as "restoration of normal expression levels" or simply "return to normal levels".

      We agree that this term could be misunderstood. This has now been changed as recommended.

      (4) The figure legend of Supplementary Figure 5F is wrongly labeled. The legend for panel F actually corresponds to panel G and vice versa. 

      This has now been corrected.  

      Reviewer #2 (Recommendations For The Authors): 

      The authors have diligently addressed most of the points raised during the review process (except the important point of "additional in vitro experiments [...] needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype" for which no additional experiments were performed), resulting in an improvement in the study. The data are now described with clarity and conciseness, enhancing the overall quality of the manuscript. Therefore, I support the publication of this work. 

      We thank the reviewer for the positive comments.

      Reviewer #3 (Recommendations For The Authors): 

      Please ensure that when the changes are made (especially for major points) by addressing the reviewer's comments, these are all appropriately incorporated in the text (for example the use of Act B as a low affinity positive control (now in Fig 4A), is not explained in the text neither the legends/methods) 

      This has now been included.

      Please ensure that all the legends correspond to the right figures (eg: Supplementary Figure with rescue experiment is 5F, but the corresponding legend in the manuscript is the S5G). 

      This has now been corrected.

      Please for future reviewing processes ensure the new parts are properly highlighted or coloured differently in the manuscript

      This has now been done more thoroughly.

    1. Author response

      Reviewer #1 (Public Review):

      The authors aimed to investigate if 2-hydroxybutyrate (2HB), a metabolite induced by exercise, influences physiological changes, particularly metabolic alterations post-exercise training. They treated young mice and cultured myoblasts with 2HB, conducted exercise tests, metabolomic profiling, gene expression analysis, and knockdown experiments to understand 2HB's mechanisms. Their findings indicate that 2HB enhances exercise tolerance, boosts branch chain amino acid (BCAA) enzyme gene expression in skeletal muscles, and increases oxidative capacity. They also highlight the role of SIRT4 in these effects. This study establishes 2HB, once considered a waste product, as a regulator of exercise-induced metabolic processes. The study's strength lies in its consistent results across in vitro, in vivo, and ex vivo analyses.

      The authors propose a mechanism in which 2HB inhibits BCAA breakdown, raises NAD+/NADH ratio, activates SIRT4, increases ADP ribosylation, and controls gene expression.

      However, some questions remain unclear based on these findings:

      This study focused on the effects of short-term exercise (1 or 5 bouts of treadmill running) and short-term 2HB treatment (1 or 4 days of treatment). Adaptations to exercise training typically occur progressively over an extended period. It's important to investigate the effects of long-term 2HB treatment and whether extended combined 2HB treatment and exercise training have independent, synergistic, or antagonistic effects.

      We agree with the reviewer that investigation of longer-term 2HB treatment may potentially yield interesting findings with more implications to exercise physiology. To investigate the effects of 2HB treatment against or in combination with a progressive exercise training protocol would require an experiment duration between 4 to 12 weeks, based on previous studies (Systematic Review by Massett et al., Frontiers in Physiology, 2021, 10.3389/fphys.2021.782695). However, our experience with these types of experiments is that such a pursuit would require a breadth of work beyond the scope of this current study. For instance, if there were evidence of weakened effect of 2HB over time, one may be compelled to investigate other organs such as the liver to find signs of metabolic adaptation to the exogenous metabolite. If there were additive or synergistic effects on exercise performance, one may be compelled to investigate changes to the cardiovascular system in addition to the skeletal muscle. Additional questions would be raised around the skeletal muscle as well, including assessment of structural and fibre-type changes. Further, these additional mechanisms would need to be characterized in a time course fashion. Rather, we view the scope of the current study to be the acute response to 2HB as an initial report on mechanistic effects of 2HB.

      Exercise training leads to significant mitochondrial changes, including increased mitochondrial biogenesis in skeletal muscle. It would be valuable to compare the impact of 2HB treatment on mitochondrial content and oxidative capacity in treated mice to that in exercised mice.

      We agree with the author that it is of interest to investigate how 2HB may affect mitochondrial biogenesis. However, our preliminary findings were that 2HB-treated MEFs, C2C12s, and mouse soleus muscles showed no change in PGC1α gene expression after four days of treatment (data not shown). As a follow-up assessment of mitochondrial protein expression, although not specific to mtDNA derived genes, we quantified the expression of the respiratory chain proteins in cells and soleus muscle and found no effect of 2HB treatment (SFig. 5,6). At this stage we conclude that there is not evidence of 2HB modifying mitochondrial biogenesis in this time frame and that further investigation would be best suited to a follow-up study such as one interested in long-term exercise training.

      The authors demonstrate that 2-ketobutyrate (2KB) can serve as an oxidative fuel, suggesting a role for the intact BCAA catabolic pathway. However, it's puzzling that the knockout of BCKDHA, a subunit crucial for the second step of BCAA catabolism, did not result in changes in oxidative capacity in cultured myoblasts.

      While we report the BCKDH complex to be dispensable for 2KB oxidation it is important to note that previous studies have reported the following: (1) that 2KB is a viable substrate for BCKDH, (2) that 2KB is a viable substrate for pyruvate dehydrogenase, and (3) that pyruvate dehydrogenase is also dispensable for 2KB oxidation (see Steele et al., J Nutr., 114: 701-710, and Paxton et al. Biochem J., 234:295-303). Collectively, these data have led previous studies to conclude that BCKDH and pyruvate dehydrogenase are redundant for the first step of 2KB oxidation, with a preference for BCKDH. The flux through either may depend upon the metabolic environment. The aim for figure 3C was to determine whether the BCAA degradation pathway was required for 2KB oxidation. We conclude that this pathway is required, first at the step of PCC.

      While these past studies were mentioned in paragraph 2 of the discussion, in light of the reviewer’s comment we have expanded this paragraph. We have added language to explain that future research interested in the presented 2HB mechanism should carefully consider BCKDH and PDH expression in the cell or tissue of interest, as the metabolism of 2KB is quite central to the presented mechanism.

      Nevertheless, this innovative model of metabolic signaling during exercise will serve as a valuable reference for informing future.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript entitled "A 2-HB-mediated feedback loop regulates muscular fatigue" by the Johnson group reports interesting findings with implications for the health benefits of exercise. The authors use a combination of metabolic/biochemical in vivo and in vitro assays to delineate a metabolic route triggered by 2-HB (a relatively stable metabolite induced by exercise in humans and mice) that controls branched-chain amino transferase enzymes and mitochondrial oxidative capacity. Mechanistically, the author shows that 2-HB is a direct inhibitor of BCAT enzymes that in turn control levels of SIRT4 activity and ADp-ribosylation in the nucleus targeting C/EBP transcription factor, affecting BCAA oxidation genes (see Fig 4i in the paper). Overall, these are interesting and novel observations and findings with relevance to human exercise, with the potential implication of using these metabolites to mimic exercise benefits, or conditions or muscular fatigue that occurs in different human chronic diseases including rheumatic diseases or long COVID.

      Weaknesses:

      There are several experiments/comments that will strengthen the manuscript-

      (1) A final model in Figure 6 integrating the exercise/mechanistic findings, expanding on Fig 4i) will clarify the findings.

      We appreciate the reviewer’s suggestion to incorporate the exercise findings into a summary figure. However, upon internal review we find that such a figure is too similar to Fig 4i to warrant a new diagram.

      (2) In some of the graphs, statistics are missing (e.g Fig 6G).

      Some figures are included primarily for the reader to visualize the data while statistical comparison is conducted in a separate figure, for example Fig 2D-G. However, we have revised the figure legends to ensure that statistical comparisons are described for all appropriate figures, including Fig 6G identified by the reviewer.

      (3) The conclusions on SIRT4 dependency should be carefully written, as it is likely that this is only one potential mechanism, further validation with mouse models would be necessary.

      We appreciate the reviewers feedback and take the point well that a NAD-dependent mechanism will likely stimulate other sirtuins, which are often in fact expressed at greater levels than SIRT4. To reflect this comment in the manuscript we have altered paragraph 5 of the discussion to now focus on sirtuins. We briefly discuss SIRT4 and highlight the need for future consideration of other sirtuins, perhaps particularly mitochondrial sirtuins.

      (4) One of the needed experiments to support the oxidative capacity effects that could be done in cultured cells, is the use of radiosotope metabolites including BCCAs to determine the ability to produce CO2. Alternatively or in combination metabolite flux using isotopes would be useful to strengthen the current results.

      We appreciate the suggestion from the reviewer and we will look to conduct such an experiment in our follow-up work.

      We sincerely thank the reviewers for their input on this study as their suggestions have led to an improved manuscript for the version of record. The reviewer comments are well taken and we are glad that they will be present alongside the final manuscript to provide an important perspective on the work.

    1. Author response:

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

      In response to Reviewer #2, we agree with the reviewer that it needs to be noted that not all forms of recognition are the same and have added the following: "However, we note that not all forms of recognition are the same; researchers may prefer to have their work featured instead of personal stories or critiques of the scientific environment."


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

      We thank both reviewers for their detailed comments and insightful suggestions. Below we summarize our responses to each concern in addition to the edits within the manuscript.

      We would also like to add a clarification to the eLife assessment, it states “This important bibliometric analysis shows that authors of scientific papers whose names suggest they are female or East Asian get quoted less often in news stories about their work.” We show that individuals with names predicted to be from women or East Asian name origins are less likely to be quoted or mentioned in Nature’s scientific news stories than expected by publication demographics. In this study, we did not compare the level of coverage of a scientific article by the demographics of the authors of the article.

      Reviewer #1

      The article is not so clearly structured, which makes it hard to follow. A better framing, contextualization, and conceptualization of their analysis would help the readers to better understand the results. There are some unclear definitions and wrong wording of key concepts.

      We have adapted our wording in the text and added a more detailed discussion which hopefully makes the paper easier to comprehend. These changes are described in the context of your reviewer's suggestions and addressed in the next section.

      Language use: Male/Female refers to sex, not to gender.

      We have now updated the language throughout the text. Thank you for pointing this out.

      Regional disparities are not the same as names' origin. While the first might relate to the academic origin of authors, inferred from their institutional belonging, the latter reflects the authors' inferred identity. Ethnic identities and the construction of prejudice against specific populations need proper contextualization.

      We have added better contextualization in the manuscript and reworded the section in our results and discussion to clarify that we are analyzing disparities related to perceived ethnicity and not regions. We also added the following text to the results section “In our analysis, we use name origin as an estimate for the perceived ethnicity of a primary source by a journalist. Our prediction is not intended to assign ethnicity to an individual, but to be used broadly as a tool to quantify representational differences in a journalist's sociologically constructed perception of a primary source's ethnicity.” We also added the following text to our Discussion: “Our use of name origins is a proxy for a journalist's or referring scholarly peer’s potential perceptions of the ethnicity of a primary source as signaled by an individual's name. We do not intend to assign an identity to an individual, but to generate a broad metric to measure possible bias for particular ethnicities during journalists' primary source gathering.”

      It would be helpful to have a clear definition of what are quotes, mentions, and citations. For me, it was not so clear and made understanding the results more difficult.

      We added the following text to the results section Extracted Data Used for Analysis: “Quoted names are any names that were attached to a quote within the article. Mentioned names are any names that were stated within the article. Cited names are all author names of a scientific paper that was cited in the news article.”

      The comparison against Nature published research articles is not perfect because journalists will also cover articles not published in Nature. If for example, the gender representation in the quoted articles is not the same between Nature journals and other journals, then this source of inequality would be missing (e.g. if the journalists are biased against women, but not as much when they published in Nature, because they are also biased towards Nature articles). Also, the gender representation among Nature authors could not be the same as in general. Nevertheless, this seems to be a fair benchmark, especially if the authors did not have access to other more comprehensive databases. But a statement of limitations including these potential issues would be good to have.

      To add better context to the generalizability of our work, we added the following text to our discussion: “Furthermore, the news articles present on "www.nature.com" are intended for a very specific readership that may not be reflective of more broad scientific news outlets. In a separate analysis, we took a cursory look into a comparison with The Guardian and found similar disparities in gender and name origin. However, it is not clear which publications should be used as a comparator for science-related articles in The Guardian, and difficult to compare relative rates of representation. While other science news outlets may not have a direct comparator, it would be useful to take a broad comparison across multiple science news outlets to compare against one another. Our existing pipeline could be easily applied to other science news outlets and identify if there exists a consistent pattern of disparity regardless of the intended readership.”

      "we select the highest probability origin for each name as the resultant assignment". Threshold based approaches for race/ethnicity name-based inference have been criticized by the literature as they might reproduce biases (see Kozlowski, D., Murray, D. S., Bell, A., Hulsey, W., Larivière, V., Monroe-White, T., & Sugimoto, C. R. (2022). Avoiding bias when inferring race using name-based approaches. Plos one, 17(3), e0264270.). The authors could use the full distribution of probabilities over names instead of selecting one. The formulae proposed (3-5) could be easily adapted to this change.

      We thank the author for pointing this out. We have updated our analysis to use the probabilities instead of hard assignments. Figure 3 and formulae 3-5 have been updated. While we observe a slight shift in the calculated values, the overall trends are unchanged.

      Is it possible to make an analysis that intersects both name origin and gender? I am not sure if the sample size would allow for this, but if some other dimensions were collapsed, it would be very important to show what happens at the intersection of these two dimensions of discrimination.

      We agree that identifying any differences in quotation patterns at the intersection of gender and name origin would be very useful to identify. To address this, we added supplemental table 5. This table identifies the number of quotes per predicted name origin and gender over all years and article types. In this table, we don’t see a significant difference in gender distribution across predicted name origins.

      Given a larger sample size, we would be able to better identify more subtle differences, but at this sample size, we cannot make more detailed inferences. Additionally, this also addresses a QC-issue, where predicted gender accuracy varies by name origin, specifically East Asian name origin. From our data, we don’t see a large difference in proportions across any name origin. We added the following text to the results section to incorporate this analysis:

      “However, it should be noted that the error rate varies by name origin with the largest decrease in performance on names with an Asian origin [@doi:10.7717/peerj-cs.156;@doi:10.5195/jmla.2021.1252]

      . In our analysis, we did not observe a large difference in names predicted to come from a man or woman between predicted East Asian and other name origins (Table 5). “

      The use of vocabulary should be more homogeneous. For example, in page 13 the authors start to use the concepts of over/under enrichment, which appeared before in a title but was not used.

      The text has been updated to remove all mentions of “over/under enrichment” with “over/under representation”

      In the discussions section, it would be important to see as a statement of limitations the problems that automatic origin and gender inference have.

      We thank the reviewer for this suggestion. We have added the following paragraph to our discussion.

      Computational tools enabled us to automatically analyze thousands of articles to identify existing disparities by gender and name origin, but these tools are not without limitations. Our tools are unable to identify non-binary people and rely on gender predictors that are known to have region-specific biases, with the largest decrease in performance on names of an Asian origin [@doi:10.7717/peerj-cs.156;@doi:10.5195/jmla.2021.1252]. Furthermore, name origin is only a proxy for externally perceived racial or ethnic origins of a source or author and is not as accurate as self-identified race or ethnicity. Self-identification better captures the lived experience of an individual that computational estimates from a name can not capture. This is highlighted in our inability to distinguish between Black and White people from the US by their names. As the collection of demographic data by publication outlets grows, we believe this will enable a more fine-grained and accurate analysis of disparities in scientific journalism.

      Figures 2a and 3a show that the affiliations of authors and their countries was going to be used in this analysis. Yet, this section is not present in the article. I would encourage the authors to add this to the analysis as it would show important patterns, and to intersect the dimensions of gender, name origin and country.

      We were interested in using this analysis in our work, but unfortunately the sample size of cited works in each country was too small to make inferences. If this work was extended to larger scientific outlets to include larger corpora such as The Guardian or New York Times, we think one could be able to make more robust inferences. Since our work only focuses on Nature, we decided not to include this analysis. However, we do include a section in our discussion for future work.

      “As a proxy for measuring possible geographical bias of a journalist, we attempted to identify if there was any geographical bias of cited authors. To do this, we identified the affiliation of each cited author and identified their affiliated country. Unfortunately, we could not robustly extract a large enough number of cited authors from different countries to make any conclusive statements. Expanding our work to other science journalism outlets could help identify possible ways in which geographic region, genders, and perceived ethnicity interact and affect scientific visibility of specific groups. While we are unable to identify that journalists have a specific geographical bias, having reporters explicitly focused on specific regional sources will broaden coverage of international opinions in science.”

      It is not clear at that point what column dependence means.

      The abstract has been updated to state, “Gender disparity in Nature quotes was dependent on the article type.”

      Reviewer #2

      We thank the reviewer for their very detailed and insightful suggestions regarding our analysis and the key caveats that needed better contextualization in our analysis. We went through each major point the reviewer brought up below and included any additional text that was needed.

      In some cases, the manuscript lacks consistency in terminology, and uses word choice that is strange (e.g., "enrichment" and "depletion" when discussion representation).

      We thank the review for pointing this out, we have removed all instances of depletion/enrichment for over/under-representation

      Caveats to Claim 1. So while Claim 1 holds, it does not hold for all comparator sets and for all years. I don't think this is critical of the paper-the authors do discuss the trend in Claim 2-but interpretation of this claim should take care of these caveats, and readers should consider the important differences in first and last authorship.

      We thank the reviewer for their detailed feedback on this section. We have added the missing contextualization of our results. In the results section, I changed the figure caption to: “Speakers predicted to be men are sometimes overrepresented in quotes, but this depends on the year and article type.” Added the following paragraph “When considering the relative proportion of authors and speakers predicted to be men, we only find a slight over-representation of men. This overrepresentation is dependent on the authorship position and the year. Before 2010, quotes predicted as from men are overrepresented in comparison to both first and last authors, but between 2010 and 2017 quotes predicted from men are only overrepresented in comparison for first authors. In 2020, we find a slight over-representation of quotes predicted to be from women relative to first and last authors, but still severely under-represented when considering the general population. The choice of comparison between first and last authors can reveal different aspects of the current state of academia. While this does not hold in all scientific fields, first authors are typically early career scientists and last authors are more senior scientists. It has also been shown that early career scientists tend to be more diverse than senior scientists [@doi:10.7554/eLife.60829; @doi:10.1096/fj.201800639]. Since we find that quotes are only slightly more likely to come from a last author, it is reasonable to compare the relative rate of predicted quotes from men to either authorship position. Comparison with last authorships may reveal more how gender bias currently exists whereas comparison with early career scientists may reveal bias in comparison to a future, more possibly diverse academic environment. We hope that increased representation and recognition of women in science, even beyond what is observed in authorship, can increase the proportion of women first and last authors such that it better reflects the general population.”

      Generalizability to other contexts of science journalism:

      We thank the reviewer for their feedback on the generalizability of our work. We have now added the following text to our discussion to provide the reader with a better context of our results: “To articles presented on "www.nature.com" are intended for a very specific readership that may not be reflective of more broad scientific news outlets. In a separate analysis, we took a cursory look into a comparison with The Guardian and found very similar disparities in gender and name origin. However, it is not clear which publications should be used as a comparator for science-related articles in The

      Guardian, and difficult to compare relative rates of representation. While other science news outlets may not have a direct comparator, it would be useful to take a broad comparison across multiple science news outlets to compare against one another. Our existing pipeline could be easily applied to other science news outlets and identify if there exists a consistent pattern of disparity regardless of the intended readership. ”

      Shallow discussion:

      The authors highlight gender parity in career features, but why exactly is there gender parity in this format

      We thank the reviewer for encouraging us to better contextualize our findings in the broader discourse. We have now added several sections to our Discussion. To address gender parity, we have added the following text: “This finding, coupled with the near equal number of articles written by journalists predicted to be men or women, argues for more diversity in topical coverage. "Career Feature" articles highlight current topics relevant to working scientists and frequently highlight systemic issues with the scientific environment. This column allows space for marginalized people to critique the current state of affairs in science or share their personal stories. This type of content encourages the journalist to seek out a diverse set of primary sources. Including more content that is not primarily focused on recent publications, but all topics surrounding the practice of science, can serve as an additional tool to rapidly achieve gender parity in journalistic recognition.”

      Representation in quotations varies by first and last author, most certainly as a result of the academic division of labor in the life sciences. However, what does it say about the scientific quotation that it appears first authors are more often to be quoted? Does this mean that the division of labor is changing such that the first authors are the lead scientists? Or does it imply that senior authors are being skipped over, or giving away their chance to comment on a study to the first author?

      We thank the reviewer for asking bringing up these important questions. We have added better context to our first author analysis in our discussion. We have included the following two sections to address this. Also, we want to state that we find last authors to be slightly more quoted than first authors, as depicted in Fig. 2d., with first author quotation percentage largely appearing below the red line. We included this text in a response above and include it again here for convenience.

      “Before 2010, quotes predicted as from men are overrepresented in comparison to both first and last authors, but between 2010 and 2017 quotes predicted from men are only overrepresented in comparison for first authors. In 2020, we find a slight over-representation of quotes predicted to be from women relative to first and last authors, but still severely under-represented when considering the general population. The choice of comparison between first and last authors can reveal different aspects of the current state of academia. While this does not hold in all scientific fields, first authors are typically early career scientists and last authors are more senior scientists. It has also been shown that early career scientists tend to be more diverse than senior scientists [@doi:10.7554/eLife.60829; @doi:10.1096/fj.201800639]. Since we find that quotes are only slightly more likely to come from a last author, it is reasonable to compare the relative rate of predicted quotes from men to either authorship position. Comparison with last authorships may reveal more how gender bias currently exists whereas comparison with early career scientists may reveal bias in comparison to a future, more possibly diverse academic environment. We hope that increased representation and recognition of women in science, even beyond what is observed in authorship, can increase the proportion of women first and last authors such that it better reflects the general population.”

      “In our analysis, we also find that there are more first authors with predicted East Asian name origin than last authors. This is in contrast to predicted Celtic/English and European name origins.

      Furthermore, we see that the amount of first author people with predicted East Asian name origins is increasing at a much faster rate than quotes are increasing. If this mismatched rate of representation continues, this could lead to an increasingly large erasure of early career scientists with East Asian name origins. As noted before, focusing on increasing engagement with early career scientists can help to reduce the growing disparity of public visibility of scientists with East Asian name origins.”

      What might be the downstream impacts on the public stemming from the under-representation of scientists with East Asian names? According to Figure 3d, not only are East Asian names under-represented in quotations, but they are becoming more under-represented over time as they appear as authors in a greater number of Nature publications; Those with European names are proportionately represented in quotations given their share of authors in Nature. Why might this be, especially seeing as Anglo names are heavily over-represented?

      To address this point, we have added the following text to our discussion: “In our analysis, we also find that there are more first authors with predicted East Asian name origin than last authors. This is in contrast to predicted Celtic/English and European name origins. Furthermore, the amount of first author people with predicted East Asian name origins is increasing at a much faster rate than quotes are increasing. If this mismatched rate of representation continues, this could lead to an increasingly large erasure of early career scientists with East Asian name origins. As noted before, focusing on increasing engagement with early career scientists can help to reduce the growing disparity of public visibility of scientists with East Asian name origins.”

      I am very confused by Figure 1B. It mixes the counts of News-related items with (non-Springer) research articles in a single stacked bar plot which makes determining the quantity of either difficult. I would advise splitting them out

      Figure 1B has been updated, and the News and Research articles have been separated.

      When querying the first 2000 or so results from the SpringerNature API, are the authors certain that they are getting a random sample of papers?

      These papers were the first 200 English language "Journal" papers returned by the Springer Nature API for each month, resulting in 2400 papers per year from 2005 through 2020. These papers are the first 200 papers published each month by a Springer Nature journal, which may not be completely random, but we believe to be a reasonably representative sample. Furthermore, the Springer Nature comparator set is being used as an additional comparator to the complete set of all Nature research papers used in our analyses.

      In all figures: the authors use capital letters to indicate panels in the caption, but lowercase letters in the figure itself and in the main text. This should be made consistent.

      This has been updated.

      In all figures: the authors should make the caption letter bold in the figure captions, which makes it much easier to find descriptions of specific panels

      This has been updated.

      In the section "coreNLP": the authors mention "co-reference resolution" but without really remarking why it is being used. This is an issue throughout the methods-the authors describe what method they are using but either they don't mention why they are using that method until later, or else not at all.

      We have added better reasoning behind our coreNLP selected methods: “We used the standard set of annotaters: tokenize, ssplit, pos, lemma, ner, parse, coref, and additionally the quote annotator. These perform text tokenization, sentence splitting, part of speech recognition, lemmatization, named entity recoginition, division of sentences into constituent phrases, co-reference resolution, and identification of quoted entities, respectively. We used the "statistical" algorithm to perform coreference resolution for speed. Each of these aspects is required to identify the names of quoted or mentioned speakers and identify any of their associated pronouns. All results were output to json format for further downstream processing.”

      We included a better description of scrapy: “Scrapy is a tool that applies user-defined rules to follow hyperlinks on webpages and return the information contained on each webpage.

      We used Scrapy to extract all web pages containing news articles and extract the text.”

      We also included our motivation for bootstrapping: “We used the boostrap method to construct confidence intervals for each of our calculated statistics.”

      In the section "Name Formatting for Gender Prediction in Quotes or Mentions", genderizeR is mentioned before an introduction to the tool

      We added the following text to provide context: “Even though genderizeR, the computational method used to predict the name's gender, only uses the first name to make the gender prediction, identifying the full name gives us greater confidence that we correctly identified the first name. “

      In the section "Name Formatting for Gender Prediction of Authors", you state that you exclude papers with only one author. How many papers is this? I assume few, in Nature, but if not I can imagine gender differences based on who writes first-authored papers.

      We find that the number excluded is roughly 7% of all papers, which is consistent across Nature and Springer Nature (1113/15013 for cited springer articles, 2899/42155 for random springer articles, 955/12459 for nature authors). We have added the following text to the manuscript for better context: “Roughly 7% of all papers were estimated to be by a single author and removed from this analysis.: 1113/15013 for cited Springer articles, 2899/42155 for random Springer articles, 955/12459 for Nature research articles.”

      In "Name Origin Analysis", for the in-text reference to Equation 3: include the prefix "Eq." or similar to mark this as referencing the equation and not something else

      This has been updated.

      The use of the word "enrichment" in reference to the representation of East Asian authors is strange and does not fit the colloquial definition of the term. I suggest just using a simpler term like "representation" instead

      Similarly, the authors use the word "depletion" to reflect the lower rate of quotes to scientists with East-Asian names, but I feel a simpler word would be more appropriate.

      We thank the reviewer for this suggestion, all instances of “enrichment/depletion” have been replaced with “over/under representation”

      The authors claim in Figure 2d that there is a steady increase in the rate of first author citations, however, this graph is not convincing. It appears to show much more noise than anything resembling a steady change.

      We have reworded our figure description to state that there is a consistent bias towards quoting last authors. Our figure description now states: “Panel d shows a consistent but slight bias towards quoting the last author of a cited article than the first author over time.”

      Supplemental Figures 1b and 1c do not seem to be mentioned in the main text, and I struggle to see their relevance.

      We thank the reviewer for identifying this error; these subpanels have been removed.

    1. Author response:

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

      Point-by-point response to concerns raised by reviewer #3:

      The manuscript has improved very substantially in revision. The authors have clearly taken the comments on board in good faith. Yet, some small concerns remain around the behavioural analysis.

      In Fig. 8H and H' average sleep/day is ~100. Is this minutes of sleep? 100 min/day is far too low, is it a typo?

      The numbers for sleep bouts are also too low to me e.g. in Fig 9 number of sleep bouts avg around 4.

      In their response to reviewers the authors say these errors were fixed, yet the figures appear not to have been changed. Perhaps the old figures were left in inadvertently?

      Indeed this correction was somehow missed and we thank the reviewer for noticing this. We have now corrected Fig 8H-H’ and Fig 9D.  

      The circadian anticipatory activity analyses could also be improved. The standard in the field is to perform eduction analyses and quantify anticipatory activity e.g. using the method of Harrisingh et al. (PMID: 18003827). This typically computed as the ratio of activity in the 3hrs preceding light transition to activity in the 6hrs preceding light transition.

      In their response to reviewers, the authors have revised their anticipation analyses by quantifying the mean activity in the 6 hrs preceding light transition. However, in the method of Harrisingh et al., anticipation is the ratio of activity in the 3hrs preceding light transition to activity in the 6hrs preceding light transition. Simply computing the activity in the 6hrs preceding light transition does not give a measure of anticipation, determining the ratio is key.

      We acknowledge the importance of obtaining accurate results in our analysis, therefore we have re-evaluated the anticipation activity by measuring the ratio of the mean activity in the 3h preceding light transition over the activity in the 6h preceding light transition. We have reported the data as percentages in Fig 8F-G and modified the figure legends accordingly.

    1. Author response:

      eLife assessment 

      This important study provides evidence for a combination of the latest generation of Oxford Nanopore Technology long reads with state-of-the art variant callers enabling bacterial variant discovery at accuracy that matches or exceeds the current "gold standard" with short reads. The evidence supporting the claims of the authors is convincing, although the inclusion of a larger number of reference genomes would further strengthen the study. The work will be of interest to anyone performing sequencing for outbreak investigations, bacterial epidemiology, or similar studies. 

      We thank the editor and reviewers for the accurate summary and positive assessment. We address the comment about increasing the number of reference genomes in the response to reviewer 2.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      The authors assess the accuracy of short variant calling (SNPs and indels) in bacterial genomes using Oxford Nanopore reads generated on R10.4 flow cells from a very similar genome (99.5% ANI), examining the impact of variant caller choice (three traditional variant callers: bcftools, freebayes, and longshot, and three deep learning based variant callers: clair3, deep variant, and nano caller), base calling model (fast, hac and sup) and read depth (using both simplex and duplex reads). 

      Strengths: 

      Given the stated goal (analysis of variant calling for reads drawn from genomes very similar to the reference), the analysis is largely complete and results are compelling. The authors make the code and data used in their analysis available for re-use using current best practices (a computational workflow and data archived in INSDC databases or Zenodo as appropriate). 

      Weaknesses: 

      While the medaka variant caller is now deprecated for diploid calling, it is still widely used for haploid variant calling and should at least be mentioned (even if the mention is only to explain its exclusion from the analysis). 

      We agree that this would be an informative addition to the study and will add it to the benchmarking.

      Appraisal: 

      The experiments the authors engaged in are well structured and the results are convincing. I expect that these results will be incorporated into "best practice" bacterial variant calling workflows in the future. 

      Thank you for the positive appraisal.

      Reviewer #2 (Public Review): 

      Summary: 

      Hall et al describe the superiority of ONT sequencing and deep learning-based variant callers to deliver higher SNP and Indel accuracy compared to previous gold-standard Illumina short-read sequencing. Furthermore, they provide recommendations for read sequencing depth and computational requirements when performing variant calling. 

      Strengths: 

      The study describes compelling data showing ONT superiority when using deep learning-based variant callers, such as Clair3, compared to Illumina sequencing. This challenges the paradigm that Illumina sequencing is the gold standard for variant calling in bacterial genomes. The authors provide evidence that homopolymeric regions, a systematic and problematic issue with ONT data, are no longer a concern in ONT sequencing. 

      Weaknesses: 

      (1) The inclusion of a larger number of reference genomes would have strengthened the study to accommodate larger variability (a limitation mentioned by the authors). 

      Our strategic selection of 14 genomes—spanning a variety of bacterial genera and species, diverse GC content, and both gram-negative and gram-positive species (including M. tuberculosis, which is neither)—was designed to robustly address potential variability in our results. Moreover, all our genome assemblies underwent rigorous manual inspection as the quality of the true genome sequences is the foundation this research is built upon. Given this, the fundamental conclusions regarding the accuracy of variant calls would likely remain unchanged with the addition of more genomes.  However, we do acknowledge that a substantially larger sample size, which is beyond the scope of this study, would enable more fine-grained analysis of species differences in error rates.

      (2) In Figure 2, there are clearly one or two samples that perform worse than others in all combinations (are always below the box plots). No information about species-specific variant calls is provided by the authors but one would like to know if those are recurrently associated with one or two species. Species-specific recommendations could also help the scientific community to choose the best sequencing/variant calling approaches.

      Thank you for highlighting this observation. The precision, recall, and F1 scores for each sample and condition can be found in Supplementary Table S4. We will investigate the samples that consistently perform below expectation to determine if this is associated with specific species, which may necessitate tailored recommendations for those species. Additionally, we will produce a species-segregated version of Figure 2 for a clearer interpretation and will place it in the supplementary materials.

      (3) The authors support that a read depth of 10x is sufficient to achieve variant calls that match or exceed Illumina sequencing. However, the standard here should be the optimal discriminatory power for clinical and public health utility (namely outbreak analysis). In such scenarios, the highest discriminatory power is always desirable and as such an F1 score, Recall and Precision that is as close to 100% as possible should be maintained (which changes the minimum read sequencing depth to at least 25x, which is the inflection point).

      We agree that the highest discriminatory power is always desirable for clinical or public health applications. In which case, 25x is probably a better minimum recommendation. However, we are also aware that there are resource-limited settings where parity with Illumina is sufficient. In these cases, 10x depth from ONT would provide sufficient data.

      The manuscript currently emphasises the latter scenario, but we will revise the text to clearly recommend 25x depth as a conservative aim in settings where resources are not a constraint, ensuring the highest possible discriminatory power for applications like outbreak analysis.

      (4) The sequencing of the samples was not performed with the same Illumina and ONT method/equipment, which could have introduced specific equipment/preparation artefacts that were not considered in the study. See for example https://academic.oup.com/nargab/article/3/1/lqab019/6193612

      To our knowledge, there is no evidence that sequencing on different ONT machines or barcoding kits leads to a difference in read characteristics or accuracy. To ensure consistency and minimise potential variability, we used the same ONT flowcells for all samples and performed basecalling on the same Nvidia A100 GPU. We will update the methods to emphasise this.

      For Illumina and ONT, the exact machines used for which samples will be added as a supplementary table. We will also add a comment about possible Illumina error rate differences in the ‘Limitations’ section of the Discussion.

      In summary, while there may be specific equipment or preparation artifacts to consider, we took steps to minimise these effects and maintain consistency across our sequencing methods.

      Reviewer #3 (Public Review): 

      Hall et al. benchmarked different variant calling methods on Nanopore reads of bacterial samples and compared the performance of Nanopore to short reads produced with Illumina sequencing. To establish a common ground for comparison, the authors first generated a variant truth set for each sample and then projected this set to the reference sequence of the sample to obtain a mutated reference. Subsequently, Hall et al. called SNPs and small indels using commonly used deep learning and conventional variant callers and compared the precision and accuracy from reads produced with simplex and duplex Nanopore sequencing to Illumina data. The authors did not investigate large structural variation, which is a major limitation of the current manuscript. It will be very interesting to see a follow-up study covering this much more challenging type of variation. 

      We fully agree that investigating structural variations (SVs) would be a very interesting and important follow-up. Identifying and generating ground truth SVs is a nontrivial task and we feel it deserves its own space and study. We hope to explore this in the future.

      In their comprehensive comparison of SNPs and small indels, the authors observed superior performance of deep learning over conventional variant callers when Nanopore reads were basecalled with the most accurate (but also computationally very expensive) model, even exceeding Illumina in some cases. Not surprisingly, Nanopore underperformed compared to Illumina when basecalled with the fastest (but computationally much less demanding) method with the lowest accuracy. The authors then investigated the surprisingly higher performance of Nanopore data in some cases and identified lower recall with Illumina short read data, particularly from repetitive regions and regions with high variant density, as the driver. Combining the most accurate Nanopore basecalling method with a deep learning variant caller resulted in low error rates in homopolymer regions, similar to Illumina data. This is remarkable, as homopolymer regions are (or, were) traditionally challenging for Nanopore sequencing. 

      Lastly, Hall et al. provided useful information on the required Nanopore read depth, which is surprisingly low, and the computational resources for variant calling with deep learning callers. With that, the authors established a new state-of-the-art for Nanopore-only variant, calling on bacterial sequencing data. Most likely these findings will be transferred to other organisms as well or at least provide a proof-of-concept that can be built upon. 

      As the authors mention multiple times throughout the manuscript, Nanopore can provide sequencing data in nearly real-time and in remote regions, therefore opening up a ton of new possibilities, for example for infectious disease surveillance. 

      However, the high-performing variant calling method as established in this study requires the computationally very expensive sup and/or duplex Nanopore basecalling, whereas the least computationally demanding method underperforms. Here, the manuscript would greatly benefit from extending the last section on computational requirements, as the authors determine the resources for the variant calling but do not cover the entire picture. This could even be misleading for less experienced researchers who want to perform bacterial sequencing at high performance but with low resources. The authors mention it in the discussion but do not make clear enough that the described computational resources are probably largely insufficient to perform the high-accuracy basecalling required. 

      We have provided runtime benchmarks for basecalling in Supplementary Figure S16 and detailed these times in Supplementary Table S7. In addition, we state in the Results section (P10 L228-230) “Though we do note that if the person performing the variant calling has received the raw (pod5) ONT data, basecalling also needs to be accounted for, as depending on how much sequencing was done, this step can also be resource-intensive.”

      Even with super-accuracy basecalling considered, our analysis shows that variant calling remains the most resource-intensive step for Clair3, DeepVariant, FreeBayes, and NanoCaller. Therefore, the statement “the described computational resources are probably largely insufficient to perform the high-accuracy basecalling required”, is incorrect. However, we will endeavour to make the basecalling component and considerations more prominent in the Results and Discussion.

    1. Author response:

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

      eLife assessment

      The authors discuss an effect, "diffusive lensing", by which particles would accumulate in high-viscosity regions, for instance in the intracellular medium. To obtain these results, the authors rely on agent-based simulations using custom rules performed with the Ito stochastic calculus convention. The "lensing effect" discussed is a direct consequence of the choice of the Ito convention without spurious drift which has been discussed before and is likely to be inadequate for the intracellular medium, causing the presented results to likely have little relevance for biology.

      We thank the editors and the reviewers for their consideration of our manuscript. We argue in this rebuttal and revision that our results and conclusions are in fact likely to have relevance for biology. While we use the Itô convention for ease of modeling considering its non-anticipatory nature upon discretization (see (Volpe and Wehr 2016) for the discretization schemes), we refer to Figure S1B to emphasize that diffusive lensing occurs not only under the Itô convention but across a wide parameter space. Indeed, it is absent only in the normative isothermal convention; note that even a stochastic differential equation conforming to the isothermal convention may be reformulated into the Itô convention by adding suitable drift terms, allowing for diffusive lensing to be seen even in case of the isothermal convention. We note in particular that the choice of the convention is a highly context-dependent one (Sokolov 2010); there is not a universally correct choice, and one can obtain stochastic differential equations consistent with Ito or Stratonovich interpretations in different regimes. Lastly, space-dependent diffusivity is now an experimentally well-recognized feature of the cellular interior, as noted in our references and as discussed further later in this response. This fact points towards the potential relevance of our model for subcellular diffusion.

      In our revised preprint, we have made changes to the text and minor changes to figures to address reviewer concerns.

      Responses to the Reviewers

      We thank the reviewers for their feedback and address the issues they raised in this rebuttal and in the revised manuscript. The central point that the reviewers raise concerns the validity of the drift-less Itô interpretation in modeling potential nonequilibrium types of subcellular transport arising from space-dependent diffusivity. If the drift term were considered, the resulting stochastic differential equation stochastic differential equation (SDE) is equivalent to one arising from the isothermal interpretation of heterogeneous diffusivity (Volpe and Wehr 2016), wherein no diffusive lensing is seen (as shown in Fig. S1B). That is, the isothermal interpretation and the drift-comprising Itô SDE produce the same uniform steady-state particle densities.

      While we agree with the reviewers that for a given interpretation, equivalent stochastic differential equations (SDEs) arising from other interpretations may be drawn, we disagree with the generalization that all types of subcellular diffusion conform to the isothermal interpretation. That is, there is no reason why any and all instances of nonequilibrium subcellular particle diffusion must be modeled using isothermal-conforming SDEs (such as the drift-comprising Itô SDE, for instance). We refer to (Sokolov 2010) which prescribes choosing a convention in a context-dependent manner. In this regard, we disagree with the second reviewer’s characterization of making such a choice merely a “choice of writing” considering that it is entirely dependent on the choice of microscopic parameters, as detailed in the discussion section of the manuscript. The following references have also been added to the manuscript: the reference from the first reviewer (Kupferman et al. 2004) proposes a prescription for choosing an appropriate convention based upon comparing the noise correlation time and the particle relaxation time. The reference notes that the Itô convention is appropriate when the particle relaxation time is large when compared to the noise correlation time and the Stratonovich convention is appropriate in the converse scenario. In (Rupprecht et al. 2018), active noise is considered and the resulting Fokker-Planck equation conforms to the Stratonovich convention when thermal noise was negligible. The related reference, (Vishen et al. 2019) compares three timescales: those of particle relaxation, noise correlation and viscoelastic relaxation, to make the choice. Indeed, as noted in the manuscript, lensing is seen in all but one interpretation (without drift additions); only its magnitude is altered by the interpretation/choice of the drift term. The appendix has been modified to include a subsection on the interchangeability of the conventions.

      Separately, with regards to the discussion on anomalous diffusion, the section on mean squared displacement calculation has been amended to avoid confusing our model with canonical anomalous diffusion which considers the anomalous exponent; how the anomalous exponent varies with space-dependent diffusivity offers an interesting future area of study.

      Responses to specific reviewer comments appear below.

      Reviewer #1 (Public Review):

      The manuscript "Diffusive lensing as a mechanism of intracellular transport and compartmentalization", explores the implications of heterogeneous viscosity on the diffusive dynamics of particles. The authors analyze three different scenarios:

      (i)   diffusion under a gradient of viscosity,

      (ii)  clustering of interacting particles in a viscosity gradient, and

      (iii) diffusive dynamics of non-interacting particles with circular patches of heterogeneous viscous medium.

      The implications of a heterogeneous environment on phase separation and reaction kinetics in cells are under-explored. This makes the general theme of this manuscript very relevant and interesting. However, the analysis in the manuscript is not rigorous, and the claims in the abstract are not supported by the analysis in the main text.

      Following are my main comments on the work presented in this manuscript:

      (a) The central theme of this work is that spatially varying viscosity leads to position-dependent diffusion constant. This, for an overdamped Langevin dynamics with Gaussian white noise, leads to the well-known issue of the interpretation of the noise term.

      The authors use the Ito interpretation of the noise term because their system is non-equilibrium.

      One of the main criticisms I have is on this central point. The issue of interpretation arises only when there are ill-posed stochastic dynamics that do not have the relevant timescales required to analyze the noise term properly. Hence, if the authors want to start with an ill-posed equation it should be mentioned at the start. At least the Langevin dynamics considered should be explicitly mentioned in the main text. Since this work claims to be relevant to biological systems, it is also of significance to highlight the motivation for using the ill-posed equation rather than a well-posed equation. The authors refer to the non-equilibrium nature of the dynamics but it is not mentioned what non-equilibrium dynamics to authors have in mind. To properly analyze an overdamped Langevin dynamics a clear source of integrated timescales must be provided. As an example, one can write the dynamics as Eq. (1) \dot x = f(x) + g(x) \eta , which is ill-defined if the noise \eta is delta correlated in time but well-defined when \eta is exponentially correlated in time. One can of course look at the limit in which the exponential correlation goes to a delta correlation which leads to Eq. (1) interpreted in Stratonovich convention. The choice to use the Ito convention for Eq. (1) in this case is not justified.

      We thank the reviewer for detailing their concerns with our model’s assumptions. We have addressed them in the common rebuttal.

      (b) Generally, the manuscript talks of viscosity gradient but the equations deal with diffusion which is a combination of viscosity, temperature, particle size, and particle-medium interaction. There is no clear motivation provided for focus on viscosity (cytoplasm as such is a complex fluid) instead of just saying position-dependent diffusion constant. Maybe authors should use viscosity only when talking of a context where the existence of a viscosity gradient is established either in a real experiment or in a thought experiment.

      The manuscript has been amended to use only “diffusivity” to avoid confusion.

      (c) The section "Viscophoresis drives particle accumulation" seems to not have new results. Fig. 1 verifies the numerical code used to obtain the results in the later sections. If that is the case maybe this section can be moved to supplementary or at least it should be clearly stated that this is to establish the correctness of the simulation method. It would also be nice to comment a bit more on the choice of simulation methods with changing hopping sizes instead of, for example, numerically solving stochastic ODE.

      The main point of this section and of Fig. 1 is the diffusive lensing effect itself: the accumulation of particles in lower-diffusivity areas. To the best of our knowledge, diffusive lensing has not been reported elsewhere as a specific outcome of non-isothermal interpretations of diffusion, with potential relevance to nonequilibrium subcellular motilities. The simulation method has been fully described in the Methods section, and the code has also been shared (see Code Availability).

      A minor comment, the statement "the physically appropriate convention to use depends upon microscopic parameters and timescale hierarchies not captured in a coarse-grained model of diffusion." is not true as is noted in the references that authors mention, a correct coarse-grained model provides a suitable convention (see also Phys. Rev. E, 70(3), 036120., Phys. Rev. E, 100(6), 062602.).

      This has been addressed in the common rebuttal.

      (d) The section "Interaction-mediated clustering is affected by viscophoresis" makes an interesting statement about the positioning of clusters by a viscous gradient. As a theoretical calculation, the interplay between position-dependent diffusivity and phase separation is indeed interesting, but the problem needs more analysis than that offered in this manuscript. Just a plot showing clustering with and without a gradient of diffusion does not give enough insight into the interplay between density-dependent diffusion and position-dependent diffusion. A phase plot that somehow shows the relative contribution of the two effects would have been nice. Also, it should be emphasized in the main text that the inter-particle interaction is through a density-dependent diffusion constant and not a conservative coupling by an interaction potential.

      The density-dependence has been added from the Methods to the main text. The goal of the work is to present lensing as a natural outcome of the parameter choices we make and present its effects as they relate to clustering and commonly used biophysical methods to probe dynamics within cells. A dense sampling of the phase space and how it is altered as a function of diffusivity, and the subsequent interpretation, lie beyond the scope of the present work but offer exciting future directions of study.

      (e) The section "In silico microrheology shows that viscophoresis manifests as anomalous diffusion" the authors show that the MSD with and without spatial heterogeneity is different. This is not a surprise - as the underlying equations are different the MSD should be different.

      The goal here is to compare and contrast the ways in which homogeneous and heterogeneous diffusion manifest in simulated microrheology measurements. We hope that an altered saturation MSD, as is observed in our simulations, provokes interest in considering lensing while modeling experimental data.

      There are various analogies drawn in this section without any justification:

      (i) "the saturation MSD was higher than what was seen in the homogeneous diffusion scenario possibly due to particles robustly populating the bulk milieu followed by directed motion into the viscous zone (similar to that of a Brownian ratchet, (Peskin et al., 1993))."

      In case of i), the Brownian ratchet is invoked as a model to explain directed accumulation. We have removed this analogy to avoid confusion as it is not delved into further over the course of our work.

      (ii) "Note that lensing may cause particle displacements to deviate from a Gaussian distribution, which could explain anomalous behaviors observed both in our simulations and in experiments in cells (Parry et al., 2014)." Since the full trajectory of the particles is available, it can be analyzed to check if this is indeed the case.

      This has been addressed in the common rebuttal.

      (f) The final section "In silico FRAP in a heterogeneously viscous environment ... " studies the MSD of the particles in a medium with heterogeneous viscous patches which I find the most novel section of the work. As with the section on inter-particle interaction, this needs further analysis.

      We thank the reviewer for their appreciation. In presenting these three sections discussing the effects of diffusive lensing, we intend to broadly outline the scope of this phenomenon in influencing a range of behaviors. Exploring the directions further comprise promising future directions of study that lie beyond the scope of this manuscript.

      To summarise, as this is a theory paper, just showing MSD or in silico FRAP data is not sufficient. Unlike experiments where one is trying to understand the systems, here one has full access to the dynamics either analytically or in simulation. So just stating that the MSD in heterogeneous and homogeneous environments are not the same is not sufficient. With further analysis, this work can be of theoretical interest. Finally, just as a matter of personal taste, I am not in favor of the analogy with optical lensing. I don't see the connection.

      We value the reviewer’s interest in investigating the causes underlying the differences in the MSDs and agree that it represents a promising future area of study. The main point of this section of the manuscript was to make a connection to experimentally measurable quantities.

      Reviewer #2 (Public Review):

      Summary:

      The authors study through theory and simulations the diffusion of microscopic particles and aim to account for the effects of inhomogeneous viscosity and diffusion - in particular regarding the intracellular environment. They propose a mechanism, termed "Diffusive lensing", by which particles are attracted towards high-viscosity regions where they remain trapped. To obtain these results, the authors rely on agent-based simulations using custom rules performed with the Ito stochastic calculus convention, without spurious drift. They acknowledge the fact that this convention does not describe equilibrium systems, and that their results would not hold at equilibrium - and discard these facts by invoking the fact that cells are out-of-equilibrium. Finally, they show some applications of their findings, in particular enhanced clustering in the high-viscosity regions. The authors conclude that as inhomogeneous diffusion is ubiquitous in life, so must their mechanism be, and hence it must be important.

      Strengths:

      The article is well-written, and clearly intelligible, its hypotheses are stated relatively clearly and the models and mathematical derivations are compatible with these hypotheses.

      We thank the reviewer for their appreciation.

      Weaknesses:

      The main problem of the paper is these hypotheses. Indeed, it all relies on the Ito interpretation of the stochastic integrals. Stochastic conventions are a notoriously tricky business, but they are both mathematically and physically well-understood and do not result in any "dilemma" [some citations in the article, such as (Lau and Lubensky) and (Volpe and Wehr), make an unambiguous resolution of these]. Conventions are not an intrinsic, fixed property of a system, but a choice of writing; however, whenever going from one to another, one must include a "spurious drift" that compensates for the effect of this change - a mathematical subtlety that is entirely omitted in the article: if the drift is zero in one convention, it will thus be non-zero in another in the presence of diffusive gradients. It is well established that for equilibrium systems obeying fluctuation-dissipation, the spurious drift vanishes in the anti-Ito stochastic convention (which is not "anticipatory", contrarily to claims in the article, are the "steps" are local and infinitesimal). This ensures that the diffusion gradients do not induce currents and probability gradients, and thus that the steady-state PDF is the Gibbs measure. This equilibrium case should be seen as the default: a thermal system NOT obeying this law should warrant a strong justification (for instance in the Volpe and Wehr review this can occur through memory effects in robotic dynamics, or through strong fluctuation-dissipation breakdown). In near-equilibrium thermal systems such as the intracellular medium (where, although out-of-equilibrium, temperature remains a relevant and mostly homogeneous quantity), deviations from this behavior must be physically justified and go to zero when going towards equilibrium.

      Considering that the physical phenomena underlying diffusion span a range of timescales (particle relaxation, noise, environmental correlation, et cetera), we disagree with the assertion that all types of subcellular diffusion processes can be modeled as occurring at thermal equilibrium: for example, one can easily imagine memory effects arising in the presence of an appropriate hierarchy of timescales. We have added references that describe in more detail the way in which the comparison of timescales can dictate the applicability of different conventions. We also refer the referee to the common rebuttal section of our response in which we discuss factors that govern the choice of the interpretation. The adiabatic elimination arguments highlighted in (Kupferman et al. 2004) provide a clear description of how relevant particle and environment-related timescales can inform the choice of stochastic calculus to use.

      With regards to the use of the term “anticipatory” to refer to the isothermal interpretation, we refer to the comment in (Volpe and Wehr 2016) of the Itô interpretation “not looking into the future”. In any case, whether anticipatory or otherwise, the interpretation’s effect on our model remains unchanged, as highlighted in the section in the Appendix on the conversion between different conventions; this section has been added to minimize confusion about the effects of the choice of convention on lensing.

      Here, drifts are arbitrarily set to zero in the Ito convention (the exact opposite of the equilibrium anti-Ito), which is the equilibrium equivalent to adding a force (with drift $- grad D$) exactly compensating the spurious drift. If we were to interpret this as a breakdown of detailed balance with inhomogeneous temperature, the "hot" region would be effectively at 4x higher temperature than the cold region (i.e. 1200K) in Fig 1A.

      Our work is based on existing observations of space-dependent diffusivity in cells (Garner et al., 2023; Huang et al., 2021; Parry et al., 2014; Śmigiel et al., 2022; Xiang et al., 2020). These papers support a definitive model for the existence of space-dependent diffusivity without invoking space-dependent temperature.

      It is the effects of this arbitrary force (exactly compensating the Ito spurious drift) that are studied in the article. The fact that it results in probability gradients is trivial once formulated this way (and in no way is this new - many of the references, for instance, Volpe and Wehr, mention this).

      Addressed in the common rebuttal.

      Enhanced clustering is also a trivial effect of this probability gradient (the local concentration is increased by this force field, so phase separation can occur). As a side note the "neighbor sensing" scheme to describe interactions is very peculiar and not physically motivated - it violates stochastic thermodynamics laws too, as the detailed balance is apparently not respected.

      The neighbor-sensing scheme used here is just one possible model of an effective attractive potential between particles. Other models that lead to density-dependent attraction between particles should also provide qualitatively similar results as ours; this offers an interesting prospect for future research.

      Finally, the "anomalous diffusion" discussion is at odds with what the literature on this subject considers anomalous (the exponent does not appear anomalous).

      This has been addressed in the common rebuttal, and the relevant part of the manuscript has been modified to avoid confusion.

      The authors make no further justification of their choice of convention than the fact that cells are out-of-equilibrium, leaving the feeling that this is a detail. They make mentions of systems (eg glycogen, prebiotic environment) for which (near-)equilibrium physics should mostly prevail, and of fluctuation-dissipation ("Diffusivity varies inversely with viscosity", in the introduction). Yet the "phenomenon" they discuss is entirely reliant on an undiscussed mechanism by which these assumptions would be completely violated (the citations they make for this - Gnesotto '18 and Phillips '12 - are simply discussions of the fact that cells are out-of-equilibrium, not on any consequences on the convention).

      Finally, while inhomogeneous diffusion is ubiquitous, the strength of this effect in realistic conditions is not discussed (this would be a significant problem if the effect were real, which it isn't). Gravitational attraction is also an ubiquitous effect, but it is not important for intracellular compartmentalization.

      The manuscript text has been supplemented with additional references that detail the ways in which the comparison of timescales can dictate how one can apply different conventions. We refer the reviewer to the common rebuttal section of our response where we detail factors that dictate the choice of the convention to use. As previously noted, the adiabatic elimination arguments highlighted in (Kupferman et al., 2004) provide a prescription for how different timescales are to be considered in deciding the choice of stochastic calculus to use.

      With regards to the strength of space-dependent diffusivity in subcellular milieu, various measurements of heterogeneous diffusivity have been made both across different model systems and via different modalities, as cited in our manuscript. (Garner et al. 2023) used single-particle tracking to determine over 100-fold variability in diffusivity within individual S. pombe cells. Single-molecule measurements in (Xiang et al. 2020) and (Śmigiel et al. 2022) reveal an order-of-magnitude variation in tracer diffusion in mammalian cells and multi-fold variation in E. coli cytoplasm respectively. Fluorescence correlation spectroscopy measurements in (Huang et al. 2022) have found a two-fold increase in short-range diffusion of protein-sized tracers in X. laevis extracts. We have also added a reference to a study that uses 3D single particle tracking in the cytosol of a multinucleate fungus, A. gossypii, to identify regions of low-diffusivity near nuclei and hyphal tips (McLaughlin et al. 2020). Many of these references deploy particle tracking and investigate how mesoscale-sized particles (i.e. tracers spanning biologically relevant size scales) are directly impacted by space-dependent diffusivity. Therefore, we base our model on not only space-dependent diffusivity being a well-recognized feature of the cellular interior, but also on these observations pertaining to mesoscale-sized particles’ motion along relevant timescales.

      These measurements are also relevant to the reviewer’s question about the strength of the effect, which depends directly on the variability in diffusivity: for ten- or a hundred-fold diffusivity variations, the effect would be expected to be significant. In case of using the Itô convention directly, the contrast in concentration gradient is, in fact, that of the diffusivity gradient.

      To conclude, the "diffusive lensing" effect presented here is not a deep physical discovery, but a well-known effect of sticking to the wrong stochastic convention.

      As detailed in the various responses above, we respectfully disagree with the notion that there exists a singular correct stochastic convention that is applicable for all cases of subcellular heterogeneous diffusion. Further, as detailed in (Volpe and Wehr 2016) and as detailed in the Appendix, it is possible to convert between conventions and that an isothermal-abiding stochastic differential equation may be suitably altered, by means of adding a drift term, to an Itô-abiding stochastic differential equation; therefore, one can observe diffusive lensing without discarding the isothermal convention if the latter were modified. Indeed, it is only the driftless (or canonical) isothermal convention that does not allow for diffusive lensing.

    1. Author response:

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

      Reviewer #1: 

      This is my first review of the article entitled "The canonical stopping network: Revisiting the role of the subcortex in response inhibition" by Isherwood and colleagues. This study is one in a series of excellent papers by the Forstmann group focusing on the ability of fMRI to reliably detect activity in small subcortical nuclei - in this case, specifically those purportedly involved in the hyper- and indirect inhibitory basal ganglia pathways. I have been very fond of this work for a long time, beginning with the demonstration of De Hollander, Forstmann et al. (HBM 2017) of the fact that 3T fMRI imaging (as well as many 7T imaging sequences) do not afford sufficient signal to noise ratio to reliably image these small subcortical nuclei. This work has done a lot to reshape my view of seminal past studies of subcortical activity during inhibitory control, including some that have several thousand citations.

      In the current study, the authors compiled five datasets that aimed to investigate neural activity associated with stopping an already initiated action, as operationalized in the classic stop-signal paradigm. Three of these datasets are taken from their own 7T investigations, and two are datasets from the Poldrack group, which used 3T fMRI.

      The authors make six chief points: 

      (1) There does not seem to be a measurable BOLD response in the purportedly critical subcortical areas in contrasts of successful stopping (SS) vs. going (GO), neither across datasets nor within each individual dataset. This includes the STN but also any other areas of the indirect and hyperdirect pathways.

      (2) The failed-stop (FS) vs. GO contrast is the only contrast showing substantial differences in those nodes.

      (3) The positive findings of STN (and other subcortical) activation during the SS vs. GO contrast could be due to the usage of inappropriate smoothing kernels.

      (4) The study demonstrates the utility of aggregating publicly available fMRI data from similar cognitive tasks. 

      (5) From the abstract: "The findings challenge previous functional magnetic resonance (fMRI) of the stop-signal task" 

      (6) and further: "suggest the need to ascribe a separate function to these networks." 

      I strongly and emphatically agree with points 1-5. However, I vehemently disagree with point 6, which appears to be the main thrust of the current paper, based on the discussion, abstract, and - not least - the title.

      To me, this paper essentially shows that fMRI is ill-suited to study the subcortex in the specific context of the stop-signal task. That is not just because of the issues of subcortical small-volume SNR (the main topic of this and related works by this outstanding group), but also because of its limited temporal resolution (which is unacknowledged, but especially impactful in the context of the stop-signal task). I'll expand on what I mean in the following.

      First, the authors are underrepresenting the non-fMRI evidence in favor of the involvement of the subthalamic nucleus (STN) and the basal ganglia more generally in stopping actions. 

      - There are many more intracranial local field potential recording studies that show increased STN LFP (or even single-unit) activity in the SS vs. FS and SS vs. GO contrast than listed, which come from at least seven different labs. Here's a (likely non-exhaustive) list of studies that come to mind:

      Ray et al., NeuroImage 2012 <br /> Alegre et al., Experimental Brain Research 2013 <br /> Benis et al., NeuroImage 2014 <br /> Wessel et al., Movement Disorders 2016 <br /> Benis et al., Cortex 2016 <br /> Fischer et al., eLife 2017 <br /> Ghahremani et al., Brain and Language 2018 <br /> Chen et al., Neuron 2020 <br /> Mosher et al., Neuron 2021 <br /> Diesburg et al., eLife 2021 

      - Similarly, there is much more evidence than cited that causally influencing STN via deep-brain stimulation also influences action-stopping. Again, the following list is probably incomplete: 

      Van den Wildenberg et al., JoCN 2006 <br /> Ray et al., Neuropsychologia 2009 <br /> Hershey et al., Brain 2010 <br /> Swann et al., JNeuro 2011 <br /> Mirabella et al., Cerebral Cortex 2012 <br /> Obeso et al., Exp. Brain Res. 2013 <br /> Georgiev et al., Exp Br Res 2016 <br /> Lofredi et al., Brain 2021 <br /> van den Wildenberg et al, Behav Brain Res 2021 <br /> Wessel et al., Current Biology 2022 

      - Moreover, evidence from non-human animals similarly suggests critical STN involvement in action stopping, e.g.: 

      Eagle et al., Cerebral Cortex 2008 <br /> Schmidt et al., Nature Neuroscience 2013 <br /> Fife et al., eLife 2017 <br /> Anderson et al., Brain Res 2020 

      Together, studies like these provide either causal evidence for STN involvement via direct electrical stimulation of the nucleus or provide direct recordings of its local field potential activity during stopping. This is not to mention the extensive evidence for the involvement of the STN - and the indirect and hyperdirect pathways in general - in motor inhibition more broadly, perhaps best illustrated by their damage leading to (hemi)ballism. 

      Hence, I cannot agree with the idea that the current set of findings "suggest the need to ascribe a separate function to these networks", as suggested in the abstract and further explicated in the discussion of the current paper. For this to be the case, we would need to disregard more than a decade's worth of direct recording studies of the STN in favor of a remote measurement of the BOLD response using (provably) sub ideal imaging parameters. There are myriads of explanations of why fMRI may not be able to reveal a potential ground-truth difference in STN activity between the SS and FS/GO conditions, beginning with the simple proposition that it may not afford sufficient SNR, or that perhaps subcortical BOLD is not tightly related to the type of neurophysiological activity that distinguishes these conditions (in the purported case of the stop-signal task, specifically the beta band). But essentially, this paper shows that a specific lens into subcortical activity is likely broken, but then also suggests dismissing existing evidence from superior lenses in favor of the findings from the 'broken' lens. That doesn't make much sense to me.

      Second, there is actually another substantial reason why fMRI may indeed be unsuitable to study STN activity, specifically in the stop-signal paradigm: its limited time resolution. The sequence of subcortical processes on each specific trial type in the stop-signal task is purportedly as follows: at baseline, the basal ganglia exert inhibition on the motor system. During motor initiation, this inhibition is lifted via direct pathway innervation. This is when the three trial types start diverging. When actions then have to be rapidly cancelled (SS and FS), cortical regions signal to STN via the hyperdirect pathway that inhibition has to be rapidly reinstated (see Chen, Starr et al., Neuron 2020 for direct evidence for such a monosynaptic hyperdirect pathway, the speed of which directly predicts SSRT). Hence, inhibition is reinstated (too late in the case of FS trials, but early enough in SS trials, see recordings from the BG in Schmidt, Berke et al., Nature Neuroscience 2013; and Diesburg, Wessel et al., eLife 2021). 

      Hence, according to this prevailing model, all three trial types involve a sequence of STN activation (initial inhibition), STN deactivation (disinhibition during GO), and STN reactivation (reinstantiation of inhibition during the response via the hyperdirect pathway on SS/FS trials, reinstantiation of inhibition via the indirect pathway after the response on GO trials). What distinguishes the trial types during this period is chiefly the relative timing of the inhibitory process (earliest on SS trials, slightly later on FS trials, latest on GO trials). However, these temporal differences play out on a level of hundreds of milliseconds, and in all three cases, processing concludes well under a second overall. To fMRI, given its limited time resolution, these activations are bound to look quite similar. 

      Lastly, further building on this logic, it's not surprising that FS trials yield increased activity compared to SS and GO trials. That's because FS trials are errors, which are known to activate the STN (Cavanagh et al., JoCN 2014; Siegert et al. Cortex 2014) and afford additional inhibition of the motor system after their occurrence (Guan et al., JNeuro 2022). Again, fMRI will likely conflate this activity with the abovementioned sequence, resulting in a summation of activity and the highest level of BOLD for FS trials. 

      In sum, I believe this study has a lot of merit in demonstrating that fMRI is ill-suited to study the subcortex during the SST, but I cannot agree that it warrants any reappreciation of the subcortex's role in stopping, which are not chiefly based on fMRI evidence. 

      We would like to thank reviewer 1 for their insightful and helpful comments. We have responded point-by-point below and will give an overview of how we reframed the paper here.  

      We agree that there is good evidence from other sources for the presence of the canonical stopping network (indirect and hyperdirect) during action cancellation, and that this should be reflected more in the paper. However, we do not believe that a lack of evidence for this network during the SST makes fMRI ill-suited for studying this task, or other tasks that have neural processes occurring in quick succession. What we believe the activation patterns of fMRI reflect during this task, is the large of amount of activation caused by failed stops. That is, that the role of the STN in error processing may be more pronounced that its role in action cancellation. Due to the replicability of fMRI results, especially at higher field strengths, we believe the activation profile of failed stop trials reflects a paramount role for the STN in error processing. Therefore, while we agree we do not provide evidence against the role of the STN in action cancellation, we do provide evidence that our outlook on subcortical activation during different trial types of this task should be revisited. We have reframed the article to reflect this, and discuss points such as fMRI reliability, validity and the complex overlapping of cognitive processes in the SST in the discussion. Please see all changes to the article indicated by red text.

      A few other points: 

      - As I said before, this team's previous work has done a lot to convince me that 3T fMRI is unsuitable to study the STN. As such, it would have been nice to see a combination of the subsamples of the study that DID use imaging protocols and field strengths suitable to actually study this node. This is especially true since the second 3T sample (and arguably, the Isherwood_7T sample) does not afford a lot of trials per subject, to begin with.

      Unfortunately, this study already comprises of the only 7T open access datasets available for the SST. Therefore, unless we combined only the deHollander_7T and Miletic_7T subsamples there is no additional analysis we can do for this right now. While looking at just the sub samples that were 7T and had >300 trials would be interesting, based on the new framing of the paper we do not believe it adds to the study, as the sub samples still lack the temporal resolution seemingly required for looking at the processes in the SST.

      - What was the GLM analysis time-locked to on SS and FS trials? The stop-signal or the GO-signal? 

      SS and FS trials were time-locked to the GO signal as this is standard practice. The main reason for this is that we use contrasts to interpret differences in activation patterns between conditions. By time-locking the FS and SS trials to the stop signal, we are contrasting events at different time points, and therefore different stages of processing, which introduces its own sources of error. We agree with the reviewer, however, that a separate analysis with time-locking on the stop-signal has its own merit, and now include results in the supplementary material where the FS and SS trials are time-locked to the stop signal as well.

      - Why was SSRT calculated using the outdated mean method? 

      We originally calculated SSRT using the mean method as this was how it was reported in the oldest of the aggregated studies. We have now re-calculated the SSRTs using the integration method with go omission replacement and thank the reviewer for pointing this out. Please see response to comment 3.

      - The authors chose 3.1 as a z-score to "ensure conservatism", but since they are essentially trying to prove the null hypothesis that there is no increased STN activity on SS trials, I would suggest erring on the side of a more lenient threshold to avoid type-2 error. 

      We have used minimum FDR-corrected thresholds for each contrast now, instead of using a blanket conservative threshold of 3.1 over all contrasts. The new thresholds for each contrast are shown in text. Please see below (page 12):

      “The thresholds for each contrast are as follows: 3.01 for FS > GO, 2.26 for FS > SS and 3.1 for SS > GO.”

      - The authors state that "The results presented here add to a growing literature exposing inconsistencies in our understanding of the networks underlying successful response inhibition". It would be helpful if the authors cited these studies and what those inconsistencies are. 

      We thank reviewer 1 for their detailed and thorough evaluation of our paper. Overall, we agree that there is substantial direct and indirect evidence for the involvement of the cortico-basal-ganglia pathways in response inhibition. We have taken the vast constructive criticism on board and agree with the reviewer that the paper should be reframed. We would like to thank the reviewer for the thoroughness of their helpful comments aiding the revising of the paper.

      (1) I would suggest reframing the study, abstract, discussion, and title to reflect the fact that the study shows that fMRI is unsuitable to study subcortical activity in the SST, rather than the fact that we need to question the subcortical model of inhibition, given the reasons in my public review.

      We agree with the reviewer that the article should be reframed and not taken as direct evidence against the large sum of literature pointing towards the involvement of the cortico-basal-ganglia pathway in response inhibition. We have significantly rewritten the article in light of this.

      (2) I suggest combining the datasets that provide the best imaging parameters and then analyzing the subcortical ROIs with a more lenient threshold and with regressors time-locked to the stop-signals (if that's not already the case). This would make the claim of a null finding much more impactful. Some sort of power analysis and/or Bayes factor analysis of evidence for the null would also be appreciated. 

      Instead of using a blanket conservative threshold of 3.1, we instead used only FDR-corrected thresholds. The threshold level is therefore different for each contrast and noted in the figures. We have also added supplementary figures including the group-level SPMs and ROI analyses when the FS and SS trials were time-locked to the stop signal instead of the GO signal (Supplementary Figs 4 & 5). But as mentioned above, due to the difference in time points when contrasting, we believe that time-locking to the GO signal for all trial types makes more sense for the main analysis.

      We have now also computed BFs on the first level ROI beta estimates for all contrasts using the BayesFactor package as implemented in R. We add the following section to the methods and updated the results section accordingly (page 8):

      “In addition to the frequentist analysis we also opted to compute Bayes Factors (BFs) for each contrast per ROI per hemisphere. To do this, we extracted the beta weights for each individual trial type from our first level model. We then compared the beta weights from each trial type to one another using the ‘BayesFactor’ package as implement in R (Morey & Rouder, 2015). We compared the full model comprising of trial type, dataset and subject as predictors to the null model comprising of only the dataset and subject as predictor. The datasets and subjects were modeled as random factors. We divided the resultant BFs from the full model by the null model to provide evidence for or against a significant difference in beta weights for each trial type. To interpret the BFs, we used a modified version of Jeffreys’ scale (Jeffreys, 1939; Lee & Wagenmakers, 2014).”

      (3) I suggest calculating SSRT using the integration method with the replacement of Go omissions, as per the most recent recommendation (Verbruggen et al., eLife 2019).

      We agree we should have used a more optimal method for SSRT estimation. We have replaced our original estimations with that of the integration method with go omissions replacement, as suggested and adapted the results in table 3.

      We have also replaced text in the methods sections to reflect this (page 5):

      “For each participant, the SSRT was calculated using the mean method, estimated by subtracting the mean SSD from median go RT (Aron & Poldrack, 2006; Logan & Cowan, 1984).”

      Now reads:

      “For each participant, the SSRT was calculated using the integration method with replacement of go omissions (Verbruggen et al., 2019), estimated by integrating the RT distribution and calculating the point at which the integral equals p(respond|signal). The completion time of the stop process aligns with the nth RT, where n equals the number of RTs in the RT distribution of go trials multiplied by the probability of responding to a signal.”

      Reviewer #2:

      This work aggregates data across 5 openly available stopping studies (3 at 7 tesla and 2 at 3 tesla) to evaluate activity patterns across the common contrasts of Failed Stop (FS) > Go, FS > stop success (SS), and SS > Go. Previous work has implicated a set of regions that tend to be positively active in one or more of these contrasts, including the bilateral inferior frontal gyrus, preSMA, and multiple basal ganglia structures. However, the authors argue that upon closer examination, many previous papers have not found subcortical structures to be more active on SS than FS trials, bringing into question whether they play an essential role in (successful) inhibition. In order to evaluate this with more data and power, the authors aggregate across five datasets and find many areas that are *more* active for FS than SS, specifically bilateral preSMA, caudate, GPE, thalamus, and VTA, and unilateral M1, GPi, putamen, SN, and STN. They argue that this brings into question the role of these areas in inhibition, based upon the assumption that areas involved in inhibition should be more active on successful stop than failed stop trials, not the opposite as they observed. 

      As an empirical result, I believe that the results are robust, but this work does not attempt a new theoretical synthesis of the neuro-cognitive mechanisms of stopping. Specifically, if these many areas are more active on failed stop than successful stop trials, and (at least some of) these areas are situated in pathways that are traditionally assumed to instantiate response inhibition like the hyperdirect pathway, then what function are these areas/pathways involved in? I believe that this work would make a larger impact if the author endeavored to synthesize these results into some kind of theoretical framework for how stopping is instantiated in the brain, even if that framework may be preliminary. 

      I also have one main concern about the analysis. The authors use the mean method for computing SSRT, but this has been shown to be more susceptible to distortion from RT slowing (Verbruggen, Chambers & Logan, 2013 Psych Sci), and goes against the consensus recommendation of using the integration with replacement method (Verbruggen et al., 2019). Therefore, I would strongly recommend replacing all mean SSRT estimates with estimates using the integration with replacement method. 

      I found the paper clearly written and empirically strong. As I mentioned in the public review, I believe that the main shortcoming is the lack of theoretical synthesis. I would encourage the authors to attempt to synthesize these results into some form of theoretical explanation. I would also encourage replacing the mean method with the integration with replacement method for computing SSRT. I also have the following specific comments and suggestions (in the approximate order in which they appear in the manuscript) that I hope can improve the manuscript: 

      We would like to thank reviewer 2 for their insightful and interesting comments. We have adapted our paper to reflect these comments. Please see direct responses to your comments below. We agree with the reviewer that some type of theoretical synthesis would help with the interpretability of the article. We have substantially reworked the discussion and included theoretical considerations behind the newer narrative. Please see all changes to the article indicated by red text.

      (1) The authors say "performance on successful stop trials is quantified by the stop signal reaction time". I don't think this is technically accurate. SSRT is a measure of the average latency of the stop process for all trials, not just for the trials in which subjects successfully stop. 

      Thank you for pointing this technically incorrect statement. We have replaced the above sentence with the following (page 1):

      “Inhibition performance in the SST as a whole is quantified by the stop signal reaction time (SSRT), which estimates the speed of the latent stopping process (Verbruggen et al., 2019).”

      (2) The authors say "few studies have detected differences in the BOLD response between FS and SS trials", but then do not cite any papers that detected differences until several sentences later (de Hollander et al., 2017; Isherwood et al., 2023; Miletic et al., 2020). If these are the only ones, and they only show greater FS than SS, then I think this point could be made more clearly and directly. 

      We have moved the citations to the correct place in the text to be clearer. We have also rephrased this part of the introduction to make the points more direct (page 2).

      “In the subcortex, functional evidence is relatively inconsistent. Some studies have found an increase in BOLD response in the STN in SS > GO contrasts (Aron & Poldrack, 2006; Coxon et al., 2016; Gaillard et al., 2020; Yoon et al., 2019), but others have failed to replicate this (Bloemendaal et al., 2016; Boehler et al., 2010; Chang et al., 2020; B. Xu et al., 2015). Moreover, some studies have actually found higher STN, SN and thalamic activation in failed stop trials, not successful ones (de Hollander et al., 2017; Isherwood et al., 2023; Miletić et al., 2020).

      (3) Unless I overlooked it, I don't believe that the author specified the criterion that any given subject is excluded based upon. Given some studies have significant exclusions (e.g., Poldrack_3T), I think being clear about how many subjects violated each criterion would be useful. 

      This is indeed interesting and important information to include. We have added the number of participants who were excluded for each criterion. Please see added text below (page 4):

      “Based on these criteria, no subjects were excluded from the Aron_3T dataset. 24 subjects were excluded from the Poldrack_3T dataset (3 based on criterion 1, 9 on criterion 2, 11 on criterion 3, and 8 on criterion 4). Three subjects were excluded from the deHollander_7T dataset (2 based on criterion 1 and 1 on criterion 2). Five subjects were excluded from the Isherwood_7T dataset (2 based on criterion 1, 1 on criterion 2, and 2 on criterion 4). Two subjects were excluded from the Miletic_7T dataset (1 based on criterion 2 and 1 on criterion 4). Note that some participants in the Poldrack_3T study failed to meet multiple inclusion criteria.”

      (4) The Method section included very exhaustive descriptions of the neuroimaging processing pipeline, which was appreciated. However, it seems that much of what is presented is not actually used in any of the analyses. For example, it seems that "functional data preprocessing" section may be fMRIPrep boilerplate, which again is fine, but I think it would help to clarify that much of the preprocessing was not used in any part of the analysis pipeline for any results. For example, at first blush, I thought the authors were using global signal regression, but after a more careful examination, I believe that they are only computing global signals but never using them. Similarly with tCompCor seemingly being computed but not used. If possible, I would recommend that the authors share code that instantiates their behavioral and neuroimaging analysis pipeline so that any confusion about what was actually done could be programmatically verified. At a minimum, I would recommend more clearly distinguishing the pipeline steps that actually went into any presented analyses.

      We thank the reviewer for finding this inconsistency. The methods section indeed uses the fMRIprep boilerplate text, which we included so to be as accurate as possible when describing the preprocessing steps taken. While we believe leaving the exact boilerplate text that fMRIprep gives us is the most accurate method to show our preprocessing, we have adapted some of the text to clarify which computations were not used in the subsequent analysis. As a side-note, for future reference, we’d like to add that the fmriprep authors expressly recommend users to report the boilerplate completely and unaltered, and as such, we believe this may become a recurring issue (page 7).

      “While many regressors were computed in the preprocessing of the fMRI data, not all were used in the subsequent analysis. The exact regressors used for the analysis can be found above. For example, tCompCor and global signals were calculated in our generic preprocessing pipeline but not part of the analysis. The code used for preprocessing and analysis can be found in the data and code availability statement.”

      (5) What does it mean for the Poldrack_3T to have N/A for SSD range? Please clarify. 

      Thank you for pointing out this omission. We had not yet found the possible SSD range for this study. We have replaced this value with the correct value (0 – 1000 ms).

      (6) The SSD range of 0-2000ms for deHollander_7T and Miletic_7T seems very high. Was this limit ever reached or even approached? SSD distributions could be a useful addition to the supplement. 

      Thank you for also bringing this mistake to light. We had accidentally placed the max trial duration in these fields instead of the max allowable SSD value. We have replaced the correct value (0 – 900 ms).

      (7) The author says "In addition, median go RTs did not correlate with mean SSRTs within datasets (Aron_3T: r = .411, p = .10, BF = 1.41; Poldrack_3T: r = .011, p = .91, BF = .23; deHollander_7T: r = -.30, p = .09, BF = 1.30; Isherwood_7T: r = .13, p = .65, BF = .57; Miletic_7T: r = .37, p = .19, BF = 1.02), indicating independence between the stop and go processes, an important assumption of the horse-race model (Logan & Cowan, 1984)." However, the independent race model assumes context independence (the finishing time of the go process is not affected by the presence of the stop process) and stochastic independence (the duration of the go and stop processes are independent on a given trial). This analysis does not seem to evaluate either of these forms of independence, as it correlates RT and SSRT across subjects, so it was unclear how this analysis evaluated either of the types of independence that are assumed by the independent race model. Please clarify or remove. 

      Thank you for this comment. We realize that this analysis indeed does not evaluate either context or stochastic independence and therefore we have removed this from the manuscript.

      (8) The RTs in Isherwood_7T are considerably slower than the other studies, even though the go stimulus+response is the same (very simple) stimulus-response mapping from arrows to button presses. Is there any difference in procedure or stimuli that might explain this difference? It is the only study with a visual stop signal, but to my knowledge, there is no work suggesting visual stop signals encourage more proactive slowing. If possible, I think a brief discussion of the unusually slow RTs in Isherwood_7T would be useful. 

      We have included the following text in the manuscript to reflect this observed difference in RT between the Isherwood_7T dataset and the other datasets (page 9).

      “Longer RTs were found in the Isherwood_7T dataset in comparison to the four other datasets. The only difference in procedure in the Isherwood_7T dataset is the use of a visual stop signal as opposed to an auditory stop signal. This RT difference is consistent with previous research, where auditory stop signals and visual go stimuli have been associated with faster RTs compared to unimodal visual presentation (Carrillo-de-la-Peña et al., 2019; Weber et al., 2024). The mean SSRTs and probability of stopping are within normal range, indicating that participants understood the task and responded in the expected manner.”

      (9) When the authors included both 3T and 7T data, I thought they were preparing to evaluate the effect of magnet strength on stop networks, but they didn't do this analysis. Is this because the authors believe there is insufficient power? It seems that this could be an interesting exploratory analysis that could improve the paper.

      We thank the reviewer for this interesting comment. As our dataset sample contains only two 3T and three 7T datasets we indeed believe there is insufficient power to warrant such an analysis. In addition, we wanted the focus of this paper to be how fMRI examines the SST in general, and not differences between acquisition methods. With a greater number of datasets with different imaging parameters (especially TE or resolution) in addition to field strength, we agree such an analysis would be interesting, although beyond the scope of this article.

      (10) The authors evaluate smoothing and it seems that the conclusion that they want to come to is that with a larger smoothing kernel, the results in the stop networks bleed into surrounding areas, producing false positive activity. However, in the absence of a ground truth of the true contributions of these areas, it seems that an alternative interpretation of the results is that the denser maps when using a larger smoothing kernel could be closer to "true" activation, with the maps using a smaller smoothing kernel missing some true activity. It seems worth entertaining these two possible interpretations for the smoothing results unless there is clear reason to conclude that the smoothed results are producing false positive activity. 

      We agree with the view of the reviewer on the interpretation of the smoothing results. We indeed cannot rule this out as a possible interpretation of the results, due to a lack of ground truth. We have added text to the article to reflect this view and discuss the types of errors we can expect for both smaller and larger smoothing kernels (page 15).

      “In the absence of a ground truth, we are not able to fully justify the use of either larger or smaller kernels to analyse such data. On the one hand, aberrantly large smoothing kernels could lead to false positives in activation profiles, due to bleeding of observed activation into surrounding tissues. On the other side, too little smoothing could lead to false negatives, missing some true activity in surrounding regions. While we cannot concretely validate either choice, it should be noted that there is lower spatial uncertainty in the subcortex compared to the cortex, due to the lower anatomical variability. False positives from smoothing spatially unmatched signal, are more likely than false negatives. It may be more prudent for studies to use a range of smoothing kernels, to assess the robustness of their fMRI activation profiles.”

    1. Author response:

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

      General response:

      We thank all the reviewers for their detailed reviews.

      All reviewers made a number of valuable comments, in particular by highlighting several points that would benefit from additional clarifications and discussion. We really appreciate the time and effort that went into the reviews. We have updated the paper to reflect the changes we have made in response to the reviewers' comments (largely by including more discussion regarding the model limitations and the effect of various modeling choices). We have also included several new supplementary figures (S7, S8, S9, S10) that provide further details of the model behavior, and show the effect of changing some of the terms in the cost. Below, we go through the individual comments, and highlight the places in which we have made changes to address the reviewers’ comments.

      Reviewer 1:

      Thank you for your review and pointing out multiple things to be discussed and clarified! Below, we go through the various limitations you pointed out and refer to the places where we have tried to address them.

      (1) It's important to keep in mind that this work involves simplified models of the motor system, and often the terminology for 'motor cortex' and 'models of motor cortex' are used interchangeably, which may mislead some readers. Similarly, the introduction fails in many cases to state what model system is being discussed (e.g. line 14, line 29, line 31), even though these span humans, monkeys, mice, and simulations, which all differ in crucial ways that cannot always be lumped together.

      That is a good point. We have clarified this in the text (Introduction and Discussion), to highlight the fact that our model isn’t necessarily meant to just capture M1. We have also updated the introduction to make it more clear which species the experiments which motivate our investigation were performed in.

      (2) At multiple points in the manuscript thalamic inputs during movement (in mice) is used as a motivation for examining the role of preparation. However, there are other more salient motivations, such as delayed sensory feedback from the limb and vision arriving in the motor cortex, as well as ongoing control signals from other areas such as the premotor cortex.

      Yes – the motivation for thalamic inputs came from the fact that those have specifically been shown to be necessary for accurate movement generation in mice. However, it is true that the inputs in our model are meant to capture any signals external to the dynamical system modeled, and as such are likely to represent a mixture of sensory signals, and feedback from other areas. We have clarified this in the Discussion, and have added this additional motivation in the Introduction.

      (3) Describing the main task in this work as a delayed reaching task is not justified without caveats (by the authors' own admission: line 687), since each network is optimized with a fixed delay period length. Although this is mentioned to the reader, it's not clear enough that the dynamics observed during the delay period will not resemble those in the motor cortex for typical delayed reaching tasks.

      Yes, we completely agree that the terminology might be confusing. While the task we are modeling is a delayed reaching task, it does differ from the usual setting since the network has knowledge of the delay period, and that is indeed a caveat of the model. We have added a brief paragraph just after the description of the optimal control objective to highlight this limitation.

      We have also performed additional simulations using two different variants of a model-predictive control approach that allow us to relax the assumption that the go-cue time is known in advance. We show that these modifications of the optimal controller yield results that remain consistent with our main conclusions, and can in fact in some settings lead to preparatory activity plateaus during the preparation epoch as often found in monkey M1 (e.g in Elsayed et al. 2016). We have modified the Discussion to explain these results and their limitations, which are summarized in a new Supplementary Figure (S9).

      (4) A number of simplifications in the model may have crucial consequences for interpretation.

      a) Even following the toy examples in Figure 4, all the models in Figure 5 are linear, which may limit the generalisability of the findings.

      While we agree that linear models may be too simplistic, much prior analyses of M1 data suggest that it is often good enough to capture key aspects of M1 dynamics; for example, the generative model underlying jPCA is linear, and Sussillo et al. (2015) showed that the internal activity of nonlinear RNN models trained to reproduce EMG data aligned best with M1 activity when heavily regularized; in this regime, the RNN dynamics were close to linear. Nevertheless, this linearity assumption is indeed convenient from a modeling viewpoint: the optimal control problem is more easily solved for linear network dynamics and the optimal trajectories are more consistent across networks. Indeed, we had originally attempted to perform the analyses of Figure 5 in the nonlinear setting, but found that while the results were overall similar to what we report in the linear regime, iLQR was occasionally trapped into local minimal, resulting in more variable results especially for inhibition-stabilized network in the strongly connected end of the spectrum. Finally, Figure 5 is primarily meant to explore to what extent motor preparation can be predicted from basic linear control-theoretic properties of the Jacobian of the dynamics; in this regard, it made sense to work with linear RNNs (for which the Jacobian is constant).

      b) Crucially, there is no delayed sensory feedback in the model from the plant. Although this simplification is in some ways a strength, this decision allows networks to avoid having to deal with delayed feedback, which is a known component of closed-loop motor control and of motor cortex inputs and will have a large impact on the control policy.

      This comment resonates well with Reviewer 3's remark regarding the autonomous nature (or not) of M1 during movement. Rather than thinking of our RNN models as anatomically confined models of M1 alone, we think of them as models of the dynamics which M1 implements possibly as part of a broader network involving “inter-area loops and (at some latency) sensory feedback”, and whose state appears to be near-fully decodable from M1 activity alone. We have added a paragraph of Discussion on this important point.

      (5) A key feature determining the usefulness of preparation is the direction of the readout dimension. However, all readouts had a similar structure (random Gaussian initialization). Therefore, it would be useful to have more discussion regarding how the structure of the output connectivity would affect preparation, since the motor cortex certainly does not follow this output scheme.

      We agree with this limitation of our model — indeed one key message of Figure 4 is that the degree of reliance on preparatory inputs depends strongly on how the dynamics align with the readout. However, this strong dependence is somewhat specific to low-dimensional models; in higher-dimensional models (most of our paper), one expects that any random readout matrix C will pick out activity dimensions in the RNN that are sufficiently aligned with the most controllable directions of the dynamics to encourage preparation.

      We did consider optimizing C away (which required differentiating through the iLQR optimizer, which is possible but very costly), but the question inevitably arises what exactly should C be optimized for, and under what constraints (e.g fixed norm or not). One possibility is to optimize C with respect to the same control objective that the control inputs are optimized for, and constrain its norm (otherwise, inputs to the M1 model, and its internal activity, could become arbitrarily small as C can grow to compensate). We performed this experiment (new Supplementary Figure S7) and obtained a similar preparation index; there was one notable difference, namely that the optimized readout modes led to greater observability compared to a random readout; thus, the same amount of “muscle energy” required for a given movement could now be produced by a smaller initial condition. In turn, this led to smaller control inputs, consistent with a lower control cost overall.

      Whilst we could have systematically optimized C away, we reasoned that (i) it is computationally expensive, and (ii) the way M1 affects downstream effectors is presumably “optimized” for much richer motor tasks than simple 2D reaching, such that optimizing C for a fixed set of simple reaches could lead to misleading conclusions. We therefore decided to stick with random readouts.

      Additional comments :

      (1) The choice of cost function seems very important. Is it? For example, penalising the square of u(t) may produce very different results than penalising the absolute value.

      Yes, the choice of cost function does affect the results, at least qualitatively. The absolute value of the inputs is a challenging cost to use, as iLQR relies on a local quadratic approximation of the cost function. However, we have included additional experiments in which we penalized the squared derivative of the inputs (Supplementary Figure S8; see also our response to Reviewer 3's suggestion on this topic), and we do see differences in the qualitative behavior of the model (though the main takeaway, i.e. the reliance on preparation, continues to hold). This is now referred to and discussed in the Discussion section.

      (2) In future work it would be useful to consider the role of spinal networks, which are known to contribute to preparation in some cases (e.g. Prut and Fetz, 1999).

      (3) The control signal magnitude is penalised, but not the output torque magnitude, which highlights the fact that control in the model is quite different from muscle control, where co-contraction would be a possibility and therefore a penalty of muscle activation would be necessary. Future work should consider the role of these differences in control policy.

      Thank you for pointing us to this reference! Regarding both of these concerns, we agree that the model could be greatly improved and made more realistic in future work (another avenue for this would be to consider a more realistic biophysical model, e.g. using the MotorNet library). We hope that the current Discussion, which highlights the various limitations of our modeling choices, makes it clear that a lot of these choices could easily be modified depending on the specific assumptions/investigation being performed.

      Reviewer 2:

      Thank you for your positive review! We very much agree with the limitations you pointed out, some of which overlapped with the comments of the other reviewers. We have done our best to address them through additional discussion and new supplementary figures. We briefly highlight below where those changes can be found.

      (1) Though the optimal control theory framework is ideal to determine inputs that minimize output error while regularizing the input norm, it however cannot easily account for some other varied types of objectives especially those that may lead to a complex optimization landscape. For instance, the reusability of parts of the circuit, sparse use of additional neurons when learning many movements, and ease of planning (especially under uncertainty about when to start the movement), may be alternative or additional reasons that could help explain the preparatory activity observed in the brain. It is interesting to note that inputs that optimize the objective chosen by the authors arguably lead to a trade-off in terms of other desirable objectives. Specifically, the inputs the authors derive are time-dependent, so a recurrent network would be needed to produce them and it may not be easy to interpolate between them to drive new movement variants. In addition, these inputs depend on the desired time of output and therefore make it difficult to plan, e.g. in circumstances when timing should be decided depending on sensory signals. Finally, these inputs are specific to the full movement chain that will unfold, so they do not permit reuse of the inputs e.g. in movement sequences of different orders.

      Yes, that is a good point! We have incorporated further Discussion related to this point. We have additionally included a new example in which we regularize the temporal complexity of the inputs (see also our response to Reviewer 3's suggestion on this topic), which leads to more slowly varying inputs, and may indeed represent a more realistic constraint and lead to simpler inputs that can more easily be interpolated between. We also agree that uncertainty about the upcoming go cue may play an important role in the strategy adopted by the animals. While we have not performed an extensive investigation of the topic, we have included a Supplementary Figure (S9) in which we used Model Predictive Control to investigate the effect of planning under uncertainty about the go cue arrival time. We hope that this will give the reader a better sense of what sort of model extensions are possible within our framework.

      (2) Relatedly, if the motor circuits were to balance different types of objectives, the activity and inputs occurring before each movement may be broken down into different categories that may each specialize into one objective. For instance, previous work (Kaufman et al. eNeuron 2016, Iganaki et al., Cell 2022, Zimnik and Churchland, Nature Neuroscience 2021) has suggested that inputs occurring before the movement could be broken down into preparatory inputs 'stricto sensu' - relating to the planned characteristics of the movement - and a trigger signal, relating to the transition from planning to execution - irrespective of whether the movement is internally timed or triggered by an external event. The current work does not address which type(s) of early input may be labeled as 'preparatory' or may be thought of as a part of 'planning' computations.

      Yes, our model does indeed treat inputs in a very general way, and does not distinguish between the different types of processes they may be composed of. This is partly because we do not explicitly model where the inputs come from, such that our inputs likely englobe multiple processes. We have added discussion related to this point.

      (3) While the authors rightly point out some similarities between the inputs that they derive and observed preparatory activity in the brain, notably during motor sequences, there are also some differences. For instance, while both the derived inputs and the data show two peaks during sequences, the data reproduced from Zimnik and Churchland show preparatory inputs that have a very asymmetric shape that really plummets before the start of the next movement, whereas the derived inputs have larger amplitude during the movement period - especially for the second movement of the sequence. In addition, the data show trigger-like signals before each of the two reaches. Finally, while the data show a very high correlation between the pattern of preparatory activity of the second reach in the double reach and compound reach conditions, the derived inputs appear to be more different between the two conditions. Note that the data would be consistent with separate planning of the two reaches even in the compound reach condition, as well as the re-use of the preparatory input between the compound and double reach conditions. Therefore, different motor sequence datasets - notably, those that would show even more coarticulation between submovements - may be more promising to find a tight match between the data and the author's inputs. Further analyses in these datasets could help determine whether the coarticulation could be due to simple filtering by the circuits and muscles downstream of M1, planning of movements with adjusted curvature to mitigate the work performed by the muscles while permitting some amount of re-use across different sequences, or - as suggested by the authors - inputs fully tailored to one specific movement sequence that maximize accuracy and minimize the M1 input magnitude.

      Regarding the exact shape of the occupancy plots, it is important to note that some of the more qualitative aspects (e.g the relative height of the two peaks) will change if we change the parameters of the cost function. Right now, we have chosen the parameters to ensure that both reaches would be performed at roughly the same speed (as a way to very loosely constrain the parameters based on the observed behavior). However, small changes to the hyperparameters can lead to changes in the model output (e.g one of the two consecutive reaches being performed using greater acceleration than the other), and since our biophysical model is fairly simple, changes in the behavior are directly reflected in the network activity. Essentially, what this means is that while the double occupancy is a consistent feature of the model, the exact shape of the peaks is more sensitive to hyperparameters, and we do not wish to draw any strong conclusions from them, given the simplicity of the biophysical model. However, we do agree that our model exhibits some differences with the data. As discussed above, we have included additional discussion regarding the potential existence of separate inputs for planning vs triggering the movement in the context of single reaches.

      Overall, we are excited about the suggestions made by the Reviewer here about using our approach to analyze other motor sequence datasets, but we think that in order to do this properly, one would need to adopt a more realistic musculo-skeletal model (such as one provided by MotorNet).

      (4) Though iLQR is a powerful optimization method to find inputs optimizing the author's cost function, it also has some limitations. First, given that it relies on a linearization of the dynamics at each timestep, it has a limited ability to leverage potential advantages of nonlinearities in the dynamics. Second, the iLQR algorithm is not a biologically plausible learning rule and therefore it might be difficult for the brain to learn to produce the inputs that it finds. It remains unclear whether using alternative algorithms with different limitations - for instance, using variants of BPTT to train a separate RNN to produce the inputs in question - could impact some of the results.

      We agree that our choice of iLQR has limitations: while it offers the advantage of convergence guarantees, it does indeed restrict the choice of cost function and dynamics that we can use. We have now included extensive discussion of how the modeling choices affect our results.

      We do not view the lack of biological plausibility of iLQR as an issue, as the results are agnostic to the algorithm used for optimization. However, we agree that any structure imposed on the inputs (e.g by enforcing them to be the output of a self-contained dynamical system) would likely alter the results. A potentially interesting extension of our model would be to do just what the reviewer suggested, and try to learn a network that can generate the optimal inputs. However, this is outside the scope of our investigation, as it would then lead to new questions (e.g what brain region would that other RNN represent?).

      (5) Under the objective considered by the authors, the amount of input occurring before the movement might be impacted by the presence of online sensory signals for closed-loop control. It is therefore an open question whether the objective and network characteristics suggested by the authors could also explain the presence of preparatory activity before e.g. grasping movements that are thought to be more sensory-driven (Meirhaeghe et al., Cell Reports 2023).

      It is true that we aren’t currently modeling sensory signals explicitly. However, some of the optimal inputs we infer may be capturing upstream information which could englobe some sensory information. This is currently unclear, and would likely depend on how exactly the model is specified. We have added new discussion to emphasize that our dynamics should not be understood as just representing M1, but more general circuits whose state can be decoded from M1.

      Reviewer #2 (Recommendations For The Authors):

      Additionally, thank you for pointing out various typos in the manuscript, we have fixed those!

      Reviewer 3:

      Thank you very much for your review, which makes a lot of very insightful points, and raises several interesting questions. In summary, we very much agree with the limitations you pointed out. In particular, the choice of input cost is something we had previously discussed, but we had found it challenging to decide on what a reasonable cost for “complexity” could be. Following your comment, we have however added a first attempt at penalizing “temporal complexity”, which shows promising behavior. We have only included those additional analyses as supplementary figures, and we have included new discussion, which hopefully highlights what we meant by the different model components, and how the model behavior may change as we vary some of our choices. We hope this can be informative for future models that may use a similar approach. Below, we highlight the changes that we have made to address your comments.

      The main limitation of the study is that it focuses exclusively on one specific constraint - magnitude - that could limit motor-cortex inputs. This isn't unreasonable, but other constraints are at least as likely, if less mathematically tractable. The basic results of this study will probably be robust with regard such issues - generally speaking, any constraint on what can be delivered during execution will favor the strategy of preparing - but this robustness cuts both ways. It isn't clear that the constraint used in the present study - minimizing upstream energy costs - is the one that really matters. Upstream areas are likely to be limited in a variety of ways, including the complexity of inputs they can deliver. Indeed, one generally assumes that there are things that motor cortex can do that upstream areas can't do, which is where the real limitations should come from. Yet in the interest of a tractable cost function, the authors have built a system where motor cortex actually doesn't do anything that couldn't be done equally well by its inputs. The system might actually be better off if motor cortex were removed. About the only thing that motor cortex appears to contribute is some amplification, which is 'good' from the standpoint of the cost function (inputs can be smaller) but hardly satisfying from a scientific standpoint.

      The use of a term that punishes the squared magnitude of control signals has a long history, both because it creates mathematical tractability and because it (somewhat) maps onto the idea that one should minimize the energy expended by muscles and the possibility of damaging them with large inputs. One could make a case that those things apply to neural activity as well, and while that isn't unreasonable, it is far from clear whether this is actually true (and if it were, why punish the square if you are concerned about ATP expenditure?). Even if neural activity magnitude an important cost, any costs should pertain not just to inputs but to motor cortex activity itself. I don't think the authors really wish to propose that squared input magnitude is the key thing to be regularized. Instead, this is simply an easily imposed constraint that is tractable and acts as a stand-in for other forms of regularization / other types of constraints. Put differently, if one could write down the 'true' cost function, it might contain a term related to squared magnitude, but other regularizing terms would by very likely to dominate. Using only squared magnitude is a reasonable way to get started, but there are also ways in which it appears to be limiting the results (see below).

      I would suggest that the study explore this topic a bit. Is it possible to use other forms of regularization? One appealing option is to constrain the complexity of inputs; a long-standing idea is that the role of motor cortex is to take relatively simple inputs and convert them to complex time-evolving inputs suitable for driving outputs. I realize that exploring this idea is not necessarily trivial. The right cost-function term is not clear (should it relate to low-dimensionality across conditions, or to smoothness across time?) and even if it were, it might not produce a convex cost function. Yet while exploring this possibility might be difficult, I think it is important for two reasons.

      First, this study is an elegant exploration of how preparation emerges due to constraints on inputs, but at present that exploration focuses exclusively on one constraint. Second, at present there are a variety of aspects of the model responses that appear somewhat unrealistic. I suspect most of these flow from the fact that while the magnitude of inputs is constrained, their complexity is not (they can control every motor cortex neuron at both low and high frequencies). Because inputs are not complexity-constrained, preparatory activity appears overly complex and never 'settles' into the plateaus that one often sees in data. To be fair, even in data these plateaus are often imperfect, but they are still a very noticeable feature in the response of many neurons. Furthermore, the top PCs usually contain a nice plateau. Yet we never get to see this in the present study. In part this is because the authors never simulate the situation of an unpredictable delay (more on this below) but it also seems to be because preparatory inputs are themselves strongly time-varying. More realistic forms of regularization would likely remedy this.

      That is a very good point, and it mirrors several concerns that we had in the past. While we did focus on the input norm for the sake of simplicity, and because it represents a very natural way to regularize our control solutions, we agree that a “complexity cost” may be better suited to models of brain circuits. We have addressed this in a supplementary investigation. We chose to focus on a cost that penalizes the temporal complexity of the inputs, as ||u(t+1) - u(t)||^2. Note that this required augmenting the state of the model, making the computations quite a bit slower; while it is doable if we only penalize the first temporal derivative, it would not scale well to higher orders.

      Interestingly, we did find that the activity in that setting was somewhat more realistic (see new Supplementary Figure S8), with more sustained inputs and plateauing activity. While we have kept the original model for most of the investigations, the somewhat more realistic nature of the results under that setting suggests that further exploration of penalties of that sort could represent a promising avenue to improve the model.

      We also found the idea of a cost that would ensure low-dimensionality of the inputs across conditions very interesting. However, it is challenging to investigate with iLQR as we perform the optimization separately for each condition; nevertheless, it could be investigated using a different optimizer.

      At present, it is also not clear whether preparation always occurs even with no delay. Given only magnitude-based regularization, it wouldn't necessarily have to be. The authors should perform a subspace-based analysis like that in Figure 6, but for different delay durations. I think it is critical to explore whether the model, like monkeys, uses preparation even for zero-delay trials. At present it might or might not. If not, it may be because of the lack of more realistic constraints on inputs. One might then either need to include more realistic constraints to induce zero-delay preparation, or propose that the brain basically never uses a zero delay (it always delays the internal go cue after the preparatory inputs) and that this is a mechanism separate from that being modeled.

      I agree with the authors that the present version of the model, where optimization knows the exact time of movement onset, produces a reasonably realistic timecourse of preparation when compared to data from self-paced movements. At the same time, most readers will want to see that the model can produce realistic looking preparatory activity when presented with an unpredictable delay. I realize this may be an optimization nightmare, but there are probably ways to trick the model into optimizing to move soon, but then forcing it to wait (which is actually what monkeys are probably doing). Doing so would allow the model to produce preparation under the circumstances where most studies have examined it. In some ways this is just window-dressing (showing people something in a format they are used to and can digest) but it is actually more than that, because it would show that the model can produce a reasonable plateau of sustained preparation. At present it isn't clear it can do this, for the reasons noted above. If it can't, regularizing complexity might help (and even if this can't be shown, it could be discussed).

      In summary, I found this to be a very strong study overall, with a conceptually timely message that was well-explained and nicely documented by thorough simulations. I think it is critical to perform the test, noted above, of examining preparatory subspace activity across a range of delay durations (including zero) to see whether preparation endures as it does empirically. I think the issue of a more realistic cost function is also important, both in terms of the conceptual message and in terms of inducing the model to produce more realistic activity. Conceptually it matters because I don't think the central message should be 'preparation reduces upstream ATP usage by allowing motor cortex to be an amplifier'. I think the central message the authors wish to convey is that constraints on inputs make preparation a good strategy. Many of those constraints likely relate to the fact that upstream areas can't do things that motor cortex can do (else you wouldn't need a motor cortex) and it would be good if regularization reflected that assumption. Furthermore, additional forms of regularization would likely improve the realism of model responses, in ways that matter both aesthetically and conceptually. Yet while I think this is an important issue, it is also a deep and tricky one, and I think the authors need considerable leeway in how they address it. Many of the cost-function terms one might want to use may be intractable. The authors may have to do what makes sense given technical limitations. If some things can't be done technically, they may need to be addressed in words or via some other sort of non-optimization-based simulation.

      Specific comments

      As noted above, it would be good to show that preparatory subspace activity occurs similarly across delay durations. It actually might not, at present. For a zero ms delay, the simple magnitude-based regularization may be insufficient to induce preparation. If so, then the authors would either have to argue that a zero delay is actually never used internally (which is a reasonable argument) or show that other forms of regularization can induce zero-delay preparation.

      Yes, that is a very interesting analysis to perform, which we had not considered before! When investigating this, we found that the zero-delay strategy does not rely on preparation in the same way as is seen in the monkeys. This seems to be a reflection of the fact that our “Go cue” corresponds to an “internal” go cue which would likely come after the true, “external go cue” – such that we would indeed never actually be in the zero delay setting. This is not something we had addressed (or really considered) before, although we had tried to ensure we referred to “delta prep” as the duration of the preparatory period but not necessarily the delay period. We have now included more discussion on this topic, as well as a new Supplementary Figure S10.

      I agree with the authors that prior modeling work was limited by assuming the inputs to M1, which meant that prior work couldn't address the deep issue (tackled here) of why there should be any preparatory inputs at all. At the same time, the ability to hand-select inputs did provide some advantages. A strong assumption of prior work is that the inputs are 'simple', such that motor cortex must perform meaningful computations to convert them to outputs. This matters because if inputs can be anything, then they can just be the final outputs themselves, and motor cortex would have no job to do. Thus, prior work tried to assume the simplest inputs possible to motor cortex that could still explain the data. Most likely this went too far in the 'simple' direction, yet aspects of the simplicity were important for endowing responses with realistic properties. One such property is a large condition-invariant response just before movement onset. This is a very robust aspect of the data, and is explained by the assumption of a simple trigger signal that conveys information about when to move but is otherwise invariant to condition. Note that this is an implicit form of regularization, and one very different from that used in the present study: the input is allowed to be large, but constrained to be simple. Preparatory inputs are similarly constrained to be simple in the sense that they carry only information about which condition should be executed, but otherwise have little temporal structure. Arguably this produces slightly too simple preparatory-period responses, but the present study appears to go too far in the opposite direction. I would suggest that the authors do what they can to address these issue via simulations and/or discussion. I think it is fine if the conclusion is that there exist many constraints that tend to favor preparation, and that regularizing magnitude is just one easy way of demonstrating that. Ideally, other constraints would be explored. But even if they can't be, there should be some discussion of what is missing - preparatory plateaus, a realistic condition-invariant signal tied to movement onset - under the present modeling assumptions.

      As described above, we have now included two additional figures. In the first one (S8, already discussed above), we used a temporal smoothness prior, and we indeed get slightly more realistic activity plateaus. In a second supplementary figure (S9), we have also considered using model predictive control (MPC) to optimize the inputs under an uncertain go cue arrival time. There, we found that removing the assumption that the delay period is known came with new challenges: in particular, it requires the specification of a “mental model” of when the Go cue will arrive. While it is reasonable to expect that monkeys will have a prior over the go time arrival cue that will be shaped by the design of the experiment, some assumptions must be made about the utility functions that should be used to weigh this prior. For instance, if we imagine that monkeys carry a model of the possible arrival time of the go cue that is updated online, they could nonetheless act differently based on this information, for instance by either preparing so as to be ready for the earliest go cue possible or alternatively to be ready for the average go cue. This will likely depend on the exact task design and reward/penalty structure. Here, we added simulations with those two cases (making simplifying assumptions to make the problem tractable/solvable using model predictive control), and found that the “earliest preparation” strategy gives rise to more realistic plateauing activity, while the model where planning is done for the “most likely go time” does not. We suspect that more realistic activity patterns could be obtained by e.g combining this framework with the temporal smoothness cost. However, the main point we wished to make with this new supplementary figure is that it is possible to model the task in a slightly more realistic way (although here it comes at the cost of additional model assumptions). We have now added more discussion related to those points. Note that we have kept our analyses on these new models to a minimum, as the main takeaway we wish to convey from them is that most components of the model could be modified/made more realistic. This would impact the qualitative behavior of the system and match to data but – in the examples we have so far considered – does not appear to modify the general strategy of networks relying on preparation.

      On line 161, and in a few other places, the authors cite prior work as arguing for "autonomous internal dynamics in M1". I think it is worth being careful here because most of that work specifically stated that the dynamics are likely not internal to M1, and presumably involve inter-area loops and (at some latency) sensory feedback. The real claim of such work is that one can observe most of the key state variables in M1, such that there are periods of time where the dynamics are reasonably approximated as autonomous from a mathematical standpoint. This means that you can estimate the state from M1, and then there is some function that predicts the future state. This formal definition of autonomous shouldn't be conflated with an anatomical definition.

      Yes, that is a good point, thank you for making it so clearly! Indeed, as previous work, we do not think of our “M1 dynamics” as being internal to M1, but they may instead include sensory feedback / inter-area loops, which we summarize into the connectivity, that we chose to have dynamics that qualitatively resemble data. We have now incorporated more discussion regarding what exactly the dynamics in our model represent.

    1. Author response:

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

      eLife assessment 

      Dasgupta and colleagues make a valuable contribution to the understanding how the guidance factor Sema7a promotes connections between mechanosensory hair cells and afferent neurons of the zebrafish lateral line system. The authors provide solid evidence that loss of Sema7a function results in fewer contacts between hair cells and afferents through comprehensive quantitative analysis. Additional work is needed to distinguish the effects of different isoforms of Sema7a to determine whether there are specific roles of secreted and membrane bound forms. 

      Public Reviews:

      Reviewer #1 (Public Review):

      Dasguta et al. have dissected the role of Sema7a in fine tuning of a sensory microcircuit in the posterior lateral line organ of zebrafish. They attempt to also outline the different roles of a secreted verses membrane-bound form of Sema7a in this process. Using genetic perturbations and axonal network analysis, the authors show that loss of both Sema7a isoforms causes abnormal axon terminal structure with more bare terminals and fewer loops in contact with presynaptic sensory hair cells. Further, they show that loss of Sema7a causes decreased number and size of both the pre- and post-synapse. Finally, they show that overexpression of the secreted form of Sema7a specifically can elicit axon terminal outgrowth to an ectopic Sema7a expressing cell. Together, the analysis of Sema7a loss of function and overexpression on axon arbor structure is fairly thorough and revealed a novel role for Sema7a in axon terminal structure. However, the connection between different isoforms of Sema7a and the axon arborization needs to be substantiated. Furthermore, the effect of loss of Sema7a on the presynaptic cell is not ruled out as a contributing factor to the synaptic and axon structure phenotypes. These issues weaken the claims made by the authors including the statement that they have identified dual roles for the GPI-anchored verses secreted forms of Sema7a on synapse formation and as a chemoattractant for axon arborization respectively. 

      Reviewer #2 (Public Review):

      In this work, Dasgupta et al. investigates the role of Sema7a in the formation of peripheral sensory circuit in the lateral line system of zebrafish. They show that Sema7a protein is present during neuromast maturation and localized, in part, to the base of hair cells (HCs). This would be consistent with pre-synaptic Sema7a mediating formation and/or stabilization of the synapse. They use sema7a loss-of-function strain to show that lateral line sensory terminals display abnormal arborization. They provide highly quantitative analysis of the lateral line terminal arborization to show that a number of specific topological parameters are affected in mutants. Next, they ectopically express a secreted form of Sema7a to show that lateral line terminals can be ectopically attracted to the source. Finally, they also demonstrate that the synaptic assembly is impaired in the sema7a mutant. Overall, the data are of high quality and properly controlled. The availability of Sema7a antibody is a big plus, as it allows to address the endogenous protein localization as well to show the signal absence in the sema7a mutant. The quantification of the arbor topology should be useful to people in the field who are looking at the lateral line as well as other axonal terminals. I think some results are overinterpreted though. The authors state: "Our findings demonstrate that Sema7A functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development." However, they have not actually demonstrated which isoform functions in HCs (also see comments below). In addition, they have to be careful in interpreting their topology analysis, as they cannot separate individual axons. Thus, such analysis can generate artifacts. They can perform additional experiments to address these issues or adjust their interpretations. 

      Reviewer #3 (Public Review):

      The data reported here demonstrate that Sema7a defines the local behavior of growing axons in the developing zebrafish lateral line. The analysis is sophisticated and convincingly demonstrates effects on axon growth and synapse architecture. Collectively, the findings point to the idea that the diffusible form of sema7a may influence how axons grow within the neuromast and that the GPI-linked form of sema7a may subsequently impact how synapses form, though additional work is needed to strongly link each form to its' proposed effect on circuit assembly. 

      The revised manuscript is significantly improved. The authors comprehensively and appropriately addressed most of the reviewers' concerns. In particular, they added evidence that hair cells express both Sema7A isoforms, showed that membrane bound Sema7A does not have long range effects on guidance, demonstrated how axons behave close to ectopic Sema7A, and analyzed other features of the hair cells that revealed no strong phenotypes. The authors also softened the language in many, but not all places. Overall, I am satisfied with the study as a whole. 

      Reviewer #4 (Public Review):

      This study provides direct evidence showing that Sema7a plays a role in the axon growth during the formation of peripheral sensory circuits in the lateral-line system of zebrafish. This is a valuable finding because the molecules for axon growth in hair-cell sensory systems are not well understood. The majority of the experimental evidence is convincing, and the analysis is rigorous. The evidence supporting Sema7a's juxtracrine vs. secreted role and involvement in synapse formation in hair cells is less conclusive. The study will be of interest to cell, molecular and developmental biologists, and sensory neuroscientists. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      In their revised manuscript, Dasgupta et al. have provided further experiments to address the role of Sema7a (sec and GPI-anchored) in regulating axon guidance in the lateral line system. Specifically, the inclusion of the heat shock controls and FM labeling to show hair cell mechanotransduction were crucial to interpretation of the results. However, there are still concerns about the specificity of the results. My primary concern is if the change in axon patterning is specifically due to loss of Sema7a in the mutant hair cells. These animals are morphologically very abnormal and, in the rebuttal, the authors state that hair cell number is reduced. This is not quantified in the manuscript and should be included. 

      Thank you for this suggestion. We have included the data in the manuscript in lines 137-139, in Figure 2—figure supplement 1B, and in the source data for Figure 2 and Figure 2-figure supplements.

      If there is not a function for Sema7a in hair cells themselves, why is the number reduced? 

      The sema7a-/- homozygous mutants are not viable and they die by 6 dpf. The loss of Sema7A protein produce other developmental defects including brain edema and a curved body axis. We believe a slight but not significant decrease in hair cell number may arise from a minute developmental delay in the morphogenesis of the neuromast. We have accordingly quantified our data at three distinct developmental stages-at 2 dpf, 3 dpf, and 4 dpf-and have incorporated them in the revised manuscript.

      Additionally, FM data should be quantified and presented in animals without a transgene in the same excitation/emission spectra for clearer interpretation of the staining.

      We have quantified the intensities of labeling with FM 4-64 styryl dye from the control and the sema7a-/- mutant larvae and incorporated the data in lines 139-146, in Figure 2—figure supplement 1D, and in source data for Figure 2 and Figure 2-figure supplements. We Kept the transgenes to concurrently show the arborization phenotype, hair cell morphology, and the FM 4-64 incorporation between the genotypes. 

      Rescue analysis using the myo6d promotor would allow the authors to ensure that the axon deficits can be rescued by putting Sema7a back into the sensory hair cells. Transient transgenesis could be useful for this approach and would not require the creation of a stable line. This could be done with both forms of Sema7a allowing the true assessment of whether or not the secreted and GPI-anchored form have disparate functions as claimed in lines 418424. 

      Although we recognize the importance of the rescue of the sema7a-/- mutant phenotype with the sema7asec and the sema7aGPI transcripts, it is not possible for us to perform that experiment at the moment, for the first author will leave the lab next week.  However, he plans to continue work on this project as an independent investigator to dissect the individual roles of the transcript variants in specifying the pattern of sensory arborization, a project that includes generation of transcript-specific knockout animals and rescue experiments with stable transgenic fish lines. 

      Other concerns:

      (1) The timeline of the heat shock experiment is confusing to me and, therefore, it makes me question the specificity of those results. Based on the speed of axon outgrowth and the time necessary for transcription and translation after heat shock induction of the transgene, it is unclear to me how the axon growth defects could occur in the timeline provided. Imaging two hours after the start of the heat shock is very rapid and speaks to either an indirect effect of the transgenesis on the axon growth or a leaky promotor/induction paradigm. It is possible I am just misunderstanding the set up but, from what I could gather, the imaging is being done 2 hrs after the start of the heat shock. This should be clarified. 

      The axons of the zebrafish posterior lateral line migrate relatively fast. The pioneering axons migrate at around 120 μm/hour (Sato et. al., 2010) and the follower axons migrate at almost 30-80 μm/hour (Sato et. al., 2010). The heat-shock promoter that we have utilized, hsp70l, is highly effective in inducing gene expression and subsequent protein formation within 30 to 60 mins. We believe an hour of heat shock and an hour of incubation post heat shock is sufficient to induce directed axon migration to a distance that spans from 27 μm to 140 μm. 

      We strongly believe that the directed arborization of the sensory axons towards the Sema7Asec source is not due to an indirect effect of transgenesis or leaky promoter induction, as in all 18 of the injected but not heat-shocked control larvae we did not observe ectopic Sema7Asec expression, and no aberrant projection was formed from the sensory arbor network. We highlight this observation in lines 297-299 and in Figure 4E.

      Sato et. al., 2010: Single-cell analysis of somatotopic map formation in the zebrafish lateral line system. Developmental Dynamics 239:2058–2065, 2010.

      Similarly, it would help to clarify if t(0) in the figure is the onset of the heat shock or onset of imaging two hours after the heat shock is started. 

      The t=0 hour in the Figure 4I denotes the onset of imaging two hours after the heat shock began. We have clarified this in the manuscript in lines 1155-1156.

      (2) In the rebuttal, the line numbers cited do not match up with the appropriate text, I believe.

      We have corrected this and updated the manuscript.

      (3) Some of the supplemental figures are not mentioned in the text, or I could not find them. For example: Figure 1 supplement 2J. 

      Thank you for pointing this. We have corrected the manuscript, and the new information is added in line 114.  

      (4) Table 1 statistics: were these adjusted for multiple comparisons using a bonferroni correction or something similar? This is necessary for statistical significance to be meaningful. 

      We did not adjust the p-values for multiple comparisons because the values correspond to only three or four statistical tests per experiment, strongly indicating the unlikelihood of erroneous significance due solely to multiple tests.

      (5) Figure 1I and 1-S3 - The legend states a positive correlation between axonal signal and sema7A signal. Correlations are 0.5, 0.6, and 0.4 (2,3, 4dpf). This is not a convincing positive correlation. At best this is no to a very weak positive correlation. 

      In lines 122-126 we mention that the basal association of the sensory arbors shows a positive correlation with Sema7A accumulation. We never emphasize on the strength of the correlation. However, a consistent positive correlation at three different developmental stages suggests that progressive Sema7A accumulation at the base of the hair cells may guide the sensory arbors to increasingly associate themselves with the hair cells.    

      Reviewer #2 (Recommendations For The Authors):

      I am a bit disappointed that the authors elected not to experimentally address the issue raised by all reviewers: whether the secreted or membrane bound isoform is active in hair cells. They rather decided to change their interpretation in the text. It is fine, given the eLife review structure. However, that would make the manuscript much stronger. Other issues were adequately addressed through textual changes as well. 

      Although we recognize the importance of the rescue of the sema7a-/- mutant phenotype with the sema7asec and the sema7aGPI transcripts, it is not possible for us to perform that experiment at the moment, for the first author will leave the lab next week.  However, he plans to continue work on this project as an independent investigator to dissect the individual roles of the transcript variants in specifying the pattern of sensory arborization, a project that includes generation of transcript-specific knockout animals and rescue experiments with stable transgenic fish lines. 

      Reviewer #3 (Recommendations For The Authors):

      Overall, I am satisfied with the study as a whole and just have a few minor comments that remain to be addressed. 

      (1) Although the authors say that they added appropriate no plasmid/heatshock-only and plasmid-only/no heatshock controls, these results need to be presented more clearly, as they are separated in the paper and only one was quantified (i.e. 100% of embryos showed no defect). Please just make it clear that no defects were observed in either control for either experiment (both secreted and membrane bound ectopic expression). 

      We have clearly stated this information in lines 297-299 and 343-345.

      (2) Please add a compass to Fig. 1A to indicate the orientation of the neuromast. It would also be helpful to add labels for developmental ages to all of the figures, rather than making the reader look it up in the legend. 

      We have updated the Figure 1A and the corresponding figure legend in lines 882883 . We have denoted the larval age in the figure legends to keep the individual images uncluttered.  

      (3) For the RT-PCR experiments in Figure 1, no negative control was included to show that supporting cell or neuronal genes are not detected in the purified hair cells and v.v. that neither isoform is detected in supporting cells or neurons. I ask only because there is a lot of immune-signal outside of the hair cells and I am curious whether that is secreted or might come from other cell types. For neurons and supporting cells, simply demonstrating absence of Sema7a overall would suffice. 

      We have utilized the transgenic line Tg(myo6b:actb1-EGFP) that expresses the fluorophore GFP specifically in the hair cells of the neuromast. Unfortunately, we do not possess a transgenic line that reliably and specifically labels the support cells in the neuromast. Hence, in our sorting experiment the GFP-negative cells that are collected from the trunk segments of the larvae contain all the non-hair cells including epidermal cells, neuronal cells, and immune cells etc. Such a mixture of varied cellular identity may not serve as a reliable negative control. 

      In Figure 7, we have plotted the normalized expression values of the sema7a gene in the neuromast. The plot clearly depicts that the source of Sema7A is the young and the mature hair cells, not the support cells. We further confirm this observation by

      immunostaining where the Sema7A signal is highly restricted to the hair cells and not in any other cell in the neuromast (Figure 1E). Immunostaining further demonstrates that the lateral line sensory arbors also do not produce the Sema7A protein (Figure 1H; Video 1).

      We agree with the reviewer that there are diverse immune cells, including macrophages in and around the neuromast. These macrophages are dynamic and possess highly ramified structure (Denans et. al., 2022). In all our Sema7A immunostainings, we never observed structures that resemble macrophages. Albeit we cannot confirm that Sema7A is not expressed in a distant immune cell, but we highly doubt that signal coming from immune cells is impacting hair cell innervation by the sensory arbors during homeostatic development.

      Denans et. al., 2022: Nature Communications volume 13, Article number: 5356 (2022).

      (4) In Figure 1, Supplement 4, I do not see the immunogen labeled in blue. 

      We have corrected the figure legend. The immunogenic region of the Sema7A protein is now clearly denoted in the figure legend of Figure 1—figure supplement 4.

      (5) In Figure 2, please add a control image as requested, as that enables direct comparison. There is ample room in the figure. 

      We have updated the Figure 2 and made the suggested change.

      (6) In Figure 2, Supplement 1, the FM4-64 data are not presented in a quantified fashion. Please report at least how many embryos showed reliable uptake and preferably how many hair cells per embryo showed reliable uptake. 

      We have quantified the FM 4-64 intensities in control and sema7a-/- mutant larvae. The new data is added to the manuscript in lines 142-146, 577-579 , and in Figure 2—figure supplement 1D.

      (7) In Figure 3, there seems to be a typo in the figure legend: "mutants in the same larvae" does not make sense to me. 

      We have corrected the error. The modified statement is represented in lines 10671068.

      (8) The text should refer more explicitly to the statistical tests reported in Table 1, i.e. as the results are presented. 

      In lines 1105 and 1109, we clearly state the statistical tests that were performed.

      (9) In Figure 6, Supplement 1, please show the raw data points not just the bar graphs

      We have updated the Figure 6—figure supplement 1.

      (10) Minor point: the authors state that they addressed the distance over which secreted Sema7A may act, but this was not evident to me in the text. Please make this finding clearer.

      We have clarified this information in lines 310-311.

      (11) Finally, the discussion contains a statement that is not supported by the data: "We have discovered dual modes of Sema7A function in vivo." They have discovered evidence that there are two isoforms, that loss of both disrupts connectivity, and that overexpression of only the secreted form can elicit growth from a distance. However, there is no direct evidence that the membrane-bound form is responsible for local effects. It is formally possible still that the phenotypes are a result of dual roles for the secreted form. It is clear that another manuscript is forthcoming that will expand on the role of the transmembrane form, but for this manuscript, the authors should make firm conclusions only about the data presented herein.

      Thank you for this suggestion. We have modified the manuscript in lines 425-434.

      Reviewer #4 (Recommendations For The Authors):

      The authors have made significant changes to the manuscript based on the comments of the reviewers. It is now suitable for publication.

    1. Author response:

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

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I have no more experiment to ask but the following errors should be corrected prior.

      (1) L. 183-198: Figure 3 panels were erroneously referred in several places.

      This has been corrected.

      (2) L.182-183: description of active/total cell numbers in main text does not match numbers in Figure 3B

      This has been corrected.

      (3) L.185-187: Figure 3C indicates significant changes of rheobase only between DMI+6OHDA versus 6-OHDA group. Statistical comparison between sham and DMI+6-OHDA was not provided, which may change the interpretation of the data in Figure 3B, C: "...these findings suggest that the 6-OHDA induced lesion of midbrain dopaminergic neurons evoked the increased firing of DRN5-HT neurons" (L.185-187).

      We thank the reviewer for highlighting this point. Indeed, a Kruskal-Wallis test comparing all three groups revealed a significantly lower rheobase in DMI + 6-OHDA mice compared to Sham while the 6-OHDA injected group was not affected. Therefore, the increased firing of DRN5-HT neurons recorded in 6-OHDA injected mice pretreated with DMI also critically involves the noradrenergic system. This is now included in the revised results section of the manuscript (lines 190-197).

      (4) L. 188: The description of "While the excitability of DRN5-HT neurons was not affected in 6-OHDA mice..." does not match the clearly increased cellular excitability shown in Figure 3G-I.

      This has been corrected and we are now referring more specifically to the rheobase, which is not affected in 6-OHDA mice.

      (5) Mann-Whitney tests were inappropriately used for statistics in Figures 3-6: Multiple comparisons (>=3 groups) should be performed one-way ANOVA or the Kruskal-Wallis test for nonparametric data.

      We thank the reviewer for the comment. We now applied the one-way ANOVA/KruskalWallis tests and the text has been modified accordingly.

      (6) It seems that the data points in some panels of Figure 4C represented a cell, but others were averaged within a mouse (Figure 4D). This needs to be clarified or corrected.

      None of the data in Figure 4 was averaged within a mouse. In the the type of chosen graph (aligned dot plot) the equal data are overlapped.

      Reviewer #2 (Recommendations For The Authors):

      The authors' revised manuscript has addressed most of my concerns. However, I'm not convinced by the authors' claim regarding Figure 5B. It would be great if the authors at least discuss in their manuscript why the DMI pretreatment group alone, not the 6OHDA group, significantly lowers the firing rate of DRN (DA) and increases the Erest of DRN (DA), compared to the sham-lesion group. These statistically significant data are not explained at all in the revised manuscript (This effect can be explained by the neuroprotection of NA-neurons from 6-OHDA toxicity?).

      We thank the reviewer for this comment. Since using a one-way ANOVA or a KruskalWallis test for comparing the three groups (as suggested by reviewer 1), the changes previously shown in Figure 5B are not significant.

    1. Author response:

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

      eLife assessment

      This manuscript represents a cleanly designed experiment for assessing biological motion processing in children (mean age = 9) with and without ADHD. The group differences concerning accuracy in global and local motion processing abilities are solid, but the analyses suggesting dissociable relationships between global and local processing and social skills, age, and IQ are inconclusive. The results are useful in terms of understanding ADHD and the ontogenesis of different components of the processing of biological motion.

      We thank the editors and reviewers for their valuable feedback and constructive comments. We have carefully considered each point raised by the reviewers and made the necessary revisions to the manuscript. Regarding the relationships between global and local BM processing, the accumulated evidence from previous studies has converged on the dissociation of the two BM components, e.g., while global BM processing is susceptible to learning and practice, local BM processing does not show a learning trend (Chang and Troje, 2009; Grossman et al., 2004), and the brain activations in response to local and global BM cues are different (Chang et al., 2018; Duarte et al., 2022). Nevertheless, we concurred with reviewers that the evidence for such dissociation from the current study by itself is not strong enough. Therefore, we have toned down on this point and no longer claimed the dissociation (including the title). Based on the current results, we focused our discussion on the different aspects of BM processing in children with and without ADHD.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The paper presents a nice study investigating the impairments of biological motion perception in individuals with ADHD in comparison with neurotypical controls. Motivated by the idea that there is a relationship between biological motion perception and social capabilities, the authors investigated the impairments of local and global (holistic) biological motion perception, the diagnosis status, and several additional behavioral variables that are affected in ADHS (IQ, social responsiveness, and attention / impulsivity). As well local as global biological motion perception is impaired in ADHD individuals. In addition, the study demonstrates a significant correlation between local biological motion perception skills and the social responsiveness score in the ADHD group, but not in controls. A path analysis in the ADHD group suggests that general performance in biological motion perception is influenced mainly by global biological motion perception performance and attentional and perceptual reasoning skills.

      Strengths:

      It is true that there exists not much work on biological motion perception and ADHD. Therefore, the presented study contributes an interesting new result to the biological motion literature, and adds potentially also new behavioral markers for this clinical group. The design of the study is straightforward and technically sound, and the drawn conclusions are supported by the presented results.

      Thanks for this positive assessment of our work.

      Weaknesses:

      Some of the claims about the relationship between genetic factors and ADHD and the components of biological motion processing have to remain speculative at this point because genetic influences were not explicitly tested in this paper. Specifically, the hypothesis that the perception of human social interaction is critically based on a local mechanism for the detection of asymmetry in foot trajectories of walkers (this is what 'BL-local' really measures), or on the detection of live agents in cluttered scenes seems not very plausible.

      Thanks for these comments. We agree that the relationship between genetic factors and BM perception remains to be further examined, as we did not test the genetic influences in this study. We have deleted relavant discussion about genetics. Based on our results, we discuss the possible mechanisms behind the relationship between local BM processing and social interaction in the revised manuscript as follows:

      “As mentioned above, we found a significant negative correlation between the SRS total score and the accuracy of local BM processing, specifically in the ADHD group. This could be due to decreased visual input related to atypical local BM processing, which further impairs global BM processing. According to the two-process theory of biological motion processing61, local BM cues guide visual attention towards BM stimuli55,62. Consequently, the visual input of BM stimuli increases, facilitating the development of the ability to process global BM cues through learning21,63. The latter is a prerequisite for attributing intentions to others and facilitating social interactions with other individuals20,64,65. Thus, atypical local BM processing may contribute to impaired social interaction through altered visual inputs. Further empirical studies are required to confirm these hypotheses.” (lines 417 - 428)

      Based on my last comments, now the discussion has been changed in a way that tries to justify the speculative claims by citing a lot of other speculative papers, which does not really address the problem. For example, the fact that chicks walk towards biological motion stimuli is interesting. To derive that this verifies a fundamental mechanism in human biological motion processing is extremely questionable, given that birds do not even have a cortex. Taking the argumentation of the authors serious, one would have to assume that the 'Local BM' mechanism is probably located in the mesencephalon in humans, and then would have to interact in some way with social perception differences of ADHD children. To me all this seems to make very strong (over-)claims. I suggest providing a much more modest interpretation of the interesting experimental result, based on what has been really experimentally shown by the authors and closely related other data, rather than providing lots of far-reaching speculations.

      In the same direction, in my view, go claims like 'local BM is an intrinsic trait' (L. 448) , which is not only imprecise (maybe better 'mechanisms of processing of local BM cues') but also rather questionable. Likely, this' local processing of BM' is a lower level mechanisms, located probably in early and mid-levels of the visual cortex, with a possible influence of lower structures. It seems not really plausible that this is related to a classical trait variables in the sense of psychology, like personality, as seems to be suggested here. Also here I suggest a much more moderate and less speculative interpretation of the results.

      We thank the reviewer for pointing out these issues. According to these comments, we have carefully revised the discussion to avoid strong (over-) claims. We have deleted the example of chicks, but substituted with more empirical studies to explain our results. We agree that the Local BM mechanism is probably located in subcortical regions in humans, which were reported by some MRI studies (Chang et al., 2018; Hirai and Senju, 2020; Loula et al., 2005). We have added some evidence that atypical local BM processing may decrease visual inputs related to social information as follows:

      “According to the two-process theory of biological motion processing61, local BM cues guide visual attention towards BM stimuli55,62. Consequently, the visual input of BM stimuli increases, facilitating the development of the ability to process global BM cues through learning21,63. The latter is a prerequisite for attributing intentions to others and facilitating social interactions with other individuals20,64,65. Thus, atypical local BM processing may contribute to impaired social interaction through altered visual inputs.” (lines 421 - 427)

      We have also deleted the clarims of 'local BM is an intrinsic trait' (originally L. 448) and related discussion as it was not conclusive based on the current study.

      Reviewer #2 (Public Review):

      Summary:

      Tian et al. aimed to assess differences in biological motion (BM) perception between children with and without ADHD, as well as relationships to indices of social functioning and possible predictors of BM perception (including demographics, reasoning ability and inattention). In their study, children with ADHD showed poorer performance relative to typically developing children in three tasks measuring local, global, and general BM perception. The authors further observed that across the whole sample, performance in all three BM tasks was negatively correlated with scores on the social responsiveness scale (SRS), whereas within groups a significant relationship to SRS scores was only observed in the ADHD group and for the local BM task. Local and global BM perception showed a dissociation in that global BM processing was predicted by age, while local BM perception was not. Finally, general (local & global combined) BM processing was predicted by age and global BM processing, while reasoning ability mediated the effect of inattention on BM processing.

      Strengths:

      Overall, the manuscript is presented in a clear fashion and methods and materials are presented with sufficient detail so the study could be reproduced by independent researchers. The study uses an innovative, albeit not novel, paradigm to investigate two independent processes underlying BM perception. The results are novel and have the potential to have wide-reaching impact on multiple fields.

      We appreciate the reviewer’s positive feedback very much.

      Weaknesses:

      The manuscript has greatly improved in clarity and methodological considerations in response to the review. There are only a few minor points which deserve the authors' attention:

      When outlining the moviation for the current study, results from studies in ADHD and ASD are used too interchangeably. The authors use a lack of evidence for contributing (psychological/developmental) factors on BM processing in ASD to motivate the present study and refer to evidence for differences between typical and non-typical BM processing using studies in both ASD and ADHD. While there are certainly overlapping features between the two conditions/neurotypes, they are not to be considered identical and may have distinct etiologies, therefore the distinction between the two should be made clearer.

      We thank the reviewer for pointing out this issue. We have removed some unnecessary citations about ASD and referred to studies about social cognition in ADHD to elaborate the motivation of this study:

      “Further exploration of a diverse range of social cognitions (e.g., biological motion perception) can provide a fresh perspective on the impaired social function observed in ADHD. Moreover, recent studies have indicated that the social cognition in ADHD may vary depending on different factors at the cognitive, pathological, or developmental levels, such as general cognitive impairment5, symptoms severity8, or age5. Nevertheless, understanding how these factors relate to social cognitive dysfunction of in ADHD is still in its infancy. Bridging this gap is crucial as it can help depict the developmental trajectory of social cognition and identify effective interventions for impaired social interaction in individuals with ADHD.” (lines 53 - 62)

      In the first/main analysis, is unclear to me why in the revised manuscript the authors changed the statistical method from ANOVA/ANCOVA to independent samples t-tests (unless the latter were only used for post-hoc comparisons, then this needs to be stated). Furthermore, although p-values look robust, for this analysis too it should be indicated whether and how multiple comparison problems were accounted for.

      Thanks for the reviewer’s comments. According to the suggestions from reviewer #3, it may be inapposite to regard gender as a covariate in ANOVA, which may violate the assumptions of ANCOVA. To ensure that gender does not influence the results, firstly, we separated boys and girls on the plots with different coloured individual data points, and there are no signs of a gender effect in their TD group. Secondly, we use t-tests to examine the difference between TD and ADHD groups. Finally, we conducted a subsampling analysis with balanced data, and the results remained consistent.

      In part 1 of the results, we aimed to compare the task accuracies between the TD and ADHD groups in three independent tasks, which assess the participants’ abilities to process three types of BM cues. We assumed that individuals with ADHD show poorer performance in three tasks compared to TD individuals. With regard to that, we consider that multiple comparisons may not be necessary.

      Reviewer #3 (Public Review):

      Strengths:

      The authors present differences between ADHD and TD children in biological motion processing, and this question has not received as much attention as equivalent processing capabilities in autism. They use a task that appears well controlled. They raise some interesting mechanistic possibilities for differences in local and global motion processing, which are distinctions worth exploring. The group differences will therefore be of interest to those studying ADHD, as well as other developmental conditions, and those examining biological motion processing mechanisms in general.

      We appreciate the reviewer’s positive assessment of this work.

      Weaknesses:

      The data are not strong enough to support claims about differences between global and lobal processing wrt social communication skills and age. The mechanistic possibilities for why these abilities may dissociate in such a way are interesting, but the crucial tests of differences between correlations do not present a clear picture. Further empirical work would be needed to test the authors' claims. Specifics:

      The authors state frequently that it was the local BM task that related to social communication skills (SRS) and not the global tasks. However, the results section shows a correlation between SRS and all three tasks. The only difference is that when looking specifically within the ADHD group, the correlation is only significant for the local task. The supplementary materials demonstrate that tests of differences between correlations present an incomplete picture. Currently they have small samples for correlations, so this is unsurprising.

      Thanks for this comment. We agree with the reviewer that the relationship between local and global processing with social communication and age needs more expirical work. Based on our results, there are only possible dissociable roles of local and global BM processing. The accumulated evidence from previous studies has converged on this dissociation, e.g., whild global BM processing is susceptible to learning and practice, local BM processing does not show a learning trend (Chang and Troje, 2009; Grossman et al., 2004), and the brain activations in response to local and global BM cues are different (Chang et al., 2018; Duarte et al., 2022). We concurred with reviewers that the evidence for such dissociation from the current study by itself is not strong enough. Therefore, we have toned down on this point and no longer emphasized the dissociation. Based on the current results, we focused our discussion on the different aspects of BM processing in children with and without ADHD. Future studies with larger sample sizes are needed to confirm this disociable relationship.

      Theoretical assumptions. The authors make some statements about local vs global biological motion processing that should still be made more tentatively. They assume that local processing is specifically genetically whereas global processing is a product of experience. These data in newborn chicks are controversial and confounded - I cannot remember the specifics but I think there an upper vs lower visual field complexity difference here.

      We appreciate the reviewer’s suggestion. We agree that the relationship between genetic factors and BM perception remains to be further examined as we didn’t perform any genetic analysis in the current study. Some speculative papers have been removed, so do the statement about newborn chicks given the controversial and confounded results. We have toned down our claims and povided a moderate interpretation of the results:

      “Sensitivity to local BM cues emerges early in life54,55 and involves rapid processing in the subcortical regions16,56-58. As a basic pre-attentive feature23, local BM cues can guide visual attention spontaneously59,60. In contrary, the ability to process global BM cues is related to slow cortical BM processing and is influenced by many factors such as attention25,26 and visual experience21,51. As mentioned above, we found a significant negative correlation between the SRS total score and the accuracy of local BM processing, specifically in the ADHD group. This could be due to decreased visual input related to atypical local BM processing, which further impairs global BM processing. According to the two-process theory of biological motion processing61, local BM cues guide visual attention towards BM stimuli55,62. Consequently, the visual input of BM stimuli increases, facilitating the development of the ability to process global BM cues through learning21,63. The latter is a prerequisite for attributing intentions to others and facilitating social interactions with other individuals20,64,65. Thus, atypical local BM processing may contribute to impaired social interaction through altered visual inputs.” (lines 413 - 427)

      “Few developmental studies have been conducted on local BM processing. The ability to process local BM cues remained stable and did not exhibit a learning trend21,25. A reasonable interpretation may be that local BM processing is a low-level mechanism, probably performed by the primary visual cortex and subcortical regions such as the superior colliculus, pulvinar, and ventral lateral nucleus14,56,61.” (lines 441- 446)

      Readability. The manuscript needs very careful proofreading and correction for grammar. There are grammatical errors throughout.

      Thank the reviewer for this feedback. We have performed thorough proofreading and corrected grammatical errors throughout the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I thank the authors for their revisions that address several of the minor points that I raised in my last review. A number of requests are still not sufficiently answered:

      L. 290 ff.: These model 'BM-local = age + gender etc ' is a pretty sloppy notation. I think what is meant that a GLM was used that uses the predictors genderetc. time appropriate beta_i values. This formulas should be corrected or one just says that a GLM was run with the predictors gender

      The same criticism applies to these other models that follow.

      This was corrected.

      However, the corrected text remains sloppy: example: 'BM-locaL = ...' What exacty is 'BM-Local' the accuracy? etc. Here a precise notation shoudl be given that clearly names which variables are used here as predictors and target variables.

      We appreciate the reviewer’s suggestion. We clarified which variables are used in our model and gived them precise notations:

      “Three linear models were built to investigate the contributing factors: (a) ACClocal = β0 + β1 * age + β2 * gender + β3 * FIQ + β4 * QbInattention, (b) ACCglobal = β0 + β1 * age + β2 * gender + β3 * FIQ + β4 * QbInattention, and (c) ACCgeneral = β0 + β1 * age + β2 * gender + β3 * FIQ + β4 * QbInattention + β5 * ACClocal + β6 * ACCglobal. ACClocal, ACCglobal and ACCgeneral refer to the response accuracies of the three tasks in the ADHD group, and QbInattention is the standardised score for sustained attention function.” (lines 337 - 343)

      All these models assume linearity of the combination of the predictors. was this assumption verified?

      We referred to the previous study of BM perception in children. They found main predictor variables, including IQ (Rutherford et al., 2012; Jones et al., 2011) and age (Annaz et al., 2010; van et al., 2016), have a linear relation with the ability of BM processing.

      This answer is insufficient and not convincing. Because a variable Y depends linearly on predictor A and B in some other study, this does not imply that is is also linear in predictor C, or does not show interactions with such predictors in the present study.

      What is needed here is the testing of models with interaction terms and verifying that such models are not better predictors. If authors do not want to do this, they need at least to clearly point out that they made the strong assumption of linearity of their model, which might be wrong and thus be a substantial limitation of their analysis.

      Thanks for the suggestion. We tried to compare each possible mode with and without relative interactions. The results showed that the change of Coefficient of Determination (R-squared, R2) between the two models was not statistically significant.

      L. 296ff.: For model (b) it looks like general BM performance is strongly driven by the predictor global BM performance in the ADHD group. Does the same observation also apply to the controls?

      The same phenomenon was not observed in TD children. We have briefly discussed this point in the Discussion section of the revised manuscript (lines 449 - 459).

      Was such a path analysis also done for the TD subjects or not? If yes, was then also predicted that the variable BM-Global largely and directedly influences the variable BM-General? (The answer refers to the general discussion section, where no such analysis is presented, as far as I understand.)

      Thank you for your comment. We also conduct a path analysis similar to that in the ADHD group. There is no statistically significant mediator effect in the TD group. Please see Figure S3 for complete statistics.

      Reviewer #2 (Recommendations For The Authors):

      (1) Please add public access to the data repository so data availability can be assessed.

      The data analyzed during the study is available at https://osf.io/37p5s/.

      (2) Lines 119-115: The differences observed in ADHD participants in the studies referenced here were relative to what group? The last sentence here also refers to two groups, and it is difficult to gather which specific groups are meant, also because the two references relate to both ADHD and ASD samples. Please clarify.

      The suggestion is well taken. We have clarified the expressions accordingly:

      “Specifically, compared with the typically developing (TD) group, children with ADHD showed reduced activity of motion-sensitive components (N200) while watching biological and scrambled motions, although no behavioural differences were observed. Another study found that children with ADHD performed worse in BM detection with moderate noise ratios than the TD group32.” (lines 100 - 105)

      (3) Line 116: I'm not sure what is meant by 'despite initial indications' - please briefly specify/summarise here why the investigation into BM processing in ADHD is warranted.

      Thank the reviewer for pointing out this issue. We rephrase this part and briefly specify “why the investigation into BM processing in ADHD is warranted”:

      “Despite initial findings about atypical BM perception in ADHD, previous studies on ADHD treated BM perception as a single entity, which may have led to misleading or inconsistent findings28. Hence, it is essential to deconstruct BM processing into multiple components and motion features.” (lines 108 -111)

      (4) Lines 290-293: Please complete the sentence.

      Thank the reviewer for pointing out this issue. Th sentence has been completed:

      “For Task 2 and 3, where children were asked to detect the presence or discriminate the facing direction of the target walker, TD group have higher accuracies than the ADHD group (Task 2 - TD: 0.70 ± 0.12, ADHD: 0.59 ± 0.12, t73 = 3.677, p < 0.001, Cohen's d = 0.861; Task 3 - TD: 0.79 ± 0.12, ADHD: 0.63 ± 0.17, t73 = 4.702, p < 0.001, Cohen's d = 1.100).” (lines 284 - 288)

      Reviewer #3 (Recommendations For The Authors):

      (1) Conclusions concerning differences between the local and global tasks wrt SRS and age (see above). I believe the authors need to reword throughout to reflect that the tests of differences between these crucial correlations did not present a clear picture.

      We have reworded throughout the paper to reflect the inconclusiveness with regard to the relationship between local and global processing with social communication based on this study only. Future studies with larger sample sizes are needed to confirm this conclusion. The mechanism for this dissociable relationship should be validated by more psychologial tests in the future studies.

      (2) I would again tone down the discussion of genetic specification of local processing, given it is highly controversial.

      We thank the reviewer for pointing out the issue. We agree the point about the genetic specification of local processing remains controversial. The interpretation of results about local BM processing has been rephrased. Please refer to our response to the point #2 mentioned.

      (3) The manuscript needs very careful proofreading and grammatical correction throughout.

      Thanks for the suggestion to check the grammar. We have carefully proofread the manuscript to correct grammatical errors

    1. Author response:

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

      eLife assessment

      Following synaptic vesicle fusion events at release sites, vesicle remnants will need to be cleared in order to allow new rounds of vesicle docking and fusion. This fundamental study of Mahapatra and Takahashi examines the role of release site clearance in synaptic transmission during repetitive activity in two types of central synapses, the giant calyx of Held and hippocampal CA1 synapses. The study uses pharmacological approaches to interfere with release site clearance by blocking membrane retrieval (endocytosis). They compare the effects on short-term plasticity with those obtained by pharmacologically inhibiting scaffold protein activity. The data presented make a compelling case for fast endocytosis as necessary for rapid site clearance and vesicle recruitment to active zones. The data reveal an unexpected, fast role for local site clearance in counteracting synaptic depression.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The study examines the role of release site clearance in synaptic transmission during repetitive activity under physiological conditions in two types of central synapses, calyx of Held and hippocampal CA1 synapses. After acute block of endocytosis by pharmacology, deeper synaptic depression or less facilitation was observed in two types of synapses. Acute block of CDC42 and actin polymerization, which possibly inhibits the activity of Intersectin, affected synaptic depression at the calyx synapse, but not at CA1 synapses. The data suggest an unexpected, fast role of the site clearance in counteracting synaptic depression.

      Strengths:

      The study uses acute block of the molecular targets with pharmacology together with precise electrophysiology. The experimental results are clear cut and convincing. The study also examines the physiological roles of the site clearance using action potential-evoked transmission at physiological Ca and physiological temperature at mature animals. This condition has not been examined.

      Weaknesses:

      Pharmacology may have some off-target effects, though acute manipulation should be appreciated and the authors have tried several reagents to verify the overall conclusions.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, Mahapatra and Takahashi report on the physiological consequences of pharmacologically blocking either clathrin and dynamin function during compensatory endocytosis or of the cortical actin scaffold both in the calyx of Held synapse and hippocampal boutons in acute slice preparations

      Strengths:

      Although many aspects of these pharmacological interventions have been studied in detail during the past decades, this is a nice comprehensive and comparative study, which reveals some interesting differences between a fast synapse (Calyx of Held) tuned to reliably transmit at several 100 Hz and a more slow hippocampal CA1 synapse. In particular the authors find that acute disturbance of the synaptic actin network leads to a marked frequency-dependent enhancement of synaptic depression in the Calyx, but not in the hippocampal synapse This striking difference between both preparations is the most interesting and novel finding.

      Weaknesses:

      Unfortunately, however, these findings concerning the different consequences of actin depolymerization are not sufficiently discussed in comparison to the literature. My only criticism concerns the interpretation of the ML 141 and Lat B data. With respect to the Calyx data, I am missing a detailed discussion of the effects observed here in light of the different RRP subpools SRP and FRP. This is very important since Lee at al. (2012, PNAS 109 (13) E765-E774) showed earlier that disruption of actin inhibits the rapid transition of SRP SVs to the FRP at the AZ. The whole literature on this important concept is missing. Likewise, the role of actin for the replacement pool at a cerebellar synapse (Miki et al., 2016) is only mentioned in half a sentence. There is quite some evidence that actin is important both at the AZ (SRP to FRP transition, activation of replacement pool) and at the peri-active zone for compensatory endocytosis and release site clearance. Both possible underlying mechanisms (SRP to FRP transition or release site clearance) should be better dissected.

      We dissected the latrunculin effect further by referring to the related literature within the scope of this study in the revised Discussion section (last paragraph).

      Reviewer #3 (Public Review):

      The manuscript by Mahapatra and Takahashi addresses the role of presynaptic release site clearance during sustained synaptic activity. The authors characterize the effects of pharmacologically interfering with SV endocytosis (pre-incubation with Dynasore or Pitstop-2) on synaptic short-term plasticity (STP) at two different CNS synapses (calyx of Held synapses and hippocampal SC to CA1 synapses) using patch-clamp recordings in acute slices under experimental conditions designed to closely mimic a physiological situation (37{degree sign}C and 1.3 mM external [Ca2+]). Endocytosis blocker-induced changes in STP and in the recovery from short-term depression (STD) are compared to those seen after pharmacologically inhibiting actin filament assembly (pre-incubation with Latrunculin-B or the selective Cdc42 GTPase inhibitor ML-141). Presynaptic capacitance (Cm) recordings in calyx terminals were used to establish the effects of the pharmacological maneuvers on SV endocytosis.

      Latrunculin-B and ML-141 neither affect SV endocytosis (assayed by Cm recordings) nor EPSC recovery following conditioning trains, but strongly enhances STD at calyx synapses. No changes in STP were observed at Latrunculin-B- or ML-141-treated SC to CA1 synapses.

      Dynasore and Pitstop-2 slow down endocytosis, limit the total amount of exocytosis in response to long stimuli, enhance STD in response to 100 Hz stimulation, but profoundly accelerate EPSC recovery following conditioning 100 Hz trains at calyx synapses. At SC to CA1 synapses, Dynasore and Pitstop-2 reduce the extend of facilitation and lower relative steady-state EPSCs suggesting a change in the facilitation-depression balance in favor of the latter.

      The authors use state-of-the art techniques and their data, which is clearly presented, leads to authors to conclude that endocytosis is universally important for clearance of release sites while the importance of scaffold protein-mediated site clearance is limited to 'fast synapses'.

      Unfortunately, and perhaps not completely unexpected in view of the pharmacological tools chosen, there are several observations which remain difficult to understand:

      (1) Blocking site clearance affects release sites that have previously been used, i.e. sites at which SV fusion has occurred and which therefore need to be cleared. Calyces use at most 20% of all release sites during a single AP, likely fewer at 1.3 mM external [Ca2+]. Even if all those 20% of release sites become completely unavailable due to a block of release site clearance, the 2nd EPSC in a train should not be reduced by >20% because ~80% of the sites cannot be affected. However, ~50% EPSC reduction was observed (Fig. 2B1, lower right panel) raising the possibility that Dynasore does more than specifically interfering with SVs endocytosis (and possibly Pitstop as well). Non-specific effects are also suggested by the observed two-fold increase in initial EPSC size in SC to CA1 synapses after Dynasore pre-incubation.

      This study compares different experimental conditions to conclude the physiological role of endocytosis on rapid neurotransmission at the large calyceal synapse in mice. A related study at the Drosophila neuromuscular junction (Kawasaki et al., Nat. Neuroscience 2000) reported similar findings in comparable experimental settings (physiological conditions and acute block of endocytosis).

      (2) More severe depression was observed at calyx synapses after blocking endocytosis which the authors attribute to a presynaptic mechanism affecting pool replenishment. When probing EPSC recovery after conditioning 100 Hz trains, a speed up was observed mediated by an "unknown mechanism" which is "masked in 2 mM [Ca2+]". These two observations, deeper synaptic depression during 100 Hz but faster recovery from depression following 100 Hz, are difficult to align and no attempt was made to find an explanation.

      By varying temperature (PT vs RT), calcium concentration (1.3 mM vs 2.0 mM), and stimulation frequency (10, 100, and 200 Hz; some data are not shown), the effect of endocytosis block on EPSC STD and recovery from STD kinetics at the post-hearing calyx were compared in these settings: (PT, 1.3 mM [Ca2+]), (PT, 2.0 mM Ca2+), and (RT, 2.0 mM [Ca2+]), to dissect their respective role.

      (3) To reconcile previous data reporting a block of Ca2+-dependent recovery (CDR) by Dynasore or Latrunculin (measured at 2 mM external [Ca2+]) with the data presented here (using 1.3 mM external [Ca2+]) reporting no effect or a speed up of recovery from depression, the authors postulate that "CDR may operate only when excessive Ca2+ enters during massive presynaptic activation" (page 10 line 244). While that is possible, such explanation ignores plenty of calyx studies demonstrating fiber stimulation-induced CDR and elucidating molecular pathways mediating fiber stimulation-induced CDR, and it also completely dismisses the strong change in recovery time course after 10 Hz conditioning (single exponential) as compared to 100 Hz conditioning (double exponential with a pronounced fast component).

      Strong presynaptic stimuli such as those illustrated in Figs. 1B,C induce massive exocytosis. The illustrated Cm increase of 2 to 2.5 pF represents fusion of 25,000 to 30,000 SVs (assuming a single SV capacitance of 80 aF) corresponding to a 12 to 15% increase in whole terminal membrane surface (assuming a mean terminal capacitance of ~16 pF). Capacitance measurements can only be considered reliable in the absence of marked changes in series and membrane conductance. Documentation of the corresponding conductance traces is therefore advisable for such massive Cm jumps and merely mentioning that the first 450 ms after stimulation were skipped during analysis or referring to previous publications showing conductance traces is insufficient.

      All bar graphs in Figures 1 through 6 and Figures S3 through S6 compare three or even four (Fig. 5C) conditions, i.e. one control and at least two treatment data sets. It appears as if repeated t-tests were used to run multiple two-group comparisons (i.e. using the same control data twice for two different comparisons). Either a proper multiple comparison test should be used or a Bonferroni correction or similar multiple-comparison correction needs to be applied.

      We updated the statistical analysis of all data using one-way ANOVA and t-test with BonferroniHolm method of p level correction and rectified one analysis in Fig 1 and 3, all major conclusions are unchanged.

      Finally, the terminology of contrasting "fast-signaling" (calyx synapses) and "slow-plastic" (SC synapses) synapses seems to imply that calyx synapses lack plasticity, as does the wording "conventional bouton-type synapses involved in synaptic plasticity" (page 11, line 251). I assume, the authors primarily refer to the maximum frequencies these two synapse types typically transmit (fast-signaling vs slow-signaling)?

      Properties of these two synapses described explicitly in updated text and they are renamed as fast and slow synapes.

      Recommendations for the authors:

      Reviewer #3 (Recommendations For The Authors):

      'SV replenishment' and 'site clearance' should not be used synonymously as it seems to be done sometimes here.

      In this revision, we described them more explicitly.

      The data presented in Fig. S6 are detached from the rest of the manuscript, not relevant and should be removed. page 4 line 95 "... to ensure sufficient Ca2+ currents to induce exo-endocytosis." ICa is large enough to induce exocytosis also at 1.3 mM Ca2+. Please clarify.

      We updated the relevant section.

      page 5, line 108 "... this slow endocytosis showed a strongly prolonged time course without accompanied by the change of Cm or presynaptic Ca2+ currents" Please fix.

      Fixed.

      page 5, line 121 "Thus, at calyces of Held, bath-application of Dynasore or Pitstop-2 can block both fast and slow endocytosis without perturbing presynaptic intracellular milieu." Bath-application never perturbs the intracellular milieu. Please clarify.

      Rephrased.

      page 6 line 128 "... physiological aCSF" is a misnomer (= physiological artificial CSF). Please fix.

      In the introduction section, it is clearly described.

      page 11, line 252 "... from hippocampal SC-CA1 pyramidal neurons" There are no "SC-CA1 pyramidal neurons". Please fix.

      Fixed.

      page 12, line 285 "In acute slices optimized to physiological conditions" The conditions are optimized, not the slices. Please fix.

      Fixed.

      page 14, line 323 same as above

      Fixed.

      page 14, line 330 LTP at SC-CA1 synapses is postsynaptic. Please clarify.

      Rephrased

      page 16, line 381 "had a series resistance of 3-4 MOhm" versus

      page 17, line 408 "The patch pipettes had a series resistance of 5-15 MOhm (less than 10 MOhm in most cells)" 3-4 is perhaps pipette resistance while 5-15 is perhaps series resistance? Please clarify.

      Fixed.

      page 17, line 398 "Cm traces were averaged at every 10 ms (for 10 Hz train stimulation) or 20 ms (for 5 ms single or 1 Hz train stimulation)." Do you mean to say that Cm traces were smoothed with a moving average using a window size of 10 or 20 ms duration? Please clarify.

      Rephrased to clarify better.

      page 18, "All values are given as mean {plus minus} SEM and significance of difference was evaluated by Student's unpaired t-test, unless otherwise noted." Please check. You cannot simply use repeated t-tests for multiple comparisons. Either a proper multiple comparison test should be used or a Bonferroni correction or similar multiple-comparison correction needs to be applied.

      All statistical analysis are updated using one-way ANOVA and t-test, with Bonferroni-Holm method of p level correction and one analysis is rectified in Fig 1 and 3, with no change in major conclusions.

    1. Author response:

      Response to Reviewer #1 (Public Review):

      We thank the reviewer for their constructive criticism of our study, their proposed solutions, and for highlighting areas of the methodology and analytical pipeline where explanations were unclear or unsatisfactory. We will take the reviewer’s feedback into account to improve the clarity and readability of the revised manuscript. We acknowledge the importance of ruling out eye movements as a potential confound. We address these concerns briefly below, but a more detailed explanation (and a full breakdown of the relevant analyses, including the corrected and uncorrected results) will be provided in the revised manuscript.

      First, the source of EEG activity recorded from the frontal electrodes is often unclear. Without an external reference, it is challenging to resolve the degree to which frontal EEG activity represents neural or muscular responses1. Thus, as a preventative measure against the potential contribution of eye movement activity, for all our EEG analyses, we only included activity from occipital, temporal, and parietal electrodes (the selected electrodes can be seen in the final inset of Figure 3).

      Second, as suggested by the reviewer, we re-ran our analyses using the activity measured from the frontal electrodes alone. If the source of the nonlinear decoding accuracy in the AV condition was muscular activity produced by eye movements, we would expect to observe better decoding accuracy from sensors closer to the source. Instead, we found that decoding accuracy from the frontal electrodes (peak d' = 0.08) was less than half that of decoding accuracy from the more posterior electrodes (peak d' = 0.18). These results suggest that the source of neural activity containing information about stimulus position was located over occipito-parietal areas, consistent with our topographical analyses (inset of Figure 4).

      Third, we compared the average eye movements between the three main sensory conditions (auditory, visual, and audiovisual). In the visual condition, there was little difference in eye movements corresponding to the five stimulus locations, likely because the visual stimuli were designed to be spatially diffuse. For the auditory and audiovisual conditions, there was more distinction between eye movements corresponding to the stimulus locations. However, these appeared to be the same between auditory and audiovisual conditions. If consistent saccades to audiovisual stimuli had been responsible for the nonlinear decoding we observed, we would expect to find a higher positive correlation between horizontal eye position and stimulus location in the audiovisual condition than in the auditory or visual conditions. Instead, we found no difference in correlation between audiovisual and auditory stimuli, indicating that eye movements were equivalent in these conditions and unlikely to explain better decoding accuracy for audiovisual stimuli.

      Finally, we note that the stricter eye movement criterion acknowledged in the Discussion section of the original manuscript resulted in significantly better audiovisual d' than the MLE prediction, but this difference did not survive cluster correction. This is an important distinction to make as, when combined with the results described above, it seems to support our original interpretation that the stricter criterion combined with our conservative measure of (mass-based) cluster correction2 led to type 2 error.

      References

      (1) Roy, R. N., Charbonnier, S., & Bonnet, S. (2014). Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms. Biomedical Signal Processing and Control, 14, 256–264.

      (2) Pernet, C. R., Latinus, M., Nichols, T. E., & Rousselet, G. A. (2015). Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Journal of Neuroscience Methods, 250, 85–93.

      Response to Reviewer #2 (Public Review):

      We thank the reviewer for their insight and constructive feedback. As emphasized in the review, an interesting question that arises from our results is that, if the neural data exceeds the optimal statistical decision (MLE d'), why doesn’t the behavioural data? We agree with the reviewer’s suggestion that more attention should be devoted to this question, and plan to provide a deeper discussion of the relationship between behavioural and neural super-additivity in the revised manuscript. We also note that while this discrepancy remains unexplained, our results are consistent with the literature. That is, both non-linear neural responses (single-cell recordings) and behavioural responses that match MLE are reliable phenomenon in multisensory integration1,2,3,4.

      One possible explanation for this puzzling discrepancy is that behavioural responses occur sometime after the initial neural response to sensory input. There are several subsequent neural processes between perception and a behavioural response5, all of which introduce additional noise that may obscure super-additive perceptual sensitivity. In particular, the mismatch between neural and behavioural accuracy may be the result of additional neural processes that translate sensory activity into a motor response to perform the behavioural task.

      Our measure of neural super-additivity (exceeding optimally weighted linear summation) differs from how it is traditionally assessed (exceeding summation of single neuron responses)2. However, neither method has yet fully explained how this neural activity translates to behavioural responses, and we think that more work is needed to resolve the abovementioned discrepancy. However, our method will facilitate this work by providing a reliable method of measuring neural super-additivity in humans, using non-invasive recordings.

      References

      (1) Alais, D., & Burr, D. (2004). The ventriloquist effect results from near-optimal bimodal integration. Current Biology, 14(3), 257–262.

      (2) Ernst, M. O., & Banks, M. S., (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415(6870), 429–433.

      (3) Meredith, M. A., & Stein, B. E. (1993). Interactions among converging sensory inputs in the superior colliculus. Science, 221, 389–391.

      (4) Stanford, T. R., & Stein, B. E. (2007). Superadditivity in multisensory integration: putting the computation in context. Neuroreport 18, 787–792.

      (5) Heekeren, H., Marrett, S. & Ungerleider, L. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 467–479.

    1. Author response:

      Thanks for the eLife assessment

      “This study employed a comprehensive approach to examining how the MT+ region integrates into a complex cognition system in mediating human visuo-spatial intelligence. While the findings are useful, the experimental evidence is incomplete and the study design, hypothesis, analyses, writing, and presentation need to be improved.” We plan to revise the manuscript according to the comments of Public Reviews.

      We are grateful for the excellent and very helpful comments, and now we address provisional author responses.

      Reviewer #1 (Public Review):

      Summary:

      The study of human intelligence has been the focus of cognitive neuroscience research, and finding some objective behavioral or neural indicators of intelligence has been an ongoing problem for scientists for many years. Melnick et al, 2013 found for the first time that the phenomenon of spatial suppression in motion perception predicts an individual's IQ score. This is because IQ is likely associated with the ability to suppress irrelevant information. In this study, a high-resolution MRS approach was used to test this theory. In this paper, the phenomenon of spatial suppression in motion perception was found to be correlated with the visuo-spatial subtest of gF, while both variables were also correlated with the GABA concentration of MT+ in the human brain. In addition, there was no significant relationship with the excitatory transmitter Glu. At the same time, SI was also associated with MT+ and several frontal cortex FCs.

      Strengths:

      (1) 7T high-resolution MRS is used.

      (2) This study combines the behavioral tests, MRS, and fMRI.

      Weaknesses:

      (1) In the intro, it seems to me that the multiple-demand (MD) regions are the key in this study. However, I didn't see any results associated with the MD regions. Did I miss something??

      Thank reviewer for pointing this out. After careful consideration, we agree with your point of view. According to the results of Melnick 2013, the motion surround suppression (SI) and the time thresholds of small and large gratings representing hMT+ functionality are correlated with Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indicators, with correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. This suggests that hMT+ does have the potential to become the core of MD system. However, due to our results only delving into “the GABA-ergic inhibition in human MT predicts visuo-spatial intelligence mediated by reverberation with frontal cortex”, it is not yet sufficient to prove that hMT+is the core node of the MD system, we will adjust the explanatory logic of the article, that is, emphasizing the de-redundancy of hMT+ in visual-spatial intelligence and the improvement of information processing efficiency, while weakening the significance of hMT+ in MD systems.

      (2) How was the sample size determined? Is it sufficient??

      Thank reviewer for pointing this out. We use G*power to determine our sample size. In the study by Melnick (2013), they reported a medium effect between SI and Perception Reasoning sub-ability (r=0.47). Here we use this r value as the correlation coefficient (ρ H1), setting the power at the commonly used threshold of 0.8 and the alpha error probability at 0.05. The required sample size is calculated to be 26. This ensures that our study has adequate power to yield valid statistical results. Furthermore, compared to earlier within-subject studies like Schallmo et al.'s 2018 research, which used 22 datasets to examine GABA levels in MT+ and the early visual cortex (EVC), our study includes a more extensive dataset.

      (3) In Schallmo elife 2018, there was no correlation between GABA concentration and SI. How can we justify the different results different here?

      Thank reviewer for pointing this out. There are several differences between us:

      a. While the earlier study by Schallmo et al. (2018) employed 3T MRS, we utilize 7T MRS, enhancing our ability to detect and measure GABA with greater accuracy.

      b. Schallmo elife 2018 choose to use the bilateral hMT+ as the MRS measurement region while we use the left hMT+. The reason why we focus on left hMT+ are describe in reviewer 1. (6). Briefly, use of left MT/V5 as a target was motivated by studies demonstrating that left MT/V5 TMS is more effective at causing perceptual effects (Tadin et al., 2011).

      c. The resolution of MRS sequence in Schallmo elife 2018 is 3 cm isotropic voxel, while we apply 2 cm isotropic voxel. This helps us more precisely locate hMT+ and exclude more white matter signal.

      (4) Basically this study contains the data of SI, BDT, GABA in MT+ and V1, Glu in MT+ and V1-all 6 measurements. There should be 6x5/2 = 15 pairwise correlations. However, not all of these results are included in Figure 1 and supplementary 1-3. I understand that it is not necessary to include all figures. But I suggest reporting all values in one Table.

      We thank the reviewer for the good suggestion, we are planning to make a correlation matrix to reporting all values.

      (5) In Melnick (2013), the IQ scores were measured by the full set of WAIS-III, including all subtests. However, this study only used the visual spatial domain of gF. I wonder why only the visuo-spatial subtest was used not the full WAIS-III?

      We thank the reviewer for pointing this out. The decision was informed by Melnick’s findings which indicated high correlations between Surround suppression (SI) and the Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indexes, with correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. It is well-established that the hMT+ region of the brain is a sensory cortex involved in visual perception processing (3D perception). Furthermore, motion surround suppression (SI), a specific function of hMT+, aligns closely with this region's activities. Given this context, the Perception Reasoning sub-ability was deemed to have the clearest mechanism for further exploration. Consequently, we selected the most representative subtest of Perception Reasoning—the Block Design Test—which primarily assesses 3D visual intelligence.

      (6) In the functional connectivity part, there is no explanation as to why only the left MT+ was set to the seed region. What is the problem with the right MT+?

      We thank the reviewer for pointing this out. The main reason is that our MRS ROI is the left hMT+, we would like to make different models’ ROI consistent to each other. Use of left MT/V5 as a target was motivated by studies demonstrating that left MT/V5 TMS is more effective at causing perceptual effects (Tadin et al., 2011). In addition, we will check the results of our localizer to confirm whether similar findings are consistently replicated.

      (7) In Melnick (2013), the authors also reported the correlation between IQ and absolute duration thresholds of small and large stimuli. Please include these analyses as well.

      We thank the reviewer for the good advice. Containing such result do help researchers compare the result between Melnick and us. We are planning to make such picture in the revised version.

      Reviewer #2 (Public Review):

      Summary:

      Recent studies have identified specific regions within the occipito-temporal cortex as part of a broader fronto-parietal, domain-general, or "multiple-demand" (MD) network that mediates fluid intelligence (gF). According to the abstract, the authors aim to explore the mechanistic roles of these occipito-temporal regions by examining GABA/glutamate concentrations. However, the introduction presents a different rationale: investigating whether area MT+ specifically, could be a core component of the MD network.

      Strengths:

      The authors provide evidence that GABA concentrations in MT+ and its functional connectivity with frontal areas significantly correlate with visuo-spatial intelligence performance. Additionally, serial mediation analysis suggests that inhibitory mechanisms in MT+ contribute to individual differences in a specific subtest of the Wechsler Adult Intelligence Scale, which assesses visuo-spatial aspects of gF.

      Weaknesses:

      (1) While the findings are compelling and the analyses robust, the study's rationale and interpretations need strengthening. For instance, Assem et al. (2020) have previously defined the core and extended MD networks, identifying the occipito-temporal regions as TE1m and TE1p, which are located more rostrally than MT+. Area MT+ might overlap with brain regions identified previously in Fedorenko et al., 2013, however the authors attribute these activations to attentional enhancement of visual representations in the more difficult conditions of their tasks. For the aforementioned reasons, It is unclear why the authors chose MT+ as their focus. A stronger rationale for this selection is necessary and how it fits with the core/extended MD networks.

      We really appreciate reviewer’s opinions. The reason why we focus on hMT+ is following: According to the results of Melnick 2013, the motion surround suppression (SI) and the time thresholds of small and large gratings representing hMT+ functionality are correlated with Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indicators, with high correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. In addition, Fedorenko et al. 2013, the averaged MD activity region appears to overlap with hMT+. Based on these findings, we assume that hMT+ does have the potential to become the core of MD system.

      (2) Moreover, although the study links MT+ inhibitory mechanisms to a visuo-spatial component of gF, this evidence alone may not suffice to position MT+ as a new core of the MD network. The MD network's definition typically encompasses a range of cognitive domains, including working memory, mathematics, language, and relational reasoning. Therefore, the claim that MT+ represents a new core of MD needs to be supported by more comprehensive evidence.

      Thank reviewer for pointing this out. After careful consideration, we agree with your point of view. Due to our results only delving into visuo-spatial intelligence, it is not yet sufficient to prove that hMT is the core node of the MD system. We will adjust the explanatory logic of the article, that is, emphasizing the de-redundancy of hMT+in visual-spatial intelligence and the improvement of information processing efficiency, while weakening the significance of hMT+ in MD systems.

      Reviewer #3 (Public Review):

      Summary:

      This manuscript aims to understand the role of GABA-ergic inhibition in the human MT+ region in predicting visuo-spatial intelligence through a combination of behavioral measures, fMRI (for functional connectivity measurement), and MRS (for GABA/glutamate concentration measurement). While this is a commendable goal, it becomes apparent that the authors lack fundamental understanding of vision, intelligence, or the relevant literature. As a result, the execution of the research is less coherent, dampening the enthusiasm of the review.

      Strengths:

      (1) Comprehensive Approach: The study adopts a multi-level approach, i.e., neurochemical analysis of GABA levels, functional connectivity, and behavioral measures to provide a holistic understanding of the relationship between GABA-ergic inhibition and visuo-spatial intelligence.

      (2) Sophisticated Techniques: The use of ultra-high field magnetic resonance spectroscopy (MRS) technology for measuring GABA and glutamate concentrations in the MT+ region is a recent development.

      Weaknesses:

      Study Design and Hypothesis

      (1) The central hypothesis of the manuscript posits that "3D visuo-spatial intelligence (the performance of BDT) might be predicted by the inhibitory and/or excitation mechanisms in MT+ and the integrative functions connecting MT+ with the frontal cortex." However, several issues arise:

      (1.1) The Suppression Index depicted in Figure 1a, labeled as the "behavior circle," appears irrelevant to the central hypothesis.

      We thank the reviewer for pointing this out. In our study, the inhibitory mechanisms in hMT+ are conceptualized through two models: the neurotransmitter model and the behavior model. The Suppression Index is essential for elucidating the local inhibitory mechanisms within behavior model. However, we acknowledge that our initial presentation in the introduction may not have clearly articulated our hypothesis, potentially leading to misunderstandings. We plan to revise the introduction to better clarify these connections and ensure the relevance of the Suppression Index is comprehensively understood.

      (1.2) The construct of 3D visuo-spatial intelligence, operationalized as the performance in the Block Design task, is inconsistently treated as another behavioral task throughout the manuscript, leading to confusion.

      We thank the reviewer for pointing this out. We acknowledge that our manuscript may have inconsistently presented this construct across different sections, causing confusion. To address this, we plan to ensure a consistent description of 3D visuo-spatial intelligence in both the introduction and the discussion sections. But we would like to maintain 'Block Design task score' within the results section to help readers clarify which subtest we use.

      (1.3) The schematics in Figure 1a and Figure 6 appear too high-level to be falsifiable. It is suggested that the authors formulate specific and testable hypotheses and preregister them before data collection.

      We thank the reviewer for pointing this out. We are planning to revise the Figure 1a and make it less abstract and more logical. For Figure 6, the schematic represents our theoretical framework of how hMT+ works in the 3D viso-spatial intelligence, we believe the elements within this framework are grounded in related theories and supported by evidence discussed in our results and discussions section, making them specific and testable.

      (2) Central to the hypothesis and design of the manuscript is a misinterpretation of a prior study by Melnick et al. (2013). While the original study identified a strong correlation between WAIS (IQ) and the Suppression Index (SI), the current manuscript erroneously asserts a specific relationship between the block design test (from WAIS) and SI. It should be noted that in the original paper, WAIS comprises Similarities, Vocabulary, Block design, and Matrix reasoning tests in Study 1, while the complete WAIS is used in Study 2. Did the authors conduct other WAIS subtests other than the block design task?

      Thanks for pointing this out. Reviewer #1 also asked this question, we copy the answers in here “The decision was informed by Melnick’s findings which indicated high correlations between Surround suppression (SI) and the Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indexes, with correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. It is well-established that the hMT+ region of the brain is a sensory cortex involved in visual perception processing (3D perception). Furthermore, motion surround suppression (SI), a specific function of hMT+, aligns closely with this region's activities. Given this context, the Perception Reasoning sub-ability was deemed to have the clearest mechanism for further exploration. Consequently, we selected the most representative subtest of Perception Reasoning—the Block Design Test—which primarily assesses 3D visual intelligence.”

      (3) Additionally, there are numerous misleading references and unsubstantiated claims throughout the manuscript. As an example of misleading reference, "the human MT ... a key region in the multiple representations of sensory flows (including optic, tactile, and auditory flows) (Bedny et al., 2010; Ricciardi et al., 2007); this ideally suits it to be a new MD core." The two references in this sentence are claims about plasticity in the congenitally blind with sensory deprivation from birth, which is not really relevant to the proposal that hMT+ is a new MD core in healthy volunteers.

      Thanks for pointing this out. We have carefully read the corresponding references and considered the corresponding theories and agree with these comments. Due to our results only delving into “the GABA-ergic inhibition in human MT predicts visuo-spatial intelligence mediated by reverberation with frontal cortex”, it is not yet sufficient to prove that hMT+ is the core node of the MD system, we will adjust the explanatory logic of the article, that is, emphasizing the de redundancy of hMT+in visual-spatial intelligence and the improvement of information processing efficiency, while weakening the significance of hMT+ in MD systems. In addition, regarding the potential central role of hMT+ in the MD system, we agree with your view that research on hMT+ as a multisensory integration hub mainly focuses on developmental processes. Meanwhile, in adults, the MST region of hMT+ is considered a multisensory integration area for visual and vestibular inputs, which potentially supports the role of hMT+ in multitasking multisensory systems (Gu et al., J. Neurosci, 26(1), 73–85, 2006; Fetsch et al., Nat. Neurosci, 15, 146–154, 2012.). Further research could explore how other intelligence sub-ability such as working memory and language comprehension are facilitated by hMT+'s features.

      Another example of unsubstantiated claim: the rationale for selecting V1 as the control region is based on the assertion that "it mediates the 2D rather than 3D visual domain (Born & Bradley, 2005)". That's not the point made in the Born & Bradley (2005) paper on MT. It's crucial to note that V1 is where the initial binocular convergence occurs in cortex, i.e., inputs from both the right and left eyes to generate a perception of depth.

      Thank you for pointing this out. We acknowledge the inappropriate citation of "Born & Bradley, 2005," which focuses solely on the structure and function of the visual area MT. However, we believe that choosing hMT+ as the domain for 3D visual analysis and V1 as the control region is justified. Cumming and DeAngelis (Annu Rev Neurosci, 24:203–238.2001) state that binocular disparity provides the visual system with information about the three-dimensional layout of the environment, and the link between perception and neuronal activity is stronger in the extrastriate cortex (especially MT) than in the primary visual cortex(V1). This supports our choice and emphasizes the relevance of MT+ in our study. We will revise our reference in the revised version.

      Results & Discussion

      (1) The missing correlation between SI and BDT is crucial to the rest of the analysis. The authors should discuss whether they replicated the pattern of results from Melnick et al. (2013) despite using only one WAIS subtest.

      We thank for reviewer’s suggestion. Now the correlation result is placed in the supplemental material, we will put it back to the main text.

      (2) ROIs: can the authors clarify if the results are based on bilateral MT+/V1 or just those in the left hemisphere? Can the authors plot the MRS scan area in V1? I would be surprised if it's precise to V1 and doesn't spread to V2/3 (which is fine to report as early visual cortex).

      We thank for reviewer’s suggestion. We plan to draw the V1 ROI MRS scanning area and use the visual template to check if the scanning area contains V2/3. If it does, we will refer to it as the early visual cortex rather than specifically V1 in our reporting.

      (3) Did the authors examine V1 FC with either the frontal regions and/or whole brain, as a control analysis? If not, can the author justify why V1 serves as the control region only in the MRS but not in FC (Figure 4) or the mediation analysis (Figure 5)? That seems a little odd given that control analyses are needed to establish the specificity of the claim to MT+

      We thank for reviewer’s suggestion. We plan to do the V1 FC-behavior connection as control analysis. For mediation analysis, since V1 GABA/Glu has no correlation with BDT score, it is not sufficient to apply mediation analysis.

      (4) It is not clear how to interpret the similarity or difference between panels a and b in Figure 4.

      We thank reviewer for pointing this out. We plan to further interpret the difference between a and b in the revised version. Panels a represents BDT score correlated hMT+-region FC, which is obviously involved in frontal cortex. While panels b represents SI correlated hMT+-region FC, which shows relatively less regions. The overlap region is what we are interested in and explain how local inhibitory mechanisms works in the 3D viso-spatial intelligence. In addition, we would like to revise Figure 4 and point out the overlap region.

      (5) SI is not relevant to the authors‘ priori hypothesis, but is included in several mediation analyses. Can the authors do model comparisons between the ones in Figure 5c, d, and Figure S6? In other words, is SI necessary in the mediation model? There seem discrepancies between the necessity of SI in Figures 5c/S6 vs. Figure 5d.

      We thank the reviewer for highlighting this point. The relationship between the Suppression Index (SI) and our a priori hypotheses is elaborated in the response to reviewer 3, section (1). SI plays a crucial role in explicating how local inhibitory mechanisms function within the context of the 3D visuo-spatial task. Additionally, Figure 5c illustrates the interaction between the frontal cortex and hMT+, showing how the effects from the frontal cortex (BA46) on the Block Design Task are fully mediated by SI. This further underscores the significance of SI in our model.

      (6) The sudden appearance of "efficient information" in Figure 6, referring to the neural efficiency hypothesis, raises concerns. Efficient visual information processing occurs throughout the visual cortex, starting from V1. Thus, it appears somewhat selective to apply the neural efficiency hypothesis to MT+ in this context.

      We thank the reviewer for highlighting this point. There is no doubt that V1 involved in efficient visual information processing. However, in our result, the V1 GABA has no significant correlation between BDT score, suggesting that the V1 efficient processing might not sufficiently account for the individual differences in 3D viso-spatial intelligence. Additionally, we will clarify our use of the neural efficiency hypothesis by incorporating it into the introduction of our paper to better frame our argument.

      Transparency Issues:

      (1) Don't think it's acceptable to make the claim that "All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary information". It is the results or visualizations of data analysis, rather than the raw data themselves, that are presented in the paper/supp info.

      We thank reviewer for pointing this out. We realized that such expression will lead to confusion. We will delete this expression.

      (2) No GitHub link has been provided in the manuscript to access the source data, which limits the reproducibility and transparency of the study.

      We thank reviewer for pointing this out. We will attach the GitHub link in the revised version.

      Minor:

      "Locates" should be replaced with "located" throughout the paper. For example: "To investigate this issue, this study selects the human MT complex (hMT+), a region located at the occipito-temporal border, which represents multiple sensory flows, as the target brain area."

      We thank reviewer for pointing this out. We will revise it.

      Use "hMT+" instead of "MT+" to be consistent with the term in the literature.

      We thank reviewer for pointing this out. We agree to use hMT+ in the literature.

      "Green circle" in Figure 1 should be corrected to match its actual color.

      We thank reviewer for pointing this out. We will revise it.

      The abbreviation for the Wechsler Adult Intelligence Scale should be "WAIS," not "WASI."

      We thank reviewer for pointing this out. We will revise it.

    1. Author Response:

      We appreciate the thorough comments from the reviewers. Before revising the manuscript, we would like to briefly reply to the main concerns raised:

      • Is pupil size a reliable proxy of effort? A vast amount of work demonstrates that pupil size sensitively scales with fluctuations in effort: for instance, the pupil dilates when increasing load in working memory, or multiple object tracking tasks, and such pupillary effects robustly explain individual differences in cognitive ability and fluctuations in performance across trials.1–4 This extends to the planning of movements as pupil dilations are observed prior to the execution of (eye) movements.5 As reviewed previously6–12 (based on vast literature each), any increase in effort is associated with an increase in pupil size. Inadvertently, we phrased as if the link between effort and pupil size was established via shared neural correlates. However, this is not the case as the link between effort and pupil size had been established well before the underlying neural circuitry of this relationship was investigated in detail. During the revision, we plan to rewrite this section to clarify that pupil size indexes effort and to provide a clear distinction between this link and putative neural underpinnings of such effort-linked modulations.

      • Is saccade latency an alternative explanation for the link between effort and saccade selection? Longer saccade latencies may imply more complex oculomotor programming (e.g. saccades with larger amplitudes require longer latencies for non-microsaccades13, and latencies increase when distractors are presented14), and latencies are indeed known to differ across directions15,16. As suggested, it is possible that saccade latencies may also predict saccade preferences. However, even if this is the case, this would not constitute an alternative explanation. As saccade latency may index oculomotor programming complexity, it can potentially be considered an alternative outcome measure of effort, albeit restricted to the context of saccades. Therefore, if saccade latencies predict saccade preferences, this would not affect our conclusion, rather it would constitute as converging evidence that supports the conclusion that effort drives saccade selection.

      A related question is why one would use pupil size as a measure of effort, given the methodological care that pupillometry requires. There are a number of points that make pupil size sensible and promising in comparison with saccade latencies. In contrast to saccade latencies, pupil size allows to capture the effort of different effector systems (e.g. head or hand movements), and potentially even the effort associated with covert shifts of attention. Moreover, pupil size is a temporally rich and continuous measure that allows to isolate processes unfolding prior to (eye) movement onset (e.g. oculomotor programming). Together, this makes pupil size a powerful tool to study the costs of visual selection more broadly. In the revision, we will add analyses incorporating latencies and other other saccade metrics. We will also discuss the differences between pupil size and saccade latencies in capturing saccade costs and effort.

      • Are the current results causal or correlational? Most of the currently reported results are indeed correlational in nature. In our first tasks, we correlated pupil size during saccade planning to saccade preferences in a subsequent task. Although the link between across tasks was correlational, the observed relationship clearly followed our previously specified hypothesis.17 Moreover, experiments 1 and 2 of the visual search data replicated and extended this relationship. We also directly manipulated cognitive demand in the second visual search experiment. In line with the hypothesis that effort affects saccade selection, participants executed less saccades overall when performing a (primary) auditory dual task, and even cut the costly saccades most. Whilst mostly correlational, we do not know of a more fitting and parsimonious explanation for our findings than effort predicting saccade selection. We will address causality in the discussion for transparency and point more clearly to the second visual search experiment for causal evidence.

      References

      (1) Alnæs, D. et al. Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. J. Vis. 14, 1 (2014).

      (2) Koevoet, D., Strauch, C., Van der Stigchel, S., Mathôt, S. & Naber, M. Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization. WIREs Cogn. Sci. e1668 (2023) doi:10.1002/wcs.1668.

      (3) Robison, M. K. & Unsworth, N. Pupillometry tracks fluctuations in working memory performance. Atten. Percept. Psychophys. 81, 407–419 (2019).

      (4) Unsworth, N. & Miller, A. L. Individual Differences in the Intensity and Consistency of Attention. Curr. Dir. Psychol. Sci. 30, 391–400 (2021).

      (5) Richer, F. & Beatty, J. Pupillary Dilations in Movement Preparation and Execution. Psychophysiology 22, 204–207 (1985).

      (6) Bumke, O. Die Pupillenstörungen Bei Geistes-Und Nervenkrankheiten. (Fischer, 1911).

      (7) Kahneman, D. Attention and Effort. (Prentice-Hall, 1973).

      (8) van der Wel, P. & van Steenbergen, H. Pupil dilation as an index of effort in cognitive control tasks: A review. Psychon. Bull. Rev. 25, 2005–2015 (2018).

      (9) Loewenfeld, I. E. Mechanisms of reflex dilatation of the pupil. Doc. Ophthalmol. 12, 185–448 (1958).

      (10) Mathôt, S. Pupillometry: Psychology, Physiology, and Function. J. Cogn. 1, 16 (2018).

      (11) Sirois, S. & Brisson, J. Pupillometry. WIREs Cogn. Sci. 5, 679–692 (2014).

      (12) Strauch, C., Wang, C.-A., Einhäuser, W., Van der Stigchel, S. & Naber, M. Pupillometry as an integrated readout of distinct attentional networks. Trends Neurosci. 45, 635–647 (2022).

      (13) Kalesnykas, R. P. & Hallett, P. E. Retinal eccentricity and the latency of eye saccades. Vision Res. 34, 517–531 (1994).

      (14) Walker, R., Deubel, H., Schneider, W. X. & Findlay, J. M. Effect of Remote Distractors on Saccade Programming: Evidence for an Extended Fixation Zone. J. Neurophysiol. 78, 1108–1119 (1997).

      (15) Hanning, N. M., Himmelberg, M. M. & Carrasco, M. Presaccadic attention enhances contrast sensitivity, but not at the upper vertical meridian. iScience 25, 103851 (2022).

      (16) Hanning, N. M., Himmelberg, M. M. & Carrasco, M. Presaccadic Attention Depends on Eye Movement Direction and Is Related to V1 Cortical Magnification. J. Neurosci. 4

      4, (2024).

      (17) Koevoet, D., Strauch, C., Naber, M. & Van der Stigchel, S. The Costs of Paying Overt and Covert Attention Assessed With Pupillometry. Psychol. Sci. 34, 887–898 (2023).

    1. Author response:

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

      We have made revisions accordingly. The following is a list of the changes we have made in this revised Version of Record:

      (1) We have added three more panels to Figure 1-figure supplement 1, showing that lipopolysaccharide-induced severe lung injury also generate some ectopic tuft cells expressing both Dclk1 and Gα-gustducin, a G protein α subunit expressed in taste bud cells and many tuft cells.

      (2) We have added a new supplemental figure, Figure 2-figure supplement 1, showing the reanalysis data of the single-cell RNAseq dataset (GSE197163) indicating the numbers of Trpm5-GFP+ ectopic tuft cells expressing Tas2r108, Tas2r105, Tas2r138, Tas2r137 and other Tas2rs, respectively. And the original “Figure 2-figure supplement 1” in the previous version has been changed to “Figure 2-figure supplement 2”.

      (3) We have added another new supplemental figure, Figure 3-figure supplement 1, showing the H1N1 infection-damaged lung tissue volumes in the Gng13-cKO mice are significantly greater than those in WT or Trpm-/- mice, which is in agreement with the data of the injured lung surface areas from these three genotypes of mice (Figure 3 C and D). And the original “Figure 3-figure supplement 1” in the previous version has been changed to “Figure 3-figure supplement 2”.

      (4) We have added to the new Figure 3-figure supplement 2 two new panels: I and J, showing the reanalysis data of the single-cell RNAseq dataset (GSE197163), indicating that about 57% of Trpm5-GFP+ ectopic tuft cells express Gγ13, some of which express Alox5, a key enzyme to the biosynthesis of pro-resolving mediators.

      (5) We have added one reference on Sytox and another on Alox5.

      (6) We have corrected two labeling errors to Figure 3 G and M, and some other typos in the article. Also, we have removed “Present address” attached to some authors since no present address was needed at all.

      Attached below is our point-by-point reply to the comments and suggestions made by the reviewers. We hope that you and the reviewers will find all concerns satisfactorily addressed.

      Responses to public reviews:

      Reviewer #1:

      Li et al. report here on the expression of a G-protein subunit Gng13 in ectopic tuft cells that develop after severe pulmonary injury in mice. By deleting this gene in ectopic tuft cells as they arise, the authors observed worsened lung injury and greater inflammation after influenza infection, as well as a decrease in the overall number of ectopic tuft cells. This was in stark contrast to the deletion of Trpm5, a cation channel generally thought to be required for all functional gustatory signaling in tuft cells, where no phenotype is observed. Strengths here include a thorough assessment of lung injury via a number of different techniques. Weaknesses are notable: confusingly, these findings are at odds with reports from other groups demonstrating no obvious phenotype upon influenza infection in mice lacking the transcription factor Pou2f3, which is essential for all tuft cell specification and development. The authors speculate that heterogeneity within nascent tuft cell populations, specifically the presence of pro- and anti-inflammatory tuft cells, may explain this difference, but they do not provide any data to support this idea.

      We thank the reviewer for pointing out the strengths of this work. The phenotypes of the Gng13 conditional knockout mice upon severe pulmonary injury seem to be severer than those of Trpm5 knockout or Pou2f3 knockout mice, which we would attribute to functionally specific tuft cell subtypes. In the intestines, tuft cells are known to promote type II innate immune responses. Those ectopic pulmonary tuft cells emerge at 12 days post infection, and may not be involved in the initial immune responses to the infection, and instead, some of them may contribute to the inflammation resolution and functional recovery. Reanalysis of the previously published single tuft cell RNAseq dataset indeed showed that Gng13 is expressed in a subset of these ectopic pulmonary tuft cells, and anti-inflammatory genes such as Alox5 are also found in some of these tuft cells (please see the newly added Figure 3 supplement 2 I and J). Together, these data suggest that while some of these tuft cells may still play a pro-inflammatory role as in the intestines, some other Gγ13-expressing tuft cells contribute to the inflammation resolution, and disruption of the latter’s function results in the severer phenotypes.

      Reviewer #2:

      The study by Li et al. aimed to demonstrate the role of the Gγ13-mediated signal transduction pathway in tuft cell-driven inflammation resolution and repairing injured lung tissue. The authors showed a reduced number of tuft cells in the parenchyma of Gγ13 null lungs following viral infection. Mice with a Gγ13 null mutation showed increased lung damage and heightened macrophage infiltration when exposed to the H1N1 virus. Their further findings suggested that lung inflammation resolution, epithelial barrier, and fibrosis were worsened in Gγ13 null mutants.

      Strengths:

      The beautiful immunostaining findings do suggest that the number of tuft cells is decreased in Gr13 null mutants.

      Weaknesses:

      The description of phenotypes, and the approaches used to measure the phenotypes are problematic. Rigorous investigation of the mouse lung phenotypes is needed to draw meaningful conclusions.

      Thank the reviewer for pointing out the major findings and strengths of our work. Regarding the approaches used to measure the phenotypes, we first did double immunostaining and validated that the lipopolysaccharide-induced DCLK1+ positive cells are indeed ectopic pulmonary tuft cells with an antibody to Gα-gustducin, a commonly expressed G protein α subunit in taste buds and tuft cells. Second, in addition to the measurements of the injured lung surface areas, we determined the injured lung tissue volumes by slicing the injured lungs into a series of tissue sections, quantifying the injured areas in each section and then reconstructing the injured volumes. Third, we reanalyzed the previously published single-tuft cell RNAseq dataset and found that a subset (i.e., ~57%) of Trpm5-GFP+ tuft cells express Gng13, some of which express anti-inflammatory genes such as Alox5. These additional data further support our finding that a subset of these Gγ13-expressing ectopic tuft cells may contribute to the inflammation resolution while others may play a proinflammatory role.

      Reply to the recommendations of Reviewer #1:

      (1) A major issue with this study is the fact that Chat-Cre mediated knockout of Gng13 leads to reduced tuft cells and impaired recovery, yet global TRPM5 deletion (this study) and global Pou2f3 deletion (Barr et al.) exhibit no apparent phenotype. One can imagine a Trpm5-independent role of Gng13 in tuft cells, but it is much harder to reconcile with the fact that Pou2f3 KO mice, which lack tuft cells entirely, exhibit no apparent phenotype. This was examined in some detail in Barr et al., demonstrating no apparent change in weight loss, dysplastic expansion (Krt5+ cells), or goblet cell metaplasia. The most parsimonious explanation is that Gng13 deletion in another Chat+ cell type, probably neurons of some sort, is leading to this phenotype. The authors really need to investigate this in some detail as the data does not really support a role of tuft cells in the phenotype they observe. Better yet, identification of the other Chat+ cell type in which Gng13 deletion promotes impaired lung recovery would be very interesting. While neurons seem likely, perhaps there is another Chat+ cell type expressing Gng13 in the respiratory tract that could be playing a role as well. In either case, the discrepancy between Pou2f3 KO (no phenotype) and Chat-Cre / Gng13 KO (impaired recovery) is difficult to reconcile.

      We agree with the reviewer, and it took us some time to make senses of the data as well. The differences in phenotypes between Trpm5-knockout versus Gng13 conditional knockout (Gng13-cKO) could be explained by that Gγ13 is a partner of Gβγ moiety of a heterotrimeric G protein (Gαβγ),which is known to act on many effector enzymes and ion channels, while Trpm5 largely regulates the influx of monovalent cations, depolarizing the plasma membrane potentials. Thus, it is understandable that nullification of Gng13 may have more profound effect on cell physiology and consequent phenotypes than that of Trpm5, and similar differential effects were also found in the intestines (Frontiers in Immunology, 2023, DOI 10.3389/fimmu.2023.1259521).

      Data from several research groups have indicated that there are subtypes of tuft cells, each of which displays unique gene expression patterns as well as input and out signal profiles. It is yet not well understood how each subtype may contribute to the inflammatory responses or inflammation resolution. Comparative analyses of our data from the Gng13-cKO mice versus those from Pou2f3-KO mice suggest that Gng13-expressing tuft cells may have a role in the inflammation resolution while other ectopic tuft cells may contribute to the maintenance of the inflammation at a certain level, impairing subsequent tissue repairing and recovery. The exact molecular and cellular mechanisms are to be revealed in our future studies.

      The central nervous system may also play a role in the impaired lung recovery. But our detailed immunochemical studies did not identify any significant number of neurons innervating the lung tissue co-expressing ChAT and Gng13, suggesting that no immediate action from these neurons may regulate the pulmonary inflammation resolution or functional recovery.

      Together, our data suggest the importance of tuft cell subtype-specific functions, which may help us further understand the role of these rare tuft cells.

      (2) Figures showing alternative injury models inducing the generation of ectopic tuft cells are not convincing and not quantified. DCLK1 can be a bit promiscuous, so verifying tuft cell expansion in these other models with other markers (especially for LPS and HDM which have not been reported elsewhere) is important.

      We agree with the reviewer that DCLK1 is not a very specific marker for tuft cells. We have also observed that chemical inductions of these ectopic tuft cells with bleomycin, HDM or LPS are not as effective as H1N1 viruses. To verify that these rare DCLK1-positive cells are indeed tuft cells, we performed double immunostaining with antibodies to DCLK1 and to Gα-gustducin, another tuft cell marker. The results showed that some of these spindle-shaped DCLK1 positive cells indeed also express Gα-gustducin (see the newly added panels in Figure 1-figure supplement 1), indicating that they are most likely the chemically induced ectopic tuft cells. We also agree with the reviewer that it would be important to further investigate the possible roles of these cells during the stages of the chemically induced injury, inflammation resolution and functional recovery.

      (3) Calcium responses in isolated post-flu tuft cells are interesting but difficult to interpret as presented. Can higher-power images be shown? Also, no statistical analysis is presented to provide any confidence in that data.

      Thank the reviewer for the suggestions. As found in taste buds, only a subset of these ectopic tuft cells expresses Tas2rs, and each of these cells may express a few of the 35 murine Tas2rs. Thus, a particular bitter tasting compound can activate only few tuft cells and we had to use low-magnification to include more responsive cells in a field under the imaging microscope. We agree with the reviewer that it would be an interesting idea to statistically correlate the response profile to bitter substances with the cell’s Tas2r expression pattern, which we have done with sperm cells before (Molecular Human Reproduction, 2013, doi:10.1093/molehr/gas040). However, the main focus of this work is on the effect of Gng13-cKO in a subset of these ectopic tuft cells on the recovery. We plan to investigate these interesting cells in more details in the future.

      (4) I am unaware of Sytox being a specific dye for pyroptotic cells. Can the authors please provide a reference or otherwise justify this?

      Sytox is a dye to stain dead cells, which has been used previously in the studies on gasdermin-mediated lytic cell death (Xi et al., Up-regulation of gasdermin C in mouse small intestine is associated with lytic cell death in enterocytes in worm-induced type 2 immunity. PNAS 2021 118(30) e2026307118 https://doi.org/10.1073/pnas.2026307118). In our work we used the dye for the same assay.

      (5) The authors perform qPCR for various taste receptor genes pre- and post-flu, but do not show that these genes are specifically induced in tuft cells. Since single-cell data and bulk RNA-Seq are available from Barr et al., the authors should validate the expression of these Tas2r genes specifically in post-flu tuft cells.

      Thank the reviewer for the suggestion. Yes, we have performed analysis of the single-cell RNAseq dataset (GSE197163, Barr et al. 2022) and found that among 613 Trpm5-GFP+ tuft cells, Tas2r108 was expressed in the greatest number of cells, i.e., 67 cells, followed by Tas2r105, Tas2R138, Tas2r137, Tas2r118 and Tas2r102, which were detected in 11, 10, 10, 5 and 4 cells, respectively (see the newly added Figure 2-figure supplement 1). This order of expressing cell numbers is very much in agreement with that of the relative Tas2r expression levels obtained with the qPCR experiment (Figure 2A), indicating the expression of these Tas2rs likely in the ectopic tuft cells. We will further validate the data by analyzing the bulk RNA-Seq dataset when it is accessible to us.

      (6) Some general editing of language throughout would be helpful to increase readability.

      Thanks for pointing out. We have carefully checked the manuscripts, corrected some typos and revised several sentences to increase its readability.

      (7) For the fibrosis analysis, trichrome staining is very heterogenous, which is reflected by the large error bars in Fig. 8B. A more quantitative, "whole lung" analysis such as hydroxyproline content or western blotting for Col1a1 would be ideal.

      The approach of Masson’s trichrome staining along with qRT-PCR assays on the fibrotic gene expression has been used previously to quantitatively analyze fibrosis (e.g., Zhang et al., Neuropilin-1 mediates lung tissue-specific control of ILC2 function in type 2 immunity. Nature Immunology 23:237-250, 2022, https://doi.org/10.1038/s41590-021-01097-8). We agree with the reviewer that there are large error bars in Fig. 8B, and hydroxyproline content assay or western blotting for Col1a1 would be ideal. But our qRT-PCR was performed on the RNA samples extracted from the “whole lungs”, and its data are also able to reflect the extent of fibrosis of the lungs.

      (8) The authors claim that only a subset of tuft cells express Gng13, but this is supported only by a single IF image in Fig. 3 supplement 1G. The authors could download the single-cell dataset from Barr et al. to confirm the heterogeneity of Gng13 expression and get a better sense of the fraction of total ectopic tuft cells that express this, as it is a critical point in their model.

      Thank the reviewer for the suggestion. Yes, we have downloaded and reanalyzed the single-cell RNAseq dataset (GSE197163), and found that out of 613 Trpm5-GFP+ tuft cells, 350 or 57% of these cells expressed Gng13 (Figure 3-figure supplement 2I). This result, together with our immunohistochemical data (Figure 3-figure supplement 2G and H) indicates that Gγ13 is expressed in a subset of these ectopic tuft cells. More comprehensive studies are needed to characterize these tuft cell subtypes and elucidate subtype-selective functions.

      Reply to the recommendations of Reviewer #2:

      The study needs more rigorous examinations of the phenotypes. For example, quantification of the injury area in Fig3C is problematic. Similarly, fibrotic phenotype and quantification in Fig 8C also have problems. This study heavily used qRT-PCR analysis to quantitate the level change of bitter/other receptors in a minor population of tuft cells which are also minor in a whole lung. Given the limited number of cells, it is difficult to appreciate that qRT-PCR can pick up the difference. In addition, how would the findings in this study reconcile with the finding by Huang (PMID: 36129169) where pou2f3 null mutants (without tuft cells) were used? Huang et al. did not observe more severe phenotypes in the mice without tuft cells than controls.

      Thank the reviewer for the recommendations. Regarding Fig 3C, please see the reply below: revisions for clarity point #2.

      Fig 8 B and C used Masson’s trichrome staining to quantitatively analyze fibrosis, which has been used by other groups as well (e.g., Zhang et al., Neuropilin-1 mediates lung tissue-specific control of ILC2 function in type 2 immunity. Nature Immunology 23:237-250, 2022, https://doi.org/10.1038/s41590-021-01097-8). Our qRT-PCR data on the fibrotic gene expression (Figure 8A) further support the Masson’s trichrome staining results.

      We realized that tuft cells make up only a minor population in the lungs. So, we performed qRT-PCR assays on the RNA samples isolated from mostly the injured tissues along with the corresponding tissues from the uninjured lungs as control. To validate our qRT-PCR data, we reanalyzed the previously published single ectopic tuft cell RNAseq dataset (GSE197163), and found that the most abundantly expressed Tas2r108 determined by qRT-PCR was also expressed in the greatest number of tuft cells, and the order of expression levels of other Tas2rs are also well in agreement between the qRT-PCR and single-cell RNAseq data (Figure 2A, Figure 2-figure supplement 1), cross-validating the data obtained by these two very different approaches.

      We have carefully studied the finding by Huang (PMID: 36129169). Our data suggest that there are subtypes of the ectopic tuft cells, some of which contribute to the inflammation resolution while others play a proinflammatory role. Indeed, the reanalysis of the aforementioned single tuft cell RNAseq dataset found that about 57% Trpm5-GFP+ ectopic tuft cells expressed Gng13, and some of which expressed Alox5, a key enzyme to the biosynthesis of pro-resolving mediators. Thus, in the Pou2f3-knockout mice, both pro- and anti-inflammatory tuft cells are ablated, it would be hard to observe any significant phenotypes. When the function of a subset of Gγ13-expressing tuft cells is disrupted, the anti-inflammatory role from these cells is eliminated, resulting severer phenotypes. More studies are needed to further understand the subtype-specific functions of these fascinating tuft cells.

      Do Gγ13 null mutants show similar phenotypes in bleomycin injury model?

      Bleomycin and other chemicals-induced injury models indeed engender much fewer ectopic pulmonary tuft cells. Thus, it is more difficult to test the effect of Gng13 mutation due to the small number of the Gng13-expressing tuft cells in either WT or mutant lungs.

      What is the cell fate of lineage labeled tuft cells in the lungs of Chat-Cre:Ai9:Gng13flox/flox mice following viral infection at different times examined? The numbers were decreased at different time points post-injury based on the data. Did these cells undergo apoptosis? It is an excellent idea to look into the cell fate of ChAT-Cre:Ai9:Gng13flox/flox. We believe that these cells would have a similar fate to other ectopic tuft cells, probably undergoing apoptosis. But our data suggest that Gng13 mutation suppresses the increase the ectopic tuft cells, or the increase of a particular subtype of these tuft cells. Further studies are needed to elucidate the molecular mechanisms of the Gγ13-mediated signal transduction pathways regulating the proliferation of a subset of ectopic tuft cells.

      Here are the revisions for clarity and coherence to the figures:

      (1) Fig 2: For the functional assessment, using tracheal tuft cells from the same ChAT-Cre:Ai9 mice would be a suitable positive control in the calcium response traces experiment. These specific cells could also serve as a control in Fig2a.

      We would agree with the reviewer that tracheal tuft cells from the same ChAT-Cre: Ai9 mice would be an ideal positive control in the calcium response experiment as well as in the qRT-PCR assay. But we have established reliable methods to calcium image primary cells expressing taste receptors and quantify their RNA expression levels, which have been used in our previous publications, e.g., (1) Functional characterization of bitter taste receptors expressed in mammalian testis. Molecular Human Reproduction, 2013, doi:10.1093/molehr/gas040; (2) Infection by the parasitic helminth Trichinella spiralis activates a Tas2r-mediated signaling pathway in intestinal tuft cells. PNAS 2019, www.pnas.org/cgi/doi/10.1073/pnas.1812901116. We thank the reviewer for the excellent suggestion.

      (2) Fig 3C: It is not clear whether the depicted areas really represent the injured area. To provide a more comprehensive view, the authors should also provide histological analysis and quantification of the injured lung. A 3D representation of the injury area would offer a more accurate presentation.

      Thank the reviewer for the point. The depicted areas in Fig 3C are indeed the injured surface areas of the lungs. Following the reviewer’s suggestion, we carried out the histological analysis to determine the injured tissue volumes of the lungs. We fixed the lungs, and sliced them into 12 μm-thick sections, which were imaged under a microscope. The injured areas in a section were identified and quantified using the ImageJ software, and then the injured volume for this section was obtained by multiplying the area by the thickness of the section, i.e., 12 μm. Statistical analyses indicate that the injured volume of the Gng13-cKO lungs is significantly more than those of WT or Trpm5-KO mice, which has been included in Figure 3-figure supplement 1, and is in agreement with the data of the injured surface areas (Fig 3C).

      (3) Fig 3 G/I/K/M: There seems to be an inconsistency in the time points. There's no indication for 14 dpi, yet two for 25 dpi. Additionally, a color legend for each sample would be helpful.

      Thank the reviewer for pointing out. There were two typos, which have been corrected. Yes, the time points should be 14 dpi, 20 dpi, 25 dpi and 50 dpi. And a color legend has been added as well.

      (4) Fig 4A: Using CD64 co-stained with Krt5 might better highlight the immune cells in the damaged region. Additionally, could you clarify the choice of the neutrophil marker CD64 over CD45 for staining the injured lung?

      We agree with the reviewer that Krt5 antibody staining can help define the damaged region. We sectioned the lung tissues with a special attention to the damaged areas, but we found that the adjacent healthy areas also had extra immune cells. Thus, we counted in all these CD64+ cells in both the damaged as well as the surrounding, seemingly healthy areas. We used CD64 instead of CD45 to label these altered immune cells because we found that CD64 can better label the differential immune cells between WT and Gng13-cKO mice following H1N1 infection. Furthermore, CD64-labeled cells could be readily related to the Gsdmd/Gsdme-expressing F4/80-labeled immune cells shown in Figure 5 and its supplemental figures.

      (5) Fig 5 and Supplemental Fig 5: It appears that the F4/80 staining exhibits notable background staining.

      Yes, there is some background staining. The antibody was the best we could find, but its quality could be further improved. On the other hand, we thought that there were some cellular debris that might be stained positive by that antibody. At a higher magnification, however, we could still identify individual cells co-expressing IL-1β.

      (6) Fig 8C: The depicted area does not seem to adequately represent the fibrosis in the injured lung.

      Masson’s trichrome staining has been previously used to quantitatively analyze fibrosis (e.g., Zhang et al., Neuropilin-1 mediates lung tissue-specific control of ILC2 function in type 2 immunity. Nature Immunology 23:237-250, 2022, https://doi.org/10.1038/s41590-021-01097-8). Our qRT-PCR assays on the fibrotic gene expression (Figure 8A) were performed on the RNA samples extracted from the whole lungs, and the resultant data are able to reflect the extent of fibrosis of the lungs, although we also agree with the reviewer that additional data would make the conclusion more convincing.

    1. Author response:

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

      We express our sincere appreciation for your insightful comments and constructive suggestions. It is with great pleasure that we submit the revised version of our manuscript. Over the past months, we have meticulously considered all the invaluable feedback provided by the three anonymous reviewers, and endeavored to incorporate significant revisions accordingly. Furthermore, we have meticulously rephrased the results section in accordance with your guidance, aiming to bolster the rigor of our manuscript. The specific changes implemented in the revised manuscript are outlined below:

      - Revised the title of the manuscript.

      - Revised the description of early mitotic and meiotic chromosome structure in the scc3 mutant (Lines 167-274).

      - Added the BiFC results illustrating the interaction between SCC3 and other cohesin proteins in Figure S10.

      - Enhanced the detail in the description of figure legends, particularly for Figures 2 and 4.

      - Refined and rephrased the language of the manuscript.

      We hope these positive revisions have substantially strengthened the manuscript. Once again, we extend our heartfelt gratitude for your invaluable input.

      eLife assessment

      This important study elucidates the function of the cohesin subunit SCC3 in impeding DNA repair between inter-sister chromatids in rice. The observation of sterility in the SCC3 weak mutant prompted an investigation of abnormal chromosome behavior during anaphase I through karyotype analysis. While the evidence presented is largely solid, the strength of support can be substantially improved in some aspects, leaving room for further investigation. This research contributes to our understanding of meiosis in rice and attracts cell biologists, reproductive biologists, and plant geneticists.

      Public Reviews:

      Reviewer #1 (Public Review):

      The manuscript describes the identification and characterization of rice SCC3, including the generation and characterization of plants containing apparently lethal null mutations in SCC3 as well as mutant plants containing a c-terminal frame-shift mutation. The weak scc3 mutants showed both vegetative and reproductive defects. Specifically, mitotic chromosomes appeared to partially separate during prometaphase, while meiotic chromosomes were diffuse during early meiosis and showed alterations in sister chromatid cohesion, homologous chromosome pairing, and recombination. The authors suggest that SCC3 acts as a cohesin subunit in mitosis and meiosis, but also plays more functions other than just cohesion.

      Reviewer #2 (Public Review):

      This manuscript shows detailed evidence of the role of cohesin regulators in rice meiosis and mitosis.

      Reviewer #3 (Public Review):

      Prior research on SCC3, a cohesin subunit protein, in yeast and Arabidopsis has underscored its vital role in cell division. This study investigated into the specific functions of SCC3 in rice mitosis and meiosis. In a weakened SCC3 mutant, sister chromatids separating was observed in anaphase I, resulting in 24 univalents and subsequent sterility. The authors meticulously documented SCC3's loading and degradation dynamics on chromosomes, noting its impact on DNA replication. Despite the loss of homologous chromosome pairing and synapsis in the mutant, chromosomes retained double-strand breaks without fragmenting. Consequently, the authors inferred that in the scc3 mutant, DNA repair more frequently relies on sister chromatids as templates compared to the wild type.

      We extend our sincere gratitude to the Editors and the Reviewers for their highly constructive and insightful suggestions. We deeply appreciate receiving both positive feedback and constructive criticism on our manuscript. In light of the reviewers’ comments, we have diligently undertaken substantial revisions to improve the manuscript. The revised version comprehensively addresses all the points raised by the reviewers.

      Below, we provide a detailed point-by-point response to the reviewers’ comments:

      Recommendations for the authors:

      Reviewer #1:

      (1) Line 170- looking at pollen formation does not specifically evaluate whether SCC3 is involved in meiosis.

      Thank you very much for this advice. We totally agree with your point of view that pollen formation defects only indicate the problem of gametogenesis. We are sorry for not accurately describing this sentence. It has been revised in the manuscript (Lines 167-176).

      (2) Lines 203-205- this seems more like discussion and is pure speculation. Another possibility described above is that the truncated SCC3 protein is partially functional and what they see is due to this partial functionality. Have the authors considered the possibility that a partially functional version of SCC3 is produced that alters its function or the function of the cohesin complex? How much of the protein epitope remains in the truncated protein?

      We are so grateful for the insightful suggestions provided. We concur with the proposition that a partially functional SCC3 may indeed be synthesized, contributing to the survivability of the mutant. Notably, the truncated version of the protein retains approximately 60% to 70% of the epitope, which ostensibly maintains a residual functionality within the weak scc3 mutant. In this manuscript, the loss of C-terminal 910-1116 aa of SCC3 contains a special protein epitope and a certain protein secondary structure, which may alter the protein’s folding and its subsequent roles within the cohesin complex.

      In this study, we encountered challenges in generating null alleles of the scc3 mutants in rice utilizing the CRISPR-Cas9 system. Consequently, it is plausible that the scc3-1 and scc3-2 variants represent null alleles of SCC3, resulting in embryonic lethality. We posit that the identification of weak alleles is paramount to facilitating the survival of the organism. Thus, selecting some weak mutants, particularly those exhibiting the most pronounced phenotype, is advantageous for conducting further research. Our findings indicate that the diminished scc3 mutant lacks only a segment of the C-terminal, yet this deficiency is adequate to ensure the plant's survival while significantly impeding the meiotic process. We cannot dismiss the likelihood that these observed defects are attributable to the unique truncated proteins. We extend our sincerest thanks once again.

      (3) Lines 212- I question whether what the authors see in Figure 2 is chromosome fragmentation. It could just as well be alterations in chromosome structure. Likewise, the authors provide little to no evidence that the mutation affects the replication process. Rather, the presence of replicated chromosomes later in mitosis and meiosis would argue that replication is not disrupted.

      We express our gratitude to the reviewer for highlighting this critical inquiry. Contrary to the scenario of chromosome fragmentation, as you astutely observed, the preservation of normal sister chromatids during prometaphase indicates that the replication process remains uninterrupted. In alignment with your insights, our study embarked on an extensive series of full-length fluorescence in situ hybridization (FISH) experiments to elucidate the underlying mechanisms contributing to the observed increase in the distance between sister chromatids, particularly during interphase. The preponderance of our findings corroborates the hypothesis that the chromosomes exhibit alterations in structure, as depicted in Figure 2A. Intriguingly, our data suggest that cohesin, upon interaction with other chromatin-bound proteins, may facilitate loop extrusion, anchoring itself in a manner that potentially alters chromosomal architecture. These alterations in chromosome structure and the subsequent defects in genome folding and cohesion establishment, particularly rely on SCC3. In response to your valuable suggestions, we have meticulously revised the relevant sections of our manuscript. We extend our sincere thanks for your insightful comments.

      (4) Line 230- what does the sentence SCC3 may enhance the interaction with DNA mean, the interaction of the cohesin complex?

      We are sorry for the ambiguity in our initial description and wish to clarify that SCC3 indeed plays a pivotal role in augmenting the interaction between the cohesin complex and DNA. Our observations revealed an upsurge in the signal intensity of SCC3 as cells transition from interphase to prophase, as depicted in Figure 2B. This enhancement correlates with the observed defects in scc3 mutants during prophase, suggesting that SCC3’s functional significance is particularly pronounced at this stage of the cell cycle. We have revised our manuscript to reflect these insights more accurately, in accordance with your valuable suggestions. We express our sincere gratitude for your guidance.

      (5) Oddly, and unexplainably the authors present data indicating that SCC3 interacts with RAD21.1, but not SMC1, SMC3, or REC8. The fact that the authors report that SCC3 only interacts with RAD21.1 but no other cohesin proteins is quite hard to explain.

      As argued in the point above, the available data do not provide compelling evidence supporting the interaction between SCC3 and other cohesin proteins. We have repeated yeast two-hybrid (Y2H) experiments yielding consistent outcomes, which also surprised us initially. In the revised manuscript, we further added the bimolecular fluorescence complementation (BiFC) results between SCC3 and other cohesin proteins in rice protoplast (Figure S10). These supplementary data affirm that SCC3 predominantly interacts with RAD21.1, excluding interactions with other cohesin proteins. While the absence of such interactions is perplexing, our investigations have failed to detect any binding between SCC3 and other cohesin proteins.

      A weak interaction between SCC3 and REC8 has been reported in Arabidopsis (Kuttig et al. bioRxiv https://doi.org/10.1101/2022.06.20.496767). We speculate that either these proteins do not interact or the yeast-hybrid assays may be inadequate for detecting their interaction, as several factors can impede interaction in a heterologous system. In Figure 7, we could only detect the interaction between SCC3 and RAD21.1 in both Y2H and BiFC experiments. This suggests potential alterations in protein folding or conformation, or the involvement of additional regulatory factors modulating the interaction between SCC3 and other cohesin proteins. Notably, given RAD21.1’s pivotal role as a core component in the cohesin complex, our supplementary findings demonstrate the interactions between SMC1/3 and RAD21.1 (data not shown). Consequently, our current data propose a model wherein RAD21.1 and SMC1/3 form a circular structure, with SCC3 positioned on the outer periphery of the ring complex, associating specifically with RAD21.1 (Figure 8A).

      Reviewer #2:

      The authors did not consider creating heterozygous mutants for the replication fork. Moderate English language editing may be required.

      We extend our gratitude to the reviewer for their valuable suggestions. Initially, we did not explore the potential relationship between SCC3 and the replication fork. Cohesin, as we understand, becomes associated with DNA prior to DNA replication. The phenomenon of sister chromatid co-entrapment arises as replication forks traverse through cohesin rings, a process intricately linked to DNA replication dynamics. In this study, we exclusively observed aberrant chromosome structures in the scc3 mutant during interphase (Figure 2). We conjecture that these anomalies may stem from alterations in chromosome structure, such as genome folding and loop extrusion, rather than being directly attributable to the DNA replication fork. However, the precise nature of these chromosome structural aberrations during interphase in the scc3 mutant remains elusive, necessitating further comprehensive investigation in future studies. We have refined the language of our manuscript in accordance with the reviewer’s suggestions. Once again, we express our sincere appreciation for the invaluable suggestions provided.

      Reviewer #3:

      While the paper's conclusions are generally well-supported, further substantiation is needed for the claim that SCC3 inhibits template choice for sister chromatids. To bolster this conclusion, I recommend that the authors perform whole-genome sequencing on parental and F1 individuals from two rice variants, subsequently calculating the allele frequencies at heterozygous sites in the F1 individuals. If SCC3 indeed inhibits inter-sister chromatid repair in the wild type, we would anticipate a higher frequency of inter-homologous chromosome repair (i.e., gene conversion). This should be manifested as a bias away from the Mendelian inheritance ratio (50:50) in the offspring of the wild type compared to the offspring of the scc3+/- mutant.

      We express our sincere appreciation for your insightful suggestions. It is really a good suggestion. We have arranged to do this experiment. As it takes long time to prepare plant materials and sequence analysis, we hope the ongoing sequencing work will get some important information supporting those hypotheses. As we have not obtained the direct evidence that SCC3 involved in sister chromatid repair, we changed the title as “SCC3 is an axial element essential for homologous chromosome pairing and synapsis”. Once again, we really extend our gratitude for your invaluable suggestions.

      A point that warrants consideration is the placement of the protein interaction experiments involving SCC3 within the paper. It is presented relatively late in the manuscript. If the authors possess information regarding the interaction between RAD21.1 and SCC3 and how it relates to the functional study of RAD21.1, it could contribute to a more comprehensive analysis. However, if this information is unrelated to the current study, it might be advisable to omit it, as it appears to diverge from the main focus of this work.

      We express our sincere gratitude for your invaluable suggestions. It has been documented in yeast that the interaction between SCC3 and SCC1 is indispensable for the efficient loading of cohesin. In our study, we endeavored to elucidate the intricate relationships among various cohesin subunits. Through our investigations, we have discerned that RAD21.1 serves as a pivotal core subunit within the cohesin complex, facilitating interactions with both SMC1/3 and SCC3 (data not shown). Additionally, our findings indicate that the interaction between RAD21.1 and SCC3 is imperative for maintaining the stability of the cohesin ring and its association with DNA (data not shown). Consequently, the interaction between these two proteins assumes paramount importance for our subsequent analyses. This study holds significant promise for future investigations.

      It's worth noting that while the title of the study claims that "SCC3 inhibits inter-sister chromatids repair during rice meiosis," the last sentence of the abstract weakens this conclusion by using the word "seems." A study's title should ideally reflect the most definitive and conclusive findings.

      We sincerely appreciate your valuable suggestions. In response, we have revised the description in our manuscript to enhance its rigor.

      In Figure 8C, it appears that cohesin is depicted between two DNA strands.

      Figure 8C illustrates the process of sister chromatid repair during meiosis in the scc3 mutant. Two gray lines and two blue lines represent the four sister chromatids of two homologous chromosomes, respectively. In the wild type, cohesin plays a crucial role in tethering together the two sister chromatids. As per your reminder, cohesin should indeed encircle the two sister chromatids, as depicted in Figure 8B. Following a thorough evaluation and to mitigate any potential confusion, we have deleted Figure 8C.

    1. Author response

      Reviewer #1 (Public Review):

      Summary:

      The authors aimed to modify the characteristics of the extracellular matrix (ECM) produced by immortalized mesenchymal stem cells (MSCs) by employing the CRISPR/Cas9 system to knock out specific genes. Initially, they established VEGF-KO cell lines, demonstrating that these cells retained chondrogenic and angiogenic properties. Additionally, lyophilized carriage tissues produced by these cells exhibited retained osteogenic properties.

      Subsequently, the authors established RUNX2-KO cell lines, which exhibited reduced COLX expression during chondrogenic differentiation and notably diminished osteogenic properties in vitro. Transplantation of lyophilized carriage tissues produced by RUNX2-KO cell lines into osteochondral defects in rat knee joints resulted in the regeneration of articular cartilage tissues as well as bone tissues, a phenomenon not observed with tissues derived from parental cells. This suggests that gene-edited MSCs represent a valuable cell source for producing ECM with enhanced quality.

      Strengths:

      The enhanced cartilage regeneration observed with ECM derived from RUNX2-KO cells supports the authors' strategy of creating gene-edited MSCs capable of producing ECM with superior quality. Immortalized cell lines offer a limitless source of off-the-shelf material for tissue regeneration.

      We thank the reviewer for the interest in our work. We however want to clarify that the present manuscript does not report the generation of ECM with “superior quality”, but rather of modulated composition and thus function.

      Weaknesses:

      Most data align with anticipated outcomes, offering limited novelty to advance scientific understanding. Methodologically, the chondrogenic differentiation properties of immortalized MSCs appeared deficient, evidenced by Safranin-O staining of 3D tissues and histological findings lacking robust evidence for endochondral differentiation. This presents a critical limitation, particularly as authors propose the implantation of cartilage tissues for in vivo experiments. Instead, the bulk of data stemmed from type I collagen scaffold with factors produced by MSCs stimulated by TGFβ.

      The chondrogenic differentiation of our MSOD-B line and their capacity of undergoing endochondral ossification has been robustly demonstrated in previous studies (Pigeot et al., Advanced Materials 2021 and Grigoryan et al., Science Translational Medicine 2022). In the present manuscript, we thus compare the chondrogenic capacity of newly established VEGF-KO and RUNX-KO lines to those of MSOD-B cells. We demonstrate by qualitative (Safranin-O staining, Collagen type 2 and Collagen type X immuno-stainings) and quantitative (glycosaminoglycans assay) assays that the generated tissues consist in cartilage grafts of similar quality than the MSOD-B counterpart. Of note, the safranin-O stainings were performed on lyophilized tissues, which can alter the staining quality/intensity. We will thus provide additional stainings of generated tissues pre-lyophilization.

      The rationale behind establishing VEGF-KO cell lines remains unclear. What specific outcomes did the authors anticipate from this modification?

      VEGF is a known master regulator of angiogenesis and a key mediator of endochondral ossification. It has also been extensively used in bone tissue engineering studies as a supplemented factor – primarily in the form of VEGFα – to increase the vascularization and thus outcome of bone formation of engineered grafts (https://www.nature.com/articles/s42003-020-01606-9, https://www.sciencedirect.com/science/article/pii/S8756328216301752). In our study, it was thus identified as a natural candidate to demonstrate the possibility to generate VEGF-KO cartilage and subsequently assess the functional impact on both the angiogenic and osteogenic potential of resulting cartilage tissue.

      Insufficient depth was given to elucidate the disparity in osteogenic properties between those observed in ectopic bone formation and those observed in transplantation into osteochondral defects. While the regeneration of articular cartilage in RUNX2-KO ECM presents intriguing results, the study lacked an exploration into underlying mechanisms, such as histological analyses at earlier time points.

      Using RUNX2-KO ECM, we aimed at demonstrating the impact on cartilage remodeling and bone formation. This was performed ectopically but also in the rat osteochondral defect as a regenerative set-up of higher clinical relevance. We agree with the reviewer that additional experimental groups and time-points (not only earlier but also longer ones) would offer a better mechanistic understanding of the ECM contribution to the joint repair. However, as stated in our manuscript this is a proof-of-concept study that successfully demonstrated the influence of the cartilage ECM modification on the in vivo skeletal regeneration. A follow-up study would need to be performed to complement existing evidence and strengthen the relevance of our approach for cartilage repair.

      Reviewer #2 (Public Review):

      The manuscript submitted by Sujeethkumar et al. describes an alternative approach to skeletal tissue repair using extracellular matrix (ECM) deposited by genetically modified mesenchymal stromal/stem cells. Here, they generate a loss of function mutations in VEGF or RUNX2 in a BMP2-overexpressing MSC line and define the differences in the resulting tissue-engineered constructs following seeding onto a type I collagen matrix in vitro, and following lyophilization and subcutaneous and orthotopic implantation into mice and rats. Some strengths of this manuscript are the establishment of a platform by which modifications in cell-derived ECM can be evaluated both in vitro and in vivo, the demonstration that genetic modification of cells results in complexity of in vitro cell-derived ECM that elicits quantifiable results, and the admirable goal to improve endogenous cartilage repair. However, I recommend the authors clarify their conclusions and add more information regarding reproducibility, which was one limitation of primary-cell-derived ECMs.

      We thank the reviewer for the positive evaluation of our work.

      Overcoming the limitations of native/autologous/allogeneic ECMs such as complete decellularization and reduction of batch-to-batch variability was not specifically addressed in the data provided herein. For the maintenance of ECM organization and complexity following lyophilization, evidence of complete decellularization was not addressed, but could be easily evaluated using polarized light microscopy and quantification of human DNA for example in constructs pre and post-lyophilization.

      We will clarify the experiments and characterization performed with lyophilized tissues versus those performed with decellularized ones. We will also provide evidence of DNA removal in our decellularized ECMs.

      It would be ideal to see minimization of batch-to-batch variability using this approach, as mitigation of using a sole cell line is likely not sufficient (considering that the sole cell line-derived Matrigel does exhibit batch-to-batch and manufacturer-to-manufacturer variability). I recommend adding details regarding experimental design and outcomes not initially considered. Inter- and intra-experimental reproducibility was not adequately addressed. The size of in vitro-derived cartilage pellets was not quantified, and it is not clear that more than one independent 'differentiation' was performed from each gene-edited MSC line to generate in vitro replicates and constructs that were implanted in vivo.

      We thank the Reviewer for the comment on variability/reproducibility concern. Using a cell line does confer higher robustness but indeed does not grant unlimited consistency of batch production. We will temper our claims in the discussion and mention the need to regularly re-characterize cell lines properties upon passages.

      In our study, our grafts have been generated from various batches and tested in more than one experimental repeat. This will be further described in the revised version of our manuscript. We will also implement data on the size variability of generated tissues.

      The use of descriptive language in describing conclusions may mislead the reader and should be modified accordingly throughout the manuscript. For example, although this reviewer agrees with the comparative statements made by the authors regarding parental and gene-edited MSC lines, non-quantifiable terms such as 'frank' 'superior' (example, line 242) are inappropriate and should rather be discussed in terms of significance. Another example is 'rich-collagenous matrix,' which was not substantiated by uniform immunostaining for type II collagen (line 189).

      I have similar recommendations regarding conclusive statements from the rat implantation model, which was appropriately used for the purpose of evaluating the response of native skeletal cells to the different cell-derived ECMs. Interpretations of these results should be described with more accuracy. For example, increased TRAP staining does not indicate reduced active bone formation (line 237). Many would not conclude that GAGs were retained in the RUNX2-KO line graft subchondral region based on the histology. Quantification of % chondral regeneration using histology is not accurate as it is greatly influenced by the location in the defect from which the section was taken. Chondral regeneration is usually semi-quantified from gross observations of the cartilage surface immediately following excision. The statements regarding integration (example line 290) are not founded by histological evidence, which should show high magnification of the periphery of the graft adjacent to the native tissue.

      We thank the Reviewer for the constructive suggestions. We will revise language accordingly throughout the manuscript.

      Reviewer #3 (Public Review):

      Summary:

      In this study, the authors have started off using an immortalized human cell line and then gene-edited it to decrease the levels of VEGF1 (in order to influence vascularization), and the levels of Runx2 (to decrease chondro/osteogenesis). They first transplanted these cells with a collagen scaffold. The modified cells showed a decrease in vascularization when VEGF1 was decreased, and suggested an increase in cartilage formation.

      In another study, the matrix generated by these cells was subsequently remodeled into a bone marrow organ. When RUNX2 was decreased, the cells did not mineralize in vitro, and their matrices expressed types I and II collagen but not type X collagen in vitro, in comparison with unedited cells. In vivo, the author claims that remodeling of the matrices into bone was somewhat inhibited. Lastly, they utilized matrices generated by RUNX2 edited cells to regenerate chondro-osteal defects. They suggest that the edited cells regenerated cartilage in comparison with unedited cells.

      Strengths:

      -The notion that inducing changes in the ECM by genetically editing the cells is a novel one, as it has long been thought that ECM composition influences cell activity.

      -If successful, it may be possible to make off-the-shelf ECMS to carry out different types of tissue repair.

      We thank the Reviewer for the critical evaluation of our work and the highlighted novelty of it.

      Weaknesses:

      -The authors have not generated histologically identifiable cartilage or bone in their transplants of the cells with a type I scaffold.

      The chondrogenic differentiation of our MSOD-B line and their capacity of undergoing endochondral ossification has been robustly demonstrated in previous studies (Pigeot et al., Advanced Materials 2021 and Grigoryan et al., Science Translational Medicine 2022). In the present manuscript, we thus compare the chondrogenic capacity of newly established VEGF-KO and RUNX-KO lines to those of MSOD-B. We demonstrate by qualitative (Safranin-O staining, Collagen type 2 and Collagen type X immuno-stainings) and quantitative (glycosaminoglycans assay) assays that the generated tissues consist in cartilage tissue of similar quality than the MSOD-B. However, the safranin-O stainings were performed on lyophilized tissues, which can alter the staining quality/intensity. We will thus provide additional stainings of generated tissues pre-lyophilization.

      On the contested formation of bone in vivo by our ECMs grafts, we have provided compelling qualitative evidence via Masson´s Trichrome stainings and quantification of mineralized volume by µCT. Both cortical bone and trabecular structures were identified ectopically. Those are standard evaluation methods in the field, we would be happy to receive additional suggestions by the Reviewer.

      -In many cases, they did not generate histologically identifiable cartilage with their cell-free-edited scaffold. They did generate small amounts of bone but this is most likely due to BMPs that were synthesized by the cells and trapped in the matrix.

      We now appreciate that the Reviewer agrees on the successful formation of bone induced by our engineered grafts. We however still respectfully disagree with the “small amount of bone” statement since our MSOD-B and MSOD-B VEGF KO cartilage grafts led to the full generation of a mature ectopic bone organ (that is, also composed of extensive marrow). This has been assessed qualitatively and quantitatively.

      We agree with the Reviewer on the key role of BMP-2 in the remodeling process into bone and bone marrow, which we have extensively described in our previous publication (Pigeot et al., Advanced Materials 2021). We previously demonstrated that the low amount of BMP-2 (in the dozens of nanogram/tissue range) embedded in the matrix is not sufficient per se to induce ectopic endochondral ossification. It is the combined presence of GAGs in the matrix -thus cartilage- that allows the success of bone formation. Since we have already demonstrated in the present manuscript that the GAGs content is the same in MSOD-B and MSOD-B edited ECMs, we will provide additional data demonstrating the maintenance of BMP-2 content in all generated cartilage tissues.

      -There is a great deal of missing detail in the manuscript.

      We will provide additional information on the MSOD-B line and the overall methodology in our revised version.

      -The in vivo study is underpowered, the results are not well documented pictorially, and are not convincing.

      We will provide additional information and pictures related to our in vivo studies. We believe our group size supports our conclusions confirmed by statistical assessment.

      -Given the fact that they have genetically modified cells, they could have done analyses of ECM components to determine what was different between the lines, both at the transcriptome and the protein level. Consequently, the study is purely descriptive and does not provide any mechanistic understanding of what mixture of matrix components and growth factors works best for cartilage or bone. But this presupposes that they actually induced the formation of bona fide cartilage, at least.

      We thank the Reviewer for the suggestion. However, our study did not aim at understanding what ECM graft composition work best for cartilage nor bone regeneration respectively. Instead, we propose the exploitation of our cellular tools to interrogate the function of key ECM constituents and their impact in skeletal regeneration. We once more confirm that we generated lyophilized cartilage grafts which will be more evidently supported by histological assessment before lyophilization.

    1. Author response:

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

      We thank the reviewers for their thorough review of and overall positive comments on our manuscript. We have revised the manuscript to address the one remaining concern raised by one of the reviewers. This is described below.

      Fig.1B-C: To give a standard deviation from 2 data points has no statistical significance. In this case it would be better to define as range/difference of the 2 data points.

      We have modified the legend for Figure 1 to now read, “The average of two experiments is plotted with the bars representing the range of each time point.”

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public review):

      In 'Systems analysis of miR-199a/b-5p and multiple miR-199a/b-5p targets during chondrogenesis', Patel et al. present a variety of analyses using different methodologies to investigate the importance of two miRNAs in regulating gene expression in a cellular model of cartilage development. They first re-analysed existing data to identify these miRNAs as one of the most dynamic across a chondrogenesis development time course. Next, they manipulated the expression of these miRNAs and showed that this affected the expression of various marker genes as expected. An RNA-seq experiment on these manipulations identified putative mRNA targets of the miRNAs which were also supported by bioinformatics predictions. These top hits were validated experimentally and, finally, a kinetic model was developed to demonstrate the relationship between the miRNAs and mRNAs studied throughout the paper.

      I am convinced that the novel relationships reported here between miR-199a/b-5p and target genes FZD6, ITGA3, and CAV1 are likely to be genuine. It is important for researchers working on this system and related diseases to know all the miRNA/mRNA relationships but, as the authors have already published work studying the most dynamic miRNA (miR-140-5p) in this biological system I was not convinced that this study of the second miRNA in their list provided a conceptual advance on their previous work.

      We believe this study is an enhancement on our previous work for two reasons, which have been alluded to in new text within the introduction. Firstly, our previous work used experimental and bioinformatic analysis to identify microRNAs with significant regulatory roles during chondrogenesis. This new manuscript additionally uses  a systems biology approaches to identify novel miRNA-mRNA interactions and capture these within an in silico model. Secondly, this work was initiated by the analysis of our previously generated data – using a novel tool we developed for this type of data (Bioconductor - TimiRGeN).  

      I was also concerned with the lack of reporting of details of the manipulation experiments. The authors state that they have over-expressed miR-199a-5p (Figure 2A) and knocked down miR-199b-5p (Figure 2B) but they should have reported their proof that these experiments had worked as predicted, e.g. showing the qRT-PCR change in miRNA expression. Similarly, I was concerned that one miRNA was over-expressed while the other was knocked down - why did the authors not attempt to manipulate both miRNAs in both directions? Were they unable to achieve a significant change in miRNA expression or did these experiments not confirm the results reported in the manuscript?

      We agree with the reviewer that some additional data were needed to demonstrate the effective regulation of miR-199-5p.  Hence, Supplementary Figure 1 is now included which provides validation of the effects of miR-199a-5p overexpression (Supplementary Figure 1A) and inhibition of miR-199a/b-5p (Supplementary Figure 1B). Within the main manuscript, Figure 2B has been amended to include the consequences of inhibition of miR-199a-5p, with 2C showing the consequences of miR-199b-5p inhibition. Further, we include new data with regards to miR-199a/b-5p inhibition on CAV1 (Figure 4A). 

      I had a number of issues with the way in which some of the data was presented. Table 1 only reported whether a specific pathway was significant or not for a given differential expression analysis but this concealed the extent of this enrichment or the level of statistical significance reported. Could it be redrawn to more similarly match the format of Figure 3A? The various shades of grey in Figure 2 and Figure 4 made it impossible to discriminate between treatments and therefore identify whether these data supported the conclusions made in the text. It also appeared that the same results were reported in Figure 3B and 3C and, indeed, Figure 3B was not referred to in the main text. Perhaps this figure could be made more concise by removing one of these two sets of panels.

      We agree with all points made here and have amended these within the manuscript. Figure 1A is now pathway enrichment plots from the TimiRGeN R Bioconductor package, and the table which previously showed the pathways enriched at each time point is now in the supplementary materials (supp. Table 1). Figure 2 and 4 now have color instead of shades of grey. Figure 3C has now been moved to supplementary materials (Supplementary Figure 2) and is referenced in the text. 

      Overall, while I think that this is an interesting and valuable paper, I think its findings are relatively limited to those interested in the role of miRNAs in this specific biomedical context.

      Reviewer #2 (Public review):

      Summary:

      This study represents an ambitious endeavor to comprehensively analyze the role of miR199a/b-5p and its networks in cartilage formation. By conducting experiments that go beyond in vitro MSC differentiation models, more robust conclusions can be achieved.

      Strengths:

      This research investigates the role of miR-199a/b-5p during chondrogenesis using bioinformatics and in vitro experimental systems. The significance of miRNAs in chondrogenesis and OA is crucial, warranting further research, and this study contributes novel insights.

      Weaknesses:

      While miR-140 and miR-455 are used as controls, these miRNAs have been demonstrated to be more relevant to Cartilage Homeostasis than chondrogenesis itself. Their deficiency has been genetically proven to induce Osteoarthritis in mice. Therefore, the results of this study should be considered in comparison with these existing findings.

      We agree with the reviewers comments. miR-455-null mice develop normally but miR-140-null (or mutated) mice and humans do have skeletal abnormalities (e.g. Nat Med. 2019 Apr;25(4):583-590. doi: 10.1038/s41591-019-0353-2), indicating a role in chondrogenesis.  We have made an addition in the description to point towards the need to assess the roles miR-199a/b-5p may play during skeletogenesis and OA. We anticipate miR-199a/b-5p to be relevant in OA and have ongoing additional work for this – but this beyond the scope of this manuscript. 

      Recommendations to Authors:

      Reviewer #1 (Recommendations to authors):

      Beyond the issues raised in the public review, I had a few minor recommendations that are largely designed to help improve the understanding of the manuscript as it is currently written.

      (1) Please provide the statistical tests used to obtain p-values in the Figure 2 and 4 legends.

      We have now added statistical test information to the figure legends of figures 2 and 4.

      (2) It is stated on p. 9 that both miRNAs may share a functional repertoire because 25 and 341 genes are interested between their inhibition experiments. Please provide statistical support that this overlap is an enrichment over the null background in this experiment. Total DE genes – chi squared. Expected / Observed. 

      A chi-squared test is now presented in the manuscript which shows that the number of significant genes which were found in common between miR-199a-5p knockdown and miR-199b-5p knockdown were significantly more than expected for day 0 or day 1 of the experiments. 

      (3) The final sentence on p. 12 (beginning 'Size of the points reflect...') seemed out of place - is it part of a legend?

      Thank you for pointing out this mistake - it was part of figure 3C and now is in the supplementary materials.

      (4) A sentence on p. 14 reads that 'FZD6 and ITGA3 levels increased significantly' but this should read decreased, rather than increased. Quite an important typo!

      Thank you for pointing this error out. It has been corrected.

      (5) Theoretical transcripts are mentioned in the legend of Figure 5A but these were not present in the figure. Please include these or remove them from the legend.

      This error has been removed form Figure 5A.

      (6) On p 20, the references 22 and 27 should I think be moved to earlier in the sentence (after 'miR-199a-5p-FZD6 has been predicted previously'). Currently, it reads as if these references support your luciferase assays which you claim are the first evidence for this target relationship.

      We agree with this change and have corrected the manuscript.

      (7) The reference to Figure 5D on p. 20 should be a reference to Figure 5C.

      Thank you for pointing this error out – this has been corrected.

      Reviewer #2 (Recommendations to authors):

      (1) The paper is based on the importance of miR-140 and miR-455 as miRNAs in chondrogenesis, citing only Barter, M. J. et al. Stem Cells 33, (2015). Considering the scope and results of this study, this citation is insufficient.

      We agree with this reviewers comments. For many year miR-140 and miR-455 have been experimented on and their importance in OA research has become apparent. We included additional references within the introduction to address this.

      (2) Analyzing chondrogenesis solely through differentiation experiments from MSCs is inadequate. It is essential to perform experiments involving the network within normal cartilage tissue and/or the generation of knockout mice to understand the precise role of miR199a/b-5p in chondrogenesis.

      We have added an additional paragraph in the discussion to state this, and do believe it is highly important that miR-199a/b-5p be tested in OA samples – however this would be beyond the intended scope of this article.

      (3) In light of the above points, it is imperative to investigate the role of miR-199a/b-5p beyond the in vitro differentiation model from MSCs, encompassing mouse OA models or human disease samples.

      In tangent with the previous address, we agree with the pretense and believe additional experiments should be performed to gain more insight to the mechanism of how miR-199a/b-5p regulate OA. But development of a new mouse line to investigate this is not in the scope of this manuscript.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      In this study the authors use an elegant set of single-molecule experiments to assess the transcriptional and post-transcriptional regulation of RecB. The question stems from a previous observation from the same lab, that RecB protein levels are low and not induced under DNA damage. The authors first show that recB transcript levels are low and have a short half-life. They further show that RecB levels are likely regulated via translational control. They provide evidence for low noise in RecB protein levels across cells and show that the translation of the mRNA increases under double-strand break conditions. Authors identify Hfq binding sites in the recbcd [recBCD] operon and show that Hfq regulates the levels of RecB protein without changing the mRNA levels. They suggest that RecB translation is directly controlled by Hfq binding to mRNA, as mutating one of the binding sites has a direct effect on RecB protein levels.

      Strengths:

      The implication of Hfq in regulation of RecB translation is important and suggests mechanisms of cellular response to DNA damage that are beyond the canonically studied mechanisms (such as transcriptional regulation by LexA). Data are clearly presented and the writing is direct and easy to follow. Overall, the study is well-designed and provides novel insights into the regulation of RecB, that is part of the complex required to process break ends.

      Weaknesses:

      Some key findings need additional support/ clarifications to strengthen the conclusions. These are suggested to the authors.

      Reviewer #2 (Public Review):

      Summary:

      The authors carry out a careful and rigorous quantitative analysis of RecB transcript and protein levels at baseline and in response to DNA damage. Using single-molecule FISH and Halo-tagging in order to achieve sensitive measurements, they provide evidence that enhanced RecB protein levels in response to DNA damage are achieved through a post-transcriptional mechanism mediated by the La-like RNA binding protein, Hhq1 [Sm-like RNA binding protein, Hfq]. In terms of biological relevance, the authors suggest that this mechanism provides a way to control the optimum level of RecB expression as both deletion and over-expression are deleterious. In addition, the proposed mechanism provides a new framework for understanding how transcriptional noise can be suppressed at the protein level.

      Strengths:

      Strengths of the manuscript include the rigorous approaches and orthogonal evidence to support the core conclusions, for example, the evidence that altering either Hhq1 [Hfq] or its recognition sequence on the RNA similarly enhance the protein to RNA ratio of RecB. The writing is clear and the experiments are well-controlled. The modeling approaches provide essential context to interpret the data, particularly given the small numbers of molecules per cell. The interpretations are careful and well supported.

      Weaknesses:

      The authors make a compelling case for the biological need to exquisitely control RecB levels, which they suggest is achieved by the pathway they have uncovered and described in this work. However, this conclusion is largely inferred as the authors only investigate the effect on cell survival in response to (high levels of) DNA damage and in response to two perturbations - genetic knock-out or over-expression, both of which are likely more dramatic than the range of expression levels observed in unstimulated and DNA damage conditions.

      In the discussion, we proposed that the post-transcriptional regulation of recB that we have uncovered could be involved in keeping RecB levels within an optimal range. We agree that testing the phenotypic impact of small changes in RecB levels would add additional strength to this suggestion. However, this is experimentally very challenging because of the low copy number of RecB molecules, which makes it difficult to slightly alter RecB levels in a controlled and homogeneous (across cells) manner. Developing the synthetic biology tools necessary for such an experiment is beyond the scope of this article. In the manuscript, we will clarify the limits of our interpretation of the role of the uncovered regulation.

      Reviewer #3 (Public Review):

      Summary:

      The work by Kalita et al. reports regulation of RecB expression by Hfq protein in E.coli cell. RecBCD is an essential complex for DNA repair and chromosome maintenance. The expression level needs to be regulated at low level under regular growth conditions but upregulated upon DNA damage. Through quantitative imaging, the authors demonstrate that recB mRNAs and proteins are expressed at low level under regular conditions. While the mRNA copy number demonstrates high noise level due to stochastic gene expression, the protein level is maintained at a lower noise level compared to expected value. Upon DNA damage, the authors claim that the recB mRNA level is not significantly affected, but RecB protein level increases due to a higher translation efficiency. [Upon DNA damage, the authors claim that the recB mRNA concentration is decreased, however RecB protein level is compensated by higher translation efficiency]. Through analyzing CLASH data on Hfq, they identified two Hfq binding sites on RecB polycistronic mRNA, one of which is localized at the ribosome binding site (RBS). Through measuring RecB mRNA and protein level in the ∆hfq cell, the authors conclude that binding of Hfq to the RBS region of recB mRNA suppresses translation of recB mRNA. This conclusion is further supported by the same measurement in the presence of Hfq sequestrator, the sRNA ChiX, and the deletion of the Hfq binding region on the mRNA.

      Strengths:

      (1) The manuscript is well-written and easy to understand.

      (2) While there are reported cases of Hfq regulating translation of bound mRNAs, its effect on reducing translation noise is relatively new.

      (3) The imaging and analysis are carefully performed with necessary controls.

      Weaknesses:

      The major weaknesses include a lack of mechanistic depth, and part of the conclusions are not fully supported by the data.

      (1) Mechanistically, it is still unclear why upon DNA damage, translation level of recB mRNA increases, which makes the story less complete. The authors mention in the Discussion that a moderate (30%) decrease in Hfq protein was observed in previous study, which may explain the loss of translation repression on recB. However, given that this mRNA exists in very low copy number (a few per cell) and that Hfq copy number is on the order of a few hundred to a few thousand, it's unclear how 30% decrease in the protein level should resides a significant change in its regulation of recB mRNA.

      While Hfq is a highly abundant protein, it has many mRNA and sRNA targets, some of which are also present in large amounts (DOI: 10.1046/j.1365-2958.2003.03734.x). As recently shown, the competition among the targets over Hfq proteins results in unequal (across various targets) outcomes, where the targets with higher Hfq affinity have an advantage over the ones with less efficient binding (DOI: 10.1016/j.celrep.2020.02.016). In line with these findings, we reason that upon DNA damage, a moderate decrease in the Hfq protein abundance (30%) can lead to a similar competition among Hfq targets where high-affinity targets outcompete low- affinity ones as well as low-abundant ones (such as recB mRNAs). Therefore, we hypothesise that the regulation of low abundant targets of Hfq by moderate perturbations of Hfq protein level is a potential explanation for the change in RecB translation that we have observed. We will expand this part of the discussion to explain our reasoning in a more explicit and coherent way.

      (2) Based on the experiment and the model, Hfq regulates translation of recB gene through binding to the RBS of the upstream ptrA gene through translation coupling. In this case, one would expect that the behavior of ptrA gene expression and its response to Hfq regulation would be quite similar to recB. Performing the same measurement on ptrA gene expression in the presence and absence of Hfq would strengthen the conclusion and model

      Indeed, based on our model, we expect PtrA expression to be regulated by Hfq in a similar manner to RecB. However, the product encoded by the ptrA gene, Protease III, (i) has been poorly characterised; (ii) unlike RecB, is located in the periplasm (DOI: 10.1128/jb.149.3.1027-1033.1982); and (iii) is not involved in any DNA repair pathway. Therefore, analysing PtrA expression would take us away from the key questions of our study.

      (3) The authors agree that they cannot exclude the possibility of sRNA being involved in the translation regulation. However, this can be tested by performing the imaging experiments in the presence of Hfq proximal face mutations, which largely disrupt binding of sRNAs.

      (4) The data on construct with a long region of Hfq binding site on recB mRNA deleted is less convincing. There is no control to show that removing this sequence region itself has no effect on translation, and the effect is solely due to the lack of Hfq binding. A better experiment would be using a Hfq distal face mutant that is deficient in binding to the ARN motifs.

      We thank the referee for these suggestions. We have performed the requested experiments, and the quantification of RecB abundance in the presence of Hfq proteins mutated in the proximal and distal face will be added to the revised version of the manuscript.

      (5) Ln 249-251: The authors claim that the stability of recB mRNA is not changed in ∆hfq simply based on the steady-state mRNA level. To claim so, the lifetime needs to be measured in the absence of Hfq.

      We agree that this statement is not fully supported by our data and will address this issue in the revised version.

      (6) What's the labeling efficiency of Halo-tag? If not 100% labeled, is it considered in the protein number quantification? Is the protein copy number quantification through imaging calibrated by an independent method? Does Halo tag affect the protein translation or degradation?

      Our previous study (DOI: 10.1038/s41598-019-44278-0) described a detailed characterisation of the HaloTag labelling technique for quantifying low-copy proteins in single E. coli cells.

      In that study, we used RecB-HaloTag as an example of a low-copy number protein. We showed a complete quantitative agreement of RecB detection between two fully independent methods: HaloTag-based labelling with cell fixation and RecB-sfGFP combined with a microfluidic device that lowers protein diffusion in the bacterial cytoplasm. This second method has previously been validated for protein quantification (DOI: 10.1038/ncomms11641) and provides detection of 80-90% of the labelled protein. Additionally, in our protocol, immediate chemical fixation of cells after the labelling and quick washing steps ensure that new, unlabelled RecB proteins are not produced. We, therefore, conclude that our approach to RecB detection is highly reliable and sufficient for comparing RecB production in different conditions and mutants.

      The RecB-HaloTag construct has been designed for minimal impact on RecB production and function. The HaloTag is translationally fused to RecB in a loop positioned after the serine present at position 47 where it is unlikely to interfere with (i) the formation of RecBCD complex (based on RecBCD structure, DOI: 10.1038/nature02988), (ii) the initiation of translation (as it is far away from the 5’UTR and the beginning of the open reading frame) and (iii) conventional C-terminal-associated mechanisms of protein degradation (DOI: 10.15252/msb.20199208). In our manuscript, we showed that the RecB-HaloTag degradation rate is similar to the dilution rate due to bacterial growth. This is in line with a recent study on unlabelled proteins, which shows that RecB’s lifetime is set by the cellular growth rate (https://doi.org/10.1101/2022.08.01.502339) and indicates that the HaloTag fusion is not affecting RecB stability.

      Furthermore, we have demonstrated (DOI: 10.1038/s41598-019-44278-0) that (i) bacterial growth is not affected by replacing the native RecB with RecB-HaloTag, (ii) RecB-HaloTag is fully functional upon DNA damage, and (iii) no proteolytic processing of the RecB-HaloTag is detected by Western blot.

      These results suggest that RecB expression and functionality are unlikely to be affected by the translational HaloTag insertion at Ser-47 in RecB. In the revised version of the manuscript, we will add information about the construct and discuss the reliability of the quantification.

      (7) Upper panel of Fig S8a is redundant as in Fig 5B. Seems that Fig S8d is not described in the text.

      Indeed, the data in the upper panel in Fig S8a was repeated (from Fig 5B) for visual purposes to facilitate comparison with the panel below. We will modify the figure legend to indicate this repetition clearly.

      In Fig S8d, we confirmed the functionality of the Hfq protein expressed from the pQE-Hfq plasmid in our experimental conditions, which was not described in the text. We will include this clarification in the updated manuscript.

    1. Author response:

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

      We are pleased that Reviewer 3 has deemed our revisions satisfactory; below, we provide responses to the remaining Recommendations for the Authors from Reviewer 2.

      Reviewer #2 (Recommendations For The Authors):

      Minor corrections:

      • Line 91: GWT should be GNWT

      Fixed, thank you.

      • Figure 2: fix the label "Participationcoefficient rank" (no space between Participation and coefficient)

      Fixed, thank you for spotting.

      • Line 317: Figure 2 should be Figure 3

      Fixed, thank you.

      • Line 360: Figure 4D, right?

      Fixed, thank you. We also confirm that Figure 4 and its caption are correct. Under anaesthesia, many regions have more Integrated Information than during Recovery (red regions), but the only changes that are consistently observed across all three contrasts are the decreases.

      • Line 375: Should be Figure 5A

      Fixed, thank you.

      • The recovery period of the anesthesia data is not described in Methods.

      We have now added the missing information:

      “Propofol was discontinued following the deep anaesthesia scan, and participants reached level 2 of the Ramsey scale approximately 11 minutes afterwards, as indicated by clear and rapid responses to verbal commands. This corresponds to the “recovery” period 176.”

      We have also expanded our discussion on the interaction between information decomposition and measures of directionality:

      “Indeed, transfer entropy can itself be decomposed into information-dynamic atoms through Partial Information Decomposition and Integrated Information Decomposition 33,34,49,151; ΦID can further decompose the Normalised Directed Transfer Entropy measure used by Deco et al 5, as recently demonstrated 152. We look forward to a more refined conceptualization of the synergistic workspace architecture that takes into account both information types and the directionality of information flow – especially in datasets with higher temporal resolution.”

    1. Author response:

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

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Pg. 3 - lines 51-53: "Once established, the canonical RdDM pathway takes over, whereby small RNAs are generated by the plant-specific polymerase IV (Pol IV). In both cases, a second plant-specific polymerase, Pol V, is an essential downstream component." The authors' intro omits an important aspect of Pol V's function in RdDM, which is quite relevant to their study. Pol V transcribes DNA to synthesize noncoding RNA scaffolds, to which AGO4-bound 24 nt siRNAs are thought to base pair, leading to DRM2 recruitment for cytosine methylation near to these nascent Pol V transcripts (Wierzbicki et al 2008 Cell; Wierzbicki et al. 2009 Nat Genet). I recommend that the authors cite these key studies.

      These citations have now been added (see line 57).

      The authors provide compelling evidence that Pol V redistributes to ectopic heterochromatin regions in h1 mutants (e.g., Fig1a browser shot). Presumably, this would allow Pol V to transcribe these regions in h1 mutants, whereas it could not transcribe them in WT plants. Have the authors detected and/or quantified Pol V transcripts in the h1 mutant compared to WT plants at the sites of Pol V redistribution (detected via NRPE1 ChIP)?

      Robust detection of Pol V transcripts can be experimentally challenging, and instead we quantify and detect NRPE1 dependent methylation at these regions (Fig 5), which occurs downstream of Pol V transcript production. However, we note detecting Pol V transcripts as a potential future direction in the discussion (see line 263).

      Pg. 5 - lines 101-102: Figure 1e - "The preferential enrichment of NRPE1 in h1 was more pronounced at TEs that overlapped with heterochromatin associated mark, H3K9me2 (Fig. 1e). Was a statistical test performed to determine that the overall differences are significant only at TE sites with H3K9me2? Can the sites without H3K9me2 also be differentiated statistically?

      Yes, there is a statistically significant difference between WT and h1 at both the H3K9me2 marked and unmarked TEs (Wilcoxon rank sum tests, see updated Fig 1e). The size of the effect is larger for the H3K9me2 marked TEs (median difference of 0.41 vs 0.16). Median values have now been added to the boxplots so that this is directly viewable to the reader (Fig 1e). This reflects the general increase in NRPE1 occupancy in h1 mutants through the genome, with the effect consistently stronger in heterochromatin. In our initial version of the manuscript, we summarise the effect as follows “We found that h1 antagonizes NRPE1 occupancy throughout the genome, particularly at heterochromatic regions” (previous version line 83, current version line 95). Although important exceptions exist (see Fig 5, NRPE1 and DNA methylation loss in h1), we now make this point even more explicit, and have updated the manuscript at several locations (abstract line 26, results line 245, discussion line 265).

      Pg. 5 - lines 108-110: The authors state, "Importantly, we found no evidence for increased NRPE1 expression at the mRNA or protein level in the h1 mutant (Suppl. Fig. 2)." But the authors did observe reduced NRPE1 transcript levels in h1 mutants, in their re-analysis of RNA-seq data and reduced NRPE1 protein signals via western blot in (Suppl. Fig. 2), which should be reported here in the results.

      As described further below, we reanalysed h1 RNA-seq from scratch, and see no evidence for significant differential gene expression of NRPE1. This table and analysis are now provided in Supplementary Table 1.

      More importantly, the above logic about NRPE1 expression in h1 mutants assumes that NRPE1 is the stoichiometrically limiting subunit for Pol V assembly and function in vivo, but this is not known to be the case:

      (1) While NRPE1's expression is somewhat reduced (and not increased) in h1 mutant plants, we cannot be certain that other genes influencing Pol V stability or recruitment are unaffected by h1 mutants. I thus recommend that the authors perform RT-qPCR directly on the WT and h1 mutant materials used in their current study, quantifying NRPE1, NRPE2, NRPE5, DRD1, DMS3, RDM1, SUVH2 and SUVH9 transcript levels.

      (2) Normalizations used to compare samples should be included with RT-qPCR and western assays. An appropriate house-keeping gene like Actin2 or Ubiquitin could be used to normalize the RT-qPCR. Protein sample loading in Suppl. Fig. 2 could be checked by Coomassie staining and/or an antibody detection of a house-keeping protein.

      We have now included a full re-analysis of h1 RNA-seq (data from Choi et al 2020) focusing on transcriptional changes of DNA methylation machinery genes in the h1 mutant. Of the 61 genes analysed, only AGO6 and AGO9 were found to be differentially expressed (2-3 fold upregulation). This analysis is now included as a table

      (Supplementary Table 1). The western blot has been moved to Supplementary Fig 3 to now illustrate antibody specificity and H1 loss in the h1 mutant lines, so NRPE1 itself serves as a loading control (Supplementary Fig 3a).

      Pg. 6 - lines 129-131: The authors state that "over NRPE1 defined peaks (where NRPE1 occupancy is strongest in WT) we observed no change in H1 occupancy in nrpe1 (Fig 2b). The results indicate that H1 does not invade RdDM regions in the nrpe1 mutant background." This conclusion assumes that the author's H1 ChIP is successfully detecting H1 occupancy. However, in Fig 2d there does not appear to be H1 enrichment or peaks as visualized across the 10766 ZF-DMS3 off-target loci, or even at the selected 451 ZFDMS3 off-target hyper DMRs, where the putative signal for H1 enrichment on the metaplot center is extremely weak/non-existent.

      As a reference for H1 enrichment in chromatin (e.g., looking where H2A.W antagonizes H1 occupancy) one can compare analyses in Bourguet et al (2021) Nat Commun, involving co-authors of the current study. Bourguet et al (2021) Fig 5b show a metaplot of H1 levels centered on H2A.W peaks with H1 ChIP signal clearly tapering away from the metaplot center point peak. To my eye, the H1 ChIP metaplots for ZF-DMS3 offtarget loci in the current manuscript (Fig 2d) resemble "shuffled peaks" controls like those in Fig 5b of Bourguet et al (2021).

      Can one definitively interpret Fig 2d as showing RdDM "not reciprocally affecting H1 localization" without first showing the specificity of the ChIP-seq results in a genotype where H1 occupancy changes? Alternatively, could this dataset be displayed with Deeptools heatmaps to strengthen the evidence that the authors are detecting H1 occupancy/enrichment genome-wide, before diving into WT/nrpe1 mutant analysis at ZF-DMS3 off-target loci?

      This is an excellent suggestion from the reviewer. We have now included several analyses that assess and demonstrate the quality of our H1 ChIP-seq profiles. First, as suggested by the reviewer, we show that our H1 profiles peak over H2A.W enriched euchromatic TEs as defined by Bourguet et al, mirroring these published findings. Next, we investigated whether our H1 profiles match Teano’s recently described pattern over genes, confirming a similar pattern with 3’ enrichment of H1 over H3K27me3 unmarked genes. Furthermore, we show that the H1 peaks defined here are similarly enriched with GFP tagged H1.2 from the Teano et al. 2023 study. These analyses that validate the quality of our H1 ChIP-seq datasets and bolster the conclusion that NRPE1 redistribution does not affect H1 occupancy. These new analysis are now presented in Supplementary Figure 3 and see line 153.

      Pg. 8 - lines 228-230: The authors state that, "As with NRPE1, SUVH1 increased in the h1 background significantly more in heterochromatin, with preferential enrichment over long TEs, cmt2 dependent hypo CHH DMRs, and heterochromatic TEs (Fig. 6b)."

      Contrary to the above statement, the violin plots in Fig. 6c show SUVH1 occupancy increasing at euchromatic TEs in the h1 mutant. What statistical test allowed the authors to determine that the increase in h1 occurs "significantly more in heterochromatin"? The authors should critically interpret Fig. 6c and 6d, which are not currently referenced in the results section. More support is needed for the claim that SUVH1 specifically encroaches into heterochromatin in the h1 mutant, rather than just TEs generally (euchromatic and heterochromatic alike).

      Similar to what we see for NRPE1, statistical tests that we have now performed show that SUVH1 is significantly enriched in h1 in all classes. Importantly however, the effect size is larger in all of the heterochromatin associated classes. We display these statistical tests and the median values on the plots so that effects are immediately viewable (see updated Fig 6).

      In addition, the authors should verify that SUVH1-3xFLAG transgenes (in the WT and h1 mutant backgrounds, respectively) and endogenous Arabidopsis genes encoding the transcriptional activator complex (SUVH1-SUVH3-DNAJ1-DNAJ2) are not overexpressed in the h1 mutant vs. WT. Higher expression of SUVH1 or limiting factors in the larger complex could explain the observation of increased SUVH1 occupancy in the h1 background.

      We do not see a difference in SUVH1/3/DNAJ1/2 complex gene expression in the h1 background (see Supplementary Table 1). However, we cannot rule out that that our SUVH1-FLAG line in h1 is more highly expressed than the corresponding SUVH1-FLAG line in WT. We now note this point in line 248.

      Pg. 8 - lines 231-232: Here the authors make a sweeping conclusion about H1 demarcating, "the boundary between euchromatic and heterochromatic methylation pathways, likely through promoting nucleosome compaction and restricting heterochromatin access." I do not see how a H1 boundary between euchromatic and heterochromatic methylation pathways is revealed based on the SUVH1-3xFLAG occupancy data, which shows increased enrichment at every category interrogated in the h1 mutant (Fig 6b,c,d) and all along the baseline too in the h1 mutant browser tracks (Fig 6a). Can the authors provide more examples of this phenomenon (similar to Fig 6a) and better explain why their SUVH1-3xFLAG ChIP supports this demarcation model?

      The general conclusion from SUVH1 about H1’s agnostic role in preventing heterochromatin access is now further supported from our findings with H3K27me3 (see Figure 6e and description from line 250). However, we agree that the demarcation model as initially presented was overly simplistic. This point was also raised by reviewer 2. We have removed the line highlighted by the reviewer in the revised version of the manuscript. In the revised version we clarify that H1 impedes RdDM and associated machinery throughout the genome (consistent with H1’s established broad occupancy across the genome) but this effect is most pronounced in heterochromatin, corresponding to maximal H1 occupancy (abstract line 26, results line 245, discussion line 265). 

      Corrections:

      Pg. 8 - lines 226-227: "We therefore wondered whether complex's occupancy might also be affected by H1." The sentence contains a typo, where I assume the authors mean to refer to occupancy by the SUVH1-SUVH3-DNAJ1-DNAJ2 transcriptional activator complex. This needs to be specified more clearly.

      The paragraph has been updated (see from line 237).

      Pg. 13 - lines 393-405: There are minor errors in the capitalization of titles and author initials in the References. I recommend that the authors proofread all the references to eliminate these issues:

      Thank you, these have been corrected.

      Choi J, Lyons DB, Zilberman D. 2021. Histone H1 prevents non-cg methylation-mediated small RNA biogenesis in arabidopsis heterochromatin. Elife 10:1-24. doi:10.7554/eLife.72676 (...)

      Du J, Johnson LM, Groth M, Feng S, Hale CJ, Li S, Vashisht A a., Gallego-Bartolome J, Wohlschlegel J a., Patel DJ, Jacobsen SE. 2014. Mechanism of DNA methylation-directed histone methylation by KRYPTONITE. Mol Cell 55:495-504. doi:10.1016/j.molcel.2014.06.009 (...)

      Du J, Zhong X, Bernatavichute Y V, Stroud H, Feng S, Caro E, Vashisht A a, Terragni J, Chin HG, Tu A, Hetzel J, Wohlschlegel J a, Pradhan S, Patel DJ, Jacobsen SE. 2012. Dual binding of chromomethylase domains to H3K9me2-containing nucleosomes directs DNA methylation in plants. Cell 151:167-80. doi:10.1016/j.cell.2012.07.034

      Reviewer #2 (Recommendations For The Authors):

      As for a normal review, here are our major and minor points.

      Major:

      (1) Lines 38 to 45 of the introduction are important for the subsequent definition of heterochromatic and non-heterochromatic transposons, but the definition is ambiguous. Is heterochromatin defined by surrounding context such as pericentromeric position or is this an autonomous definition? Can a TE with the chromosomal arms be considered heterochromatic provided that it is long enough and recruits the right machinery? These cases should be more explicitly introduced. Ideally, a supplemental dataset should provide a key to the categories, genomic locations and overlapping TEs as they were used in this analysis, even if some of the categories were taken from another study.

      We have now added all the regions used for analysis in this study to Supplementary Table 3.

      (2) Line 80: This would be the first chance to cite Teno et al. and the "encroachment" of

      PcG complexes to TEs in H1 mutants

      Done - “H1 also plays a key role in shaping nuclear architecture and preventing ectopic polycomb-mediated H3K27me3 deposition in telomeres (Teano et al., 2023).” See line 83

      (3) It is "only" a supplemental figure but S2 but it should still follow the rules: Indicate the number of biological replicates for the RNA-seq data, and perform a statistical test. In case of WB data, provide a loading control.

      We are now using the western blot to illustrate antibody specificity and H1 loss in the h1 mutant lines, so NRPE1 itself serves as a loading control (Supplementary Fig 3a). For NRPE1 mRNA expression, we have now replaced this with a more comprehensive transcriptome analysis of methylation machinery in h1 (see Supplementary Table 1). 

      (4) Lines 115 to 124 and corresponding data: Here, the goal is to exclude other changes to heterochromatin structure other than "increased access" in H1 mutants; however, only one feature, H3K9me2, is tested. Testing this one mark does not necessarily prove that the nature of the chromatin does not change, e.g. H2A.W could be differently redistributed, DDM1 may change, VIM protein, and others. Either more comprehensive testing for heterochromatin markers should be performed, or the conclusions moderated.

      We have moderated the text accordingly (see line 135).

      (5) Lines 166ff and Figure 1, a bit out of order also Figure 5: The general hypothesis is that NRPE1 redistributes to heterochromatic regions in h1 mutants (as do other chromatin modifiers), but the data seem to only support a higher occurrence at target sites.

      a. The way the NRPE1 data is displayed makes it seem like there is much more NRPE1 in the h1 samples, even at peaks that should not be recruiting more as they do not represent "long" TEs. It would be good to present more gbrowse shots of all peak classes.

      We now clarify that h1 does result in a general increase of NRPE1 throughout the genome, but the effect is strongest at heterochromatin. In our initial version of the manuscript, we summarise the effect as follows “We found that h1 antagonizes NRPE1 occupancy throughout the genome, particularly at heterochromatic regions” (previous version line 83, current version line 95). We have modified the language at several locations throughout the manuscript to make this point more clearly (abstract line 26, results line 245, discussion line 265). We include several browser shots in Supp Fig. 8.

      b. The data are "normalized" how exactly?

      c. One argument of observing "gaining" and "losing" peaks is that there is redistribution of NRPE1 from euchromatic to heterochromatic sites. There should be an analysis and figure to corroborate the point (e.g. by comparing FRIP values). Figure 1b shows lower NRPE1 signals at the TE flanking regions. This could reflect a redistribution or a flawed normalization procedure.

      The data are normalised using a standardised pipeline by log2 fold change over input, after scaling each sample by mapped read depth using the bamCompare function in deepTools. This is now described in detail in the Materials and Methods line 365, with full code and pipelines available from GitHub (https://github.com/Zhenhuiz/H1-restrictseuchromatin-associated-methylation-pathways-from-heterochromatic-encroachment).

      d. Figure 1d and f show similar profiles comparing "long" and "short" TEs or "CMT2 dependent hypo-CHH" and "DRM2 dependent CHH". How do these categories relate to each other, how many fragments are redundant?

      The short vs long TEs were defined in Liu et al 2018 (doi: 10.1038/s41477-017-0100-y) and the DMRs were defined in Zhang et al. 2018 (DOI: 10.1073/pnas.1716300115). There is likely to be some degree of overlap between the categories, but numbers are very different (short TEs (n=820), long TEs (n=155), drm2 DMRs (n=5534), CMT (n=21784)) indicating that the different categories are informative. We have now listed all the regions used for analysis in this study as in Supplementary Table 3.

      e. The purpose of the data presented in Figure 1 b is to compare changes of NRPE1 association in H3K9me3 non-overlapping and overlapping TEs between wild-type and background, yet the figure splits the categories in two subpanels and does neither provide a fold-change number nor a statistical test of the comparison. As before, the figure does not really support the idea that NPRE1 somehow redistribute from its "normal" sites towards heterochromatin as both TE classes seem to show higher NRPE1 binding in h1 mutants.

      There is a statistically significant difference between WT and h1 at both the H3K9me2 marked and unmarked TEs, however, the size of the effect is larger for the H3K9me2 marked TEs (median difference of 0.41 vs 0.16). Median values have now been added to the boxplots so that this is directly viewable to the reader (Fig 1e). Although important exceptions exist (see Fig 5 – regions that lose NRPE1 and DNA methylation), this reflects the general increase in NRPE1 occupancy in h1 mutants throughput the genome, with a consistently stronger effect in heterochromatin. As noted above, we have updated the manuscript to make this point more clearly (abstract line 26, results line 245, discussion line 265).

      f. Panel g is the only attempt to corroborate the redistribution towards heterochromatic regions, but at this scale, the apparent reduction of binding in the chromosome arms may be driven by off-peak differences and normalization problems between different ChIP samples with different signal-to-noise-ratio.

      We describe our normalisation and informatic pipeline in more detail in the Materials and Methods line 365. It is also important to note that the reduction is not only observed at the chromosomal level, but also at specific sites. We called differential peaks between WT and h1 mutant. The "Regions that gain NRPE1 in h1" peaks are more enriched in heterochromatic regions, while " Regions that lose NRPE1 in h1" peaks are more enriched outside heterochromatic regions.

      g. Figure 5: how many regions gain vs lose NRPE1 in h1 mutants? If the "redistribution causes loss" scenario applies, the numbers should overall be balanced but that does not seem the case. The loss case appears to be rather exceptional judging from the zigzagging meta-plot. Are these sites related to the sites taken over by PcG-mediated repression in h1 mutants?

      As described in line 222 (previous version of the manuscript line 206), there are 15,075 sites that gain and 1,859 sites that lose NRPE1 in h1. Comparing these sites to

      H3K27me3 in the Teano et al. study was an excellent suggestion. We compared sites that gain NRPE1 to sites that gain H3K27me3 in h1, finding a statistically significant overlap (2.4 fold enrichment over expected, hypergeometric test p-value 2.1e-71). Reciprocally, sites that lose NRPE1 were significantly enriched for overlap with H3K27me3 loss regions (1.6 fold over expected, hypergeometric test p-value 1.4e-4). This indicates that RdDM and H3K27me3 patterning are similarly modulated by H1. To directly test this, we reanalysed the H3K27me3 ChIP-seq data from Teano et al., finding coincident gain and loss of H3K27me3 at sites that gain and lose NRPE1 in h1. These results are described from line 250 and in Fig 6e, which supports a general role for H1 in preventing heterochromatin encroachment.

      (6) Lines 166ff and Figure 3: The data walk towards the scenario of pathway redistribution but actually find that RdDM plays a minor role overall as a substantial increase in heterochromatin regions occurs in all contexts and is largely independent of RdDM.

      a. How exactly are DNA-methylation data converted across regions to reach a fraction score from 0 to 1? There is no explanation in the legend for the methods that allow to recapitulate.

      We now explain our methods in full in the Materials and Methods and all the code for generating these has now been deposited on GitHub (https://github.com/Zhenhuiz/H1restricts-euchromatin-associated-methylation-pathways-from-heterochromaticencroachment). Briefly, BSMAP is used to calculate the number of reads that are methylated vs unmethylated on a per-cytosine basis across the genome. Next, the DNA methylation fraction in each region is calculated by adding all the methylation fractions per cytosine in a given window, and divided by the total number of cytosines in that same window (ie mC/(unmC+mC)) i.e. this is expressed as a fraction ranging from 0 to 1.

      “0” indicates this region is not methylated, and “1” indicates this region is fully methylated (every cytosine is 100% methylated).  

      b. Kernel plots? These are slang for experts and should be better described. In addition, nothing is really concluded from these plots in the text, although they may be quite informative.

      Kernel density plots show the proportion of TEs that gain or lose methylation in a particular mutant, rather than the overall average as depicted in the methylation metaplots above. We now describe the kernel density plots in more detail in the Figure 3 legend. 

      (7) Figure 4: This could be a very interesting analysis if the reader could actually understand it.

      a. The legend is minimal. What is the meaning of hypo and hyper regions indicated to the right of Figure 4c?

      b. The color scale represents observed/expected values. What exactly does this mean? Mutant vs WT?

      c. Some comparisons in 4a are cryptic, e.g. h1 nrpe1 nrpe1 vs CHH?

      d. Figure 4d focuses on a correlation square of relevance, but why? Interestingly the square does not correspond to any "hypo" or "hyper" label?

      Thank you, we have revised Figure 4 and legend based on these suggestions to clarify all of the above.

      (8) Lines 226 and Figure 6B. De novo (or increased) targeting of SUVH1 to heterochromatic sites in h1 mutants, similar to NRPE1, is used to support the argument that more access allows other chromatin modifiers to encroach. SUVH1 strongly depends on RdDM for its in vivo binding and may be the least conclusive factor to argue for a "general" encroachment mechanism.

      We appreciate the reviewers point here. Something that is entirely independent of RdDM following the same pattern would be stronger evidence in favour of general encroachment. Excitingly, this is exactly what we provide evidence for when investigating the interrelationship with H3K27me3 and we appreciate the reviewer’s suggestion to check this! This data is now described in Figure 6e and line 250.

      Minor:

      (1) Line 23: "Loss of H1 resulted in heterochromatic TE enrichment by NRPE1." This does not seem right. NRPE enrichment as TEs

      Modified, (line 26) thank you.

      (2) Lines 73-74: The idea that DDM1 displaces H1 in heterochromatic TEs is somewhat counterintuitive to model that heterochromatic TEs are unavailable for RdDM because of the presence of H1. Is this displacement non-permanent and directly linked to interaction with CMT2/3 Met1?

      This is a very good question and we agree with the reviewer that the effect of DDM1 may only be transient or insufficient to allow for full RdDM assembly, or indeed there may be a direct interaction between DDM1 and CMTs/MET1. During preparation of these revisions, a structure of Arabidopsis nucleosome bound DDM1 was published, which provides some insight by showing that DDM1 promotes DNA sliding. This is at least consistent with the idea of DDM1 causing transient / non-permanent displacement of H1 that would be insufficient for RdDM establishment. We incorporate discussion of these ideas at line 80.

      (3) Line 85: A bit more background on the Reader activator complex should be given. In fact, the reader may not really care that it was more recently discovered (not really recent btw) but what does it actually do?

      We have quite extensively reconfigured this paragraph to take into account our new finding with H3K27me3, such that there is less emphasis on the reader activator complex. The sentence now reads as follows:

      “We found that h1 antagonizes NRPE1 occupancy throughout the genome, particularly at heterochromatic regions. This effect was not limited to RdDM,  similarly impacting both the methylation reader complex component, SUVH1 (Harris et al., 2018) and polycomb-mediated H3K27me3 (Teano et al., 2023).” (line 95). 

      Also, when describing the experiment the results section (line 241), we now provide more background on SUVH1’s function.

      (4) Lines 80-81: Since it is already shown that RdDM associated small RNAs are more enriched in h1 at heterochromatin, help us to know what is precisely the added value of studying the enrichment of NRPE1 at these sites.

      Good point. We have the following line: ‘...small RNAs are not a direct readout of functional RdDM activity and Pol IV dependent small RNAs are abundant in regions of the genome that do not require RdDM for methylation maintenance and that do not contain Pol V (Stroud et al., 2014).’ (line 90)

      (5) Line 99: This seems to be the only time where the connection between long TEs and heterochromatic regions is mentioned but no source is cited.

      We have added the following appropriate citations: (Bourguet et al., 2021; Zemach et al., 2013). (line 110).

      (6) Line 100: DMRs is used for the first time here without explanation and full text. The abbreviation is introduced later in the text (Line 187).

      Thank you, we now describe DMRs upon first use, line 112.

      (7) Figure 2: Panels 2 c and d should show metaplots for WT and transgenes in one panel. There is something seriously wrong with the normalization in d or the scale for left and right panel is not the same. Neither legend nor methods describe how normalization was performed.

      Thank you for pointing this out, the figure has been corrected. We have updated the Materials and Methods (line 365) and have added codes and pipelines to GitHub to explain the normalisation procedure in more detail (https://github.com/Zhenhuiz/H1restricts-euchromatin-associated-methylation-pathways-from-heterochromaticencroachment).

    1. Author response:

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

      We are very grateful to the reviewers for their constructive comments. Here is a summary of the main changes we made from the previous manuscript version, based on the reviewers’ comments:

      (1) Introduction of a new model, based on a Markov chain, capturing within-trial evolution in search strategy .

      (2) Addition of a new figure investigating inter-animal variations in search strategy.

      (3) Measurement of model fit consistency across 10 simulation repetitions, to prevent the risk of model overfitting.

      (4) Several clarifications have been made in the main text (Results, Discussion, Methods) and figure legends.

      (5) We now provide processed data and codes for analyses and models at GitHub repository

      (6) Simplification of the previous modeling. We realized that the two first models in the previous manuscript version were simply special cases of the third model. Therefore, we retained only the third model, which has been renamed as the ‘mixture model’.

      (7) Modification of Figure 4-6 and Supplementary Figure 7-8 (or their creation) to reflect the aforementioned changes

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors design an automated 24-well Barnes maze with 2 orienting cues inside the maze, then model what strategies the mice use to reach the goal location across multiple days of learning. They consider a set of models and conclude that one of these models, a combined strategy model, best explains the experimental data.

      This study is written concisely and the results presented concisely. The best fit model is reasonably simple and fits the experimental data well (at least the summary measures of the data that were presented).

      Major points:

      (1) One combined strategy (once the goal location is learned) that might seem to be reasonable would be that the animal knows roughly where the goal is, but not exactly where, so it first uses a spatial strategy just to get to the first vestibule, then switches to a serial strategy until it reaches the correct vestibule. How well would such a strategy explain the data for the later sessions? The best combined model presented in the manuscript is one in which the animal starts with a roughly 50-50 chance of a serial (or spatial strategy) from the start vestibule (i.e. by the last session before the reversal the serial and spatial strategies are at ~50-50m in Fig. 5d). Is it the case that even after 15 days of training the animal starts with a serial strategy from its starting point approximately half of the time? The broader point is whether additional examination of the choices made by the animal, combined with consideration of a larger range of possible models, would be able to provide additional insight into the learning and strategies the animal uses.

      Our analysis focused on the evolution of navigation strategies across days and trials. The reviewer raises the interesting possibility that navigation strategy might evolve in a specific manner within each trial, especially on the later days once the environment is learned. To address this possibility, we first examined how some of the statistical distributions, previously analyzed across days, evolved within trials. Consistent with the reviewer’s intuition, the statistical distributions changed within trials, suggesting a specific strategy evolution within trials. Second, we developed a new model, where strategies are represented as nodes of a Markov chain. This model allows potential strategy changes after each vestibule visit, according to a specific set of transition probabilities. Vestibules are chosen based on the same stochastic processes as in the previous model. This new model could be fitted to the experimental distributions and captured both the within-trial evolution and the global distributions. Interestingly, the trials were mostly initiated in the random strategy (~67% chance) and to a lesser extent in the spatial strategy (~25% chance), but rarely in the serial strategy (~8% chance). This new model is presented in Figure 6.

      (2) To clarify, in the Fig. 4 simulations, is the "last" vestibule visit of each trial, which is by definition 0, not counted in the plots of Fig. 4b? Otherwise, I would expect that vestibule 0 is overrepresented because a trial always ends with Vi = 0.

      The last vestibule visit (vestibule 0 by definition) is counted in the plots of Fig.4b. We initially shared the same concern as the reviewer. However, upon further consideration, we arrived at the following explanation: A factor that might lead to an overrepresentation of vestibule 0 is the fact that, unlike other vestibules, it has to be contained in each trial, as trials terminated upon the selection of vestibule 0. Conversely, a factor that might contribute to an underrepresentation of vestibule 0 is that, unlike other vestibules, it cannot be counted more than once per trial. Somehow these two factors seem to counterbalance each other, resulting in no discernible overrepresentation or underrepresentation of vestibule 0 in the random process. 

      Reviewer #2 (Public Review):

      This paper uses a novel maze design to explore mouse navigation behaviour in an automated analogue of the Barnes maze. Overall I find the work to be solid, with the cleverly designed maze/protocol to be its major strength - however there are some issues that I believe should be addressed and clarified.

      (1) Whilst I'm generally a fan of the experimental protocol, the design means that internal odor cues on the maze change from trial to trial, along with cues external to the maze such as the sounds and visual features of the recording room, ultimately making it hard for the mice to use a completely allocentric spatial 'place' strategy to navigate. I do not think there is a way to control for these conflicts between reference frames in the statistical modelling, but I do think these issues should be addressed in the discussion.

      It should be pointed out that all cues on the maze (visual, tactile, odorant) remained unchanged across trials, since the maze was rotated together with goal and guiding cues. Furthermore, the maze was equipped with an opaque cover to prevent mice from seeing the surrounding room (the imaging of mouse trajectories was achieved using infrared light and camera). It is however possible that some other cues such as room sounds and odors could be perceived and somewhat interfered with the sensory cues provided inside the maze. We have now mentioned this possibility in the discussion.

      (2) Somewhat related - I could not find how the internal maze cues are moved for each trial to demarcate the new goal (i.e. the luminous cues) ? This should be clarified in the methods.

      The luminous cues were fixed to the floor of the arena. Consequently, they rotated along with the arena as a unified unit, depicted in figure 1. We have added some clarifications in Figure 1 legend and methods.

      (3) It appears some data is being withheld from Figures 2&3? E.g. Days 3/4 from Fig 2b-f and Days 1-5 on for Fig 3. Similarly, Trials 2-7 are excluded from Fig 3. If this is the case, why? It should be clarified in the main text and Figure captions, preferably with equivalent plots presenting all the data in the supplement.

      The statistical distributions for all single days/trials are shown in the color-coded panels of Figure2&3. In the line plots of Figure2&3, we show only the overlay of 2-3 lines for the sake of clarity. The days/trials represented were chosen to capture the dynamic range of variability within the distributions. We have added this information in the figure legends.

      (4) I strongly believe the data and code should be made freely available rather than "upon reasonable request".

      Matrices of processed data and various codes for simulations and analyses are now available at https://github.com/ sebiroyerlab/Vestibule_sequences.

      Reviewer #3 (Public Review):

      Royer et al. present a fully automated variant of the Barnes maze to reduce experimenter interference and ensure consistency across trials and subjects. They train mice in this maze over several days and analyze the progression of mouse search strategies during the course of the training. By fitting models involving stochastic processes, they demonstrate that a model combined of the random, spatial, and serial processes can best account for the observed changes in mice's search patterns. Their findings suggest that across training days the spatial strategy (using local landmarks) was progressively employed, mostly at the expense of the random strategy, while the serial strategy (consecutive nearby vestibule check) is reinforced from the early stages of training. Finally, they discuss potential mechanistic underpinnings within brain systems that could explain such behavioral adaptation and flexibility.

      Strength:

      The development of an automated Barnes maze allows for more naturalistic and uninterrupted behavior, facilitating the study of spatial learning and memory, as well as the analysis of the brain's neural networks during behavior when combined with neurophysiological techniques. The system's design has been thoughtfully considered, encompassing numerous intricate details. These details include the incorporation of flexible options for selecting start, goal, and proximal landmark positions, the inclusion of a rotating platform to prevent the accumulation of olfactory cues, and careful attention given to atomization, taking into account specific considerations such as the rotation of the maze without causing wire shortage or breakage. When combined with neurophysiological manipulations or recordings, the system provides a powerful tool for studying spatial navigation system.

      The behavioral experiment protocols, along with the analysis of animal behavior, are conducted with care, and the development of behavioral modeling to capture the animal's search strategy is thoughtfully executed. It is intriguing to observe how the integration of these innovative stochastic models can elucidate the evolution of mice's search strategy within a variant of the Barnes maze.

      Weakness:

      (1) The development of the well-thought-out automated Barnes maze may attract the interest of researchers exploring spatial learning and memory. However, this aspect of the paper lacks significance due to insufficient coverage of the materials and methods required for readers to replicate the behavioral methodology for their own research inquiries.

      Moreover, as discussed by the authors, the methodology favors specialists who utilize wired recordings or manipulations (e.g. optogenetics) in awake, behaving rodents. However, it remains unclear how the current maze design, which involves trapping mice in start and goal positions and incorporating angled vestibules resulting in the addition of numerous corners, can be effectively adapted for animals with wired implants.

      The reviewer is correct in pointing out that the current maze design is not suitable for performing experiments with wired implant, particularly due to the maze’s enclosed structure and the access to the start/goal boxes through side holes. Instead, pharmacogenetics and wireless approaches for optogenetic and electrophysiology would need to be used. We have now mentioned this limitation in the discussion.

      (2) Novelty: In its current format, the main axis of the paper falls on the analysis of animal behavior and the development of behavioral modeling. In this respect, while it is interesting to see how thoughtfully designed models can explain the evolution of mice search strategy in a maze, the conclusions offer limited novel findings that align with the existing body of research and prior predictions.

      We agree with the reviewer that our study is weakly connected to previous researches on hippocampus and spatial navigation, as it consists mainly of animal behavior analysis and modeling and addresses a relatively unexplored topic. We hope that the combination of our behavioral approach with optogenetic and electrophysiology will allow in the future new insights that are in line with the existing body of research.

      (3) Scalability and accessibility: While the approach may be intriguing to experts who have an interest in or are familiar with the Barnes maze, its presentation seems to primarily target this specific audience. Therefore, there is a lack of clarity and discussion regarding the scalability of behavioral modeling to experiments involving other search strategies (such as sequence or episodic learning), other animal models, or the potential for translational applications. The scalability of the method would greatly benefit a broader scientific community. In line with this view, the paper's conclusions heavily rely on the development of new models using custom-made codes. Therefore, it would be advantageous to make these codes readily available, and if possible, provide access to the processed data as well. This could enhance comprehension and enable a larger audience to benefit from the methodology.

      The current approach might indeed extend to other species in equivalent environments and might also constitute a general proof of principle regarding the characterization of animal behaviors by the mixing of stochastic processes. We have now mentioned these points in the discussion.

      As suggest by the reviewer, we have now provided model/simulation codes and processed data to replicate the figures, at https://github.com/sebiroyerlab/Vestibule_sequences

      (4) Cross-validation of models: The authors have not implemented any measures to mitigate the risk of overfitting in their modeling. It would have been beneficial to include at least some form of cross-validation with stochastic models to address this concern. Additionally, the paper lacks the presence of analytics or measures that assess and compare the performance of the models.

      To avoid the risk of model overfitting, the most appropriate solution appeared to be repeating the simulations several times and examining the consistency of the obtained parameters across repetitions. For the mixture model, we now show in Supplementary figure 7 the probabilities obtained from 10 repetitions of the simulation. Similarly, for the Markov chain model, the probabilities obtained from 10 repetitions of the simulation are shown in Figure 6.

      Regarding model comparison, we have simplified our mixture model into only one model, as we realized the 2 other models in the previous manuscript version were simply special cases of the 3rd model. Nevertheless, comparison was still needed for the estimation for the best value of N (the number of consecutive segments that a strategy lasts) in the mixture model. We now show the comparison of mean square errors obtained for different values of N, using t-test across 10 repetitions of the simulations (Figure 5c).

      (5) Quantification of inter-animal variations in strategy development: It is important to investigate, and address the argument concerning the possibility that not all animals recruit and develop the three processes (random, spatial, and serial) in a similar manner over days of training. It would be valuable to quantify the transition in strategy across days for each individual mouse and analyze how the population average, reflecting data from individual mice, corresponds to these findings. Currently, there is a lack of such quantification and analysis in the paper.

      We have added a figure (Supplementary figure 8) showing the mixture model matching analyses for individual animals. A lot of variability is indeed observed across animals, with some animals displaying strong preferences for certain strategies compare to others. The average across mouse population showed a similar trend as the result obtained with the pooled data.

      Recommendations for the authors:

      Summary of Reviewer Comments:

      (1) In its present form, the manuscript lacks sufficient coverage of the materials and methods necessary for readers to replicate the behavioral methodology in their own research inquiries. For instance, it would be beneficial to clarify how the cues are rotated relative to the goal.

      (2) The models may be over-fitted, leading to spurious conclusions, and cross-validation is necessary to rule out this possibility.

      (3) The specific choice of the three strategies used to fit behavior in this model should be better justified, as other strategies may account for the observed behavior.

      (4) The study would benefit from an analysis of behavior on an animal-by-animal basis, potentially revealing individual differences in strategies.

      (5) Spatial behavior is not necessarily fully allocentric in this task, as only the two cues in the arena can be used for spatial orientation, unlike odor cues on the floor and sound cues in the room. This should be discussed.

      (6) Making the data and code fully open source would greatly strengthen the impact of this study.

      In addition, each reviewer has raised both major and minor concerns which should be addressed if possible.

      Reviewer #1 (Recommendations For The Authors):

      Minor points:

      (1) Change "tainted" to "tinted" in Fig. 1a

      (2) Should note explicitly in Fig. 2d that the goal is at vestibule 0, and also in the legend

      (3) Fig. 3 legend should say "c-e)", not "c-f)"

      (4) Supplementary Fig. 8 legend repeats "d)" twice

      Reviewer #2 (Recommendations For The Authors):

      Packard & McGaugh 1996 is cited twice as refs 5 and 14

      Reviewer #3 (Recommendations For The Authors):

      - Figure 3: Please correct the labels referenced as "c-f)" in the figure's legend.

      - Rounding numbers issue on page 4: 82.62% + 17.37% equals 99.99%, not 100%.

      We fixed all minor points. We are very thankful to the reviewers for their constructive comments.

    1. Author response:

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

      We are thankful to the reviewers and the editor for their detailed feedback, insightful suggestions, and thoughtful assessment of our work. Our point-by-point responses to the comments and suggestions are below.

      The revised manuscript has taken into account all the comments of the three reviewers. Modifications include corrections to errors in spelling and unit notation, additional quantification, improvements to the clarity of the language in some places, as well as additional detail in the descriptions of the methods, and revisions to the figures and figure legends.

      We have also undertaken additional analyses and added materials in response to reviewer suggestions. In brief:

      In response to a suggestion from Reviewer #1, we added Figure 6-1 to show examples of the calcium traces of individual fish and individual ROIs from the condensed data in Figure 6. We revised Figure 7 as follows:

      • We added an analysis of the duration of the response to shock to address comments from Reviewers #2 and #3.

      • In response to Reviewer #3, we added histograms showing the distribution of the amplitudes of the calcium signals in the gsc2 and rln3a neurons to show, without relying on the detection of peaks in the calcium trace, that the rln3a neurons have more oscillations in activity.

      We added Figure 8-2 in response to the suggestion from Reviewer #3 to analyze turning behavior in larvae with ablated rln3a neurons.

      To address Reviewer #2’s suggestion to show how the ablated transgenic animals compare to the non-ablated transgenic animals of the same genotype, we have added this analysis as Figure 8-3.

      A detailed point-by-point is as follows:

      The reviewers agree that the study of Spikol et al is important, with novel findings and exciting genetic tools for targeting cell types in the nucleus incertus. The conclusions are overall solid. Results could nonetheless be strengthened by performing few additional optogenetic experiments and by consolidating the analysis of calcium imaging and behavioral recordings as summarized below.

      (1) Light pulses used for optogenetic-mediated connectivity mapping were very long (5s), which could lead to non specific activation of numerous population of neurons than the targeted ones. To confirm their results, the authors should repeat their experiments with brief 5-50ms (500ms maximum) -long light pulses for stimulation.

      As the activity of the gsc2 neurons is already increased by 1.8 fold (± 0.28) within the first frame that the laser is activated (duration ~200 msec), it is unlikely that that the observed response is due to non-specific activation induced by the long light pulse.

      (2) In terms of analysis, the authors should improve :

      a) The detection of calcium events in the "calcium trace" showing the change in fluorescence over time by detecting the sharp increase in the signal when intracellular calcium rises;

      We have added an additional analysis to Figure 7 that does not rely on detection of calcium peaks. See response to Reviewer #3.

      b) The detection of bouts in the behavioral recordings by measuring when the tail beat starts and ends, thereby distinguishing the active swimming during bouts from the immobility observed between bouts.

      Our recordings capture the entire arena that the larva can explore in the experiment and therefore lack the spatial resolution to capture and analyze the tail beat. Rather, we measured the frequency and length of phases of movement in which the larva shows no more than 1 second of immobility. To avoid confusion with studies that measure bouts from the onset of tail movement, we removed this term from the manuscript and refer to activity as phases of movement.

      (3) The reviewers also ask for more precisions in the characterization of the newly-generated knock-in lines and the corresponding anatomy as explained in their detailed reports.

      Please refer to the point-by-point request for additional details that have now been added to the manuscript.

      Reviewer #1 (Recommendations For The Authors):

      The conclusions of this paper are mostly well supported by data, but some technical aspects, especially about calcium imaging and data analysis, need to be clarified.

      (1) Both the endogenous gsc2 mRNA expression and Tg(gsc2:QF2) transgenic expression are observed in a neuronal population in the NI, but also in a more sparsely distributed population of neurons located more anteriorly (for example, Fig. 2B, Fig. 5A). The latter population is not mentioned in the text. It would be necessary to clarify whether or not this anterior population is also considered as the NI, and whether this population was included for the analysis of the projection patterns and ablation experiments.

      The sparsely distributed neurons had been mentioned in the Results, line 134, but we have now added more detail. In line 328, we have clarified that: “As the sparsely distributed anterior group of gsc2 neurons (Fig. 2B, C) are anatomically distinct from the main cluster and not within the nucleus incertus proper, they were excluded from subsequent analyses.”

      (2) Both Tg(gsc2:QF2) and Tg(rln3a:QF2) transgenic lines have the QF genes inserted in the coding region of the targeted genes. This probably leads to knock out of the gene in the targeted allele. Can the authors mention whether or not the endogenous expression of gsc2 and rln3a was affected in the transgenic larvae? Is it possible that the results they obtained using these transgenic lines are affected by the (heterozygous or homozygous) mutation of the targeted genes?

      Figure 8-1 includes in situ hybridization for gsc2 and rln3a in heterozygous Tg(gsc2:QF2)c721; Tg(QUAS:GFP)c578 and Tg(rln3a:QF2; he1.1:YFP)c836; Tg(QUAS:GFP)c578 transgenic larvae.

      The expression of gsc2 is unaffected in Tg(gsc2:QF2)c721; Tg(QUAS:GFP)c578 heterozygotes

      (Fig. 8-1A), whereas the expression of rln3a is reduced in Tg(rln3a:QF2; he1.1:YFP)c836; Tg(QUAS:GFP)c578 heterozygous larvae (Fig. 8-1D), as mentioned in the legend for Figure 8-1. We confirmed these findings by comparing endogenous gene expression between transgenic and non-transgenic siblings that were processed for RNA in situ hybridization in the same tube.

      The behavioral results we obtained are not due to rln3a heterozygosity because comparisons were made with sibling larvae that are also heterozygous for Tg(rln3a:QF2; he1.1:YFP)c836; Tg(QUAS:GFP)c578, as stated in the Figure 8 legend.

      (3) Optogenetic activation and simultaneous calcium imaging is elegantly designed using the combination of the orthogonal Gal4/UAS and QF2/QUAS systems (Fig. 6). However, I have some concerns about the analysis of calcium responses from a technical point of view. Their definition of ΔF/F in this manuscript is described as (F-Fmin)/(Fmax-Fmin) (see line 1406). This is confusing because it is different from the conventional definition of ΔF/F, which is F-F0/F0, where F0 is a baseline GCaMP fluorescence. Their way of calculating the ΔF/F is inappropriate for measuring the change in fluorescence relative to the baseline signal because it rather normalizes the amplitude of the responses across different ROIs. The same argument applies to the analyses done for Fig. 7.

      We have taken a careful look at our analyses and replotted the data using F-F0/F0. However, this only changes Y-axis values and does not change the shape of the calcium trace or the change in signal upon stimulation. Both metrics (F-F0/F0 and (F-Fmin)/(Fmax-Fmin)) adjust the fluorescence values of each ROI to its own baseline.

      (4) The %ΔF/F plots shown in Fig.6 are highly condensed showing the average of different ROIs (cells) within one fish and then the average of multiple fish. It would be helpful to see example calcium traces of individual ROIs and individual fish to know the variability across ROIs and fish. Also, It would be helpful to know how much laser power (561 nm laser) was used to photostimulate ReaChR.

      Laser power (5%) was added to the section titled Calcium Signaling in Methods.

      In Figure 6, shading in the %ΔF/F plots (D, D’, E, E’, F, F’, G, G’, H, H’) represents the variability across ROIs, and the dot plots (D’’, E’’, F’’, G’’, H’’) show the variability across fish (where each data point represents an individual fish). We have now also added Figure 6-1 with examples of calcium traces from individual fish and individual ROIs.

      (5) Some calcium traces presented in Fig. 6 (Fig. 6D, D', F, H, H') show discontinuous fluctuations at the onset and offset of the photostimulation period. Is this caused by some artifacts introduced by switching the settings for the photostimulation? The authors should mention if there are some alternative explanations for this discontinuity.

      As noted by the reviewer, this artifact does result from switching the settings for photostimulation, which we mention in the legend for Figure 6.

      (6) In the introduction, they mention that the griseum centrale is a presumed analogue of the NI (lines 74-75). It would be helpful for the readers to better understand the brain anatomy if the authors could discuss whether or not their findings on the gsc2 and rln3a NI neurons support this idea.

      Our findings on the gsc2 and rln3a neurons support the idea that the griseum centrale of fish is the analogue of the mammalian NI. We have now edited the text in the third paragraph of the discussion, line 1271, to make this point more clearly: “By labeling with QUAS-driven fluorescent reporters, we determined that the anatomical location, neurotransmitter phenotype, and hodological properties of gsc2 and rln3a neurons are consistent with NI identity, supporting the assertion that the griseum centrale of fish is analogous to the mammalian NI. Both groups of neurons are GABAergic, reside on the floor of the fourth ventricle and project to the interpeduncular nucleus.”

      Reviewer #2 (Recommendations For The Authors):

      Major comments:

      (1) Throughout the figures a need for more precision and reference in the anatomical evidence:

      • Specify how many planes over which height were projected for each Z-projection in Figure 1,2,3, ....

      We added this information to the last paragraph of the section titled Confocal Imaging within the Materials and Methods.

      • Provide the rhombomere numbers, deliminate the ventricles & always indicate on the panel the orientation (Rostral Caudal, Left Right or Ventral Dorsal) for Figure 1 panels D-F , Figure 2-1B-G, Figure 2-2A-C in the adult brain, Figure 3.

      We annotated Figures 2-1 and 2-2 as suggested. We also indicated the orientation (anterior to the top or anterior to the left) in all figure legends. For additional context on the position of gsc2 and rln3a neurons within the larval brain, refer to Fig. 1A-C’, Fig. 1-2A, Fig. 2, Fig. 4 and Fig. 5.

      • Add close up when necessary: Figure 2-2A-C, specify in the text & in the figure where are the axon bundles from the gsc2+ neurons in the adult brain- seems interesting and is not commented on?

      We added a note to the legend of Figure 2-2: Arrowheads in B and B’ indicate mApple labeling of gsc2 neuronal projections to the hypothalamus. We also refer to Fig 2-2B, B’ in the Results section titled Distinct Projection Patterns of gsc2 and rln3a neurons.

      • keep the same color for one transgene within one figure: example, glutamatergic neurons should always be the same color in A,B,C - it is confusing as it is.

      We have followed the reviewer’s suggestion and made the color scheme consistent in Figure 3.

      • Movies: add the labels (which transgenic lines in which color, orientation & anatomical boundaries for NI, PAG, any other critical region that receives their projections and the brain ventricle boundaries) on the anatomical movies in supplemental (ex Movie 4-1 for gsc2 neurons and 4-2 for rln3 neurons: add cerebellum, IPN, raphe, diencephalon, and rostral and caudal hypothalamus, medulla for 4-1 as well as lateral hypothalamus and optic tectum for 42); add the ablated region when necessary.

      We added more detail to the movie legends. Please refer to Figure 4 for additional anatomical details.

      • for highlighting projections from NI neurons and distinguish them from the PAG neurons, the authors elegantly used 2 Photon ablation of one versus the other cluster: this method is valid but we need more resolution that the Z stacks added in supplemental by performing substraction of before and after maps.

      We are not sure what the author meant by subtraction as there are no before and after images in this experiment. Larvae underwent ablation of cell bodies and were imaged one day later in comparison to unablated larvae.

      In particular, it is not clear to me if both PAG and NI rln3a neurons project to medulla - can the authors specify this point & the comparison between intact & PAG vs NI ablation maps? The authors should resolve better the projections to all targeted regions of NI gsc2 neurons and differentiate them from other PAG gsc2 neurons, same for rln3a neurons.

      We have clarified this point on line 549.

      Make sure to mention in the result section the duration between ablation & observation that is key for the axons to degrade.

      We always assessed degeneration of neuronal processes at 1-day post-ablation.

      (“2) calcium imaging experiments:

      a) with optogenetic connectivity mapping:

      the authors combine an impressive diverse set of optogenetic actuators & sensors by taking advantage of the QUAS/QF2 and UAS/GAL4 systems to test connectivity from Hb-IPN onto gsc2 and rln3 neurons.

      The experiments are convincing but the choice of the duration of the stimulation (5s) is not adequate to test for direct connectivity: the authors should make sure that response in gsc2 neurons is observed with short duration (50ms-1s max).

      As noted above:

      “As the activity of the gsc2 neurons is already increased by 1.8 fold (± 0.28) within the first frame that the laser is activated (duration ~200 msec), it is unlikely that that the observed response is due to non-specific activation induced by the long light pulse.”

      note: Specify that the gsc2 neurons tested are in NI.

      We have edited the text accordingly in the Results section titled Afferent input to the NI from the dHb-IPN pathway.

      b) for the response to shock: in the example shown for rln3 neurons, the activity differs before and after the shock with long phases of inhibition that were not seen before. Is it representative? the authors should carefully stare at their data & make sure there is no difference in activity patterns after shock versus before.

      We reexamined the responses for each of the rln3a neurons individually and confirmed that, although oscillations in activity are frequent, the apparent inhibition (excursions below baseline) are an idiosyncratic feature of the particular example shown.

      (3) motor activity assay:

      a) there seems to be a misconception in the use of the word "bout" to estimate in panels H and I bout distance and duration and the analysis should be performed with the criterion used by all in the motor field:

      As we know now well based on the work of many labs on larval zebrafish (Orger, Baier, Engert, Wyart, Burgess, Portugues, Bianco, Scott, ...), a bout is defined as a discrete locomotor event corresponding to a distance swam of typically 1-6mm, bout duration is typically 200ms and larvae exhibit a bout every s or so during exploration (see Mirat et al Frontiers 2013; Marques et al Current Biology 2018; Rajan et al. Cell Reports 2022).

      Since the larval zebrafish has a low Reynolds number, it does not show much glide and its movement corresponds widely to the active phase of the tail beats.

      Instead of detecting the active (moving) frames as bouts, the authors however estimate these values quite off that indicate an error of calibration in the detection of a movement: a bout cannot last for 5-10s, nor can the fish swim for more than 1 cm per bout (in the definition of the authors, bout last for 5-10 s, and bout correspond to 10 cm as 50 cm is covered in 5 bouts).

      The authors should therefore distinguish the active (moving) from inactive (immobile) phase of the behavior to define bouts & analyze the corresponding distance travelled and duration of active swimming. They would also benefit from calculating the % of time spent swimming in order to test whether the fish with ablated rln3 neurons change the fraction of the time spent swimming.

      As noted above:

      Our recordings capture the entire arena that the larva can explore in the experiment and therefore lack the spatial resolution to capture and analyze the tail beat. Rather, we measured the frequency and length of phases of movement in which the larva shows no more than 1 second of immobility. To avoid confusion with studies that measure bouts from the onset of tail movement, we removed this term from the manuscript and refer to activity as phases of movement.

      Note that a duration in seconds is not a length and that the corresponding symbol for seconds in a scientific publication is "s" and not "sec".

      We have corrected this.

      b) controls in these experiments are key as many clutches differ in their spontaneous exploration and there is a lot of variation for 2 min long recordings (baseline is 115s). The authors specify that the control unablated are a mix of siblings; they should show us how the ablated transgenic animals compare to the non ablated transgenic animals of the same clutch.

      The unablated Tg(gsc2:QF2)c721; Tg(QUAS:GFP)c578 and Tg(rln3a:QF2, he1.1:YFP)c836; Tg(QUAS:GFP)c578 larvae in the control group are siblings of ablated larvae. We repeated the analyses using either the Tg(gsc2:QF2)c721; Tg(QUAS:GFP)c578 or Tg(rln3a:QF2, he1.1:YFP)c836; Tg(QUAS:GFP)c578 larvae only as controls and added the results in Figure 8-3. Although the statistical power is slightly reduced due to a smaller number of samples in the control group, the conclusions are the same, as the behavior of Tg(gsc2:QF2)c721; Tg(QUAS:GFP)c578 and Tg(rln3a:QF2, he1.1:YFP)c836; Tg(QUAS:GFP)c578 unablated larvae is indistinguishable.

      Minor comments:

      (1) Anatomy :

      • Add precision in the anatomy in Figure 1:

      • Improve contrast for cckb.

      The contrast is determined by the signal to background ratio from the fluorescence in situ hybridization. Increasing the brightness would increase both the signal and the background, as any modification must be applied to the whole image.

      • since the number of neurons seems low in each category, could you quantify the number of rln3+, nmbb+, gsc2+, cckb+ neurons in NI?

      Quantification of neuronal numbers has been added to the first Results section titled Identification of gsc2 neurons in the Nucleus Incertus, lines 219-224.

      note: indicate duration for the integral of the DF/F in s and not in frames.

      We have added this in the legends for Figures 6 and 7 and in Materials and Methods.

      (2) Genetic tools:

      To generate a driver line for the rln3+ neurons using the Q system, the authors used the promoter for the hatching gland in order to drive expression in a structure outside of the nervous system that turns on early and transiently during development: this is a very elegant approach that should be used by many more researchers.

      If the her1 construct was integrate together with the QF2 in the first exon of the rln3 locus as shown in Figure 2, the construct should not be listed with a ";" instead of a "," behind rln3a:QF2 in the transgene name. Please edit the transgene name accordingly.

      We have edited the text accordingly.

      (3) Typos:

      GABAergic neurons is misspelled twice in Figure 3.

      Thank you for catching this. We have corrected the misspellings.

      Reviewer #3 (Recommendations For The Authors):

      • More analysis should be done to better characterize the calcium activity of gsc2 and rln3a populations. Specifically:

      Spontaneous activity is estimated by finding peaks in the time-series data, but the example in Fig7 raises concerns about this process: Two peaks for the gsc2 cell are identified while numerous other peaks of apparently similar SNR are not detected. Moreover, the inset images suggest GCaMP7a expression might be weaker in the gsc2 transgenic and as such, differences in peak count might be related to the SNR of the recordings rather than underlying activity. Overall, the process for estimating spontaneous activity should be more rigorous.

      To not solely rely on the identification of peaks in the calcium traces, we also plotted histograms of the amplitudes of the calcium signals for the rln3a and gsc2 neurons. The histograms show that the amplitudes of the rln3a calcium signals frequently occur at small and large values (suggesting large fluctuations in activity), whereas the amplitudes of the gsc2 calcium signals occur most frequently at median values. We added this analysis to a revised Figure 7.

      Interestingly, there are a number of large negative excursions in the calcium data for the rln3a cell - what is the authors' interpretation of these? Could it be that presynaptic inhibition via GABA-B receptors in dIPN might influence dIPN-innervating rln3a neurons?

      As noted above:

      We reexamined the responses for each of the rln3a neurons individually and confirmed that, although oscillations in activity are frequent, the apparent inhibition (excursions below baseline) are an idiosyncratic feature of the particular example shown.

      Regarding shock-evoked activity, the authors state "rln3a neurons showed ... little response to shock", yet the immediate response after shock appears very similar in gsc2 vs rln3a cells (approx 30 units on the dF/F scale). The subsequent time-course of the response is what appears to distinguish gsc2 versus rln3a; it might thus be useful to separately quantify the amplitude and decay time constant of the shock evoked response for the two populations.

      The reviewer is correct that the difference between the gsc2 and rln3a neurons in the response to shock is dependent on the duration of time post-shock that is analyzed. Thus, the more relevant feature is the length of the response rather than the size. To reflect this, we compared the average length of responses for the gsc2 and rln3a neurons. We have now added this analysis to Figure 7 and updated the text accordingly.

      • The difference in spontaneous locomotor behavior is interesting and the example tracking data suggests there might also be differences in turn angle distribution and/or turn chain length following rln3 NI ablations. I would recommend the authors consider exploring this.

      Thank you for this suggestion. We wrote additional code to quantify turning behavior and found that larvae with rln3a NI neurons ablated do indeed have a statistically significant increase in turning compared to other groups. We now show this analysis as Figure 8-2 and we added an explanation of the quantification of turning behavior to the Methods section titled Locomotor assay.

      • I didn't follow the reasoning in the discussion that activity of rln3a cells may control transitions between phases of behavioral activity and inactivity. The events (at least those that are detected) in Fig7 occur with an average interval exceeding 30 s, yet swim bouts occur at a frequency around 1 Hz. The authors should clarify their hypothesis about how these disparate timescales might be connected.

      As noted above:

      Our recordings capture the entire arena that the larva can explore in the experiment and therefore lack the spatial resolution to capture and analyze the tail beat. Rather, we measure the frequency and length of phases of movement in which the larva shows no more than 1 second of immobility. To avoid confusion with studies that measure bouts from the onset of tail movement, we removed this term from the manuscript and refer to activity as phases of movement.

      • Fig2-2: Images are ordered from (A, B, C) anterior to (A', B', C') posterior. Its not clear what this means and images appear to be in sequence A, A', B, B'.... please clarify and consider including a cartoon of the brain in sagittal view showing location of sections indicated.

      We clarified the text in the Figure 2-2 legend and added a drawing of the brain showing the location of the sections.

      • In Fig7, why are 300 frames analyzed pre/post shock? Even for gsc2, the response appears complete in ~100 frames.

      Reviewer #2 also pointed out that the difference between the gsc2 and rln3a neurons in the response to shock is dependent on the duration of time post-shock that is analyzed. Thus, the more relevant feature is the length of the response rather than the size. To reflect this, we compared the average length of response for the gsc2 and rln3a neurons and modified the text and Figure as described above.

      • What are the large negative excursions in the calcium signal in the rln3a data (Fig7E)?

      See response to Reviewer # 2, repeated below:

      We looked through each of the responses of individual rln3a neuron and confirmed that, although oscillations in activity are frequent among the rln3a neurons, the apparent inhibition (excursions below baseline) are an idiosyncratic feature of the particular example shown.

      • There are several large and apparently perfectly straight lines in the fish tracking examples (Fig8) suggestive of tracking errors (ie. where the tracked centroid instantaneously jumps across the camera frame). Please investigate these and include analysis of the distribution of swim velocities to support the validity of the tracking data.

      The reason for this is indeed imperfect tracking resulting in frames in which the tracker does not detect the larva. The result is that the larva appears to move 1 cm or more in a single frame. However, analysis of the distribution of distances across all frames shows that these events (movement of 1 cm or more in a single frame) are rare (less than 0.04%), and there are no systematic differences that would explain the differences in locomotor behavior presented in Fig. 8. A summary of the data is as follows:

      Controls: 0.0249% of distances 1 cm or greater gsc2 neurons ablated: 0.0302% of distances 1 cm or greater rln3a NI neurons ablated: 0.0287% of distances 1 cm or greater rln3a PAG neurons ablated: 0.0241% of distance 1 cm or greater

      • Insufficient detail is provided in the methods about how swim bouts are detected (and their durations extracted) from the centroids tracking data. Please expand detail in this section.

      We added an explanation to the Methods section titled Locomotor assay.

    1. Author response:

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

      eLife assessment:

      This study uses carefully designed experiments to generate a useful behavioural and neuroimaging dataset on visual cognition. The results provide solid evidence for the involvement of higher-order visual cortex in processing visual oddballs and asymmetry. However, the evidence provided for the very strong claims of homogeneity as a novel concept in vision science, separable from existing concepts such as target saliency, is inadequate.

      We appreciate the positive and balanced assessment from the reviewers. We agree that visual homogeneity is similar to existing concepts such as target saliency. We have tried our best to articulate our rationale for defining it as a novel concept. However, the debate about whether visual homogeneity is novel or related to existing concepts is completely beside the point, since that is not the key contribution of our study.

      Our key contribution is our quantitative model for how the brain could be solving generic visual tasks by operating on a feature space. In the literature there are no theories regarding the decision-making process by which the brain could be solving generic visual tasks. In fact, oddball search tasks, same-different tasks and symmetry tasks are never even mentioned in the same study because it is tacitly assumed that the underlying processes are completely different! Our work brings together these disparate tasks by proposing a specific computation that enables the brain to solve both types of tasks and providing evidence for it. This specific computation is a well-defined, falsifiable model that will need to be replicated, elaborated and refined by future studies.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in the human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

      Thank you for your concise summary. We appreciate your careful reading and thoughtful and constructive comments.

      Strengths:

      The authors present carefully designed experiments, combining multiple types of visual judgments and multiple types of visual stimuli with concurrent fMRI measurements. This is a rich dataset with many possibilities for analysis and interpretation.

      Thank you for your accurate assessment of the strengths of our study.

      Weaknesses:

      The datasets presented here should provide a rich basis for analysis. However, in this version of the manuscript, I believe that there are major problems with the logic underlying the authors' new theory of visual homogeneity (VH), with the specific methods they used to calculate VH, and with their interpretation of psychophysical results using these methods. These problems with the coherency of VH as a theoretical construct and metric value make it hard to interpret the fMRI results based on searchlight analysis of neural activity correlated with VH.

      We appreciate your concerns, and have tried our best to respond to them fully against your specific concerns below.

      In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of the visual cortex, that underlies a wide variety of visual tasks and functions.

      We agree with you that the VH regions defined using symmetry task and search task do not overlap completely (as we have shown in Figure S13). However this is to be expected for several reasons. First, the images in the symmetry task were presented at fixation, whereas the images in the visual search task were presented peripherally. Second, the lack of overlap could be due to variations across individuals. Indeed, considerable individual variability has been observed in the location of category-selective regions such as VWFA (Glezer and Riesenhuber 2013) and FFA (Weiner and Grill-Spector, 2012). We propose that testing the same participants on both search and symmetry tasks would reveal overlapping VH regions. We now acknowledge these issues in the Results (p. 26).

      Maybe I have missed something, or there is some flaw in my logic. But, absent that, I think the authors should radically reconsider their theory, analyses, and interpretations, in light of the detailed comments below, to make the best use of their extensive and valuable datasets combining behavior and fMRI. I think doing so could lead to a much more coherent and convincing paper, albeit possibly supporting less novel conclusions.

      We appreciate your concerns. We have tried our best to respond to them fully against your specific concerns below.

      THEORY AND ANALYSIS OF VH

      (1) VH is an unnecessary, complex proxy for response time and target-distractor similarity. VH is defined as a novel visual quality, calculable for both arrays of objects (as studied in Experiments 1-3) and individual objects (as studied in Experiment 4). It is derived from a center-to-distance calculation in a perceptual space. That space in turn is derived from the multi-dimensional scaling of response times for target-distractor pairs in an oddball detection task (Experiments 1 and 2) or in a same-different task (Experiments 3 and 4).

      The above statements are not entirely correct. Experiments 1 & 3 are oddball visual search experiments. Their purpose was to estimate the underlying perceptual space of objects.

      Proximity of objects in the space is inversely proportional to response times for arrays in which they were paired. These response times are higher for more similar objects. Hence, proximity is proportional to similarity. This is visible in Fig. 2B as the close clustering of complex, confusable animal shapes.

      VH, i.e. distance-to-center, for target-present arrays, is calculated as shown in Fig. 1C, based on a point on the line connecting the target and distractors. The authors justify this idea with previous findings that responses to multiple stimuli are an average of responses to the constituent individual stimuli. The distance of the connecting line to the center is inversely proportional to the distance between the two stimuli in the pair, as shown in Fig. 2D. As a result, VH is inversely proportional to the distance between the stimuli and thus to stimulus similarity and response times. But this just makes VH a highly derived, unnecessarily complex proxy for target-distractor similarity and response time. The original response times on which the perceptual space is based are far more simple and direct measures of similarity for predicting response times.

      We agree that VH brings no explanatory power to target-present searches, since target-present response times are a direct estimate of target-distractor similarity. However, we are additionally explaining target-absent response times. Target-absent response times are well known to vary systematically with image properties, but why they do so have not been clear in the literature.

      Our key conceptual advance lies in relating the neural response to a search array to the neural response of the constituent elements, and in proposing a decision variable using which participants can make both target-present and target-absent judgements on any search array.

      (2) The use of VH derived from Experiment 1 to predict response times in Experiment 2 is circular and does not validate the VH theory.

      The use of VH, a response time proxy, to predict response times in other, similar tasks, using the same stimuli, is circular. In effect, response times are being used to predict response times across two similar experiments using the same stimuli. Experiment 1 and the target present condition of Experiment 2 involve the same essential task of oddball detection. The results of Experiment 1 are converted into VH values as described above, and these are used to predict response times in Experiment 2 (Fig. 2F). Since VH is a derived proxy for response values in Experiment 1, this prediction is circular, and the observed correlation shows only consistency between two oddball detection tasks in two experiments using the same stimuli.

      We agree that it would be circular to use oddball search times in Experiment 1 to explain only target-present search times in Experiment 2, since they basically involve the same searches. However, we are explaining both target-present and target-absent search times in a unified framework; systematic variations in target-absent search times have been noted in the literature but never really explained. One could still simply say that target-absent search times are some function of the target-present search times, but this still doesn’t provide an explanation for how participants are making target-present and absent decisions. The existing literature contains models for how visual search might occur for a specific target and distractor but does not elucidate how participants might perform generic visual search where target and distractors are not known in advance.

      Our key conceptual advance lies in relating the neural response to a search array to the neural response of the constituent elements, and in proposing a decision variable using which participants can make both target-present and target-absent judgements on any search array.

      (3) The negative correlation of target-absent response times with VH as it is defined for target-absent arrays, based on the distance of a single stimulus from the center, is uninterpretable without understanding the effects of center-fitting. Most likely, center-fitting and the different VH metrics for target-absent trials produce an inverse correlation of VH with target-distractor similarity.

      We see no cause for concern with the center-fitting procedure, for several reasons. First, the best-fitting center remained stable despite many randomly initialized starting points. Second, the best-fitting center derived from one set of objects was able to predict the target-absent and target-present responses of another set of objects. Finally, the VH obtained for each object (i.e. distance from the best-fitting center) is strongly correlated with the average distance of that object from all other objects (Figure S1A). We have now clarified this in the Results (p. 11).

      The construction of the VH perceptual space also involves fitting a "center" point such that distances to center predict response times as closely as possible. The effect of this fitting process on distance-to-center values for individual objects or clusters of objects is unknowable from what is presented here. These effects would depend on the residual errors after fitting response times with the connecting line distances. The center point location and its effects on the distance-to-center of single objects and object clusters are not discussed or reported here.

      While it is true that the optimal center needs to be found by fitting to the data, there no particular mystery to the algorithm: we are simply performing a standard gradient-descent to maximize the fit to the data. We have described the algorithm clearly and are making our codes public. We find the algorithm to yield stable optimal centers despite many randomly initialized starting points. We find the optimal center to be able to predict responses to entirely novel images that were excluded during model training. We are making no assumption about the location of centre with respect to individual points. Therefore, we see no cause for concern regarding the center-finding algorithm.

      Yet, this uninterpretable distance-to-center of single objects is chosen as the metric for VH of target-absent displays (VHabsent). This is justified by the idea that arrays of a single stimulus will produce an average response equal to one stimulus of the same kind. However, it is not logically clear why response strength to a stimulus should be a metric for homogeneity of arrays constructed from that stimulus, or even what homogeneity could mean for a single stimulus from this set. It is not clear how this VHabsent metric based on single stimuli can be equated to the connecting line VH metric for stimulus pairs, i.e. VHpresent, or how both could be plotted on a single continuum.

      Most visual tasks, such as finding an animal, are thought to involve building a decision boundary on some underlying neural representation. Even visual search has been portrayed as a signal-detection problem where a particular target is to be discriminated from a distractor. However none of these formulations work in the case of generic visual tasks, where the target and distractor identities are unknown. We are proposing that, when we view a search array, the neural response to the search array can be deduced from the neural responses to the individual elements using well known rules, and that decisions about an oddball target being present or absent can be made by computing the distance of this neural response from some canonical mean firing rate of a population of neurons. This distance to center computation is what we denote as visual homogeneity. We have revised our manuscript throughout to make this clearer and we hope that this helps you understand the logic better.

      It is clear, however, what should be correlated with difficulty and response time in the target-absent trials, and that is the complexity of the stimuli and the numerosity of similar distractors in the overall stimulus set. The complexity of the target, similarity with potential distractors, and the number of such similar distractors all make ruling out distractor presence more difficult. The correlation seen in Fig. 2G must reflect these kinds of effects, with higher response times for complex animal shapes with lots of similar distractors and lower response times for simpler round shapes with fewer similar distractors.

      You are absolutely correct that the stimulus complexity should matter, but there are no good measures for stimulus complexity. But considering what factors are correlated with target-absent response times is entirely different from asking what decision variable or template is being used by participants to solve the task.

      The example points in Fig. 2G seem to bear this out, with higher response times for the deer stimulus (complex, many close distractors in the Fig. 2B perceptual space) and lower response times for the coffee cup (simple, few close distractors in the perceptual space). While the meaning of the VH scale in Fig. 2G, and its relationship to the scale in Fig. 2F, are unknown, it seems like the Fig. 2G scale has an inverse relationship to stimulus complexity, in contrast to the expected positive relationship for Fig. 2F. This is presumably what creates the observed negative correlation in Fig. 2G.

      Taken together, points 1-3 suggest that VHpresent and VHabsent are complex, unnecessary, and disconnected metrics for understanding target detection response times. The standard, simple explanation should stand. Task difficulty and response time in target detection tasks, in both present and absent trials, are positively correlated with target-distractor similarity.

      Respectfully, we disagree with your assessment. Your last point is not logically consistent though: response times for target-absent trials cannot be correlated with any target-distractor similarity since there is no target in the first place in a target-absent array. We have shown that target-absent response times are in fact, independent of experimental context, which means that they index an image property that is independent of any reference target (Results, p. 15; Section S4). This property is what we define as visual homogeneity.

      I think my interpretations apply to Experiments 3 and 4 as well, although I find the analysis in Fig. 4 especially hard to understand. The VH space in this case is based on Experiment 3 oddball detection in a stimulus set that included both symmetric and asymmetric objects. However, the response times for a very different task in Experiment 4, a symmetric/asymmetric judgment, are plotted against the axes derived from Experiment 3 (Fig. 4F and 4G). It is not clear to me why a measure based on oddball detection that requires no use of symmetry information should be predictive of within-stimulus symmetry detection response times. If it is, that requires a theoretical explanation not provided here.

      We are using an oddball detection task to estimate perceptual dissimilarity between objects, and construct the underlying perceptual representation of both symmetric and asymmetric objects. This enabled us to then ask if some distance-to-center computation can explain response times in a symmetry detection task, and obtain an answer in the affirmative. We have reworked the text to make this clear.

      (4) Contrary to the VH theory, same/different tasks are unlikely to depend on a decision boundary in the middle of a similarity or homogeneity continuum.

      We have provided empirical proof for our claims, by showing that target-present response times in a visual search task are correlated with “different” responses in the same-different task, and that target-absent response times in the visual search task are correlated with “same” responses in the same-different task (Section S3).

      The authors interpret the inverse relationship of response times with VHpresent and VHabsent, described above, as evidence for their theory. They hypothesize, in Fig. 1G, that VHpresent and VHabsent occupy a single scale, with maximum VHpresent falling at the same point as minimum VHabsent. This is not borne out by their analysis, since the VHpresent and VHabsent value scales are mainly overlapping, not only in Experiments 1 and 2 but also in Experiments 3 and 4. The authors dismiss this problem by saying that their analyses are a first pass that will require future refinement. Instead, the failure to conform to this basic part of the theory should be a red flag calling for revision of the theory.

      We respectfully disagree – by no means did we dismiss this problem! In fact, we have explicitly acknowledged this by saying that VH does not explain all the variance in the response times, but nonetheless explains substantial variance and might form the basis for an initial guess or a fast response. The remaining variance might be explained by processes that involve more direct scrutiny. Please see Results, page 10 & 22.

      The reason for this single scale is that the authors think of target detection as a boundary decision task, along a single scale, with a decision boundary somewhere in the middle, separating present and absent. This model makes sense for decision dimensions or spaces where there are two categories (right/left motion; cats vs. dogs), separated by an inherent boundary (equal left/right motion; training-defined cat/dog boundary). In these cases, there is less information near the boundary, leading to reduced speed/accuracy and producing a pattern like that shown in Fig. 1G.

      The key conceptual advance of our study is that we show that even target/present, same/different or symmetry judgements can be fit into the standard decision-making framework.

      This logic does not hold for target detection tasks. There is no inherent middle point boundary between target present and target absent. Instead, in both types of trials, maximum information is present when the target and distractors are most dissimilar, and minimum information is present when the target and distractors are most similar. The point of greatest similarity occurs at the limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with the similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors.

      Unfortunately, your logic does not boil down to any quantitative account, since you are using vague terms like “maximum information”. Further, any argument based solely on item similarity to explain visual search or symmetry responses cannot explain systematic variations observed for target-absent arrays and for symmetric objects, for the reasons below.

      If target-distractor dissimilarity were the sole driver of response times, target-absent judgements should always take the longest time since the target and distractor have zero similarity, with no variation from one image to another. This account does not explain why target-absent response times vary so systematically.

      Similarly, if symmetry judgements are solely based on comparing the dissimilarity between two halves of an object, there should be no variation in the response times of symmetric objects since the dissimilarity between their two halves is zero. However we do see systematic variation in the response times to symmetric objects.

      DEFINITION OF AREA VH USING fMRI

      (1) The area VH boundaries from different experiments are nearly completely non-overlapping.

      In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity. • Actually VHsymmetry is apparent even in a simple subtraction between symmetric and asymmetric objects (Figure S10). The VH regions identified using the visual search task and symmetry task have a partial overlap, not zero overlap as you are incorrectly claiming.

      We have noted that it is not straightforward to interpret the overlap, since there are many confounding factors. One reason could simply be that the stimuli in the symmetry task were presented at fixation, whereas the visual search arrays contained items exclusively in the periphery. Another that the participants in the two tasks were completely different, and the lack of overlap is simply due to inter-individual variability. Testing the same participants in two tasks using similar stimuli would be ideal but this is outside the scope of this study. We have acknowledged these issues in the Results (p. 26) and in the Supplementary Material (Section S8).

      (2) It is hard to understand how neural responses can be correlated with both VHpresent and VHabsent.

      The main paper results for VHdetection are based on both target-present and target-absent trials, considered together. It is hard to interpret the observed correlations, since the VHpresent and VHabsent metrics are calculated in such different ways and have opposite correlations with target similarity, task difficulty, and response times (see above). It may be that one or the other dominates the observed correlations. It would be clarifying to analyze correlations for target-present and target-absent trials separately, to see if they are both positive and correlated with each other.

      Thanks. The positive correlation between VH and neural response holds even when we do the analysis separately for target-present and -absent searches (correlation between neural response in VH region and visual homogeneity (n = 32, r = 0.66, p < 0.0005 for target-present searches & n = 32, r = 0.56, p < 0.005 for target-absent searches).

      (3) The definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

      Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. The cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in the cortex anterior to LO, rather than treating them as the defining purpose for a large area of the visual cortex.

      We totally agree with you that reporting a new brain region would require careful interpretation and abundant and converging evidence. However, this requires many studies worth of work, and historically category-selective regions like the FFA have achieved consensus only after they were replicated and confirmed across many studies. We believe our proposal for the computation of a quantity like visual homogeneity is conceptually novel, and our study represents a first step that provides some converging evidence (through replicable results across different experiments) for such a region. We have reworked our manuscript to make this point clearer (Discussion, p 32).

      Reviewer #2 (Public Review):

      Summary:

      This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are the same, or judging if an object is symmetric. In Experiment 1, the reaction times on several objects were measured in human subjects. In Experiment 2, the visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

      Strengths:

      (1) The writing is very clear. The presentation of the study is informative.

      (2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

      We are grateful to you for your balanced assessment and constructive comments.

      Weaknesses:

      (1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.

      We disagree with you since the same logic applies to any curve-fitting procedure. When we fit data to a straight line, we are finding the slope and intercept that minimizes the error between the data and the straight line, but we would hardly consider the process circular when a good fit is achieved – in fact we take it as a confirmation that the data can be fit linearly. In the same vein, we would not have observed a good fit to the data, if there did not exist any good reference point relative to which the distances of the target-present and target-absent search arrays predicted these response times.

      In Section S1, we have already reported that the visual homogeneity estimates for each object is strongly correlated with the average distance of each object to all other objects (r = 0.84, p<0.0005, Figure S1). Second, to confirm that the results we obtained are not due to overfitting, we have already reported a cross-validation analysis, where we removed all searches involving a particular image and predicted these response times using visual homogeneity. This too revealed a significant model correlation confirming that our results are not due to overfitting.

      (2) On page 11, lines 214-221. It says: "these findings are non-trivial for several reasons". However, the first reason is confusing. It is unclear to me why "it suggests that there are highly specific computations that can be performed on perceptual space to solve oddball tasks". In fact, these two sentences provide no specific explanation for the results.

      We have now revised the text to make it clearer (Results, p. 11).

      (3) The second reason is interesting. Reaction times in target-present trials can be easily explained by target-distractor similarity. But why does reaction time vary substantially across target-absent stimuli? One possible explanation is that the objects that are distant from the feature distribution elicit shorter reaction times. Here, all objects constitute a statistical distribution in the feature (perceptual) space. There is certainly a mean of this distribution. Some objects look like outliers and these outliers elicit shorter reaction times in the target-absent trials because outlier detection is very salient.

      One might argue that the above account is merely a rephrasing of the idea of visual homogeneity proposed in this study. If so, feature saliency is not a new account. In other words, the idea of visual homogeneity is another way of reiterating the old feature saliency theory.

      Thank you for this interesting point. We don’t necessarily see a contradiction. However, we are proposing a quantitative decision variable that the brain could be using to make target present/absent judgements.

      (4) One way to reject the feature saliency theory is to compare the reaction times of the objects that are very different from other objects (i.e., no surrounding objects in the perceptual space, e.g., the wheel in the lower right corner of Fig. 2B) with the objects that are surrounded by several similar objects (e.g., the horse in the upper part of Fig. 2B). Also, please choose the two objects with similar distance from the reference point. I predict that the latter will elicit longer reaction times because they can be easily confounded by surrounding similar objects (i.e., four-legged horses can be easily confounded by four-legged dogs). If the density of object distribution per se influences the visual homogeneity score, I would say that the "visual homogeneity" is essentially another way of describing the distributional density of the perceptual space.

      We agree with you, and we have indeed found that visual homogeneity estimates from our model are highly correlated with the average distance of an object relative to all other objects. However, we performed several additional experiments to elucidate the nature of target-absent response times. We find that they are unaffected by whether these searches are performed in the midst of similar or dissimilar objects (Section S4, Experiment S6), and even when the same searches are performed among nearby sets of objects with completely uncorrelated average distances (Section S4, Experiment S7). We have now reworked the text to make this clearer.

      (5) The searchlight analysis looks strange to me. One can easily perform a parametric modulation by setting visual homogeneity as the trial-by-trial parametric modulator and reaction times as a covariate. This parametric modulation produces a brain map with the correlation of every voxel in the brain. On page 17 lines 340-343, it is unclear to me what the "mean activation" is.

      We have done something similar. For each region we took the mean activation at each voxel as the average activation 3x3x3 voxel neighborhood in the brain, and took its correlation with visual homogeneity. We have now reworked this to make it clearer (Results, p. 16).

      Minor points

      (1) In the intro, it says: "using simple neural rules..." actually it is very confusing what "neural rules" are here. Better to change it to "computational principles" or "neural network models"??

      We have now replaced this with “using well-known principles governing multiple object representations”.

      (2) In the intro, it says: "while machine vision algorithms are extremely successful in solving feature-based tasks like object categorization (Serre, 2019), they struggle to solve these generic tasks (Kim et al., 2018; Ricci et al. 2021). These are not generic tasks. They are just a specific type of visual task-judging relationship between multiple objects. Moreover, a large number of studies in machine vision have shown that DNNs are capable of solving these tasks and even more difficult tasks. Two survey papers are listed here.

      Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.

      Małkiński, M., & Mańdziuk, J. (2022). Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices. arXiv preprint arXiv:2201.12382.

      Thank you for sharing these references. In fact, a recent study has shown that specific deep networks can indeed solve the same-different task (Tartaglini et al, 2023). However our broader point remains that the same-different or other such visual tasks are non-trivial for machine vision algorithms.

      Reviewer #1 (Recommendations For The Authors):

      Nothing to add to the public review. If my concerns turn out to be invalid, I apologize and will happily accept correction. If they are valid, I hope they will point toward a new version of this paper that optimizes the insights to be gained from this impressive dataset.

      Reviewer #2 (Recommendations For The Authors):

      My suggestions are as follows:

      (1) Analyze the fMRI data using the parametric modulation approach first at the single-subject level and then perform group analysis.

      To clarify, we have obtained image-level activations from each subject, and used it for all our analyses.

      (2) Think about a way to redefine visual homogeneity from a purely image-computable approach. In other words, visual homogeneity should be first defined as an image feature that is independent of any empirical response data. And then use the visual homogeneity scores to predict reaction times.

      While we understand what you mean, any image-computable representation such as from a deep network may carry its own biases and may not be an accurate representation of the underlying object representation. By contrast, neural dissimilarities in the visual cortex are strongly predictive of visual search oddball response times. That is why we used visual search oddball response times as a proxy for the underlying neural representation, and then asked whether some decision variable can be derived from this representation to explain both target present and absent judgements in visual search.

    1. Author response:

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

      eLife assessment

      The authors provide convincing experimental evidence of extended motivational signals encoded in the mouse anterior cingulate cortex (ACC) that are implemented by the orbitofrontal cortex (OFC)-to-ACC signaling during learning. The results are valuable to the field of motivation and cognition. The experimental methods used were state-of-the-art. The manuscript would further benefit from theory-driven analyses to inform a mechanistic understanding, particularly for the single-cell calcium imaging results. These results will be of interest to those interested in cortical function, learning, and/or motivation.

      We thank the reviewers for their thoughtful reading of our paper and providing constructive feedback. We have made the relevant changes to the manuscript to improve the writing and figures. We provide responses below to each of the reviewer’s comments.

      Reviewer #1 (Public Review):

      (1) An important conclusion (Figure 4) is that when mice are trained to run through no reward (N) cues in order to reach reward (R) cues, the OFC neurons projecting to ACC each respond to different specific events in a manner that ensures that collectively they tile the extended behavioural sequence. What I was less sure of was whether the ACC neurons do the same or not. Figure 3 suggests that on average ACC neurons maintain activity across N cues in order to get to R cues but I was not sure whether this was because all individual neurons did this or whether some had activity patterns like the OFC neurons projecting to ACC.

      We agree that it remains uncertain what individual ACC neurons do during the extended behavioral sequence. We now include a few sentences in the discussion about what we hypothesize, as we did not perform the cellular resolution imaging to determine this:

      “While we did not perform single-cell imaging of ACC in our task, we hypothesize that individual ACC neurons could encode the distribution of actions/opportunities47 (i.e. stop, run, lick, suppress lick) taken during R or N cues. ACC neurons could compute the relative value of the action taken such that more ACC neurons become recruited once mice learn to run out of N cues. The sustained increase in bulk ACC activity across N cue trials (Figure 2) could come from a stable sequence of individual neurons that encode the timescale of the actions taken. In this way, OFC projections would encode current motivation across N cues before learning, which then triggers ACC to compute the valuebased actions. Motivational signals in OFC would thus represent state since past rewards/goals, while in ACC these signals represent actions taken to pursue rewards/goals in the future.”

      (2) Figure 1 versus Figure 2: There does not seem to be a particular motivation for whether chemogenetic inactivation or optogenetic inhibition were used in different experiments. I think that this is not problematic but, if I am wrong and there were specific reasons for performing each experiment in a certain way, then further clarification as to why these decisions were made would be useful. If there is no particular reason, then simply explaining that this is the case might stop readers from seeking explanations.

      Thank you for this comment and we agree that clarification on this is important. We performed chemogenetic inhibition of ACC in Figure 1 to take a broad survey of behavioral effects throughout a 40-min long behavioral session, and performed optogenetic inhibition in Figure 2 because we wanted to restrict our inhibition to the few seconds of cue presentation during a behavioral session and across days. Furthermore, we wanted to combat any potential off-target effects that would come from repeated administration of CNO over the several days of training (Manvich et al 2018). We have included a couple sentences on page 4 to clarify this:

      “We proceeded to test whether these motivation related signals in ACC are required for learning. To restrict our inhibition to cue presentation portions of our task, and combat any potential off-target effects of CNO31 from repeated administration across several days of training, we used optogenetic inhibition.”

      (3) P5, paragraph 2. The authors argue that OFC and anteriomedial (AM) thalamic inputs into ACC are especially important for mediating motivation through N cues in order to reach R cues. Is this based on a statistical comparison between the activity in OFC or AM inputs as opposed to the other inputs?

      We determined that OFC and AM thalamic inputs to ACC are particularly important by comparing the pre-cue activity in a reward-no reward-reward trial sequence (RNR; Figure 3B). Specifically, we performed paired t-tests comparing pre-cue activity between N and R cues, and found a statistically significant increase for R cues but only for the OFC and AM inputs, not for the BLA or LC inputs.

      (4) P3, paragraph 2. Some papers by Khalighinejad and colleagues (eg Neuron 2020, Current Biology, 2022) might be helpful here in as much as they assess ACC roles in determining action frequency, initiation, and speed and mediating the relationship between reward availability and action frequency and speed.

      We thank the reviewer for bringing these relevant papers to our attention. We have included these papers in our citations in this paragraph.

      (5) Paragraph 1 "This learning is of a more deliberate, informed nature than habitual learning, as they are sensitive to the current value of outcomes and can lead to a novel sequence of actions for a desired outcome1-3." Should "they" be "it"?

      This is correct, we have edited this in the manuscript.

      Reviewer #2 (Public Review):

      Impact:

      The findings will be valuable for further research on the impact of motivational states on behaviour and cognition. The authors provided a promising concept of how persistent motivational states could be maintained, as well as established a novel, reproducible task assay. While experimental methods used are currently state-of-the-art, theoretical analysis seems to be incomplete/not extensive. We thank the reviewer for these comments. In our paper, we performed single-cell calcium imaging of OFC projection neurons to ACC to build a mechanistic understanding for the bulk ramp-like response we identified in these neurons with photometry. We identified ensembles of neurons that tile sequences of trials that match the bulk response, in particular a subset of neurons that are active at the time a reward (R) cue is reached after 2 no-reward (N) cues. We included a paragraph in the discussion to address future theory-driven analyses to address how computation is achieved by OFC projection neurons:

      “We linked the ramp-like increase in neural activity in OFC to motivation, but several questions still remain about how motivation is computed and why it would be represented as a ramp. Motivation could be computed as a combination of several variables such as time since last reward, value of reward, and effort to reach future rewards. Future theorydriven analyses could determine how motivation is computed, and whether individual variables of time, value, and effort, are encoded as clusters of similar tuned neurons, or mixed and collectively represented at the population level. In either case, it is likely that a combined map of task space and value-information carried by OFC are being used to inform downstream regions, such as ACC, for adjusting behavior. ”

      Reviewer #2 (Recommendations for the Authors):

      Overall, the layout of the figures seems a little bit chaotic and makes it hard to understand the boundaries between panels.

      We agree that the figure layout could be improved upon to aid the reader in moving from panel to panel. We have edited two of the main figures with layouts that are most irregular (Figures 2 and 4) to help with this.

      Figures/text should include the promoters used for protein expression so that readers understand which cell types would be affected.

      We have made sure to edit the figures to include the promoter of the viruses we used, and edited the text to include both the AAV serotype and promoter.

      Discuss why it is necessary for multiple prefrontal areas to be involved in maintaining motivational signals.

      We thank the reviewer for this comment. We believe that prefrontal areas would be recruited as tasks to study motivational states become more complex and require animals to keep track of task structure and perform value-guided actions. We have included a couple sentences in the final paragraph of the discussion about this:

      “Our work showed the recruitment of multiple frontal cortical areas in this process, which is to be expected as animals are required to build, maintain, and use representations of task structure and value to drive learned, motivated behaviors47. Future work can build upon the task we developed here to determine how the frontal cortex maintains motivational states across many more cue-outcome associations, and how these associations may dynamically change across time48”.

      Additionally, we included a short discussion on how in motivational signals differ between OFC and ACC in our work. We suggest OFC encodes current motivation before and after learning, which then leads ACC to represent learned actions taken and thus have a longer timescale motivational response (see response to Reviewer 1).

      Minor: Page 4, Line 1: "increase" instead of "increases".

      This is correct, we have edited this in the manuscript.

    1. Author response:

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

      eLife assessment

      This study provides important insights into the role of neurexins as regulators of synaptic strength and timing at the glycinergic synapse between neurons of the medial nucleus of the trapezoid body and the lateral superior olive, key components of the auditory brainstem circuit involved in computing sound source location from differences in the intensity of sounds arriving at the two ears. Through an elegant combination of genetic manipulation, fluorescence in-situ hybridization, ex vivo slice electrophysiology, pharmacology, and optogenetics, the authors provide convincing evidence to support their claims. While further work is needed to reveal the mechanistic basis by which neurexins influence glycinergic neurotransmission, this work will be of interest to both auditory and synaptic neuroscientists.

      We appreciate the recognition of the significance of our study in shedding light on the role of neurexins in regulating synaptic strength and timing at the glycinergic synapse. Indeed, further investigations are warranted to delve deeper into the specific role of each different variant of neurexins in the future. We hope that our work will spark more interest and collaboration in unraveling the complexities of molecular codes of synaptic function.

      Public Reviews:

      Reviewer #1 (Public Review):

      Jiang et al. demonstrated that ablating Neurexins results in alterations to glycinergic transmission and its calcium sensitivity, utilizing a robust experimental system. Specifically, the authors employed rAAV-Cre-EGFP injection around the MNTB in Nrxn1/2/3 triple conditional mice at P0, measuring Glycine receptor-dependent IPSCs from postsynaptic LSO neurons at P13-14. Notably, the authors presented a clear reduction of 60% and 30% in the amplitudes of opto- and electric stimulation-evoked IPSCs, respectively. Additionally, they observed changes in kinetics, alterations in PPR, and sensitivity to lower calcium and the calcium chelator, EGTA, indicating solid evidence for changes in presynaptic properties of glycinergic transmission.

      Furthermore, the authors uncovered an unexpected increase in sIPSC frequency without altering amplitude. Despite the reduction in evoked IPSC, immunostaining revealed an increase in GlyT2 and VGAT in TKO mice, supporting the notion of an increase in synapse number. However, the reviewer expresses caution regarding the authors' conclusion that "glycinergic neurotransmission likely by promoting the synapse formation/maintenance, which is distinct from the phenotypes observed in glutamatergic and GABAergic neurons (Chen et al., 2017; Luo et al., 2021)", as outlined in lines 173-175. The reviewer suggests that this statement may be overstated, pointing out the authors' own discussion in lines 254-265, which acknowledges multiple possibilities, including the potential that the increase in synapses is a consequence rather than a causal effect of Nrxn deletion.

      We appreciate the reviewer’s thoughtful evaluation of our study. We agree that our conclusion regarding the promotion of synapse formation/maintenance may have been overstated and recognize the need for a more nuanced interpretation of our findings. Accordingly, we have revised our interpretation by discussing carefully the various possibilities that may cause the observed increase in synapse number in line 256-266.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, Jiang et al., explore the role of neurexins at glycinergic MNTB-LSO synapses. The authors utilize elegant and compelling ex vivo slice electrophysiology to assess how the genetic conditional deletion of Nrxns1-3 impacts inhibitory glycinergic synaptic transmission and found that TKO of neurexins reduced electrically and optically evoked IPSC amplitudes, slowed optically evoked IPSC kinetics and reduced presynaptic release probability. The authors use classic approaches including reduced [Ca2+] in ACSF and EGTA chelation to propose that changes in these evoked properties are likely driven by the loss of calcium channel coupling. Intriguingly, while evoked transmission was impaired, the authors reported that spontaneous IPSC frequency was increased, potentially due to an increased number of synapses in LSO. Overall, this manuscript provides important insight into the role of neurexins at the glycinergic MNTP-LSO synapse and further emphasizes the need for continued study of both the non-redundant and redundant roles of neurexins.

      We thank the reviewer for the strong comments and support of our work.

      Strengths:

      This well-written manuscript seamlessly incorporates mouse genetics and elegant ex vivo electrophysiology to identify a role for neurexins in glycinergic transmission at MNTB-LSO synapses. Triple KO of all neurexins reduced the amplitude and timing of evoked glycinergic synaptic transmission. Further, spontaneous IPSC frequency was increased. The evoked synaptic phenotype is likely a result of reduced presynaptic calcium coupling while the spontaneous synaptic phenotype is likely due to increased synapse numbers. While neuroligin-4 has been identified at glycinergic synapses, this study, to the best of my knowledge, is the first to study Nrxn function at these synapses.<br />

      We again appreciate the positive feedback on the strengths of our study. We agree that the observed reduction in evoked synaptic transmission and the increase in spontaneous IPSC frequency provide intriguing insights into the function of neurexins in regulating glycinergic synaptic activity.

      Weaknesses:

      The data are compelling and report an intriguing functional phenotype. The role of Neurexins redundantly controls calcium channel coupling has been previously reported. Mechanistic insight would significantly strengthen this study.

      We wholeheartedly agree with the reviewer that understanding how neurexins control calcium channel coupling at the presynaptic active zone is crucial for elucidating their role in synaptic transmission. While our current study has provided compelling evidence for the functional phenotypes of pan-neurexin deletion, we recognize the importance of investigating the underlying molecular mechanisms in future research. Exploring these mechanisms would undoubtedly enhance our understanding of neurexin function at various synapses and contribute to advancing the field.

      The claim that triple KO of Nrxns from MNTB increases the number of synapses in LSO is not strongly supported.

      We agree. Echoing the suggestion made by reviewer 1 (as mentioned above), we acknowledge that the claim regarding the increase in synapse numbers in the LSO following the triple knockout of neurexins from the MNTB was overstated. Consequently, we have revised our conclusions more carefully to reflect this adjustment.

      Despite the stated caveats of measuring electrically evoked currents and the more robust synaptic phenotypes observed using optically evoked transmission, the authors rely heavily on electrical stimulation for most measurements.

      We acknowledge that optogenetic stimulation offers crucial advantages, and we have provided a balanced discussion of the caveats associated with both methods in our manuscript. Additionally, we have conducted new optogenetic experiments specifically for measuring the paired-pulse ratio in control and Nrxn123 TKO mice. These results have been included as a new supplementary figure (Figure S2).

      For experiments involving EGTA and low Ca2+ manipulations, we opted for electrical stimulation due to concerns regarding potential side effects of optogenetics, including the phototoxicity and photobleaching during prolonged light exposure.

      The differential expression of individual neurexins might indicate that specific neurexins may dominantly regulate synaptic transmission, however, this possibility is not discussed in detail.

      We thank the reviewer for bringing up this important point. The differential expression of individual neurexins indeed suggests that specific neurexins may play dominant roles in regulating synaptic transmission. While our study primarily focused on the collective impact of ablating all neurexins, we acknowledge the significance of exploring the specific contributions of individual neurexin isoforms in the future. Understanding the distinct roles of each neurexin isoform could provide valuable insights into the precise mechanisms underlying synaptic function and plasticity. We have added discussion in our revised manuscript Line223-230.

      Reviewer #3 (Public Review):

      Summary:

      The authors investigate the hypothesis that neurexins serve a crucial role as regulators of the synaptic strength and timing at the glycinergic synapse between neurons of the medial nucleus of the trapezoid body (MNTB) and the lateral superior olivary complex (LSO). It is worth mentioning that LSO neurons are an integration station of the auditory brainstem circuit displaying high reliability and temporal precision. These features are necessary for computing interaural cues to derive sound source location from comparing the intensities of sounds arriving at the two ears. In this context, the authors' findings build up according to the hypothesis first by displaying that neurexins were expressed in the MNTB at varying levels. They followed this up with the deletion of all neurexins in the MNTB through the employment of a triple knock-out (TKO). Using electrophysiological recordings in acute brainstem slices of these TKO mice, they gathered solid evidence for the role of neurexins in synaptic transmission at this glycinergic synapse primarily by ensuring tight coupling of Ca2+ channels and vesicular release sites. Additionally, the authors uncovered a connection between the deletion of neurexins and a higher number of glycinergic synapses in TKO mice, for which they provided evidence in the form of immunostainings and related it to electrophysiological data on spontaneous release. Consequently, this investigation expands our knowledge on the molecular regulation of synaptic transmission at glycinergic synapses, as well as on the auditory processing at the level of the brainstem.

      Strengths:

      The authors demonstrate substantial results in support of the hypothesis of a critical role of neurexins for regulating glycinergic transmission in the LSO using various techniques. They provide evidence for the expression of neurexins in the MNTB and consecutively successfully generate and characterize the neurexin TKO. For their study on LSO IPSCs the authors transduced MNTB neurons by co-injection of virus-carrying Cre and ChR2 and subsequently optogenetically evoke release of glycine. As a result, they observed a significant reduction in amplitude and significantly slower rise and decay times of the IPSCs of the TKO in comparison with control mice in which MNTB neurons were only transduced with ChR2. Furthermore, they observed an increased paired pulse ratio (PPR) of LSO IPSCs in the TKO mice, indicating lower release probability. Elaborating on the hypothesis that neurexins are essential for the coupling of synaptic vesicles to Ca2+ channels, the authors show lowered Ca2+ sensitivity in the TKO mice. Additionally, they reveal convincing evidence for the connection between the increased frequency of spontaneous IPSC and the higher number of glycinergic synapses of the LSO in the TKO mice, revealed by immunolabeling against the glycinergic presynaptic markers GlyT2 or VGAT.

      We thank the reviewer for the thoughtful and thorough evaluation of the significance of investigating the role of neurexins in glycinergic transmission at the MNTB-LSO synapse, particularly in the context of auditory processing and sound localization. The positive feedback is greatly appreciated.

      Weaknesses:

      The major concern is novelty as this work on the effects of pan-neurexin deletion in a glycinergic synapse is quite consistent with the authors' prior work on glutamatergic synapses (Luo et al., 2020). The authors might want to further work out novel aspects and strengthen the comparative perspective. Conceptually, the authors might want to be more clear about interpreting the results on the altered dependence of release on voltage-gated Ca2+ influx (Ca2+ sensitivity, coupling).

      Regarding the reviewer’s concern about the novelty of our work, we acknowledge that our previous work has explored the effects of pan-neurexin deletion on glutamatergic synapses (Luo et al., 2020). However, we would like to point out that a novelty of our present study indeed stems from the exploration of how different types of synapses converge to employ the same mechanism of synaptic function, particularly in the context of neurexin-mediated regulation. Our previous study focused on glutamatergic synapses, the current study delves into the realm of glycinergic synapses, which represent a distinct population with unique properties and functions. Despite the differences between these synapse types, our findings reveal a commonality in the underlying mechanisms of synaptic regulation mediated by neurexins. This convergence of mechanisms across different synapse types highlights the fundamental role of neurexins in synaptic function and plasticity. By elucidating how neurexins regulate synaptic transmission at both excitatory and inhibitory synapses, we provide valuable insights into the general principles governing synaptic function. In addition, this comparative perspective may shed light on the complex interplay between excitatory and inhibitory neurotransmission, which is crucial for maintaining the balance of neuronal activity and network dynamics.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      During the developmental period spanning P3-P12, the MNTB-LSO synapses undergo a transition from GABAergic to glycinergic transmission. It is well-established that Neurexin plays a role in modulating GABAergic transmission. In the authors' experimental system, AAV was injected at P0, likely impacting GABAergic transmission, including potentially influencing synapse number, before subsequently affecting glycinergic transmission. A thoughtful discussion of how the experimental interventions might have influenced this developmental process and glycinergic transmission would enhance the clarity and interpretation of their findings.

      We thank the reviewer for raising the interesting topic of the transmitter switch during neurodevelopment. Strong evidence using gerbils and rats as animal models demonstrates that the MNTB-LSO synapses undergo a shift from GABAergic to glycinergic during the early development. However, in a more recent study by Friauf and colleagues (Fisher et al., 2019), patch-clamp recordings in acute mouse brainstem slices at P4-P11 combined with pharmacological blockade of GABAA receptors and/or glycine receptors clearly demonstrated no GABAergic synaptic component on LSO principal neurons, suggesting the transmitter subtype switch may be species different. We add a discussion in our revision to clarify this topic.

      Reviewer #2 (Recommendations For The Authors):

      The data are compelling and report an intriguing functional phenotype. Mechanistic insight into how this phenotype manifests would significantly strengthen this study. For example, which neuroligin is found at these MNTB-LSO synapses?

      We agree that investigating the underlying molecular mechanisms, particularly the specific function of each variant of neurexins and their respective ligands on the postsynaptic neurons, is crucial. Exploring these mechanisms, which extend beyond the scope of our current study, would undoubtedly enhance our understanding of neurexin function at various synapses and foster advancements in the field.

      Does the TKO alter the ability of MNTB inputs to induce AP firing in LSO neurons?

      Activation of the MNTB inputs does not directly induce AP firing in LSO neurons, because the MNTB-LSO synapses are glycinergic and serve to inhibit neuronal activity.

      We think the reviewer was to ask whether pan-neurexin deletion in the MNTB neurons alter their ability to impact the firing of LSO neurons. Indeed, the weakening of glycinergic transmission due to pan-neurexin ablation in MNTB neurons could potentially alter the excitation-inhibition (E/I) balance, thereby impacting the overall excitability of LSO neurons. We have conducted preliminary experiments to investigate this aspect and found that the E/I balance at LSO neurons was notably increased in TKO mice. We are currently preparing a manuscript to comprehensively address the role of neurexins at the auditory circuit and behavior levels.

      Additional calcium measurements using GECIs would provide insight into whether nanodomain calcium or total calcium is altered at these synapses.

      We appreciate the valuable suggestion provided by the reviewer. However, distinguishing between Ca2+ nanodomain and Ca2+ microdomain using Ca2+ imaging techniques requires advanced systems such as two-photon STED microscopy, which are beyond the scope of our current research.

      It is unclear why fluorescence intensity is quantified instead of the number of synaptic clusters in LSO. In addition to changes in synapse numbers, fluorescent intensity can indicate a number of other possible morphological changes.

      We appreciate the valuable suggestion from the reviewer. We have re-analyzed our imaging data to compare synaptic density. The results, as included in Fig.3f and 3h, confirm an increase in the number of glycinergic synapses after pan-neurexin deletion.

      The most robust synaptic phenotypes were produced by measuring light-evoked oIPSCs and the authors acknowledge that electrically-evoked eIPSCs might be contaminated by uninfected fibers or by other sources of glycinergic inputs. I suggest that IPSC PPRs, EGTA, and low Ca2+ experiments be performed using optogenetics.

      As discussed in our response to Public Reviews, we acknowledge that optogenetic stimulation offers crucial advantages, and we have provided a balanced discussion of the caveats associated with both methods in our manuscript. Additionally, following the reviewer’s suggestion, we have conducted new optogenetic experiments specifically for measuring the paired-pulse ratio in control and Nrxn123 TKO mice. We included this new dataset in supplementary Figure S2, which is consistent with our result obtained with electrically fiber stimulation.

      For experiments involving EGTA and low Ca2+ manipulations, we opted for electrical stimulation due to major concerns regarding potential side effects of optogenetics, including the phototoxicity and photobleaching during prolonged light exposure.

      It is sometimes confusing which type of evoked stimulation is being used (e.g. PPR, EGTA, and low Ca2+ experiments). To aid in the interpretations of these experiments, it would help to clarify.

      We appreciate the reviewer's suggestion regarding the clarity of the evoked stimulation methods used in our experiments. We have revised the manuscript to provide clearer descriptions of the specific types of evoked stimulation employed in each experiment. Thank you for guiding towards this clarification.

      The comparisons to Chen et al 2017 and the senior author's 2020 paper seem disjointed and do not contribute to the findings, which alone, are quite interesting. Given the prevailing notion that neurexins control different synaptic properties depending on the brain region and/or synapse studied, is it surprising that the findings observed here differ from previous studies of different synapses (glutamatergic and GABAergic)?

      By comparing previous studies at different types of neurons/synapses, our findings reveal a commonality in the underlying mechanisms of synaptic regulation mediated by neurexins. This convergence of mechanisms across different synapse types highlights the fundamental role of neurexins in synaptic function and plasticity. In addition, this comparative perspective may shed light on the complex interplay between excitatory and inhibitory neurotransmission, which is crucial for maintaining the balance of neuronal activity and network dynamics.

      Despite Nrxn3 being the most abundant Nrxn mRNA in MNTB neurons, the possible contributions of this highly expressed protein are not discussed.

      We thank the reviewer for bringing up this important point. The differential expression of individual neurexins indeed suggests that specific neurexins may play dominant roles in regulating synaptic transmission. While our study primarily focused on the collective impact of ablating all neurexins, we acknowledge the significance of exploring the specific contributions of individual neurexin isoforms in the future. Understanding the distinct roles of each neurexin isoform could provide valuable insights into the precise mechanisms underlying synaptic function and plasticity. We have added discussion in our revised manuscript Line223-230.

      Reviewer #3 (Recommendations For The Authors):

      • There are several instances of spaces missing and typos, please carefully check the manuscript.

      We greatly appreciate the reviewer's helpful feedback on the text that could be clarified or improved. We have meticulously edited the manuscript to address these concerns.

      • While studying the properties of IPSC, apart from optogenetic stimulation, the authors performed experiments with electrical fiber stimulation. Their findings showed a slightly significant reduction of the IPSC amplitude and no effect on the IPSCs kinetics when comparing the TKO and control. One weakness is the discrepancy between the results from the optogenetic and fiber stimulation experiments, which the authors contribute to inefficient transfection in the fiber stimulation experiments. The authors state that they tried to optimize their protocols for virus injection protocols. However, they do not elaborate on how the transfection rates could be improved in the discussion section. Moreover, it would be good to further address the reasons for the difference in amplitude between the control IPSCs in the optogenetic and fiber stimulation experiments.

      Echoing the suggestion by Reviewer 2 (see above), we acknowledge that optogenetic stimulation offers certain advantages, and we have provided a balanced discussion of the caveats associated with both methods in our manuscript. In addition, we have performed a new set of optogenetic experiment for the paired-pulse ratio measurement in control and Nrxn123 TKO mice and included as a new figure in supplementary figure S2.

      For experiments involving EGTA and low Ca2+ manipulations, we opted for electrical stimulation due to major concerns regarding potential side effects of optogenetics, including the phototoxicity and photobleaching during prolonged light exposure.

      We added the detail of virus injection strategy that optimized the transfection rates in the method section “To enhance virus infection efficiency, we decreased the dosage per injection while increasing the frequency of injections. Additionally, we ensured the pipette remained immobilized for 20-30 seconds to guarantee virus absorption at injection sites. As a result of this strategy, we estimated that the vast majority of MNTB neurons were inoculated by AAVs.” See line288-290.

      • Abstract: "ablation of all neurexins in MNTB neurons reduced not only the amplitude but also altered the kinetics of the glycinergic synaptic transmission at LSO neurons."

      Changed as suggested.

      • Consider revising to "The synaptic dysfunctions primarily resulted from an altered dependence of release on voltage-gated Ca2+ influx."

      We appreciate the reviewer's suggestion, which helps improve the clarity of our manuscript. We have revise the phrasing as follows: "The synaptic dysfunctions primarily resulted from an impaired calcium sensitivity of release and a loosened coupling between voltage-gated calcium channels and synaptic vesicles."

      • Line 39 should be vertebrates.

      Revised as suggested.

      • Line 49 it would sound better to say "which further points to the diverse actions of neurexins in specific neurons."

      Revised as suggested.

      • Line 60 - this paragraph could include information about GABA signaling from the MNTB to the LSO, because on line 113 you mention LSO neurons receive inhibitory GABAergic/glycinergic inputs, but when you do not mention blocking of GABA currents to isolate the glycinergic ones.

      We thank the reviewer for the thoughtful and detailed suggestion. We revised the text in line 60 to “In the mature mammalian auditory brainstem” and in line 113, we removed GABAergic to emphasize the nature of glycinergic synapse, particularly in the mouse brainstem where no GABAergic components are found (Fisher et al., 2019).

      • Line 72/73 it should be adeno-associated virus; line 73: "combining this with the RNAScope technique" sounds better.

      Changed as suggested.

      • Line 91 using the RNAScope technique; lines 97, 119 as a control; line 108 the functional organization.<br />

      Changed as suggested.

      • Line 113 should be a pharmacological approach; line 122 optogenetically evoked.

      Changed as suggested.

      • Line 132, 160: the control.

      Changed as suggested.

      • Line 147 thus were infected; line 148 likely to be present but were obscured .

      Changed as suggested.

      • Line 154 which has been routinely used.

      Changed as suggested.

      • Line 155 It is not supposed to be Figure 2h but 2i; following that Figure 2i should be 2j; in my opinion, Figure 2i does not display a strong depression for the TKO mice.

      Changed as suggested.

      • Line 171 a better flow is achieved by saying: together these data show.

      Changed as suggested.

      • EC50 rather than IC50 of [Ca2+].

      Changed as suggested.

      • 180 it is better to say "we approached the matter by..."; line 183 while recording;

      Changed as suggested.

      • Line 203 were much stronger than the effect at control synapses; line 206 tightly clustering.

      Changed as suggested.

      • Line 212 sounds like they provide evidence for retina and spinal cord as well, should be made clear.

      Changed as suggested.

      • Line 289 previously.

      Changed as suggested.

      • Line 295 should be 30 min.

      Changed as suggested.

      • Line 336, 337 confocal microscope.

      Changed as suggested.

      • Please provide the number of data points also in figure captions or in the results section.

      Added in the captions as suggested.

      • Line 533, a better phrasing would be: the blocking effect of 0.2 mM Ca on IPSC amplitude.

      Changed as suggested.

      • Explain either in the methods or result section how was the EC50 of Ca2+ calculated.

      Added in the methods as suggested.

    1. Author response:

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

      Reviewer #1 (Comments to the Author):

      Summary:

      In this study, Xie and colleagues aimed to explore the function and potential mechanisms of the gut microbiota in a hamster model of severe leptospirosis. The results demonstrated that Leptospira infection was able to cause intestine damage and inflammation. Leptospira infection promoted an expansion of Proteobacteria, increased gut barrier permeability, and elevated LPS levels in the serum. Thus, they proposed an LPS-neutralization therapy which improved the survival rate of moribund hamsters combined with antibody therapy or antibiotic therapy.

      Strengths:

      The work is well-designed and the story is interesting to me. The gut microbiota is essential for immunity and systemic health. Many life-threatening pathogens, such as SARS-CoV-2 and other gut-damaged infection, have the potential to disrupt the gut microbiota in the later stages of infection, causing some harmful gut microbiota-derived substances to enter the bloodstream. It is emphasized that in addition to exogenous pathogenic pathogens, harmful substances of intestinal origin should also be considered in critically ill patients.

      Weaknesses:

      Q1: There are many serotypes of Leptospira, it is suggested to test another pathogenic serotype of Leptospira to validate the proposed therapy.

      That’s a constructive suggestion. We have tested another pathogenic serotype of Leptospira (L. interrogans serovar Autumnalis strain 56606) to verify the LPS-neutralization therapy combined with antibiotic therapy (Supplementary Fig. S9B). The results showed that the combination of the LPS-neutralization therapy with antibody therapy or antibiotic therapy also significantly improved the survival rate of hamsters infected by 56606.

      Q2: Authors should explain why the infective doses of leptospires was not consistent in different study.

      Thank you for your comment. To examine the role of the gut microbiota on acute leptospirosis, the infective doses of leptospires was chosen for 106, while in other sections of the study, the infective doses of leptospires was chosen for 107. In fact, we also used 107 leptospires to infect hamsters, however, the infective doses of 107 leptospires might be overdose, there was no significant difference on the survival rate between the control group and the Abx-treated group. A previous study also highlighted that the infective doses of leptospires was important in the investigating the sex on leptospirosis, as male hamsters infected with L. interrogans are more susceptible to severe leptospirosis after exposure to lower infectious doses than females (103 leptospires but not 104 leptospires) (1).

      Reference

      (1) GOMES C K, GUEDES M, POTULA H H, et al. Sex Matters: Male Hamsters Are More Susceptible to Lethal Infection with Lower Doses of Pathogenic Leptospira than Female Hamsters (J). Infect Immun, 2018, 86(10).

      Q3: In the discussion section, it is better to supplement the discussion of the potential link between the natural route of infection and leptospirosis.

      Thank for your suggestion. We have supplemented it in the discussion (line 523-527 in the track change PDF version).

      Q4: Line 231, what is the solvent of thioglycolate?

      We have supplemented it in the manuscript (line 242-243 in the track change PDF version).

      Q5: Lines 962-964, there are some mistakes which are not matched to Figure 7.

      Thank you for pointing that out, we have corrected it in the manuscript.

      Reviewer #2 (Comments to the Author):

      Summary:

      Severe leptospirosis in humans and some mammals often meet death in the endpoint. In this article, authors explored the role of the gut microbiota in severe leptospirosis. They found that Leptospira infection promoted a dysbiotic gut microbiota with an expansion of Proteobacteria and LPS neutralization therapy synergized with antileptospiral therapy significantly improved the survival rates in severe leptospirosis. This study is well-organized and has potentially important clinical implications not only for severe leptospirosis but also for other gut-damaged infections.

      Weaknesses:

      Q1: In the Introduction section and Discussion section, the authors should describe and discuss more about the differences in the effect of Leptospira infection between mice and hamsters, so that the readers can follow this study better.

      Thank you for your suggestion, we have supplemented it in the manuscript (line 62-66 in the track change PDF version).

      Q2: Lines 92-95, the authors should explain why they chose two different routines of infection.

      Thank you for your comment, we have explained it in the manuscript (line 100 in the track change PDF version).

      Q3: Line 179-180, the concentration of PMB and Dox is missed, and 0.016 μg/L is just ok.

      We have corrected it in the manuscript.

      Q4: "μL" or "μl" and "mL" or "ml' should be uniform in the manuscript.

      Thank you for your suggestion, we have revised it in the manuscript.

      Q5: In the culture of primary macrophages, how many cells are inoculated in the plates should be described clearly.

      We have supplemented it in the manuscript (line 250 in the track change PDF version).

      Q6: Line 271, it is better to list primers used for leptospiral detection in the text. Because it allows readers to find the information they need more directly.

      Thank you for your suggestions, we have supplemented it in the manuscript (line 281-284 in the track change PDF version).

      Q7: Line 366-369, Lactobacillus seems to be a kind of key bacteria during Leptospira infection. A previous study (doi: 10.1371/journal.pntd.0005870) also demonstrated that pre-treatment with Lactobacillus plantarum prevented severe pathogenesis in mice. The authors should discuss the potential probiotic for leptospirosis prevention.

      We have discussed it in the manuscript (line 564-566 in the track change PDF version).

      Q8: Lines 450-451, not all concentrations of fecal filtration from two groups upregulated all gene expression mentioned in the text, the authors should correct it.

      Thank you for pointing that out, we have corrected it in the manuscript (line 461-462 in the track change PDF version).

      Reviewer #3 (Comments to the Author):

      Summary:

      This is a well-prepared manuscript that presented interesting research results. The only defect is that the authors should further revise the English language.

      Strengths:

      The omics method produced unbiased results.

      Weaknesses:

      Q1: LPS neutralization is not a new method for treating leptospiral infection.

      Thank you for your comment. Yes, LPS neutralization is not a new method for treating leptospiral infection, most of which might focus on leptospiral LPS. In addition, Leptospira seemed to be naturally resistant to polymyxin B (1). Recently, neutralizing gut-derived LPS was applied in other diseases which significantly relieved diseases (2-3). In this study, we found that Leptospira infection promoted an expansion of Proteobacteria, increased gut barrier permeability, and elevated LPS levels in the serum. Thus, we proposed an LPS-neutralization therapy which improved the survival rate of moribund hamsters combined with antibody therapy or antibiotic therapy.

      Reference

      (1) LIEGEON G, DELORY T, PICARDEAU M. Antibiotic susceptibilities of livestock isolates of leptospira (J). Int J Antimicrob Agents, 2018, 51(5):693-699.

      (2) MUNOZ L, BORRERO M J, UBEDA M, et al. Intestinal Immune Dysregulation Driven by Dysbiosis Promotes Barrier Disruption and Bacterial Translocation in Rats With Cirrhosis (J). Hepatology, 2019, 70(3):925-938.

      (3) ZHANG X, LIU H, HASHIMOTO K, et al. The gut-liver axis in sepsis: interaction mechanisms and therapeutic potential (J). Crit Care, 2022, 26(1):213.

      Q2: The authors should further revise the English language used in the text.

      Thank you for your suggestion, our manuscript has been polished by American Journal Experts (certificate number: 81C8-C5C1-9D5D-109D-3F23).

    1. Author response:

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

      eLife assessment

      In their valuable study, Chen et al. aim to define the neuronal role of HMMR, a microtubule-associated protein typically associated with cell division. Their findings suggest that HMMR is necessary for proper neuronal morphology and the generation of polymerizing microtubules within neurites, potentially by promoting the function of TPX2. While the study is recognized as a first step in deciphering the influence of HMMR on microtubule organization in neurons, reviewers note the current work has important gaps and would benefit from further exploration of the mechanism of microtubule stability by HMMR, the link between HMMR-mediated microtubule generation and morphogenesis, and the physiological implications of disrupting HMMR during neuronal morphogenesis.

      Public Reviews:

      Reviewer #1 (Public Review):

      The microtubule cytoskeleton is essential for basic cell functions, enabling intracellular transport, and establishment of cell polarity and motility. Microtubule-associated proteins (MAPs) contribute to the regulation of microtubule dynamics and stability - mechanisms that are specifically important for the development and physiological function of neurons. Here, the authors aimed to elucidate the neuronal function of the MAP Hmmr, which they had previously identified in a quantitative study of the proteome associated with neuronal microtubules.

      The authors conduct well-controlled experiments to demonstrate the localization of endogenous as well as exogenous Hmmr on microtubules within the soma as well as all neurites of hippocampal neurons. Functional analysis using gain- and loss-of-function approaches demonstrates that Hmmr levels are crucial for neuronal morphogenesis, as the length of both dendrites and axons decreases upon loss of Hmmr and increases upon Hmmr overexpression. In addition to length alterations, the branching pattern of neurites changes with Hmmr levels. To uncover the mechanism of how Hmmr influences neuronal morphology, the authors follow the lead that Hmmr overexpression induces looped microtubules in the soma, indicative of an increase in microtubule stability. Microtubule acetylation indeed decreases and increases with Hmmr LOF and GOF, respectively. Together with a rescue of nocodazole-induced microtubule destabilization by Hmmr GOF, these results argue that Hmmr regulates microtubule stability. Highlighted by the altered movement of a plus-end-associated protein, Hmmr also has an effect on the dynamic nature of microtubules. The authors present evidence suggesting that the nucleation frequency of neuronal microtubules depends on Hmmr's ability to recruit the microtubule nucleator Tpx2. Together, these data add novel insight into MAP-mediated regulation of microtubules as a prerequisite for neuronal morphogenesis. While the data shown support the author's conclusions, the study also has several weaknesses:

      • The study appears incomplete as the initial proteomics analysis which is referenced as an entry into the study is not presented. This surely is the authors' choice, however, without presenting this data set, it would make more sense if the authors first showed the localization of Hmmr on neuronal microtubules and then started with the functional analysis.

      The reviewer suggests moving the Hmmr localization data in front of the loss- and gain-of-function data because we did not present the proteomics data. However, we still believe placing the loss- and gain-of-function data in the beginning is the better arrangement. This is because it allows the audience to see the drastic changes on neuronal morphology when HMMR is depleted or overly abundant. It also provides a better linkage between HMMR’s localization on microtubules and its effect on the stability and dynamics of microtubules.

      • Neurite branching is quantified, but the methods used are not consistent (normalized branch density vs. Sholl analysis) and there is no distinction between alterations of branching in dendrites vs. axons. This information should be added as it could prove informative with respect to the physiological function of Hmmr in neurite branching.

      Sholl analysis is considered the gold standard in neurite branching analyses. However, in the knockdown experiment (Figure 1A~1E), HMMR-depleted neurons exhibited extremely short axons (<100 μm) and dendrites (<40 μm). Using Sholl analysis to assess the branching of these Hmmrdepleted neurons became unsuitable. That is why we used normalized branch density (Figure 1E) in the knockdown experiment and Sholl analysis (Figure 1J) in the overexpression experiment.

      Regarding the branching difference between axons and dendrites, only axons exhibit branches at 4 DIV. Therefore, the branching analysis focuses on axons rather than on dendrites. We have revised the manuscript to clarify this.

      • The authors show that altered Hmmr levels affect neurite branching and identify an effect on microtubule stability and dynamics as a molecular mechanism. However, how branching correlates with or is regulated by Hmmr-mediated microtubule dynamics is neither addressed experimentally nor discussed by the authors. The physiological significance of altered neuronal morphogenesis also lacks discussion.
      • To discuss how branching correlates with or is regulated by HMMR-mediated microtubule dynamics, we have added the following paragraph into the Discussion section:

      “It has been shown that compromising microtubule nucleation in neurons by SSNA1 mutant overexpression prevents proper axon branching (Basnet et al., 2018). Additionally, dendritic branching in Drosophila sensory neurons depends on the orientation of microtubule nucleation. Nucleation that results in an anterograde microtubule growth leads to increased branching, while nucleation that results in a retrograde microtubule growth leads to decreased branching (Yalgin et al., 2015). These results demonstrate the importance of microtubule nucleation on neurite branching. It is conceivable that overexpressing a microtubule nucleation promoting protein such as HMMR results in an increase of branching complexity.”

      • In terms of discussing the physiological significance of altered neuronal morphogenesis. We have added the following paragraph to the Discussion section:

      “Neurons are the communication units of the nervous system. The formation of their intricate shape is therefore crucial for the physiological function. Alterations in neuronal morphogenesis have a profound impact on how nerve cells communicate, leading to a variety of physiological consequences. These consequences include impaired neural circuit formation and function, compromised signal transmission between neurons, as well as altered anatomical structure of the CNS. Depending on the specific type and location of the morphogenetically altered neurons, the physiological consequences can include neurological disorders such as autism spectrum disorder (Berkel et al., 2012) and schizophrenia (Goo et al., 2023), as well as learning and memory deficits (Winkle et al., 2016). However, due to the involvement of HMMR on mitosis, most HMMR mutations are associated with familial cancers (based on ClinVar data).”

      • Multiple times, the manuscript lacks a rationale for an experimental approach, choice of cell type, time points, regions of interest, etc. Also, a meaningful description of the methods and for how data were analyzed is missing, making the paper hard to read for someone not directly from the field.

      We understand the reviewer’s comments regarding the lack of rationale for choosing the experimental approach, choice of cell type, time points, regions of interest, etc. As a result, we have added the rationales where appropriate to help readers from other fields to better understand the choice of cell type, time points, regions of interest, etc. A brief explanation is shown below:

      • Approach and timing: We employed both electroporation (immediate but milder expression) and lipofectamine transfection (delayed but stronger expression). We prioritized knocking down HMMR early in development, so electroporation was used. For overexpression experiments, we chose lipofectamine which allows high protein expression level to be achieved.

      • Cell selection: Hippocampal neurons were chosen in experiments that involve morphological quantification due to their homogeneous morphology. On the other hand, cortical neurons were selected in experiments that require large amounts of neurons and/or experiments where we want to demonstrate the universality of a proposed hypothesis.

      • Regions of interest (ROIs): In our previous publication (Chen et al., 2017), it was discovered that a significant reduction of EB3 emanation frequency can be detected at the tip and the base of the neurite but not in the middle of the neurite in TPX2-depleted neurons. The reason for this difference is due to the presence of GTP-bound Ran GTPase (RanGTP) at the tip and the base of the neurite. Since RanGTP has also been shown to regulate the interaction between HMMR and TPX2 in the cell-free system (Scrofani et al., 2015), it is possible that the same phenomenon can be observed in HMMR-depleted neurons. This is why we examined those 3 ROIs in Figure 4.

      Reviewer #2 (Public Review):

      The mechanism of microtubule formation, stabilization, and organization in neurites is important for neuronal function. In this manuscript, the authors examine the phenotype of neurons following alteration in the level of the protein HMMR, a microtubule-associated protein with established roles in mitosis. Neurite morphology is measured as well as microtubule stability and dynamic parameters using standard assays. A binding partner of HMMR, TPX2, is localized. The results support a role for HMMR in neurons.

      The work presented in this manuscript seeks to determine if a MAP called HMMR contributes to microtubule dynamics in neurons. Several steps, including validation of the RNAi, additional statistical analysis, use of cells at the same age in culture, and better documentation in figures, would increase the impact of the work.

      In many places, the data can be improved which might make the story more convincing. As presented, the results show that HMMR is distributed as puncta on neurons with data coming from a single HMMR antibody, and some background staining that was not discussed. In the discussion the authors state that HMMR impacts microtubule stability, which was evaluated by the presence of post-translational modification and resistance to nocodazole; the data are suggestive but not entirely convincing. The discussion also states that HMMR increases the “amount” of growing microtubules which was measured as the frequency of comet appearance. The authors did not comment on how the number of growing microtubules results in the observed morphological changes.

      We actually tested several HMMR antibodies, including E-19 (Santa Cruz, sc-16170), EPR4054 (Abcam, ab124729), and a variety of antibodies provided by Prof. Eva Turley. E-19 performed the best in immunofluorescence (IF) staining and knockdown validation. The other antibodies either failed to detect HMMR in IF staining or generate excessive background signal. We understand that the final images are produced using a single antibody. But since we meticulous validated this antibody and that the localization of overexpressed HMMR is consistent with the endogenous HMMR, we are very confident about our data generated using this single antibody.

      We have added the following paragraph in the Discussion section to elucidate how the number of growing microtubules result in the observed morphological changes such as an increase of axon branches:

      “It has been shown that compromising microtubule nucleation in neurons by SSNA1 mutant overexpression prevents proper axon branching (Basnet et al., 2018). Additionally, dendritic branching in Drosophila sensory neurons depends on the orientation of microtubule nucleation. Nucleation that results in an anterograde microtubule growth leads to increased branching, while nucleation that results in a retrograde microtubule growth leads to decreased branching (Yalgin et al., 2015). These results demonstrate the importance of microtubule nucleation on neurite branching. It is conceivable that overexpressing a microtubule nucleation promoting protein such as HMMR results in an increase of branching complexity.

      Reviewer #1 (Recommendations for The Authors):

      (1) The manuscript jumps extensively between main figures and supplementary figures. Please check whether parts of the supplement could be moved to the main figures.

      We understand the frustration of moving back and forth between the main figures and supplementary figures. After examining the manuscript, we decided to combine Figure 2A with Figure S3.

      (2) In Figure 1, total neurite length between days 3 and 4 DIV does not appear to change - can this be true?

      Please check or else explain.

      We carefully re-examined our raw data and found out the total neurite length of 4 DIV hippocampal neurons expressing non-targeting shRNA (Figure 1B) and that of 3 DIV hippocampal neurons expressing AcGFP (Figure 1G) are indeed very similar. The explanation is that the 3 DIV hippocampal neurons used for Figure 1G was cultured in low-density and in the presence of cortical neuron-conditioned neurobasal medium (as written in Methods, Neuron culture and transfection section). The low-density culture with minimal overlapping neurites allowed us to better quantify total neurite length, because neurons expressing AcGFP-mHMMR sprouted long and highly branched axons. However, the addition of cortical neuron-conditioned neurobasal medium promoted neurite elongation. This is the reason why the total neurite length of 4 DIV hippocampal neurons expressing non-targeting shRNA (Figure 1B) and that of 3 DIV hippocampal neurons expressing AcGFP (Figure 1G) is similar.

      (3) Groen et al. have shown that Hmmr also bundles microtubules, a mechanism that surely is important for neuronal microtubules. Please discuss.

      We thank the reviewer for pointing out that HMMR also bundles microtubules and have added this to our revised Discussion section:

      “It has been shown that the Xenopus HMMR homolog XRHAMM bundles microtubules in vitro (Groen et al., 2004). In addition, deleting proteins which promote microtubule bundling (e.g., doublecortin knockout, MAP1B/MAP2 double knockout) leads to impaired neurite outgrowth (Bielas et al., 2007; Teng et al., 2001). These observations are consistent with our data that overexpressing HMMR leads to the increased axon and dendrite outgrowth, while depleting it results in the opposite phenotype (Figure 1).”

      (4) Please explain why in Figure 4, cortical neurons were chosen for analysis and why and how the three different ROIs were picked.

      To answer the question why we chose cortical neurons for the analyses in Figure 4, it will be important to explain why we used hippocampal neurons for other figures. Primary hippocampal neurons have a high homogeneity in terms of their morphology. This uniform morphology allows more consistent morphological quantification. Figure 4, however, does not involve morphological quantification. We are more confident to conclude that HMMR regulates microtubule dynamics if this effect can be detected in the relatively heterogeneous cortical neurons. These are the reasons why we chose to analyze cortical neurons in Figure 4.

      In our previous publication (Chen et al., 2017), it was discovered that a significant reduction of EB3 emanation frequency can be detected at the tip and the base of the neurite but not in the middle of the neurite in TPX2-depleted neurons. The reason for this difference is due to the presence of GTP-bound Ran GTPase (RanGTP) at the tip of the neurite and in the soma. Since RanGTP has also been shown to regulate the interaction between HMMR and TPX2 in the cell-free system (Scrofani et al., 2015), it is possible that the same phenomenon can be observed in HMMR-depleted neurons. This was why we examined those 3 ROIs in Figure 4.

      (5) Microtubule looping has been shown to occur in regions prior to branch formation (e.g. Dent et al. 2004). As the authors identify increased looping upon Hmmr GOF, this should be discussed.

      We thank the reviewer for pointing out that microtubule looping occurs in regions of branch formation and have added this to our revised discussion:

      “It is worth noting that the elevated level of HMMR increases the branching density of axons (Figure 1J) and promotes the formation of looped microtubules (Figure 3A). This is consistent with the observations that looped microtubules are often detected in regions of axon branch formation (Dent et al., 1999; Dent and Kalil, 2001; Purro et al., 2008).”

      Reviewer #2 (Recommendations for The Authors):

      (1) The work seeks to gain insight into microtubule behavior in neurons, an important issue.

      (2) Several steps, including validation of the RNAi, additional statistical analysis, use of cells at the same age in culture, and better documentation in figures, would increase the impact of the work.

      (3) Figure 1 documents the results of experiments in which the HMMR protein was depleted using shRNA. A western blot of cell extracts from control and depleted cells is needed to verify that the protein level is reduced; alternatively, documentation of the reduction in RNA levels in treated cells could be provided. Neurite, axon, and dendrite length and branch density are measured. The neurite length is in microns, and the axon length is normalized to 100% of the non-treated cells. Please use the same for measures for easier comparison. Looking at the images in Figure 1, the length of the dendrites does not look different in the examples shown, whereas the axon appears shorter. This impression is not supported by the quantification. Are representative images shown? Additionally, the authors should report the values for each replicate of the experiment and compare the three averages rather than comparison of lengths from all measurements. A related issue is that the dendrites do not look longer in panel F, following overexpression of HMMR. For examples of using averages of replicates see: https://pubmed.ncbi.nlm.nih.gov/32346721/

      The reviewer mentioned that Western blot of cell extracts or RNA quantification from control and depleted cells are needed to verify that the protein level is reduced.

      Unfortunately, these assays are extremely difficult to perform in primary neurons due to the low transfection efficiency. We believe that the consistent knockdown phenotype from 3 different shRNA sequences (Figure 1A-D) and the immunofluorescence staining in depleted primary neurons (Figure S2) are sufficient to confirm that HMMR level is reduced.

      We revised Figure 1C, 1D, 1H, 1I so that axon and dendrite lengths are all in micron.

      We selected another image for the non-targeting control in Figure 1A to better demonstrate the reduction of dendrite length when HMMR is knocked down.

      We thank the reviewer for the suggestion of comparing the three average values rather than comparing all measurements. We have performed statistical analyses for all our data using the average values and revised the graphs accordingly. While the P-values changed, our conclusions remain the same.

      We thank the reviewer for pointing out this discrepancy and have selected another image of the AcGFP control for Figure 1F to better demonstrate the increase of dendrite length when HMMR is overexpressed.

      (4) Given the changes in neurite morphology, the authors examine the localization of endogenous and overexpressed. The supplemental figures (see S2 and S3) show evidence that HMMR is present in a punctate pattern by conventional immunofluorescence. This is reasonable evidence that the protein is in a linear pattern along cytoskeletal microtubules and that the signal is present in puncta. Please move this to the main text, perhaps replacing Figure 2A, which is low magnification and very hard to see the HMMR staining. Additionally, the level of overexpression of HMMR is not mentioned. Please address this; were cells with similar levels of overexpression selected? Did the result depend on the overexpression? A related issue is the DIV for the cells - some are examined earlier and some at later times; does this impact the results? Please provide information or perform experiments with consistent timing. For the immunofluorescence, were multiple antibodies tried to see if the result was the same with each? Were different fixations, in addition to methanol, utilized?

      We have replaced Figure 2A with Figure S3 based on the reviewer’s suggestion.

      In the HMMR overexpression experiments, we used HMMR antibody and immunofluorescence staining to confirm that the overexpression is achieved. However, we did not quantify to what extend HMMR was overexpressed.

      We performed all the depletion experiments on 4 DIV to maximize knockdown efficiency and performed all the overexpression experiments on 3 DIV to prevent excessive axon fasciculation. Nonetheless, we examined the effect of HMMR depletion on neuronal morphology on 3 DIV. The trend of reduced total neurite length, axon length, and dendrite length can be observed, but no statistical significance can be detected. We also examined the effect of HMMR overexpression on neuronal morphology on 4 DIV and did observe an increase of total neurite length, axon length, and dendrite length. But the overlapping and bundled axons made reliable quantification extremely difficult.

      We actually tested multiple HMMR antibodies, such as E-19 (Santa Cruz, sc-16170), EPR4054 (Abcam, ab124729), and a variety of antibodies provided by Prof. Eva Turley. E19 performed the best in immunofluorescence (IF) staining and knockdown validation. The other antibodies either failed to detect HMMR in IF staining or generate excessive background signal. We also tested various fixation methods, including 37°C formaldehyde fixation, -20°C methanol fixation, 37°C formaldehyde followed by -20°C methanol fixation. All fixation methods generated similar IF staining pattern using the E-19 antibody, but 3.7% formaldehyde fixation produced the highest signal.

      (5) In Figure 2 C it is hard to see DAPI fluorescence. Are the white areas in the merge with bright cell nuclei? Is Figure 2C control or overexpressing cells? If this is endogenous, is there less signal in PLA compared with S4, which was in culture longer and is overexpressed prior to using PLA for detection?

      The white areas in Figure 2C the reviewer mentioned are not cell nuclei, they are actually bubbles formed within the mounting medium.

      HMMR detected in Figure 2C is endogenous. We did not quantitatively compare the PLA signals in Figure 2C and those in Figure S4. This is because the PLA signals in Figure 2C are generated using anti-HMMR (to detect endogenous HMMR) and anti-β-III-tubulin antibodies while those in Figure S4 are generated using anti-AcGFP (to detect overexpressed AcGFP-mHMMR) and anti-β-III-tubulin antibodies. Since the affinity of the two antibodies (i.e., anti-HMMR and anti-AcGFP) toward their antigens is different, comparing the PLA signals is not informative.

      (6) The images of the endogenous HMMR (Fig S3) and the PLA with tubulin and HMMR antibodies are not the same (2C). The "dots" in PLA are widely separated; gauging from the marker bar length of 50 μm, the small clusters of dots are about 10 μm apart. In Figure S3, the puncta are much more closely spaced, appearing almost in a linear fashion along the microtubules. Enlarging the PLA image shows that each dot is very small - just a few pixels - please provide additional explanation including the minimal detection limit for the method, and why the images differ. If the standard immunofluorescence signal was enhanced, for example with the use of two secondaries, what is observed? Is the distribution of HMMR similar for both dendrites and axons? Microtubule polarity differs in these locations, so greater attention to this point seems of interest. There is a significant amount of punctate HMMR in the cytoplasm (or outside the cytoplasm?) in Figure S5; this is concerning. Please outline the cell edge for ease of visualization. What is the distribution of HMMR in a cell that has been treated with cold and/or nocodazole to disassemble the microtubules? is the signal lost?

      The reasons images of the endogenous HMMR (Figure S3) and the PLA with tubulin and HMMR antibodies (Figure 2C) differ are due to the following reasons. o PLA utilizes two primary antibodies to target two different epitopes on HMMR and βIII-tubulin. It is conceivable that not every anti-HMMR antibody has the correct orientation and/or proximity (<40 nm) toward the anti-β-III-tubulin antibody to enable DNA amplification. This results in the shortage of PLA puncta compared to immunofluorescence signals.

      • The creator of PLA has pointed out that in situ PLA is a method based upon equilibrium reactions and several enzymatic steps. Therefore, only a fraction of the inter-acting molecules is detected (Weibrecht et al., 2010).

      We have not used signal enhancing immunofluorescence staining methods [e.g., using tertiary antibodies or tyramide signal amplification (TSA)] to detect HMMR. This is mainly because HMMR signal is strong enough to be detected using standard immunofluorescence staining.

      Regarding the question “Is the distribution of HMMR similar for both dendrites and axons?” The reviewer raised a very important issue about the polarity difference of microtubules in axons (uniform) and dendrites (mixed). We were aware of such issue and very carefully examined the distribution and signal intensity of HMMR in axons vs dendrites. However, no differences were detected.

      The reviewer mentioned that “there is a significant amount of punctate HMMR in the cytoplasm (or outside the cytoplasm?) in Figure S5; this is concerning. Please outline the cell edge for ease of visualization.” Instead of outlining the cell edge, we have selected another image to facilitate the visualization of HMMR signals. There are indeed HMMR signals outside the cell. However, these outside signals are usually weaker and smaller in size compared to those inside the cell.

      After the examination of neurons expressing AcGFP-mHMMR with or without 100 nM nocodazole treatment, we did not notice any difference of AcGFP-mHMMR in distribution. We did not examine the distribution and signal intensity of the endogenous HMMR.

      (7) To determine if HMMR alters microtubule stability, the authors examine the distribution of acetylated tubulin and resistance to nocodazole-induced microtubule disassembly. In Figure 3 please show immunofluorescence images of the acetylated tubulin staining, not just the ratio images; the color is not obviously different in the various panels shown. For statistical analysis, see the comment above for Figure 1. For the nocodazole experiment, a similar change in neurite length following drug treatment was observed (Figure 3H), for the experimental and control, even though the starting length was greater in the overexpressing cells. Please consider the possibility that in both cases the microtubules are only partially resistant to nocodazole and that HMMR is not changing the fraction of microtubules that are sensitive to the drug. The cells were treated at 3 DIV; the authors note that more stable microtubules accumulate with time; how does time in culture impact stability? Often, acute treatment with a high concentration of nocodazole is used to assay microtubule stability; here the authors used a low (nM) concentration for 2 days (chronic). Why not use a higher concentration (1-10 μM) for a shorter incubation? The data show that overexpression of HMMR results in curved, buckled microtubules are these microtubules more acetylated and/or retained after nocodazole treatment?

      The reviewer suggested that we show immunofluorescence images of the acetylated tubulin staining, not just the ratio images. But we still believe showing the ratio images is the better approach. This is because the microtubules density can be different from neuron to neuron. Showing acetylated tubulin may provide a false impression when the overall microtubule density is higher or lower in a particular neuron. We realized that “16 colors” pseudo-color scheme has the cyan color at the lower intensity which can sometimes be confused with the white color at the higher intensity. Therefore, we changed the pseudocolor from “16 colors” to “fire” for Figure 3B and 3E to better visualize these images so that they appear more consistent with the quantitative data.

      The reviewer raised a very good question regarding the possibility that HMMR is not changing the fraction of microtubules that are sensitive to nocodazole. We re-conducted the same experiment and used a series of different nocodazole concentrations. While the addition of nocodazole causes a concentration-dependent reduction of total neurite length in both AcGFP and AcGFP-mHMMR expressing neurons, there are subtle differences in the susceptibility of neurite length to the concentration of nocodazole. 1) 10 nM nocodazole treatment causes a significant reduction of neurite length in AcGFP expressing neurons, but not in AcGFP-mHMMR expressing neurons. This result indicates that AcGFP-mHMMR expression increases the tolerance of neurite elongation toward 10 nM nocodazole treatment. 2) 50 nM and 100 nM nocodazole treatment exhibits no statistical significance in AcGFP expressing neurons, suggesting that 50 nM nocodazole has reached maximal effectiveness. In AcGFP-mHMMR expressing neurons, 100 nM nocodazole further reduces the neurite length compared to the 50 nM group. These results argue against the possibility that HMMR does not change the fraction of microtubules that are sensitive to nocodazole. We have revised Figure 3H accordingly.

      The reviewer asked why we did not use the acute nocodazole treatment (μM concentration) to assess the effect of Hmmr on microtubule stability. This is because we used the neurite length as an indicator for microtubule stability. That is why the chronic treatment was chosen to produce a more detectable effect on neurite length.

      The reviewer asked whether the looped microtubules caused by HMMR overexpression are more acetylated and/or nocodazole resistant. While we do not have direct evidence to answer the reviewer’s question, we can deduce the answer from our observations. We noticed that looped microtubules are only present when HMMR is highly expressed (i.e., using lipofection to introduce HMMR-expressing plasmid) but not when HMMR is mildly expressed (i.e., using electroporation to introduce HMMR-expressing plasmid). From these observations, we can conclude that HMMR is more abundantly present on looped microtubules. Since HMMR overexpression leads to higher microtubule acetylation (Figure 3E), looped microtubules which contains more HMMR are most likely to be more acetylated.

      (8) An additional measure of microtubule dynamics is to measure the growth of microtubules using a live cell marker for microtubule plus ends. Such experiments were performed, using tagged EB3. The images are rather fuzzy. Parameters of microtubule dynamics were measured at three locations - is there data that the authors can cite about any differences in dynamics in control cells at these locations? They look very similar, so it is not clear why the different locations were used. It is not possible to learn much from the kymographs which look similar for all panels; I would remove these unless they can be changed or labeled to help the reader. Data is presented for three shRNA reagents. No data are presented to document the extent to which the protein is depleted with these reagents. This should be fixed. Alternatively, an RNAi pool could be utilized. Is there a control for off-target effects? For the analysis were all the comets used to generate the average values? What about a comparison of the average of each trial - not each comet?

      In our previous publication (Chen et al., 2017), it was discovered that a significant reduction of EB3 emanation frequency can be detected at the tip and the base of the neurite but not in the middle of the neurite in TPX2-depleted neurons. The reason for this difference is due to the presence of RanGTP at the tip and the base of the neurite. Since RanGTP has also been shown to regulate the interaction between HMMR and TPX2 in the cell-free system (Scrofani et al., 2015), it is possible that the same phenomenon can be observed in HMMR-depleted neurons. This is why we examined those 3 ROIs in Figure 4.

      We notice that photobleaching causes the EB3-mCherry signal to diminish at later time points, which made it difficult to observe the differences amongst kymographs. In the revised Figure 4B and 4D, we removed the second half of all the kymographs to make the differences more obvious.

      The reviewer mentioned that there are no data documenting the extent to which the protein is depleted with the shRNAs. These data are shown in Figure S2, in which we quantified the HMMR protein level in the soma and along the neurite in neurons expressing different shRNA molecules.

      The reviewer asked whether there is a control for off-target effects. The answer is yes. We performed the rescue experiment to control for off-target effects, which is shown in Figure S1.

      We revised Figure 4 so that the dynamic properties of EB3 are quantified using the average of each experimental repetition.

      (9) In a final experiment, the authors examine the distribution of TPX2, a binding partner of HMMR. Include a standard immunofluorescence in addition to PLA to illustrate the distribution of TPX2. The quantification used was the inter puncta distance; please quantify the signal in control and treated cells.

      The reviewer asked us to include a standard immunofluorescence staining to illustrate the distribution of TPX2. We have done that in our previous publication (Chen et al., 2017) and TPX2 localizes primarily to the centrosome (https://www.nature.com/articles/srep42297/figures/2). In order to enhance the weak signal of TPX2 along the neurite, we actually needed to use PLA in that publication (https://www.nature.com/articles/srep42297/figures/3).

      Proximity ligation assay (PLA) generates fluorescent signals based on a local enzymatic reaction which catalyzes the amplification of a specific DNA sequence that can then be detected using a red fluorescent probe. Because this enzymatic reaction is not linear, the amount of amplified DNA nor the intensity of the fluorescence does not correlate with the strength of the interaction (Soderberg et al., 2006). As a result, quantification of PLA is typically done by counting the number of fluorescent puncta per unit area or by calculating the area containing fluorescent signal (not signal intensity) per unit area in the case that PLA signals are too strong and coalesced. That is why our quantification is based on the distance between PLA fluorescent puncta, not the fluorescent signal intensity.

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    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      The authors set up a pipeline for automated high-throughput single-molecule fluorescence imaging (htSMT) in living cells and analysis of molecular dynamics

      Strengths:

      htSMT reveals information on the diffusion and bound fraction of molecules, dose-response curves, relative estimates of binding rates, and temporal changes of parameters. It enables the screening of thousands of compounds in a reasonable time and proves to be more sensitive and faster than classical cell-growth assays. If the function of a compound is coupled to the mobility of the protein of interest, or affects an interaction partner, which modulates the mobility of the protein of interest, htSMT allows identifying the modulator and getting the first indication of the mechanism of action or interaction networks, which can be a starting point for more in-depth analysis.

      Weaknesses:

      While elegantly showcasing the power of high-throughput measurements, the authors disclose little information on their microscope setup and analysis procedures. Thus, reproduction by other scientists is limited. Moreover, a critical discussion about the limits of the approach in determining dynamic parameters, the mechanism of action of compounds, and network reconstruction for the protein of interest is missing. In addition, automated imaging and analysis procedures require implementing sensitive measures to assure data and analysis quality, but a description of such measures is missing.

      The reviewer rightly highlights both the power and complexity in high throughput assay systems, and as such the authors have spent significant effort in first developing quality control checks to support screening. We discuss some of these as part of the description and characterization of the platform. We added additional details into the manuscript to help clarify. The implementation of our workflow for image acquisition, processing and analysis relies heavily on the specifics of our lab hardware and software infrastructure. We have added additional details to the text, particularly in the Methods section, and believe we have added enough information that our results can be reproduced using the suite of tools that already exist for single molecule tracking.

      The reviewer also points out that all assays have limitations, and these have not been clearly identified as part of our discussion of the htSMT platform. We have also added some comments on the limitations of the current system and our approach.

      Reviewer #2 (Public Review):

      Summary:

      McSwiggen et al present a high throughput platform for SPT that allows them to identify pharmaceutics interactions with the diffusional behavior of receptors and in turn to identify potent new ligands and cellular mechanisms. The manuscript is well written, it provides a solid new mentor and a proper experimental foundation

      Strengths:

      The method capitalizes and extends to existing high throughput toolboxes and is directly applied to multiple receptors and ligands. The outcomes are important and relevant for society. 10^6 cells and >400 ligands per is a significant achievement.

      The method can detect functionally relevant changes in transcription factor dynamics and accurately differentiate the ligand/target specificity directly within the cellular environment. This will be instrumental in screening libraries of compounds to identify starting points for the development of new therapeutics. Identifying hitherto unknown networks of biochemical signaling pathways will propel the field of single-particle live cell and quantitative microscopy in the area of diagnostics. The manuscript is well-written and clearly conveys its message.

      Weaknesses:

      There are a few elements, that if rectified would improve the claims of the manuscript.

      The authors claim that they measure receptor dynamics. In essence, their readout is a variation in diffusional behavior that correlates to ligand binding. While ligand binding can result in altered dynamics or /and shift in conformational equilibrium, SPT is not recording directly protein structural dynamics, but their effect on diffusion. They should correct and elaborate on this.

      This is an excellent clarifying question, and we have tried to make it more explicit in the text. The reviewer is absolutely correct; we’re not using SPT to directly measure protein structural dynamics, but rather the interactions a given protein makes with other macromolecules within the cell. So when an SHR binds to ligand it adopts conformations that promote association with DNA and other protein-protein interactions relevant to transcription. This is distinct from assays that directly measure conformational changes of the protein.

      L 148 What do the authors mean 'No correlation between diffusion and monomeric protein size was observed, highlighting the differences between cellular protein dynamics versus purified systems'. This is not justified by data here or literature reference. How do the authors know these are individual molecules? Intensity distributions or single bleaching steps should be presented.

      The point we were trying to make is that the relative molecular weights for the monomer protein (138 kDa for Halo-AR, 102 kDa for ER-Halo, 122 kDa for Halo-GR, and 135 kDa for Halo-PR) is uncorrelated with its apparent free diffusion coefficient. Were we to make this measurement on purified protein in buffer, where diffusion is well described by the Stokes Einstein equation, one would expect to see monomer size and diffusion related. We’ve clarified this point in the manuscript.

      Along the same lines, the data in Figs 2 and 4 show that not only the immobile fraction is increased but also that the diffusion coefficient of the fast-moving (attributed to free) is reduced. The authors mention this and show an extended Fig 5 but do not provide an explanation.

      This is an area where there is still more work to do in understanding the estrogen receptor and other SHRs. As the reviewer says, we see not only an increase in chromatin binding but also a decrease in the diffusion coefficient of the “free” population. A potential explanation is that this is a greater prevalence of freely-diffusing homodimers of the receptor, or other protein-protein interactions (14-3-3, P300, CBP, etc) that can occur after ligand binding. Nothing in our bioactive compound screen shed light on this in particular, and so we can only speculate and have refrained from drawing further conclusions in the text.

      How do potential transient ligand binding and the time-dependent heterogeneity in motion (see comment above) contribute to this? Also, in line 216 the authors write "with no evidence" of transient diffusive states. How do they define transient diffusive states? While there are toolboxes to directly extract the existence and abundance of these either by HMM analysis or temporal segmentation, the authors do not discuss or use them.

      Throughout the analysis in this work, we consider all of tracks with a 2-second FOV as representative of a single underlying population and have not looked at changes in dynamics within a single movie. As we show in the supplemental figures we added (see Figure 3, figure supplement 1), this appears to be a reasonable assumption, at least in the cases we’ve encountered in this manuscript. For experiments involving changes in dynamics over time, these are experiments where we’ve added compound simultaneous with imaging and collect many 2-second FOVs in sequence to monitor changes in ER dynamics. In this case when we refer to “transient states,” we are pointing out that we don’t observe any new states in the State Array diagram that exist in early time points but disappear at later time point.

      The reviewer suggests track-level analysis methods like hidden Markov models or variational Bayesian approaches which have been used previously in the single molecule community. These are very powerful techniques, provided the trajectories are long (typically 100s of frames). In the case of molecules that diffuse quickly and can diffuse out of the focal plane, we don’t have the luxury of such long trajectories. This was demonstrated previously (Hansen et al 2017, Heckert el al 2022) and so we’ve adopted the State Array approach to inferring state occupations from short trajectories. As the reviewer rightly points out, this approach potentially loses information about state transitions or changes over time, but as of now we are not aware of any robust methods that work on short trajectories.

      The authors discuss the methods for extracting kinetic information of ligand binding by diffusion. They should consider the temporal segmentation of heterogenous diffusion. There are numerous methods published in journals or BioRxiv based on analytical or deep learning tools to perform temporal segmentation. This could elevate their analysis of Kon and Koff.

      We’re aware of a number of approaches for analyzing both high framerate SMT as well as long exposure residence time imaging. As we say above, we’re not aware of any methods that have been demonstrated to work robustly on short trajectories aside from the approaches we’ve taken. Similarly, for residence time imaging there are published approaches, but we’re not aware of any that would offer new insight into the experiments in this study. If the reviewer has specific suggestions for analytical approaches that we’re not aware of we would happily consider them.

      Reviewer #3 (Public Review):

      Summary:

      The authors aim to demonstrate the effectiveness of their developed methodology, which utilizes super-resolution microscopy and single-molecule tracking in live cells on a high-throughput scale. Their study focuses on measuring the diffusion state of a molecule target, the estrogen receptor, in both ligand-bound and unbound forms in live cells. By showcasing the ability to screen 5067 compounds and measure the diffusive state of the estrogen receptor for each compound in live cells, they illustrate the capability and power of their methodology.

      Strengths:

      Readers are well introduced to the principles in the initial stages of the manuscript with highly convincing video examples. The methods and metrics used (fbound) are robust. The authors demonstrate high reproducibility of their screening method (R2=0.92). They also showcase the great sensitivity of their method in predicting the proliferation/viability state of cells (R2=0.84). The outcome of the screen is sound, with multiple compounds clustering identified in line with known estrogen receptor biology.

      Weaknesses:

      • Potential overstatement on the relationship of low diffusion state of ER bound to compound and chromatin state without any work on chromatin level.

      We appreciate the reviewers caution in over-interpreting the relationship between an increase in the slowest diffusing states that we observe by SMT and bona fide engagement with chromatin. In the case of the estrogen receptor there is strong precedent in the literature showing increases in chromatin binding and chromatin accessibility (as measured by ChIP-seq and ATAC-seq) upon treatment with either estradiol or SERM/Ds. Taken together with the RNA-seq, we felt it reasonable to assume all the trajectories with a diffusion coefficient less that 0.1 µm2/sec were chromatin bound.

      • Could the authors clarify if the identified lead compound effects are novel at any level?

      Most of the compounds we characterize in the manuscript have not previously been tested in an SMT assay, but many are known to functionally impact the ER or other SHRs based on other biochemical and functional assays. We have not described here any completely novel ER-interacting compounds, but to our knowledge this is the first systematic investigation of a protein showing that both direct and indirect perturbation can be inferred by observing the protein’s motion. Especially for the HSP90 inhibitors, the observation that inhibiting this complex would so dramatically increase ER chromatin-binding as opposed to increasing the speed of the free population is counterintuitive and novel.

      • More video example cases on the final lead compounds identified would be a good addition to the current data package.

      Reviewer #1 (Recommendations For The Authors):

      General:

      • More information on the microscope setup and analysis procedures should be given. Since custom code is used for automated image registration, spot detection, tracking, and analysis of dynamics, this code should be made publicly available.

      Results:

      • line 97: more details about the robotic system and automatic imaging, imaging modalities, and data analysis procedures should be given directly in the text.

      Additional information added to text and methods

      • line 100: we generated three U2OS cell lines --> how?

      Additional information added to text and methods

      • line 101: ectopically expressing HaloTag fused proteins --> how much overexpression did cells show?

      The L30 promoter tends to produce fairly low expression levels. The same approach was used for all ectopic expression plasmids, and for the SHRs the expression levels were all comparable to endogenous levels. We have not checked this for H2B, Caax and free Halo but given that the necessary dye concentration to achieve similar spot densities is within a 10-fold range for all constructs, its reasonable to say that those clonal cell lines will also have modest Halotag expression.

      • line 107: Single-molecule trajectories measured in these cell lines yielded the expected diffusion coefficients --> how was data analysis performed?

      Additional information added to text and methods

      • line 109: how was the localization error determined?

      Additional information added to text and methods

      • line 155: define occupation-weighted average diffusion coefficient.

      Additional information added to text and methods

      • line 157: with 34% bound in basal conditions and 87% bound after estradiol treatment  contradicts figure 2b, where the bound fraction is up to 50% after estradiol treatment.

      Line 157 is the absolute fraction bound, figure 2b is change in fbound

      • line 205: Figure 2c is missing.

      Fixed

      • line 215: within minutes --> how was this data set obtained? which time bins were taken?

      Additional information added to text and methods

      • line 216: with no evidence of transient diffusive states  What is meant by transient diffusive state? It seems all time points have a diffusive component, which decreases over time.

      Additional information added to text and methods

      The diffusive peak decreases, the bound peak increases but no other peaks emerge during that time (e.g. neither super fast nor super slow)

      • line 225: it seems that fbound of GDC-0810 and GDC-0927 are rather similar in FRAP experiments, please comment, how was FRAP done?

      FRAP is in the methods section. The curves and recovery times are quite distinct, is the reviewer looking at

      • line 285: reproducibly: how often was this repeated?

      Information added to the manuscript

      • line 285: it would be necessary to name all of the compounds that were tested, e.g. with an ID number in the graph and a table. This also refers to extended data 7 and 8.

      Additional supplemental file with the list of bioactive compounds tested will be included.

      • line 290/1: what is meant by vendor-provided annotation was poorly defined?

      Additional information added to text and methods. Specifically, the “other” category is the most common category, and it includes both compounds with unknown targets/functions as well as compound where the target and pathway are reasonably well documented. Hence, we applied our own analysis to better understand the list of active compounds.

      Figures:

      • fig. 2-6: detailed statistics are missing (number of measured cells, repetitions, etc.).

      We have added clarifying information, including an “experiment design and sample size” section in the Methods.

      • fig. 3: the authors need to give a list with details about the 5067 compounds tested,

      Additional supplemental file with the list of bioactive compounds tested will be included.

      • extended data 1c: time axis does not correspond to the 1.5s of imaging in the text, results line 127.

      Axes fixed

      • extended data 3: panel c and d are mislabeled.

      Panel labels fixed

      Methods:

      • line 746: HILO microscope: the authors need to explain how they can get such large fields of view using HILO

      Additional details added to the materials and methods. The combination of the power of the lasers, the size of the incident beam out of the fiber optic coupling device and the sCMOS camera are the biggest components that enable detection over a larger field of view.

      • line 761: it is common practice to publish the analysis code. Since the authors wrote their own code, they should publish it

      Our software contains proprietary information that we cannot yet release publicly. Comparable results can be achieved with HILO data using publicly-available tools like utrack. State Arrays code is distributed and the parameters used are listed in the M&M.

      Reviewer #2 (Recommendations For The Authors):

      The writing and presentation are coherent, concise, and easy to follow.

      The authors should consider justifying the following:

      Why is 1.5s imaging time selected? Topological and ligand variations may last significantly longer than this. The authors should present at least for one condition the same effect images for longer.

      Related to the similar comment above, we added a figure examining the jump length distribution as a function of frame. Over the 6 seconds of data collection the jump length distribution is unchanged, suggesting it is reasonable to consider all the trajectories within an FOV as representative of the same underlying dynamical states.

      The authors miss the k test or T test in their graphs.

      We chose to apply the Kurskal-Wallis test in the context of the bioactive screen to assess whether a grouping of compounds based on their presumed cellular target was significantly different from the control even when individual compounds might not by themselves raise to significance. In this case many of the pathway inhibitors are subtle and not necessarily obvious in their difference. In the other cases throughout the manuscript, whether two conditions are statistically distinguishable is rarely in question and of far less importance to the conclusions in the manuscript than the magnitude of the difference. We’ve added statistical tests where appropriate.

      The overall integrated area of Fig 4a appears to reduce upon ligand addition. Data appear normalized but the authors should also add N (number of molecules) on top of the graphs.

      While the integrated area may appear to decrease, all State Array analysis is performed by first randomly sampling 10,000 trajectories from the assay well and inferring state distribution on those 10,000. This has been clarified in the figure legend and in the Methods.

      Minor

      Extended Figure 3 legend c, d appear swapped and incorrectly named in the text.

      Panel labels fixed

      L 197 but this appears not to BE a general feature of SHRs (maybe missing Be).

      Error fixed

      L205 authors refer to Figure 2c, which does not exist.

      Panel reference fixed

      Reviewer #3 (Recommendations For The Authors):

      Among minor issues:

      In Figure 1B, if the authors could specify how they discriminate the specific cell lines from the mixed context, it would enhance clarity. Could they perform additional immunofluorescence to understand how the assignment is determined? Alternatively, could they also show the case with isolated cell lines in an unmixed context?

      Immunofluorescence would be a challenge given that there is not a good epitope to distinguish the three ectopically-expressed genes from each other or from endogenous proteins in the case of H2B and CaaX. We are really reliant on the single cell dynamics to determine the likely cell identity. That said, we’ve added graphs of a number of individual cell State Arrays from the same data graphed in 1A which support the notion that it’s reasonable to assume a cells identity given the observed dynamics.

      In Extended Figure 2F: possibly a CHip-Seq experiment would be more directly qualified to state the effect of ER ligand on ER ability to bind chromatin.

      This is true. Presumably ER that is competent at activating transcription of ER-responsive genes is also capable of binding DNA. ChIP would be the more direct measure, but would not address whether the protein was functional. We chose to balance these measuring these two aspects of ER biology by pairing dynamics with the end-point transcription readout.

      In Figure 3: A representation with plate-by-plate orientation along the x-axis, with controls included in each plate, would be more appropriate to reflect the consistency of the controls used in the assay across different plates. Currently, all controls are pooled in one location, and we cannot appreciate how the controls vary from plate to plate.

      Figure added to the supplement

      Also in this figure, a general workflow of the screen down to segmentation/analysis would be a great add-on.

      New figure added to the supplement and reflected in the textual description of the platform

      In Extended Figures 3B and C an add-on of the positive and negative control would make the figure more convincing.

      Addressed as part of figure added to the supplement

      Is there any description of compound leads identified that is novel in nature in relation to impact on ER, and if so could it be stated more clearly in the text as novel finding?

      To our knowledge, the impact of HSP inhibition in increasing ER-chromatin association has never been described, neither has the link between inhibition post-translation modifying enzymes like the CDKs or mTOR and ER dynamics ever been described. We added clarifying text to the manuscript

    1. Author response:

      We thank the reviewers for their positive assessments and constructive feedback.

      In light of their comments, we will aim to improve the explanation of the methods and interpretation of results, as well as their relation to well-established literature in this research area.

      The major contributions of our work are threefold:

      • First, we introduce a novel way of analyzing codas that specifically targets subcoda structures by considering inter-click intervals within codas in terms of transition probabilities. By describing codas’ click patterns via Variable Length Markov Chains, we do not need to consider codas in their entirety, but we can detect coda subunits.This enables a new dimension for quantitatively comparing differences among various individuals, social units, and clans; which we term ‘vocal style’.

      • Using this approach, we reinforce findings from past research, including the idea that identity codas function as symbolic markers of vocal clan identity (Hersh et al., 2022; Sharma et al., 2024). More importantly, we offer new insights into the function of non-identity codas, which comprise the majority of coda types produced by sperm whales but have been largely uncharacterized. 

      • Our work reveals that non-identity coda vocal styles are more similar for spatially overlapped clans, and suggests that this similarity in style may be maintained by social learning across clan boundaries. This opens up a paradigm shift in our understanding of between-clan acoustic interactions.

      From a broader perspective, our work builds on two well-established research areas: the form and function of sperm whale codas, and statistical generative models, specifically Variable Length Markov Chains on finite data spaces. Our methods, results, and interpretations are grounded in theories and concepts from these fields.

      For clarity, we will ensure that our terminology aligns with field standards and existing research. We will clearly introduce each key theory or concept at first mention and justify its relevance. In particular, we will clarify the definition and meaning of the distance between subcoda trees for a general audience. We agree with the reviewers’ comments on the broader implications and will refine our work accordingly.

    1. Author response:

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

      eLife assessment

      Both reviewers positively received the manuscript, in general. The agreement was that the manuscript presented valuable findings, using solid techniques and approaches, that shed additional light into how the canine distemper virus hemagglutinin might engage cellular receptors and how that engagement impacts host tropism. While both reviewers appreciated the X-ray crystallographic data, they also felt that the AFM experiments could have been performed at a higher standard and that the interpretation of the results ensuing from those AFM experiments could have been explained more thoroughly and in simpler terms. An additional missed opportunity of the current manuscript is the lack of comparison of the crystal structure to that of the already published cryo-EM structure, for context.

      Thank you very much for constructive comments of the editor and reviewers. Following your comments, we have changed the text related to the AFM experiments with simpler terms as follows.

      “When CDV-H was loaded onto a mica substrate and scanned with a cantilever to acquire images of attached molecules, the CDV-H dimer was observed as two globules clustered together in most cases, but sometimes, each domain moved independently (Fig. 7B and Supplementary Movie). Time-course analysis of the dynamics of the representative CDV-H dimer showed that CDV-H could adopt both associated and dissociated forms (Fig. 7C). The distances between the domains were calculated by measuring those between the centers of mass of each domain. Finally, the distribution of distances between each head domain in the CDV-H dimers showed approximately 15 nm as a major peak (Fig. 7D). This is a reasonable length for the linker between the head domain dimers.” in Page 11, Lines 8-17.

      With regards to the structural comparison between cryo-EM structure published in Proc. Natl. Acad. Sci. U. S. A. (2023) 120, e2208866120 and our crystal structure, we have compared these structures for Cα on page 6 and added the following text. “A recent cryo-EM structure of the wild-type CDV-H ectodomain revealed that the head dimer is located on one side of the stalk region in solution (Proc. Natl. Acad. Sci. U. S. A. (2023) 120, e2208866120)” in Page 14, Lines 22-24.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Fukuhara, Maenaka, and colleagues report a crystal structure of the canine distemper virus (CDV) attachment hemagglutinin protein globular domain. The structure shows a dimeric organization of the viral protein and describes the detailed amino-acid side chain interactions between the two protomers. The authors also use their best judgement to comment on predicted sites for the two cellular receptors - Nectin-4 and SLAM - and thus speculate on the CDV host tropism. A complementary AFM study suggests a breathing movement at the hemagglutinin dimer interface.

      Strengths:

      The study of CDV and related Paramyxoviruses is significant for human/animal health and is very timely. The crystallographic data seem to be of good quality.

      Thank you very much for the constructive comment of the reviewer.

      Weaknesses:

      While the recent CDV hemagglutinin cryo-EM structure is mentioned, it is not compared to the present crystal structure, and thus the context of the present study is poorly justified. Additionally, the results of the AFM experiment are not unexpected. Indeed, other paramyxoviral RBP/G proteins also show movement at the protomer interface.

      Thank you very much for constructive comments of the reviewer. When we submitted our manuscript to e-life, cryo-EM structure just published in Proc. Natl. Acad. Sci. U. S. A. (2023) 120, e2208866120 a week ago was not able to be available. Following the comment of the reviewer, we have added the text about the structural comparison between the cryo-EM structure and our crystal structure. We also have changed the text related to the AFM experiments to tone down the movement of the protomer interfaceas follows.

      “This observation raises the possibility that each head domain of CDV-H also dissociates and moves flexibly, as shown in the structure of Nipah virus (NiV)-G protein, previously (Science (2022) 375, 1373–1378).” in Page 11, Lines 4-6.

      Reviewer #2 (Public Review):

      Summary:

      The authors solved the crystal structure of CDV H-protein head domain at 3,2 A resolution to better understand the detailed mechanism of membrane fusion triggering. The structure clearly showed that the orientation of the H monomers in the homodimer was similar to that of measles virus H and different from other paramyxoviruses. The authors used the available co-crystal strictures of the closely related measles virus H structures with the SLAM and Nectin4 receptors to map the receptor binding site on CDV H. The authors also confirmed which N-linked sites were glycosylated in the CDV H protein and showed that both wildtype and vaccine strains of CDV H have the same glycosylation pattern. The authors documented that the glycans cover a vast majority of the H surface while leaving the receptor binding site exposed, which may in part explain the long-term success of measles virus and CDV vaccines. Finally, the authors used HS-AFM to visualize the real-time dynamic characteristics of CDV-H under physiological conditions. This analysis indicated that homodimers may dissociate into monomers, which has implications for the model of fusion triggering.

      The structural data and analysis were thorough and well-presented. However, the HS-AFM data, while very exciting, was not presented in a manner that could be easily grasped by readers of this manuscript. I have some suggestions for improvement.

      (1) The authors claim their structure is very similar to the recently published croy-EM structure of CDV H. Can the authors provide us with a quantitative assessment of this statement?

      Thank you very much for constructive comments of the reviewer. When we submitted our manuscript to e-life, cryo-EM structure just published in Proc. Natl. Acad. Sci. U. S. A. (2023) 120, e2208866120 a week ago was not able to be available. Following the comment of the reviewer, we have added the text about the structural comparison between the cryo-EM structure and our crystal structure. We also have changed the text related to the AFM experiments to tone down the movement of the protomer interface as follows.

      “This observation raises the possibility that each head domain of CDV-H also dissociates and moves flexibly, as shown in the structure of Nipah virus (NiV)-G protein, previously (Science (2022) 375, 1373–1378).” in Page 11, Lines 4-6.

      (2) The results for the HS-AFM are difficult to follow and it is not clear how the authors came to their conclusions. Can the authors better explain this data and justify their conclusions based on it?

      Thank you very much for constructive comments of the reviewer. Following your comments, we have changed the text related to the AFM experiments with simpler terms as follows.

      “When CDV-H was loaded onto a mica substrate and scanned with a cantilever to acquire images of attached molecules, the CDV-H dimer was observed as two globules clustered together in most cases, but sometimes, each domain moved independently (Fig. 7B and Supplementary Movie). Time-course analysis of the dynamics of the representative CDV-H dimer showed that CDV-H could adopt both associated and dissociated forms (Fig. 7C). The distances between the domains were calculated by measuring those between the centers of mass of each domain. Finally, the distribution of distances between each head domain in the CDV-H dimers showed approximately 15 nm as a major peak (Fig. 7D). This is a reasonable length for the linker between the head domain dimers.” in Page 11, Lines 8-17.

      (3) The fusion triggering model in Figure 8 is ambiguous as to when H-F interactions are occurring and when they may be disrupted. The authors should clarify this point in their model.

      Thank you very much for constructive comments of the reviewer. Following your comments, we have changed the Figure 8 and its legend.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) AFM experiments with SLAM or Nectin-4 immobilized on the cantilever would be much more informative.

      Thank you very much for the constructive comment of the reviewer. We will try this experiment in the next paper.

      (2) The authors should compare their crystal structure to that of the reported cryo-EM structure.

      With regards to the structural comparison between cryo-EM structure published in Proc. Natl. Acad. Sci. U. S. A. (2023) 120, e2208866120 and our crystal structure, we have added the text.

      (3) Figure 1D - why does the beta2 MG negative control have such a high SPR signal?

      Thank you very much for the constructive comment of the reviewer. The immobilization levels for b 2-microglobulin (beta2 MG), CDV-OP-H and CDV-5VD-H were similar, 1204.7 RU, 1235.7 RU, and 1504.5 RU, respectively. We applied relatively high concentrations (5 mM) of dNectin4 and hNectin4 onto the chip to determine low-affinity dissociation constants. Then, the signals for beta2 MG (negative control) were high. In other SPR experiments for cell surface receptors, such high signals for beta2 MG were often observed in our previous paper, Kuroki et al., J. Immunol. 2019 Dec 15;203(12):3386-3394. doi: 10.4049/jimmunol.1900562. Therefore, we think that these SPR signals are not unusual.

      (4) Figure 1C - please indicate the Ve volume for the peak and add in Ve for standard.

      Thank you very much for the constructive comment of the reviewer. We have indicated the Ve volume for the peak and added in Ve for standard in Figure 1C.

      (5) The authors mention that one of the chains in the asymmetric unit was better resolved than the other. Please show regions of the atomic model fit regions of the electron density to convince the reader of the quality of your data.

      Thank you very much for the constructive comment of the reviewer. We have added new Supplementary figure 2 for comparison of electron density maps of chains A and B.

      (6) Table 2 indicates that the difference between Rw and Rf values is larger than 5% which indicates slight overfitting during refinement. Please provide details of your refinement strategy and attempt simulated annealing as a strategy to reduce this delta.

      Thank you very much for the constructive comment of the reviewer. We further introduced TLS and NCS parameters for the refinement. Consequently, the R/Rfree factors became 0.2645/0.3092. Simulated annealing had been already carried out. All the refinement statistics in the table 2 are updated.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors' fusion triggering model was difficult to follow. For example, this sentence was difficult to understand: "The other possible models may include the monomer-dimer-tetramer transition facilitated by receptor binding for the fusion."

      Thank you very much for the constructive comment of the reviewer. Following your comments, we have removed the above sentences and have added the detail mechanism of the proposed model in Discussion. Furthermore, we have changed the Figure 8 and its legend for readers to understand more clearly.

      (2) Figure 5A is not called out in the main text.

      Thank you very much for the constructive comment of the reviewer. Following your comments, we have added the text as follows.

      “the crystal structure of MeV-H in complex with hNectin-4 showed that the H-SLAM interaction consists of three main sites (Fig. 5A) (Nat. Struct. Mol. Biol. (2013) 20, 67–72).” in Page 11, Lines 4-6.

      (3) Page 9, Line 4: interspaces? Perhaps interphases.

      Thank you very much for the constructive comment of the reviewer. We have changed the term “interspaces” to “internal spaces”.

      (4) Page 12, penultimate line: The authors mention "epitopes for anti-MeV-H Abs." Do they mean anti-CDV-H Abs?

      Thank you very much for the constructive comment of the reviewer. Following your comments, we have changed the “anti-MeV-H Abs” to “anti-morbillivirus H neutralizing antibodies”.

      (5) The paper will benefit from an English language editor to help clarify what the authors are trying to convey.

      Thank you very much for the constructive comment of the reviewer.

      We have asked a English proof reading company to check.

    1. Author response:

      We are grateful to the reviewers for their interest and enthusiasm about the work, and deeply appreciate their constructive comments and suggestions. Our responses are below

      (1) Do mice with BCR-ABL/MSI2-HOXA9 leukemia have an increased pool of leukemic stem cells (LSC), or do they have an increased propensity to develop blast cells? Is it the number of LSCs that has increased, or is it the function of LSC to give rise to the disease that has increased? It is not clear if the detected differences in Lineage-negative cells (Figure S1D) were detected in vitro in retrovirally transduced cells or were detected in vivo in transplanted mice. If the differences were detected in vitro, could the author confirm the same findings in vivo? This will greatly enhance the understanding of in vivo disease pathogenesis and could directly link the aggressivity of the disease (shortened survival) with an increased stem cell-like population.

      We find that BCR-ABL/MSI2-HOXA9 leads to a marked increase in Lineage negative (Lin-) cells which contains the LSC fraction. Specifically, the LSC containing fraction represented 14.1% of the BCR-ABL driven disease and 56.7% of the BCR-ABL and MSI2-HOXA9 driven disease (p<.0001). This suggests that MSI2-HOXA9 triggers the expansion of the undifferentiated LSC containing pool. In addition, the blast frequency was also increased albeit to a lesser extent, with 63.8% blasts (SEM 1.1) for BCR-ABL and 83.3% (SEM 3.1) for BCR-ABL/MSI2-HOXA9 (p=.0001). This suggests that the resulting aggressive disease seen with MSI2-HOXA9 is a consequence of a large increase in undifferentiated  LSC containing cells, as well as the resulting increase in the blast count. The Lin- cells were analyzed from fully established leukemias in vivo (Fig. S1D)

      (2) The authors suggest that BCR-ABL/MSI2-HOXA9 leads to the development of blast crisis-CML. One of the main characteristics of blast crisis-CML is drug resistance. Is BCR-ABL/MSI2-HOXA9 leukemia resistant to classical CML treatment drugs?

      The sensitivity to Imatinib is a very interesting question. In general, while differentiated cells in CML are sensitive to Imatinib, the more undifferentiated cells (LSCs) are resistant1,2. Based on the fact that therapy resistance in blast crisis is largely driven by the undifferentiated fraction of leukemia cells, and given that BCR-ABL/MSI2-HOXA9 driven disease harbors a larger fraction of these undifferentiated cells, we would predict that BCR-ABL/MSI2-HOXA9 leukemia would also be more resistant to imatinib. However, this would need to be experimentally demonstrated and is an important question to address.

      (3) The authors have emphasized the heightened expression of Polrmt in delineating the mitochondrial phenotype of BCR-ABL/MSI2-HOXA9 leukemia cells. However, the regulatory mechanism governing the expression of Polrmt by MSI2-HOXA9 has not been clearly demonstrated by the authors. Unveiling this mechanism would constitute a novel finding and significantly elevate the quality of the research.

      Since Polrmt and mitochondrial genes are transcribed in the nucleus we explored whether MSI2-HOXA9 may control mitochondrial gene expression by triggering expression of Polrmt and other key transcription factors. Consistent with this possibility, MSI2-HOXA9 was preferentially found in the nucleus relative to MSI2. In addition, there were 10 occurrences of the minimal MSI2 RRM1 consensus binding sequence UAGU within the Polrmt transcript. While this is consistent with the possibility that Polrmt expression can be post-transcriptionally modulated by MSI2-HOXA9, this needs to be experimentally validated using Clip Seq analysis with wild type MSI2 as well as the MSI2-HOXA9 fusion protein in context of blast crisis CML.

      (4) Did the authors observe any survival differences between BCR-ABL/NUP98-HOXA9 and BCR-ABL/MSI2-HOXA9?

      In previous work from our lab we have found that the median survival for BCR-ABL/NUP98-HOXA9 was 17 days, and with BCR-ABL/ MSI2-HOXA9 was 18.5 days (p value of 0.22). This suggests that there is not a significant difference in survival times between the leukemias driven by the distinct alleles, and they may be equally aggressive.

      (1) MSI2-HOXA9 fusion is extremely rare as it has been only found in a handful of patients and it is not clear whether other MSI2 fusions function in a similar manner.

      We were very surprised and excited to see the large number of translocations in solid cancers that involve MSI2.  Interestingly, MSI2 translocations occurred both at the N and the C terminus.  Distinct translocations are likely to have unique roles in each disease context. For example, if MSI2’s 5 prime end is part of a translocation, it may functionally contribute via its promoter to drive expression in immature cells and could thus activate oncogenic signals (e.g. controlled by the partner gene) in immature cells which are inherently more susceptible to transformation (Eµ-myc is an example of such a translocation). If Msi2’s RRM domains are part of the fusion, they could bind and target RNAs aberrantly (such as in the wrong cell and the wrong time) and lead to activation of downstream oncogenic mediators. To fully understand the role of each of these translocations in each specific cancer, we would need to experimentally test their impact by ectopic expression in the appropriate cell of origin and domain mapping the basis of any impact in the relevant cancer models as we have done for MSI2-HOXA9 in blast crisis CML in the work we report here.   While this is an intensive undertaking, it is nonetheless important future work as it will undoubtedly lead to new insight about MSI2 linked translocations in diverse solid cancers such as breast cancer and lung cancer.

      (2) The mechanism needs to be strengthened since MSI2 alone or the HOXA9 mutant may not be linked to the mitochondrial mechanism. (3) It is not clear that the mitochondrial pathway is sufficient for the MSI2-HOXA9 oncogenic mechanism.

      Our observation that MSI2-HOXA9 triggered changes in mitochondrial function was of particular interest as it was (to our knowledge) uncharted in context of Msi2 signaling in cancer, thus leading us to explore this further.  However, multiple other signals are likely downstream regulators and these may well act cooperatively with, or independently of, the heightened­­ mitochondrial function we report here. Among these pathways, the most likely mediators included oncogenic programs related to the Wnt pathway including Wnt, Fzd 3 and Frat1, and those related to the Notch pathway including-Tribbles and Hey1 as well as other stem cell genes such as Aldh1. These programs have been previously implicated in the regulation of myeloid leukemia3-11 and could well mediate the impact of the MSI2-HOXA9 translocation. The relative contribution of mitochondrial metabolism and that of developmental and stem cell signals to the onset of MSI2-HOXA9 driven blast crisis CML is an important avenue of future work.

      References

      (1) Corbin, A. S., Agarwal, A., Loriaux, M., Cortes, J., Deininger, M. W. & Druker, B. J. 2011. Human chronic myeloid leukemia stem cells are insensitive to imatinib despite inhibition of BCR-ABL activity. J Clin Invest 121: 396-409. PMC3007128.

      (2) Graham, S. M., Jørgensen, H. G., Allan, E., Pearson, C., Alcorn, M. J., Richmond, L. & Holyoake, T. L. 2002. Primitive, quiescent, Philadelphia-positive stem cells from patients with chronic myeloid leukemia are insensitive to STI571 in vitro. Blood 99: 319-325.

      (3) Gurska, L. M., Ames, K. & Gritsman, K. 2019. Signaling Pathways in Leukemic Stem Cells. Adv Exp Med Biol 1143: 1-39. PMC7249489.

      (4) Narendra, G., Raju, B., Verma, H. & Silakari, O. 2021. Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis. Med Oncol 38: 123.

      (5) Ran, D., Schubert, M., Pietsch, L., Taubert, I., Wuchter, P., Eckstein, V., Bruckner, T., Zoeller, M. & Ho, A. D. 2009. Aldehyde dehydrogenase activity among primary leukemia cells is associated with stem cell features and correlates with adverse clinical outcomes. Exp Hematol 37: 1423-1434.

      (6) Reya, T., Duncan, A. W., Ailles, L., Domen, J., Scherer, D. C., Willert, K., Hintz, L., Nusse, R. & Weissman, I. L. 2003. A role for Wnt signalling in self-renewal of haematopoietic stem cells. Nature 423: 409-414.

      (7) Riether, C., Schürch, C. M., Bührer, E. D., Hinterbrandner, M., Huguenin, A. L., Hoepner, S., Zlobec, I., Pabst, T., Radpour, R. & Ochsenbein, A. F. 2017. CD70/CD27 signaling promotes blast stemness and is a viable therapeutic target in acute myeloid leukemia. J Exp Med 214: 359-380. PMC5294846.

      (8) Riether, C., Schürch, C. M., Flury, C., Hinterbrandner, M., Drück, L., Huguenin, A. L., Baerlocher, G. M., Radpour, R. & Ochsenbein, A. F. 2015. Tyrosine kinase inhibitor-induced CD70 expression mediates drug resistance in leukemia stem cells by activating Wnt signaling. Sci Transl Med 7: 298ra119.

      (9) Venton, G., Pérez-Alea, M., Baier, C., Fournet, G., Quash, G., Labiad, Y., Martin, G., Sanderson, F., Poullin, P., Suchon, P., Farnault, L., Nguyen, C., Brunet, C., Ceylan, I. & Costello, R. T. 2016. Aldehyde dehydrogenases inhibition eradicates leukemia stem cells while sparing normal progenitors. Blood Cancer J 6: e469. PMC5056970.

      (10) Yin, D. D., Fan, F. Y., Hu, X. B., Hou, L. H., Zhang, X. P., Liu, L., Liang, Y. M. & Han, H. 2009. Notch signaling inhibits the growth of the human chronic myeloid leukemia cell line K562. Leuk Res 33: 109-114.

      (11) Kang, Y. A., Pietras, E. M. & Passegué, E. 2020. Deregulated Notch and Wnt signaling activates early-stage myeloid regeneration pathways in leukemia. J Exp Med 217. PMC7062512.

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      Will the nanobody be available to the TB research community?

      Yes, we will make E11rv available upon request. Please see our materials availability statement.

      Reviewer #2 (Recommendations For The Authors):

      (1) It would be interesting to test the potential impact of residual ASB-14 contaminant on the biochemical behavior of ESAT-6-CFP10 heterodimer and ESAT-6 homodimer or tetramer and their hemolytic activity in comparison with the ones without ASB-14.

      We agree that this is an interesting line of questioning. Based on the study by Refai et al. that we cite in the text, ESAT-6 treated with nonionic detergents ASB-14 or LDAO, but not other common detergents, undergoes a conformational change that increases its cytotoxicity in cell assays, hemolytic activity, and ability to dimerize with CFP-10. What is not known at this point, is how similar the ASB-bound conformation is to anything seen physiologically.

      (2) Building on the progress in making anti-ESAT-6 nanobodies and their anti-Mtb effects in the cells, it could have been tested in human or mouse primary macrophages infected with Mtb and a mouse model of Mtb infection for its anti-Mtb efficiency.

      We thank the reviewer for this suggestion, and we agree that these would be very informative next steps for determining the therapeutic potential of anti-ESAT-6 nanobodies.

      Reviewer #3 (Recommendations For The Authors):

      Minor comments:

      Line 133: "It is well established that Mm-induced hemolysis is ESX-1 dependent, but our results suggest that Mtb must lack one or more factors necessary for efficient hemolysis.". I would tone this down a bit, as it is also known that M. tuberculosis escapes much later than M. marinum from the phagosome, which could indicate different kinetics.

      We thank the reviewer for their insightful comments. We agree that the kinetics of Mtb and Mm infection are quite different and that this may impact the hemolysis assay. As described by Augenstreich et al. some hemolysis by Mtb is observed at 48 hours, though the method of normalization makes it impossible to determine absolute amount of hemolysis that occurred in their experiment. Our findings just show that the absolute amount of Mtb hemolysis in 2 hours is negligible, setting it apart from Mm. We have edited the wording of this statement in the manuscript to avoid any confusion.

      Line 155: "Because Mtb often exists in an acidified compartment". First of all, the reference used here does not discuss anything about Mtb, secondly, papers that do measure the acidification of Mtb-loaded phagosomes indicate that this acidification is very mild (typically to pH 6.2).

      We agree that this point should be articulated more precisely. We have added additional clarification that the pH of Mtb-containing compartments in macrophages can fall in a broad range depending on the activation state of the macrophages, and that non-activated macrophages are typically only mildly acidic. We have updated our references to better describe the current state of knowledge on this topic.

      Line 339: "Whereas most of these functions rely only on the secretion of ESAT-6 into the cytoplasm, the ability of E11rv to access Mtb suggests that this communication is likely two-way." No, not necessary, there are many processes in which ESX-1 substrates affect the macrophage. This nanobody could affect EsxA functioning only once the bacteria reach the cytoplasm. I think checking phagosomal escape in these cells is therefore crucial.

      We agree that phagosomal escape and subsequent direct secretion of ESAT-6 into the cytoplasm is a reasonable alternative hypothesis. We have added this point to our discussion, and we agree that looking directly at phagosomal escape is an important next step.

      Figure 7 is not mentioned in the text (mistake for Fig 6).

      This has been corrected.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      The study is highly interesting and the applied methods are target-oriented. The biophysical characterization of viable N-protein species and several representative N-protein mutants is supported by the data, including polarity, hydrophobicity, thermodynamic stability, CD spectra, particle size, and especially protein self-association. The physicochemical parameters for viable N-protein and related coronavirus are described for comparison in detail. However, the conclusion becomes less convincing that the interaction of peptides or motifs was judged by different biophysical results, with no more direct data about peptide interaction. Additionally, the manuscript could benefit from more results involving peptide interaction to support the author's opinions or make expression more accurate when concerning the interaction of motifs. Although the authors put a lot of effort into the study, there are still some questions to answer.

      We thank the Reviewer for this assessment and wholeheartedly agree that there are still many questions. The main thrust of the present work was not intended to unravel the detailed mechanistic origin of all observations, but rather to juxtapose the different observations made with different viable N-protein species across the mutant spectrum, in order to get a sense of how narrowly the biophysical phenotype is confined to ensure virus viability. Such a study has become possible for the first time with the unprecedented genomic database of SARS-CoV-2. This has led to observations of non-local effects of individual mutations that are not independent and non-additive relative to the effects of other mutations, and in that sense we have inferred ‘interactions’. These might be mediated by direct contacts or indirectly through altered chain configurations. In the revised manuscript we have clarified this point.

      Meanwhile, a number of documented direct physical intra-molecular and intra-dimer interactions provide a context to our study of mutation effects. The flexibility of the IDRs provides a rich variety of contacts that have been observed in molecular dynamics and single-molecule fluorescence studies (Rozycki & Boura, Biophys Chem. 2022 and Cubuk et al, Nat Communs 2021). We have previously carried out detailed hydrodynamic studies of self-association interfaces located in the leucine-rich region. More recently, NMR data just published by the Blackledge laboratory (Botova et al., bioRxiv 2024) extend the list of intra-molecular contacts with the observation of long-range intra-molecular interactions between the NTD and the CTD, NTD and the phosphorylated SR-rich region, and NTD and the previously studied leucine-rich region. The latter contacts require the C-terminal region of the linker to loop back onto the NTD, which may well introduce susceptibility to any of the linker mutations. However, detailed linker configurations are beyond the scope of the present work.

      With regard to the effects of the Omicron mutations in the N-arm IDR, we have shown hydrodynamic data directly demonstrating peptide self-association, and we are currently working on a more detailed functional follow-up study which we hope to communicate soon.

      Reviewer #2 (Public Review):

      Summary: This work focuses on the biochemical features of the SARS-CoV-2 Nucleocapsid (N)protein, which condenses the large viral RNA genome inside the virus and also plays other roles in the infected cell. The N protein of SARS-CoV-2 and other coronaviruses is known to contain two globular RNA-binding domains, the NTD and CTD, flanked by disordered regions. The central disordered linker is particularly well understood: it contains a long SR-rich region that is extensively phosphorylated in infected cells, followed by a leucine-rich helical segment that was shown previously by these authors to promote N protein oligomerization.

      In the current work, the authors analyze 5 million viral sequence variants to assess the conservation of specific amino acids and general sequence features in the major regions of the N protein. This analysis shows that disordered regions are particularly variable but that the general hydrophobic and charge character of these regions are conserved, particularly in the SR and leucine-rich regions of the central linker. The authors then construct a series of N proteins bearing the most prevalent mutations seen in the Delta and Omicron variants, and they subject these mutant proteins to a comprehensive array of biophysical analyses (temperature sensitivity, circular dichroism, oligomerization, RNA binding, and phase separation).

      Strengths:

      The results include a number of novel findings that are worthy of further exploration. Most notable are the analyses of the previously unstudied P31L mutation of the Omicron variant. The authors use ColabFold and sedimentation analysis to suggest that this mutation promotes the self-association of the disordered N-terminal region and stimulates the formation of N protein condensates. Although the affinity of this interaction is low, it seems likely that this mutation enhances viral fitness by promoting N-terminal interactions. The work also addresses the impact of another unstudied mutation, D63G, that is located on the surface of the globular NTD and has no significant effect on the properties analyzed here, raising interesting questions about how this mutation enhances viral fitness. Finally, the paper ends with studies showing that another common mutant, R203K/G204R,disrupts phase separation and might thereby alter N protein function in a way that enhances viral fitness.

      Thank you for highlighting the strengths of our paper.

      Weaknesses:

      In general, the results in the paper confirm previous ideas about the role of N protein regions. The key novelty of the paper lies in the identification of point mutations, notablyP13L, that suggest previously unsuspected functions of the N-terminal disordered region in protein oligomerization. The paper would benefit from further exploration of these possibilities.

      We agree that the bioinformatic results confirm previous ideas about the role of the N protein regions. However, we believe our results go beyond the previous thinking in a crucial aspect, which is that we examine the full (so far known) mutant spectrum of N-protein. Properties previously inferred from the inspection of single consensus sequences can be misleading because of the quasispecies nature of RNA viruses. By considering the mutant spectrum we can obtain a sense for how significant differences in the physicochemical properties of the different regions are, and how much variation is possible without jeopardizing essential protein functions.

      With regard to the N-arm IDR mutations we believe this deserves a separate study focusing on the apparent N-arm function. Our rationale for presenting some initial N-arm results in the current paper was to highlight how the variability of N-protein species in the mutant spectrum can even include differences in the type and number of protein self-association interfaces.

      Reviewer #3 (Public Review):

      Nguyen, Zhao, et al. used bioinformatic analysis of mutational variants of SARS-CoV-2Nucleocapsid (N) protein from the large genomic database of SARS-CoV-2 sequences to identify domains and regions of N where mutations are more highly represented and computationally determined the effects of these mutations on the physicochemical properties of the protein. They found that the intrinsically disordered regions (IDRs) of N protein are more highly mutated than structured regions and that these mutations can lead to higher variability in the physical properties of these domains. These computational predictions are compared to in vitro biophysical experiments to assess the effects of identified mutations on the thermodynamic stability, oligomeric state, particle formation, and liquid-liquid phase separation of a few exemplary mutants.

      The paper is well-written and easy to follow, and the conclusions drawn are supported by the evidence presented. The analyses and conclusions are interesting and will be of value to virologists, cell biologists, and biophysicists studying SARS-CoV-2 function and assembly. It would be nice if some further extrapolation or comments could be made regarding the effects of the observed mutations on the in vivo behavior and properties of the virus, but I appreciate that this is much higher-order than could be addressed with the approaches employed here.

      We thank the Reviewer for this positive assessment. With regard to the possible in vivo behavior of mutant species, we agree that this would require additional data beyond the scope of the present work.

      However, for the N:G215C mutant we can point to a very recent preprint by Kubinski et al. (bioRxiv 2024) that describes reverse genetics experiments where the isolated N:G215C mutation caused altered in vivo pathology, enhanced viral replication, and altered virion morphology. We have cited this work in the revised manuscript.

      As mentioned above, for the P13L mutation we hope to communicate a more detailed follow-up study that will allow us to extrapolate on its in vivo behavior.

      Recommendations For The Authors:

      Reviewer #1:

      (1) Given the structure organization of N-protein in Figure 1, the authors should explain why linker region 180-247 is different from linker (175-247) mentioned in the first result.

      We thank the reviewer for bringing up this point, which we agree deserves clarification. While often the NTD has been assigned a C-terminal limit of 180 (e.g., in the NMR structure by Dinesh et al, Plos Pathogens 2020), the last several residues in the NTD are already disordered and contain the S176/R177 pair and therefore may be ascribed to the beginning of the SR-rich portion of the linker. In order not to artificially truncate functional sequences of either NTD or linker, we have decided to allow the designations of the NTD and linker regions to overlap. We believe this is conservative in that possible NTD or linker properties extending into this transition region will be preserved. In order to explain this in the manuscript, we have modified Figure 1 and inserted a brief sentence “(Due to ambiguity in delineation between NTD and linker, designations overlapping in 175-180 were used to avoid artificial truncation and permit conservative evaluation of the properties of each domain.)”.

      (2) Please specify the "physicochemical requirements" in the fourth paragraph of the first result, and its physicochemical meaning and references.

      Thank you for pointing this out; we agree this was not well expressed. We have rephrased this (including new references) to “…we find that hydrophobicity is uniformly high and polarity correspondingly low in the folded NTD and CTD domains, which is consistent with the expectation that folded structures are stabilized by buried hydrophobic residues (Eisenberg and McLachlan, 1986; Kauzmann, 1959)”.

      (3) The authors should clarify the biological meaning of the net charge and phosphorylation charge in the first result, just like the description in the results of polarity and hydrophobicity.

      We agree this will improve readability, and have inserted an introductory sentence to the study of charges in the mutant spectrum: “Charges in proteins can control multiple properties related to electrostatic interactions, from functions of active sites to protein solubility, protein interactions, and conformational ensembles in IDRs (Garcia-Viloca et al., 2004; Gerstein and Chothia, 1996; Gitlin et al., 2006; Mao et al., 2010).”.

      (4) The authors should clarify the calculation method and meaning of the column "occurs in % of all genomes" in Table 2.

      We have inserted a footnote specifying that this is the “Percentage of all sequenced genomes carrying the specific mutation.”.

      (5) Please specify what information or conclusion we can get for the shift of the intrinsic fluorescent spectrum of N: D63G in the third result paragraph 2.

      We have rephrased the second sentence of this paragraph to “The presence of the N:D63G mutation in the NTD is highlighted in the shift of the intrinsic fluorescence quantum yield of this mutant in comparison to Nref ”. It confirms the structural prediction, which positions D63G at the protein surface near the NA binding site, and sets up the question whether this obligatory mutation of Delta-variant N-protein affects NA binding and thereby possibly assembly. Unexpectedly, we did not find any impact of the D63G mutation on NA binding, although we observed a modest impact on temperature-dependent particle formation by DLS.

      (6) The conclusion, "some epistatic interaction between mutation of the linker and N-arm" in the third result paragraph 4, is over-interpreted from the result of the CD spectra because they didn't detect peptide interaction between mutation of the linker and N-arm.

      Thank you for raising this point. We did not mean to make a strong conclusion here, and have now deleted this statement.

      (7) The parallel assay for N: G215C and Nδ in SV-AUC experiments is recommended to be conducted with other groups to avoid experimental error.

      I believe this may be a misunderstanding: Indeed we had carried out SV-AUC experiments for all the mutants, as shown in Figure 5A. However, since all but the N:G215C and Nδ formed only dimers as the reference protein, we did not comment on these in the results text. We have rectified this omission in the revision by inserting the sentence: “…The same behavior is observed for N:D63G, No, N:R203K/G204R, as well as N:P13L/Δ31-33 at low micromolar concentrations (Figure 5A). By contrast, the G215C mutation promotes the formation of higher oligomers…”

      With regard to experimental error, SV-AUC is an absolute method based on first principles and we have maintained our instruments by performing regular calibrations, using methods developed by us and colleagues at NIST, as described in the literature (Anal Biochem 2013, PLOS ONE 2018, Eur. Biophys. J. 2021). Previously we have critically examined the accuracy of s-values by SV-AUC before and after calibration in a large multi-laboratory study (PLOS ONE 2015), and found that the accuracy of s-values is ~1%. This allows detailed comparisons of results from different runs and different points in time. To alleviate any concerns we have now mentioned our calibration methods in the methods section.

      (8) The authors did not test the function of Nδ R203M mutation, so they should not mention about it like in the third result paragraph 5, which is over-interpreted from result 5A.

      We accept the criticism that we have not yet examined the R203M mutation in isolation. However, we believe some speculation is in order: Nδ consists of D63G, R203M, G215C, and D377Y, of which D63G is unlikely to impact oligomeric state based on our data of N:D63G. It is therefore reasonable to assume that R203M and/or D377Y interfere with the observed promotion of oligomerization that we have observed with N:G215C. In previous work, we have traced the 215C-incuded oligomerization to the transient helix in the leucine-rich region of the linker 215-235 (Science Advances, 2023), Since 377Y is quite far away, the more proximal 203M appears to be the most plausible origin of the modulation of dimerization.

      In the revision we have more clearly outlined this speculation: “ Of the three additional mutations of Nδ relative to N:G215C, we speculate that D63G does not impact dimerization (as in N:D63G, Figure 5A), and that therefore either the distant D377Y and/or R203M might cause this reduction of helicity and oligomerization relative to N:G215C, noting that R203M is proximal to the L-rich region (215-235) reshaped by 215C. ”. Later we refer to this as “any potential inhibitory role suspected of the R203M mutation on self-association…”.

      (9) The description of LLPS formation lacks reference in the third result paragraph 6.

      Thank you. To improve the transition to this new paragraph in the results, we have inserted “As outlined in the introduction, …” and repeated the 8 references to the fact that N-protein undergoes LLPS. The two additional, separate references refer to just those published studies that examined the temperature-dependence of LLPS, which I believe is now clearer.

      (10) The authors did not test the interaction between the N-arm IDR mutation and linker IDR, it is not exponible that interaction promoted particle formation of No in the third result paragraph 8, which is over-interpreted from result 5B.

      We thank the Reviewer for raising this point. In fact, we did not want to imply a direct physical interaction (in terms of binding) between the N-arm IDR mutation and that in the linker. But clearly there are non-additive effects in particle formation since P13L/Δ31-33 inhibits slightly and R203K/G204R inhibits almost completely, whereas the combination of the two (constituting No) promotes particle formation. We have rephrased this to “alter the effect of”, avoiding the term “interact with” not to suggest a picture of direct binding and invoke instead the idea of epistatic interactions.

      (11) In the third result paragraph 9, why did the authors choose to examine the role of the N-arm mutations of the Omicron variants in greater detail? This reason should be added to the manuscript.

      Thank you for this suggestion. Naturally, we were curious how the defining N-arm mutations of Omicron variants could impact particle formation. Even though no obvious enhancement of self-association by either Omicron N-arm or linker mutations was observed at low micromolar concentrations in SV-AUC (Figure 5A), we knew from experience with the study of the leucine-rich transient helix in the linker IDR that even weak interfaces with mM Kd can be highly relevant in the context of multivalent assemblies (Science Advances, 2023). Therefore we followed the same roadmap and focused on IDR peptides with the goal to study them at higher concentrations that might reveal weak interactions.

      We have described this motivation as follows: “We were curious whether IDR mutations might alter particle formation through modulation of existing or introduction of new protein-protein interfaces. We focused on Omicron mutations as these are obligatory an all currently circulating strains, and specifically on N-arm mutations, which have recently been implicated in altered intramolecular interactions with NA-occupied NTD (Cubuk et al., 2023). Even though SV-AUC showed no indication of self-association of N:P13L/Δ31-33 at low micromolar concentrations, weak interactions with Kd > mM would not be detectable under these conditions yet could be highly relevant in the context of multi-valent complexes (Zhao et al., 2024). Following the roadmap used previously for the study of the weak self-association of the leucine-rich linker IDR (Zhao et al., 2023), we restricted the protein to the N-arm peptide such that it can be studied at much higher concentrations. To this end, we …”

      (12) Why were different proteins dissolved in either high-salt buffer or low-salt buffer for biophysical experiments? Did this affect the experimental results? Explanations and evidence are required.

      We appreciate this is an important point. Unfortunately, for practical reasons of available sample concentrations and quantities, it was not always possible to dialyze protein into both buffers. For example, the DSF data in Figure 4B show all proteins in low-salt buffer except N:R203K/G204R, which is in high-salt buffer. We had previously reported the absence of changes in Ti in DSF for Nref in the two buffers, which we have documented better in the revised manuscript by providing an additional Supplementary Figure S7: “As a buffer control, the difference in Ti for Nref in LS and HS buffer was measured and found to be within error of data acquisition (Supplementary Figure S7A).” This new Supplementary Figure provides an overlay of low-salt and high-salt DSF data for Nref, N:D63G, and No, which have variations in the Ti values for different buffers on the order of 0.1 °C. This is comparable to the precision of the measurement, and significantly smaller than the changes in Ti values between the different mutant protein species. Finally, we note that the one species for which we were unable to collect DSF data in low-salt buffer, N:R203K/G204R, was unremarkable relative to Nref, No, and N:P13L/Δ31-33.

      In the case of CD, the only species for which we could not collect spectra in low-salt buffer was No. Again, this spectrum was similar to the group including Nref, along with N:P13L/Δ31-33, and N:D63G. In the results we interpreted significant differences from Nref for N:G215C and N:R203K/G204R.

      Similarly, SV-AUC experiments were carried out in high-salt buffer, except Nref, Nδ , and N:G215C. In this case, we could observe a ≈ 5% difference in s-value for the same protein in different buffers, but the magnitude of this change is negligible compared to the ≈ 60-90% increase observed for altered oligomeric states. To clarify this we have inserted a sentence “Proteins for self-association studies were in buffer HS, except Nref, Nδ , and N:G215C were in LS, the latter causing a ≈5% increase in s-value (Supplementary Figure S7B).”, with the new Supplementary Figure S7B showing a comparison of sedimentation coefficient distributions of Nref and N:D63G in low- and high-salt buffers. Whether the small differences in s-values are indeed significant and reflective of salt-dependent conformational ensembles of IDRs will require a more detailed follow-up study, but is outside the scope of the present work.

      All other experiments were carried out with uniform buffer conditions for all protein species.

      (13) DLS data of N from other research suggests oligomers beyond dimer. Please address this discrepancy.

      Unfortunately several previous studies in the literature did not recognize the importance of eliminating nucleic acid contaminations in the N-protein preparations, and/or did not succeed in completely removing nucleic acid from the protein. We and others have repeatedly commented on this issue. For example, Tarczewska et al (IJBM 188 (2021) 391-403) clearly demonstrate this in much detail in a study dedicated to this problem.

      The clarify this point we have included a sentence in the paragraph describing the protein preparation “…the ratio of absorbance at 260 nm and 280 nm of ~0.50-0.55 confirmed absence of nucleic acid. The latter is important to eliminate higher order N-protein oligomers induced by nucleic acid binding (Carlson et al., 2020; Tarczewska et al., 2021; Zhao et al., 2021)” .

      In order to strengthen the statement in the Results that the ancestral N-protein is dimeric we have added additional references from other labs that have carried out detailed biophysical analyses: “As reported previously, the ancestral N-protein at micromolar concentrations in NA-free form is a tightly linked dimer sedimenting at ≈4 S , without significant populations of higher oligomers (Forsythe et al., 2021; Ribeiro-Filho et al., 2022; Tarczewska et al., 2021; Zhao et al., 2022, 2021).”

      Reviewer #2:

      The key novel finding of the work lies in the evidence that P31L promotes N-terminal interactions. The paper would be strengthened by additional studies of the impact of P31Lon the oligomerization of full-length N protein. The sedimentation analysis in Fig 6 shows that high concentrations of the N arm alone self-associate, while the analysis in Fig 5 argues that P31L does not have an effect on the oligomerization of the full-length protein. Perhaps there are specific conditions or mutation combinations that would provide evidence that P31L has an effect on protein behavior that might explain the prevalence of this mutation.

      We agree that the finding of P13L promoting N-terminal interactions is of great interest, and we thank the Reviewer for the suggestion to examine cross-correlations of N-arm mutations with other mutations as a tool to study its function and relevance.

      The observation of self-association in Figure 6 at high concentrations is not necessarily at odds with the absence of self-association at 100fold lower concentrations. Rather, it seems to show that the interaction mediated by the N-terminal mutation P13L is weak with an effective Kd in the mM range. It will likely not be possible to reach sufficiently high protein concentrations with the full-length protein to visualize the oligomerization of N-terminal IDR. But even if it was possible to concentrate the protein enough, very likely other assembly processes would take place, including LLPS, obscuring potential P13L interfaces. Nonetheless we believe the protein-protein interface created by the N-arm IDR is highly relevant in the context of multi-valent complexes, where entropic co-localization enhances the effective N-arm IDR concentration that then can provide additional binding energy and strengthen the assembly of multi-protein complexes.

      We are currently pursuing further experiments examining the properties and relevance of the N-arm mutations and intend to publish this in a separate study, not to distract from the thrust of the current work exploring of the extent of the biophysical phenotype space.

      The R203K/G204R mutations have a surprising impact on LLPS in Figure 7: it is not clear how such limited mutations would alter the many nonspecific, multivalent interactions that presumably lead to phase separation. The paper would benefit from a more extensive analysis of LLPS in this mutant and in the P31L mutant, perhaps by performing the analysis at various protein concentrations and times.

      Following this recommendation we have expanded the study of LLPS of Figure 7 by comparison of two different time points for Nref, N:R203K/G204R, and N:P13L in a new Supplementary Figure S6. We have also quantified the droplet distributions as shown in the new Supplementary Figure S5. Both clearly confirm the strong inhibitory effect of the R203K/G204R mutation on LLPS under our experimental conditions. What this shows is not that this protein could not undergo LLPS per se, but that the phase boundaries have shifted such that under the experimental conditions we applied LLPS does not occur yet. (In this context it is interesting to note that ≈50,000 genomes in the GISAID database have R203K/G204R as the sole N-protein mutation, without impact on viral viability.)

      That individual point-mutations in IDRs can have significant impact on LLPS has been observed previously for several other proteins. Examples include SPOP [Bouchard et al., Mol Cell 72 (2018) 19-36.e8], SHP2 [Zhu et al., Cell 183 (2020) 490-502.e18], FUS [Niaki et al., Mol Cell 77 (2020) 82-94.e4], and CAPRIN1 [Kim et al., PNAS 118 (2021) 1-11]. The latter work applies NMR and reveals that promotion of LLPS is not uniform but centered in hot-spot residues of CAPRIN1.

      While the precise molecular mechanism for LLPS of the N-protein is unclear, we can speculate how the effect of 203K/204R might be amplified. As shown by the coarse-grained MD simulations from Rozycki & Boura (Biophys. Chem. 2022), the linker IDR is highly flexible and the 203/204 residues make transient contacts to other residues throughout the linker as well as to distinct sites on the NTD. Furthermore, recent NMR data from the Blackledge lab (Botova et al., bioRxiv 2024, doi:10.1101/2024.02.22.579423) have revealed intra-molecular interactions, including a state where the L-rich (C-terminal) portion of the linker IDR interacts with a site on the distant NTD. (We have included a reference to this preprint in the discussion.) This intra-molecular contact observed in NMR must cause significant chain compaction and may thereby modulate the accessibility of portions of the linker IDR available to inter-molecular interactions contributing to LLPS. The residues 203/204 are in the middle between the SR-rich and L-rich region where bending of the chain must occur to allow for the intra-molecular contacts. The 203K/204R mutation may alter the dynamics or population of this intra-molecular bound state, especially considering the introduction of a bulky positively charged R replacing G204.

      In summary, considering the dynamics of intra-molecular contacts and considering precedent of several other disordered proteins, we believe it is not unreasonable that the local mutation in the IDR R203K/G204R may cause a significant shift in LLPS phase boundaries. We note that this mutant also shows a very distinct behavior in the temperature-dependent DLS, entirely lacking particle formation below 70 °C. This observation seems consistent with altered inter-molecular interactions.

      Reviewer #3:

      I have only a few minor specific comments:

      (1) Page 4, last paragraph - typo: "The large number of structural and non-structural N-protein functions poses the question of how they are conserved...". This either needs a colon or to be changed to "... poses the question of how they are conserved...".

      Thank you – we have changed this sentence accordingly.

      (2) Page 7, 2nd and 3rd paragraphs of "Physicochemical properties" section: why is Figure2B discussed before Figure 2A?

      Initially when we present the results of polarity and hydrophobicity we refer more generally to Figure 2, as the two properties are so closely related. Later, in the section on related coronaviruses we do refer once more to Figure 2. Here we begin this section by discussing Figure 2B since in this plot the symbols for the different viruses are most recognizable.

      (3) Page 11, lines 1-2: "Since this is a tell-tale of weak protein..." -> "tell-tale sign of ...".

      We thank the reviewer for pointing this out and have fixed this sentence.

      (4) Further down in the same paragraph, the meaning of "SV-AUC" should be spelled out at its first use.

      We have double checked that SV-AUC is spelled out at its first use.

      (5) Figures 1 and 2. Is there a good reason that the color scheme for the IDRs (magenta and cyan) is so close to the color scheme for the identifying mutations of Omicron and Delta (magenta and blue)? This initially led me to try to search for some connection, and it remains unclear to me if there is.

      We apologize for this confusion. This was indeed a poor color choice, and we have rectified this in the revised manuscript by changing the colors of the identifying mutations of Omicron and Delta to dashed green and dotted red, respectively, so that there is no connection to the shading of the IDRs. Thank you very much for pointing this out!

      (6) Figure 1: The physical limits of the subdomains, e.g. SR-rich, L-rich, C-arm1, and N3 could be more clearly delineated with lines, or some other visual representation.

      Once more, we thank the reviewer for pointing this out. We have revised Figure 1 to indicate the limits between these subdomains.

      (7) Figures 4, 5, and 6: are there any kind of error bars or confidence intervals on these measurements?

      We appreciate this concern and have addressed it in different ways for the different methods.

      For the spectra of intrinsic fluorescence in Figure 4A, we have now plotted an overlay of three acquired spectra, from which the experimental error as a function of wavelength may be assessed. It is clear that the differences between Nref and N:D63G are far greater than the measurement error.

      With regard to DSF, we have provide an error estimate of 0.3 °C for the Ti-values, a value that we have revised from the previously reported errors of sequential replicates to now include Ti variation observed with different preparations of the same protein over long time periods.

      For CD spectra we have included a new Supplementary Figure S3 that shows standard deviations of triplicate measurements as a function of wavelength. Since an overlay including errors for all species would be too crowded, we have created separate plots for all species in comparison with Nref. (On this occasion we discovered a 3% error in the magnitude of the Nref spectrum due to previously incorrect conversion to MRE, which we have now fixed.)

      In SV-AUC, for data with typical signal-noise ratio, the statistical error is very small due to the large number (> 104 ) of raw data points included in the calculation of each c(s) trace, which each data point carrying a statistical error that is usually better than 1%. Therefore, the dominant error is systematic. In the past we have carried out large studies quantifying the accuracy of the major peaks of the sedimentation coefficient distributions, and found they are typically ≈1% in s-value and 1-2% for relative peak areas. In the AUC methods section we have now included the sentence “Typical accuracy of c(s) peaks are on the order of ≈1% for peak s-values and ≈1-2% for relative peak areas (Zhao et al., 2015).”

      Finally, for the temperature-dependent DLS data we have to resort to the scatter in the temperature-dependent Rh-values. The calculated Rh-values can exhibit fluctuations once particles start to form and the distribution becomes highly polydisperse. As is characteristic for DLS under those conditions, individual Rh-values can be dominated by adventitious diffusion of few large particles into the laser focal spot. Although customarily autocorrelation functions can be filtered out through software filters (e.g., setting baseline and amplitude thresholds), this still presents the largest source of error in the Rh-values. These are systematic for the individual autocorrelation functions. We believe that the variation of Rh-values at similar temperatures outside the transition region provides a reasonable estimate for the experimental error.

      (8) Figure 7: My most major comment. It would be good to somehow quantify the differences between these images. The claim is made that the LLPS droplets are different sizes, or for the P13L/\Delta31-33 variant that droplets are coalescing or changing shape over time. It would be good to quantify this rather than rely on eyeballing the pictures.

      We are grateful to the Reviewer for this suggestion. As mentioned above, to improve the LLPS analysis we have now carried out segmentation of the images in Figure 7 to quantify the droplet numbers and areas. Histograms and statistical analyses are now provided in the new Supplementary Figure S5. In addition, we have added a comparison of the droplet numbers and sizes at two time-points for Nref, N:R203K/G204R, in addition to the previously shown N:P13L/Δ31-33, provided in the new Supplementary Figure S6. The results corroborate the previous conclusions, and depict how droplets in the N:P13L/Δ31-33 merge and grow in area more strongly than those from Nref.

    1. Author response:

      eLife assessment

      This study represents a fundamental contribution to our understanding of how gene expression levels are controlled in bacteria. Through a series of compelling and careful experiments, relying on a mutant that blocks DNA replication but permits growth, and using various methods, the authors reveal how genome concentration rapidly becomes limiting for growth when replication is inhibited. This work contributes to our understanding of the contributions and limiting roles of DNA, mRNA, and ribosomes for growth in bacteria, and will be of considerable interest within both systems biology and microbial physiology.

      Thank you!

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript by Mäkelä et al. presents compelling experimental evidence that the amount of chromosomal DNA can become limiting for the total rate of mRNA transcription and consequently protein production in the model bacterium Escherichia coli. Specifically, the authors demonstrate that upon inhibition of DNA replication the single-cell growth rate continuously decreases, in direct proportion to the concentration of active ribosomes, as measured indirectly by single-particle tracking. The decrease of ribosomal activity with filamentation, in turn, is likely caused by a decrease of the concentration of mRNAs, as suggested by an observed plateau of the total number of active RNA polymerases. These observations are compatible with the hypothesis that DNA limits the total rate of transcription and thus translation. The authors also demonstrate that the decrease of RNAp activity is independent of two candidate stress response pathways, the SOS stress response and the stringent response, as well as an anti-sigma factor previously implicated in variations of RNAp activity upon variations of nutrient sources.

      Remarkably, the reduction of growth rate is observed soon after the inhibition of DNA replication, suggesting that the amount of DNA in wild-type cells is tuned to provide just as much substrate for RNA polymerase as needed to saturate most ribosomes with mRNAs. While previous studies of bacterial growth have most often focused on ribosomes and metabolic proteins, this study provides important evidence that chromosomal DNA has a previously underestimated important and potentially rate-limiting role for growth.

      Thank you for the excellent summary of our work.

      Strengths:

      This article links the growth of single cells to the amount of DNA, the number of active ribosomes and to the number of RNA polymerases, combining quantitative experiments with theory. The correlations observed during depletion of DNA, notably in M9gluCAA medium, are compelling and point towards a limiting role of DNA for transcription and subsequently for protein production soon after reduction of the amount of DNA in the cell. The article also contains a theoretical model of transcription-translation that contains a Michaelis-Menten type dependency of transcription on DNA availability and is fit to the data. While the model fits well with the continuous reduction of relative growth rate in rich medium (M9gluCAA), the behavior in minimal media without casamino acids is a bit less clear (see comments below).

      At a technical level, single-cell growth experiments and single-particle tracking experiments are well described, suggesting that different diffusive states of molecules represent different states of RNAp/ribosome activities, which reflect the reduction of growth. However, I still have a few points about the interpretation of the data and the measured fractions of active ribosomes (see below).

      Apart from correlations in DNA-deplete cells, the article also investigates the role of candidate stress response pathways for reduced transcription, demonstrating that neither the SOS nor the stringent response are responsible for the reduced rate of growth. Equally, the anti-sigma factor Rsd recently described for its role in controlling RNA polymerase activity in nutrient-poor growth media, seems also not involved according to mass-spec data. While other (unknown) pathways might still be involved in reducing the number of active RNA polymerases, the proposed hypothesis of the DNA substrate itself being limiting for the total rate of transcription is appealing.

      Finally, the authors confirm the reduction of growth in the distant Caulobacter crescentus, which lacks overlapping rounds of replication and could thus have shown a different dependency on DNA concentration.

      Weaknesses:

      There are a range of points that should be clarified or addressed, either by additional experiments/analyses or by explanations or clear disclaimers.

      First, the continuous reduction of growth rate upon arrest of DNA replication initiation observed in rich growth medium (M9gluCAA) is not equally observed in poor media. Instead, the relative growth rate is immediately/quickly reduced by about 10-20% and then maintained for long times, as if the arrest of replication initiation had an immediate effect but would then not lead to saturation of the DNA substrate. In particular, the long plateau of a constant relative growth rate in M9ala is difficult to reconcile with the model fit in Fig 4S2. Is it possible that DNA is not limiting in poor media (at least not for the cell sizes studied here) while replication arrest still elicits a reduction of growth rate in a different way? Might this have something to do with the naturally much higher oscillations of DNA concentration in minimal medium?

      We note that the total RNAP activity (abundance x active fraction) was also significantly reduced in poor media (Figure 3 -- supplement 4G and H) similarly to rich medium (Figure 3H). This is consistent with DNA being limiting. The main difference between rich and poor medium conditions is that the total ribosome activity in poor media (Figure 2 -- supplement 4G and H) was less affected in comparison to rich media (Figure 2H). Our interpretation of these results is that while DNA is limiting in all medium conditions (as shown by the RNAP data), changes in ribosome activity or mRNA degradation can compensate for the reduction in transcription in poor media and hence maintain better scaling of growth rates under DNA limitation. We understand how our current presentation made it confusing. We will reorganize the text and figures to better explain our results and interpretations. 

      The authors argue that DNA becomes limiting in the range of physiological cell sizes, in particular for M9glCAA (Fig. 1BC). It would be helpful to know by how much (fold-change) the DNA concentration is reduced below wild-type (or multi-N) levels at t=0 in Fig 1B and how DNA concentration decays with time or cell area, to get a sense by how many-fold DNA is essentially 'overexpressed/overprovided' in wild-type cells.

      We will provide an estimate.

      Fig. 2: The distribution of diffusion coefficients of RpsB is fit to Gaussians on the log scale. Is this based on a model or on previous work or simply an empirical fit to the data? An exact analytical model for the distribution of diffusion constants can be found in the tool anaDDA by Vink, ..., Hohlbein Biophys J 2020. Alternatively, distributions of displacements are expressed analytically in other tools (e.g., in SpotOn).

      We use an empirical fit of Gaussian mixture model (GMM) of three states to the data and extract the fractions of molecules in each state. This avoids making too many assumptions on the underlying processes, e.g. a Markovian system with Brownian diffusion. The model in anaDDA (Vink et al.) is currently limited to two-transitioning states with a maximal step number of 8 steps per track for a computationally efficient solution (longer tracks are truncated). Using a short subset of the trajectories is less accurate than using the entire trajectory and because of this, we consider full tracks with at least 9 displacements. Meanwhile, Spot-On supports a three-state model but it is still based on a semi-analytical model with a pre-calculated library of parameters created by fitting of simulated data. Neither of these models considers the effect of cell confinement, which plays a major role on single-molecule diffusion in small-sized cells such as bacteria. For these reasons, we opted to use an empirical fit to the data. We note that the fractions of active ribosomes in WT cells grown in different media, which we extracted from these diffusion measurements, are consistent with estimates obtained by others using similar or different approaches (Forchhammer and Lindhal 1971; Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014).

      The estimated fraction of active ribosomes in wild-type cells shows a very strong reduction with decreasing growth rate (down from 75% to 30%), twice as strong as measured in bulk experiments (Dai et al Nat Microbiology 2016; decrease from 90% to 60% for the same growth rate range) and probably incompatible with measurements of growth rate, ribosome concentrations, and almost constant translation elongation rate in this regime of growth rates. Might the different diffusive fractions of RpsB not represent active/inactive ribosomes? See also the problem of quantification above. The authors should explain and compare their results to previous work.

      We agree that our measured range is somewhat larger than the estimated range from Dai et al, 2016. However, they use different media, strains, and growth conditions. We also note that Dai et al did not make actual measurements of the active ribosome fraction. Instead, they calculate the “active ribosome equivalent” based on a model that includes growth rate, protein synthesis rate, RNA/protein abundance, and the total number of amino acids in all proteins in the cell. Importantly, our measurements show the same overall trend as Dai et al, 2016. Furthermore, our results are in quantitative agreements with previous experimental measurements that use ribosome profiling (Forchhammer and Lindhal 1971) or single-ribosome tracking (Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014), which, we believe, validates our approach. We will clarify this point in the revised manuscript.

      To measure the reduction of mRNA transcripts in the cell, the authors rely on the fluorescent dye SYTO RNAselect. They argue that 70% of the dye signal represents mRNA. The argument is based on the previously observed reduction of the total signal by 70% upon treatment with rifampicin, an RNA polymerase inhibitor (Bakshi et al 2014). The idea here is presumably that mRNA should undergo rapid degradation upon rif treatment while rRNA or tRNA are stable. However, work from Hamouche et al. RNA (2021) 27:946 demonstrates that rifampicin treatment also leads to a rapid degradation of rRNA. Furthermore, the timescale of fluorescent-signal decay in the paper by Bakshi et al. (half life about 10min) is not compatible with the previously reported rapid decay of mRNA (24min) but rather compatible with the slower, still somewhat rapid, decay of rRNA reported by Hamouche et al.. A bulk method to measure total mRNA as in the cited Balakrishnan et al. (Science 2022) would thus be a preferred method to quantify mRNA. Alternatively, the authors could also test whether the mass contribution of total RNA remains constant, which would suggest that rRNA decay does not contribute to signal loss. However, since rRNA dominates total RNA, this measurement requires high accuracy. The authors might thus tone down their conclusions on mRNA concentration changes while still highlighting the compelling data on RNAp diffusion.

      Thank you for bringing the Hamouche et al 2022 paper to our attention. We will address this point in the revised manuscript.

      The proteomics experiments are a great addition to the single-cell studies, and the correlations between distance from ori and protein abundance is compelling. However, I was missing a different test, the authors might have already done but not put in the manuscript: If DNA is indeed limiting the initiation of transcription, genes that are already highly transcribed in non-perturbed conditions might saturate fastest upon replication inhibition, while genes rarely transcribed should have no problem to accommodate additional RNA polymerases. One might thus want to test, whether the (unperturbed) transcription initiation rate is a predictor of changes in protein composition. This is just a suggestion the authors may also ignore, but since it is an easy analysis, I chose to mention it here.

      Thank you for the suggestion. We will provide the suggested analysis in the revised manuscript.

      Related to the proteomics, in l. 380 the authors write that the reduced expression close to the ori might reflect a gene-dosage compensatory mechanism. I don't understand this argument. Can the authors add a sentence to explain their hypothesis?

      We apologize for the confusion. This will be addressed in the revised manuscript.

      In Fig. 1E the authors show evidence that growth rate increases with cell length/area. While this is not a main point of the paper it might be cited by others in the future. There are two possible artifacts that could influence this experiment: a) segmentation: an overestimation of the physical length of the cell based on phase-contrast images (e.g., 200 nm would cause a 10% error in the relative rate of 2 um cells, but not of longer cells). b) time-dependent changes of growth rate, e.g., due to change from liquid to solid or other perturbations. To test for the latter, one could measure growth rate as a function of time, restricting the analysis to short or long cells, or measuring growth rate for short/long cells at selected time points. For the former, I recommend comparison of phasecontrast segmentation with FM4-64-stained cell boundaries.

      As the reviewer notes, the small increase in relative growth was just a minor observation that does not affect our story whether it is biologically meaningful or the result of a technical artefact. But we agree with the reviewer that others might cite it in future works and thus should be interpreted with caution.

      An artefact associated with time-dependent changes (e.g. changing from liquid cultures to more solid agarose pads) is unlikely for two reasons. 1. We show that varying the time that cells spend on agarose pads relative to liquid cultures does not affect the cell size-dependent growth rate results (Figure 1 -- supplement 5B). 2. We show that the growth rate is stable from the beginning of the time-lapse with no transient effects upon cell placement on agarose pads for imaging (Figure 1 -- supplement 5B). These results were described in the Methods section where they could easily be missed. We will revise the text to discuss these controls more prominently in the Results section.

      As for cell segmentation, we have run simulations and agree with the reviewer that a small overestimation of cell area (which is possible with any cell segmentation methods including ours) could lead to a small increase in relative growth with increasing cell areas. Since the finding is not important to our story, we will simply alert the readers to the possibility that the observation may be due to a small cell segmentation bias.

      Reviewer #2 (Public Review):

      In this work, the authors uncovered the effects of DNA dilution on E. coli, including a decrease in growth rate and a significant change in proteome composition. The authors demonstrated that the decline in growth rate is due to the reduction of active ribosomes and active RNA polymerases because of the limited DNA copy numbers. They further showed that the change in the DNA-tovolume ratio leads to concentration changes in almost 60% of proteins, and these changes mainly stem from the change in the mRNA levels.

      Thank you for the support and accurate summary!

      Reviewer #3 (Public Review):

      Summary:

      Mäkelä et al. here investigate genome concentration as a limiting factor on growth. Previous work has identified key roles for transcription (RNA polymerase) and translation (ribosomes) as limiting factors on growth, which enable an exponential increase in cell mass. While a potential limiting role of genome concentration under certain conditions has been explored theoretically, Mäkelä et al. here present direct evidence that when replication is inhibited, genome concentration emerges as a limiting factor.

      Strengths:

      A major strength of this paper is the diligent and compelling combination of experiment and modeling used to address this core question. The use of origin- and ftsZ-targeted CRISPRi is a very nice approach that enables dissection of the specific effects of limiting genome dosage in the context of a growing cytoplasm. While it might be expected that genome concentration eventually becomes a limiting factor, what is surprising and novel here is that this happens very rapidly, with growth transitioning even for cells within the normal length distribution for E. coli. Fundamentally, it demonstrates the fine balance of bacterial physiology, where the concentration of the genome itself (at least under rapid growth conditions) is no higher than it needs to be.

      Weaknesses:

      One limitation of the study is that genome concentration is largely treated as a single commodity. While this facilitates their modeling approach, one would expect that the growth phenotypes observed arise due to copy number limitation in a relatively small number of rate-limiting genes. The authors do report shifts in the composition of both the proteome and the transcriptome in response to replication inhibition, but while they report a positional effect of distance from the replication origin (reflecting loss of high-copy, origin-proximal genes), other factors shaping compositional shifts and their functional effects on growth are not extensively explored. This is particularly true for ribosomal RNA itself, which the authors assume to grow proportionately with protein. More generally, understanding which genes exert the greatest copy number-dependent influence on growth may aid both efforts to enhance (biotechnology) and inhibit (infection) bacterial growth.

      We agree but feel that identifying the specific limiting genes is beyond the scope of the study. However, to examine other potential contributing factors and identify limiting gene candidates, we plan to carry out new correlation analyses between our proteomic/transcriptomic datasets and published genome-wide datasets that report various variables under unperturbed conditions (e.g., mRNA/protein concentration, mRNA degradation rates, fitness cost, transcription/translation initiation rates, and essentiality).

      Overall, this study provides a fundamental contribution to bacterial physiology by illuminating the relationship between DNA, mRNA, and protein in determining growth rate. While coarse-grained, the work invites exciting questions about how the composition of major cellular components is fine-tuned to a cell's needs and which specific gene products mediate this connection. This work has implications not only for biotechnology, as the authors discuss, but potentially also for our understanding of how DNA-targeted antibiotics limit bacterial growth.

      Good point about the DNA-targeted antibiotics. Thank you!

    1. Author response:

      Public Reviews: 

      Reviewer #1 (Public Review): 

      As a reviewer for this manuscript, I recognize its significant contribution to understanding the immune response to saprophytic Leptospira exposure and its implications for leptospirosis prevention strategies. The study is well-conceived, addressing an innovative hypothesis with potentially high impact. However, to fully realize its contribution to the field, the manuscript would benefit greatly from a more detailed elucidation of immune mechanisms at play, including specific cytokine profiles, antigen specificity of the antibody responses, and long-term immunity. Additionally, expanding on the methodological details, such as immunophenotyping panels, qPCR normalization methods, and the rationale behind animal model choice, would enhance the manuscript's clarity and reproducibility. Implementing functional assays to characterize effector T-cell responses and possibly investigating the microbiota's role could offer novel insights into the protective immunity mechanisms. These revisions would not only bolster the current findings but also provide a more comprehensive understanding of the potential for saprophytic Leptospira exposure in leptospirosis vaccine development. Given these considerations, I believe that after substantial revisions, this manuscript could represent a valuable addition to the literature and potentially inform future research and vaccine strategy development in the field of infectious diseases. 

      We have been interested in understanding how both pathogenic and non-pathogenic Leptospira species affect each other on a mammalian reservoir host. With the current study we continue to elucidate the immune mechanisms engaged by pathogenic Leptospira interrogans versus non-pathogenic L. biflexa, as a follow up to our previous work (Shetty et al, 2021 PMID: 34249775, and Kundu et al 2022 PMID 35392072). We found that both species engaged partially overlapping myeloid immune cells and inflammatory signatures of infection. For example, some chemokines were increased, and macrophage and dendritic cells were engaged at 24h post inoculation with both species of Leptospira (PMID: 34249775). Thus, we questioned whether this robust innate immune response raised to eliminate an immunogenic but rather non-pathogenic bacterium, could also help restrain L. interrogans pathogenesis. In this study we show that L. biflexa pre-exposure to L. interrogans challenge mediates improved kidney homeostasis, mitigates leptospirosis severity and leads to increased shedding of L. interrogans in urine. This suggests an interspecies symbiotic commensalistic process that facilitates survival of the pathogenic species. These findings have high impact on the lives of millions of people in areas endemic for leptospirosis that are naturally exposed to non-pathogenic Leptospira species.

      We will expand on the methodological details and will update the introduction and discussion to include answers to questions raised by the three reviewers to further clarify the importance and impact of our study.

      Reviewer #2 (Public Review): 

      Summary: 

      The authors try to achieve a method of protection against pathogenic strains using saprophytic species. It is undeniable that the saprophytic species, despite not causing the disease, activates an immune response. However, based on these results, using the saprophytic species does not significantly impact the animal's infection by a virulent species. 

      We separate concepts of exposure to a non-virulent bacterium that establishes a brief infection with engagement of an immune response (L. biflexa), from infection established by a virulent species of Leptospira that leads to pathogenesis (L. interrogans). While trying to understand how both pathogenic and non-pathogenic Leptospira species affect each other on a mammalian reservoir host, we previously found that L. biflexa induces immune responses that should affect immunity of populations naturally exposed to this spirochete. Thus, we designed this study to answer that question.

      Strengths: 

      Exposure to the saprophytic strain before the virulent strain reduces animal weight loss, reduces tissue kidney damage, and increases cellular response in mice.

      Weaknesses: 

      Even after the challenge with the saprophyte strain, kidney colonization and the release of bacteria through urine continue. Moreover, the authors need to determine the impact on survival if the experiment ends on the 15th. 

      Another novel and unexpected aspect of our findings in the single exposure experiment was that L. biflexa pre-exposure mediated a homeostatic environment in the kidney (lower ColA1, healthier renal physiology) that restrained pathogenesis of L. interrogans after challenge, which resulted in better health outcomes and increased shedding of L. interrogans in urine; in contrast, if the kidney is compromised (high ColA1) by L. interrogans (without L. biflexa pre-exposure) there was lower shedding L. interrogans in urine. Interestingly, this suggests an interspecies symbiotic commensalistic process that facilitates survival of the pathogenic species. Thus, these data suggest that higher shedding of L. interrogans in urine may not be a hallmark of increased disease, but rather it could be the opposite.

      We will include these concepts in the updated discussion.

      We don’t think that extending this experiment to d21 or d28 would add relevant data to our findings. We provide survival curves for both experiments up to d15 post infection.

      Reviewer #3 (Public Review): 

      Summary: 

      Kundu et al. investigated the effects of pre-exposure to a non-pathogenic Leptospira strain in the prevention of severe disease following subsequent infection by a pathogenic strain. They utilized a single or double exposure method to the non-pathogen prior to challenge with a pathogenic strain. They found that prior exposure to a non-pathogen prevented many of the disease manifestations of the pathogen. Bacteria, however, were able to disseminate, colonize the kidneys, and be shed in the urine. This is an important foundational work to describe a novel method of vaccination against leptospirosis. Numerous studies have attempted to use recombinant proteins to vaccinate against leptospirosis, with limited success. The authors provide a new approach that takes advantage of the homology between a non-pathogen and a pathogen to provide heterologous protection. This will provide a new direction in which we can approach creating vaccines against this re-emerging disease. 

      Strengths: 

      The major strength of this paper is that it is one of the first studies utilizing a live non-pathogenic strain of Leptospira to immunize against severe disease associated with leptospirosis. They utilize two independent experiments (a single and double vaccination) to define this strategy. This represents a very interesting and novel approach to vaccine development. This is of clear importance to the field. 

      The authors use a variety of experiments to show the protection imparted by pre-exposure to the non-pathogen. They look at disease manifestations such as death and weight loss. They define the ability of Leptospira to disseminate and colonize the kidney. They show the effects infection has on kidney architecture and a marker of fibrosis. They also begin to define the immune response in both of these exposure methods. This provides evidence of the numerous advantages this vaccination strategy may have. Thus, this study provides an important foundation for future studies utilizing this method to protect against leptospirosis. 

      Weaknesses: 

      Although they provide some evidence of the utility of pretreatment with a non-pathogen, there are some areas in which the paper needs to be clarified and expanded. 

      The authors draw their conclusions based on the data presented. However, they state the graphs only represent one of two independent experiments. Each experiment utilized 3-4 mice per group. In order to be confident in the conclusions, a power analysis needs to be done to show that there is sufficient power with 3-4 mice per group. In addition, it would be important to show both experiments in one graph which would inherently increase the power by doubling the group size, while also providing evidence that this is a reproducible phenotype between experiments. Overall, this weakens the strength of the conclusions drawn and would require additional statistical analysis or additional replicates to provide confidence in these conclusions. 

      We will take these suggestions into consideration and will address as many of these issues as possible in the revised manuscript.

      A direct comparison between single and double exposure to the non-pathogen is not able to be determined. The ages of mice infected were different between the single (8 weeks) and double (10 weeks) exposure methods, thus the phenotypes associated with LIC infection are different at these two ages. The authors state that this is expected, but do not provide a reasoning for this drastic difference in phenotypes. It is therefore difficult to compare the two exposure methods, and thus determine if one approach provides advantages over the other. An experiment directly comparing the two exposure methods while infecting mice at the same age would be of great relevance to and strengthen this work. 

      Both experiments need to be analyzed as separate but complementary as they provide different hind sights into L. interrogans pathogenesis and potential solutions to the problem. Optimal measurements of disease progression (weight loss, survival curves) require infection of mice at 8 weeks. Based on this, a new L. biflexa double exposure experiment would have to start when mice are 4 weeks old which is just after weaning, and before the mouse immune system is fully developed.

    1. Author response:

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

      eLife assessment

      This is a valuable contribution to the electric fish community, and to studies of active sensing more generally, in that it provides evidence that a well-studied behavior (chirping) may serve in active sensing rather than communication. For the most part, the evidence is solid. In particular, the evidence showing increased chirping in more cluttered environments and the relationship between chirping and movement are convincing. Nevertheless, evidence to support the argument that chirps are mostly used for navigation rather than communication is incomplete.

      Thank you for the comment. In response to what seemed to be a generalized need for more evidence to support our hypothesis, we have extensively reviewed the manuscript, changed the existing figures and added new ones (3 new figures in the main text and 4 in the supplementary information section). Our edits include:

      (1) changes to the written text to remove categorical statements ruling out the possible communication function of chirps. When necessary, we have also added details on why we believe a social communication function of chirps could interfere with a role in electrolocation.

      (2) new experiments (and related figures) adding details on the behavioral correlates of chirping, on the effects of chirps on electric images (which are a way to represent current flow on the fish skin), and behavioral responses to ramp frequency playback EODs (used to test a continuous range of beat frequencies and fill the sampling gaps left by our experiments using real fish).

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors investigate the role of chirping in a species of weakly electric fish. They subject the fish to various scenarios and correlate the production of chirps with many different factors. They find major correlations between the background beat signals (continuously present during any social interactions) or some aspects of social and environmental conditions with the propensity to produce different types of chirps. By analyzing more specifically different aspects of these correlations they conclude that chirping patterns are related to navigation purposes and the need to localize the source of the beat signal (i.e. the location of the conspecific).

      We thank the Reviewer for the extensive feedback received. Hereby we respond to each of the points raised.

      We have better clarified that our intention is not to propose chirps as tools for “conspecific localization” intended as the pinpointing of its particular location. Instead, based on our observation of chirps being employed at very close ranges, we suggest that chirps may serve to assess other parameters related to “conspecific positioning” (which in a wide sense, it is still “electrolocation”), and that could be derived from the beat. These parameters might include size, relative orientation, or subtle changes in position during movement. While the experiments discussed in the manuscript do not provide a conclusive answer in this regard, we prioritize here the presentation of broader evidence for a different use of chirping. We are actively working on another manuscript that explores this aspect more in detail, but, due to space limitations, additional results had to be excluded.

      In the abstract we mention a role of chirps in the enhancement of “electrolocation”, but - as above mentioned - it is here meant only in a broad sense. In the introduction (at the very end) we propose chirps as self-directed signals (homeoactive sensing). In the result paragraph dedicated to the novel environment exploration experiment the following lines were added “Most chirps (90%) in fact are produced within a distance corresponding to 1% of the maximum field intensity (i.e. roughly 30 cm; Figure S12B), indicating that chirping occurs way below the threshold range for beat detection (i.e. roughly in the range of 60-120 cm, depending on the study; see appendix 1: Detecting beats at a distance) and likely does not represent a way to improve it”. We conclude this paragraph mentioning “This further corroborates the hypothesized role of chirps in beat processing.”. The last result paragraph (on chirping in cluttered environments) ends with “This supports the notion of chirps as self-referenced probing cues, potentially employed to optimize short-range aspects of conspecific electrolocation, such as conspecific size, orientation, and swimming direction - a hypothesis that will certainly be explored in future studies.”. In the discussion paragraph entitled “probing with chirps”, we do provide hints to possible mechanisms implied in the role of chirps in beat processing. As mentioned, we have planned to add further details in another manuscript, currently in preparation.

      The study provides a wealth of interesting observation of behavior and much of this data constitute a useful dataset to document the patterns of social interactions in these fish. Some data, in particular the high propensity to chirp in cluttered environments, raises interesting questions. Their main hypothesis is a useful addition to the debate on the function of these chirps and is worth being considered and explored further. However, the data they provide does not support strong conclusion statements arguing that these chirps are used for localization purposes and is even less convincing at rejecting previously established hypotheses on the communication purpose of the chirps.

      We intentionally framed our aims a bit provocatively to underscore that, to date, the role of chirps in social communication has been supported solely by correlative evidence. While the evidence we provide to support the role of chirps as probes is also correlative, it opens at the same time critical questions on the long assumed role of chirps in social communication. In fact, chirping is strongly dependent on fish reciprocal positioning, highly constrained by beat frequency, and patterned in such ways that - in our opinion - makes the existence of links between chirp types and internal states less likely, as suggested instead by the current view. Moreover, the use of different chirp types does not appear specific to any of the social contexts analyzed but is primarily explained by DF (beat frequency). This observation, coupled with the analysis of chirp transitions (more self-referenced than reflecting an actual exchange between subjects), leads us to hypothesize with greater confidence that chirp production may be more related to sensing the environment, rather than transmitting information about a specific behavioral state.

      Nevertheless, the Reviewer's comment is valid. We've tempered the study's conclusions by introducing the possibility of chirps serving both communication and electrolocation functions, as stated in the conclusion paragraph: "While our results do not completely dismiss the possibility of chirps serving a role in electrocommunication—probing cues could, for instance, function as proximity signals to signal presence, deter approaches, or coordinate behaviors like spawning (Henninger et al., 2018).". Nonetheless, we do emphasize that our hypothesis is more likely to apply - based on our data. We refrain from categorically excluding a communicative function for chirps (between subjects), but we hypothesize that this communication - if occurring - may contain the same type of information as the self-directed signaling implied by the “chirps as probes” idea (i.e. spatial information).

      In response to the Reviewer's feedback, we've revised the end of the introduction, removing suggestions of conclusiveness: "Finally, by recording fish in different conditions of electrical 'visibility,' we provide evidence supporting a previously neglected role of chirps: homeoactive sensing." (edit: the word “validating” has been removed to give a less “conclusive” answer to the open functional questions about chirping).

      I would suggest thoroughly revising the manuscript to provide a neutral description of the results and leaving any speculations and interpretations for the discussion where the authors should be careful to separate strongly supported hypotheses from more preliminary speculations. I detail below several instances where the argumentation and/or the analysis are flawed.

      Following to the reviewer’s comment, we have revised the manuscript to emphasize the following points: 1) the need for a revision of the current view on chirping, 2) our proposal of an alternative hypothesis based on correlations between chirping and behavior, which were previously unexplored, and 3) our acknowledgment that while we offer evidence supporting a probing role of chirps (e.g., lack of behavioral correlation, DF-dependency, stereotypy in repeated trials, modulation by clutter and distance), we do not present here conclusive evidence for chirps detecting specific details of conspecific positioning. Neither do we exclude categorically a role of chirps in social communication.

      They analyze chirp patterning and show that, most likely, a chirp by an individual is followed by a chirp in the same individual. They argue that it is rare that a chirp elicits a "response" in the other fish. Even if there are clearly stronger correlations between chirps in the same individual, they provide no statistical analysis that discards the existence of occasional "response" patterns. The fact that these are rare, and that the authors don't do an appropriate analysis of probabilities, leads to this unsupported conclusion.

      We employed cross-correlation indices, calculated and assessed with a 3 standard deviation symmetrical boundary (which is a statistically sound and strict criterion). Median values were utilized to depict trends in each group/pair. To support our findings, we added new experiments and new figures: 1) a correlation analysis between chirps and behaviors, providing more convincing evidence of how chirps are employed during "scanning" swimming activity (backward swimming); 2) a text mining approach to underscore chirp-behavior correlations, employing alternative and statistically more robust methods.

      One of the main pieces of evidence that chirps can be used to enhance conspecific localization is based on their "interference" measure. The measure is based on an analysis of "inter-peak-intervales". This in itself is a questionable choice. The nervous system encodes all parts of the stimulus, not just the peak, and disruption occurring at other phases of the beat might be as relevant. The interference will be mostly affected by the summed duration of intervals between peaks in the chirp AM. They do not explain why this varies with beat frequency. It is likely that the changes they see are simply an artifact of the simplistic measure. A clear demonstration that this measure is not adequate comes from the observation in Fig7E-H. They show that the interference value changes as the signal is weaker. This measure should be independent of the strength of the signal. The method is based on detecting peaks and quantifying the time between peaks. The only reason this measure could be affected by signal strength is if noisy recordings affect how the peak detection occurs. There is no way to argue that this phenomenon would happen the same way in the nervous system. Furthermore, they qualitatively argue that patterns of chirp production follow patterns of interference strength. No statistical demonstration is done. Even the qualitative appraisal is questionable. For example, they argue that there are relatively few chirps being produced for DFs of 60 or -60 Hz. But these are DF where they have only a very small sample size. The single pair of fish that they recorded at some of these frequencies might not have chirped by chance and a rigorous statistical analysis is necessary. Similarly, in Fig 5C they argue that the position of the chirps fall on areas of the graph where the interferences are strongest (darker blue) but this is far from obvious and, again, not proven.

      We would like to clarify that the estimation of the effects of chirps on the beat (referred to as “beat interference”) was not intended to serve as the primary evidence supporting a different use of chirping. In fact, all the experiments conducted prior to that calculation already provide substantial evidence supporting the hypothesis we have proposed. In an attempt to address the Reviewer’s concern and to avoid misleading interpretations, we moved this part now to the Supplementary Information (see now Figures S8 and S9), in agreement with the non crucial relevance of this approach. We also added the following statement to the result paragraph entitled “Chirps significantly interfere with the beat and enhance electric image contrast”: “Obviously, measuring chirp-triggered beat interferences by using an elementary outlier detection algorithm on the distribution of beat cycles does not reflect any physiological process carried out by the electrosensory system and can be therefore used only as an oversimplified estimate.”.

      Regarding the meaning of “beat interference” (as here estimated) from a perspective of brain physiology: chirp interference was calculated using the beat cycles as a reference. Beat peaks were used only to estimate beat cycle duration. Regardless of whether or not a beat peak is represented in the brain, beat cycle duration (estimated using the peaks) is the main determinant of p-unit rhythmic response to a beat. Regarding the effect of signal amplitude, this is also not very relevant. It is obvious that a chirp creates more - or less - interference based on the chirp FM and its duration (but also the sign of the DF and the magnitude of the amplitude modulation). If electroreceptor responses are entrained in waves of beat AMs and if “interference” is a measure of how such waves are scrambled, then “interference” is a measure of how chirps scramble waves of electroreceptor activity by affecting beat AMs.

      The reason why the interference fades with the signal (previous figure 7, now Figure S12) is because it is weighted on the signal strength (the signals used as carrier for chirps are recalculated based on real measurements of signal strength at different distances). Nonetheless, the Reviewer is right: mathematically speaking interference would not change at all because it is just the result of an outlier detection algorithm. This outlier detection is actually set to have a 1% threshold (percent of beat contrast).

      Regarding the comparison “chirps vs interference”, we did not make a statistical analysis because we wanted to just show a qualitative observation. Similar results can be obtained for slightly shorter or longer time windows, within certain limits of course (see added Figure S9, in the Supplementary Information). We hope that moving this analysis to the supplementary information makes it clear that this approach is not central to make our point.

      The Reviewer’s point on the DF sampling is correct, we have reconsidered the low chirping at 60Hz as potentially the result of sampling bias and edited the respective result paragraph.

      They relate the angle at which one fish produces chirps relative to the orientation of the mesh enclosing. They argue that this is related to the orientation of electric field lines by doing a qualitative comparison with a simplified estimate of field lines. To be convincing this analysis should include a quantitative comparison using the exact same body position of the two fish when the chirps are emitted.

      We agree with the Reviewer, this type of experiment would be much better suited to illustrate the correlations between chirping and reciprocal positioning in fish. What we can see is that chirping occurs at certain orientations more often than others. This could have something to do with either field geometry or with locomotion in the particular test environment we have used. As mentioned earlier, we are currently editing a second manuscript which will include the type of analysis/experiment the Reviewer is thinking of. We preferred to focus in this first study on the broader behavioral correlates of chirping. We removed the mention to the field current lines because - we agree - the argument is vague as presented here.

      They show that the very vast majority of chirps in Fig 6 occur when the fish are within a few centimeters (e.g. very large first bin in Fig6E-Type2). This is a situation when the other fish signal will be strongest and localization will be the easiest. It is hard to understand why the fish would need a mechanism to enhance localization in these conditions (this is the opposite of difficult conditions e.g. the "cluttered" environment).

      Agreed, in fact we do not explicitly propose chirps as means to improve “electrolocation” (this word is used only broadly in the abstract) but instead as probes to extract spatial information (e.g. shape, motion, orientation) from a beat source. In a broader sense, all these spatial parameters contribute to any given instance of "localization." Because we were unable to explore all these aspects in greater detail, we chose to maintain a broader perspective. If chirps contribute to a better resolution of fine spatial attributes of conspecific locations, it is reasonable to expect higher chirping rates in proximity to the target fish.

      The argumentation aimed at rejecting the well-established role of chirp in communication is weak at best. First, they ignored some existing data when they argue that there is no correlation between chirping and behavioral interactions. Particularly, Hupe and Lewis (2008) showed a clear temporal correlation between chirps and a decrease in bites during aggressive encounters. It could be argued that this is "causal evidence" (to reuse their wording) that chirps cause a decrease in attacks by the receiver fish (see Fig 8B of the Hupe paper and associated significant statistics). Also, Oboti et al. argue that social interactions involve "higher levels of locomotion" which would explain the use of chirps since they are used to localize. But chirps are frequent in "chirp chamber" paradigms where no movement is involved. They also point out that social context covaries with beat frequency and thus that it is hard to distinguish which one is linked to chirping propensity and then say that it is hard to disentangle this from "biophysical features of EOD fields affecting detection and localization of conspecific fish". But they don't provide any proof that beat frequency affects detection and localization so their argument is not clear. Last, they argue that tests in one species shouldn't be extrapolated to other species. But many of the studies arguing for the role of chirps in communication was done on brown ghost. In conclusion of this point, they do not provide any strong argument that rejects the role of chirps as a communication signal. A perspective that would be better supported by their data and consistent with past research would be to argue that, in addition to a role in communication, chirps could sometimes be used to help localize conspecifics.

      We did not intend to disregard the extensive body of literature supporting a role of chirps in social communication. Rather, the primary goal of this study was to present a valid alternative perspective to this prevailing view. The existence of a well-established hypothesis does not imply that new evidence cannot change it; it simply indicates that changing it may be challenging either because it's genuinely difficult or because the idea has not been thoroughly explored. Whatever the case may be, proposing new hypotheses, whether complementary or alternative to established theories, is a challenging undertaking for a single study. We judged that starting from broad correlations would be the most desirable approach.

      We did not ignore data from Hupé and Lewis 2008. We cited this study repeatedly and compared their findings to those of others, not only for the correlation chirp-behaviors but also for chirping distance considerations. However, following the Reviewer’s comment, we now cite this study in the context of the behavioral analysis recently added (data from the PSTH plots could possibly confirm the observation of lower chirps during attacks). We also cited the study by Triefenbach and Zakon 2008, which reports something along the same lines. See the statement: “Overall, these results provided mutually reinforcing evidence indicating that chirps are produced more often during locomotion or scanning-related motor activity and confirm previous reports of a lower occurrence of chirping during more direct aggressive contact (as shown also by Triefenbach and Zakon, 2008; Hupé and Lewis, 2008).”, in the result paragraph related to the behavioral correlates of chirping.

      In our study we make it clear how we distinguish causal evidence (i.e. providing evidence that A is required for B) from correlation (i.e. evidence for A simply occurring together with B). We also make it clear that we are not going to provide causal evidence but we are going to provide new evidence for correlations that were so far not considered, in order to propose a new unexplored function of chirps.

      The Reviewer's point on chirping during motion and while caged in a chirp chamber is valid. Indeed at first we were also puzzled by this finding. However, under the “chirps as probes” paradigm, chirping in a chirp-chamber can be explained by the need to obtain spatial information from an otherwise unreachable beat source (brown ghosts are typically exploring new environmental objects or conspecifics by actively swimming around them - something caged fish can’t do). So, eventually the observation of chirping under conditions of limited movement (such as in a chirp chamber experiment) is not in contradiction with our hypothesis, rather it can be used to support it. Further experiments are required - as rightfully pointed out - to evaluate the effects of beat frequency on beat detection. We added a note about this in the “probing with chirps” discussion paragraph.

      The Reviewer's comment regarding generalization is unclear. We acknowledge that most studies are conducted in brown ghosts, as stated in the abstract. Our intention was to highlight that insights gained from this species have been applied to broaden the understanding of chirps in other species. Specifically, the "behavioral meaning idea" of chirping has been extended to other gymnotiform species producing EOD frequency modulations .

      Our study's aim is not to dismiss the idea of chirps being used for communication but to present an alternative hypothesis and to provide supporting evidence. While our results may not align well with the communication theory, our intention is not dismissal but rather engaging in a discussion and exploration of alternative perspectives.

      The discussion they provide on the possible mechanism by which chirps could help with localization of the conspecific is problematic. They imply that chirps cause a stronger response in the receptors. For most chirps considered here, this is not true. For a large portion of the beat frequencies shown in this paper, chirps will cause a de-synchronization of the receptors with no increase in firing rate. They cannot argue that this represents an enhanced response. They also discuss a role for having a broader frequency spectrum -during the chirp- in localization by making a parallel with pulse fish. There is no evidence that a similar mechanism could even work in wave-type fish.

      We have already commented on the “localization” idea in our previous responses. The Reviewer is right in saying that we have provided only vague descriptions of the potential mechanisms implied by our hypothesis. The studies by Benda and others (2005, 2006) demonstrate a clear synchronizing effect of chirps on p-unit firing rates, especially at low DFs (at ranges similar to those considered in this study). This synchronization could lead to an enhanced response at the electroreceptor level, as described in these very studies, which in turn would result in a higher probability of firing in downstream neurons (E-cells in the ELL).

      As also reported within the same works, chirps may also exert an opposite effect on p-units (i.e. desynchronization). This is what happens for large chirps at high DFs. Desynchronization may cause temporary lapses of p-unit firing, which in turn may lead to increased activity of I-cells in the ELL (which are indeed specifically tuned to p-unit lack of activity).

      So, in general, if we consider both ON and OFF pyramidal cells (in the ELL) and small and large chirps, we could state that chirps can be potentially used to enhance the activity of peripheral electrosensory circuits through different mechanisms, contingent on the chirp type and beat frequency. Unfortunately, space constraints limited our ability to dig into these details in the present study.

      However, to address the Reviewer’s rightful point, we now mention this in the manuscript: Since the beat AMs generated by the chirps always trigger reliable responses in primary electrosensory circuits (pyramidal cells in the ELL respond to both increases and decreases in beat AM), any chirp-triggered AM causing a sudden change in p-unit firing could potentially amplify the downstream signal (Marsat and Maler, 2010) and thus enhance EI contrast.” (see result paragraph on beat interference and electric images).

      They write the whole paper as if males and females had been identified in their experiments. Although EOD frequency can provide some guess of the sex the method is unreliable. We can expect a non-negligible percentage of error in assigning sex.

      We agree and in fact, in the method section we state:

      “The limitation of this approach is that females cannot be distinguished from immature males with absolute certainty, since no post-mortem gonadal inspection was carried out.”

      to this we added:

      “Although a more accurate way to determine the sex of brown ghosts would be to consider other morphological features such as the shape of the snout, the body size, the occurrence of developing eggs, EOD frequency has been extensively used for this purpose.”

      Moreover, the consistent behavioral differences observed in low frequency fish, measured with those behavioral experiments aimed at assessing responses to playback stimuli and swimming behavior in novel environments, could also be caused by a younger age (as opposed to femaleness). However, the size ranges of our fish (an admittedly unreliable proxy of age) were all comparable, making this possibility perhaps less likely.

      Reviewer #2 (Public Review):

      Studying the weakly electric brown ghost knifefish, the authors provide evidence that 'chirps' (brief modulations in the frequency and amplitude of the ongoing electric signal) function in active sensing (specifically homeoactive sensing) rather than communication. This is a behavior that has been very well studied, including numerous studies on the sensory coding of chirps and the neural mechanisms for chirp generation. Chirps are largely thought to function in communication behavior, so this alternative function is a very exciting possibility that could have a great impact on the field. The authors do provide convincing evidence that chirps may function in homeoactive sensing. However, their evidence arguing against a role for chirps in communication is not as strong, and neglects a large body of research. Ultimately, the manuscript has great potential but suffers from framing these two possibilities as mutually exclusive and dismissing evidence in favor of a communicative function.

      We thank the Reviewer for the comment. Overall, we have edited the manuscript to soften our conclusions and avoid any strong categorical statement excluding the widely accepted role of chirps in social communication. We have added some new experiments with the aim to add more detail to the behavioral correlates of chirping and to the DF dependency of the production of different types of chirps. Nonetheless, based on our results, we are prone to conclude that the communication idea - although widely accepted - is not as well substantiated as it should be.

      Although we do not dismiss the bulk of literature supporting a role of chirps in social communication, we think that our hypothesis (i.e. decoding of spatial parameters from the beat) may be not fully compatible with the social communication hypothesis for the following reasons:

      (1) Chirp type dependency on DF makes chirps likely to be adaptive responses to beat frequency. While this idea is compatible with a role of chirps in the detection of beat parameters, their concurrent role in social communication would imply that chirps interacting at given beat frequencies (DFs) would communicate only (or mainly) by delivering a very limited range of “messages”. For instance, assuming type 2 chirps are related to aggression (as widely suggested), are female-male pairs - with larger DFs - interacting less aggressively than same sex pairs? Our experiments often suggested this is not the case. In addition, large DFs are not always indicative of opposite sex interactions, while they are very often characterized by the emission of large chirps. Not to mention that, despite the fact that opposite sex interactions in absence of breeding-like conditions, cannot be considered truly courtship-related, large chirps are often considered courtship signals, regardless of the reproductive state of the emitting fish.

      (2) Chirping is highly affected by locomotion (consider female/male pairs with or without mesh divider) and distance (as shown in the novel environment exploration experiment). While the involvement of both parameters is compatible with a role of chirps in active sensing, a role of chirps in social communication implies that such signaling would occur only when fish are in very close proximity to each other. In this case, the beat is therefore heavily distorted not only by fish position/locomotion but also by chirps. Which means that when fish are close to each other, the 2 different types of information relayed by the beat (electrolocation and electrocommunication) would certainly interfere (this idea has been better phrased in the Introduction paragraph).

      (3) In our playback experiments we could not see any meaningful matching (e.g. angry-chirp → angry-chirp or sexy-chirp → approach) between playback chirps and evoked chirps, raising doubts on the meaning associated so far with the different types. Considering that playback experiments are typically used to assess signal meaning based on how animals respond to them, this result is suggesting quite strongly that such meaning cannot be assigned to chirps.

      (4) In playback experiments in which the same stimulus is provided multiple times, chirp type transitions (i.e. emission of a different chirp type after a given chirp) become predictable (as shown in the added playback experiments using ramping signals). This confirms that the choice to emit a given chirp type has something to do with beat frequency (or a change in this parameter) and not a communication of internal states. It would be otherwise unclear how a fish could change its internal state so quickly - and so reliably - even in the span of a few seconds.

      Despite this evidence against a semantic content of chirps in the context of social communication, we conclude the manuscript reminding that we are not providing strong evidence dismissing the communication hypothesis, and that both could coexist (see the example of “proximity signals” in the mating context given in the concluding paragraph).

      (1) The specific underlying question of this study is not made clear in the abstract or introduction. It becomes apparent in reading through the manuscript that the authors seek to test the hypothesis that chirps function in active sensing (specifically homeoactive sensing). This should be made explicitly clear in both the abstract and introduction, along with the rationale for this hypothesis.

      In the abstract we state “Despite the success of this model in neuroethology over the past seven decades, the underlying logic of their electric communication remains unclear. This study re-evaluates this view, aiming to offer an alternative, and possibly complementary, explanation for why these freshwater bottom dwellers emit electric chirps.”. This statement is meant as a summary of our aims. However, in order to convey a clearer message, we have revised the whole manuscript to more explicitly articulate our objectives. In particular we stress that with our experiments we intend to provide correlative evidence for a different role of chirps (previously unexplored) with the idea to stimulate a discussion and possibly a revision of the current theory about the functional role of chirps.

      In the introduction we have added a paragraph explaining our aim and also why we think that communicating through chirps could potentially interfere with efficient electrolocation: “Since both chirps and positional parameters (such as size, orientation or motion) can only be detected as perturbations of the beat (Petzold et al., 2016; Yu et al., 2012; Fotowat et al., 2013), and via the same electroreceptors, the inputs relaying both types of information are inevitably interfering. Moreover, as the majority of chirps are produced within a short range (< 50 cm; Zupanc et al., 2006; Hupé and Lewis 2008; Henninger et al., 2018; see appendix 1) this interference is likely to occur consistently during social interactions.

      Under the communication-hypothesis, the assumption that chirps and beats are conveying different types of information (i.e. semantic value as opposed to position and related geometrical parameters) is therefore leaving this issue unresolved.”.

      (2) My biggest issue with this manuscript is that it is much too strong in dismissing evidence that chirping correlates with context. This is captured in this sentence in the introduction, "We first show that the choice of different chirp types does not significantly correlate with any particular behavioral or social context." This very strong conclusion comes up repeatedly, and I disagree with it, for the following reasons:

      In your behavioral observations, you found sex differences in chirping as well as differences between freely interacting and physically separated fish. Your model of chirp variability found that environmental experience, social experience, and beat frequency (DF) are the most important factors explaining chirp variability. Are these not all considered "behavioral or social context"? Beat frequency (DF) in particular is heavily downplayed as being a part of "context" but it is a crucial part of the context, as it provides information about the identity of the fish you're interacting with.

      In your playback experiments, fish responded differently to small vs. large DFs, males chirped more than females, type 2 chirps became more frequent throughout a playback, and rises tended to occur at the end of a playback. These are all examples of context-dependent behavior.

      We agree with the Reviewer’s comment and we think that probably we have been unclear in what the meaning of that statement was. We also agree with the Reviewer about what is defined as “context”, and that a given beat frequency (DF) can in the end represent a “behavioral context” as well. In order to make it clearer, we have rephrased this statement and changed it to: “We first show that the relative number of different chirp types in a given recording does not significantly correlate with any particular behavioral or social context.”. This new form refers specifically to the observation that - in all different social conditions examined - the relative amounts of different types of chirps is unchanged (see Figure S2). We thought the Reviewer maybe interpreted our statement as if we suggested that chirp type choice is random or unaffected by any social variable. We agree with the Reviewer that this is not the case. We also reported that sex differences in chirping are present, but we have emphasized they may have something to do with the propensity of the brown ghosts of either sex to swim/explore as opposed to seek refuge and wait (as suggested by our experiments in which FM pairs were either divided or freely interacting and our novel environment exploration experiments).

      We agree DF is important, in fact it is the 3rd most important factor explaining chirp variance in our model. In our fish pair recordings, we see a strong correlation of chirp total variance with tank experience (one naïve, one experienced, both fish equally experienced) and social context (novel to each other/familiar to each other, subordinate/dominant, breeding/non breeding, accessible/not accessible) although data clustering seems to better distinguish “divided” vs “freely moving” conditions (and sex may also play a role as well because of the reversal of sexual dimorphism in chirp rates in precisely this case) more than other variables. However, we do not see a specific effect of these variables on the proportion of different types of chirps in any recording (see Figure S2).

      We also edited the beginning of the first result paragraph and changed it to “Thus, if behavioral meaning can be attributed to different types of chirps, as posed by the prevailing view (e.g., Hagedorn and Heiligenberg, 1985; Larimer and MacDonald, 1968; Rose, 2004), one should be able to identify clear correlations between behavioral contexts characterizing different internal states and the relative amounts of different types of chirp”, to emphasize we are here assessing the meaning of different types of chirps (not of the total amount of chirping in general).

      Further, you only considered the identity of interacting fish or stimulated fish, not their behavior during the interaction or during playback. Such an analysis is likely beyond the scope of this study, but several other studies have shown correlations between social behavior and chirping. In the absence of such data here, it is too strong to claim that chirping is unrelated to context.

      We agree with the Reviewer, in fact this analysis was previously carried out but purposely left out in an attempt to limit the manuscript length. We have now made space for this experimental work which is now added (see the new Figure 6).

      In summary, it is simply too strong to say that chirping does not correlate with context. Importantly, however, this does not detract from your hypothesis that chirping functions in homeoactive sensing. A given EOD behavior could serve both communication and homeoactive sensing. I actually suspect that this is quite common in electric fish. The two are not mutually exclusive, and there is no reason for you to present them as such. I recommend focusing more on the positive evidence for a homeoactive function and less on the negative evidence against a communication function.

      We aimed to clarify that our reference was to the lack of correlation between "chirp type relative numbers" and the analyzed context. Regarding the communication function, we tempered negative statements. However, as this study stems from evidence within the established paradigm of "chirps as communication signals", and aims at proposing an alternative hypothesis, eliminating all references to it could undermine the study's purpose.

      (3) The results were generally challenging to follow. In the first 4 sections, it is not made clear what the specific question is, what the approach to addressing that question is, and what specific experiment was carried out (the last two sections of the results were much clearer). The independent variables (contexts) are not clearly established before presenting the results. Instead they are often mentioned in passing when describing the results. They come across as an unbalanced hodgepodge of multiple factors, and it is not made clear why they were chosen. This makes it challenging to understand why you did what you did, the results, and their implications. For each set of major results, I recommend: First, pose a clear question. Then, describe the general approach to answering that question. Next, describe the specifics of the experimental design, with a rationale that appeals to the general approach described. Finally, describe the specific results.

      The introductory sentences of the first result paragraphs have been edited, rendering the aim of the experiments more explicit.

      (4) Results: "We thus predicted that, if behavioral meaning can be attributed to different types of chirps, as posed by the prevailing view (e.g., Hagedorn and Heiligenberg, 1985; Larimer and MacDonald, 1968; Rose, 2004)..." It should be made clear why this is the prevailing view, and this description should likely be moved to the introduction. There is a large body of evidence supporting this view and it is important to be complete in describing it, especially since the authors seem to seek to refute it.

      We understand the Reviewer’s question and we tried to express in the introduction the main reasons for why this is the current view. We state “Different types of chirps are thought to carry different semantic content based on their occurrence during either affiliative or agonistic encounters (Larimer and MacDonald 1968; Bullock 1969; Hopkins 1974; Hagedorn and Heiligenberg 1985; Zupanc and Maler 1993; Engler et al. 2000; Engler and Zupanc 2001; Bastian et al., 2001).”. To this we added: “Although supported mainly by correlative evidence, this idea gained popularity because it is intuitive and because it matches well enough with the numerous behavioral observations of interacting brown ghosts.”.

      We believe the prevailing view is based on intuition and a series of basic observed correlations repeated throughout the years. The crystallization of this idea is not due to negligence but mainly to technical limitations existing at the time of the first recordings. In order to assess the role of chirps in behaving fish a tight and precise temporal control over synched video-EOD recordings is most likely necessary, and this is a technical feature probably available only much later than the 50-60ies, when electric communication was first described.

      (5) I am not convinced of the conclusion drawn by the analysis of chirp transitions. The transition matrices show plenty of 1-2 and 2-1 transitions occurring. Further, the cross-correlation analysis only shows that chirp timing between individuals is not phase-locked at these small timescales. It is entirely possible that chirp rates are correlated between interacting individuals, even if their precise timing is not.

      We agree with the Reviewer: chirp repertoires recorded in different social contexts are not devoid of reciprocal chirp transitions (i.e. fish 1 chirp - to - fish 2 chirp, or vice versa). Yet our point is to emphasize that their abundance is way more limited when compared to the self-referenced ones (i.e. 1-1 and 2-2). This is a fair concern and in order to further address this point, we have added a whole new set of analyses and new experiments (see chirp-behavior correlations, PSTHs and more analysis based on more solid statistical methods; see Figure 6).

      Reviewer #3 (Public Review):

      Summary:

      This important paper provides the best-to-date characterization of chirping in weakly electric fish using a large number of variables. These include environment (free vs divided fish, with or without clutter), breeding state, gender, intruder vs resident, social status, locomotion state and social and environmental experience, as well as with playback experiments. It applies state-of-the-art methods for reducing dimensionality and finding patterns of correlation between different kinds of variables (factor analysis, K-means). The exceptional strength of the evidence, collated from a large number of trials with many controls, leads to the conclusion that a number of commonly accepted truths about which variable affects chirping must be carefully rewritten or nuanced. Based on their extensive analyses, the authors suggest that chirps are mainly used as probes that help detect beats and objects.

      Strengths:

      The work is based on completely novel recordings using interaction chambers. The amount of new data and associated analyses is simply staggering, and yet, well organized in presentation. The study further evaluates the electric field strength around a fish (via modelling with the boundary element method) and how its decay parallels the chirp rate, thereby relating the above variables to electric field geometry.

      The main conclusions are that the lack of any significant behavioural correlates for chirping, and the lack of temporal patterning in chirp time series, cast doubt on a communication goal for most chirps. Rather, the key determinants of chirping are the difference frequency between two interacting conspecifics as well as individual subjects' environmental and social experience. These conclusions by themselves will be hugely useful to the field. They will also allow scientists working on other "communication" systems to at least reconsider, and perhaps expand the precise goal of the probes used in those senses. There are a lot of data summarized in this paper, and thorough referencing to past work. For example, the paper concludes that there is a lack of evidence for stereotyped temporal patterning of chirp time series, as well as of sender-received chirp transitions beyond the known increase in chirp frequency during an interaction.

      The alternative hypotheses that arise from the work are that chirps are mainly used as environmental probes for better beat detection and processing and object localization.

      The authors also advance the interesting idea that the sinusoidal frequency modulations caused by chirps are the electric fish's solution to the minute (and undetectable by neural wetware) echo-delays available to it, due to the propagation of electric fields at the speed of light in water.

      Weaknesses:

      My main criticism is that the alternative putative role for chirps as probe signals that optimize beat detection could be better developed. The paper could be clearer as to what that means precisely.

      We appreciate the Reviewer's kind comments. While we acknowledge that our exploration of chirp function in this study may be limited and not entirely satisfying, we made this decision due to space constraints, opting for a broader and diversified approach. We hope that future studies will build on these data and start filling the gaps. We are also working on another manuscript which is addressing this point more in detail.

      Nonetheless, we considered the Reviewer’s criticism and added not only a new figure (to show more explicitly what chirps can do to the perceived electric fields, as simulated by electric images) but also more descriptive parts explaining how we think chirps may act to improve the spatial resolution of beat processing (see the discussion paragraph “probing with chirps”). In this paragraph we rendered more clearly how chirps could improve beat processing by phase shifting EODs and recovering eventual blind-spots on the fish skin caused by disruptive EOD interferences (resulting in lower beat contrast). We also mention that enhancement of electrosensory input triggered by chirps, could be localized not only at the level of electroreceptors (consider the synchronizing effects small chirps have on p-units at low frequency beats) but also at the level of ON and OFF pyramidal cells in the ELL. Looked at from the perspective of these neurons, any chirp would enhance the activity of these input lines, yet in opposite ways.

      And there is an egg-and-chicken type issue as well, namely, that one needs a beat in order to "chirp" the beating pattern, but then how does chirping optimize the detection of the said beat? Perhaps the authors mean (as they wrote elsewhere in the paper) that the chirps could enhance electrosensory responses to the beat.

      According to the Reviewer’s comment, we have now revised several instances of the misleading phrasing identified.

      In the results on novel environment exploration: “If chirps enhance beat processing, for instance, chirping should occur within beat detection range but at a certain distance.”.

      “This, in turn, could be used to validate our beat-interference estimates as meaningfully related to beat processing.” and “In all this, rises may represent an exception as their locations are spread over larger distances and even in presence of obstacles potentially occluding the beat source (such as shelters, plants, or walls), all of which are conditions in which beat detection or beat processing could be more difficult (this, could be coherent with the production of rises right at the end of EOD playbacks; Figure S5).”

      Last result paragraph (clutter experiment): “Overall, these results indicate that chirping is significantly affected by the presence of environmental clutter partially disrupting - or simply obstructing - the processing of beat related information during locomotion”.

      In the probing with chirps discussion paragraph “In theory, chirps could also be used to improve electrolocation of objects as well (as opposed to the processing of the beat).”.

      In the conclusions: “optimizing the otherwise passive responses to the beat”.

      A second criticism is that the study links the beat detection to underwater object localization. I did not see a sufficiently developed argument in this direction, nor how the data provided support for this argument. It is certainly possible that the image on the fish's body of an object in the environment will be slightly modified by introducing a chirp on the waveform, as this may enhance certain heterogeneities of the object in relation to its environment. The thrust of this argument seems to derive more from the notion of Fourier analysis with pulse type fish (and radar theory more generally) that the higher temporal frequencies in the beat waveform induced by the chirp will enable a better spatial resolution of objects. It remains to be seen whether this is significant.

      The Reviewer is correct in noting that this point is not addressed in the manuscript. We introduced it as a speculative discussion point to mention alternative possibilities. These could be subject to further testing in future studies.

      I would also have liked to see a proposal for new experiments that could test these possible new roles.

      We have added clearer suggestions for future experiments throughout the discussion: these may be aimed at 1) improving playback experiments using more realistic copies of the brown ghost’s EODs (including harmonics), 2) assess fish reciprocal positioning during chirping in better detail and 3) test the use of chirping during target-reaching tasks in order to better assess the probing function of chirps.

      The authors should recall for the readers the gist of Bastian's 2001 argument that the chirp "can adjust the beat frequency to levels that are better detectable" in the light of their current. Further, at the beginning of the "Probing with chirps" section, the 3rd way in which chirps could improve conspecific localization mentions the phase-shifting of the EOD. The authors should clarify whether they mean that the tuberous receptors and associated ELL/toral circuitry could deal with that cue, or that the T_unit pathway would be needed?

      We thank the Reviewer for identifying this unclear point. We added reference to the p-units “Yet, this does not exclude the possibility that chirps could be used to briefly shift the EOD phase in order to avoid disruptive interferences caused by phase opposition (at the level of p-units)” in the above mentioned paragraph. We would prefer to omit a more detailed reference to t-units in order to avoid lengthy descriptions required to discuss the different electroreceptor types.

      On p.17 I don't understand what is meant by most chirps being produced, possibly aligned with the field lines, since field lines are everywhere. And what is one to conclude from the comparison of Fig.6D and 7A? Likewise it was not clear what is meant by chirps having a detectable effect on randomly generated beats.

      We agree on the valid point raised by the Reviewer and we have removed reference to current lines from the text.

      In the section on Inconsistencies between behaviour and hypothesized signal meaning, the authors could perhaps nuance the interpretation of the results further in the context of the unrealistic copy of natural stimuli using EOD mimics. In particular, Kelly et al. 2008 argued that electrode placement mattered in terms of representation of a mimic fish onto the body of a real fish, and thus, if I properly understand the set up here, the movement would cause the mimic to vary in quality. This may nevertheless be a small confounding issue.

      We agree with the Reviewer and added a comment at the beginning of the paragraph mentioned. “Nonetheless, it's plausible that playback stimuli, as employed in our study and others, may not faithfully replicate natural signals, thus potentially influencing the reliability of the observed behaviors. Future studies might consider replicating these findings using either natural signals or improved mimics, which could include harmonic components (excluded in this study).”

      Recommendations for the authors:

      8Reviewer #2 (Recommendations For The Authors):*

      (1) Abstract: "...is probably the most intensely studied species..." is a weak, unsupported, and unnecessary statement. Just state that it has been heavily studied, or is one of the most well-studied,...

      rephrased

      (2) Abstract: "...are thus used as references to specific internal states during recordings - of either the brain or the electric organ..." This was not clear to me.

      rephrased

      (3) Abstract: "...the logic underlying this electric communication..." It is not clear to me what the authors mean here by "logic".

      rephrased

      (4) I strongly recommend clearly defining homeoactive sensing and distinguishing it from allocative sensing when this term is first introduced in the introduction. This is not a commonly used term. Most readers likely think they understand what is meant by the term active sensing, however I recommend first defining it, and then distinguishing amongst these two different types of active sensing.

      rephrased

      (5) Introduction: "Together with a few other species (Rose, 2004),..." More than a few. There are hundreds of species with electric organs. It is certainly not a "unique" capability.

      rephrased

      (6) Introduction: "But the real advantage of active electrolocation can be appreciated in the context of social interaction." This is unclear. Why is this the "real advantage" of active electrolocation when an electrically silent fish could detect an electrically communicating fish just fine without interference? Active electrolocation is needed to detect objects that are not actively emitting an electric field. It is not needed to detect signaling individuals.

      rephrased

      (7) Introduction: why is active sensing using EODs limited to distances of 6-12 cm? Why does it not work at closer range?

      Here we meant to give a range based on published data. We rephrased it to “up to 12”.

      (8) Introduction: electric fields decay with the cubed of distance, as you show in appendix 1.

      rephrased

      (9) Introduction: it is not clear what is meant by "blurred EOD amplitude".

      rephrased (“noisy”)

      (10) Figure 2C is very challenging to interpret. I recommend spending more time in the manuscript walking the reader through this analysis and its presentation.

      We are grateful for the comment as we probably overlooked this point. We now added a small paragraph to explain these data in better detail.

      (11) Results: "This was done by calculating the ratio between the duration of the beat cycles affected by the chirp (beat interpeak intervals) and the total duration of the beat cycles detected within a fixed time window (roughly double the size of the maximum chirp duration, 700 ms)." This was not clear to me.

      We now rephrased to “Estimates of beat interference were made by calculating the ratio between the cumulative duration of the beat cycles affected by a given chirp (1 beat cycle corresponding to the beat comprised by two consecutive beat peaks, or - more simply - the beat inter-peak interval) over the cumulative duration of all the beat cycles within the time window used as a reference (700 ms; other analysis windows were tested Figure S9)” to clarify this method.

      (12) Results: "For each chirp, the interference values obtained for 4 different phases (90{degree sign} steps) were averaged." Why was this done?

      To consider an average effect across phases. Although it is true that chirp parameters may have a different impact on the beat, depending on EOD phase, including this parameter in our figure/s would have considerably increased the volume of data reported giving too much emphasis to an analysis we judged not crucially important. In addition, since we did not consider EOD phase in our recordings, we opted for an average estimate encompassing different phase values.

      (13) Discussion: "Third, observations in a few species are generalized to all other gymnotiforms without testing for species differences (Turner et al., 2007; Smith et al., 2013; Petzold et al., 2016)." I strongly disagree with this statement. First, the studies referenced here do explicitly compare chirps across species. Second, you only studied one species here, so it is not clear to me how this is a relevant concern in interpreting your findings.

      Here we have probably been unclear in the writing: the point we wanted to make is that the idea of chirps having semantic content has been generalized to other species without investigating the nature of their chirping with as much detail as done for brown ghosts.

      We have now rephrased the statement and changed it to: “Second, observations in a few species are generalized to all other gymnotiforms without testing whether chirping may have similar functions in other species (Turner et al., 2007; Smith et al., 2013; Petzold et al., 2016)”

      (14) Discussion: "The two beats could be indistinguishable (assuming that the mechanism underlying the discrimination of the sign of DF at low DFs, and thought to be the basis of the so called jamming avoidance response (JAR; Metzner, 1999), is not functional at higher DFs)." Why would you assume this?

      What we meant here is that it is unlikely that the two DFs are not discriminated by the same mechanisms implied in the JAR, even if the DF is higher than the levels at which usually JARs are detected (i.e. DF = 1-10 Hz?). To improve clarity, we rephrased this statement. “The two beats could be indistinguishable (assuming - perhaps not realistically - that the same mechanism involved in DF discrimination at lower DF values would not work in this case; Metzner, 1999)”.

      (15) Discussion: "...an idea which seems congruent with published electrophysiological studies..." How so?

      Rephrased to “Based on our beat interference estimates, we propose that the occurrence of the different types of chirps at more positive DFs (such as in male-to-female chirping) may be explained by their different effect on the beat (Figure 5D; Benda et al., 2006; Walz et al., 2013).”

      Reviewer #3 (Recommendations For The Authors):

      On p.2 there is a discrepancy between the quoted ranges for active sensing of objects, first 10-12 cm, and then 6-12 cm further down. And in the following paragraph right below this passage, electric fields are said to decay with the squared distance (appendix 1). That expression has a cos(theta) which is inversely proportional to the distance, and so one is really dealing, as expected for dipolar fields, with a drop-off that decays with the distance cubed.

      We thank the Reviewer for the comment, we have now corrected the mistake and added “cubed”. We also removed the imprecise reference to the range 6-12 cm, rephrased to “up to 12 cm”.

      At the end of the section on Inconsistencies..., it is not clear what "activity levels" refers to. It should also be made clearer at the outset, and reminded in this section too, that for the authors, behavioural context does not include social experience, which is somewhat counter-intuitive.

      We now specified we meant “locomotor activity levels”. Regarding the social experience we included it as “behavioral context”, we now made it clearer in the first result paragraph. We hope we resolved the confusion.

      The caption of Fig.8 could use more clarity in terms of what is being compared in (C) (and is "1*2p" a typo?)

      We corrected the typo and edited the figure to make the references more clear.

      The concept of "high self-correlation of chirp time series" is presented only in the Conclusion using those words. The word self-correlation is not used beforehand. This needs to be fixed so the reader knows clearly what is being referred to.

      Thank you for noting this. We have now changed the wording using the term “auto-correlation” and changed a statement at the beginning of the “interference” result paragraph accordingly, removing references to self-correlation.

    1. Author response: 

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Meissner-Bernard et al present a biologically constrained model of telencephalic area of adult zebrafish, a homologous area to the piriform cortex, and argue for the role of precisely balanced memory networks in olfactory processing.

      This is interesting as it can add to recent evidence on the presence of functional subnetworks in multiple sensory cortices. It is also important in deviating from traditional accounts of memory systems as attractor networks. Evidence for attractor networks has been found in some systems, like in the head direction circuits in the flies. However, the presence of attractor dynamics in other modalities, like sensory systems, and their role in computation has been more contentious. This work contributes to this active line of research in experimental and computational neuroscience by suggesting that, rather than being represented in attractor networks and persistent activity, olfactory memories might be coded by balanced excitation-inhibitory subnetworks.

      Strengths:

      The main strength of the work is in: (1) direct link to biological parameters and measurements, (2) good controls and quantification of the results, and (3) comparison across multiple models.

      (1) The authors have done a good job of gathering the current experimental information to inform a biological-constrained spiking model of the telencephalic area of adult zebrafish. The results are compared to previous experimental measurements to choose the right regimes of operation.

      (2) Multiple quantification metrics and controls are used to support the main conclusions and to ensure that the key parameters are controlled for - e.g. when comparing across multiple models.

      (3) Four specific models (random, scaled I / attractor, and two variant of specific E-I networks - tuned I and tuned E+I) are compared with different metrics, helping to pinpoint which features emerge in which model.

      Weaknesses:

      Major problems with the work are: (1) mechanistic explanation of the results in specific E-I networks, (2) parameter exploration, and (3) the functional significance of the specific E-I model.

      (1) The main problem with the paper is a lack of mechanistic analysis of the models. The models are treated like biological entities and only tested with different assays and metrics to describe their different features (e.g. different geometry of representation in Fig. 4). Given that all the key parameters of the models are known and can be changed (unlike biological networks), it is expected to provide a more analytical account of why specific networks show the reported results. For instance, what is the key mechanism for medium amplification in specific E/I network models (Fig. 3)? How does the specific geometry of representation/manifolds (in Fig. 4) emerge in terms of excitatory-inhibitory interactions, and what are the main mechanisms/parameters? Mechanistic account and analysis of these results are missing in the current version of the paper.

      We agree with the reviewer that a mechanistic analysis of manifold geometry is of high interest and we will address this issue in our revisions. We are currently exploring approaches to better understand how amplification of activity is controlled in E/I assemblies, and how geometric modifications can be described in terms of elementary excitatory and inhibitory interactions. We expect these approaches to provide new mechanistic insights into representational manifolds.

      (2) The second major issue with the study is a lack of systematic exploration and analysis of the parameter space. Some parameters are biologically constrained, but not all the parameters. For instance, it is not clear what the justification for the choice of synaptic time scales are (with E synaptic time constants being larger than inhibition: tau_syn_i = 10 ms, tau_syn_E = 30 ms). How would the results change if they are varying these - and other unconstrained - parameters? It is important to show how the main results, especially the manifold localisation, would change by doing a systematic exploration of the key parameters and performing some sensitivity analysis. This would also help to see how robust the results are, which parameters are more important and which parameters are less relevant, and to shed light on the key mechanisms.

      We varied neuronal and network parameters in the past and we are currently performing additional systematic parameter variations to further address this comment. Preliminary results indicate that networks with similar properties can be obtained with equal synaptic time constants and biophysical parameters for all E and I neurons, thus supporting the notion that representational geometry is determined primarily by connectivity. Results of parameter variations will be reported in the revised manuscript.

      (3) It is not clear what the main functional advantage of the specific E-I network model is compared to random networks. In terms of activity, they show that specific E-I networks amplify the input more than random networks (Fig. 3). But when it comes to classification, the effect seems to be very small (Fig. 5c). Description of different geometry of representation and manifold localization in specific networks compared to random networks is good, but it is more of an illustration of different activity patterns than proving a functional benefit for the network. The reader is still left with the question of what major functional benefits (in terms of computational/biological processing) should be expected from these networks, if they are to be a good model for olfactory processing and learning.

      One possibility for instance might be that the tasks used here are too easy to reveal the main benefits of the specific models - and more complex tasks would be needed to assess the functional enhancement (e.g. more noisy conditions or more combination of odours). It would be good to show this more clearly - or at least discuss it in relation to computation and function.

      We agree that further insights into potential benefits of manifold representations would be interesting. In the initial manuscript we performed analyses of pattern classification primarily to examine whether the structured E/I networks studied here can support pattern classification at all, given that they do not exhibit discrete attractor states or global pattern completion. As structured E/I networks still support pattern classification when activity is read out from neuronal subsets, we concluded that structured E/I networks are not in conflict with the general notion of pattern classification by autoassociation. In addition, manifold representations may support a variety of other computations that we discussed only superficially.  In the revised we are planning to address this issue in more depth by additional discussion and analyses. In particular, we are planning to address the hypothesis that manifold geometry provides a continuous distance metric to analyze relationships between inputs and relevant stimuli (learned odors) in the presence of irrelevant stimulus components (non-learned odors).

      Reviewer #2 (Public Review):

      Summary:

      The authors conducted a comparative analysis of four networks, varying in the presence of excitatory assemblies and the architecture of inhibitory cell assembly connectivity. They found that co-tuned E-I assemblies provide network stability and a continuous representation of input patterns (on locally constrained manifolds), contrasting with networks with global inhibition that result in attractor networks.

      Strengths:

      The findings presented in this paper are very interesting and cutting-edge. The manuscript effectively conveys the message and presents a creative way to represent high-dimensional inputs and network responses. Particularly, the result regarding the projection of input patterns onto local manifolds and continuous representation of input/memory is very Intriguing and novel. Both computational and experimental neuroscientists would find value in reading the paper.

      Weaknesses:

      Intuitively, classification (decodability) in discrete attractor networks is much better than in networks that have continuous representations. This could also be shown in Figure 5B, along with the performance of the random and tuned E-I networks. The latter networks have the advantage of providing network stability compared to the Scaled I network, but at the cost of reduced network salience and, therefore, reduced input decodability. The authors may consider designing a decoder to quantify and compare the classification performance of all four networks.

      As suggested by the reviewer, we will explicitly examine decodability by different types of networks in the revised manuscript.

      Networks featuring E/I assemblies could potentially represent multistable attractors by exploring the parameter space for their reciprocal connectivity and connectivity with the rest of the network. However, for co-tuned E-I networks, the scope for achieving multistability is relatively constrained compared to networks employing global or lateral inhibition between assemblies. It would be good if the authors mentioned this in the discussion. Also, the fact that reciprocal inhibition increases network stability has been shown before and should be cited in the statements addressing network stability (e.g., some of the citations in the manuscript, including Rost et al. 2018, Lagzi & Fairhall 2022, and Vogels et al. 2011 have shown this).

      We thank the reviewer for this comment and will revise the manuscript accordingly.

      Providing raster plots of the pDp network for familiar and novel inputs would help with understanding the claims regarding continuous versus discrete representation of inputs, allowing readers to visualize the activity patterns of the four different networks. (similar to Figure 1B).

      We will follow the suggestion by the reviewer and include raster plots of responses to both familiar and novel inputs in the revised manuscript.

      Reviewer #3 (Public Review):

      Summary:

      This work investigates the computational consequences of assemblies containing both excitatory and inhibitory neurons (E/I assembly) in a model with parameters constrained by experimental data from the telencephalic area Dp of zebrafish. The authors show how this precise E/I balance shapes the geometry of neuronal dynamics in comparison to unstructured networks and networks with more global inhibitory balance. Specifically, E/I assemblies lead to the activity being locally restricted onto manifolds - a dynamical structure in between high-dimensional representations in unstructured networks and discrete attractors in networks with global inhibitory balance. Furthermore, E/I assemblies lead to smoother representations of mixtures of stimuli while those stimuli can still be reliably classified, and allow for more robust learning of additional stimuli.

      Strengths:

      Since experimental studies do suggest that E/I balance is very precise and E/I assemblies exist, it is important to study the consequences of those connectivity structures on network dynamics. The authors convincingly show that E/I assemblies lead to different geometries of stimulus representation compared to unstructured networks and networks with global inhibition. This finding might open the door for future studies for exploring the functional advantage of these locally defined manifolds, and how other network properties allow to shape those manifolds.

      The authors also make sure that their spiking model is well-constrained by experimental data from the zebrafish pDp. Both spontaneous and odor stimulus triggered spiking activity is within the range of experimental measurements. But the model is also general enough to be potentially applied to findings in other animal models and brain regions.

      Weaknesses:

      I find the point about pattern completion a bit confusing. In Fig. 3 the authors argue that only the Scaled I network can lead to pattern completion for morphed inputs since the output correlations are higher than the input correlations. For me, this sounds less like the network can perform pattern completion but it can nonlinearly increase the output correlations. Furthermore, in Suppl. Fig. 3 the authors show that activating half the assembly does lead to pattern completion in the sense that also non-activated assembly cells become highly active and that this pattern completion can be seen for Scaled I, Tuned E+I, and Tuned I networks. These two results seem a bit contradictory to me and require further clarification, and the authors might want to clarify how exactly they define pattern completion.

      We believe that this comment concerns a semantic misunderstanding and apologize for any lack of clarity. The reviewer is correct that “pattern completion” in morphing experiments can be described as a nonlinear increase in output correlations in response to related inputs. This is different from the results obtained by simulated current injections because currents were targeted to subsets of assembly neurons and the analysis focused on firing rates within and outside assemblies. We referred to results of both experiments as “pattern completion” because this has been standard in the neurobiological and in the computer science literature, respectively. However, we agree that this can cause confusion and we will revise the manuscript to clarify this issue.

      The authors argue that Tuned E+I networks have several advantages over Scaled I networks. While I agree with the authors that in some cases adding this localized E/I balance is beneficial, I believe that a more rigorous comparison between Tuned E+I networks and Scaled I networks is needed: quantification of variance (Fig. 4G) and angle distributions (Fig. 4H) should also be shown for the Scaled I network. Similarly in Fig. 5, what is the Mahalanobis distance for Scaled I networks and how well can the Scaled I network be classified compared to the Tuned E+I network? I suspect that the Scaled I network will actually be better at classifying odors compared to the E+I network. The authors might want to speculate about the benefit of having networks with both sources of inhibition (local and global) and hence being able to switch between locally defined manifolds and discrete attractor states.

      As pointed out already in response to reviewer 1, we agree that the potential computational benefits of continuous manifold representations in comparison to discrete attractor states is an important point that merits further exploration and discussion. We are therefore planning to include a more in-depth discussion and to perform further analyses. The specific suggestions of the reviewer will be addressed.

      At a few points in the manuscript, the authors use statements without actually providing evidence in terms of a Figure. Often the authors themselves acknowledge this, by adding the term "not shown" to the end of the sentence. I believe it will be helpful to the reader to be provided with figures or panels in support of the statements.

      Thank you for this comment. We shall be happy to include additional data figures in the revised manuscript.

    1. Author response:

      eLife assessment

      The authors present an algorithm and workflow for the inference of developmental trajectories from single-cell data, including a mathematical approach to increase computational efficiency. While such efforts are in principle useful, the absence of benchmarking against synthetic data and a wide range of different single-cell data sets make this study incomplete. Based on what is presented, one can neither ultimately judge if this will be an advance over previous work nor whether the approach will be of general applicability.

      We thank the eLife editor for the valuable feedback. We wish to emphasize that both, benchmarking against other methods and validation on a synthetic dataset (“dyntoy”) are indeed presented in Supplementary Note, although we failed to sufficiently emphasize it in the main text. 

      We will extend the benchmarking to more TI methods and we will improve the results and discussion sections to present those facts more clearly to the reader.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors present tviblindi, a computational workflow for trajectory inference from molecular data at single-cell resolution. The method is based on (i) pseudo-time inference via expecting hitting time, (ii) sampling of random walks in a directed acyclic k-NN where edges are oriented away from a cell of origin w.r.t. the involved nodes' expected hitting times, and (iii) clustering of the random walks via persistent homology. An extended use case on mass cytometry data shows that tviblindi can be used elucidate the biology of T cell development.

      Strengths:

      - Overall, the paper is very well written and most (but not all, see below) steps of the tviblindi algorithm are explained well.

      - The T cell biology use case is convincing (at least to me: I'm not an immunologist, only a bioinformatician with a strong interest in immunology).

      We thank the reviewer for feedback and suggestions that we will accommodate, we respond point-by-point below

      Weaknesses:

      - The main weakness of the paper is that a systematic comparison of tviblindi against other tools for trajectory inference (there are many) is entirely missing. Even though I really like the algorithmic approach underlying tviblindi, I would therefore not recommend to our wet-lab collaborators that they should use tviblindi to analyze their data. The only validation in the manuscript is the T cell development use case. Although this use case is convincing, it does not suffice for showing that the algorithms's results are systematically trustworthy and more meaningful (at least in some dimension) than trajectories inferred with one of the many existing methods.

      We have compared tviblindi to several trajectory inference methods (Supplementary note section 8.2: Comparison to state-of-the-art methods, namely Monocle3 (v1.3.1) Cao et al. (2019), Stream (v1.1) Chen et al. (2019), Palantir (v1.0.0) Setty et al. (2019), VIA (v0.1.89) Stassen et al. (2021) and PAGA (scanpy==1.9.3) Wolf et al. (2019).) We will add thorough and systematic comparisons to the other algorithms mentioned by reviewers. We will include extended evaluation on publically available datasets.

      Also, we have successfully used tviblindi to investigate human B-cell development in primary immunodeficiency (manuscript in revisions), double negative T-cells development in ALPS (Autoimmune Lymphoproliferative Syndrome) by mass cytometry (project in progress).

      - The authors' explanation of the random walk clustering via persistent homology in the Results (subsection "Real-time topological interactive clustering") is not detailed enough, essentially only concept dropping. What does "sparse regions" mean here and what does it mean that "persistent homology" is used? The authors should try to better describe this step such that the reader has a chance to get an intuition how the random walk clustering actually works. This is especially important because the selection of sparse regions is done interactively. Therefore, it's crucial that the users understand how this selection affects the results. For this, the authors must manage to provide a better intuition of the maths behind clustering of random walks via persistent homology.

      In order to satisfy both reader types: the biologist and the mathematician, we explain the mathematics in detail in the Supplementary Note, section 4. We will improve the Results text to better point the reader to the mathematical foundations in the Supplementary Note.

      - To motivate their work, the authors write in the introduction that "TI methods often use multiple steps of dimensionality reduction and/or clustering, inadvertently introducing bias. The choice of hyperparameters also fixes the a priori resolution in a way that is difficult to predict." They claim that tviblindi is better than the original methods because "analysis is performed in the original high-dimensional space, avoiding artifacts of dimensionality reduction." However, in the manuscript, tviblindi is tested only on mass cytometry data which has a much lower dimensionality than scRNA-seq data for which most existing trajectory inference methods are designed. Since tviblindi works on a k-NN graph representation of the input data, it is unclear if it could be run on scRNA-seq data without prior dimensionality reduction. For this, cell-cell distances would have to be computed in the original high-dimensional space, which is problematic due to the very high dimensionality of scRNA-seq data. Of course, the authors could explicitly reduce the scope of tviblindi to data of lower dimensionality, but this would have to be stated explicitly.

      In the manuscript we tested the framework on the scRNA-seq data from Park et al 2020 (DOI: 10.1126/science.aay3224). To illustrate that tviblindi can work directly in the high-dimensional space, we applied the framework successfully on imputed 2000 dimensional data.

      The idea behind tviblindi is to be able to work without the necessity to use non-linear dimensionality reduction techniques, which reduce the dimensionality to a very low number of dimensions and whose effects on the data distribution are difficult to predict. On the other hand the use of (linear) dimensionality reduction techniques which effectively suppress noise in the data such as PCA is a good practice (see also response to reviewer 2). We will emphasize this in the revised version and add the results of the corresponding analysis.

      - Also tviblindi has at least one hyper-parameter, the number k used to construct the k-NN graphs (there are probably more hidden in the algorithm's subroutines). I did not find a systematic evaluation of the effect of this hyper-parameter.

      Detailed discussion of the topic is presented in the Supplementary Note, section 8.1, where Spearman correlation coefficient between pseudotime estimated using k=10 and k=50 nearest neighbors was 0.997.   The number k however does affect the number of candidate endpoints. But even when larger k causes spurious connection between unrelated cell fates, the topological clustering of random walks allows for the separation of different trajectories. We will expand the “sensitivity to hyperparameters section” also in response to reviewer 2.

      Reviewer #2 (Public Review):

      Summary:

      In Deconstructing Complexity: A Computational Topology Approach to Trajectory Inference in the Human Thymus with tviblindi, Stuchly et al. propose a new trajectory inference algorithm called tviblindi and a visualization algorithm called vaevictis for single-cell data. The paper utilizes novel and exciting ideas from computational topology coupled with random walk simulations to align single cells onto a continuum. The authors validate the utility of their approach largely using simulated data and establish known protein expression dynamics along CD4/CD8 T cell development in thymus using mass cytometry data. The authors also apply their method to track Treg development in single-cell RNA-sequencing data of human thymus.

      The technical crux of the method is as follows: The authors provide an interactive tool to align single cells along a continuum axis. The method uses expected hitting time (given a user input start cell) to obtain a pseudotime alignment of cells. The pseudotime gives an orientation/direction for each cell, which is then used to simulate random walks. The random walks are then arranged/clustered based on the sparse region in the data they navigate using persistent homology.

      We thank the reviewer for feedback and suggestions that we will accommodate, we respond point-by-point below.

      Strengths:

      The notion of using persistent homology to group random walks to identify trajectories in the data is novel.

      The strength of the method lies in the implementation details that make computationally demanding ideas such as persistent homology more tractable for large scale single-cell data.

      This enables the authors to make the method more user friendly and interactive allowing real-time user query with the data.

      Weaknesses:

      The interactive nature of the tool is also a weakness, by allowing for user bias leading to possible overfitting for a specific data.

      tviblindi is not designed as a fully automated TI tool (although it implements a fully automated module), but as a data driven framework for exploratory analysis of unknown data. There is always a risk of possible bias in this type of analysis - starting with experimental design, choice of hyperparameters in the downstream analysis, and an expert interpretation of the results. The successful analysis of new biological data involves a great deal of expert knowledge which is difficult to a priori include in the computational models.

      tvilblindi tries to solve this challenge by intentionally overfitting the data and keeping the level of resolution on a single random walk. In this way we aim to capture all putative local relationships in the data. The on-demand aggregation of the walks using the global topology of the data allows researchers to use their expert knowledge to choose the right level of detail (as demonstrated in the Figure 4 of the manuscript) while relying on the topological structure of the high dimensional point cloud. At all times tviblindi allows to inspect the composition of the trajectory to assess the variance in the development, possible hubs on the KNN-graph etc.

      The main weakness of the method is lack of benchmarking the method on real data and comparison to other methods. Trajectory inference is a very crowded field with many highly successful and widely used algorithms, the two most relevant ones (closest to this manuscript) are not only not benchmarked against, but also not sited. Including those that specifically use persistent homology to discover trajectories (Rizvi et.al. published Nat Biotech 2017). Including those that specifically implement the idea of simulating random walks to identify stable states in single-cell data (e.g. CellRank published in Lange et.al Nat Meth 2022), as well as many trajectory algorithms that take alternative approaches. The paper has much less benchmarking, demonstration on real data and comparison to the very many other previous trajectory algorithms published before it. Generally speaking, in a crowded field of previously published trajectory methods, I do not think this one approach will compete well against prior work (especially due to its inability to handle the noise typical in real world data (as was even demonstrated in the little bit of application to real world data provided).

      We provide comparisons of tviblindi and vaevictis in the Supplementary Note, section 8.2, where we compare it to Monocle3 (v1.3.1) Cao et al. (2019), Stream (v1.1) Chen et al. (2019), Palantir (v1.0.0) Setty et al. (2019), VIA (v0.1.89) Stassen et al. (2021) and PAGA (scanpy==1.9.3) Wolf et al. (2019). We use two datasets: artificial Dyntoy and real mass cytometry thymus+peripheral blood dataset. We thank the reviewer for suggesting specific methods.  CellRank was excluded from the benchmarking as it was originally designed for RNA-velocity data (not available in mass cytometry data), but will include recent upgrade CellRank2 (preprint at doi.org/10.1101/2023.07.19.549685) which offers more flexibility.

      We will add further benchmarking as suggested by the reviewer in the course of revisions.

      Beyond general lack of benchmarking there are two issues that give me particular concern. As previously mentioned, the algorithm is highly susceptible to user bias and overfitting. The paper gives the example (Figure 4) of a trajectory which mistakenly shows that cells may pass from an apoptotic phase to a different developmental stage. To circumvent this mistake, the authors propose the interactive version of tviblindi that allows users to zoom in (increase resolution) and identify that there are in fact two trajectories in one. In this case, the authors show how the author can fix a mistake when the answer is known. However, the point of trajectory inference is to discover the unknown. With so much interactive options for the user to guide the result, the method is more user/bias driven than data-driven. So a rigorous and quantitative discussion of robustness of the method, as well as how to ensure data-driven inference and avoid over-fitting would be useful.

      Local directionality in expression data is a challenge which is not, to our knowledge, solved. And we are not sure it can be solved entirely, even theoretically. The random walks passing “through” the apoptotic phase are biologically infeasible, but it is an (unbiased) representation of what the data look like based on the diffusion model. It is a property of the data (or of the panel design), which has to be interpreted properly rather than a mistake. Of note, except for Monocle3 (which does not provide the directionality) other tested methods did not discover this trajectory at all.

      The “zoom in” has in fact nothing to do with “passing through the apoptosis”. We show how the researcher can investigate the suggested trajectory to see if there is an additional structure of interest and/or relevance. This investigation is still data driven (although not fully automated). Anecdotally in this particular case this branching was discovered by an bioinformatician, who knew nothing about the presence of beta-selection in the data. 

      We show that the trajectory of apoptosis of cortical thymocytes consists of 2 trajectories corresponding to 2 different checkpoints (beta-selection and positive/negative selection). This type of structure, where 2 (or more) trajectories share the same path for most of the time, then diverge only to be connected at a later moment (immediately from the point of view of the beta-selection failure trajectory) is a challenge for TI algorithms and none of tested methods gave a correct result. More importantly there seems to be no clear way to focus on these kinds of structures (common origin and common fate) in TI methods.

      Of note, the “zoom in” is a recommended and convenient method to look for an inner structure, but it does not necessarily mean addition of further homological classes. Indeed, in this case the reason that the structure is not visible directly is the limitation of the dendrogram complexity (only branches containing at least 10% of simulated random walks are shown by default).

      In summary, tviblindi effectively handled all noise in the data that obscured biologically valid trajectories for other methods. We will improve the discussion of the robustness in the reviewed version. 

      Second, the paper discusses the benefit of tviblindi operating in the original high dimensions of the data. This is perhaps adequate for mass cytometry data where there is less of an issue of dropouts and the proteins may be chosen to be large independent. But in the context of single-cell RNA-sequencing data, the massive undersampling of mRNA, as well as high degree of noise (e.g. ambient RNA), introduces very large degree of noise so that modeling data in the original high dimensions leads to methods being fit to the noise. Therefore ALL other methods for trajectory inference work in a lower dimension, for very good reason, otherwise one is learning noise rather than signal. It would be great to have a discussion on the feasibility of the method as is for such noisy data and provide users with guidance. We note that the example scRNA-seq data included in the paper is denoised using imputation, which will likely result in the trajectory inference being oversmoothed as well.

      We agree with the reviewer. In our manuscript we wanted to showcase that tviblindi can directly operate in high-dimensional space (thousands of dimensions) and we used MAGIC imputation for this purpose. This was not ideal. More standard approach, which uses 30-50 PCs as input to the algorithm resulted in equivalent trajectories. We will add this analysis to the study.

      In summary, the fact that tviblindi scales well with dimensionality of the data and is able to work in the original space does not mean that it is always the best option. We will emphasize in the revised paper that we aim to avoid the non-linear dimensional reduction techniques as a data preprocessing tool, as the effect of the reduction is difficult to predict. We will also discuss the preprocessing of scRNA-seq data in greater detail.

      Reviewer #3 (Public Review):

      Summary:

      Stuchly et al. proposed a single-cell trajectory inference tool, tviblindi, which was built on a sequential implementation of the k-nearest neighbor graph, random walk, persistent homology and clustering, and interactive visualization. The paper was organized around the detailed illustration of the usage and interpretation of results through the human thymus system.

      Strengths:

      Overall, I found the paper and method to be practical and needed in the field. Especially the in-depth, step-by-step demonstration of the application of tviblindi in numerous T cell development trajectories and how to interpret and validate the findings can be a template for many basic science and disease-related studies. The videos are also very helpful in showcasing how the tool works.

      Weaknesses:

      I only have a few minor suggestions that hopefully can make the paper easier to follow and the advantage of the method to be more convincing.

      (1) The "Computational method for the TI and interrogation - tviblindi" subsection under the Results is a little hard to follow without having a thorough understanding of the tviblindi algorithm procedures. I would suggest that the authors discuss the uniqueness and advantages of the tool after the detailed introduction of the method (moving it after the "Connectome - a fully automated pipeline".

      We thank the reviewer for the suggestion and we will accommodate it to improve readability of the text.

      Also, considering it is a computational tool paper, inevitably, readers are curious about how it functions compared to other popular trajectory inference approaches. I did not find any formal discussion until almost the end of the supplementary note (even that is not cited anywhere in the main text). Authors may consider improving the summary of the advantages of tviblindi by incorporating concrete quantitative comparisons with other trajectory tools.

      We provide comparisons of tviblindi and vaevictis in the Supplementary Note, section 8.2, where we compare it to Monocle3 (v1.3.1) Cao et al. (2019), Stream (v1.1) Chen et al. (2019), Palantir (v1.0.0) Setty et al. (2019), VIA (v0.1.89) Stassen et al. (2021) and PAGA (scanpy==1.9.3) Wolf et al. (2019). We use two datasets: artificial Dyntoy and real mass cytometry thymus+peripheral blood dataset. We will also add CellRank2 into comparisons and we will strengthen the message of the benchmarking results in the Discussion section.

      (2) Regarding the discussion in Figure 4 the trajectory goes through the apoptotic stage and reconnects back to the canonical trajectory with counterintuitive directionality, it can be a checkpoint as authors interpret using their expert knowledge, or maybe a false discovery of the tool. Maybe authors can consider running other algorithms on those cells and see which tracks they identify and if the directionality matches with the tviblindi.

      We have indeed used the thymus dataset for comparison of all TI algorithms listed above. Except for Monocle 3 they failed to discover the negative selection branch (Monocle 3 does not offer directionality information). Therefore, a valid topological trajectory with incorrect (expert-corrected) directionality was partly or entirely missed by other algorithms.

      (3) The paper mainly focused on mass cytometry data and had a brief discussion on scRNA-seq. Can the tool be applied to multimodality data such as CITE-seq data that have both protein markers and gene expression? Any suggestions if users want to adapt to scATAC-seq or other epigenomic data?

      The analysis of multimodal data is the logical next step and is the topic of our current research. At this moment tviblindi cannot be applied directly to multimodal data. It is possible to use the KNN-graph based on multimodal data (such as weighted nearest neighbor graph implemented in Seurat) for pseudotime calculation and random walk simulation. However, we do not have a fully developed triangulation for the multimodal case yet.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript focuses on the role of the deubiquitinating enzyme UPS-50/USP8 in endosome maturation. The authors aimed to clarify how this enzyme drives the conversion of early endosomes into late endosomes. Overall, they did achieve their aims in shedding light on the precise mechanisms by which UPS-50/USP8 regulates endosome maturation. The results support their conclusions that UPS-50 acts by disassociating RABX-5 from early endosomes to deactivate RAB-5 and by recruiting SAND-1/Mon1 to activate RAB-7. This work is commendable and will have a significant impact on the field. The methods and data presented here will be useful to the community in advancing our understanding of endosome maturation and identifying potential therapeutic targets for diseases related to endosomal dysfunction. It is worth noting that further investigation is required to fully understand the complexities of endosome maturation. However, the findings presented in this manuscript provide a solid foundation for future studies.

      We thank this reviewer for the instructive suggestions and encouragement.

      Strengths:

      The major strengths of this work lie in the well-designed experiments used to examine the effects of UPS-50 loss. The authors employed confocal imaging to obtain a picture of the aftermath of the USP-50 loss. Their findings indicated enlarged early endosomes and MVB-like structures in cells deficient in USP-50/USP8.

      We thank this reviewer for the instructive suggestions and encouragement.

      Weaknesses:

      Specifically, there is a need for further investigation to accurately characterize the anomalous structures detected in the ups-50 mutant. Also, the correlation between the presence of these abnormal structures and ESCRT-0 is yet to be addressed, and the current working model needs to be revised to prevent any confusion between enlarged early endosomes and MVBs.

      Excellent suggestions. The EM imaging indeed revealed an increase in enlarged cellular vesicles containing various contents in usp-50 mutants. However, the detailed molecular features of these vesicles remain unclear. Therefore, we plan to utilize ESCRT components for double staining with early or late endosome markers. This will enable us to accurately characterize the anomalous structures detected in the usp-50 mutants.

      Reviewer #2 (Public Review):

      Summary:

      In this study, the authors study how the deubiquitinase USP8 regulates endosome maturation in C. elegans and mammalian cells. The authors have isolated USP8 mutant alleles in C. elegans and used multiple in vivo reporter lines to demonstrate the impact of USP8 loss-of-function on endosome morphology and maturation. They show that in USP8 mutant cells, the early endosomes and MVB-like structures are enlarged while the late endosomes and lysosomal compartments are reduced. They elucidate that USP8 interacts with Rabx5, a guanine nucleotide exchange factor (GEF) for Rab5, and show that USP8 likely targets specific lysine residue of Rabx5 to dissociate it from early endosomes. They also find that the localization of USP8 to early endosomes is disrupted in Rabx5 mutant cells. They observe that in both Rabx5 and USP8 mutant cells, the Rab7 GEF SAND-1 puncta which likely represents late endosomes are diminished, although Rabex5 is accumulated in USP8 mutant cells. The authors provide evidence that USP8 regulates endosomal maturation in a similar fashion in mammalian cells. Based on their observations they propose that USP8 dissociates Rabex5 from early endosomes and enhances the recruitment of SAND-1 to promote endosome maturation.

      We thank this reviewer for the instructive suggestions and encouragement.

      Strengths:

      The major highlights of this study include the direct visualization of endosome dynamics in a living multi-cellular organism, C. elegans. The high-quality images provide clear in vivo evidence to support the main conclusions. The authors have generated valuable resources to study mechanisms involved in endosome dynamics regulation in both the worm and mammalian cells, which would benefit many members of the cell biology community. The work identifies a fascinating link between USP8 and the Rab5 guanine nucleotide exchange factor Rabx5, which expands the targets and modes of action of USP8. The findings make a solid contribution toward the understanding of how endosomal trafficking is controlled.

      We thank this reviewer for the instructive suggestions and encouragement.

      Weaknesses:

      -The authors utilized multiple fluorescent protein reporters, including those generated by themselves, to label endosomal vesicles. Although these are routine and powerful tools for studying endosomal trafficking, these results cannot tell whether the endogenous proteins (Rab5, Rabex5, Rab7, etc.) are affected in the same fashion.

      Good suggestion. Indeed, to test whether the endogenous proteins (Rab5, Rabex5, Rab7, etc.) are affected in the same fashion as fluorescent protein reporters, we supplemented our approach with the utilization of endogenous markers. These markers, including Rab5, RAB-5, Rabex5, RABX-5, and EEA1 for early endosomes, as well as RAB-7, Mon1a, and Mon1b for late endosomes, were instrumental in our investigations (refer to Figure 3, Figure 6, Sup Figure 4, Sup Figure 5, and Sup Figure 7). Our comprehensive analysis, employing various methodologies such as tissue-specific fused proteins, CRISPR/Cas9 knock-in, and antibody staining, consistently highlights the critical role of USP8 in early-to-late endosome conversion.

      -The authors clearly demonstrated a link between USP8 and Rabx5, and they showed that cells deficient in both factors displayed similar defects in late endosomes/lysosomes. However, the authors didn't confirm whether and/or to which extent USP8 regulates endosome maturation through Rabx5. Additional genetic and molecular evidence might be required to better support their working model.

      Excellent point. We plan to conduct additional genetic analyses, including the construction of double mutants between usp-50 and various rabex-5 mutations, to further elucidate the extent to which USP8 regulates endosome maturation via Rabex5.

      Reviewer #3 (Public Review):

      Summary:

      The authors were trying to elucidate the role of USP8 in the endocytic pathway. Using C. elegans epithelial cells as a model, they observed that when USP8 function is lost, the cells have a decreased number and size in lysosomes. Since USP8 was already known to be a protein linked to ESCRT components, they looked into what role USP8 might play in connecting lysosomes and multivesicular bodies (MVB). They observed fewer ESCRT-associated vesicles but an increased number of "abnormal" enlarged vesicles when USP8 function was lost. At this specific point, it's not clear what the objective of the authors was. What would have been their hypothesis addressing whether the reduced lysosomal structures in USP8 (-) animals were linked to MVB formation? Then they observed that the abnormally enlarged vesicles, marked by the PI3P biosensor YFP-2xFYVE, are bigger but in the same number in USP8 (-) compared to wild-type animals, suggesting homotypic fusion. They confirmed this result by knocking down USP8 in a human cell line, and they observed enlarged vesicles marked by YFP-2xFYVE as well. At this point, there is quite an important issue. The use of YFP-2xFYVE to detect early endosomes requires the transfection of the cells, which has already been demonstrated to produce differences in the distribution, number, and size of PI3P-positive vesicles (doi.org/10.1080/15548627.2017.1341465). The enlarged vesicles marked by YFP-2xFYVE would not necessarily be due to the loss of UPS8. In any case, it appears relatively clear that USP8 localizes to early endosomes, and the authors claim that this localization is mediated by Rabex-5 (or Rabx-5). They finally propose that USP8 dissociates Rabx-5 from early endosomes facilitating endosome maturation.

      Weaknesses:

      The weaknesses of this study are, on one side, that the results are almost exclusively dependent on the overexpression of fusion proteins. While useful in the field, this strategy does not represent the optimal way to dissect a cell biology issue. On the other side, the way the authors construct the rationale for each approximation is somehow difficult to follow. Finally, the use of two models, C. elegans and a mammalian cell line, which would strengthen the observations, contributes to the difficulty in reading the manuscript.

      The findings are useful but do not clearly support the idea that USP8 mediates Rab5-Rab7 exchange and endosome maturation, In contrast, they appear to be incomplete and open new questions regarding the complexity of this process and the precise role of USP8 within it.

      We thank this reviewer for the insightful comments. Fluorescence-fused proteins serve as potent tools for visualizing subcellular organelles both in vivo and in live settings. Specifically, in epidermal cells of worms, the tissue-specific expression of these fused proteins is indispensable for studying organelle dynamics within living organisms. This approach is necessitated by the inherent limitations of endogenously tagged proteins, whose fluorescence signals are often weak and unsuitable for live imaging or genetic screening purposes. Acknowledging concerns raised by the reviewer regarding potential alterations in organelle morphology due to overexpression of certain fused proteins, we supplemented our approach with the utilization of endogenous markers. These markers, including Rab5, RAB-5, Rabex5, RABX-5, and EEA1 for early endosomes, as well as RAB-7, Mon1a, and Mon1b for late endosomes, were instrumental in our investigations (refer to Figure 3, Figure 6, Sup Figure 4, Sup Figure 5, and Sup Figure 7). Our comprehensive analysis, employing various methodologies such as tissue-specific fused proteins, CRISPR/Cas9 knock-in, and antibody staining, consistently highlights the critical role of USP8 in early-to-late endosome conversion. Specifically, we discovered that the recruitment of USP-50/USP8 to early endosomes is depending on Rabex5. However, instead of stabilizing Rabex5, the recruitment of USP-50/USP8 leads to its dissociation from endosomes, concomitantly facilitating the recruitment of the Rab7 GEF SAND-1/Mon1. In cells with loss-of-function mutations in usp-50/usp8, we observed enhanced RABX-5/Rabex5 signaling and mis-localization of SAND-1/Mon1 proteins from endosomes. Consequently, this disruption impairs endolysosomal trafficking, resulting in the accumulation of enlarged vesicles containing various intraluminal contents and rudimentary lysosomal structures.

      Through an unbiased genetic screen, verified by cultured mammalian cell studies, we observed that loss-of-function mutations in usp-50/usp8 result in diminished lysosome/late endosomes. To elucidate the underlying mechanisms, we investigated the formation of multivesicular bodies (MVBs), a process tightly linked to USP8 function. Extensive electron microscopy (EM) analysis indicated that MVB-like structures are largely intact in usp-50 mutant cells, suggesting that USP8/USP-50 likely regulate lysosome formation through alternative pathways in addition to their roles in MVB formation and ESCRT component function. USP8 is known to regulate the endocytic trafficking and stability of numerous transmembrane proteins. Interestingly, loss-of-function mutations in usp8 often lead to the enlargement of early endosomes, yet the mechanisms underlying this phenomenon remain unclear. Given that lysosomes receive and degrade materials generated by endocytic pathways, we hypothesized that the abnormally enlarged MVB-like vesicular structures observed in usp-50 or usp8 mutant cells correspond to the enlarged vesicles coated by early endosome markers. Indeed, in the absence of usp8/usp-50, the endosomal Rab5 signal is enhanced, while early endosomes are significantly enlarged. Given that Rab5 guanine nucleotide exchange factor (GEF), Rabex5, is essential for Rab5 activation, we further investigated its dynamics. Additional analyses conducted in both worm hypodermal cells and cultured mammalian cells revealed an increase of endosomal Rabex5 in response to usp8/usp-50 loss-of-function. Live imaging studies further demonstrated active recruitment of USP8 to newly formed Rab5-positive vesicles, aligning spatiotemporally with Rabex5 regulation. Through systematic exploration of putative USP-50 binding partners on early endosomes, we identified its interaction with Rabex5. Comprehensive genetics and biochemistry experiments demonstrated that USP8 acts through K323 site de-ubiquitination to dissociate Rabex5 from early endosomes and promotes the recruitment of the Rab7 GEF SAND-1/Mon1. In summary, our study began with an unbiased genetic screen and subsequent examination of established theories, leading to the formulation of our own hypothesis. Through multifaceted approaches, we unveiled a novel function of USP8 in early-to-late endosome conversion.

    1. Author response:

      We would like to thank all the reviewers and editors for their thoughtful and detailed comments, critiques and suggestions. We will revise our manuscript in accordance with all the points raised by the reviewers. Here we summarize some of the main points that we intend to address in our revised manuscript.

      The reviewers noted that we were not sufficiently careful in identifying possible exogenous cues that the mice might be using to locate the cues and that we did not consider why such cues might be ineffective. As the reviewers point out, the mice may be ignoring the visual landmarks (and floor scratches) because they are not reliable cues and their relation to the food varies with the entrance the mice have used. In particular, a reviewer refers to papers that show that “in environments with 'unreliable' landmarks, place cells are not controlled by landmarks”. These papers were known to the authors but failed to make final cut of our extensive discussion. This important point will be thoroughly addressed.

      Another critical point was the mice were often doing thigmotaxis. The literature on thigmotaxis was known to us and we will now directly refer to this point. We do note that the final average start to food trajectory (TEV) is directly to the food. In other words, the thigmotaxic trajectories and “towards the center” trajectories effectively average out.

      There was a very cogent point about the difficulty of totally eliminating odor cues that we will now address. Finally, based on studies using a virtual reality environment, one reviewer questioned the use of “path integration” as a signal that encodes goal location. The relevance of path integration to spatial learning and performance is a very difficult issue that, to our knowledge, has never been entirely settled in the vast spatial learning literature. We do not think that our data can “settle’ this issue but will try to at least be explicit re the complexity of the path integration hypothesis as it applies to both our own data and the virtual reality literature. In particular, we will discuss the potential roles of optic flow versus proprioceptive and vestibular inputs to a putative path integration mechanism.

      Finally, the reviewers raised many important technical points re statistics reporting and how the figures are presented. In our revision, we will completely comply with all these helpful critiques.

    1. Author response:

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

      eLife assessment

      Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments provide compelling evidence that conditional deletion of Vglut2 in noradrenergic neurons does not impact steadystate breathing or metabolic activity in room air, hypercapnia, or hypoxia. This study provides an important contribution to our understanding of how noradrenergic neurons regulate respiratory homeostasis in conscious adult mice.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments show that conditional deletion of Vglut2 in NA neurons does not impact steady-state breathing or metabolic activity in room air, hypercapnia, or hypoxia. Their observations challenge the importance of glutamatergic signaling from Vglut2 expressing NA neurons in normal respiratory homeostasis in conscious adult mice.

      Strengths:

      The comprehensive Vglut1, Vglut2, and Vglut3 co-expression profiles in the central noradrenergic system and the combined measurements of breathing and oxygen consumption are two major strengths of this study. Observations from these experiments provide previously undescribed insights into (1) expression patterns for subtypes of the vesicular glutamate transporter protein in the noradrenergic system and (2) the dispensable nature of Vglut2-dependent glutamate signaling from noradrenergic neurons to breathing responses to physiologically relevant gas challenges in adult conscious mice.

      Weaknesses:

      Although the cellular expression profiles for the vesicular glutamate transporters are provided, the study fails to document that glutamatergic-based signaling originating from noradrenergic neurons is evident at the cellular level under normal, hypoxic, and/or hypercapnic conditions. This limits the reader's understanding of why conditional Vglut2 knockdown is dispensable for breathing under the conditions tested.

      We thank the reviewers for their positive evaluation of our work. First, we would like to highlight that multiple studies have provided anatomical evidence of innervation of multiple cardio-respiratory nuclei by Vglut2+ noradrenergic fibers. Thus, the anatomical substrates are present for noradrenergic based Vglut2 signaling to either play a direct role in breathing control or, upon perturbation, to indirectly affect breathing through disrupted metabolic or cardiovascular control. We have included supplemental table 1 that summarizes central noradrenergic Vglut2+ innervations of respiratory and autonomic nuclei. Additionally, Ultrastructural evidence shows asymmetric synaptic contacts assuming glutamatergic transmission between C1 neurons and LC, A1, A2 and the dorsal motor nucleus of the vagus (DMV) (Milner et al., 1989; Abbott et al., 2012; Holloway et al., 2013; DePuy et al., 2013).

      Functionally, electrophysiological evidence showed that photostimulating C1 neurons activate LC, A1, A2 noradrenergic neurons monosynaptically by releasing glutamate (Holloway et al., 2013; DePuy et al., 2013) and optogenetic stimulation of LC neurons excite the downstream parabrachial nucleus (PBN) neurons by releasing glutamate. Thus, at least the glutamatergic signaling from C1 and LC noradrenergic neurons (two noradrenergic nuclei that have been shown to play a role in breathing control) is evident at the cellular level under normal conditions. Other evidence, highlighted in our manuscript, is more circumstantial.

      Reviewer #2 (Public Review):

      The authors characterized the recombinase-based cumulative fate maps for vesicular glutamate transporters (Vglut1, Vglut2 and Vglut3) expression and compared those maps to their real-time expression profiles in central NA neurons by RNA in situ hybridization in adult mice. Authors have revealed a new and intriguing expression pattern for Vglut2, along with an entirely uncharted co-expression domain for Vglut3 within central noradrenergic neurons. Interestingly, and in contrast to previous studies, the authors demonstrated that glutamatergic signaling in central noradrenergic neurons does not exert any influence on breathing and metabolic control either under normoxic/normocapnic conditions or after chemoreflex stimulation. Also, they showed for the first-time the Vglut3-expressing NA population in C2/A2 nuclei. In addition, they were also able to demonstrate Vglut2 expression in anterior NA populations, such as LC neurons, by using more refined techniques, unlike previous studies.

      A major strength of the study is the use of a set of techniques to investigate the participation of NA-based glutamatergic signaling in breathing and metabolic control. The authors provided a full characterization of the recombinase-based cumulative fate maps for Vglut transporters. They performed real-time mRNA expression of Vglut transporters in central NA neurons of adult mice. Further, they evaluated the effect of knocking down Vglut2 expression in NA neurons using a DBH-Cre; Vglut2cKO mice on breathing and control in unanesthetized mice. Finally, they injected the AAV virus containing Cre-dependent Td tomato into LC of v-Glut2 Cre mice to verify the VGlut2 expression in LC-NA neurons. A very positive aspect of the article is that the authors combined ventilation with metabolic measurements. This integration holds particular significance, especially when delving into the exploration of respiratory chemosensitivity. Furthermore, the sample size of the experiments is excellent.

      Despite the clear strengths of the paper, some weaknesses exist. It is not clear in the manuscript if the experiments were performed in males and females and if the data were combined. I believe that the study would have benefited from a more comprehensive analysis exploring the sex specific differences. The reason I think this is particularly relevant is the developmental disorders mentioned by the authors, such as SIDS and Rett syndrome, which could potentially arise from disruptions in central noradrenergic (NA) function, exhibit varying degrees of sex predominance. Moreover, some of the noradrenergic cell groups are sexually dimorphic. For instance, female Wistar rats exhibit a larger LC size and more LC-NA neurons than male subjects (Pinos et al., 2001; Garcia-Falgueras et al., 2005). More recently, a detailed transcriptional profiling investigation has unveiled the identities of over 3,000 genes in the LC. This revelation has highlighted significant sexual dimorphisms, with more than 100 genes exhibiting differential expression within LC-NA neurons at the transcript level. Furthermore, this investigation has convincingly showcased that these distinct gene expression patterns have the capacity to elicit disparate behavioral responses between sexes (Mulvey et al., 2018). Therefore, the authors should compare the fate maps, Vglut transporters in males and females, at least considering LC-NA neurons. Even in the absence of identified sex differences, this information retains significant importance.

      All experiments contained both males and females as described in the original submission. In our analysis of breathing and metabolism, sex was included in the analysis and no significant phenotypic difference was observed. For the fate map and in situ experiments, we did not see obvious differences in the expression patterns in the three glutamate transporters between females and males, though the group size is small. Though all the anatomical and phenotypic data in this manuscript are presented as combined graphs, we have differentially labeled our data points by sex. The reviewer does raise important questions regarding possible sexual dimorphisms in the central noradrenergic system and whether such dimorphisms may extend to glutamate transporter co-expression. Our thorough interrogation of respiratory-metabolic parameters fails to reveal any sex specific differences in control or experimental mice. Thus, it is unclear if any of the previously described and cited dimorphisms are functionally relevant in this setting. Given the large differences in the real time expression and cumulative fate maps of Vglut2, a worthwhile interrogation of differential glutamate transporter expression would be best served by longitudinal studies with large group sizes across age as it is not clear what underlies the dynamic VGlut2 expression changes. Such changes may at times be greater in males and other times in females, driven by experience or physiological challenges etc., but resulting in averaged cumulative fatemaps that are similar between sexes. Such a longitudinal quantitative study of real-time and fatemapped cell populations across the central NA system would be of a scale that is beyond the scope of this report, especially when no phenotypic changes have been observed in our respiratory data.

      An important point well raised by the authors is that although suggestive, these experiments do not definitively rule out that NA-Vglut2 based glutamatergic signaling has a role in breathing control. Subsequent experiments will be necessary to validate this hypothesis.

      As noted, we discuss that we only address requirement, not sufficiency, of NA Vglut2 in breathing. Functional sufficiency experiments usually involve increasing the relevant output. However, these experiments can lead to non-specific, pleiotropic effects that would be difficult to disambiguate, even if done with high cellular specificity. Viral or genetic overexpression of Vglut2 in NA neurons may be a feasible approach. Conditional ablation of TH or DBH with concurrent chemo or optogenetic stimulation may also be informative. These approaches would require significant investments in mouse model generation and suffer additional experimental limitations.

      An improvement could be made in terms of measuring body temperature. Opting for implanted sensors over rectal probes would circumvent the need to open the chamber, thereby preventing alterations in gas composition during respiratory measurements. Further, what happens to body temperature phenotype in these animals under different gas exposures? These data should be included in the Tables.

      While surgical implantation of sensors would provide a more direct assessment of temperature, it requires components that were not available at the time of the study and addresses a question (temperature changes during a time course of gas exposure) that go beyond the scope of the current work focused on respiratory response. As we have done for prior experiments (Martinez et al., 2019; Ray et al., 2011), the body temperature was measured immediately before and after measuring breathing only. Our flow through system using inline gas sensors (AEI P-61B CO2 sensor and AEI N-22M O2 sensor) ensure that gas challenges were constant and consistent across all measurements. Any disruption in gas composition would have been noted by our software analysis system, Breathe Easy, and the data rejected. We did not observe any such perturbations.

      Is it plausible that another neurotransmitter within NA neurons might be released in higher amounts in DBH-Cre; Vglut2 cKO mice to compensate for the deficiency in glutamate and prevent changes in ventilation?

      We agree that compensation is always a possibility at the synaptic, cellular, and circuit levels that may involve a variety of transcriptional, translational, cellular, and circuit mechanisms (i.e., synaptic strength). This could be interrogated by combining multiple conditional alleles and recombinase drivers for various transmitters and receptors, but would, in our experience, take multiple years for the requisite breeding to be completed.

      Continuing along the same line of inquiry is there a possibility that Vglut2 cKO from NA neurons not only eliminates glutamate release but also reduces NA release? A similar mechanism was previously found in VGLUT2 cKO from DA neurons in previous studies (Alsio et al., 2011; Fortin et al., 2012; Hnasko et al., 2010). Additionally, does glutamate play a role in the vesicular loading of NA? Therefore, could the lack of effect on breathing be explained by the lack of noradrenaline and not glutamate?

      These are all excellent points, but prior studies suggest that reductions in NA signaling would itself have an apparent effect (Zanella et al., 2006; Kuo et al., 2016). Although several studies showed that LC and C1 NA neurons co-release noradrenaline and glutamate, no direct evidence yet makes clear that glutamate facilitates NA release or vice versa. However, it would be of great interest to test if reduced or lack of NA compensated for loss of glutamate in the future. We do fully acknowledge that compensation in the manuscript that any number of compensatory events could be at play in these findings.

      Reviewer #3 (Public Review):

      Summary:

      The authors, Y Chang and colleagues, have performed elegant studies in transgenic mouse models that were designed to examine glutamatergic transmission in noradrenergic neurons, with a focus on respiratory regulation. They generated 3 different transgenic lines, in which a red fluorophore was expressed in dopamine-B-hydroxylase (DBH; noradrenergic and adrenergic neurons) neurons that did not express a vesicular glutamate transporter (Vglut) and a green fluorophore in DBH neurons that did express one of either Vglut1, Vglut2 or Vglut3.

      Further experiments generated a transgenic mouse with knockout of Vglut2 in DBH neurons. The authors used plethysmography to measure respiratory parameters in conscious, unrestrained mice in response to various challenges.

      Strengths:

      The distribution of the Vglut expression is broadly in agreement with other studies, but with the addition of some novel Vglut3 expression. Validation of the transgenic results, using in situ hybridization histochemistry to examine mRNA expression, revealed potential modulation of Vglut2 expression during phases of development. This dataset is comprehensive, wellpresented and very useful.

      In the physiological studies the authors observed that neither baseline respiratory parameters, nor respiratory responses to hypercapnea (5, 7, 10% CO2) or hypoxia (10% O2) were different between knockout mice and littermate controls. The studies are well-designed and comprehensive. They provide observations that are supportive of previous reports using similar methodology.

      Weaknesses:

      In relation to the expression of Vglut2, the authors conclude that modulation of expression occurs, such that in adulthood there are differences in expression patterns in some (nor)adrenergic cell groups. Altered sensitivity is provided as an explanation for different results between studies examining mRNA expression. These are likely explanations; however, the conclusion would really be definitive with inclusion of a conditional cre expressing mouse. Given the effort taken to generate this dataset, it seems to me that taking that extra step would be of value for the overall understanding of glutamatergic expression in these catecholaminergic neurons

      The seemingly dynamic Vglut2 expression pattern across the NA system is intriguing. As noted in our comments to reviewer 2, a robust age dependent interrogation would require a large magnitude study. The reviewer correctly points out that a temporally controlled recombinase fate mapping experiment would offer greater insight into the dynamic expression of Vglut2. We strongly agree with that idea and did work to develop a Vglut2-CreER targeted allele that, despite our many other successes in mouse genetic engineering (Lusk et al., 2022; Sun and Ray, 2016), did not succeed on the first attempt. We aim to complete the line in the near future so that we may better understand the Vglut2 expression pattern in central noradrenergic neurons in a time-specific manner and sex specific manner.

      The respiratory physiology is very convincing and provides clear support for the view that Vglut2 is not required for modulation of the respiratory parameters measured and the reflex responses tested. It is stated that this is surprising. However, comparison with the data from Abbott et al., Eur J Neurosci (2014) in which the same transgenic approach was used, shows that they also observed no change in baseline breathing frequency. Differences were observed with strong, coordinated optogenetic stimulation, but, as discussed in this manuscript, it is not clear what physiological function this is relevant to. It just shows that some C1 neurons can use glutamate as a signaling molecule. Further, Holloway et al., Eur J Neurosci (2015), using the same transgenic mouse approach, showed that the respiratory response to optogenetic activation of Phox2 expressing neurons is not altered in DBH-Vglut2 KO mice. The conclusion seems to be that some C1 neuron effects are reliant upon glutamatergic transmission (C1DMV for example), and some not.

      We agree that activation of C1 neurons may be sufficient to modulate breathing when artificially stimulated and that such stimulation relies on glutamatergic transmission for its effect. This is why we find our results surprising and important in clarifying for the field that glutamatergic signaling in noradrenergic cells is dispensable for breathing and hypoxic and hypercapnic responses under physiological conditions.

      Further contrast is made in this manuscript to the work of Malheiros-Lima and colleagues (eLife 2020) who showed that the activation of abdominal expiratory nerve activity in response to peripheral chemoreceptor activation with cyanide was dependent upon C1 neurons and could be attenuated by blockade of glutamate receptors in the pFRG - i.e. the supposition that glutamate release from C1 neurons was responsible for the function. However, it is interesting to observe that diaphragm EMG responses to hypercapnia (10% CO2) or cyanide, and the expiratory activation to hypercapnia, were not affected by the glutamate receptor blockade. Thus, a very specific response is affected and one that was not measured in the current study.

      As we mention above, we do not dispute that glutamate signaling can be manipulated to create a response in non-physiological conditions – we suggest that framing the interpretation around the glutamatergic role in a model that better matches physiological conditions should inform our interpretation. Furthermore, we do include an examination of expiratory flow – which was not impacted by loss of glutamatergic activity in NA neurons – which would be likely to have been impacted if abdominal expiratory nerve activity was modified.

      These previous published observations are consistent with the current study which provides a more comprehensive analysis of the role of glutamatergic contributions respiratory physiology. A more nuanced discussion of the data and acknowledgement of the differences, which are not actually at odds, would improve the paper and place the information within a more comprehensive model.

      Thank you for the comments. As noted in the original and extended discussion, we respectfully disagree with the perspective that our results align with prior results.

      Recommendations for the authors:

      The three reviewers believe this is an important study. They have numerous suggestions for improvement of the manuscript (outlined below), but no new experiments are required. The Editor requests some nomenclature changes as indicated in attachment 1.

      Reviewer #1 (Recommendations For The Authors):

      Abstract/Introduction: Although the need for this study is obvious, it is important that the authors explicitly communicate their working hypothesis < before the start of the work> to the reader. In the current form, it is unclear whether the authors aimed to test the hypothesis that glutamatergic signaling from noradrenergic neurons is important to breathing or whether to test the hypothesis that glutamatergic signaling from noradrenergic neurons is not important to breathing. If it is the latter-it is not important-then the study (related to the breathing measurements) is poorly justified and designed, as additional orthogonal approaches (e.g., actual measurements of glutamatergic signaling at the cellular level) are almost requisite. If the authors' hypothesis was originally based on existing literature suggesting that glutamatergic signaling from noradrenergic neurons is important to breathing, then the experimental design appropriate.

      Thank you for the suggestion. The working hypothesis has been added in the abstract (line 2425) and the introduction (line 92-94)), making clear that we initially hypothesized that glutamatergic signaling from noradrenergic neurons is important in breathing.

      Results: While the steady state measurements for breathing metrics are clearly important in defining how glutamatergic signaling may contribute to be pulmonary function, the role of glutamatergic signaling may have a greater role in the dynamics of patterns (i.e., regularity of the breathing rhythms) such traits can be described using SD1 and SD2 from Poincare maps, and/or entropy measurements. Such an analysis should be performed.

      Thank you for the suggestion. The dynamic patterns of respiratory rate (Vf), tidal volume (VT), minute ventilation (VE), inspiratory duration (TI), expiratory duration (TE), breath cycle duration (TTOT), inspiratory flow rate (VT/TI), expiratory flow rate (VT/TE) have been shown as Poincaré plots and quantified and tested using the SD1 and SD2 statistics in the supplemental figures of Figure 4-7.

      Results: Analyses of Inspiratory time (Ti) and flow rate (i.e., Tidal Volume / Ti) should be assessed and included.

      Thank you for the suggestion. Inspiratory duration (Ti), expiratory duration (TE), breath cycle duration (TTOT), inspiratory flow rate (VT/Ti), and expiratory flow rate (VT/TE) have been included in the Figures 4-7.

      Results/Methods: If similar analytical approaches were used in the current study as to that in Lusk et al. 2022, it appears that data was discontinuously sampled, rejecting periods of movement and only including periods of quiescent breathing. Were the periods of quiescent breathing different? Information should be provided to describe the total sampling duration included.

      For room air, the entire gas condition was used for data analysis. For hypercapnia (5% CO2, 7% CO2, 10% CO2), only the last 5 minutes of the gas challenge period was used for data analysis. For hypoxia (10% O2), we analyzed the breathing trace of three 5-minute epochs following initiation of the gas exposure separately, e.g., epoch 1 = 5-10min, epoch 2 = 10-15min, and epoch 3 = 15-20min. All breaths included as quiescent breathing were analyzed in the aggregate for each group and experimental condition, we did not compare individual periods of quiescent breathing within or across an animal(s)/group(s)/experimental condition(s). We have added the details in the Materials and Methods (line 637-642).

      Results: As mice were conscious in this study, were sniff periods (transient periods of fast breathing, i.e.,>8Hz) included in the analysis?

      No, only regular quiescent breathing periods were included in the analysis.

      Discussion: The authors need to discuss the limitations of their findings.

      • How should the reader interpret the findings? Concluding that glutamatergic signaling is dispensable implies that it occurs in room air, hypoxia, and hypercapnia.

      We have edited our discussion for clarity to highlight our conclusions that Vglut2-based glutamatergic signaling from noradrenergic neurons is ultimately dispensable for baseline breathing and hypercapnia and hypoxic chemoreflex in unanesthetized and unrestrained mice.

      • Assuming that glutamatergic signaling is active during the conditions tested, then the authors should discuss what may be the potential compensations.

      We have provided additional discussion surrounding potential compensatory events that may have taken place and could result in the unchanged phenotype in the experimental group.

      • The authors need to discuss how age and state of consciousness may play a role in their finds. The current discussion gives the impression that their findings are broadly applicable in all cases, but the lack of differences in this study may not hold true under different conditions.

      The study was done in adult (6–8-week-old) unanesthetized and unrestrained mice. In the discussion (line 472-474), we highlight that in our unpublished results, loss of NA-expressed Vglut2 does not change the survival curve in P7 neonate mice undergoing repeated bouts of autoresuscitation until death. Thus, we believed that Vglut2-based glutamatergic signaling in central NA neurons is dispensable for baseline breathing and the hypercapnic and hypoxic chemoreflexes in unanesthetized and unrestrained mice across different ages. Otherwise, we do not imply that we have interrogated any other aspects of breathing in our discussion.

      Methods: Further description of the analysis window for the respiratory metrics should be provided. Were breath values for each condition taken throughout the entire condition? This is particularly important for hypoxia, where the stereotypical respiratory response is biphasic.

      For room air, the entire gas condition was used for data analysis. For hypercapnia (5% CO2, 7% CO2, 10% CO2), only the last 5min of the gas challenge period was used for data analysis. For hypoxia (10% O2), we analyzed the breathing trace of three 5min time periods separately including 5-10min, 10-15min, and 15-20min during the hypoxic challenge as noted in our original manuscript, we graph and assess three 5min epochs during hypoxic exposure to capture the dynamic nature of the hypoxic ventilatory response. We have added the details in the Materials and Methods (line 637-642).

      Methods: How was consciousness determined?

      The conscious mice mentioned in the manuscript refer to the mice without anesthesia. We have replaced “awake” and “conscious” with “unanesthetized” in the text.

      Reviewer #2 (Recommendations For The Authors):

      Since no EEG/EMG recording was performed it would be more appropriate to remove "awake" and "conscious" throughout the manuscript and include the term "unanesthetized".

      Thank you for the suggestion. “Awake” and “conscious” have been replaced by “unanesthetized” in the text.

      Line 545: Why 32C? Isn't this temperature too high for animals?

      30-32°C is the thermoneutral zone for mice. It is the range of ambient temperature where mice can maintain a stable core temperature with their minimal metabolic rate (Gordon, 1985). Whole-body plethysmography uses the barometric technique to detect pressure oscillations caused by changes in temperature and humidity with each breathing act when an animal sits in a sealed chamber (Mortola et al., 2013). Thus, maintaining the chamber temperature near the thermoneutral zone during the plethysmography assay is required to maintain constancy in respiratory and metabolic parameters from trial to trial as well as to maintain linearity of ventilatory pressure changes due to humidification, rarefaction, and thermal expansion and contraction during inspiration and expiration (Ray et al., 2011). The chamber temperature that has been used for adult plethysmography has been set across a range 30-34°C (Hodges et al., 2008; Ray et al., 2011; Hennessy et al., 2017). We use 32°C in this manuscript which is consistent with previously published literature from other groups and our own work (Sun et al., 2017; Lusk et al., 2022).

      I would include the units of the physiological variables in the tables.

      Thank you for the suggestion. The units of the physiological variables have been added in all the tables.

      Reviewer #3 (Recommendations For The Authors):

      Why is the C3 group not considered in this study?

      The C3 adrenergic group, best characterized in rat, is only seen in rodents but not in many other species including primates (including human) (Kitahama et al., 1994). Thus, the C3 group is not the focus of this study where we aim to discuss if glutamate derived from noradrenergic neurons could be the potential therapeutic target of human respiratory disorders. The C3 adrenergic group is typically described as a population containing only about 30 neurons. We have added the fate map data and the adult expression pattern for the three vesicular glutamate transporters for the C3 group in the figure 1 and 2 supplements for reference.

      Sub CD/CV does not appear to be defined in the manuscript.

      Thank you for the point. The definition of sub CD/CV has been added in the text (line 126).

      The data on line 131-133 is interesting but could be described more effectively and clearly.

      Thank you for the suggestion. The text has been modified accordingly.

      The end of the paragraph at lines 140 onwards is rather repeated in the paragraph that starts at line 146.

      The repeated text has been removed accordingly.

      Whilst anterior and posterior are correct anatomical terms, for a quadraped, rostral and caudal are more widely used - particularly in the brainstem field. Is there a particular reason for using anterior/posterior?

      We followed the anatomical terminations in the Robertson et al. (2013) where they used anterior/posterior to describe C2/A2 and C1/A1.

      On the protocol lines include in Figure 4-7 it would be worth adding the test day. This seems a little strange. Why wait up to one week after the habituation to perform the stimulation. How many mice were left for each day between habituation and experimentation, and does this timing affect responses? Do mice forget the habituation after a period?

      Thank you for the point. We have added the test day for plethysmography in figures 4-7. After the 5 days of habituation, we began the plethysmography recordings on the sixth day. A maximum of 6 mice can be assayed for plethysmography per day due to the limited number of barometric flow through plethysmography and metabolic measurement systems we have. Thus, all animals were finished with plethysmography “within” one week of the last day of habituation. This protocol is consistent with our previous published work (Martinez et al., 2019; Lusk et al., 2022; Lusk et al., 2023). For the experiments in this manuscript, mice were assayed within 3 days after habituation. As noted in our methods and figures, each mouse is given as much as 40 mins to acclimate to the chamber (determined by directly observed quiet breathing) before data acquisition. We have no reason or evidence that indicates testing order and thus timing was a factor. The detailed explanation for the plethysmography protocol has been added in the material and methods section (line 606-625).

      Please state clearly that each mouse is only exposed to one gas mixture (what I interpret is the case), or could one mouse be exposed to several different stimuli?

      Each mouse is only exposed to one gas challenge (5% CO2, 7% CO2, 10% CO2, or 10% O2) in a testing period. Each testing period for an individual mouse was separated by 24hs to allow for a full recovery. The protocol is to put the mouse under room air for 45mins, switch to one gas challenge for 20mins, and switch back to room air for 20mins.

      With apologies if I missed this, but did each of the respiratory stimuli produce a statistically significant response in the control mice? For example, the response to 10%O2?

      Yes, each respiratory stimuli including 5/7/10% CO2 and 10% O2 produced a statistically significant response in both mutant and control mice. We have labeled the statistical significance in the Figures 4-7. Thank you for pointing this out.

      Line 312: Optogenetic stimulation induced an increase from 130 to 180 breaths per min (Abbott et al., EJN 2014). It is surprising that this is called "modest". Baseline respiratory frequency was presented.

      Thank you for the point. The word “modest” has been removed and the discussion has been changed accordingly (line 355-360).

      Line 338: This discussion is not sufficiently nuanced. It is the increased Dia amplitude (to KCN only, not 10%CO2 ) and the stimulation of active expiration, to both stimuli, that is blocked by kyn in pFRG. There is no effect of breathing frequency. The current study would not detect such differences in active expiration.

      Thank you for the suggestion. The discussion has been modified accordingly (line 382-388).

    1. Author response:

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

      eLife assessment

      In this important paper, Blin and colleagues develop a high-throughput behavioral assay to test spontaneous swimming and olfactory preference in individual Mexican cavefish larvae. The authors present compelling evidence that the surface and cave morphs of the fish show different olfactory preferences and odor sensitivities and that individual fish show substantial variability in their spontaneous activity that is relevant for olfactory behaviour. The paper will be of interest to neurobiologists working on the evolution of behaviour, olfaction, and the individuality of behaviour.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors posed a research question about how an animal integrates sensory information to optimize its behavioral outputs and how this process evolved. Their data (behavioral output analysis with detailed categories in response to the different odors in different concentrations by comparing surface and cave populations and their hybrid) partially answer this tough question. They built a new low-disturbance system to answer the question. They also found that the personality of individual fish is a good predictor of behavioral outputs against odor response. They concluded that cavefish evolved to specialize their response to alanine and histidine while surface fish are more general responders, which was supported by their data.

      Strengths:

      With their new system, the authors could generate clearer results without mechanical disturbances. The authors characterize multiple measurements to score the odor response behaviors, and also brought a new personality analysis. Their conclusion that cavefish evolved as a specialist to sense alanine and histidine among 6 tested amino acids was well supported by their data.

      Weaknesses:

      The authors posed a big research question: How do animals evolve the processes of sensory integration to optimize their behavioral outputs? I personally feel that, to answer the questions about how sensory integration generates proper (evolved) behavior, the authors at least need to show the ecological relevance of their response. For the alanine/histidine preference in cavefish, they need data for the alanine and other amino acid concentrations in the local cave water and compare them with those of surface water.

      We agree with the reviewer. This is why, in the Discussion section, we had written: “…Such significant variations in odor preferences or value may be adaptive and relate to the differences in the environmental and ecological conditions in which these different animals live. However, the reason why Pachón cavefish have become “alanine specialists” remains a mystery and prompts analysis of the chemical ecology of their natural habitat. Of note, we have not found an odor that would be repulsive for Astyanax so far, and this may relate to their opportunist, omnivorous and detritivore regime (Espinasa et al., 2017; Marandel et al., 2020).” This is also why we currently develop field work projects aimed at clarifying this question. However, such experiments and analyses are challenging, practically and technically. We hope we can reach some conclusions in the future.

      To complete the discussion we have also added an important hypothesis: “Alternatively, specialization for alanine may not need to be specific for an olfactory cue present only, or frequently, or in high amounts in caves. Bat guano for example, which is probably the main source of food in the Pachón cave, must contain many amino acids. Enhanced recognition of one of them - in the present case alanine but evolution may have randomly acted for enhanced recognition of another amino acid – should suffice to confer cavefish with augmented sensitivity to their main source of nutriment.”

      Also, as for "personality matters", I read that personality explains a large variation in surface fish. Also, thigmotaxis or wall-following cavefish individuals are exceeded to respond well to odorants compared with circling and random swimming cavefish individuals. However, I failed to understand the authors' point about how much percentages of the odorant-response variations are explained (PVE) by personality. Association (= correlation) was good to show as the authors presented, but showing proper PVE or the effect size of personality to predict the behavioral outputs is important to conclude "personality is matter"; otherwise, the conclusion is not so supported.

      From the above, I recommend the authors reconsider the title also their research questions well. At this moment, I feel that the authors' conclusions and their research questions are a little too exaggerated, with less supportive evidence.

      Thank you for this interesting suggestion, which we have fully taken into consideration. We have therefore now calculated and plotted PVE (the percentage of variation explained on the olfactory score) as a function of swimming speed or as a function of swimming pattern. The results are shown in modified Figure 8 of our revised ms and they suggest that the personality (here, swimming patterns or swimming speed) indeed predicts the olfactory response skills. Therefore, we would like to keep our title as we provide support for the fact that “personality matters”.

      Also, for the statistical method, Fisher's exact test is not appropriate for the compositional data (such as Figure 2B). The authors may quickly check it at https://en.wikipedia.org/wiki/Compositional_data or https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-042720-124436.

      The authors may want to use centered log transformation or other appropriate transformations (Rpackage could be: https://doi.org/10.1016/j.cageo.2006.11.017). According to changing the statistical tests, the authors' conclusion may not be supported.

      Actually, in most cases, the distributions are so different (as seen by the completely different colors in the distribution graphs) that there is little doubt that swimming behaviors are indeed different between surface and cavefish, or between ‘before’ and ‘after’ odor stimulation. However, it is true that Fisher’s exact test is not fully appropriate because data can be considered as compositional type. For this kind of data, centered log transformation have been suggested. However, our dataset contains many zeros, and this is a case where log transformations have difficulty handling.

      To help us dealing with our data, the reviewer proposed to consider the paper by Greenacre (2021) (https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-042720-124436). In his paper, Greenacre clearly wrote: "Zeros in compositional data are the Achilles heel of the logratio approach (LRA)."

      Therefore, we have now tested our data using CA (Correspondence Analysis), that can deal with table containing many zeros and is a trustable alternative to LRA (Cook-Thibeau, 2021; Greenacre, 2011).

      The results of CA analysis are shown in Supplemental figure 8 and they fully confirm the difference in baseline swimming patterns between morphs as well as changes (or absence of changes) in behavioral patterns after odor stimulation suggested by the colored bar plots in main figures, with confidence ellipses overlapping or not overlapping, depending on cases. Therefore, the CA method fully confirms and even strengthens our initial interpretations.

      Finally, we have kept our initial graphical representation in the ms (color-coded bar plots; the complete color code is now given in Suppl. Fig7), and CA results are shown in Suppl. Figure 8 and added in text.

      Reviewer #2 (Public Review):

      In their submitted manuscript, Blin et al. describe differences in the olfactory-driven behaviors of river-dwelling surface forms and cave-dwelling blind forms of the Mexican tetra, Astyanax mexicanus. They provide a dataset of unprecedented detail, that compares not only the behaviors of the two morphs but also that of a significant number of F2 hybrids, therefore also demonstrating that many of the differences observed between the two populations have a clear (and probably relatively simple) genetic underpinning.

      To complete the monumental task of behaviorally testing 425 six-week-old Astyanax larvae, the authors created a setup that allows for the simultaneous behavioral monitoring of multiple larvae and the infusion of different odorants without introducing physical perturbations into the system, thus biasing the responses of cavefish that are particularly fine-tuned for this sensory modality. During the optimization of their protocol, the authors also found that for cave-dwelling forms one hour of habituation was insufficient and a full 24 hours were necessary to allow them to revert to their natural behavior. It is also noteworthy that this extremely large dataset can help us see that population averages of different morphs can mask quite significant variations in individual behaviors.

      Testing with different amino-acids (applied as relevant food-related odorant cues) shows that cavefish are alanine- and histidine-specialists, while surface fish elicit the strongest behavioral responses to cysteine. It is interesting that the two forms also react differently after odor detection: while cave-dwelling fish decrease their locomotory activity, surface fish increase it. These differences are probably related to different foraging strategies used by the two populations, although, as the observations were made in the dark, it would be also interesting to see if surface fish elicit the same changes in light as well.

      Thank you for these nice comments.

      Further work will be needed to pinpoint the exact nature of the genetic changes that underlie the differences between the two forms. Such experimental work will also reveal how natural selection acted on existing behavioral variations already present in the SF population.

      Yes. Searching for genetic underpinnings of the sensory-driven behavioral differences is our current endeavor through a QTL study and we should be able to report it in the near future.

      It will be equally interesting, however, to understand what lies behind the large individual variation of behaviors observed both in the case surface and cave populations. Are these differences purely genetic, or perhaps environmental cues also contribute to their development? Does stochasticity provided by the developmental process has also a role in this? Answering these questions will reveal if the evolvability of Astyanax behavior was an important factor in the repeated successful colonization of underground caves.

      Yes. We will also access (at least partially) responses to most of these questions in our current QTL study.

      Reviewer #3 (Public Review):

      Summary:

      The paper explores chemosensory behaviour in surface and cave morphs and F2 hybrids in the Mexican cavefish Astyanax mexicanus. The authors develop a new behavioural assay for the longterm imaging of individual fish in a parallel high-throughput setup. The authors first demonstrate that the different morphs show different basal exploratory swimming patterns and that these patterns are stable for individual fish. Next, the authors test the attraction of fish to various concentrations of alanine and other amino acids. They find that the cave morph is a lot more sensitive to chemicals and shows directional chemotaxis along a diffusion gradient of amino acids. For surface fish, although they can detect the chemicals, they do not show marked chemotaxis behaviour and have an overall lower sensitivity. These differences have been reported previously but the authors report longer-term observations on many individual fish of both morphs and their F2 hybrids. The data also indicate that the observed behavior is a quantitative genetic trait. The approach presented will allow the mapping of genes' contribution to these traits. The work will be of general interest to behavioural neuroscientists and those interested in olfactory behaviours and the individual variability in behavioural patterns.

      Strengths:

      A particular strength of this paper is the development of a new and improved setup for the behavioural imaging of individual fish for extended periods and under chemosensory stimulation. The authors show that cavefish need up to 24 h of habituation to display a behavioural pattern that is consistent and unlikely to be due to the stressed state of the animals. The setup also uses relatively large tanks that allow the build-up of chemical gradients that are apparently present for at least 30 min.

      The paper is well written, and the presentation of the data and the analyses are clear and to a high standard.

      Thank you for these nice comments.

      Weaknesses:

      One point that would benefit from some clarification or additional experiments is the diffusion of chemicals within the behavioural chamber. The behavioural data suggest that the chemical gradient is stable for up to 30 min, which is quite surprising. It would be great if the authors could quantify e.g. by the use of a dye the diffusion and stability of chemical gradients.

      OK. We had tested the diffusion of dyes in our previous setup and we also did in the present one (not shown). We think that, due to differences of molecular weight and hydrophobicity between the tested dyes and the amino acid molecules we are using, their diffusion does not constitute a proper read-out of actual amino acid diffusion. We anticipate that amino acid diffusion is extremely complex in the test box, possibly with odor plumes diffusing and evolving in non-gradient patterns, in the 3 dimensions of the box, and potentially further modified by the fish swimming through it, the flow coming from the opposite water injection side and the borders of the box. This is the reason why we have designed the assay with contrasting “odor side” and “water control side”. Moreover, our question here is not to determine the exact concentration of amino acid to which the fish respond, but to compare the responses in cavefish, surface fish and F2 hybrids. Finally and importantly, we have performed dose/response experiments whereby varying concentrations have been presented for 3 of the 6 amino acids tested, and these experiments clearly show a difference in the threshold of response of the different morphs.

      The paper starts with a statement that reflects a simplified input-output (sensory-motor) view of the organisation of nervous systems. "Their brains perceive the external world via their sensory systems, compute information and generate appropriate behavioral outputs." The authors' data also clearly show that this is a biased perspective. There is a lot of spontaneous organised activity even in fish that are not exposed to sensory stimulation. This sentence should be reworded, e.g. "The nervous system generates autonomous activity that is modified by sensory systems to adapt the behavioural pattern to the external world." or something along these lines.

      Done

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      In addition to my comments in the "weakness" section above, here are my other comments.

      How many times fish were repeatedly assayed and what the order (alanine followed by cysteine, etc) was, is not clear (Pg 24, Materials and Methods). I am afraid that fish memorize the prior experience to get better/worse their response to the higher conc of alanine, etc. Please clarify this point.

      Many fish were tested in different conditions on consecutive days, indeed. Most often, control experiments (eg, water/nothing; water/water; nothing/nothing) were followed by odor testing. In such cases, there is no risk that fish memorize prior experience and that such previous experience interferes with response to odor. In other instances, fish were tested with a low concentration of one amino acid, followed by a high concentration of another amino acid, which is also on the safe side. Of note, on consecutive days, the odors were always perfused on alternate sides of the test box, to avoid possibility of spatial memory. Finally, in the few cases where increasing concentrations of the same amino acids were perfused consecutively, 1) they were perfused on alternate sides, 2) if the fish does not detect a low concentration below threshold / does not respond, then prior experience should not interfere for responding to higher concentrations, and 3) we have evidence (unpublished, current studies) that when a fish is given increasing concentrations of the same amino acid above detection threshold, then the behavioral response is stable and reproducible (eg does not decrease or increase).

      Minor points:

      Thygmotaxis and wall following.

      Classically, thigmotaxis and wall following are treated as the same (sharma et al., 2009; https://pubmed.ncbi.nlm.nih.gov/19093125/) but the authors discriminate it in thigmotaxis at X-axis and Y-axis because fish repeatedly swam back and forth on x-axis wall or y-axis wall. I understand the authors' point to discriminate WF and T but present them with more explanations (what the differences between them) in the introduction and result sections.

      Done

      Pg5 "genetic architecture" in the introduction.

      "Genetic architecture" analysis needs a more genomic survey, such as GWAS, QTL mapping, and Hi-C. Phenotype differences in F2 generation can be stated as "genetic factor(s)" "genetic component(s)", etc. please revise.

      Done

      Pg10 At the serine treatment, the authors concluded that "...suggesting that their detection threshold for serine is lower than for alanine." I believe that the 'threshold for serine is higher' according to the authors' data. Their threshold-related statement is correct in Pg21 "as SF olfactory concentration detection threshold are higher than CF,..." So the statement on page 10 is a just mistake, I think. Please revise.

      Done (mistake indeed)

      Pg11 After explaining Fig5, the statement "In sum, the responses of the different fish types to different concentrations of different amino acids were diverse and may reflect complex, case-bycase, behavioral outputs" does not convey any information. Please revise.

      OK. Done : “In sum, the different fish types show diverse responses to different concentrations of different amino acids.”

      For the personality analysis (Fig 7)

      The index value needs more explanation. I read the materials and methods three times but am still confused. From the equation, the index does not seem to exceed 1.0, unless the "before score" was a negative value, and the "after score" value was positive. I could not get why the authors set a score of 1.5 as the threshold for the cumulative score of these different behavior index values (= individual score). Please provide more description. Currently, I am skeptical about this index value in Fig 7.

      Done, in results and methods.

      Pg15 the discussion section

      Please discuss well the difference between the authors' finding (cavefish respond 10^-4M for position and surface fish responded 10^-4 for thig-Y; Fig 4AB), and those in Hinaux et al. 2016 (cavefish responded 10^-10M alanine but surface fish responded 10^-5M or higher). It seems that surface fish could respond to the low conc of alanine as cavefish do, which is opposed to the finding in Hinaux 2016.

      The increase in NbrtY at population level for surface fish with 10-4M alanine (~10-6M in box) was most probably due to only a few individuals. Contrarily to cavefish, all other parameters were unchanged in surface fish for this concentration. Moreover, at individual level, only 3.2% of surface fish had significant olfactory scores (to be compared to 81.3% for cavefish). Thus, we think that globally this result does not contradict our previous findings in Hinaux et al (2016), and solely represent the natural, unexplained variations inherent to the analysis of complex animal behaviors – even when we attempt to use the highest standards of controlled conditions.

      Of note, in the revised version, we have now included a full dose/response analysis for alanine concentration ranging from 10-2M to 10-10M, on cavefish. Alanine 10-5M has significant effects (now shown in Suppl Fig2 and indicated in text; a column has been added for 10-5M in Summary Table 1). Lower concentrations have milder effects (described in text) but confirm the very low detection threshold of cavefish for this amino acid.

      Pg19, "In sum, CF foraging strategy has evolved in response to the serious challenge of finding food in the dark"

      My point is the same as explained in the 'weakness' section above: how this behavior is effective in the cave life, if they conclude so? Please explain or revise this statement.

      The present manuscript reports on experiments performed in “artificial” and controlled laboratory conditions. We are fully aware that these conditions are probably distantly related to conditions encountered in the wild. Note that we had written in original version (page 20) “…for 6-week old juveniles in a rectangular box - but the link may be more elusive when considering a fish swimming in a natural, complex environment.” As the reviewer may know, we also perform field studies in a more ethological approach of animal behaviors, thus we may be able to discuss this point more accurately in the future.

      Pg20 "To our knowledge, this is the first time individual variations are taken into consideration in Astyanax behavioral studies."

      This is wrong. Please see Fernandes et al., 2022. (https://pubmed.ncbi.nlm.nih.gov/36575431/).

      OK. The sentence is wrong if taken in its absolute sense, i.e., considering inter-individual variations of a given parameter (e.g., number of neuromasts per individual or number of approaches to vibrating rod in Fernandez et al, 2022). In this same sense, Astyanax QTL studies on behaviors in the past also took into account variations among F2 individuals. Here, we wanted to stress that personality was taken into consideration. The sentence has been changed: “To our knowledge, this is the first time individual temperament is taken into consideration in Astyanax behavioral studies.”

      Figure 2B and others.

      The order of categories (R, R-TX, etc) should match in all columns (SF, F2, and CF). Currently, the category orders seem random or the larger ratio categories at the bottom, which is quite difficult to compare between SF, F2, and CF. Also, the writings in Fig 2A (times, Y-axis labels, etc), and the bargraphs' writings are quite difficult to read in Fig 2B, Fig 3B 4H, 5GN, 6EFG. Also, no need to show fish ID in Fig 2C in the current way, but identify the fish data points of the fish in Fig 2D (SF#40, CF#65, and F2#26) in Fig 2C if the authors want to show fish ID numbers in the boxplots. Fish ID numbers in other boxplot figures are recommended to be removed too.

      We have thought a lot on how to best represent the distributions of swimming patterns in graphs such as Fig 2B and others. The difficulty is due to the existence of many combinations (33 possibilities in total, see new Suppl Fig7), which are never the same in different plots/conditions because individual tested fish are different. We decided that that the best way was to represent, from bottom to top, the most used to the less used swimming patterns, and to use a color code that matches at best the different combinations. It was impossible to give the full color code on each figure, therefore it was simplified, and we believe that the results are well conveyed on the graphs. We would like to keep it as it is. To respond (partially) to the reviewer’s concern, we have now added a full color code description in a new Supplemental Figure 7 (associated to Methods).

      Size of lettering has been modified in all pattern graphs like Fig2A. Thanks for the suggestion, it reads better now.

      Finally, we would like to keep the fish ID numbers because this contributes to conveying the message of the paper, that individuality matters.

      Raw data files were not easy to read in Excel or LibreOffice. Please convert them into the csv format to support the rigor in the authors' conclusion.

      We do not understand this request. Our very large dataset must be analysed with R, not excel for stats or for plotting and pattern analysis. However, raw data files can be opened in excel with format conversion.

      Reviewer #2 (Recommendations For The Authors):

      I think most of the experimental procedures (with few exceptions, see below) are well-defined and nicely described, so the majority of my suggestions will be related to the visualization of the data. I think the authors have done a great job in presenting this complex dataset, but there are still some smaller tweaks that could be used to increase the legibility of the presented data.

      First and perhaps foremost, a better definition of the swimming pattern subsets is needed. I have no problem understanding the main behavioral types, but whereas the color codes for these suggest that there is continuous variance within each pattern, it is not clear (at least to me), what particular aspect(s) of the behaviors vary. Also, whereas the sidebars/legends suggest a continuum within these behaviors, the bar charts themselves clearly present binned data. I did not find a detailed description of how the binning was done. As this has been - according the Methods section - a manual process, more clarity about the details of the binning would be welcome. I would also suggest using binned color codes for the legends as well.

      Done, in Results and Methods. We hope it is now clear that there is no “continuum”, rather multiple combinations of discrete swimming patterns. The gradient aspect in color code in figures has been removed to avoid the idea of continuum. According to the chosen color code, WF is in red, R in blue, T in yellow and C in green. Then, combination are represented by colors in between, for example, R+WF is purple. We have now added a full color code description for the swimming patterns and their combinations in a new Supplemental Figure 7 (associated to Methods).

      Also, to better explain the definition of the swimming patterns and the graphical representation, it now reads (in Methods):

      “The determination of baseline swimming patterns and swimming patterns after odor injection was performed manually based on graphical representations such as in Figure 2A or Figure 3A. Four distinctive baseline behaviors clearly emerged: random swim (R; defined as haphazard swimming with no clear pattern, covering entirely or partly the surface of the arena), wall following (WF; defined as the fish continuously following along the 4 sides of the box and turning around it, in a clockwise or counterclockwise fashion), large or small circles (C; self explanatory), and thigmotactism (T, along the X- or the Y-axis of the box; defined as the fish swimming back and forth along one of the 4 sides of the box). On graphical representations of swimming pattern distributions, we used the following color code: R in blue, WF in red, C in green, T in yellow. Of note, many fish swam according to combination(s) of these four elementary swimming patterns (see descriptions in the legends of Supplemental figures, showing many examples). To fully represent the diversity and the combinations of swimming patterns used by individual fish, we used an additional color code derived from the “basic” color code described above and where, for example R+WF is purple. The complete combinatorial color code is shown in Suppl. Fig7.”

      It would be also easier to comprehend the stacked bar charts, presenting the particular swimming patterns in each population, if the order of different swimming patterns was the same for all the plots (e.g. the frequency of WF always presented at the bottom, R on the top, and C and T in the middle). This would bring consistency and would highlight existing differences between SF, CF, and F2s. Furthermore, such a change would also make it much easier to see (and compare) shifts in behaviors.

      We have thought a lot on how to best represent the distributions of swimming patterns in graphs such as Fig 2B and others. The difficulty is due to the existence of many combinations, which are never the same in different plots/conditions because the individual fish tested are different. We decided to keep it as it currently stands, because we think re-doing all the graphs and figures would not significantly improve the representation. In fact, we think that the differences between morphs (dominant blue in SF, dominant red in CF) and between conditions (bar charts next to each other) are easy to interpret at first glance in the vast majority of cases. Moreover, they are now completed by CA analyses (Suppl Figure 8).

      While the color coding of the timeline in the "3D" plots presented for individual animals is a nice feature, at the moment it is slightly confusing, as the authors use the same color palette as for the stacked bar charts, representing the proportionality of the particular swimming patterns. As the y-axis is already representing "time" here, the color coding is not even really necessary. If the authors would like to use a color scheme for aesthetic reasons, I would suggest using another palette, such as "grey" or "viridis".

      We would like to keep the graphical aspect of our figures as they are, for aesthetic reasons. To avoid confusion with stacked bar chart color code, we have added a sentence in Methods and in the legend of Figure 2, where the colors first appear:

      “The complete combinatorial color code is shown in Suppl. Figure 7. Of note, in all figures, the swimming pattern color code does not relate whatsoever with the time color code used in the 2D plus time representation of swimming tracks such as in Figure 2A”.

      I would also suggest changing the boxplots to violin-plots. Figure 7 clearly shows bimodality for F2 scores (something, as the authors themselves note, not entirely surprising given the probably poligenic nature of the trait), but looking at SF and CF scores I think there are also clear hints for non-normal distributions. If non-normal distribution of traits is the norm, violin-plots would capture the variance in the data in a more digestible way. (The existence of differently behaving cohorts within the population of both SF and CF forms would also help to highlight the large pre-existing variance, something that was probably exploited by natural selection as well, as mentioned briefly in the Discussion by the authors, too.)

      The bimodal distribution of scores shown by F2s in Figure 7B is indeed probably due to the polygenic nature of the trait. However, such distribution is rather the exception than the norm. Moreover, the boxplot representations we have used throughout figures include all the individual points, and outliers can be identified as they have the fish ID number next to them. This allows the reader to grasp the variance of the data. Again, redoing all graphs and figures would constitute a lot of work, for little gain in term of conveying the results. Therefore, we choose not to change the boxplot for violin plots.

      The summary data of individual scores in Table 1B shows some intriguing patterns, that warrant a bit further discussion, in my opinion. For example, we can see opposite trends in scores of SF and CF forms with increasing alanine concentration. Is there an easy explanation for this? Also, in the case of serine, the CF scores do not seem to respond in a dose-dependent manner and puzzlingly at 10^(-3)M serine concentration F2 scores are above those of both grandparental populations.

      That is true. However, we have no simple explanation for this. To begin responding to this question, we have now performed full dose/responses expts for alanine (concentrations tested from 10-2M to 10-10M on cavefish; confirm that CF are bona fide “alanine specialists”) and for serine (10-2M to 104M tested on both morphs; confirm that both morphs respond well to this amino acid). These complementary results are now included in text and figures (partially) and in the summary table 1.

      If anything is known about this, I would also welcome some discussion on how thigmotactic behavior, a marker of stress in SF, could have evolved to become the normal behavior of CF forms, with lower cortisol levels and, therefore lower anxiety.

      We actually think thigmotactism is a marker of stress in both morphs. See Pierre et al, JEB 2020, Figure S3A: in both SF and CF thigmotaxis behavior decreases after long habituation times. In our hands, the only difference between the two morphs is that surface fish (at 5 month of age) express stress by thigmotactism but also freezing and rapid erratic movements, while cavefish have a more restricted stress repertoire.

      This is why in the present paper we have carefully made the distinction between thigmotactism (= possible stress readout) and wall following (= exploratory behavior). Our finding that WF and large circles confers better olfactory response scores to cavefish is in strong support of the different nature of these two swimming patterns. Then, why is swimming along the 4 walls of a tank fundamentally different from swimming along one wall? The question is open, although the number of changes of direction is probably an important parameter: in WF the fish always swims forward in the same direction, while in T the fish constantly changes direction when reaching the corner of the tank – which is similar to erratic swim in stressed surface fish.

      Finally two smaller suggestions:

      • When referring to multiple panels on the same figure it would be better to format the reference as "Figure 4D-G" instead of "Figure 4DEFG";

      Done

      • On page 4, where the introduction reads as "although adults have a similar olfactory rosette with 2025 lamellae", in my opinion, it would be better to state that "while adults of the two forms have a similar olfactory rosette with 20-25 lamellae".

      Done

      Reviewer #3 (Recommendations For The Authors):

      Consider moving Figure 3 to be a supplement of Figure 4. This figure shows a water control and therefore best supplements the alanine experiment.

      We would like to keep this figure as a main figure: we consider it very important to establish the validity of our behavioral setup at the beginning of the ms, and to establish that in all the following figures we are recording bona fide olfactory responses.

      "sensory changes in mecano-sensory and gustatory systems " - mechano-sensory.

      Done

      Figure 2 legend: "(3) the right track is the 3D plus time (color-coded)" - shouldn't it be 2D plus time or 3D (x,y, time).

      True! Thanks for noting this, corrected.

      Figure 4 legend "E, Change in swimming patterns" should be H.

      Done

      "suggesting that their detection threshold for serine is lower than for alanine" - higher?

      Done

      In the behavioural plots, I assume that the "mean position" value represents the mean position along the X-axis of the chamber - this should be clarified and the axis label updated accordingly.

      That is correct and has been updated in Methods and Figures and legends.

      "speed, back and forth trips in X and Y, position and pattern changes (see Methods; Figure 7A)." - here it would be helpful to add an explanation like "to define an olfactory score for individual fish."

      This has been changed in Results and more detailed explanations on score calculations are now given in Methods.

      "possess enhanced mecanosensory lateral line" - mechanosensory.

      Done

    1. Author response:

      Reviewer #1 (Public Review):

      (1) Deleting ICP34.5 from the HSV construct has a very strong effect on HIV reactivation. Why is no eGFP readout given in Figure 1C as for WT HSV? The mechanism underlying increased activation by deleting ICP34.5 is only partially explored. Overexpression of ICP34.5 has a much smaller effect (reduction in reactivation) than deletion of ICP34.5 (strong activation); so the story seems incomplete.

      Thank you for your comment. (1) In Figure 1c, "HSV-wt" refers to the virus rescued from pBAC—GFP-HSV (as mentioned in the “Method” section), which carries GFP itself. Therefore, detecting GFP cannot distinguish between HSV infection and HIV reactivation. Hence, we assess the reactivation effect by measuring the mRNA levels of HIV LTR. (2) Our data indicate that overexpression of ICP34.5 inhibits the reactivation of the HIV latent reservoir, but this effect is not equivalent to the activation observed in HSV-1 with ICP34.5 deletion. There are some possible reasons: one is that the overexpression of ICP34.5 by lentivirus is randomly integrated into the genome of J-Lat cell line, which will potentially activate HIV latency to some extent. The other is that ICP34.5 mainly inhibited HIV reactivation through modulation of host NF-κB or HSF1 pathways, while PMA, TNF-a, and HSV-1 with deleted ICP34.5 can reactivate HIV latency by other mechanisms that have yet to be determined. Thereby, exerting a synergistic small inhibitory effect. We will further discuss this issue in the revised version. Thank you.

      (2) No toxicity data are given for deleting ICP34.5. How specific is the effect for HIV reactivation? An RNA seq analysis is required to show the effect on cellular genes.

      Thank you for your comment. We plan to conduct several experiments to demonstrate a reduction in HSV-1 replication after ICP34.5 deletion: (1) Detect the growth curve of HSV-1 deleted with ICP34.5 in Vero cells. The virus growth curve of HSV-1 with deleted ICP34.5 may be lower than that of wild-type HSV-1, which could demonstrate a reduction in HSV-1 replication after ICP34.5 deletion. (2) Detect the level of inflammatory factors in tumor cells after infection with HSV-1 deleted with ICP34.5.

      We believe that the effect is specific, as we previously tested poxviruses and adenoviruses and found no activation of the latent reservoir. We consider the activation observed with HSV-1 virus and HSV-1 with deleted ICP34.5 to be specific. We will supplement relevant data in the revised version.

      In addition, we will provide the corresponding RNA-seq data to assess its effect on cellular genes.

      (3) The primate groups are too small and the results to variable to make averages. In Figure 5, the group with ART and saline has two slow rebounders. It is not correct to average those with a single quick rebounder. Here the interpretation is NOT supported by the data.

      We agree with you that this is a pilot study of limited numbers of rhesus macaques. There were only 3 monkeys per group in this study, but our results were encouraging. Although the number of macaques was relatively limited, these nine macaques were distributed very carefully based on age, sex, weight and genotype. All SIV-infected macaques used in this study had a long history of SIV infection and had several courses of ART therapy, which mimics treatment of chronic HIV-1 infection in humans. These macaques were infected with SIVmac239 for more than 5 years, and highly pathogenic SIV-infected macaques have been well-validated as a stringent model to recapitulate HIV-1 pathogenesis and persistence during ART therapy in humans. Indeed, in our rhesus model, ART treatment effectively suppressed SIV infection to undetectable levels in plasma, and upon ART discontinuation, virus rapidly rebounded, which is very similar with that in ART-treated HIV patients. Our further studies will be expanded the scale of animals and then to preclinical and clinical study in our next projects. Thank you for your understanding.

      Discussion

      HSV vectors are mainly used in cancer treatment partially due to induced inflammation. Whether these are suitable to cure PLWH without major symptoms is a bit questionable to me and should at least be argued for.

      We will provide more data about the safety assessment of HSV-1 vector in SIV-infected macaques, and also further discuss the potential of inflammatory HSV vector in PLWH in the revised manuscript.

      Reviewer #2 (Public Review):

      (1) While the mechanism of ICP34.5 interaction and modulation of the NF-kB and HSF1 pathways are shown, this only proves ICP34.5 interactions but does not give away the mechanism of how the HSV-deltaICP-34.5 vector purges HIV-1 latency. What other components of the vector are required for latency reversal? Perhaps serial deletion experiments of the other ORFs in the HSV-deltaICP-34.5 vector might be revealing.

      We agree with your suggestion. In fact, we are currently further exploring some viral genes of HSV-1 that play a role in activation. We have found that the ICP0 gene of HSV-1 virus can activate HIV, and the specific mechanism is under investigation.

      (2) The efficacy of the HSV vaccine vectors was evaluated in Rhesus Macaque model animals. Animals were chronically infected with SIV (a parent of HIV), treated with ART, challenged with bi-functional HSV vaccine or controls, and discontinued treatment, and the resulting virus burden and immune responses were monitored. The animals showed SIV Gag and Env-specific immune responses, and delayed virus rebound (however rebound is still there), and below-detection viral DNA copies. What would make a more convincing argument to this reviewer will be data to demonstrate that after the bi-functional vaccine, the animals show overall reduction in the number of circulating latent cells. The feasibility of obtaining such a result is not clearly demonstrated.

      Thank you for your suggestion. We will plan to conduct IPDA experiments to further supplement data on the overall reduction in circulating latent cell numbers in animals.

      (3) The authors state that the reduced virus rebound detected following bi-functional vaccine delivery is due to latent genomes becoming activated and steady-state neutralization of these viruses by antibody response. This needs to be demonstrated. Perhaps cell-culture experiments from specimens taken from animals might help address this issue. In lab cultures one could create environments without antibody responses, under these conditions one would expect a higher level of viral loads to be released in response to the vaccine in question.

      We plan to use primary cells for related experiments to further validate the results of the cell experiments.

      (4) How do the authors imagine neutralizing HIV-1 envelope epitopes by a similar strategy? A discussion of this point may also help.

      Thank you for your comments. In fact, our study adopts the "shock and kill" strategy, with a focus on the "kill" aspect leaning towards T-cell therapy. Although the vaccine in the paper also utilizes Env antigen, we believe these antibodies are insufficient for neutralizing the mutated SIV virus. We strongly agree with your suggestion that in HIV/AIDS treatment, effective T-cell killing combined with broad-spectrum neutralizing antibodies would be more effective. This aligns with our findings, as our treatment has partially delayed viral rebound but with a relatively short duration of suppression. This may indicate insufficient killing activity. In future research, we will further consider the role of broad-spectrum neutralizing antibodies. Our revised manuscript will elaborate on this in the discussion section.

      (5) I thought the empty HSV-vector control also elicited somewhat delayed kinetics in virus rebound and neutralization, can the authors comment on why this is the case?

      We agree with you that the HSV-1 empty vector does exhibit somewhat a delayed rebound. The reason is that our treatment simultaneously utilizes both the HSV vector vaccine and ART therapy. Although the empty HSV-vector cannot elicit SIV-specific CTL response, it effectively activates the latent SIV reservoirs and then these activated virions can be partially killed by ART, Therefore, even without carrying antigens, the slight delay may be achieved.

    1. Author response:

      We would like to thank the eLife Editors and Reviewers for their positive assessment and constructive comments, and for the opportunity to revise our manuscript. We greatly appreciate the Reviewers’ recommendations and believe that they will further improve our manuscript.

      In revising the manuscript, our primary focus will be enhancing the clarity surrounding testing procedures and addressing corrections for multiple comparisons. Additionally, we intend to offer more explicit information about the statistical tests employed, along with the details about the number of models/comparisons for each test. We will also include an extended discussion on potential limitations of the dopaminergic receptor mapping methods used, addressing the Reviewers’ comments relating to the quality of PET imaging with different dopaminergic tracers in mesiotemporal regions such as the hippocampus. While the code used for connectopic mapping is publicly available through the ConGrads toolbox, we will provide the additional code we have used for data processing and analysis, visualization of hippocampal gradients, and the cortical projections. The data used in the current study is not publicly available due to ethical considerations concerning data sharing, but can be shared upon reasonable request from the senior author. Additional plans include clarifying and discussing which findings were successfully replicated, and addressing Reviewers’ suggestions for using other openly available cohorts for replication, and implementing alternative coordinate systems to quantify connectivity change along gradients.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript "comparative transcriptomics reveal a novel tardigrade specific DNA binding protein induced in response to ionizing radiation" aims to provide insights into the mediators and mechanisms underlying tardigrade radiation tolerance. The authors start by assessing the effect of ionizing radiation (IR) on the tardigrade lab species, H. exemplaris, as well as the ability of this organism to recover from this stress - specifically, they look at DNA double and single-strand breaks. They go on to characterize the response of H. exemplaris and two other tardigrade species to IR at the transcriptomic level. Excitingly, the authors identify a novel gene/protein called TDR1 (tardigrade DNA damage response protein 1). They carefully assess the induction of expression/enrichment of this gene/protein using a combination of transcriptomics and biochemistry - even going so far as to use a translational inhibitor to confirm the de novo production of this protein. TDR1 binds DNA in vitro and co-localizes with DNA in tardigrades.

      Reverse genetics in tardigrades is difficult, thus the authors use a heterologous system (human cells) to express TDR1 in. They find that when transiently expressed TDR1 helps improve human cell resistance to IR.

      This work is a masterclass in integrative biology incorporating a holistic set of approaches spanning next-gen sequencing, organismal biology, biochemistry, and cell biology. I find very little to critique in their experimental approaches.

      Strengths:

      (1) Use of trans/interdisciplinary approaches ('omics, molecular biology, biochemistry, organismal biology)

      (2) Careful probing of TDR1 expression/enrichment

      (3) Identification of a completely novel protein seemingly involved in tardigrade radio-tolerance.

      (4) Use of multiple, diverse, tardigrade species of 'omics comparison.

      Weaknesses:

      (1) No reverse genetics in tardigrades - all insights into TDR1 function from heterologous cell culture system.

      (2) Weak discussion of Dsup's role in preventing DNA damage in light of DNA damage levels measured in this manuscript.

      (3) Missing sequence data which is essential for making a complete review of the work.

      Overall, I find this to be one of the more compelling papers on tardigrade stress-tolerance I have read. I believe there are points still that the authors should address, but I think the editor would do well to give the authors a chance to address these points as I find this manuscript highly insightful and novel.

      We thank the reviewer for his comments.

      We agree that it will be important to further investigate the role of Dsup in radio-tolerance. We briefly mentioned this point in the discussion (p14). Our findings show that tardigrades undergo DNA damage at levels roughly similar to radio-sensitive organisms and therefore support a major role for DNA repair in the maintenance of genome integrity after exposure to IR. Nevertheless, we believe that more precise quantification of DNA damage may still reveal a contribution of genome protection to radio-tolerance of tardigrades compared to radio-sensitive organisms. Dsup loss of function experiments in tardigrades would clearly be the best way to assess this possibility. In the absence of experiments directly addressing the function of Dsup, we prefer to refrain from drawing any firm conclusion on prevention of DNA damage by Dsup and thus to keep a more open position. In any case, as discussed in the text, we note that Dsup has only been reported in Hypsibioidea and other molecular players, such as TDR1, are likely involved in radio-tolerance in other tardigrade species.

      The sequence data can be accessed at the NCBI SRA database with Bioproject ID PRJNA997229.

      Reviewer #3 (Public Review):

      Summary:

      This paper describes transcriptomes from three tardigrade species with or without treatment with ionizing radiation (IR). The authors show that IR produces numerous single-strand and double-strand breaks as expected and that these are substantially repaired within 4-8 hours. Treatment with IR induces strong upregulation of transcripts from numerous DNA repair proteins including Dsup specific to the Hypsobioidea superfamily. Transcripts from the newly described protein TDR1 with homologs in both Hypsibioidea and Macrobiotoidea supefamilies are also strongly upregulated. They show that TDR1 transcription produces newly translated TDR1 protein, which can bind DNA and co-localizes with DNA in the nucleus. At higher concentrations, TDR appears to form aggregates with DNA, which might be relevant to a possible function in DNA damage repair. When introduced into human U2OS cells treated with bleomycin, TDR1 reduces the number of double-strand breaks as detected by gamma H2A spots. This paper will be of interest to the DNA repair field and to radiobiologists.

      Strengths:

      The paper is well-written and provides solid evidence of the upregulation of DNA repair enzymes after irradiation of tardigrades, as well as upregulation of the TRD1 protein. The reduction of gamma-H2A.X spots in U2OS cells after expression of TRD1 supports a role in DNA damage.

      Weaknesses:

      Genetic tools are still being developed in tardigrades, so there is no mutant phenotype to support a DNA repair function for TRD1, but this may be available soon.

      We thank the reviewer for his comments.

      Reviewer #4 (Public Review):

      The manuscript brings convincing results regarding genes involved in the radio-resistance of tardigrades. It is nicely written and the authors used different techniques to study these genes. There are sometimes problems with the structure of the manuscript but these could be easily solved. According to me, there are also some points which should be clarified in the result sections. The discussion section is clear but could be more detailed, although some results were actually discussed in the results section. I wish that the authors would go deeper in the comparison with other IR-resistant eucaryotes. Overall, this is a very nice study and of interest to researchers studying molecular mechanisms of ionizing radiation resistance.

      I have two small suggestions regarding the content of the study itself.

      (1) I think the study would benefit from the analyses of a gene tree (if feasible) in order to verify if TDR1 is indeed tardigrade-specific.

      (2) It would be appreciated to indicate the expression level of the different genes discussed in the study, using, for example, transcript per millions (TPMs).Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

      We thank the reviewer for his comments.

      (1) To identify TDR1 homologous sequences in non-tardigrade species, we conducted extensive homology searches using multiple homology-based approaches (Blastp and Diamond against the NCBI non-redundant protein sequences (nr) database and hmmsearch against the EBI reference proteomes), which failed to identify TDR1 homologs in non-tardigrade ecdysozoans, thus strongly supporting that TDR1 is indeed tardigrade-specific.

      To be clearer in the manuscript, we now state the absence of hits for TDR1 in non-tardigrade ecdysozoans. Given the absence of homologs in non-tardigrade species, it is not possible to make a gene tree with non-tardigrade species.

      (2) To further document expression levels (which were already available from the Tables in the initial submission), we added MAplots (representing log2foldchange and logNormalized read counts) in the supplementary materials (Supp Figure 3 and Supp Figure 8). These additional figures clearly document that the DNA repair genes discussed in the main text and TDR1 are highly expressed genes after IR and after Bleomycin treatment.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      We thank the reviewer for his comments.

      (1) It has always seemed strange to me that tardigrades accumulate just as much DNA damage as any other organism when irradiated and yet their Dsup protein is supposed to shield and protect their DNA from damage. Perhaps this is an appropriate time for this idea to be reconsidered given the Dsup was NOT induced by IR in this study and the authors found that their animals incurred just as much damage as other biological systems. While Dsup is clearly not the focus of this manuscript, it is the protein most associated with tardigrade radio-tolerance and I would argue this new paper would call into question previous conclusions made about Dsup.

      We agree that it will be important to further investigate the role of Dsup in radio-tolerance. We briefly mentioned this point in the discussion (p14). Our findings show that tardigrades undergo DNA damage at levels roughly similar to radio-sensitive organisms and therefore support a major role for DNA repair in the maintenance of genome integrity after exposure to IR. Nevertheless, we believe that more precise quantification of DNA damage may still reveal a contribution of genome protection to radio-tolerance of tardigrades compared to radio-sensitive organisms. Dsup loss of function experiments in tardigrades would clearly be the best way to assess this possibility. In the absence of experiments directly addressing the function of Dsup, we prefer to refrain from drawing any firm conclusion on prevention of DNA damage by Dsup and thus to keep a more open position. In any case, as discussed in the text, we note that Dsup has only been reported in Hypsibioidea and other molecular players, such as TDR1, are likely involved in radio-tolerance in other tardigrade species.

      (2) While reverse genetics are difficult in tardigrades, they are not impossible, and RNAi can be used to good effect in these animals. In fact several authors on this manuscript have used RNAi to examine the necessity of genes in tardigrade stress tolerance in the past. Was an attempt made to RNAi TDR1? If not, why? With the large amount of work that the authors put into showing the sufficiency of TDR1 for increasing radiotolerance in cell culture, one would think looking at necessity in tardigrades would be of great interest. If RNAi was performed, what were the results? Even a negative result here is informative since a protein can be sufficient but not necessary for a function - if this were the case it would mean tardigrades have some redundant mechanism(s) for surviving radiation exposure beyond TDR1.

      We have attempted RNAi experiments targeting TDR1 or a mix of DNA repair genes (including XRCC5) and examined response to a bleomycin treatment of 2 weeks. Unfortunately, we could not distinguish any difference between uninjected animals and animals injected with TDR1 dsRNAs , or the mix of DNA repair genes dsRNAs. We concluded that, bleomycin treatment, that we used because it is much easier to perform than irradiation, was perhaps not the best way to assay a potential impact of RNAi on survival since it required long term treatment for several days during which the effect of RNAi may have waned. Another attempt was therefore made injecting with TDR1 or control GFP dsRNAs and exposing animals to a 2000Gy IR treatment. We noticed that the viability was lower after injection with GFP dsRNAs than with TDR1 dsRNAs (likely due to problems we had with the injection needle during injections). The next day, animals were irradiated and we observed after 24h that animals injected with GFP dsRNAs exhibited higher lethality rates than animals injected with TDR1 dsRNAs or uninjected animals. We found that this set of experiments were not conclusive. Our current experimental set up will make it difficult to distinguish lethality due to injections from lethality due to potentially decreased resistance to IR. In particular, many key controls are difficult to make (in particular, we could not confirm the efficiency of target gene knockdown, as it is very challenging given the low amount of biological material available and the poor expression of these genes without irradiation). From a practical point of view, performing these experiments is thus very challenging. We nevertheless agree that, in future work, further experimentation is needed to examine the impact of knock-down by RNAi of TDR1 or of other genes such as DNA repair genes or Dsup, in tardigrade DNA repair and survival after IR. Gene knock-out with CRISPR-Cas9 is a very promising alternative to RNAi given that studies in mutant lines will eliminate the confounding effect of lethality due to injections.

      (3) Regarding the U2OS experiments. I have several questions/points of clarification:

      a. Were survival/proliferation levels tested or only H2AX foci? I think that showing decreased H2AX foci (fewer double-stranded breaks) correlates with higher survival rates would be important.

      In the experiments reported in Figure 6, cells were transiently transfected with expression vectors and we did not examine the impact on survival rates. U2OS cells are resistant to high doses of Bleomycin and testing survival would require longer exposure at much higher concentrations (Buscemi et al, 2014, PMID: 25486478). In order to try and better address an impact on cell survival, we therefore generated populations of cells stably expressing the candidate tardigrade proteins fused to GFP. Despite trying different experiment conditions for treatment with Bleomycin, we could not detect a reproducibly significant benefit on cell survival for any of the tardigrade proteins tested, including RvDsup which was used as a positive control (since it was previously reported to improve cell survival in response to X-rays). One possibility is that the analysis should be performed in clones and not in populations of cells with heterogeneous expression levels of the tardigrade protein tested. For example, expression levels of the tardigrade protein needed to reduce the number of phospho-H2AX foci in response to DNA damage may interfere with cell division. We note that in the original Dsup paper, the benefit of RvDsup on cell survival was reported in specific transgenic clones. Experiments in different biological systems have also started to document toxic effects of RvDsup expression, illustrating the challenge, when performing experiments in heterologous systems, to achieve suitable expression levels of the tested protein. Trying to perform such a finer analysis, in our opinion, would go beyond the scope of our manuscript and will be best addressed in future studies. We are therefore careful in the text not to make any claim on the benefit of TDR1 expression on cell survival in response to Bleomycin in human cultured cells.

      (b) From the methods I am a bit confused as to how the images were treated/foci quantified. With the automatic segmentation and foci identification, is this done through the entire Z-series or a single layer? If the latter then I am not sure the results are meaningful, since we do not know how many foci might be present in other layers of the nuclei analyzed. If the former, please clarify this in the method since it is a very important consideration.

      We have acquired images throughout the entire Z-series and edited the text to make it more clear ; We now write: “ Z-stacks were maximum projected and analyzed with Zen Blue software (v2.3)...”. To limit the time needed for image analysis, we have generated an artificial image by projecting the entire Z-series into a single image and counted foci in that single maximum projection image. Although there are potential drawbacks, such as potentially only counting one focus when two foci are superposed along the Z axis, this approach overcomes the limitations of quantification from a single layer. We further ensured statistical robustness of the analysis by performing quantification from several independent fields of the labelled cells and several independent biological replicates (n>=3 as now specified in the legend of figure 6a).

      (c) RvDsup reduced levels of HXA1 foci in these experiments, however, HeDsup was not found to be enriched in the transcriptomic analysis performed here. Was there a reason HeDsup was not used in the cell-based experiments? One could argue that RvDsup is from a different species of tardigrade, but it is a bit concerning that an ortholog of a protein found NOT to be induced by radiation exposure seems to perform as well (if not better) than some versions of TDR1.

      RvDsup is the protein initially shown to increase survival of human HEK293 cells treated with X-rays and reduce the number of phospho-H2AX foci induced: it was therefore used as a positive control in our experiments. The sequence of HeDsup is only poorly similar to RvDsup (with 26% identity) and activity of HeDsup in cultured cells has not been reported before. We therefore believe that HeDsup is not well suited to provide a positive control for the experiments performed in our manuscript.

      (d) From the methods, it seems that cells were treated with Bleomycin and then immediately fixed without any sort of recovery time. In this short timeframe, the presence of TDR1 appears to be enough to deal with a substantial amount of double-stranded breaks (as evidenced by the reduced number of HXA1 foci). Does this make sense? How quickly could one expect DNA repair machinery to make significant progress in resolving damaged DNA? This response seems much faster than what was observed in tardigrades. Perhaps the authors to comment on this.

      Kinetic studies in human cells show extremely rapid repair of DNA double-strand breaks. Sensing of DNA double strand breaks by PARP proteins takes place within seconds after irradiation by IR (Pandey and Black, 2021, PMID: 33674152). NHEJ is then observed to take place by formation of 53BP1 foci within 15 minutes (Schultz et al, 2000, PMID: 11134068). The number of phospho-H2AX and 53BP1 foci peaks at 30 minutes and starts declining thereafter, showing that at a significant number of sites, DNA repair is proceeding very rapidly (by NHEJ). Although we are not aware of any studies of DNA repair kinetics in U2OS cells after addition of Bleomycin, DNA damage must be instantaneous and further take place during exposure to the drug in parallel to DNA repair, which would be expected to have similar kinetics than after irradiation with IR.

      In our experiments, several mechanisms may be involved in reducing the number of phospho-H2AX foci induced by Bleomycin, such as DNA protection (for Dsup expression) or stimulation of DNA repair (for RNF146 expression). For TDR1, the molecular mechanism involved remains to be determined. Given our finding that TDR1 can form aggregates with DNA, an additional possibility is that clustering of phospho-H2AX foci is induced.

      (4) I could not find the sequences of the TDR1 proteins studied here. I did find the cDNA sequence of HeTDR1 in the final supplementary file, but not the other TDR1 orthologs. In the place where it appeared the TDR1 sequences from other tardigrades should be there were very short segments of the HETDR1 sequence. All sequences of proteins used in this study should be easily accessible to the reader and reviewers as it is not possible to review this work without accessing the sequences.

      Our apologies for the inappropriate documentation of TDR1 sequences in the original manuscript. As requested, we have now included the TDR1 sequences in the Supplementary Table 4.

      (5) Likewise, the RNA sequence data is said to be deposited in NCBI under PRJNA997229, but I do not find this available on NCBI.

      The RNA sequence data was deposited in NCBI under the indicated reference before submission of the manuscript. The data has now been released and is fully available on NCBI.

      (6) A few typographical errors: e.g., Page 10 - sentence 4 has two periods ". ." or page 14 which has an open parenthesis that is not closed.

      These typos have been corrected in the revised manuscript.

      Reviewer #3 (Recommendations For The Authors):

      We thank the reviewer for his comments.

      In Figure 4C, what fraction of the 50 genes upregulated in all species and treatments are DNA repair genes? Is there any other notable commonality between these 50 genes? The bulk of upregulated genes are specific to a species and to treatment with IR or bleomycin. What fraction of DNA repair genes are specific to a species or treatment?

      The results in Figure 4C on the 50 putative orthologous genes upregulated in all species and treatments are further detailed in supp Figure 10. The legend to supp Figure 10 now provides the requested information: 14/50 genes are DNA repair genes and the other notable commonality is that 21/50 are “stress response genes”. We did not further breakdown the analysis to evaluate the fraction of DNA repair genes specific to a species or treatment. It will be interesting to gather data in more species to hed light on the evolutionary history of DNA repair gene regulation in response to IR.

      How does the suite of upregulated tardigrade DNA repair proteins after IR or bleomycin compare with DNA or repair proteins upregulated under similar treatments in human cells? Are they quantitatively or qualitatively different, or both?

      There is a great wealth of studies documenting genes differentially expressed in human cells in response to IR (e.g. Borras-Fresneda et al, 2016, PMID: 27245205; Rieger and Chu, 2004, PMID: 15356296; Budwoeth et al, 2012, PMID: 23144912 ; Rashi-Elkeles et al, 2011, PMID: 21795128; Jen and Cheung, 2003, PMID: 12915489...). Upregulation of DNA repair and cell cycle genes is commonly found. However, the number of DNA repair genes induced is always very limited and fold stimulation very modest compared to the massive upregulation observed in tardigrades.

      On page 14, please explain the acronym BER. Do the authors mean Base Excision Repair? Or something else?

      As assumed by the reviewer, the acronym BER stands for Base Excision Repair. The acronym has been removed from the main text and replaced by the full name.

      Reviewer #4 (Recommendations For The Authors):

      We thank the reviewer for his comments.

      Abstract:

      The abstract is fine. What was hard to grasp at the beginning is why TDR1 gene was named that way. It should be clearer that this study decided to further focus on that gene, one of the most overexpressed gene after IR, with an unknown function. Then maybe introduce that it was found to be unique to tardigrade and to interact with DNA. Therefore, it was named TDR1.

      Introduction:

      The introduction has been modified according to the suggestions of Reviewer#4 below. One of the suggested references, Nicolas et al 2023 from the Van Doninck lab, was published while our manuscript was under review and cannot be considered as background information for our study.

      1st paragraph:

      The study is on tardigrades, I found it strange that the first paragraph is on D. radiodurans. I think it is fine to mention what is known in bacteria and eucaryotes but we should already know what will be the main topic in the first paragraph of the introduction. Some details about D. radiodurans seem less important and distracting from the main topic (3D conformation).

      2nd paragraph:

      When mentioning radio-resistant eurcaryotes the authors do not mention the larvae of the anhydrobiotic insect Polypedilum vanderplanki. Stating that the mechanisms of resistance are poorly characterized should perhaps be nuanced. There are some recent studies on D. radiodurans (Ujaoney et al., 2017) the insect P. vanderplanki (Ryabova et al., 2017), tardigrades (Kamilari et al., 2019), and rotifers (Nicolas et al., 2023, Moris et al., 2023). Perhaps these papers are worth indicating that if mechanisms are not elucidated yet, recent studies suggest some actors involved in their resistance. Regarding the sentence stating that DNA repair rather than DNA protection plays a predominant role in the radio-resistance of bdelloid rotifers should also be nuanced. Indeed, many chaperones, antioxidants were mentioned to play a role in the radio-resistance of bdelloid rotifers (Moris et al., 2023). The authors mentioned the reference Hespeels et al., 2023 which is not found in their list of references, I am not sure which paper they refer to. The last sentence of the second paragraph does not mean much. I am not sure what the authors want to state with this. Perhaps they should specify if they mean that the function of many other genes overexpressed after IR remains unknown.

      Still, in the second paragraph, the authors focus on rotifers. They also do not mention what is known in the insect P. vanderplanki, which should be added. They still do not mention tardigrades. I think it is nice to first start with eucaryotes and then focus on tardigrades but as I mentioned before it would help to understand the aim of the paper if the first paragraph mentioned briefly the tardigrades and then could go into detail in the third paragraph.

      3rd paragraph:

      The sentence starting "with over 1400 species" best to remove from it "but they can differ in their resistance" and start the next sentence with that.

      4th paragraph:

      Very clear, we finally understand what is the focus of the manuscript.

      5th paragraph:

      Very clear. The authors should mention the names of the three studied species. Here, A. antarcticus is missing. The sentence "Further analyses in H. exemplaris... showed that TDR1 protein is present and upregulated". The authors should mention in which conditions the protein is upregulated. In that paragraph the authors mention phospho-H2AX: it might be good to introduce its functions before in the introduction (it is mentioned in the second sentence of the results: best to move it to the introduction).

      Results:

      There are a few sentences in this section which rather discuss the results than describe them. I think the manuscript might gain in quality if these interpretations of the results are moved into the discussion section. That would make the result section more concise and the discussion enriched.

      For instance, I suggest to move these sentences into the discussion:

      • "the finding of persistent DSBs in gonads at 72h.... likely explains...".

      • "suggesting that (i) DNA synthesis..."

      • " Phospho-H2AX....also suggested"

      • "Moreover, expression of TDR1-GFP..., supporting the potential role of TDR1 proteins..."

      • "our results suggest that RNF146 upreguation could contribute..."

      • "AMNP gene g12777 was shown to increase...Based on our results, it is possible that..."

      Interpretations mentioned here above were always introduced cautiously (-"suggesting that (i) DNA synthesis..." ; -" Phospho-H2AX....also suggested" ; -"Moreover, expression of TDR1-GFP..., supporting the potential role of TDR1 proteins..." ; -"our results suggest that RNF146 upreguation could contribute..." ). These cautious interpretations were usually important in deciding next steps of the work. We therefore believe it is important to mention these interpretations in the results section to clearly expose the milestones marking the progression of the study.

      For some results, they were directly discussed in the results section for the sake of concision (for example -"the finding of persistent DSBs in gonads at 72h.... likely explains..."; -"AMNP gene g12777 was shown to increase...Based on our results, it is possible that..." ) since, in our opinion, there was no need to mention them again in the main discussion.

      Some other parts could be good to be moved into the introduction:

      • "Previous studies have indicated that irradiation with IR increases expression of Rad51,..." none of the actors involved in DNA repair are mentioned in the introduction. Also, change resistant into resistance

      • "A. antarcticus ..., known for its resistant to high doses of UV....

      We have moved these parts to the introduction as recommended.

      It was in O. areolatus.... that the first demonstration..."

      This piece of information is somewhat anecdotical. We choose to keep it it here in the results section. This information on the radio-resistance of the species P. areolatus is only relevant at this specific step of the study because it encouraged us to consider that P. fairbanksi, which we isolated fortuitously, would be a good model species for studying radio-resistance of tardigrades.

      Here are some additional comments/suggestions on the result section:

      1st section

      • Remove the Gross et al., 2018 from the sentence "using confocal microscopy", it looks otherwise that these results are from their study, not yours.

      We have changed the text to make it clear that this is indeed a finding of Gross et al which was previously made in non-irradiated tardigrades. We replicated this finding, which showed that the protocol was working appropriately, and that we could use this control result for comparison with irradiated animals. We apologize for this confusion.

      The text now states: “Using confocal microscopy, we could detect DNA synthesis in replicating intestinal cells of control animals, as previously shown by (Gross et al. 2018).”

      2nd section

      • It is confusing what has been found induced by IR and/or by Bleomycin.

      • I think it might help if the authors first present what is induced after IR, then write if it is similar after Bleomycin. Especially since they start to do it in the first paragraph of that section. However, they only mention TDR1 in the second paragraph dedicated to Bleomycin treatment which is confusing as it is also overexpressed after IR. It is also not clear if RNF146 is also induced by Bleomycin.

      As recommended, the text presents first what is induced after IR and then what is induced by Bleomycin in the following paragraph. When reporting results with Bleomycin, we have provided a global assessment of what is common to both treatments in Supp Figure 3 and in Supp Table 3. In this figure, we also specifically highlighted several key genes of DNA repair induced by both treatments. These are also mentioned in the text (p8) to illustrate the point that many key DNA repair genes are common to both treatments. We have now added RNF146 to that list as recommended.

      • Regarding TDR1, it is not clear when introduced in the text as "promising candidate" why it is the case. It is clear in the figures but perhaps the authors should explain why they chose these genes for further analyses: high log2foldchange and expression level for instance. Regarding that last comment, it would be interesting to have an idea about the expression level of the genes with high log2foldchange. In Figures 2, 3, and 4 the pvalue and log2foldchange are represented but not the expression level (ideally Transcript per Millions). These values would give an additional idea on the importance of that gene. While looking at the figures, it is unclear why you did not further characterize other genes with high log2foldchange (some with even hints of their function): the mentioned RNF146, macroH2A1 (not even mentioned in the results), some genes unannotated in the figures with likely unknown functions,

      When selecting genes of interest, we did indeed take into account high expression levels. To more clearly document expression levels (which were already available from the Tables), we added MAplots (representing log2foldchange and logNormalized read counts) in the supplementary materials (Supp Figure 3 and Supp Figure 8).

      • It is also unclear at that stage why you named it "Tardigrade DNA damage response protein", as it is characterized as DNA repair/damage proteins by specific GO id or is it based on your downstream analyses, I think it might be worth to quickly mention the reason of that name.

      The name illustrates two points which were already characteristic at this point in time of the study i.e. 1) it is a tardigrade specific protein and 2) it is induced in response to DNA damage.

      • Regarding the BLAST analyses the protein was searched in C. elegans, D. melanogaster and H. sapiens. Why only these three species? What were the threshold evalues used for these analyses. As mentioned in the main comment, it would be worth searching species phylogenetically close to tardigrades to verify if it is well-tardigrade specific. Did you try to make a gene tree, after looking for a conserved domain (using hmmersearch)?

      As indicated in the methods section, the “Tardigrade-specific" annotation was determined by absence of hits after high-throughput alignment (with diamond using –ultrasensitive-option) on the NCBI nr database and absence of hits after blast search on C. elegans, D. melanogaster and H. sapiens proteomes as a complementary criterion (the latter blast search was primarily performed to enrich for functional annotations). Based on these criteria, TDR1 was annotated as “Tardigrade-specific”. As stated in the text, we also searched for TDR1 related sequences with 1) blastp (which is more sensitive than diamond) on the NCBI nr database and 2) HMMER on Reference Proteomes, and no hits were found among non-tardigrade ecdysozoans organisms, confirming TDR1 is specific to tardigrades. For Blast search for example, there were five hits in non-ecdysozoans organisms (two cephalochordates, one mollusc and two echinoderma). The blastp and HMMER results are now included in the revised supplementary material (Supp Table 5). These very few hits in species phylogenetically distant from tardigrades cannot be taken to support the existence of TDR1 genes outside tardigrades.

      To be clearer in the manuscript, we now state the absence of hits for TDR1 in non-tardigrade ecdysozoans. Given the absence of homologs in non-tardigrade species, it is not possible to make a gene tree with non-tardigrade species.

      • Page 9: "Proteins extracts from H. exemplaris... at 4h and 24h..." I think this sentence can be removed as this is mentioned again 2 paragraphs after: "...we conducted an unbiased proteome analysis... at 4h..." The log2foldchange threshold mentioned for the proteomic analyses is 0.3: why this threshold, was it chosen randomly?

      This is threshold is commonly used when considering log2foldchange with the technology used in our study, an isobaric multiplexed quantitative proteomic strategy which is known to compress ratios (Hogrebe et al. 2018).

      • Page 10:

      It would be good for more clarity to indicate at the beginning of the new section which species were investigated after IR or Bleomycin treatment.

      TDR1 homologs in the other tardigrade species were identified based on what? Best reciprocal hit?

      As indicated in the methods section of the manuscript, we searched for homologs in other tardigrade species by BLAST. A best reciprocal hit approach was not performed to try to determine which homologs might be orthologs. In particular, most TDR1 homologs identified are known from transcriptome assemblies and high-contiguity genome assemblies are needed to more confidently identify orthology (using synteny). The results of the BLASTP search are now provided as supplementary material (Supp Table 5).

      Preliminary experiments indicated that A. antarcticus and P. fairbanski survived exposure to 1000 Gy: is there a supplementary graph showing this?

      We have corrected the text to avoid any confusion. We have not rigorously examined the dose-dependent survival of P. fairbanksi in response to irradiation. Text was changed to: “We found by visual inspection of animals after IR that A. antarcticus and P. fairbanksi readily survived exposure to 1000 Gy.”

      • Page 11:

      "A set of 50 genes was upregulated in the three species": please be precise if only after IR.

      Done

      These genes cannot be the same as they are from different species. Did the author mean that they are coding for similar proteins? It might be good to give some more details even if the supplementary figure is mentioned.

      Obviously, these genes are putative orthologs. We have changed the text to:

      ” a set of 50 putative orthologous genes was upregulated in response to IR in all three species”

      Discussion:

      • General comment: the discussion is focused mainly on TDR1, it would be nice to also discuss the other results: DNA repair genes, RNF146.

      A whole paragraph is devoted to discussion of results on DNA repair genes and RNF146. We have extended that discussion following on the suggestion of the reviewer. In particular, we have explicitly mentioned the apparent paradox that XRCC5 and XRCC6, which are among the most highly stimulated genes at the mRNA level, only display modest upregulation at the protein level. Although further studies would be needed to examine the mechanisms involved, we propose that upregulation of RNF146, whose human homolog has been shown to drive degradation of PARylated XRCC5 and XRCC6 proteins in response to IR (Kang et al. 2011), may be responsible for higher degradation rates and may thus counterbalance increased levels of protein synthesis.

      • Pulse field electrophoresis would be nice to be performed. It has been used to assess DSBs in bdelloid rotifers, is it possible in tardigrades?

      As stated in the discussion, we believe that it would be challenging to perform pulse field electrophoresis in tardigrades. However, if possible, these experiments would certainly bring invaluable information to complement our analysis of DNA damage induced by IR.

      • "By comparative transcriptomics": please rephrase that sentence.

      • Proteins acting early in DNA repair: I am not sure I understand this sentence. Actors as ligases act not at the beginning of the repair pathways.

      Well noted. We have removed ligases from the list.

      • It is confusing that the authors mention NHEJ and double-strand break repair pathways as different pathways. There are 2 main pathways to repair DBSs: NHEJ and HR. It would be nice to add a reference to the sentence "PARP proteins act as sensors of DNA damage etc."

      A typo in the sentence gave rise to the misleading suggestion that NHEJ is not a double strand repair pathway. It has been corrected.

      A reference has been added for PARP proteins.

      • It would be nice if the authors can explain deeper their suggestion that degradation of DNA repair actors is essential for tardigrade IR resistance.

      We have expanded this part of the discussion and hope that it is clearer.

      “For XRCC5 and XRCC6, our studyestablished, by two independent methods, proteomics and Western blot analysies, that the stimulation at the protein level could be much more modest (6 and 20-fold at most (Supp Figure 6) than at the RNA level (420 and 90 fold respectively). This finding suggests that the abundance of DNA repair proteins does not simply increase massively to quantitatively match high numbers of DNA damages. Interestingly, in response to IR, the RNF146 ubiquitin ligase was also found to be strongly upregulated. RNF146 was previously shown to interact with PARylated XRCC5 and XRCC6 and to target them for degradation by the ubiquitin-proteasome system (Kang et al. 2011). To explain the lower fold stimulation of XRCC5 and XRCC6 at the protein levels, it is therefore tempting to speculate that, XRCC5 and XRCC6 protein levels (and perhaps that of other scaffolding complexes of DNA repair as well) are regulated by a dynamic balance of synthesis, promoted by gene overexpression, and degradation, made possible by RNF146 upregulation. Consistent with this hypothesis, we found that, similar to human RNF146 (Kang et al. 2011), He-RNF146 expression in human cells reduced the number of phospho-H2AX foci detected in response to Bleomycin (Figure 6).”

      • Page 15: Please add a reference for the sentence "Functional analysis of promotor sequences in transgenic tardigrades etc."

      The reference has been added to fix this omission.

      Material and Methods:

      Small comments:

      • 40 μm mesh: space missing

      • 100 μm mesh: space missing

      • (for Bleomycin)): parenthesis missing

      • remove "as indicated in the text"

      • The investigated time points after radiation need to be clearly stated in the method section. It is also unclear in the IR and Bleomycin section which tardigrades were treated with what. Not all were treated with Bleomycin.

      The small comments above have been fixed in the revised version of the manuscript.

      • Page 21: please precise the coverage of the RNA sequencing

      Statistics on mapping of RNAseq reads are now provided in Supp Table 10.

      • Page 22: Was any read trimming performed? Anything about the quality check of the reads?

      Trimming was conducted using trimmomatic (v0.39) and quality check using FastQC (v. ?) This information has been added to the Methods section.

      • Were the analyses confirmed by a second approach: for instance, EdgeR? Deseq2 and EdgeR do not always have the same results. For more robust analyses it is advised to use both.

      Differential transcriptome analyses were conducted with DESeq2 only. The robustness of our identification of differentially expressed genes in response to IR stems from performing comparative analyses in three different species, rather than from using two bioinformatics pipelines in a single species. We also note that benchmarking reported in the initial DEseq2 paper showed that identification of differentially expressed genes with large log fold changes (which, as reported in our manuscript, is characteristic of many DNA repair genes in response to IR) is very consistent between DEseq2 and EdgeR.

      Figures:

      • Figure 2: Legend vertical dotted line does not indicate log2foldchange value of 4 in all panels: it would be good to indicate for panels a and c as well.

      Figure 2has been improved following on the suggestions of the reviewer. Dotted lines now show log2foldchange value of 2 in all panels (ie Fold Change of 4 as mentioned in the main text).

      • Figure 2C: There are a few points with high log2foldchange which are not annotated: was it because nothing was found in the blast research? If yes, it would be good to indicate their functions. If not, it would be good to mention in the discussion that there are some genes with still unknown functions which might play an important role in the resistance of tardigrades to IR.

      The few points which are not annotated in figure 2c can now be found in Supp Table 3 Some of them have no hit in Blast search, some others such as BV898_09662 or BV898_07145 have hits on DNA repair genes as RBBP8/CtIP or XRCC6 respectively but are not annnotated as such by eggnog in KEGG pathway.

      • Figure 4C: Why not have included the response of P. fairbanski to bleomycin? I guess it was not done, but it is unclear in the results and methods sections.

      P.fairbanksi response to bleomycin wasn’t assessed as we didn’t get enough animals to run the study. The method section has been modified to precise this point.

    1. Author response:

      Reviewer #1 (Public Review):

      This study makes a substantial contribution to our understanding of the molecular evolutionary dynamics of microbial genomes by proposing a model that incorporates relatively frequent adaptive reversion mutations. In many ways, this makes sense from my own experience with evolutionary genomic data of microbes, where reversions are surprisingly familiar as evidence of the immense power of selection in large populations.

      One criticism is the reliance on one major data set of B. fragilis to test fits of these models, but this is relatively minor in my opinion and can be caveated by discussion of other relevant datasets for parallel investigation.

      We analyze data from 10 species of the Bacteroidales family, and we compare it to a dataset of Bacteroides fragilis. We have now added a reference to a recent manuscript from our group showing phenotypic alteration by reversion of a stop codon and further breaking of the same pathway through stop codons in other genes in Burkholderia dolosa on page 9, and have added a new analysis of codon usage in support of the reversion model on page 14.

      We have chosen not to analyze other species as there are no large data sets with rigorous and evenly-applied quality control across scales. We anticipate the reversion model would be able to fit the data in these cases. We now note that this work remains to be done in the discussion.

      Another point is that this problem isn't as new as the manuscript indicates, see for example https://journals.asm.org/doi/10.1128/aem.02002-20 .

      Loo et al puts forward an explanation similar to the purifying model proposed by Rocha et al, which we refute here. Quoting from Loo et al: “Our results confirm the observation that nonsynonymous SNPs are relatively elevated under shorter time periods and that purifying selection is more apparent over longer periods or during transmission.” While there is some linguistic similarity between the weak purifying model and our model of strong local adaptation model and strong adaptive reversion, we believe that the dynamical and predictive implications suggested by the reversion model are an important conceptual leap and correction to the literature. We now cite Loo et al and additional works cited therein. We have updated the abstract, introduction, and discussion to further emphasize the distinction of the reversion model from previous models: namely the implication of the reversion model that long-time scale dN/dS hides dynamics.

      Nonetheless, the paper succeeds by both developing theory and offering concrete parameters to illustrate the magnitudes of the problems that distinguish competing ideas, for example, the risk of mutational load posed in the absence of frequent back mutation.

      Reviewer #2 (Public Review):

      This manuscript asks how different forms of selection affect the patterns of genetic diversity in microbial populations. One popular metric used to infer signatures of selection is dN/dS, the ratio of nonsynonymous to synonymous distances between two genomes. Previous observations across many bacterial species have found dN/dS decreases with dS, which is a proxy for the divergence time. The most common interpretation of this pattern was proposed by Rocha et al. (2006), who suggested the excess in nonsynonymous mutations on short divergence times represent transient deleterious mutations that have not yet been purged by selection.

      In this study, the authors propose an alternative model based on the population structure of human gut bacteria, in which dN is dominated by selective sweeps of SNPs that revert previous mutations within local populations. The authors argue that contrary to standard population genetics models, which are based on the population dynamics of large eukaryotes, the large populations in the human gut mean that reversions may be quite common and may have a large impact on evolutionary dynamics. They show that such a model can fit the decrease of dN/dS in time at least as well as the purifying selection model.

      Strengths

      The main strength of the manuscript is to show that adaptive sweeps in gut microbial populations can lead to small dN/dS. While previous work has shown that using dN/dS to infer the strength of selection within a population is problematic (see Kryazhimskiy and Plotkin, 2008, cited in the paper) the particular mechanism proposed by the authors is new to my knowledge. In addition, despite the known caveats, dN/dS values are still routinely reported in studies of microbial evolution, and so their interpretation should be of considerable interest to the community.

      The authors provide compelling justification for the importance of adaptive reversions and make a good case that these need to be carefully considered by future studies of microbial evolution. The authors show that their model can fit the data as well as the standard model based on purifying selection and the parameters they infer appear to be plausible given known data. More generally, I found the discussion on the implications of traditional population genetics models in the context of human gut bacteria to be a valuable contribution of the paper.

      Thank you for the kind words and appreciation of the manuscript.

      Weaknesses

      The authors argue that the purifying selection model would predict a gradual loss in fitness via Muller's ratchet. This is true if recombination is ignored, but this assumption is inconsistent with the data from Garud, et al. (2019) cited in the manuscript, who showed a significant linkage decrease in the bacteria also used in this study.

      We now investigate the effect of recombination on the purifying selection model on page 8 and in Supplementary Figure S6. In short, we show that reasonable levels of recombination (obtained from literature r/m values) cannot rescue the purifying selection model from Muller’s ratchet when s is so low and the influx of new deleterious mutations is so high. We thank the reviewers for prompting this improvement.

      I also found that the data analysis part of the paper added little new to what was previously known. Most of the data comes directly from the Garud et al. study and the analysis is very similar as well. Even if other appropriate data may not currently be available, I feel that more could be done to test specific predictions of the model with more careful analysis.

      In addition to new analyses regarding recombination and compensatory mutations using the Garud et al data set, we have now added two new analyses, both using Bacteroides fragilis . First, we show that de novo mutations in Zhao & Lieberman et al dataset include an enrichment of premature stop codons (page 9). Second we show that genes expected to be under fluctuating selection in B. fragilis displays a significant closeness to stop codons, consistent with recent stop codons and reversions. We thank the reviewer for prompting the improvement.

      Finally, I found the description of the underlying assumptions of the model and the theoretical results difficult to understand. I could not, for example, relate the fitting parameters nloci and Tadapt to the simulations after reading the main text and the supplement. In addition, it was not clear to me if simulations involved actual hosts or how the changes in selection coefficients for different sites was implemented. Note that these are not simply issues of exposition since the specific implementation of the model could conceivably lead to different results. For example, if the environmental change is due to the colonization of a different host, it would presumably affect the selection coefficients at many sites at once and lead to clonal interference. Related to this point, it was also not clear that the weak mutation strong selection assumption is consistent with the microscopic parameters of the model. The authors also mention that "superspreading" may somehow make a difference to the probability of maintaining the least loaded class in the purifying selection model, but what they mean by this was not adequately explained.

      We apologize for leaving the specifics of the implementation from the paper and only accessible through the Github page and have corrected this. We have added a new section in the methods further detailing the reversion model and the specifics of how nloci and Tadapt (now tau_switch as of the edits) are implemented in the code.

      The possibility for clonal interference is indeed included in the simulation. Switching is not correlated with transmissions in our main figure simulations (Figure 4a). When we run simulations in which transmission and selection are correlated, the results remain essentially the same, barring higher variance at lower divergences (new Figure S10). We have now clarified these points in the results, and have also better clarified the selection only at transmission model in the main results.

      Reviewer #3 (Public Review):

      The diversity of bacterial species in the human gut microbiome is widely known, but the extensive diversity within each species is far less appreciated. Strains found in individuals on opposite sides of the globe can differ by as little as handfuls of mutations, while strains found in an individual's gut, or in the same household, might have a common ancestor tens of thousands of years ago. What are the evolutionary, ecological, and transmission dynamics that established and maintain this diversity?

      The time, T, since the common ancestor of two strains, can be directly inferred by comparing their core genomes and finding the fraction of synonymous (non-amino acid changing) sites at which they differ: dS. With the per-site per-generation mutation rate, μ, and the mean generation times roughly known, this directly yields T (albeit with substantial uncertainty of the generation time.) A traditional way to probe the extent to which selection plays a role is to study pairs of strains and compare the fraction of non-synonymous (amino acid or stop-codon changing) sites, dN, at which the strains differ with their dS. Small dN/dS, as found between distantly related strains, is attributed to purifying selection against deleterious mutations dominating over mutations that have driven adaptive evolution. Large dN/dS as found in laboratory evolution experiments, is caused by beneficial mutations that quickly arise in large bacterial populations, and, with substantial selective advantages, per generation, can rise to high abundance fast enough that very few synonymous mutations arise in the lineages that take over the population.

      A number of studies (including by Lieberman's group) have analyzed large numbers of strains of various dominant human gut species and studied how dN/dS varies. Although between closely related strains the variations are large -- often much larger than attributable to just statistical variations -- a systematic trend from dN/dS around unity or larger for close relatives to dN/dS ~ 0.1 for more distant relatives has been found in enough species that it is natural to conjecture a general explanation.

      The conventional explanation is that, for close relatives, the effects of selection over the time since they diverged has not yet purged weakly deleterious mutations that arose by chance -- roughly mutations with sT<1 -- while since the common ancestor of more distantly related strains, there is plenty of time for most of those that arose to have been purged.

      Torrillo and Lieberman have carried out an in-depth -- sophisticated and quantitative -- analysis of models of some of the evolutionary processes that shape the dependence of dN/dS on dS -- and hence on their divergence time, T. They first review the purifying selection model and show that -- even ignoring its inability to explain dN/dS > 1 for many closely related pairs -- the model has major problems explaining the crossover from dN/dS somewhat less than unity to much smaller values as dS goes through -- on a logarithmic scale -- the 10^-4 range. The first problem, already seen in the infinite-population-size deterministic model, is that a very large fraction of non-synonymous mutations would have to have deleterious s's in the 10^-5 per generation range to fit the data (and a small fraction effectively neutral). As the s's are naturally expected (at least in the absence of quantitative analysis to the contrary) to be spread out over a wide range on a logarithmic scale of s, this seems implausible. But the authors go further and analyze the effects of fluctuations that occur even in the very large populations: ~ >10^12 bacteria per species in one gut, and 10^10 human guts globally. They show that Muller's ratchet -- the gradual accumulation of weakly deleterious mutations that are not purged by selection - leads to a mutational meltdown with the parameters needed to fit the purifying selection model. In particular, with N_e the "effective population size" that roughly parametrizes the magnitude of stochastic birth-death and transition fluctuations, and U the total mutation rate to such deleterious mutations this occurs for U/s > log(sN_e) which they show would obtain with the fitted parameters.

      Torrillo and Lieberman promise an alternate model: that there are a modest number of "loci" at which conditionally beneficial mutations can occur that are beneficial in some individual guts (or other environmental conditions) at some times, but deleterious in other (or the same) gut at other times. With the ancestors of a pair of strains having passed through one too many individuals and transmissions, it is possible for a beneficial mutation to occur and rise in the population, only later to be reverted by the beneficial inverse mutation. With tens of loci at which this can occur, they show that this process could explain the drop of dN/dS from short times -- in which very few such mutations have occurred -- to very long times by which most have flipped back and forth so that a random pair of strains will have the same nucleotide at such sites with 50% probability. Their qualitative analysis of a minimally simple model of this process shows that the bacterial populations are plenty big enough for such specific mutations to occur many times in each individual's gut, and with modest beneficials, to takeover. With a few of these conditionally beneficial mutations or reversions occurring during an individuals lifetime, they get a reasonably quantitative agreement with the dN/dS vs dS data with very few parameters. A key assumption of their model is that genetically exact reversion mutations are far more likely to takeover a gut population -- and spread -- than compensatory mutations which have a similar phenotypic-reversion effect: a mutation that is reverted does not show up in dN, while one that is compensated by another shows up as a two-mutation difference after the environment has changed twice.

      Strengths:

      The quantitative arguments made against the conventional purifying selection model are highly compelling, especially the consideration of multiple aspects that are usually ignored, including -- crucially -- how Muller's ratchet arises and depends on the realistic and needed-to-fit parameters; the effects of bottlenecks in transmission and the possibility that purifying selection mainly occurs then; and complications of the model of a single deleterious s, to include a distribution of selective disadvantages. Generally, the author's approach of focusing on the simplest models with as few as possible parameters (some roughly known), and then adding in various effects one-by-one, is outstanding and, in being used to analyze environmental microbial data, exceptional.

      The reversion model the authors propose and study is a simple general one and they again explore carefully various aspects of it -- including dynamics within and between hosts -- and the consequent qualitative and quantitative effects. Again, the quantitive analysis of almost all aspects is exemplary. Although it is hard to make a compelling guess of the number of loci that are subject to alternating selection on the needed time-scales (years to centuries) they make a reasonable argument for a lower bound in terms of the number of known invertible promoters (that can genetically switch gene expression on and off).

      We are very grateful for the reviewer’s kind words and careful reading.

      Weaknesses:

      The primary weakness of this paper is one that the author's are completely open about: the assumption that, collectively, any of possibly-many compensatory mutations that could phenotypically revert an earlier mutation, are less likely to arise and takeover local populations than the exact specific reversion mutation. While detailed analysis of this is, reasonably enough, beyond the scope of the present paper, more discussion of this issue would add substantially to this work. Quantitatively, the problem is that even a modest number of compensatory mutations occurring as the environmental pressures change could lead to enough accumulation of non-synonymous mutations that they could cause dN/dS to stay large -- easily >1 -- to much larger dS than is observed. If, say, the appropriate locus is a gene, the number of combinations of mutations that are better in each environment would play a role in how large dN would saturate to in the steady state (1/2 of n_loci in the author's model). It is possible that clonal interference between compensatory and reversion mutations would result in the mutations with the largest s -- eg, as mentioned, reversion of a stop codon -- being much more likely to take over, and this could limit the typical number of differences between quite well-diverged strains. However, the reversion and subsequent re-reversion would have to both beat out other possible compensatory mutations -- naively less likely. I recommend that a few sentences in the Discussion be added on this important issue along with comments on the more general puzzle -- at least to this reader! -- as to why there appear to be so little adaptive genetic changes in core genomes on time scales of human lifetimes and civilization.

      We now directly consider compensatory mutations (page 14, SI text 3.2, and Supplementary Figure 12). We show that as long as true reversions are more likely than compensatory mutations overall, (adaptive) nonsynonymous mutations will still tend to revert towards their initial state and not contribute to asymptotic dN/dS, and show that true reversions are expected in a large swath of parameter space. Thank you for motivating this improvement!

      We note in the discussion that directional selection could be incorporated into the parameter alpha (assuming even more of the genome is deleterious) on page 16.

      An important feature of gut bacterial evolution that is now being intensely studied is only mentioned in passing at the end of this paper: horizontal transfer and recombination of core genetic material. As this tends to bring in many more mutations overall than occur in regions of a pair of genomes with asexual ancestry, the effects cannot be neglected. To what extent can this give rise to a similar dependence of dN/dS on dS as seen in the data? Of course, such a picture begs the question as to what sets the low dN/dS of segments that are recombined --- often from genetic distances comparable to the diameter of the species.

      We now discuss the effect of recombination on the purifying selection model on page 8 and in Supplementary Figure S6. In short, we now show that reasonable levels of recombination cannot rescue the purifying selection model from Muller’s ratchet when s is so low and the influx of new deleterious mutations is so high. We thank the reviewers for prompting this improvement

    1. Author response:

      Reviewer #1 (Public Review):

      1) Napthylamine (1NA), an industrial reagent used in the manufacturing of dyes and pesticides is harmful to humans and the environment. In the current manuscript, the authors report the successful isolation of a Pseudomonas strain from a former naphthylamine manufacturing site that is capable of degrading 1NA. Using genetic and enzymatic analysis they identified the initial stages of 1NA degradation and the enzymes responsible for downstream processing of 1,2-dihydroxynapthalene and Salicylate. The authors determined the molecular structure of NpaA1, the first enzyme in the pathway responsible for glutamylation of 1NA. NpaA1 has a border substrate specificity compared to previously characterized enzymes involved in aromatic amine degradation. They carried out structural comparison of NpaA1 with glutamine synthase structures, alfa-fold models of similar enzymes and put forth hypothesis to explain the broad substrate specificity of NpaA1.

      The manuscript is well written and easy to understand. The authors carried out careful genetic analysis to identify the genes/enzymes responsible for degradation of 1NA to catechol. They characterized the first enzyme in the pathway, NpaA1 which is responsible glutamylation of 1NA. and determined the molecular structure of apo-NpaA1, NpaA1 - AMPPNP complex and Npa1 - ADP - Met-Sox-P complex using X-ray crystallography.

      The proposed mechanism of broad substrate specificity of NpaA1, however, is based on comparison of 1NA docked NpaA1 structure with St-GS (Glutamate synthase) and Alphafold2 predicted model of AtdA1 from an aniline degrading strain of Acinetobacter sp. Lack of molecular structure or mutational studies to back the proposed mechanism makes it difficult to agree with the proposed mechanism.

      We appreciate your valuable comments. To further demonstrate that the structure of the aromatic amine binding tunnel and active pocket determines the broad substrate specificity of NpaA1, we have conducted additional experiments with several key residue mutants of the binding tunnel for naphthylamine and monocyclic aniline activities. The results provide a more detailed elucidation of the reasons for NpaA1's broad substrate specificity. Specific results and analyses are provided in the subsequent response.

      Reviewer #2 (Public Review):

      Microbial degradation of synthetic organic compounds is the basis of bioremediation. Biodegradation of 1NA has not been previously reported. The report describes a complete study of 1NA biodegradation by a new isolate Pseudomonas sp. strain JS3066. The study includes the enrichment and isolation of the 1NA-degrading bacterium Pseudomonas sp. strain JS3066, the identification of the genes and enzymes involved in 1NA degradation, and the detailed characterization of γ-glutamylorganoamide synthetase by using biochemical and structural analysis. In the discussion, the potential evolution of 1NA degradation pathway, the similarity and difference between γ-glutamylorganoamide synthetase and glutamine synthetase, and the significance were explained. The conclusions were well supported by the results presented.

      We deeply appreciate the reviewer’s comments on the manuscript. We have responded to the recommendations one by one in the later section.

    1. Author response:

      Reviewer #1 (Public Review):

      “… it remains unclear how ninein reduction causes bone defects …”

      We have added several control experiments that permit us to conclude that osteoblast numbers remain unaltered in the ninein-knockout embryos, and that bone abnormalities in vivo are caused by fusion defects of osteoclast precursor cells, whereas the proliferation, viability, or the adhesion of these precursor cells remain unaffected. For details, please see our comments below.

      “Discussion includes several unfounded potential mechanisms that really need to be thoroughly analyzed to gain a mechanistic understanding of the bone defects…”

      The new data back up our claim of fusion defects as a cause for limited osteoclast function. We have re-written parts of the discussion, to take into account our new findings.

      “Data showing normal osteoblasts in ninein-null mice was qualitative and requires further in-depth analysis and quantification of osteoblast …”

      To address this point, quantification of osteoblast numbers in tibiae at E16.5 and E18.5 was performed in control and ninein-deleted mouse embryos. The data are presented in the new Figures 3G and J.

      “In ninein knock-out mice, reduced TRAP+ve multinuclear cells were observed (Figure 6A and 6B). However, the magnitude of difference (about 5% decrease in multinucleated cells) is not consistent with the skeletal deformities reported in Figures 2-4, potentially suggesting the contribution of additional mechanisms.”

      We agree that the difference appears to be small at first glance, but nevertheless it remains statistically significant (a more than three-fold difference). We would like to recall that these observations (Fig. 6A) were performed at E14.5, i.e. at a stage when no ossification has occurred yet. We are looking at the first fusion events of myeloid precursors, likely derived from the fetal liver, that colonize the area of the first bone to form, and small differences in the number of functional osteoclasts may account for different timing of ossification. We think that differences in osteoclast fusion also account for the premature appearance of ossification centers for other skeletal elements, at later time points during development.

      “The fusion assay in Figure 6C needs further clarification. How was the syncytia perimeter defined to measure cell surface? The x-axis suggests that there are syncytia that contain up to 160 nuclei at day 3. How were the nuclei differentially stained and quantified?”

      We provide now additional information on the experimental approach in the revised manuscript, on pages 16-17 (Materials and Methods). For information: high numbers of syncytial nuclei in cultures were also observed by other groups in the past (Tiedemann et al., 2017, Front Cell Dev Biol. 5:54). In addition, we performed new experiments and quantified the fusion of osteoclast precursors by staining for actin and nuclei (new Figure 7C). This allowed us to quantify several additional parameters related to cell fusion (as initially performed in Raynaud-Messina et al., 2018, PNAS, 115:E2556-E2565).

      “Some text needs clarification. … What is the definition of "large syncytia"? Is the fusion index increase by day 5 diminished in later days? A graph of the syncytia size/ nuclei number or fusion index in the above-mentioned days will be helpful.”

      Information on the definition of “large syncytia” is now provided on page 10 (1st paragraph). We added further experimental details on osteoclast size for days 3, 4, and 5 in the supplemental Figures 7A and B. Most importantly, we performed additional assays of the fusion index by quantifying syncytial versus non-syncytial nuclei in a semi-automated manner. The new data are presented in Figure 7C, and the methods are explained on page 17. Together with our new analysis of cell proliferation, cell viability, and cell adhesion (Figure 7C, D, suppl. Fig. 7C-G), we provide now solid evidence for a fusion defect at the origin of impaired formation of ninein del/del osteoclasts.

      “Assessment of resorption was qualitative in Figure 6E and since the fusion deficiencies are transient, quantification of a corresponding resorption activity is needed. This should be described in the Materials and Methods section.”

      Quantifications of the bone resorption activities are now provided in the new Figure 7E, and a reference for the methods is provided on page 16.

      “Further experiments are needed to show connections between reduced centrosome clustering and reduced osteoclast formation as there is no evidence to date that suggest centrosome clustering is required for cell fusion. Multi-color live imaging and dynamic analysis can be used to determine if the ninein deficient cells show defective movement/migration/ fusion dynamics.”

      We agree that it is an important question, and studying potential links between centrosomal microtubule organization and osteoclast fusion is an ongoing project of the team. However, we estimate that in order to obtain conclusive results this will require 1-2 additional years of research activity, and we intend to present this as a separate project in the future. At the current point of our investigation, we think that providing a solid link between ninein, osteoclast fusion, and controlled timing of ossification, as shown in this manuscript, represents valuable progress to understand previously published bone abnormalities in patients with ninein mutations.

      “Quantification of the % of multinucleated osteoclasts that contain clustered and dispersed centrosomes is needed.”

      New quantification experiments on centrosome clustering are now provided in Figure 8H. These quantifications demonstrate that the potential of centrosome clustering is almost completely lost in osteoclasts without ninein.

      Reviewer #2 (Public Review):

      “Based on the decrease in the number of osteoclasts (Fig 5E, G, and also per coverslip after 2 days in culture), the authors suggest that the loss of ninein impacts osteoclast proliferation. First, proliferation can be directly quantified using Ki67 staining or EdU incorporation. Second, other interpretations are also plausible and can also be experimentally tested. These include less adhesion and attachment of the mutants to the coverslips, but perhaps more relevant in vivo is cell death of the ninein mutant osteoclasts. It has been established that the loss of centrosome function activates p53- dependent cell death and osteoclasts might be a vulnerable cell population. Quantifying p53 immunoreactivity and/or cell death in osteoclasts might help clarify the phenotype of osteoclast reduction.”

      In response to the reviewers, we have performed a series of new experiments that include

      1) A careful analysis of the fusion index, using a semi-automated approach, indicating significant differences in the fusion of precursor cells into osteoclasts (Fig. 7C).

      2) We have repeated the quantification of cell numbers prior to fusion and find variations between samples from different mice (also among mice of the same genotype), but we see on average comparable cell adhesion between samples from control mice and ninein-del/del mice. The data are provided in the supplemental Figure 7F. Moreover, we have quantified the expression of three main beta-integrins at the surface of control and ninein del/del osteoclast precursors (suppl. Fig. 7G), without detecting significant differences. Altogether, these data suggest the cell adhesion is comparable for the two genotypes.

      3) We have addressed the question of altered cell proliferation, by performing flow cytometry experiments and by quantifying the different cell cycle stages (Fig. 7D), and by quantifying Ki67 expression (suppl. Fig. 7C). We see no significant differences between samples from control and ninein-del/del mice.

      4) We have addressed the question of cell death, by performing Annexin V staining and flow cytometry (suppl. Fig. 7D), and by immunoblotting for cleaved caspase 3 and PARP (suppl. Fig. 7E). These experiments reveal no significant differences between the control and ninein del/del samples. Our data permit us to exclude cell death as a likely cause for the reduction of fused osteoclasts in the absence of ninein.

      Overall, the new experiments show that the defects in osteoclast formation from ninein-deleted samples are due to defects in cell fusion, but not in cell proliferation, cell adhesion or viability.

      Reviewer #3 (Public Review):

      “The authors put much emphasis on the centrosome in the Introduction session. However, it was not until Figure 7 did they show abnormal centriole clustering in osteoclasts. The introduction should include more background on osteoclast and osteoblast balance during skeletal development.”

      To address this, we included more background on the role of osteoclasts and osteoblasts in the revised introduction (page 4).

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      Results showing reactivation for near and far items separately are now included in Fig. 5 and convincingly suggest a simultaneous reactivation. For me, the open question remaining (see public) review is the degree to which the methods used here to show clustered vs sequential reactivation are mutually exclusive; and if the pre-selection of a time window of peak reactivation (based on all future items) biases the analyses towards clustered reactivation. The discussion would benefit from a brief discussion of these issues.

      We have added a brief discussion of the issues. However, we want to clarify a minor point of the public review: While our interpretation implies that replay and reactivation are probably mutually exclusive within a single retrieval event, it does not imply that strategies cannot vary within different retrieval events of the same participant. Nevertheless, we want to address this raised concern (that is, if we understand correctly, that replay events that are contained within the time window of the reactivation analysis could not be distinguished by the chosen methods) and have added it to the discussion.

      The corresponding sentence reads:

      “[…] Finally, we want to acknowledge that by selecting a time window for the clustered reactivation we cannot distinguish very fast replay events (<=30ms) from clustered reactivation if they are contained exactly within the specific reactivation analysis time window..

      Reviewer #2 (Recommendations For The Authors):

      Figure 5D shows the difference scores between near vs. distant items for learning and retrieval. Similar to Figure 5 from the first version of your paper, the difference score does not show whether reactivation of the near vs. distant items change from learning to retrieval. You could show this change in a 2 (near vs. distant) x 2 (learning vs. retrieval) box plot (corresponding to Figure 5A).

      We have added the requested plot as supplement 9 and referred to it in the figure description. However comparing absolute, raw probabilities between different blocks is tricky, as baseline probabilities are varying over time (e.g. due to shift in distance to sensors), therefore, differential reactivation might be better suited as it is a relative measure to compare between blocks.

      At the end of the results section, you state: "On average, differential reactivation probability increased from pre to post resting state (Figure 5D).". I would suggest providing some statistical comparison and the corresponding values.

      We have calculated and added respective p-value statistics of a T-Test and reported that the increase is only descriptive and not statistically significant.

    1. Author response:

      We thank both the reviewers for their thorough reading of our manuscript and insightful suggestions. We thank the editors for their assessment of our article. We will submit a revised manuscript that addresses several comments and include a point-by-point response to the reviewers.

      (1) With respect to how our data compares with previously published datasets, we will provide a table comparing cell numbers. Study differences such as read depth, strain of animals used (including pigmented vs albino), method of cell isolation (including drug exposure), and number of cells profiled raise a significant impediment to integration with previously published datasets. We would like to highlight that ours is the first SEC single cell study that uses pigmented mouse eyes on C57BL/6J background. Integrating with the albino mouse data (Thompson et al. 2021) hindered pathway analyses possibly due to the variable drop out of genes across studies that was likely impacted by differences in method of cell isolation and increased representation of stress response genes in their dataset. We also attempted an integrated analysis with published mouse data (Van Zyl et al. 2020) but did not obtain additional meaningful information due to their low SEC numbers.

      (2) The reviewers commented that our integration of single cell and single nuc data should be done with caution: we agree and had given careful consideration to the integration process. We will demonstrate the contribution of different samples and datasets to show how our datasets have integrated.

      (3) To address the purity of bulk RNA seq, we will add more details for isolation of SECs for bulk seq. The markers to distinguish the three cell types were informed by immunofluorescence. Using these markers, we performed FACS using gates that were well separated. We have provided a heatmap with hierarchical clustering based on Euclidean distance of the EC subtypes (Figure 1B) analyzed by bulk RNA seq in addition to number of DE genes between subtypes.

      (4) To address the immunostaining of NPNT and CCL21A, since both our antibodies are derived from the same species (goat), a co-labeling wasn’t possible. To be prudent, we used adjacent sections, flat-mounts, and RNAscope and provided further evidence of the anterior/posterior “bias” in supplemental figures. Although we agree on its importance, work with human tissue will be a focus of future work.

      (5) Regarding the reviewer’s comments on substructure and that profiling may still not be comprehensive, we agree that further even more comprehensive studies are still needed. Profiling more cells will determine the robustness of the detected cell state difference and will help to resolve the cause of substructure within clusters as due to either lack of completely comprehensive profiling of cell types/states or more stochastic differences. We will add a comment to the discussion.

    1. Author response:

      Reviewer #1:

      The phenomenon of stress-inducible mutagenesis in bacterial evolution remains a topic of heated debate. Consequently, the emergence of genetically encoded resistance may stem from either microevolution or the dissemination of pre-existing variants from polyclonal infections under drug pressure. We believe that the Introduction presents both of these hypotheses in a balanced manner to elucidate the rationale behind our mutation accumulation investigations.

      While we acknowledge the well-known existence of phenotypic antibiotic resistance, it's worth noting that conclusions regarding mutation rates are often drawn from fluctuation assays without confirmation of genetic-level changes. This discrepancy persists despite fluctuation assays accounting for both phenotypic and genotypic alterations. Combining genome sequencing with fluctuation assays underscores the importance of making this distinction.

      Thank you for the suggestion regarding improving the figures; we will incorporate these changes accordingly in the revised version. Additionally, we will address the rationale for using sub-lethal doses of antibiotics and compare our results with the referenced papers.

      Reviewer #2:

      Thank you for acknowledging the values of the manuscript and for the insightful suggestions for improvement. We agree on the necessity to directly connect the mutation accumulation experiments with the tolerance assay, and we have already initiated additional experiments to integrate into a revised version.

      We also agree with and have been aware of the notion that cell death affects the calculation of the mutation rate. However, the error in the estimation of the generation time leads to an overestimation of the mutation rate, which, in our case, reinforces the conclusion that no discernible increase in mutation rate occurs in our mutation accumulation experiment. In the revised version, we aim to address i) the source of variation in cell death degree and ii) its influence on calculations.

      The SNPs identified from the lineages of each treatment are compiled in the "unique muts.xls" file within the Figshare document bundle we included with the manuscript. We regret not providing a detailed reference to this in the manuscript; instead, the Figshare files were merely mentioned under the Data Availability section (No. 6) without specifics. As advised, we will create a supplementary table containing this data.

      Reviewer #3:

      Thank you for appreciating the manuscript's merits and for the instructive suggestions (also articulated in the specific comments). We agree that we should show the data on reduced colony growth on agar plates to demonstrate that the drug concentrations used in the study are relevant. We will include this in the revised version, as well as changes in response to all specific comments.

      We acknowledge that the observed upregulation of DNA repair enzymes and the low mutation rates under drug pressure represent correlative data. Therefore, we opted against presenting the qPCR results as a mechanistic explanation. In the manuscript, we carefully stated: "The observed upregulation of the relevant DNA repair enzymes might account for the low mutation rate even under drug pressure." We did not establish a mechanistic link or emphasize the repair activation in the title, abstract, or discussion. We recognize the necessity for a new series of targeted experiments to provide mechanistic explanations. In this paper, our aim is to convincingly demonstrate that antibiotic pressure did not induce the occurrence of new adaptive mutations.

    1. Author response:

      eLife assessment

      This paper presents a valuable optimization algorithm for determining the spatio-temporal organization of chromatin. The algorithm identifies the polymer model that best fits population averaged Hi-C data and makes predictions about the spatio-temoral organization of specific genomic loci such as the oncogenic Myc locus. While the algorithm will be of value to biologists and physicists working in the field of genome organization, the provided methodological details and evidence are incomplete to fully substantiate the conclusions. In particular, the following would be beneficial: analysis of single-cell data, the inclusion of loci beyond Myc, testing the dependence of results on the chosen parameters, providing more details on CTCF occupancy at loop anchors, and better substantiating the claim about predictions of single-cell heterogeneity.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors of this study aim to use an optimization algorithm approach, based on the established Nelder-Mead method, to infer polymer models that best match input bulk Hi-C contact data. The procedure infers the best parameters of a generic polymer model that combines loop-extrusion (LE) dynamics and compartmentalization of chromatin types driven by weak biochemical affinities. Using this and DNA FISH, the authors investigate the chromatin structure of the MYC locus in leukemia cells, showing that loop extrusion alone cannot explain local pathogenic chromatin rearrangements. Finally, they study the locus single-cell heterogeneity and time dynamics.

      Strengths:

      • The optimization method provides a fast computational tool that speeds up the parameter search of complex chromatin polymer models and is a good technical advancement.

      • The method is not restricted to short genomic regions, as in principle it can be applied genome-wide to any input Hi-C dataset, and could be potentially useful for testing predictions on chromatin structure.

      Weaknesses:

      (1) The optimization is based on the iterative comparison of simulated and Hi-C contact matrices using the Spearman correlation. However, the inferred set of the best-fit simulation parameters could sensitively depend on such a specific metric choice, questioning the robustness of the output polymer models. How do results change by using different correlation coefficients?

      This is an important question. We have tested several metrics in the process of building the fitting procedure. We will showcase side-by-side comparisons of the fitting results obtained using these different metrics in an upcoming version of the preprint.

      (2) The best-fit contact threshold of 420nm seems a quite large value, considering that contact probabilities of pairs of loci at the mega-base scale are defined within 150nm (see, e.g., (Bintu et al. 2018) and (Takei et al. 2021)).

      This is a good point. Unfortunately, there is no established standard distance cutoff to map distances to Hi-C contact frequency data. Indeed, previous publications have used anywhere between 120 nm to 500 nm (see e.g. (Cardozo Gizzi et al. 2019), (Cattoni et al. 2017) , (Mateo et al. 2019), (Hafner et al. 2022), (Murphy and Boettiger 2022), (Takei et al. 2021), (Fudenberg and Imakaev 2017) , (Wang et al. 2016), (Su et al. 2020), (Chen et al. 2022), (Finn et al. 2019)). We will include a supplementary table in the upcoming revised preprint listing these values to demonstrate the lack of consensus. This large variation could reflect different chromatin compaction levels across distinct model systems, and different spatial resolutions in DNA FISH experiments performed by different labs. The variance in the threshold choice is also likely partially explained by Hi-C experimental details, e.g. the enzyme used for digestion, which biases the effective length scale of interactions detected (Akgol Oksuz et al. 2021). Among commonly used restriction enzymes, HindIII has a relatively low cutting frequency which results in a lower sensitivity to short-range interactions; on the other hand, MboI has a higher cutting frequency which results in a higher sensitivity to short-range interactions (Akgol Oksuz et al. 2021). Because the Hi-C data we used for the Myc locus in (Kloetgen et al. 2020) was generated using HindIII, we chose a distance cutoff close to the larger end of published values (420 nm).

      (3) In their model, the authors consider the presence of LE anchor sites at Hi-C TAD boundaries. Do they correspond to real, experimentally found CTCF sites located at genomic positions, or they are just assumed? A track of CTCF peaks of the considered chromatin loci would be needed.

      We apologize this was not clear. The LE anchor sites in the simulation model were chosen because they correspond to experimental CTCF sites and ChIP-seq peaks located at the corresponding genomic positions. Representative CTCF ChIP-seq tracks from (Kloetgen et al. 2020) will be added to figure 2 in the revised preprint version to emphasize this point.

      (4) In the model, each TAD is assigned a specific energy affinity value. Do the different domain types (i.e., different colors) have a mutually attractive energy? If so, what is its value and how is it determined? The simulated contact maps (e.g., Figure 2C) seem to allow attractions between different blocks, yet this is unclear.

      Sorry this was not explicit. The attraction energy between a pair of monomers in the simulation is determined using the geometric mean of the affinities of the two monomers. This applies to both monomers within the same domain and in different domains. This detail will be clarified in the upcoming revised preprint.

      (5) To substantiate the claim that the simulations can predict heterogeneity across single cells, the authors should perform additional analyses. For instance, they could plot the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions and check whether the models can recapitulate the FISH-observed variance or standard deviation. They could also add other testable predictions, e.g., on gyration radius distributions, kurtosis, all-against-all comparison of single-molecule distance matrices, etc,.

      We agree that heterogeneity prediction is a key advantage of the simulations. We do note that the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions measured by FISH were plotted in Fig. 3C as empirical cumulative probability distributions (as is standard in the field), side by side with the simulation predictions. Simulations indeed recapitulate the variance observed by FISH. We also had emphasized this important point in the main text: “Importantly, not just the average distances, but the shape of the distance distribution across individual cells closely matches the predictions of the simulations in both cell types, further confirming that the simulations can predict heterogeneity across cells.”

      (6) The authors state that loop extrusion is crucial for enhancer function only at large distances. How does that reconcile, e.g., with Mach et al. Nature Gen. (2022) where LE is found to constrain the dynamics of genomically close (150kb) chromatin loci?

      This is an interesting question. In (Mach et al. 2022), the authors tracked the physical distance between two fluorescent labels positioned next to either anchor of a ~150 kb engineered topological domain using live-cell imaging. They found that abrogation of the loop anchors by ablation of the CTCF binding motifs, or knock-down of the cohesin subunit Rad21 resulted in increased physical distance between the loci. HMM Modeling of the distance over time traces suggests that the increased distance resulted from rarer and shorter contacts between the anchors. While this might seem at odds with the results of Fig. 4L, we note a key difference between the loci. While (Mach et al. 2022) observed the dynamics of the distance separating two CTCF loop anchors, in our model only the MYC promoter is proximal to a loop anchor, while the position of the second locus is varied, but remains far from the other anchor. The deletion of the CTCF sites at both anchors in (Mach et al. 2022) indeed results in a lowered sensitivity of the physical distance to Rad21 knock-down, reminiscent of the results of Fig. 4L in our work. This result demonstrates that loop extrusion disruption disproportionately impacts distances between loci close to loop anchors, consistent with Hi-C results (Rao et al. 2017; Nora et al. 2017). We therefore believe that the models in our work and (Mach et al. 2022) are not at odds, but simply reflect that loop extrusion perturbations impact distances between loop anchors the most. Enhancer-Promoter loops are generally distinct from CTCF-mediated loops (Hsieh et al. 2020, 2022). While (Mach et al. 2022) represents a landmark study in our understanding of the dynamics of genomic folding by loop extrusion, we therefore believe that the locus we chose here - which matches the endogenous MYC architecture - may more accurately represent Enhancer-Promoter dynamics than a synthetic CTCF loop. To better articulate the similarities between model predictions and differences between the two loci, we will simulate a locus matching that of (Mach et al. 2022) in the upcoming revised preprint, and test the sensitivity of contact frequency and duration to in silico cohesin knock-down. This will also serve to extend the generality of our conclusions to different categories of genomic architectures, and the text will be clarified accordingly.

      Reviewer #2 (Public Review):

      Summary:

      The authors Fu et al., developed polymer models that combine loop extrusion with attractive interactions to best describe Hi-C population average data. They analyzed Hi-C data of the MYC locus as an example and developed an optimization strategy to extract the parameters that best fit this average Hi-C data.

      Strengths:

      The model has an intuitive nature and the authors masterfully fitted the model to predict relevant biology/Hi-C methodology parameters. This includes loop extrusion parameters, the need for self-interaction with specific energies, and the time and distance parameters expected for Hi-C capture.

      Weaknesses:

      (1) We are no longer in the age in which the community only has access to population average Hi-C. Why was only the population average Hi-C used in this study?

      Can single-cell data: i.e. single-cell Hi-C/Dip-C data or chromatin tracing data (i.e. see Tan et al Science 2018 - for Dip-C, Bintu et al Science 2018, Su et al Cell 2020 for chromatin tracing, etc.) or even 2 color DNA FISH data (used here only as validation) better constrain these models? At the very least the simulations themselves could be used to answer this essential question.

      I am expecting that the single-cell variance and overall distributions of distances between loci might better constrain the models, and the authors should at least comment on it.

      We agree that it is possible to recapitulate single-cell Hi-C or chromatin tracing data with simulations, and that these data modalities have a superior potential to constrain polymer models because they provide an ensemble of single allele structures rather than population-averaged contact frequencies. However, these data remain out of reach for most labs compared to Hi-C. Our goal with this work was to provide an approachable method that anyone interested could deploy on their locus of choice, and reasoned that Hi-C currently remains the data modality available to most. We envision this strategy will help reach labs beyond the small number of groups expert in single cell chromatin architecture, and thus hopefully broaden the impact of polymer simulations in the chromatin organization field.

      Nevertheless, we do agree that the comparison of single-cell chromatin architectures to simulations is a fertile ground for future studies. We will include a brief discussion of the potential of single-cell architectures in an upcoming version of the manuscript.

      (2) The authors claimed "Our parameter optimization can be adapted to build biophysical models of any locus of interest. Despite the model's simplicity, the best-fit simulations are sufficient to predict the contribution of loop extrusion and domain interactions, as well as single-cell variability from Hi-C data. Modeling dynamics enables testing mechanistic relationships between chromatin dynamics and transcription regulation. As more experimental results emerge to define simulation parameters, updates to the model should further increase its power." The focus on the Myc locus in this study is too narrow for this claim. I am expecting at least one more locus for testing the generality of this model.

      We note that we used two distinct loci in the study, the MYC locus in leukemia vs T cells (Figs. 2-3) and a representative locus in experiments comparing WT CTCF with a mutant that leads to loss of a subset of CTCF binding sites (Fig. 1L). To further demonstrate generality, we will add to the upcoming revised preprint a demonstration of the simulation fitting to other loci acquired in different cell types.

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    1. Author response:

      The authors express their gratitude to the reviewers for their insightful comments.

      Reviewer #1: We are uncertain about the reference to an overjudgement of the recovery of spermatogonial stem cells, as we did not draw any conclusions on this in the current study. Additionally, we have received feedback mentioning the multitude and diversity of datasets as both a strength and a weakness. However, we would appreciate clarification on which datasets may have been insufficiently reviewed and how our selection of highlights may have introduced bias to the interpretation and conclusion of the study. It is important to note that we did not select any patients/ data; all patient data were incorporated into our results section. We acknowledge the need for clarification regarding our study population for the germ cell stainings. As stated in our Materials and Methods section, our current study population includes the cohort from our previous publication (Vereecke et al., 2020), supplemented by nine additional participants, totaling n=106 trans women. While Fig. 1C incorporates both previous and new data on germ cells, we understand the need to clarify this to avoid confusion. Additionally, we will include information on the Tanner stages of the trans women in our cohort (all G5), as well as details on the selection criteria for our controls and their Tanner stages. As briefly touched upon in the discussion, a marker such as delta-like homolog 1 would indeed be valuable to assess the presence of truly immature Leydig cells. Unfortunately, our attempts to optimize the immunofluorescence protocol for this marker were unsuccessful, resulting in a double staining instead of a triple staining for the Leydig cells. The suboptimal resolution of Fig.1 will be solved.

      Reviewer #2 raises concerns regarding the suitability of rejuvenated testicular tissue for research purposes. However, we emphasize that this tissue source holds significant value. Although there is a wide availability of adult testicular tissue (coming from prostate cancer patients or vasectomy reversal patients), we are especially looking for alternatives for the scarce prepubertal/ pubertal tissue for research on in vitro spermatogenesis. While we acknowledge that transgender tissue with severe hyalinization or without spermatogonia may not be suitable for such research, the abundance of transgender tissue without these issues emphasizes the value of this tissue source.

    1. Author response:

      Reviewer #1

      […] it seems that the readout units are not operating in continuous time, and that interval discrimination relies in part on external information. Specifically, the readout units only look at the spike counts during the window delta_t_w.

      In the first version of the review, the reviewer implied that each readout unit only receives input during a small window around the interval it represents. However, this is not the case. The small window that is depicted in Fig. 16 is a sliding window that is used to compute the states (i.e., an estimate of the instantaneous firing rate) at each point in time. The fact that the readout units indeed do operate in continuous time is apparent from Fig. 2A, showing the activity of all output units as a function of time: There is gradually changing activity with a peak at the represented interval. If each unit would only receive input during a window of a couple milliseconds, there would be a single peak of activity at the represented interval, and near-zero activity at any other time.

      This misunderstanding has been cleared out in the current version of the review (see last paragraph of review #1).

      Stimulus onset occurs at 1500 ms in order to allow the network to stabilize. Ideally, this value should be randomized across trials to ensure performance generalizes across initial states.

      This is a valid point which we will address in the revision. However, we note that experimentation with different onset values did not change the dynamics of the network systematically in previous studies (i.e., Hass et al., 2022).

      Why does StDev saturate? Is that because subjective time saturates as well?

      Indeed, the two phenomena are closely related. In section “Deviations from the scalar property and the origin on Vierordt’s law”, we discuss that both is caused by the broadening of the tuning curves of the readout units (Fig 1A) as the longest time constants of the network are exceeded.

      In the discussion, it would be nice to explain that dopaminergic modulation of subjective timing is not as universally observed as the linear psychophysical law or the scalar property, and I believe somewhat controversial (e.g., Ward, ..., Balsam, 2009).

      We are thankful for this advice and will adapt the discussion accordingly in the revision. Still, we note that dopaminergic modulation of subjective timing is one of the more robust effects observed in several time perception experiments.

      Reviewer #2:

      (1) Lack of Empirical Data: […] The paper would benefit from quantitative and qualitative simulations of results from specific, large-sample studies to anchor the model's predictions in concrete empirical evidence.

      While it is correct that this study does not attempt the replicate a concrete empirical study, we note that do compare the model's results with specific studies wherever possible. The comparison is done on the level of parameters of functional relationships: For the linear psychophysical law, we compare the slope and the indifference point of the model with those from experimental studies. For the scalar property, we compare the Weber fraction of the model to those computed from experiments. For dopaminergic modulation of subjective duration, no direct comparison with experimental data is possible, as the levels of modulation are estimated from in vitro experiments and cannot be directly compared with modulations in vivo. However, we discuss a range of qualitative observations in experiments that are reproduced (and explained) by the model.

      The above arguments notwithstanding, one can discuss whether the presentation of the experimental results and the comparison with the simulations is appropriate, and we do plan to extend this presentation in a revision.

      (2) Methodological Ambiguities: The training and testing procedures lack robust checks for generalization, leading to potential overfitting issues.

      It is correct that formal checks for generalization, such as cross-validation protocols, are missing, and we will include them in the revision. However, as we obtained a mechanistic understanding of how the model tells time, we are confident that our results are not due to overfitting.

      (3) Inadequate Visualization of Empirical Data: References to empirical data are vague and not directly visualized alongside model outputs. Future iterations should include empirical data, not general trends from psychophysics, in figures for a clear comparison.

      As mentioned above, the comparison between simulation and empirical data will be extended in a revision. However, we argue that the “general trends”, namely adherence of the model to the often-reported psychophysical regularities, are of greater importance compared to the replication of, e.g. one specific slope of the linear psychophysical law, which does vary a lot between experiments.

      (4) Limitations in Model Scope and Dynamics: […] Expanding the model limitations to consider isochronous pulse processing and the emergence of limit-cycle behaviors after prolonged stimulation would provide a more comprehensive understanding of the model's capabilities and limitations.

      The current research focuses on the estimation of a single duration rather than the processing of sequences of durations. Sequence processing is a vast field, and it has been argued that it comprises different mechanisms compared to duration estimation. Thus, we feel that including sequences processing would be beyond the scope of the already quite extensive paper. However, we will discuss a possible extension of the model to sequence processing in the revision.

      Additionally, the justification for using(N_{Poisson}\) as a proxy for more connections is unclear and warrants a more direct approach.

      We considered different means to vary the noise input into the network, including changes in the number of connections. We ultimately chose to vary the firing rate of a fixed number of Poisson input neurons. As the sum of the firing rates of N independent Poisson neurons with the same f is simply N*f and the synaptic contributions from each spike also linearly add up, this is equivalent to adding more Poisson neurons and thus, more connections.

      (5) Omissions and Redundancies: Certain omissions, such as the lack of a condition in Figure 7A or missing references to relevant models and reviews, detract from the paper's thoroughness.

      The reviewer refers to a condition where everything is ablated except NMDA. We will include such a condition in the revision. Regarding missing references, the reviewer requests including references that focus on sequence processing. While the focus of the current work is on estimating a single duration rather than a sequence of durations (see above), we will include a review on this topic as an outlook on this possible extension of the model.

      Moreover, some statements and terms like "internal clock" are used without a clear mechanistic definition within the model.

      We are thankful for this advice and will adapt the revision accordingly.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Recommendations For The Authors):

      In this revision the authors address some of the key concerns, including clarification of the balanced nature of the RL driven pitch changes and conducting analyses to control for the possible effects of singing quantity on their results. The paper is much improved but still has some sources of confusion, especially around Fig. 4, that should be fixed. The authors also start the paper with a statistically underpowered minor claim that seems unnecessary in the context of the major finding. I recommend the authors may want to restructure their results section to focus on the major points backed by sufficient n and stats.

      Major issues.

      (1) The results section begins very weak - a negative result based on n=2 birds and then a technical mistake of tube clogging re-spun as an opportunity to peak at intermittent song in the otherwise muted birds. The logic may be sound but these issues detract from the main experiment, result, analysis, and interpretation. I recommend re-writing this section to home in on, from the outset, the well-powered results. How much is really gained from the n=2 birds that were muted before ANY experience? These negative results may not provide enough data to make a claim. Nor is this claim necessary to motivate what was done in the next 6 birds. I recommend dropping the claim?

      We thank the reviewer for the recommendation. We moved the information to the Methods.

      (2) Fig. 4 is very important yet remains very confusing, as detailed below.

      Fig. 4a. Can the authors clarify if the cohort of WNd birds that give rise to the positive result in Fig 4 ever experienced the mismatch in the absence of ongoing DAF reinforcement pre-deafening? Fig4a does nor the next clearly specifies this. This is important because we know that there are day timescale delays in LMAN-dependent bias away from DAF and consolidation into the HVC-RA pathway (Andalman and Fee, 2009). Thus, if birds experienced mismatch pre-deafening in the absence of DAF, then an earnly learning phase in Area X could be set in place. Then deafening occurs, but these weight changes in X could result in LMAN bias that expresses only days later -independent of auditory feedback. Such a process would not require an internal model as the authors are arguing for here. It would simply arise from delays in implementing reinforcement-driven feedback. If the birds in Fig 4 always had DAF on before deafening, then this is not an issue. But if the birds had hours of singing with DAF off before deafening, and therefore had the opportunity to associate DA error signals with the targeted time in the song (e.g. pauses on the far-from-target renditions (Duffy et al, 2022), then the return-to-baseline would be expected to be set in place independent of auditory feedback. Please clarify exactly if the pitch-contingent DAF was on or off in the WNd cohort in the hours before deafening. In Fig. 3b it looks like the answer is yes but I cannot find this clearly stated in the text.

      We did not provide DAF-free singing experience to the birds in Fig. 4 before deafening. Thus, according to the reviewer, the concern does not apply.

      Note that we disagree with the reviewer’s premise that there is ‘day timescale delay in LMAN-dependent bias away from DAF and consolidation into the HVC-RA pathway’. More recent data reveals immediate consolidation of the anterior forebrain bias without a night-time effect (Kollmorgen, Hahnloser, Mante 2020; Tachibana, Lee, Kai, Kojima 2022). Thus, the single bird in (Andalman and Fee 2009) seems to be somewhat of an outlier.

      Hearing birds can experience the mismatch regardless of whether they experience DAF-free singing (provided their song was sufficiently shifted): even the renditions followed by white noise can be assessed with regards to their pitch mismatch, so that DAF imposes no limitation on mismatch assessment.

      We disagree with their claim that no internal model would be needed in case consolidation was delayed in Area X. If indeed, Area X stores the needed change and it takes time to implement this change in LMAN, then we would interpret the change in Area X as the plan that birds would be able to implement without auditory feedback. Because pitch can either revert (after DAF stops) or shift further away (when DAF is still present), there is no rigid delay that is involved in recovering the target, but a flexible decision making of implementing the plan, which in our view amounts to using a model.

      Fig 4b. Early and Late colored dots in legend are both red; late should be yellow? Perhaps use colors that are more distinct - this may be an issue of my screen but the two colors are difficult to discern.

      We used colors yellow to red to distinguish different birds and not early and late. We modified the markers to improve visual clarity: Early is indicated with round markers and late with crosses.

      Fig 4b. R, E, and L phases are only plotted for 4c; not in 4b. But the figure legend says that R, E and L are on both panels.

      In Fig. 4b E and L are marked with markers because they are different for different birds. In Fig. 4c the phases are the same for all birds and thus we labeled them on top. We additionally marked R in Fig. 4b as in Fig. 4c.

      Fig 4e. Did the color code switch? In the rest of Fig 4, DLO is red and WND is blue. Then in 4e it swaps. Is this a typo in the caption? Or are the colors switch? Please fix this it's very confusing.

      Thank you for pointing out the typo in the caption. We corrected it.

      The y axes in Fig 4d-e are both in std of pitch change - yet they have different ylim which make it visually difficult to compare by eye. Is there a reason for this? Can the authors make the ylim the same for fig 4d-e?.

      We added dashed lines to clarify the difference in ylim.

      Fig 4d-3 is really the main positive finding of the paper. Can the others show an example bird that showcases this positive result, plotted as in Fig 3b? This will help the audience clearly visualize the raw data that go into the d' analyses and get a more intuitive sense of the magnitude of the positive result.

      We added example birds to figure 4, one for WNd and one for dLO.

      Please define 'late' in Fig.4 legend.

      Done

      Minor

      Define NRP In the text with an example. Is an NRP of 100 where the birds was before the withdrawal of reinforcement?

      We added the sentence to the results:

      "We quantified recovery in terms of 𝑵𝑹𝑷 to discount for differences in the amount of initial pitch shift where 𝑵𝑹𝑷 = 𝟎% corresponds to complete recovery and 𝑵𝑹𝑷 = 𝟏𝟎𝟎% corresponds pitch values before withdrawal of reinforcement (R) and thus no recovery."

      Reviewer #3 (Recommendations For The Authors):

      The use of "hierarchically lower" to refer to the flexible process is confusing to me, and possibly to many readers. Some people think of flexible, top-down processes as being _higher_ in a hierarchy. Regardless, it doesn't seem important, in this paper, to label the processes in a hierarchy, so perhaps avoid using that terminology.

      We reformulated the paragraph using ‘nested processes’ instead of hierarchical processes.

      In the statement "a seeming analogous task to re-pitching of zebra finch song, in humans, is to modify developmentally learned speech patterns", a few suggestions: it is not clear whether "re-pitching" refers to planning or feedback-dependent learning (I didn't see it introduced anywhere else). And if this means planning, then it is not clear why this would be analogous to "humans modifying developmentally learned speech patterns". As you mentioned, humans are more flexible at planning, so it seems re-pitching would _not_ be analogous (or is this referring to the less flexible modification of accents?).

      We changed the sentence to:

      "Thus, a seeming analogous task to feedback-dependent learning of zebra finch song, in humans, is to modify developmentally learned speech patterns."

    1. Author response:

      We would first like to thank the editor for considering our findings for publication in eLife. Furthermore, we thank the reviewers and editors for their encouraging reviews and for providing helpful and insightful comments.

      Reviewer #1 (Public Review):

      Summary:

      The pituitary gonadotropins, FSH and LH, are critical regulators of reproduction. In mammals, synthesis and secretion of FSH and LH by gonadotrope cells are controlled by the hypothalamic peptide, GnRH. As FSH and LH are made in the same cells in mammals, variation in the nature of GnRH secretion is thought to contribute to the differential regulation of the two hormones. In contrast, in fish, FSH and LH are produced in distinct gonadotrope populations and may be less (or differently) dependent on GnRH than in mammals. In the present manuscript, the authors endeavored to determine whether FSH may be independently controlled by a distinct peptide, cholecystokinin (CCK), in zebrafish.

      Strengths:

      The authors demonstrated that the CCK receptor is enriched in FSH-producing relative to LH-producing gonadotropes, and that genetic deletion of the receptor leads to dramatic decreases in gonadotropin production and gonadal development in zebrafish. Also, using innovative in vivo and ex vivo calcium imaging approaches, they show that LH- and FSH-producing gonadotropes preferentially respond to GnRH and CCK, respectively. Exogenous CCK also preferentially stimulated FSH secretion ex vivo and in vivo.

      Weaknesses:

      The concept that there may be a distinct FSH-releasing hormone (FSHRH) has been debated for decades. As the authors suggest that CCK is the long-sought FSHRH (at least in fish), they must provide data that convincingly leads to such a conclusion. In my estimation, they have not yet met this burden. In particular, they show that CCK is sufficient to activate FSH-producing cells, but have not yet demonstrated its necessity. Their one attempt to do so was using fish in which they inactivated the CCK receptor using CRISPR-Cas9. While this manipulation led to a reduction in FSH, LH was affected to a similar extent. As a result, they have not shown that CCK is a selective regulator of FSH.

      Our conclusion regarding the necessity of CCK signaling for FSH secretion is based on the following evidence:

      (1) CCK-like receptors are expressed in the pituitary gland predominantly on FSH cells.

      (2) Application of CCK to pituitaries elicits FSH cell activation and FSH release, and, to a lesser degree, activation of LH cells.

      (3) Mutating the CCK-like receptor causes a decrease in fsh and lh mRNA synthesis.

      (4) Mutating the CCK-like receptor gives rise to a phenotype which is identical to that caused by mutation of both lh and fsh genes in zebrafish.

      (5) Mutating the FSH-specific CCK receptor in a different species of fish (medaka) also causes a complete shutdown of FSH production and phenocopies a fsh-mutant phenotype (Uehara et al, BioRxiv, DOI: 10.1101/2023.05.26.542428).

      Taken together, we believe that this data strongly supports the conclusion that CCK is necessary for FSH production and release from the fish pituitary. Admittedly, the overlapping effects of CCK on both FSH and LH cells in zebrafish (evident in both our calcium imaging experiments and the KO phenotype) complicates the interpretation of the phenotype. We speculate that the effect of CCK on LH cells in zebrafish can be caused either by paracrine signaling within the gland or by the effects of CCK on higher levels of the axis. In our revised manuscript we will make sure to highlight the overlapping effects of CCK on LH cells rather than portray it as a selective activator of FSH cells.

      Moreover, they do not yet demonstrate that the effects observed reflect the loss of the receptor's function in gonadotropes, as opposed to other cell types.

      Although there is evidence for the expression of CCK receptor in other tissues, we do show a direct decrease of FSH and LH expression in the gonadotrophs of the pituitary of the mutant fish; taken together with its significant expression in FSH cells, it is the most reasonable and forward explanation for the mutant phenotype. Unfortunately, unlike in mice, technologies for conditional knockout of genes in specific cell types are not yet available for our model and cell types. However, in the revised manuscript we will add a supplementary figure describing the distribution of this receptor in other tissues.

      It also is not clear whether the phenotypes of the fish reflect perturbations in pituitary development vs. a loss of CCK receptor function in the pituitary later in life. Ideally, the authors would attempt to block CCK signaling in adult fish that develop normally. For example, if CCK receptor antagonists are available, they could be used to treat fish and see whether and how this affects FSH vs. LH secretion.

      While the observed gonadal phenotype of the KO (sex inversion) should have a developmental origin since it requires a long time to manifest, the effect of the KO on FSH and LH cells is probably more acute.

      In the Discussion, the authors suggest that CCK, as a satiety factor, may provide a link between metabolism and reproduction. This is an interesting idea, but it is not supported by the data presented. That is, none of the results shown link metabolic state to CCK regulation of FSH and fertility. Absent such data, the lengthy Discussion of the link is speculative and not fully merited.

      In the revised manuscript, we will address this comment by either providing data to link cck with metabolic status or tuning down the Discussion of this topic.

      Also in the Discussion, the authors argue that "CCK directly controls FSH cells by innervating the pituitary gland and binding to specific receptors that are particularly abundant in FSH gonadotrophs." However, their imaging does not demonstrate innervation of FSH cells by CCK terminals (e.g., at the EM level).

      Innervation of the fish pituitary does not imply a synaptic-like connection between axon terminals and endocrine cells. In fact, such connections are extremely rare, and their functionality is unclear. Instead, the mode of regulation between hypothalamic terminals and endocrine cells in the fish pituitary is more similar to "volume transmission" in the CNS, i.e. peptides are released into the tissue and carried to their endocrine cell targets by the circulation or via diffusion.

      Moreover, they have not demonstrated the binding of CCK to these cells. Indeed, no CCK receptor protein data are shown.

      Our revised manuscript will include detailed experiments showing the activation of the receptor by its ligand. Unfortunately, no antibody is available against this fish- specific receptor (one of the caveats of working with fish models); therefore, we cannot present receptor protein data.

      The calcium responses of FSH cells to exogenous CCK certainly suggest the presence of functional CCK receptors therein; but, the nature of the preparations (with all pituitary cell types present) does not demonstrate that CCK is acting directly in these cells.

      We agree with the reviewer that there are some disadvantages in choosing to work with a whole-tissue preparation. However, we believe that the advantages of working in a more physiological context far outweigh the drawbacks as it reflects the natural dynamics more precisely. Since our transcriptome data as well as our ISH staining, show that the CCK receptor is exclusively expressed on FSH cells, it is improbable that the observed calcium response is mediated via a different pituitary cell type.

      Indeed, the asynchrony in responses of individual FSH cells to CCK (Figure 4) suggests that not all cells may be activated in the same way. Contrast the response of LH cells to GnRH, where the onset of calcium signaling is similar across cells (Figure 3).

      The difference between the synchronization levels of LH and FSH cells activity stems from the gap-junction mediated coupling between LH cells that does not exist between FSH cells (Golan et al 2016, DOI: 10.1038/srep23777). Therefore, the onset of calcium response in FSH cells is dependent on the irregular diffusion rate of the peptide within the preparation, whereas the tight homotypic coupling between LH cells generates a strong and synchronized calcium rise that propagates quickly throughout the entire population; we will make sure this is clear in the final revision.

      Finally, as the authors note in the Discussion, the data presented do not enable them to conclude that the endogenous CCK regulating FSH (assuming it does) is from the brain as opposed to other sources (e.g., the gut).

      We agree with the reviewer that, for now, we are unable to determine whether hypothalamic or peripheral CCK are the main drivers of FSH cells. While the strong innervation of the gland by CCK-secreting hypothalamic neurons strengthens the notion of a hypothalamic-releasing hormone and also fits with the dogma of the neural control of the pituitary gland in fish (Ball, 1981; doi: 10.1016/0016-6480(81)90243-4.), more experiments are required to resolve this question.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript builds on previous work suggesting that the CCK peptide is the releasing hormone for FSH in fishes, which is different than that observed in mammals where both LH and FSH release are under the control of GnRH. Based on data using calcium imaging as a readout for stimulation of the gonadotrophs, the researchers present data supporting the hypothesis that CCK stimulates FSH- containing cells in the pituitary. In contrast, LH-containing cells show a weak and variable response to CCK but are highly responsive to GnRH. Data are presented that support the role of CCK in the release of FSH. Researchers also state that functional overlap exists in the potency of GnRH to activate FSH cells, thus the two signalling pathways are not separate.

      The results are of interest to the field because for many years the assumption has been that fishes use the same signalling mechanism. These data present an intriguing variation where a hormone involved in satiation acts in the control of reproduction.

      Strengths:

      The strengths of the manuscript are that researchers have shed light on different pathways controlling reproduction in fishes.

      Weaknesses:

      Weaknesses are that it is not clear if multiple ligand/receptors are involved (more than one CCK and more than one receptor?). The imaging of the CCK terminals and CCK receptors needs to be reinforced.

      Reviewer consultation summary:

      • The data presented establish sufficiency, but not necessity of CCK in FSH regulation. The paper did not show that CCK endogenously regulates FSH in fish. This has not been established yet.

      This is a very important comment, also raised by reviewer 1. To avoid repetition, please see our detailed response to the comment above.

      • The paper presents the pharmacological effects of CCK on ex vivo preparations but does not establish the in vivo physiological function of the peptide. The current evidence for a novel physiological regulatory mechanism is incomplete and would require further physiological experiments. These could include the use of a CCK receptor antagonist in adult fish to see the effects on FSH and LH release, the generation of a CCK knockout, or cell-specific genetic manipulations.

      As detailed in the responses to the first reviewer,we cannot conduct conditional, cell- specific gene knockout in our model.

      • Zebrafish have two CCK ligands: ccka, cckb and also multiple receptors: cckar, cckbra and cckbrb. There is ambiguity about which CCK receptor and ligand are expressed and which gene was knocked out.

      In the revised manuscript, we will clarify which of the receptors are expressed and which receptor is targeted. We will also provide data showing the specificity of the receptors (both WT and mutant) to the ligands.

      • Blocking CCK action in fish (with receptor KO) affects FSH and LH. Therefore, the work did not demonstrate a selective role for CCK in FSH regulation in vivo and any claims to have discovered FSHRH need to be more conservative.

      We agree with the reviewer that the overlap in the effect of CCK measured in the calcium activation of cells and in the KO model does not allow us to conclude selectivity. In this context, it is crucial to highlight that CCK-R exhibits high expression on FSH cells but not on LH cells. Therefore, the effect of CCK on LH cells is likely paracrine rather than solely endocrine. We will tone down our claims of selectivity in the revised manuscript.

      • The labelling of the terminals with anti-CCK looks a lot like the background and the authors did not show a specificity control (e.g. anti-CCK antibody pre-absorbed with the peptide or anti-CCK in morphant/KO animals).

      We will update the colors of the image for better clarity. Also, The same antibody had been previously used to mark CCK-positive cells in the gut of the red drum fish (K.A. Webb, Jr. 2010; DOI: https://doi.org/10.1016/j.ygcen.2009.10.010), where a control (pre-absorbed with the peptide) experiment had been conducted.

    1. Author response:

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

      The authors have addressed my comments. As a final minor point, regarding comment 2, these condensates are likely viscoelastic rather than purely viscous. It is prudent to indicate that the data may refer to an apparent viscosity.

      We added the following text to the manuscript to highlight the viscoelastic nature of ELP condensates, and the relationship of reported values with the steady state viscosity. “It is worth noting that the reported values, although related, may not quantitatively represent the steady-state viscosity. This discrepancy arises from the slow relaxation timescale inherent in ELP condensates with viscoelastic properties.”

    1. Author response:

      We thank eLife and the reviewers for the thoughtful summary and valuable review of our manuscript. We largely agree with the summary and review and have provided our responses to the comments below. We believe BADGER is a significant new tool for identifying associated risk factors for complex diseases, and the associations we observed in the analysis provide insights into the genetic basis of Alzheimer's disease.

      Reviewer #1 (Public Review):

      The major aim of the paper was a method for determining genetic associations between two traits using common variants tested in genome-wide association studies. The work includes a software implementation and application of their approach. The results of the application of their method generally agree with what others have seen using similar AD and UKB data.

      The paper has several distinct portions. The first is a method for testing genetic associations between two or more traits using genome-wide association tests statistics. The second is a python implementation of the method. The last portion is the results of their method using GWAS from AD and UK Biobank.

      We thank the reviewer for the conclusion and positive comments.

      Regarding the method, it seems like it has similarities to LDSC, and it is not clear how it differs from LDSC or other similar methods. The implementation of the method used python 2.7 (or at least was reportedly tested using that version) that was retired in 2020. The implementation was committed between Wed Oct 3 15:21:49 2018 to Mon Jan 28 09:18:09 2019 using data that existed at the time so it was a bit surprising it used python 2.7 since it was initially going to be set for end-of-life in 2015. Anyway, trying to run the package resulted in unmet dependency errors, which I think are related to an internal package not getting installed. I would expect that published software could be installed using standard tooling for the language, and, ideally, software should have automated testing of key portions.

      We thank the reviewer for their comments. To clarify, the primary difference between our proposed method, BADGERS, and LDSC lies in their respective objectives and applications. LDSC is designed to estimate heritability and genetic correlations between traits by utilizing GWAS summary statistics, thereby aiding in the elucidation of the genetic architecture of complex traits and diseases. Conversely, BADGERS is specifically developed to explore causal relationships between risk factors, such as biomarkers, and diseases of interest. It employs genetic variants as variables to deduce causality, thereby addressing the challenges of confounding and reverse causation that are common in observational studies. Although BADGERS utilizes the LD reference panel derived from LDSC, the LD reference panel is used to obtain the predicted trait expression. The ultimate goal is to focus on linking biobank traits with Alzheimer’s disease and building causal relationships instead of identifying genetic architecture.

      Regarding the technical aspects mentioned, we acknowledge the concerns about the use of Python 2.7 and the issues encountered during the package installation. We are in the process of updating the software to ensure compatibility with current versions of Python and to enhance the installation process with standard tooling and automated testing for a more user-friendly experience. We have provided tests for each portion of the software so the user can test if the software is working properly.

      Regarding the main results, they find what has largely been shown by others using the same data or similar data, which add prima facie validity to the work The portions of the work dealing with AD subgroups, pathology, biomarkers, and cognitive traits of interest. I was puzzled why the authors suggested surprise regarding parental history and high cholesterol not associated with MCI or cognitive composite scores since the this would seem like the likely fallout of selection of the WRAP cohort. The discussion paragraph that started "What's more, environmental factors may play a big role in the identified associations." confused me. I think what the authors are referring to are how selection, especially in a biobank dataset, can induce correlations, which is not what I think of as an environmental effect.

      We thank the reviewer very much for their comment. We're glad that our findings align with existing research using similar data, increasing the validity of our work and the proposed BADGER algorithm. Your point about the lack of association between parental history, high cholesterol, and mild cognitive impairment (MCI) or cognitive composite scores in the WRAP cohort is well-taken. We agree that the selection criteria of the WRAP cohort may influence these findings, as it consists of individuals with a specific risk profile for Alzheimer's disease. This selection could indeed mitigate the observed association between these factors and cognitive outcomes, which we initially found surprising.

      Regarding the environmental factors, we appreciate your clarification and understand the confusion. Our intention was to discuss the potential for selection bias and confounding factors in biobank datasets for the identified associations, which might not necessarily be direct environmental effects.

      Overall, the work has merit, but I am left without a clear impression of the improvement in the approach over similar methods. Likewise, the results are interesting, but similar findings are described with the data that was used in the study, which are over 5 years old at the time of this review.

      We thank the reviewer a lot for their endorsement of the BADGER framework. We believe that our method, BADGER, improves on existing approaches by effectively linking genetic data with the detailed phenotypic information in biobanks and large disease GWAS. This enhances our ability to detect associations without needing individual-level data, offering clearer insights while reducing issues like reverse causality and confounding factors.

      Even though the IGAP dataset is over five years old, it remains one of the largest publicly available datasets for Alzheimer’s Disease. Likewise, the UK biobank is one of the largest publicly available human traits datasets, which researchers continue to use. These datasets' continued utility demonstrates their value in the research community. Additionally, the versatility of the BADGER framework makes it suitable for future research investigating the relationship between human traits and various diseases using different datasets.

      Reviewer #2 (Public Review):

      Summary:

      Yan, Hu, and colleagues introduce BADGERS, a new method for biobank-wide scanning to find associations between a phenotype of interest, and the genetic component of a battery of candidate phenotypes. Briefly, BADGERS capitalizes on publicly available weights of genetic variants for a myriad of traits to estimate polygenic risk scores for each trait, and then identify associations with the trait of interest. Of note, the method works using summary statistics for the trait of interest, which is especially beneficial for running in population-based cohorts that are not enriched for any particular phenotype (ie. with few actual cases of the phenotype of interest).

      Here, they apply BADGERS on Alzheimer's disease (AD) as the trait of interest, and a battery of circa 2,000 phenotypes with publicly available precalculated genome-wide summary statistics from the UK Biobank. They run it on two AD cohorts, to discover at least 14 significant associations between AD and traits. These include expected associations with dementia, cognition (educational attainment), and socioeconomic status-related phenotypes. Through multivariate modelling, they distinguish between (1) clearly independent components associated with AD, from (2) by-product associations that are inflated in the original bivariate analysis. Analyses stratified according to APOE inclusion show that this region does not seem to play a role in the association of some of the identified phenotypes. Of note, they observe overlap but significant differences in the associations identified with BADGERS and other Mendelian randomization (MR), hinting at BADGERS being more powerful than classical top variant-based MR approaches. They then extend BADGERS to other AD-related phenotypes, which serves to refine the hypotheses about the underlying mechanisms accounting for the genetic correlation patterns originally identified for AD. Finally, they run BADGERS on a pre-clinical cohort with mild cognitive impairment. They observe important differences in the association patterns, suggesting that this preclinical phenotype (at least in this cohort) has a different genetic architecture than general AD.

      We thank the reviewer a lot for the conclusion and positive comments.

      Strengths:

      BADGERS is an interesting new addition to a stream of attempts to "squeeze" biobank data beyond pure association studies for diagnosis. Increasingly available biobank cohorts do not usually focus on specific diseases. However, they tend to be data-rich, opening for deep explorations that can be useful to refine our knowledge of the latent factors that lead to diagnosis. Indeed, the possibility of running genetic correlation studies in specific sub-settings of interest (e.g. preclinical cohorts) is arguably the most interesting aspect of BADGERS. Classical methods like LDSC or two-sample MR capitalize on publicly available summary statistics from large cohorts, or having access to individual genotype data of large cohorts to ensure statistical power. Seemingly, BADGERS provides a balanced opportunity to dissect the correlation between traits of interest in settings with small sample size in which other methods do not work well.

      We thank the reviewer a lot for the conclusion and positive comments.

      Weaknesses:

      However, the increased statistical power is just hinted, and for instance, they do not explore if LDSC would have identified these associations. Although I suspect that is the case, this evidence is important to ensure that the abovementioned balance is right. Finally, as discussed by the authors, the reliance on polygenic risk scoring necessarily undermines the causality evidence gained through BADGERS. In this sense, BADGERS provides an alternative to strict instrumental-variable based analysis, which can be particularly useful to generate new mechanistic hypotheses.

      We thank the reviewer a lot for the comments. We understand the importance of comparing BADGER to other methods. The comparison with LDSC, while not directly relevant to BADGER’s causal inference aims, is indeed an interesting aspect to consider for future studies. In this paper, we focused on comparing BADGER with Mendelian Randomization (MR), which shares its causal inference objective.

      As a result, BADGERS identified a total of 48 traits that reached Bonferroni-corrected statistical significance. In contrast, MR-IVW only identified nine traits with Bonferroni-corrected statistical significance. Among these nine traits, seven were also identified by BADGERS. This demonstrates that BADGER holds higher power in detecting causal relationships.

      Regarding the use of polygenic risk scoring, we agree that it holds challenges in directly inferring causality. While BADGERS offers an innovative way to explore genetic correlations and can help generate new hypotheses about disease mechanisms, it does not replace the causal inferences that can be drawn from instrumental-variable-based analyses. Instead, it should be viewed as a complementary tool that can illuminate potential genetic relationships and guide further causal investigations.

      In summary, after 15 years of focus on diagnosis that would require having individual access to large patient cohorts, BADGERS can become an excellent tool to dig into trait heterogeneity, especially if it turns out to be more powerful than other available methodologies.

      We thank the reviewer a lot for the conclusion and positive comments.

    1. Author response:

      We thank the reviewers and editors for their time and effort reviewing and improving this manuscript. We also thank them for their support.

      Following the guidelines received by eLife we submit here the preliminary author’s response to the Public review with our planned changes to the manuscript.

      Reviewer 1.

      Comment 1. Issue on cross-reactivities of MafB antibodies.

      We are confident that our description of MafB V1 interneurons is correct despite some cross-reactivity with one of the antibodies used. We test all antibodies we use, and unfortunately, we found an inverse relationship between sensitivity and specificity with the two MafB antibodies used in this study. We chose for quantification the one with highest sensitivity, despite the presence of some cross-reactivity in interneurons other than the dorsal and ventral (Renshaw) V1 populations we focus on. The dorsal and ventral (Renshaw) V1 populations we describe here are also reactive with the more specific antibody (although with lower sensitivity) and both are neatly labeled in a MafB-GFP reporter mouse as described in Figure 3. We will add an image to the supplement with MafB-GFP V1 Interneurons at P5 showing the immunoreactivity of both MafB antibodies as suggested by the reviewer. We agree with the reviewer that this will give further support to the characterization of these populations by either immunocytochemical or genetic means at P5.

      Unfortunately, we cannot show lack of immunoreactivity for MafB antibodies in MafB GFP/GFP knockout mice at P5 because MafB global KOs die at birth as a result of respiratory failure. This is due to removal of inhibitory interneurons in brainstem centers critical for respiration (Blanchi at al. 2003 MafB deficiency causes defective respiratory rhythmogenesis and fatal central apnea at birth. Nat Neurosci. 6(10):1091-100. doi: 10.1038/nn1129. PMID: 14513037). This is why we used tissues from late embryos for testing antibody specificity in KO spinal cords. We will make this clearer in the text.

      Comment 2. Overlap of V1 clades with lineage labeled Foxp2-V1s at P5.

      We collected the data requested by the reviewer for P5 Foxp2-V1 interneurons and this will be added to an updated version of this figure. In comparison to the results with the OTP mouse, we only found marginal overlap at P5 with Renshaw cells, Pou6f2, and Sp8 V1s in our genetic intersection to label Foxp2-V1s. We apologize for not showing the data. We will make this clearer.

      Reviewer 2.

      Comment 1. Paper VERY hard to read.

      We will make every effort to make the paper more readable by moving methodological discussions to supplementary materials. We strive to keep our methods as rigorous, clean, and replicable as possible, and that sometimes requires lengthy explanations of the details and reasoning behind our approaches. We will make sure this does not distract from the principal scientific messages we want to convey. We agree with the reviewer that these should be emphasized over methodological detail, and we will correct any mistakes in the text that lead to confusion. Thank you for pointing out this problem that we hope to correct in a new version. Why focus on Foxp2 V1s? We focus in the Foxp2 population for several reasons: 1) This is the largest population of V1s, and it is the one with a close spatial association to motoneurons, in particular limb motoneurons; 2) Given previous results (Benito-Gonzalez and Alvarez, 2012, cited in bibliography) it likely includes many reciprocal inhibitory interneurons; 3) We do not have the mice for studying the Pou6f2 (or Sp8) population, but similar studies are now being carried out in the Bikoff lab.

      Comment 2. Lack of functional studies.

      Functional studies are currently being carried out, both during development of limb function in postnatal mice as well as in adult animals. These studies required the creation of several new animal models and reagents. As with the present manuscript, we thoroughly characterize all animals and methods. This takes time and space. These studies are beyond the goals and length of the current manuscript, but we agree with the reviewer that these are the critical next experiments that need to be performed. We are now finalizing studies on the role of Foxp2-V1 interneurons in the postnatal development of limb coordination and validating approaches for silencing them in the adult while also optimizing behavioral assays and recordings. The data presented here on Foxp2-V1 interneuron heterogeneity and relations with limb motoneurons gives the necessary context for raising stronger hypotheses and aiding in the interpretation of future results in functional studies.

      Synapse counts.

      We respectfully disagree with the reviewer’s comments on our synapse density estimates. To fully explain the reasons and prevent any ambiguity, we need to focus on detailed methodological aspects. We apologize for the lengthy response. Two major issues were raised:

      (1) Focus on the cell body.

      The issue pointed by the reviewer of potential synapses in distal dendrites from V1 subgroups not projecting proximally was already discussed in the text. The reason we focus on the cell body is because 1) it is not feasible to study the full dendritic arbor of so many different types of motoneurons and 2) it allows us to identify V1 subpopulations that likely exert stronger modulation of motoneuron firing by targeting the proximal somatodendritic membrane. The fact that synaptic organization on motoneurons is similar on cell bodies and proximal dendrites (first 100 µm) suggests that inputs from V1 clades other than Renshaw cells are likely further away, and therefore there is limited benefit to include analyses of proximal dendrites in these data. Additionally, dendrites would be difficult to consistently follow in Chat immunostained tissue. We are currently using novel viral approaches to obtain labeling of single motoneurons and their full dendritic trees for more in depth dendritic analyses in the mouse. The classical method based on single cell in vivo intracellular labeling using micropipettes is presently very low yield in the adult mouse. We are experienced with detailed single motoneuron dendritic arbor analyses in cat and rat motoneurons (Alvarez et al. 1997 Cell-type specific organization of glycine receptor clusters in the mammalian spinal cord. J Comp Neurol. 379(1):150-70; Alvarez et al., 1998 Distribution of 5-hydroxytryptamine-immunoreactive boutons on alpha-motoneurons in the lumbar spinal cord of adult cats. J Comp Neurol. 393(1):69-83; Rotterman et al., 2014. Normal distribution of VGLUT1 synapses on spinal motoneuron dendrites and their reorganization after nerve injury. J Neurosci. 34(10):3475-92. doi: 10.1523/JNEUROSCI.4768-13.2014). Based on this experience, we do not believe it is feasible to include similar analyses to compare all motor columns throughout 6 segments of the spinal cord in this study. We agree with the reviewer that these are important data sets that need to be collected and they are planned for future experiments. These analyses will address different questions than the ones posed and answered in our current manuscript.

      (2) Number of motoneurons analyzed.

      We disagree with the reviewer assessment that our conclusions might be biased because of the numbers of motoneurons analyzed. We sampled a total of 295 motoneurons in 5 different mice (117 LMC/HMC, 99 MMC, and 79 PGC motoneurons), and we used stringent methods for synapse detection. Due to a technical error, Mouse 3 lacked data in upper lumbar and Th13, but all other mice included data in almost all motor columns and segments. We disagree with the characterization that these are small samples. For full transparency, all motoneurons analyzed were identified in Figure 6D. Each of the nearly 300 motoneuron cell bodies was carefully reconstructed through several optical planes to obtain an accurate estimate of synapse density. More automatic methods in current use in the literature sometimes analyze larger samples, but our methods are designed to avoid methodological biases inherent to these automatic methods. We do not use image thresholding to extract synaptic contacts because they lack accuracy identifying single synapses. Thus, estimates using this technique frequently refer to coverage, not synapse density. In addition, it is hard to keep threshold criteria consistent across multiple optical planes to analyze enough section thickness to estimate a motoneuron surface. This is because tissue light diffraction alters thresholding levels continuously across optical planes. Thus, many authors present data as linear densities across a perimeter (in a single plane) measuring many cells in one field in one plane. We avoid cell body linear densities (or coverage) because they bias counts towards larger synapses that have higher probability of being present at any single confocal plane. Moreover, estimates along a surface reduces synapse sampling variability and better estimate synaptic coverage compared to estimates derived from analyzing single cross-sections. We also confirm each genetically labeled varicosity as a likely synapse by accumulation of VGAT. In this manner we restrict our counts to synaptic boutons and not axons or intervaricose regions. Previously, we used bassoon to show the accuracy of our methods (Wootz et al. 2013 Alterations in the motor neuron-Renshaw cell circuit in the Sod1(G93A) mouse model. J Comp Neurol. 521(7):1449-69. doi: 10.1002/cne.23266). That means that our densities are true synaptic densities, which are difficult to extract from automatic methods that estimate fluorescence coverage over larger samples of somatic profiles but fail to individualize synapses and frequently bias results. These bulk methods introduce significant confounds in data interpretation: Is higher coverage due to bigger synapses or more synapses? Do threshold structures represent true synapses or also include axons? To what extent does sub- or over-thresholding in different planes affect identification of structures in contact with the motoneuron surface? We avoid all these problems. Not surprisingly, a nested ANOVA demonstrated consistent significant differences among motor columns and segments.

      In summary, while more automatic methods allow larger samples, they disregard true synaptic densities and are based on thresholding methods with high variability in different motoneurons, optical planes and histological sections, thereby they require much larger numbers of motoneurons to overcome their many biases and sources of error. This is not our case. Our sample size is large enough considering the accuracy of our methods and data quality. This is demonstrated by consistency in statistical results across motor columns in different segments and mice.

      Comment 3. Possibility of anterograde transsynaptic labeling from primary afferents infected with rabies virus.

      This is a fair question that we did not clearly explain. The reviewer compares our results with those of Pimpinella et al., 2022. The methods used are different. To obtain anterograde tracing, these authors used Cre lines to achieve high levels of expression of TVA and RV glycoprotein in specific subtypes of sensory neurons including proprioceptors. Then EnVa-coated Rabies virus was injected directly inside the spinal cord for cell-type specificity. This method transynaptically labeled in the anterograde direction interneurons receiving inputs from specific types of sensory afferents, but the method does not have the muscle specificity required in our analyses. In our case, we used intramuscular injections at P5 of AAV1-G for transcomplementation with Rabies virus delta G injected in the same muscles later, at P15. In previous studies in which we used the RV-delta G virus without AAV1G, we analyzed motoneuron and primary afferent infection rates and found both to be considerably reduced with injection age. In our hands, there is almost no RV infection of primary afferents when Rabies virus is injected i.m. at P15, but there is some limited motoneuron infection remaining (that we used to our advantage in this paper to avoid primary afferent and developmental confounds).

      Unfortunately, these methodological studies are presently communicated only in abstract form (GomezPerez et al., 2015 and 2016; Program Nos. 242.08 and 366.06). Therefore, we will add to the supplementary information some images from serial sections to those illustrated in the paper and that will show a few “start” LG motoneurons that remained labeled at this survival time point and the lack of any dorsal horn primary afferent labeling. This is consistent with our yet unpublished data that is based on a larger number of animals and more extensive time courses.

      Comment 4. Temporal resolution of birth-dating.

      We agree with the reviewer, and that is the reason we explicitly discuss that temporal resolution is not perfect (we also add a few more caveats that affect temporal resolution beyond the reviewers’ comments). However, the method is good enough to differentiate temporal sequences of neurogenesis with close to 12-hour resolution, once enough animals are analyzed to compensate for methodological temporal overlaps. That is the reason for our Figure 1D.

      Reviewer 3

      Comment 1. Text is too long and main message buried in technical details.

      We agree and similar to our response to the first comment of Reviewer 2, we will revise the writing to make it more straightforward while moving some of the information on methods and technical discussion to supplementary materials. As demonstrated by reviewer 2 comments, methodological discussions are still important to best interpret the data presented in this paper.

    1. Author response:

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

      Public Reviews:

      Reviewer #2 (Public Review):

      In this revised manuscript Aguillon and collaborators convincingly demonstrating that CLK is required for free-running behavioral rhythms under constant conditions in the Cnidarian Nematostella. The results also convincingly show that CLK impacts rhythmic gene expression in this organism. This original work thus demonstrates that CLK was recruited very early during animal evolution in the circadian clock mechanism to optimize behavior and gene expression with the time-of-day. The manuscript could still benefit from some improvements so that it is more accessible for a wide readership.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      Aguillon and collaborators have deeply revised, and in the progress significantly improved the presentation of their interesting results with the first Cnidarian circadian gene mutant. Results are now very convincingly demonstrating that CLK is required for free-running behavioral rhythms under constant conditions. The results also now more convincingly show that CLK impact rhythmic gene expression, although interpretation of the transcriptomics data is not straightforward. I think there is still improvements that are needed to make the manuscript more accessible. We authors need to keep in mind that a broad audience will read their report, not just chronobiologists. I have listed below several issues that I think should be addressed, and some editing suggestions.

      General comment to Editor and Reviewers:

      We are genuinely grateful to both reviewers and editors about all the feedback which helped us to make the best of our data, to question our analysis to the point we redefined our approach and end up with a great article we are proud of it. Only the name of authors is visible on the article, and considering how much the reviewing system help to improve the research it seems almost unfair. As such, we thank all of you and really appreciate the new eLife system. Bravo all.

      Abstract:

      (1) Line 40" It should read "transcript levels" instead of "transcription". There is no measurement of transcription rates in this manuscript, only mRNA levels.

      Modified accordingly.

      (2) Line 41: the authors mention "constant light". Does this refer to previous work? Their data in Figure 4 were in constant darkness, not in LL.

      Modified accordingly.

      (3) Line 46 and throughout the manuscript, the allelic nomenclature is not standard. 1-/- seems to indicate there are two different alleles. Since the allele might not be a null, I would suggest simply using 1/1, or perhaps delta/delta since the mutation results in a truncates CLK.

      NvClk1-/- became NvClkΔ/Δ. Except in the .xls supplementary table were the mutant kept the NvClk-/- nomenclature. It is not possible to replace only part of a word with a different font, here generating delta sign would require to do it one by one.

      (4) The last sentence of the abstract needs to be rephrased, as it suggests that CLK evolved to maintain circadian rhythms under constant conditions. Constant conditions very rarely exist on Earth, and thus cannot be an evolutionary driving force. Different explanations have been proposed on why a self-sustained clock is the evolutionary solution to timekeeping, but the purpose of the clock and of clock genes is not to maintain oscillations in constant conditions. Actually, this sentence conflicts with the title.

      Modified to: the Clock gene has evolved in cnidarians to sustain 24-hour rhythmic physiology and behavior in absence of diel environmental conditions. From my actual understanding, you are right, the purpose of clock gene is not to maintain oscillation in constant conditions (this is simply the result of the experiment), but to synchronize the physiology to the day/night rhythm, and surely to sustain 24h oscillations in case the environment challenges the perception of the diel cues. The DD or LL is just an artificial experimental design to reveal the endogenous time-keeping pacemaker.

      Results:

      (1) Line 148 and elsewhere in the MS: I would not use the word "lower" or "higher" to qualify acrophases. I would suggest advanced/delayed or earlier/later.

      Modified accordingly.

      (2) Line 157-9: The introductory sentence does not clearly present the rationale for the 6/6 experiments.

      We modified the paragraph accordingly: The presence of a 24-hour rhythm of NvClkΔ/Δ polyps under LD conditions could be attributed to either a direct light-response or the partial functioning of the circadian clock due to the nature of the mutation….

      (3) At the end of the behavior section, or perhaps at the end of each paragraph in this section, it would be helpful to have a summary of the results and more clearly explain their interpretation. The authors need to guide the readers, particularly non-chronobiologist, so that they can understand what the really neat data that were obtained mean. For example, what does it mean that the acrophase is different between mutant and wild-type, why are Clk mutants rhythmic under LD12/12 or 6/6, etc.

      We added a conclusion sentence to help non-specialist to understand each result.

      (4) Line 172 and elsewhere" "true rhythmic genes" sounds odd to me. Either they are, or they are not rhythmic.

      Modified to “rhythmic genes.”

      (5) Paragraph starting with line 184: I do not follow what is important about the number of genes per time cluster. What does it tell us, beyond the simple fact that less genes are rhythmic in the Clk mutants?

      We rewrote the result paragraph to make it clearer why we performed this clustering analysis. This clustering analysis became Extended Data Fig.2 with modification of the figures (see my comments in your review about Figure 3).

      (6) Line 197: The authors need to explain what they saw with circadian clock genes and their expression in CLk mutants. In some case, amplitude increased in LD. This surprising observation deserves some explanations. "Complex regulatory effect" is too vague.

      We replaced the vague “complex regulatory effect” by a more thorough description of the figure 3.a.

      (7) Line 198-203: Again, help the reader understand the significance of these observations.

      We rewrote the paragraph to help the reader to better understand the significance of these observations.

      Discussion:

      (1) Line 236-40. Careful with the use of -/-, which implies that an allele is a null. The first CLk mutants in mammals and flies, which the authors refer to. were actually dominant negatives.

      I went over the citations we used for this paragraph and this first mutation in fly dClkar is null, no dominant negative. Flies are still rhythmic in the dark. Unless there is an older mutation? However, you right the first mutation identified in mouse was a dominant-negative with loss of rhythmicity, while the gene deletion did not show any effect on the behavior, suggesting compensation by a paralog. I removed two references which were not relevant to the discussion.

      (2) Line 265-268 are not very clear. Do the authors mean that the lack of overlap for non-cricadian pacemaker genes is because of different experimental conditions? What would be those differences? It is reassuring that the Leach/Reitzel study and the present share pacemaker genes as rhythmic, but it is also surprising that there is almost no overlap beyond these genes. How robust are those other rhythms compared to circadian clock genes?

      We revised this paragraph and raised major points regarding the raising condition of our polyps between labs and their potential genetic differences which could explain these differences.

      (3) Line 270. I am not sure "compensation" is the right word, since there is no overlap between the rhythmic genes in mutants under LD and wild-type under either LD or DD. Also, saying on line 273 that the transcriptional pattern is not fully reproduced is a rather striking understatement, given the absence of rhythm gene overlap

      We rewrote the paragraph accordingly. We replaced by “alternative way to drive rhythmicity under LD condition”.

      (4) Line 279. The authors mention the possibility of false positives. Based on the FDR, is there more rhythmic genes than by chance?

      The possibility of false-positive is a risk to consider when you do not perform multiple-testing. We added within the results paragraph the number of rhythmic genes identified with BH.Q or p.adj. which both are the multiple testing for each algorithm (RAIN and JTK) we used.

      (5) Line 279-82. The references to the Ray study is rather obscure. What is the point the authors are trying to make here?

      Eventually, we removed the reference from this article and modify the paragraph of the discussion. Indeed, the discussion around the Ray study did not gave an interesting direction to discuss our results and analysis approach.

      (6) Line 284: define BHQ and p.adj

      Defined and referenced.

      (7) The way Lines 283-288 are worded do not provide a good rationale for how transcriptional rhythms were analyzed. The idea to combine two different approaches (JTK and RAIN) to be selective with rhythmicity was great. The authors need however to justify these choices in a more convincing manner. The goal is to detect rhythmic genes in a reliable manner, irrespective of the number of rhythmic genes observed Also, explaining the choice of methodology belongs to the result section.

      We explained our choice of methodology and moved it to the result section as suggested.

      (8) Line 292-3. There are known mechanisms that explain how transcriptional time clusters are generated. In particular, the use of interlocked feedback loop with antiphase peaks of transcriptions is well documented. Actually, it seems to me the clustering shown in Fig 4 might hint at such a mechanism.

      Indeed you are right the clustering shown in Fig 3 (former Fig 4) revealed such mechanism.

      Figures:

      Figure 2: Define relative amplitude

      We added the definition of the relative amplitude within the results. If this is what you asked for?

      Figure 3: Some of the cycles look odd (first row of graphs in panel C). Why would the first and last data point be so different in three of these graphs?

      We decided to modify this figure as we realized it was not informative and not objective enough, as we selected among multiple patterns few “representatives”. In the new figure we combined the cluster analysis to the behavior. Thus, readers can now pick a cluster according to a specific behavior activity level (or ZT/CT) and reach in supp. Table 4 the “genes of potential interest”. However generally speaking this figure does not explain more the consequences of the mutation, so we moved it into the Extended data Fig.2

      Figure4: define the color coding in the correlation panels (blue to red)

      These values from -1 to 1 are the Pearson correlation values. Now indicated on the figure with the color coding legend.

    1. Author response:

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

      Reviewer #1 (Public Review):

      (1) The authors' findings are primarily rooted in a series of well-conducted in vitro experiments using two CML cell lines, K562 and MEG-01. While the findings are interesting and novel, further work to corroborate these findings in primary CML samples would have greatly strengthened the potential real-world relevance of these discoveries. The authors appear to have some PBMCs from primary CML patients and a BM sample from a Ph+ ALL in which they performed western blot analyses (Fig 1). Couldn't these samples have been used to at least confirm some of the key discoveries? For example, the neddylation of BCR-ABL, or; sensitivity of primary leukemic cells to RAPSYN knockdown, and/or; phosphorylation of RAPSYN by SRC?

      We agree with your points and really appreciate your comments. To demonstrate the clinical relevance, we have conducted a series of experiments to address your concerns.

      (1) after a thorough optimization on the transduction process, we have managed to show that shRNA-mediated gene silencing of RAPSYN impaired the growth of primary CML samples. These additional data are presented as Figure 1D in the revised manuscript with its corresponding figure legend and description, lines 136-141.

      (2) we have invested tremendous time and effort to deal with “key discoveries” regardless of the almost impossible task with a great technical difficulty. With 5 mL (ethical approval) of PBMCs on hands, we have finally managed to confirm BCR-ABL neddylation by IP from two newly acquired CML patients. The results are as presented in Figure 2F in the revised manuscript with its corresponding figure legend and description, lines 186-187.

      (2) The authors initially interrogated a fairly dated (circa 2009) microarray-based primary dataset to show that the increase in RAPSYN is primarily a post-transcriptional event, as mRNA levels are not different between healthy and CML samples. It would be interesting to see whether differences might be more readily seen in more recent RNA-seq datasets from CML patients, given the well-known differences in sensitivity between the two platforms. Additionally, I wonder if there would be transcriptional signatures of increased NEDDylation (or RAPSYN-induced NEDDylation) that could be interrogated in primary samples? Furthermore, there are proteomics datasets of CML cells made resistant to TKIs (through in vitro selection experiments) that could be interrogated for independent validation of the authors' discoveries. For example: from K562 cells, PMID: 30730747 or PMID: 34922009).

      Thank you very much for your constructive comments. Based on your suggestion, we have 1) analyzed mRNA level of RAPSYN in RNA-seq datasets GSE13159 (2009), GSE138883 (2020) and GSE140385 (2020), indicating no difference between CML patients and healthy donors. We have included the results in Figure1-figure supplementary 1A and in the revised manuscript (lines 123-127); 2) examined the RNA levels of RAPSYN-related neddylation enzymes, including E1 (NAE1), E2 (UBE2M), NEDD8 and NEDP1 in these databases, and no significant differences of these neddylation-related genes were found between CML patients and healthy donors as well (Supplementary Figure 2C, lines 168-172).

      We have also analyzed the proteomics datasets from PMID: 30730747 and PMID: 34922009 according to your suggestion. Unfortunately, no information on RAPSYN expression is available in these datasets. To avoid potential negligence, we have examined all CML-related proteomics datasets from 2002 to 2024, still resulting in no information about protein expression of RAPSYN. Consequently, our finding on the higher expression of RAPSYN in the PBMCs of Ph+ patients in this study appears to be an observation for the first time. And we believe that our results should be more clinically relevant than those, if any, from the cells by in vitro selection.

      Reviewer #2 (Public Review):

      Most of the conclusions drawn in this paper are well supported by data, but some aspects of the data need to be clarified and extended:

      (1) The authors propose that targeting RAPSYN in Ph+ leukemia could have a high therapeutic index, suggesting that inhibition of RAPSYN may lead to cytotoxicity in Ph+ leukemia with high specificity and minimal side effects. To substantiate this assertion, the authors should investigate the impact on cell viability upon RAPSYN knockdown in non-Ph leukemic cell lines or HS-5 cells (similar to Figure 1C), despite their lower RAPSYN protein levels.

      We appreciate your valuable comments. When we used shRNA to knockdown the expression of RAPSYN in HS-5 cells, it did not affect the cell growth of HS-5 cells. We have included the data in Figure 1C, modified its figure legend, and added corresponding description, lines 136-141.

      (2) The authors intriguingly show that the protein levels of RAPSYN are significantly enriched in Ph+ patient samples and cell lines (Figure 1A, B), even though the mRNA levels remain unchanged (Supplementary Figure 1 A-C). This observation merits a clear explanation in the context of the presented results. The data in the manuscript does imply a feedforward loop mechanism (Figure 7), where BCR-ABL activates SRC, which subsequently stabilizes RAPSYN, which in turn helps protect BCR-ABL from c-CBL-mediated degradation. If this is the working hypothesis, it would be beneficial for the reader to see supporting evidence.

      Thank you very much for pointing out the issue. We have realized the inappropriateness of Figure 7, which was originally placed as a summarizing figure. To avoid potential confusion and misleading, this figure has been deleted, which does not affect the results and conclusions of this study. In addition, the differences on mRNA levels and protein expressions have been responded to Reviewer #1.

      (3) The authors present compelling evidence to suggest that RAPSYN may possess direct NEDD8-ligase activity on BCR-ABL. To strengthen this claim, it may be valuable to conduct further assays involving a ligase-deficient mutant, such as C366A, beyond its use in Figure 2J. Incorporating this mutant into the in vitro assay illustrated in Figure 2K, for instance, could offer substantial validation for the claim. In addition, showing whether the ligase-deficient mutant is capable of phenocopying the phosphorylation-mutant Y336F, as showcased in Figures 5E, F, and 6D, F, would be beneficial.

      We are grateful to your comments. In the manuscript, we have provided sufficient data to support the direct neddylation of BCR-ABL by RAPSYN, as you commented “The authors present compelling evidence to suggest that RAPSYN may possess direct NEDD8-ligase activity on BCR-ABL.”. Cys366 was previously demonstrated as the catalytic residue essential for E3 activity of RAPSYN (Li et al. 2016, PMID: 27839998), and the phosphorylation at Phe336 was thoroughly verified by site-directed mutagenesis and the treatments of SRC-specific inhibitor saracatinib in present cellular experiments. Therefore, while we fully respect your opinions, we do not think it would be necessary to perform tedious in vitro reactions for expected negative results, which was the reason for us not to conduct enzymatic reactions with known inactive mutants, such as C366A and Y336F, in the first place.

      (4) The observations presented in Figures 6 C-G require additional clarification. Notably, there are discrepancies in relative cell viability effects in K562 cells, and to some extent in MEG-01 cells, under conditions that are indicated as being either identical or highly similar. For instance, this inconsistency is observable when comparing the left panels of Figure 6C and 6D in the case of NC overexpression + shSRC#2, and the left panels of Figure 6E and 6G with NC overexpression or shNC, respectively. Listing potential causes of these discrepancies would strengthen the overall validity of the findings and their subsequent interpretation.

      Thank you for your comments and apologize for the confusion. To make a meaningful comparison, we have revised the method part “Preparation of stable RAPSYNWT, RAPSYNY336F or SRC expression cell lines” (lines 625-627) and reorganized Figure 6 to reflect the differences on the negative controls. In fact, we first used LV6 (EF-1a/Puro; OE-NC1) vector for the overexpression of RAPSYNWT and SRC. Due to low expression level with LV6 and long period of time for subsequent selection, we switched to LV18 (CMV/Puro; OE-NC2) for the overexpression of RAPSYNY336F. Since the sensitivities of K562/MEG01-OE-NC cells to shSRC transduction in Figure 6C (now revised to K562/MEG01-OE-NC1) and 6D (now revised to K562/MEG01-OE-NC2) were noticeably different, we have separated RAPSYNWT and RAPSYNY336F cells as 6C and 6D with their own corresponding empty vector as negative control, instead of merging the results into a single figure with one negative control of OE-NC. In addition, given the fact that K562/MEG01 cells reacted differently upon saracatinib treatments after transduction with the empty vector, we have also distinguished the negative controls as OE-NC1 in Figure 6E, OE-NC2 in Figure 6F and shNC in Figure 6G. Afterall, the transduction of K562/MEG01 cells with different expression vectors and viral particles caused the discrepancies in the experiments of cell viability, which has been clarified by reorganizing Figure 6 in the revision.

      (5) Throughout the manuscript, immunoblots which showcase immunoprecipitations of BCR-ABL or His-BCR-ABL depict poly-neddylation (e.g. Figures 2E-M, 3D-G, and 5A-E) and poly-ubiquitination (e.g. Figures 3D-G) patterns/smears where these patterns seem to extend below the molecular weight of BCR-ABL. To enhance clarity, it would be valuable for the authors to provide an explanation in the text or the figure legend for this observation. Is it reflective of potential degradation of BCR-ABL or is there another explanation behind it?

      Thank you for your valuable comments. After carefully checking original immunoblots, we have ascertained that the protein band of BCR-ABL was at 250 KDa and the smear bands appeared to be higher than 250 KDa were likely caused by the conjugation of NEDD8 (neddylation) or Ubiquitin (ubiquitination) onto BCR-ABL. Regarding the molecular weight of modified BCR-ABL lower than expected, whether it is a common feature as previously reported (Mao, J., et al, 2010, PMID: 21118980) or possible degradation during the modification process or sample preparation requires further investigation. We have corrected the labeling of figures in the revised manuscript.

      Reviewer #1 (Recommendations For The Authors):

      (1) It would really nail the real-world relevance of these nice findings if the authors are able to confirm some aspects of their cell line-based discoveries in publicly available 'omics datasets generated from primary CML samples. I have suggested some of these in the public review as well.

      Alternatively, if they are able to investigate samples from murine CML models (eg. BALB/c CML models), it would represent a step towards real-world relevance.

      Thank you very much for your constructive comments. According to your suggestion, we have examined and analyzed RAPSYN mRNA and protein in updated and publicly available datasets as replied in the public response.

      (2) The Discussion repeats some of the information already presented in the Introduction (for example, lines 311-327 of the merged document, or lines 349-358). I would urge the authors to instead expand more about how RAPSYN might be upregulated at the post-transcriptional level, or its potential post-translational regulation by SRC-mediated phosphorylation.

      Thanks for your constructive suggestion. We have re-written this part according to your suggestion and marked in red color in the revised manuscript, lines 319-325 and lines 351-378.

      (3) There are instances of clunky phrases/grammatical mistakes in the manuscript which detract from its readability (eg: lines 142-143: "...empty body transduced shRAPSN#3 or K562 cells into...."; lines 163-164: "Despite AChR subunits α7, M2, M3, and M4 were expressed in all tested cells, no change..."; line 178: "Preeminent BCR-ABL neddylation was detected in..."). A closer proof-reading of the final manuscript is advisable.

      We appreciate the valuable comments. We have made changes for improvement, which is marked in red color in the revised manuscript, lines 145-147, lines 166-168 and line 185.

      (4) The western blot in Fig 5C (particularly the control "OE-NC" of K562) looks drastically different from the corresponding control lanes in Figs 5A and 5B. Similarly, the cell viability curves presented in Fig 6D and 6F (for both K562 and MEG-01, control conditions) look very different from the corresponding curves in Figs 6A and 6B.

      We appreciate for your valuable comments. Because we accidently used the imagines with different exposure time, the western blots in Fig 5C (particularly the control "OE-NC" of K562) look very different from corresponding control lanes in Figs 5A and 5B. We have replaced images with the same exposure time in the revised manuscript.

      For readers to clearly understand, we have revised the method part “Preparation of stable RAPSYNWT, RAPSYNY336F or SRC expression cell lines” (lines 625-627) and related figure legends to reflect the differences.

      We have publicly responded the discrepancy on cell viability.

      Reviewer #2 (Recommendations For The Authors):

      In reviewing your study, I must insist that the completeness and robustness of your work would significantly benefit from a more exhaustive listing of the antibodies used for immunoblotting and immunoprecipitation within the Materials and Methods section. A number of antibodies have been accounted for, however, crucial ones targeting BCR-ABL, c-CBL, Ubiquitin, NEDD8, HA, Myc, and others appear to be omitted. To maintain rigorous scientific standards, I strongly encourage you to include these.

      We appreciate your comments. We have carefully checked the section of Methods and added detailed information of antibodies for Immunoblotting and Immunoprecipitation in the revised manuscript, lines 502-516.

    1. Author response:

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

      eLife assessment

      The authors have made important contributions to our understanding of the pathogenesis of erectile dysfunction (ED) in diabetic patients. They have identified the gene Lbh, expressed in pericytes of the penis and decreased in diabetic animals. Overexpression of Lbh appears to counteract ED in these animals. The authors also confirm Lbh as a potential marker in cavernous tissues in both humans and mice. While solid evidence supports Lbh's functional role as a marker gene, further research is needed to elucidate the specific mechanisms by which it exerts its effects. This work is of interest to those working in the fields of ED and angiogenesis.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

      Comments on revised version:

      For Figure 4, immunofluorecent staining of LBH following intracavernous injections with lentiviruses is required to justify overexpression and tissue specificity.

      We agree with this claims. Therefore, we have performed the immunofluorecent staining of LBH in cavernous tissues after infection with LBH O/E lentiviruses. And we found the LBH expression is significantly decreased in DM or DM+NC groups, however, after infection with LBH O/E lentiviruses, the LBH expression is significantly increased, shown as Supplementary Fig. 10. (Please see revised ‘Result’ and ‘Supplementary Fig. 10’)

      Reviewer #3 (Public Review):

      Bae et al. described the key roles of pericytes in cavernous tissues in diabetic erectile dysfunction using both mouse and human single-cell transcriptomic analysis. Erectile dysfunction (ED) is caused by dysfunction of the cavernous tissue and affects a significant proportion of men aged 40-70. The most common treatment for ED is phosphodiesterase 5 inhibitors; however, these are less effective in patients with diabetic ED. Therefore, there is an unmet need for a better understanding of the cavernous microenvironment, cell-cell communications in patients with diabetic ED, and the development of new therapeutic treatments to improve the quality of life.

      Pericytes are mesenchymal-derived mural cells that directly interact with capillary endothelial cells (ECs). They play a vital role in the pathogenesis of erectile function as their interactions with ECs are essential for penile erection. Loss of pericytes has been associated with diabetic retinopathy, cancer, and Alzheimer's disease and has been investigated in relation to the permeability of cavernous blood vessels and neurovascular regeneration in the authors' previous studies. This manuscript explores the mechanisms underlying the effect of diabetes on pericyte dysfunction in ED. Additionally, the cellular landscape of cavernous tissues and cell type-specific transcriptional changes were carefully examined using both mouse and human single-cell RNA sequencing in diabetic ED. The novelty of this work lies in the identification of a newly identified pericyte (PC)-specific marker, LBH, in mouse and human cavernous tissues, which distinguishes pericytes from smooth muscle cells. LBH not only serves as a cavernous pericyte marker, but its expression level is also reduced in diabetic conditions. The LBH-interacting proteins (Cryab and Vim) were further identified in mouse cavernous pericytes, indicating that these signaling interactions are critical for maintaining normal pericyte function. Overall, this study demonstrates the novel marker of pericytes and highlights the critical role of pericytes in diabetic ED.

      Comments on revised version:

      Bae and colleagues substantially improved the data quality and revised their manuscript "Pericytes contribute to pulmonary vascular remodeling via HIF2a signaling". While these revisions clarify some of the concerns raised, others remain. In my view, the following question must be addressed.

      In my prior question on #3, I completely disagree with the statement that "identified cells with pericyte-like characteristics in the walls of large blood vessels". The staining that authors provided for LBH, was clearly stained for SMCs, not pericytes. Per Fig 2E, the authors are correct that LBH is colocalized with SMA+ cells( SMCs). However, the red signal from LBH clearly stains endothelial cells. In the rest of 2E and 2D, LBH is CD31- and their location suggests LBH stained for SMCs in the Aorta, Kidney vasculature, Dorsal vein, and Dorsal Artery.

      We respect the reviewer's comments and provide further justification for the reviewer's concerns. We first performed double staining of LBH and CD31 on dorsal artery and dorsal vein tissues. We found that LBH-expressing cells are completely different from CD31-expressing cells (Figrue 2D, indicated by arrows, and Supplementary Fig. 10A) and that expression is higher in veins than in arteries. This is consistent with previous understanding. In addition, in the double staining of LBH and α-SMA, we also found that there was no overlap between LBH-expressing cells and α-SMA-expressing smooth muscle cells in the cavernosum tissues, but there was some overlap in dorsal artery and dorsal vein (Figrue 2E, indicated by arrows). This may indicate that LBH is expressed slightly different types of blood vessels. This requires further experiments to prove in the future. In addition, to avoid confusion among other readers. We modify our previous discussion regarding the identification of cells with pericyte-like characteristics in the walls of large blood vessels. We removed the associated immunofluorescence staining in the aorta and kidneys replaced them with dorsal artery and dorsal vein (Please see revised ‘Result’ and ‘Figure 2’ and ‘Supplementary Fig. 10A’)

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this work, Qiu and colleagues examined the effects of preovulatory (i.e., proestrous or late follicular phase) levels of circulating estradiol on multiple calcium and potassium channel conductances in arcuate nucleus kisspeptin neurons. Although these cells are strongly linked to a role as the "GnRH pulse generator," the goal here was to examine the physiological properties of these cells in a hormonal milieu mimicking late proestrus, the time of the preovulatory GnRH-LH surge. Computational modeling is used to manipulate multiple conductances simultaneously and support a role for certain calcium channels in facilitating a switch in firing mode from tonic to bursting. CRISPR knockdown of the TRPC5 channel reduced overall excitability, but this was only examined in cells from ovariectomized mice without estradiol treatment. The patch clamp experiments are comprehensive and overall solid but a direct demonstration of the role of these conductances in being necessary for surge generation (or at least having a direct physiological consequence on surge properties) is lacking, substantially reducing the impact of the findings.

      Strengths:

      (1) Examination of multiple types of calcium and potassium currents, both through electrophysiology and molecular biology.

      (2) Focus on arcuate kisspeptin neurons during the surge is relatively conceptually novel as the anteroventral periventricular nucleus (AVPV) kisspeptin neurons have received much more attention as the "surge generator" population.

      (3) The modeling studies allow for direct examination of manipulation of single and multiple conductances, whereas the electrophysiology studies necessarily require examination of each current in isolation. The construction of an arcuate kisspeptin neuron model promises to be of value to the reproductive neuroendocrinology field.

      We thank the reviewer for recognizing our comprehensive examination of Kiss-ARH neurons through electrophysiological, molecular and computational modeling of their activity during the preovulatory surge, which as the reviewer pointed out is “conceptually novel.” We will bolster our argument that Kiss1-ARH neurons transition from synchronized firing to burst firing with the E2-mediated regulation of channel expression with the addition of new experiments. We will address the weaknesses as follows:

      Weaknesses:

      (1) The novelty of some of the experiments needs to be clarified. This reviewer's understanding is that prior experiments largely used a different OVX+E2 treatment paradigm mimicking periods of low estradiol levels, whereas the present work used a "high E2" treatment model. However, Figures 10C and D are repeated from a previous publication by the same group, according to the figure legend. Findings from "high" vs. "low" E2 treatment regimens should be labeled and clearly separated in the text. It would also help to have direct comparisons between results from low E2 and high E2 treatment conditions.

      We will revise Figures 10C and 10D to include new findings on Tac2 and Vglut2 expression in OVX and E2-treated Kiss1ARH. We did show the previously published data (Qiu, eLife 2018) to contrast with Figures 10E, F showing the downregulation of TRPC5 and GIRK2 channels following E2 treatment. Most importantly, our E2 treatment regime is clearly stated in the Methods and is exactly the same that was used previously (Qiu, eLife 2016 and Qiu, eLife 2018) for the induction of the LH surge in OVX mice (Bosch, Molecular and Cellular Endocrinology 2013) .

      (2) In multiple places, links are made between the changes in conductances and the transition from peptidergic to glutamatergic neurotransmission. However, this relationship is never directly assessed. The data that come closest are the qPCR results showing reduced Tac2 and increased Vglut2 mRNA, but in the figure legend, it appears that these results are from a prior publication using a different E2 treatment regimen.

      In the revised Figure 1, we will now include a clear depiction of the transition from synchronized firing driven by NKB signaling in OVX females to burst firing driven by glutamate in E2-treated females. We have used the same E2 treatment paradigm as previously published (Qiu, eLife 2018).

      (3) Similarly, no recordings of arcuate-AVPV glutamatergic transmission are made so the statements that Kiss1ARH neurons facilitate the GnRH surge via this connection are still only conjecture and not supported by the present experiments.

      Using a horizontal hypothalamic slice preparation, we have shown that Kiss1-ARH neurons excite GnRH neurons via Kiss1ARH glutaminergic input to Kiss1AvPV neurons (summarized in Fig. 12, Qiu, eLife 2016). We do not think that it is necessary to repeat these experiments in the current manuscript.

      (4) Figure 1 is not described in the Results section and is only tenuously connected to the statement in the introduction in which it is cited. The relevance of panels C and D is not clear. In this regard, much is made of the burst firing pattern that arises after E2 treatment in the model, but this burst firing pattern is not demonstrated directly in the slice electrophysiology examples.

      We will revised Figure 1 to include new whole-cell, current clamp recordings documenting the burst firing in response to glutamate in E2-treated, OVX females.

      (5) In Figure 3, it would be preferable to see the raw values for R1 and R2 in each cell, to confirm that all cells were starting from a similar baseline. In addition, it is unclear why the data for TTA-P2 is not shown, or how many cells were recorded to provide this finding.

      Before initiating photo-stimulation for each Kiss1-ARH neuron, we adjust the resting membrane potential to -70 mV, as noted in each panel in Figure 3, through current injections. We will include new findings on the effects of the T-channel blocker TTA-P2 on slow EPSP in the revised Figure 3. The number of cells tested with each calcium channel blocker is depicted in each of the bar graphs summarizing the effects of the blockers.

      (6) In Figure 5, panel C lists 11 cells in the E2 condition but panel E lists data from 37 cells. The reason for this discrepancy is not clear.

      In Figure 5E, we measured the L-, N-, P/Q and R channel currents after pretreatment with TTA-P2 to block the T-type current, whereas in Figure 5C, we measured the current without TTA-P2.

      (7) In all histogram figures, it would be preferable to have the data for individual cells superimposed on the mean and SEM.

      In all revised Figures we will include the individual data points for the individual neurons.

      (8) The CRISPR experiments were only performed in OVX mice, substantially limiting interpretation with respect to potential roles for TRPC5 in shaping arcuate kisspeptin neuron function during the preovulatory surge.

      The TRPC5 channels are most important for generating slow EPSPs when expression of NKB is high in the OVX state. Conversely, the glutamatergic response becomes more significant when the expression of NKB and TRPC5 channel are muted. Therefore, the CRISPR experiments were specifically conducted in OVX mice to maximize the effects.

      (9) Furthermore, there are no demonstrations that the CRISPR manipulations impair or alter the LH surge.

      In this manuscript, our focus is on the cellular electrophysiological activity of the Kiss1ARH neurons in ovx and E2-treated females. Exploration of CRISPR manipulations related to the LH surge is certainly slated for future experiments, but these in vivo experiments are beyond the scope of these comprehensive cellular electrophysiological and molecular studies.

      (10) The time of day of slice preparation and recording needs to be specified in the Methods.

      We will provide the times of slice preparation and recordings in the revised Methods and Materials.

      Reviewer #2 (Public Review):

      Summary:

      Kisspeptin neurons of the arcuate nucleus (ARC) are thought to be responsible for the pulsatile GnRH secretory pattern and to mediate feedback regulation of GnRH secretion by estradiol (E2). Evidence in the literature, including the work of the authors, indicates that ARC kisspeptin coordinate their activity through reciprocal synaptic interactions and the release of glutamate and of neuropeptide neurokinin B (NKB), which they co-express. The authors show here that E2 regulates the expression of genes encoding different voltage-dependent calcium channels, calcium-dependent potassium channels, and canonical transient receptor potential (TRPC5) channels and of the corresponding ionic currents in ARC kisspeptin neurons. Using computer simulations of the electrical activity of ARC kisspeptin neurons, the authors also provide evidence of what these changes translate into in terms of these cells' firing patterns. The experiments reveal that E2 upregulates various voltage-gated calcium currents as well as 2 subtypes of calcium-dependent potassium currents while decreasing TRPC5 expression (an ion channel downstream of NKB receptor activation), the slow excitatory synaptic potentials (slow EPSP) elicited in ARC kisspeptin neurons by NKB release and expression of the G protein-associated inward-rectifying potassium channel (GIRK). Based on these results, and on those of computer simulations, the authors propose that E2 promotes a functional transition of ARC kisspeptin neurons from neuropeptide-mediated sustained firing that supports coordinated activity for pulsatile GnRH secretion to a less intense firing in glutamatergic burst-like firing pattern that could favor glutamate release from ARC kisspeptin. The authors suggest that the latter might be important for the generation of the preovulatory surge in females.

      Strengths:

      The authors combined multiple approaches in vitro and in silico to gain insights into the impact of E2 on the electrical activity of ARC kisspeptin neurons. These include patch-clamp electrophysiology combined with selective optogenetic stimulation of ARC kisspeptin neurons, reverse transcriptase quantitative PCR, pharmacology, and CRIPR-Cas9-mediated knockdown of the Trpc5 gene. The addition of computer simulations for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.

      The authors add interesting information on the complement of ionic currents in ARC kisspeptin neurons and on their regulation by E2 to what was already known in the literature. Pharmacological and electrophysiological experiments appear of the highest standards. Robust statistical analyses are provided throughout, although some experiments (illustrated in Figures 7 and 8) do have rather low sample numbers.

      The impact of E2 on calcium and potassium currents is compelling. Likewise, the results of Trpc5 gene knockdown do provide good evidence that the TRPC5 channel plays a key role in mediating the NKB-mediated slow EPSP. Surprisingly, this also revealed an unsuspected role for this channel in regulating the membrane potential and excitability of ARC kisspeptin neurons.

      We thank the reviewer for recognizing that the “pharmacological and electrophysiological experiments appear of the highest standards” and “the addition of the computer modeling for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength. However, we agree with the reviewer that we need to provide a direct demonstration of “burst-like” firing of Kiss1-ARH neurons. We will address the weaknesses as follows:

      Weaknesses:

      The manuscript also has weaknesses that obscure some of the conclusions drawn by the authors.

      One has to do with the fact that "burst-like" firing that the authors postulate ARC kisspeptin neurons transition to after E2 replacement is only seen in computer simulations, and not in slice patch-clamp recordings. A more direct demonstration of the existence of this firing pattern, and of its prominence over neuropeptide-dependent sustained firing under conditions of high E2 would make a more convincing case for the authors' hypothesis.

      We will provide a more direct demonstration of the existence of this firing pattern in the whole-cell current clamp experiments in the revised Figure 1.

      In addition, and quite importantly, the authors compare here two conditions, OVX versus OVX replaced with high E2, that may not reflect the physiological conditions (the diestrous [low E2] and proestrous [high E2] stages of the estrous cycle) under which the proposed transition between neuropeptide-dependent sustained firing and less intense burst firing might take place. This is an important caveat to keep in mind when interpreting the authors' findings. Indeed, that E2 alters certain ionic currents when added back to OVX females, does not mean that the magnitude of these ionic currents will vary during the estrous cycle.

      We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle and the similarity to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016). Moreover, TRPC5 channel mRNA expression, similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch, Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

      Lastly, the results of some of the pharmacological and genetic experiments may be difficult to interpret as presented. For example, in Figure 3, although it is possible that blockade of individual calcium channel subtypes suppresses the slow EPSP through decreased calcium entry at the somato-dendritic compartment to sustain TRPC5 activation and the slow depolarization (as the authors imply), a reasonable alternative interpretation would be that at least some of the effects on the amplitude of the slow EPSP result from suppression of presynaptic calcium influx and, thus, decreased neurotransmitter and neuropeptide secretion. Along the same lines, in Figure 12, one possible interpretation of the observed smaller slow EPSPs seen in mice with mutant TRPC5 could be that at least some of the effect is due to decreased neurotransmitter and neuropeptide release due to the decreased excitability associated with TRPC5 knockdown.

      The reviewer raises a good point, but our previous findings clearly demonstrate that chelating intracellular calcium with BAPTA in whole-cell current clamp recordings abolishes the slow EPSP and persistent firing (Qiu, J. Neurosci 2021), which we have noted is the rationale for dissecting out the contribution of T, R, N, L and P/Q calcium channels to the slow EPSP in our current studies (revised Figure 3 will include the effects of T-channel blocker).

      However, to further bolster the argument for the post-synaptic contribution of the calcium channels to the slow EPSP and eliminate the potential presynaptic effects of calcium channel blockers on the postsynaptic slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter release, we will utilized an additional strategy. Specifically, we will measure the response to the externally administered TACR3 agonist senktide under conditions in which the extracellular calcium influx, as well as neurotransmitter and neuropeptide release, are blocked (new Figure 3).

    1. Author response:

      eLife assessment

      Unlocking the potential of molecular genetic tools (optogenetics, chemogenetics, sensors, etc.) for the study of systems neuroscience in nonhuman primates requires the development of effective regulatory elements for cell-type specific expression to facilitate circuit dissection. This study provides a valuable building block, by carefully characterizing the laminar expression profile of two viral vectors, one designed for general GABA+ergic neurons and the second for parvalbumin+ cell-type selective expression in the marmoset primary visual cortex. The authors provide solid evidence for the first enhancer S5E2 and incomplete evidence for the second one, h56D. This study contributes to our understanding of these tools but is limited by the understandably small number of animals used.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Federer et al. tested AAVs designed to target GABAergic cells and parvalbumin-expressing cells in marmoset V1. Several new results were obtained. First, AAV-h56D targeted GABAergic cells with >90% specificity, and this varied with serotype and layer. Second, AAV-PHP.eB.S5E2 targeted parvalbumin-expressing neurons with up to 98% specificity. Third, the immunohistochemical detection of GABA and PV was attenuated near viral injection sites.

      Strengths:

      Vormstein-Schneider et al. (2020) tested their AAV-S5E2 vector in marmosets by intravenous injection. The data presented in this manuscript are valuable in part because they show the transduction pattern produced by intraparenchymal injections, which are more conventional and efficient.

      Our manuscript additionally provides detailed information on the laminar specificity and coverage of these viral vectors, which was not investigated in the original studies.

      Weaknesses:

      The conclusions regarding the effects of serotype are based on data from single injection tracks in a single animal. I understand that ethical and financial constraints preclude high throughput testing, but these limitations do not change what can be inferred from the measurements. The text asserts that "...serotype 9 is a better choice when high specificity and coverage across all layers are required". The data presented are consistent with this idea but do not make a strong case for it.

      We are aware of the limitations of our results on the AAV-h56D. We agree with the Reviewer that a single injection per serotype does not allow us to make strong statements about differences between the 3 serotypes. Therefore, in the revised version of the manuscript we will temper our claims about such differences and use more caution in the interpretation of these data. Despite this weakness, we feel that these data still demonstrate high efficiency and specificity across cortical layers of transgene expression in GABA cells using the h56D promoter, at least with two of the 3 AAV serotypes we tested. We feel that in itself this is sufficiently useful information for the primate community, worthy of being reported. Due to cost, time and ethical considerations related to the use of primates, we chose not to perform additional experiments to determine precise differences among serotypes. Thus, for example, while it is possible that had we replicated these experiments, serotype 7 would have proven equally efficient and specific as the other two serotypes, we felt answering this question did not warrant additional experiments in this precious species.

      A related criticism extends to the analysis of injection volume on viral specificity. Some replication was performed here, but reliability across injections was not reported. My understanding is that individual ROIs were treated as independent observations. These are not biological replicates (arguably, neither are multiple injection tracks in a single animal, but they are certainly closer). Idiosyncrasies between animals or injections (e.g. if one injection happened to hit one layer more than another) could have substantial impacts on the measurements. It remains unclear which results regarding injection volume or serotype would hold up had a large number of injections been made into a large number of marmosets.

      For the AAV-S5E2, we made a total of 7 injections (at least 2 at the same volume), all of which, irrespective of volume, resulted in high specificity and efficiency for PV interneurons. Our conclusion is that larger volumes are slightly less specific, but the differences are minimal and do not warrant additional injections. Additionally, all of our injections involved all cortical layers, and the ROIs we selected for counts encompassed reporter protein expression across all layers. To provide a better sense of the reliability of the results across injections, in the revised version of the manuscript we will provide a supplementary table with results for each injection case separately.

      Reviewer #2 (Public Review):

      This is a straightforward manuscript assessing the specificity and efficiency of transgene expression in marmoset primary visual cortex (V1), for 4 different AAV vectors known to target transgene expression to either inhibitory cortical neurons (3 serotypes of AAV-h56D-tdTomato) or parvalbumin (PV)+ inhibitory cortical neurons in mice. Vectors are injected into the marmoset cortex and then postmortem tissue is analyzed following antibody labeling against GABA and PV. It is reported that: "in marmoset V1 AAV-h56D induces transgene expression in GABAergic cells with up to 91-94% specificity and 80% efficiency, depending on viral serotype and cortical layer. AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86-90% efficiency."

      These claims are largely supported but slightly exaggerated relative to the actual values in the results presented. In particular, the overall efficiency for the best h56D vectors described in the results is: "Overall, across all layers, AAV9 and AAV1 showed significantly higher coverage (66.1{plus minus}3.9 and 64.9%{plus minus}3.7)". The highest coverage observed is just in middle layers and is also less than 80%: "(AAV9: 78.5%{plus minus}9.1; AAV1: 76.9%{plus minus}7.4)".

      In the abstract, we indeed summarize the overall data and round up the decimals, and state that these parentages are upper bound and that they vary by serotype and layer, while in the Results we report the detailed counts with decimals. To clarify this, in the revised version of the Abstract we will change 80% to 79% and emphasize even more clearly the dependence on serotype and layer. We will amend this sentence of the Abstract as follows: “We show that in marmoset V1 AAV-h56D induces transgene expression in GABAergic cells with up to 91-94% specificity and 79% efficiency, but this depends on viral serotype and cortical layer.”

      For the AAV-PHP.eB-S5E2 the efficiency reported in the abstract ("86-90%) is also slightly exaggerated relative to the results: "Overall, across all layers coverage ranged from 78%{plus minus}1.9 for injection volumes >300nl to 81.6%{plus minus}1.8 for injection volumes of 100nl."

      Indeed, the numbers in the Abstract are upper bounds, for example efficiency in L4A/B with S5E2 reaches 90%. To further clarify this important point, in the revised abstract we will state ”AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86-90% efficiency, depending on layer”.

      These data will be useful to others who might be interested in targeting transgene expression in these cell types in monkeys. Suggestions for improvement are to include more details about the vectors injected and to delete some comments about results that are not documented based on vectors that are not described (see below).

      Major comments:

      Details provided about the AAV vectors used with the h56D enhancer are not sufficient to allow assessment of their potential utility relative to the results presented. All that is provided is: "The fourth animal received 3 injections, each of a different AAV serotype (1, 7, and 9) of the AAV-h56D-tdTomato (Mehta et al., 2019), obtained from the Zemelman laboratory (UT Austin)." At a minimum, it is necessary to provide the titers of each of the vectors. It would also be helpful to provide more information about viral preparation for both these vectors and the AAVPHP.eB-S5E2.tdTomato. Notably, what purification methods were used, and what specific methods were used to measure the titers?

      We thank the Reviewer for this comment. In the revised version of the manuscript, we will provide a Table with titers of each viral vector injected as well as more information regarding viral preparation methods. In fact, the methods for viral preparation and purification are detailed in the original publications so we feel it may be sufficient to cite the original papers?

      The first paragraph of the results includes brief anecdotal claims without any data to support them and without any details about the relevant vectors that would allow any data that might have been collected to be critically assessed. These statements should be deleted. Specifically, delete: "as well as 3 different kinds of PV-specific AAVs, specifically a mixture of AAV1-PaqR4-Flp and AAV1-h56D-mCherry-FRT (Mehta et al., 2019), an AAV1-PV1-ChR2-eYFP (donated by G. Horwitz, University of Washington)," and delete "Here we report results only from those vectors that were deemed to be most promising for use in primate cortex, based on infectivity and specificity. These were the 3 serotypes of the GABA-specific pAAV-h56D-tdTomato, and the PV-specific AAVPHP.eB-S5E2.tdTomato." These tools might in fact be just as useful or even better than what is actually tested and reported here, but maybe the viral titer was too low to expect any expression.

      This data is indeed anecdotal, and while we could delete it from the manuscript, as suggested by the Reviewer, we feel it could be useful information for the scientific community. It could prevent other labs from wasting resources, animals and time, particularly, as some of these vectors have been reported to be selective and efficient in the primate cortex, which we have not been able to confirm. We made several injections in several animals of those vectors that failed either to infect a sufficient number of cells or turned out to be poorly specific. Therefore, the negative results have been consistent. But we agree with the Reviewer that our negative results could have depended on factors such as titer. In the revised version of the manuscript, we will provide a supplementary Methods section in which we will report the specifics of the vectors that failed in our hands (i.e. number of injections made in how many animals, volumes, survival time, and titers).

      Based on the description in the Methods it seems that no antibody labeling against TdTomato was used to amplify the detection of the transgenes expressed from the AAV vectors. It should be verified that this is the case - a statement could be added to the Methods.

      That is indeed the case. We used no immunohistochemistry to enhance the reporter proteins as this was unnecessary. The native / non-emplified tdT signal was strong.

      Reviewer #3 (Public Review):

      Summary:

      Federer et al. describe the laminar profiles of GABA+ and of PV+ neurons in marmoset V1. They also report on the selectivity and efficiency of expression of a PV-selective enhancer (S5E2). Three further viruses were tested, with a view to characterizing the expression profiles of a GABA-selective enhancer (h56d), but these results are preliminary.

      Strengths:

      The derivation of cell-type specific enhancers is key for translating the types of circuit analyses that can be performed in mice - which rely on germline modifications for access to cell-type specific manipulation - in higher-order mammals. Federer et al. further validate the utility of S5E2 as a PV-selective enhancer in NHPs.

      Additionally, the authors characterize the laminar distribution pattern of GABA+ and PV+ cells in V1. This survey may prove valuable to researchers seeking to understand and manipulate the microcircuitry mediating the excitation-inhibition balance in this region of the marmoset brain.

      Weaknesses:

      Enhancer/promoter specificity and efficiency cannot be directly compared, because they were packaged in different serotypes of AAV.

      The three different serotypes of AAV expressing reporter under the h56D promoter were only tested once each, and all in the same animal. There are many variables that can contribute to the success (or failure) of a viral injection, so observations with an n=1 cannot be considered reliable.

      This is an important point that was also brought up by the Reviewer 1, which we thoroughly addressed in our comments. For clarity and convenience, we copied our response to Reviewer 1 below:.

      We are aware of the limitations of our results on the AAV-h56D. We agree with the Reviewer that a single injection per serotype does not allow us to make strong statements about differences between the 3 serotypes. Therefore, in the revised version of the manuscript we will temper our claims about such differences and use more caution in the interpretation of these data. Despite this weakness, we feel that these data still demonstrate high efficiency and specificity across cortical layers of transgene expression in GABA cells using the h56D promoter, at least with two of the 3 AAV serotypes we tested. We feel that in itself this is sufficiently useful information for the primate community, worthy of being reported. Due to cost, time and ethical considerations related to the use of primates, we chose not to perform additional experiments to determine precise differences among serotypes. Thus, for example, while it is possible that had we replicated these experiments, serotype 7 would have proven equally efficient and specific as the other two serotypes, we felt answering this question did not warrant additional experiments in this precious species.

      The language used throughout conflates the cell-type specificity conferred by the regulatory elements with that conferred by the serotype of the virus.

      In the revised version of the manuscript we will correct ambiguous language.

    1. Author response:

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

      General responses to the weaknesses of this work:

      The two reviewers mentioned two major weaknesses of this work:

      (1) The one unexplained step in this intricately described mechanism is how HSCB functions to promote TACC3 degradation. It appears that the proteasome is involved since MG-132 reverses the effect of HSCB deficiency, but no other details are provided. Does HSCB target TACC3 for ubiquitination somehow? Future studies will be required to understand this portion of the mechanism.

      We totally agree that the detailed mechanisms through which HSCB promotes TACC3 degradation should be clarified. We tried to find the ubiquitin ligases involved in this regulatory process but could not identify such a key protein so far. We also investigated whether HSCB itself is a ubiquitin ligase but found that the protein does not possess this activity. We therefore consider this weakness another limitation of this research and have added one sentence to the penultimate paragraph of the Discussion section to address this issue.

      (2) This study only uses cell models. The significance of this work may be broadened by further studies using animal models.

      We totally agree that in vivo models should be adopted to validate the major findings of this study. As we stated in the penultimate paragraph of the Discussion section, we did not have access to biological samples from the patient harboring the HSCB mutation. Additionally, HSCB constitutive knockout mice died during the embryonic stage, while conditional knockout did not cause embryonic death but resulted in almost no erythroid cells in the bone marrow. Therefore, we were not able to further validate our findings in in vivo models.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      • Figure 3A - Should include FOG1 on the total cell lysate blots to show if total FOG1 is changing or only the cytoplasmic/nuclear ratio. This is shown later but would be good to include here.

      We would like to thank the reviewer for the nice suggestion. We have added the blots for total FOG1 to updated Figure 3A as requested.

      • Figures 3C and 4F - Should include the qPCR results from control cultures on the graphs (EPO + CRISPR NC and shNC, respectively).

      We would like to thank the reviewer for the good suggestion. We have added the control groups for all qPCR assays to the updated figures throughout the study.

      • Figure 4 - The addition of genetic manipulation of TACC3 to confirm its role in the cytoplasmic retention of FOG1 and failed erythroid differentiation in HSCB-deficient cells would strengthen the conclusions of this figure.

      We would like to thank the reviewer for the good suggestion. We initially tried to knock down TACC3 expression through siRNAs to confirm its role in the cytoplasmic retention of FOG1. However, we found that siRNAs that worked well in untreated K562 and erythroid progenitor cells as well as several other cell lines had poor efficiency of knocking down gene expression upon HSCB deficiency. This happened not only to siRNAs targeting TACC3, but also to those targeting several other genes. Interestingly, gene overexpression plasmids worked especially well in HSCB-deficient cells. We were not able to explain these phenomena and chose to use an inhibitor of TACC3 to study its functional implications in this research.

      • Text should be added to discuss the implications of this work for the lineage-specifying function of GATA-1. There are papers by John Crispino and Alan Cantor/Stu Orkin using the FOG-binding mutant of GATA-1 that implicate FOG1-dependent GATA-1 activity as Meg/Ery specifying, whereas FOG1-independent GATA-1 activity promotes mast cell or eosinophil fate. This work suggests that GATA1-expressing myeloid progenitors where FOG1 is kept cytoplasmic (no EPO signaling) would be driven towards the mast cell fate.

      We would like to thank the reviewer for the valuable suggestion. We have added a new paragraph in the Discussion section of the updated manuscript to discuss the implication of this work for the lineage-specifying function of GATA-1.

      Reviewer #2 (Recommendations For The Authors):

      Minor comments:

      (1) In the model provided in Figure 7H, HSCB and FOG1 bind TACC3 simultaneously. However based on the data provided in Figure 6B and other figures, it seems that their interactions are more likely to be mutually exclusive. Is there a possibility that, besides inducing the degradation of TACC3, the binding of HSCB can inhibit the interaction between TACC3 and FOG1?

      We would like to thank the reviewer for the insightful comment. According to the data presented in the updated Figure 5F, TACC3 can simultaneously bind with HSCB and FOG1 in E 2-day HSCs. That is why we depict the simultaneous binding pattern in the model provided in Figure 7H. However, we agree that there is a possibility that the binding of HSCB can inhibit the interaction between TACC3 and FOG1 and have mentioned this possibility in the “Phosphorylation of HSCB by PI3K was necessary for its functionalization during human erythropoiesis” subsection of the “Results” section in the updated manuscript.

      (2) Whether the decreased TACC3 protein abundance (Figure 5D) during erythroblast differentiation is mainly due to the effect of HSCB. Can silencing of HSCB block this reduction?

      We would like to thank the reviewer for the great question. We have analyzed the protein abundance of TACC3 in HSCB-deficient hematopoietic stem cells induced for erythropoiesis for 0, 2 and 4 days and summarized the results as a new Figure 5E. According to the results, TACC3 protein abundance in HSCB-deficient hematopoietic stem cells exhibited no obvious change when the cells were induced for erythropoiesis for 0, 2 and 4 days. These results suggest that the decreased TACC3 protein abundance during early erythroblast differentiation was indeed due to the effect of HSCB. We only investigated the effect of HSCB on TACC3 abundance in early erythroid progenitors because, as shown in Figure 1, HSCB-deficient hematopoietic stem cells stopped differentiation at an early phase of their erythropoiesis. We have also mentioned these data in the “HSCB facilitated FOG1 nuclear translocation by binding with and mediating the proteasomal degradation of TACC3 upon activation of the EPO/EPOR signaling” subsection of the “Results” section in the updated manuscript.

      (3) This study shows that HSCB can be phosphorylated by PI3K, and this modification is important for its role in regulating FOG1 distribution. Does the phosphorylation of HSCB also affect its function in ISC biogenesis?

      We would like to thank the reviewer for the instructive question. We have analyzed the mitochondrial and cytosolic aconitase activities in wortmannin-treated K562 and E 2-day HSCs and their respective controls. The results have been summarized as a new Figure S5. According to the results, wortmannin treatment did not significantly affect mitochondrial and cytosolic aconitase activities. Therefore, it seems that HSCB phosphorylation does not affect its function in ISC biogenesis. We have also mentioned these data in the “Phosphorylation of HSCB by PI3K was necessary for its functionalization during human erythropoiesis” subsection of the “Results” section in the updated manuscript.

      (4) The method of isolation of nuclear fraction needs to be provided in the "Materials and Methods" section.

      We would like to thank the reviewer for the thoughtful suggestion. We have added the required information to the “Nuclear proteomics analysis” subsection of the "Materials and Methods" section in the updated manuscript.

    1. Author response:

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

      eLife assessment

      Following small molecule screens, this study provides convincing evidence that 7,8 dihydroxyflavone (DHF) is a competitive inhibitor of pyridoxal phosphatase. These results are important since they offer an alternative mechanism for the effects of 7,8 dihdroxyflavone in cognitive improvement in several mouse models. This paper is also significant due to the interest in the protein phosphatases and neurodegeneration fields.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Zink et al set out to identify selective inhibitors of the pyridoxal phosphatase (PDXP). Previous studies had demonstrated improvements in cognition upon removal of PDXP, and here the authors reveal that this correlates with an increase in pyridoxal phosphate (PLP; PDXP substrate and an active coenzyme form of vitamin B6) with age. Since several pathologies are associated with decreased vitamin B6, the authors propose that PDXP is an attractive therapeutic target in the prevention/treatment of cognitive decline. Following high throughput and secondary small molecule screens, they identify two selective inhibitors. They follow up on 7, 8 dihydroxyflavone (DHF). Following structure-activity relationship and selectivity studies, the authors then solve a co-crystal structure of 7,8 DHF bound to the active site of PDXP, supporting a competitive mode of PDXP inhibition. Finally, they find that treating hippocampal neurons with 7,8 DHF increases PLP levels in a WT but not PDXP KO context. The authors note that 7,8 DHF has been used in numerous rodent neuropathology models to improve outcomes. 7, 8 DHF activity was previously attributed to activation of the receptor tyrosine kinase TrkB, although this appears to be controversial. The present study raises the possibility that it instead/also acts through modulation of PLP levels via PDXP, and is an important area for future work.

      Strengths:

      The strengths of the work are in the comprehensive, thorough, and unbiased nature of the analyses revealing the potential for therapeutic intervention in a number of pathologies.

      Weaknesses:

      Potential weaknesses include the poor solubility of 7,8 DHF that might limit its bioavailability given its relatively low potency (IC50= 0.8 uM), which was not improved by SAR. However, the compound has an extended residence me and the co-crystal structure could aid the design of more potent molecules and would be of interest to those in the pharmaceutical industry. The images related to crystal structure could be improved.

      Reviewer #2 (Public Review):

      Summary:

      In this study, the authors performed a screening for PDXP inhibitors to identify compounds that could increase levels of pyridoxal 5'- phosphate (PLP), the co-enzymatically active form of vitamin B6. For the screening of inhibitors, they first evaluated a library of about 42,000 compounds for activators and inhibitors of PDXP and secondly, they validated the inhibitor compounds with a counter-screening against PGP, a close PDXP relative. The final narrowing down to 7,8-DHF was done using PLP as a substrate and confirmed the efficacy of this flavonoid as an inhibitor of PDXP function. Physiologically, the authors show that, by acutely treating isolated wild-type hippocampal neurons with 7,8-DHF they could detect an increase in the ratio of PLP/PL compared to control cultures. This effect was not seen in PDXP KO neurons.

      Strengths:

      The screening and validation of the PDXP inhibitors have been done very well because the authors have performed crystallographic analysis, a counter screening, and mutation analysis. This is very important because such rigor has not been applied to the original report of 7,8 DHF as an agonist for TrkB. Which is why there is so much controversy on this finding.

      Weaknesses:

      As mentioned in the summary report the study may benefit from some in vivo analysis of PLP levels following 7,8-DHF treatment, although I acknowledge that it may be challenging because of the working out of the dosage and timing of the procedure.

      Reviewer #3 (Public Review):

      This is interesting biology. Vitamin B6 deficiency has been linked to cognitive impairment. It is not clear whether supplements are effective in restoring functional B6 levels. Vitamin B6 is composed of pyridoxal compounds and their phosphorylated forms, with pyridoxal 5-phosphate (PLP) being of particular importance. The levels of PLP are determined by the balance between pyridoxal kinase and phosphatase activities. The authors are testing the hypothesis that inhibition of pyridoxal phosphatase (PDXP) would arrest the age-dependent decline in PLP, offering an alternative therapeutic strategy to supplements. Published data illustrating that ablation of the Pdxp gene in mice led to increases in PLP levels and improvement in learning and memory trials are consistent with this hypothesis.

      In this report, the authors conduct a screen of a library of ~40k small molecules and identify 7,8dihydroxyflavone (DHF) as a candidate PDXP inhibitor. They present an initial characterization of this micromolar inhibitor, including a co-crystal structure of PDXP and 7,8-DHF. In addition, they demonstrate that treatment of cells with 7,8 DHP increases PLP levels. Overall, this study provides further validation of PDXP as a therapeutic target for the treatment of disorders associated with vitamin B6 deficiency and provides proof-of-concept for inhibition of the target with small-molecule drug candidates.

      Strengths include the biological context, the focus on an interesting and under-studied class of protein phosphatases that includes several potential therapeutic targets, and the identification of a small molecule inhibitor that provides proof-of-concept for a new therapeutic strategy. Overall, the study has the potential to be an important development for the phosphatase field in general.

      Weaknesses include the fact that the compound is very much an early-stage screening hit. It is an inhibitor with micromolar potency for which mechanisms of action other than inhibition of PDXP have been reported. Extensive further development will be required to demonstrate convincingly the extent to which its effects in cells are due to on-target inhibition of PDXP.

      Recommendations for the authors:

      There is general agreement that the study represents an advance regarding the mechanisms of pyridoxal phosphatase and 7,8 DHF. From the reviewers' comments, several major questions and considerations are raised, followed by their detailed remarks:

      (1) More analysis of the solubility and dose of 7,8 DHF with regard to the 50% inhibition and the salt bridge of the B protomer, as raised by the reviewers.

      (2) Is there a possible involvement of another phosphatase?

      (3) Does 7,8 DHF cause an effect upon TrkB tyrosine phosphorylation?

      We thank the Reviewers and Editors for their fair and constructive comments and suggestions. We have performed additional experiments to address these questions and considerations. In addition, we have generated two new high-resoling (1.5 Å) crystal structures of human PDXP in complex with 7,8-DHF that substantially expand our understanding of 7,8-DHF-mediated PDXP inhibition. The scientist who performed this work for the revision of our manuscript has been added as an author (shared first authorship).

      We believe that the insights gained from these new data have further strengthened and improved the quality of our manuscript. Together, our data provide compelling evidence that 7,8-dihydroxyflavone is a direct and competitive inhibitor of pyridoxal phosphatase.

      Please find our point-by-point responses to the Public Reviews that are not addressed in the Recommendations for the Authors, and the Recommendations for the Authors below.

      Reviewer #2:

      As mentioned in the summary report the study may benefit from some in vivo analysis of PLP levels following 7,8-DHF treatment, although I acknowledge that it may be challenging because of the working out of the dosage and timing of the procedure.

      We agree that an in vivo analysis of PLP levels following 7,8-DHF treatment could be informative for the further evaluation of a possible mechanistic link between the reported effects of this compound and PDXP/vitamin B6. However, we currently do not have a corresponding animal experimentation permission in place and are unlikely to obtain such a permit within a reasonable me frame for this revision.

      Recommendations For The Authors:

      Reviewer #1:

      The work is already well-written, comprehensive, and convincing.

      Suggestions that could improve the manuscript.

      (1) Include a protein tyrosine phosphatase (PTP) in the selectivity analysis. One possibility is that 7,8 DHF acts on a PTP (such as PTP1B), leading to TrkB activation by preventing dephosphorylation. I note that a previous study has looked at SAR for flavones with PTP1B (PMID: 29175190), which is worth discussion.

      We thank the reviewer for bringing this interesting possibility to our attention. We were not aware of the SAR study for flavonoids with PTP1B by Proenca et al. but have now tested the effect of 7,8-DHF on PTP1B, referring to this paper. As shown in Figure 2d, PTP1B was not inhibited by 7,8-DHF at a concentration of 5 or 10 µM. At the highest tested concentration of 40 µM, 7,8-DHF inhibited PTP1B merely by ~20%. For comparison, compound C13 (3-hydroxy-7,8-dihydroxybenzylflavone-3’,4’dihydroxymethyl-phenyl), which emerged as the most active flavonoid in the SAR study by Proenca et al. inhibited PTP1B with an IC50 of 10 µM. Consistent with the results of these authors, our finding confirms that less polar substituents, such as O-benzyl groups at positions 7 and 8, and O-methyl groups at positions 3’ and 4’ of the flavone scaffold, are important for the ability of flavonoids to effectively inhibit PTP1B. We conclude that PTP1B inhibition by 7,8-DHF is unlikely to be a primary contributor to the reported cellular and in vivo effects of this flavone.

      In addition to PTP1B, we have now additionally tested the effect of 7,8-DHF on the serine/threonine protein phosphatase calcineurin/PP2B, the DNA/RNA-directed alkaline phosphatase CIP, and three other metabolite-directed HAD phosphatases, namely NANP, NT5C1A and PNKP. PP2B, CIP and NANP were not inhibited by 7,8-DHF. Similar to PTP1B, PNKP activity was attenuated (~30%) only at 40 µM 7,8-DHF. In contrast, 7,8-DHF effectively inhibited NT5C1A (IC50 ~10 µM). NT5C1A is an AMP hydrolase expressed in skeletal muscle and heart. To our knowledge, a role of NT5C1A in the brain has not been reported. Based on currently available information, the inhibition of NT5C1A therefore appears unlikely to contribute to 7,8-DHF effects in the brain.

      The results of these experiments are shown in the revised Figure 2d. Taken together, the extended selectivity analysis of 7,8-DHF on a total of 12 structurally and functionally diverse protein- and nonprotein-directed phosphatases supports our initial conclusion that 7,8-DHF preferentially inhibits PDXP.

      (2) Line 144: It is unclear how fig 2c supports the statement here. Remove call out for clarity.

      Our intention was to highlight the fact that 7,8-DHF concentrations >12.5 µM could not be tested in the BLI assay (shown in Figure 2c) due to 7,8-DHF solubility issues under these experimental conditions. However, since this is discussed in the text, but not directly visible in Figure 2c, we agree with the Reviewer and have removed this call out.

      (3) Figure 3a. It is difficult to see the pink 7,8 DHF on top of the pink ribbon backbone. A better combination of colours could be used. Likewise in Figure 3b it is pink on pink again.

      We have improved the combination of colors to enhance the visibility of 7,8-DHF and have consistently color-coded murine and the new human PDXP structures throughout the manuscript.

      (4) Figure 3c and d. These are the two protomers I believe, but the colour coding is not present in 3c where the ribbon is now gray. Please choose colours that can be used to encode protomers throughout the figure.

      Please see response to point 3 above.

      (5) Figure 3f. I think this is the same protomer as 3c but a 180-degree rotation. Could this be indicated, or somehow lined up between the two figures for clarity? It would also be useful to have 3e in the same orientation as 3f, to better visualise the overlap with PLP binding. PLP and 7,8 DHF could be labelled similarly to the amino acids in 3f (the colour coding here is helpful).

      Please see response to point 3 above. We have substantially revised the structural figures and have used consistent color coding and the same perspective of 7,8-DHF in the PDXP active sites.

      (6) Figure 3g. The colours of the bars relating to specific mutations do not quite match the colours in Figure 3f, which I think was the aim and is very helpful.

      We have adapted the colours of the residues in Figure 3f (now Fig. 3b and additionally Fig. 3 – figure supplement 1e) so that they exactly match the colours of the bars in Figure 3g (now Fig. 3d).

      Reviewer #2:

      No further comments.

      Reviewer #3:

      Page 4: The authors describe 7,8DHF as a "selective" inhibitor of PDXP - in my opinion, they do not have sufficient data to support such a strong assertion. Reports that 7,8DHF may act as a TRK-B-agonist already highlight a potential problem of off-target effects. Does 7,8DHF promote tyrosine phosphorylation of TRK-B in their hands? The selectivity panel presented in Figure 2, focusing on 5 other HAD phosphatases, is much too limited to support assertions of selectivity.

      We agree with the Reviewer that our previous selectivity analysis with six HAD phosphatases was limited. To further explore the phosphatase target spectrum of 7,8-DHF, we have now analyzed six other enzymes: three other non-HAD phosphatases (the tyrosine phosphatase PTP1B, the serine/threonine protein phosphatase PP2B/calcineurin, and the DNA/RNA-directed alkaline phosphatase/CIP) and three other non-protein-directed C1/C0-type HAD phosphatases (NT5C1A, NANP, and PNKP). The C1-capped enzymes NT5C1A and NANP were chosen because we previously found them to be sensitive to small molecule inhibitors of the PDXP-related phosphoglycolate phosphatase PGP (PMID: 36369173). PNKP was chosen to increase the coverage of C0-capped HAD phosphatases (previously, only the C0-capped MDP1 was tested).

      We found that calcineurin, CIP and NANP were not inhibited by up to 40 µM 7,8-DHF. The activities of PTP1B or PNKP activity were attenuated (by ~20 or 30%, respectively) only at 40 µM 7,8-DHF. In contrast, 7,8-DHF effectively inhibited NT5C1A (IC50 ~10 µM). We have previously found that NT5C1A was sensitive to small-molecule inhibitors of the PDXP paralog PGP, although these molecules are structurally unrelated to 7,8-DHF (PMID: 36369173). NT5C1A is an AMP hydrolase expressed in skeletal muscle and heart (PMID: 12947102). To our knowledge, a role of NT5C1A in the brain has not been reported. Based on currently available information, the inhibition of NT5C1A therefore appears unlikely to contribute to 7,8-DHF effects in the brain. The results of these experiments are shown in the revised Figure 2d. Taken together, the extended selectivity analysis of 7,8-DHF on a total of 12 structurally and functionally diverse protein- and non-protein-directed phosphatases supports our initial conclusion that 7,8-DHF preferentially inhibits PDXP. To nevertheless avoid any overstatement, we have now also replaced “selective” by “preferential” in this context throughout the manuscript.

      We have not tested if 7,8-DHF promotes tyrosine phosphorylation of TRK-B. Being able to detect 7,8- DHF-induced TRK-B phosphorylation in our hands would not exclude an additional role for PDXP/vitamin B6-dependent processes. Not being able to detect TRK-B phosphorylation may indicate absence of evidence or evidence of absence. This would neither conclusively rule out a biological role for 7,8-DHF-induced TRK-B phosphorylation in vivo, nor contribute further insights into a possible involvement of vitamin B6-dependent processes in 7,8-DHF induced effects.

      Page 6: The authors report that they obtained only two PDXP-selective inhibitor hits from their screen; 7,8DHF and something they describe as FMP-1. For the later, they state that it "was obtained from an academic donor, and its structure is undisclosed for intellectual property reasons". In my opinion, this is totally unacceptable. This is an academic research publication. If the authors wish to present data, they must do so in a manner that allows a reader to assess their significance; in the case of work with small molecules that includes the chemical structure. In my opinion, the authors should either describe the compound fully or remove mention of it altogether.

      We are unable to describe “FMP-1” because its identity has not been disclosed to us. The academic donor of this molecule informed us that they were not able to permit release of any details of its structure or general structural class due to an emerging commercial interest.

      We mentioned FMP-1 simply to highlight the fact that the screening campaign yielded more than one inhibitor. FMP-1 was also of interest due its complete inhibition of PDXP phosphatase activity.

      Because the structure of this molecule is unknown to us, we have now removed any mention of this compound in the manuscript. For the same reason, we have removed the mention of the inhibitor hits “FMP-2” and “FMP-3” in Figure 2 – figure supplement 1 and Figure 2 – figure supplement 2. The number of PDXP inhibitor hits in the manuscript has been adapted accordingly.

      Page 7: The observed plateau at 50% inhibition requires further explanation. It is not clear how poor solubility of the compound explains this observation. For example, the authors state that "due to the aforementioned poor solubility of 7,8DHF, concentrations higher than 12.5µM could not be evaluated". Yet on page 8, they describe assays against the specificity panel at concentrations of compound up to 40µM. Do the analogues of 7,8DHF (Fig 2b) result in >50% inhibition at higher concentrations? Further explanation and data on the solubility of the compounds would be of benefit.

      We currently do not have a satisfactory explanation for the apparent plateau of ~50% PDXP inhibition by 7,8-DHF. Resolving this question will likely require other approaches, including computational chemistry such as molecular dynamics simulations, and we feel that this is beyond the scope of the present manuscript.

      We previously speculated that the limited solubility of 7,8-DHF may counteract a complete enzyme inhibition if higher concentrations of this molecule are required. Specifically, we referred to Todd et al. who have performed HPLC-UV-based solubility assays of 7,8-DHF (ref. 35). These authors found that immediately after 7,8-DHF solubilization, nominal 7,8-DHF concentrations of 5, 20 or 50 µM resulted in 0.5, 3.0 or 13 µM of 7,8-DHF in solution of (i.e., 10, 15 or 26% of the respective nominal concentration). Seven hours later, 46, 26 or 26% of the respective nominal 7,8-DHF concentrations were found in solution. Hence, above a nominal concentration of 5 µM, 7,8-DHF solubility does not increase linearly with the input concentration, but plateaus at ~20% of the nominal concentration. This phenomenon could potentially contribute to the apparent plateau of human or murine PDXP inhibition by 7,8-DHF in vitro.

      However, experiments performed during the revision of our manuscript show that they HAD phosphatase NT5C1A can be effectively inhibited by 7,8-DHF with an IC50-value of 10 µM (see revised Fig. 2). Together with the fact that the activity of the PDXP-Asn61Ser variant can be completely inhibited by 7,8-DHF (see Fig. 3d), we conclude that the reason for the observed plateau of PDXP inhibition is likely to be primarily structural, with Asn61 impeding 7,8-DHF binding. We have therefore removed the mention of the limited solubility of 7,8-DHF here. On p.14, we now say: “These data also suggest that Asn61 contributes to the limited efficacy of 7,8-mediated PDXP inhibition in vitro.”

      The solubility of 7,8-DHF is dependent on the specific assay and buffer conditions. In BLI experiments, interference patterns caused by binding of 7,8-DHF in solution to biotinylated PDXP immobilized on the biosensor surface are measured. In phosphatase selectivity assays, phosphatases are in solution, and the effect of 7,8-DHF on the phosphatase activity is measured via the quantification of free inorganic phosphate.

      In BLI experiments, we observed that the sensorgrams obtained with the highest tested 7,8-DHF concentration (25 µM) showed the same curve shapes as the sensorgrams obtained with 12.5 µM 7,8-DHF. This contrasts with the expected steeper slope of the curves at 25 µM vs. 12.5 µM 7,8-DHF. The same behavior was observed for the reference sensors (i.e., the SSA sensors that were not loaded with PDXP, but incubated with 7,8-DHF at all employed concentrations for referencing against nonspecific binding of 7,8-DHF to the sensors). The sensorgrams at 25 µM 7,8-DHF were therefore not included in the analysis (this is now specified in the Materials and Methods BLI section on p.27). To clarify this point, we now state that “As a result of the poor solubility of the molecule, a saturation of the binding site was not experimentally accessible” (p.7).

      In contrast, the phosphatase selectivity assays described on p.8 could be performed with nominal 7,8-DHF concentrations of up to 40 µM. Although the effective 7,8-DHF concentration in solution is expected to be lower (see ref. 35 and discussed above), the limited solubility of 7,8-DHF in phosphatase assays does not prevent the quantification of free inorganic phosphate. Nevertheless, we cannot exclude some interference with this absorbance-based assay (e.g., due to turbidity caused by insoluble compound). Indeed, 5,6-dihydroxyflavone and 5,6,7-trihydroxyflavone caused an apparent increase in PDXP activity at concentrations above 10 µM (see Figure 2b), which may be related to compound solubility issues. Alternatively, these flavones may activate PDXP at higher concentrations.

      We have tested the 7,8-DHF analogue 3,7,8,4’-tetrahydroxyflavone at concentrations of 70 and 100 µM. At concentrations >100 µM, the DMSO concentration required for solubilizing the flavone interferes with PDXP activity. PDXP inhibition by 3,7,8,4’-tetrahydroxyflavone was slightly increased at 70 µM compared to 40 µM (by ~18%) but plateaued between 70 and 100 µM. These results are now mentioned in the text (p.7): “The efficacy of PDXP inhibition by 3,7,8,4’-tetrahydroxyflavone was not substantially increased at concentrations >40 µM (relative PDXP activity at 40 µM: 0.46 ± 0.05; at 70 µM: 0.38 ± 0.15; at 100 µM: 0.37 ± 0.09; data are mean values ± S.D. of n=6 experiments).”

      Page 9: The authors report that PDXP crystallizes as a homodimer in which 7,8DHF is bound only to one protomer. Is the second protomer active? Does that contribute to the 50% inhibition plateau? If Arg62 is mutated to break the salt bridge, does inhibition go beyond 50%?

      We have no way to measure the activity of the second, inhibitor-free protomer in murine PDXP. We know that PDXP functions as a constitutive homodimer, and based on our current understanding, both protomers are active. We have previously shown that the experimental monomerization of PDXP (upon introduction of two-point mutants in the dimerization interface) strongly reduces its phosphatase activity. Specifically, PDXP homodimerization is required for an inter-protomer interaction that mediates the proper positioning of the substrate specificity loop. Thus, homodimerization is necessary for effective substrate coordination and -dephosphorylation (PMID: 24338687).

      In the murine structure, we observed that 7,8-DHF binding to the second subunit (the B-protomer) is prevented by a salt bridge between Arg62 and Asp14 of a symmetry-related A-protomer in the crystal lace (i.e., this is not a salt bridge between Arg62 in the B-protomer and Asp14 in the A-protomer of a PDXP homodimer). As suggested, we have nevertheless tested the potential role of this salt bridge for the sensitivity of the PDXP homodimer to 7,8-DHF.

      The mutation of Arg62 is not suitable to answer this question, because this residue is involved in the coordination of 7,8-DHF (see Figure 3b), and the PDXP-Arg62Ala mutant is inhibitor resistant (see Figure 3d). We have therefore mutated Asp14, which is not involved in 7,8-DHF coordination. As shown in the new Figure 3 – figure supplement 1d, the 7,8-DHF-mediated inhibition of PDXPAsp14Ala again reached a plateau at ~50%. This result suggests that while an Arg62-Asp14 salt bridge is stabilized in the murine crystal, it is not a determinant of the active site accessibility of protomer B in solution.

      To address this important question further, we have now also generated co-crystals of human PDXP bound to 7,8-DHF, and refined two structures to 1.5 Å. We found that in human PDXP, both protomers bind 7,8-DHF. These new, higher resolution data are now shown in the revised Figure 3 and its figure supplements, and we have moved the panels referring to the previously reported murine PDXP structure to the Figure 3 – figure supplement 1. Thus, both protomers of human PDXP, but only one protomer of murine PDXP bind 7,8-DHF in the crystal structure, yet the 7,8-DHFmediated inhibition of human and murine PDXP plateaus at ~50% under the phosphatase assay conditions (see Figure 2a). We conclude that 7,8-DHF binding efficiency in the PDXP crystal does not necessarily reflect its inhibitory efficiency in solution.

      Taken together, these data indicate that the apparent partial inhibition of murine and human PDXP phosphatase activity by 7,8-DHF in our in vitro assays is not explained by an exclusive binding of 7,8DHF to just one protomer of the homodimer.

      Page 10-12; Is it possible to generate a mutant form of PDXP in which activity is maintained but inhibition is attenuated - an inhibitor-resistant mutant form of PDXP? Can such a mutant be used to assess on-target vs off-target effects of 7,8DHF in cells?

      This is an excellent point, and we agree with the Reviewer that such an approach would provide further evidence for cellular on-target activity of 7,8-DHF. Indeed, the verification of the PDXP-7,8DHF interaction sites has led to the generation of catalytically active, inhibitor-resistant PDXP mutants, such as Tyr146Ala and Glu148Ala (Fig. 3d). However, the biochemical analysis of such mutants in primary hippocampal neurons is a very difficult task.

      Primary hippocampal neurons are derived from pooled, isolated hippocampi of mouse embryos and are subsequently differentiated for 21 days in vitro. The resulting cellular yield is typically low and variable, and the viability (and contamination of the respective cultures with e.g. glial cells) varies from batch to batch. Although such cell preparations are suitable for electrophysiological or immunocytochemical experiments, they are far from ideal for biochemical studies. A meaningful experiment would require the efficient expression of a catalytically active, but inhibitor-resistant PDXP-mutant in PDXP-KO neurons. In parallel, PDXP-KO cells reconstituted with PDXP-WT (at phosphatase activity levels comparable with the PDXP mutant cells) would be needed for comparison. Unfortunately, the generation of (a) sufficient numbers of (b) viable cells that (c) efficiently express (d) functionally comparable levels of PDXP-WT or -mutant for downstream analysis (PLP/PL-levels upon inhibitor treatment) is currently not possible for us.

      Human iPSC-derived (hippocampal) spheroids are at present no alternative, due to the necessity of generating PDXP-KO lines first, and the difficulties with transfecting/transducing them. Such a system would require extensive validation. We have attempted to use SH-SY5Y cells (a metastatic neuroblastoma cell line), but PDXK expression in these cells is modest and they produce too little PLP. We therefore feel that this question is beyond the scope of our current study.

    1. Author response:

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

      Reviewer #3 (Public Review):

      Summary:

      It has been proposed in the literature, that the ATP release channel Panx1 can be activated in various ways, including by tyrosine phosphorylation of the Panx1 protein. The present study reexamines the commercial antibodies used previously in support of the phosphorylation hypothesis and the presented data indicate that the antibodies may recognize proteins unrelated to Panx1. Consequently, the authors caution about the use and interpretation of results obtained with these antibodies.

      Strengths:

      The manuscript by Ruan et al. addresses an important issue in Panx1 research, i.e. the activation of the channel formed by Panx1 via protein phosphorylation. If the authors' conclusions are correct, the previous claims for Panx1 phosphorylation on the basis of the commercial anti-phospho-Panx1 antibodies would be in question.

      This is a very detailed and comprehensive analysis making use of state-of-the-art techniques, including mass spectrometry and phos-tag gel electrophoresis.

      In general, the study is well-controlled as relating to negative controls.

      The value of this manuscript is, that it could spawn new, more function-oriented studies on the activation of Panx1 channels.

      Weaknesses:

      Although the manuscript addresses an important issue, the activation of the ATP-release channel Panx1 by protein phosphorylation, the data provided do not support the firm conclusion that such activation does not exist. The failure to reproduce published data obtained with commercial anti-phospho Panx1 antibodies can only be of limited interest for a subfield.

      (1) The title claiming that "Panx1 is NOT phosphorylated..." is not justified by the failure to reproduce previously published data obtained with these antibodies. If, as claimed, the antibodies do not recognize Panx1, their failure cannot be used to exclude tyrosine phosphorylation of the Panx1 protein. There is no positive control for the antibodies.

      The full title of our manuscript is “Human Pannexin 1 Channel is NOT Phosphorylated by Src Tyrosine Kinase at Tyr199 and Tyr309”. The major conclusion of our manuscript shall not be extended to the claim that “Panx1 is NOT phosphorylated”. This is by no means our conclusion. In fact, the LC-MS/MS data from both ours and others have shown that PANX1 is phosphorylated at both serine and tyrosine sites1. However, we provided solid evidence that Tyr199 and Tyr309 of human PANX1 are not effective substrate of the Src kinase.

      We did provide several positive controls for the antibodies in our study. We showed that the anti-PANX1 and anti-Src antibodies unambiguously recognized PANX1 and Src, respectively (Figure 3A), and that a pan-specific phosphotyrosine antibody (P-Tyr-100) unambiguously recognized phosphorylated Src (Figure 3A)—as expected—but did not recognize PANX1. In addition, we demonstrated that the two antibodies in question (anti-PANX1-pY198 and anti-PANX1-pY308) did produce signals in our western blot analysis, but we provided compelling evidence that the bands produced by these antibodies do not correspond to PANX1 (Figure 2B).

      (2) The authors claim that exogenous SRC expression does not phosphorylate Y198. DeLalio et al. 2019 show that Panx1 is constitutively phosphorylated at Y198, so an effect of exogenous SRC expression is not necessarily expected.

      We have unambiguously identified peptide fragments containing non-phosphorylated Y198 in our LC-MS/MS experiment, none corresponds to a phosphorylated Y198. Therefore, our LC-MS/MS data doesn’t support the notion that Panx1 is constitutively phosphorylated at Y198.

      (3) The authors argue that the GFP tag of Panx1at the COOH terminus does not interfere with folding since the COOH modified (thrombin cleavage site) Panx1 folds properly, forming an amorphous glob in the cryo-EM structure. However, they do not show that the COOH-modified Panx1 folds properly. It may not, because functional data strongly suggest that the terminal cysteine dives deep into the pore. For example, the terminal cysteine, C426, can form a disulfide bond with an engineered cysteine at position F54 (Sandilos et al. 2012).

      Our manuscript included results of using a non-GFP tagged PANX1 construct (Figure 2-figure supplement 1). We didn’t notice any difference for PANX1 phosphorylation between GFP-tagged and non-GFP-tagged PANX1. Therefore, the folding of the C-terminal tail of PANX1 doesn’t affect the conclusion of our study.

      (4) The authors dismiss the additional arguments for tyrosine phosphorylation of Panx1 given by the various previous studies on Panx1 phosphorylation. These studies did not, as implied, solely rely on the commercial anti-phospho-Panx1 antibodies, but also presented a wealth of independent supporting data. Contrary to the authors' assertion, in the previous papers the pY198 and pY308 antibodies recognized two protein bands in the size range of glycosylated and partial glycosylated Panx1.

      We didn’t dismiss additional arguments for the Src-dependent PANX1 regulation. In fact, in the discussion of our manuscript, we acknowledged the fact that Src may still be involved in PANX1 regulation, but probably through indirect mechanisms. In the two previous studies2,3, it’s unclear if the multimeric bands detected by pY198/pY308 antibodies correspond to glycosylated PANX1 or not, as the authors did not overlay the protein markers with their blots. In particular, the migration pattern of PANX1 changes across different western blot images from DeLalio et al2. It’s also worth noting that none of these “independent supporting data” in the two previous studies provided direct evidence that Src can phosphorylate pY198/pY308.

      (5) A phosphorylation step triggering channel activity of Panx1 would be expected to occur exclusively on proteins embedded in the plasma membrane. The membrane-bound fraction is small in relation to the total protein, which is particularly true for exogenously expressed proteins. Thus, any phosphorylated protein may escape detection when total protein is analyzed. Furthermore, to be of functional consequence, only a small fraction of the channels present in the plasma membrane need to be in the open state. Consequently, only a fraction of the Panx1 protein in the plasma membrane may need to be phosphorylated. Even the high resolution of mass spectroscopy may not be sufficient to detect phosphorylated Panx1 in the absence of enrichment processes.

      We agree with the reviewer that only plasma membrane-residing Panx1 phosphorylation is functionally relevant. Interestingly, however, previous studies actually analyzed total protein from cell lysate and concluded that PANX1 is phosphorylated at Y198 and Y3082,3. This has motivated our analysis, in which we found that the phosphorylation events cannot be detected when using whole cell lysate. Therefore, we have also conducted an electrophysiology experiment by comparing conditions with/without active Src kinase (Figure 7). Our result indicates that PANX1 current is not affected by the presence of Src. This result suggests that even if there might be minor Src kinase phosphorylation beyond detection limit of western blot or mass spectrometry, they may not be functionally significant as well.

      (6) In the electrophysiology experiments described in Figure 7, there is no evidence that the GFP-tagged Panx1 is in the plasma membrane. Instead, the image in Figure 7a shows prominent fluorescence in the cytoplasm. In addition, there is no evidence that the CBX-sensitive currents in 7b are mediated by Panx1-GFP and are not endogenous Panx1. Previous literature suggests that the hPanx1 protein needs to be cleaved (Chiu et al. 2014) or mutated at the amino terminus (Michalski et al 2018) to see voltage-activated currents, so it is not clear that the currents represent hPANX1 voltage-activated currents.

      Our previous analysis has already shown that endogenous current of non-transfected cells is not sensitive to CBX4. Therefore, the CBX-sensitive current in cells overexpressed PANX1 is from PANX1-GFP. It should be noted that when protein is overexpressed, it tends to accumulate at different intracellular membranes during protein synthesis/maturation. However, this doesn’t affect a portion of the protein to be trafficked to the plasma membrane. In the paper from Michalski et al 2018, it was shown that WT human/mouse PANX1 displayed voltage-dependent activation5. Although the current is relatively small, it is clearly distinguishable from non-transfected HEK and CHO cells. This voltage-dependent activation is also sensitive to CBX, consistent with our measurement (Figure 7)4. When GS is introduced at the N-terminus, the voltage-dependent activation of human/mouse PANX1 is significantly boosted, likely due to the altered NTH conformation resulting from the N-terminal extension.

      Recommendations for the authors:

      Reviewer #3 (Recommendations For The Authors):

      Literature quotes are still problematic. Why are secondary papers quoted instead of the original work? At least quote reviews by authors who published the original findings.

      We appreciate the reviewer pointing this out. We have carefully checked our references and made sure that the original literature is cited.

      Why does wtPanx1 run close to the 37 kD marker (Figure 2 supplement 1) instead of close to 50 kD as shown in the previous papers using the pY198 and pY308 antibodies?

      It is a common observation that membrane proteins migration in SDS-PAGE gel doesn’t correlate with their formula molecular weight, also known as “gel shifting”6–8. The molecular mechanism of this phenomenon remains complex. Therefore, simply relying on protein molecular standard could not unambiguously identify PANX1 protein band. This is an issue for identifying PANX1 band, especially in light of the fact that some antibodies may not be very specific (see Figure 6B). In our experiment, we have correlated the in-gel fluorescence and western blot signal which allowed us to determine the protein band corresponding to PANX1. It is worth noting that in Figure S3 of DeLalio 2019, the PANX1 is detected at 37 kDa2. However, in many other panels of the paper, PANX1 is detected at close to 50 kDa (for example, Figure S2B).

      Figure 6, supplement 1: why are there oligomers observed in the absence of crosslinking? Why is there no shift in the size of the "oligomers" in response to glycosidase F?

      It is common to observe multimeric membrane proteins, including PANX1, forming oligomeric bands in SDS-PAGE gels, likely because they are not fully denatured or disassembled. PANX1 also contains several free cysteines, which may non-specifically crosslink subunits. There is actually a small shift for the 75 kDa band (dimer) in Figure 6, supplement 1. For higher molecular weight bands, this small shift may not be apparent due to the limited resolution of the gel.

      A positive control for the antibodies used is missing. The authors argue that such controls are not available, since these commercial antibodies are "proprietary".

      We did provide several positive controls for the antibodies in our study. We showed that the anti-PANX1 and anti-Src antibodies unambiguously recognized PANX1 and Src, respectively (Figure 3A), and that a pan-specific phosphotyrosine antibody (P-Tyr-100) unambiguously recognized phosphorylated Src (Figure 3A)—as expected—but did not recognize PANX1. In addition, we demonstrated that the two antibodies in question (anti-PANX1-pY198 and anti-PANX1-pY308) did produce signals in our western blot analysis, but we provided compelling evidence that the bands produced by these antibodies do not correspond to PANX1 (Figure 2B).

      Unfortunately, the epitopes that Millipore Sigma used to generate anti-PANX1-pY198 and anti-PANX1-pY308 are not available. The description of the immunogen from Millipore Sigma website states that “A linear peptide corresponding to 12 amino acids surrounding phospho-Tyr198 of murine Pannexin-1” and “A linear peptide corresponding to 13 amino acids surrounding phosphotyrosine 308 of rat pannexin-1”. However, these immunogen peptides are not available for us to purchase.

      References

      (1) Nouri-Nejad, D. et al. Pannexin 1 mutation found in melanoma tumor reduces phosphorylation, glycosylation, and trafficking of the channel-forming protein. Mol Biol Cell 32, (2021).

      (2) DeLalio, L. J. et al. Constitutive SRC-mediated phosphorylation of pannexin 1 at tyrosine 198 occurs at the plasma membrane. Journal of Biological Chemistry 294, (2019).

      (3) Weilinger, N. L. et al. Metabotropic NMDA receptor signaling couples Src family kinases to pannexin-1 during excitotoxicity. Nat Neurosci 19, (2016).

      (4) Ruan, Z., Orozco, I. J., Du, J. & Lü, W. Structures of human pannexin 1 reveal ion pathways and mechanism of gating. Nature 584, (2020).

      (5) Michalski, K., Henze, E., Nguyen, P., Lynch, P. & Kawate, T. The weak voltage dependence of pannexin 1 channels can be tuned by N-terminal modifications. Journal of General Physiology 150, (2018).

      (6) Rath, A., Cunningham, F. & Deber, C. M. Acrylamide concentration determines the direction and magnitude of helical membrane protein gel shifts. Proc Natl Acad Sci U S A 110, (2013).

      (7) Rath, A. & Deber, C. M. Correction factors for membrane protein molecular weight readouts on sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Anal Biochem 434, (2013).

      (8) Rath, A., Glibowicka, M., Nadeau, V. G., Chen, G. & Deber, C. M. Detergent binding explains anomalous SDS-PAGE migration of membrane proteins. Proc Natl Acad Sci U S A 106, (2009).

    1. Author response:

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

      Public Reviews:

      Reviewer #1:

      Summary:

      The evolution of non-shivering thermogenesis is of fundamental importance to understand. Here, in small mammals, the contractile apparatus of the muscle is shown to increase energy expenditure upon a drop in ambient temperature. Additionally, in the state of torpor, small hibernators did not show an increase in energy expenditure under the same challenge.

      Strengths:

      The authors have conducted a very well-planned study that has sampled the muscles of large and small hibernators from two continents. Multiple approaches were then used to identify the state of the contractile apparatus, and its energy expenditure under torpor or otherwise.

      Weaknesses:

      There was only one site of biopsy from the animals used (leg). It would be interesting to know if non-shivering thermogenesis is something that is regionally different in the animal, given the core body and distal limbs have different temperatures.

      We thank the reviewer for their time and effort in reviewing our manuscript. Furthermore, we agree that it would be of interest to perform similar experiments upon different muscle sites in these animals. This is of particular interest as in some mammals, such as mice, distal limbs do not shiver and therefore non-shivering thermogenesis may play a more prominent role in heat regulation. A paper from Aydin et al., demonstrated that when shivering muscles (soleus) were prevented undergoing non-shivering thermogenesis via knock-out of UCP1 and were then exposed to cold temperatures, the force production of these muscles was significantly reduced due to prolonged shivering [1]. These results do suggest that even in shivering muscle, non-shivering thermogenesis plays a key role in the generation of heat for survival and for the maintenance of muscle performance. Furthermore, there is evidence from garden dormice that muscle temperature during torpor is slightly warmer than abdominal temperature and slighter cooler that heart temperature which is 7-8°C than abdominal suggesting the existence of non-shivering thermogenesis in skeletal and cardiac muscles (Giroud et al. in prep) [2]. We have added this information and reference into our discussion to reflect this important point (Discussion, paragraph 6, “As the biopsies which were used…”).

      Reviewer #2:

      Summary:

      The authors utilized (permeabilized) fibers from muscle samples obtained from brown and black bears, squirrels, and Garden dormice, to provide interesting and valuable data regarding changes in myosin conformational states and energetics during hibernation and different types of activity in summer and winter. Assuming that myosin structure is similar between species then its role as a regulator of metabolism would be similar and not different, yet the data reveal some interesting and perplexing differences between the selected hibernating species.

      Strengths:

      The experiments on the permeabilized fibers are complementary, sophisticated, and well-performed, providing new information regarding the characteristics of skeletal muscle fibers between selected hibernating mammalian species under different conditions (summer, interarousal, and winter).

      The studies involve complementary assessments of muscle fiber biochemistry, sarcomeric structure using X-ray diffraction, and proteomic analyses of posttranslational modifications.

      Weaknesses:

      It would be helpful to put these findings on permeabilized fibers into context with the other anatomical/metabolic differences between the species to determine the relative contribution of myosin energetics (with these other contributors) to overall metabolism in these different species, including factors such as fat volume/distribution.

      We thank the reviewer for the time and effort they have put into reviewing our paper and are grateful for the helpful suggestions which we believe, enhances our work (please see below for detailed answers to critics).

      Reviewer #3:

      Summary and strengths:

      The manuscript, "Remodelling of skeletal muscle myosin metabolic states in hibernating mammals", by Lewis et al, investigates whether myosin ATP activity may differ between states of hibernation and activity in both large and small mammals. The study interrogates (primarily) permeabilized muscle strips or myofibrils using several state-of-the-art assays, including the mant-ATP assay to investigate ATP utilization of myosin, X-ray diffraction of muscles, proteomics studies, metabolic tests, and computational simulations. The overall data suggests that ATP utilization of myosin during hibernation is different than in active conditions.

      A clear strength of this study is the use of multiple animals that utilize two different states of hibernation or torpor. Two large animal hibernators (Eurasian Brown Bear, American Black Bear) represent large animal hibernators that typically undergo prolonged hibernation. Two small animal hibernators (Garden Dormouse, 13 Lined Ground Squirrel) undergo torpor with more substantial reductions in heart rate and body temperature, but whose torpor bouts are interrupted by short arousals that bring the animals back to near-summer-like metabolic conditions.

      Especially interesting, the investigators analyze the impact that body temperature may have on myosin ATP utilization by performing assays at two different temperatures (8 and 20 degrees C, in 13 Lined Ground Squirrels).

      The multiple assays utilized provide a more comprehensive set of methods with which to test their hypothesis that muscle myosins change their metabolic efficiency during hibernation.

      We thank this reviewer for the effort and time they have put into carefully reviewing our manuscript and have taken on board their valuable suggestions to improve our manuscript (please see below for detailed answers to critics).

      Suggestions and potential weaknesses:

      While the samples and assays provide a robust and comprehensive coverage of metabolic needs and testing, the data is less categorical. Some of these may be dependent on sample size or statistical analysis while others may be dependent on interpretation.

      (1) Statistical Analysis

      (1a) The results of this study often cannot be assessed properly due to a lack of clarity in the statistical tests.

      For example, the results related to the large animal hibernators (Figure 1) do not describe the statistical test (in the text of the results, methods, or figure legends). (Similarly for figure 6 and Supplemental Figure 1). Further, it is not clear whether or when the analysis was performed with paired samples. As the methods described, it appears that the Eurasian Brown Bear data should be paired per animal.

      We thank the reviewer for these important points and have added information upon the statistical tests used where previously missing in each figure legend. Details on the statistical testing used for figure 6 are listed in the methods section, paragraph 18, “All statistical analysis of TMT derived protein expression data…”

      (1b) The statistical methods state that non-parametric testing was utilized "where data was unevenly distributed". Please clarify when this was used.

      We have now clariid all statistical tests used in the figure legends.

      (1c) While there are two different myosin isoforms, the isoform may be considered a factor. It is unclear why a one-way ANOVA is generally used for most of the mant-ATP chase data.

      The reviewer is right, in our analysis, we haven’t considered ‘myosin isoforms’ as a factor. One of the main reasons for that is because we have decided to treat fibres expressing different myosin heavy chain isoforms as totally separated entities (not interconnected).

      (1d) While the technical replicates on studies such as the mant-ATP chase assay are well done, the total biological replicates are small. A consideration of the sample power should be included.

      Unfortunately, obtaining additional biological samples from these unique species is challenging. Hence, we have added a statement in the Discussion section. This statement focuses on the potential benefits of increasing sample size to increase statistical power (Discussion, paragraph 2, “In contrast to our study hypothesis…”

      (1e) An analysis of the biological vs statistical significance should be considered, especially for the mant-ATP chase data from the American Black Bear, where there appear to be shifts between the summer and winter data.

      We agree that it is important to be careful when drawing conclusions from data only based on p-values. We agree that the modest differences observed in these data on American Black bear, whilst not significant, are worth noting and we have added these considerations into the manuscript (Discussion, paragraph 2, “In contrast to our study hypothesis…).

      (2) Consistency of DRX/SRX data.

      (2a) The investigators performed both mant-ATP chase and x-ray diffraction studies to investigate whether myosin heads are in an "on" or "off" state. The results of these two studies do not appear to be fully consistent with each other, which should not be a surprise. The recent work of Mohran et al (PMID 38103642) suggests that the mant-ATP-predicted SRX:DRX proportions are inconsistent with the position of the myosin heads. The discussion appears to lack a detailed assessment of this prior work and lack a substantive assessment contrasting the differing results of the two assays in the current study. i.e. why the current study's mant-ATP chase and x-ray diffraction results differ.

      Prior works on skeletal muscle (observing discrepancies between Mant-ATP chase assay and X-ray diffraction) are rather scarce. Adding a comprehensive discussion about this may be beyond the scope of current study and would distract the reader from the main topic. For this reason, we have not added any section. Note that, we have other manuscripts in preparation that are specifically dedicated to the discrepancy.

      (2b) The discussion of the current study's x-ray diffraction data relating to the I_1,1/I_1,0 ratio and how substantially different this is to the M6 results merits discussion. i.e. how can myosin both be more primed to contract during IBA versus torpor (according to intensity ratio), but also have less mass near the thick filament (M6).

      The I1,1/I1,0 ratio indicates a subtle mass shift towards the myosin thick filament whilst the M6 spacing shows a more compliant thick filament. These results are not incompatible and rely on interpretation of the X-ray diffraction patterns. To avoid any confusion and avoid distracting the reader from the main topic, we have decided not to speculate there.

      (3) Possible interactions with Heat Shock Proteins

      Heat Shock Proteins (HSPs), such as HSP70, have been shown to be differential during torpor vs active states. A brief search of HSP and myosin reveals HPSs related to thick filament assembly and Heat Shock Cognate 70 interacting with myosin binding protein C. Especially given the author's discussion of protein stability and the potential interaction with myosin binding protein C and the SRX state, the limitation of not assessing HSPs should be discussed. (While HSP's relation to thick filament assembly might conceivably modify the interpretation of the M3 x-ray diffraction results, this reviewer acknowledges that possibility as a leap.)

      The reviewer raises an interesting and potentially important of the potential impact of HSP and their interaction with the thick filament during hibernation. We have added a section into the discussion of this manuscript regarding this, with particular impact upon the HSP70 acting as a chaperone for myosin binding protein, however we feel that it is important to point out that HSPs have only been shown to interact with MYBPC3, a cardiac isoform of this protein which is not present in skeletal muscle [3]. (Discussion, paragraph 5, “Of potential further interest to the regulation of myosin…”).

      Despite the above substantial concerns/weaknesses, this reviewer believes that this manuscript represents a valuable data set.

      Other comments related to interpretation:

      (4) The authors briefly mention the study by Toepfer et al [Ref 25] and that it utilizes cardiac muscles. There would benefit from increased discussion regarding the possible differences in energetics between cardiac and skeletal muscle in these states.

      As this manuscript focuses solely on skeletal muscle. We believe that introducing comparisons between cardiac and skeletal muscles would confuse the reader. These types of muscles have very different regulations of SRX/DRX as an example. Note that we are preparing a manuscript focusing on cardiac muscle and hibernation.

      (5) The author's analysis of temperature is somewhat limited.

      (5a) First, the authors use 20 degrees C (room temperature), not 37 degrees C, a more physiologic body temperature for large mammals. While it is true that limbs are likely at a lower temperature, 20 degrees C seems substantially outside of a normal range. Thus, temperature differences may have been minimized by the author's protocol.

      The authors agree that the experimental set up to perform these single fiber studies at slightly higher temperatures may have been more beneficial to replicate the physiological conditions of these hind leg muscle in the analyzed animals. However, previous work has shown that the resting myosin dynamics are in fact stable at temperatures between 20-30 degrees Celsius in type I, type II and cardiac mammalian muscle fibers [4].

      (5b) Second, the authors discuss the possibility of myosin contributing to non-shivering thermogenesis. The magnitude of this impact should be discussed. The suggestion of myosin ATP utilization also implies that there is some basal muscle tone (contraction), as the myosin ATPase utilizes ATP to release from actin, before binding and hydrolyzing again. Evidence of this tone should be discussed.

      The reviewer is raising an interesting point and it would indeed be interesting to assess the magnitude of the impact and whether a basal muscle tone exists. Assessing the magnitude of the impact, is not an easy task and would require very advanced simulations which we are not experts in unfortunately. As for basal muscle tone, this is difficult to say as myosin is not actually binding to actin but hydrolyzing ATP at a faster pace during hibernation. We then think that the relation between our data and basal muscle tone is unclear. Hence, we have decided not to discuss these points in the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      This is a very interesting paper. I have some minor suggestions to help improve it.

      Is there any way to estimate the contribution of contractile apparatus to energy expenditure in reference to what is being generated at SERCA in the resting muscle under the various states examined?

      This is an interesting idea however, as far as we know, this would be challenging experimentally (in the hibernating mammals) and difficult to achieve in a reliable manner.

      It is important to emphasize that while BAT has been traditionally seen to be the site of NST, the skeletal muscle is very important, especially in large mammals, where BAT is going to be a very small % of the body and unlikely to be able to adequately provide heat. The addition of the contractile apparatus to SERCA as a heat generator at rest is very important -- also, the activation of ryanodine receptor Ca2+ to increase the local [Ca2+] at SERCA to generate heat has also recently been shown and should be mentioned (Meizoso-Huesca et al 2022, PNAS; Singh et al 2023, PNAS) alongside the work of Bal et al 2012 etc...

      We have included these mechanisms and references into the manuscript discussion [5, 6]. Discussion, paragraph 4, “A critical difference between the large hibernators…”

      Are you able to report the likely proportion of type II fibers in the muscles you have sampled?

      The fiber type breakdown for all animals used in this study is reported in supplementary table 1.

      The sampling of muscle from the legs of live animals is sensible and convenient. Is it possible different muscles in the body have different levels of NST, changes in energy expenditure in torpor, and other states?

      As discussed in the public review we have added to the discussion of this manuscript to reflect upon this important point of potentially different results from different muscle sites in these animals.

      Reviewer #2 (Recommendations For The Authors):

      Is it likely that the proportion of fast and slow myosin-heavy chains within the selected sample of myofibers from the different mammals contributes to the overall differences in the energetics of different conformational states? In living animals, how does the relative contribution of the energetics from different muscle fiber types compare with the contribution from other organs to the overall regulation of metabolism during activities in summer, winter, or periods of intermittent arousal?

      Fiber types in mammals can be vastly different between species as well as having a considerable amount of plasticity to change within each species upon specific stimuli. Furthermore, some mammals also have specific myosin heavy chain isoforms which have considerable expression, for example, myosin heavy chain 2B which is expressed in rodents such as mice but not larger mammals such as humans.

      In the manuscript, we demonstrate that there is no significant change in the ATP usage by myosin in resting muscle in any of the species which we examined (Fig 1 F, L; Fig 2 E, J). The relatively high mitochondrial density of type I fibers when compared to type II fibers may contribute to a higher overall requirement of energy storage primarily via lipid oxidation. However, mitochondrial respiration is heavily suppressed during hibernation, so questions remain over the overall energy demand in hibernating muscle beyond myosin [7]. The fact that myosin ATP demand is relatively preserved in hibernating muscle suggests that skeletal muscle may be a relatively energy-demanding organ even during hibernation, we speculate in the manuscript this may be due to the requirement of maintaining muscular tone and function during this period of prolonged immobilization. This may be of relevance when one considers the almost complete shutdown of organs involved with food intake and breakdown such as the stomach and liver during hibernation. Furthermore, heart rate and breathing rates are vastly suppressed. Altogether, whilst is it difficult at this point to make an accurate estimate of energy demands between the different organs of hibernators, our data points to skeletal muscle to be a relatively high energy demand organ during these periods. When considering the difference between fiber type, again our data suggests that both type I and type II fibers have relatively similar energy demands during hibernation.

      The supplementary data are quite revealing as to how the myosin isoform composition is stable in some species but highly plastic in others in response to the same environmental/metabolic challenges. Why is the myosin heavy chain isoform (I and II) composition stable for brown bears but not for black bears between summer and winter? This is very interesting. For the Ground squirrel, there is remarkable plasticity between myosin heavy chain isoforms ( I and II) between summer, interbout arousal, and torpor. Yet in the Garden Dormouse, the myosin heavy chain isoform (I and II) composition is stable between these three activity states. The inconsistencies between and within species are perplexing and worthy of closer interrogation.

      The measurements and role of myosin energetics in different conformational states are interesting but need to be explained in context with other metabolic regulators for these hibernating mammals, especially because some species show remarkable plasticity whereas others show remarkable stability. For example, compare brown and black bears which show differences in the response of myosin composition the activity, interbout arousal, and torpor. Ground squirrels show remarkable plasticity in myosin isoform composition between activity states (and likely metabolic differences), but the Garden Dormouse has a remarkably stable myosin isoform composition during the three metabolic/environmental challenges. What mechanisms facilitate these modifications in some but not other mammals, even those of similar size? The differences are very interesting, worthy of follow-up, and may well contribute to further understanding the significance of the energetics of different myosin conformational states.

      We agree that the changes seen between these species are very interesting and worthy of further investigation. What would be of further interest would be to look at methods which would allow for even deeper phenotyping, such as single fiber proteomics, to allow for the assessment of the percentage of hybrid fibers and fibers undergoing any fiber type switch during hibernating periods. Our results do observe a modest, albeit not significant, increase in the number of type I muscle fibers in 13-lined ground squirrels and Garden dormice during torpor which is consistent with previous studies[8]. Previous studies have demonstrated that lower temperatures may promote a shift towards more oxidative type I muscle fibers in mammals[9]. This could be an explanation for why we see this specifically in the smaller hibernators, however as we demonstrate and discuss, these lower temperatures are vital for the survival of these smaller mammals during hibernation so it would be inconsistent to hypothesize that these shifts are for heat-production purposes. Further studies are warranted to understand the relevance of these shifts further, particularly those with a higher sample size. It would also be on interest to examine fiber type percentages during the progression these long hibernating periods to observe if these changes are progressive.

      As for the triggers and mechanisms which facilitate these changes to myosin dynamics, this is of current investigation by the field. One which may be of particular relevance to the changes seen during hibernation would that of steroid hormones previous research has demonstrated that steroid hormone levels in make and female bears change differentially[10]. This may be of relevance as the steroid hormone estradiol has been shown to slow the resting myosin ATP turnover via the binding of myosin RLC[11]. Considering these studies, future work which looks at hibernating animals of each sex as different groups may be fruitful.

      Reviewer #3 (Recommendations For The Authors):

      i. PDF Pg 8- Results- 'Myosin temperature sensitivity is lost in relaxed skeletal muscles fibers of hibernating Ictidomys tridecemlineatus.': An extra comma appears to be placed between "temperature, decrease".

      ii. PDF Pg 9- Results- 'Hyper-phosphorylation of Myh2 predictably stabilizes myosin backbone in hibernating Ictidomys tridecemlineatus.' (last paragraph): A parenthesis needs to be closed upon the first reference to "supplemental figures 2 and 3".

      iii. PDF Pg 15- Methods- 'Samples collection and cryo-preservation'- The authors use the term "individuals" in the 2nd line. Consider using "subjects".

      iv. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (2nd paragraph)- define "subadult" in approximate months or years.

      v. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (2nd paragraph)- The authors state that brown bears were located in "February and again ... in late June". Was this order of operations always held? If so, a comment about how the potential ageing from the hibernation (especially if sub-adult transitions to adulthood in this period) should be included.

      All samples were collected during the subadult period of the lifespan of each bear and therefore we do not think that there would be a potential aging affect observed considering the lifespan of this species to be 20-30 years.

      vi. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (3rd paragraph)- The justification for deprivation of feeding of black bears 24 hours prior to euthanasia should be included. A comment on how this might impact post-translational modifications or gene expression should be included.

      Animals are starved prior to prevent aspiration during euthanasia. Considering these samples are to be compared to animals which have not consumed food or water for five months the impact relative impact on PTMs and gene expression would be considered negligible.

      vii. PDF Pg 17- Methods- 'Mant-ATP chase experiments' (just after normalized fluorescence equation): The "Where" may be lowercase.

      viii. PDF Pg 17- Methods- 'Mant-ATP chase experiments' (last paragraph): The protocol for myosin staining, along with the antibody identification (source, catalog number) should be included.

      ix. PDF Pg 18- Methods- 'Post-translational Modification Peptide mapping': Define the makeup of the acrylamide gel and/or the source and catalog number.

      x. PDF Pg 18- Methods- 'Post-translational Modification Peptide mapping': The authors state that "Gel bands were washed..." Please specify which protein bands and if multiple bands (i.e. multiple isoforms) were isolated.

      We thank this reviewer for their careful reading of our manuscript, we have made the changes above as relevant.

      Reference list

      (1) Aydin, J., et al., Nonshivering thermogenesis protects against defective calcium handling in muscle. Faseb j, 2008. 22(11): p. 3919-24.

      (2) Stickler, S., Regional body temperatures and fatty acid compositions in hibernating garden dormice: a focus on cardiac adaptions. 2022, Vienna: Vienna. p. v, 49 Seiten, Illustrationen.

      (3) Glazier, A.A., et al., HSC70 is a chaperone for wild-type and mutant cardiac myosin binding protein C. JCI Insight, 2018. 3(11).

      (4) Walklate, J., et al., Exploring the super-relaxed state of myosin in myofibrils from fast-twitch, slow-twitch, and cardiac muscle. Journal of Biological Chemistry, 2022. 298(3).

      (5) Meizoso-Huesca, A., et al., Ca<sup>2+</sup> leak through ryanodine receptor 1 regulates thermogenesis in resting skeletal muscle. Proceedings of the National Academy of Sciences, 2022. 119(4): p. e2119203119.

      (6) Singh, D.P., et al., Evolutionary isolation of ryanodine receptor isoform 1 for muscle-based thermogenesis in mammals. Proceedings of the National Academy of Sciences, 2023. 120(4): p. e2117503120.

      (7) Staples, J.F., K.E. Mathers, and B.M. Duffy, Mitochondrial Metabolism in Hibernation: Regulation and Implications. Physiology, 2022. 37(5): p. 260-271.

      (8) Xu, R., et al., Hibernating squirrel muscle activates the endurance exercise pathway despite prolonged immobilization. Exp Neurol, 2013. 247: p. 392-401.

      (9) Yu, J., et al., Effects of Cold Exposure on Performance and Skeletal Muscle Fiber in Weaned Piglets. Animals (Basel), 2021. 11(7).

      (10) Frøbert, A.M., et al., Differential Changes in Circulating Steroid Hormones in Hibernating Brown Bears: Preliminary Conclusions and Caveats. Physiol Biochem Zool, 2022. 95(5): p. 365-378.

      (11) Colson, B.A., et al., The myosin super-relaxed state is disrupted by estradiol deficiency. Biochemical and biophysical research communications, 2015. 456(1): p. 151-155.

    1. Author response:

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

      Reviewer #1:

      Comments on revised version:

      The authors have satisfactorily addressed my concerns.

      I suggest some minor edits, however. Line 747 does not mention MARK3 and neither does the figure 8 legend (just MARK2). It would be helpful if the authors could include references to the papers reporting the shown structures in the Figure 8 legend

      We have added MARK3 and related references in the revised Figure 8 legend.

      Reviewer #2:

      I would recommend that the catalog numbers from the different antibodies used in the study, mainly CST and Invitrogen are depicted in material and methods (see Methods/Recombinant proteins and general reagents).

      Thank you for the comment. We have now added the antibody catalog numbers in the revised methods section.

      I have one remark related to question number 5 (my question was not clear enough). I meant if the authors did look at the functional relevance of the residues implicated in the identified salt-bridge network/tethers. What happens to the proteins functionally when you mutate those residues? (represented on Fig. 8).

      Otherwise, the authors have satisfactorily addressed my concerns.

      Yes, we have analyzed the stability of the salt bridge interaction in the context of cysteine mutations, and our findings are described in the results section titled “Cysteine mutations alter critical structural interactions required for kinase allosteric regulation Figure 6)”. However, we have not performed mutational analysis of the salt bridge residues as we feel this would be beyond the scope of the current study.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review): Weaknesses:

      However, the molecular mechanisms leading to NPC dysfunction and the cellular consequences of resulting compartmentalization defects are not as thoroughly explored. Results from complementary key experiments using western blot analysis are less impressive than microscopy data and do not show the same level of reduction. The antibodies recognizing multiple nucleoporins (RL1 and Mab414) could have been used to identify specific nucleoporins that are most affected, while the selection of Nup98 and Nup107 is not well explained.

      The results for the Western blots are less impressive than single nuclei imaging analysis because the protocol for isolating brain nuclei is heterogeneous and includes non-neuronal cells. For this reason, we selected specific nucleoporins for Western blot studies to complement the nonspecificity of pan-NPC antibodies for which the detection is based on the glycosylated moieties. We reasoned that a combination of pan-NPC and select NUPs will give the strongest complementary validation for the mutant phenotype. We have discussed the rationale of NUP selection in discussion. In brief, we selected NUP107 as it is a major component of the Yscaffold complex and is a long-lived subunit of the NPCs (Boehmer et al., 2003; D'Angelo et al., 2009). NUP98 is a mobile nucleoporin and is associated with the central pore, nuclear basket and cytoplasmic filaments. Both NUPs have been implicated in degenerative disorders. (Eftekharzadeh et al., 2018; Wu et al., 2001).

      There is also no clear hypothesis on how Aβ pathology may affect nucleoporin levels and NPC function. All functional NCT experiments are based on reporters or dyes, although one would expect widespread mislocalization of endogenous proteins, likely affecting many cellular pathways.

      We agree that the interaction between Aβ pathology and the NPC remains a work in progress. We decided to rigorously characterize Aβ-mediated deficits in App KI neurons – using different approaches and in more than one animal model – before moving on to explore mechanisms in subsequent studies, which we think deserves more extensive experiments. We seek your understanding and have included in the discussion, possible mechanisms for direct and indirect Aβ-mediated disruption of NPCs. We have also included an additional study to show the disruption in the localization of an endogenous nucleocytoplasmic protein – CRTC1 (cAMP Regulated Transcriptional Coactivator), which is CREB coactivator responsive to neural activity. We observed under basal and also in tetrodotoxin-silenced conditions, there is much higher CRTC1 in the nucleus in App KI neurons relative to WT. This reflects the compromised permeability barrier that we observed via FRAP studies. (Supplementary Figure S15).

      The second part of this manuscript reports that in App KI neurons, disruption in the permeability barrier and nucleocytoplasmic transport may enhance activation of key components of the necrosome complex that include receptor-interacting kinase 3 (RIPK3) and mixed lineage kinase domain1 like (MLKL) protein, resulting in an increase in TNFα-induced necroptosis. While this is of potential interest, it is not well integrated in the study. This potential disease pathway is not shown in the very simple schematic (Fig. 8) and is barely mentioned in the Discussion section, although it would deserve a more thorough examination.

      The study of necroptosis is meant to showcase a single cellular pathway that requires nucleocytoplasmic transport for activation that is compromised and is relevant for AD. We agree there is much more to explore in this pathway but feel is outside the scope of this study. We have included a new illustration that models how damage to NPCs and permeability barrier results in enhanced vulnerability of App KI neurons for necroptosis (Supplemental figure S12).

      Reviewer #2 (Public Review):

      (1) Adding statistics and comparisons between wild-type changes at different times/ages to determine if the nuclear pore changes with time in wild-type neurons. The images show differences in the Nuclear pore in neurons from the wild-type mice, with time in culture and age. However, a rigorous statistical analysis is lacking to address the impact of age/development on NUP function. Although the authors state that nuclear pore transport is reported to be altered in normal brain aging, the authors either did not design their experiments to account for the normal aging mechanisms or overlooked the analysis of their data in this light.

      All our quantifications and statistical comparisons in neuron cocultures are time-matched between WT and App KI neurons, and thus independent of age and maturity of the neurons in culture. The accelerated loss of NUP expression is evident across all time groups. However, we cannot compare across age groups in cultured neurons as the time-matched WT and App KI samples for each time point were processed and imaged separately as neurons matured over time (Fig. 1B-C). An experiment must be done simultaneously across all age groups to compare agerelated effects for WT and App KI neurons in order to account for time-dependent changes. Given the unique challenges of studying “aging” in culture systems, we opted to be more conservative in our interpretation of the results and as such, we were careful to describe the accelerated nuclear pore deficits in App KI neurons relative to time-matched WT expression and speculate its relationship to normal brain aging only in the discussion section. We seek your understanding in this matter. That said, we are able to capture the decline of the NPC in histology of brain sections and observed a statistically significant drop in WT NUP levels in animal sections across age groups where we quantified and compared the raw nuclear intensities from brain sections that were processed and imaged simultaneously across independent experiments (Fig. 1D-E). We have included a statement in the results section to highlight that point.

      (2) Add experiments to assess the contribution of wild-type beta-amyloid accumulation with aging. It was described in 2012 (Guix FX, Wahle T, Vennekens K, Snellinx A, Chávez-Gutiérrez L, Ill-Raga G, Ramos-Fernandez E, Guardia-Laguarta C, Lleó A, Arimon M, Berezovska O, Muñoz FJ, Dotti CG, De Strooper B. 2012. Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer's disease. EMBO Mol Med 4:660-673, doi:10.1002/emmm.201200243) and 2021 (Burrinha T, Martinsson I, Gomes R, Terrasso AP, Gouras GK, Almeida CG. 2021. Upregulation of APP endocytosis by neuronal aging drives amyloid-dependent synapse loss. J Cell Sci 134. doi:10.1242/jcs.255752), 28 DIV neurons are senescent and accumulate beta-amyloid42. In addition, beta-amyloid 42 accumulates normally in the human brain (Baker-Nigh A, Vahedi S, Davis EG, Weintraub S, Bigio EH, Klein WL, Geula C. 2015. Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138:1722-1737. doi:10.1093/brain/awv024), thus, it would be important to determine if it contributes to NUP dysfunction. Unfortunately, the authors tested the Abeta contribution at div14 when wild-type Abeta accumulation was undetected. It would enrich the paper and allow the authors to conclude about normal aging if additional experiments were performed, namely, treating 28Div neurons with DAPT and assessing if NUP is restored.

      Your point is well-noted. We are intrigued at the potential contribution of WT Aβ to the decline in NUPs and NPC but decided to focus on mutant Aβ for this manuscript. We have observed negligible MOAB2-positive Aβ signals in WT neurons across all age groups (data not shown) but acknowledge the potential contributions of aging toward a reduction in NPC function. Instead, we have included a section in the discussion to highlight the aging-related expression of Aβ in WT neurons and a subset of the citations above to indicate a possible link with normal decay of NPCs.

      Reviewer #3 (Public Review):

      Weaknesses:

      (1) It does not consider the relationship of the findings here to other published work on the intraneuronal perinuclear and nuclear accumulation of amyloid in other transgenic mouse models and in humans.

      We have updated the discussion to further elaborate on intraneuronal and perinuclear accumulation of amyloid and how that relates to our NPC phenotype.

      (2) It appears to presume that soluble, secreted Abeta is responsible for the effect rather than the insoluble amyloid fibrils.

      At present, our data cannot fully discount the role of fibrils or other forms of Aβ causing the NPC deficits, but our studies do show that external presence of Aβ (e.g. addition of synthetic oligomeric Aβ or App KI conditioned media) leads to intracellular accumulation and NPC dysfunction. We are aware that endogenous formation of fibrils could also contribute to the NPC dysfunction but refrained from drawing any conclusions without further studies. We have stated this in the discussion.

      (5) It is not clear when the alteration in NUP expression begins in the KI mice as there is no time at which there is no difference between NUP expression in KI and Wt and the earliest time shown is 2 months. If NUP expression is decreased from the earliest times at birth, then this makes the significance of the observation of the association with amyloid pathology less clear.

      The phenotype we observed early in neuronal cultures and in very young animals is subtle and in all our studies, the severity of the NUP phenotypes consistently correlates with elevated intracellular Aβ. We expect that by looking at earlier/younger neurons, the deficits will not be present. However, neurons before DIV7 are immature, and hence we chose not to include those in our observations. In animals, we observed Aβ expression in neuronal soma in young mice (2 mo.), but it is not clear when the deficits manifests and how early to look. While the NUP expression is reduced at an early stage, we speculate in discussion that cellular homeostatic mechanisms can compensate for any compromised nuclear functions and to maintain viability to the point where age-dependent degradation of cellular mechanisms will eventually lead to progression of AD.

      Reviewer #1 (Recommendations For The Authors):

      While the App KI model is suitable for modeling one key aspect of human AD, the use of the term "AD neurons" throughout the manuscript is misleading and should be avoided when describing experiments with "App KI neurons".

      Noted and corrected.

      The claim that Aβ pathology causes NPC dysfunction via reduced nucleoporin protein expression would be stronger if it was better supported by biochemical evidence based on western blots (WBs) to complement the strong microscopy data. The results shown in Figure 2H show a very weak effect compared to microscopy data that does not appear to match the quantification (e.g. Lamin-B1 staining appears reduced after 2 months in WB but not the graph). It is also not clear why nuclear fractionation is required. WB analyses with RL1 and MAB414 (that recognizes multiple FG-Nupsin ICCs and WBs) would help identify Nups that are most affected by Aβ pathology.

      The weaker Western blot results is due to the heterogeneity of the nuclei we isolated from the whole brain which includes non-neuronal cells. We reasoned that isolating the nuclear fraction would give us a cleaner Western blot with fewer background bands as the input lysate is more specific. We also decided to use antibodies against specific NUPs as a way to complement the pan-NPC antibodies that detect glycosylation-enriched epitopes in the nucleus. We reasoned that Western blot identification of individual subunits should provide complementary and stronger evidence for the reduction of NUPs at the peptide level. Overall, we used four different nuclear pore antibodies (RL1, Mab414, NUP98, NUP107) to demonstrate the same mutant phenotype in App KI neurons.

      While the observed NCT defects are discussed in detail, the authors do not present any potential mechanisms to be tested, how intracellular Aβ may impact NPCs. Does Aβ pathology affect nucleoporin expression or stability?

      We have observed the presence of Aβ adjacent to the nuclear membrane and also in the cytosol via high resolution confocal microscopy (Supplementary Figure S14). Our primary goal in this paper is to provide convincing evidence – using different assays and in more than one mouse model – for the reduction of NUPs and lower NPC counts. We feel mechanistic details of Aβdriven NPC disruption requires more extensive experimentation more suitable for subsequent publications.

      The very simple schematic just represents the loss of compartmentalization, without illustrating more complex concepts. It would also be improved by representing the outer and inner nuclear membrane fusing around the NPCs with a much wider perinuclear space between the membranes. As shown now, the nuclear envelope almost looks like a single membrane, while >60kDa proteins are shown at a similar size as the 125MDa NPC.

      We have updated the illustration along with a new schematic for necroptosis (Supplementary Figure S12). We have refrained from giving specific details of the damage to the nuclear pore complex because it is not yet clear the nature of these deficits.

      Misspelling of "Hoechst" as "Hochest" in several figures (Fig. 1, 2, S5, S7).

      Noted and corrected

      Reviewer #2 (Recommendations For The Authors):

      (1) Additional data analysis is required concerning the wild-type controls. The figures show clear differences in the wild-type neurons with time in culture (referring to figures 1A, 1B, 1C; 2A, 2B, 2C, 2D,6E, 6F, 6G, s4) and in different ages (2E, 2F, 2G, 5B, 5C, 5D). The data analysis is shown for knockin vs the time-matched wild-type condition. The effect of time in wild-type neurons/mice should also be analyzed. All the data is suggested to be normalized to 7 DIV/2month wild-type neurons/mice. Were these experiments done with different time points of the same culture? This would be the best to conclude on the effect of time.

      We have noted a decline of NUPs in WT neurons over time in primary cultures and in animal sections. This is not surprising since the NPC and nuclear signaling pathways deteriorate with age (Liu and Hetzer, 2022; Mertens et al., 2015). However, we are unable to do a direct comparison across age groups in cultured neurons as the time-matched WT and App KI neuronal samples for each time point were processed and imaged separately as neurons matured over time (Fig. 1B-C). Hence, we perform statistical analysis for each time-matched WT and App KI neurons. To be clear, multiple independent experiments across different cultures were performed at each time point. Given the inherent challenges of studying aging in culture systems, we opted to be more conservative in our interpretation of the results and as such, we were careful to describe the accelerated nuclear pore deficits in App KI neurons relative to WT levels without inferring the effect of time and speculate its relationship to normal brain aging only in the discussion section. That said, we are able to capture the decline of the nuclear pore complex across different age groups in histology of brain sections where we observed a drop in WT NUP levels in animal sections when we quantified and compared the raw nuclear intensities from brain sections that were processed and imaged simultaneously across independent experiments (Fig. 1D-E).

      Similarly, in Figure 2H, why aren't 2 months compared with 14 months? Why were these ages chosen? 2 months is a young adult, and 14 months is a middle-aged adult. To conclude, aging should have included an age between 18 and 24 months old.

      As with cultures, we isolated age-matched WT and App KI animals separately. We chose 2 to 14 months as they represent young and middle-aged adults as we wanted to showcase the nuclear pore deficits induced by the presence of Aβ without drawing a conclusion on the effects of age or time. That said, we do show histology of brain sections at 18 months of age with individual NUPs. We agree that the temporal aspects of NPC loss in WT neurons is interesting, however, given our experimental parameters, we cannot draw conclusions across different age groups at the moment.

      In Figure 3, statistics between wild type should have been included.

      Similar to the above comment, samples were processed and imaged independently across different groups, hence we cannot compare the datapoints across time.

      (4) Additional quantification: The intensity of MOAB2 at 2 and 13 months should be measured as in Figure 3C.

      Intracellular Aβ signal in 2-mo. old App KI mice is diffuse throughout the soma but in older animals, they are punctate. This observation was similarly described by Lord et al. for tgAPPArcSwe mice (Lord et al., 2006). We have included a confocal micrograph of MOAB-2 immunocytochemistry of a 13-mo. App KI brain section in supplemental figures (Supplementary Figure S13). We found it challenging to differentiate whether the signal is localized intracellularly or as an extracellular aggregate. Regardless, the differences in the quality and uneven distribution of Aβ signal makes any direct comparison of soma intensity across the different age groups harder to interpret in the context of the mutant phenotype.

      (5) Additional experiments: Because primary neurons differentiate, mature, and age with time in culture, they are required to control for the developmental stage of your cultures. Analyzing neuronal markers such as doublecortin for neuronal precursors, MAP2 (or Tau) for dendritic/axonal maturation, synapsin for synaptic maturation, and accumulation of senescenceassociated beta-galactosidase (SA-Beta-Gal) as an aging marker.

      As part of the maintenance of cultures, we stain cultures for axodendritic markers (e.g. MAP2), glial cell distribution (e.g GFAP) and excitatory vs. inhibitory neuronal subpopulations (e.g. Gad65) and synaptic markers (e.g. PSD95) to ensure that growth, survival and viability of neurons are not compromised (data not shown). These markers for maturity are routinely tracked to ensure proper development. We also test the health of the cultures (e.g. apoptosis, necrosis) and to look for cytoskeletal disruption or fragmentation for neuronal processes.

      (6) Additional methods: The quantification of Abeta intensity in Figure 3 is not clearly explained in the methods. Was the intensity measured per field, per cell body?

      The quantifications for Aβ are done for each MAP2-positive cell body and have included that statement in the methods.

      (7) Missing in discussion integration and references to these papers:

      a. Mertens J, Paquola ACM, Ku M, Hatch E, Böhnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy JR, Gonçalves JT, Toda T, Kim Y, Winkler J, Yao J, Hetzer MW, Gage FH. 2015. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell 17:705-718. doi:10.1016/j.stem.2015.09.001

      b. Guix FX, Wahle T, Vennekens K, Snellinx A, Chávez-Gutiérrez L, Ill-Raga G, Ramos-Fernandez E, Guardia-Laguarta C, Lleó A, Arimon M, Berezovska O, Muñoz FJ, Dotti CG, De Strooper B. 2012. Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer's disease. EMBO Mol Med 4:660-673. doi:10.1002/emmm.201200243

      c. Burrinha T, Martinsson I, Gomes R, Terrasso AP, Gouras GK, Almeida CG. 2021. Upregulation of APP endocytosis by neuronal aging drives amyloid-dependent synapse loss. J Cell Sci 134. doi:10.1242/jcs.255752),

      Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138:1722-1737. doi:10.1093/brain/awv024).

      We have cited a subset of the papers in the discussion section and also expanded the discussion to include the possibility of time-dependent changes for Aβ expression in WT neurons.

      Reviewer #3 (Recommendations For The Authors):

      Specific comments:

      (1) Fig. 1D,E. Fig. 2E, F. This shows the change in NUP IR with time for the APP-KI, but there is also a difference between Wt and KI from the earliest time shown. How early is this difference apparent? From birth? The study should go back to the earliest time possible as the timing of the staining for NUP is important to correlate this with other events of intraneuronal Abeta and amyloid IR. Is the difference between 4 and 7-month ko mice in Figures 2G and 2F statistically significant? If not, perhaps we need a larger N to determine the timing accurately.

      The point is well taken. We have not examined the WT and App KI brains before 2-mo. of age. At this early time point, the extracellular amyloid deposits are very low but intracellular Aβ can be readily detected in neuronal soma. We expect that as the animal ages, the Aβ inside cells will directly impact the NPC mutant phenotype, but it is unclear how early this phenotype manifests in animals and when we should look. To be clear, in less mature neurons (DIV7), the phenotype is very subtle and can only be observed via high resolution microscopy. The differences between 4-7 mo. old animals (Fig. 2F and G) in terms of severity of the reduction cannot be assessed as the age-matched animals for each time point were processed separately, but at each time point, we observed a significant reduction of NPC relative to WT. Nevertheless, in Figure 1E, we performed immunohistochemistry experiments with pan-NPC antibodies and quantified raw intensities to show a difference between 4/7-mo. with 13-mo. old animals.

      (2) Similarly, the increase in Abeta IR is only shown for cultured neurons and only a single time point of 2 months is shown for CA1 in KI brain. Since a major point is that the decrease in NUP IR is correlated with an increase in Abeta IR, a more convincing approach would be to stain for both simultaneously in KI brain, especially since Abeta IR is quite sensitive to conformational variation between APP, Abeta, and aggregated forms and whether they are treated with denaturants for "antigen retrieval". The entire brain hemisphere should be shown as the pathology is not limited to CA1. There are many different Abeta antibodies that are specific to the amyloid state so it should be possible to come up with a set of antibodies and conditions that work for both Abeta and NUP staining.

      The intracellular Aβ signal in 2-mo. old App KI mice is diffuse throughout the soma but in older animals, they are punctate. We have included a confocal micrograph of MOAB-2 immunocytochemistry of a 13-mo. App KI brain section (Supplementary Figure S13). We did not quantify Aβ as it was challenging to differentiate if the signal is intracellular Aβ or amyloid β plaques. Regardless, the differences in the quality and uneven distribution of Aβ signal makes any direct comparison of soma intensity across the different age groups much harder to interpret.

      (3) Figure 3A. The staining with MOAB 2 and 82E1 appears qualitatively different with 82E1 exhibiting larger perinuclear puncta. Both antibodies appear to stain puncta inside the nucleus consistent with previously published reports of intranuclear amyloid IR. If these are flattened images, then 3D Z stacks should be shown to clarify this. Figure 3H shows what appears to be Abeta immunofluorescence quantitation in DAPT-treated cells, but the actual images are apparently not shown. The details of this experiment aren't clear or what antibody is used, but this may not be Abeta as many APP fragments that are not Abeta also react with antibodies like MOAB2.

      Since 82E1 detects a larger epitope (aa1-16 as compared to 1-4 in MOAB-2), it is possible some forms of Aβ are differentially detected inside the cell. MOAB-2 is shown to detect the different forms of Aβ40 and 42, with a stronger selectivity for the latter. However, it is not known to react with APP or APP/CTFs (Youmans et al., 2012). DAPT-treated cells were processed and imaged as with other experiments in figure 3 using MOAB-2 antibodies to detect Aβ. We have included that information in the figure legends.

      The way we image the cell is to collect LSM800 confocal stacks and use IMARIS software to render the nucleus in a 3D object prior to quantifying the intensity or coverage. In this way, we are capturing and quantifying the entire volume of the nucleus and not just a single plane. The majority of signal for MOAB-2 positive Aβ are punctate signals in the cytosol with a subset adjacent to the nucleus (Supplementary Figure 14; Airyscan; single plane). We also detected MOAB-2 signals coming from within the nucleus. The nature of this interaction between Aβ and the nuclear membrane/perinuclear space/nucleoplasm remains unclear.

      (4) P20 L12. "We demonstrate an Aβ-driven loss of NUP expression in hippocampal neurons both in primary cocultures and in AD mouse models" It isn't clear that exogenous or extracellular Abeta drives this in living animals. All the data that demonstrate this is derived from cell culture and things may be very different (eg. Soluble Abeta concentration) in vivo. It is OK to speculate that the same thing happens in vivo, but to say it has been demonstrated in vivo is not correct.

      We have rewritten the opening statement in the paragraph to narrowly define our observations in the context of App KI. We understand the caveats of our studies in primary cultures, but we have done our due diligence to study the phenomenon in different assays, using at least four different nuclear pore antibodies, and in more than one mouse model to show the deficits. We mentioned Aβ-driven loss but did not conclude which Aβ peptide (e.g. 40 vs. 42) or form (e.g. fibrillar) that drives the deficits. However, we have shown some data that oligomers and not monomers as well as extracellular Aβ can accumulate in the soma and trigger NPC deficits. We also state in the discussion that other possible mechanisms of action, mainly via indirect interactions of Aβ with the cell, could result in the deficits.

      (5) P21, L21 "Inhibition of γ-secretase activity prevented cleavage of mutant APP and generation of Aβ, which led to the partial restoration of NUP levels". What the data actually shows is that treatment of the cells with DAPT led to partial restoration of NUP levels. Other studies have shown that DAPT is a gamma secretase inhibitor, so it is reasonable to suspect that the effect to gamma secretase activity, but the substrates and products are assumed rather than measured, so a little caution is a good idea here. For example, CTF alpha is also a substrate, producing P3, which is not considered abeta. The products Abeta and P3 also typically are secreted, where they can be further degraded. Abeta and P3 can also aggregate into amyloid, so whether the effect is really due to Abeta per se as a monomer or Abeta-containing aggregates isn't clear.

      The point is noted. DAPT inhibition of -secretase can impact more than one substate as the complex can cleave multiple substrates. However, we have measured Aβ intensity which increases with DAPT, and while a singular experiment is insufficient to show direct Aβ involvement, we have performed other experiments that show a correlation of Aβ levels inside the soma and the degree of NPC reduction. This includes the direct application of synthetic Aβ42 oligomers. We agree the data cannot fully exclude the involvement of other -secretase cleavage products, but we feel there is strong enough evidence that Aβ – in whatever form - is at least partially if not, the main driver that promote these deficits.

      (6) Discussion. The authors point to "intracellular Abeta" as a potential causative agent for decreased NUP expression and function and cite a number of papers reporting intracellular Abeta. (D'Andrea et al., 2001; Iulita et al., 2014; Kimura et al., 2003; LaFerla et al., 1997; Oddo et al., 2003b; Takahashi et al., 2004; Wirths et al., 2001). Most of these papers report immunoreactivity with Abeta antibodies and argue about whether this is really Abeta40 or 42 and not APP or APP-CTF immunoreactivity. What is missing from these papers and the discussion in this manuscript is that this is not just soluble Abeta, but Abeta amyloid of the same type that ends up in plaques because it has the same immunoreactivity with Abeta amyloid fibril-specific antibodies and even the classical anti-Abeta antibodies 6E10 and 4G8 after antigen retrieval as shown in papers by Pensalfini, et al., 2014 and Lee, et al., 2022 (1,2) who describe the evolution of neuritic plaques and their amyloid core beginning inside neurons. The term "dystrophic neurite" is a misnomer because the structures that resemble "neurites" morphologically are actually autophagic vesicles packed with Abeta and APP immunoreactive material which has the detergent insolubility properties of amyloid plaques. See (1,2). The apparent intranuclear IR of MOAB2 and 82E1 mentioned in comment 3 is relevant here. In Lee et al., the 3D serial section EM reconstruction of one of these neurons with perinuclear and nuclear amyloid shows abundant amyloid fibrils in the remnant of the nucleus. The nuclear envelope appears to break down as evidenced by the redistribution of NeuN immunoreactivity (Pensalfini et al.,) and other nuclear markers and the EM evidence (Lee et al.,). These papers are also improperly cited as evidence for a hypothetical intracellular source for soluble Abeta.

      We have devoted a section of the discussion to highlight some of these findings in the context of Pensalfini et al. 2014 and Lee et al. 2022. Lee et al. tested multiple animal strains to observe the Panthos structures but did not use the App KI mouse model. Since none of our experiments directly tested their observations (e.g. perinuclear fibrils or acidity of autophagic vesicles) in App KI, we decided to take a more conservative approach in our interpretations by framing the NPC deficits without specifying the nature of the intracellular Aβ. We note in discussion that it is entirely possible that App KI animals also show the same Panthos phenotypes and the perinuclear accumulation of Aβ which results in damaged NUPs. To do that, the Panthos phenotype must first be established in App KI mice.

      (7) The authors also cite the work of Ditaranto et al., 2001 and Ji et al., 2002 for Aβ-induced lysosomal leakage from these vesicular structures but overlook the original publications on Abeta-induced lysosomal leakage by Yang et al., (3) who further show that this is correlated with aggregation of Abeta42 upon internalization which also leads to the co-aggregation of APP and APP-CTFs in a detergent-insoluble form (4) and pulse-chase studies demonstrate that metabolically-labeled APP ultimately ends up as insoluble Abeta that have "ragged" N-termini (5). This work seems relevant to the results reported here as the perinuclear amyloid that the authors report here is likely to be the same insoluble, aggregated APP and APP-CTF-containing amyloid as that reported in references 1 and 2.

      We have included the literature references in the discussion, highlighting the possibility of lysosomal leakage contributing to the NPC damage.

      Minor points.

      (1) P2, L28 "permeability barrier facilities passive" should be 'facilitates'.

      (2) P7, L24 "homogenate and grounded for 5 additional strokes" One of the peculiarities of English is that the past tense of grind is ground. Grounded means something else.

      (3) P8, L9 "For synthetic Aβ experiments," Abeta what? 42? 40? It makes a difference and if it is Abeta42, you should be specific in the rest of the text where it is used.

      (4) P11, L14. "To determine if Aβ can trigger changes in nuclear structure and function" It seems a little early to start by presupposing that it is Abeta that triggers changes in nuclear structure and function. It sounds like you are starting out with a bias.

      (5) P11, L16,17 "While Aβ pathology is robustly detected in App KIs" At some point in the manuscript, either here or in the introduction, it would be useful to include a couple of sentences about what the pathology is in these mice along with the timing of the development of the pathology to compare with the results presented here. There are several types of amyloid deposits, "neuritic" plaques, diffuse plaques, and cerebrovascular amyloid. This is important because the early "neuritic" plaques are intraneuronal at least early on before the neuron dies. See (1,2).

      (6) P19, L10. "LMB is an inhibitor or CRM-1 mediated" should be of

      All minor points have been addressed in the manuscript and figures.

      References

      (1) Pensalfini, A., Albay, R., 3rd, Rasool, S., Wu, J. W., Hatami, A., Arai, H., Margol, L., Milton, S., Poon, W. W., Corrada, M. M., Kawas, C. H., and Glabe, C. G. (2014) Intracellular amyloid and the neuronal origin of Alzheimer neuritic plaques. Neurobiol Dis 71C, 53-61

      (2) Lee, J. H., Yang, D. S., Goulbourne, C. N., Im, E., Stavrides, P., Pensalfini, A., Chan, H., Bouchet-Marquis, C., Bleiwas, C., Berg, M. J., Huo, C., Peddy, J., Pawlik, M., Levy, E., Rao, M., Staufenbiel, M., and Nixon, R. A. (2022) Faulty autolysosome acidification in Alzheimer’s disease mouse models induces autophagic build-up of Abeta in neurons, yielding senile plaques. Nat Neurosci 25, 688-701

      (3) Yang, A. J., Chandswangbhuvana, D., Margol, L., and Glabe, C. G. (1998) Loss of endosomal/lysosmal membrane impermeability is an early event in amyloid Aß1-42 pathogenesis. J. Neurosci. Res. 52, 691-698

      (4) Yang, A. J., Knauer, M., Burdick, D. A., and Glabe, C. (1995) Intracellular A beta 1-42 aggregates stimulate the accumulation of stable, insoluble amyloidogenic fragments of the amyloid precursor protein in transfected cells. J Biol Chem 270, 14786-14792

      (5) Yang, A., Chandswangbhuvana, D., Shu, T., Henschen, A., and Glabe, C. G. (1999) Intracellular accumulation of insoluble, newly synthesized Aßn-42 in APP transfected cells that have been treated with Aß1-42. J. Biol. Chem. 274, 20650-20656

      References

      Boehmer, T., Enninga, J., Dales, S., Blobel, G., and Zhong, H. (2003). Depletion of a single nucleoporin, Nup107, prevents the assembly of a subset of nucleoporins into the nuclear pore complex. Proc Natl Acad Sci U S A 100, 981-985.

      D'Angelo, M.A., Raices, M., Panowski, S.H., and Hetzer, M.W. (2009). Age-dependent deterioration of nuclear pore complexes causes a loss of nuclear integrity in postmitotic cells. Cell 136, 284-295.

      Eftekharzadeh, B., Daigle, J.G., Kapinos, L.E., Coyne, A., Schiantarelli, J., Carlomagno, Y., Cook, C., Miller, S.J., Dujardin, S., Amaral, A.S., et al. (2018). Tau Protein Disrupts Nucleocytoplasmic Transport in Alzheimer's Disease. Neuron 99, 925-940 e927.

      Liu, J., and Hetzer, M.W. (2022). Nuclear pore complex maintenance and implications for agerelated diseases. Trends Cell Biol 32, 216-227.

      Lord, A., Kalimo, H., Eckman, C., Zhang, X.Q., Lannfelt, L., and Nilsson, L.N. (2006). The Arctic Alzheimer mutation facilitates early intraneuronal Abeta aggregation and senile plaque formation in transgenic mice. Neurobiol Aging 27, 67-77.

      Mertens, J., Paquola, A.C., Ku, M., Hatch, E., Bohnke, L., Ladjevardi, S., McGrath, S., Campbell, B., Lee, H., Herdy, J.R., et al. (2015). Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell stem cell 17, 705-718.

      Wu, X., Kasper, L.H., Mantcheva, R.T., Mantchev, G.T., Springett, M.J., and van Deursen, J.M. (2001). Disruption of the FG nucleoporin NUP98 causes selective changes in nuclear pore complex stoichiometry and function. Proc Natl Acad Sci U S A 98, 3191-3196.

      Youmans, K.L., Tai, L.M., Kanekiyo, T., Stine, W.B., Jr., Michon, S.C., Nwabuisi-Heath, E., Manelli, A.M., Fu, Y., Riordan, S., Eimer, W.A., et al. (2012). Intraneuronal Abeta detection in 5xFAD mice by a new Abeta-specific antibody. Molecular neurodegeneration 7, 8.

  2. Apr 2024
    1. Author response:

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

      We would like to express our gratitude to the reviewers for their suggestions and critiques as we continually strive to enhance the quality of the manuscript. We improved it, by incorporating the reviewers’ suggestions, changing the content and numbering of figures (Figs 1, 3S1 were edited; 4 figures were moved to supplemental materials), and adding several analyses suggested by the reviewers along with accompanying figures (1S2, 1S3) and tables (1 and 2). These analyses include investigating the link between freezing behavior and 44-kHz calls as well as their sound mean power and duration. Also, we have introduced detailed information regarding the experiments performed as well as expanded the description and discussion of the results section. Finally, we added the information about 44-kHz calls reported by another group – which was inspired by our findings.

      Below is the point-by-point response to the reviewers’ comments.

      Reviewer #1 (Public Review):

      Olszyński and colleagues present data showing variability from canonical "aversive calls", typically described as long 22 kHz calls rodents emit in aversive situations. Similarly long but higher-frequency (44 kHz) calls are presented as a distinct call type, including analyses both of their acoustic properties and animals' responses to hearing playback of these calls. While this work adds an intriguing and important reminder, namely that animal behavior is often more variable and complex than perhaps we would like it to be, there is some caution warranted in the interpretation of these data. The authors also do not provide adequate justification for the use of solely male rodents. With several reported sex differences in rat vocal behaviors this means caution should be exercised when generalizing from these findings.

      We fully agree that our data should be interpreted with caution and we followed the Reviewer’s suggestions along these lines (see below). Also, we appreciate the suggestion to explore the prevalence of 44-kHz calls in female subjects, which would indeed represent an important and intriguing extension of our research. However, due to present financial constraints, we can only plan such experiments. To address the comment, we have added the sentence: “Here we are showing introductory evidence that 44-kHz vocalizations are a separate and behaviorally-relevant group of rat ultrasonic calls. These results require further confirmations and additional experiments, also in form of repetition, including research on female rat subjects.”

      It is important to note that the data presented in the current manuscript originates primarily from previously conducted experiments. These earlier experiments employed male subjects only; it was due to established evidence indicating that the female estrus cycle significantly influences ultrasonic vocalization (Matochik et al., 1992). Adhering to controls for the estrus cycle would require a greater number of female subjects than males, which would not only increase animal suffering but also escalate the demands of human labor and financial costs.

      Firstly, the authors argue that the shift to higher-frequency aversive calls is due to an increase in arousal (caused by the animals having received multiple aversive foot shocks towards the end of the protocols). However, it cannot be ruled out that this shift would be due to factors such as the passage of time and increase in fatigue of the animals as they make vocalizations (and other responses) for extended periods of time. In fact the gradual frequency increase reported for 22 kHz calls and the drop in 44 kHz calls the next day in testing is in line with this.

      Answer: We would like to point out that the “increased-arousal” hypothesis, declared in the manuscript, is only a hypothesis – as reflected by the wording used. However, we changed the beginning of the sentence in question from “It could be argued” to “We would like to propose a hypothesis” to emphasize the speculative aspect of the proposed explanation behind the increase of 44-kHz ultrasonic emissions.

      Also, we do agree that other factors could contribute to the increased emission of 44kHz calls. These factors could include: heightened fear, stress/anxiety, annoyance/anger, disgust/boredom, grief/sadness, despair/helplessness, and weariness/fatigue. We are listing these potential factors in the discussion. Also, we added: “It is not possible, at this stage, to determine which factors played a decisive role. Please note that the potential contribution of these factors is not mutually exclusive”. However, we propose a list of arguments supporting the idea that 44-kHz vocalizations communicate an increased negative emotional state. Among these arguments were the conclusions drawn from additional analyses – mostly inspired by the fatigue hypothesis proposed by the Reviewer #1. In particular, we investigated changes in the sound mean power and duration of 22-kHz and 44-kHz calls. Specifically, we showed that the mean power of 44-kHz vocalizations did not change, and was higher than that of 22-kHz vocalizations (Fig. 1S2EF).

      Finally, the Reviewer #1 listed “the gradual frequency increase reported for 22 kHz calls and the drop in 44 kHz calls the next day” as arguments for the fatigue hypothesis. We do not agree that the “increase” should be interpreted as a sign of fatigue [Producing and maintaining higher frequency calls require greater effort from the vocalizer, on which we elaborated in the manuscript], also we are not sure what “drop in 44 kHz calls” the Reviewer is referring to [We assume it refers to less 44-kHz calls during testing vs. training; we suppose that the levels of arousal are lower in the test due to shorter session time and lack of shocks, which additionally contributes to fear extinction].

      Secondly, regarding the analysis where calls were sorted using DBSCAN based on peak frequency and duration, it is not surprising that the calls cluster based on frequency and duration, i.e. the features that are used to define the 44 kHz calls in the first place. Thus presenting this clustering as evidence of them being truly distinct call types comes across as a circular argument.

      Answer: The DBSCAN sorting results were to convey that when changing the clustering ε value, the degree of cluster separation, the 44-kHz vocalizations remained distinct from the 22-kHz and various short-call clusters that merged. In other words: 44-kHz calls remained separate from long 22-kHz, short 22-kHz and 50-kHz vocalizations, which all consolidated into one common cluster. As a result, in this mathematical analysis, 44-kHz vocalizations remained distinct without applying human biases. Additionally, frequency and duration are the two most common features used to define all types of calls (Barker et al., 2010; Silkstone & Brudzynski, 2019a, 2019b; Willey & Spear, 2013). In summary, we did not expect the analysis to isolate out the 44-kHz calls, and we were surprised by this result.

      The sparsity of calls in the 30-40 kHz range (shown in the individual animal panels in Figure 2C) could in theory be explained by some bioacoustics properties of rat vocal cords, without necessarily the calls below and above that range being ethologically distinct.

      Answer: We respectfully disagree with the argument regarding sparsity. It is important to note that, during prolonged fear conditioning experiments, we observed an increased incidence of 44-kHz calls (Fig. 1E-G) of up to >19% (Fig. 1S2AB) of the total ultrasonic vocalizations during specific inter-trial intervals. Also, it is possible that in observed experimental circumstances almost every fifth call could be attributed to the vocal apparatus as an artifact of its functioning (assuming we are interpreting the Reviewer’s argument correctly). While we do not believe this to be the case, we acknowledge the importance of considering such a hypothesis.

      The behavioral response to call playback is intriguing, although again more in line with the hypothesis that these are not a distinct type of call but merely represent expected variation in vocalization parameters. Across the board animals respond rather similarly to hearing 22 kHz calls as they do to hearing 44 kHz calls, with occasional shifts of 44 kHz call responses to an intermediate between appetitive and aversive calls. This does raise interesting questions about how, ethologically, animals may interpret such variation and integrate this interpretation in their responses. However, the categorical approach employed here does not address these questions fully.

      Answer: We are unsure of the Reviewer’s critique in this paragraph and will attempt to address it to the best of our understanding. Our finding of up to >19% of long seemingly aversive, 44-kHz calls, at a frequency in the define appetitive ultrasonic range (usually >32 kHz) is unexpected rather than “expected”. We would agree that aversive call variation is expected, but not in the appetitive frequency range.

      Kindly note the findings by Saito et al. (2019), which claim that frequency band plays the main role in rat ultrasonic perception. It is possible that the higher peak frequency of 44kHz calls may be a strong factor in their perception by rats, which is, however, modified by the longer duration and the lack of modulation.

      Also, from our experience, it is quite challenging to demonstrate different behavioral responses of naïve rats to pre-recorded 22-kHz (aversive) vs. 50-kHz (appetitive) vocalizations. Therefore, to demonstrate a difference in response to two distinct, potentially aversive, calls, i.e., 22-kHz vs. 44-kHz calls, to be even more difficult (as to our knowledge, a comparable experiment between short vs. long 22-kHz ultrasonic vocalizations, has not been done before).

      Therefore, we do not take lightly the surprising and interesting finding that “animals respond rather similarly to hearing 22 kHz calls as they do to hearing 44 kHz calls, with occasional shifts of 44 kHz call responses to an intermediate between appetitive and aversive calls”. We would rather put this description in analogous words: “the rats responded similarly to hearing 44-kHz calls as they did to hearing aversive 22-kHz calls, especially regarding heartrate change, despite the 44-kHz calls occupying the frequency band of appetitive 50-kHz vocalizations” and “other responses to 44-kHz calls were intermediate, they fell between response levels to appetitive vs. aversive playback” – which we added to the Discussion.

      Finally, we acknowledge that our findings do not present a finite and complete picture of the discussed aspects of behavioral responses to the presented ultrasonic stimuli (44-kHz vocalizations). Therefore, we have incorporated the Reviewer’s suggestion in the discussion. The added sentence reads: “Overall, these initial results raise further questions about how, ethologically, animals may interpret the variation in hearing 22-kHz vs. 44-kHz calls and integrate this interpretation in their responses.”

      In sum, rather than describing the 44kHz long calls as a new call type, it may be more accurate to say that sometimes aversive calls can occur at frequencies above 22 kHz. Individual and situational variability in vocalization parameters seems to be expected, much more so than all members of a species strictly adhering to extremely non-variable behavioral outputs.

      Answer: The surprising fact that there are presumably aversive calls that are beyond the commonly applied thresholds, i.e. >32 kHz, while sharing some characteristics with 22-kHz calls, is the main finding of the current publication. Whether they be finally assigned as a new type, subtype, i.e. a separate category or become a supergroup of aversive calls with 22-kHz vocalizations is of secondary importance to be discussed with other researchers of the field of study.

      However, we would argue – by showing a comparison – that 22-kHz calls occur at durations of <300 ms and also >300 ms, and are, usually, referred to in literature as short and long 22-kHz vocalizations, respectively (not introduced with a description that “sometimes 22kHz calls can occur at durations below 300 ms”). These are then regarded and investigated as separate groups or classes usually referred to as two different “types” (e.g., Barker et al., 2010) or “subtypes” (e.g., Brudzynski, 2015). Analogously, 44-kHz vocalizations can also be regarded as a separate type or a subtype of 22-kHz calls. The problem with the latter is that 22-kHz vocalizations are traditionally and predominantly defined by 18–32 kHz frequency bandwidth (Araya et al., 2020; Barroso et al., 2019; Browning et al., 2011; Brudzynski et al., 1993; Hinchcliffe et al., 2022; Willey & Spear, 2013).

      Reviewer #2 (Public Review):

      Olszyński et al. claim that they identified a "new-type" ultrasonic vocalization around 44 kHz that occurs in response to prolonged fear conditioning (using foot-shocks of relatively high intensity, i.e. 1 mA) in rats. Typically, negative 22-kHz calls and positive 50-kHz calls are distinguished in rats, commonly by using a frequency threshold of 30 or 32 kHz. Olszyński et al. now observed so-called "44-kHz" calls in a substantial number of subjects exposed to 10 tone-shock pairings, yet call emission rate was low (according to Fig. 1G around 15%, according to the result text around 7.5%).

      Answer: We are thankful for praising the strengths. Please note Figure 1G referred to 10-trial Wistar rats during delay fear conditioning session in which 44-kHz constituted 14.1% of ultrasonic vocalizations. The 7.5% number in results refers to the total of vocalizations analyzed across all animal groups used in fear conditioning experiments. These values have been updated in the current version of the manuscript. Also, please note – 44-kHz calls constituted up to 19.4% of calls, on average, in one of the ITI during fear conditioning session. However, the prevalence of aversive calls and of 44-kHz vocalizations in particular varied. It varied between individual rats; we added the text: “for n = 3 rats, 44-kHz vocalizations accounted for >95% of all calls during at least one ITI (e.g., 140 of total 142, 222 of 231, and 263 of 265 tallied 44-kHz calls), and in n = 9 rats, 44-kHz vocalizations constituted >50% of calls in more than one ITI.” See also further for the description of the array of experiments analyzed and the prevalence/percentage of 44-kHz calls encountered (Tab. 1, Fig. 1S3).

      Weaknesses: I see a number of major weaknesses.

      While the descriptive approach applied is useful, the findings have only focused importance and scope, given the low prevalence of "44 kHz" calls and limited attempts made to systematically manipulate factors that lead to their emission. In fact, the data presented appear to be derived from reanalyses of previously conducted studies in most cases and the main claims are only partially supported. While reading the manuscript, I got the impression that the data presented here are linked to two or three previously published studies (Olszyński et al., 2020, 2021, 2023). This is important to emphasize for two reasons:

      (1) It is often difficult (if not impossible) to link the reported data to the different experiments conducted before (and the individual experimental conditions therein). While reanalyzing previously collected data can lead to important insight, it is important to describe in a clear and transparent manner what data were obtained in what experiment (and more specifically, in what exact experimental condition) to allow appropriate interpretation of the data. For example, it is said that in the "trace fear conditioning experiment" both single- and grouphoused rats were included, yet I was not able to tell what data were obtained in single- versus group-housed rats. This may sound like a side aspect, however, in my view this is not a side aspect given the fact that ultrasonic vocalizations are used for communication and communication is affected by the social housing conditions.

      Answer: Preparing the current manuscript, we indeed used data collected during fear conditioning experiments which were described previously (Olszyński et al., 2021; Olszyński et al., 2022). Please note, however, that vocalization behavior during the fear conditioning itself was not the main subject of these publications. Our previous publications (Olszyński et al., 2020; Olszyński et al., 2021; Olszyński et al., 2022) present primarily ultrasonic-vocalization data from playback-part of experiments whereas here we analyze recordings obtained during fear conditioning experiments, thus we are analyzing new parts, i.e., not yet analyzed, of previously published studies. Also, we have performed additional experiments.

      In the first version of the current manuscript, we did not attempt to demonstrate exactly which calls were recorded in which conditions as the focus was to demonstrate that 44-kHz calls were emitted in several different fear-conditioning experiments. Also, as the experiments were not performed simultaneously and are results from different experimental situations, we would prefer to not compare these results directly.

      However, in the current version of the manuscript, we have introduced an additional reference system, based on Tab. 1, to more clearly indicate which rats have been employed in each analysis, e.g. the group of “Wistar rats that undergone 10 trials of fear conditioning” are described as “Tab. 1/Exp. 1-3/#2,4,8,13; n = 46”, i.e., these are the rats listed in rows 2, 4, 8, and 13 of Tab. 1.

      We have also tried to unify the analyses, in terms of rats used, as much as possible. Finally, we have also introduced Fig. 1S3 to demonstrate the prevalence of 44-kHz calls in all experiments analyzed with the note that “the experiments were not performed in parallel”.

      Regarding the Reviewer’s concerns about analyzing single- and pair-housed rats together. We have examined ultrasonic vocalizations emitted and freezing behavior in these two groups.

      • Ultrasonic vocalizations; when comparing the number of vocalizations, their duration, peak frequency and latency to first occurrence, equally for all types of calls and divided into types (short 22-kHz, long 22-kHz, 44-kHz, 50-kHz), the only difference was observed in peak frequency in 50-kHz vocalizations (50.7 ± 2.8 kHz for paired vs. 61.8 ± 3.1 kHz for single rats; p = 0.0280, Mann-Whitney). Since 50-kHz calls are not the subject of the current publication, we did not investigate this difference further. Also, this difference was not observed during playback experiments (Olszyński et al., 2020, Tab. 1).

      • Freezing. There were no differences between single- and pair-housed groups in freezing behavior, both in the time before first shock presentation and during fear conditioning training (Mann-Whitney).

      In summary, since the two groups did not differ in relevant ultrasonic features and freezing, we decided to present the results obtained from these rats together. However, we agree with the Reviewer, and it is possible that social housing conditions may in fact affect the emission of 44-kHz vocalizations, which could be a subject of another project – involving, e.g., larger experimental groups observed under hypothesis-oriented and defined conditions.

      (2) In at least two of the previously published manuscripts (Olszyński et al., 2021, 2023), emission of ultrasonic vocalizations was analyzed (Figure S1 in Olszyński et al., 2021, and Fig. 1 in Olszyński et al., 2023). This includes detailed spectrographic analyses covering the frequency range between 20 and 100 kHz, i.e. including the frequency range, where the "newtype" ultrasonic vocalization, now named "44 kHz" call, occurs, as reflected in the examples provided in Fig. 1 of Olszyński et al. (2023). In the materials and methods there, it was said: "USV were assigned to one of three categories: 50-kHz (mean peak frequency, MPF >32 kHz), short 22-kHz (MPF of 18-32 kHz, <0.3 s duration), long 22-kHz (MPF of 18-32 kHz, >0.3 s duration)". Does that mean that the "44 kHz" calls were previously included in the count for 50-kHz calls? Or were 44 kHz calls (intentionally?) left out? What does that mean for the interpretation of the previously published data? What does that mean for the current data set? In my view, there is a lack of transparency here.

      Answer: As mentioned above, we indeed used data collected during fear conditioning experiments which were described previously (Olszyński et al., 2021; Olszyński et al., 2022). However, in these publications, ultrasonic vocalizations emitted during playback experiments were the main subject, while the ultrasonic calls emitted during fear conditioning (performed before the playback) were only analyzed in a preliminary way. As a result, the 44-kHz vocalizations analyzed in the current manuscript were not included in the previous analyses. In particular, in Olszyński et al. (2021), we counted the overall number of ultrasonic vocalizations before fear conditioning session to determine the basal ultrasonic emissions (Fig. S1). Then, our next article (Olszyński et al., 2022), we analyzed again the number of all ultrasonic vocalizations before fear conditioning (Fig. S1) and restricted the analysis of vocalizations during fear conditioning to 22-kHz calls (Tab. S1 and S2).

      Also, we re-reviewed all the data used in our previous playback publications. Overall, 44-kHz calls were extremely rare in playback parts of the experiments. There were no 44-kHz calls in the playback data used in Olszyński et al. (2022) and Olszyński et al. (2020). In Olszyński et al. (2021), one rat produced eight 44-kHz calls. These 44-kHz calls constituted 0.03% of all vocalizations analyzed in the experiment (8/24888) and were included in the total number of calls analyzed (but not in the 50-kHz group), they were not described in further detail in that publication.

      Moreover, whether the newly identified call type is indeed novel is questionable, as also mentioned by the authors in their discussion section. While they wrote in the introduction that "high-pitch (>32 kHz), long and monotonous ultrasonic vocalizations have not yet been described", they wrote in the discussion that "long (or not that long (Biały et al., 2019)), frequency-stable high-pitch vocalizations have been reported before (e.g. Sales, 1979; Shimoju et al., 2020), notably as caused by intense cholinergic stimulation (Brudzynski and Bihari, 1990) or higher shock-dose fear conditioning (Wöhr et al., 2005)" (and I wish to add that to my knowledge this list provided by the authors is incomplete). Therefore, I believe, the strong claims made in abstract ("we are the first to describe a new-type..."), introduction ("have not yet been described"), and results ("new calls") are not justified.

      Answer: We would argue that 44-kHz vocalizations were indeed reported but not described. As far as we are concerned, an in-depth analysis of the properties and experimental circumstance of emission of long, high-frequency calls has not yet been performed. These researchers have observed, at least to a degree, similar calls to the ones we observed – as we mentioned in the discussion section. However, since these reported 44-kHz vocalizations were not fully described, we can only guess that they may be similar to ours. We speculate that perhaps like us, these researchers unknowingly recorded 44-kHz calls in their experiments and may also be able to describe them more extensively when re-analyzing their data as we have done here.

      Possibly, it was difficult to find reports on vocalizations, similar to the 44-kHz calls that we observed, because of the canonical and accepted definitions of ultrasonic vocalization types. Biały et al. (2019) allocated them as a part of 22-kHz group, perhaps because their calls were often of a step variation having both low and high components. Shimoju et al. (2020) grouped them along with 50-kHz vocalizations because they appeared during stroking rats held vertically; this procedure was compared to tickling which usually elicits appetitive calls.

      The Reviewer #2 states there are other publications to complete the list. We are aware of other articles authored by the same team as Shimoju et al. (2020) with different first authors. However, they are reporting similar findings to the cited article. Otherwise, we would gladly cite a more complete list of publications showing atypical, long, monotonous highfrequency vocalizations, similar to those observed in our experiments. Therefore, we would argue that ultrasonic vocalizations which were long, flat, high in frequency, and repeatedly occurring in a defined behavioral situation, have not been reported before. However, concerning the strong claims of novelty of our finding, we toned them down where we found this was warranted.

      In general, the manuscript is not well written/ not well organized, the description of the methods is insufficient, and it is often difficult (if not impossible) to link the reported data to the experiments/ experimental conditions described in the materials and methods section.

      Answer: The description of the methods has been adjusted and expanded. We added the requested link to each particular experiment as a formula “Tab. 1/Exp. nos./# nos.” which shows, each time, which experiments and experimental groups were analyzed. The list of the experiments and groups is found in the Tab. 1.

      For example, I miss a clear presentation of basic information: 1) How many rats emitted "44 kHz" calls (in total, per experiment, and importantly, also per experimental condition, i.e. single- versus group-housed)?

      Answer: We now clearly show which experiments were performed and how many animals were tested in each condition (Tab. 1), while the prevalence of 44-kHz calls amongst experimental conditions and animal groups is shown in Fig. 1S3. Also, we included information regarding the number of animals and treatment of each group of rats when reporting results. For example, we are stating that:

      (1a) “53 of all 84 conditioned Wistar rats (Tab. 1/Exp. 1-3/#2,4,6-8,13, Figs 1B, 1E, 1S1BC) displayed” 44-kHz vocalizations – as a general assessment; these numbers are different from those in the first version of the Ms, when we are mentioning Wistar rats conditioned 6 or 10 times only.

      (1b) “From this group of rats (n = 46), n = 41 (89.1%) emitted long 22-kHz calls, and 32 of them (69.6%) emitted 44-kHz calls” – this time referring only to 10-times conditioned Wistar rats as the biggest group that could be analyzed together (Figs 1F, 1G, 1S2A).

      (1c) “for n = 3 rats, 44-kHz vocalizations accounted for >95% of all calls during at least one ITI (e.g., 140 of total 142, 222 of 231, and 263 of 265 tallied 44-kHz calls), and in n = 9 rats, 44kHz vocalizations constituted >50% of calls in more than one ITI.”

      (2) Out of the ones emitting "44 kHz" calls, what was the prevalence of "44 kHz" calls (relative to 22- and 50-kHz calls, e.g. shown as percentage)?

      Answer: The prevalence of 44-kHz vocalizations in all investigated experiments and groups is shown in Fig. 1S3CD. Also, more information regarding the percentage of 44-kHz calls was demonstrated in Fig. 1S2AB where we calculated the distribution of 44-kHz calls to 22-kHz calls in Wistar rats, in 10-trial fear conditioning, across the length of the session.

      Additionally, the values are listed in the sentence regarding all Wistar rats which underwent 10 trials of fear conditioning: “these vocalizations were less frequent following the first trial (1.2 ± 0.4% of all calls), and increased in subsequent trials, particularly after the 5th (8.8 ± 2.8%), through the 9th (19.4 ± 5.5%, the highest value), and the 10th (15.5 ± 4.9%) trials, where 44-kHz calls gradually replaced 22-kHz vocalizations in some rats (Fig. 1F, 1S2B, Video 1; comp Fig. 1D vs. 1E).”

      (3) How did this ratio differ between experiments and experimental conditions?

      Answer: The prevalence of 44-kHz vocalizations in all experimental conditions is shown in Fig. 1S3. However, the direct comparison of results obtained in different conditions was not the goal of the present work. Also, we would argue, that such direct comparisons of results of different experiments would not be allowed. These experiments were done with different groups of animals, at different times, with different timetables of experimental manipulations.

      However, we are comfortable to state that:

      • There were more 44-kHz vocalizations during fear conditioning training than testing in all fear-conditioned Wistar rats;

      • We observed more 44-kHz vocalizations in Wistar rats compared to SHR.

      (4) Was there a link to freezing? Freezing was apparently analyzed before (Olszyński et al., 2021, 2023) and it would be important to see whether there is a correlation between "44-kHz" calls and freezing. Moreover, it would be important to know what behavior the rats are displaying while such "44-kHz" calls are emitted? (Note: Even not all 22-kHz calls are synced to freezing.) All this could help to substantiate the currently highly speculative claims made in the discussion section ("frequency increases with an increase in arousal" and "it could be argued that our prolonged fear conditioning increased the arousal of the rats with no change in the valence of the aversive stimuli"). Such more detailed analyses are also important to rule out the possibility that the "new-type" ultrasonic vocalization, the so-called "44 kHz" call, is simply associated with movement/ thorax compression.

      Answer: We analyzed freezing behavior and its association with ultrasonic emissions. The emission of 44-kHz vocalizations was associated with freezing. The results are now described and presented in the manuscript, i.e., Tab. 2, its legend and the description in Results: “Freezing during the bins of 22-kHz calls only (p < 0.0001, for both groups) and during 44-kHz calls only bins (p = 0.0003) was higher than during the first 5 min baseline freezing levels of the session. Also, the freezing associated with emissions of 44-kHz calls only was higher than during bins with no ultrasonic vocalizations (p = 0.0353), and it was also 9.9 percentage points higher than during time bins with only long 22-kHz vocalizations, but the difference was not significant (p = 0.1907; all Wilcoxon)” and “To further investigate this potential difference, we measured freezing during the emission of randomly selected single 44-kHz and 22-kHz vocalizations. The minimal freezing behavior detection window was reduced to compensate for the higher resolution of the measurements (3, 5, 10, or 15 video frames were used). There was no difference in freezing during the emission of 44-kHz vs. 22-kHz vocalizations for ≥150ms-long calls (3 frames, p = 0.2054) and for ≥500-ms-long calls (5 frames, p = 0.2404; 10 frames, p = 0.4498; 15 frames, p = 0.7776; all Wilcoxon, Tab. 2B).”

      Please note, that the general observation that "frequency increases with an increase in arousal" is not our claim but a general rule derived from large body of observations and proposed by the others (Briefer et al., 2012); we changed the wording of this statement to: “frequency usually increases with an increase in arousal (Briefer et al., 2012)”.

      The figures currently included are purely descriptive in most cases - and many of them are just examples of individual rats (e.g. majority of Fig. 1, all of Fig. 2 to my understanding, with the exception of the time course, which in case of D is only a subset of rats ("only rats that emitted 44-kHz calls in at least seven ITI are plotted" - is there any rationale for this criterion?)), or, in fact, just representative spectrograms of calls (all of Fig. 3, with the exception of G, all of Fig. 4).

      Answer: Please note, the former figures 2, 4, 6, and 8 have been now moved to supplementary figures 1S1, 2S1, 3S1, and 4S1 – to better organize the presentation of data. Figures 1, 3, 5, 7 are now 1, 2, 3, 4 respectively. In regards to presenting data from individual rats, this was to show the general patterns of ultrasonic-calls distributions observed. Showing the full data set as seen in Fig. 5A (now Fig. 3A) would obscure the readability of the graph without using mathematical clustering techniques such as DBSCAN.

      Concerning the Reviewer’s #2 question regarding the criterion of “minimum seven ITI”, we selected the highest vocalizers by taking animals above the 75th percentile of the number of ITI with 44-kHz calls. However, in the current version of the manuscript, we decided to omit this part of the analysis and the accompanying part of the figure, since it did not provide any additional informative value (apart from employing questionable criterion).

      Moreover, the differences between Fig. 5 and Fig. 6 are not clear to me. It seems Fig. 5B is included three times - what is the benefit of including the same figure three times?

      Answer: We hope that designating Fig. 6 as supplementary to Fig. 5 (now Figs 3S1 and 3, respectively) will make interpreting them more streamlined. Fig. 6A (now Fig. 3S1A) is a more detailed look on information presented in Fig. 5B (now Fig. 3B) with spectrogram images of ultrasonic vocalizations from different areas of the plot. Also, Fig. 3B (former Fig. 5B) was removed from Fig. 3S1B (former Fig. 6B).

      A systematic comparison of experimental conditions is limited to Fig. 7 and Fig. 8, the figures depicting the playback results (which led to the conclusion that "the responses to 44-kHz aversive calls presented from the speaker were either similar to 22-kHz vocalizations or in between responses to 22-kHz and 50-kHz playbacks", although it remains unclear to me why differences were seen b e f o r e the experimental manipulation, i.e. the different playback types in Fig. 8B).

      Answer: There were indeed instances of such before-differences. Such differences were observed in our previous studies (Olszyński et al., 2020, Tabs S9-12; Olszyński et al., 2021, Tabs S7; Olszyński et al., 2022, Tabs S4, S9, S13, S17, S18) and were most likely due to analyzing multiple comparisons. However, we think that the carry-over effect, mentioned by the Reviewer #2 (see below), also played a role.

      Related to that, I miss a clear presentation of relevant methodological aspects: 1) Why were some rats single-housed but not the others?

      Answer: As stated before, data were collected from our previous experiments and the observation of 44-kHz vocalizations in fear conditioning was an emergent discovery as we decided to analyze ultrasonic recordings from fear conditioning procedures. Single-housed animals were part of our experiment comparing fear conditioning and social situation on the perception of ultrasonic playback as described in Olszyński et al. (2020). Aside from this experiment, all other rats were housed in pairs.

      (2) Is the experimental design of the playback study not confounded? It is said that "one group (n = 13) heard 50-kHz appetitive vocalization playback while the other (n = 16) 22-kHz and 44kHz aversive calls". How can one compare "44 kHz" calls to 22- and 50-kHz calls when "44 kHz" calls are presented together with 22-kHz calls but not 50-kHz calls? What about carry-over effects? Hearing one type of call most likely affects the response to the other type of call. It appears likely that rats are a bit more anxious after hearing aversive 22-kHz calls, for example. Therefore, it would not be very surprising to see that the response to "44 kHz" calls is more similar to 22-kHz calls than 50-kHz calls.

      Of note, in case of the other playback experiment it is just said that rats "received appetitive and aversive ultrasonic vocalization playback" but it remains unclear whether "44 kHz" calls are seen as appetitive or aversive. Later it says that "rats were presented with two 10-s-long playback sets of either 22-kHz or 44-kHz calls, followed by one 50-kHz modulated call 10-s set and another two playback sets of either 44-kHz or 22-kHz calls not previously heard" (and wonder what data set was included in the figures and how - pooled?). Again, I am worried about carry-over effects here. This does not seem to be an experimental design that allows to compare the response to the three main call types in an unbiased manner.

      Answer: We apologize for being confounding and brief in our original description of the playback experiments. We wanted to avoid confusion associated with including several additional playback signals (please note some are not related to the current comparisons and include different 50-kHz ultrasonic subtypes and two different subtypes of short 22-kHz calls). We lengthened the description of these playback experiments in the current version.

      In general, including more than one type of ultrasonic calls as playback has a risk of a carry-over effect as well as a habituation effect (the responses become weak). However, it greatly reduces the number of required animals. Finally, regarding the first experiment, we chose 3 playbacks to compare the rats’ reactions, as this was the most conservative choice we thought of.

      We would like to highlight that we wanted to compare specifically the rats’ responses to 22-kHz vs. 44-kHz playback (as well as the effects of playback of different subtypes 50-kHz calls, which is not the subject of the current work). Therefore, we would argue, that the design of both experiments is actually unbiased regarding this key comparison (responses to 22-kHz vs. 44-kHz playback). In both experiments, 22-kHz and 44-kHz playbacks were included in the same sequences of stimuli and counterbalanced regarding their order (i.e., taking into account possible carry-over effects), and presented to the same rats. We regarded the group of rats that heard 50-kHz recordings as a baseline/control, since we know from previous playback studies what reactions to expect from rats exposed to these vocalizations (and 22-kHz playback), while in the second experiment, we reduced the 50-kHz playback to one set in order to minimize possible habituation to multiple playbacks.

      We agree that the design of both experiments does not allow for full comparison of the effects of aversive playbacks to 50-kHz playback. Also, we agree that some carry-over effects could play a role. It was mentioned in the discussion: ”Please factor in potential carryover effects (resulting from hearing playbacks of the same valence in a row) in the differences between responses to 50-kHz vs. 22/44-kHz playbacks, especially, those observed before the signal (Fig. 4AB).” However, we would still argue that the observed lack of difference in heartrate response (Fig. 4A) and the differences regarding the number of 50-kHz calls emitted (e.g., Fig. 4S1F) are void of the constraints raised by the Reviewer #2.

      We acknowledge that our studies do not give a complete picture of 44-kHz ultrasonic perception in relation to other ultrasonic bands and, given the possibility, we would like to perform more in-depth and focused experiments to study this aspect of 44-kHz calls in the future.

      Finally, regarding the second experiment, the description of the rats now includes that they “received 22-kHz, 44-kHz, and 50-kHz ultrasonic vocalization playback”, while the description of the experiment itself includes: “Responses to the pairs of playback sets were averaged”.

      Of note, what exactly is meant by "control rats" in the context of fear conditioning is also not clear to me. One can think of many different controls in a fear conditioning experiment.

      More concrete information is needed.

      Answer: This information was included in our previous publications. However, it was now provided in the method section of the current version of the manuscript. In general, control rats were subjected to the same procedures but did not receive electric shocks.

      Literature included in the answers

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      Olszyński, K. H., Polowy, R., Wardak, A. D., Grymanowska, A. W., & Filipkowski, R. K. (2021). Increased Vocalization of Rats in Response to Ultrasonic Playback as a Sign of Hypervigilance Following Fear Conditioning. Brain Sci, 11(8). https://doi.org/10.3390/brainsci11080970

      Olszyński, K. H., Polowy, R., Wardak, A. D., Grymanowska, A. W., Zieliński, J., & Filipkowski, R. K. (2022). Spontaneously hypertensive rats manifest deficits in emotional response to 22-kHz and 50-kHz ultrasonic playback. Prog Neuropsychopharmacol Biol Psychiatry, 120, 110615. https://doi.org/10.1016/j.pnpbp.2022.110615

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      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      Additional considerations:

      The discussion of the "perfect fifth" and the proposition that this observation could be evidence of an evolutionary mechanism underlying it is rather far-fetched, especially for being presented in the Results section (with no supporting non-anecdotal evidence).

      Answer: We agree with the Reviewer #1. The text was modified, the word “evolutionary” was deleted. Instead, we expended on the possible reason for prevalence of the perfect fifth in the current version of the manuscript; we added that the prevalence of the perfect fifth: “could be explained by the observation that all physical objects capable of producing tonal sounds generate harmonic vibrations, the most prominent being the octave, perfect fifth, and major third (Christensen, 1993, discussed in Bowling and Purves, 2015).”

      It is not clear why Sprague-Dawleys were used as "receivers" in the playback experiment, when presumably the calls were recorded from Wistars and SHRs. While this does not critically impact the conclusions, within the species rats should be able to respond appropriately to calls made by rats of different genetic backgrounds, it adds an unnecessary source of variance.

      Answer: Sprague-Dawley rats were used to test another normotensive strain of rats. Regarding the Reviewer’s main point – we beg to differ as we think that it is worth testing playback stimuli in different strains. Diverging the stimuli between different rat strains would add unnecessary variance and it seemed logical to use the same recordings to test effects in different strains. Please note that finally, in spite of this additional variance, the results of both playback experiments are, in general, similar – which may point to a universal effect of 44-kHz playback across rat strains.

      It is pertinent to note that for the trace fear conditioning experiment, the rats had previously been exposed to a vocalization playback experiment. While such a pre-exposure is unlikely to be a very strong stressor, the possibility for it to influence the vocal behaviors of these rats in later experiments cannot be ruled out. It is also not clear what the control rats in this experiment experienced (home cage only?), nor what they were used for in analyses.

      Answer: In the current version of the manuscript, we have described in greater detail all the experiments performed and analyzed. We would like to emphasize that both delay and trace fear conditioning experiments with radiotelemetric transmitters were not performed specifically to elicit any particular response during fear conditioning, rather that our observation of 44-kHz vocalizations emerged as a result of re-examining the audio recordings. As a result, this work summarizes our observations of 44-kHz calls from several different experiments. It is relevant to note, that 44-kHz vocalizations were observed “in rats which were exposed to vocalization playback experiment”, in rats before the playback experiments as well as in naïve rats, without transmitters implemented, trained in fear conditioning (Tab. 1/Exp. 1-3).

      Our main message is that 44-kHz vocalizations were present in several experiments, with different conditions and subjects, while we are not attempting to compare in detail the results across the different experiments. In other words, we agree that pre-exposure to playback (and even more likely – transmitters implantation) could influence, but are not necessary, for 44-kHz ultrasonic emissions by the rats. To demonstrate this, we added a prolonged fear conditioning group with naïve Wistar rats (Exp. 3) to verify the emission of 44kHz calls in the absence of those experimental factors.

      We modified the methods section to clarify the circumstances under which these discoveries were made, such as including the information regarding the control rats in trace fear conditioning. In particular we mention that: “Control rats were subjected to the exact same procedures but did not receive the electric shock at the end of trace periods”.

      For Figure 1A-E, only example call distributions from individual rats are shown. It would perhaps be more informative to see the full data set displayed in this manner, with color/shape codes distinguishing individuals if desired.

      Answer: Please note the Fig. 1S1 shows more examples of ultrasonic call distribution. Showing all the data would make it more difficult to read and interpret. The problem is partly amended in Fig. 3A.

      It is not clear what is presented in Figure 2D vs. E, i.e. panel D is shown only for "selected rats" but the legend does not clarify how and why these rats were selected. It is also not clear why the legend reports p-values for both Friedman and Wilcoxon tests; the latter is appropriate for paired data which seems to be the case when the question is whether the call peak frequency alters across time, but the Friedman assumes non-paired input data.

      Answer: The question refers to the current Fig. 1S2C panel (former Fig. 2E panel) and the former Fig. 2D panel. The latter was not included in the current version of the manuscript, since both reviewers opposed the presentation of “selected rats” only (see above). The full description of the Fig. 1S2C panel is now in the results section together with p-values for Friedman and Wilcoxon test. We used the latter to investigate the difference between the first and the last ITI (selected paired data), while the Friedman to investigate the presence of change within the chain of ten ITI – since it is a suitable test for a difference between two or more paired samples.

      Reviewer #2 (Recommendations For The Authors):

      The weaknesses listed in the public review need to be addressed.

      Answer: We have done our best to address the weaknesses.

      Notes: 1) Page and line numbers would have been useful.

      Answer: We are including a separate manuscript version with page and line numbers.

      .(2) English language needs to be improved.

      Answer: The text has been checked by two native English speakers (one with a scientific background). Both only identified minor changes to improve the text which we applied.

      (3) I am a bit unsure whether the comment about the Star Wars movie (1997) and the Game of Thrones series (2011) is supposed to be a joke.

      Answer: These are indeed two genuine examples of the perfect fifth in human music that we hope are easily recognizable and familiar to readers. Parts of the same examples of the perfect fifth can also heard in the rat voice files provided.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      During the last decades, extensive studies (mostly neglected by the authors), using in vitro and in vivo models, have elucidated the five-step mechanism of intoxication of botulinum neurotoxins (BoNTs). The binding domain (H chain) of all serotypes of BoNTs binds polysialogangliosides and the luminal domain of a synaptic vesicle protein (which varies among serotypes). When bound to the synaptic membrane of neurons, BoNTs are rapidly internalized by synaptic vesicles (SVs) via endocytosis. Subsequently, the catalytic domain (L chain) translocates, a process triggered by the acidification of these organelles. Following translocation, the disulfide bridge connecting the H chain with the L chain is reduced by the thioredoxin reductase/thioredoxin system, and it is refolded by the chaperone Hsp90 on SV's surface. Once released into the cytosol, the L chains of different serotypes cleave distinct peptide bonds of specific SNARE proteins, thereby disrupting neurotransmission. In this study, Yeo et al. extensively revise the neuronal intoxication model, suggesting that BoNT/A follows a more complex intracellular route than previously thought. The authors propose that upon internalization, BoNT/A-containing endosomes are retro-axonally trafficked to the soma. At the level of the neuronal soma, this serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. The ER SEC61 translocon complex facilitates the translocation of BoNT/A's LC from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic L chain. Subsequently, the L chain diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses. Strengths:

      I appreciate the authors' efforts to confirm that the newly established methods somehow recapitulate aspects of the BoNTs mechanism of action, such as toxin binding and uptake occurring at the level of active synapses. Furthermore, even though I consider the SNAPR approach inadequate, the genome-wide RNAi screen has been well executed and thoroughly analyzed. It includes well-established positive and negative controls, making it a comprehensive resource not only for scientists working in the field of botulinum neurotoxins but also for cell biologists studying endocytosis more broadly. Weaknesses:

      I have several concerns about the authors' main conclusions, primarily due to the lack of essential controls and validation for the newly developed methods used to assess toxin cleavage and trafficking into neurons. Furthermore, there is a significant discrepancy between the proposed intoxication model and existing studies conducted in more physiological settings. In my opinion, the authors have omitted over 20 years of work done in several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc.). I want to emphasize that I support changes in biological dogma only when these changes are supported by compelling experimental evidence, which I could not find in the present manuscript.

      We thank the reviewer for his reading and comments and for pointing out the discrepancy between our proposed model and the existing model. However, we respectfully disagree with the phrase of “extensive studies have elucidated the five-steps mechanism of intoxication…”. This sentence and the following imply that the model is well-established and demonstrated. It also highlights how the reviewer is convinced about this previous model.

      We contest this model for theoretical reasons and contest the strength of evidences that support it. We previously included references to previous work showing that the model is also being challenged by others. In light of the reviewer’s comments, we incluced more references in the introduction and we also explicit our main theoretical concern in the introduction:

      “Arguably, the main problem of the model is its failure to propose a thermodynamically consistent explanation for the directional translocation of a polypeptidic chain across a biologial membrane. Other known instances of polypeptide membrane translocation such as the co-translational translocation into the ER indicate that it is an unfavorable process, which consumes significant energy (Alder and Theg 2003). ”

      We also added the following text in the Discussion to address with the reviewer’s concerns: “Our study contradicts the long-established model of BoNT intoxication, which is described in several reviews specifically dedicated to the subject 1–4. In short, these reviews support the notion that BoNT are molecular machines able to mediate their own translocation across membranes; this notion has convinced some cell biologists interested in toxins and retrograde traffic, who describe BoNT mode of translocation in their reviews 5,6.

      But is this notion well supported by data? A careful examination of the primary literature reveals that early studies indeed report that BonTs form ion channels at low pH values 7,8. These studies have been extended by the use of patch-clamp 9,10. These works and others lead to various suppositions on how the toxin forms a channel and translocate the LC 1,11 .

      However, only a single study claims to reconstitute in vitro the translocation of BonT LC across membranes 12. In this paper, the authors report using a system of artificial membranes separating two aqueous compartments. They load the toxin in the cis compartment and measure the protease activity in the trans compartment after incubation. However, when the experimental conditions described are actually converted in terms of molarity, it appears that the cis compartment was loaded at 10e-8M BonT and that the reported translocated protease activity is equivalent to 10e-17 M (Figure 3D, 12). Thus, in this experiment, about 1 LC molecule in 100 millions has crossed the membrane. Such extremely low transfert rate does not tally with the extreme efficiency of intoxication in vivo, even while taking into account the difference between artificial and biological membranes.

      In sum, a careful analysis of the primary literature indicate that while there is ample evidence that BoNTs have the ability to affect membranes and possibly create ion channels, there is actually no credible evidence that these channels mediate translocation of the LC. As mentioned earlier, it is not clear how such a self-translocation mechanism would function thermodynamically. By contrast, our model proposes a mechanism without a thermodynamic problem, is consistent with current knowledge about other protein toxins, such as PE, Shiga and Ricin, and can help explain previously puzzling features of BonT effects. It is worth noting that a similar self-translocation model was proposed for other protein toxins such as Pseudomonas exotoxin, which have similar molecular organisation as BonT (68). However, it has since been demonstrated that the PE toxins require cellular machinery, in particular in the ER, for intoxication (21,69,70).”

      Reviewer #2 (Public Review):

      Summary:

      The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after the release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

      In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, does the LC traffic back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

      To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

      SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

      In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 hours after intoxication and progresses along the axon towards the nerve terminus during an additional 24 hours. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 hours. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

      In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication.

      Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists of those of the retromer, and the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to the Golgi apparatus, and then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

      Strengths:

      The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.

      These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation, and appearance of central effects.

      Weaknesses:

      The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), whose mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

      Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

      We thank the reviewer for the careful reading and comments of our manuscript. The reference to “a vast amount of observation” is a similar argument to the Reviewer 1 and used to suggest that our study may not be applicable as a general mechanism.

      We respectfully disagree as described above and posit on the contrary that the model we propose is much more likely to be general than the model presented in current reviews for the several reasons cited (see added text in Introduction and Discussion). While we agree that more work is needed to confirm the proposed mechanisms of BonT translocation in other models, these experiments fall outside the perimeter of our study.

      The fact that nerve/muscle preparations of BonT activity have relatively fast kinetics does not pose a contradiction to our model. Our model reveals primarily the requirement for trafficking to the ER membranes. This ER targeting requires trafficking through the Golgi complex, in turn explaining the requirement for trafficking to the soma of neurons in the experimental system we used. However, in neuronal cells in vivo, Golgi bodies can be found along the lenght of the axon, thus BonT may not always require trafficking to the soma of the affected cells. The time required for intoxication could thus vary greatly depending on the neuronal structural organisation.

      TenT is proposed to transfer from excitatory neurons into inhibitory neurons before exerting its action. While the detailed mechanism of this fascinating mechanism remain to be explored, it clearly falls beyond the purview of this manuscript.

      Regarding the use of drugs, we agree that it would be a nice addition; unfortunately we are unable to perform such experiments at this stage. Setting up a large scale siRNA screen for BonT mechanism of action is challenging as it requires a special facility with controlled access and police authorisation (in Singapore) given the high toxicity of this molecule. Unfortunately, the authorisations have now lapsed.

      Reviewer #3 (Public Review): Summary:

      The manuscript by Yao et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identified over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

      Strengths:

      The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

      Weaknesses:

      To my opinion, the main weakness of the paper is in the interpretation of the data equating loss of tGFP signal (when using the Red SNAPR assay) with proteolytic cleavage by the toxin. Indeed, the first step for loss of tGFP signal by degradation of the cleaved part is the actual cleavage. However, this needs to be degraded (by the proteasome, I presume), a process that could in principle be affected (in speed or extent) by the toxin.

      We thank the reviewer for his comments and careful reading of our manuscript.

      Regarding the read-out of the assay, we agree that the assay could be sensitive to alteration in the protein degradation pathway. We have added the following sentence in the Discussion to take it into account:

      “As noted by one reviewer, the assay may be sensitive to perturbation in the general rate of protein degradation, a consideration to keep in mind when evaluating the results of large scale screens.”

      While this may be valid for some hits in the general list, it is important to note that the main hits have been shown to affect toxin trafficking by an independent, orthogonal assay based on the split GFP reconstitution.

      Recommendations to authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) To assess the activity of BoNT/A in neurons, Yeo et al. have generated a neuronal stem line referred to as SNAPR. This cell line stably expresses a chimeric reporter protein that consists of SNAP25 flanked at its N-terminus with a tagRFPT and at its C-terminus with a tagGFP. After exposure to BoNT/A, SNAP25 is cleaved and, the C-terminal tGFP-containing moiety is rapidly degraded. I have many doubts about the validity of the described method. Indeed, BoNT/A activity is analysed in an indirect way by quantifying the degradation of the GFP moiety generated after toxin cleavage (Fig. 2). In this regard, the authors should consider that their approach is dependent, not only on the toxin's metalloprotease activity but also on the functionality of the proteasome in neurons. Therefore, considering the current dataset, it is impossible to rule out the possibility that the progression of GFP signal loss from the soma to the neurite terminals may be attributed to the different proteasome activity in these compartments. Is it conceivable that the GFP fragment generated upon toxin cleavage degrades more rapidly in the soma in comparison to axonal terminals? This alternative explanation could challenge the conclusion drawn in Fig. 2.

      The reviewer’s alternative explanation disregards the experiments performed with the split-GFP complementation approach, which indicate translocation in the soma first. The split GFP reporter is not dependent on the proteasome activity. It also disregard the genetic data implicating many genes involved in membrane retrograde traffic, which are also not consistent with the hypothesis of the reviewer. These genes depletions not only affect SNAPR degradation but also BoNT/A-mNG11 trafficking: thus, their effect cannot be attributed to an completely hypothetical spatial heterogeneous distribution of the proteasome.

      For this reason, I strongly suggest using a more physiological approach that does not depend on proteasomal degradation or on the expression of the sensor in neurons. The authors should consider performing a time course experiment following intoxication and staining BoNT/A-cleaved SNAP25 by using specific antibodies (see Antonucci F. et al., Journal of Neuroscience, 2008 or Rheaume C. et al., Toxins 2015).

      For the above reason, we do not agree with the pressing importance of confirming by a third method using specific antibodies; especially considering that BonT is very difficult to detect in cells when incubated at physiological levels. By the way, the cited paper, by Antonucci F; et al. documents long distance retrograde traffic of BonT/A, which is in line with our data.

      An alternative approach could involve the use of microfluidic devices that physically separate axons from cell bodies. Such a separation will allow us to test the authors' primary conclusion that SNAP25 is initially cleaved in the soma. The suggested experiments will also rule out potential overexpression artifacts that could influence the authors' conclusions when using the newly developed SNAPR approach. Without these additional experiments, the authors' main conclusion that SNAP25 is cleaved first in the neuronal soma rather than at the nerve terminal is inadequate.

      As discussed above we disagree about the doubts raised by the reviewer: we present three types of evidences (SNAPR, split GFP and genetic hits) and they all point in the same direction. Thus, we respectfully doubt that a fourth approach would convince this reviewer. To note, we have attempted to use microfluidics devices as suggested by the reviewer, however, the Ren-VM neurons were not able to extend axons long enough across the device.

      (2) To detect BoNT/A translocation into the cytosol, the authors have used a complementation assay by intoxicating ReNcell VM cell expressing a cytosolic HA-tagged split monomeric NeonGreen (Cyt-mNG1-10) with an engineered BoNT/A, where the catalytic domain (LC) was fused to mNG1-11. When drawing conclusions regarding the detection of cytosolic LC in the neuronal soma, the authors should highlight the limitations of this assay and explicitly describe them to the readers. Firstly, the authors need to investigate whether the addition of mNG1-11 to the LC affects the translocation process itself (by comparing with a WT, not tagged, LC).

      Additionally, from the data shown in Fig. 2C, it is evident that the Cyt-mNG1-10 is predominantly expressed in the cytosol and less detected in neurites. This raises the question of whether there might be a bias for the cell soma in this assay. To address this important concern, I suggest quantifying MFI per cell (Fig. 2D) taking into consideration the amount of HA-tagged Cyt-mNG1-10. Furthermore, I strongly suggest targeting mNG1-10 to synapses and performing a similar time course experiment to observe when LC translocation occurs at nerve terminals. Alternative experiments, to prove that BoNT/A requires retrograde trafficking before it can translocate, may be done to repeat the experiments shown in Fig. 2D in the presence of inhibitors (or by KD some of the hits identified as microtubule stabilizers) that should interfere with BoNT/A trafficking to the neuronal somata. Without these additional experiments, the authors' main conclusion that the BoNT/A catalytic domain is first detected in the neuronal soma rather than at the nerve terminal is very preliminary.

      Similarly as for the SNAPR assay, the reviewer is raising the level of doubt to very high levels. We respect his thoroughness and eagerness to question the new model. However, we note that a similar level of scrutiny does not apply to the prevalent competitive model. Indeed, the data supporting the self-translocation model is based on a single in vitro experiment published in one panel as we have explain din the discussion (see above).

      (3) In the genome-wide RNAi screening, rather than solely assessing SV2 surface levels, it would have been beneficial to directly investigate BoNT/A binding to the neuronal membrane. For instance, this could have been achieved by using a GFP-tagged HC domain of BoNT/A. At present, the authors cannot exclude the possibility that among the 135 hits that did not affect SV2 levels, some might still inhibit BoNT/A binding to the neuronal surface. These concerns, already exemplified by B4CALT4 (which is known to be involved in the synthesis of GT1b), should be explicitly addressed in the main text.

      We agree with the reviewer that perturbation of binding of BonT is possible. We added the following text:

      “Network analysis reveals regulators of signaling, membrane trafficking and thioreductase redox state involved in BoNT/A intoxication

      Among the positive regulators of the screen, 135 hits did not influence significantly surface SV2 levels and are thus likely to function in post-endocytic processes (Supplementary Table 2). However, we cannot formerly exclude that they could affect binding of BonT to the cell surface independently of SV2.”

      (4) The authors should clearly state which reagents they have tried to use in order to explain the challenges they faced when directly testing the trafficking of BoNT/A. The accumulation of Dendra-SV2 bulbous structures at the neurite tips in VPS35-depleted cells could be interpreted as a sign of neuronal stress/death. Have the authors investigated other proteins that do not undergo retro-axonal trafficking in a retromer-dependent manner? This control is essential. In this regard, the use of a GFP-tagged HC domain of BoNT/A could prove to be quite helpful.

      We tried multiple commercially available antibodies against BonT but we could not get a very good signal. The postdoc in charge of this project has now gone to greener pastures and we are not in the capacity to provide the details corresponding to these antibodies. We di dnot observe significant cell death after VPS-35 knockdown at the time of the experiment, however longe rterm treatment might result in toxicity indeed.

      (5) Considering my concerns related to the SNAPR system and the complementation assay to study SNAP25 cleavage and BoNT/A trafficking, I suggest validating some of their major hits (ex. VPS34 and Sec61) by performing WB or IF analysis to examine the cleavage of endogenous SNAP25. Furthermore, the authors should test VPS35 depletion in the context of the experiments performed in Fig. 6G-H, by validating that this protein is essential for BoNT/A retrograde trafficking.

      The reviewer concerns are well noted but as discussed above, the two systems we used are completely orthogonal. Thus, for the reviewer’s concerns to be valid, it would have to be two completely independent artefacts giving rise to the same result. The alternative explanation is that BonT/A translocates in the soma. The Ockham razor principle dictates that the simplest explanation is the likeliest.

      (6) The introduction and the discussion section of this paper completely disregard more than 20 years of research conducted by several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc). The authors should make an effort to contextualize their data within the framework of these studies and address the significant discrepancies between their proposed intoxication model and existing research that clearly demonstrates BoNTs translocating upon the endocytic retrieval of SVs at presynaptic sites. Nevertheless, even assuming that the model proposed by the authors is accurate, numerous questions emerge. One such question is: How can the authors explain the exceptional toxicity of botulinum neurotoxin in an ex vivo neuromuscular junction preparation devoid of neuronal cell bodies (see Cesare Montecucco and Andreas Rummel's seminal studies)?

      Please see above in the answer to public reviews.

      (7) Scale bars should be added to all representative pictures.

      This has been done. Thank you for the thorough reading of our manuscript.

      Reviewer #2(Recommendations For The Authors):*

      (1) The title overstates the results. It may be indicated "in differenciated ReNcell VM".

      Title changed to: “Botulinum toxin intoxication requires retrograde transport and membrane translocation at the ER in RenVM neurons”

      (2) In the provided manuscript there are two Figure 2 and no Figure 3. This made the reading and understanding extremely difficult and should be corrected. As a result, the Figure legends do not fit the numbering. There are also discrepancies between some Figure panels (A, B, C, etc), the text, and the Legends. All this needs to be carefully checked.

      We apologize for the confusion as the manuscript as followed multiple rounds of revisions. We have carefully verified labels and legends.

      (3) The BoNT/A-mNG11x3 may introduce some bias that could be discussed. Would these additional peptides block LC translocation from synaptic vesicles in the nerve termini? In addition, the mNG peptides that are unfolded before complementation may direct LC towards Sec61. These aspects should be discussed.

      The comment would be valid if BoNT/A-mNG11x3 was the only approach used in the paper, however the SNAPR reporter is used with native BonT and shows data consistent with the split GFP approach.

      (4) In the Figure about SV2 (Fig 3 or 4): The authors did not locate SV2. The cells seem not to have the same differentiated phenotype as in Figure 1 and Figure 2/3A.

      We apologized above for the mislabeling. It is not clear what is the question here.

      (5) The authors should check whether BoNT/A wt cleaves the endogeneous SNAP25 by western blot for instance in the original ReNcell VN before SNAPR engineering. This should be compared with wt SNAP25 cleavage by the BoNT/A-LC-mNG.

      It is likely that BoNT/A-LC-mNG11 should have similar activity as it is only adding a small peptide at the end of the LC. At any rate, it is not clear why this is so important since both molecules translocate in the cytosol, with the same kinetics and in the same subcellular locale.

      (6) Perhaps I did not understand. How can the authors exclude that what is observed is the kinetic overproduction of the reporter substrate SNAPR?

      The authors could use SLO toxin (PNAS 98, 3185-3190, 2001) to permeabilize the cells all along their body and axon to introduce BoNT/A or LC (wt) and observe synchronized SNAPR cleavage throughout the cells.

      The concept mentioned here is not very clear to us. The reviewer is proposing that the SNAPR is produced much more efficiently at the tips of the neurites and thus its cleavage takes longer to be detected and is apparent first in the soma?? With all due respect, this is a strange hypothesis, at odds with what we know of protein dynamics in the neurons (i.e. most proteins are largely made in the soma and transported or diffuse into the neurites).

      Again, the two orthogonal approaches: split GFP and SNAPR reporter use different constructs and methods, yet converge on similar results. Perhaps, the incredulity of the reviewer might be more productively directed at the current data “demonstrating” the translocation of LC in the synaptic button?

      (7) The authors could also use an essay on neurotransmitter release monitoring by electrophysiology measurements to check the functional consequences of the kinetic diffusion of LC activity along the axon. Can the authors exclude that some toxin molecules translocate from the endocytic vesicles and block neurotransmission within minutes or a few hours?

      It is well established that inhibition of neurotransmission does not occur within minutes in vivo and in vitro, but rather within hours or even days. This kinetic delay is experienced by many patients and is one of the key argument against the current model of self-translocation at the synaptic vesicle level.

      Minor remarks

      Thank you for pointing out all these.

      (1) Please check typos. There are many. Check space before the parenthesis, between numbers and h (hours), reference style etc.

      Thank you. We have reviewed the text and try to eliminate all these instances.

      (2) Line 90: The C of HC should be capitalized.

      Fixed

      (3) Line 107: add space between "neurons(Donato".

      Fixed

      (4) Line 109: space "72 h".

      Fixed

      (5) Line 115: a word is missing ? ...to show retro-axonal... ? Please clarify this sentence.

      Fixed

      (6) Figure 1E: does nm refer to nM (nanomolar)? Please correct. No mention of panel F.

      Fixed

      (7) Line 161: do you mean ~16 µm/h? Please correct.

      Fixed

      (8) Line 168, words are missing.

      Fixed, thank you

      We verified that Cyt-mNG1-10 was expressed using the HA tag, the expression was homogeneously distributed in differentiated neurons and we observed no GFP signal (Figure2C).

      (9) Line 171: Isn't mNG 11 the eleventh beta strand of the neon green fluorescent protein, not alpha helix? Otherwise, can the authors confirm it acquires the shape of an alpha helix? Same at line 326.

      We have corrected the mistake; thanks for pointing it out.

      (10) Figure 2 is doubled. The legend of Fig 2 refers to Figure 3. There is no legend for Figure 2. Then, some figures are shifted in their numbering.

      Fixed

      (11) The fluorescence in the cell body must appear before the fluorescence in the axon due to higher volume. Please discuss.

      The fluorescence progresses in the neurites extensions in a centripetal fashion. The volume of the neurite near the cell body is not significantly different from the end of the neurite. Thus the fluorescence data is consistent with translocation in soma and not with an effect due to higher volume in the soma.

      (12) Figure 2D, right: the term intoxication is improper for this experiment. Rather, it is the presence of the BoNT/A-mNG11 that is detected. I believe the authors should be particularly careful about the use of terms: intoxication means blockade of neurosecretion, SNAPR cleavage means activity etc.

      While the reviewer is correct that it is the presence of BoNT/A-mNG11 that is detected, it remains that it is an active toxin, so the neurons are effectively intoxicated; as they are when we use the wild type toxin. We do not imply that we are measuring intoxication, but simply that the neurons are put into contact with a toxin.

      (13) Line 196: Should we read TXNRD1 is required for BoNT/A LC translocation? TXNRD1 in the current model of translocation is located in the cytoplasm and is supposed to play a role in the cleavage of the disulfide bond linking LC to HC. In the model proposed by this study, LC is translocated through the Sec61 translocon. In this case, I would assume that the protein disulfide isomerase (PDI) in the endoplasmic reticulum would reduce the LC-HC disulfide bond. In that case, TXNRD1 would not be required anymore. Please discuss.

      Why should we assume that a PDI is involved in the reduction of the LC-HC disulfide bond? In our previous studies on A-B toxins (PE and Ricin), different reduction systems seemed to be at play. There is no conceptual imperative to assume reduction in the ER because the Sec61 translocon is implicated. Reduction might occur on the cytosolic side by TXNRD1 or the effect of this reductase could be indirect.

      (14) The legend of Figure 4 (in principle Figure 5?) is not matching with the panels and panel entries are missing (Figure 4F in particular).

      Fixed

      (15) Figure 6 panels E and H, please match colors with legend (grey and another color).

      Not clear

      (16) Please indicate BoNT/A construct concentrations in all Figure legends.

      Done

      (17) Line 416: isn't SV2 also involved in epilepsy?

      Yes it is.

      (18) Line 433: as above, shouldn't the disulfide bond linking LC to HC be cleaved by PDI in the ER in this model (as for other translocating bacterial toxins) rather than by thioredoxin reductases in the cytoplasm? Please discuss.

      See above

      (19) Identification of vATPase in the screen could be consistent with the endocytic vesicle acidification model of translocation.

      Yes

      (20) Did the authors add KCl in screening controls without toxins? This should be detailed in the Materials and Methods. Could there be a KCl effect on the cells? KCl exposure for 48 hours may be highly stressful for cells. The KCl exposure should last only several minutes for toxin entry.

      We did not observe significant cell detah with the cell culture conditions used. Cell viability was controlled at multiple stages using nuclei number for instance

      Reviewer #3 (Recommendations For The Authors):

      Main comments: (1) In Figure 1B: could you devise a means to prevent proteosomal degradation of the tGFP cleaved part to assess whether this is formed?

      We have also used a FRET assay after tintoxication and obtained similar results

      (2) Line 152: Where it reads "was not surprising", maybe I missed something, but to me, this is indeed surprising. If the toxin is rapidly internalized and translocated (therefore, it is able to cleave SNAP25), the fact that tGFP requires 48 hours to be degraded seems surprising to me. Or does it mean that the toxin also slows down the degradation of the tGFP fragment? So, how can you differentiate between the effect being on cleavage of the fragment or in tGFP degradation?

      The reviewer is correct, the “not” was a typo due to re-writting; the long delay between adding the toxin and observing cleavage was suprising indeed. Our interpretation is that it is trafficking that takes time, indeed, the split-GFP data kinetics indicates that the toxin takes about 48h to fill up the entire cytosol (Fig. 2D).

      (3) Regarding the effect of Sec61G knockdown, is it possible that the observed effects are indirect and not due to the translocon being directly responsible for translocating the protein?

      As discussed in the last part of the results,Sec61 knock-down results in block of intoxication, but does not prevent BonT from reaching the lumen of the ER (Figure 6G,H). Thus, Sec61 is “is instrumental to the translocation of BoNT/A LC into the neuronal cytosol at the soma.”

      Minor comments:

      (1) Fig. 3E: in the legend I think one of the NT3+ should be NT3-.

      Yes, thanks for spotting it

      (2) Would you consider adding Figure S4 as a main figure?

      Thanks for the suggestion

      (3) Please, check that all microscopy image panels have scale bars.

      Done

      (4) Figure 6B (bottom panes): why does it seem that there is a lot of mNeonGreen positive signal in regions that are not positive for HA? Shouldn't complementation keep HA in the complemented protein.

      Our assumption i sthat there is an excess of receptor protein (HA tag) over reconstituted protein (GFP protein) given the relatively low concentration of toxin being internalized and translocated Refs: (1) Pirazzini M, Azarnia Tehran D, Leka O, Zanetti G, Rossetto O, Montecucco C. On the translocation of botulinum and tetanus neurotoxins across the membrane of acidic intracellular compartments. Biochim Biophys Acta. 2016 Mar;1858(3):467–474. PMID: 26307528

      (2) Pirazzini M, Rossetto O, Eleopra R, Montecucco C. Botulinum Neurotoxins: Biology, Pharmacology, and Toxicology. Pharmacol Rev. 2017 Apr;69(2):200–235. PMCID: PMC5394922

      (3) Dong M, Masuyer G, Stenmark P. Botulinum and Tetanus Neurotoxins. Annu Rev Biochem. Annual Reviews; 2019 Jun 20;88(1):811–837.

      (4) Rossetto O, Pirazzini M, Fabris F, Montecucco C. Botulinum Neurotoxins: Mechanism of Action. Handb Exp Pharmacol. 2021;263:35–47. PMCID: 6671090

      (5) Williams JM, Tsai B. Intracellular trafficking of bacterial toxins. Curr Opin Cell Biol. 2016 Aug;41:51–56. PMCID: PMC4983527

      (6) Mesquita FS, van der Goot FG, Sergeeva OA. Mammalian membrane trafficking as seen through the lens of bacterial toxins. Cell Microbiol. 2020 Apr;22(4):e13167. PMCID: PMC7154709

      (7) Hoch DH, Romero-Mira M, Ehrlich BE, Finkelstein A, DasGupta BR, Simpson LL. Channels formed by botulinum, tetanus, and diphtheria toxins in planar lipid bilayers: relevance to translocation of proteins across membranes. Proc Natl Acad Sci U S A. 1985 Mar;82(6):1692–1696. PMCID: PMC397338

      (8) Donovan JJ, Middlebrook JL. Ion-conducting channels produced by botulinum toxin in planar lipid membranes. Biochemistry. 1986 May 20;25(10):2872–2876. PMID: 2424493

      (9) Fischer A, Montal M. Single molecule detection of intermediates during botulinum neurotoxin translocation across membranes. Proc Natl Acad Sci U S A. 2007 Jun 19;104(25):10447–10452. PMCID: PMC1965533

      (10) Fischer A, Nakai Y, Eubanks LM, Clancy CM, Tepp WH, Pellett S, Dickerson TJ, Johnson EA, Janda KD, Montal M. Bimodal modulation of the botulinum neurotoxin protein-conducting channel. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1330–1335. PMCID: PMC2635780

      (11) Fischer A, Montal M. Crucial role of the disulfide bridge between botulinum neurotoxin light and heavy chains in protease translocation across membranes. J Biol Chem. 2007Oct 5;282(40):29604–29611. PMID: 17666397

      (12) Koriazova LK, Montal M. Translocation of botulinum neurotoxin light chain protease through the heavy chain channel. Nature structural biology. 2003. p. 13–18. PMID: 12459720

      (13) Moreau D, Kumar P, Wang SC, Chaumet A, Chew SY, Chevalley H, Bard F.Genome-wide RNAi screens identify genes required for Ricin and PE intoxications. Dev Cell. 2011 Aug 16;21(2):231–244. PMID: 21782526

      (14) Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Chen S, Mann M, Hyman AA, Leproust EM, McManus MT, Weissman JS. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell. 2013 Feb 14;152(4):909–922. PMCID: PMC3652613

      (15) Tian S, Muneeruddin K, Choi MY, Tao L, Bhuiyan RH, Ohmi Y, Furukawa K, Furukawa K, Boland S, Shaffer SA, Adam RM, Dong M. Genome-wide CRISPR screens for Shiga toxins and ricin reveal Golgi proteins critical for glycosylation. PLoS Biol. 2018 Nov;16(11):e2006951. PMCID: PMC6258472

    1. Author response:

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

      Reviewer #1:

      (1) Importantly, it would be useful to have provided more detailed information on the structure and histological properties of the murine cysts and how such findings relate to human lung cysts. Also, the authors should examine whether there is any information on Bmpr1a in human cyst formation (i.e GWAS data).

      We fully agree that it is important to examine Bmpr1a in human cyst pathology. Unfortunately, there is no GWAS data on this. From the published RNA-seq data, which were obtained from postnatal lung specimen of congenital pulmonary airway malformation (CPAM) patients, “integrated suppression of BMP signaling pathway” was reported although altered expression of BMPR1A was not presented. We speculate that (1) BMPR1A is critical in embryonic development and a germline deficiency of BMPR1A may lead to early embryonic lethality prior to lung formation as supported by mouse data; (2) As suggested by our previously published study related to TGF-beta signaling and prenatal pulmonary cysts (Miao et al., Am J Physiol Lung Cell Mol Physiol 2021), dysregulation of BMPR1A-mediated signaling in a particular time window of fetal lung development may be sufficient to cause cyst formation, so that BMPR1A alteration may not be persistent to postnatal lung specimens.

      (2) Throughout the paper, there is a lack of quantification for the histological findings. Littermate controls should also be clearly defined genetically,

      We thank the reviewer for this suggestion and acknowledge the importance of quantitative measurement for the changes. We now add quantitative data on branching number and size of the airway tips to define the difference between wild-type and Bmpr1a CKO mouse lungs in Fig.1. “The littermate controls were the mice without any gene deletion due to lack of transgenes Tbx4-rtTA and/or TetO-Cre”, which is now added in Materials and Methods.

      (3) Figure 1 suppl: "Doxycycline" is misspelled.

      This has been corrected.

      (4) Figure1c Suppl: Hard to discern clear-cut expression of Bmpr1a protein in mesenchyme in WT. Comparable images with similar sizes of airways should be used.

      To provide a clearer comparison of Bmpr1a expression patterns between Bmpr1a CKO and control lungs, we enlarge the fluorescent stained lungs presented in Supplemental Figure 1C as suggested by the editor. Additionally, dotted lines have been added to delineate the airway boundaries from the surrounding mesenchyme to better visualize the Bmpr1a distribution in lung mesenchyme. Bmpr1a expression in fetal lung mesenchyme is easily detected at E15.5 when significant dilation of airways is presented in Bmpr1a CKO lung. It is rare to have comparable sizes of peripheral airways in the Bmpr1a CKO lung at this point.

      (5) Figure 2a: Expression of several genes studied and altered should be identified on scatter plot.

      As suggested by the reviewer, we now highlight the related genes, including Acta2, Myocd, Eln, Bmp4, Sox2, etc., in the scatter plot. In addition, we also highlight these critical genes in the heatmap (Fig. 2B and Fig. 7B).

      (6) Figure 2c: Authors should also consider staining for other smooth muscle markers.

      We now include a panel of Myh11 immunostaining in Figure 2E. Myh11 is another common marker for smooth muscle cells. Lack of Myh11 staining in Bmpr1a CKO lung airways further supports our conclusion that loss of mesenchymal Bmpr1a leads to defective airway smooth muscle growth.

      (7) Figure 3: ELN expression should be defined in a clear quantitative manner.

      We have presented RNA-seq data, Real-time PCR results, immunostaining, and western blot data for in vivo samples. Additionally, we have included in vitro experiment illustrating that Bmp4 induces Eln expression, suggesting that BMP signaling regulates Eln expression. We believe that these datasets collectively support our conclusion.

      (8) Figure 4: Additional information on p38 dependent signaling (Including in vivo studies) would potentially help to understand key molecular events and perhaps could help to address key mechanistic events, including their location and identity.

      We sincerely appreciate the insightful suggestion from the reviewer. While the study of p38-dependent signaling is definitely important to dissect the entire mechanisms, we are not going to include such experiments in this manuscript due to time constraints associated with in vivo studies.

      (9) Figure 6: Would be helpful to know whether Bmpr1a receptor is expressed in Myocd KO.

      Bmpr1a expression is not changed in Myocd KO lungs, which is now included as Figure 6C. Together with other data, this suggests that Myocd is a downstream target directly mediating Bmpr1a-regulated airway smooth muscle development.

      (10) Figure 7: Not clear how these findings, though interesting, relate to the body of studies and the pathogenesis of cyst formation. Other points: 1) The authors should re-examine/repeat co-staining in the KO mouse lung (right 2 images in the top group of 4) for Foxj1, Sox2, and CDH (right 2 images, Figure 7A). For one thing, the cadherin stain in the 2 KO images seems localized to the lumen. Secondly, the pattern of cadherin staining looks exactly the same in both KO images, suggesting an error and/or duplication 2) authors should place arrows on the heat map showing the location of SPC, Sox2, Sox9, and FoxJ1 bands 3) figure 7D graph needs numbers on y axis.

      Fig.7 provides an additional potential mechanism by which deficient Bmp signaling leads to abnormally increased Bmp ligand expression, which disrupts the formation of epithelial proximal-distal axis, and results in cystic defects. Further in vivo experiments are needed to test this, which is beyond the scope of this paper.

      The E-cadherin staining signal in the lumen is caused by the tissue section positioned at an interface between lumen and the apical membrane of the lining epithelial cells where the E-cadherin is localized.

      Triple immunostaining of E-Cadherin, Sox2, and FoxJ1 was performed for the same tissue section (upper two panels of Figure 7A) as these antibodies were derived from different species, but the images are presented in two different combinations for simplicity and clarity. For the lower two panels of Figure 7A, double immunostaining of Sox9/E-Cadherin and Spc/E-Cadherin were performed separately on different tissue sections due to both anti-Sox9 and anti-Spc antibodies were produced from rabbits.

      The genes listed in the heatmap are canonical and putative marker genes for differential lung epithelial cell lineages, such as Scgb1a1 for Clara cells and FoxJ1 for ciliated cells. Therefore, progenitor cell marker Sox2 and Sox9 were not included. In the updated heatmap, four widely acknowledged epithelial cell markers—Scgb1a1, FoxJ1, Sftpb, and Sftpc have been distinguished by utilizing a distinct font color (red) to enhance their visibility.

      Label for the y axis of Fig.7D is now added.

      Reviewer #2 (Public Review):

      (1) The authors may be aware that a recent paper (https://doi.org/10.1038/s41598-022-24858-3) reported on transcriptional changes seen in human CPAM. It would seem that some of the molecular changes seen in human CPAM move in the opposite direction of what is reported in mice lacking mesenchymal Bmrp1a. Perhaps the authors could comment on these differences in the discussion and whether they potentially explain the etiology of CPAM or branching morphogenesis in general.

      We thank the reviewer for referring this paper regarding human CPAM study. CPAM has a variety of histopathology. The type 1 CPAM is assumed to develop from more proximal bronchial/bronchiolar airways while type 2 CPAM is developed from relatively distal bronchiolar airways. In that publication, surgical resected lung specimens were collected from type 1 CPAM patients postnatally (0.5-1 year), in which the cysts were lined with ciliated pseudostratified columnar epithelial cells. Gene expression was compared between cystic lung tissues and adjacent non-cystic lung tissues. Interestingly, integrated suppression of BMP signaling pathway was shown by their data analysis. In our mouse model, the histopathology appears as human type 2 CPAM, such as back-to-back cysts lining with a simple layer of epithelial cells. Therefore, several factors could explain the differences between their published data and our study at the molecular level: (1) Different types of CPAM based on the histopathology; (2) Different sampling time points, developing cysts at fetal stage in mouse sample vs. developed cysts in postnatal huma samples; (3) Different comparison of diseased and normal tissues: separate normal lungs vs. cystic lungs in mice while in human cystic tissues vs. non-cystic tissues in the same lungs. We now include this reference in the Discussion.

      (2) Figure 4 shows that BMP4 increases SMADs, p38, and several muscle genes in mesenchymal cells. Figure 5 extends this finding with a clever strategy to label airway and vascular smooth muscle with different fluorescent molecules used to isolate different types of mesenchymal cells. It shows that non-vascular smooth muscle cells but not perivascular smooth muscles are responsive to BMP4 signaling as defined by increased expression of Myh11. Are there cell-restricted responses to the other genes shown in Figure 4? Given the lack of SMAD signaling and the increase seen in p38 signaling, would blocking p38 signaling influence the BMP responsiveness of these nonvascular smooth muscle cells?

      We thank the reviewer for this constructive comment. As we have addressed above, we will leave p38-mediated signaling and cyst formation to next step study due to time constraints associated with these studies.

      (3) Figure 6 shows that mesenchymal loss of Myocd causes a deficiency of airway smooth muscle cells, but this was not sufficient to create cysts. Did the authors ever check to see if it changed Sox2-Sox9 staining in the airway epithelium?

      There is no significant change in Sox2 expression in proximal airway epithelia of Myocd CKO lungs as detected by immunostaining. The result was not included in this manuscript.

      (4) Figure 7 shows that mesenchymal loss of Bmpr1a proximalizes the distal airway as defined by loss of Sox2 and FoxJ1 (a ciliated marker) and gain in (Sox9 and SP-C) staining. But Club cells expressing Scgb1a1 and Cyp2F2 are the predominant epithelial cells in the distal airway. The transcriptomics data in panel B shows expression of these genes is less in the mutant mice. Does this mean they fail to generate Club cells or there is just less expression per cell? In other words, what are the primary epithelial cells present in the airways of mice with loss of mesenchymal Bmpr1a?

      As shown in the heatmap of Fig.7b, the dysregulated gene expression in the Bmpr1a CKO extends beyond the featured epithelial cell markers, encompassing alterations in numerous putative marker genes. For example, several putative Club cell markers in addition to Scgb1a1 and Cyp2F2 were reduced in the Bmpr1a CKO lungs, suggesting a compromised differentiation of Club cells. Additionally, we observed upregulations of some molecular markers for distal progenitors and differentiated cells in the proximal region of airways, again suggesting a significant disruption in epithelial differentiation in the Bmpr1a CKO lungs. These abnormal cells can be further defined by a single cell transcriptomic approach in future.

      Recommendations for Authors:

      Reviewer #1 (Recommendations For The Authors):

      As discussed above, there may be an issue with the histological images and staining in 2 images in Figure 7A. The precise images, problems and suggestions to resolve the issue are in the Review.

      Please see our response to Reviewer 1 above.

      Reviewer #2 (Recommendations For The Authors):

      Minor Weaknesses:

      (1) Please enlarge the fluorescent stained lungs presented in Supplemental Figure 1C.

      We have revised this panel accordingly.

      (2) Figure 1D and E show that loss of Bmpr1a does not change proliferation or apoptosis on E15.5. Was that also seen through E18.5?

      We thank the reviewer for the thoughtful question about proliferation and apoptosis at later embryonic stages. Our focus here was to elucidate the mechanisms underlying abnormal branching morphogenesis and lung cyst initiation that occur prior to E15.5 in our model. Measuring the dynamic changes in cell proliferation and apoptosis at later timepoints will help to understand cyst progression, which will be our next focus.

      (3) BMP inhibitors used in Figure 4 show that BMP signaling regulates mesenchymal myogenesis independent of SMAD. But the experiments don't show how the inhibitors impact the control cells.

      We have examined the effects of the BMPR1 inhibitor LDN on the control cells. At the same dose (200 nM) and serum-free culture condition, LDN did not affect the basal level of BMP signaling (data not included) but blocked exogeneous BMP4-induced signaling elevation (Fig.4E).

      (4) Bmpr1a was deleted by administering doxycycline to pregnant dams prior to lung bud formation. It caused cystic disorders by disrupting proximal airspace. Could the authors speculate on why it does not impact tracheal and bronchiolar development? In other words, does the TBX4 promoter not target these cells? Do these cells not express Bmpr1a?

      The Tbx4 enhancer does target mesenchymal cells surrounding the trachea and bronchioles. Deletion of Bmpr1a in tracheal mesenchymal cells result in disruption of tracheal cartilage formation and smooth muscle differentiation. These phenotypes are evident in the gross view of lungs from E15.5 and later (Fig.1A). However, our manuscript is focusing on the phenotype of prenatal lung cysts, and we have chosen not to include complex data on tracheal development.

    1. Author response:

      We would like to thank the reviewers for their helpful comments. We note that both reviews are strongly supportive with comments including, “a biophysical tour de force” (rev #1), “the study is exemplary” (rev #2), and “represents a roadmap for future work” (rev #2). Below we respond to each reviewer comment.

      Reviewer #1

      This study provides a detailed and quantitative description of the allosteric mechanisms resulting in the paradoxical activation of BRAF kinase dimers by certain kinase inhibitors. The findings provide a much needed quantiative basis for this phenomenon and may lay the foundation for future drug development efforts aimed at the important cancer target BRAF. The study builds on very evidence obtained by multiple independent biophysical methods.

      Summary:

      The authors quantitatively describe the complex binding equilibria of BRAF and its inhibitors resulting in some cases in the paradoxical activation of BRAF dimer when bound to ATP competitive inhibitors. The authors use a biophysical tour de force involving FRET binding assays, NMR, kinase activity assays and DEER spectroscopy.

      We are gratified by the reviewer’s supportive summary.

      Strengths:

      The strengths of the study are the beautifully conducted assays that allow for a thorough characterization of the allostery in this complex system. Additionally, the use of F-NMR and DEER spectroscopy provide important insights into the details of the process. The resulting model for binding of inhibitors and dimerization (Fig.4) is very helpful.

      Weaknesses:

      This is a complex system and its communication is inherently challenging. It might be of interest to the broader readership to understand the implications of the model for drug development and therapy.

      We agree with the reviewer that this is a complicated system. With regard to inhibitor development, a key insight is that designing aC-in state inhibitors that avoid paradoxical activation may be non-trivial because these molecules not only induce dimers but also tend to bind the second dimer subunit more weakly than the first, due to allosteric asymmetry and/or inherently different affinities for each RAF isoform. We feel the full implications for future therapeutic development are an extensive topic that is beyond the scope of our work, which is focused on the properties of current inhibitors.

      Recommendations for the author:

      The experimental work, analysis and resulting model are excellent. I had some difficulty following the complex model in some instances and it may be useful to review the description of the model and see whether it can be made more palatable to the broader readership. I think it would be useful to discuss the model presented in reference 40 (Kholodenko) and to compare it to the presented model here.

      We regret any confusion with regards to the nature of the model. Our analysis was built upon the model developed by Boris Kholodenko as reported in his 2015 Cell Reports paper. This formed the theoretical framework that combined with our experimental data allowed us to parameterize this model to obtain experimental values for the equilibrium constants and allosteric coupling factors.

      Reviewer #2

      This manuscript combines elegant biophysical solution measurements to address paradoxical kinase activation by Type II BRAF inhibitors. The novel findings challenge prevailing models, through experiments that are rigorous and carefully controlled. The study is exemplary in the breadth of strategies it uses to address protein kinase dynamics and inhibitor allostery.

      Summary:

      This manuscript uses FRET, 19F-NMR and DEER/EPR solution measurements to examine the allosteric effects of a panel of BRAF inhibitors (BRAFi). These include first-generation aC-out BRAFi, and more recent Type I and Type II aC-in inhibitors. Intermolecular FRET measurements quantify Kd for BRAF dimerization and inhibitor binding to the first and second subunits. Distinct patterns are found between aC-in BRAFi, where Type I BRAFi bind equally well to the first and second subunits within dimeric BRAF. In contrast, Type II BRAFi show stronger affinity for the first subunit and weaker affinity for the second subunit, an effect named "allosteric asymmetry". Allosteric asymmetry has the potential for Type II inhibitors to promote dimerization while favoring occupancy of only one subunit (BBD form), leading to enrichment of an active dimer.

      Measurements of in vitro BRAF kinase activity correlate amazingly well with the calculated amounts of the half site-inhibited BBD forms with Type II inhibitors. This suggests that the allosteric asymmetry mechanism explains paradoxical activation by this class of inhibitors. DEER/EPR measurements further examine the positioning of helix aC. They show systematic outward movement of aC with Type II inhibitors, relative to the aC-in state with Type I inhibitors, and further show that helix aC adopts multiple states and is therefore dynamic in apo BRAF. This makes a strong case that negative cooperativity between sites in the BRAF dimer can account for paradoxical kinase activation by Type II inhibitors by creating a half site-occupied homodimer, BBD. In contrast, Type I inhibitors and aC-out inhibitors do not fit this model, and are therefore proposed to be explained by previous proposed models involving negative allostery between subunits in BRAF-CRAF heterodimers, RAS priming, and transactivation.

      Strengths:

      This study integrates orthogonal spectroscopic and kinetic strategies to characterize BRAF dynamics and determine how it impacts inhibitor allostery. The unique combination of approaches presented in this study represents a road map for future work in the important area of protein kinase dynamics. The work represents a worthy contribution not only to the field of BRAF regulation but protein kinases in general.

      Weaknesses:

      Some questions remain regarding the proposed model for Type II inhibitors and its comparison to Type I and aC-out inhibitors that would be useful to clarify. Specifically, it would be helpful to address whether the activation of BRAF by Type II inhibitors, while strongly correlated with BBD model predictions in vitro, also depends on CRAF via BRAF-CRAF in cells and therefore overlaps with the mechanisms of paradoxical activation by Type I and aC-out inhibitors.

      We agree with the reviewer that this is a worthy question to be pursued. However, given the substantial experimental effort required for such an endeavor, and the highly supportive nature of the reviewer comments, including that “This is a strong manuscript that I feel is well above the bar for publication”, we believe this effort is more appropriate for a future study.

      This is a strong manuscript that I feel is well above the bar for publication. Nevertheless, it is recommended that the authors consider addressing the following points in order to support their major conclusions.

      (1) Fig 3D shows similar effects of Type II and Type I inhibitors in the biphasic increase of cellular pMEK/pERK. From this, the authors argue that Type II inhibitors are explained by negative allostery in the BRAF homodimer (based on Fig 2E), while Type I inhibitors are not. But it seems possible that despite the terrific correlation between BBD and BRAF kinase activities measured in vitro, CRAF is still important to explain pathway activation in cells. It also seems conceivable that the calculated %BBD between different Type II inhibitors may not correlate as well with their effects on pathway activation in cells. These possibilities should be addressed.

      We agree with the reviewer that it is likely that CRAF contributes to paradoxical activation by type II inhibitors in cells. It is also likely that other cellular factors such as RAS-priming and membrane recruitment play a role in activation. However, we note that for the type II inhibitors there is good agreement between the biophysical predictions and the concentration regimes in which activation is observed in cells, suggesting that these predictions are capturing a key part of the activation process that occurs in cells.

      (2) In Fig 2A, is it possible to report the activity of dimeric BRAF-WT in the absence of inhibitor? This would help confirm that the maximal activity measured after titrating inhibitor is indeed consistent with the predicted %BBD population, which would be expected to have half of the specific activity of BB.

      In principle, it is possible to determine the catalytic activity of apo dimers (BB) by combining our model predictions for the concentration of BB dimers and our activity measurements. However, because the activity assays are performed at nanomolar kinase concentrations, whereas the baseline dimerization affinity of BRAF is in the micromolar range, the observed activity of apo BRAF arises from a small subpopulation of dimers (on the order of 4 percent under the conditions of our experiments) and is therefore difficult to define accurately. As a result, we deemed it more suitable to compare our results to published activity measurements derived from 14-3-3-activated dimers which should represent fully dimerized BRAF. This analysis, as reported in Figure 2E, suggests that the BBD activity is approximately half of that of BB.

      (3) The 19F-NMR experiments make a good case for broadening of the helix aC signal in the BRAF dimer. From this, the study proposes that after inhibitor binds one subunit, the second unoccupied subunit retains dynamics. It would be useful to address this experimentally, if possible. For example, can the 19F-NMR signal be measured in the presence of inhibitor, to support the prediction that the unoccupied subunit is indeed dynamic and samples multiple conformations as in apo BRAF?

      We agree with the reviewer that it would be interesting to determine the dynamic response of BRAF to inhibitor binding. However, this is a challenging undertaking due to the biochemical heterogeneity that occurs at sub saturating inhibitor concentrations. For example, at any given inhibitor concentration, BRAF exists as a mixture of monomers, apo dimers, dimers with one inhibitor molecule, and dimers with two inhibitor molecules bound. This makes it challenging to relate the 19F NMR signal to a single biochemical state. Addressing this would require a substantial experimental effort that we feel is beyond the scope of this study.

    1. Author response:

      Reviewer 1:

      The paper “Quantifying gliding forces of filamentous cyanobacteria by self-buckling” combines experiments on freely gliding cyanobacteria, buckling experiments using two-dimensional V-shaped corners, and micropipette force measurements with theoretical models to study gliding forces in these organisms. The aim is to quantify these forces and use the results to perhaps discriminate between competing mechanisms by which these cells move. A large data set of possible collision events are analyzed, bucking events evaluated, and critical buckling lengths estimated. A line elasticity model is used to analyze the onset of buckling and estimate the effective (viscous type) friction/drag that controls the dynamics of the rotation that ensues post-buckling. This value of the friction/drag is compared to a second estimate obtained by consideration of the active forces and speeds in freely gliding filaments. The authors find that these two independent estimates of friction/drag correlate with each other and are comparable in magnitude. The experiments are conducted carefully, the device fabrication is novel, the data set is interesting, and the analysis is solid. The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion. While consistent with the data, this conclusion is inferred.

      We thank the reviewer for the positive evaluation of our work.

      Summary:

      The paper addresses important questions on the mechanisms driving the gliding motility of filamentous cyanobacteria. The authors aim to understand these by estimating the elastic properties of the filaments, and by comparing the resistance to gliding under a) freely gliding conditions, and b) in post-buckled rotational states. Experiments are used to estimate the propulsion force density on freely gliding filaments (assuming over-damped conditions). Experiments are combined with a theoretical model based on Euler beam theory to extract friction (viscous) coefficients for filaments that buckle and begin to rotate about the pinned end. The main results are estimates for the bending stiffness of the bacteria, the propulsive tangential force density, the buckling threshold in terms of the length, and estimates of the resistive friction (viscous drag) providing the dissipation in the system and balancing the active force. It is found that experiments on the two bacterial species yield nearly identical values of f (albeit with rather large variations). The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion.

      We appreciate this comprehensive summary of our work.

      Strengths of the paper:

      The strengths of the paper lie in the novel experimental setup and measurements that allow for the estimation of the propulsive force density, critical buckling length, and effective viscous drag forces for movement of the filament along its contour – the axial (parallel) drag coefficient, and the normal (perpendicular) drag coefficient (I assume this is the case, since the post-buckling analysis assumes the bent filament rotates at a constant frequency). These direct measurements are important for serious analysis and discrimination between motility mechanisms.

      We thank the reviewer for this positive assessment of our work.

      Weaknesses:

      There are aspects of the analysis and discussion that may be improved. I suggest that the authors take the following comments into consideration while revising their manuscript.

      The conclusion that adhesion via focal adhesions is the cause for propulsion rather than slime protrusion is consistent with the experimental results that the frictional drag correlates with propulsion force. At the same time, it is hard to rule out other factors that may result in this (friction) viscous drag - (active) force relationship while still being consistent with slime production. More detailed analysis aiming to discriminate between adhesion vs slime protrusion may be outside the scope of the study, but the authors may still want to elaborate on their inference. It would help if there was a detailed discussion on the differences in terms of the active force term for the focal adhesion-based motility vs the slime motility.

      We appreciate this critical assessment of our conclusions. Of course we are aware that many different mechanisms may lead to similar force/friction characteristics, and that a definitive conclusion on the mechanism would require the combination of various techniques, which is beyond the scope of this work. Therefore, we were very careful in formulating the discussion of our findings, refraining, in particular, from a singular conclusion on the mechanism but instead indicating “support” for one hypothesis over another, and emphasizing “that many other possibilities exist”.

      The most common concurrent hypotheses for bacterial gliding suggest that either slime extrusion at the junctional pore complex [A1], rhythmic contraction of fibrillar arrays at the cell wall [A2], focal adhesion sites connected to intracellular motor-microtubule complexes [A3], or modified type-IV pilus apparati [A4] provide the propulsion forces. For the slime extrusion hypothesis, which is still abundant today, one would rather expect an anticorrelation of force and friction: more slime extrusion would generate more force, but also enhance lubrication. The other hypotheses are more conformal to the trend we observed in our experiments, because both pili and focal adhesion require direct contact with a substrate. How contraction of fibrilar arrays would micromechanically couple to the environment is not clear to us, but direct contact might still facilitate force transduction. Please note that these hypotheses were all postulated without any mechanical measurements, solely based on ultra-structural electron microscopy and/or genetic or proteomic experiments. We see our work as complementary to that, providing a mechanical basis for evaluating these hypotheses.

      We agree with the referee that narrowing down this discussion to focal adhesion should have been avoided. We rewrote the concluding paragraph (page 8):

      “…it indicates that friction and propulsion forces, despite being quite vari able, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

      Can the authors comment on possible mechanisms (perhaps from the literature) that indicate how isotropic friction may be generated in settings where focal adhesions drive motility? A key aspect here would probably be estimating the extent of this adhesion patch and comparing it to a characteristic contact area. Can lubrication theory be used to estimate characteristic areas of contact (knowing the radius of the filament, and assuming a height above the substrate)? If the focal adhesions typically cover areas smaller than this lubrication area, it may suggest the possibility that bacteria essentially present a flat surface insofar as adhesion is concerned, leading to a transversely isotropic response in terms of the drag. Of course, we will still require the effective propulsive force to act along the tangent.

      We thank the referee for suggesting to estimate the dimensions of the contact region. Both pili and focal adhesion sites would be of sizes below one micron [A3, A4], much smaller than the typical contact region in the lubricated contact, which is on the order of the filament radius (few microns). So indeed, isotropic friction may be expected in this situation [A5] and is assumed frequently in theoretical work [A6–A8]. Anisotropy may then indeed be induced by active forces [A9], but we are not aware of measurements of the anisotropy of friction in bacterial gliding.

      For a more precise estimate using lubrication theory, rheology and extrusion rate of the secreted polysaccharides would have to be known, but we are not aware of detailed experimental characterizations.

      We extended the paragraph in the buckling theory on page 5 regarding the assumption of isotropic friction:

      “We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t− η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Presumably, this friction is dominated by the lubrication drag from the contact with the substrate, filled by a thin layer of secreted polysaccharide slime which is much more viscous than the surrounding bulk fluid. Speculatively, the motility mechanism might also comprise adhering elements like pili (Khayatan et al., 2015 ) or foci (Mignot et al., 2007 ) that increase the overall friction (Pompe et al., 2015 ). Thus, the drag due to the surrounding bulk fluid can be neglected (Man and Kanso, 2019 ), and friction is assumed to be isotropic, a common assumption in motility models (Fei et al., 2020; Tchoufag et al., 2019; Wada et al., 2013 ). We assume…”

      We also extended the discussion regarding the outcome of isotropic friction (page 7):

      “…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

      I am not sure why the authors mention that the power of the gliding apparatus is not rate-limiting. The only way to verify this would be to put these in highly viscous fluids where the drag of the external fluid comes into the picture as well (if focal adhesions are on the substrate-facing side, and the upper side is subject to ambient fluid drag). Also, the friction referred to here has the form of a viscous drag (no memory effect, and thus not viscoelastic or gel-like), and it is not clear if forces generated by adhesion involve other forms of drag such as chemical friction via temporary bonds forming and breaking. In quasi-static settings and under certain conditions such as the separation of chemical and elastic time scales, bond friction may yield overall force proportional to local sliding velocities.

      We agree with the referee that the origin of the friction is not easily resolved. Lubrication yields an isotropic force density that is proportional to the velocity, and the same could be generated by bond friction. Importantly, both types of friction would be assumed to be predominantly isotropic. We explicitly referred to lubrication drag because it has been shown that mutations deficient of slime extrusion do not glide [A4].

      Assuming, in contrast, that in free gliding, friction with the environment is not rate limiting, but rather the internal friction of the gliding apparatus, i.e., the available power, we would expect a rather different behavior during early-buckling evolution. During early buckling, the tangential motion is stalled, and the dynamics is dominated by the growing buckling amplitude of filament regions near the front end, which move mainly transversely. For geometric reasons, in this stage the (transverse) buckling amplitude grows much faster than the rear part of the filament advances longitudinally. Thus that motion should not be impeded much by the internal friction of the gliding apparatus, but by external friction between the buckling parts of the filament and the ambient. The rate at which the buckling amplitude initially grows should be limited by the accumulated compressive stress in the filament and the transverse friction with the substrate. If the latter were much smaller than the (logitudinal) internal friction of the gliding apparatus, we would expect a snapping-like transition into the buckled state, which we did not observe.

      In our paper, we do not intend to evaluate the exact origin of the friction, quantifying the gliding force is the main objective. A linear force-velocity relation agrees with our observations. A detailed analysis of friction in cyanobacterial gliding would be an interesting direction for future work.

      To make these considerations more clear, we rephrased the corresponding paragraph on page 7 & 8:

      “…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

      For readers from a non-fluids background, some additional discussion of the drag forces, and the forms of friction would help. For a freely gliding filament if f is the force density (per unit length), then steady gliding with a viscous frictional drag would suggest (as mentioned in the paper) f ∼ v! L η||. The critical buckling length is then dependent on f and on B the bending modulus. Here the effective drag is defined per length. I can see from this that if the active force is fixed, and the viscous component resulting from the frictional mechanism is fixed, the critical buckling length will not depend on the velocity (unless I am missing something in their argument), since the velocity is not a primitive variable, and is itself an emergent quantity.

      We are not sure what “f ∼ v! L η||” means, possibly the spelling was corrupted in the forwarding of the comments.

      We assumed an overdamped motion in which the friction force density ff (per unit length of the filament) is proportional to the velocity v0, i.e. ff ∼ η v0, with a friction coefficient η. Overdamped means that the friction force density is equal and opposite to the propulsion force density, so the propulsion force density is f ∼ ff ∼ η v0. The total friction and propulsion forces can be obtained by multiplication with the filament length

      L, which is not required here. In this picture, v0 is an emergent quantity and f and η are assumed as given and constant. Thus, by observing v0, f can be inferred up to the friction coefficient η. Therefore, by using two descriptive variables, L and v0, with known B, the primitive variable η can be inferred by logistic regression, and f then follows from the overdamped equation of motion.

      To clarify this, we revised the corresponding section on page 5 of the paper:

      “The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

      Reviewer 2:

      In the presented manuscript, the authors first use structured microfluidic devices with gliding filamentous cyanobacteria inside in combination with micropipette force measurements to measure the bending rigidity of the filaments.

      Next, they use triangular structures to trap the bacteria with the front against an obstacle. Depending on the length and rigidity, the filaments buckle under the propulsive force of the cells. The authors use theoretical expressions for the buckling threshold to infer propulsive force, given the measured length and stiffnesses. They find nearly identical values for both species, f ∼ (1.0 ± 0.6) nN/µm, nearly independent of the velocity.

      Finally, they measure the shape of the filament dynamically to infer friction coefficients via Kirchhoff theory. This last part seems a bit inconsistent with the previous inference of propulsive force. Before, they assumed the same propulsive force for all bacteria and showed only a very weak correlation between buckling and propulsive velocity. In this section, they report a strong correlation with velocity, and report propulsive forces that vary over two orders of magnitude. I might be misunderstanding something, but I think this discrepancy should have been discussed or explained.

      We regret the misunderstanding of the reviewer regarding the velocity dependence, which indicates that the manuscript should be improved to convey these relations correctly.

      First, in the Buckling Measurements section, we did not assume the same propulsion force for all bacteria. The logistic regression yields an ensemble median for Lc (and thus an ensemble median for f ), along with the width ∆Lc of the distribution (and thus also the width of the distribution of f ). Our result f ∼ (1.0 ± 0.6) nN/µm indicates the median and the width of the distribution of the propulsion force densities across the ensemble of several hundred filaments used in the buckling measurements. The large variability of the forces found in the second part is consistently reflected by this very wide distribution of active forces detected in the logistic regression in the first part.

      We did small modifications to the buckling theory paragraph to clarify that in the first part, a distribution of forces rather than a constant value is inferred (page 6)

      “Inserting the population median and quartiles of the distributions of bending modulus and critical length, we can now quantify the distribution of the active force density for the filaments in the ensemble from the buckling measurements. We obtain nearly identical values for both species, f ∼ (1.0±0.6) nN/µm, where the uncertainty represents a wide distribution of f across the ensemble rather than a measurement error.”

      The same holds, of course, when inferring the distribution of the friction coefficients (page 5):

      “The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

      The (naturally) wide distribution of force (and friction) leads to a distribution of Lc as well. However, due to the small exponent of 1/3 in the buckling threshold Lc ∼ f 1/3, the distribution of Lc is not as wide as the distributions of the individually inferred f or η. This is visualized in panel G of Figure 3, plotting Lc as a function of v0 (v0 is equivalent to f , up to a proportionality coefficient η). The natural length distribution, in contrast, is very wide. Therefore, the buckling propensity of a filament is most strongly characterized by its length, while force variability, which alters Lc of the individual, plays a secondary role.

      In order to clarify this, we edited the last paragraph of the Buckling Measurements section on page 5 of the manuscript:

      “…Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

      Second, in the Profile analysis section, we did not report a correlation between force and velocity. As can be seen in Figure 4—figure Supplement 1, neither the active force nor the friction coefficient, as determined from the analysis of individual filaments, show any significant correlation with the velocity. This is also written in the discussion (page 7):

      We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B, C and Figure 4—figure Supplement 1 ).

      Note that this is indeed consistent with the logistic regression: Using v0 as a second regressor did not significantly reduce the width of the distribution of Lc as compared to the simple logistic regression, indicating that force and velocity are not strongly correlated.

      In order to clarify this in the manuscript, we modified that part (page 7):

      “…We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B,C and Figure 4— figure Supplement 1 ). This is consistent with the logistic regression, where using v0 as a second regressor did not significantly reduce the width of the distribution of critical lengths or active forces. The two estimates of the friction coefficient, from logistic regression and individual profile fits, are measured in (predominantly) orthogonal directions: tangentially for the logistic regression where the free gliding velocity was used, and transversely for the evolution of the buckling profiles. Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic…”

      From a theoretical perspective, not many new results are presented. The authors repeat the well-known calculation for filaments buckling under propulsive load and arrive at the literature result of buckling when the dimensionless number (f L3/B) is larger than 30.6 as previously derived by Sekimoto et al in 1995 [1] (see [2] for a clamped boundary condition and simulations). Other theoretical predictions for pushed semi-flexible filaments [1–4] are not discussed or compared with the experiments. Finally, the Authors use molecular dynamics type simulations similar to [2–4] to reproduce the buckling dynamics from the experiments. Unfortunately, no systematic comparison is performed.

      [1]        Ken Sekimoto, Naoki Mori, Katsuhisa Tawada, and Yoko Y Toyoshima. Symmetry breaking instabilities of an in vitro biological system. Physical review letters, 75(1):172, 1995.

      [2]       Raghunath Chelakkot, Arvind Gopinath, Lakshminarayanan Mahadevan, and Michael F Hagan. Flagellar dynamics of a connected chain of active, polar, brownian particles. Journal of The Royal Society Interface, 11(92):20130884, 2014.

      [3]       Rolf E Isele-Holder, Jens Elgeti, and Gerhard Gompper. Self-propelled worm-like filaments: spontaneous spiral formation, structure, and dynamics. Soft matter, 11(36):7181–7190, 2015.

      [4]       Rolf E Isele-Holder, Julia J¨ager, Guglielmo Saggiorato, Jens Elgeti, and Gerhard Gompper. Dynamics of self-propelled filaments pushing a load. Soft Matter, 12(41):8495–8505, 2016.

      We thank the reviewer for pointing us to these publications, in particular the work by Sekimoto we were not aware of. We agree with the referee that the calculation is straight forward (basically known since Euler, up to modified boundary conditions). Our paper focuses on experimental work, the molecular dynamics simulations were included mainly as a consistency check and not intended to generate the beautiful post-buckling patterns observed in references [2-4]. However, such shapes do emerge in filamentous cyanobacteria, and with the data provided in our manuscript, simulations can be quantitatively matched to our experiments, which will be covered by future work.

      We included the references in the revision of our manuscript, and a statement that we do not claim priority on these classical theoretical results.

      Introduction, page 2:

      “…Self-Buckling is an important instability for self-propelling rod-like micro-organisms to change the orientation of their motion, enabling aggregation or the escape from traps (Fily et al., 2020; Man and Kanso, 2019; Isele-Holder et al., 2015; Isele-Holder et al., 2016 ). The notion of self-buckling goes back to work of Leonhard Euler in 1780, who described elastic columns subject to gravity (Elishakoff, 2000 ). Here, the principle is adapted to the self-propelling, flexible filaments (Fily et al., 2020; Man and Kanso, 2019; Sekimoto et al., 1995 ) that glide onto an obstacle. Filaments buckle if they exceed a certain critical length Lc ∼ (B/f)1/3, where B is the bending modulus and f the propulsion force density…”

      Buckling theory, page 5:

      “…The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments. We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t − η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 )…”

      Further on page 6:

      “To derive the critical self-buckling length, Equation 5 can be linearized for two scenarios that lead to the same Lc: early-time small amplitude buckling and late-time stationary rotation at small and constant curvature (Fily et al., 2020; Chelakkot et al., 2014 ; Sekimoto et al., 1995 ). […] Thus, in physical units, the critical length is given by Lc = (30.5722 B/f)1/3, which is reproduced in particle based simulations (Appendix Figure 2 ) analogous to those in Isele-Holder et al. (2015, 2016).”

      Discussion, page 7 & 8:

      “…This, in turn, has dramatic consequences on the exploration behavior and the emerging patterns (Isele-Holder et al., 2015, 2016; Abbaspour et al., 2021; Duman et al., 2018; Prathyusha et al., 2018; Jung et al., 2020 ): (L/Lc)3 is, up to a numerical prefactor, identical to the flexure number (Isele-Holder et al., 2015, 2016; Duman et al., 2018; Winkler et al., 2017 ), the ratio of the Peclet number and the persistence length of active polymer melts. Thus, the ample variety of non-equilibrium phases in such materials (Isele-Holder et al., 2015, 2016; Prathyusha et al., 2018; Abbaspour et al., 2021 ) may well have contributed to the evolutionary success of filamentous cyanobacteria.”

      Reviewer 3:

      Summary:

      This paper presents novel and innovative force measurements of the biophysics of gliding cyanobacteria filaments. These measurements allow for estimates of the resistive force between the cell and substrate and provide potential insight into the motility mechanism of these cells, which remains unknown.

      We thank the reviewer for the positive evaluation of our work. We have revised the manuscript according to their comments and detail our replies and modifications next to the individual points below.

      Strengths:

      The authors used well-designed microfabricated devices to measure the bending modulus of these cells and to determine the critical length at which the cells buckle. I especially appreciated the way the authors constructed an array of pillars and used it to do 3-point bending measurements and the arrangement the authors used to direct cells into a V-shaped corner in order to examine at what length the cells buckled at. By examining the gliding speed of the cells before buckling events, the authors were able to determine how strongly the buckling length depends on the gliding speed, which could be an indicator of how the force exerted by the cells depends on cell length; however, the authors did not comment on this directly.

      We thank the referee for the positive assessment of our work. Importantly, we do not see a significant correlation between buckling length and gliding speeds, and we also do not see a correlation with filament length, consistent with the assumption of a propulsion force density that is more or less homogeneously distributed along the filament. Note that each filament consists of many metabolically independent cells, which renders cyanobacterial gliding a collective effort of many cells, in contrast to gliding of, e.g., myxobacteria.

      In response also to the other referees’ comments, we modified the manuscript to reflect more on the absence of a strong correlation between velocity and force/critical length. We modified the Buckling measurements section on page 5 of the paper:

      “The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over-damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E, F show the buckling behavior…”

      Further, we edited the last paragraph of the Buckling measurements section on page 5 of the manuscript:

      “Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

      We also rephrased the corresponding discussion paragraph on page 7:

      “…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

      Weaknesses:

      There were two minor weaknesses in the paper.

      First, the authors investigate the buckling of these gliding cells using an Euler beam model. A similar mathematical analysis was used to estimate the bending modulus and gliding force for Myxobacteria (C.W. Wolgemuth, Biophys. J. 89: 945-950 (2005)). A similar mathematical model was also examined in G. De Canio, E. Lauga, and R.E Goldstein, J. Roy. Soc. Interface, 14: 20170491 (2017). The authors should have cited these previous works and pointed out any differences between what they did and what was done before.

      We thank the reviewer for pointing us to these references. The paper by Wolgemuth is theoretical work, describing A-motility in myxobacteria by a concentrated propulsion force at the rear end of the bacterium, possibly stemming from slime extrusion. This model was a little later refuted by [A3], who demonstrated that focal adhesion along the bacterial body and thus a distributed force powers A-motility, a mechanism that has by now been investigated in great detail (see [A10]). The paper by Canio et al. contains a thorough theoretical analysis of a filament that is clamped at one end and subject to a concentrated tangential load on the other. Since both models comprise a concentrated end-load rather than a distributed propulsion force density, they describe a substantially different motility mechanism, leading also to substantially different buckling profiles. Consequentially, these models cannot be applied to cyanobacterial gliding.

      We included both citations in the revision and pointed out the differences to our work in the introduction (page 2):

      “…A few species appear to employ a type-IV-pilus related mechanism (Khayatan et al., 2015; Wilde and Mullineaux, 2015 ), similar to the better- studied myxobacteria (Godwin et al., 1989; Mignot et al., 2007; Nan et al., 2014; Copenhagen et al., 2021; Godwin et al., 1989 ), which are short, rod-shaped single cells that exhibit two types of motility: S (social) motility based on pilus extension and retraction, and A (adventurous) motility based on focal adhesion (Chen and Nan, 2022 ) for which also slime extrusion at the trailing cell pole was earlier postulated as mechanism (Wolgemuth et al., 2005 ). Yet, most gliding filamentous cyanobacteria do not exhibit pili and their gliding mechanism appears to be distinct from myxobacteria (Khayatan et al., 2015 ).”

      And in Buckling theory, page 5:

      “….The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments.”

      The second weakness is that the authors claim that their results favor a focal adhesion-based mechanism for cyanobacterial gliding motility. This is based on their result that friction and adhesion forces correlate strongly. They then conjecture that this is due to more intimate contact with the surface, with more contacts producing more force and pulling the filaments closer to the substrate, which produces more friction. They then claim that a slime-extrusion mechanism would necessarily involve more force and lower friction. Is it necessarily true that this latter statement is correct? (I admit that it could be, but is it a requirement?)

      We thank the referee for raising this interesting question. Our claim regarding slime extrusion is based on three facts: i. mutations deficient of slime extrusion do not glide, but start gliding as soon as slime is provided externally [A4]. ii. A positive correlation between speed and slime layer thickness was observed in Nostoc [A11]. iii. The fluid mechanics of lubricated sliding contacts is very well understood and predicts a decreasing resistance with increasing layer thickness.

      We included these considerations in the revision of our manuscript (page 8):

      “…it indicates that friction and propulsion forces, despite being quite variable, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

      Related to this, the authors use a model with isotropic friction. They claim that this is justified because they are able to fit the cell shapes well with this assumption. How would assuming a non-isotropic drag coefficient affect the shapes? It may be that it does equally well, in which case, the quality of the fits would not be informative about whether or not the drag was isotropic or not.

      The referee raises another very interesting point. Given the typical variability and uncertainty in experimental measurements (cf. error Figure 4 A), a model with a sightly anisotropic friction could be fitted to the observed buckling profiles as well, without significant increase of the mismatch. Yet, strongly anisotropic friction would not be consistent with our observations.

      Importantly, however, we did not conclude on isotropic friction based on the fit quality, but based on a comparison between free gliding and early buckling (Figure 4 D). In early buckling, the dominant motion is in transverse direction, while longitudinal motion is insignificant, due to geometric reasons. Thus, independent of the underlying model, mostly the transverse friction coefficiont is inferred. In contrast, free gliding is a purely longitudinal motion, and thus only the friction coefficient for longitudinal motion can be inferred. These two friction coefficients are compared in Figure 4 D. Still, the scatter of that data would allow to fit a certain anisotropy within the error margins. What we can exclude based on out observation is the case of a strongly anisotropic friction. If there is no ab-initio reason for anisotropy, nor a measurement that indicates it, we prefer to stick with the simplest

      assumption. We carefully chose our wording in the Discussion as “mainly isotropic” rather

      than “isotropic” or “fully isotropic”.

      We added a small statement to the Discussion on page 7 & 8:

      “... Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces ...”

      Recommendations for the authors

      The discussion regarding how the findings of this paper imply that cyanobacteria filaments are propelled by adhesion forces rather than slime extrusion should be improved, as this conclusion seems questionable. There appears to be an inconsistency with a buckling force said to be only weakly dependent on the gliding velocity, while its ratio with the velocity correlates with a friction coefficient. Finally, data and source code should be made publicly available.

      In the revised version, we have modified the discussion of the force generating mechanism according to the reviewer suggestions. The perception of inconsistency in the velocity dependence of the buckling force was based on a misunderstanding, as we detailed in our reply to the referee. We revised the corresponding section to make it more clear. Data and source code have been uploaded to a public data repository.

      Reviewer #2 (recommendations for the authors)

      Despite eLife policy, the authors do not provide a Data Availability Statement. For the presented manuscript, data and source code should be provided “via trusted institutional or third-party repositories that adhere to policies that make data discoverable, accessible and usable.” https://elifesciences.org/inside-elife/51839f0a/for-authors-updates- to-elife-s-data-sharing-policies

      Most of the issues in this reviewer’s public review should be easy to correct, so I would strongly support the authors to provide an amended manuscript.

      We added the Data Availability Statement in the amended manuscript.

      References

      [A1] E. Hoiczyk and W. Baumeister. “The junctional pore complex, a prokaryotic secretion organelle, is the molecular motor underlying gliding motility in cyanobacteria”. In: Curr. Biol. 8.21 (1998), pp. 1161–1168. doi: 10.1016/s0960-9822(07)00487-3.

      [A2] N. Read, S. Connell, and D. G. Adams. “Nanoscale Visualization of a Fibrillar Array in the Cell Wall of Filamentous Cyanobacteria and Its Implications for Gliding Motility”. In: J. Bacteriol. 189.20 (2007), pp. 7361–7366. doi: 10.1128/jb.00706- 07.

      [A3] T. Mignot, J. W. Shaevitz, P. L. Hartzell, and D. R. Zusman. “Evidence That Focal Adhesion Complexes Power Bacterial Gliding Motility”. In: Science 315.5813 (2007), pp. 853–856. doi: 10.1126/science.1137223.

      [A4] Behzad Khayatan, John C. Meeks, and Douglas D. Risser. “Evidence that a modified type IV pilus-like system powers gliding motility and polysaccharide secretion in filamentous cyanobacteria”. In: Mol. Microbiol. 98.6 (2015), pp. 1021–1036. doi: 10.1111/mmi.13205.

      [A5] Tilo Pompe, Martin Kaufmann, Maria Kasimir, Stephanie Johne, Stefan Glorius, Lars Renner, Manfred Bobeth, Wolfgang Pompe, and Carsten Werner. “Friction- controlled traction force in cell adhesion”. In: Biophysical journal 101.8 (2011), pp. 1863–1870.

      [A6] Hirofumi Wada, Daisuke Nakane, and Hsuan-Yi Chen. “Bidirectional bacterial gliding motility powered by the collective transport of cell surface proteins”. In: Physical Review Letters 111.24 (2013), p. 248102.

      [A7] Jo¨el Tchoufag, Pushpita Ghosh, Connor B Pogue, Beiyan Nan, and Kranthi K Mandadapu. “Mechanisms for bacterial gliding motility on soft substrates”. In: Proceedings of the National Academy of Sciences 116.50 (2019), pp. 25087–25096.

      [A8] Chenyi Fei, Sheng Mao, Jing Yan, Ricard Alert, Howard A Stone, Bonnie L Bassler, Ned S Wingreen, and Andrej Kosmrlj. “Nonuniform growth and surface friction determine bacterial biofilm morphology on soft substrates”. In: Proceedings of the National Academy of Sciences 117.14 (2020), pp. 7622–7632.

      [A9] Arja Ray, Oscar Lee, Zaw Win, Rachel M Edwards, Patrick W Alford, Deok-Ho Kim, and Paolo P Provenzano. “Anisotropic forces from spatially constrained focal adhesions mediate contact guidance directed cell migration”. In: Nature communications 8.1 (2017), p. 14923.

      [A10] Jing Chen and Beiyan Nan. “Flagellar motor transformed: biophysical perspectives of the Myxococcus xanthus gliding mechanism”. In: Frontiers in Microbiology 13 (2022), p. 891694.

      [A11] Samia Dhahri, Michel Ramonda, and Christian Marliere. “In-situ determination of the mechanical properties of gliding or non-motile bacteria by atomic force microscopy under physiological conditions without immobilization”. In: PLoS One 8.4 (2013), e61663.

    1. Author response:

      We extend our gratitude to the two reviewers and the editors at eLife for their meticulous examination of our manuscript, as well as for their valuable feedback and positive assessment. We are particularly pleased to observe in both the reviews and the editorial evaluation the recognition of the importance of our findings. Through this provisional response, we wish to convey to the editors, reviewers, and the readership of eLife our intention to enhance the paper by incorporating a detailed description of the sections pertaining to MAD analysis, data interpretation with combined HS-AFM and PCA methods, and specific portions of the discussions. This will involve editing the manuscript accordingly and providing separate explanations in the "author response”. We acknowledge that such additions will strengthen the comprehensiveness of our work and render it more self-contained.

      Moreover, in alignment with the recommendations from the review team, we will provide a thorough discussion of published data and offer a clearer explanation of our utilized methods, thereby providing a more robust foundation for our conclusions.

    1. Author response:

      eLife assessment

      This study investigates associations between retrotransposon element expression and methylation with age and inflammation, using multiple public datasets. The study is valuable because a systematic analysis of retrotransposon element expression during human aging has been lacking. However, the data provided are incomplete due to the sole reliance on microarray expression data for the core analysis of the paper.

      Both reviewers found this study to be important. We have selected the microarray datasets of human blood adopted by a comprehensive study of ageing published in Nature Communications (DOI: doi: 10.1038/ncomms9570). We only included the datasets specifically collected for ageing studies. Therefore, the large RNA-seq cohorts for cancer, cardiovascular, and neurological diseases were not relevant to this study and cannot be included.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Tsai and Seymen et al. investigate associations between RTE expression and methylation and age and inflammation, using multiple public datasets. The concept of the study is in principle interesting, as a systematic analysis of RTE expression during human aging is lacking.

      We thank the reviewer for the positive comment.

      Unfortunately, the reliance on expression microarray data, used to perform the core analysis of the paper places much of the study on shaky ground. The findings of the study would not be sufficiently supported until the authors validate them with more suitable methods.

      In our discussion section in the manuscript, we have clarified that “we are aware of the limitations imposed by using microarray in this study, particularly the low number of intergenic probes in the expression microarray data. Our study can be enriched with the advent of large RNA-seq cohorts for aging studies in the future.” However, the application of microarray for RTE expression analysis was introduced previously. In fact, in a manuscript published by Reichmann et al. (DOI: 10.1371/journal.pcbi.1002486) which was cited 76 times, the authors showed and experimentally verified that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. Inspired by this methodological manuscript with reasonable acceptance by other researchers, we trusted that the RTE microarray probes could accurately quantify RTE expression at class and family levels.

      Strengths:

      This is a very important biological problem.

      Weaknesses:

      RNA microarray probes are obviously biased to genes, and thus quantifying transposon analysis based on them seems dubious. Based on how arrays are designed there should at least be partial (perhaps outdated evidence) that the probe sites overlap a protein-coding or non-coding RNA.

      We disagree with the reviewer that quantifying transposon analysis based on microarray data is dubious. As previously shown by Reichmann et al., the quantification is reliable as long as the probes do not overlap with annotated genes and they are in the correct orientation to detect sense repetitive element transcripts. Reichman et al. identified 1,400 repetitive element probes in version 1.0, version 1.1 and version 2.0 of the Illumina Mouse WG-6 Beadchips by comparing the genomic locations of the probes with the Repeatmasked regions of the mouse genome. We applied the same criteria for Illumina Human HT-12 V3 (29431 probes) and V4 (33963) to identify the RTE-specific probes.

      The authors state they only used intergenic probes, but based on supplementary files, almost half of RTE probes are not intergenic but intronic (n=106 out of 264).

      All our identified RTE probes overlap with intergenic regions. However, due to their repetitive natures, some probes overlap with intronic regions, too. We can replace "intergenic" with "noncoding" in our revision to show that they do not overlap with the exons of protein-coding genes. However, we do not rule out the possibility that some of our detected RTE probes might overlap noncoding RNAs. In fact, the border between coding and non-coding genomes has recently become very fuzzy with new annotations of the genome. RTE RNAs can be easily considered as non-coding RNAs if we challenge our junk DNA view.

      This is further complicated by the fact that not all this small subset of probes is available in all analyzed datasets. For example, 232 probes were used for the MESA dataset but only 80 for the GTP dataset. Thus, RTE expression is quantified with a set of probes which is extremely likely to be highly affected by non-RTE transcripts and that is also different across the studied datasets. Differences in the subsets of probes could very well explain the large differences between datasets in multiple of the analyses performed by the authors, such as in Figure 2a, or 3a. It is nonetheless possible that the quantification of RTE expression performed by the authors is truly interpretable as RTE expression, but this must be validated with more data from RNA-seq. Above all, microarray data should not be the main type of data used in the type of analysis performed by the authors.

      In this study, we did not compare MESA with GTP etc. We have analysed each dataset separately based on the available data for that dataset. Therefore, sacrificing one analysis because of the lack of information from the other does not make sense. We would do that if we were after comparing different datasets. Moreover, the datasets are not comparable because they were produced from different blood cell types.

      Reviewer #2 (Public Review):

      Summary:

      Yi-Ting Tsai and colleagues conducted a systematic analysis of the correlation between the expression of retrotransposable elements (RTEs) and aging, using publicly available transcriptional and methylome microarray datasets of blood cells from large human cohorts, as well as single-cell transcriptomics. Although DNA hypomethylation was associated with chronological age across all RTE biotypes, the authors did not find a correlation between the levels of RTE expression and chronological age. However, expression levels of LINEs and LTRs positively correlated with DNA demethylation, and inflammatory and senescence gene signatures, indicative of "biological age". Gene set variation analysis showed that the inflammatory response is enriched in the samples expressing high levels of LINEs and LTRs. In summary, the study demonstrates that RTE expression correlates with "biological" rather than "chronological" aging.

      Strengths:

      The question the authors address is both relevant and important to the fields of aging and transposon biology.

      We thank the reviewer for finding this study relevant and important.

      Weaknesses:

      The choice of methodology does not fully support the primary claims. Although microarrays can detect certain intergenic transposon sequences, the authors themselves acknowledge in the Discussion section that this method's resolution is limited. More critical considerations, however, should be addressed when interpreting the results. The coverage of transposon sequences by microarrays is not only very limited (232 unique probes) but also predetermined. This implies that any potential agerelated overexpression of RTEs located outside of the microarray-associated regions, or of polymorphic intact transposons, may go undetected. Therefore, the authors should be more careful while generalising their conclusions.

      This is a bioinformatics study, and we have already admitted and discussed the limitations in the discussion section of this manuscript. All technologies have their own limitations, and this should not stop us from shedding light on scientific facts because of inadequate information. In the manuscript, we have discussed that all large and proper ageing studies were performed using microarray technology. Peters et al. (DOI: doi: 10.1038/ncomms9570) adopted all these microarray data in their transcriptional landscape of ageing manuscript. Our study essentially applies the Reichmann et al. method to the peripheral blood-related data from the Peters et al. manuscript. Since hypomethylation due to ageing is a well-established and broad epigenetic reprogramming, it is unlikely that only a fraction of RTEs is affected by this phenomenon. Therefore, the subsampling of RTEs should not affect the result so much. Indeed, this is supported in our study by the inverse correlation between DNA methylation and RTE expression for LINE and SINE classes despite having limited numbers of probes for LINE and SINE expressions.

      Additionally, for some analyses, the authors pool signals from RTEs by class or family, despite the fact that these groups include subfamilies and members with very different properties and harmful potentials. For example, while sequences of older subfamilies might be passively expressed through readthrough transcription, intact members of younger groups could be autonomously reactivated and cause inflammation. The aggregation of signals by the largest group may obscure the potential reactivation of smaller subgroups. I recommend grouping by subfamily or, if not possible due to the low expression scores, by subgroup. For example, all HERV subfamilies are from the ERVL family.

      We agree with the reviewer that different subfamilies of RTEs play different roles through their activation. However, we will lose our statistical power if we study RTE subfamilies with a few probes. Global epigenetic alteration and derepression of RTEs by ageing have been observed to be genome-wide. While our systematic analysis across RTE classes and families cannot capture alterations in subfamilies due to statistical power, it is still relevant to the research question we are addressing.

      Next, Illumina arrays might not accurately represent the true abundance of TEs due to non-specific hybridization of genomic transposons. Standard RNA preparations always contain traces of abundant genomic SINEs unless DNA elimination is specifically thorough. The problem of such noise should be addressed.

      We have checked the RNA isolation step from MESA, GTP, and GARP manuscripts. The total RNA was isolated using the Qiagen mini kit following the manufacturer’s recommendations. The authors of these manuscripts did not mention whether they eliminated genomics DNA, but we assumed they were aware of the DNA contamination and eliminated it based on the manufacturer’s recommendations. We have looked up the literature about non-specific hybridization of RTEs but could not find any evidence to support this observation. We would appreciate the reviewers providing more evidence about such RTE contaminations.

      Lastly, scRNAseq was conducted using 10x Genomics technology. However, quantifying transposons in 10x sequencing datasets presents major challenges due to sparse signals.

      Applying the scTE pipeline (https://www.nature.com/articles/s41467-021-21808-x), we have found that the statical power of quantifying RTE classes (LINE, SINE, and LTR) or RTE families (L1, L2, All, ERVK, etc.) are as good as each individual gene. However, our proposed method cannot analyse RTE subfamilies, and we did not do that.

      Smart-seq single-cell technology is better suited to this particular purpose.

      We agree with the reviewer that Smart-seq provides higher yield than 10x, but there is no Smart-seq data available for ageing study.

      Anyway, it would be more convincing if the authors demonstrated TE expression across different clusters of immune cells using standard scRNAseq UMAP plots instead of boxplots.

      Since the number of RTE reads per cell is low, showing the expression of RTEs per cell in UMAP may not be the best statistical approach to show the difference between the aged and young groups. This is why we chose to analyse with pseudobulk and displayed differential expression using boxplot rather than UMAP for each immune cell type.

      I recommend validating the data by RNAseq, even on small cohorts. Given that the connection between RTE overexpression and inflammation has been previously established, the authors should consider better integrating their observations into the existing knowledge.

      Until recently, there were no publicly-available, non-cancerous, large cohort of RNA-seq data for ageing studies. We tried to gain access to the two RNA-seq datasets suggested by reviewer 2: Marquez et al. 2020 (phs001934.v1.p1, controlled access) and Morandini et al. 2023 (GSE193141, public access).

      Unfortunately, Marquez et al. 2020 data is not accessible because the authors only provide the data for projects related to cardiovascular diseases. However, we did analyse Morandini et al. 2023 data, and we can confirm that no association was observed between any class and family of RTEs with chronological ageing, which is the second strong piece of evidence supporting the statement in the manuscript. However, as expected, we found a positive correlation between RTE expression and IFNI signature score.

    1. Author response:

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

      eLife assessment

      This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing although certain caveats need to be taken into consideration as the authors acknowledged in the paper. The work will be of interest to the research community working on metabolism and on kidney cancer especially.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

      Strengths:

      The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

      We thank the Reviewer for their careful evaluation of the study and for their supportive comments.

      Weaknesses:

      The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

      We agree with the Reviewer that these are important caveats to consider when interpreting the results of this study.

      The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on a sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

      The Reviewer raises another important point that our interpretation of KIF vs TIF is limited to the ~300 metabolites we measured. We agree it would be worthwhile quantifying more metabolites where technically feasible to further characterize similarities and differences in nutrient availability between tumor and normal tissues.

      In vitro, tissue culture is recognized to suffer from ‘non-physiological’ nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

      We thank the Reviewer for calling attention to the limitations of cell culture media in studying tumor metabolism. While both in vitro and in vivo approaches have inherent limitations, formulating culture media based on metabolite concentrations measured here and in other studies provides a tool to study the influence of nutrient availability on kidney cell or kidney cancer cell phenotypes in vitro. We also agree with the Reviewer that determining whether the findings in our study are recapitulated in mouse models of kidney cancer, as this might enable investigation into the factors that modulate nutrient availability in this tissue context.

      Reviewer #2 (Public Review):

      The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

      We thank the Reviewer for their evaluation of our work and for their supportive comments.

      The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

      We agree with the Reviewer that an important caveat in considering the study findings is that we do not have KIF values from healthy individuals. Since resection of normal kidney is not a common procedure, obtaining KIF samples from healthy patients was not possible to complement our analysis. We further agree that the metabolite levels we measured in KIF or plasma are plausibly impacted by the presence of RCC. We did compare the composition of polar metabolites in the plasma from RCC, lung cancer, and healthy patients, highlighting how cystine is affected by tumor presence and/or sample collection methodology. We also point out that factors such as diet will impact metabolites in both blood and tissues.

      Reviewer #3 (Public Review):

      In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

      We thank the Reviewer for their time considering our paper and for their supportive comments.

      In Figures 1D and 1E, there were about 30% of polar metabolites and 25% of lipids significantly different between TIF and KIF, which could be key factors for RCC tumors. This reviewer considers that the authors should make comments on this.

      We agree with the Reviewer that the metabolites that significantly differ between TIF and KIF are of interest, particularly for those studying RCC tumor metabolism. We comment on some of the metabolites driving differences between TIF and KIF in our discussion of Figure 2, and in the revised manuscript we now include a new figure showing a heatmap that enables visualization of these metabolites (Figure 2-Supplement 1A-B).

      Recommendations for the authors:

      From the Reviewing Editor:

      Figure 2 needs to plot heatmaps for both upregulated and downregulated metabolites in TIF.

      We agree and now include heatmaps for significantly differing polar metabolites and lipids in TIF vs KIF as requested by Reviewer 3 (Figure 2-Supplement 1A-B). For completeness, we also include heatmaps for metabolites differing between healthy and RCC plasma (Figure 2-Supplement 2C) and for NSCLC and RCC plasma (Figure 2-Supplement 2D).

      There is a need to show whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals.

      Unfortunately, it has not been possible for us to collect KIF from healthy individuals. Since resection of normal kidney is not a common procedure, we have no way to obtain sufficient KIF samples from healthy patients for this measurement. We discuss this as a limitation of the study.

      Reviewer #1 (Recommendations For The Authors):

      a. The authors should provide additional details about the methodology to separate the KIF and TIF. Contaminating metabolites from surrounding tissue or the peritoneal fluids could impact interpretation and it would be helpful to understand how these challenges were addressed during tissue collection for this study. Additionally, was the collected tissue minced or otherwise dissociated? If so, could these procedures cause tissue lysis and contaminate the KIF/TIF with intracellular components?

      We thank the Reviewer for the suggestions to include more information about the sampling methodology. Care was taken to minimize cell lysis incurred by the processing methodology as tissues were not minced, smashed, nor dissociated, however there is still a possibility of some level of tissue lysis that is pre-existing or occurs during the isolation procedure. We note this caveat in the text (lines 218-220) and have updated the Methods with more details of the sampling and processing of the samples.

      b. Although the authors focus on metabolites that are elevated in TIF (relative to KIF and plasma), it would be equally relevant to consider the converse. Metabolites that are reduced in TIF, either due to underproduction or overconsumption, could render the tumors auxotrophic for some critical dependencies and identify some novel metabolic vulnerabilities. In this regard, Figure 2 could have a heatmap of the top metabolites that are elevated and depleted specifically in the TIF.

      We agree with the Reviewer it is useful to include heatmaps to better display the metabolites that significantly differ between TIF and KIF and now include these in Figure 2-Supplement 1A-B.

      c. The future utilization of this knowledge would depend on our ability to model these differences. Would interstitial tissue from a normal mouse kidney or tumor-bearing mouse kidney recapitulate the same differences relative to mouse plasma?

      We agree with the Reviewer that it would be worth determining whether the findings in our study are recapitulated in mouse models of kidney cancer, which would support future investigation into the factors that modulate nutrient availability. This is an interesting question, but we did not have access to endogenously arising models of RCC, which have been a limitation for the field, and comparison of normal mouse kidney metabolite data to human metabolite data is problematic for obvious reasons. Thus, we had no choice but to discuss this as a limitation of the study.

      Reviewer #2 (Recommendations For The Authors):

      In this study, Abbott et al. investigated the metabolic profile of renal cell carcinoma (RCC) by analyzing the tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from patients. The results indicate that nutrient composition in TIF closely resembles that of KIF, suggesting that tissue-specific factors, rather than tumor-driven alterations, have a more significant impact on nutrient levels. These findings are interesting. The study is overall well-constructed, including appropriate analysis, and the manuscript is written clearly and supported by high-quality figures. However, some issues are raised which if addressed, would strengthen the paper.

      We thank the Reviewer for their suggestions to improve the paper.

      The authors found a difference in the number of metabolites when comparing TIF or KIF lipid composition with plasma. The discoveries are intriguing; however, I am keen to understand whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals. I am particularly interested in identifying which metabolites could serve as potential diagnostic markers, intervention targets, or potentially reshape the tumor microenvironment. Because, even though some metabolite levels show no difference between TIF and KIF in RCC patients, I wonder if these metabolite levels in KIF increase or decrease compared to the interstitial fluid in healthy individuals. I am intrigued by the metabolites that simultaneously increase or decrease in both TIF and KIF compared to the kidney interstitial fluid in healthy individuals.

      We agree with the Reviewer that it would be interesting to measure kidney interstitial fluid from healthy patients to be able to compare metabolites changing due to the presence of RCC tumor. As we discuss in response to the public review, this was not possible as we could not obtain material from healthy individuals for analysis. Nevertheless we agree it warrants future study if material were available.

      The analysis conducted using plasma from healthy donors, as applauded by the author, is noteworthy. The author seems to have found that cystine levels do not differ between RCC patient plasma and tissue interstitial fluid. However, considering that in patient plasma, the cystine concentration is approximately two-fold higher than in plasma from healthy individuals, likely, cystine levels in patient tissue fluid have also increased nearly two-fold compared to levels in the interstitial fluid of normal kidney tissues. This finding aligns with the discovery of elevated GSH levels in cancer cells.

      We agree with the Reviewer that a higher cystine concentration in RCC patient plasma and interstitial fluid is interesting, and also considered this in relationship to past findings including reports of elevated GSH levels in RCC. However, we think this observation is driven at least in part by the fasting status of the patients pre-surgery. This does not rule out some part being related to the presence of the tumor, as this would be consistent with elevated GSH levels as noted by the Reviewer. Future studies will be needed to further delineate the factors that impact elevated cystine levels in both interstitial fluid and plasma.

      Some minor typos, such as "HIF1􀀀-driven" should be corrected.

      We thank the Reviewer for pointing out this typo and we have corrected it in the revised manuscript.

    1. Author response:

      eLife assessment

      This study provides valuable evidence indicating that Syngap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of Syngap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

      We are happy that the editors found that our study provides valuable evidence and that the central question is timely. We thank the reviewers for their detailed comments and suggestions. Below, we provide a point-by-point answer (in blue) to the specific comments and indicate the changes to the manuscript and the additional experiments we plan to perform to answer these comments.

      Public Reviews:

      Reviewer #1 (Public Review):

      The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

      Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

      Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

      We agree with the reviewer on the proposed open questions, which we will certainly discuss in the revised manuscript we are preparing. We do have experimental evidence suggesting that Syngap1 mRNA is expressed by PV+ and SST+ neurons in different cortical areas, during early postnatal development and in adulthood; therefore, we agree that it will be important, in future experiments, to tackle the question of when the observed phenotypes arise.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

      Strengths:

      The questions are novel

      Weaknesses:

      Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

      Major issues:

      (1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

      We understand the reviewer’s perspective; indeed, we asked ourselves the very same question regarding why the sEPSC and mEPSC frequency fall within a similar range when we analysed neuron means (bar graphs). We have already recorded sEPSCs followed by mEPSCs from several PV neurons (control and cHet) and are in the process of analyzing the data. We will add this data to the revised version of the manuscript. We will also rephrase the manuscript to present multiple potential interpretations of the data.

      We hope that we have correctly interpreted the reviewer's concern. However, if the question is why sEPSC amplitude but not frequency is affected in cHet vs ctrl then the reviewer’s comment is perhaps based on the assumption that the amplitude and frequency of miniature events should be lower for all events compared to those observed for spontaneous events. However, it's essential to note that changes in the mean amplitude of sEPSCs are primarily driven by alterations in large sEPSCs (>9-10pA, as shown in cumulative probability in Fig. 1b right), with smaller ones being relatively unaffected. Consequently, a reduction in sEPSC amplitude may not necessarily result in a significant decrease in frequency since their values likely remain above the detection threshold of 3 pA. This could explain the lack of a significant decrease in average inter-interval event of sEPSCs (as depicted in Fig. 1b left).

      If the question is whether we should see the same parameters affected by the genetic manipulation in both sEPSC and mEPSC, then another critical consideration is the involvement of the releasable pool in mEPSCs versus sEPSCs. Current knowledge suggests that activity-dependent and -independent release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites. This concept has been extensively explored (reviewed in Kavalali, 2015). Consequently, while we may have traditionally interpreted activity-dependent and -independent data assuming they utilize the same pool, this is no longer accurate. The current discussion in the field revolves around understanding the mechanisms underlying such phenomena. Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. For a rigorous analysis, particularly in this context involving thousands of events, it is essential to assess these data sets (mEPSCs vs sEPSCs) separately and provide cumulative probability curves. This approach allows for a more comprehensive understanding of the underlying distributions and helps to elucidate any potential differences between the two types of events. We will rephrase the text, and as mentioned above, add additional data, to better reflect these considerations.

      (2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells. Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here. Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

      We apologize, as it appears that the images we provided have caused confusion. The selected images represent a single focal plane of a confocal stack, which was visually centered on the PV cell somata. We chose just one confocal plane because we thought it showed more clearly the apposition of presynaptic and postsynaptic immunolabeling around the somata. In the revised version of the manuscript, we will provide higher magnification images, which will clearly show how we identified and selected the region of interest for the quantification of colocalized synaptic markers. In our confocal stacks, we can also identify PV immunolabeled dendrites and colocalized vGlut1/PSD95 or vGlut2/PSD95 puncta on them; but these do not appear in the selected images because, as explained, only one focal plane, centered on the PV cell somata, was shown.

      We acknowledge the reviewer's point that in PV+ cells the majority of excitatory inputs are formed onto dendrites; however, we focused on the somatic excitatory inputs to PV cells, because despite their lower number, they produce much stronger depolarization in PV neurons than dendritic excitatory inputs (Hu et al., 2010; Norenberg et al., 2010). Further, quantification of perisomatic putative excitatory synapses is more reliable since by using PV immunostaining, we can visualize the soma and larger primary dendrites, but smaller, higher order dendrites are not be always detectable. Of note, PV positive somata receive more excitatory synapses than SST positive and pyramidal neuron somata as found by electron microscopy studies in the visual cortex (Hwang et al., 2021; Elabbady et al., 2024).

      Regarding the comment on the density of vGlut1 and vGlut2 puncta, the reason that the numbers appear high and similar between the two markers is because we present normalized data (cHet normalized to their control values for each set of immunolabelling) to clearly represent the differences between genotypes. This information is present in the legends but we apologize for not clearly explaining it the methods section. We will provide a more detailed explanation of our methods in the revised manuscript.

      Briefly, immunostained sections were imaged using a Leica SP8-STED confocal microscope, with a 63x (NA 1.4) at 1024 X 1024, z-step =0.3 μm, stack size of ~15 μm. Images were acquired from the auditory cortex from at least 3 coronal sections per animal. All the confocal parameters were maintained constant throughout the acquisition of an experiment. All images shown in the figures are from a single confocal plane. To quantify the number of vGlut1/PSD95 or vGlut2/PSD95 putative synapses, images were exported as TIFF files and analyzed using Fiji (Image J) software. We first manually outlined the profile of each PV cell soma (identified by PV immunolabeling). At least 4 innervated somata were selected in each confocal stack. We then used a series of custom-made macros in Fiji as previously described (Chehrazi et al, 2023). After subtracting background (rolling value = 10) and Gaussian blur (σ value = 2) filters, the stacks were binarized and vGlut1/PSD95 or vGlut2/PSD95 puncta were independently identified around the perimeter of a targeted soma in the focal plane with the highest soma circumference. Puncta were quantified after filtering particles for size (included between 0-2μm2) and circularity (included between 0-1). Data quantification was done by investigators blind to the genotype, and presented as normalized data over control values for each experiment.

      (3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

      We agree with the reviewer, please see answer to point below.

      (‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

      We agree with the reviewer and we will perform additional paired-pulse ratio experiments at different intervals. We will rephrase the discussion and our interpretation and potential hypothesis according to the data obtained from this new experiment.

      (5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.

      We acknowledge that a higher sampling rate could offer a more detailed analysis of the action potential waveform. However, in the context of action potential analysis, it is acceptable to use sampling rates ranging from 10 kHz to 20 kHz (Golomb et al., 2007; Stevens et al., 2021; Zhang et al., 2023), which are considered adequate in the context of the present study. Indeed, our study aims to evaluate "relative" differences in the electrophysiological phenotype when comparing groups following a specific genetic manipulation. A sampling rate of 10 kHz is commonly employed in similar studies, including those conducted by our collaborator and co-author S. Kourrich (e.g., Kourrich and Thomas 2009, Kourrich et al., 2013), as well as others (Russo et al., 2013; Ünal et al., 2020; Chamberland et al., 2023).

      Despite being acquired at a lower sampling rate than potentially preferred by the reviewer, our data clearly demonstrate significant differences between the experimental groups, especially for parameters that are negligibly or not affected by the sampling rate used here (e.g., #spikes/input, RMP, Rin, Cm, Tm, AP amplitude, AP latency, AP rheobase).

      Regarding the phase-plots, we agree that a higher sampling rate would have resulted in smoother curves and more accurate absolute values. However, the differences were sufficiently pronounced to discern the relative variations in action potential waveforms between the experimental groups.

      A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

      We indeed performed bridge balance and neutralized the capacitance before starting every recording. We will add the information in the methods.

      (6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

      We are uncertain if we have correctly interpreted the reviewer's comment. While we observed distinct impacts on the rheobase (Fig. 7d and 7i), there seems to be a common effect on the AP threshold (Fig. 7c and 7h), as interpreted and indicated in the final sentence of the results section for Figure 7 (page 12). If our response does not address the reviewer's comment adequately, we would greatly appreciate it if the reviewer could rephrase their feedback.

      (7) Design issue:

      The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.

      Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

      While we acknowledge the reviewer's point that Kv1 currents and density weren't specifically tested, an important insight provided by Fig. 5 is the prolonged action potential latency. This delay is significantly influenced by slowly inactivating subthreshold potassium currents, namely the D-type K+ current. It's worth noting that D-type current is primarily mediated by members of the Kv1 family. The literature supports a role for Kv1.1-containing channels in modulating responses to near-threshold stimuli in PV cells (Wang et al., 1994; Goldberg et al., 2008; Zurita et al., 2018). However, we recognize that besides the Kv1 family, other families may also contribute to the observed changes.

      To address this concern, we will revise our interpretation. We will opt for the more accurate term "D-type K+ current" and only speculate about the involved channel family in the discussion. It is not our intention to open unnecessary controversy, but present the data we obtained. We believe this approach and rephrasing the discussion as proposed will prevent unnecessary controversy and instead foster fruitful discussions.

      (8) Writing issues:

      Abstract:

      The auditory system is not mentioned in the abstract.

      One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.

      We agree with the reviewer and we will rephrase the abstract accordingly.

      Introduction:

      There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

      We agree with the reviewer and we will rephrase more specifically.

      (9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

      In the initial experiments, we handled all PV cell data together because we wanted to be rigorous and not make assumptions/biases on the different PV cells, which in later experiments we were to distinguish based on the intrinsic properties alone. We will make this point clear in the revised manuscript.

      (10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

      We agree with the reviewer and apologize for the lack of clarity. Our unpublished work is in revision right now. We will provide more information and update references in the revised version of this manuscript.

      (11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

      We apologize for the error, we will change SOM with SST (SOM and SST are two commonly used acronyms for Somatostatin expressing interneurons).

      Reviewer #3 (Public Review):

      This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

      (1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

      The reviewer raises an interesting and pertinent open question which we will address in the discussion of the revised paper.

      (2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences.

      We respectfully disagree, we do not think the text and figures are inconsistent. In the cited example, large apparent difference in mean values does not show significance due to the large variability in the data; further, we did not exclude any data points, because we wanted to be rigorous. In particular, for Fig.1g, statistical analysis shows a significant increase in the inter-mEPSC interval (*p=0.027, LMM) when all events are considered (cumulative probability plots), while there is no significant difference in the inter-mEPSCs interval for inter-cell mean comparison (inset, p=0.354, LMM). Inter-cell mean comparison does not show difference with Mann-Whitney test either (p=0.101, the data are not normally distributed, hence the choice of the Mann-Whitney test). For Fig. 3f (eNMDA), the higher mean value for the cHet versus the control is driven by two data points which are particularly high, while the other data points overlap with the control values. The Mann-Whitney test show also no statistical difference (p=0.174).

      In the manuscript, discussion of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. In the supplemental tables, we provided the results of the statistical analysis done with both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.

      Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

      We apologize for our lack of clarity. In legend 9, we reported the statistical comparisons between 1) cHET mice in absence of a-DTX and control mice and 2) cHET mice in presence of a-DTX and control mice. We will rephrase result description and the legend of the figure to avoid confusion.

      (3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

      We agree with the reviewer that there appears to be a qualitative difference in eNMDA decay between conditions, although quantified eNMDA decay itself is similar between groups. We have used a cutoff of 15 % for the series resistance (Rs), which is significantly more stringent as compared to the cutoff typically used in electrophysiology, which are for the vast majority between 20 and 30%. To answer this concern, we re-examined the Rs, we compared Rs between groups and found no difference for Rs in eAMPA (13.2±0.5 in WT n=16 cells, 7 mice vs 13.7±0.3 in cHet n=14 cells, 7 mice, p=0.432 LMM) and eNMDA (12.7±0.7 in WT n=6 cells, 3 mice vs 13.8±0.7 in cHet n=6 cells, 5 mice, p=0.231, LMM). Thus, the apparent qualitative difference in eNMDA decay stems from inter-cell variability rather than inter-group differences. Notably, this discrepancy between the trace (Fig. 3a) and the data (Fig. 3f, right) is largely due to inter-cell variability, particularly in eNMDA, where a higher but non-significant decay rate is driven by a couple of very high values (Fig. 3f, right). In the revised manuscript, we will show traces that better represent our findings.

      (4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

      We acknowledge the variability in AP threshold data, with some APs appearing too depolarized to generate healthy spikes. However, we meticulously examined each AP that spiked at these depolarized thresholds and found that other intrinsic properties (such as Rin, Vrest, AP overshoot, etc.) all indicate that these cells are healthy. Therefore, to maintain objectivity and provide unbiased data to the community, we opted to include them in our analysis. It's worth noting that similar variability has been observed in other studies (Bengtsson Gonzales et al., 2020; Bertero et al., 2020).

      Further, we conducted a significance test on AP threshold excluding these potentially unhealthy cells and found that the significant differences persist. After removing two outliers from the cHet group with values of -16.5 and 20.6 mV, we obtain: -42.6±1.01 mV in control, n=33, 15 mice vs -36.2±1.1 mV in cHet, n=38 cells, 17 mice, ***p<0.001, LMM. Thus, whether these cells are included or excluded, our interpretations and conclusions remain unchanged.

      We would like to clarify that these data have not been corrected with the junction potential. We will add this info in the revised version.

      (5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

      We apologize for our lack of clarity. Although the analysis was done at high resolution, the figures were focused on showing multiple PV somata receiving excitatory inputs. We will add higher magnification figures and more detailed information in the methods of the revised version. Please also see our response to reviewer #2.

      (6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

      While we acknowledge the theoretical expectation that changes in intrinsic parameters should correlate with alterations in neuronal firing, the absence of differences in the parameters analyzed in this study should not overshadow the clear and significant decrease in firing rate observed in cHet SST+ cells. This decrease serves as a compelling indication of reduced intrinsic neuronal excitability. It's certainly possible that other intrinsic factors, not assessed in this study, may have contributed to this effect. However, exploring these mechanisms is beyond the scope of our current investigation. We will rephrase the discussion and add this limitation of our study in the revised version.

      (7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

      This study utilized a sampling rate of 10 kHz, which is a standard rate for action potential analysis in the present context. We will describe more extensively the technical details in the method section of the revised manuscript we are preparing. While we acknowledge that a higher sampling rate could have enhanced the clarity of the phase plot, our recording conditions, as detailed in our response to Rev#2/comment#5, were suitable for the objectives of this study.

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    1. Author response:

      Reviewer #1

      The first is that data on the general health of mice with single and double knockouts is not shown, nor is there any data on effects in any other tissues. This gives the impression that the only phenotype is in the male reproductive system, which would be misleading if there were phenotypes in other tissues that are not reported.

      We thank the reviewer for helpful and constructive suggestions that we plan to implement in the revision. We agree with this point and we will add a statement that the effect on the urogenital system was not the only observed phenotype, although it was the most striking histological feature that we found. We did notice some other physiological differences that we are examining in detail and determining their mechanisms, for future publications.

      Furthermore, data for the genitourinary system in single knockouts are very sparse; data are described for fertility in Figure 1H, ploidy, and cell number in Figures 2B and C, plasma testosterone and luteinizing hormone levels in Figures 5C and 5D, and morphology of testis and prostate tissue for single Cdk8 knockout in Supplementary Figure 1C (although in this case the images do not appear very comparable between control and CDK8 KO, thus perhaps wider fields should be shown), but, for example, there is no analysis of different meiotic stages or of gene expression in single knockouts. It is worth mentioning that single knockouts seem to show a corresponding upregulation of the level of the paralogue kinase, indicating that any lack of phenotypes might be due to feedback compensation, which would be an interesting finding if confirmed; this has not been mentioned.

      We agree that a description of the single KO could be beneficial, but we expect no big differences with the WT or Cre-Ert. We found neither histological differences nor changes in cell counts or ratios of cell types. Our ethical committee also has concerns about sacrificing mice without major phenotypic changes, without a well formulated hypothesis about the observed effects. We plan to add histological pictures to the next version of the article.

      We thank the reviewer for raising an important point about the paralog upregulation. Indeed, our data on primary cells (supplementary 1B) suggests the upregulation of CDK19 in CDK8KO and vice versa. We will point this out in disc We plan to examine the data for the testis as soon as more tissues are available.

      The second major weakness is that the correlation between double knockout and reduced expression of genes involved in steroid hormone biosynthesis is portrayed as a causal mechanism for the phenotypes observed. While this is a possibility, there are no experiments performed to provide evidence that this is the case. Furthermore, there is no evidence showing that CDK8 and/or CDK19 are directly responsible for the transcription of the genes concerned.

      We agree with the reviewer that the effects on CDK8/CDK19/CCNC could lead to the observed transcriptional changes in multiple indirect steps. There are, however, major technical challenges in examining the binding of transcription factors in the tissue, especially in Leydig cells which are a relatively minor population. We will clarify it in the revision, and strengthen this point in the discussion.

      Finally, the authors propose that the phenotypes are independent of the kinase activity of CDK8 or CDK19 because treatment of mice for a month with an inhibitor does not recapitulate the effects of the knockout, and nor does expression of two steroidogenic genes change in cultured Leydig cells upon treatment with an inhibitor. However, there are no controls for effective target inhibition shown.

      We thank the reviewer for raising this concern, which we will address in the revision. This study used the same CDK8/19 inhibitor (SNX631-6) as in the recently published study on prostate cancer (doi: 10.1172/JCI176709). That study describes the inhibitor, its target engagement in cell-free and cell-based assays, its anticancer potency, and its transcriptomic effects in vivo, the same dosage strength as in the present study, which phenocopy the effects of CDK8/19 knockdown. Additional data will be included in the revision.

      Reviewer #2

      The claim of reproductive defects in the induced double knockout of CDK8/19 resulted from the loss of CCNC via a kinase-independent mechanism is interesting but was not supported by the data presented. While the construction and analysis of the systemic induced knockout model of Cdk8 in Cdk19KO mice is not trivial, the analysis and data are weakened by the systemic effect of Cdk8 loss, making it difficult to separate the systemic effect from the local testis effect.

      We agree with the reviewer that the effects on the testis could be due to the systemic loss of CDK8 rather than specifically in the testis, and we will clarify it in the revision. We will also clarify that although our results are suggestive that the effects of CDK8/19 knockout are kinase-independent, and that the loss of Cyclin C is a likely explanation for the kinase independence but we do not claim that it is the mechanism.

      The analysis of male sterile phenotype is also inadequate with poor image quality, especially testis HE sections. The male reproductive tract picture is also small and difficult to evaluate.

      Unfortunately, during the submission process through Biorxiv the quality of the image worsened. We uploaded the high resolution pictures for the journal but probably they were not presented for the reviewer. We will re-send the high resolution images.

      The mice crossing scheme is unusual as you have three mice to cross to produce genotypes, while we could understand that it is possible to produce pups of desired genotypes with different mating schemes, such a vague crossing scheme is not desirable and of poor genetics practice.

      We thank the reviewer for this suggestion. Indeed, our scheme is not a representation of the actual breeding scheme but just a brief explanation of lineages used for the acquisition of the triple transgenic mice. We will include the full crossing scheme into the revision.

      Also using TAM-treated wild type as control is ok, but a better control will be TAM-treated ERT2-cre; CDK8f/f or TAM-treated ERT2 Cre CDK19/19 KO, so as to minimize the impact from the well-recognized effect of TAM.

      We used TAM-treated ERT2-cre for most of the experiments, and did not observe any major histological or physiological differences with the WT+TAM. We will make sure to present them in the revision.

      While the authors proposed that the inducible loss of CDK8 in the CDK19 knockout background is responsible for spermatogenic defects, it was not clear in which cells CDK8/19 genes are interested and which cell types might have a major role in spermatogenesis. The authors also put forward the evidence that reduction/loss of Testosterone might be the main cause of spermatogenic defects, which is consistent with the expression change in genes involved in steroigenesis pathway in Leydig cells of inducible double knockout. However it is not clear how the loss of Testosterone contributed to the loss of CcnC protein.

      We agree with the reviewer that the spermatogenic defects could be caused by the effects on gene expression in tissues other than Leydig cells. Nevertheless, this is our primary hypothesis since these changes resemble the effects of chemical castration in rats (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408499/), and in SCARKO mice (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968405/).

      Our hypothesis is actually the reversed scenario proposed by the reviewer. We think that the loss of steroidogenic gene expression is caused by the loss of CDK8/19 and Cyclin C in Leydig cells. This, in turn, leads to a drop of testosterone levels. We will expand this explanation for clarity.

      The authors should clarify or present the data on where CDK8 and CDK19 as well as CcnC are expressed so as to help the readers understand which tissues both CDK might be functioning in and cause the loss of CcnC. It should be easier to test the hypothesis of CDK8/19 stabilizing CcnC protein using double knock-out primary cells, instead of the whole testis.

      The stabilizing effect of Cdk8/19 on CcnC has been previously discovered and reported in cell culture (doi: 10.1093/nar/gkad538.), and here we have confirmed it at the level of whole tissue. Due to a limited sensitivity of single cell sequencing (only ~5,000 transcripts are sequenced from total of average 500,000 transcripts per cell, so the low expressed transcripts are not sequenced in all cells) it is challenging to firmly establish CDK8/19 positive and -negative tissues from single cell data because both transcripts are minor. This image will be included in the next version. We plan to resolve this matter using two approaches. First, we will try immunohistochemistry. If this method is not sufficiently sensitive we will analyze published single cell sequencing data from mouse databases and re-analyze our data. So far the former approach was challenging for us due to the absence of anti-mouse antibodies which are specific for CDK8 and CDK19 and work on tissue sections. We and others could not produce a tissue-specific staining, with the currently available commercially available antibodies. The only published specific antibody is currently not available.

      Since CDK8KO and CDK19KO have significantly reduced fertility compared to the wild type, it might be important to measure the sperm quantity and motility among CDK8 KO, CDK19KO, and induced DKO to evaluate spermatogenesis based on their sperm production.

      We agree that this is an interesting question. We did not do spermograms for single KOs but we don’t think that a decreased sperm count would explain CDK8KO infertility as the vasectomized males are able to produce copulative plugs in females whereas CDK8KO males do not, suggesting the absence of mating behavior as a reason for low fertility in the latter genotype.

      Some data for the inducible knockout efficiency of Cdk8 were presented in Supplemental Figure 1, but there is no legend for the supplemental figures, it was not clear which band represented the deletion band, and which tissues were examined. Tail or testis?

      We apologize for the accidental loss of supplementary figure legends, which will be presented in the next version. The efficiency of CDK8 KO in different tissues was previously examined by us in https://www.ncbi.nlm.nih.gov/gene/264064. The western blot in the MS represents deletion data for the testis.

      It seems that two months after the injection of Tam, all the Cdk8 were completely deleted, indicating extremely efficient deletion of Tam induction by two months post administration. Were the complete deletion of Cdk8 happening even earlier?

      The complete deletion of CDK8 occurs within a week or even as early as 2-3 days in culture, and at least after at two weeks in vivo. We chose the two mo. period to prevent the effect of tamoxifen on gene expression. We examined other time points (Figure 6) and registered the beginning of effects at 2 weeks and maximum effect by one mo.

      The authors found that Sertoli cells re-entered the cell cycle in the inducible double knockout but stopped short of careful characterization other than increased expression of cell cycle genes.

      We agree with the reviewer, and we will add Ki67 (or equivalent) staining along with Sertoli cell markers.

      Dko should be appropriately named iDKO (induced dKO).

      We will make the corresponding change.

      We performed necropsy ? not the right wording here. Colchicine-lke apoptotic bodies ? what does this mean? Not clear.

      We will amend the next version to address these minor points, and we thank the reviewer for careful reading of the manuscript.

      Images throughout the manuscript suffer from poor resolution and are often blurry and hard to evaluate.

      As mentioned above, we had a problem with image quality during the submission through Biorxiv and we will provide high resolution images in the next version.

      To pinpoint the meiotic stage defect of iDKO, it is better to use the meiotic chromosome spread approach.

      Unfortunately, meiotic spreads would not be feasible or informative, due to a low number of surviving cells in iDKO and the fact that there were evidently no cells in stages after SYCP3+.

    1. Author response:

      eLife assessment

      This is an important study describing a neuromuscular junction co-culture system using human cells that the authors use to study the synaptic consequences of ALS mutations. The data supporting the system are solid and show the value of using myotubes and motor neurons from the same donor. The study will be of interest to researchers who model neuromuscular junction disorders, however, the authors could more comprehensively compare and contrast their system with previous literature describing other similar models. There are also technical weaknesses that limit the interpretation of specific findings.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors propose an improved neuro-muscle co-culture system to study ALS-related functional differences in human pluripotent stem cell lines.

      Strengths:

      A simple co-culture system with functional readout.

      We appreciate the recognition that this is a simplified co-culture system with a straight-forward functional evaluation.

      Weaknesses:

      There are concerns about the lack of novelty, rigor, and clarity in the approach. The strength of the study is undermined by its reliance on transcription factors used more than a decade ago, low myocyte activity, and inadequate validation methods, such as the lack of single-cell transcriptome analysis and detailed neuromuscular synapse characterization. The evidence presented requires substantial validation through rigorous experimental approaches and resolution of the identified concerns for the study's findings to be considered significant and reliable.

      The muscle differentiation protocol used in our work is an adaptation of the Albini S, et al. Cell Rep. 2013. This protocol was selected due to its efficiency to differentiate skeletal muscles from pluripotent stem cells (PSCs). Modifications from the original publications were made in the plasmids (MYOD and BAF60C) used, such as the inclusion of selection genes, puromycin and blasticidin, to improve efficiency. Moreover, a criticism of the previously used overexpression system, especially overexpression of MYOD, is that it introduces artificial expression of this gene throughout muscle differentiation, when it is only supposed to be expressed early in myogenesis. Thus, the constructs used in our work are dox inducible, which enables us to control the expression of MYOD and restrict it to the first 48 hours. This protocol resulted in a highly efficient skeletal muscle differentiation, as noted in our manuscript. “The PSC-derived skeletal muscles were characterized by the presence of Desmin (DES) and Myosin Heavy Chain (MHC), and as early as day 8 of differentiation nearly 100% of the cells co-expressed these markers.” We agree with the reviewer that the myocyte activity identified in our work is lower compared to Albini et al. (2013), mostly explained by the modification we made to the method, from a 3D to a 2D culture. In Albini et al. (2013) the electrophysiological properties were assayed in skeletal myospheres (3D), which are known to improve contractility measurements. Conversely, in 2D cultures when the contractility intensifies the cells detach from the plate. Thus, a tight regulation of cell concentration for optimal maturation and formation of contractile skeletal muscle culture without premature detachment of the cells is required. We believe that single-cell or single-nuclei transcriptome analysis from the co-culture setting of two well-defined cell types might yield little value for method characterization, however, as part of a follow up study we are performing morphological NMJ characterization and applying single-nuclei transcriptome analysis in the fALS disease context to identify specific molecular mechanisms that result in synaptic dysfunction.

      Reviewer #2 (Public Review):

      The manuscript by Chen et al from the group of Helen Miranda aims to describe an improved neuromuscular junction (NMJ) model to study synaptic dysfunction in several cases of familial ALS. Overall, the system described in the paper appears as a valid platform to study disease phenotypes with exciting results showing specific effects of GDNF on non-SOD1 ALS patient lines. The strength of the paper lies in the use of myotubes, and motor neurons derived from the same donor. However, the current study: (1) lacks a clear comparison of the current system with numerous previously described systems; (2) is limited by the number of repeat experiments in the study and (3) has no description of the synaptic phenotype observed in the study. These major points are discussed in more detail below.

      We appreciate the recognition that “the system described in the paper appears as a valid platform to study disease phenotypes with exciting results showing specific effects of GDNF on non-SOD1 ALS patient lines” and the careful evaluation of our work. We plan to address the points raised by this reviewer in the revision.

      Major points:

      (1) In the introduction the authors state (p. 4): "Finally, recent human NMJ models have been established from PSCs by differentiating these cells into both skeletal muscles and motor neurons in 2D and 3D formats. These previous systems present a remarkable advancement to the studies of human NMJs, however, they require long NMJ formation and maturation time (40 to 60 days), which, restricts their sensitivity and scalability [42]"

      In fact, a number of studies have described various in-vitro NMJ systems, with the same timeframes for NMJ formation. For example, in studies by Osaki et al, 2018, Sci Adv; Bellmann et al, 2019, Biomat; Demestre et al, 2015, Stem Cell Res; Badu-Mensah et al, 2022, Biomat (this is just an exemplar selection of the papers); NMJ formation was observed as early as 14 d in culture, in line with or at least slightly longer than reported by Chen et al. With the exception of the study by Osaki et al, all co-culture systems cited above are 2D-based. The authors need to expand on this further or provide a quantitative assessment of why their system is better compared to previously published models.

      Indeed, there are previous publications that have described neuromuscular junctions (NMJs) in cocultures of iPSC-derived skeletal muscles and motor neurons. Some of the publications mentioned above did show NMJ formation within ~20ish days, albeit with several caveats such as culture heterogeneity, i.e. 50% motor neuron differentiation efficiency. We agree with the reviewer that this needs to be expanded and clarified, and we will address this concern in the revision.

      (2) Further, when comparing their results with other work it is hard to claim how the current system is (p. 5) "more reproducible, and offers a 6-fold increase in scalability compared to previous models [40-43]".

      The authors need to expand on this further.

      This is an important aspect of this work, and we believe that our protocol offers a higher reproducibility due to, at least partially, the homogeneity of the starting cultures of iPSC-derived skeletal muscles and iPSC-derived motor neurons, and that the direct 2D co-culture approach is more suitable for miniaturization compared to 3D cultures or microfluidic chamber devices. Thus, we will expand on this idea in the revision.

      (3) Although mentioned, there were no examples of the modularity of the system, which of course would strengthen the paper and help to uncover ALS mechanisms of synaptic formation, for example by combining WT myotubes and fALS motor neurons (see point 4 below). The authors should show how they would adapt to 96 well plate format to showcase the scalability of the system. Based on their data on the efficacy of synaptic formation (60 per 0.7 cm2 area), is further miniaturization allowed?

      We appreciate the points raised by the reviewer. The “mix-and-match” approach to co-culture wild-type and affected iPSC-derived skeletal muscles with iPSC-derived motor neurons is a main focus of our lab and an advantage to protocols like ours, where cells are differentiated independently and later co-cultured together; however, a comprehensive characterization of various mix-match combinations is beyond the scope of this Tools and Resources article. Since the initial submission of this manuscript, we have extensively optimized the scalability of the co-cultures from the initial 0.7 cm2 to 0.32 cm2 (96-well plates). Further miniaturization is also being optimized to 0.136 cm2 (384-well plates). This point will be clarified in the revision.

      (4) A lot of a-bungarotoxin staining corresponds to AChR clusters that do not seem to be associated with muscle and do not form normal rings of clustering (pretzel-like) associated with the NMJ in vivo. This is seen clearly in Figure 3B and Figure 5B. Figures 3B and 5B only show low-magnification images which makes it difficult to assess the specificity of localization of the pre-/post-synaptic markers. The authors should clearly show the morphologies of the NMJs formed in WT and fALS lines at high magnification. In addition, the authors should show co-localization images for a-bungarotoxin and myosin-heavy chains to confirm the localization of the bungarotoxin signal on the myotubes.

      In addition to that, the authors report that the number of functional synapses formed on a plate varies from 30 (fASL) to 60 (Ctrl) per 10,000 neurons spread over the 0.7 cm2 area (0.6%). How do the authors explain an extensive loss of a-bungarotoxin signal in Figure 5B the majority of which likely corresponds to AChR clusters that are formed outside of neuronal connections? Such clustering can be usually observed in immature co-cultures and in vivo prior to the innervation of myotubes. One explanation could be that myotubes derived from fALS PSC are less capable of synaptic formation. Noteworthy, a study of PSCderived myotubes and motor neurons from PSC lines with various SOD1 mutations has already been published, but not cited by Chen et al (Badu-Mensah et al). Given the importance of those confounding factors, the authors should test cell-intrinsic (motor neuron-related) vs non-cell-intrinsic mechanisms by co-culturing healthy myotubes with fALS-derived motor neurons followed by NMJ quantification.

      The iPSC-derived skeletal muscle cultures were plated as a monolayer and even though the abungarotoxin staining does not show the pretzel-like shape NMJs, similar to other in vitro NMJ protocols (Badu-Mensah et al, Biomat 2023; Pereira et al., Nat Commun 2021; Uzel et al., Sci Adv 2016), abungarotoxin does show association with the muscles. For quantification purposes we omitted the MHC staining to decrease background, however we will include it in the revision in response to the reviewer’s concern.

      We agree with the reviewer that the suggested approaches would yield insight into disease mechanism but are beyond the scope of this method development study. In fact, we are very excited about our follow up study pursuing a more in-depth analysis of cell-autonomous vs non-cell autonomous pathogenesis to understand the NMJ dysfunction in fALS. We apologize that the “Badu-Mensah et al” work was not included, this was our oversight and will be added in the revision.

      (5) The authors present the advantage of optogenetic stimulation, but they only show the proof-ofprinciple and never really apply it to their studies. Specifically, with regard to Figure 6, are motor units derived from fALS PSCs incapable of being ontogenetically activated to the same extent as control motor units? Does the dysfunction stem from fALS motor neurons or fALS myotubes?

      We agree that these are important questions to be addressed and are actively pursuing these experiments as part of the natural follow up investigation from the present Tools and Resources article.

      (6) Figures 6 B and C appear to be identical except for the addition of the GDNF effect on the fALS lines. This should all be put in one figure. The authors should also show whether GDNF-induced functional recovery is associated with recovery in the number of motor units or with merely synaptic function by quantifying the NMJ number in the presence of GDNF.

      We will combine Figures 6B and 6C in the revision. Our follow up study also includes the interrogation of the mechanism through which GDNF rescues fALS NMJ dysfunction.

      (7) Figure 5 and Figure 6. The authors only use one line per fALS mutation and their corresponding isogenic controls. They state that the n=6 for these experiments represents the technical replication of the experiment. These experiments should be performed at least n=3 times starting from neuronal differentiation, and not by seeding replicate wells representing a true replication of each experiment. This would significantly strengthen their argument that their method is robust and the results are easily reproducible.

      We will clarify that the technical replicates originated from independent differentiations in the revision.

      (8) In the discussion the authors may want to mention that the lack of function of GDNF on the SOD1 lines may relate to the fact that SOD1 mutations do not lead to TDP43 pathology. Although speculative this suggests that in cases with TDP43 mutations (their data) or sporadic disease GDNF may be effective.

      We appreciate this suggestion and will highlight this as possible inclusion criteria for GDNF treatment in the discussion of our revised version of the manuscript.

      (9) Although beyond the scope of this paper, it would of course be interesting to see if sporadic forms of ALS had this same phenotype.

      We agree with the reviewer and we hope to include iPSC derived NMJs from sporadic ALS patients in a future study.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Strengths:

      This work (almost didactically) demonstrates how to develop, calibrate, validate and analyze a comprehensive, spatially resolved, dynamical, multicellular model. Testable model predictions of (also non-monotonic) emergent behaviors are derived and discussed. The computational model is based on a widely-used simulation platform and shared openly such that it can be further analyzed and refined by the community.

      Weaknesses:

      While the parameter estimation approach is sophisticated, this work does not address issues of structural and practical non-identifiability (Wieland et al., 2021, DOI:10.1016/j.coisb.2021.03.005) of parameter values, given just tissue-scale summary statistics, and does not address how model predictions might change if alternative parameter combinations were used. Here, the calibrated model represents one point estimate (column "Value" in Suppl. Table 1) but there is specific uncertainty of each individual parameter value and such uncertainties need to be propagated (which is computationally expensive) to the model predictions for treatment scenarios.

      We thank the reviewer for the excellent suggestions and observations. The CaliPro parameterization technique applied puts an emphasis on finding a robust parameter space instead of a global optimum. To address structural non-identifiability, we utilized partial rank correlation coefficient with each iteration of the calibration process to ensure that the sensitivity of each parameter was relevant to model outputs. We also found that there were ranges of parameter values that would achieve passing criteria but when testing the ranges in replicate resulted in inconsistent outcomes. This led us to further narrow the parameters into a single parameter set that still had stochastic variability but did not have such large variability between replicate runs that it would be unreliable. Additional discussion on this point has been added to lines 623-628. We acknowledge that there are likely other parameter sets or model rules that would produce similar outcomes but the main purpose of the model was to utilize it to better understand the system and make new predictions, which our calibration scheme allowed us to accomplish.

      Regarding practical non-identifiability, we acknowledge that there are some behaviors that are not captured in the model because those behaviors were not specifically captured in the calibration data. To ensure that the behaviors necessary to answer the aims of our paper were included, we used multiple different datasets and calibrated with multiple different output metrics. We believe we have identified the appropriate parameters to recapitulate the dominating mechanisms underlying muscle regeneration. We have added additional discussion on practical non-identifiability to lines 621-623.

      Suggested treatments (e.g. lines 484-486) are modeled as parameter changes of the endogenous cytokines (corresponding to genetic mutations!) whereas the administration of modified cytokines with changed parameter values would require a duplication of model components and interactions in the model such that cells interact with the superposition of endogenous and administered cytokine fields. Specifically, as the authors also aim at 'injections of exogenously delivered cytokines' (lines 578, 579) and propose altering decay rates or diffusion coefficients (Fig. 7), there needs to be a duplication of variables in the model to account for the coexistence of cytokine subtypes. One set of equations would have unaltered (endogenous) and another one have altered (exogenous or drugged) parameter values. Cells would interact with both of them.

      Our perturbations did not include delivery of exogenously delivered cytokines and instead were focused on microenvironmental changes in cytokine diffusion and decay rates or specific cytokine concentration levels. For example, the purpose of the VEGF delivery perturbation was to test how an increase in VEGF concentrations would alter regeneration outcome metrics with the assumption that the delivered VEGF would act in the same manner as the endogenous VEGF. We have clarified the purpose of the simulations on line 410. We agree that exploring if model predictions would be altered if endogenous and exogenous were represented separately; however, we did not explore this type of scenario.

      This work shows interesting emergent behavior from nonlinear cytokine interactions but the analysis does not provide insights into the underlying causes, e.g. which of the feedback loops dominates early versus late during a time course.

      Indeed, analyzing the model to fully understand the time-varying interactions between the multiple feedback loops is a challenge in and of itself, and we appreciate the opportunity to elaborate on our approach to addressing this challenge. First: the crosstalk/feedback between cytokines and the temporal nature was analyzed in the heatmap (Fig. 6) and lines 474-482. Second: the sensitivity of cytokine parameters to specific outputs was included in Table 9 and full-time course sensitivity is included in Supplemental Figure 2. Further correlation analysis was also included to demonstrate how cytokine concentrations influenced specific output metrics at various timepoints (Supplemental Fig. 3). We agree that further elaboration of these findings is required; therefore, we added lines 504-509 to discuss the specific mechanisms at play with the combined cytokine interactions. We also added more discussion (lines 637-638) regarding future work that could develop more analysis methods to further investigate the complex behaviors in the model.

      Reviewer #2 (Public Review):

      Strengths:

      The manuscript identified relevant model parameters from a long list of biological studies. This collation of a large amount of literature into one framework has the potential to be very useful to other authors. The mathematical methods used for parameterization and validation are transparent.

      Weaknesses:>

      I have a few concerns which I believe need to be addressed fully.

      My main concerns are the following:

      (1) The model is compared to experimental data in multiple results figures. However, the actual experiments used in these figures are not described. To me as a reviewer, that makes it impossible to judge whether appropriate data was chosen, or whether the model is a suitable descriptor of the chosen experiments. Enough detail needs to be provided so that these judgements can be made.

      Thank you for raising this point. We created a new table (Supplemental table 6) that describes the techniques used for each experimental measurement.

      (2) Do I understand it correctly that all simulations are done using the same initial simulation geometry? Would it be possible to test the sensitivity of the paper results to this geometry? Perhaps another histological image could be chosen as the initial condition, or alternative initial conditions could be generated in silico? If changing initial conditions is an unreasonably large request, could the authors discuss this issue in the manuscript?

      We appreciate your insightful question regarding the initial simulation geometry in our model. The initial configuration of the fibers/ECM/microvascular structures was kept consistent but the location of the necrosis was randomly placed for each simulation. Future work will include an in-depth analysis of altered histology configuration on model predictions which has been added to lines 618-621. We did a preliminary example analysis by inputting a different initial simulation geometry, which predicted similar regeneration outcomes. We have added Supplemental Figure 5 that provides the results of that example analysis.

      (3) Cytokine knockdowns are simulated by 'adjusting the diffusion and decay parameters' (line 372). Is that the correct simulation of a knockdown? How are these knockdowns achieved experimentally? Wouldn't the correct implementation of a knockdown be that the production or secretion of the cytokine is reduced? I am not sure whether it's possible to design an experimental perturbation which affects both parameters.

      We appreciate that this important question has been posed. Yes, in order to simulate the knockout conditions, the cytokine secretion was reduced/eliminated. The diffusion and decay parameters were also adjusted to ensure that the concentration within the system was reduced. Lines 391-394 were added to clarify this assumption.

      (4) The premise of the model is to identify optimal treatment strategies for muscle injury (as per the first sentence of the abstract). I am a bit surprised that the implemented experimental perturbations don't seem to address this aim. In Figure 7 of the manuscript, cytokine alterations are explored which affect muscle recovery after injury. This is great, but I don't believe the chosen alterations can be done in experimental or clinical settings. Are there drugs that affect cytokine diffusion? If not, wouldn't it be better to select perturbations that are clinically or experimentally feasible for this analysis? A strength of the model is its versatility, so it seems counterintuitive to me to not use that versatility in a way that has practical relevance. - I may well misunderstand this though, maybe the investigated parameters are indeed possible drug targets.

      Thank you for your thoughtful feedback. The first sentence (lines 32-34) of the abstract was revised to focus on beneficial microenvironmental conditions to best reflect the purpose of the model. The clinical relevance of the cytokine modifications is included in the discussion (lines 547-558) with additional information added to lines 524-526. For example, two methods to alter diffusion experimentally are: antibodies that bind directly to the cytokine to prevent it from binding to its receptor on the cell surface and plasmins that induce the release of bound cytokines.

      (5) A similar comment applies to Figure 5 and 6: Should I think of these results as experimentally testable predictions? Are any of the results surprising or new, for example in the sense that one would not have expected other cytokines to be affected as described in Figure 6?

      We appreciate the opportunity to clarify the basis for these perturbations. The perturbations included in Figure 5 were designed to mimic the conditions of a published experiment that delivered VEGF in vivo (Arsic et al. 2004, DOI:10.1016/J.YMTHE.2004.08.007). The perturbation input conditions and experimental results are included in Table 8 and Supplemental Table 6 has been added to include experimental data and method description of the perturbation. The results of this analysis provide both validation and new predictions, because some the outputs were measured in the experiments while others were not measured. The additional output metrics and timepoints that were not collected in the experiment allow for a deeper understanding of the dynamics and mechanisms leading to the changes in muscle recovery (lines 437-454). These model outputs can provide the basis for future experiments; for example, they highlight which time points would be more important to measure and even provide predicted effect sizes that could be the basis for a power analysis (lines 639-640).

      Regarding Figure 6, the published experimental outcomes of cytokine KOs are included in Table 8. The model allowed comparison of different cytokine concentrations at various timepoints when other cytokines were removed from the system due to the KO condition. The experimental results did not provide data on the impact on other cytokine concentrations but by using the model we were able to predict temporally based feedback between cytokines (lines 474-482). These cytokine values could be collected experimentally but would be time consuming and expensive. The results of these perturbations revealed the complex nature of the relationship between cytokines and how removal of one cytokine from the system has a cascading temporal impact. Lines 533-534 have been added to incorporate this into the discussion.

      (6) In figure 4, there were differences between the experiments and the model in two of the rows. Are these differences discussed anywhere in the manuscript?

      We appreciate your keen observation and the opportunity to address these differences. The model did not match experimental results for CSA output in the TNF KO and antiinflammatory nanoparticle perturbation or TGF levels with the macrophage depletion. While it did align with the other experimental metrics from those studies, it is likely that there are other mechanisms at play in the experimental conditions that were not captured by simulating the downstream effects of the experimental perturbations. We have added discussion of the differences to lines 445-454.

      (7) The variation between experimental results is much higher than the variation of results in the model. For example, in Figure 3 the error bars around experimental results are an order of magnitude larger than the simulated confidence interval. Do the authors have any insights into why the model is less variable than the experimental data? Does this have to do with the chosen initial condition, i.e. do you think that the experimental variability is due to variation in the geometries of the measured samples?

      Thank you for your insightful observations and questions. The lower model variability is attributed to the larger sample size of model simulations compared to experimental subjects. By running 100 simulations it narrows in the confidence interval (average 2.4 and max 3.3) compared to the experiments that typically had a sample size of less than 15. If the number of simulations had been reduced to 15 the stochasticity within the model results in a larger confidence interval (average 7.1 and max 10). There are also several possible confounding variables in the experimental protocols (i.e. variations in injury, different animal subjects for each timepoint, etc.) that are kept constant in the model simulation. We have added discussion of this point to the manuscript (lines 517519). Future work with the model will examine how variations in conditions, such as initial muscle geometry, injury, etc, alter regeneration outcomes and overall variability. This discussion has been incorporated into lines 640-643.

      (8) Is figure 2B described anywhere in the text? I could not find its description.

      Thank you for pointing that out. We have added a reference for Fig. 2B on line 190.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The model code seems to be available from https://simtk.org/projects/muscle_regen but that website requests member status ("This is a private project. You must be a member to view its contents.") and applying for membership could violate eLife's blind review process. So, this reviewer liked to but couldn't run the model her/himself. To eLife: Can the authors upload their model to a neutral server that reviewers and editors can access anonymously?

      The code has been made publicly available on the following sites:

      SimTK: https://simtk.org/docman/?group_id=2635

      Zendo: https://zenodo.org/records/10403014

      GitHub: https://github.com/mh2uk/ABM-of-Muscle-Regeneration-with-MicrovascularRemodeling

      Line 121 has been updated with the new link and the additional resources were added to lines 654-657.

      (2) The muscle regeneration field typically studies 2D cross-sections and the present model can be well compared to these other 2D models but cells as stochastic and localized sources of diffusible cytokines may yield different cytokine fields in 3D vs. 2D. I would expect more broadened and smoothened cytokine fields (from sources in neighboring cross-sections) than what the 2D model predicts based on sources just within the focus cross-section. Such relations of 2D to 3D should be discussed.

      We thank the reviewer for the excellent suggestions and observations. It has been reported in other Compucell3D models (Sego et al. 2017, DOI:10.1088/17585090/aa6ed4) that the convergence of diffusion solutions between 2D and 3D model configurations had similar outcomes, with the 3D simulations presenting excessive computational cost without contributing any noticeable additional accuracy. Similarly, other cell-based ABMs that incorporate diffusion mechanisms (Marino et al. 2018, DOI:10.3390/computation6040058) have found that 2D and 3D versions of the model both predict the same mechanisms and that the 2D resolution was sufficient for determining outcomes. Lines 615-618 were added to elaborate on this topic.

      (3) Since the model (and title) focuses on "nonlinear" cytokine interactions, what would change if cytokine decay would not be linear (as modeled here) but saturated (with nonlinear Michaelis-Menten kinetics as ligand binding and endocytosis mechanisms would call for)?

      Thank you for raising an intriguing point. The model includes a combination of cytokine decay as well as ligand binding and endocytosis mechanisms that can be saturated. For a cytokine-dependent model behavior to occur the cytokines necessary to induce that action had to reach a minimum threshold. Once that threshold was reached, that amount of the cytokine would be removed at that location to simulate ligand-receptor binding and endocytosis. These ligand binding and endocytosis mechanisms behave in a saturated way, removing a set amount when above a certain threshold or a defined ratio when under the threshold. Lines 313-315 was revised to clarify this point. There were certain concentrations of cytokines where we saw a plateau in outputs likely as a result of reaching a saturation threshold (Supplemental Fig. 3). In future work, more robust mathematical simulation of binding kinetics of cytokines (e.g., using ODEs) could be included.

      (4) Limitations of the model should be discussed together with an outlook for model refinement. For example, fiber alignment and ECM ultrastructure may require anisotropic diffusion. Many of the rate equations could be considered with saturation parameters etc. There are so many model assumptions. Please discuss which would be the most urgent model refinements and, to achieve these, which would be the most informative next experiments to perform.

      We appreciate your thoughtful consideration of the model's limitations and the need for a comprehensive discussion on model refinements and potential future experiments. The future direction section was expanded to discuss additional possible model refinements (lines 635-643) and additional possible experiments for model validation (lines 630-634).

      (5) It is not clear how the single spatial arrangement that is used affects the model predictions. E.g. now the damaged area surrounds the lymphatic vessel but what if the opposite corner was damaged and the lymphatic vessel is deep inside the healthy area?

      Thank you for highlighting the importance of considering different spatial arrangements in the model and its potential impact on predictions. We previously tested model perturbations that included specifying the injury surrounding the lymphatic vessel versus on the side opposite the vessel. Since this paper focuses more on cytokine dynamics, we plan to include this perturbation, along with other injury alterations, in a follow-on paper. We added more context about this in the future efforts section lines 640-643.

      (6) It seems that not only parameter values but also the initial values of most of the model components are unknown. The parameter estimation strategy does not seem to include the initial (spatial) distributions of collagen and cytokines and other model components. Please discuss how other (reasonable) initial values or spatial arrangements will affect model predictions.

      We appreciate your thoughtful consideration of unknown initial values/spatial arrangements and their potential influence on predictions. Initial cytokine levels prior to injury had a low relative concentration compared to levels post injury and were assumed to be negligible. Initial spatial distribution of cytokines was not defined as initial spatial inputs (except in knockout simulations) but are secreted from cells (with baseline resident cell counts defined from the literature). The distribution of cytokines is an emergent behavior that results from the cell behaviors within the model. The collagen distribution is altered in response to clearance of necrosis by the immune cells (decreased collagen with necrosis removal) and subsequent secretion of collagen by fibroblasts. The secretion of collagen from fibroblast was included in the parameter estimation sweep (Supplemental Table 1).

      We are working on further exploring the model sensitivity to altered spatial arrangements and have added this to the future directions section (lines 618-621), as well as provided Supplemental Figure 5 to demonstrate that model outcomes are similar with altered initial spatial arrangements.

      (7) Many details of the CC3D implementation are missing: overall lattice size, interaction neighborhood order, and "temperature" of the Metropolis algorithm. Are the typical adhesion energy terms used in the CPM Hamiltonian and if so, then how are these parameter values estimated?

      Thank you for bringing attention to the missing details regarding the CC3D implementation in our manuscript. We have included supplemental information providing greater detail for CPM implementation (Lines 808-854). We also added two additional supplemental tables for describing the requested CC3D implementation details (Supplemental Table 4) and adhesion energy terms (Supplemental Table 5).

      (8) Extending the model analysis of combinations of altered cytokine properties, which temporal schedules of administration would be of interest, and how could the timing of multiple interventions improve outcomes? Such a discussion or even analysis would further underscore the usefulness of the model.

      In response to your valuable suggestion, lines 558-562 were added to discuss the potential of using the model as a tool to perturb different cytokine combinations at varying timepoints throughout regeneration. In addition, this is also included in future work in lines 636-637.

      (9) The CPM is only weakly motivated, just one sentence on lines 142-145 which mentions diffusion in a misleading way as the CPM just provides cells with a shape and mechanical interactions. The diffusion part is a feature of the hybrid CompuCell3D framework, not the CPM.

      Thank you for bringing up this distinction. We removed the statement regarding diffusion and updated lines 143-146 to focus on CPM representation of cellular behavior and interactions. We also added a reference to supplemental text that includes additional details on CPM.

      (10) On lines 258-261 it does not become clear how the described springs can direct fibroblasts towards areas of low-density collagen ECM. Are the lambdas dependent on collagen density?

      Thank you for highlighting this area for clarification. The fibroblasts form links with low collagen density ECM and then are pulled towards those areas based on a constant lambda value. The links between the fibroblast and the ECM will only be made if the collagen is below a certain threshold. We added additional clarification to lines 260-264.

      (11) On line 281, what does the last part in "Fibers...were regenerating but not fully apoptotic cells" mean? Maybe rephrase this.

      The last of part of that line indicates that there were some fibers surrounding the main injury site that were damaged but still had healthy portions, indicating that they were impacted by the injury and are regenerating but did not become fully apoptotic like the fiber cells at the main site of injury. We rephrased this line to indicate that the nearby fibers were damaged but not fully apoptotic.

      (12) Lines 290-293 describe interactions of cells and fields with localized structures (capillaries and lymphatic vessel). Please explain in more detail how "capillary agents...transport neutrophiles and monocytes" in the CPM model formalism. Are new cells added following rules? How is spatial crowding of the lattice around capillaries affecting these rules? Moreover, how can "lymphatic vessel...drain the nearby cytokines and cells"? How is this implemented in the CPM and how is "nearby" calculated? We appreciate your detailed inquiry into the interactions of cells and fields with localized structures. The neutrophils and monocytes are added to the simulation at the lattice sites above capillaries (within the cell layer Fig. 2B) and undergo chemotaxis up their respective gradients. The recruitment of the neutrophils and monocytes are randomly distributed among the healthy capillaries that do not have an immune cell at the capillary location (a modeling artifact that is a byproduct of only having one cell per lattice site). This approach helped to prevent an abundance of crowding at certain capillaries. Because immune cells in the simulation are sufficiently small, chemotactic gradients are sufficiently large, and the simulation space is sufficiently large, we do not see aggregation of recruited immune cells in the CPM.

      The lymphatic vessel uptakes cytokines at lattice locations corresponding to the lymphatic vessel and will remove cells located in lattice sites neighboring the lymphatic vessel. In addition, we have included a rule in our ABM to encourage cells to migrate towards the lymphatic vessel utilizing CompuCell3D External Potential Plugin. The influence of this rule is inversely proportional to the distance of the cells to the lymphatic vessel.

      We have updated lines 294-298 and 305-309 to include the above explanation.

      (13) Tables 1-4 define migration speeds as agent rules but in the typical CPM, migration speed emerges from random displacements biased by chemotaxis and other effects (like the slope of the cytokine field). How was the speed implemented as a rule while it is typically observable in the model?

      We appreciate your inquiry regarding the implementation of migration speeds. To determine the lambda parameters (Table 7) for each cell type, we tested each in a simplified control simulation with a concentration gradient for the cell to move towards. We tuned the lambda parameters within this simulation until the model outputted cell velocity aligned with the literature reported cell velocity for each cell type (Tables 1-4). We have incorporated clarification on this to lines 177-180.

      (14) Line 312 shows the first equation with number (5), either add eqn. (1-4) or renumber.

      We have revised the equation number.

      (15) Typos: Line 456, "expect M1 cell" should read "except M1 cell".

      Line 452, "thresholds above that diminish fibroblast response (Supplemental Fig 3)." remains unclear, please rephrase.

      Line 473, "at 28." should read "at 28 days.".

      Line 474, is "additive" correct? Was the sum of the individual effects calculated and did that match?

      Line 534, "complexity our model" should read "complexity in our model".

      We have corrected the typos and clarified line 452 (updated line 594) to indicate that the TNF-α concentration threshold results in diminished fibroblast response. We updated terminology line 474 (updated line 512) to indicate that there was a synergistic effect with the combined perturbation.

      (16) Table 7 defines cell target volumes with the same value as their diameter. This enforces a strange cell shape. Should there be brackets to square the value of the cell diameter, e.g. Value=(12µm)^2 ?

      The target volume parameter values were selected to reflect the relative differences in average cell diameter as reported in the literature; however, there are no parameters that directly enforce a diameter for the cells in the CPM formalism separate from the volume. We have observed that these relative cell sizes allow the ABM to effectively reproduce cell behaviors described in the literature. Single cells that are too large in the ABM would be unable to migrate far enough per time step to carry out cell behaviors, and cells that are too small in the CPM would be unstable in the simulation environment and not persist in the simulation when they should. We removed the units for the cell shape values in Table 7 since the target volume is a relative parameter and does not directly represent µm.

      (17) Table 7 gives estimated diffusion constants but they appear to be too high. Please compare them to measured values in the literature, especially for MCP-1, TNF-alpha and IL-10, or relate these to their molecular mass and compare to other molecules like FGF8 (Yu et al. 2009, DOI:10.1038/nature08391).

      We utilized a previously published estimation method (Filion et al. 2004, DOI:10.1152/ajpheart.00205.2004) for estimating cytokine diffusivity within the ECM. This method incorporates the molecular masses and accounts for the combined effects of the collagen fibers and glycosaminoglycans. The paper acknowledged that the estimated value is faster than experimentally determined values, but that this was a result of the less-dense matrix composition which is more reflective of the tissue environment we are simulating in contrast to other reported measurements which were done in different environments. Using this estimation method also allowed us to more consistently define diffusion constants versus using values from the literature (which were often not recorded) that had varied experimental conditions and techniques (such as being in zebrafish embryo Yu et al. 2009, DOI:10.1038/nature08391 as opposed to muscle tissue). This also allowed for recalculation of the diffusivity throughout the simulation as the collagen density changed within the model. Lines 318-326 were updated to help clarify the estimation method.

      (18) Many DOIs in the bibliography (Refs. 7,17,20,31,40,47...153) are wrong and do not resolve because the appended directory names are not allowed in the DOI, just with a journal's URL after resolution.

      Thank you for bringing this to our attention. The incorrect DOIs have been corrected.

      Reviewer #2 (Recommendations For The Authors):

      Minor comments:

      (9) On line 174, the authors say "We used the CC3D feature Flip2DimRatio to control the number of times the Cellular-Potts algorithm runs per mcs." What does this mean? Isn't one monte carlo timestep one iteration of the Cellular Potts model? How does this relate to physical timescales?

      We appreciate your attention to detail and thoughtful question regarding the statement about the use of the CC3D feature Flip2DimRatio. Lines 175-177 were revised to simplify the meaning of Flip2DimRatio. That parameter alters the number of times the Cellular-Potts algorithm is run, which is the limiting factor for cell movement. The physical timescale is kept to a 15-minute timestep but a high Flip2DimRatio allows more flexibility and stability to allow the cells to move faster in one timestep.

      (10) Has the costum matlab script to process histology images into initial conditions been made available?

      The Matlab script along with CC3D code for histology initialization with documentation has been made available with the source code on the following sites:

      SimTK: https://simtk.org/docman/?group_id=2635

      Zendo: https://zenodo.org/records/10403014

      GitHub: https://github.com/mh2uk/ABM-of-Muscle-Regeneration-with-MicrovascularRemodeling

      (11) Equation 5 is provided without a reference or derivation. Where does it come from and what does it mean?

      Thank you for highlighting the diffusion equation and seeking clarification on its origin and significance. Lines 318-326 were revised to clarify where the equation comes from. This is a previously published estimation method that we applied to calculate the diffusivity of the cytokines considering both collagen and glycosaminoglycans.

      (12) Line 326: "For CSA, experimental fold-change from pre-injury was compared with fold-change in model-simulated CSA". Does this step rely on the assumption that the fold change will not depend on the CSA? If so, is this something that is experimentally known, or otherwise, can it be confirmed by simulations?

      We appreciate the opportunity to clarify our rationale. The fold change was used as a method to normalize the model and experiment so that they could be compared on the same scale. Yes, this step relies on the assumption that fold change does not depend on pre-injury CSA. Experimentally it is difficult to determine the impact of initial fiber morphology on altered regeneration time course. This fold-change allows us to compare percent recovery which is a common metric utilized to assess muscle regeneration outcomes experimentally. Line 340-343 was revised to clarify.

      (13) Line 355: "The final passing criteria were set to be within 1 SD for CSA recovery and 2.5 SD for SSC and fibroblast count" Does this refer to the experimental or the simulated SD?

      The model had to fit within those experimental SD. Lines 371-372 was edited to specify that we are referring the experimental SD.

      (14) "Following 8 iterations of narrowing the parameter space with CaliPro, we reached a set that had fewer passing runs than the previous iteration". Wouldn't one expect fewer passing runs with any narrowing of the parameter space? Why was this chosen as the stopping criterion for further narrowing?

      We appreciate your observation regarding the statement about narrowing the parameter space with CaliPro. We started with a wide parameter space, expecting that certain parameters would give outputs that fall outside of the comparable data. So, when the parameter space was narrowed to enrich parts that give passing output, initially the number of passing simulations increased.

      Once we have narrowed the set of possible parameters into an ideal parameter space, further narrowing will cut out viable parameters resulting in fewer passing runs. Therefore, we stopped narrowing once any fewer simulations passed the criteria that they had previously passed with the wider parameter set. Lines 375-379 have been updated to clarify this point.

      (15) Line 516: 'Our model could test and optimize combinations of cytokines, guiding future experiments and treatments." It is my understanding that this is communicated as a main strength of the model. Would it be possible to demonstrate that the sentence is true by using the model to make actual predictions for experiments or treatments?

      This is demonstrated by the combined cytokine alterations in Figure 7 and discussed in lines 509-513. We have also added in a suggested experiment to test the model prediction in lines 691-695.

      (16) Line 456, typo: I think 'expect' should be 'except'.

      Thank you for pointing that out. The typo has been corrected.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      The authors collected genomic information from public sources covering 423 eukaryote genomes and around 650 prokaryote genomes. Based on pre-computed CDS annotation, they estimated the frequency of alternative splicing (AS) as a single average measure for each genome and computed correlations with this measure and other genomic properties such as genome size, percentage of coding DNA, gene and intergenic span, etc. They conclude that AS frequency increases with genome complexity in a somewhat directional trend from "lower" organisms to "higher" organisms.

      Strengths:

      The study covers a wide range of taxonomic groups, both in prokaryotes and eukaryotes.

      Weaknesses:

      The study is weak both methodologically and conceptually. Current high throughput sequencing technologies, coupled with highly heterogeneous annotation methods, can observe cases of AS with great sensitivity, and one should be extremely cautious of the biases and rates of false positives associated with these methods. These issues are not addressed in the manuscript. Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

      We are aware of the bias that may exist in annotation files. Since the source of noise can be highly variable, we have assumed that most of the data has a similar bias. However, we agree with the reviewer that we could perform some analysis to test for these biases and their association to different methodologies. Thus, we will measure the uncertainty present in the data. From one side, we will be more explicit about the data limitations and the biases it can generate in the results. On the other side, while analyzing the false positives in the data is out of our scope, we will perform a statistical test to detect possible biases regarding different methods of sequencing and annotation, and types of organisms (model or non-model organisms). If positive, we will proceed, as far as possible, to normalize the data or to estimate a confidence interval.

      Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

      Beyond taking into account the differential bias that may exist in the data, we do not consider that our AS measure is problematic. The NCBI database is one of the most reliable databases that we have to date and is continuously updated from all scientific community. So, the use of this data and the corresponding procedures for deriving the AS measure are perfectly acceptable for a comparative analysis on such a huge global scale. Furthermore, the proposal of a new genome-level measure of AS that allows to compare species spanning the whole tree of life is part of the novelty of the study. We understand that small-scale studies require a high specificity about the molecular processes involved in the study. However, this is not the case, where we are dealing with a large-scale problem. On the other side, as we have previously mention, we agree with the reviewer to analyze the degree of uncertainty in the data to better interpret the results.

      There is no mention of the possibility that AS could be largely caused by random splicing errors, a possibility that could very well fit with the manuscript's data. Instead, the authors adopt early on the view that AS is regulated and functional, generally citing outdated literature.

      There is no question that some AS events are functional, as evidenced by strongly supported studies. However, whether all AS events are functional is questionable, and the relative fractions of functional and non-functional AS are unknown. With this in mind, the authors should be more cautious in interpreting their data.

      Many studies suggest that most of the AS events observed are the result of splicing errors and are therefore neither functional nor conserved. However, we still have limited knowledge about the functionality of AS. Just because we don’t have a complete understanding of its functionality, doesn’t mean there isn’t a fundamental cause behind these events. AS is a highly dynamic process that can be associated with processes of a stochastic nature that are fundamental for phenotypic diversity and innovation. This is one of the reasons why we do not get into a discussion about the functionality of AS and consider it as a potential measure of biological innovation. Nevertheless, we agree with the reviewer’s comments, so we will add a discussion about this issue with updated literature and look at any possible misinterpretation of the results.

      The "complexity" of organisms also correlates well (negatively) with effective population size. The power of selection to eliminate (slightly) deleterious mutations or errors decreases with effective population size. The correlation observed by the authors could thus easily be explained by a non-adaptive interpretation based on simple population genetics principles.

      We appreciate the observation of the reviewer. We know well the M. Lynch’s theory on the role of the effective population size and its eventual correlation with genomic parameters, but we want to emphasize that our objective is not to find an adaptive or non-adaptive explanation of the evolution of AS, but rather to reveal it. Nevertheless, as the reviewer suggests, we will look at the correlation between the AS and the effective population size and discuss about a possible non-adaptive interpretation.

      The manuscript contains evidence that the authors might benefit from adopting a more modern view of how evolution proceeds. Sentences such as "... suggests that only sophisticated organisms optimize alternative splicing by increasing..." (L113), or "especially in highly evolved groups such as mammals" (L130), or the repeated use of "higher" and "lower" organisms need revising.

      As the reviewer suggests, we will proceed with the corresponding linguistic corrections.

      Because of the lack of controls mentioned above, and because of the absence of discussion regarding an alternative non-adaptive interpretation, the analyses presented in the manuscript are of very limited use to other researchers in the field. In conclusion, the study does not present solid conclusions.

      Reviewer #2 (Public Review):

      Summary:

      In this contribution, the authors investigate the degree of alternative splicing across the evolutionary tree and identify a trend of increasing alternative splicing as you move from the base of the tree (here, only prokaryotes are considered) towards the tips of the tree. In particular, the authors investigate how the degree of alternative splicing (roughly speaking, the number of different proteins made from a single ORF (open reading frame) via alternative splicing) relates to three genomic variables: the genome size, the gene content (meaning the fraction of the genome composed of ORFs), and finally, the coding percentage of ORFs, meaning the ratio between exons and total DNA in the ORF. When correlating the degree of alternative splicing with these three variables, they find that the different taxonomic groups have a different correlation coefficient, and identify a "progressive pattern" among metazoan groups, namely that the correlation coefficient mostly increases when moving from flowering plants to arthropods, fish, birds, and finally mammals. They conclude that therefore the amount of splicing that is performed by an organismal group could be used as a measure of its complexity.

      Weaknesses:

      While I find the analysis of alternative splicing interesting, I also find that it is a very imperfect measure of organismal complexity and that the manuscript as a whole is filled with unsupported statements. First, I think it is clear to anyone studying evolution over the tree of life that it is the complexity of gene regulation that is at the origin of much of organismal structural and behavioral complexity. Arguably, creating different isoforms out of a single ORF is just one example of complex gene regulation. However, the complexity of gene regulation is barely mentioned by the authors.

      We disagree with the reviewer with that our measure of AS is imperfect. Just as we responded to the first reviewer, we will quantify the uncertainty in the data and correct for differential biases caused by annotation and sequencing methods. Thus, beyond correcting relevant biases in the data, we consider that our measure is adequate for a comparative analysis at a global scale. A novelty of our study is the proposal of a genome-level measure of AS that takes into account data from the entire scientific community. 

      We want also to emphasize that we assume from the beginning that AS may reflect some kind of biological complexity, it is not a conclusion from the results. An argument in favor of such an assumption is that AS is associated with stochastic processes that are fundamental for phenotypic diversity and innovation. Of course, we agree with the reviewer that it is not the only mechanism behind biological complexity, so we will emphasize it in the manuscript. On the other side, we will be more explicit about the assumptions and objectives, and will correct any unsupported statement.

      Further, it is clear that none of their correlation coefficients actually show a simple trend (see Table 3). According to these coefficients, birds are more complex than mammals for 3 out of 4 measures.

      An evolutionary trend is broadly defined as the gradual change in some characteristic of organisms as they evolve or adapt to a specific environment. Under our context, we define an evolutionary trend as the gradual change in genome composition and its association with AS across the main taxonomic groups. If we look at Figure 4 and Table 3 we can conclude that there is a progressive trend. We will be more precise about how we define an evolutionary trend and correct any possible misinterpretation of the results. On the other side, we do not assume that mammals should be more complex than birds. First, we will emphasize that our results show that birds have the highest values of such a trend. Second, after reading the reviewer’s comments, we have decided that we will perform an additional analysis to correct for differences in the taxonomic group sizes, which will allow us to have more confidence in the results.

      It is also not clear why the correlation coefficient between alternative splicing ratio and genome length, gene content, and coding percentage should display such a trend, rather than the absolute value. There are only vague mechanistic arguments.

      The study analyzes the relationship of AS with genomic composition for the large taxonomic groups. We assume that significant differences in these relationships are indicators of the presence of different mechanisms of genome evolution. However, we agree with the reviewer that a correlation does not imply a causal relation, so we will be more cautious when interpreting the results.

      To quantify the relationships we use correlation coefficients, the slopes of such correlations, and the relation of variability. Although the absolute values of AS are also illustrated in Table 4, we consider that they are less informative than if we include how it relates to the genomic composition. For example, we observe that plants have a different genome composition and relation with AS if compared to animals, which suggest that they follow different mechanisms of genome evolution. On the other hand, we observe a trend in animals, where high values of AS are associated to a large percentage of introns and a percentage of intergenic DNA of about the 50% of genomes.

      Much more troubling, however, is the statement that the data supports "lineage-specific trends" (lines 299-300). Either this is just an ambiguous formulation, or the authors claim that you can see trends *within* lineages.

      We agree with the reviewer that this statement is not correct, so we will proceed to correct it.

      The latter is clearly not the case. In fact, within each lineage, there is a tremendous amount of variation, to such an extent that many of the coefficients given in Table 3 are close to meaningless. Note that no error bars or p-values are presented for the values shown in Table 3. Figure 2 shows the actual correlation, and the coefficient for flowering plants there is given as 0.151, with a p-value of 0.193. Table 3 seems to quote r=0.174 instead. It should be clear that a correlation within a lineage or species is not a sign of a trend.

      The reviewer is not understanding correctly the results in Table 3. It is precisely the variation of the genome variables what we are measuring. Given the standardization of these values by the mean values, we have proceeded to compare the variability between groups, which is the result shown in Table 3. In this case there are no error bars or p-values associated. On the other hand, we agree that a correlation is not a sign of a trend. But the relations of variability, together with the results obtained in Figure 3, are indicators of a trend. As we mentioned before, we will proceed to analyze whether the variation in the group sizes is causing a bias in the results.

      There are several wrong or unsupported statements in the manuscript. Early on, the authors state that the alternative splicing ratio (a number greater or equal to one that can be roughly understood as the number of different isoforms per ORF) "quantifies the number of different isoforms that can be transcribed using the same amount of information" (lines 51-52). But in many cases, this is incorrect, because the same sequence can represent different amounts of information depending on the context. So, if a changed context gives rise to a different alternative splice, it is because the genetic sequence has a different meaning in the changed context: the information has changed.

      We agree that there are not well supported statements, so we will proceed to revise them.

      In line 149, the authors state that "the energetic cost of having large genomes is high". No citation is given, and while such a statement seems logical, it does not have very solid support.

      We will also revise the bibliography and support our statements with updated references.

      If there was indeed a strong selective force to reduce genome size, we would not see the stunning diversity of genome sizes even within lineages. This statement is repeated (without support) several times in the manuscript, apparently in support of the idea that mammals had "no choice" to increase complexity via alternative splicing because they can't increase it by having longer genomes. I don't think this reasoning can be supported.

      We agree with the reviewer in this issue, so we will carefully revise the statements that indirectly (or directly) assume the action of selective forces on the genome composition.

      Even more problematic is the statement that "the amount of protein-coding DNA seems to be limited to a size of about 10MB" (line 219). There is no evidence whatsoever for this statement.

      In Figure 1A we observe a one-to-one relationship between the genome size and the amount of coding. However, in multicellular organisms, although the genome size increases we observe that the amount of coding does not increase by more than 10Mb, which suggest the presence of some genomic limitation. Of course, this is not an absolute or general statement, but rather a suggestion. We are only describing our results.

      The reference that is cited (Choi et al 2020) suggests that there is a maximum of 150GB in total genome size due to physiological constraints. In lines 257-258, the authors write that "plants are less restricted in terms of storing DNA sequences compared to animals" (without providing evidence or a citation).

      We will revise the bibliography and add updated references.

      I believe this statement is made due to the observation that plants tend to have large intergenic regions. But without examining the functionality of these interagency regions (they might host long non-coding RNA stretches that are used to regulate the expression of other genes, for example) it is quite adventurous to use such a simple measure as being evidence that plants "are less restricted in terms of storing DNA sequences", whatever that even means. I do not think the authors mean that plants have better access to -80 freezers. The authors conclude that "plant's primary mechanism of genome evolution is by expanding their genome". This statement itself is empty: we know that plants are prone to whole genome duplication, but this duplication is not, as far as we understand, contributing to complexity. It is not a "primary mechanism of genome evolution".

      We will revise these statements.

      In lines 293-294, the authors claim that "alternative splicing is maximized in mammalian genomes". There is no evidence that this ratio cannot be increased. So, to conclude (on lines 302-303) that alternative splicing ratios are "a potential candidate to quantify organismal complexity" seems, based on this evidence, both far-fetched and weak at the same time.

      Our results show the highest values of AS in mammals, but we understand that the results are limited to the availability and accuracy of data, which we will emphasize in the manuscript. As we previously mention, we will also proceed to analyze the uncertainty in data and carry out the appropriate corrections.

      I am also not very comfortable with the data analysis. The authors, for example, say that they have eliminated from their analysis a number of "outlier species". They mention one: Emmer wheat because it has a genome size of 900 Mb (line 367). Since 900MB does not appear to be extreme, perhaps the authors meant to write 900 Gb. When I consulted the paper that sequenced Triticum dicoccoides, they noted that 14 chromosomes are about 10GB. Even a tetraploid species would then not be near 900Gb. But more importantly, such a study needs to state precisely which species were left out, and what the criteria are for leaving out data, lest they be accused of selecting data to fit their hypothesis.

      The reviewer is right, we wanted to say 900Mb, which is approximately 7.2Gb. We had a mistake of nomenclature. This value is extreme compared to the typical values, so it generates large deviations when applying measures of central tendency and dispersion. We want to obtain mean values that are representative of the most species composing the taxonomic groups, so we find appropriate to exclude all outlier values in the study. Nevertheless, we will specify the criteria that we have used to select the data in a rigorous way.

      I understand that Methods are often put at the end of a manuscript, but the measures discussed here are so fundamental to the analysis that a brief description of what the different measures are (in particular, the "alternative splicing ratio") should be in the main text, even when the mathematical definition can remain in the Methods.

      We agree with the reviewer, so we will add a brief description of the genomic variables at the beginning of the Results section.

      Finally, a few words on presentation. I understand that the following comments might read differently after the authors change their presentation. This manuscript was at the border of being comprehensible. In many cases, I could discern the meaning of words and sentences in contexts but sometimes even that failed (as an example above, about "species-specific trends", illustrates). The authors introduced jargon that does not have any meaning in the English language, and they do this over and over again.

      Note that I completely agree with all the comments by the other reviewer, who alerted me to problems I did not catch, including the possible correlation with effective population size: a possible non-adaptive explanation for the results.

    1. Author response:

      Overall recommendations.

      A brief summary of the main reviewers' recommendations that should be prioritized is listed below. Detailed recommendations as suggested by each individual reviewer are also included.

      -Better justification of the choice of the substitutions for the mutations should be added. In addition, authors should strongly consider adding more mutations to enable mechanistic tests of the proposed model for lipid conduction.

      We will characterize more mutations to the key residues at the TM4-TM6 interface. In addition to the TM4 lysine mutations shown in the original manuscript, we will include mutations to alanine and glutamate, and justify our choice of the substitutions in the revised manuscript. Furthermore, we will also test if introducing lysine mutations in TM6 will convert the ion channels into lipid scramblases. These additional experiments will greatly strengthen our conclusion.

      -Blockers to validate the concern that the recorded currents indeed arise from TMEM16A or OSCA/TMEM63 channels should be tested. Do the pore blockers also block scramblase activity in the gating mutants?

      TMEM16A and OSCA1.2 are readily expressed on cell surface. OSCA1.2 also has large conductance. This is the reason why we can record huge current even with inside-out patches. We will include TMEM16A inhibitor Ani9 and a non-specific inhibitor of OSCA channels to further validate. We have reported that Ani9 can inhibit a TMEM16A-derived lipid scramblase (L543K in TM4) in our previo3us publication (PMID: 31015464). We will test if Ani9 can also inhibit other TMEM16A scramblases reported in this study. We will also examine if Gd3+ is capable of blocking lipid scrambling of the OSCA1.2 gating mutations.

      -Include details of missing experimental conditions for scramblase activity.

      We will conduct a thorough revision to include detailed experimental conditions for scramblase activity measurement.

      -Additional mutants above and below the putative lysine gate as suggested by reviewer 3 to better assess the model.

      As we explained in Response #1, we will extend our mutations around the putative activation gate.

      -Concern about whether osmolarity changes are in fact activating OSC and TMEM63. As suggested by reviewers 1 and 3. This could be addressed by assessing scramblase activity and currents at different osmolarity levels.

      We will test the engineered OSCA1.2 scramblases in response to solutions with different osmolarity.

      Reviewer #1 (Public Review):

      Summary:

      TMEM16, OSCA/TMEM63, and TMC belong to a large superfamily of ion channels where TMEM16 members are calcium-activated lipid scramblases and chloride channels, whereas OSCA/TMEM63 and TMCs are mechanically activated ion channels. In the TMEM16 family, TMEM16F is a well-characterized calcium-activated lipid scramblase that plays an important role in processes like blood coagulation, cell death signaling, and phagocytosis. In a previous study, the group demonstrated that lysine mutation in TM4 of TMEM16A can enable the calcium-activated chloride channel to permeate phospholipids too. Based on this they hypothesize that the energy barrier for lipid scramblase in these ion channels is low, and that modification in the hydrophobic gate region by introducing a charged side chain between the TM4/6 interface in TMEM16 and OSCA/TMEM63 family can allow lipid scramblase. In this manuscript, using scramblase activity via Annexin V binding to phosphatidylserine, and electrophysiology, the authors demonstrate that lysine mutation in TM4 of TMEM16F and TMEM16A can cause constitutive lipid scramblase activity. The authors then go on to show that analogous mutations in OSCA1.2 and TMEM63A can lead to scramblase activity.

      Strengths:

      Overall, the authors introduce an interesting concept that this large superfamily can permeate ions and lipids.

      Weaknesses:

      The electrophysiology data does not entirely support their claims.

      We appreciate your positive comments. We will conduct more experiments including more electrophysiology characterizations as suggested.

      Reviewer #2 (Public Review):

      This concise and focused study by Lowry and colleagues identifies a motif in the pores of three families of channel/scramblase proteins that regulate exclusive ion permeation and lipid transport. These three ion channel families, which include the TMEM16s, the plant-expressed and stress-gated cation channel OSCA, and the mammalian homolog and mechanosensitive cation channel, TMEM63 share low sequence similarity between them and have seemingly differing functions, as anion (TMEM16s), or stress-activated cation channels (OSCA/TMEM63). The study finds that in all three families, mutating a single hydrophobic residue in the ion permeation pathway of the channels confers lipid transport through the pores of the channels, indicating that TMEM16 and the related OSCA and TMEM63 channels have a conserved potential for both ion and lipid permeation. The authors interpret the findings as revealing that these channel/scramblase proteins have a relatively low "energetic barrier for scramblase" activity. The experiments themselves seem to be done with a high level of rigor and the paper is well written. A weakness is the limited scope of the experiments which, if fixed, could open up a new line of inquiry.

      We appreciate the positive comments from the reviewer. We will conduct more experiments listed in our responses to the Overall Recommendations to improve the scope and quality of our study.

      Reviewer #3 (Public Review):

      This study was focused on the conserved mechanisms across the Transmembrane Channel/Scramblase superfamily, which includes members of the TMEM16, TMEM63/OSCA, and TMC families. The authors show that the introduction of lysine residues at the TM4-TM6 interface can disrupt gating and confer scramblase activity to non-scramblase proteins. Specifically, they show this to be true for conserved TM4 residues across TMEM16F, TMEM16A, OSCA1.2, and TMEM63A proteins. This breadth of data is a major strength of the paper and provides strong evidence for an underlying linked mechanism for ion conduction and phospholipid transport. Overall, the confocal imaging experiments, patch clamping experiments, and data analysis are performed well.

      However, there are several concerns regarding the scope of experiments supporting some claims in the paper. Although the authors propose that the TM4/TM6 interface is critical to ion conduction and phospholipid scramblase activity, in each case, there is very narrow evidence of support consisting of 1-3 lysine substitutions at specific residues on TM4. Given that the authors postulate that the introduction of a positive charge via the lysine side chain is essential to the constitutive activity of these proteins, additional mutation controls for side chain size (e.g. glutamine/methionine) or negative charge (e.g. glutamic acid), or a different positive charge (i.e. arginine) would have strengthened their argument. To more comprehensively understand the TM4/TM6 interface, mutations at locations one turn above and one turn below could be studied until there is no phenotype. In addition, the equivalent mutations on the TM6 side should be explored to rule out the effects of conformational changes that arise from mutating TM4 and to increase the strength of evidence for the importance of side-chain interactions at the TM6 interface. The experiments for OSCA1.2 osmolarity effects on gating and scramblase in Figure 4 could be improved by adding different levels of osmolarity in addition to time in the hypotonic solution.

      We appreciate the positive and constructive comments from the reviewer. As we outlined in our responses to the Overall Recommendations, we will include more mutations at the TM4 and TM6 interface to further strengthen our conclusion.

    1. Author response:

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

      The reviewer comments have been helpful, and we have revised the manuscript to address the concerns of reviewer 2. In addition to text changes, we also added a negative control to Figure 1 to address concerns about photobleaching or DNA unwrapping.

      Reviewer #1:

      This manuscript presents an extremely exciting and very timely analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. Intriguingly, SWR1 loses activity almost completely if any of the acidic patches are absent. To my knowledge, this makes SWR1 the first remodeler with such a unique and pronounced requirement for the acidic patch. The authors demonstrate that SWR1 affinity is dramatically reduced if at least one of the acidic patches is absent, pointing to a key role of the acidic patch in SWR1 binding to the nucleosome. The authors also pinpoint a specific subunit - Swc5 - that can bind nucleosomes, engage the acidic patch, and obtain a cryo-EM structure of Swc5 bound to a nucleosome. They also identify a conserved arginine-rich motif in this subunit that is critical for nucleosome binding and histone exchange in vitro and for SWR1 function in vivo. The authors provide evidence that suggests a direct interaction between this motif and the acidic patch.

      Strengths:

      The manuscript is well-written and the experimental data are of outstanding quality and importance for the field. This manuscript significantly expands our understanding of the fundamentally important and complex process of H2A.Z deposition by SWR1 and would be of great interest to a broad readership.

      We thank the reviewer for their enthusiastic and positive comments on our work.

      Reviewer #2:

      Summary:

      In this study, Baier et al. investigated the mechanism by which SWR1C recognizes nucleosomal substrates for the deposition of H2A.Z. Their data convincingly demonstrate that the nucleosome's acidic patch plays a crucial role in the substrate recognition by SWR1C. The authors presented clear evidence showing that Swc5 is a pivotal subunit involved in the interaction between SWR1C and the acidic patch. They pared down the specific region within Swc5 responsible for this interaction. However, two central assertions of the paper are less convincing. First, the data supporting the claim that the insertion of one Z-B dimer into the canonical nucleosome can stimulate SWR1C to insert the second Z-B dimer is somewhat questionable (see below). Given that this claim contradicts previous observations made by other groups, this hypothesis needs further testing to eliminate potential artifacts. Secondly, the claim that SWR1C simultaneously recognizes the acidic patch on both sides of the nucleosome also needs further investigation, as the assay used to establish this claim lacks the sensitivity necessary to distinguish any difference between nucleosomal substrates containing one or two intact acidic patches.

      Strengths:

      As mentioned in the summary, the authors presented clear evidence demonstrating the role of Swc5 in recognition of the nucleosome acidic patch. The identification of the specific region in Swc5 responsible for this interaction is important.

      We thank the reviewer for their careful critique of our work. Below we address each major concern.

      Major comments: (1) Figure 1B: It is unclear how much of the decrease in FRET is caused by the bleaching of fluorophores. The authors should include a negative control in which Z-B dimers are omitted from the reaction. In the absence of ZB dimers, SWR1C will not exchange histones. Therefore, any decrease in FRET should represent the bleaching of fluorophores on the nucleosomal substrate, allowing normalization of the FRET signal related to A-B eviction.

      In this manuscript, as well as in our two previous publications (Singh et al., 2019; Fan et al.,2022), we have presented the results of no enzyme controls, +/- ZB dimers, no ATP controls, or AMP-PNP controls for our FRET-based, H2A.Z deposition assay (see also Figure S3). We do not observe significant levels of photobleaching in this assay, either during ensemble measurements or in an smFRET experiment. To aid the reader, we have added the AMP-PNP data for the experiment shown in Figure 1B. The results show there is less than a 10% decrease in FRET over 30’, and the signal from the double acidic patch disrupted nucleosome is identical to this negative control.

      (2) Figure S3: The authors use the decrease in FRET signal as a metric of histone eviction. However, Figure S3 suggests that the FRET signal decrease could be due to DNA unwrapping. Histone exchange should not occur when SWR1C is incubated with AMP-PNP, as histone exchange requires ATP hydrolysis (10.7554/eLife.77352). And since the insertion of Z-B dimer and the eviction of A-B dimer are coupled, the decrease of FRET in the presence of AMP-PNP is unlikely due to histone eviction or exchange. Instead, the FRET decrease is likely due to DNA unwrapping (10.7554/eLife.77352). The authors should explicitly state what the loss of FRET means.

      We agree with the reviewer, that loss of FRET can be due to DNA unwrapping from the nucleosome. We have previously demonstrated this activity by SWR1C in our smFRET study (Fan et al., 2022). However, DNA unwrapping is highly reversible and has a time duration of only 1-3 seconds. We and others have not observed stable unwrapping of nucleosomes by SWR1C, but rather the stable loss of FRET reports on dimer eviction. We assume the reviewer is concerned about the rather large decrease in FRET signal shown in the AMP-PNP controls for Figure S3, panels A and D. For the other 7 panels, the decrease in FRET with AMP-PNP are minimal. In fact, if we average all of the AMP-PNP data points, the rate of FRET loss is not statistically different from no enzyme control reactions (nucleosome plus ZB dimers).

      Data for panels A and D used a 77NO nucleosomal substrate, with Cy3 labeling the linker distal dimer. This is our standard DNA fragment, and it was used in Figure 1B. The only difference between data sets is that the data shown in Fig 1B used nucleosome reconstituted with a Cy5-labelled histone octamer, rather than the hexasome assembly method used for Fig S3. Three points are important. First, for all of these substrates, we assembled 3 independent nucleosomes, and the results are highly reproducible. Two, we performed a total of 6 experiments for the 77NO-Cy5 substrates to ensure that the rates were accurate (+/-ATP). Third, and most important, we do not see this decrease in FRET signal in the absence of SWR1C (no enzyme control). This data was included in the data source file. Thus, it appears that there is significant SWR1C-induced nucleosome instability for these two hexasome-assembled substrates. We now note this in the legend to Figure S3. Key for this work, however, is that there is a large increase in the rate of FRET loss in the presence of ATP, and this rate is faster when a ZB dimer was present at the linker proximal location. In response to the last point, we state in the first paragraph of the results: “The dimer exchange activity of SWR1C is monitored by following the decrease in the 670 nm FRET signal due to eviction of the Cy5-labeled AB-Cy5 dimer (Figure 1A).”

      (3) Related to point 2. One way to distinguish nucleosomal DNA unwrapping from histone dimer eviction is that unwrapping is reversible, whereas A-B eviction is not. Therefore, if the authors remove AMP-PNP from the reaction chamber and a FRET signal reappears, then the initial loss of FRET was due to reversible DNA unwrapping. However, if the removal of AMP-PNP did not regain FRET, it means that the loss of FRET was likely due to A-B eviction. The authors should perform an AMP-PNP and/or ATP removal experiment to make sure the interpretation of the data is correct.

      See response to item 2 above

      (4) The nature of the error bars in Figure 1C is undefined; therefore, the statistical significance of the data is not interpretable.

      We apologize for not making this more explicit for each figure. The error bars report on 95% confidence intervals from at least 3 sets of experiments. This statement has been added to the legend.

      (5) The authors claim that the SWR1C requires intact acidic patches on both sides of the nucleosomes to exchange histone. This claim was based on the experiment in Figure 1C where they showed mutation of one of two acidic patches in the nucleosomal substrate is sufficient to inhibit SWR1C-mediated histone exchange activity. However, one could argue that the sensitivity of this assay is too low to distinguish any difference between nucleosomes with one (i.e., AB/AB-apm) versus two mutated acidic patches (i.e., AB-apm/AB-apm). The lack of sensitivity of the eviction assay can be seen when Figure 1B is taken into consideration. In the gel-shift assay, the AB-apm/AB-apm nucleosome exhibited a 10% SWR1C-mediated histone exchange activity compared to WT. However, in the eviction assay, the single AB/AB-apm mutant has no detectable activity. Therefore, to test their hypothesis, the authors should use the more sensitive in-gel histone exchange assay to see if the single AB/AB-apm mutant is more or equally active compared to the double AB-apm/AB-apm mutant.

      Our pincher model is based on three, independent sets of data, not just Figure 1C. First, as noted by the reviewer, we find that disruption of either acidic patch cripples the dimer exchange activity of SWR1C in the FRET-based assay. Whether the defect is identical to that of the double APM mutant nucleosome does not seem pertinent to the model. In a second set of assays, we used fluorescence polarization to quantify the binding affinity of SWR1C for wildtype nucleosomes, a double APM nucleosome, or each single APM nucleosome. Consistent with the pincher model, each single APM disruption decreases binding affinity at least 10-fold (below the sensitivity of the assay). Finally, we monitored the ability of different nucleosomes to stimulate the ATPase activity of SWR1C. Consistent with the pincher model, a single APM disruption was sufficient to eliminate nucleosome stimulation.

      (6) The authors claim that the AZ nucleosome is a better substrate than the AA nucleosome. This is a surprising result as previous studies showed that the two insertion steps of the two Z-B dimers are not cooperative (10.7554/eLife.77352 and 10.1016/J.CELREP.2019.12.006). The authors' claim was based on the eviction assay shown in Fig 1C. However, I am not sure how much variation in the eviction assay is contributed by different preparations of nucleosomes. The authors should use the in-gel assay to independently test this hypothesis.

      For all data shown in our manuscript, at least three different nucleosome preparations were used. The impact of a ZB dimer on the rates of dimer exchange was highly reproducible among different nucleosome preparations and experiments. We also see reproducible ZB stimulation for three different substrates – with ZB on the linker proximal side, the linker distal side, and on one side of a core particle. We do not believe that our data are inconsistent with previous studies. First, the previous work referenced by the reviewer performed dimer exchange reactions with a large excess of nucleosomes to SWR1C (catalytic conditions), whereas we used single turnover reactions. Secondly, our study is the first to use a homogenous, ZA heterotypic nucleosome as a substrate for SWR1C. All previous studies used a standard AA nucleosome, following the first and second rounds of dimer exchange that occur sequentially. And finally, we observe only a 20-30% increase in rate by a ZB dimer (e.g. 77N0 substrates), and such an increase was unlikely to have been detected by previous gel-based assays.

      Minor comments:

      (1) Abstract line 4: To say 'Numerous' studies have shown acidic patch impact chromatin remodeling enzymes activity may be too strong.

      Removed

      (2) Page 15, line 15: The authors claim that swc5∆ was inviable on formamide media. However, the data in Figure 8 shows cell growth in column 1 of swc5∆.

      The term ‘inviable’ has been replaced with ‘poor’ or ‘slow growth’

      (3) The authors should use standard yeast nomenclature when describing yeast genes and proteins. For example, for Figure 8 and legend, Swc5∆ was used to describe the yeast strain BY4741; MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; YBR231c::kanMX4. Instead, the authors should describe the swc5∆ mutant strain as BY4741 MAT a his3∆1 leu2∆0 met15∆0 ura3∆0 swc5∆::kanMX4. Exogenous plasmid should also be indicated in italics and inside brackets, such as [SWC5-URA3] or [swc5(R219A)-URA3].

      We apologize for missing this mistake in the Figure 8 legend. We had inadvertently copied this from the euroscarf entry and forgot to edit the entry. We decided not to add all the plasmid names to the figure, as it was too cluttered. We state in the figure legend that the panels show growth of swc5 deletion strains harboring the indicated swc5 alleles on CEN/ARS plasmids.

      (4) According to Lin et al. 2017 NAR (doi: 10.1093/nar/gkx414), there is only one Swc5 subunit per SWR1C. Therefore, the pincher model proposed by the authors would suggest that there is a missing subunit that recognizes the second acidic patch. The authors should point out this fact in the discussion. However, as mentioned in Major comment 6, I am not sure if the pincer model is substantiated.

      In our discussion, we had noted that the published cryoEM structure had suggested that the Swc2 subunit likely interacts with the acidic patch on the dimer that is not targeted for replacement, and we proposed that Swc5 interacts with the acidic patch on the exchanging H2A/H2B dimer. We have now made this more clear in the text.

    1. Author response:

      We thank the reviewers for the feedback on our manuscript; we are planning to address the raised concerns in the following manner:

      We will be more explicit about the novelty of this method framing it more concretely within the scope of current research. From some comments of the reviewers, we understand that it is not clear that our method is an extension of an already existing method and model that has been extensively validated with pre-trained models brought online. Consequently, the details of the model as well as the training cohort are only covered briefly, referencing relevant published works on this topic. We will improve the clarity in this respect in the full responses. Nevertheless, we agree that the work would benefit from a simulation study that formally evaluates the performance of our method compared with more traditional approaches and will add it in our full responses. We will take care specifically of investigating the effect of assumptions like the centile-stability in healthy controls as suggested by the Reviewer 2.

      The novelty of this work lies in introducing a mathematically transparent method to use normative modelling for evaluating studies with a longitudinal design, using normative models trained on cross sectional data. We emphasise strongly that this is otherwise not possible using current methods. Furthermore, by building on a pre-trained model, this method enjoys the benefits of big (cross-sectional) data (by the pre-trained model being fitted on an extensive population sample) without the need to have direct access to them, or a ‘big’ longitudinal dataset from the cohort at hand. This is crucial in neuroimaging, where longitudinal data are much more scarce than cross-sectional data.

      We strongly disagree with the notion raised by Reviewer 1 that after the first episode cortical thickness alterations are expected to become more severe. There is now increasing evidence that: (i) trajectories of cortical thickness are highly variable across different individuals after the first psychotic episode and (ii) that individuals treated with second-generation antipsychotics and with careful clinical follow-up can show normalisation of cortical thickness atypicalities after the first episode. Indeed, we can provide evidence for this in an independent cohort, with different analytical methodologies, where precisely this occurs (https://www.medrxiv.org/content/10.1101/2024.04.19.24306008v1, https://pubmed.ncbi.nlm.nih.gov/36805840/). In the full revision, we would be happy to provide further discussion of evidence in support of this.

      We  would also like to re-emphasise  that the data were processed with the utmost rigour using state of the art processing pipelines including quality control.

      We will take care to improve the flow of the manuscript with special attention to the theoretical part and sections highlighted by the Reviewer 2. 

      We agree with the challenge outlined by the Reviewer 2 regarding the limitations in interpretation of overall trends when the position in the visit one is different between the subjects. However, this is a much broader challenge and is not specific to this study. The non-random sampling of large cohort studies is problematic for nearly all studies using such cohorts, and regardless of the  statistical approach used. We will explicitly acknowledge these limitations in the full response.

    1. Author response:

      Reviewer #1 (Public Review):

      Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known.

      Most published studies are from men living with HIV-1 subtype B and the studies are not from the hyperacute infection phase and therefore a direct head-to-head comparison with the FRESH study is difficult. However, we can cite/highlight and contrast our study with a few examples from other acute infection studies as follows.

      (1) Peluso et. al., JCI, 2020, showed that in Caucasian men (SCOPE study), with subtype B infection, initiating ART during chronic infection virus intact genomes decayed at a rate of 15.7% per year, while defective genomes decayed at a rate of 4% per year. In our study we showed that in chronic treated participants genomes decreased at a rate of 25% (intact) and 3% (defective) per month for the first 6 months of treatment.

      (2) White et. al., PNAS, 2021, demonstrated that in a cohort of African, white and mixed-race American men treated during acute infection, the rate of decay of intact viral genomes in the first phase of decay was <0.3 logs copies in the first 2-3 weeks following ART initiation. In the FRESH cohort our data from acute treated participants shows a comparable decay rate of 0.31 log copies per month for virus intact genomes.

      (3) A study in Thailand (Leyre et. al., 2020, Science Translational Medicine), of predominantly HIV-1 CRF01-AE subtype compared HIV-reservoir levels in participants starting ART at the earliest stages of acute HIV infection (in the RV254/SEARCH 010 cohort) and participants initiating ART during chronic infection (in SEARCH 011 and RV304/SEARCH 013 cohorts). In keeping with our study, they showed that the frequency of infected cells with integrated HIV DNA remained stable in participants who initiated ART during chronic infection, while there was a sharp decay in these infected cells in all acutely treated individuals during the first 12 weeks of therapy. Rates of decay were not provided and therefore a direct comparison with our data from the FRESH cohort is not possible.

      (4) A study by Bruner et. al., Nat. Med. 2016, described the composition of proviral populations in acute treated (within 100 days) and chronic treated (>180 days), predominantly male subtype B cohort. In comparison to the FRESH chronic treated group, they showed that in chronic treated infection 98% (87% in FRESH) of viral genomes were defective, 80% (60% in FRESH) had large internal deletions and 14% (31% in FRESH) were hypermutated. In acute treated 93% (48% in FRESH) were defective and 35% (7%) in FRESH were hypermutated. The differences frequency of hypermutations could be explained by the differences in timing of infection specifically in the acute treated groups were FRESH participants initiate ART at a median of 1 day after infection. It is also possible that sex- or race-based differences in immunological factors that impact the reservoir may play a role.

      This study also showed that large deletions are non-random and occur at hotspots in the HIV-1 genome. The design of the subtype B IPDA assay (Bruner et. al., Nature, 2019) is based on optimal discrimination between intact and deleted sequences - obtained with a 5′ amplicon in the Ψ region and a 3′ amplicon in Envelope. This suggest that Envelope is a hotspot for large while deletions in Ψ is the site of frequent small deletions and is included in larger 5′ deletions. In the FRESH cohort of HIV-1 subtype C, genome deletions were most frequently observed between Integrase and Envelope relative to Gag (p<0.0001–0.001).

      (5) In 2017, Heiner et. al., in Cell Rep, also described genetic characteristics of the latent HIV-1 reservoir in 3 acute treated and 3 chronic treated male study participants with subtype B HIV. Their data was similar to Bruner et. al. above showing proportions of intact proviruses in participants who initiated therapy during acute/early infection at 6% (94% defective) and chronic infection at 3% (97% defective). In contrast the frequencies in FRESH in acute treated were 52% intact and 48% defective and in chronic infection were 13% intact and 87% defective. These differences could be attributed to the timing of treatment initiation where in the aforementioned study early treatment ranged from 0.6-3.4 months after infection.

      Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size.

      We will edit the manuscript text to describe the term peak viraemia in hyperacute treated participants more clearly. We will perform an analysis of area under the curve to compare viral burden in the two study groups.

      Reviewer #2 (Public Review):

      Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections.)

      We agree that these factors could play a role however participants in this study were of similar age (18-23), and information on co-morbidities and coinfections are not known.

      Reviewer #3 (Public Review):

      The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

      We agree and will amend the use of the word reservoir to only refer to the proviral DNA load after full viral suppression, i.e., during undetectable viral load.

      All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

      We will make all relevant data available and accessible to interested parties.

      The legend of Supplementary Figure 2 should list when samples were taken.

      The data in this figure represents an overall analysis of all sequences available for each participant at all time points. This will be explained more clearly in the manuscript and added to the figure legend.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This is a follow-up study to the authors' previous eLife report about the roles of an alpha-arrestin called protein thioredoxin interacting protein (Txnip) in cone photoreceptors and in the retinal pigment epithelium. The findings are important because they provide new information about the mechanism of glucose and lactate transport to cone photoreceptors and because they may become the basis for therapies for retinal degenerative diseases.

      Strengths:

      Overall, the study is carefully done and, although the analysis is fairly comprehensive with many different versions of the protein analyzed, it is clearly enough described to follow. Figure 4 greatly facilitated my ability to follow, understand and interpret the study. The authors have appropriately addressed a few concerns about statistical significance and the relationship between their findings and previous studies of the possible roles of Txnip on GLUT1 expression and localization on the surfaces of RPE cells.

      We are delighted that Reviewer #1 is satisfied with this revised version.

      Reviewer #2 (Public Review):

      The hard work of the authors is much appreciated. With overexpression of a-arrestin Txnip in RPE, cones and the combined respectively, the authors show a potential gene agnostic treatment that can be applied to retinitis pigmentosa. Furthermore, since Txnip is related to multiple intracellular signaling pathway, this study is of value for research in the mechanism of secondary cone dystrophy as well.

      There are a few areas in which the article may be improved through further analysis and application of the data, as well as some adjustments that should be made in to clarify specific points in the article.

      Strengths

      • The follow-up study builds on innovative ground by exploring the impact of TxnipC247S and its combination with HSP90AB1 knockdown on cone survival, offering novel therapeutic pathways.

      • Testing of different Txnip deletion mutants provides a nuanced understanding of its functional domains, contributing valuable insights into the mechanism of action in RP treatment.

      • The findings regarding GLUT1 clearance and the differential effects of Txnip mutants on cone and RPE cells lay the groundwork for targeted gene therapy in RP.

      Weaknesses

      • The focus on specific mutants and overexpression systems might overlook broader implications of Txnip interactions and its variants in the wider context of retinal degeneration.

      Txnip is not expressed in WT or RP cones, as described in our previous study (Xue et al., 2021, eLife), so we could not perform loss of function assays. We thus chose overexpression, and assayed various alleles, based upon the literature, as we describe in our manuscript.

      • The study's reliance on cell count and GLUT1 expression as primary outcomes misses an opportunity to include functional assessments of vision or retinal health, which would strengthen the clinical relevance.

      In our previous study, we demonstrated that the optomotor response of Txnip-treated RP mice improved (Xue et al., 2021, eLife). Also, as described in our previous Txnip study, as well as an independent study (Xue et al., 2021, eLife; Xue et al., 2023, PNAS), ERG assays of Txnip-treated RP cones were no different than the controls. Other therapies that prolong RP cone survival and the optomotor response in our lab also failed to save the ERG, suggesting that there are other pathways that need to be addressed, e.g. the visual cycle. A combination therapy addressing multiple problems is one of our goals.

      • The paper could benefit from a deeper exploration of why certain treatments (like Best1-146 Txnip.C247S) do not lead to cone rescue and the potential for these approaches to exacerbate disease phenotypes through glucose shortages.

      This system is more complicated than we currently understand, and more work needs to be done.

      • Minor inconsistencies, such as the missing space in text references and the need for clarification on data representation (retinas vs. mice), should be addressed for clarity and accuracy.

      The missing spaces are added.

      We described the strategy of injecting the same mouse in each eye, one eye with control and one with the experimental vector. However, the following sentence has been added to the Materials and Methods to better assist the reader:

      “In almost all experiments, other than as noted, one eye of the mouse was treated with control (AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye), and the other eye was treated with the experimental vector plus AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye.”

      • The observation of promoter leakage and potential vector tropism issues raise questions about the specificity and efficiency of the gene delivery system, necessitating further discussion and validation.

      The following sentences have been added to the Results. We do not think this phenomenon affects the practice of the experiments or the interpretation of the results in this study.

      “To enable automated cone counting and trace the infection, we co-injected an AAV (AAV8-RedO-H2BGFP-WPRE-bGHpA) encoding an allele of GFP fused to histone 2B (H2BGFP), which localized to the nucleus. As the red opsin promoter was used to express this gene, H2BGFP was seen in cone nuclei, but not in the RPE, if AAV8-RedO-H2BGFP-WPRE-bGHpA was injected alone. However, when an AAV that expressed in the RPE, i.e. AAV8-Best1-Sv40intron-(Gene)-WPRE-bGHpA, was co-injected with AAV8-RedO-H2BGFP-WPRE-bGHpA, H2BGFP was expressed in the RPE, along with expression in cones (Figure 2A). We speculate that this is due to concatenation or recombination of the two genomes, such that the H2BGFP comes under the control of the RPE promoter. This may be due to the high copy number of AAV in the RPE, as it did not happen in the reverse combination, i.e. AAV with an RPE promoter driving GFP and a cone promoter driving another gene, perhaps due to the observation that the AAV genome copy number is »10 fold lower in cones than in the RPE (Wang et al., 2020).”

    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      This paper provides a straightforward mechanism of how mycobacterial cAMP level is increased under stressful conditions and shows that the increase is important for the survival of the bacterium in animal hosts. The cAMP level is increased by decreasing the expression of an enzyme that degrades cAMP.

      We thank the reviewer for these extremely encouraging comments.

      Strengths:

      The paper shows that under different stresses the response regulator PhoP represses a phosphodiesterase (PDE) that degrades cAMP specifically. Identification of PhoP as a regulator of cAMP is significant progress in understanding Mtb pathogenesis, as increase in cAMP apparently increases bacterial survival upon infection. On the practical side, reduction of cAMP by increasing PDE can be a means to attenuate the growth of the bacilli. The results have wider implications since PhoP is implicated in controlling diverse mycobacterial stress responses and many bacterial pathogens modulate host cell cAMP level. The results here are straightforward, internally consistent, and of both theoretical and applied interests. The results also open considerable future work, especially how increases in cAMP level help to increase survival of the pathogen.

      Weaknesses:

      It is not clear whether PhoP-PDE Rv0805 is the only pathway to regulate cAMP level under stress.

      Reviewer 1 (Recommendations for the authors):

      (1) L.1: "maintenance of" or 'regulating'- I thought change in cAMP level upon stress is the whole point of the paper. Also, can replace "intracellular survival" with 'survival in host macrophages' if you want to be more specific.

      We agree with the reviewer, and therefore, we have now replaced “maintenance of” with “regulating cAMP level” in the title. However, we feel more comfortable with “intracellular survival” rather than being more specific with ‘survival in host macrophages’ as we have also shown animal experiments to demonstrate ‘in vivo’ effect in mice lung and spleen.

      (2) L.26: ---requires the bacterial virulence regulator –

      The suggested change has been made to the text.

      (3) L.30: Replace "phoP locus since the" with 'PhoP since this'. (The product, not the locus, is the regulator). The same comment for l.113.

      We agree with the reviewer. The suggested changes have been made to the text.

      (4) L.31: Change represtsor to repressor.

      We are sorry for the embarrassing spelling mistake. We have rectified the mistake in the revised version.

      (5) L.32: "hydrolytically degrades" or hydrolyses? (lytic and degrade sound like tautology). Same comment for l.117.

      We agree. The suggested change has been made to the text in both places of the revised manuscript.

      (6) L.35: I would also suggest changing "intra-mycobacterial" to 'intra bacterial' because you are talking about one bacterium here. The same change is recommended in l.29.

      Following reviewer’s recommendation, we have made the changes in the revised manuscript.

      (7) L.37: bacillus unless use of the plural form is the norm in the field.

      We agree. The suggested change has been made to the text.

      (8) L.43: Delete "intracellular" and change "intracellular" to host in l.44.

      The suggested changes have been made to the text.

      (9) L.66: --that a burst--

      We have corrected the mistake in the revised manuscript.

      (10) L.76: Receptor or receptor?

      We have corrected the mistake in the revised manuscript.

      (11) L.86: -- mechanisms of regulation of mycobacterial cAMP level. (homeostasis needs to be introduced first, and not used in the concluding statement for the first time).

      The suggested changes have been made to the text.

      (12) L.96: "essential" or 'a requirement'. (reduction is not the same as elimination)

      We understand the reviewer’s concern. However, several studies have independently established that phoPR remains an essential requirement for mycobacterial virulence.

      (13) L.97: Moreover, a mutant

      The suggested change has been made to the text.

      (14) L.113: --locus since PhoP has been –

      The suggested change has been made to the text.

      (15) L.119: mechanism or manner? (you are stating a fact, not a mechanism)

      We agree. We have now replaced ‘mechanism’ with ‘manner’ in the revised manuscript.

      (16) L.130: --lacking copies of both phoP and phoR (I am assuming you don't have two copies of each gene)

      We understand the reviewer’s concern. For better clarity, we have now clearly mentioned that the phoPR-KO mutant lacks both the single copies of phoP and phoR genes.

      (17) L.156: Indicate why GroEL2? - cells as another cytoplasmic protein, GroEL2 was also undetectable

      We have now mentioned it in the secretion experiments that mycobacterial cells did not undergo autolysis. To prove this point, we have used cytoplasmic GroEL2 as a marker protein. The absence of detectable GroEL2 in the culture filtrates (CFs) suggests absence of autolysis. To this end, we have modified the sentence in the revised manuscript (duplicated below):

      “Fig. 1C confirms absence of autolysis of mycobacterial cells as GroEL2, a cytoplasmic protein, was undetectable in the culture filtrates (CF).”

      (18) L.266: May delete "Together". Start with These data--, which would draw more attention to integrated view. In l.268-270, a reminder that intracellular pH is acidic in the normal course would enhance the physiological significance of the present results.

      We agree. We have made the suggested changes to the text. In view of the second comment of the reviewer, we have modified the text (duplicated below):

      “These data represent an integrated view of our results suggesting that PhoP-dependant repression of rv0805 regulates intra-mycobacterial cAMP level. In keeping with these results, activated PhoP under acidic pH conditions significantly represses rv0805, and intracellular mycobacteria most likely utilizes a higher level of cAMP to effectively mitigate stress for survival under hostile environment including acidic pH of the phagosome.”

      (19) L.272: Delete "and intracellular survival" (?) (I am assuming the survival is due to stress tolerance; also the section talks about stress only). No period in l.273.

      Following reviewer’s recommendations, the suggested changes have been made to the text.

      (20) L.295: Start the sentence thus: It appears that at least one of ---. (This would put more emphasis on the inference)

      We agree. We have now incorporated the recommended changes in the revised version.

      (21) L.301: No parenthesis.

      The parenthesis has been removed in the revised manuscript.

      (22) L.306: Together already implies these. Either delete Together (which I would prefer) or say 'Together, the results suggest that strains expressing wild type and mutant----properties, and the results are

      We agree. We have now deleted ‘Together’ in the revised manuscript.

      (23) L.311: These results support our view that higher---- (to avoid repetition of l.266)

      We agree. We have now incorporated the suggested change in the revised manuscript.

      (24) L.316: Using or with?

      We think “with” goes well with the statement.

      (25) L.329: Rephrase thus: Effect of intra-bacterial cAMP level on in vivo--

      The recommended change has been made to the text.

      (26) L.333: I would use ~, if you want to indicate about.

      We agree. We have now used ‘~’ in the revised version. Changes were incorporated in lines 328, 330 and 333 of the revised manuscript.

      (27) L.350: Change "somewhat functionally" to phenotypically?

      We thank the reviewer for this suggestion. We have changed “somewhat functionally” to “phenotypically” in the revised manuscript.

      (28) L.361: Change "is connected to" to 'regulates'.

      The suggested change has been made to the text.

      (29) L.365: ACs (to be parallel with PDEs)

      We agree. The suggested change has been made to the text.

      (30) L.366: delete "very" (let the readers decide how recent from the reference date).

      The suggested change has been made to the text.

      (31) L.382: level remained unknown before the present study.

      The recommended change has been made to the text.

      (32) L.399: add at the end of the sentence 'under stress'. Also, represent, not represents.

      The recommended changes have been made to the text.

      (33) L.560 and 571: Section headings formatted differently from the rest. Similar problem in l.900.

      We have rectified the issue and all of the section headings are now formatted in the same style.

      Reviewer #2 (Public Review):

      Summary:

      In the manuscript, the authors have presented new mechanistic details to show how intracellular cAMP levels are maintained linked to the phosphodiesterase enzyme which in turn is controlled by PhoP. Later, they showed the physiological relevance linked to altered cAMP concentrations.

      Strengths:

      Well thought out experiments. The authors carefully planned the experiments well to uncover the molecular aspects of it diligently.

      We thank the reviewer for these extremely encouraging comments.

      Weaknesses:

      Some fresh queries were made based on the author's previous responses and hope to get satisfactory answers this time.

      We provide below a point-by-point response to the fresh queries.

      (2) Line 134: please describe the complementation strain features as it is mentioned for the first time (plasmid, copy number, promoter etc.) in the manuscript. Especially under NO stress what could be the authors' justification regarding the high cAMP concentration in the complementation strain?

      As recommended by the reviewer, the details of construction of the complemented strain have been incorporated in the 'Materials and Methods' section of the revised manuscript (duplicated below): "To complement phoPR expression, pSM607 containing a 3.6-kb DNA fragment of M. tuberculosis phoPR including 200-bp phoP promoter region, a hygromycin resistance cassette, attP site and the gene encoding phage L5 integrase, as detailed earlier (Walters et al., 2006) was used to transform phoPR mutant to integrate at the L5 attB site.

      " To address the reviewer's other concern, we have now included the following sentence in the 'Results' section of the revised manuscript (duplicated below): "A higher cAMP level in the complemented strain under NO stress is possibly attributable to reproducibly higher phoP expression in the complemented mutant under specific stress condition (Khan et al., 2022)."

      Reference: Khan et al. (2022) Convergence of two global regulators to coordinate expression of essential virulence determinants of Mycobacterium tuberculosis. eLife 2022, 11:e80965.

      New query: The complemented gene (in pSM607 plasmid) becomes a single copy after chromosomal integration, so it should ideally behave like a WT strain. How could authors still justify the high cAMP concentration under NO stress?

      We agree with the reviewer. We are unable to provide a cogent justification regarding this result. We speculate that PhoP is strikingly activated under NO stress by a non-canonical mechanism and strongly represses rv0805 expression. As a result, there is a significantly higher cAMP concentration in case of the complemented mutant under NO stress.

      (13) Line 292: There is a difference between red and green bars. Authors should do statistical analysis and then comment on whether overexpression of WT and mutant pde are different or similar, to me they are different; also, explain why the WT-Rv0805 strain is different than the phoPR-KO strain in the context of cell wall metabolism.

      As recommended by the reviewer, we have now included statistical significance of the data in the revised version, and modified the text accordingly in the manuscript.

      New query: Authors are asked to put a statistical significance test between WT-Rv0805 and WT-Rv0805M.

      We have included it in the modified figure. Also, to explain it we incorporated new text in the legend to Fig. 4C of the revised manuscript (duplicated below):

      “Note that similar to phoPR-KO, WT-Rv0805 shows a comparably higher sensitivity to CHP relative to WT bacilli. However, WT-Rv0805M expressing a mutant Rv0805, shows a significantly lower sensitivity to CHP relative to WT-Rv0805, as measured by the corresponding CFU values.”

      (14) Line 299-303: Authors should explain how the colocalization % are calculated. Also, in the figure 4D merge panel please highlight the difference.

      As suggested by the reviewer, we have now explained the methodology used to calculate percent colocalization in greater details. Also, we have modified Figure 4D to highlight the difference between samples shown in merge panel. Please see our response to comment # 33 from the Reviewer 1.

      New query: In the figure legend it should be mentioned that the white arrow indicates non-co-localization which is visibly higher in WT and WT Rvo805M.

      We thank the reviewer for this very important suggestion. We have now included the following text in the legend to Fig. 4D of the revised manuscript.

      “White arrowheads in the merge panels indicate non-colocalization, which remains higher in WT-H37Rv and WT-Rv0805M relative to phoPR-KO or WT-Rv0805.”

    1. Author Response

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

      Reviewer #1 (Public Review):

      Summary:

      Through an unbiased genomewide KO screen, the authors identified loss of DBT to suppress MG132-mediated death of cultured RPE cells. Further analyses suggested that DBT reduces ubiquitinated proteins by promoting autophagy. Mechanistic studies indicated that DBT loss promotes autophagy via AMPK and its downstream ULK and mTOR signaling. Furthermore, loss of DBT suppresses polyglutamine- or TDP-43-mediated cytotoxicity and/or neurodegeneration in fly models. Finally, the authors showed that DBT proteins are increased in ALS patient tissues, compared to non-neurological controls.

      Strengths:

      The idea is novel, the evidence is mostly convincing, and the data are clean. The findings have implications for human diseases.

      Reply: We thank the reviewer for the supportive comments.

      Weaknesses:

      More experiments are needed to establish the connections between DBT and autophagy. The mechanistic studies are somewhat biased, and it's unclear whether the same mechanism (i.e., AMPK-->mTOR) can be applied to TDP-43-mediated neurodegeneration. Also, some data interpretation has to be more accurate.

      Reply: We thank the reviewer for raising these questions, and we have provided additional evidence in the revised manuscript to support the model that DBTKO can enhance autophagy and induce resistance to TDP-43-associated toxicity. This is described in greater detail below.

      (1) To provide further evidence for the connection between DBT and autophagy, we have introduced additional controls. For the additional controls, we have included the AMPK shRNA and drug treatment controls (Fig.4D, Fig.S4B), and these results suggest that reducing the AMPK level renders DBTKO cells sensitive to MG132 toxicity. We also added the TSC1 shRNA and mTOR agonist treatment controls (Fig.5E, Fig.S4G), and the results show that increasing mTOR levels also make the DBTKO cells sensitive to MG132.

      (2) To further confirm the roles of AMPK and mTOR in DBTKO cells, we introduced the AMPK agonist (EX229) and mTOR inhibitors (RAD001 and AZD8055) in co-treatment experiments with MG132 and then measured cell survival (Fig.S4D, S4G). The results indicate that promoting AMPK activation or inhibiting mTOR can enhance cell resistance to MG132-induced toxicity.

      (3) Additionally, we included the overexpression and rescue experiments for DBT and analyzed the AMPK-ULK1 signaling in WT RPE1 and DBTKO cells (Fig.S5D, S5E). The results indicate that the increase of DBT can significantly reduce the phosphorylation of AMPK/ULK1 and the levels of the autophagy marker LC3II. Together, these results suggest that DBT plays an important role in autophagy.

      (4) We had shown in the original version of the manuscript that DBTKO renders cells more resistant to TDP-43-associated toxicity, similar to the tolerance of MG132-induced toxicity. Here we further show that expression of TDP-43M337V enhances the phosphorylation of AMPK in the DBTKO cells (Fig. S7A), similar to the effect of the MG132 treatment. These results suggest that the resistance of DBTKO cells to MG132 or TDP-43-assoicated toxicity shares a similar mechanism of activated the AMPK signaling.

      Reviewer #2 (Public Review):

      Summary:

      Hwang, Ran-Der et al utilized a CRISPR-Cas9 knockout in human retinal pigment epithelium (RPE1) cells to evaluate for suppressors of toxicity by the proteasome inhibitor MG132 and identified that knockout of dihydrolipoamide branched chain transacylase E2 (DBT) suppressed cell death. They show that DBT knockout in RPE1 cells does not alter proteasome or autophagy function at baseline. However, with MG132 treatment, they show a reduction in ubiquitinated proteins but with no change in proteasome function. Instead, they show that DBT knockout cells treated with MG132 have improved autophagy flux compared to wildtype cells treated with MG132. They show that MG132 treatment decreases ATP/ADP ratios to a greater extent in DBT knockout cells, and in accordance causes activation of AMPK. They then show downstream altered autophagy signaling in DBT knockout cells treated with MG132 compared to wild-type cells treated with MG132. Then they express the ALS mutant TDP43 M337 or expanded polyglutamine repeats to model Huntington's disease and show that knockdown of DBT improves cell survival in RPE1 cells with improved autophagic flux. They also utilize a Drosophila model and show that utilizing either a RNAi or CRISPR-Cas9 knockout of DBT improves eye pigment in TDP43M337V and polyglutamine repeat-expressing transgenic flies. Finally, they show evidence for increased DBT in postmortem spinal cord tissue from patients with ALS via both immunoblotting and immunofluorescence.

      Strengths:

      This is a mechanistic and well-designed paper that identifies DBT as a novel regulator of proteotoxicity via activating autophagy in the setting of proteasome inhibition. Major strengths include careful delineation of a mechanistic pathway to define how DBT is protective. These conclusions are largely justified, but additional experiments and information would be useful to clarify and extend these conclusions.

      Reply: We thank the reviewer for the supportive comments.

      Weaknesses:

      The large majority of the experiments are evaluating suppression of drug (MG132) toxicity in an in vitro epithelial cell line, so the generalizability to disease is unclear. Indeed, MG132 itself has been shown to modulate autophagy, and off-target effects of MG132 are not addressed. While this paper is strengthened by the inclusion of mouse-induced motor neurons, Drosophila models, and postmortem tissue, the putative mechanisms are minimally evaluated in these models.

      Also, this effect is only seen with MG132 treatment, at a dose that causes markedly impaired cell survival. In this setting, it is certainly plausible that changes in autophagy could be the result of differences in cell survival, as opposed to an underlying mechanism for cell survival. Additional controls would be useful to increase confidence that DBT knockdown is protective via modulation of autophagy.

      While the authors report increased DBT in postmortem ALS tissue as suggestive that DBT may modulate proteotoxicity in neurodegeneration, this point would be better supported with the evaluation of overexpression of DBT in their model.

      Reply: We appreciate the reviewer for raising these questions, and we have provided further evidence in the revised manuscript to support the proposed mechanism that DBTKO confers resistance to MG132-induced toxicity through activation of autophagy. This is discussed in greater detail below.

      (1) To provide further mechanistic analysis, we have included additional controls for the analysis of AMPK signaling in Fig. 4D and Fig. S4B. These results demonstrate that using drugs or shRNAs to reduce AMPK activity can decrease DBTKO survival. We have also shown that that an increasing the AMPK activity with an activator enhances the survival of both WT and DBTKO cells under MG132 treatment (Fig. S4D), suggesting that DBTKO cells resist MG132-induced toxicity through the activation of AMPK signaling.

      (2) We have included additional controls for the analysis of mTOR signaling in Fig. 5E and Fig. S4F. The results in Fig. 5E show that reducing TSC1 using shRNAs can decrease DBTKO survival. We also added the experiments with mTOR agonist MHY1485 as a control in Fig. S4F. These results indicate that mTOR activation can promote DBTKO cells' sensitivity to MG132 toxicity. To further confirm the importance of mTOR in DBTKO-mediated resistance to MG132 toxicity, we included the mTOR inhibitors RAD001 and AZD8055 in the co-treatment experiments with MG132, and then measured cell survival (Fig. S4G). The results show that both mTOR inhibitors can enhance cell resistance to MG132-induced toxicity (Fig. S4G). These findings suggest that mTOR inhibition is required for DBTKO-mediated cell survival under MG132 treatment.

      (3) To further test the hypothesis that DBT knockdown is protective via modulation of autophagy, we have introduced the overexpression of DBT and the rescue of DBT in DBTKO cells to analyze the AMPK signaling that regulates autophagy (Fig. S5E). The results demonstrate that overexpression of DBT significantly reduced the phosphorylation of AMPK and ULK1 (Fig. S5E). In the rescue experiment, the results mirror those of the overexpression experiment, showing a significant reduction in the phosphorylation of AMPK and ULK1 (Fig. S5E). We also analyzed the autophagy marker LC3II in both the overexpression and rescue experiments, and the results indicate that increasing the DBT level specifically reduces the LC3II level (Fig. S5D). These results support the model that loss of DBT promotes the activation of autophagy.

      (4) To test the hypothesis that DBT may modulate proteotoxicity in neurodegeneration, we included the studies with TDP-43M337V and found that the expression of the mutant TDP43 enhanced the phosphorylation of AMPK in the DBTKO cells (Fig. S7A), consistent with the observations made with MG-132 treatment. Together with other findings in the manuscript, these results indicate that DBTKO can sensitize the activation of the AMPK signaling and confer the resistance to TDP-43-associated toxicity.

    1. Author response:

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

      Suggestions to the authors:

      • Please re-analyze findings by omitting from all Tables and Figures all data of comparators who were not randomized (BAC). I understand the difficulties of running this trial but the results of excess reduction of mortality do not allow the publication of a trial where comparators do not come from the randomized patient population.

      We wish to thank the editors and reviewers for their useful comments. Given that the study was designed with both randomised and CC participants we can’t easily exclude the CC analysis from the paper. However, we do provide graphs for both randomised only and randomised and CC participants for the primary and secondary endpoints. The fact that the primary endpoint (CRP) results are mirrored in both instances is also informative form a trial design perspective and indicative of the effect of dornase alfa therapy on inflammation being robust enough to yield the same results with small and larger cohorts.

      We agree that there are potential drawbacks of using contemporary controls. To address these potential biases we used CC patients recruited at the same time period at single site using the same selection criteria as the randomised group, which minimised potential bias. However, the enrolment and comparison of CRP in CC-BAC participants to concurrent randomised control R-BAC patients indicated that the two groups responded to BAC treatment in the same manner (Table 2, LS means log(CRP) 3.78 vs 3.53, P=0.386), whereas the R-BAC+DA vs R-BAC group comparison yielded significant differences (Table 2, LS means log(CRP) 3.1 vs 3.59, P=0.041). These comparisons mitigate to a large degree these potential problems.

      Still, to make easy to distinguish the groups we now use the following unique nomenclature throughout the manuscript which is clearly defined on ln. 111 and state that comparisons of treated participants were performed with both control groups separately and combined.

      R-BAC: Randomised BAC CC-BAC: Contemporary control BAC R-BAC+DA : Randomised BAC+ dornase alfa T-BAC: R-BAC + CC-BAC

      In fact, the most important bias in our study, might actually be the placebo effect, given that participants randomised to BAC did not receive a nebulized control substance. We now discuss these points in more detail in the manuscript and modified the title by removing the reference to a randomised trial and clinical outcomes.

      • The presentation remains confusing and the manuscript should be critically revised for clarity. There is a repetition of methods (e.g. lines 176-187 repeat 160-175) and redundant results (e.g. Figure S2, Table 3).

      We apologise for the repetition. We removed the repeated text in the Exclusion criteria (lines 176-187 in the old manuscript).

      Figure S2 is not related to Table 3. Figure S2 depicts baseline characteristics, whereas Table 3 complements the graph in Figure 3A but lists the mean daily value of the primary endpoint as requested by Reviewer 1 in the first round of revision.

      At Table 4: the authors should select one method of illustration for lab results, either Table or figure, without repetitions

      We agree and have removed Table 4 leaving the graphs instead.

      • Regarding inclusion criteria, it is unclear whether high radiological suspicion is sufficient for inclusion or whether PCR based confirmation is required in all instances (differences in wording between lines 153 and 191), and under which oxygen requirements (lines 155 and 192)

      We thank the reviewer for pointing this out. Indeed, radiological suspicion was not sufficient and all participants in this study had a positive PCR test as part of their diagnosis prior to inclusion in the study. The entire eligibility section was rewritten to reflect this important point.

      • Table 1 should be merged with Table S2 and a better description of cohort baseline severity (P/F, SOFA, APACHE, organ support, number of patients in each point of the WHO severity score) and treatments should be made available.

      We thank the reviewer for this suggestion. We have now merged Table 1 and S2 and included WHO ordinal severity information in Table 1, with median, average, SD, min and max values which reflect the participant distribution. Unfortunately, although the additional requested information was recorded, it was not systematically collected for the analysis of the trial and it was not straight forward to compile at this stage.

    1. Author response:

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

      Reviewer #1 (Recommendation for the authors):

      (1) On a few occasions, I found that the authors would introduce a concept, but provide evidence much later on. For example, in line 57, they introduced the idea that feedback timing modulates engagement of the hippocampus and striatum, but they provided the details much later on around line 99. There are a few instances like these, and the authors may want to go through the manuscript critically to bridge such gaps to improve the flow of reading.

      First, we thank the reviewer for acknowledging the contribution of our study and the methodological choices. We acknowledge the concern raised about the flow of information in the introduction. We have critically reviewed the manuscript, especially on writing style and overall structure, to ensure a smoother transition between the introduction of concepts and the provision of supporting evidence. In the case of the concept of feedback timing and memory systems, lines 46-58 first introduce the concept enhanced with evidence regarding adults, and we then pick up the concept around line 103 again to relate it to children and their brain development to motivate our research question. To further improve readability, we have included an outline of what to expect in the introduction. Specifically, we added a sentence in line 66-68 that provides an overview of the different paragraphs: “We will introduce the key parameters in reinforcement learning and then we review the existing literature on developmental trajectories in reinforcement learning as well as on hippocampus and striatum, our two brain regions of interest.”

      This should prepare the reader better when to expect more evidence regarding the concepts introduced. We included similar “road-marker” outline sentences in other occasions the reviewer commented on, to enhance consistency and readability.

      (2) I am curious as to how they think the 5-second delay condition maps onto real-life examples, for example in a classroom setting feedback after 5 seconds could easily be framed as immediate feedback.

      The authors may want to highlight a few illustrative examples.

      Thank you for asking about the practical implications of a 5-second delay condition, which may be very relevant to the reader. We have modified the introduction example in line 39-41 towards the role of feedback timing in the classroom to point out its practical relevance early on: “For example, children must learn to raise their hand before speaking during class. The teacher may reinforce this behavior immediately or with a delay, which raises the question whether feedback timing modulates their learning”.

      We have also expanded a respective discussion point in lines 720-728 to pick up the classroom example and to illustrate how we think timescale differences may apply: “In scenarios such as in the classroom, a teacher may comment on a child’s behavior immediately after the action or some moments later, in par with our experimental manipulation of 1 second versus 5 seconds. Within such short range of delay in teachers’ feedback, children’s learning ability during the first years of schooling may function equally well and depend on the striatal-dependent memory system. However, we anticipate that the reliance on the hippocampus will become even more pronounced when feedback is further delayed for longer time. Children’s capacity for learning over longer timescales relies on the hippocampal-dependent memory system, which is still under development. This knowledge could help to better structure learning according to their development.”

      (3) In the methods section, there are a few instances of task description discrepancies which make things a little bit confusing, for example, line 173 reward versus punishment, or reward versus null elsewhere e.g. line 229. In the same section, line 175, there are a few instances of typos.

      We appreciate your attention to detail in pointing out discrepancies in task descriptions and typos in the method section. We have revised the section, corrected typos, and now phrased the learning outcomes consistently as “reward” and “punishment”.

      (4). I wasn't very clear as to why the authors did not compute choice switch probability directly from raw data but implemented this as a model that makes use of a weight parameter. Former would-be much easier and straightforward for data plotting especially for uninformed readers, i.e., people who do not have backgrounds in computational modelling.

      Thank you for asking for clarification on the calculation of switching behavior. Indeed, in the behavioral results, switching behavior was directly calculated from the raw data. We now stressed this in the methods in lines 230-235, also by naming win-stay and lose-shift as “proportions” instead of as “probabilities”:“As a first step, we calculated learning outcomes diretly from the raw data, which where learning accuracy, win-stay and lose-shift behavior as well as reaction time.

      Learning accuracy was defined as the proportion to choose the more rewarding option, while win-stay and lose-shift refer to the proportion of staying with the previously chosen option after a reward and switching to the alternative choice after receiving a punishment, respectively.”

      In contrast to the raw data switching behavior, the computational heuristic strategy model indeed uses a weight for a relative tendency of switching behavior. We have also stressed the advantage of the computational measure and its difference to the raw data switching behavior in lines 248-252 and believe that the reader can now clearly distinguish between the raw data and the computational results: “Note that these model-based outcomes are not identical to the win-stay and lose-shift behavior that were calculated from the raw data. The use of such model-based measure offers the advantage in discerning the underlying hidden cognitive process with greather nuance, in contrast to classical approaches that directly use raw behavioral data.”

      (5) I agree with the authors' assertion that both inverse temperature and outcome sensitivity parameters may lead to non-identifiability issues, but I was not 100% convinced about their modelling approach exclusively assessing a different family of models (inv temperature versus outcome sensitivity). Here, I would like to make one mid-way recommendation. They may want to redefine the inverse temperature term in terms of reaction time, i.e., B=exp^(s+g(RT-mean (RT)) where s and g are free parameters (see Webb, 2019), and keep the outcome sensitivity parameter in the model with bounds [0,2] so that the interpretation could be % increase or decrease in actual outcome. Personally, in tasks with binary outcomes i.e. [0,1: null vs reward] I do not think outcome sensitivity parameters higher than 2 are interpretable as these assign an inflated coefficient to outcomes.

      We appreciate the mid-way recommendation regarding the modeling approach for inverse temperature and outcome sensitivity parameters. We have carefully revised our analysis approach by considering alternative modeling choices. Regarding the suggestion to redefine the inverse temperature in terms of reaction time by B=exp^(s+g(RT-mean (RT)), we unfortunately were not able to identify the reference Webb (2019), nor did we find references to the suggested modeling approach. Any further information that the reviewer could provide will be greatly appreciated. Regardless, we agree that including reaction times through the implementation of drift-diffusion modeling may be beneficial. However, changing the inverse temperature model in such a way would necessitate major changes in our modeling approach, which unfortunately would result in non-convergence issues in our MCMC pipeline using Rstan. Hence, this approach goes beyond the scope of the manuscript. Nonetheless, we have decided to mention the use of a drift-diffusion model, along with other methodological considerations, as future recommendation for disentangling outcome sensitivity from inverse temperature in lines 711-712: “Future studies might shed new light by examining neural activations at both task phases, by additionally modeling reaction times using a drift-diffusion approach, or by choosing a task design that allows independent manipulations of these phases and associated model parameters, e.g., by using different reward magnitudes during reinforcement learning, or by studying outcome sensitivity without decisionmaking.“

      Regarding the upper bound of outcome sensitivity, we agree that traditionally, limiting the parameter values at 2 is the choice for the parameter to be best interpretable. During model fitting, we had experienced non-convergence issues and ceiling effects in the outcome sensitivity parameter when fixing the inverse temperature at 1. The non-convergence issue was not resolved when we fixed the inverse temperature at 15.47, which was the group mean of the winning inverse temperature family. Model convergence was only achieved after increasing the outcome sensitivity upper bound to 20, with inverse temperature again fixed at 1. Since this model also performed well during parameter and model recovery, we argue that the parameter is nevertheless meaningful, despite the more extreme trial-to-trial value fluctuations under higher outcome sensitivity. We described our choice for this model in the methods section in lines 282-288: “Even though outcome sensitivity is usually restricted to an upper bound of 2 to not inflate outcomes at value update, this configuration led to ceiling effects in outcome sensitivity and non-converging model results. Further, this issue was not resolved when we fixed the inverse temperature at the group mean of 15.47 of the winning inverse temperature family model. It may be that in children, individual differences in outcome sensitivity are more pronounced, leading to more extreme values. Therefore, we decided to extend the upper bound to 20, parallel to the inverse temperature, and all our models converged with Rhat < 1.1.”.

      (6) I think the authors reporting optimal parameters for the model is very important (line 464), but the learning rate they report under stable contingencies is much higher than LRs reported by for example Behrens et al 2007, LRs around 0.08 for the optimal learning behaviour. The authors may want to discuss why their task design calls for higher learning rates.

      Thank you for appreciating our optimal parameter analysis, and for the recommendation to discuss why optimal learning rates in our task design may call for higher learning rates compared to those reported in some other studies. As largely articulated in Zhang et al (2020; primer piece by one of our co-authors), the optimal parameter combination is determined by several factors, such as the reward schedule (e.g., 75:25, vs 80:20) and task design (e.g., no reversal, one reversal, vs multiple reversal) and number of trials (e.g., 80, vs 100, vs, 120). Notably, in these taskrelated regards, our task is different from Behrens et al. (2007), which hinders a quantitative comparison among the optimal parameters in the two tasks. We have now included more details in our discussion in lines 643-656: “However, the differences in learning rate across studies have to be interpreted with caution. The differences in the task and the analysis approach may limit their comparability. Task proporties such as the trial number per condition differed across studies. Our study included 32 trials per cue in each condition, while in adult studies, the trials per condition ranged from 28 to 100. Optimal learning rates in a stable learning environment were at around 0.25 for 10 to 30 trials, another study reported a lower optimal learning rate of around 0.08 for 120 trials. This may partly explain why in our case of 32 trials per condition and cue, optimal learning rates called for a relatively high optimal learning rate of 0.29, while in other studies, optimal learning rates may be lower. Regarding differences in the analysis approach, the hierarchical bayesian estimation approach used in our study produces more reliable results in comparison to maximum likelihood estimation, which had been used in some of the previous adult studies and may have led to biased results towards extreme values. Taken together, our study underscores the importance of using longitudinal data to examine developmental change as well as the importance of simulation-based optimal parameters to interpret the direction of developmental change.”

      (7) The authors may want to report degrees of freedom in t-tests so that it would be possible to infer the final sample size for a specific analysis, for example, line 546.

      We appreciate the recommendation to include degrees of freedom, which are now added in all t-test results, for example in line 579: “Episodic memory, as measured by individual corrected object recognition memory (hits - false alarms) of confident (“sure”) ratings, showed at trend better memory for items shown in the delayed feedback condition (𝛽!""#$%&’(#")%*"# = .009, SE =.005, t(df = 137) = 1.80, p = .074, see Figure 5A).”

      (8) I'm not sure why reductions in lose shift behaviour are framed as an improvement between 2 assessment points, e.g. line 578. It all depends on the strength of the contingency so a discussion around this point should be expanded.

      We acknowledge that a reduction in lose-shift behavior only reflect improvements under certain conditions where uncertainty is low and the learning contingencies are stable, which is the case in our task. We have added Supplementary Material 4 to illustrate the optimality of win-stay and lose-shift proportions from model simulation and to confirm that children’s longitudinal development was indeed towards more optimal switching behavior. In the manuscript, we refer to these results in lines 488-490: “We further found that the average longitudinal change in win-stay and lose-shift proportion also developed towards more optimal value-based learning (Supplementary Material 4).”

      (9) If I'm not mistaken, the authors reframe a trend-level association as weak evidence. I do not think this is an accurate framing considering the association is strictly non-significant, therefore should be omitted line 585.

      We thank for the point regarding the interpretation of a trend-level association as weak evidence. We changed our interpretation, corrected in lines 581-585: “The inclusion of poor learners in the complete dataset may have weakend this effect because their hippocampal function was worse and was not involved in learning (nor encoding), regardless of feedback timing. To summarize, there was inconclusive support for enhanced episodic memory during delayed compared to immediate feedback, calling for future study to test the postulation of a selective association between hippocampal volume and delayed feedback learning.” as well as lines 622-623: “Contrary to our expectations, episodic memory performance was not enhanced under delayed feedback compared to immediate feedback.”

      Reviewer # 2 (Public Review):

      We thank the reviewer for acknowledging the strength of our study and pointing out its weaknesses.

      Weaknesses:

      There were a few things that I thought would be helpful to clarify. First, what exactly are the anatomical regions included in the striatum here?

      We appreciate the clarification question regarding the anatomical regions included in the striatum. The striatum included ventral and dorsal regions, i.e., accumbens, caudate and putamen. We have now specified the anatomical regions that were included in the striatum in lines 211-212: “We extracted the bilateral brain volumes for our regions of interest, which were striatum and hippocampus. The striatum regions included nucleus accumbens, caudate and putamen.”

      Second, it was mentioned that for the reduced dataset, object recognition memory focused on "sure" ratings. This seems like the appropriate way to do it, but it was not clear whether this was also the case for the full analyses in the main text.

      Thank you for pointing out that in the full dataset analysis, the use of “sure” ratings for object recognition memory was previously not mentioned. Including only “sure” ratings was used consistently across analyses. This detail is now described under methods in lines 332-333: “Only confident (“sure”) ratings were included in the analysis, which were 98.1 % of all given responses.”

      Third, the children's fitted parameters were far from optimal; is it known whether adults would be closer to optimal on the task?

      We thank for your question on whether adult learning rates in the task have been reported to be more optimal than those of the children in our study. This indeed seems to be the case, and we added this point in our discussion in line 639-643: “Adult studies that examined feedback timing during reinforcement learning reported average learning rates range from 0.12 to 0.34, which are much closer to the simulated optimal learning rates of 0.29 than children’s average learning rates of 0.02 and 0.05 at wave 1 and 2 in our study. Therefore, it is likely that individuals approach adult-like optimal learning rates later during adolescence.”

      The main thing I would find helpful is to better integrate the differences between the main results reported and the many additional results reported in the supplement, for example from the reduced dataset when excluding non-learners. I found it a bit challenging to keep track of all the differences with all the analyses and parameters. It might be helpful to report some results in tables side-by-side in the two different samples. And if relevant, discuss the differences or their implication in the Discussion. For example, if the patterns change when excluding the poor learners, in particular for the associations between delayed feedback and hippocampal volume, and those participants were also those less well fit by the value-based model, is that something to be concerned about and does that affect any interpretations? What was not clear to me is whether excluding the poor learners at one extreme simply weakens the general pattern, or whether there is a more qualitative difference between learners and non-learners. The discussion points to the relevance of deficits in hippocampaldependent learning for psychopathology and understanding such a distinction may be relevant.

      We appreciate the feedback that it might seem challenging to keep track of differences between the analyses of the full and the reduced dataset. We have now gathered all the analyses for the reduced dataset in Supplementary Material 6, with side-by-side tables for comparison to the full dataset results. Whenever there were differences between the results, they were pointed out in the results section, see lines 557-560: “In the results of the reduced dataset, the hippocampal association to the delayed learning score was no longer significant, suggesting a weakened pattern when excluding poor learners (Supplementary Material 6). It is likely that the exclusion reduced the group variance for hippocampal volume and delayed learning score in the model.” and lines 579-581: “Note that in the reduced dataset, delayed feedback predicted enhanced item memory significantly (Supplementary Material 6).”

      The found differences were further included in our discussion in lines 737-740 in the context of deficits in hippocampal-dependent learning and psychopathology: “Interestingly, poor learners showed relatively less value-based learning in favor of stronger simple heuristic strategies, and excluding them modulated the hippocampal-dependent associations to learning and memory in our results. More studies are needed to further clarify the relationship between hippocampus and psychopathology during cognitive and brain development.”

    1. Author response:

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

      Reviewer #1 (Public Review):

      (1) There appears to be a flaw in the exploration of cortical inputs. the authors never show that HFS of cortical inputs has no effect in the absence of thalamic stimulation. It appears that there is a citation showing this, but I think it would be important to show this in this study as well.

      We understand that the reviewer would like us to induce an HFS protocol on cortical input and then test if there is any change in synaptic strength in thalamic input. We have done this experiment which shows that without a footshock, high-frequency stimulation (HFS) of the cortical inputs did not induce synaptic potentiation on the thalamic pathway (Extended Data Fig. 4d).

      (2) t is somewhat confusing that the authors refer to the cortical input as driving heterosynaptic LTP, but this is not shown until Figure 4J, that after non-associative conditioning (unpaired shock and tone) HFS of the cortex can drive freezing and heterosynaptic LTP of thalamic inputs.

      We agree with the reviewer that it is in figure 4j and figure 5,b,c which we show electrophysiological evidence for cortical input driving heterosynaptic LTP. It is only to be consistent with our terminology that initially we used behavioral evidence as the proxy for heteroLTP (figure 3c).

      …, the authors are 'surprised' by this outcome, which appears to be what they predict.

      We removed the phrase “To our surprise”.

      (3) 'Cortex' as a stimulation site is vague. The authors have coordinates they used, it is unclear why they are not using standard anatomical nomenclature.

      We replaced “cortex” with “auditory/associative cortex”.

      (4) The authors' repeated use of homoLTP and heteroLTP to define the input that is being stimulated makes it challenging to understand the experimental detail. While I appreciate this is part of the goal, more descriptive words such as 'thalamic' and 'cortical' would make this much easier to understand.

      We agree with the reviewer that a phrase such as “an LTP protocol on thalamic and cortical inputs” would be more descriptive. We chose the words “homoLTP” and “heteroLTP” only to clarify (for the readers) the physiological relevance of these protocols. We thought by using “thalamic” and “cortical” readers may miss this point. However, when for the first time we introduce the words “homoLTP” and “heteroLTP”, we describe which stimulated pathway each refers to.

      Reviewer #2 (Public Review):

      (1) …The experimental schemes in Figs. 1 and 3 (and Fig. 4e and extended data 4a,b) show that one group of animals was subjected to retrieval in the test context at 24 h, then received HFS, which was then followed by a second retrieval session. With this design, it remains unclear what the HFS impacts when it is delivered between these two 24 h memory retrieval sessions.

      We understand that the reviewer has raised the concern that the increase in freezing we observed after the HFS protocol (ex. Fig. 1b, the bar labeled as Wth+24hHFSth) could be caused or modulated by the recall prior to the HFS (Fig. 1a, top branch). To address this concern, in a new group of mice, 24 hours after weak conditioning, we induced the HFS protocol, followed by testing (that is, no testing prior to the HFS protocol). We observed that homoLTP was as effective in mice that were tested prior to the induction protocol as those that were not (Fig. 1b, Extended Data Fig. 1d,e).

      It would be nice to see these data parsed out in a clean experimental design for all experiments (in Figs 1, 3, and 4), that means 4 groups with different treatments that are all tested only once at 24 h, and the appropriate statistical tests (ANOVA). This would also avoid repeating data in different panels for different pairwise comparisons (Fig 1, Fig 3, Fig 4, and extended Fig 4).

      While we understand the benefit of the reviewer’s suggestion, the current presentation of the data was done to match the flow of the text and the delivery of the information throughout the manuscript. We think it is unlikely that the retrieval test prior to the HFS impacts its effectiveness, as confirmed by homosynaptic HFS data (Extended Data Fig. 1d,e). It is beyond the scope of current manuscript to investigate the mechanisms and manipulations related to reconsolidation and retrieval effects.

      (2) … It would be critical to know if LFPs change over 24 h in animals in which memory is not altered by HFS, and to see correlations between memory performance and LFP changes, as two animals displayed low freezing levels. … They would suggest that thalamo-LA potentiation occurs directly after learning+HFS (which could be tested) and is maintained over 24 h.

      We have performed the experiment where we recorded the evoked LFP 2hrs and 24hrs following the weak conditioning protocol. We observed that a weak conditioning protocol that was not followed by an optical LTP protocol on the cortical inputs failed to produce synaptic potentiation of the thalamic inputs (tested 2hrs and 24hrs after the LTP protocol; Extended Data Fig. 5d,e).

      (3) The statistical analyses need to be clarified. All statements should be supported with statistical testing (e.g. extended data 5c, pg 7 stats are missing). The specific tests should be clearly stated throughout. For ANOVAs, the post-hoc tests and their outcomes should be stated. In some cases, 2-way ANOVAs were performed, but it seems there is only one independent variable, calling for one-way ANOVA.

      All the statistical analyses have been revised and the post-hoc tests performed after the ANOVAs are mentioned in the relevant figure legends.

      Reviewer #2 (Recommendations For The Authors):

      The wording "transient" and "persistent" used here in the context of memory seems a bit misleading, as only one timepoint was assessed for memory recall (24 h), at which the memory strength (freezing levels) seem to change.

      As the reviewer mentioned, we have tested memory recall only at one time point. For this reason, throughout the text we used “transient” exclusively to refer to the experience (receiving footshock) and not to the memory. We replaced “persistence” with “stabilization” where it refers to a memory (“the induction of plasticity influences the stabilization of the memory”).

      For the procedures in which the CS and US were not paired, the term "unpairing" is used (which is probably the more adequate one), but the term "non-associative conditioning" appears in the text, which seems a bit misleading, as this term may have another connotation. There is also literature that an unpairing of CS and US could lead to the formation of a safety memory to the CS, that may be disrupted by HFS stimulation.

      We replaced "non-associative" with “unpaired”.

      Validation of viral injection sites for all experiments: Only representative examples are shown, it would be nice to see all viral expression sites.

      For this manuscript, we have used 155 mice. For this reason, including the injection sites for all the animals in the manuscript is not feasible. Except for the mice that have been excluded, (please see exclusion criteria added in the methods), the expression pattern we observed was consistent across animals and therefore the images shown are true representatives.

      Extended Data 1b: Please explain what N, U, W, and S behavioral groups mean. To what groups mentioned in the text (pg 2,3) do these correspond?

      The requested clarifications are implemented in the figure legend.

      Please elaborate on the following aspects of your methods and approaches:

      • Please explain if the protocol for HFS to manipulate behavior was the same as the one used for the LTP experiments (Fig 1d, Fig 4j) and was identical for homo/hetero inputs from thal and ctx?

      We used the same HFS protocol for all the HFS inductions. We included this information in the methods section.

      • Please state when the HFS was given in respect to the conditioning (what means immediately before and after?) and in which context it was given. Were animals subjected to HFS exposed to the context longer (either before or after the conditioning while receiving HFS) than the other groups? When the HFS was given in another context (for the 24 h group)- how was this controlled for?

      Requested information has been added to the methods section. The control and intervention groups were treated in the same way.

      • When were the footshocks given in the anesthesized recordings (Fig. 4j) and how was the temporal relationship to the HFS? Was the timing the same as for the HFS in the behavioral experiments?

      Requested information has been added to the methods section.

      • Please add information on how the LFP was stimulated and how the LFP- EPSP slope was determined in in vivo recordings, likewise for the whole cell recordings of EPSPs in Fig. 5d-f.

      Requested information has been added to the methods section.

      Here, the y-Axis in Fig. 5e should be corrected to EPSP slope rather than fEPSP slope if these are whole-cell recordings.

      This has been corrected.

      • Please include information if the viral injections and opto-manipulations were done bilateral or unilateral and if so in which hemisphere. Likewise, indicate where the LFP recordings were done.

      Requested information has been added to the methods section.

      • Were there any exclusion criteria for animals (e.g. insufficient viral targeting or placement of fibers and electrodes), other than the testing of the optical CS for adverse effects?

      Requested information has been added to the methods section.

      Statistics: In addition to clarifying analytical statistics, please clarify n-numbers for slice recordings (number of animals, number of slices, and number of cells if applicable).

      Requested information has been added to the methods section.

      It would be nice to scrutinize the results in extended data 4b. The freezing levels with U+24h HFS show a strong trend towards an increase, the effect size may be similar to immediate HFS Fig 4f and extended data 4a) if n was increased.

      We agree with the reviewer. To address this point, we added “HomoLTP protocol when delivered 24hrs later, produced an increase in freezing; however, the value was not statistically significant.” To show this point, we used the same scale for freezing in Extended Data Fig. 4a and b.

      In the final experiment (Fig. 5a-c), Fig. 5b seems to show results from only one animal, but behavioral results are from 4 animals (Fig 5c). It would be helpful to see the quantification of potentiation in each animal.

      The results (now with error bar) include all mice.

      Please spell out the abbreviation "STC".

      Now, it is spelled out.

      Page 8 last sentence of the discussion does not seem to fit there.

      The sentence has been removed.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors did not determine how WTh affects Th-LA synapses, as field EPSPs were recorded only after HFS. WTh was required for the effects of HFS, as HFS alone did not produce CR in naïve and/or unpaired controls. As such the effects of the WTh protocol on synaptic strength must be investigated.

      We have performed the experiment where we recorded the evoked LFP 2hrs and 24hrs following the weak conditioning protocol. We observed that a weak conditioning protocol that was not followed by an optical LTP protocol on the cortical inputs failed to produce synaptic potentiation of the thalamic inputs (tested 2hrs and 24hrs after the LTP protocol; Extended Data Fig. 5d,e).

      (2) The authors provide some evidence that their dual opsin approach is feasible, particularly the use of sustained yellow light to block the effects of blue light on ChrimsonR. However, this validation was done using single pulses making it difficult to assess the effect of this protocol on Th input when HFS was used. Without strong evidence that the optogenetic methods used here are fault-proof, the main conclusions of this study are compromised. Why did the authors not use a protocol in which fibers were placed directly in the Ctx and Th while using soma-restricted opsins to avoid cross-contamination?

      We understand that the reviewer raises the possibility that our dual-opsin approach, although effective with single pulses, may fail in higher frequency stimulation protocols (10Hz and 85Hz). To address this concern, in a new group of mice we applied our approach to 10Hz and 85Hz stimulation protocols. We show that our approach is effective in single-pulse as well as in 10Hz and 85Hz stimulation protocols (Fig. 2d-h).

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      Zhang et al. demonstrate that CD4+ single positive (SP) thymocytes, CD4+ recent thymic emigrants (RTE), and CD4+ T naive (Tn) cells from Cd11c-p28-flox mice, which lack IL-27p28 selectively in Cd11c+ cells, exhibit a hyper-Th1 phenotype instead of the expected hyper Th2 phenotype. Using IL-27R-deficient mice, the authors confirm that this hyper-Th1 phenotype is due to IL-27 signaling via IL-27R, rather than the effects of monomeric IL-27p28. They also crossed Cd11c-p28-flox mice with autoimmune-prone Aire-deficient mice and showed that both T cell responses and tissue pathology are enhanced, suggesting that SP, RTE, and Tn cells from Cd11c-p28-flox mice are poised to become Th1 cells in response to self-antigens. Regarding mechanism, the authors demonstrate that SP, RTE, and Tn cells from Cd11c-p28-flox mice have reduced DNA methylation at the IFN-g and Tbx21 loci, indicating 'de-repression', along with enhanced histone tri-methylation at H3K4, indicating a 'permissive' transcriptional state. They also find evidence for enhanced STAT1 activity, which is relevant given the well-established role of STAT1 in promoting Th1 responses, and surprising given IL-27 is a potent STAT1 activator. This latter finding suggests that the Th1-inhibiting property of thymic IL-27 may not be due to direct effects on the T cells themselves.

      Strengths:

      Overall the data presented are high quality and the manuscript is well-reasoned and composed. The basic finding - that thymic IL-27 production limits the Th1 potential of SP, RTE, and Tn cells - is both unexpected and well described.

      Weaknesses:

      A credible mechanistic explanation, cellular or molecular, is lacking. The authors convincingly affirm the hyper-Th1 phenotype at epigenetic level but it remains unclear whether the observed changes reflect the capacity of IL-27 to directly elicit epigenetic remodeling in developing thymocytes or knock-on effects from other cell types which, in turn, elicit the epigenetic changes (presumably via cytokines). The authors propose that increased STAT1 activity is a driving force for the epigenetic changes and resultant hyper-Th1 phenotype. That conclusion is logical given the data at hand but the alternative hypothesis - that the hyper-STAT1 response is just a downstream consequence of the hyper-Th1 phenotype - remains equally likely. Thus, while the discovery of a new anti-inflammatory function for IL-27 within the thymus is compelling, further mechanistic studies are needed to advance the finding beyond phenomenology.

      Thanks for the comments. Following the suggestions of the reviewer, further studies will be performed to test whether developing thymocytes are the direct targets of IL-27 using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras of wildtype and IL-27ra knockout cells.

      To address the potential autocrine loop in the STAT1 hyperactivation, we added IFN-γ antibody into CD4+ T cell cultures and saw no obvious impact on STAT1 phosphorylation. If deemed necessary, we could further test this possibility in vivo using Cd4-Ifng and CD11c-p28 double knockout mice.

      The detailed mechanisms underlying the hyperactivation of STAT1 remain to be determined. IL-27p28 has recently been shown to act as an antagonist of gp130-mediated signaling. In addition, structural studies have demonstrated that IL-27p28 has the interface with EBI3, as well as the two receptor subunits IL-27Rα and gp130. Taken into consideration of these findings and the fact that p28 and IL-27ra deficiency exhibits similar phenotype, we speculate that deficiency in either p28 or IL-27ra makes more gp130 available to transduce signals elicited by other cytokines. We will next focus on gp130 related cytokines to search for the candidate(s) which ultimately leads to enhanced STAT1 activation in the absence of p28. Alternatively, release of EBI3 in the absence of p28 may facilitate its coupling with other cytokine subunits. IL-35, which is composed of EBI3 and p35, is of particular interest as IL-27Rα is also involved in its signaling.

      To narrow down the candidate cytokines, we will first examine the expression of IL-35 and gp130 related cytokines, including IL-6, IL-11, LIF, CT1, OSM, IL-31, CLCF1, CNTF in the thymus and thymocyte-depleted thymic stromal cells by mining public databases and by RT-PCR. Similarly, CD4+ thymocytes will be examined for the expression of receptor subunits which can couple with gp130, including IL-6R, IL-11R, LIFR, OSMRβ, IL-31Rα, CNTFRα, IL-23R, and IL-12Rβ2.

      We next will select those cytokines expressed in the thymus or thymic stromal cells with cognate receptor expression in CD4+ thymocytes and test their effect on STAT1 phosphorylation of wildtype and p28-deficient CD4+ thymocytes. If deemed necessary, double knockout mice will be engaged to rescue the hyper-Th1 phenotype.

      Reviewer #2 (Public Review):

      Summary:

      Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

      Strengths:

      Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

      Weaknesses:

      The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding. In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

      Indeed, the present study is based on the finding of enhanced IFN-γ production by CD4+ T cells from CD11c-p28 floxed mice, which was originally reported by Zhang et al. and repeatedly cited in the our manuscript. We revisited this phenomenon in the context of functional bias of newly generated CD4+ T cells and sought to reveal the mechanisms underlying the hyper-Th1 phenotype in the absence of thymic DC-derived IL-27. We showed that deletion of p28 resulted in an unexpected hyperactivation of STAT1, which was accompanied by epigenetic changes in favor of Th1 bias. However, the gap remains between p28 deficiency and STAT1 activation.

      Altogether, the major issues of this study remain unresolved:

      (1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

      Thanks for the suggestions. Further studies will be performed to test the potential autocrine loop for IFN-γ production in vivo using Cd4-Ifng and CD11c-p28 double knockout mice. This model should also be helpful to exclude the possibility of indirect role of IFN- production by such cells as iNKT.

      As pointed out by the reviewer, a critical unanswered question is what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells. Several lines of evidence point to the possibility that p28 deficiency increases the responsiveness of developing thymocytes to STAT1-activating cytokines. Firstly, IL-27p28 has recently been shown to act as an antagonist of gp130-mediated signaling. Secondly, structural studies have demonstrated that IL-27p28 is centrally positioned in the complex formed with EBI3, as well as the two receptor subunits IL-27Rα and gp130. Thirdly, we observed similar hyper-Th1 phenotype in the absence of either p28 and IL-27ra. Therefore, it is speculated that more gp130 should be available to transduce signals elicited by other cytokines in such a scenario. We will next seek to determine the candidate cytokine(s) responsible for the enhanced STAT1 activation in the absence of p28 as outlined in the response to Reviewer 1.

      (2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

      Thanks for the suggestions. Further studies will be performed to test whether developing thymocytes are the direct targets of IL-27 using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras of wildtype and IL-27ra knockout cells.

    1. Author Response:

      We thank the editors for their assessment of our manuscript. We appreciate the reviewers’ thoughtful comments and plan to incorporate their feedback into a revised manuscript. We agree that incorporating an additional, more common ablation tool would be highly complementary to our Kir2.1 ablation studies. We also agree that images across timepoints should be expanded for contact analyses, connectomics data can be better leveraged, additional quantifications can be performed as suggested by the reviewers to better support claims, and that the introduction and discussion can be revised to better position our work in the context of previous studies. We also strongly agree that providing data on receptor RNA and protein expression in the GF across timepoints would be extremely informative, however we have found acquiring these data, at the necessary resolution, would require new approaches and tools that may be outside the scope of the project.

    1. Author response:

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

      Reviewer #1 (Public Review):

      Farhat-Younis and colleagues demonstrate tumor-specific IgM's capacity to induce tumor cell death in monocyte-derived dendritic cell cultures. They subsequently designed a chimeric receptor based on high-affinity FcRI. However, the authors found that the transfection process was more efficient when either the variable light or heavy chain was transfected individually rather than the entire scFv. This scFv construct led to an endoplasmic reticulum (ER) stress response and scFv degradation. A considerable portion of the manuscript is dedicated to the negative scFv expression results. The authors pivoted to a modified FcgRI capable of transmitting IgM signals. This represents a tremendous amount of work in the development of this chimeric receptor, the critical experiment showing efficacy in vivo was not presented, and instead various in vitro assays are shown. Thus, this manuscript will markedly benefit from showing improved responses to tumors in vivo when macrophages express FcgRI-IgM.

      We deeply thank the reviewer for his thoughtful comments and overall favorable review of our manuscript.

      1) In a mouse tumor model, the authors demonstrated that monocyte-derived dendritic cells (MoDCs) treated with IgG immune complexes (ICs) were more effective at preventing tumor growth compared to those treated with IgM ICs (as shown in Figure 1B). In Figure 1C, their in vitro experiments revealed that IgM resulted in tumor cell death, as well as increased production of nitric oxide (NO) and granzyme B. How do the authors reconcile IgG IC-treated MoDCs performing better in preventing tumors in vivo than IgM IC-treated MoDCs, despite the in vitro results with IgM-ICs. The authors speculate that IgG IC-treated MoDCs might trigger T cell immunity but do not show T cell involvement.

      We apologize for not making this point clearer. We have extensively studied this phenomenon and published two papers that detailed the underlying mechanism in two consecutive papers (PMID: 27812544, PMID: 25924063). Briefly, we showed that DC activated with IgM-IC DC undergo cell death concomitantly to their release of lytic granules and lysis of tumor cells. As a result, they do not migrate to the lymph nodes where they should induce reactive T cell clones. In contrast, DC activated with IgG-IC do not elicit in vitro cytotoxicity but rather process the IC to present its derived antigens of MHC-II. We addressed that issue in the revised version and cited the relevant paper to further clarify it.

      (2) The authors report distinct functional consequences of MoDCs incubated with tumor-IgG complexes and tumor IgM complexes. Tumor growth was inhibited and T cell immunity induced with the former. The latter, however, elicited robust anti-tumor killing. What happens if MoDCs are incubated with both IgG and IgM complexes? If this combined treatment induces effective killing and T cell memory, would this impact the design of the chimeric receptor to include IgG responsiveness as well?

      This is a very interesting point. As mentioned above, our previous publications strongly suggest that tumor binding IgG and IgM induce different processes in myeloid cells. Yet, since MoDC naturally express the high affinity receptors for IgG FcRI, we speculate that treating tumor-bearing mice modified monocyte, alone or in combination with tumor-binding IgG, would shed some light into that. Indeed, such treatment elicit a strong T cell immunity in these mice and the data was added to Supplementary Data Figure S4J. With that being said, a complete analysis of this question is very complicated and extent beyond the scope of this work. We would like to emphasize that the purpose of this work is to highlight some of the challenges unique to genetic manipulation in myeloid cells and to suggest one alternative scaffold for integrating signaling in these cells. We do not argue that the specific solution presented here is the most potent one and more work is required before promoting such treatment into the clinic. We have added a sentence to the Discussion section that stress that issue.

      (3) In Figure 5H, the authors demonstrate the ability of the chimeric receptor construct to deplete tumor cells in vitro. The ms would improve if the authors could show the chimeric receptor construct results in tumor cell death and/or prevention in an in vivo model. Similarly, if combined stimulation with IgG and IgM complexes enhances tumor response, this should be incorporated into the therapeutic strategy.

      This is a wonderful suggestion. To address that, we challenged C57Bl/6 mice with B16F10 melanoma and allowed them to grow until it reached a palpable size of approximately 25 mm2. Concomitantly, we cultured bone marrow dendritic cells from syngeneic mice and transfected them with a linear mRNA of the alpha/mu construct. Tumor bearing mice were then treated with alpha/mu and sham transduced BMDC alone, or in combination with antibody against the melanoma antigen Trp1 (TA99). The results were added as Figure 5K and to Supplementary Figure S4h-S4I.

      Reviewer #2 (Public Review):

      Summary:

      While a significant portion of immunotherapy research has focused on the pivotal role of T cells in tumor immunity, their effectiveness may be limited by the suppressive nature of the tumor environment. On the other hand, myeloid cells are commonly found within tumors and can withstand these adverse conditions. However, these cells often adopt an immunosuppressive phenotype when infiltrating tumors. Therefore, manipulating myeloid cells could potentially enhance the anti-tumor potential of immunotherapy.

      In this manuscript, Farhat-Younes and colleagues have demonstrated that activating the IgM receptor signaling in myeloid cells induces an oxygen burst, the secretion of Granzyme B, and the lysis of adjacent tumor cells. Furthermore, they have outlined a strategy to utilize these features to generate CAR macrophages. However, they have identified a limitation: the expression of scFv in myeloid cells induces ER stress and the degradation of misfolded proteins. To address this issue, chimeric receptors were designed based on the high-affinity FcγRI for IgG. When macrophages transfected with these receptors were exposed to tumor-binding IgG, extensive tumor cell killing, and the release of reactive oxygen species and Granzyme B were observed.

      Strengths:

      In general, I consider this work to be significant, and the results are compelling. It emphasizes the specific considerations and requirements for successful manipulation in myeloid cells, which could further advance the field of cellular engineering for the benefit of immunotherapy

      We thank the reviewer for his thoughtful comments and overall appreciation of our findings.

      Weaknesses:

      Nevertheless, there are several minor issues that should be addressed:

      (1) TCR fragments are commonly used to induce ER stress in non-immune cells. Therefore, it would be interesting to investigate whether TCR fragments can be expressed in myeloid cells and if they induce ER stress. Addressing this issue would support the notion that these cells lack the ER chaperones required for folding immunoglobulin variable chains.

      This is a wonderful suggestion. To assess that possibility, we cloned the alpha chain of anti-Trp1 TCR and transfected RAW 264.7 macrophages. Importantly, we could not detect expression on this construct in macrophages, further supporting our findings with scFv in these cells. We added this result to Figure 4J and Supplementary Figure S3C.

      (2) It would be valuable to determine whether, after the degradation of scFv fragments by myeloid cells, they are presented on MHC-I and MHC-II.

      This is a very interesting point. To address that, we generated a genetic construct where we fused the anti-CD19 scFv to a polypeptide composed from the MHCI and the MHCII fragments of Ova Albumin. Next, DC 2.4 were transfected with this construct and measured their capacity to stimulate the proliferation of CD8+ T cells from OT-I and CD4+ from OT-II mice. DC transfected with this construct efficiently stimulated the proliferation of both T cells, suggesting that both Ova fragments are indeed presented on MHCI and MHCII. Nonehteless, DC transfected with polypeptide of MHCI and MHCII fragments of Ova Albumin only (with no scFv), were almost equally effective in stimulating OT-I and OT-II T cell proliferation. We added that result to Supplementary Figure S3D-S3E.

      (3) Some methodological details, such as the vaccination protocol and high-resolution microscopy procedures, are missing from the text.

      We thank the reviewer for pointing out these issues. We added the missing details to the revised version of the manuscript.

    1. Author response:

      We thank both reviewers for their feedback and for underlying the potential of our new tool and experimental approach to identify signalling molecules that can improve the in vitro derivation of specific cell types from human pluripotent stem cells. To address the reviewers' points we plan to carry out further analysis that should solidify our conclusions. We will also edit the text to temper conclusions where appropriate.

    1. Author response:

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

      We sincerely appreciate the reviewer’s dedication to evaluating our manuscript and raising essential considerations regarding the classification of the migration behavior we described. While the reviewer suggests that this behavior aligns with the concept of itinerancy, we contend that it represents a distinct phenomenon, albeit with similarities, as both involve the non-breeding movements of birds. We acknowledge that our manuscript did not adequately address this distinction and have considered the reviewer’s feedback. In our response, we clarify the difference between the described phenomenon and itinerancy. Our revised manuscript will include a new section in the Discussion to address this issue comprehensively.

      In the first part of the review, the reviewer emphasizes that the pattern we are describing is consistent with itinerancy. Regardless of the terminology used, we want to highlight the existence of two different types of migratory behavior, both of which involve movement in non-breeding areas.

      The first type, called itinerancy, was first described by Moreau in 1972 in “The Palaearctic-African Bird Migration Systems.” As noted by the reviewer, this behavior involves an alternation of stopovers and movements between different short-term non-breeding residency areas. They usually occur in response to food scarcity in one part of the non-breeding range, causing birds to move to another part of the same range. These movements typically cover distances of 10 to 100 kilometers but are neither continuous nor directional. Moreau (1972) defined itinerancy as prolonged stopovers, normally lasting several months, primarily in tropical regions. He noted observations of certain species disappearing from his study areas in sub-Saharan Africa in December and others appearing, suggesting they may have multiple home ranges during the non-breeding season. Subsequent research, as mentioned by the reviewer, has confirmed itinerancy in many species, particularly among Palaearctic-African migrants in sub-Saharan Africa. In particular, the Montagu’s Harrier has been extensively studied in this regard. The reviewer rightly points out that our study does not include recent findings on this species. In our revised version, we will include references to recent studies, such as those by Trierweiler et al. (2013, Journal of Animal Ecology, 82:107-120) and Schlaich et al. (2023, Ardea, 111:321-342), which show that Montagu’s Harrier has an average of 3-4 home ranges separated by approximately 200 kilometers. These studies suggest that the species spends approximately 1.5 months at each site, with the most extended period typically observed at the last site before migrating to the breeding grounds.

      In the second type, birds undertake a post-breeding migration, arrive in their non-breeding range, and then gradually move in a particular direction throughout the season. This continuous directional movement covers considerable distances and continues throughout the non-breeding period. In our study, this movement covered about 1000 km, comparable to the total migration distance of Rough-legged Buzzards of about 1500 km. As observed in our research, these movements are influenced by external factors such as snow cover. In such cases, the progression of snow cover in a south-westerly direction during winter can prevent birds from finding food, forcing them to continue migrating in the same direction. In essence, this movement represents a prolonged phase of the migration process but at a slower pace. Similar behavior has been documented in buzzards, as reported by Strandberg et al. (2009, Ibis 151:200-206). Although several transmitters in their study stopped working in mid-winter, the authors observed a phenomenon they termed ‘prolonged autumn migration.’

      In the second part of the review, the reviewer questions the need to distinguish between the two behaviors we have discussed. However, we believe these behaviors differ in their structure (with the first being intermittent and often non-directional, whereas the second is continuous and directional) and in their causes (with the first being driven by seasonal food resource cycles and the second by advancing snow cover). We therefore argue that it is worth distinguishing between them. To differentiate these forms of non-breeding movement, we propose to use ‘itinerancy’ for the first type, as described initially by Moreau in 1972, and introduce a separate term for the second behavior. Although ‘slow directional itinerancy’ could be considered, we find it too cumbersome.

      Moreover, ‘itinerancy’ in the literature refers not only to non-breeding movements but also to the use of different nesting sites, e.g., Lislevand et al. (2020, Journal of Avian Biology: e02595), reinforcing its association with movements between multiple sites within habitats. We, therefore, propose that the second behavior be given a distinct name. We acknowledge the reviewer’s point that we did not adequately address this distinction in the Discussion and plan to include a separate section in our paper’s revised version. In the third part of his review, the reviewer suggests an alternative title. Another reviewer, Dr Theunis Piersma, suggested the current title during the first round of reviewing, and we have chosen his version.

      In the fourth part of the review, the reviewer questions whether it is appropriate to discuss the conservation aspect of this study. This type of non-breeding movement raises concerns about accurately determining non-breeding ranges and population dynamics for species that exhibit this behavior. We believe that accurate determination of range and population dynamics is critical to conservation efforts. While this may be less important for species breeding in Europe and migrating to Africa, for which monitoring breeding territories is more feasible, it’s essential for Arctic and sub-Arctic breeding species. Large-scale surveys in these regions have historically been challenging and have become even more so with the end of Arctic cooperation following Russia’s war with Ukraine (Koivurova, Shibata, 2023). For North America and Europe, non-breeding abundance is typically estimated once per season in mid-winter. In North America, these are the so-called Christmas counts (which take place once at the end of December), and in Europe, they are the IWC counts mentioned by the reviewer (as follows from their official website - “The IWC requires a single count at each site, which should be repeated each year. The exact dates vary slightly from region to region, but take place in January or February”). Because of such a single count in mid-winter, non-breeding habitats occupied in autumn and spring will be listed as ‘uncommon’ at best, while south-western habitats where birds are only present in mid-winter will be listed as ‘common.’ However, the situation will be reversed if we consider the time birds spend in these habitats.

      The reviewer also highlights the introduction’s unconventional structure and information redundancy at the beginning. We have chosen this structure and provided basic explanations to improve readability for a wider audience, given eLife’s readership. At the same time, we will certainly take the reviewers’ feedback into account in the revised version. We plan to include the references to modern itinerancy research mentioned above and to add a section on itinerancy to the Discussion.

      We appreciate the reviewer’s input and sincerely thank them for their time and effort in reviewing our paper. While we may not fully agree on the classification of the behavior we describe, we value the opportunity to engage in discussion and believe that presenting arguments and counterarguments to the reader is beneficial to scientific progress.


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

      Reviewer #1 (Recommendations For The Authors):

      I much enjoyed reading this manuscript, that is, once I understood what it is about. Titles like "Conserving bird populations in the Anthropocene: the significance of non-breeding movements" are a claim to so-called relevance, they have NOTHING to do with the content of the paper, so once I understood that this paper was about the "Quick quick slow: the foxtrot migration of rough-legged buzzards is a response to habitat and snow" (an alternative title), it was becoming very interesting. So the start of the abstract as well as the introduction is very tedious, as clearly much trouble is taken here to establish reputability. In my eyes this is unnecessary: eLife should be interested in publishing such a wonderful description of such a wonderful migrant in a study that comes to grips with limiting factors on a continental scale!

      We sincerely appreciate your time and effort in reviewing our manuscript. Thank you for your appreciation of our study.

      We agree that the focus of the article should be changed from conservation to migration patterns. We have rewritten the Introduction and Discussion as suggested. We have added the application of this pattern including conservation at the end of the Discussion by completely changing Figure 5. We have also changed the title to the suggested one.

      Not sure that the first paragraph statements that seek to downplay what we know about wintering vs breeding areas are valid (although I see what purpose they serve). Migratory shorebirds have extensively been studied in the nonbreeding areas, for example, including movement aspects (see, as just one example, Verhoeven, M.A., Loonstra, A.H.J., McBride, A.D., Both, C., Senner, N.R. & Piersma, T. (2020) Migration route, stopping sites, and non breeding destinations of adult Black tailed Godwits breeding in southwest Fryslân, The Netherlands. Journal of Ornithology 162, 61-76) and there are very impressive studies on the winter biology of migrants across large scale (for example in Zwarts' Living on the Edge book on the Sahel wetlands). Think also about geese and swans and about seabirds!

      We have rewritten the first paragraph and it now talks about patterns of migratory behavior. We have also rewritten the second paragraph, now it is devoted to studies of movements in the non-breeding period. We explain how our pattern differs from those already studied and give references to the papers you mentioned.

      Directional movements in nonbreeding areas as a function of food (in this case locusts) have really beautifully been described by Almut Schlaich et al in JAnimEcol for Montagu's harriers.

      We have added Montagu's harrier example in the second paragraph of the Introduction and the Discussion. We have added a reference to Schlaich and to Garcia and Arroyo, who suggested that Montagu's harriers have long directional migrations during the non-breeding period.

      Once the paper starts talking buzzards, and the analyses of the wonderful data, all is fine. It is a very competent analysis with a description of a cool pattern.

      Thank you for your appreciation of our study. We hope the revised version is better and clearer.

      However, i would say that it is all a question of spatial scale. The buzzards here respond to changes in food availability, but there is not an animal that doesn't. The question is how far they have to move for an adequate response: in some birds movements of 100s of meters may be enough, and then anything to the scale of rough-legged buzzards.

      In the new version of the manuscript, we emphasize that this is a large distance (about 1000 km), comparable to the distance of the fall and spring migrations (about 1400 km) in lines 70-72 of the Introduction and 379-383 of the Discussion.

      And actually, several of the shorebirds I know best also do a foxtrot, such as red knots and bar-tailed godwits moulting in the Wadden Sea, then spending a few months in the UK estuaries, before returning to the Wadden Sea before the long migrations to Arctic breeding grounds. The publication of the rough-legged buzzard story may help researchers to summarize patterns such as this too. Mu problem with this paper is the framing. A story on the how and why of these continental movements in response to snow and other habitat features would be a grand contribution. Drop Anthropocene, and rethink whether foxtrot should be introduced as a hypothesis or a summary of cool descriptions. I prefer the latter, and recommend eLife to go with that too, rather than encourage "disconnected frames that seek 'respectability'" Good luck, theunis piersma

      We thank the reviewer again for his valuable comments and suggestions. We have changed the framing to the suggested one and removed the Anthropocene from the article.

      Reviewer #2 (Recommendations For The Authors):

      We sincerely appreciate the time and effort you have taken to review our manuscript. We have carefully considered all of your comments, including both public and author comments, and provided detailed responses to each of them below. In addition, we would like to address the most important public comments.

      We agree with the suggestion to shift the focus of the article from conservation to migration patterns. Accordingly, we have rewritten both the Introduction and Discussion sections to focus on migration behavior rather than conservation.

      However, we respectfully disagree with the suggestion that the migration patterns we describe are synonymous with itinerancy. We acknowledge that our original presentation may have been unclear and may have hindered full understanding. In the revised version, we provide a detailed analysis of migratory behavior in the Introduction that describes how our pattern differs from itinerancy. We also revisit this distinction in the Discussion section. We have also carefully revised Figure 1 to improve clarity and avoid potential misunderstandings.

      Regarding the applicability of the described migration pattern, we acknowledge that the Rough-legged Buzzard is not listed as an endangered species. However, we believe that our findings have practical implications. We have moved our discussion of this issue to the end of the Discussion section and have completely revised Figure 5. While the overall population of Rough-legged Buzzards is not declining, certain regions within its range are experiencing declines. We show that this decline does not warrant listing the species as endangered. Instead, it may represent a redistribution within the non-breeding range - a shift in range dynamics. We use the example of the Rough-legged Buzzard to illustrate this concept and emphasize the importance of considering such dynamics when assessing the conservation status of species in the future.

      We also acknowledge that the hypothesis of this form of behavior has been proposed previously for Montagu's Harrier, and we have included this information in the revised manuscript. In addition, we agree that the focus on the Anthropocene is unnecessary in this context and have therefore removed it.

      We believe that these revisions significantly improve the clarity and robustness of the manuscript, and we are grateful for your insightful comments and suggestions.

      As a general comment, please note that including line numbers (as it is the standard in any manuscript submission) would facilitate reviewers providing more detailed comments on the text.

      We apologize for this oversight and have added line numbers to our revised manuscript.

      Dataset: unclear what is the frequency of GPS transmissions. Furthermore, information on relative tag mass for the tracked individuals should be reported.

      We have included this information in our manuscript (L 157-163). We also refer to the study in which this dataset was first used and described in detail (L 164).

      Data pre-processing: more details are needed here. What data have been removed if the bird died? The entire track of the individual? Only the data classified in the last section of the track? The section also reports on an 'iterative procedure' for annotating tracks, which is only vaguely described. A piecewise regression is mentioned, but no details are provided, not even on what is the dependent variable (I assume it should be latitude?).

      Regarding the deaths. We only removed the data when the bird was already dead. We have corrected the text to make this clear (L 170).

      Regarding the iterative procedure. We have added a detailed description on lines 175-188.

      Data analysis: several potential issues here:

      (1) Unclear why sex was not included in all mixed models. I think it should be included.

      Our dataset contains 35 females and eight males. This ratio does not allow us to include sex in all models and adequately assess the influence of this factor. At the same time, because adult females disperse farther than males in some raptor species, we conducted a separate analysis of the dependence of migration distance on sex (Table S8) and found no evidence for this in our species. We have written a separate paragraph about this. This paragraph can be found on lines 356-360 of the new manuscript.

      (2) Unclear what is the rationale of describing habitat use during migration; is it only to show that it is a largely unsuitable habitat for the species? But is a formal analysis required then? Wouldn't be enough to simply describe this?

      Habitat use and snow cover determine the two main phases (quick and slow) of the pattern we describe. We believe that habitat analysis is appropriate in this case and that a simple description would be uninformative and would not support our conclusions.

      (3) Analysis of snow cover: such a 'what if' analysis is fine but it seems to be a rather indirect assessment of the effect of snow cover on movement patterns. Can a more direct test be envisaged relating e.g. daily movement patterns to concomitant snow cover? This should be rather straightforward. The effectiveness of this method rests on among-year differences in snow cover and timing of snowfall. A further possibility would be to demonstrate habitat selection within the entire non-breeding home range of an individual in relation snow cover. Such an analysis would imply associating presence-absence of snow to every location within the non-breeding range and testing whether the proportion of locations with snow is lower than the proportion of snow of random locations within the entire non-breeding home range (95% KDE) for every individual (e.g. by setting a 1/10 ratio presence to random locations).

      The proposed analysis will provide an opportunity to assess whether the Rough-legged Buzzard selects areas with the lowest snow cover, but will not provide an opportunity to follow the dynamics and will therefore give a misleading overall picture. This is especially true in the spring months. In March-April, Rough-legged Buzzards move northeast and are in an area that is not the most open to snow. At this time, areas to the southwest are more open to snow (this can be seen in Figure 4b). If we perform the proposed analysis, the control points for this period would be both to the north (where there is more snow) and to the south (where there is less snow) from the real locations, and the result would be that there is no difference in snow cover.

      A step-selection analysis could be used, as we did in our previous work (Curk et al 2020 Sci Rep) with the same Rough-legged Buzzard (but during migration, not winter). But this would only give us a qualitative idea, not a quantitative one - that Rough-legged Buzzards move from snow (in the fall) and follow snowmelt progression (in the spring).

      At the same time, our analysis gives a complete picture of snow cover dynamics in different parts of the non-breeding range. This allows us to see that if Rough-legged Buzzards remained at their fall migration endpoint without moving southwest, they would encounter 14.4% more snow cover (99.5% vs. 85.1%). Although this difference may seem small (14.4%), it holds significance for rodent-hunting birds, distinguishing between complete and patchy snow cover. Simultaneously, if Rough-legged Buzzards immediately flew to the southwest and stayed there throughout winter, they would experience 25.7% less snow cover (57.3% vs. 31.6%). Despite a greater difference than in the first case, it doesn't compel them to adopt this strategy, as it represents the difference between various degrees of landscape openness from snow cover.

      We write about this in the new manuscript on lines 385-394.

      Results: it is unclear whether the reported dispersion measures are SDs or SEs. Please provide details.

      For the date and coordinates of the start and end of the different phases of migration, we specified the mean, sd, and sample size. We wrote this in line 277. For the values of the parameters of the different phases of the migration (duration, distance, speed, and direction), we used the mean, the standard error of the mean, and the confidence interval (obtained using the ‘emmeans’ package). We have indicated this in lines 302-303 and the caption of Table 1 (L 315) and Figure 2 (L 293-294). For the values of habitat and snow cover experienced by the Rough-legged Buzzards, we used the mean and the error of the mean. We reported this on lines 322 and 337 and in Figures 3 (L 332-333) and 4 (L 355-356).

      Discussion: in general, it should be reshaped taking into account the comments. It is overlong, speculative and quite naive in several passages. Entire sections can be safely removed (I think it can be reduced by half without any loss of information). I provide some examples of the issues I have spotted below. For instance, the entire paragraph starting with 'Understanding....' is not clear to me. What do you mean by 'prohibited management' options? Without examples, this seems a rather general text, based on unclear premises when related to the specific of this study. Some statements are vague, derive from unsubstantiated claims, and unclear. E.g. "Despite their scarcity in these habitats, forests appear to hold significant importance for Rough-legged buzzards for nocturnal safety". I could not find any day-night analysis showing that they actually roost in forests during nighttime. Being a tundra species, it may well be possible that rough-legged buzzards perceive forests as very dangerous habitats and that they prefer instead to roost in open habitats. Analysing habitat use during day and night during the non-breeding period may be of help to clarify this. Furthermore, considering the fast migration periods, what is the flight speed during day and night above forests? Do these birds also migrate at night or do they roost during the night? Perhaps a figure visualizing day and night track segments could be of help (or an analysis of day vs. night flight speed) (there are several R packages to annotate tracks in relation to day and night). This is an example of another problematic statement: "The progression of snow cover in the wintering range of Rough-legged buzzards plays a significant role in their winter migration pattern." The manuscript does not contain any clear demonstration of this, as I wrote in my previous comments. Without such evidence, you must considerably tone down such assertions. But since providing a direct link is certainly possible, I think that additional analyses would clearly strengthen your take-home message.

      The paragraph starting with "The quantification of environmental changes that could prove fatal to bird species presents yet another challenge for conservation efforts in an era of rapid global change." is quite odd. Take the following statement "For instance, the presence of small patches of woodland in the winter range might appear crucial to the survival of the Rough-legged buzzard. Elimination of these seemingly minor elements of vegetation cover through management actions could have dire consequences for the species.". It is based on the assumption that minor vegetation elements play a key role in the ecology of the species, without any evidence supporting this. Does it have any sense? I could safely say exactly the opposite and I would believe it might even be more substantiated.

      We agree with these comments.

      We have completely rewritten this section. As suggested, we have shortened it by removing statements that were not supported by the research. We have completely removed the statements about "prohibited management". We have also removed the statement that "forests appear to be of significant importance to Rough-legged buzzards for nocturnal safety" and everything associated with that statement, e.g. the statement about "small elements of vegetation cover", etc. We do believe that this statement is true in substance, but we also agree that it is not supported by the results and requires separate analysis. At the same time, we believe that this is a topic for a separate study and would be redundant here. Therefore, we leave it for a separate publication.

      Conclusion paragraph: I believe this severely overstates the conservation importance of this study. That the results have "crucial implications for conservation efforts in the Anthropocene, where rapidly changing environmental factors can severely impact bird migration" seems completely untenable to me. What is the evidence for such crucial implications? For instance, these results may suggest that climate change, because global warming is predicted to reduce snow cover in the non-breeding areas, might well be beneficial for populations of this species, by reducing non-breeding energy expenditure and improving non-breeding survival. I think statements like these are simply not necessary, and that the study should be more focused on the actual results and evidence provided.

      We have completely rewritten this section. We removed the reference to the Anthropocene and focused on migratory behavior and migration patterns.

    1. Author response:

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

      Public Review:

      Summary:

      In this manuscript, the authors set out to understand how different TLR4 agonists trigger Myddosome assembly and seek to examine how the potent LPS agonist induces a heightened TLR4 response. A strength of the study is that the authors employ a novel light sheet imaging modality coupled to nanopipette delivery of TLR4 ligands. The authors use this technological innovation to resolve the dynamics of Myddosome formation within the whole cell volume of macrophage cell lines expressing MyD88-YFP. The main finding is that the kinetics of Myddosome formation is slower for the weaker agonist Abeta than LPS. However, Abeta amyloids resulted in the formation of larger MyD88-YFP puncta that persisted for longer. The authors suggest the slower kinetics of formation and larger puncta size reflect how Abeta amyloids are a less efficient TLR4 agonist. Many Toll-like receptors are now known to recognize endogenous produced danger signals and microbially derived molecules. This work is the first to compare the signaling kinetics of endogenous versus microbially derived TLR agonists.

      Strengths:

      A key strength of this work is the technological achievement of imaging Myddosomes within the entire cell volume and using a nanopipette to administer ligands directly to single cells. The authors also combine this light sheet microscopy with STORM imaging to gain a super-resolved view of the assembly of Myddosomes. These findings suggest that Myddosomes formed in response to Abeta have a more irregular morphology. We conclude that these technological achievements are significant in improving our understanding of the dynamics of TLR4 signaling in response to diverse agonists. Given the limited literature on the molecular dynamics of innate immune signal transduction, this study is an important addition to the field.

      Weaknesses:

      One limitation of the paper is that a suitable explanation for how larger Myddosomes would contribute to an attenuated downstream signaling response. Do the larger clusters of nucleated MyD88 polymers reflect inefficiency in assembling fully formed Myddosomes that contain IRAK4/2? Could the MyD88-GFP puncta be stained with antibodies against IRAK4 (or IRAK2) to determine the frequency and probably of the two ligands to stimulate signal transduction beyond MyD88 assembly?

      A second weakness is the discussion. The authors should explore other explanations for the observed differences in Myddosome formation between TLR4 agonists. For example, could the observed delay in Myddosome assembly in response to Abeta be due to different binding affinity or kinetics to TLR4? Can this be ruled out?

      We thank the reviewer for these comments.

      To address the first comment we have added a section on the limitations of the current study and suggested that future work could use IRAK4 or 2 staining to identify Myddosomes that are functional as well as working with cells where the Myddosome expression levels is at physiological levels, which may reduce the formation of larger Myddosomes.

      The reviewer is correct that the difference in delay time for Myddosome formation could be due slow formation of a TLR4 dimer or binding to the TLR4 dimer, rather than the time take to assemble the Myddosome after TLR4 dimerisation and binding since we have only measured the delay time for Myddosome formation when triggered by LPS or Aβ aggregates. This delay times involves dimerization of TLR4, binding of LPS or Aβ aggregates to the TLR4 dimer followed by Myddosome formation. These other processes might contribute to the difference in delay time that we observed between LPS or Aβ aggregates. It is worth noting that in our experiments we deliver the LPS or Aβ aggregates directly onto the surface for 5 seconds and that we previously showed the presence of the preformed TLR4 dimers on the cell surface (Latty et al., 2018). The affinity of Aβ aggregates for TLR4 is not known but LPS has a high affinity for TLR4, estimated to ∼3 nM for lipid A–TLR4-MD-2 (Akashi et al., 2003). However, even with this high affinity which implies fast binding, direct delivery directly onto the surface and the presence of preformed TLR4 dimers on the cell surface we observed that it took 80 s to observe Myddosome formation. This indicates that Myddosome formation is the slow step for LPS triggering. This is likely to be the case Aβ aggregates, since pM concentrations of aggregates can trigger TLR4 signalling (Hughes et al., 2020) indicating high affinity. However, it is not possible to rule out a contribution of a difference in affinity to observed difference in delay time without measuring the affinity directly.

      We have added both these points to a new paragraph on the limitations of the study in the Discussion.

    1. Author response:

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

      eLife assessment

      Connelly and colleagues provide convincing genetic evidence that importation from mainland Tanzania is a major source of Plasmodium falciparum lineages currently circulating in Zanzibar. This study also reveals ongoing local malaria transmission and occasional near-clonal outbreaks in Zanzibar. Overall, this research highlights the role of human movements in maintaining residual malaria transmission in an area targeted for intensive control interventions over the past decades and provides valuable information for epidemiologists and public health professionals.

      Reviewer #1 (Public Review):

      Zanzibar archipelago is close to achieving malaria elimination, but despite the implementation of effective control measures, there is still a low-level seasonal malaria transmission. This could be due to the frequent importation of malaria from mainland Tanzania and Kenya, reservoirs of asymptomatic infections, and competent vectors. To investigate population structure and gene flow of P. falciparum in Zanzibar and mainland Tanzania, they used 178 samples from mainland Tanzania and 213 from Zanzibar that were previously sequenced using molecular inversion probes (MIPs) panels targeting single nucleotide polymorphisms (SNPs). They performed Principal Component Analysis (PCA) and identity by descent (IBD) analysis to assess genetic relatedness between isolates. Parasites from coastal mainland Tanzania contribute to the genetic diversity in the parasite population in Zanzibar. Despite this, there is a pattern of isolation by distance and microstructure within the archipelago, and evidence of local sharing of highly related strains sustaining malaria transmission in Zanzibar that are important targets for interventions such as mass drug administration and vector control, in addition to measures against imported malaria.

      Strengths:

      This study presents important samples to understand population structure and gene flow between mainland Tanzania and Zanzibar, especially from the rural Bagamoyo District, where malaria transmission persists and there is a major port of entry to Zanzibar. In addition, this study includes a larger set of SNPs, providing more robustness for analyses such as PCA and IBD. Therefore, the conclusions of this paper are well supported by data.

      Weaknesses:

      Some points need to be clarified:

      (1) SNPs in linkage disequilibrium (LD) can introduce bias in PCA and IBD analysis. Were SNPs in LD filtered out prior to these analyses?

      Thank you for this point. We did not filter SNPs in LD prior to this analysis. In the PCA analysis in Figure 1, we did restrict to a single isolate among those that were clonal (high IBD values) to prevent bias in the PCA. In general, disequilibrium is minimal only over small distances <5-10kb without selective forces at play. This is much less than the average spacing of the markers in the panel. If there is minimal LD, the conclusions drawn on relative levels and connections at high IBD are unlikely to be confounded by any effects of disequilibrium.

      ( 2) Many IBD algorithms do not handle polyclonal infections well, despite an increasing number of algorithms that are able to handle polyclonal infections and multiallelic SNPs. How polyclonal samples were handled for IBD analysis?

      Thank you for this point. We added lines 157-161 to clarify. This section now reads:

      “To investigate genetic relatedness of parasites across regions, identity by descent (IBD) estimates were assessed using the within sample major alleles (coercing samples to monoclonal by calling the dominant allele at each locus) and estimated utilizing a maximum likelihood approach using the inbreeding_mle function from the MIPanalyzer package (Verity et al., 2020). This approach has previously been validated as a conservative estimate of IBD (Verity et al., 2020).”

      Please see the supplement in (Verity et al., 2020) for an extensive simulation study that validates this approach.

      Reviewer #1 (Recommendations For The Authors):

      (3) I think Supplementary Figures 8 and 9 are more visually informative than Figure 2.

      Thank you for your response. We performed the analysis in Figure 2 to show how IBD varies between different regions and is higher within a region than between.

      Reviewer #2 (Public Review):

      This manuscript describes P. falciparum population structure in Zanzibar and mainland Tanzania. 282 samples were typed using molecular inversion probes. The manuscript is overall well-written and shows a clear population structure. It follows a similar manuscript published earlier this year, which typed a similar number of samples collected mostly in the same sites around the same time. The current manuscript extends this work by including a large number of samples from coastal Tanzania, and by including clinical samples, allowing for a comparison with asymptomatic samples.

      The two studies made overall very similar findings, including strong small-scale population structure, related infections on Zanzibar and the mainland, near-clonal expansion on Pemba, and frequency of markers of drug resistance. Despite these similarities, the previous study is mentioned a single time in the discussion (in contrast, the previous research from the authors of the current study is more thoroughly discussed). The authors missed an opportunity here to highlight the similar findings of the two studies.

      Thank you for your insights. We appreciated the level of detail of your review and it strengthened our work. We have input additional sentences on lines 292-295, which now reads:

      “A recent study investigating population structure in Zanzibar also found local population microstructure in Pemba (Holzschuh et al., 2023). Further, both studies found near-clonal parasites within the same district, Micheweni, and found population microstructure over Zanzibar.”

      Strengths:

      The overall results show a clear pattern of population structure. The finding of highly related infections detected in close proximity shows local transmission and can possibly be leveraged for targeted control.

      Weaknesses:

      A number of points need clarification:

      (1) It is overall quite challenging to keep track of the number of samples analyzed. I believe the number of samples used to study population structure was 282 (line 141), thus this number should be included in the abstract rather than 391. It is unclear where the number 232 on line 205 comes from, I failed to deduct this number from supplementary table 1.

      Thank you for this point. We have included 282 instead of 391 in the abstract. We added a statement in the results at lines 203-205 to clarify this point, which now reads:

      “PCA analysis of 232 coastal Tanzanian and Zanzibari isolates, after pruning 51 samples with an IBD of greater than 0.9 to one representative sample, demonstrates little population differentiation (Figure 1A).”

      (2) Also, Table 1 and Supplementary Table 1 should be swapped. It is more important for the reader to know the number of samples included in the analysis (as given in Supplementary Table 1) than the number collected. Possibly, the two tables could be combined in a clever way.

      Thank you for this advice. Rather than switch to another table altogether, we appended two columns to the original table to better portray the information (see Table 1).

      Methods

      (3) The authors took the somewhat unusual decision to apply K-means clustering to GPS coordinates to determine how to combine their data into a cluster. There is an obvious cluster on Pemba islands and three clusters on Unguja. Based on the map, I assume that one of these three clusters is mostly urban, while the other two are more rural. It would be helpful to have a bit more information about that in the methods. See also comments on maps in Figures 1 and 2 below.

      Cluster 3 is a mix of rural/urban while the clusters 2, 4 and 5 are mostly rural. This analysis was performed to see how IBD changes in relation to local context within different regions in Zanzibar, showing that there is higher IBD within locale than between locale.

      (4) Following this point, in Supplemental Figure 5 I fail to see an inflection point at K=4. If there is one, it will be so weak that it is hardly informative. I think selecting 4 clusters in Zanzibar is fine, but the justification based on this figure is unclear.

      The K-means clustering experiment was used to cluster a continuous space of geographic coordinates in order to compare genetic relatedness in different regions. We selected this inflection point based on the elbow plot and based the number to obtain sufficient subsections of Zanzibar to compare genetic relatedness. This point is added to the methods at lines 174-178, which now reads:

      “The K-means clustering experiment was used to cluster a continuous space of geographic coordinates in order to compare genetic relatedness in different regions. We selected K = 4 as the inflection point based on the elbow plot (Supplemental Figure 5) and based the number to obtain sufficient subsections of Zanzibar to compare genetic relatedness.”

      (5) For the drug resistance loci, it is stated that "we further removed SNPs with less than 0.005 population frequency." Was the denominator for this analysis the entire population, or were Zanzibar and mainland samples assessed separately? If the latter, as for all markers <200 samples were typed per site, there could not be a meaningful way of applying this threshold. Given data were available for 200-300 samples for each marker, does this simply mean that each SNP needed to be present twice?

      Population frequency is calculated based on the average within sample allele frequency of each individual in the population, which is an unbiased estimator. Within sample allele frequency can range from 0 to 1. Thus, if only one sample has an allele and it is at 0.1 within sample frequency, the population allele frequency would be 0.1/100 = 0.001. This allele is removed even though this would have resulted in a prevalence of 0.01. This filtering is prior to any final summary frequency or prevalence calculations (see MIP variant Calling and Filtering section in the methods). This protects against errors occurring only at low frequency.

      Discussion:

      (6) I was a bit surprised to read the following statement, given Zanzibar is one of the few places that has an effective reactive case detection program in place: "Thus, directly targeting local malaria transmission, including the asymptomatic reservoir which contributes to sustained transmission (Barry et al., 2021; Sumner et al., 2021), may be an important focus for ultimately achieving malaria control in the archipelago (Björkman & Morris, 2020)." I think the current RACD program should be mentioned and referenced. A number of studies have investigated this program.

      Thank you for this point. We have added additional context and clarification on lines 275-280, which now reads:

      “Thus, directly targeting local malaria transmission, including the asymptomatic reservoir which contributes to sustained transmission (Barry et al., 2021; Sumner et al., 2021), may be an important focus for ultimately achieving malaria control in the archipelago (Björkman & Morris, 2020). Currently, a reactive case detection program within index case households is being implemented, but local transmission continues and further investigation into how best to control this is warranted (Mkali et al. 2023).”

      (7) The discussion states that "In Zanzibar, we see this both within and between shehias, suggesting that parasite gene flow occurs over both short and long distances." I think the term 'long distances' should be better defined. Figure 4 shows that highly related infections rarely span beyond 20-30 km. In many epidemiological studies, this would still be considered short distances.

      Thank you for this point. We have edited the text at lines 287-288 to indicate that highly related parasites mainly occur at the range of 20-30km, which now reads:

      “In Zanzibar, highly related parasites mainly occur at the range of 20-30km.”

      (8) Lines 330-331: "Polymorphisms associated with artemisinin resistance did not appear in this population." Do you refer to background mutations here? Otherwise, the sentence seems to repeat lines 324. Please clarify.

      We are referring to the list of Pfk13 polymorphisms stated in the Methods from lines 146-148. We added clarifying text on lines 326-329:

      “Although polymorphisms associated with artemisinin resistance did not appear in this population, continued surveillance is warranted given emergence of these mutations in East Africa and reports of rare resistance mutations on the coast consistent with spread of emerging Pfk13 mutations (Moser et al., 2021). “

      (9) Line 344: The opinion paper by Bousema et al. in 2012 was followed by a field trial in Kenya (Bousema et al, 2016) that found that targeting hotspots did NOT have an impact beyond the actual hotspot. This (and other) more recent finding needs to be considered when arguing for hotspot-targeted interventions in Zanzibar.

      We added a clarification on this point on lines 335-345, which now reads:

      “A recent study identified “hotspot” shehias, defined as areas with comparatively higher malaria transmission than other shehias, near the port of Zanzibar town and in northern Pemba (Bisanzio et al., 2023). These regions overlapped with shehias in this study with high levels of IBD, especially in northern Pemba (Figure 4). These areas of substructure represent parasites that differentiated in relative isolation and are thus important locales to target intervention to interrupt local transmission (Bousema et al., 2012). While a field cluster-randomized control trial in Kenya targeting these hotspots did not confer much reduction of malaria outside of the hotspot (Bousema et al. 2016), if areas are isolated pockets, which genetic differentiation can help determine, targeted interventions in these areas are likely needed, potentially through both mass drug administration and vector control (Morris et al., 2018; Okell et al., 2011). Such strategies and measures preventing imported malaria could accelerate progress towards zero malaria in Zanzibar.”

      Figures and Tables:

      (10) Table 2: Why not enter '0' if a mutation was not detected? 'ND' is somewhat confusing, as the prevalence is indeed 0%.

      Thank you for this point. We have put zero and also given CI to provide better detail.

      (11) Figure 1: Panel A is very hard to read. I don't think there is a meaningful way to display a 3D-panel in 2D. Two panels showing PC1 vs. PC2 and PC1 vs. PC3 would be better. I also believe the legend 'PC2' is placed in the wrong position (along the Y-axis of panel 2).

      Supplementary Figure 2B suffers from the same issue.

      Thank you for your comment. A revised Figure 1 and Supplemental Figure 2 are included, where there are separate plots for PC1 vs. PC2 and PC1 vs. PC3.

      (12) The maps for Figures 1 and 2 don't correspond. Assuming Kati represents cluster 4 in Figure 2, the name is put in the wrong position. If the grouping of shehias is different between the Figures, please add an explanation of why this is.

      Thank you for this point. The districts with at least 5 samples present are plotted in the map in Figure 1B. In Figure 2, a totally separate analysis was performed, where all shehias were clustered into separate groups with k-means and the IBD values were compared between these clusters. These maps are not supposed to match, as they are separate analyses. Figure 1B is at the district level and Figure 2 is clustering shehias throughout Zanzibar.

      The figure legend of Figure 1B on lines 410-414 now reads:

      “B) A Discriminant Analysis of Principal Components (DAPC) was performed utilizing isolates with unique pseudohaplotypes, pruning highly related isolates to a single representative infection. Districts were included with at least 5 isolates remaining to have sufficient samples for the DAPC. For plotting the inset map, the district coordinates (e.g. Mainland, Kati, etc.) are calculated from the averages of the shehia centroids within each district.”

      The figure legend of Figure 2 on lines 417-425 now reads:

      “Figure 2. Coastal Tanzania and Zanzibari parasites have more highly related pairs within their given region than between regions. K-means clustering of shehia coordinates was performed using geographic coordinates all shehias present from the sample population to generate 5 clusters (colored boxes). All shehias were included to assay pairwise IBD between differences throughout Zanzibar. Pairwise comparisons of within cluster IBD (column 1 of IBD distribution plots) and between cluster IBD (column 2-5 of IBD distribution plots) was done for all clusters. In general, within cluster IBD had more pairwise comparisons containing high IBD identity.”

      (13) Figure 2: In the main panel, please clarify what the lines indicate (median and quartiles?). It is very difficult to see anything except the outliers. I wonder whether another way of displaying these data would be clearer. Maybe a table with medians and confidence intervals would be better (or that data could be added to the plots). The current plots might be misleading as they are dominated by outliers.

      Thank you for this point and it greatly improved this figure. We changed the plotting mechanisms through using a beeswarm plot, which plots all pairwise IBD values within each comparison group.

      (14) In the insert, the cluster number should not only be given as a color code but also added to the map. The current version will be impossible to read for people with color vision impairment, and it is confusing for any reader as the numbers don't appear to follow any logic (e.g. north to south).

      Thank you very much for these considerations. We changed the color coding to a color blind friendly palette and renamed the clusters to more informative names; Pemba, Unguja North (Unguja_N), Unguja Central (Unguja_C), Unguja South (Unguja_S) and mainland Tanzania (Mainland).

      (15) The legend for Figure 3 is difficult to follow. I do not understand what the difference in binning was in panels A and B compared to C.

      Thank you for this point. We have edited the legend to reflect these changes. The legend for Figure 3 on lines 427-433 now reads:

      “Figure 3. Isolation by distance is shown between all Zanzibari parasites (A), only Unguja parasites (B) and only Pemba parasites (C). Samples were analyzed based on geographic location, Zanzibar (N=136) (A), Unguja (N=105) (B) or Pemba (N=31) (C) and greater circle (GC) distances between pairs of parasite isolates were calculated based on shehia centroid coordinates. These distances were binned at 4km increments out to 12 km. IBD beyond 12km is shown in Supplemental Figure 8. The maximum GC distance for all of Zanzibar was 135km, 58km on Unguja and 12km on Pemba. The mean IBD and 95% CI is plotted for each bin.”

      (16) Font sizes for panel C differ, and it is not aligned with the other panels.

      Thank you for pointing this out. Figure 3 and Supplemental Figure 10 are adjusted with matching formatting for each plot.

      (17) Why is Kusini included in Supplemental Figure 4, but not in Figure 1?

      In Supplemental Figure 4, all isolates were used in this analysis and isolates with unique pseudohaplotypes were not pruned to a single representative infection. That is why there are additional isolates in Kusini. The legend for Supplemental Figure 4 now reads:

      “Supplemental Figure 4. PCA with highly related samples shows population stratification radiating from coastal Mainland to Zanzibar. PCA of 282 total samples was performed using whole sample allele frequency (A) and DAPC was performed after retaining samples with unique pseudohaplotypes in districts that had 5 or more samples present (B). As opposed to Figure 1, all isolates were used in this analysis and isolates with unique pseudohaplotypes were not pruned to a single representative infection.”

      (18) Supplemental Figures 6 and 7: What does the width of the line indicate?

      The sentence below was added to the figure legends of Supplemental Figures 6 and 7 and the legends of each network plot were increased in size:

      “The width of each line represents higher magnitudes of IBD between pairs.”

      (19) What was the motivation not to put these lines on the map, as in Figure 4A? This might make it easier to interpret the data.

      Thank you for this comment. For Supplemental Figure 8 and 9, we did not put these lines that represent lower pairwise IBD to draw the reader's attention to the highly related pairs between and within shehias.

      Reviewer #2 (Recommendations For The Authors):

      (1) There is a rather long paragraph (lines 300-323) on COI of asymptomatic infections and their genetic structure. Given that the current study did not investigate most of the hypotheses raised there (e.g. immunity, expression of variant genes), and the overall limited number of asymptomatic samples typed, this part of the discussion feels long and often speculative.

      Thank you for your perspective. The key sections highlighted in this comment, regarding immunity and expression of variant genes, were shortened. This section on lines 300-303 now reads:

      “Asymptomatic parasitemia has been shown to be common in falciparum malaria around the globe and has been shown to have increasing importance in Zanzibar (Lindblade et al., 2013; Morris et al., 2015). What underlies the biology and prevalence of asymptomatic parasitemia in very low transmission settings where anti-parasite immunity is not expected to be prevalent remains unclear (Björkman & Morris, 2020).”

      (2) As a detail, line 304 mentions "few previous studies" but only one is cited. Are there studies that investigated this and found opposite results?

      Thank you for this comment. We added additional studies that did not find an association between clinical disease and COI. These changes are on lines 303-308, which now reads:

      “Similar to a few previous studies, we found that asymptomatic infections had a higher COI than symptomatic infections across both the coastal mainland and Zanzibar parasite populations (Collins et al., 2022; Kimenyi et al., 2022; Sarah-Matio et al., 2022). Other studies have found lower COI in severe vs. mild malaria cases (Robert et al., 1996) or no significant difference between COI based on clinical status (Earland et al. 2019; Lagnika et al. 2022; Conway et al. 1991; Kun et al. 1998; Tanabe et al. 2015)”

      (3) Table 2: Percentages need to be checked. To take one of several examples, for Pfk13-K189N a frequency of 0.019 for the mutant allele is given among 137 samples. 2/137 equals to 0.015, and 3/137 to 0.022. 0.019 cannot be achieved. The same is true for several other markers. Possibly, it can be explained by the presence of polyclonal infections. If so, it should be clarified what the total of clones sequenced was, and whether the prevalence is calculated with the number of samples or number of clones as the denominator.

      Thank you for this point. We mistakenly reported allele frequency instead of prevalence. An updated Table 2 is now in the manuscript. The method for calculating the prevalence is now at lines 148-151:

      “Prevalence was calculated separately in Zanzibar or mainland Tanzania for each polymorphism by the number of samples with alternative genotype calls for this polymorphism over the total number of samples genotyped and an exact 95% confidence interval was calculated using the Pearson-Klopper method for each prevalence.”

    1. Author response:

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

      Reviewer #1 (Public Review):

      Granados-Aparici et al., investigate somatic-germline interactions in female mice. Mammalian oocytes are nurtured in multi-cellular ovarian follicles and communication with surrounding somatic cells is critical for oocyte development. This study focused on transzonal projections (TZP) extending from granulosa cells to the surface of oocytes and documented the importance of SMAD4, a TGF- β mediator, in regulating the TZPs. They propose a model in which individual TZPs contact the surface of the oocyte and stably attach if there is sufficient N-cadherin. In SMAD4-depleted cells, there is insufficient N-cadherin to stabilize the attachment. The TZP continues to elongate but eventually retracts. Their model is well supported by their experimental evidence and the manuscript is both well-formulated and written.

      Reviewer #2 (Public Review):

      Summary:

      This study proposed a new mechanism by which the TGF-beta signaling pathway promotes contacts between oocytes and the surrounding somatic cells in mice, by regulating the numbers of transzonal projections (TZPs).

      Strengths:

      The conditional Smad4 knockout and three-dimensional observation of transzonal projections are solid and sufficiently support the major conclusions.

      Weaknesses:

      The physiological significance of SMAD4-dependent formation of transzonal projection networks is not assessed in this study.

      Previous studies have shown that physical contact and gap junctional communication with the granulosa cells is essential for normal oocyte development. A recent study has also shown that depleting Myo10 in granulosa cells reduces the number of TZPs and leads to abnormalities in oocyte and embryo development. Thus, the importance of TZPs is well-established. These findings, which were insufficiently brought out in the Introduction of the original manuscript, have now been made more clearly (Introduction, 2nd paragraph). We recognize that these reports do not directly test a role for SMAD4-dependent TZPs. Unfortunately, it is beyond our technical capacity to obtain embryos following meiotic maturation and fertilization of oocytes that have grown in vitro, which wold be necessary for us to fully test the physiological role of SMAD4-dependent TZPs.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The authors switch from Amhr2-cre to ER-cre to increase the number of GFP-positive granulosa cells in 12 d/o ovaries. To avoid disruption of FSH secretion by SMAD4, they use an in vitro model that requires 6 days in GEO culture (1 d tamoxifen + 5 d). Could it be that Amhr2-cre didn't work because most follicles would not have reached the atretic preantral stage in 12 d/o ovaries? Did the authors consider 6 days in vitro GEO culture to determine if Amhr2-cre would be efficient and avoid exposure to tamoxifen?

      Please see below.

      When is Amhr2 expressed?

      Previous studies (Jorgez et al, 2004; Pangas et al, 2006) report that Amhr2 is expressed in growing follicles that have progressed beyond a single layer of granulosa cells (often defined as secondary and primary follicles, respectively). As shown in Fig. 1C, we did not observe evidence of widespread Cre activity in multilayer follicles. At least two factors may contribute why we observed relatively weak Cre activity. One possibility is that, on the genetic background our mice, Amhr2 is expressed relatively late during follicular growth. Thus, we might have observed more GFP-positive granulosa cells in antral or pre-ovulatory follicles. Because the granulosa cells of these late-stage follicles would already have produced many TZPs, the number of new TZPs generated in wild-type but not SMAD4-depleted cells after Amhr2 activation would be a relatively small proportion of the total population. This would make it more difficult to detect a reduction in TZP number in the absence of SMAD4.

      A second point is that we used pre-puberal mice whereas Jorgez et al examined Amhr2 expression in ovaries of adult mice. Pangas et al evaluated both prepuberal and adult females. It may be that Amhr2 is expressed earlier or more strongly in granulosa cells of adult mice. Regarding the suggestion to culture complexes obtained from mice on the Amhr2-Cre background, as this might allow widespread expression of Cre without the need for tamoxifen, this is an excellent idea. If there is considerable heterogeneity among cells in the timing of Amhr2-Cre activity, though, this may further cloud efforts to uncover the role of SMAD4 in the production or stability of TZPs, as noted above.

      (2) Did most of the GEO cultured in vitro reach the antral follicle stage after 6 days?

      Since GOCs were treated with collagenase, the thecal layer was removed. Therefore, development of an antrum does not occur. We observed that, in some cases, the oocyte was extruded from the granulosa cell mass. These abnormal complexes were discarded.

      (3). Was the development/diameter of the oocyte in the GEO comparable to the oocyte growing in vivo?

      We did not compare the diameter of the oocytes grown in vitro to those grown in vivo. Thus, we cannot say whether the oocytes grown in vitro reached the same size as those grown in vivo. We did, however, compare the diameter of the oocytes in the wt and ko groups and observed no difference (Figure 2). This indicates that depletion of SMAD4 in the granulosa cells does not impair oocyte growth. Importantly for our studies, it excludes the possibility that the reduction in TZP-number is simply due to a smaller surface area of the oocyte.

      (4) SMAD4 depletion in granulosa cells disrupts steroidogenesis leading to increased progesterone levels and precocious luteinization of granulosa cells (Pangas et al., 2006). Did the authors determine the expression level of luteal markers of granulosa cells in the in vitro GEO culture Smad4 knockout model? Are their observations direct effects of the absence of SMAD4?

      This is an excellent point. We checked our previously performed RNA-seq analysis of the wild-type and knockout granulosa cells, but found no difference in the quantities of Cyp11a1, Sfrp4, Star or Ptgfr. This is now described in the Discussion (4th paragraph). One potentially important difference between our study and that of Pangas et al (2006) is that they observed premature luteinization when prepuberal (3-week old) mice were injected with the FSH analogue, equine serum gonadotropin, whereas we studied granulosa-oocyte complexes cultured in vitro. This could underlie the apparent differences with respect to luteinization.

      (5) Could the reduced number of TZPs in ER-cre+; Smad4fl/fl GOCs be explained by luteinization?

      This interesting and logical possibility is related to the previous point. In other words, luteinization could be considered as a default pathway of differentiation that is suppressed by SMAD signaling. It is possible that luteinized cells are unable to generate or maintain TZPs. This model offers a potential mechanistic basis for our observation, and we now raise it in the Discussion (3rd paragraph).

      Reviewer #3 (Recommendations For The Authors):

      The expression and localization of N-cadherin should be observed in Smad4 and control granulosa cell-oocyte complexes.

      We agree that this would be an excellent approach to confirm the decreased expression of N-cadherin in the granulosa cells that was observed by immunoblotting. We were confronted by two challenges, however. First, we were unable to consistently obtain strong staining of granulosa cell membranes in the inner layers of multilayer granulosa-oocyte complexes. Other antibodies are able to stain structures at the oocyte surface, indicating that antibodies are not physically blocked from penetrating the complex. More likely, the anti-N-cadherin does not bind its target strongly enough to generate a robust signal that can be detected through multiple overlying layers of cells. Second, whereas for immunoblotting we collect all granulosa cells from culture complexes, for immunofluorescence we are only able to examine those that remain in the complex. This means that, for immunofluorescence, we essentially but unavoidably select against cells that are only loosely attached – as would be expected for N-cadherin-deficient cells – to their neighbours. Given these challenges, we believe that the immunoblotting approach, which produced highly reproducible results over six biological replicates (Fig. 6), is the most reliable.

    1. Author response:

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

      eLife assessment

      This study presents useful findings regarding the role of formin-like 2 in mouse oocyte meiosis. The submitted data are supported by incomplete analyses, and in some cases, the conclusions are overstated. If these concerns are addressed, this paper would be of interest to reproductive biologists.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The presented study focuses on the role of formin-like 2 (FMNL2) in oocyte meiosis. The authors assessed FMNL2 expression and localization in different meiotic stages and subsequently, by using siRNA, investigated the role of FMNL2 in spindle migration, polar body extrusion, and distribution of mitochondria and endoplasmic reticulum (ER) in mouse oocytes.

      Strengths:

      Novelty in assessing the role of formin-like 2 in oocyte meiosis.

      Weaknesses:

      Methods are not properly described.

      Overstating presented data.

      It is not clear what statistical tests were used.

      My main concern is that there are missing important details of how particular experiments and analyses were done. The material and methods section are not written in the way that presented experiments could be repeated - it is missing basic information (e.g., used mouse strain, timepoints of oocytes harvest for particular experiments, used culture media, image acquisition parameters, etc.). Some of the presented data are overstated and incorrectly interpreted. It is not clear to me how the analysis of ER and mitochondria distribution was done, which is an important part of the presented data interpretation. I'm also missing important information about the timing of particular stages of assessed oocytes because the localization of both ER and mitochondria differs at different stages of oocyte meiosis. The data interpretation needs to be justified by proper analysis based on valid parameters, as there is considerable variability in the ER and mitochondria structure and localization across oocytes based on their overall quality and stage.

      Thank you for your comment. We regret the oversight of omitting critical information in the manuscript. In the revised manuscript, we have included essential details such as mouse strains, culture media, stages of oocyte and statistical methods in the materials and methods section. Please find our details responses in the “Recommendations for the authors” part.

      Reviewer #2 (Public Review):

      Summary:

      This research involves conducting experiments to determine the role of Fmnl2 during oocyte meiosis I.

      Strengths:

      Identifying the role of Fmnl2 during oocyte meiosis I is significant.

      Weaknesses:

      The quantitative analysis and the used approach to perturb FMNL2 function are currently incomplete and would benefit from more confirmatory approaches and rigorous analysis.

      (1) Most of the results are expected. The new finding here is that FMNL2 regulates cytoplasmic F-actin in mouse oocytes, which is also expected given the role of FMNL2 in other cell types. Given that FMNL2 regulates cytoplasmic F-actin, it is very expected to see all the observed phenotypes. It is already established that F-actin is required for spindle migration to the oocyte cortex, extruding a small polar body and normal organelle distribution and functions.

      Thank you for your comment. In the recent decade, Arp2/3 complex (Nat Cell Biol 2011), Formin2 (Nat Cell Biol 2002, Nat Commun 2020), and Spire (Curr Biol 2011) were reported to be 3 key factors to involve into this process. These factors regulate actin filaments in different ways. However, how they cross with each other for the subcellular events were still fully clear. Our current study identified that FMNL2 played a critical role in coordinating these molecules for actin assembly in oocytes. Our findings demonstrate that FMNL2 interacts with both the Arp2/3 complex and Formin2 to facilitate actin-based meiotic spindle migration. Additionally, we discovered a novel role for FMNL2 in determining the distribution and function of the endoplasmic reticulum and mitochondria, which may in turn influence meiotic spindle migration in oocytes. Our results not only uncover the novel functions of FMNL2-mediated actin for organelle distribution, but also extend our understanding of the molecular basis for the unique meiotic spindle migration in oocyte meiosis.

      (2) The authors used Fmnl2 cRNA to rescue the effect of siRNA-mediated knockdown of Fmnl2. It is not clear how this works. It is expected that the siRNA will also target the exogenous cRNA construct (which should have the same sequence as endogenous Fmnl2) especially when both of them were injected at the same time. Is this construct mutated to be resistant to the siRNA?

      Thank you for your question. We regret any misunderstanding that may have been caused by the inappropriate description in our manuscript. In the rescue experiments, we initially injected FMNL2 siRNA into oocytes, followed by the microinjection of FMNL2 mRNA 18-20 hours later. After conducting our previous experiments, we have verified through Western blotting that endogenous FMNL2 is effectively suppressed 18-20 hours following the microinjection of FMNL2 siRNA. Additionally, we observed a significant increase in exogenous FMNL2 protein expression 2 hours after the injection of FMNL2 mRNA. We believe that the exogenous FMNL2 could compensate the decrease by FMNL2 knockdown, and this approach was adopted in many oocyte studies.

      (3) The authors used only one approach to knockdown FMNL2 which is by siRNA. Using an additional approach to inhibit FMNL2 would be beneficial to confirm that the effect of siRNA-mediated knockdown of FMNL2 is specific.

      Thank you for your question. Yes, the specificity is always the concern for siRNA or morpholino microinjection due to the off-target issue. Due to the limitation we could not generate the knock out model, and there are no known inhibitors with specific targeting capabilities for FMNL2. To solve this, we performed the rescue study with exogenous mRNA to confirm the effective knock down of FMNL2. These measures provide reassurance regarding the credibility of the experimental outcomes, and this is also the general way to avoid the off-target of siRNA or morpholino.

      Reviewer #3 (Public Review):

      Summary:

      The authors focus on the role of formin-like protein 2 in the mouse oocyte, which could play an important role in actin filament dynamics. The cytoskeleton is known to influence a number of cellular processes from transcription to cytokinesis. The results show that downregulation of FMNL2 affects spindle migration with resulting abnormalities in cytokinesis in oocyte meiosis I.

      Weaknesses:

      The overall description of methods and figures is overall dismissively poor. The description of the sample types and number of replicate experiments is impossible to interpret throughout, and the quantitative analysis methods are not adequately described. The number of data points presented is unconvincing and unlikely to support the conclusions. On the basis of the data presented, the conclusions appear to be preliminary, overstated, and therefore unconvincing.

      Thank you for your comment. We regret the oversight of omitting critical information in the manuscript. In the revised manuscript, we have incorporated your suggestions for modification, particularly regarding the Materials and Methods section. Please see the detailed revision and responses in the “Recommendations for the authors” part.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for The Authors):

      My main concern is that there are missing important details of how particular experiments and analyses were done. The material and methods section is not written in the way that presented experiments could be repeated - it is missing basic information (e.g., used mouse strain, timepoints of oocytes harvest for particular experiments, used culture media, image acquisition parameters, etc.). Some of the presented data are overstated and incorrectly interpreted. It is not clear to me how the analysis of ER and mitochondria distribution was done, which is an important part of the presented data interpretation. I'm also missing important information about the timing of particular stages of assessed oocytes because the localization of both ER and mitochondria differs at different stages of oocyte meiosis. The data interpretation needs to be justified by proper analysis based on valid parameters, as there is considerable variability in the ER and mitochondria structure and localization across oocytes based on their overall quality and stage. My specific comments are listed below.

      (1) Information about statistical tests that were used needs to be provided for all quantification experiments.

      Thank you for your suggestion. Based on your suggestions, we revised the statistical analysis description in the Materials and Methods section. Additionally, we also included a description of the statistical methods in the legends of the relevant result figures.

      (2) I recommend replacing the plunger plots, used in most quantification data, with alternatives allowing evaluation of the distribution of the data (dot plots, box plots, whisker plots).

      Thank you for your suggestion. Following your suggestion, we replaced the plunger plots in Fig 2C, D, H, I and Fig3 B, C with dot plots.

      (3) Can the authors provide information about particular time points when were individual oocyte stages (GVBD, meiosis I, and meiosis II) harvested/used for immunofluorescence protein detection, western blotting, microinjection, and ER and mitochondria staining? Were the time points always the same in all presented experiments and experimental vs control group? If not, this needs to be clarified.

      Thank you for your suggestion. We used oocytes in the metaphase I (MI) stage for the statistical analysis of spindle migration, actin filament aggregation, endoplasmic reticulum localization, and mitochondrial localization. In the Western blot analysis, GV stage oocytes were utilized to evaluate the efficiency of knockdown and rescue experiments. The protein expression levels of Arp2, Formin2, INF2, Cofilin, Grp78, and Chop in different treatment groups were detected using MI-stage oocytes. In the revised version, we provided all the detailed information about the stages.

      (4) Figure 1B: Can the authors comment on why there is a missing representative image of MII oocyte FMBL2-Ab? I recommend including this in the figure to have a complete view of comparing overexpressed and endogenous FMNL2 localization in oocyte meiosis.

      Thank you for your suggestion. In the revised manuscript, we added immunostaining images of FMNL2 antibody in MII stage oocytes.

      (5) Figure 1C: The figure legend says, "FMNL2 and actin overlapped in cortex and spindle surrounding". In MI oocytes, there is usually no accumulated actin signal around the spindle, which is also true in the presented images, so there cannot be overlapping with the FMNL2 signal. The interpretation should be changed.

      We apologize for this inappropriate description that was used, and we deleted this sentence.

      (6) Figure 2B: What were the parameters of the "large" and "normal" polar bodies for performing the analysis?

      Thank you for your question. In order to assess the size of the polar body, we conducted a comparison between the diameter of the polar body and that of the oocyte. If the diameter of the polar body was found to be less than 1/3 of the oocyte's diameter, we categorized it as normal-sized polar body. Conversely, if the polar body's diameter exceeded 1/3 of the oocyte's diameter, we categorized it as a large polar body. We have included these details in the Results section of the manuscript.

      (7) Figure 2F: Can the authors comment on what can be the second band in the rescue group?

      Thank you for your question. In the rescue experiment, we microinjected exogenous FMNL2-EGFP mRNA into the oocytes. As a result, compared to endogenous FMNL2, the protein size increased due to the addition of the EGFP tag, approximately 27 kDa. Hence, in the Western blot bands of the rescue group, the upper band represents the expression of exogenous FMNL2-EGFP, while the lower band corresponds to the expression of endogenous FMNL2. We have provided annotations in the revised Figure 2F to clarify this.

      (8) Can the authors comment on the variability of PBE between 2C and 2H in the FMNL2-KD groups? In panel C, the PBE in the KD group was 59.5 {plus minus} 2.82%; in panel H, the PBE in the KD group was 48.34 {plus minus} 4.2%, and in the rescue group, the PBE was 62.62 {plus minus} 3.6%. The rescue group has a similar PBE rate as the KD group in panel C. How consistent was the FMNL2 knockdown across individual replicates? Can the authors provide more details on how the rescue experiment was performed?

      Thank you for your question. We believe that the difference in PBE observed in Figure 2C and 2H of the FMNL2-KD group was due to the microinjection times and the duration of in vitro arrest. The results shown in Figure 2C depict the outcome of a single injection of FMNL2 siRNA into GV stage oocytes, followed by 18 hours of in vitro arrest; the results shown in Figure 2H contain a subsequent additional injection of FMNL2-EGFP mRNA with another 2 hours of arrest. The two rounds of microinjection and the extended period of in vitro arrest both affect oocyte maturation rates.

      (9). Figure 2J and K: What groups were compared together? The used statistic needs to be properly described.

      Thank you for your question. The FMNL2-KD, FMNL3-KD, and FMNL2+3-KD groups were all compared to the Control group, therefore, t-test was used for analysis. We have provided explanations in the revised manuscript.

      (10) Figure 4B and C: Can the authors provide representative images without oversaturated actine signal?

      Thank you for your question. For the analysis of oocyte F-actin, the F-actin are divided into cortex actin and cytoplasmic actin. Due to the contrast during imaging, the strong cortex actin signals affected the detection of cytoplasmic actin, therefore, it is necessary to increase the scanning index, which will cause the overexpose the cortex actin signal. This is for the better observation of the cytoplasmic signals.

      (11) Figure 4G + 5H: Can the authors comment on why they used as a housekeeping gene actin instead of tubulin, which was used in the rest of the WB experiments?

      Thank you for your question. In most of the western blot experiments conducted in this study, we used tubulin as a housekeeping gene. However, due to the supply of antibodies by delivery period, we had GAPDH and actin as well for some experiments. These housekeeping genes were all valid for the study.

      (12) Based on what parameters was ER considered normally or abnormally distributed, and what stages of oocytes were assessed?

      Thank you for your question. In this study, we employed oocytes at the MI stage for the analysis of ER localization. In the MI stage, the ER localized around the spindle, which is regarded as the typical localization pattern. The ER displayed a dispersed distribution throughout the cytoplasm or clustered were categorized as aberrant positioning. We included relevant descriptions in the revised version of the manuscript.

      (13) Figure 5H: As a housekeeping gene was used actin - the quantification is labeled as a Grp78 to tubulin ratio.

      Thank you for pointing out the error. This is a label mistake and we corrected it.

      (14) Information about how JC-1 staining was done needs to be provided.

      Thank you for your carefully reading. We included a description of JC1 staining in the Materials and Methods section.

      (15). Line 231-232: "As shown in Figure 4A" - the text doesn't correspond to the figure.

      Thank you for pointing out the error. We revised this mistake in the revised manuscript by correcting "Fig3A" to "Fig4A."

      (16) Line 265: there is probably a missing word "Formin2".

      Thank you and we corrected the error and made the necessary changes in the revised manuscript.

      Reviewer #2 (Recommendations for The Authors):

      (1) Quantification and analysis:

      • Fig. 3B: The rate of spindle migration should be quantified based on the distance from the spindle to the cortex. Also, the orientation of the spindle (Z-position) needs to be taken into consideration.

      • Fig. 5C, D: It is unclear how the rate of ER distribution was calculated.

      • Western blot: In many experiments (such as Fig. 5H), the bands are saturated which will prevent accurate intensity measurements and quantifications.

      For spindle migration, we specifically focused on spindles exhibiting a distinctive spindle-like shape with clear bipolarity to eliminate any statistical discrepancies potentially caused by variations in Z-axis alignment. Our criterion for determining successful migration was based on the contact between the spindle pole and the cortical region of the oocyte. Therefore, we think that the rate is better to reflect the phenotype than the distance.

      For the examination of ER localization, Reviewer 1 also raised this issue. We utilized oocytes at the MI stage in this study. The ER localized around the spindle in MI stage. The ER displayed a dispersed distribution throughout the cytoplasm or clustered were categorized as aberrant positioning. We included relevant descriptions in the revised version of the manuscript.

      For the bands of the western blot results, during the experimental procedure we typically capture multiple images at different exposure levels (3-5 images). In the revised manuscript, we have replaced the inappropriate images with more suitable ones.

      (2) Given that all Immunoprecipitation experiments in this manuscript were performed on the whole ovary which contains more somatic cells than oocytes, the results do not necessarily reflect meiotic oocytes. Please consider this possibility during the interpretation.

      Thank you for your suggestion. Yes, we agree with you. In the revised manuscript, we made appropriate modifications to the relevant descriptions.

      (3) 351-365: The conclusion that Arp2/3 compensates for the decreased formin 2 in FMNL2 knockdown oocytes is a bit unconvincing. 1- In mouse oocytes, it is already known that Arp2/3 and formin 2 regulate different pools of F-actin nucleation. 2- The authors found an increase in Arp2/3 in FMNL2 knockdown oocytes compared to control oocytes without any change in cortical F-actin. Given that Arp2/3 is primarily promoting cortical F-actin, it is expected to see an increase in cortical F-actin in FMNL2 knockdown oocytes, which was not the case.

      Thank you for your question. Yes, previous studies showed that formin2 localizes to the cytoplasm of oocytes and accumulates around the spindle, which facilitate cytoplasmic actin assembly. While Arp2/3 is primarily responsible for actin assembly at the cortex region of oocytes. In invasive cells, FMNL2 is mainly localized in the leading edge of the cell, lamellipodia and filopodia tips, to improve cell migration ability by actin-based manner (Curr Biol 2012). We showed that FMNL2 localized both at spindle periphery and cortex, but depletion of FMNL2 did not affect cortex actin intensity. We think that FMNL2 and Arp2/3 both contribute to the cortex actin dynamics, when FMNL2 decreased, ARP2 increased to compensate for this, which maintained the cortex actin level. In the revised manuscript, we have made modifications to avoid excessive extrapolation from our results, ensuring that our conclusions are presented in a more objective manner.

      (4) Lines 195-197: The spindle is initially formed soon after the GVBD, so there is no spindle during GVBD. Also, I can't see oocytes at anaphase I or telophase I in this figure. Please revise.

      Thank you for your suggestion. We apologize for the inappropriate descriptions that were used. In the revised manuscript, we have made modifications to the respective descriptions in the Results part.

      (5) Fig. 2E: It seems that the control oocyte is abnormal with mild cytokinesis defects. Please replace or delete it since this information is already included in Fig. 3A.

      Thank you for your suggestion. Based on our observations, during the extrusion of the first polar body in oocytes, there is a temporary occurrence of cellular morphological fragmentation due to cortical reorganization (11h in control oocyte from Fig 2E). However, after the extrusion of the first polar body, the oocyte morphology returns to normal. Figure 2E illustrates the meiotic division process of oocytes, while Figure 3A primarily focuses on the process of oocyte spindle migration. We think that it is better to retain both to present our results.

      Reviewer #3 (Recommendations for The Authors):

      In the case of the observed phenotype, the stage of GV is important. The phenotypes presented also occur in meiotic or developmentally incompetent oocytes. In addition, the images of GV oocytes appear as NSN, which also show the KD phenotype in Figs. 2 and 3.

      Thank you for your concern. As the oocyte grows, the proportion of SN-type oocytes gradually increases. When the oocyte diameter reaches 70-80 μm, the proportion of SN oocytes is approximately 52.7% (Mol Reprod Dev. 1995). In our study, both the control and knockdown groups collected oocytes with a diameter of around 80 μm, which is considered as fully-grown oocytes, predominantly in the SN phase. Since the collection period and size of the oocytes were consistent, we can sure that the observed differences between the control and knockdown groups in phenotype analysis could be solid and reliable.

      MII is absent in Fig. 1B.

      In the revised manuscript, we added immunostaining images of FMNL2 in MII stage oocytes.

      The result of KD is not convincing. Also, discuss whether the heterozygous effect of Fmnl2 deletion affects reproductive fitness.

      Thank you for your concern. In our investigation, limited to the setup of knock out model, we employed siRNA to knockdown FMNL2 expression, to avoid the risk of off-target, we performed rescue experiment with exogenous mRNA, which we believe that it could solve this issue. When designing siRNA sequences, we ensured their specificity for binding to FMNL2 mRNA only, and we assessed the levels of FMNL2 and FMNL3 mRNA in oocytes after injection of FMNL2 siRNA. The results showed that, compared to the control group, the expression of FMNL2 mRNA decreased by approximately 70% after 18 hours of FMNL2 siRNA injection, while the level of FMNL3 mRNA was not decreased.

      Fig. 2F rescue experiment with double bands. What bands are seen here? Did the authors inject tagged or untagged FMNL2? Or does endogenous FMNL2 appear higher in the sample after KD?

      Thank you for your question. In the rescue experiment, we microinjected exogenous FMNL2-EGFP mRNA into the oocytes. As a result, compared to endogenous FMNL2, the protein size increased due to the addition of the EGFP tag, approximately 27 kDa. Hence, in the Western blot bands of the rescue group, the upper band represents the expression of exogenous FMNL2-EGFP, while the lower band corresponds to the expression of endogenous FMNL2. We provided annotations in the revised Figure 2F to clarify this.

      Variability in mitochondria and ER distribution patterns is also known in healthy and developing oocytes, although the authors described only a single phenotype.

      Thank you for your concern. Yes, mitochondria and ER show dynamic localization in different stage of oocyte maturation. However, in this study we employed oocyte MI stage for the analysis of ER and mitochondria localization, and in MI stage, both the ER and mitochondria localize around the spindle. This pattern is considered as the normal localization. Several studies showed that dispersed or clustered localization contributed to maturation defects. We included relevant descriptions in the revised manuscript.

      What exactly is meant by input in the IP experiments? Why is the target missing in the input sample?

      Thank you for your question. We subjected the input samples to electrophoresis on a single channel, all the analyzed proteins demonstrated normal expression, thereby confirming the viability of the input sample. However, upon simultaneous exposure with the IP samples, we observed a lack of clear signal for certain proteins in the input group. This phenomenon is due to the excessive signal intensity resulting from protein enrichment in the IP group, which caused the low exposure of proteins in input group.

      Explain the rationale for using, actin or tubulin as loading or normalization controls in the study focusing on the cytoskeleton.

      Thank you for your question. Actin and tubulin are both widely used as the control due to their stable expression. For actin, there are α-actin and β-actin isoforms. Formins and Arp2/3 complex regulate the polymerization of α-actin and β-actin to form F-actin, not isoform expression. In our study F-actin (the functional type) was examined. While α-tubulin and β-tubulin are two subtypes of tubulin, and they interact with each other to form stable α/β-tubulin heterodimers. The changes of cytoskeleton dynamics could not change the expression of α/β-tubulin. Therefore, β-actin and α-tubulin could be used as normalization controls.

      Fig. 6E shows only , but the legend says *.

      Thank you for pointing out the error. We correct the mistake in the revised manuscript.

      Spindle positioning appears to differ between control and KD. Does this affect the quantification of Fig. 6F? Adequate nomenclature should be used here.

      Thank you for your question. Yes, spindle positioning was affected by FMNL2 depletion. However, central spindle or cortex spindle all belong to MI stage, and JC1 is not related with the stage difference. To avoid misunderstanding we replaced the representative images and corresponding description in Figure 6F.

      The description of the methods and legends should be significantly improved.

      Thank you for your suggestion. Reviewer 1 and 2 also raised the similar concern. We enriched the description of methods and legends in the revised manuscript.

    1. Author response:

      We extend our sincere gratitude to the editor and three reviewers for their invaluable feedback, which not only included positive comments but also provided constructive suggestions for enhancing the quality of our manuscript.

      Of potential interest to you is our forthcoming investigation into vaccine efficacy, where we will compare the effectiveness of our live-attenuated vaccine with an mRNA-based alternative.

      Moreover, we acknowledge and fully endorse the recommendation to elucidate why immunization with our live-attenuated vaccine confers protection against viral challenge, even in the absence of sufficient neutralizing antibodies. As pointed out by the reviewers, this phenomenon may be attributed to mucosal immunity. Consequently, we have outlined plans to investigate whether the attenuated live vaccine elicits mucosal immunity as part of our ongoing research.

      We are currently working to gather the necessary data to address these inquiries comprehensively, and are aiming to resubmit our manuscript at the earliest opportunity.

      Reviewer #1: We sincerely appreciate the insightful comments provided by Reviewer #1. In response to this feedback, we will conduct a comparative analysis of efficacy between our live-attenuated vaccine and an mRNA-based alternative. Furthermore, we will thoroughly examine and delineate the advantages and limitations of this/our live-attenuated vaccine in our discussion.

      Reviewer #2: We express our sincere appreciation to Reviewer #2 for invaluable suggestions. In light of the insightful observation concerning the weakness of our study, related to the poor assessment/evaluation of the induction of mucosal immunity by our vaccine candidate, we have resolved to undertake a comprehensive analysis in this regard.

      Furthermore, we will take into account this reviewer's recommendation to compare BK2102 results with those of an mRNA vaccine. We are currently in the process of planning additional experiments to thoroughly address this aspect.

      Reviewer #3: We are very grateful to Reviewer #3 for the positive feedback and invaluable suggestions. In order to further explore the immune mechanisms underlying the protection against the Omicron variant in the absence of detectable neutralizing antibodies, we are currently devising plans for experiments focused on evaluating mucosal immunity.

      Moreover, in accordance with Reviewer #3's suggestion, we are considering the incorporation of an ELISPOT assay experiment. However, we acknowledge uncertainties regarding the feasibility of establishing an experimental system for this purpose.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Day et al. present a high-throughput version of expansion microscopy to increase the throughput of this well-established super-resolution imaging technique. Through technical innovations in liquid handling with custom-fabricated tools and modifications to how the expandable hydrogels are polymerized, the authors show robust ~4-fold expansion of cultured cells in 96-well plates. They go on to show that HiExM can be used for applications such as drug screens by testing the effect of doxorubicin on human cardiomyocytes. Interestingly, the effects of this drug on changing DNA organization were only detectable by ExM, demonstrating the utility of HiExM for such studies.

      Overall, this is a very well-written manuscript presenting an important technical advance that overcomes a major limitation of ExM - throughput. As a method, HiExM appears extremely useful, and the data generally support the conclusions.

      Strengths:

      Hi-ExM overcomes a major limitation of ExM by increasing the throughput and reducing the need for manual handling of gels. The authors do an excellent job of explaining each variation introduced to HiExM to make this work and thoroughly characterize the impressive expansion isotropy. The dox experiments are generally well-controlled and the comparison to an alternative stressor (H2O2) significantly strengthens the conclusions.

      Weaknesses:

      (1) Based on the exceedingly small volume of solution used to form the hydrogel in the well, there may be many unexpanded cells in the well and possibly underneath the expanded hydrogel at the end of this. How would this affect the image acquisition, analysis, and interpretation of HiExM data?

      The hydrogel footprint covers approximately 5% of the surface within an individual well and only cells within this area are embedded in the polymerized hydrogel for subsequent processing steps. Cells that are outside of this footprint are not incorporated into the gel, meaning that these cells are digested by Proteinase K and subsequently washed away by the excess water exchange in the gel swelling step. Note that different cell types may require higher or lower concentrations of Proteinase K to adequately digest cells for expansion while maintaining fluorescence signal. Given the compatibility of HiExM with 96-well plates, this titration can be performed rapidly in a single experiment. Although cells outside of the hydrogel footprint are removed prior to imaging, we do occasionally observe Hoechst signal that appears to be underneath the gels. We believe this signal is likely from excess DNA from digested cells that was not fully washed out in the gel swelling step. This signal is both spatially and morphologically distinct from the nuclear signal of intact cells and it does not affect image acquisition, analysis, or data interpretation.

      (2) It is unclear why the expansion factor is so variable between plates (e.g., Figure 2H). This should be discussed in more detail.

      The variability in expansion factor across plates can likely be attributed to the small volume (~250 nL) deposited by the device posts. Small variations in gel volume could impact gel polymerization compared to standard ExM gels. For example, gels in HiExM are more sensitive to evaporation because they are ~1000x smaller than standard expansion gel preparations due to an increased air-liquid-interface. Evaporation in HiExM gels increases monomer and cross linker concentrations, leading to variation in expansion factor across plates. We note that expansion factor is robust within well plates and that variance is slightly increased between plates. These differences will be discussed in the revised manuscript.

      (3) The authors claim that CF dyes are more resistant to bleaching than other dyes. However, in Figure. S3, it appears that half of the CF dyes tested still show bleaching, and no data is shown supporting the claim that Alexa dyes bleach. It would be helpful to include data supporting the claim that Alexa dyes bleach more than CF dyes and the claim that CF dyes in general are resistant to bleaching should be modified to more accurately reflect the data shown.

      We did not show data using Alexa dyes because these fluorophores are highly sensitive to photobleaching using Irgacure and thus we could not obtain images. In contrast, some CF dyes are more robust to bleaching in HiExM including CF488A, CF568, and CF633 dyes. We have recently adapted our protocol to PhotoExM chemistry which is compatible with a wider range of fluorophores as described by Günay et al. (2023) and as shown in current Fig. S11.

      (4) Related to the above point, it appears that Figure S11 may be missing the figure legend. This makes it hard to understand how HiExM can use other photo-inducible polymerization methods and dyes other than CF dyes.

      The following figure legend will be included in the revised manuscript. Fig. S11: Example of a cell expanded in HiExM using Photo-ExM gel chemistry. Photo-ExM does not require an anoxic environment for gel deposition and polymerization, improving ease of use of HiExM. Mitochondria were stained with an Alexa 647 conjugated secondary antibody, indicating that HiExM is compatible with additional fluorophores when combined with Photo-ExM.

      (5) The use of automated high-content imaging is impressive. However, it is unclear to me how the increased search space across the extended planar area and focal depths in expanded samples is overcome. It would be helpful to explain this automated imaging strategy in more detail.

      We imaged plates on the Opera Phenix using the PreciScan Acquisition Software in Harmony. In brief, each well is imaged at 5x magnification in the Hoechst channel to capture the full well at low resolution. Hoechst is used for this step given its signal brightness, ubiquity across established staining protocols, and spectral independence from most fluorophores commonly conjugated to secondary antibodies. Using this information, the microscope detects regions of interest (nuclei) based on criteria including size, brightness, circularity, etc. Finally, the positional information for each region is stored, and the microscope automatically images those regions at 63x magnification. The working distance for the objective used in this study is 600 µm which is sufficient to capture the entirety of expanded cells in the Z direction. This strategy allows minimizes off-target imaging and allows robust image acquisition even in cultures with lower seeding density. A detailed description of the automated imaging strategy will be included in the revised manuscript.

      (6) The general method of imaging pre- and post-expansion is not entirely clear to me. For example, on page 5 the authors state that pre-expansion imaging was done at the center of each gel. Is pre-expansion imaging done after the initial gel polymerization? If so, this would assume that the gelation itself has no effect on cell size and shape if these gelled but not yet expanded cells are used as the reference for calculating expansion factor and isotropy.

      Pre-expansion imaging is performed after staining is complete, but prior to the application of AcX, which is the first step of the HiExM protocol. Following staining and imaging, plates can be sealed with paraffin and stored at 4˚C for up to a week prior to starting the expansion protocol. We typically image 61 fields of view at the center of the well plate (where the gel will be deposited) to obtain sufficient pre-expansion images as shown in Figure 2b (left). After pre-expansion imaging, we perform the HiExM protocol followed by image acquisition. We then tile all the images, as shown in Figure 2b, and compare tiled images from the same well pre- and post-expansion to manually identify the same cells. Comparisons of the pre- and post-expansion images of the same cell are then used to calculate expansion factor and isotropy measurements as described. This detailed description will be included in the revised manuscript.

      (7) In the dox experiments, are only 4 expanded nuclei analyzed? It is unclear in the Figure 3 legend what the replicates are because for the unexpanded cells, it says the number of nuclei but for expanded it only says n=4. If only 4 nuclei are analyzed, this does not play to the strengths of HiExM by having high throughput.

      We performed the DOX titration assay across four different well plates (i.e. n=4). For each condition, the total number of nuclei measured was 56, 71, 64, 92, and 62 for DMSO, 1nM, 10nM, 100nM, and 1µM, respectively. For SEM calculations, we included the number of technical replicates to avoid underestimating error. We have revised the Figure 3 legend to better reflect the experimental details.

      (8) I am not sure if the analysis of dox-treated cells is accurate for the overall phenotype because only a single slice at the midplane is analyzed. It would be helpful to show, at least in one or two example cases, that this trend of changing edge intensity occurs across the whole 3D nucleus.

      We will repeat our analysis on a subset of images using multiple optical sections for each nucleus reported. These new data will be included in the revised manuscript.

      (9) It would be helpful to provide an actual benchmark of imaging speed or throughput to support the claims on page 8 that HiExM can be combined with autonomous imaging to capture thousands of cells a day. What is the highest throughput you have achieved so far?

      The parameters that dictate imaging speed in HiExM include exposure time, z-stack height, and number of channels. Depending on the signal intensity for a given channel, exposure times vary from 200ms to 1000ms. For z-stack height, we found that imaging 65 sections with 1µm spacing allowed for robust identification of each region of interest in the 5x pre-scan. As an example, collecting images for a full well plate (e.g., 20 images per well with 4 channels) requires approximately 24 hours of autonomous image acquisition using the Opera Phenix. Depending on cell size, this yields imaging data for between 1200 cells (1 cell per field of view) to 6000 cells (5 cells per field of view). Different autonomous imagers as well as improving staining techniques that increase signal:noise can be expected to significantly decrease the exposure time as it will reduce the number of z-stacks needed for each region.

      Reviewer #2 (Public Review):

      Summary:

      In the present work, the authors present an engineering solution to sample preparation in 96-well plates for high-throughput super-resolution microscopy via Expansion Microscopy. This is not a trivial problem, as the well cannot be filled with the gel, which would prohibit the expansion of the gel. A device was engineered that can spot a small droplet of hydrogel solution and keep it in place as it polymerizes. It occupies only a small portion of space at the center of each well, the gel can expand into all directions, and imaging and staining can proceed by liquid handling robots and an automated microscope.

      Strengths:

      In contrast to Reference 8, the authors' system is compatible with standard 96 well imaging plates for high-throughput automated microscopy and automated liquid handling for most parts of the protocol. They thus provide a clear path towards high-throughput ExM and high-throughput super-resolution microscopy, which is a timely and important goal.

      Weaknesses:

      The assay they chose to demonstrate what high-throughput ExM could be useful for, is not very convincing. But for this reviewer that is not important.

      We appreciate this reviewer’s point. We believe the data provide an example of the power of HiExM for collecting thousands of nanoscale images that would benefit experiments that require many samples (e.g., conditions, replicates, timepoints, etc.). The ability to generate large data sets also enables quantitative analysis of images with appropriate statistical power. The intention of this experiment was to provide a proof-of-concept example of the robustness, accessibility, and experimental design flexibility of HiExM.

      Reviewer #3 (Public Review):

      Summary:

      Day et al. introduced high-throughput expansion microscopy (HiExM), a method facilitating the simultaneous adaptation of expansion microscopy for cells cultured in a 96-well plate format. The distinctive features of this method include 1) the use of a specialized device for delivering a minimal amount (~230 nL) of gel solution to each well of a conventional 96-well plate, and 2) the application of the photochemical initiator, Irgacure 2959, to successfully form and expand the toroidal gel within each well.

      Strengths:

      This configuration eliminates the need for transferring gels to other dishes or wells, thereby enhancing the throughput and reproducibility of parallel expansion microscopy. This methodological uniqueness indicates the applicability of HiExM in detecting subtle cellular changes on a large scale.

      Weaknesses:

      To demonstrate the potential utility of HiExM in cell phenotyping, drug studies, and toxicology investigations, the authors treated hiPS-derived cardiomyocytes with a low dose of doxycycline (dox) and quantitatively assessed changes in nuclear morphology. However, this reviewer is not fully convinced of the validity of this specific application. Furthermore, some data about the effect of expansion require reconsideration.

      The application we chose was intended as a proof of concept. We believe the data provide an example of the power of HiExM for collecting thousands of nanoscale images that would benefit experiments that require many samples (e.g., conditions, replicates, timepoints, etc.). The ability to generate large data sets also enables quantitative analysis of images with appropriate statistical power. The intention of this experiment was to provide a proof-of-concept example of the robustness, accessibility, and experimental design flexibility of HiExM.

      The variability in expansion factor across plates can likely be attributed to the small volume (~250 nL) deposited by the device posts. Small variations in gel volume could impact gel polymerization compared to standard ExM gels. For example, gels in HiExM are more sensitive to evaporation because they are ~1000x smaller than standard expansion gel preparations due to an increased air-liquid-interface. Evaporation in HiExM gels increases monomer and cross linker concentrations, leading to variation in expansion factor across plates. We note that expansion factor is robust within well plates and that variance is slightly increased between plates. These differences will be discussed in the revised manuscript.

    1. Author response:

      eLife assessment

      This study presents valuable information on the mechanism of how birnavirus VP3 protein interacts with PI3P in early endosomes. Evidence supporting the proposed two-stage mechanism is incomplete and would benefit from additional supporting experiments, and additional experimentation would also address concerns about data consistency.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Zanetti et al. use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that the association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

      Strengths:

      Supports previously published claims that VP3 may associate with early endosomes and bind to PI3P-containing membranes. The claim that mutating a single residue (R200) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

      Weaknesses:

      The manuscript is relatively narrowly focused: one bimolecular interaction between a host cell lipid and one protein of an unusual avian virus (VP3-PI3P). Aspects of this interaction have been described previously. Additional data would strengthen claims about the specificity and some technical issues should be addressed. Many of the core claims would benefit from additional experimental support to improve consistency.

      We focused our efforts on the characterization of the molecular interaction between the birnaviral protein VP3 and the anionic lipid PI3P, which is found in the host cell. This decision was motivated by our previous research, which made use of cell biology and virology techniques to demonstrate that VP3 facilitates the formation of the viral replication machinery on the cytosolic leaflet of early endosomes due to its inherent endosome-targeting capability (J Virol. 2018 May 14;92(11):e01964-17). Additionally, our previous findings indicated that PI3P, present in early endosomal membranes, is a critical host factor enabling VP3's association with these membranes, thereby promoting viral replication (J Virol. 2021 Feb 24;95(6):e02313-20). Consequently, an in-depth characterization of the VP3/PI3P interaction was necessary and motivated the present work. We plan to incorporate specific recommendations to further substantiate our assertions in the revised version of our manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Birnavirus replication factories form alongside early endosomes (EEs) in the host cell cytoplasm. Previous work from the Delgui lab has shown that the VP3 protein of the birnavirus strain infectious bursal disease virus (IBDV) interacts with phosphatidylinositol-3-phosphate (PI3P) within the EE membrane (Gimenez et al., 2018, 2020). Here, Zanetti et al. extend this previous work by biochemically mapping the specific determinants within IBDV VP3 that are required for PI3P binding in vitro, and they employ in silico simulations to propose a biophysical model for VP3-PI3P interactions.

      Strengths:

      The manuscript is generally well-written, and much of the data is rigorous and solid. The results provide deep knowledge into how birnaviruses might nucleate factories in association with EEs. The combination of approaches (biochemical, imaging, and computational) employed to investigate VP3-PI3P interactions is deemed a strength.

      Weaknesses:

      (1) Concerns about the sources, sizes, and amounts of recombinant proteins used for co-flotation: Figures 1A, 1B, 1G, and 4A show the results of co-flotation experiments in which recombinant proteins (control His-FYVE v. either full length or mutant His VP3) were either found to be associated with membranes (top) or non-associated (bottom). However, in some experiments, the total amounts of protein in the top + bottom fractions do not appear to be consistent in control v. experimental conditions. For instance, the Figure 4A western blot of His-2xFYVE following co-flotation with PI3P+ membranes shows almost no detectable protein in either top or bottom fractions.

      Liposome-based methods, such as the co-flotation assay, are well-known and preferred to study protein-phosphoinositide interaction because the phosphoinositides are incorporated in a membrane, the composition of which can mimic cellular membranes. Additionally, by modifying the phosphoinositide incorporated in the liposomes, this technique allows for determining the specificity of the protein binding. However, this approach is rather qualitative, meaning that, after density gradient separation, the protein is found in the top fractions (bound to liposomes) or in the bottom fractions (not bound to liposomes), and our quantifications have the aim of showing the difference in the bound fraction between liposome populations with or without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the conditions are homogeneous, it’s not surprising to observe differences in the protein level between each one. Indeed, our revised version of the manuscript will include membranes with more similar band intensities.

      Reading the paper, it was difficult to understand which source of protein was used for each experiment (i.e., E. coli or baculovirus-expressed), and this information is contradicted in several places (see lines 358-359 v. 383-384). Also, both the control protein and the His-VP3-FL proteins show up as several bands in the western blots, but they don't appear to be consistent with the sizes of the proteins stated on lines 383-384. For example, line 383 states that His-VP3-FL is ~43 kDa, but the blots show triplet bands that are all below the 35 kDa marker (Figures 1B and 1G). Mass spectrometry information is shown in the supplemental data (describing the different bands for His-VP3-FL) but this is not mentioned in the actual manuscript, causing confusion. Finally, the results appear to differ throughout the paper (see Figures 1B v. 1G and 1A v. 4A).

      We used two sources of recombinant VP3: baculovirus and Escherichia coli. Initially, we opted for the baculovirus system based on evidence from previous studies that it was suitable for ectopic expression of VP3. Subsequently, we successfully produced VP3 using Escherichia coli and chose to transition to this system due to several technical advantages. Moreover, mass spectrometry analysis did not reveal any post-translational modifications that may have favored retaining the baculoviral system. We confirmed that VP3, produced in either system, exhibited similar behavior in our co-flotation assays. We will clarify all this in the revised version of our manuscript.

      (2) Possible "other" effects of the R200D mutation on the VP3 protein. The authors performed mutagenesis to identify which residues within patch 2 on VP3 are important for association with PI3P. They found that a VP3 mutant with an engineered R200D change (i) did not associate with PI3P membranes in co-floatation assays, and (ii) did not co-localize with EE markers in transfected cells. Moreover, this mutation resulted in the loss of IBDV viability in reverse genetics studies. The authors interpret these results to indicate that this residue is important for "mediating VP3-PI3P interaction" (line 211) and that this interaction is essential for viral replication. However, it seems possible that this mutation abrogated other aspects of VP3 function (e.g., dimerization or other protein/RNA interactions) aside from or in addition to PI3P binding. Such possibilities are not mentioned by the authors.

      The arginine amino acid at position 200 of VP3 is not located in any of the protein regions associated with its other known functions. VP3 has a dimerization domain located in the second helical domain, where different amino acids across the three helices form a total of 81 interprotomeric close contacts; however, R200 is not involved in these contacts (Structure. 2008 Jan;16(1):29-37). VP3 also has an oligomerization domain mapped within the 42 C-terminal residues of the polypeptide, i.e., the segment of the protein composed by the residues at positions 216-257 (J Virol. 2003 Jun;77(11):6438–6449). Regarding VP3’s ability to bind RNA, it is facilitated by a region of positively charged amino acids, identified as P1, which includes K99, R102, K105, and K106 (PLoS One. 2012;7(9):e45957). Furthermore, our findings indicate that the R200D mutant retains a folding pattern similar to the wild-type protein, as shown in Figure 4B. All these lead us to conclude that the loss of replication capacity of R200D viruses results from impaired, or even lost, VP3-PI3P interaction.

      (3) Interpretations from computational simulations. The authors performed computational simulations on the VP3 structure to infer how the protein might interact with membranes. Such computational approaches are powerful hypothesis-generating tools. However, additional biochemical evidence beyond what is presented would be required to support the authors' claims that they "unveiled a two-stage modular mechanism" for VP3-PI3P interactions (see lines 55-59). Moreover, given the biochemical data presented for R200D VP3, it was surprising that the authors did not perform computational simulations on this mutant. The inclusion of such an experiment would help tie together the in vitro and in silico data and strengthen the manuscript.

      We acknowledge that the language used may have overstated the "unveiling" of the two-stage binding mechanism for VP3 on membranes containing PI3P. We intended to propose, rather than confirm, this mechanism, largely based on our coarse-grained simulations. Accordingly, we will revise the manuscript to temper our claims and frame them more appropriately. Regarding the absence of computer simulations for the R200D VP3 mutant, these were indeed conducted, and the results are detailed in Figure 14 of the supplementary material. We realize this was not adequately emphasized in the main manuscript, an oversight we will correct in the revised version.

      Reviewer #3 (Public Review):

      Summary:

      Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2.

      This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

      Strengths: Detailed characterization of the interaction between VP3 and PIP3 identified R200D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

      Weaknesses:

      The work does not directly show that the identified R200 residues are directly involved in VP3-early endosome recruitment during infection. The majority of work is done with transfected VP3 protein (or in vitro) and not in virus-infected cells. Additional controls such as the use of PIP3 antagonizing drugs in infected cells together with a colocalization study of VP3 with early endosomes would strengthen the study. In addition, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow ruling out any unspecific binding that might not be detected on WB.

      The authors also do not propose how their findings could be translated into drug development that could be applied to protect poultry during an outbreak. The title of the manuscript is broad and would improve with rewording so that it captures what the authors achieved.

      In previous works from our group, we demonstrated the crucial role of the VP3 P2 region in targeting the early endosomal membranes and for viral replication, including the use of PI3K inhibitors to deplete PI3P, showing that both the control RFP-2xFYVE and VP3 lost their ability to associate with the early endosomal membranes (J Virol. 2018 May 14;92(11):e01964-17; J Virol. 2021 Feb 24;95(6):e02313-20). In the present work, to further characterize the role of R200 in binding to early endosomes and for viral replication, we show that: i) the transfected VP3 R200D protein loses the ability to bind to early endosomes in immunofluorescence assays (Figure 2E and Figure 3); ii) the recombinant VP3 R200D protein loses the ability to bind to liposomes PI3P(+) in co-flotation assays (Figure 4A); and, iii) the mutant virus R200D loses replication capacity (Figure 4C).

      Regarding the cryo-EM comment: we will include images where we used liposomes PIP3(-) in the revised version of our manuscript.

      We will also modify the title of the manuscript.

      Regarding the question of how our findings could be translated into drug development, indeed, VP3-PI3P binding constitutes a good target for drugs that counteract infectious bursal disease. However, we did not mention this idea in the manuscript, first because it is somewhat speculative and second because infected farms do not implement any specific treatment. The control is based on vaccination. We will mention these aspects of the infection in the revised version of our manuscript.

    1. Author Response

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

      Thank you once again for your patience and guidance through this revision process. I would like to add an important aspect to our previous discussion regarding the identification and impact of potential contaminants in our study.

      In recent years, advanced tools such as SCRuB (recently published in Nature Biotechnology, DOI:10.1038/s41587-023-01696-w) and the widely-used tool decontam have been developed to address the issue of contaminants in metagenomic studies. These tools primarily operate based on sequence similarity, identifying potential contaminants by marking and removing those found in only a minority of samples or those that display patterns indicative of laboratory contamination.

      As the reviewer rightly pointed out, contaminants are often rare species that appear in very few samples. Our study, focusing on high-abundance species in the vaginal microbiome, is less susceptible to the influences of such rare contaminants. This approach aligns with the methodology employed by leading research groups in the field, such as Professor Jacques Ravel's lab. Their decision not to use blank controls in several of their studies on the female reproductive tract microbiome likely stems from a similar understanding — that the impact of rare contaminants is minimal on the study's conclusions, especially when high-abundance species are the main focus.

      We believe that the methodologies and tools currently available for contaminant identification and removal, while highly effective for their intended purpose, reinforce our decision to focus on high-abundance species. This focus minimizes the potential impact of rare contaminants on our study's conclusions. In light of this, our study's methodology remains robust and well-suited for achieving our research objectives.

      In our revised manuscript, we will include a discussion of these points, further clarifying our approach and the rationale behind our methodological choices. We hope that this additional information will address the concerns raised and provide a clearer understanding of the context and reliability of our findings.

      Thank you for considering these additional points. We look forward to your feedback on our revised manuscript.

    1. Author response:

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

      We thank the reviewers for their thoughtful comments. We were pleased that they thought our study was "well crafted and written", "important", and that it provides a "valuable resource for researchers studying color vision". They also expressed several constructive criticisms, concerning – among other things – the lack of details regarding experimental procedures and analysis, the challenge in relating retinal data to cortical recordings, and consistency of results across animals. In response to the reviewers’ comments and following their suggestions, we performed additional analyses, and substantially revised the paper:

      We added a section in the Discussion about "Limitations of the stimulus paradigm". In addition, we added a new Suppl. Figure that illustrates the effect of deconvolution of calcium traces on our results and clarified in the text why we use deconvolved signals for all analyses. The new Suppl. Figure also shows an additional analysis with a more conservative threshold of neuron exclusion.

      We now clarify how retinal signaling relates to our cortical results and rewrote the text to be more conservative regarding our conclusions.

      In addition, we added a new Suppl. Figure showing the key analyses from Figures 2 and 4 separately for each animal. We now mention consistency across animals in the Results section and clearly state which analyses were performed an data pooled across animals.

      We are positive that these additions address the issues raised by the reviewers. Please find our point-by-point replies to all comments below.

      eLife assessment

      Franke et al. explore and characterize the color response properties in the mouse primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The data is solid; however, the evidence supporting some conclusions and details about some procedures are incomplete. In its current form, the paper makes a useful contribution to how color is coded in mouse V1. Significance would be enhanced with some additional analyses and resolution of some technical issues.

      We thank the reviewers for appreciating our manuscript and their thoughtful comments.

      Referee 1 (Remarks to the Author):

      Summary:

      In this study, Franke et al. explore and characterize the color response properties across the primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The authors use awake-behaving 2P imaging to define the spectral response properties of visual interneurons in Layer 2/3. They find that opponent responses are more prominent at photopic light levels, and diversity in color opponent responses exists across the visual science, with green ON/ UV OFF responses being stronger represented in the upper visual field. This is argued to be relevant for detecting certain features that are more salient when the chromatic space is used, possibly due to noise reductions.

      Strengths:

      The work is well crafted and written and provides a thorough characterization that reveals an uncharacterized diversity of visual properties in V1. I find this characterization important because it reveals how strongly chromatic information can modulate the response properties in V1. In the upper visual field, 25% of the cells differentially relay chromatic information, and one may wonder how this information will be integrated and subsequently used to aid vision beyond the detection of color per see. I personally like the last paragraph of the discussion that highlights this fact.

      We thank the reviewer for appreciating our manuscript.

      Weaknesses: One major point highlighted in this paper is the fact that Green ON/UV OFF responses are not generated in the retina. But glancing through the literature, I saw this is not necessarily true. Fig 1. of Joesch and Meister, a paper cited, shows this can be the case. Thus, I would not emphasize that this wasn’t present in the retina. This is a minor point, but even if the retina could not generate these signals, I would be surprised if the diversity of responses would only arise through feed-forward excitation, given the intricacies of cortical connectivity. Thus, I would argue that the argument holds for most of the responses seen in V1; they need to be further processed by cortical circuitries.

      We thank the reviewer for this comment. When analyzing available data from the retina using a similar center-surround color flicker stimulus (Szatko et al. 2020), we found that Green On/UV Off color opponency is very rare in the RF center of retinal ganglion cells (Suppl. Fig. 5). This suggests that center Green On/UV Off color opponency in V1 neurons is not inherited by the RF center of retinal neurons. However, we agree with the reviewer that retinal neurons might still contribute to V1 color opponency, for example by being center-surround color opponent (e.g. Joesch et al. 2016 and Szatko et al. 2020). We rephrased the text to acknowledge this fact.

      This takes me to my second point, defining center and surround. The center spot is 37.5 deg of visual angle, more than 1 mm of the retinal surface. That means that all retinal cells, at least half and most likely all of their surrounds will also be activated. Although 37.5 deg is roughly the receptive field size previously determined for V1 neurons, the one-to-one comparison with retinal recording, particularly with their center/surround properties, is difficult. This should be discussed. I assume that the authors tried a similar approach with sparse or dense checker white noise stimuli. If so, it would be interesting if there were better ways of defining the properties of V1 neurons on their complex/simple receptive field properties to define how much of their responses are due to an activation of the true "center" or a coactivation of the surround. Interestingly, at least some of the cells (Fig. 1d, cells 2 and 5) don’t have a surround. Could it be that in these cases, the "center" and "surround" are being excited together? How different would the overall statistics change if one used a full-filed flicker stimulus instead of a center/surround stimulus? How stable are the results if the center/surround flicker stimulus is shifted? These results won’t change the fact that chromatic coding is present in the VC and that there are clear differences depending on their position, but it might change the interpretation. Thus, I would encourage you to test these differences and discuss them.

      Thanks for this comment. We agree with the reviewer that a one-to-one comparison of retina and V1 data is challenging, due to differences in both RF and stimulus size. We rephrased the Results text to clarify this point and now also mention it in the Discussion.

      To be able to record from many V1 neurons simultaneously, we used a stimulus size of 37.5 degree visual angle in diameter, which is slightly larger than center RFs of single V1 neurons. As the reviewer mentions, the disadvantage of this approach is that the stimulus is only roughly centered on the neurons’ center RFs. To reduce the impact of potential stimulus misalignment on our results, we used the following steps:

      For each recording, we positioned the monitor such that the mean RF across all neurons lies within the center of the stimulus field of view.

      We confirmed that this procedure results in good stimulus alignment for the large majority of recorded neurons within individual recording fields by using a sparse noise stimulus (Suppl. Fig. 1a-c). Specifically, we found that for 83% of tested neurons, more than two thirds of their center RF, determined by the sparse noise stimulus, overlapped with the center spot of the color noise stimulus.

      For analysis, we excluded neurons without a significant center STA, which may be caused by misalignment of the stimulus.

      Together, we believe these points strongly suggest that the center spot and the surround annulus of the noise stimulus predominantly drive center (i.e. classical RF) and surround (i.e. extraclassical RF), respectively, of the recorded V1 neurons. This is further supported by the fact that color response types identified using an automated clustering method were robust across mice (Suppl. Fig. 6c), indicating consistent stimulus centering.

      Nevertheless, we cannot exclude that the stimulus was misaligned for a subset of the recorded neurons used for analysis. We agree with the reviewer that such misalignment might have contributed to cells not having surround STAs, due to simultaneous activation of antagonistic center and surround RF components by the surround stimulus. While a full-field stimulus would get rid of the misalignment problem, it would not allow to study color tuning in center and surround RF components separately. Instead, one could compare the results of our approach with an approach that centers the stimulus on individual neurons. However, we believe that performing these additional experiments is out of the scope of the current study.

      To acknowledge the experimental limitations of our study and the concerns brought up by the reviewer, we now explicitly mention the steps we perform to reduce the effects of stimulus misalignment in the Results section and discuss the problem of stimulus alignment in the Discussion. We believe these changes will help the reader to interpret our results.

      Referee 2 (Remarks to the Author):

      Summary:

      Franke et al. characterize the representation of color in the primary visual cortex of mice and how it changes across the visual field, with a particular focus on how this may influence the ability to detect aerial predators. Using calcium imaging in awake, head-fixed mice, they characterize the properties of V1 neurons (layer 2/3) using a large center-surround stimulation where green and ultra-violet were presented in random combinations. Using a clustering approach, a set of functional cell-types were identified based on their preference to different combinations of green and UV in their center and surround. These functional types were demonstrated to have varying spatial distributions in V1, including one neuronal type (Green-ON/UV-OFF) that was much more prominent in the posterior V1 (i.e. upper visual field). Modelling work suggests that these neurons likely support the detection of predator-like objects in the sky.

      Strengths:

      The large-scale single-cell resolution imaging used in this work allows the authors to map the responses of individual neurons across large regions of the visual cortex. Combining this large dataset with clustering analysis enabled the authors to group V1 neurons into distinct functional cell types and demonstrate their relative distribution in the upper and lower visual fields. Modelling work demonstrated the different capacity of each functional type to detect objects in the sky, providing insight into the ethological relevance of color opponent neurons in V1.

      We thank the reviewer for appreciating our manuscript.

      Weaknesses:

      While the study presents solid evidence a few weaknesses exist, including the size of the dataset, clarity regarding details of data included in each step of the analysis and discussion of caveats of the work. The results presented here are based on recordings of 3 mice. While the number of neurons recorded is reasonably large (n > 3000) an analysis that tests for consistency across animals is missing. Related to this, it is unclear how many neurons at each stage of the analysis come from the 3 different mice (except for Suppl. Fig 4).

      Thank you for this comment. We apologize that the original manuscript did not clearly indicate the consistency of our results across animals. We have revised the manuscript in the following ways:

      We have added an additional Suppl. Figure, which shows the variability of the data within and across animals (Suppl. Fig. 4). Specifically, we show the distribution of color and luminance selectivity for (i) center and surround components of V1 RFs and (ii) for upper and lower visual field. This data is used for all analyses shown in Figures 2-4. The figure legend of this figure also states the number of neurons per animal.

      We now clearly state in the Results section that all analyses in the main figures were performed by pooling data across animals, and refer to the Suppl. Figures for consistency across animals.

      We believe these changes help the reader to interpret our results.

      Finally, the paper would greatly benefit from a more in depth discussion of the caveats related to the conclusion drawn at each stage of the analysis. This is particularly relevant regarding the caveats related to using spike triggered averages to assess the response preferences of ON-OFF neurons, and the conclusions drawn about the contribution of retinal color opponency.

      Thanks. We substantially revised the text to discuss caveats and limitations of the approach. For example, we added a section into the Discussion called "Limitations of the stimulus paradigm". In addition, we clarified how retinal signals relate to cortical ones and phrased our conclusions more conservatively.

      The authors provide solid evidence to support an asymmetric distribution of color opponent cells in V1 and a reduced color contrast representation in lower light levels. Some statements would benefit from more direct evidence such as the integration of upstream visual signals for color opponency in V1.

      Based on the comments from Reviewer 1, we have rephrased the statements regarding the integration of upstream visual signals for color opponency in V1. We think these revisions increase the clarity of the results and help the reader with interpretation.

      Overall, this study will be a valuable resource for researchers studying color vision, cortical processing, and the processing of ethologically relevant information. It provides a useful basis for future work on the origin of color opponency in V1 and its ethological relevance.

      Thanks! We thank the reviewer again for the helpful comments.

      Referee 3 (Remarks to the Author):

      This paper studies chromatic coding in mouse primary visual cortex. Calcium responses of a large collection of cells are measured in response to a simple spot stimulus. These responses are used to estimate chromatic tuning properties - specifically sensitivity to UV and green stimuli presented in a large central spot or a larger still surrounding region. Cells are divided based on their responses to these stimuli into luminance or chromatic sensitive groups. Several technical concerns limit how clearly the data support the conclusions. If these issues can be fixed, the paper would make a valuable contribution to how color is coded in mouse V1.

      We thank the reviewer for the helpful comments.

      Analysis: The central tool used to analyze the data is a "spike triggered average" of the responses to randomly varying stimuli. There are several steps in this analysis that are not documented, and hence evaluating how well it works is difficult. Central to this is that the paper does not measure spikes. Instead, measured calcium traces are converted to estimated spike rates, which are then used to estimate STAs. There are no raw calcium traces shown, and the approach to estimate spike rates is not described in any detail. Confirming the accuracy of these steps is essential for a reader to be able to evaluate the paper. Further, it is not clear why the linear filters connecting the recorded calcium traces and the stimulus cannot be estimated directly, without the intermediate step of estimating spike rates.

      Thank you for this comment. We have used the genetically encoded calcium sensor GCaMP6s in our recordings. This sensor is a very sensitive GCaMP6 variant, but also one with slow kinetics. To remove the effect of the slow sensor kinetics from recorded calcium responses, the recorded traces are commonly deconvolved with the impulse function of the sensor to obtain the deconvolved calcium traces. We now include this reasoning in the Results section. To illustrate the effect of the deconvolution, we added a new Suppl. Figure (Suppl. Fig. 2) showing raw calcium and deconvolved traces, and the STAs estimated from both types of traces. This illustrates that the results regarding neuronal color preferences are consistent across raw and deconvolved calcium traces.

      We agree with the reviewer that the term STA might be confusing. We have replaced it with the term "even-triggered-average" (ETA). In addition, we have replaced the phrase "estimated spike rate" with "deconvolved calcium trace" throughout the manuscript because the unit of the deconvolved traces is not interpretable, like spike rate would be (spikes per second). In the revised version, we now clarify in the Methods section that we estimate the ETAs based on deconvolved calcium traces, which is correlated with and an approximation for spike rate.

      A further issue about the STAs is that the inclusion criterion (correlation of predicted vs measured responses of 0.25) is pretty forgiving. It would be helpful to see a distribution of those correlation values, and some control analyses to check whether the STA is providing a sufficiently accurate measure to support the results (e.g. do the central results hold for the cells with the highest correlations).

      We thank the reviewer for this comment. To exclude noisy neurons from analysis, we used the following procedure:

      For each of the four stimulus conditions (center and surround for green and UV stimuli), kernel quality was measured by comparing the variance of the STA with the variance of the baseline, defined as the first 500 ms of the STA. Only cells with at least 10-times more variance of the kernel compared to baseline for UV or green center STA were considered for further analysis.

      We have added the distribution of quality values to a new Suppl. Figure (Suppl. Fig. 2d,e). We now also show the percentage of neurons above threshold, given different quality thresholds. Finally, we have repeated the analysis shown in Figure 2 for a much more conservative threshold, including only the top 25% of neurons (Suppl. Fig. 2e,f). We now mention this new analysis in the Methods and Results section.

      Limitations of stimulus choice: The paper relies on responses to a large (37.5 degree diameter) modulated spot and surrounding region. This spot is considerably larger than the receptive fields of both V1 cells and retinal ganglion cells. As a result, the spot itself is very likely to strongly activate both center and surround mechanisms, and responses of cells are likely to depend on where the receptive fields are located within the spot (and, e.g., how much of the true neural surround samples the center spot vs the surround region). The impact of these issues on the conclusions is considered briefly at the start of the results but needs to be evaluated in considerably more detail. This is particularly true for retinal ganglion cells given the size of their receptive fields (see also next point).

      We agree with the reviewer that the centering of the stimulus is critical and apologize if this point was not discussed sufficiently. To be able to record from many V1 neurons simultaneously, we used a stimulus size of 37.5 degree visual angle in diameter, which is slightly larger than center RFs of single V1 neurons. As the reviewer mentions, the disadvantage of this approach is that the stimulus is only roughly centered on the neurons’ center RFs. To reduce the impact of potential stimulus misalignment on our results, we have used different experimental and analysis steps and controls (see also second comment of Reviewer 1):

      For each recording, we positioned the monitor such that the mean RF across all neurons lies within the center of the stimulus field of view.

      We confirmed that this procedure results in good stimulus alignment for the large majority of recorded neurons within individual recording fields by using a sparse noise stimulus (Suppl. Fig. 1a-c). Specifically, we found that for 83% of tested neurons, more than two thirds of their center RF, determined by the sparse noise stimulus, overlapped with the center spot of the color noise stimulus.

      For analysis, we excluded neurons without a significant center STA, which may be caused by misalignment of the stimulus.

      We now mention those clearly in the Results section and added the limitations of our approach to the Discussion section.

      Comparison with retina: A key conclusion of the paper is that the chromatic tuning in V1 is not inherited from retinal ganglion cells. This conclusion comes from comparing chromatic tuning in a previously-collected data set from retina with the present results. But the retina recordings were made using a considerably smaller spot, and hence it is not clear that the comparison made in the paper is accurate. This issue may be handled by the analysis presented in the paper, but if so it needs to be described more clearly. The paper from which the retina data is taken argues that rod-cone chromatic opponency originates largely in the outer retina. This mechanism would be expected to be shared across retinal outputs. Thus it is not clear how the Green-On/UV-Off vs Green-Off/UV-On asymmetry could originate. This should be discussed.

      We agree with the reviewer that a one-to-one comparison of retina and V1 data is challenging, due to differences in both RF and stimulus size. We rephrased the Results text to clarify this point and now also mention it in the Discussion.

      When analyzing available data from the retina using a similar center-surround color flicker stimulus (Szatko et al. 2020), we found that Green On/UV Off color opponency is very rare in the RF center of retinal ganglion cells (Suppl. Fig. 5). This suggests that center Green On/UV Off color opponency in V1 neurons is not inherited by the RF center of retinal neurons. However, we agree with the reviewer that retinal neurons might still contribute to V1 color opponency, for example by being center-surround color opponent (e.g. Joesch et al. 2016 and Szatko et al. 2020). We rephrased the text to acknowledge this fact.

      Residual chromatic cells at low mesopic light levels The presence of chromatically tuned cells at the lowest light level probed is surprising. The authors describe these conditions as rod-dominated, in which case chromatic tuning should not be possible. This again is discussed only briefly. It either reflects the presence of an unexpected pathway that amplifies weak cone signals under low mesopic conditions such that they can create spectral opponency or something amiss in the calibrations or analysis. Data collected at still lower light levels would help resolve this.

      Thank you for this comment. We call the lowest light level "low mesopic" and "rod-dominated" because the spectral contrast of V1 center responses in posterior recording fields is green-shifted for this light level (Fig. 3a). This is only expected if responses in the UV-cone dominant ventral retina are predominantly driven by rod photoreceptors. We now explain this rationale in the Results section. In addition, we mention in the Discussion that future studies are required to test whether cone signals need to be amplified for low light levels. While we agree with the reviewer that it would be exciting to use even lower light levels during recordings, we believe this is out of the scope of the current study due to the technical challenges involved in achieving scotopic stimulation.

    1. Author response:

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

      We have revised the manuscript mainly in the following aspects: (1) the data of electrophysiological and behavioral responses of larvae and adults to trehalose have been added, and the related figures and texts have been modified accordingly; (2) the photos of taste organs of larvae and adults indicating the position of recorded sensilla have been added; (3) the potential off-target effects of GR knock-out on other GR expressions has been carefully explained and revised in the relevant text; (4) the abstract has been revised to present the findings more technically in a limited number of words; (5) some details of experiments in Materials and Methods and some new literatures have been added; (6) a new figure (Figure 8) summarizing the main findings of the study has been added.

      In the following, we respond to the reviewers’ comments and suggestions one by one. We hope that our answers will satisfy you and the three reviewers. We are also very happy to get further valuable advices from you.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The process of taste perception is significantly more intricate and complex in Lepidopteran insects. This investigation provides valuable insights into the role of Gustatory receptors and their dynamics in the sensation of sucrose, which serves as a crucial feeding cue for insects. The article highlights the differential sensitivity of Grs to sucrose and their involvement in feeding and insect behavior.

      Strengths:

      To support the notion of the differential specificity of Gr to sucrose, this study employed electrophysiology, ectopic expression of Grs in Xenopus, genome editing, and behavioral studies on insects. This investigation offers a fundamental understanding of the gustation process in lepidopteran insects and its regulation of feeding and other gustation-related physiological responses. This study holds significant importance in advancing our comprehension of lepidopteran insect biology, gustation, and feeding behavior.

      Thank you for your recognition of our research.

      Weaknesses:

      While this manuscript demonstrates technical proficiency, there exists an opportunity for additional refinement to optimize comprehensibility for the intended audience. Several crucial sugars have been overlooked in the context of electrophysiology studies and should be incorporated. Furthermore, it is imperative to consider the potential off-target effects of Gr knock-out on other Gr expressions. This investigation focuses exclusively on Gr6 and Gr10, while neglecting a comprehensive narrative regarding other Grs involved in sucrose sensation.

      We accept the reviewer's suggestion. Because trehalose is a main sugar in insect blood, and it is converted by insects after feeding on plant sugars, we have added the new data on electrophysiological and behavioral responses of larvae and adults of Helicoverpa armigera to trehalose (see Figure 1-2, Figure 1-figure supplement 1, Figure 2-figure supplement 1). Now, the total eight sugars include 2 pentoses (arabinose and xylose), 4 hexoses (fructose, fucose, galactose and glucose), and 2 disaccharides (sucrose and trehalose), which were chosen because they are mainly present in host-plants of H. armigera and/or representative in the structure and source of sugars.

      We fully agree to the reviewer’s opinion and have already taken the potential off-target effects of CRISPR/Cas9 knockout of Gr on other GR expressions into consideration. To predict the potential off-target sites of sgRNA of Gr6 and Gr10 establishing homozygous mutants using CRISPR/Cas9 technology, we first use online software CasOFFinder (http://www.rgenome.net/cas-offinder/) to blast the genome of the wild type cotton bollworm and set the mismatch number less than or equal to 3. We found that Gr10 sgRNA had no potential potential off-target site, and the sgRNA of Gr6 had only one potential off-target site. Therefore, we designed primers according to the sequence of potential off-target sites of Gr6 sgRNA, and conducted PCR using genomic DNA of homozygous mutant as a template, performed Sanger sequencing on the PCR products obtained, and found that the potential off-target sites of Gr6 sgRNA were no different from those of the wild type. Particularly, concerning the sgRNA of Gr6 and Gr10 may produce off-target effects on other sugar receptor genes of H. armigera, we conducted the same off-target site analysis with the designed sgRNA on each of the other eight sugar receptor genes, and found that there were no off-target sites on these receptor genes (see Line254-256).

      Reviewer #2 (Public Review):

      Summary:

      To identify sugar receptors and assess the capacity of these genes the authors first set out to identify behavioral responses in larvae and adults as well as physiological response. They used phylogenetics and gene expression (RNAseq) to identify candidates for sugar reception. Using first an in vitro oocyte system they assess the responses to distinct sugars. A subsequent genetic analysis shows that the Gr10 and Gr6 genes provide stage specific functions in sugar perception.

      Strengths:

      A clear strength of the manuscript is the breadth of techniques employed allowing a comprehensive study in a non-canonical model species.

      Thank you for your recognition of our research.

      Weaknesses:

      There are no major weaknesses in the study for the current state of knowledge in this species. Since it is much basic work to establish a broader knowledge, context with other modalities remains unknown. It might have been possible to probe certain contexts known from the fruit fly, which would have strengthened the manuscript.

      Thank you so much for your suggestion. According to this suggestion, we further added some sentences probing sugar sensing and behaviors of fruit fly larvae in the Introduction and discussion sections (Line 68-71 in Introduction section, Line 395-399 in Discussion section).

      Reviewer #3 (Public Review):

      In this study, the authors combine electrophysiology, behavioural analyses, and genetic editing techniques on the cotton bollworm to identify the molecular basis of sugar sensing in this species.

      The larval and adult forms of this species feed on different plant parts. Larvae primarily consume leaves, which have relatively lower sugar concentrations, while adults feed on nectar, rich in sugar. Through a series of experiments-spanning electrophysiological recordings from both larval and adult sensillae, qPCR expression analysis of identified GRs from these sensillae, response profiles of these GRs to various sugars via heterologous expression in Xenopus oocytes, and evaluations of CRISPR mutants based on these parameters-the authors discovered that larvae and adults employ distinct GRs for sugar sensing. While the larva uses the highly sensitive GR10, the adult uses the less sensitive and broadly tuned GR6. This differential use of GRs are in keeping with their behavioral ecology.

      The data are cohesive and consistently align across the methodologies employed. They are also well presented and the manuscript is clearly written.

      Recommendations for the authors:

      While appreciating the quality of the work and its presentation, we have a few comments for the authors, should they wish to consider them, that would significantly improve the presentation of the work.

      Title: Could the authors please revisit their title to better reflect the main finding of their work?

      The title has been changed into “The larva and adult of Helicoverpa armigera use differential gustatory receptors to sense sugars”.

      Text: There are a few comments related to the text, and these are listed below:

      (1) Could the authors place their work in the context of what's known about sugar sensing in Drosophila larva and adult?

      In the Introduction section, we added the status of research on sugar perception in Drosophila larvae, pointing out "No external sugar-sensing mechanism in Drosophila larvae has yet been characterized." (Line 70-71); in the Discussion section, the research progress of sugar sensing in Drosophila adults and larvae was also summarized (Line 397-399).

      (2) For each results section, could the authors please include a sentence or two that interprets the data in the context of previously presented data?

      We accept the reviewer's suggestion. In order to make it easy for readers to follow up, we included a sentence interprets the above data at the beginning of each part of the Results on the premise of avoiding duplication.

      (3) Could the authors please provide details of the generation and screening of the CRISPR mutants?

      We have added more details on mutant establishment and screening in the Materials and Methods section (Line 722-726, 729-732).

      Figures: Could the authors please include images and schematics wherever possible? For example, a schematic depicting the position of the sense organs and one summarising the main findings of the studies.

      In Figure 1 we added the photo of each taste organ, on which the recorded sensilla were indicated. We also added a new figure, Figure 8, summarizing the main findings of the study.

      Choice of Sugars: Could the authors please justify their choice of sugars they have used in the analyses?

      In the first paragraph of the Results section of the article, we further explain the reasons for using the sugars in the study. “We first investigated the electrophysiological responses of the lateral and medial sensilla styloconica in the larval maxillary galea to eight sugars. These sugars were chosen because they are mostly found in host-plants of H. armigera or are representative in the structure and source of sugars.”

      In addition to this, there are several specific comments in the detailed reviewers comments below, which the authors could consider responding to.

      Reviewer #1 (Recommendations For The Authors):

      The article titled "Sucrose taste receptors exhibit dissimilarities between larval and adult stages of a moth" by Shuai-Shuai Zhang and colleagues provides an intriguing analysis. The authors have conducted a meticulously planned and executed study. However, I do have some inquiries.

      (1) What precisely does the term "differ" signify in the title? It can be expounded upon in terms of differing in expression or sensitivity. The title could benefit from being more informative. The authors should appropriately specify the insect species in the title of the paper. This would make it more comprehensible to readers. Merely mentioning the term "moth" does not provide any information about the model organism. Hence, it would be preferable to mention Helicoverpa armigera instead of using the generic term "moth" in the title.

      Thank you for your suggestions. We considered it better to emphasize that the receptors for sucrose are different, and we have accepted the suggestion of adding the name of the animal. The title has been changed into “The larva and adult of Helicoverpa armigera use differential gustatory receptors to sense sugars”.

      (2) The abstract is written in a simple and easily understandable manner, but it overlooks important findings from a technical standpoint.

      We add some key experimental techniques to illustrate some important findings in the Abstract.

      (3). Almost all herbivorous insects are said to consume plants and utilize sucrose as a stimulus for feeding, as stated by the authors. Sucrose, glucose, and fructose sugar are among the commonly observed stimulants for feeding in numerous insects. It would be appropriate to incorporate not only sucrose but also glucose and fructose as feeding stimulants for almost all herbivorous insects.

      Thank you for your suggestion. Sucrose is the major sugar in plants, and its concentration varies greatly from tissue to tissue, while the concentration of the hexose sugars is much lower and the concentration does not change much. In Line 48, we state that sucrose, glucose, and fructose are feeding stimuli for herbivorous insects. From the previous studies, it seems that sucrose is the strongest, followed by fructose, and finally glucose. The cotton bollworm larvae showed no electrophysiological and behavioral response to glucose.

      (4) The reason why trehalose is not considered in the electrophysiology analysis is unclear. Given that trehalose is a major sugar in insects and plants, it would be intriguing to include it in the analysis.

      We have accepted the reviewer's suggestion, and supplemented the electrophysiological responses of taste organs in larvae and adults of Helicoverpa armigera to trehalose (Figure 1, Figure 1-Figure Supplement 1), and also tested the behavioral responses of the larvae and adults to trehalose (Figure 2, Figure 2-Figure Supplement 1). Therefore, all the related figures have been changed.

      (5) The author's intention regarding the co-receptor relationship between Gr5 and Gr6 (line 211) is unclear. If this is indeed the case, then the reason for considering Gr5 in further studies remains uncertain.

      We have changed the sentence as follows: “Since Gr5 was highly expressed with Gr6 in the proboscis and tarsi (Figure 3D-3E, Figure 3—figure supplement 1), we suspected that Gr5 and Gr6 might be expressed in the same cells, and then tested the response profile of their co-expression in oocytes.”

      (6) The homologous nature of Grs is emphasized by the authors. It is not specified how the author ensured that the guide RNA targeting Gr6 or Gr10 did not result in off-target effects on other Grs.

      Thank you so much for your suggestion. We have rewritten the relevant paragraph (Line 238-251), detailing our tests and the results on the potential off-target effects of knocking out GRs by CRISPR/Cas9: “In order to predict the potential off-target sites of sgRNA of Gr6 and Gr10, we used online software Cas-OFFinder (http://www.rgenome.net/cas-offinder/) to blast the genome of H. armigera, and the mismatch number was set to less than or equal to 3. According to the predicted results, the Gr10 sgRNA had no potential off-target region but Gr6 sgRNA had one. Therefore, we amplified and sequenced the potential off-target region of Gr6-/- and found there was no frameshift or premature stop codon in the region compared to WT (Figure 5—figure supplement 2). It is worth mentioning that there was no potential off-target region of Gr6 and Gr10 sgRNA in other sugar receptor genes of H. armigera, Gr4, Gr5, Gr7, Gr8, Gr9, Gr11 and Gr12. We further found there was no difference in the response to xylose of the medial sensilla styloconica among WT, Gr10-/- and Gr6-/- (Figure 5—figure supplement 2). Furthermore, WT, Gr10-/- and Gr6-/- did not show differences in the larval body weight, adult lifespan, and number of eggs laid per female (Figure 5—figure supplement 2). All these results suggest that no off-target effects occurred in the study.”

      (7) Is it possible that knocking out Gr10 is not compensated for by the overexpression of Gr6 or other sucrose sensing Grs? Similarly, would the vice versa scenario hold true?

      In the Discussion section, we have added some sentences to discuss this issue: “From our results, knocking out Gr10 or Gr6 is unlikely to be compensated by overexpression of other sugar GRs. One of our recent studies showed that Orco knockout had no significant effect on the expression of most OR, IR and GR genes in adult antennae of H. armigera, but some genes were up- or down-regulated (Fan et al., 2022).”

      (8) What was the rationale for selecting nine candidate GR genes for expression analysis?

      Based on the reviewer's suggestion, we expanded the relevant paragraphs to illustrate the rationale for selecting nine candidate GR genes for expression analysis: “To reveal the molecular basis of sugar reception in the taste sensilla of H. armigera, we first analyzed the putative sugar gustatory receptor genes based on the reported gene sequences of GRs in H. armigera and their phylogenetic relationship of D. melanogaster sugar gustatory receptors (Jiang et al., 2015; Pearce et al., 2017; Xu et al., 2017). Nine putative sugar GR genes, Gr4–12 were identified, and their full-length cDNA sequences were cloned (The GenBank accession number is provided in Appendix—Table S1).” (Line 155-161)

      (9) What is the potential reason for the difference between the major larval sugar receptors of Drosophila and Lepidopterans?

      The difference between the major larval sugar receptors of Drosophila and Lepidopterans is probably due to differences in the food their larvae feed on. Fruit fly larvae feed on rotten fruit, the main sugar of which is fructose. The larvae of Lepidoptera mainly feed on plants, and the main sugar is sucrose. In the Discussion section, we have added a sentence “This is most likely due to fruit fly larvae feeding on rotten fruits, which contain fructose as the main sugar.” (Line 399-401)

      (10) There is a disparity in GRs, specifically GR5 and GR6, between the female antenna, proboscis, and tarsi. What could be the possible justification and significance of this?

      Thank you so much for this question. We have added a sentence in the Discussion section, “In this study, the expression patterns of 9 sugar GRs in three taste organs of adult H. armigera show that there is a disparity in GRs, specifically GR5 and GR6, between the female antenna, tarsi and proboscis, which may be an evolutionary adaptation reflecting subtle differentiation in the function of these taste organs in adult foraging. Antennae and tarsi play a role in the exploration of potential sugar sources, while the proboscis plays a more precise role in the final decision to feed.” (Line 433-438)

      (11) I suggest that a visual representation illustrating the positioning of GSNs, particularly the lateral and medial sensilla, in both larva and adult stages would enhance the correlation with the results.

      In Figure 1 we added the photo of each taste organ and the position of the recorded sensilla, and also added a new figure, Figure 8 summarizing the main findings of the studies.

      (12) Further experiments can be conducted to elucidate the precise molecular mechanisms, particularly the downstream effects of GRs, in order to establish the specificity of GRs more convincingly.

      Thank you so much for your suggestion. We have discussed the further experiments in the Discussion section, “To elucidate the precise molecular mechanisms of sugar reception in H. armigera is necessary to compare a series of single, double and even multiple Gr knock-out lines and investigate the downstream effects of the GRs.” (Line 363-369)

      (13) Figure 6 caption: In Figure 6 (D to I), the percentage of PER is depicted. There is redundancy in the Y-axis title (Percentage of PER) and the legend. This appears to be repetitive. I suggest that it would be better to include the Y-axis title only in Figure D or in Figures D and G.

      We accept the suggestion. Figure 7 (not Figure 6) has been revised accordingly.

      (14) In Figures 6A and 6C, there is inconsistency in the colors used for WT, Gr6, and Gr10. This could potentially confuse the reader. I recommend using the same colors in both figures instead of using a blue color. Please specify how the authors calculated the feeding area in Figure 6.

      We accept the reviewer's suggestion and have changed the color of Figure 7A, B. We have also added the detail method for calculating feeding area (Line 541-545).

      (15) In Two-choice tests, why did the authors use 0.01% Tween 80? Please provide comments on this.

      Use of 0.01% Tween 80 is to reduce the surface tension and increase the malleability of the solution. We have given detailed explanation in the Method section and cite the reference. (Line538-540)

      (16) It would be valuable if the authors could comment on the prospects of this study, considering that GRs play a vital role in controlling behavior and developmental pathways. What are the potential consequences of blocking or disrupting these receptors in terms of behavioral and developmental phenotypic deformities? Could this potentially lead to increased insect mortality?

      Thank you so much for your suggestions. In the last paragraph of the Discussion section, we have added the following perspectives, “Knockout of Gr10 or Gr6 led to a significant decrease in sugar sensitivity and food preference of the larvae and adults of H. armigera, respectively, which is bound to bring adverse consequences to survival and reproduction of the insects. Therefore, studying the molecular mechanisms underlying sugar perception in phytophagous insects may provide new insights into the behavioral ecology of this important and highly diverse group of insects, and measures blocking or disrupting sugar receptors could also have applications to control agricultural pests and improve crop yields worldwide” (Line 449-456).

      Reviewer #2 (Recommendations for The Authors):

      There are a few comments, that I feel would be beneficial to be addressed.

      • The authors used 7 different sugars for their experimental approach. While I agree that this is a sufficiently large collection for a study, I was wondering why they specifically chose these sugars; an explanatory section might be helpful for a reader to follow the reasoning.

      According to reviewer 1's suggestion, we increased trehalose to 8 sugars in experiments. Trehalose is a main sugar in insect blood. It is converted by insects after feeding on plant sugars. The 8 sugars were chosen because they are present in host-plants of H. armigera or are representative in the structure and source of sugars. They contain 2 pentoses (arabinose and xylose), 4 hexoses (fructose, fucose, galactose and glucose), and 2 disaccharides (sucrose and trehalose).

      • It might be beneficial to provide some broader overview on the gustatory system in the cotton bollworm, particularly at the larval stage since this may not be common knowledge. Along these lines eg. the complexity of sensilla types, organs and overall number (or estimation) of neurons might be good to know, a graphical representation of the sense organs might be informative.

      In the Introduction section, we give a more specific description on sugar sensitive GSNs in the taste system of the larva and adult of H. armigera, and cite the corresponding references.

      • Concerning phylogeny of GRs, it might be relevant to know how complete the genome information is and some more general background on GR diversity in the cotton bollworm.

      We agree to your opinion. According to this idea, we got the putative sugar GRs from the previously published genome (Pearce et al. 2017) and the related annotation of GRs (Jiang et al. 2015, Xu et al. 2012). We have made a more detailed explanation about this in the new version of the manuscript, “We first analyzed the putative sugar gustatory receptor genes based on the genome data of H. armigera (Pearce et al. 2017), the reported gene sequences of sugar GRs in H. armigera and their phylogenetic relationship of D. melanogaster sugar gustatory receptors (Jiang et al. 2015, Xu et al. 2012). All nine putative sugar GR genes in H. armigera, Gr4–12 were validated, and their full-length cDNA sequences were cloned (The GenBank accession number is provided in Appendix—Table S1).” (Line 155-161).

      • Generation of mutants based on CRISPR is intriguing and a powerful step. While the techniques are well described in the method section, there is no information concerning efficiency or broader feasibility of the approach. I feel it would be quite interesting to learn about how feasible or laborious the approach is to generate mutants (e.g. number of initial injected eggs, the resulting F0 offspring, number of back-crosses, number of screened F1s ....).

      In the Materials and Methods section, we have added specific success rates for each step in the process of building the two mutants (Line 722-726, 729-732).

      Reviewer #3 (Recommendations For The Authors):

      I want to congratulate the authors on this very nice study and have only minor comments for them.

      (1) It would be very nice to include pictures of the larva and adult of H. armigera. It would also help to have schematics of where the sensilla they are recording from are.

      We have added photos of four taste organs on which the recoded sensilla were indicated (Figure 1), and picture of the larva and adult on which the stimulating site was indicated (Figure 2).

      (2) A schematic summarising their findings, including the relevance to the animal's behavioural ecology, will greatly improve interpretations for the broader audience.

      A schematic summarizing the findings has been added.

      (3) The manner in which PIs are represented in figure 2A, B (among others) is confusing. Can the authors please plot the PI and not the feeding area? From the PI values listed beside the plot, it actually suggests that the larvae don't really show a preference. Could the authors please comment on this?

      Yes, sucrose has a significant stimulating effect on larva feeding, but the effect is not as large as the predicted based on the sensitivity of the sensillum, the main reasons are as follows: (1) there are many factors affecting larva feeding, sucrose is only one of them; (2) due to the substrate leaf discs also contain sugar, the effect of newly added sucrose may be reduced. After careful consideration, we think it is better to display the feeding area and PI together so that readers have a complete understanding of the data.

      (4) The heterologous expression experiments suggest that co-expression of GR6 with either GR10 or GR5 somehow suppress the response of the GR6 alone to fucose. Am I reading the data correctly? Why would this be? Perhaps the authors could discuss this. In this context, it would help to reproduce all the GR6 data together.

      Your interpretation is reasonable to a certain extent. The result of co-injection might be that Gr10 or Gr5 inhibited the response of Gr6. However, there is another possibility that the amount of Gr6 sRNA was diluted by co-injection of two GRs, resulting in a reduced response of Gr6 to fucose.

      (5) In general, for each results section, it would help to have a sentence or two that interprets the data in the context of previously presented data. This would help the reader digest the data and interpret it as they read along. Currently, the authors summarise the observations and leave all the interpretation to the discussion section.

      We accept the suggestion. In each part of the results, we have added a sentence to explain the above data, which will help readers to clarify the context of the research more easily.

      (6) Is the GR6 data in 4C not lined up correctly?

      Yes, it is right.

      (7) Line 228 suggests that the mutants were validating with qPCRs - I don't see that data.

      The mutants were not validating with qPCR. We used the ordinary PCR technology at the mRNA level to verify whether the related sequences were really deleted in the mutants.

    1. Author response:

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

      Public Reviews:

      Reviewer #3 (Public Review):

      Kang, Huang, and colleagues have provided new data to address concerns regarding confirmation of LRRK1 and LRRK2 deletion in their mouse model and the functional impact of the modest loss of TH+ neurons observed in the substantia nigra of their double KO mice. In the revised manuscript, the new data around the characterization of the germline-deleted LRRK1 and LRRK2 mice add confidence that LRRK1 and LRRK2 can be deleted using the genetic approach. They have also added new text to the discussion to try and address some of the comments and questions raised regarding how LRRK1/2 loss may impact cell survival and the implications of this work for PD-linked variants in LRRK2 and therapeutic approaches targeting LRRK2.

      The new data provides additional support for the author's claims. I have provided below some suggestions for clarification/additions to the text that can be addressed without additional experiments.

      (1) The authors added additional text highlighting that more studies are warranted in mice where LRRK1/2 are deleted in other CNS cell types (microglia/astrocytes) to understand cell extrinsic drivers of the autophagy deficits observed in their previous work. It still remains unclear how loss of LRRK1/2 leads to increased apoptosis and gliosis in dopaminergic neurons in a cell-intrinsic manner, and, as suggested in the original review, it would be helpful to add some text to the discussion speculating on potential mechanisms by which this might occur.

      (2) Revisions have been made to the discussion to clarify their rationale around how variants in LRRK2 associated with PD may be loss-of-function to support the relevance of this mouse model to phenotypes observed in PD. However, as written, the argument that PD-linked variants are loss-offunction is based on the fact that the double KO mice have a mild loss of TH+ neurons while the transgenic mice overexpressing PD-linked LRRK2 variants often do not and that early characterization of kinase activity was done in vitro are relatively weak. Given that the majority of evidence generated by many labs in the field supports a gain-of-function mechanism, the discussion should be further tempered to better highlight the uncertainty around this (rather than strongly arguing for a loss-offunction effect). This could include the mention of increased Rab phosphorylation observed in cellular and animal models and opposing consequences on lysosomal function observed in cellular studies in KO and pathogenic variant expressing cells. Further, a reference to the Whiffen et al. 2020 paper mentioned by another reviewer should be included in the discussion for completeness.

      We thank the reviewer for the comments. The discussion has been further revised and expanded to explain the cell extrinsic microglial response to pathophysiological changes in DA neurons of cDKO mice and propose future studies of single-cell RNA-sequencing to identify molecular changes within DA neurons of cDKO mice that may drive their apoptotic death during aging.

      We also added paragraphs summarizing existing experimental evidence for the toxic gain-of-function mechanism (biochemical data of increased kinase activity but the lack of evidence for the elevated pRabs and the altered pLRRK2 driving dopaminergic neurodegeneration) and for the loss-of-function mechanism (genetic data of relevant physiological roles) as well as the relationships between LRRK1 and LRRK2 (functional homologues sharing functional domains and overlapping roles in dopaminergic neuron survival) and how dominantly inherited missense mutations can confer a loss of function mechanism (impairing its function in cis and inhibiting wild-type protein function in trans). We also provided a brief summary and discussion of the Whiffen et al. 2020 paper.

    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      This study identifies a family of solute transports in the enteric protist, Blastocystis, that may mediate the transport of glycolytic intermediates across the mitochondrial membrane. The study builds on previous observations suggesting that Blastocystis (and other Stramenopiles) are unusual in having a compartmentalized glycolytic pathway with enzymes involved in upper and lower glycolysis being located in the cytosol and mitochondria, respectively. In this study, the authors identified two putative Stamenopile metabolite transporters that are related to plant di/tricarboxylic acid transporters that might mediate the transport of glycolytic intermediates across the mitochondrial membrane. These GIC-transporters were localized to the Blastocystis mitochondrion using specific rabbit antibodies and shown to bind several glycolytic intermediates (including GAP, DHAP, and PEP) based on thermostability shift assays. Direct evidence for transport activity was obtained by reconstituting native proteins in proteoliposomes and measuring the uptake of 14C-malate or 35S-sulphate against unlabelled substrates. This assay showed that GIC-2 transported DHAP, GAP, and PEP. However, significant transport activity was not observed for bGIC-2. Overall, the study provides strong, but not conclusive evidence that bGIC-1 is involved in transporting glycolytic intermediates across the inner membrane of the mitochondria, while the function of GIC-2 remains unclear, despite exhibiting the same metabolite binding properties as bGIC-2 in thermostability assays.

      Strengths:

      Overall, the findings are of interest in the context of understanding the diversity of core metabolic pathways in evolutionarily diverse eukaryotes, as well as the process by which cytosolic glycolysis evolved in most eukaryotes. The experiments are carefully performed and clearly described.

      We thank the reviewer for their constructive comments. We note that bGIC-2 is the identified glycolytic intermediate transporter, not bGIC-1.

      Weaknesses:

      The main weakness of the study is the lack of direct evidence that either bGIC-1 and/or bGIC2 are active in vivo. While it is appreciated that the genetic tools for disrupting GIC genes in Blastocystis are limited/lacking, are there opportunities to ectopically express or delete these genes in other Stamenopiles, such as Phaeodactylum triconuteum, to demonstrate function in vivo?

      Here, we have identified a transport protein, unique to stramenopiles, which is present in mitochondria of Blastocystis and can bind and transport glycolytic intermediates. We agree that it would have been desirable to confirm that they function as glycolytic intermediate transporters in vivo. However, the reviewer is correct in saying that the genetic tools for disrupting GIC genes in Blastocystis in vivo are not available. While the reviewer mentions the possibility of performing these analyses in Phaeodactylum tricornutum, it is important to note that this species possesses aerobic mitochondria and that the pay-off phase of glycolysis is present in both the mitochondrial matrix and the cytosol. Consequently, any data obtained from this species might not be conclusive and would also not be relevant to the glycolytic metabolism in Blastocystis, the subject of this study.

      The authors demonstrate that both bGIC-1 and bGIC-2 are targeted to the mitochondrion, based on immunofluorescence studies. However, the precise localization and topology of these carriers in the inner or outer membrane are not defined. The conclusions of the study would be strengthened if the authors could show that one/both transporters are present in the inner membrane using protease protection experiments following differential solubilization of the outer and inner mitochondrial membranes.

      The protein is a member of the mitochondrial carrier family, which are extremely hydrophobic membrane proteins. Those with an established transport function are known to localise consistently to the mitochondrial inner membrane, which is impermeable to charged molecules, whereas the outer membrane is porous through VDAC. Furthermore, when the carriers are overproduced in Saccharomyces cerevisiae, the protein is found in the enriched mitochondrial fraction, adding further support to the idea that they are localised to the inner membrane, as the outer membrane has a limited surface area.

      It is not clear why hetero-exchange reactions were not performed for bGIC-1 (only for bGIC-2).

      Unfortunately, bGIC-1 did not display transport activity when tested in [14C]-malate/malate, [35S]-sulphate/sulphate or [33P]-phosphate/phosphate homo-exchange reactions, as shown in Figure 6 (Figure 5 in the revised manuscript). Phosphoenolpyruvate and dihydroxyacetone phosphate are not available in a radiolabelled form and glyceraldehyde-3-phosphate is prohibitively expensive, so we were unable to test glycolytic intermediates directly in homo-exchange reactions. Hetero-exchange reactions, as performed in Figure 5 (Figure 6 in the revised manuscript) for bGIC-2, are conclusive, as accumulation of the radio-labelled substrate inside the proteoliposomes can only occur, when the internal substrate is exported. It seems that Blastocystis has multiple copies, some of which are coding for dysfunctional carriers, being possible pseudo-genes.

      The summary slide depicted in Fig 7 is somewhat simplified and inaccurate. First, the authors show that TPI is located in the mitochondria in this study, while in the summary figure, TPI is shown to be present in both the cytosol and mitochondrial matrix. A cytosolic localization for TPI provides a functional rationale for having a triose-P carrier in the inner membrane - however, this is not supported by the data shown here. Second, if bGIC1/2 uses PEP as a counter ion to import GA3P and DHAP into the mitochondrion, as proposed in Fig 7, the lower glycolytic pathway would be effectively truncated at PEP, removing substrate for pyruvate kinase and formation of pyruvate/ATP. Third, the authors suggest that DHAP may have other functions in the mitochondria although these are not shown in the figure.

      Figure 7 presents a schematic comparison of the localisation of glycolysis in humans and Blastocystis, specifically focused on the transport steps of either pyruvate (humans) or glycolytic intermediates (Blastocystis) into the mitochondrial matrix. Most of the metabolism of Blastocystis has been inferred from the presence or absence of genes, encoding for particular enzymes, with the exception of the unusual glycolytic pathway. We feel that overcomplicating this schematic figure would detract from the main message of this analysis. Although the transport data show that PEP, another glycolytic intermediate, is transported, we agree with the reviewer that PEP export cannot be rationalised in the context of our current understanding of the metabolism, and we have changed the figure accordingly.

      We have not suggested that DHAP has other functions in mitochondria; on line 230, we state that ‘we have not found any evidence for the presence of dihydroxyacetone phosphate inside mitochondria in the literature. It is possible that it is not transported under physiological conditions in competition with dicarboxylates or other substrates.’

      Reviewer #2 (Public Review):

      In this manuscript, the authors set out to identify transporters that must exist in Stramenophiles due to the fact that the second half of glycolysis appears to be conducted in the mitochondria. They hypothesize that a Stramenophile-specific clade of transporters related to the dicarboxylate carriers is likely the relevant family and then go on to test two proteins from Blastocystis due to the infectious disease relevance of this organism. They show rather convincingly that these two proteins are expressed and are localized to the mitochondria in the native organism. The purified proteins bind to glycolytic intermediates and one of them, GIC-2, transports several glycolytic intermediates in vitro. This is a very solid and well-executed study that clearly demonstrates that bCIC-2 can transport glycolytic intermediates.

      We thank the reviewer for their positive comments on the manuscript, and their careful analyses of the presented data.

      (1) The major weakness is that the authors aren't able to show that this protein actually has this function in the native organism. This could be impossible due to the lack of genetic tools in Blastocystis, but it leaves us without absolute confidence that bGIC-2 is the important glycolytic intermediate mitochondrial transporter (or even that it has this function in vivo).

      Unfortunately, genetic manipulation in Blastocystis is currently not feasible and thus we cannot conduct a comparative metabolic study with the appropriate controls. The gold standard for identification is to prove the function with purified protein directly, which we have done here by using binding studies and transport assays.

      (2) It's atypical that the figures and figure panels don't really follow the order of their citation in the text. It's not a big deal, but mildly annoying to have to skip around in the figures (e.g. Figure 3D-E are described in the same paragraph as Figure 5). In addition, to facilitate the flow and a proper understanding I would encourage a reordering between figures 5D and 6 since Figure 6 is needed to understand the results shown in panel 5D, which may lead to confusion.

      We agree with the reviewer and have reordered the figures, switching Figure 5 and 6, which makes the manuscript easier to follow.

      (3) My impression is that the authors under-emphasize the fact that the hDIC also binds (and is stabilized by) glycolytic intermediates (G3P and 3PG). In the opinion of this reviewer, this might change the interpretation about the uniqueness of the bGIC proteins. They act on additional glycolytic intermediates, but it's not unique.

      The reviewer is correct that hDIC is stabilized by both G3P and 3PG, but neither are transported, as shown in Figure 5B (Figure 6B in the revised manuscript). It is not uncommon for compounds to bind to some extend without being transported, as they share certain structural and chemical features with the substrates, which result in stabilisation in thermostability analyses. For example, GTP stabilises the ADP/ATP carrier in thermostability analyses to some extent (Majd et al, 2018), although it is not a transported substrate of the carrier (King et al, 2020). Although thermostability assays are very useful for screening of potential substrates, it is always necessary to carry out transport assays, which are the gold standard for transporter identification.

      Reviewer #3 (Public Review):

      Summary:

      Unlike most eukaryotes, Blastocystis has a branched glycolysis pathway, which is split between the cytoplasm and the mitochondrial matrix. An outstanding question was how the glycolytic intermediates generated in the 'preparatory' phase' are transported into the mitochondrial matrix for the 'pay off' phase. Here, the authors use bioinformatic analysis to identify two candidate solute carrier genes, bGIC-1, and bGIC-2, and use biochemical and biophysical methods to characterise their substrate specificity and transport properties. The authors demonstrate that bGIC-2 can transport dihydroxyacetone phosphate, glyceraldehyde-3-phosphate, 3-phosphoglycerate, and phosphoenolpyruvate, establishing this protein as the 'missing link' connecting the two split branches of glycolysis in this branch of single-celled eukaryotes. The authors also present their data on bGIC-1, which suggests a role in anion transport and bOGC, which is a close functional homologue of the human oxoglutarate carrier (hOGC, SLC25A11) and human dicarboxylate carrier (hDIC, SLC25A10).

      Strengths:

      The results are presented in a clear and logical arrangement, which nicely leads the reader through the process of gene identification and subsequent ligand screening and functional reconstitution. The results are compelling and well supported - the thermal stabilisation data is supported by the exchange studies. Caveats, where apparent, are discussed and rational explanations are given.

      We thank the reviewer for their positive and constructive comments on the manuscript.

      Weaknesses:

      The study does not contain any significant weaknesses in my view. I would like to see the authors include the initial rate plots used in the main figures (possibly as insets), so we can observe the data points used for these calculations. It would also have been interesting to include the AlphaFold models for bGIC-1 and bGIC-2 and a discussion/rationalisation for the substrate specificity discussed in the study.

      We have shown uptake curves in both Figure 3 and Figure 6 (Figure 5 in the revised manuscript) to provide the typical uptake curves that we record by our robot, and we also show how we calculate the initial rates. We feel that the inclusion of uptake curves for each compound for each carrier (96 uptake curves in total) would make figure 5 (Figure 6 in the revised manuscript) extremely complicated.

      It would also have been interesting to include the AlphaFold models for bGIC-1 and bGIC-2 and a discussion/rationalisation for the substrate specificity discussed in the study.

      Whilst AlphaFold is an important step forward in the prediction of protein structures, it is not accurate enough at this time to be used for the rationalisation of the substrate specificity. For instance, there are the significant structural differences between the predicted AlphaFold structure of the human uncoupling protein (https://alphafold.ebi.ac.uk/entry/P25874), by and large based on the mitochondrial ADP/ATP carrier, and the experimentally determined structure, especially for the central cavity where the substrate recognition takes place (Jones et al, 2023; Kang & Chen, 2023). More importantly, it is believed that the optimal binding of the substrate takes place in the occluded state (Klingenberg, 2007; Springett et al, 2017), for which we have no structure.

      References

      Jones SA, Gogoi P, Ruprecht JJ, King MS, Lee Y, Zögg T, Pardon E, Chand D, Steimle S, Copeman DM et al (2023) Structural basis of purine nucleotide inhibition of human uncoupling protein 1. Sci Adv 9: eadh4251

      Kang Y, Chen L (2023) Structural basis for the binding of DNP and purine nucleotides onto UCP1. Nature 620: 226-231

      King MS, Tavoulari S, Mavridou V, King AC, Mifsud J, Kunji ERS (2020) A single cysteine residue in the translocation pathway of the mitosomal ADP/ATP carrier from Cryptosporidium parvum confers a broad nucleotide specificity. Int J Mol Sci 21: 8971

      Klingenberg M (2007) Transport viewed as a catalytic process. Biochimie 89: 1042-1048

      Majd H, King MS, Palmer SM, Smith AC, Elbourne LD, Paulsen IT, Sharples D, Henderson PJ, Kunji ER (2018) Screening of candidate substrates and coupling ions of transporters by thermostability shift assays. Elife 7: e38821

      Springett R, King MS, Crichton PG, Kunji ERS (2017) Modelling the free energy profile of the mitochondrial ADP/ATP carrier. Biochim Biophys Acta 1858: 906-914

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors present a detailed study of a nearly complete Entomophthora muscae genome assembly and annotation, along with comparative analyses among related and non-related entomopathogenic fungi. The genome is one of the largest fungal genomes sequenced, and the authors document the proliferation and evolution of transposons and the presence/absence of related genetic machinery to explore how this may have occurred. There has also been an expansion in gene number, which appears to contain many "novel" genes unique to E. muscae. Functionally, the authors were interested in CAZymes, proteases, circadian clock related genes (due to entomopathogenicity/ host manipulation), other insect pathogenspecific genes, and secondary metabolites. There are many interesting findings including expansions in trahalases, unique insulinase, and another peptidase, and some evidence for RIP in Entomophthoralean fungi. The authors performed a separate study examining E. muscae species complex and related strains. Specifically, morphological traits were measured for strains and then compared to the 28S+ITSbased phylogeny, showing little informativeness of these morpho characters with high levels of overlap.

      This work represents a big leap forward in the genomics of non-Dikarya fungi and large fungal genomes. Most of the gene homologs have been studied in species that diverged hundreds of millions of years ago, and therefore using standard comparative genomic approaches is not trivial and still relatively little is known. This paper provides many new hypotheses and potential avenues of research about fungal genome size expansion, entomopathogenesis in zygomycetes, and cellular functions like RIP and circadian mechanisms.

      Strengths:

      There are many strengths to this study. It represents a massive amount of work and a very thorough functional analysis of the gene content in these fungi (which are largely unsequenced and definitely understudied). Too often comparative genomic work will focus on one aspect and leave the reader wondering about all the other ways genome(s) are unique or different from others. This study really dove in and explored the relevant aspects of the E. muscae genome.

      The authors used both a priori and emergent properties to shape their analyses (by searching for specific genes of interest and by analyzing genes underrepresented, expanded, or unique to their chosen taxa), enabling a detailed review of the genomic architecture and content. Specifically, I'm impressed by the analysis of missing genes (pFAMs) in E. muscae, none of which are enriched in relatives, suggesting this fungus is really different not by gene loss, but by its gene expansions.

      Analyzing species-level boundaries and the data underlying those (genetic or morphological) is not something frequently presented in comparative genomic studies, however, here it is a welcome addition as the target species of the study is part of a species complex where morphology can be misleading and genetic data is infrequently collected in conjunction with the morphological data.

      Thank you for your careful reading of our work. We’re glad that you identified these areas as strengths.

      Weaknesses:

      The conclusions of this paper are mostly well supported by data, but a few points should be clarified.

      In the analysis of Orthogroups (OGs), the claim in the text is that E. muscae "has genes in multi-species OGs no more frequently than Enotomophaga maimaiga. (Fig. 3F)" I don't see that in 3F. But maybe I'm really missing something.

      Thank you for catching this. You were, in fact, not missing anything at all. There was a mismatch between the data plotted in F and G and how the caption described these data. We very much apologize for the confusion that this must have caused. We have corrected these plots and also made changes to improve interpretability (see below).

      Also related, based on what is written in the text of the OG section, I think portions of Figure 3G are incorrect/ duplicated. First, a general question, related to the first two portions of the graph. How do "Genes assigned to an OG" and "Genes not assigned to an OG" not equal 100% for each species? The graph as currently visualized does not show that. Then I think the bars in portion 3 "Genes in speciesspecific OG" are wrong (because in the text it says "N. thromboides had just 16.3%" species-specific OGs, but the graph clearly shows that bar at around 50%. I think portion 3 is just a duplicate of the bars in portion 4 - they look exactly the same - and in addition, as stated in the text portion 4 "Potentially speciesspecific genes" should be the simple addition of the bars in portion 2 and portion 3 for each species.

      As mentioned above, we sincerely regret the error made in the plot and for the confusion that this caused. F now reflects the percentage of orthogroups (OGs) that possess at least one representative from the indicated species (left) and the percentage of OGs that are species-specific (only possess genes from one species; right). The latter is a subset of the former. G now reflects the percentage of annotated genes that were assigned an OG, per species, as well as the inverse of this - genes that were not assigned to any OG. These should, and now do, sum to 100%. The “Within species-specific OG” data summed with the “Not assigned OG” data yields the “Potentially species-specific data” in the rightmost column.

      In the introduction, there is a name for the phenomenon of "clinging to or biting the tops of plants," it's called summit disease. And just for some context for the readers, summit disease is well-documented in many of these taxa in the older literature, but it is often ignored in modern studies - even though it is a fascinating effect seen in many insect hosts, caused by many, many fungi, nematodes (!), etc. This phenomenon has evolved many times. Nice discussions of this in Evans 1989 and Roy et al. 2006 (both of whom cite much of the older literature).

      You’re right. We have now clarified that this behavior is called “summit disease” and referenced the suggested articles, along with a more recent review.

      Reviewer #2 (Public Review):

      In their study, Stajich and co-authors present a new 1.03 Gb genome assembly for an isolate of the fungal insect parasite Entomophthora muscae (Entomophthoromycota phylum, isolated from Drosophila hydei). Many species of the Entomophthoromycota phylum are specialised insect pathogens with relatively large genomes for fungi, with interesting yet largely unexplored biology. The authors compare their new E. muscae assembly to those of other species in the Entomophthorales order and also more generally to other fungi. For that, they first focus on repetitive DNA (transposons) and show that Ty3 LTRs are highly abundant in the E. muscae genome and contribute to ~40% of the species' genome, a feature that is shared by closely related species in the Entomophthorales. Next, the authors describe the major differences in protein content between species in the genus, focusing on functional domains, namely protein families (pfam), carbohydrate-active enzymes, and peptidases. They highlight several protein families that are overrepresented/underrepresented in the E. muscae genome and other

      Entomophthorales genomes. The authors also highlight differences in components of the circadian rhythm, which might be relevant to the biology of these insect-infecting fungi. To gain further insights into E. muscae specificities, the authors identify orthologous proteins among four Entomophthorales species. Consistently with a larger genome and protein set in E. muscae, they find that 21% of the 17,111 orthogroups are specific to the species. To finish, the authors examine the consistency between methods for species delineation in the genus using molecular (ITS + 28S) or morphological data (# of nuclei per conidia + conidia size) and highlight major incongruences between the two.

      Although most of the methods applied in the frame of this study are appropriate with the scripts made available, I believe there are some major discrepancies in the datasets that are compared which could undermine most of the results/conclusions. More precisely, most of the results are based on the comparison of protein family content between four Entomophthorales species. As the authors mention on page 5, genome (transcriptome) assembly and further annotation procedures can strongly influence gene discovery. Here, the authors re-annotated two assemblies using their own methods and recovered between 30 and 60% more genes than in the original dataset, but if I understand it correctly, they perform all downstream comparative analyses using the original annotations. Given the focus on E. muscae and the small sample size (four genomes compared), I believe performing the comparisons on the newly annotated assemblies would be more rigorous for making any claim on gene family variation.

      Thank you for this comment. While we did compare gene model predictions for two of these assemblies to assess if this difference could account for discrepancies in gene counts, completely reannotating all non-E. muscae datasets was outside of the scope of this study. In our opinion, the total number of predicted genes in a genome is not a best representation of differences since splitting or fusing gene models can inflate seeming differences; the orthology and domain counts are a more accurate assessment of the content. It’s possible that annotation differences may have inflated some gene family counts, however we will note that similar domain trends were observed between the closest species to E. muscae, Entomophaga maimaiga, suggesting that these differences were not sufficient to prevent us from detecting real biological signals. We look forward to continued improvement of our genome through additional sequencing and more clarity on total gene content of E. muscae.

      The authors also investigate the putative impact of repeat-induced point mutation on the architecture of the large Entomophthorales genomes (for three of the eight species in Figure 1) and report low RIP-like dinucleotide signatures despite the presence of RID1 (a gene involved in the RIP process in Neurospora crassa) and RNAi machinery. They base their analysis on the presence of specific PFAM domains across the proteome of the three Entomophthorales species. In the case of RID1, the authors searched for a DNA methyltransferase domain (PF00145), however other proteins than RID1 bear such functional domain (DNMT family) so that in the current analysis it is impossible to say if the authors are actually looking at RID1 homologs (probably not, RID1 is monophyletic to the Ascomycota I believe). Similar comments apply to the analysis of components of the RNAi machinery. A more reliable alternative to the PFAM analysis would be to work with full protein sequences in addition to the functional domains.

      While we understand this concern regarding domain vs. full length protein, the advantage of the domain search is that HMM-based searches are sensitive to detecting more distantly related homologs. Entomophthoralean fungi are distantly related from the ascomycetes in which these mechanisms have been characterized, so we chose a broader search approach that may identify proteins with similar domain structure, but are not necessarily homologs. These searches are presented in the manuscript as preliminary, but worth further investigation. However, our RID-based analysis did not identify convincing homologs for RID1 in entomophthoralean fungi included in our investigation, and we reported low homology (i.e., 12-14%) among our orthogroup of interest and RID1. We have further edited this section to clarify our understanding that these candidates are not RID1 homologs. We had hoped to avoid this implication, but we felt this investigation and null result were worth reporting.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific points:

      Results:

      "1.03 Gb genome consisting of 7,810 contigs (N50 = 301.1 kb). Additional... resulted in a final contig count of 7,810 (N50 = 329.6 kb)" So you started and ended with the same contig count but a different N50? Is this a typo?

      Yes, this was a typo. Thank you for bringing this to our attention.

      Figure 1D.

      The colors of Complete1x and Complete2x are too similar to tell them apart.

      The colors have been made more distinct.

      Figure 4B.

      I know C. rosea has been found from insects before, but it's mostly a mycoparasite and occasionally an endophyte, and has bioactivity against a lot of things. I just saw that it's listed as an entomopathogen, and I was surprised. Anyway, leave it as is if you want to, but it's definitely better studied and better known (Google Scholar) as a mycoparasite.

      Thanks for this comment. For the sake of including a more diverse representation of entomopathogenic fungi, we have opted to leave this as is.

      Full references (from the public comment)

      Evans, H.C., 1989. Mycopathogens of insects of epigeal and aerial habitats. Insect-fungus interactions, pp.205-238.

      Roy, H.E., Steinkraus, D.C., Eilenberg, J., Hajek, A.E. and Pell, J.K., 2006. Bizarre interactions and endgames: entomopathogenic fungi and their arthropod hosts. Annu. Rev. Entomol., 51, pp.331-357.

      Reviewer #2 (Recommendations For The Authors):

      I believe the manuscript could largely benefit from restructuring the results section to enhance clarity. The results section reads like a lot of descriptive back and forth, so that the reader lacks a clear rationale. The absence of a consistent dataset used for the different comparisons made all along the manuscript makes it hard to follow.

      Minor comments:

      (No line numbers were available so I refer to page numbers).

      p1

      • not sure about the use of "allied" to describe other fungal species in the title and after (sister species?).

      We didn’t want to use the word sister because not all of these species could be considered sister.

      • Genomic defence against transposable elements rather than "anti"?

      We have rephrased to genomic defense.

      p3

      • Extra parenthesis at Bronski et al.

      This is now corrected.

      • What does newly-available mean here?

      We mean recent. A lot of the datasets we used were very new, and we wanted to emphasize that point.

      • The back and forth between genomes and transcriptomes makes it hard to follow, would clarify from the beginning (in addition to the sequencing method - short vs long-read assemblies as in Figure 1B) or perhaps use a consistent dataset for all subsequent comparative analysis in the Entomophthorales.

      We have denoted our transcriptomic datasets in Fig 1C using parentheses.

      p5

      • Perhaps clarify that class II DNA transposons can also "copy" (single-strand excisions can be repaired by the host machinery).

      We have now included mention of “copy” as well as “jump” mechanisms of Class II transposons per your suggestion.

      p6

      • "beginning roughly concurrently", not clear what "began".

      This is now corrected.

      • "control" rather than "protect against"?

      We’ve changed “protect against” to “counter”.

      • I believe RIP has only been observed (experimentally) in a handful of fungal species, all from the Ascomycota phylum.

      Hood et al, 2005 found signatures of RIP in anther-smut fungus and Horns et al, 2012, found evidence of hypermutability across repeat elements within several Pucciniales species.

      • "RID1 contains two DNA_methylase domains", RID1 has one methyltransferase domain according to the reference Freitag et al, 2002.

      Thank you for drawing this to our attention. It is true RID1 has one methyltransferase region; however, the sequence deposited by Freitag et al, 2002 (AAM27408) is predicted by HMMer to have two adjacent Pfam DNA_methylase domains (i.e., PF00145). In this exploratory analysis, we tried to leverage this characteristic to identify candidate proteins of interest. We have reworded this section to clarify this.

      p8

      • Here and after I would use more informative titles for each paragraph.

      With the exception of the headings for Pfam, CAZy and MEROPs analyses, we believe the other headings are informative. We appreciate this comment, but opt to leave the heading titles as is.

      • I believe presenting the orthology analysis before the more in-depth protein family domain search.

      We leveraged the OG analysis mostly as a way to identify potentially unique genes in E. muscae, so we think the current order makes the most sense.

      p10

      • Figures 3F and G are confusing. The legend for Figure 3F mentions "OGs with >= 2 species" while the figure shows "multi-species OGs", and reads as redundant with the "species-specific" OGs. For the "OGs within species" do I understand it correctly that it represents the number of genes assigned to OGs for each species? If yes, the numbers are in contradiction with Figure 3G. And in Figure 3G shouldn't the sum of "genes assigned in OGs" and "genes nor assigned in OGs" add up to 100? I'm probably missing something here, but I would clarify what the different sets of orthogroups are in the figure and in the text (perhaps adopting a pangenome-like nomenclature).

      Thanks for this comment. This legend, unfortunately, reflected an earlier version of the figure and was overlooked prior to submission. We have since amended this and sincerely apologize for the error on our part.

      p12

      • The whole first paragraph reads more like it should be part of an introduction/discussion.

      We’ve moved some of this paragraph to the discussion but left the background information necessary for the reader to understand why we were looking for homologs of wc and frq.

      p13

      • The last paragraph reads like discussion.

      We have revised this paragraph so it now reads: “Because E. muscae is an obligate insect-pathogen only living inside live flies, we investigate the presence of canonical entomopathogenic enzymes in the genome. We find that E. muscae appear to have an expanded group of acid-trehalases compared to other entomopathogenic and non-entomopathogenic Entomophthorales (Fig. 4A), which correlates with the primary sugar in insect blood (hemolymph) being trehalose (Thompson, 2003). The obligate insectpathogenic lifestyle is also evident when comparing the repertoire of lipases, subtilisin-like serine proteases, trypsins, and chitinases in our focal species versus Zoopagomycota and Ascomycota fungi that are not obligate insect pathogens (Fig. 4B). Sordariomycetes within Ascomycota contains the other major transition to insect-pathogenicity within the kingdom Fungi (Araújo and Hughes, 2016). Based on our comparison of gene numbers, Entomophthorales possess more enzymes suitable for cuticle penetration than Sordariomycetes (Fig. 4B). In contrast, insect-pathogenic fungi within Hypocreales possess a more diverse secondary metabolite biosynthesis machinery as evidenced by the absence of polyketide synthase (PKS) and indole pathways in Entomophthorales (Fig. 4C).”

      p15 and 16

      • This all reads as redundant with the previous protein family domain analysis. I would try to merge them.

      Thank you for this comment, however we have opted to maintain the current structure.

      p18

      • In the first sentence, I'm not sure about what was performed here.

      This has been reworded to clarify.

      p20

      • Regarding the assembly, do I understand it correctly that a nuclear genome can be partially haploid / diploid?

      Thanks for your comment. The genome itself is, of course, some integer multiple of n, but based on BUSCO scores our assembly doesn’t appear to have completely collapsed into a haploid genome. We think it makes more sense here to say “partially haploid” than “partially diploid” so have altered this.

      p21

      • RIP has only been observed in a couple of Ascomycetes. RIP-like genomic signatures (GC bias) have been observed elsewhere.

      Hood et al, 2005 found signatures of RIP in anther-smut fungus and Horns et al, 2012, found evidence of hypermutability across repeat elements within several Pucciniales species.

      p23

      • Interesting that the peptidase A2B domain is found uniquely in E. muscae genome and is associated with Ty3 activity. Does the domain often overlap with annotated Ty3 in E. muscae genome? Or how come the domain is not present in other sister species with large genomes full of Ty3 transposons? Could it relate to a new active transposon in E. muscae specifically?

      Thanks for this comment. The domain-based analysis was only performed on the predicted transcriptome of the genome assembly, which does not include the repeat elements (e.g., Ty3). It could be that this peptidase reflects a new active transposon that’s specific to E. muscae, which would certainly be very interesting. We’ve now included this idea in the discussion.

      p26

      • In the case of fungal genomes, I would not advise masking the assembly for repeated sequences prior to gene annotation (in particular given the current focus on protein family variation).

      Thank you for this comment, however we disagree with this assertion as a typical approach for genome annotation in fungi and eukaryotic genomes is to use soft masking of transposable elements before performing gene prediction to avoid over-prediction. While there could be alternative approaches that compare masked or unmasked. This is a recommended protocol for underlying tools like Augustus (10.1002/cpbi.57) and in general descriptions of genome annotation (10.1002/0471250953.bi0401s52). The false positive rate of genes predicted through TE regions is likely to be more a problem than false negatives of missed genes in our experience. Further it seems appropriate to use consistent approach to annotation throughout when including genomes from other sources (e.g., Joint Genome Institute annotated genomes) which also use a repeat masking approach first before annotation. It seems most appropriate to use consistent methods when generating datasets to be used for comparative analyses. It is outside the scope of this project to reannotate all genomes with and without repeat masking.

      p27

      • Interrupted sentence at "Classification of DNA and LTR .. by similarity The".

      This was an unnecessary partial phrase as the information on classification of elements via RepBase was made a few sentences above this.

      p28

      • Enriched/depleted rather than "significantly different"?

      Thank you for this comment, however we have opted to maintain the current phrasing.

    1. Author response:

      Reviewer #2 (Public Review):

      In this study, the authors report that both mice and human patients carrying function-disrupting mutations in the OFD1 gene exhibited ectopic brown adipose tissue formation in the malformed tongue. The OFD1 gene is located on the X-chromosome and encodes a protein product required for the formation and function of the primary cilium, which is required for cells to properly receive and activate several signaling pathways, particularly the hedgehog signaling pathway. Loss of OFD1 function causes prenatal lethality of male fetuses and mosaic disruption of tissues in females due to random inactivation of the X-chromosome carrying either the mutant or wildtype allele. Using cell type-specific gene inactivation and genetic lineage labeling, the manuscript shows that the ectopic brown adipose tissue in the mutant tongue was not derived from cranial neural crest cells (CNCCs). Additional genetic and embryological studies led to the conclusion that loss of Ofd1 function in the CNCC cells in the embryonic hypoglossal cord, via which the tongue myoblast precursor cells migrate from anterior somites to the tongue primordia, caused disruption of cell-cell interactions between the CNCCs and migrating muscle precursor cells, resulting in altered differentiation of those myoblast precursor cells into brown adipocytes. The authors provided data that disruption of Smo in a subset of CNCCs also resulted in ectopic adipose tissue formation in the tongue, indicating that this phenotype in the Ofd1 mutant mice was likely caused by disruption of hedgehog signaling in CNCCs. However, no experimental evidence is provided to support a major conclusion of the manuscript regarding altered differentiation of the tongue myoblast precursor cells into brown adipocytes in the Ofd1 mutant mice. Since it is well established that hedgehog signaling in the CNCCs is required for them to direct tongue myoblast cell migration as well as for tongue muscle differentiation/organization after the myoblasts arrived in the tongue primordia, the finding of tongue muscle defects in the Ofd1 mutant mice is not surprising. However, if proven true that disruption of Ofd1 function in CNCCs caused tongue myoblast precursor cells to alter their fate and differentiate into brown adipocytes, it would be an interesting new finding. Further identification of the signals produced by the Ofd1 mutant CNCCs for directing the cell fate switch will be a highly significant new advance in understanding the cellular and molecular mechanisms regulating tongue morphogenesis.

      Many in vitro and in vivo data have been added as new data. We hope that these are enough for our conclusion. It is extremely difficult to identify the signals produced by the Ofd1 mutant CNCCs for directing the cell fate switch of mesodermal cells after activation of Hh signaling in CNCC. Instead, our new findings raise the possibility that Hh signaling in mesodermal cells is also important for their differentiation as well as Hh signaling in CNCC, which has been added in revised paper. However, we think that it is beyond the scope of this study to deepen these.

      Reviewer #3 (Public Review):

      The authors observed phenotypes of ciliopathy model mice and they seem to coincide with those in human patients. They used mutants in which cilial function genes are deleted in cranial neural crest cells, and found the mutants exhibit abnormal cell differentiation in both neural crest- and mesoderm-lineage cells. The finding clearly shows the importance of tissue/cell interaction. The authors mainly observed the mouse in which Ofd1 gene that is coded on the X chromosome is deleted, therefore, Ofd1fl/WT;Wnt1Cre(HET) mice show that about one-fourth of neural crest cells can exhibit Ofd1 function whereas Ofd1fl;Wnt1Cre (HM) shows null Ofd1 function and show severer phenotypes than HET.

      For ectopic brown adipose tissue in the tongue is derived from mesoderm and the authors tried to show that the hypoglossal cord failed to obtain myogenic lineage after entering branchial arches in HET and HM due to lack of communication with neural crest cells. For ectopic bone formation, they found that it is due to the lack of Hedgehog signaling in neural crest cells, which was consistent with the reports in the Smofl/fl;Wnt1-Cre (Xu et al., 2019) and Ift88fl/fl;Wnt1Cre (Kitamura et al. 2020). The ectopic bone is connected to the original mandibular bone. The authors attribute the ectopic bone formation to the migration of mandibular bone neural crest cells into the tongue-forming area.

      For the poor tongue frenum formation, the authors found the importance of cell migration from the lateral sides of the branchial arch to the midline and its formation relies on non-canonical Wnt signaling. The authors observed similar phenotypes in the human patients as those in the mutants. The adipose tissue in the tongue area is normally found in the salivary gland region and intermuscular space, and it is intriguing to find the brown adipose tissue anterior to the cervical area in which the most anterior brown adipose tissue develops. qRT-PCR indicates that some of the marker genes are expressed in the laser micro-dissected sections of the ectopic brown adipose tissue. However, histology does not show the typical brown adipose tissue feature. In addition, brown adipose tissue is normally recognized in the sixth pharyngeal region as the cervical brown tissue from around E14.5 (Schulz and Tseng 2013), not E12 as the authors observe. Although the mutants develop under abnormal conditions, is it possible to say they are brown adipose tissue? The point has to be further investigated with more marker expression by immunohistochemical detection and other methods. Since the mutants seem to show impaired midline formation (which is consistent with the condition of human ciliopathy), is it possible to hypothesize that the adipose-like tissue is derived from the mesoderm of posterior branchial arch levels if the tissue is brown adipose tissue?

      Immunohistochemistry data has been added as new Figure S4 and S5.

      We agree reviewer’s comment. Histology of ectopic adipose in Ofd1 cKO is slightly different from typical images of brown adipose. Molecular characters of ectopic adipose in Ofd1 mutant tongue are similar to these of low thermogenic adipocyte. Histological features of low thermogenic is known to be different from that of typical brown adipose tissue. Histological features of low thermogenic adipocyte is similar to that of ectopic adipose in Ofd1 mutant mice. This has been mentioned in Results section.

      The cervical brown adipose tissue in Ofd1 mutant should be shrinked or be connected to ectopic adipose in mutant tongue, if ectopic adipose in mutant tongue was derived from the cervical brown adipose tissue due to mis-migration. However, any significant changes of the cervical brown adipose tissue or conection between cervical brown adipose and tongue adipose could not be detected in Ofd1 mutant mice. We think that ectopic adipose in mutant tongue is unlikely derived from cervical brown adipose tissue. These have been added in Result section.

      Cranial neural crest cells start migrating around E8.0 and reach their destination by E9.5. The authors show the lack of neural crest cells in the midline, the fluorescence is absent from the midline in HM, however, they studied it in the E11 mandible (Fig. 4E), almost more than two days after neural crest migration completes. Since the mandibular arch seems to form at the beginning in the mutants, is there a failure in allocating the neural crest and mesoderm at the beginning of the mandibular arch formation?

      It is difficult to prove how much migration is affected in mutant mice. Therefore, sentence describing migration has been deleted in revised paper

      The authors tried to disturb the interaction between the hypoglossal cord and neural crest cells by making incisions in the dorsal area of the branchial arches. That area contains both neural crest and mesoderm but not the hypoglossal cord-derived mesoderm. The hypoglossal cord passed through the posterior edge of the caudal (6th) pharyngeal arch, along the lateral side of the pericardium towards the anterior, ventral to branchial arches, and then inside the 2nd and 1st branchial arches (Adachi et al., 2018). It expresses Pax3 before entering the branchial arches, then Myf5 in the branchial arches. It seems that the migration of the hypoglossal cord does not require interaction with neural crest cells but it has to be confirmed as well as neural crest migration into the branchial arches from the beginning. Although the hypoglossal cord migrates mostly in mesoderm-derived mesenchyme, we cannot exclude the possibility that hypoglossal cord migration is affected.

      Cutting region in original Figure 2Q was not accurate. It has been changed in new Figure 3Q. We agree reviewer’s comment “we cannot exclude the possibility that hypoglossal cord migration is affected”. However, It is difficult to prove how much migration is affected in mutant mice. Therefore, sentence describing migration has been deleted in revised paper

      The lack of Myf5 expression in Ofd1fl;Wnt1Cre (HM) was explained as a failure in the differentiation of the hypoglossal cord into myoblasts on entrance into the branchial arches. Most of the cervical brown adipose tissue is derived from either Myf5- or Pax3- expressing lineage (Sanchez-Gurmaches and Guertin, 2014). Although the authors suggest that brown adipose cells are fate-changed mesoderm in the branchial arches, how do they explain the association with Myf5- or Pax3- expression?

      As reviewer mentioned, the cervical brown adipose tissue is derived from either Myf5- or Pax3- expressing lineage. However, these cells lost Myf5- or Pax3 expression when they differentiate into brawn adipocytes. Although ectopic adipose in Ofd1 mutant tongue showed Pax3 expression at early stage, they likely loose Pax3 expression soon after. There is another possibility that ectopic adipocytes retain Pax3 expression, if they would be abnormal adipocytes. If so, it's not surprised when expression pattern of ectopic adipocytes in Ofd1 mutant is different from these of normal brown adipose tissue. Anything can be possible in these situation. Therefore, we don’t mention anything about these in the text

      In addition, the cervical brown tissue is supposed to be derived from the branchial arch mesoderm (Mo et al., 2017). Is the formation of the cervical brown tissue affected in the Ofd1fl/WT;Wnt1Cre(HET) or Ofd1fl;Wnt1Cre (HM) if dysfunction of neural crest cells results in the cell fate change of mesoderm?

      Any significant morphological changes of the cervical brown adipose tissue could not be detected in Ofd1 mutant mice. Ectopic adipose tissue in Ofd1 cKO was found from E115, while cervical adipose tissue form from E14.5. We think that dysfunction of CNCC at E14.5 does not affect mesodermal cells for the cervical adipose tissue.

      For the tongue frenum development, it is hard to understand to hypothesize that its formation is unlikely to associate with midline formation. Although Lgr5 and Tbx22 are not expressed in the midline, the defect in midline formation could cause unnecessary interaction between the right and left tissues.

      We agree reviewer’s comment. The sentences have been changed in new manuscript.

      Tissue morphogenesis takes place in three dimensions, which were not considered in the data, especially in the labeling experiments. When the authors labelled the cells, which cells in which area were labelled? In the textbook, tongue formation is a result of the fusion of the midline processes derived from the branchial arches, therefore, it is important to identify which cells in which area are labelled.

      Data of Lgr5 and Tbx22 in situ hybridization has been added as new Figure 10-S1D and -S1E, since we labelled cells within Lgr5 and Tbx22 expression domain. Data showing section of explant with DiD injection before and after culture has been added as new Figure 10-S1F and -S1G, which showed DiD labelled cells were located within Lgr5 and Tbx22 expression domain before culture and at tongue frenum region after culture.

      The weakest point is that the authors demonstrate many interesting phenotypes but fail to show the mechanism of altered cell differentiation and direct evidence of the tissue origin of ectopic brown tissue. Without the data, suggestion from the authors' argument is weak, which is reflected in the conclusion of the abstract.

      Many in vitro and in vivo data have been added as new data. We hope that these are enough for our conclusion.

    1. Author response:

      Reviewer #2 (Public Review):

      (1) Some changes to statistical analyses are needed in this study.

      Fig. 1B, 1D, 2A, 3E, and 3F report the QL.d phenotype as a percentage of animals scored that were defective in migration. The methods make it clear this data is categorical rather than quantitative. Therefore, a t-test or any test designed for quantitative data is not appropriate. I suggest that the authors should investigate using a chi-squared or Fisher's exact test.

      For the reasons mentioned above, the calculation of standard deviation (as shown in error bars) is also not appropriate for Fig. 1B, 1D, 2A, 3E, and 3F. Of course, it is excellent that the authors scored multiple trials. For experiments with mutants, I suggest the authors might combine these trials or show separate results of each trial. For experiments using RNAi (Fig. 1B), each trial should be plotted separately because RNAi effectiveness can vary. If there is not enough space to show multiple trials, then I would ask that a representative trial be shown in the main figure and additional trials in a supplement.

      We thank the reviewer for pointing out the statistical mistake. For all figures assessing the QL.d migration phenotype (Fig.1B, 1D, 2A, 4A (former 3E), 4D (former 3F) and Fig.1 – figure supplement 1, Fig.2 – figure supplement 1, Fig.4 – figure supplement 2) the statistical significance was evaluated using Fisher’s exact test. For RNAi experiments (Fig. 1B) results from a representative experiment is shown and two additional trials are shown in Figure 1 – figure supplement 1. For experiments with mutants, results from separate trials were pooled and are presented in the main figures.

      In Fig. 1, 2, 3, and 5, it is not specified whether/how p-values were adjusted for multiple tests.

      We have applied Bonferroni correction for multiple testing in all Figures where it was relevant (Fig. 1, 2, 4, 5 and 6 and in their supplements) and this is now stated in all corresponding Figure legends.

      (2) I felt the author's interpretation of the sel-5 mutant phenotypes in EXC, and the genetic interactions with Wnt signaling mutants, might be improved. The authors show convincing data that the sel-5 mutants display a shortened EXC outgrowth phenotype. Conversely, mutants with reduced Wnt signaling, such as the lin-17 or lin-44 mutants, displayed lengthened EXC outgrowth. The authors show that in double mutants, loss of sel-5 partially suppressed the EXC overgrowth defects of lin-17 or lin-44 mutants (Fig. 5). In my opinion, this data is consistent with a model where SEL-5 acts to inhibit Wnt signaling in EXC. An inhibitory role in a Wnt-receiving cell would be consistent with the known activity for human AAK1 in promoting negative feedback and endocytosis of LPR6. Interestingly, the authors mention in their discussion that a mutant of plr-1, which acts in the internalization of Frizzled receptors, has a shortened EXC phenotype similar to that of sel-5 mutants. These observations all seem consistent with an inhibitory role, yet the authors do not state this as their conclusion. A clarification of their interpretation is needed.

      We thank the reviewer for this feedback. Indeed, the above interpretation of the excretory cell migration data is plausible, however, we think that several lines of evidence argue against this possibility. First, measurements of the posterior canal length during L1/L2 larval stages show that LIN-44/LIN-17 signalling is not required for the early stages of excretory canal outgrowth, unlike SEL-5/VPS-29 (Fig. 5E, 6D). This suggests that SEL-5 and VPS-29 are required earlier than LIN-44 and LIN-17 and therefore should not act at the level of Wnt receptor internalization. Our new data with more mutant combinations revealed canal shortening in cwn-1; cfz-2 and cwn-2; cfz-2 mutants. This would rather suggest a positive role for SEL-5 and VPS-29 in Wnt pathway regulation. Either SEL-5/VPS-29 employ two different mechanisms of Wnt pathway regulation or alternatively, act prior to any Wnt-dependent step in the excretory canal outgrowth. The observed partial rescue of the lin-17 or lin-44 overgrowth defect by sel-5 could then be explained for example by a reduced speed of canal outgrowth in sel-5 mutants. Based on new findings about CWN-1, CWN-2 and CFZ-2 involvement we have also modified our model now presented in Fig.7.

      For changes to the Results section, see Response to Reviewer 1, point 4b. The Discussion part has been substantially rewritten and is presented below:

      LINE 428 “Our analysis of single Wnt and Frizzled mutants revealed that while loss of cwn-2 or cfz-2 expression resulted in a very mild shortening of the excretory canal, loss of lin-44 or lin-17 led to profound canal overgrowth (summarized in Fig. 7A). These findings suggested that two independent Wnt pathways could be employed to establish proper excretory canal length – one promoting canal extension and one generating the stop signal for growth termination. Further analyses of double mutants and other Wnt signalling components revealed that the extension-promoting pathway includes cwn-1 in addition to cwn-2 and cfz-2, while the stop-signal pathway encompasses lin-44, lin-17, dsh-1, mig-5 and mig-14. A similar repulsive role of LIN-44/LIN-17 complex has been described in the case of a posterior axon of C. elegans GABAergic DD6 motor neuron (Maro et al., 2009) or PLM, ALN and PLN neurons (Zheng et al., 2015). Loss of lin-44 or lin-17 expression promoted outgrowth of the posterior neurites of these neurons implicating that in wild type animals, LIN-44 serves as a repulsive cue. On the other hand, cwn-2 and cfz-2 were shown to positively regulate the posterior neurite outgrowth of RMED/V neurons with cwn-2 acting as an attractive cue (Song et al., 2010). The role of two other Wnt signalling components, egl-20 and mig-1, is less clear. No effect (mig-1) or only very mild overgrowth defect (egl-20) is observed in single mutants. However, both egl-20 and mig-1 significantly rescue the overgrowth phenotype of lin-17 mutants, while at the same time, mig-1 can suppress the shortening of canals in cfz-2 mutants. EGL-20-producing cells are localized around the rectum (Whangbo et al., 1999; Harterink et al., 2011), exactly where the excretory canals stop, while LIN-44 is expressed more posteriorly (Herman et al., 1995; Harterink et al., 2011). A possible explanation could thus be that while LIN-44 provides a general posterior repulsive signal, EGL-20 fine-tunes the exact stopping position of the growing canal. The role of different Wnts and Frizzleds in excretory canal outgrowth is summarized in Fig. 7B. Further investigation will be required to decipher the exact way how SEL-5 and the retromer crosstalk with Wnt signalling during excretory cell outgrowth. It is clear though that more than one mechanism is likely involved. First, sel-5 vps-29 mutants display canal shortening similarly to cwn-1; cfz-2 or cwn-2; cfz-2 suggesting a positive regulatory role. Mutants in lin-17 and lin-44 display canal overgrowth, yet sel-5 is partially able to suppress this phenotype. This would imply a negative regulatory role of sel-5 and be in agreement with the role of AAK1 in Wnt pathway regulation (Agajanian et al., 2019). However, sel-5 and vps-29 are required already during the initial larval outgrowth while the LIN-44/LIN-17 signal is required later. The observed rescue might thus also be explained by a delayed growth of the canal and not by a direct impact of sel-5 and vps-29 on LIN-44 or LIN-17 levels or localization.”

    1. Author response:

      Reviewer #2 (Public Review):

      The manuscript entitled " Multimodal HLA-I genotypes regulation by human cytomegalovirus US10 and resulting surface patterning" by Gerke et al describes the biochemical analysis of US10-mediated down regulation of HLA-I molecules. The authors systemically examine the surface expression of different HLA-I alleles in cells expressing US10 and interactions of US10 with HLA-I and antigen presentation machinery. Further, studies examined genotypic and allotypic differences during expression of US10/US11 transcripts suggest a different allelic class I downregulation. In general, the authors have included data supporting the major claims. Yet, the conclusions and findings of the study only marginally advance the overall understanding of HCMV viral evasion and the mechanism of US10 function.

      Strengths:

      The studies are well characterized and the studies utilize diverse HLA-I and HCMV viral molecules. The biochemistry is excellent and is of high quality. Importantly, the study describes HLA-I allelic specific HCMV down regulation at the cell surface and molecular levels.

      Weaknesses:

      (1) The authors use over expressive language such as "strong binding" that does not have a quantitative value and it is relative to the specific assay with only small differences among the factors.

      We have changed the language to avoid non-quantitative expressions.

      (2) The US10 binding to the HLA-I did not correlate with class I surface levels suggesting that binding to the APC machinery (Figure 1); hence, why does the binding of US10 to the APC define its mechanism of action.

      We hypothesized that since binding to HLA-I allomorphs did not correlate with surface expression, further factors could be involved in regulation. Since the PLC (APC machinery) plays a major role for HLA-I expression, it was relevant to investigate this. The new data underlines the importance of the PLC for US10-mediated HLA-I regulation.

      (3) The innovative and significant aspects of the study are limited. The study does not delineate the US10 mechanism of action or show data in which US10-mediated MHC class I down regulation impacts adaptive or innate immune function.

      These remarks are important. We want to emphasize the variable impact of US10 on HLA-I. To our knowledge previous studies have not uncovered genotype-dependent effects on HLA-I as distinct as those observed with US10, indicating that US10 may exploit aspects of HLA-I that are yet to be fully elucidated. Therefore, confirming these findings is crucial for our study. The quantitative analysis of the HeLa HLA-I ligandome in US10-expressing cells strongly supports this conclusion. The precise quantification of HLA-I peptide ligands was made possible through collaboration with Dr. Andreas Schlosser from Würzburg, Germany, who possesses profound expertise in this specific method. Thus, in our opinion, this revision has enabled us to advance innovation and, importantly, enhance the significance of our study.

    1. Author response:

      Reviewer #3 (Public Review):

      Software UX design is not a trivial task and a point-and-click interface may become difficult to use or misleading when such design is not very well crafted. While Phantasus is a laudable effort to bring some of the out-of-the box transcriptomics workflows closer to the broader community of point-and-click users, there are a number of shortcomings that the authors may want to consider improving.

      Thank you for such an in-depth review. We really appreciate this feedback and have tried to address all of the concerns in the new version of Phantasus.

      Here I list the ones I found running Phantasus locally through the available Bioconductor package:

      (1) The feature of loading in one click one of the thousands of available GEO datasets is great. However, one important use of any such interfaces is the possibility for the users to analyze his/her own data. One of the standard formats for storing tables of RNA-seq counts are CSV files. However, if we try to upload from the computer a CSV file with expression data, such as the counts stored in the file GSE120660_PCamerge_hg38.csv.gz from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120660, a first problem is that the system does not recognize that the CSV file is compressed. A second problem is that it does not recognize that values are separated by commas, the very original CSV format, giving a cryptic error "columnVector is undefined". If we transform the CSV format into tab-separated values (TSV) format, then it works, but this constitutes already a first barrier for the target user of Phantasus.

      Thank you for highlighting this issue of file formats support. We acknowledge the commonality of CSV and CSV.gz files in gene expression analysis. As a response, we have updated our data loading procedure to support these file formats. Moreover, the most recent version of our web application is able to recognize gzip-archived file in any of supported table formats: GCT, TSV, CSV and XLSX.

      (2) Many RNA-seq processing pipelines use Ensembl annotations, which for the purpose of downstream interpretation of the analysis, need to be translated into HUGO gene symbols. When I try to annotate the rows to translate the Ensembl gene identifiers, I get the error

      "There is no AnnotationDB on server. Ask administrator to put AnnotationDB sqlite databases in cacheDir/annotationdb folder"

      Thank you for revealing this issue. Indeed, locally installed instances of the Phantatus might lose some functionality in absence of some auxiliary files. For example, gene annotation mapping is unavailable without annotation databases. Previously, the user had to perform additional setup steps to unlock a few features, which might be confusing and unclear. In order to overcome this we have revised significantly the installation procedure. Newly added ‘setupPhantasus’ function is able to create all necessary configuration files and provides an interactive dialog with the user that helps to load all necessary data files from our official cache mirror (https://alserglab.wsutl.edu/files/phantasus/minimal-cache/). Docker-based installation follows the same approach, however it is configured to install everything by default. Thus, with help of the new installation procedure locally installed Phantasus now has the whole functionality available at the official mirrors. The comprehensive installation description is now available at https://ctlab.github.io/phantasus-doc/installation.

      (3) When trying to normalize the RNA-seq counts, there are no standard options such as within-library (RPKM, FPKM) or between-library (TMM) normalization procedures.

      Appreciating your feedback, we've expanded the available normalization options in the updated version of Phantasus. We added support for TMM normalization as suggested by the edgeR package and voom normalization from the limma package. However, certain strategies like RPKM/FPKM or TPM rely on gene-specific effective lengths, which are challenging to infer without protocol and alignment details. As Phantasus operates on gene expression matrices and doesn't execute alignment steps, the implementation of these normalization seems infeasible. On the other hand, if the user has the matrix with FPKM or TPM gene values (for example from a core facility), such a matrix can be loaded into Phantasus and used for the analysis.

      If I take log2(1+x) a new tab is created with the normalized data, but it's not easy to realize what happened because the tab has the same name as the previous one and while the colors of the heatmap changed to reflect the new scale of the data, this is quite subtle. This may cause that an unexperienced user to apply the same normalization step again on the normalized data. Ideally, the interface should lead the user through a pipeline, reducing unnecessary degrees of freedom associated with each step.

      Thank you for your comment. Indeed our approach to create a new tab for each alteration to the expression values preserving the name might be the source of confusion for a user. On the other hand, generating informative tab names without overwhelming users with too much detail is also challenging. As a compromise we have an option for the user to manually rename the tab. Still, we agree that this remains an area for improvement. We also consider it to be a part of a larger issue: for example, the loaded data can already be log-scaled, so that even one round of log-scale transformation in Phantasus would be incorrect. Accordingly, we are exploring ways to address this issue in the future by adding automated checks for the tools or, as you suggested, implementing stricter pipelines.

      (4.4) Phantasus allows one to filter out lowly-expressed genes by averaging expression of genes across samples and discarding/selecting genes using some cutoff value on that average. This strategy is fine, but to make an informed decision on that cutoff it would be useful to see a density plot of those averages that would allow one to identify the modes of low and high expression and decide the cutoff value that separates them.

      Thank you for the suggestion. Indeed a density plot might help users to make informed decisions during gene filtration. We have added such a plot into the ‘Plot/Chart’ tool as a ‘histogram’ chart type.

      It would be also nice to have an interface to the filterByExpr() function from the edgeR package, which provides more control on how to filter out lowly-expressed genes.

      Thank you for proposing the inclusion of an interface for the filterByExpr() function from the edgeR package. In the recent update we have incorporated filterByExpr() as part of the voom normalization tool. For now, for simplicity, we have decided to keep only the default parameter values. However, we will explore the addition of the dedicated filtering tool in the future.

      (5) When attempting a differential expression (DE) analysis, a popup window appears saying:

      "Your dataset is filtered. Limma will apply to unfiltered dataset. Consider using New Heat Map tool."

      One of the main purposes of filtering lowly-expressed genes is mainly to conduct a DE analysis afterwards, so it does not make sense that the tool says that such an analysis will be done on the unfiltered dataset. The reference to the "New Heat Map tool" is vague and unclear where should the user look for that other tool, without any further information or link.

      Thank you for highlighting this issue. We agree that the message in the popup window and the default action were confusing. In response to your feedback, we've updated the default behavior of our DE tools to automatically use the filtered data in a new tab. Additionally, we've clarified the warning message to ensure a better understanding of this process.

      (6) The DE analysis only allows for a two-sample group comparison, which is an important limitation in the question we may want to address. The construction of more complex designs could be graphically aided by using the ExploreModelMatrix Bioconductor package (Soneson et al, F1000Research, 2020).

      Indeed, the ability to create complex designs and various comparisons is important for many applications for gene expression analysis. Accordingly, in the latest Phantasus version, we've introduced an advanced design feature for the DE analysis, enabling the utilization of multiple column annotations for the design matrix. Combined with the existing ability to create new annotations, this update facilitates the setup of diverse design matrices. While at the moment we do not allow setting a complex contrast, we hope that the current interface will cover most of the differential expression use cases.

      (7) When trying to perform a pathway analysis with FGSEA, I get the following error:

      "Couldn't load FGSEA meta information. Please try again in a moment. Error: cannot open the connection In call: file(file, "rt")

      We hope that this issue should be resolved after we have implemented a more streamlined setup process. Among others, the new approach aims to eliminate the unexpected absence of metafiles in local installations. The latest Phantasus package version explicitly prompts the user to load necessary additional files automatically during the initial run, reducing options for an invalid setup.

      Finally, there have been already some efforts to approach R and Bioconductor transcriptomics pipelines to point-and-click users, such as iSEE (Rue-Albrecht et al, 2018) and GeneTonic (Marini et al, 2021) but they are not compared or at least cited in the present work.

      Indeed, our comparison was focused toward tools that offer non-programmatic functionalities for gene expression data analysis. While tools like iSEE and GeneTonic are adept at visualizing data and hold their own in providing extensive abilities, they do necessitate additional data preparation using R, distinguishing them from the specific scope of tools we assessed.

      One nice features of these two tools that I missed in Phantasus is the possibility of generating the R code that produces the analysis performed through the interface. This is important to provide a way to ensure the reproducibility of the analyses performed.

      The ability to generate R code within tools like these indeed aids in ensuring analysis reproducibility. Moreover, we have previously attempted implementing this functionality in Phantasus, however it proved to be hard to do in a useful fashion due to potential complex interactions between user and the client-side part of Phantasus. Nevertheless, we acknowledge the significance of such a feature and aim to introduce it in the future.

    1. Author response:

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

      We thank the reviewers for a careful review of the manuscript and for their comments, which we address below.

      Reviewer #1:

      (1) …the authors could examine division in a population of cells with only one centrosome. Seeing some restoration of mitotic progression in the absence of SAC-dependent delays would suggest that even one centrosome with uninhibited Eg5 is sufficient to negate SAC-dependent delays, and would limit models for what exactly centrosomes contribute.

      We agree that the one-centrosome question (i.e. whether cells with a single centriole, and therefore a single centrosome, have the same SAC dependence) would be interesting to address. It is known that cells with a single centriole generated through centrinone treatment also have elongated mitoses, like cells lacking centrioles (see Chinen, et. al. 2021, compare Fig 2C to Fig 2D), We have tried this experiment in RPE-1 cells with preliminary results confirming that there is a mitotic delay. It is not known whether this delay requires SAC activity, and we hope to address that in future work. In addition, we note that we show in Fig. 4b-c that cells with the normal centrosome number but with a single focus of microtubules due to Eg5 inhibition, were also sensitive to MPS1 inhibition. This suggests that centrosome presence alone cannot overcome the requirement for SAC activity, rather, the centrosomes need to be able to separate in a timely fashion.

      Reviewer #2:

      (1) An example is how to interpret the effect of Aurora B inhibition, which does not block acentrosomal cell division. If Aurora B is required for SAC activity, it suggests this effect of MPS1 may be a function other than SAC. Given the complexity of the SAC, it would be informative to test other SAC components. Instead, the authors conclude that the mitotic delay caused by MPS is required for acentrosomal cell division. I don't think they have ruled out, or even addressed other functions of MPS1.

      We agree that it is possible that functions of the MPS1 kinase other than those involved in the SAC could be important. Although we have not directly tested other SAC components, we did “mimic” SAC activity by delaying anaphase onset using APC/C inhibition while also inhibiting MPS1 (Fig. 2b-b’’). The fact that this restored division suggests that it is the SAC function of MPS1 kinase activity that is relevant to this delay. 

      (2) The authors find that when both the APC and MPS1 are inhibited, the cells eventually divide. These results are intriguing, but hard to interpret. The authors suggest that the failure to divide in MPS1-inhibited cells is because they enter anaphase, and then must back out. This is hard to understand and there is not data supporting some kind of aborted anaphase. Is the division observed with double inhibition some sort of bypass of the block caused by MPS1 inhibition alone? It is not clear why inhibition of APC causes increased cell division when MPS1 is inhibited.

      As described in the response to 1), we believe that reinstating the delay to anaphase onset by APC/C inhibition provided the time needed to establish a functional bipolar spindle even in the absence of the SAC, and that cells eventually overcome the proTAME block and proceed through mitosis, as observed in control cells in our experiments. We note that we chose concentrations of proTAME specifically for each cell line (RPE-1 and U2OS) that would result only in a temporary block, following on the work of Lara-Gonzalez and Taylor (2012), who reported similar findings for HeLa cells.

      (3) The authors characterize MTOC formation in these cells, which is also interesting. MTOCs are established after NEB in acentrosomal cells. Indeed, forming these MTOCs is probably a key mechanism for how these cells complete a division, like mouse oocytes.

      We agree that the observed intermediates of MTOCs are interesting and likely crucial to the mechanism of cell division in acentrosomal somatic cells. We are investigating further the differences and similarities between somatic cell MTOC formation in the absence of centrosomes and the naturally-occurring form of that process in oocytes.

    1. Author response:

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

      We would like to thank the editors and reviewers for providing feedback and suggestions for our manuscript.

      In response to reviewers comments we changed several main Figures and added new tables and supplementary figures. We also made edits to the Discussion.

      Reviewer #1 (Public Review):

      Weaknesses:

      Limited data is shown on the let-7afdLOF mice. Does this mouse respond similarly to nCB as the let-7bc2LOF.

      In the revised manuscript, we have added a baseline lung phenotypic assessment for the let-7afdLOF mice up to 6-months of age within Figure 4-figure supplement 1. The data supports our original statement and observation that let-7afdLOF mice do not exhibit lung pathology, inflammation, or changes in T cell subsets at baseline. Our view is that current manuscript addresses the importance of let-7bc2-cluster in experimental emphysema and the let-7afd-cluster mice is used to validate Rorc as a direct target of let-7. In the future, new grant funding will make it possible to ascertain whether absence of the let-7afd-cluster also sensitizes mice to experimentally induced emphysema.

      Because the authors validate their findings from a previously published RNA-seq dataset in subjects with and without emphysema, the authors should include patient demographics from the data presented in Figure 1C-D.

      We thank the reviewers for their recommendation. In address of this, the revised manuscript contains a new Supplementary Table 1 with the human subject demographic information that corresponds with Figure 1D.

      To validate their mouse models, the absence of Let-7 or enhanced Let-7 expression needs to be shown in isolated T cells from exposed mice.

      In the case of let-7bc2-cluster, we have included Figure 2-figure supplement 2 which shows pri-let7bc2 expression assessed by qPCR from selected CD8+ lung T cells of control and let-7bc2LOF mice exposed to PBS vehicle or nCB. The let-7g GOF model used in our studies has been validated for the induction of let-7g in thymic and peripheral T cells and elicitation of gain-of-function phenotypes (Pobezinskaya et al. 2019; Angelou et al. 2020; Wells et al. 2023).

      In Figure 3, the authors are missing the unexposed let-7bc2LOF group from all panels.

      We emphasize that our exhaustive characterization of control and let-7bc2LOF mice in absence of challenge showed no phenotype. The baseline data was collectively shown in Figure 2-figure supplement 1.

      Why did the authors choose to overexpress Let-7g, the rational is not clear?

      We concur that ideal GOF experiments can be carried out with let-7b or let-7c. Unfortunately, let-7b/c2 transgenic mice are not currently available, so we elected to use the well characterized let-7g T cell GOF mouse model (Pobezinskaya et al. 2019; Angelou et al. 2020; Wells et al. 2023). Furthermore, it is worth noting that the binding/seed sequence of let-7g is identical to let-7a/b/c and other members. Nonetheless, we have edited our Discussion section to reflect this as a potential caveat that can confound the utilization of this let-7GOF mouse model.

      The purity of the CD4+ and CD8+ T cells is not shown and the full gating strategy should be included.

      In the revision, we included the flow gating strategy and display the representative population with purities in Supplementary Figure 1 of the revised manuscript.

      Reviewer #2 (Public Review):

      Weaknesses:

      The functional analyses are unusually focused on IL-17 producing CD8 T cells, but it is not made clear whether these cells are an important player in emphysema pathogenesis in the nCB and CS models. The data shown reveal that they are far less numerous than IL-17-producing CD4 T cells. It is also notable that the Figure 1 expression data from human subjects used sorted CD4+ T cells. And as the author mentioned, prior work on let-7 showed that it regulated Th17 (CD4) responses.

      As we showed that the let-7bc2LOF had enhanced the Tc17 cell population without any significant impact on Th17 cells, we elected to focus our analysis on this population. Furthermore, the connection of let-7 with the generation of a Tc17 inflammatory response is a novel finding, which so far remained unappreciated in the field and instigates new lines of inquiry.

      Compared with Let7bc2 deletion, Let7afd deletion had a much larger effect on IL17 production by CD8 T cells in vitro, and it also had a larger effect on RORgt expression in untreated mice in vivo, especially in the lung. It would be valuable to more thoroughly characterize the let7afd mice. RORgt expression should be shown in the in vitro assays. In the results, the authors state that let7afdLOF mice "did not exhibit lung histopathology nor inflammatory changes" up to 6 months of age. Similarly, it is stated in the conclusion that "the let-7afdLOF mice ... did not exhibit changes in Tc17/Th17 subpopulations" in vivo. All these data should be shown, and if no baseline changes are apparent, then I also recommend challenging these mice with nCB and/or cigarette smoke.

      We concur that additional phenotypic characterization on the let-7afdLOF mice will contribute valuable information in the future. Reviewer 1 had a similar comment. As described above in response to Reviewer 1, we added comprehensive phenotypic analysis of let-7afdLOF mice within Figure 4-figure supplement 1 in the revised manuscript. The new data indicates that there is no overt lung pathology in the let-7afdLOF mice despite the subtle induction of RORγt expression in T cells. Furthermore, we have now included flow cytometric analysis of RORγt expression from in vitro polarized Tc0 and Tc17 cells from let-7afdLOF mice within revised Figure 5H.

      This brings up the larger issue of redundancy among the let-7 family members and genomic clusters. This should be discussed, including some explanation of the relative expression of each mature family member in T cells, and how that maps to the clusters studied here (and those that were not investigated). It would also be helpful to explain the relationship between mouse Let7bc2 and human Let7a3b, since Let7bc2 is the primary focus of emphysema experiments in this manuscript. This is especially important because the study of individual let-7 clusters is the core novelty of this body of work, as described in the first paragraph of the discussion. The regulation of let-7 expression has been reported before and its functional role has been investigated with a variety of tools.

      We appreciate the interest and suggestion to expand the discussion on the let-7 family and their expression regulation. To address these points, we included additional references and expanded the Discussion section of the revised manuscript.

      Let7g overexpression caused a marked reduction in Rorgt expression in T cells at baseline and in the setting of nCB challenge, and it reduced the frequency of IL17+ producing CD8 T cells in the lung to baseline levels. Yet there was no change in the MLI measurement of histopathology. Is this a robust result? The responses in the experiment shown in Fig. 6C-D are quite muted compared to those shown in Figure 2. The latter also shows a larger number of replicates, and it is unclear whether the data in 6D include measurement from all of the mice tested (e.g. pooled from 2 small experiments) or only mice from one experiment.

      We appreciate the reviewer inquiry into the data presented in Figure 6C-D. The data is representative of a single experiment and the number of experiments has been added to the revised Figure 6 legend. We note that all let-7GOF and associated control mice in Figure 6 are exposed to doxycycline as part of the let7g induction model, whereas mice in Figure 2 are not. It has been previously reported that doxycycline, a member of the tetracycline family of molecules, has anti-inflammatory properties (Di Caprio et al. 2015), which we speculate could account for the differences in the magnitude of emphysemic response.

      Reviewer #3 (Public Review):

      Weaknesses:

      The authors show no change in frequencies of Treg cells in let-7bc2LOF mice exposed to nCB. Do these Treg cells also express higher levels of RORgt and IL-17? The major question that was not addressed in this study is how let-7 expression is regulated in emphysema. The other recommendation is that the authors include the sequences of the let-7 mimic oligos used in the luciferase assay.

      We did not have the opportunity to address whether RORγt is in fact also upregulated in Treg cells. It remains unclear what upstream mechanisms drive the downregulation of the let-7 clusters in T cells with exposure to smoke/nCB. However, we agree that this an important question and we therefore updated the Discussion section of manuscript by including several citations that could explain how let-7 clusters become repressed in a coordinated fashion. Regarding the last point, the sequence of the duplex used in luciferase assay corresponds to the canonical mature let-7b in NCBI and has been added to Supplementary Table 3.

      Reviewer #2 (Recommendations For The Authors):

      The authors state that "Recent evidence suggests the let-7 family is downregulated in patients with COPD, however, how they cause emphysema remains unclear." This should be reworded. Its downregulation in disease does not necessarily indicate that let-7 causes emphysema. Also, recommend rewording "Overall, our findings shed light on the let-7/RORγt axis as a braking and driving regulatory circuit in the generation of Tc17 cells..." What does it mean to be a "braking and driving" circuit? These terms seem contradictory.

      We recognize that the sentences were not phrased clearly. We have rephrased these statements as “Recent evidence suggests the let-7 miRNA family is downregulated in patients with COPD, however, whether this repression conveys a functional consequence in emphysema pathology has not been elucidated.” and “Overall, our findings shed light on the let-7/RORγt axis with let-7 acting as a molecular brake in the generation of Tc17 cells…”

      Experimental details are needed for the human miRNA expression studies. Too little information is provided in the methods section, and the article cited there (Yuan et al 2020) is not listed in the bibliography.

      We expanded the Materials and Methods section for the collection, isolation, and qPCR analysis of human subject lung T cells. We have corrected the bibliography and added the missing citation.

      The claim of novelty for miRNA-mediated silencing of Rorc in the discussion section is unnecessary and incorrect (https://pubmed.ncbi.nlm.nih.gov/23359619).

      Thank you for bringing the publication to our attention. Close inspection of this publication indicates that the authors did not experimentally validate Rorc as a direct target of let-7 itself. Plus the work was limited to immortalized in vitro cell cultures. We amended the sentence in the Discussion section highlighting the novelty of our findings which is the demonstration of Rorc as an in vivo target of let-7 in T cells.

      Citations

      Angelou, Constance C., Alexandria C. Wells, Jyothi Vijayaraghavan, Carey E. Dougan, Rebecca Lawlor, Elizabeth Iverson, Vanja Lazarevic, et al. 2020. “Differentiation of Pathogenic Th17 Cells Is Negatively Regulated by Let-7 MicroRNAs in a Mouse Model of Multiple Sclerosis.” Frontiers in Immunology 10: 3125. https://doi.org/10.3389/fimmu.2019.03125.

      Di Caprio, Roberta, Serena Lembo, Luisa Di Costanzo, Anna Balato, and Giuseppe Monfrecola. 2015. “Anti-Inflammatory Properties of Low and High Doxycycline Doses: An in Vitro Study.” Mediators of Inflammation 2015: 329418. https://doi.org/10.1155/2015/329418.

      Pobezinskaya, Elena L., Alexandria C. Wells, Constance C. Angelou, Eric Fagerberg, Esengul Aral, Elizabeth Iverson, Motoko Y. Kimura, and Leonid A. Pobezinsky. 2019. “Survival of Naïve T Cells Requires the Expression of Let-7 miRNAs.” Frontiers in Immunology 10 (May). https://doi.org/10.3389/fimmu.2019.00955.

      Wells, Alexandria C., Kaito A. Hioki, Constance C. Angelou, Adam C. Lynch, Xueting Liang, Daniel J. Ryan, Iris Thesmar, et al. 2023. “Let-7 Enhances Murine Anti-Tumor CD8 T Cell Responses by Promoting Memory and Antagonizing Terminal Differentiation.” Nature Communications 14 (1): 5585. https://doi.org/10.1038/s41467-023-40959-7.

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      (1) The description of the wing phenotype that results from combinations of wingless and delex alleles at the bottom of page 4 (figure 1) is quite confusing. Are the trans-hets suppressed to wt or enhanced? The images in the Fig look enhanced.

      We thank the reviewer for this thoughtful observation regarding the wing phenotype description in combination with wg and dx alleles. We understand the confusion and appreciate the opportunity to clarify.

      In response to the concern raised, the trans-heterozygous indeed enhanced rather than suppressed to wild type. We acknowledge that the description would have been clearer. We have revised the relevant section to explicitly state that trans-heterozygous exhibit an enhanced wing phenotype in the updated version of the manuscript.

      (2) Use of Cut as a Wg readout in Fig1 is problematic since it is also a Notch target. Perhaps a more direct measure of Arm activity would be a better choice here, e.g., naked-lacZ.

      We appreciate the reviewer’s insightful comment regarding the use of Cut as a Wg readout. The point about being Cut as a Notch target raises a valid concern. To address this issue and provide a more direct measurement of Arm activity, we agree that incorporating a specific Arm readout, such as naked lacZ, would be a more suitable choice.

      We will incorporate this valuable feedback into our future research endeavors to augment the comprehensiveness of our study.

      (3) The dx allele effects on Sens and Vg in Fig 2C appear greater at two points along the DV margin (arrows). Do these match the expression pattern of dx mRNA?

      We thank the reviewer for this thoughtful observation. We understand that the effect of the dx LOF allele on Sens and Vg seems more pronounced at two specific points along the D/V margin. As far as our understanding Dx shows a homogeneous expression pattern throughout the Wg disc which has been reported earlier (Busseau et al., 1994., Mukherjee et al., 2005).

      (4) It really looks to my eye that dx loss lowers Wg expression in source cells in Fig 2. To confirm the model that Dx controls the spread of Wg protein, it would be ideal to rule out txnal effects with a wg-lacZ reporter.

      We appreciate the reviewer for raising this important point. In the revised version of the manuscript, we have introduced Wg-lacZ staining for both Wg-lacZ/+ and dx152/Y; Wg-lacZ/+ combination in Figure 2. This additional information eliminates the possibility of Deltex influencing Wg transcriptional regulation in source cells, thus reinforcing our hypothesis that the reduction of Deltex leads to a decline in Wg protein levels in the source cells, given Dx essential role in wingless gradient formation.

      (5) The drop in DV Wg and expansion of Vg domain in dx mutants seem paradoxical but could be explained by accelerated Wg spread and uptake. This could be tested by depleting the dally-like glypican that promotes long-range Wg diffusion in dx mutants, and seeing if this restores Wg levels at the DV margin.

      This is indeed a very thoughtful comment and we thank the reviewer for this insightful suggestion for further exploration. We believe that depleting dally-like glypican in dx mutants could possibly restore Wg levels at the DV margin.

      We recognize the importance of this experiment in providing a more comprehensive understanding of the underlying mechanisms, and we will give major emphasis on incorporating this suggestion in our future research.

      (6) The authors describe the effect of Dx over-expression as "reducing" the Wg gradient when they actually mean "flattening". Please be careful with this word choice as they mean different things.

      We thank the reviewer for the insightful feedback. The suggested modifications have been incorporated into the revised version of the manuscript.

      (7) The combined effects of Rab5dn and Dx o/e on Wg protein loc/levels are interesting but need to be followed up by testing whether the endogenous Dx/Rab5 show genetic interactions in control of Wg protein levels/localization.

      We acknowledge the reviewer's comment and in addressing it, we wish to highlight that the over-expression of Dx with endogenous Rab5 or Rab7 does not affect Wg protein levels or localization. We have mentioned the supporting data for this control in Figure 5(G, H).

      (8) The ability of MG132 to restore Arm levels in en-Dx discs is very promising. However, MG132 will also block Arm degradation by the Slmb-APC destruction complex, so this result could be non-specific. Tests of whether Dx drives poly-ub of Arm, and how much Dx is redundant to Slmb in this role, would be needed to solidify the authors' conclusion.

      We thank the reviewer for this insightful comment. We understand that the concern about MG132 blocking Arm degradation by Slmb-APC destruction complex adds an important layer of complexity to the interpretation of the results. We agree with the reviewer's comment that conducting these experiments will indeed offer valuable insight into the specificity of MG132 effects and further strengthen our conclusion.

      We are interested to see how future experiments addressing the points raised by the reviewer will shape our understanding of the intricate mechanisms involved in Wg signaling and Arm/-catenin degradation. Once again, we thank the reviewer for the thoughtful engagement with the research, and the comments will undoubtedly stimulate further investigation and discussion in this area.

      Reviewer #2 (Recommendations For The Authors):

      The work really needs more experiments to further provide a mechanistic understanding and distinguish between direct and indirect action (via Notch signaling) on Wingless, but instead switches in the second half to a second interaction with β-catenin, leaving the conclusions of the first part hanging. More mechanistic information on the cell biology of how Deltex might affect wingless endocytic trafficking directly would be beneficial, for example involving some cell culture experiments where the action of deltex on Notch and wingless could be more clearly separated and a more detailed study of the consequences on wingless trafficking could be explored.

      Wingless is secreted into an extracellular compartment and so won't be accessible for a direct interaction with cytoplasmic deltex. Therefore are the authors proposing Deltex interacts with a membrane-bound wingless receptor such as frizzled in order to mediate its effects? These avenues could be explored further experimentally to derive a more mechanistic conclusion.

      The colocalisation images are not high resolution and colocalisation is not quantified, and no differences ( +/- Deltex) in wingless subcellular localisation, which would aid mechanistic interpretation, are shown.

      We thank the reviewer for the insightful feedback on our work. We appreciate the suggestion for more experiments to provide a mechanistic understanding and to distinguish between direct and indirect actions of Notch on Wingless signaling. We acknowledge the importance of clarifying these aspects and agree that further experiments could help separate the effects of Deltex on Notch and Wingless signaling, allowing for a more detailed examination of their respective trafficking and ubiquitination mechanisms.

      We will consider your valuable input in our future research efforts to enhance the comprehensiveness of our study.

      Other specific points

      Figure 2: Narrowing and broadening of different marker gene expression patterns in dx mutants needs to be quantified so that variation is taken into account and the numbers of wings imaged should be clearly stated.

      We greatly appreciate this valuable suggestion from the reviewer. As a response, we have incorporated quantification data to address the observed variations. We have also provided information regarding the number of wing discs that were imaged for the purpose of quantification.

      Figure 3: The number of discs imaged in total should be mentioned

      We express our appreciation to the reviewer for the input. We have taken their comment into account and have subsequently included details regarding the number of discs imaged in the figure legend section of the manuscript.

      Figure 6: There is no description of (E5-E6) in the figure legend. F1 to F5 eye size phenotypes require quantification.

      We are grateful to the reviewers for bringing this to our attention. In response, we have included a description of E5-E6 in the figure legend. Also, as per the reviewer’s suggestions, we have incorporated the quantification data of the eye size phenotype.

      Discussion

      Links between Notch and wingless pathway should be more comprehensively discussed, including previous work that has previously linked Notch/Deltex to β-catenin degradation e.g.

      Acar et al. .Sci Rep 2021 Apr 27;11(1):9096. doi: 10.1038/s41598-021-88618-5

      Hayward et al. Development 2005 Apr;132(8):1819-30. doi: 10.1242/dev.01724;

      Kwon et al Nat Cell Biol 2011 Aug 14;13(10):1244-51. doi: 10.1038/ncb2313.

      Sanders et al. PLoS Biol 2009 Aug;7(8):e1000169. doi:10.1371/journal.pbio.1000169. Epub 2009 Aug 11.

      The links between endocytic trafficking and wingless gradient formation could also be further discussed eg.

      Marois et al. Development 2006 Jan;133(2):307-17.doi: 10.1242/dev.02197. Epub 2005 Dec 14

      Yamazaki et al Nat Cell Biol 2016 Apr;18(4):451-7. doi: 10.1038/ncb3325. Epub 2016 Mar 14.

      We appreciate the reviewer's valuable suggestions and we have now included these references in the discussion section of the revised manuscript.

    1. Author response:

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

      Reviewer #1 (Public Review):

      (1) The data strongly suggest that iron depletion in urine leads to conditional essentiality of some genes. It would be informative to test the single gene deletions (Figure 3G) for growth in urine supplemented with iron, to determine how many of those genes support growth in urine due to iron limitation.

      We appreciate this suggestion. We have now included this suggested experiment as a new panel (Figure 5G).

      (2) Line 641. The authors raise the intriguing possibility that some mutants can "cheat" by benefitting from the surrounding cells that are phenotypically wild-type. Growing a fepA deletion strain in urine, either alone or mixed with wild-type cells, would address this question. Given that other mutants may be similarly "masked", it is important to know whether this phenomenon occurs.

      We thank the reviewer for this suggestion but believe that this would be very difficult to ascertain in K. pneumoniae as several redundant iron uptake systems exist. This would require significantly more time to construct sequential/combinatorial iron-uptake mutants to exactly determine this “cheating” and “masking” phenomenon and such work is beyond the scope of the current study.

      (3) In cases where there are disparities between studies, e.g., for genes inferred to be essential for serum resistance, it would be informative to test individual deletions for genes described as essential in only one study.

      We thank the reviewer for this suggestion, and we agree that deleting conditionally essential genes (i.e. serum resistance) could help identify discrepancies in methodology with other studies but this is beyond the scope of this study. Furthermore, we do not have these other strains readily available to us and importing these strains into Australia is challenging due to the strict import/quarantine laws.

      Reviewer #1 (Recommendations For The Authors)

      (4) Line 529. Why was 50 chosen as the read count threshold?

      This was chosen as the minimum threshold needed to exclude essential genes from the comparative analysis, as these can contribute false positive results where a change from, for example, 2 to 5 reads between conditions is considered a >2-fold change. We have updated the manuscript text to highlight this: “were removed from downstream analysis to exclude confounding essential genes and minimize the effect of stochastic mutant loss” (line 539

      (5) The titles for Figure 5 and Figure 6 appear to be switched.

      Thank you, we have now corrected this error.

      (6) Line 381. "Forty-six of these regions contain potential open reading frames that could encode proteins". How is a potential ORF defined?

      This was based on submitting the selected 145bp regions to BLASTx using default parameters and listing the top hit (if one was found). We have now edited the manuscript text to make this clearer. (Line 394)

      (7) Two previous TnSeq studies looking at Escherichia coli and Vibrio cholerae suggest that H-NS can prevent transposon insertion, leading to false positive essentiality calls. Is there any evidence of this phenomenon here? A/T content could be used as a proxy for H-NS occupancy.

      We thank the reviewer for this point and also agree that H-NS or other DNA-binding proteins could indeed lead to false-positive essentiality calls using TraDIS. Based on this, we have now included a sentence in the conclusion section mentioning this methodological caveat (Line 631). We believe that A/T content could potentially be used as a proxy for H-NS occupancy,

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors may wish to reformat the manuscript by decanting a number of panels and figures as supplementary material. These include the panels related to the description of TraDIS (for example Fig 1D, 1E, 1F. 1G, Fig 2A, Fig 3C, 3D, 3E, 3F, Fig 5C, Fig 6D). This is a well-established method.

      We thank the reviewer for this suggestion but believe that these panels allow the methodology and resulting insertion plots to be more followable and allow other researchers, of varying expertise, to better understand this functional genetic screen technique.

      (2) The authors need to indicate how relevant the strain they have probed is. Is it a good reference strain of the KpI group?

      This is a great suggestion and we have now included a new figure illustrating the genetic context and relatedness of K. pneumoniae ECL8 within the KpI phylogroup (New Figure 3).

      (3) The authors need to provide an extensive comparison between the data obtained and those reported testing other Klebsiella strains. A Table identifying the common and different genes, as well as a figure, may suffice. I would encourage authors to compare also their data against E. coli and Salmonella. For example, igaA seems to be not essential in Kebsiella although data indicates it is in Salmonella.

      We thank the reviewer for their comment and appreciate that our data could be extended and compared to other relevant Enterobacteriaceae members. However, we believe this is beyond the scope of this study as the focus is more on K. pneumoniae.

      (4) None of the mutants tested further are complemented. Without these experiments, it cannot be rigorously claimed that these loci play any role in the phenotypes investigated.

      We agree that complementation is an important tenet for validation of mutant gene phenotypes to specific gene loci, in this case wbbY has already been complemented and believe complementation for an already known molecular mechanism would be redundant. Please refer to our response in point 6.

      We complemented isolated transposon mutants hns7::Tn5 and hns18::Tn5 with a mid-copy IPTG inducible . We observed a slight increase in serum susceptibility but not full rescue of the WT phenotype (i.e. serum susceptibility). We suspect that the imperfect rescue of the serum-resistance phenotype observed could be due to the expression levels and copy number of the complement hns plasmid used. As hns is a known global regulator its possible pleiotropic role is complex as many aspects of stress response, metabolism or capsule could be affected in Klebsiella (doi.org/10.1186/1471-2180-6-72, doi.org/10.3389/fcimb.2016.00013). We have now included in the text our efforts in complementation and have included a new supplementary figure (Figure S11).

      (5) The contribution of siderophores to survival in urine is not conclusively established. Authors may wish to test the transcription of relevant genes, and to assess whether the expression is fur dependent in urine. Also, authors may wish to identify the main siderophore needed for survival in urine by probing a number of mutants; this will allow us to assess whether there is a degree of selection and redundancy.

      We thank the reviewer for their comment and agree siderophore uptake is important. We have now included an additional panel (Figure 5G) interrogating the importance of iron-uptake genes grown in urine which is iron limited. We do appreciate that further experiments looking into the Fur regulon and siderophore biosynthesis would be interesting but believe this is outside the scope of this study.

      (6) The role of wbbY is intriguing, pointing towards the importance of high molecular weight O-polysaccharide. In this mutant background, the authors need to assess whether the expression of the capsule, and ECA is affected. Authors need also to complement the mutant. Which is the mechanism conferring resistance?

      We thank the reviewer for their comment and would like to mention that wbbY has already been shown to play a role in LPS profile/biosynthesis and serum-resistance (10.3389/fmicb.2014.00608 ). Furthermore, blast analysis shows that the wbbY gene between the NTUH-K2044 (strain used in aforementioned study) and ECL8 shares 100% sequence identity and also shares lps operon structure. Hence, we do not find it pertinent to complement this mutant as we believe its molecular mechanism has already been established. We have now in the text more prominently highlighted the results of this study and how our screen was robust enough to also identify this gene for serum resistance.

      (7) hns and gnd mutants most likely will have their capsule affected. The authors need to assess whether this is the case. Which is the mechanism conferring resistance?

      As mentioned in point 6, we believe that the serum resistance phenotype is attributable to the LPS phenotype. Previous studies have listed hns and gnd mutants would likely have differences in capsule but due to hns being pleiotropic and gnd being intercalated/adjacent to the LPS/O-antigen biosynthesis it would be difficult to exactly delineate which cellular surface structure is involved.

      (8) The conclusion section can be shortened significantly as much of the text is a repetition of the results/discussion section.

      We thank the reviewer for their suggestion and have made edits to limit repetition in the conclusion section.

      Reviewer #3 (Public Review):

      Below I include several comments regarding potential weaknesses in the methodology used:

      • The study was done with biological duplicates. In vitro studies usually require 3 samples for performing statistical robust analysis. Thus, are two duplicates enough to reach reproducible results? This is important because many genes are analyzed which could lead to false positives. That said, I acknowledge that genes that were confirmed through targeted mutagenesis led to similar phenotypic results. However, what about all those genes with higher p and q values that were not confirmed? Will those differences be real or represent false positives? Could this explain the differences obtained between this and other studies?

      We thank the reviewer for their comment and apologize for the confusion, data were only pooled for the statistical analysis of gene essentiality. Here, two technical replicates of the input library were sequenced and the number of insertions per gene quantified (insertion index scores). These replicates had a correlation coefficient of r2 = 0.955, and the insertions per gene data were pooled to give total insertions index scores to predict gene essentiality. For conditional analyses (growth in urine or serum), replicate data were not combined. As mentioned previously, differences between this and other studies could also be attributed to inherent genomic differences or due to differences in experimental methodology, computational approaches, or the stringency of analysis used to categorize these genes.

      • Two approaches are performed to investigate genes required for K. pneumoniae resistance to serum. In the first approach, the resistance to complement in serum is investigated. And here a total of 356 genes were identified to be relevant. In contrast, when genes required for overall resistance to serum are studied, only 52 genes seem to be involved. In principle, one would expect to see more genes required for overall resistance to serum and within them identify the genes required for resistance to complement. So this result is unexpected. In addition, it seems unlikely that 356 genes are involved in resistance to complement. Thus, is it possible false positives account for some of the results obtained?

      We thank the reviewer for their comment and do believe false positives may account for some of the identified genes. Specifically, to the large contrast in genes, we believe this is due to the methodology as alluded to in our conclusion section. For overall resistance to serum, we used a longer time point (180 min exposure) where fewer surviving mutants are recovered hence fewer overall genes will be identified, whereas strains with short killing windows will have more (i.e. complement-mediated killing, 90 minute exposure).

      Reviewer #3 (Recommendations For The Authors):

      • In Figure 4 it is shown that genes important for growth in urine include several that are required for enterobactin uptake. Moreover, an in vitro experiment shows that the complementation of urine with iron increases K. pneumoniae growth. It would have been informative to do a competition experiment between the WT and Fep mutants in urine supplemented with iron. This could demonstrate that the genes identified are only necessary for conditions in which iron is in limiting concentrations and confirm that the defect of the mutants is not due to other characteristics of urine.

      We appreciate this suggestion. We have now included a new panel (Figure 5G) addressing the supplementation of iron in urine for these select mutants.

      • Considering the results section, the title for Figure 6 seems to be more appropriate for Figure 5.

      Thank you, this has now been corrected.

      Other points:

      • Line 44: treat instead of treating

      Thank you, this has now been corrected.

      • Line 63: found that only 3 genes played a role instead of "found only 3 genes played a role"

      Thank you, this has now been corrected.

      • Line 105: is there any reason for only using males? Since UTIs are frequent in women? Why not use urine from women volunteers?

      Due to accessibility of willing volunteers and human ethic application processes, only male samples were available. We are currently undertaking further studies to understand how male and female urine influences growth of uropathogens.

      • Line 105: since the urine was filter-sterilized, maybe the authors can comment that another point that is missing in urine - and that it may be important to study - will be the presence of the urine microbiome and how this affects growth of K. pneumoniae.

      We again thank the reviewer for this comment and have now edited the manuscript discussing how the absence of urine microbiome could affect growth (Line 659). As an aside, future studies in our lab are interested in looking at the role of commensal/microbiome co-interactions for essentiality/pathogenesis using TraDIS.

      • Line 116: I understand that the 8 healthy volunteers combined males and females

      Thank you, we have now edited this methods line to make this clearer.

      • Line 120: incubate in serum 90 min and 180 RPM shaking: any reasons for using these conditions, any reference supporting these conditions?

      Thank you for pointing this out, we were mirroring a previous K. pneumoniae serum-resistance study (doi.org/10.1128/iai.00043-).

      • Line 156: space after the dot.

      Thank you, we have now corrected this in the manuscript.

      • Line 164: resulting reads were mapped to the K. pneumoniae: what are the parameters used for mapping (e.g. % of identity...)?

      Thank you for bringing this to our attention, we have now included in our manuscript that we used the default parameters of BWA-MEM for mapping for minimum seed length (default -k =20bp exact match)

      • Line 180: it will be good to upload to a repository the In-house scripts used or indicate the link beside the reference for those scripts.

      Our scripts are derived from the pioneering TraDIS study (doi: 10.1101/gr.097097.109). We are currently still optimizing our scripts and intend to upload these to be publicly available. However, in the meantime we are more than happy to share them with other parties upon request.

      • Line 191: why were genes classified as 12 times more likely to be situated in the left mode? Any particular reason for using this threshold?

      We opted for a more-stringent threshold for classifying essential genes, in keeping with previous and comparable studies (doi.org/10.1371/journal.pgen.1003834).

      • Line 209: do you mean Q-value of <0.05 instead of >0.05 ? How is this Q value is calculated, and which specific tests are applied?

      Thank you for pointing out this Q value error, we have now corrected this in the manuscript. These values were generated using the biotradis tradis_comparison.R script which uses the EdgeR package. For further reading please see DOI: 10.1093/bioinformatics/btp616. The Q-values are from P values corrected for multiple testing by the Benjamini-Hochberg method.

      • Line 212: again, which type of test is used? What about the urine growth analysis? The same type of tests were applied?

      Thank you for bringing this to our attention, we have now indicated in the referenced method section the use of which package for which datasets (i.e. or serum). Line 212 refers to our use of the AlbaTraDIS package, which builds on the biotradis toolkit, to identify gene commonalities/differences in the selected growth conditions again using multiple testing by the Benjamini-Hochberg methods. For further reading, please refer to DOI: 10.1371/journal.pcbi.1007980

      • Line 226: do the authors mean Sanger sequencing instead of SangerSanger sequencing?

      Thank you, we have now corrected this in the manuscript.

      • Line 239: does the WT strain contain another marker for differentiating this strain from the mutant? Or is the calculation of the number of WT CFUs done by subtracting the number of CFUs in media with antibiotics from the total number of CFUs in media without antibiotics? The former will be a more accurate method.

      The calculation was based on the latter assumption, “number of WT CFUs done by subtracting the number of CFUs in media with antibiotics from the total number of CFUs in media without antibiotics”. We have now updated the methods section to make this clearer.

      • Line 266: can you indicate approximately how many CFUs you have in this OD?

      Thank you, we have now also indicated an approximate CFU for this mentioned OD600 (OD600 1 = 7 × 108 cells).

      • Line 309: besides indicating Figure 1D please indicate here Dataset S1 (the table where one can see the list of essential and non-essential genes). This table is shown afterwards but I think it will be more appropriate to show it at the begging of the section.

      Thank you, we have now taken on this recommendation and have now edited the manuscript to also indicate Dataset S1 earlier.

      • Table 3. regarding the comparison of essential genes between different strains. I think it will be more clear if a Venn diagram was drawn including only genes that have homologs in all the studied strains (i.e. defining the core genome essentially).

      We would like to thank the reviewer for suggesting a venn diagram and have now removed Table 3 which has been replaced with a new Figure 3.

      • Line 461: replicates were combined for downstream analyses? But are replicates combined for doing the statistical analysis? If so, how is the statistical analysis performed? How is it taken into account the potential variability in the abundance in each library? An r of 0.9 is high but not perfect.

      Technical replicates of the sequenced input library were combined following identification of a correlation coefficient of r2 = 0.955, for the calculation of insertion index scores used in gene essentiality analysis. While r2 = 0.955 is not perfect, discrepancies here can be attributed to higher variance in insertion index scores when sampling small genes, as these are represented by fewer insertions and the stochastic absence of a single insertion event has a greater effect on the overall IIS. Replicate data were not pooled for statistical analysis of mutant fitness (growth in urine and serum).

      • Line 487: is there any control strain containing the kanamycin gene in a part of the genome that does not affect the growth of K. pneumoniae? This could be used to show that having the kanamycin gene does not provide any defect in urine growth.

      We thank the reviewer for this suggestion but argue that introduction of the kanamycin gene into each unique loci may result in various levels of gene fitness that would be incomparable to a single control strain. Instead, we culture the ECL8 mutant library in urine and ensure that its kinetics are comparable to the wildtype. As the library contains thousands of kanamycin cassettes uniquely positioned across most of the genome with no observable growth defect, we do not anticipate the presence or expression of the cassette to have an appreciable impact.

      • Line 569: in the methodology it was indicated that control cells were incubated in PBS for the same amount of time. I think this is an important control that is not cited in the results section. Please can you indicate?

      We apologise for this misunderstanding due to how the methodology was written. The experiment did not sequence the PBS incubated samples as this was solely used a check for viability of the used K. pneumoniae ECL8 stock solution.

      • Line 597: "Mutants in igaA are enriched in our experiments". Can you show this data?

      We have now included this as a supplementary (Figure S11A)

      • Line 615: when doing this calculation, I guess the authors take into account only genes that are also present in the other strains.

      That is correct, we were aiming to highlight the high conservation of “essential genes” among all the selected strains.

      • Line 627: why surprisingly? Because is too low. Then indicate.

      Thank you, we have now edited this sentence to indicate that.

      • Figure 4: please, for clarity, can you indicate the meaning of the colors in the figure itself besides indicating it in the figure legend?

      Thank you, we have now included a color legend in these figure panels for clarity.

    1. Author response:

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

      Reviewer #3:

      Summary:

      The receptor tyrosine kinase Anaplastic Lymphoma Kinase (ALK) in humans is nervous system expressed and plays an important role as an oncogene. A number of groups have been studying ALK signalling in flies to gain mechanistic insight into its various roles. In flies, ALK plays a critical role in development, particularly embryonic development and axon targeting. In addition, ALK also was also shown to regulate adult functions including sleep and memory. In this manuscript, Sukumar et al., used a suite of molecular techniques to identify downstream targets of ALK signalling. They first used targeted DamID, a technique that involves a DNA methylase to RNA polymerase II, so that GATC sites in close proximity to PolII binding sites are marked. They performed these experiments in wild type and ALK loss of function mutants (using an Alk dominant negative ALkDN), to identify Alk responsive loci. Comparing these loci with a larval single cell RNAseq dataset identified neuroendocrine cells as an important site of Alk action. They further combined these TaDa hits with data from RNA seq in Alk Loss and Gain of Function manipulations to identify a single novel target of Alk signalling - a neuropeptide precursor they named Sparkly (Spar) for its expression pattern. They generated a mutant allele of Spar, raised an antibody against Spar, and characterised its expression pattern and mutant behavioural phenotypes including defects in sleep and circadian function.

      Strengths:

      The molecular biology experiments using TaDa and RNAseq were elegant and very convincing. The authors identified a novel gene they named Spar. They also generated a mutant allele of Spar (using CrisprCas technology) and raised an antibody against Spar. These experiments are lovely, and the reagents will be useful to the community. The paper is also well written, and the figures are very nicely laid out making the manuscript a pleasure to read.

      We thank the reviewer for this analysis.

      Weaknesses:

      The manuscript has improved substantially in the revision. Yet, some concerns remain around the genetics and behavioural analysis which is incomplete and confusing. The authors generated a novel allele of Spar - Spar ΔExon1 and examined sleep and circadian phenotypes of this allele and of RNAi knockdown of Spar. The RNAi knockdown is a welcome addition. However, the authors only show one parental control the GAL4 / +, but leave out the other parental control i.e. the UAS RNAi / + e.g. in Fig. 9. It is important to show both parental controls.

      We would like to express our gratitude for your insightful comments and feedback on our manuscript. We acknowledge the concerns raised regarding the genetics and behavioural analysis, and we appreciate the opportunity to address these issues. We have added the reciprocal UAS Spar-RNAi control in addition to the GAL4/+ control and we have incorporated both controls in the revised Figure 9, Figure 9 Supplementary Figure 1 and Figure 9 Supplementary Figure 2. Figure legends have been modified accordingly.

      Further, the sleep and circadian characterisation could be substantially improved. It is unclear how sleep was calculated - what program was used or what the criteria to define a sleep bout was.

      The data underwent analysis utilizing an Excel macro, as outlined in the study by Berlandi et al. (2017) (PMID: 28912696). As previously indicated in the methodology, sleep is characterized as 5 minutes of inactivity. The raw data acquired from the Trikenetics DAM system was input into an Excel spreadsheet, and the parameters, encompassing sleep and activity, were computed for each day of the trial as an average derived from the data of all living animals at that time. Subsequently, these parameters were exhibited over the course of the experiment. We have further detailed this part in the methods section to avoid confusion (Page 32 of revised MS).

      In the legend for Fig 8c, it says sleep was shown as "percentage of time flies spend sleeping measured every 5min across a 24h time span". Sleep in flies is (usually) defined as at least 5 min of inactivity. With this definition, I'm not sure how one can calculate the % time asleep in a 5 min bin! Typically people use 30min or 60min bins.

      We thank the reviewer for bringing this to our attention. As previously stated, in our experiments, sleep is defined as 5 minutes of inactivity. We have now modified the wording in the figure legend (Figure 8, Page 41), which was previously misleading.

      The sleep numbers for controls also seem off to me e.g. in Fig. 8H and H' average sleep / day is ~100. Is this minutes of sleep? 100 min / day is far too low, is it a typo? The same applies to Figure 8, figure supplement 2. Other places e.g. Fig 8 figure supplement 1, avg sleep is around 1000 min / day.

      The numbers for sleep bouts are also too low to me e.g. in Fig 9 number of sleep bouts avg around 4, and in Fig. 8 figure supplement 2 they average 1 sleep bout. There are several free software packages to analyse sleep data (e.g. Sleep Mat, PMID 35998317, or SCAMP). I would recommend that the authors reanalyse their data using one of these standard packages that are used routinely in the field. That should help resolve many issues.

      We thank the reviewer for pointing this out. There was indeed a typo “missing 0”, resulting in 0 values as only 3 days of raw data were chosen for the analysis of the average sleep in the mentioned figures. We have corrected this mistake in all figures.

      The circadian anticipatory activity analyses could also be improved. The standard in the field is to perform eduction analyses and quantify anticipatory activity e.g. using the method of Harrisingh et al. (PMID: 18003827). This typically computed as the ratio of activity in the 3hrs preceding light transition to activity in the 6hrs preceding light transition. The programs referenced above should help with this.

      For consistency purposes we used the same macro excel (Berlandi et al, 2017) (PMID: 28912696) and followed the methodology of Harrisingh et al. (PMID: 18003827) to assess the anticipatory activity. We selected the activity in the 6 h period before lights on and defined it as a.m. anticipation, and the activity in the 6h period preceding the lights off and defined as p.m. anticipation (Figure 8 f-g).

      Finally, in many cases I'm not sure that the appropriate statistical tests have been used e.g. in Fig 8c, 8e, 8h t-tests have been used when are three groups in the figure. The appropriate test here would an ANOVA, followed by post-hoc comparisons.

      We agree with the reviewer’s comments. We have re-evaluated the data in Figure 8 b, c, e, h and h’ and Figure 8 Supplement 2 and 4 using a One-Way ANOVA followed by Tukey post-hoc test and we have indicated this in all legends.

    1. Author response:

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

      Reviewer #1 (Recommendations for The Authors):

      (1) Since the data suggests that the degradation of Mecp2 is a crucial event in the exit from quiescence, gaining a better understanding of the underlying mechanism would improve the significance of the study. In this regard, the authors should take advantage of the serum stimulated degradation of Mecp2 (Fig. 3D) to identify the signaling pathway(s) required for the degradation.

      Thank you for this suggestion. To decipher the molecular mechanisms underlying Mecp2-regulated quiescence exit, we performed RNA-seq combined with ChIP-seq to identify the Mecp2-dependent transcriptome genome-wide during the early stage of liver regeneration (Figure S6C). There were 2658 Mecp2 direct target genes, in which 537 were PHx-activated and 2121 were PHx-repressed genes (Figure 6A). GO analysis showed that PHx-activated Mecp2 targets were highly enriched in proliferation-associated biological processes such as ribosome biogenesis, rRNA metabolic process, ncRNA metabolic process, and regulation of transcription by RNA polymerase I, whereas PHx-repressed Mecp2 targets were associated with several metabolic processes including carboxylic acid catabolic process, cellular amino acid metabolic process, fatty acid metabolic process and steroid metabolic process (Figure 6B). These results suggest that Mecp2 plays a negative regulatory role during quiescence exit by activating metabolism-associated genes while repressing proliferation-associated genes in quiescent cells.

      Given the more rapid decay of Mecp2 at the protein compared to the mRNA level during the quiescence-proliferation transition, we speculated that Mecp2 is targeted by posttranslational regulation. This hypothesis was supported by proteasome inhibition with the proteasome inhibitor MG132, which attenuated the reduction of Mecp2 in quiescent cells after S.R. (Figure S5A). To identify the signaling pathway that regulate Mecp2 degradation during the G0/G1 transition, we performed immunoprecipitation followed by mass spectrometry (IP-MS) using Mecp2 antibody in quiescent 3T3 cells treated with or without S.R. (Figure S5B). A total of 647 proteins were identified as putative Mecp2 interactors. We were particularly interested in the proteins involved in proteasome-mediated ubiquitin-dependent protein catabolic process which was one of the enriched Gene Ontology (GO) items in the Mecp2 interactome (Table S1).

      (2) The authors suggest that Mecp2 downregulation accelerates the induction of pRb, which serves as a key marker for G0/G1 transition. However, their data only show increased magnitudes of the expression in Mecp2 downregulated cells at the timepoints when samples were collected (Figs. 2B and 4B). In the in vitro experiments, the authors should investigate earlier timepoints to demonstrate that induction of pRB during the quiescence exit occurs earlier in Mecp2 deficient cells compared to control cells. Likewise, a later induction of pRB in Mecp2 overexpression cells, in comparison to normal cells, should be demonstrated.

      Thank you for these valuable suggestions. We have, accordingly, collected cell samples re-entered the cell cycle at 30-, 60-, 90- and 120-minutes post-S.R. We examined the pRb expression and found that phosphorylation of retinoblastoma protein (pRb) at Ser807/811 occurs earlier (about 90 minutes) in Mecp2 deficient cells compared to control cells (Figure S4C). Compared to the EV, Mecp2 OE resulted in the delayed induction of pRB (about 60 minutes) upon S.R. (Figure S4D). These data indicate that enhanced reduction of Mecp2 stimulates exit from quiescence.

      (3) There are three well-known phosphorylation sites in Mecp2, including S80, S229, and S423. As protein ubiquitination and degradation are often triggered by phosphorylation, it would be interesting to examine whether phosphorylation at these sites of Mecp2 is required for its downregulation during quiescence exit. This can be achieved using non-phosphorylate mutants of Mecp2.

      This is a very good question. Indeed, the 26S ubiquitin-proteasome system (26S UPS) is responsible for the breakdown of MeCP2 (PMID: 28394263, 28973632). In 2009, the bona fide PEST (enriched in proline, glutamic acid, serine, and threonine) domains have been identified, which are highly conserved across vertebrate evolution (PMID: 19319913). Consensus sequences enriched in PEST residues have been found to predispose proteins containing them for rapid proteolytic degradation (PMID: 8755249, 2876518). In addition, phosphorylation within PEST motifs precedes ubiquitination of proteins (PMID: 15229225). One of the best characterized sites of MeCP2 phosphorylation (S80) (PMID: 19225110), as well as one of the identified ubiquitination sites (K82/K99) (PMID: 22615490), both fall within one of these regions. It is still noteworthy that most of the MeCP2 phosphorylation sites were found in close proximity to potential ubiquitylation sites. For example, Rett syndrome missense mutations in Rett syndrome affecting three (K82R, K135A, K256S) of the ubiquitination sites (PMID: 25165434) and S80 (within one of the PEST sequences) and K82 have been shown to be phosphorylated and ubiquitinated.

      Based on the above discussion, we providing a potential hypothesis that the MeCP2 turnover during cell cycle re-entry is achieved by an initial phosphorylation signal (phosphorylated at S80, S229, or S421) that triggers the ubiquitination of a close lysine residue. We hope to solve these issues and be able to present the findings in future work. Thanks again for your professional suggestions.

      (4) It would be interesting if the authors could also examine the effect of altered expression of Mecp2 on the maintenance of quiescence. For example, whether the downregulation of Mecp2 sensitizes quiescent cells for entry of the cell cycle in response to serum stimulation or delays withdrawal from the cell cycle upon serum starvation or contact inhibition.

      Thank you for your suggestions. Cell cycle synchronization was induced with serum deprivation. When nutrients are exhausted, altered expression of Mecp2 have no statistical influence on the maintenance of quiescence as analyzed by Flow cytometric (Figure 4D and H). This suggests that the altered expression of Mecp2 alone may not be sufficient for cell cycle exit. In the presence of growth factors or nutrients, loss of MeCP2 only accelerates the rate of cell cycle re-entry.

      Minor points:

      For Figs. 2D, 2H, and 2L, it would be more intuitive if the percentage of changes in liver index rather than the relative index values were used. Also, the values listed in the figures should start from time zero after partial hepatectomy rather than pre-surgery.

      Liver weight have the corresponding change with body weight. The liver index (ratio of regenerate liver weight/body weight) is tightly regulated and depends on metabolic demands of the organism. During the course of liver regeneration, reestablishment of liver volume after resection is regulated by the functional needs of the organism. Using the percentage of regenerate liver weight/body weight as a liver growth index could reflect the regenerative function. Next, we agree with the data presentation form and the values listed in the figures have been modified in the revised version.

      Reviewer #2 (Recommendations for The Authors):

      My concerns are as follows:

      (1) The authors note that the decrease in Mecp2 protein levels was more pronounced than the decrease in mRNA levels, suggesting the presence of post-translational regulation of Mecp2 during the early stages of G0 exit. Could the decrease in MeCP2 levels be related to autophagy flux?

      Thank you for your valuable comments. Also, we have compared the cells extracts from untreated and chloroquine-treated cells (to block lysosomal degradation). Chloroquine did not cause any accumulation of MeCP2 (Figure S5B). The results suggest that autophagy activity do not involve in the decrease the MeCP2 protein.

      (2) In addition to Cyclin D1, how about other cell cycle-related proteins (cyclin A, cyclin B, and cyclin E) were changed when MeCP2 was lost during cell cycle re-entry? Protein expression should be examined by western blot.

      We appreciate your valuable suggestions. The expression of cell cycle related protein cyclin A2, cyclin B1 and cyclin E1 were evaluated by Western blotting. The expression of cyclin A2, cyclin B1 and cyclin E1 was enhanced by the knockdown of MeCP2 (Figure 4B). Conversely, the repressed expression of cyclin A2, cyclin B1 and cyclin E1 was observed by the over-expression of MeCP2 (Figure 4F).

      (3) By combining MeCP2 ChIP-seq and RNA-seq of genes regulated by MeCP2, the authors uncovered the dual role of Mecp2 in preventing quiescence exit by targeting Rara and Nr1h3. All they show are the Q-PCR results. The authors should show the protein level of Rara and Nr1h3 when MeCP2 was lost during cell cycle re-entry.

      Thank you for your advice. In Figure 7C, the knockdown efficiency of Rara and Nr1h3 were checked by Western blot analysis.

      (4) The authors performed lentiviral and AAV-mediated gene knockdown to target Rara and Nr1h3 in Cells and Mecp2-cKO livers, respectively. The Knockdown efficacy should be verified by western blots (Fig 7 C and F).

      In Figure 7F, the consequences of the Rara and Nr1h3 knockdown efficiency was verified by Western blot analysis.

      (5) The other major concern is regarding the lack of quantitative assessments of MeCP2 WB results (Fig 2, Fig 4, and Fig 7).

      Thank you for this suggestion. We added supplementary figures to Figure 2B, 2F and 2J to show the quantification membrane signal of MeCP2 protein in liver regeneration. And Fig S4A and 4B showing the quantification signal of MeCP2 protein in NIH3t3 cell cycle re-entry model.

      (6) In the Figure legends of Fig 4 B and Fig 4F, the authors should delete the statistical descriptions, as there are no statistical results. In Fig 5F, Fig 5J, Fig 6D, Fig 7D and Fig7H, there are no statistical results of p < 0.01, p < 0.05 or *p < 0.0001, respectively. The authors should check the description in the figure legends. In Fig S2C, the level of significance should be annotated.

      We would like to express our heartfelt thanks for your thorough reading of our manuscript. We have made corrections to make manuscript clearer and more accurate. The level of significance have been annotated in Fig S2C.

      (7) In Fig S4A, there are no WB results of Cyclin D1 and pRb, the authors should check the description.

      Thank you for pointing this out. We have deleted the confusing statements in the revised manuscript.

    1. Author response:

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

      eLife assessment

      The authors build upon prior data implicating the secreted peptidoglycan hydrolase SagA produced by Enterococcus faecium in immunotherapy. Leveraging new strains with sagA deletion/complementation constructs, the investigators reveal that sagA is non-essential, with sagA deletion leading to a marked growth defect due to impaired cell division, and sagA being necessary for the immunogenic and anti-tumor effects of E. faecium. In aggregate, the study utilizes compelling methods to provide both fundamental new insights into E. faecium biology and host interactions and a proof-of-concept for identifying the bacterial effectors of immunotherapy response.

      We thank the Reviewers for their positive feedback on our manuscript. We also appreciate their helpful comments/critiques and have revised the manuscript as indicated below.

      Public Reviews:

      Reviewer #1 (Public Review):

      Klupt, Fam, Zhang, Hang, and colleagues present a novel study examining the function of sagA in E. faecium, including impacts on growth, peptidoglycan cleavage, cell separation, antibiotic sensitivity, NOD2 activation, and modulation of cancer immunotherapy. This manuscript represents a substantial advance over their prior work, where they found that sagA-expressing strains (including naturally-expressing strains and versions of non-expressing strains forced to overexpress sagA) were superior in activating NOD2 and improving cancer immunotherapy. Prior to the current study, an examination of sagA mutant E. faecium was not possible and sagA was thought to be an essential gene.

      The study is overall very carefully performed with appropriate controls and experimental checks, including confirmation of similar densities of ΔsagA throughout. Results are overall interpreted cautiously and appropriately.

      I have only two comments that I think addressing would strengthen what is already an excellent manuscript.

      In the experiments depicted in Figure 3, the authors should clarify the quantification of peptidoglycans from cellular material vs supernatants. It should also be clarified whether the sagA need to be expressed endogenously within E. faecium, and whether ambient endopeptidases (perhaps expressed by other nearby bacteria or recombinant enzymes added) can enzymatically work on ΔsagA cell wall products to produce NOD2 ligands?

      We mentioned in the main text that peptidoglycan was isolated from bacterial sacculi and digested with mutanolysin for LC-MS analysis. We have now also included “mutanolysin-digested” sacculi in the Figure 3 legend as well.

      We have added the following text “We next evaluated live bacterial cultures with mammalian cells to determine their ability to activate the peptidoglycan pattern recognition receptor NOD2” and “our analysis of these bacterial strains” to indicate live cultures were evaluated for NOD2 activation.

      We have also added the following text “Our results also demonstrated that while many enzymes are required for the biosynthesis and remodeling of peptidoglycan in E. faecium, SagA is essential for generating NOD2 activating muropeptides ex vivo.”

      In the murine experiments depicted in Figure 4, because the bacterial intervention is being performed continuously in the drinking water, the investigators have not distinguished between colonization vs continuous oral dosing of the mice peptidoglycans. While I do not think additional experimentation is required to distinguish the individual contributions of these 2 components in their therapeutic intervention, I do think the interpretation of their results should include this perspective.

      We have added the following text “We note that by continuous oral administration in the drinking water, live E. faecium and soluble muropeptides that are released into the media during bacterial growth may both contribute to NOD2 activation in vivo.” and revised the following text “Nonetheless, these results demonstrate SagA is not essential for E. faecium colonization, but required for promoting the ICI antitumor activity through NOD2 in vivo.

      Reviewer #2 (Public Review):

      Summary:

      The gut microbiome contributes to variation in the efficacy of immune checkpoint blockade in cancer therapy; however, the mechanisms responsible remain unclear. Klupt et al. build upon prior data implicating the secreted peptidoglycan hydrolase SagA produced by Enterococcus faecium in immunotherapy, leveraging novel strains with sagA deleted and complemented. They find that sagA is non-essential, but sagA deletion leads to a marked growth defect due to impaired cell division. Furthermore, sagA is necessary for the immunogenic and anti-tumor effects of E. faecium. Together, this study utilizes compelling methods to provide fundamental new insights into E. faecium biology and host interactions, and a proof-of-concept for identifying the bacterial effectors of immunotherapy response.

      Strengths:

      Klupt et al. provide a well-written manuscript with clear and compelling main and supplemental figures. The methods used are state-of-the-art, including various imaging modalities, bacterial genetics, mass spectrometry, sequencing, flow cytometry, and mouse models of immunotherapy response. Overall, the data supports the conclusions, which are a valuable addition to the literature.

      Weaknesses:

      Only minor revision recommendations were noted.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      General comments - the number/type of replicates and statistics are missing from some of the figure panels. Please be sure to add these throughout - all main figure panels should have replicates. I've also noted some specific cases below.

      Abstract - sagA is non-essential, need to edit text at "essential functions".

      This change has been made.

      "small number of mutations" - specify how many in the text.

      We revised the text. “Small number” is changed to “11”.

      "under control of its native promoter" - what was the plasmid copy number? It looks clearly overexpressed in Figure 1d despite using a native promoter, although it's a bit hard to know for sure without a loading control.

      pAM401 has p15A origin of replication, therefore the plasmid copy number ~20-30 copies (Lutz R. et al Nucleic Acids Res. 1997). Total protein was visualized by Stain-Free™ imaging technology (BioRad) and serves as protein loading control and has been relabeled accordingly.

      "decrease levels of small muropeptides" - the asterisks are missing from Figure 3a.

      Green asterisks for peaks 2, 3, 7 and purple asterisks for peaks 13, 14 were added.

      The use of "Com 15 WT" in the figures is confusing - just replace it with "wt" and specify the strain in the text. Presumably, all of the strains are on the Com 15 background.

      “Com15 WT” was replaced to “WT” in figures and main text.

      Change 1d to 1b so that the panels are in order (reading left to right and then top to bottom).

      Figure 1 legend is missing a number of replicates and statistics for 1a.

      Number of replicates were added.

      Figure 1b - it's unclear to me what to look at here, could add arrows indicating the feature or interest and expand the relevant text.

      Arrows pointing to cell clusters were added.

      Figure 1d - what is "stain free"? It would be preferable to show a loading control using an antibody against a constitutive protein to allow for normalization of the loading control.

      Stain-Free Imaging technology (BioRad) utilizes gel-containing trihalo compound to make proteins fluorescent directly in the gel with a short photoactivation, allowing the immediate visualization of proteins at any point during electrophoresis and western blotting. Stain-Free total protein measurement serves as a reliable loading control comparable to Coomassie Blue Staining. This has been relabeled a “Total protein” in the Figure and Stain-free imaging technology is noted in the legend.

      ED Figure 1 - representative of how many biological replicates?

      Legends are updated.

      ED Figure 2a - I would replace this with a table, it's not necessary to show the strip images. Also, please specify the number of replicates per group.

      Additional Extended Data Table 2 was added.

      ED Figure 2b - This data was not that convincing since the sagA KO has a marked growth defect and the time points are cut off too soon to know if growth would occur later. The MIC definition is potentially misleading. Should specific a % growth cutoff (i.e. <10% of vehicle control) and the metric used (carrying capacity or AUC). Then assign MIC to the tested concentration, not a range. The empty vector also seems to impact MIC, which is concerning and complicates the interpretation. Specify the number of replicates and add statistics. Given these various concerns, I might suggest removing this figure, as it doesn't really add much to the story.

      We appreciate this comment from the Reviewer, but believe this data is helpful for paper and have included longer time points for the growth data. The definition of MIC for ED Fig. 2b has been included in the legend.

      Figure 2 - specify the type of replicate. Number of cells? Number of slices? Number of independent cultures?

      For Cryo-ET experiments single bacterial cultures were prepared. Number of cells and slices for analysis are indicated in the legend. Legends are updated.

      Figure 4e - missing the water group, was it measured?

      Water (αPD-L1) group was not included in immune profiling of tumor infiltrating lymphocytes (TILs) experiment, as we have previously demonstrated limited impact on ICI anti-tumor activity and T cell activation in this setting (Griffin M et al Science 2021).

      Figure 4d - is this media specific to your strains? If not, qPCR may be a better method using strain-specific primers.

      Yes, HiCrome™ Enterococcus faecium agar plates (HIMEDIA 1580) are selective for Enterococcus species, moreover the agar is chromogenic allowing to identify E. faecium as yellow colonies among other Enterococcus species.

    1. Author response:

      We are planning to extend our results of the Jurkat model system to primary T cells, as requested by the referees and eLife’s Senior Editor. This will involve the inclusion of new figures, including super-resolution/STED images to reinforce our results and to satisfy the referees’ points. In addition, we will improve and/or replace all the mentioned images to solve the raised caveats, including further quantification and analyses.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      [...] This study is a fundamental step towards our better understanding of the mechanisms underlying light effects on cognition and consequently optimising lighting standards.

      Strengths:

      While it is still impossible to distinguish individual hypothalamic nuclei, even with the high-resolution fMRI, the authors split the hypothalamus into five areas encompassing five groups of hypothalamic nuclei. This allowed them to reveal that different parts of the hypothalamus respond differently to an increase in illuminance. They found that higher illuminance increased the activity of the posterior part of the hypothalamus encompassing the MB and parts of the LH and TMN, while decreasing the activity of the anterior parts encompassing the SCN and another part of TMN. These findings are somewhat in line with studies in animals. It was shown that parts of the hypothalamus such as SCN, LH, and PVN receive direct retinal input in particular from ipRGCs. Also, acute chemogenetic activation of ipRGCs was shown to induce activation of LH and also increased arousal in mice.

      Weaknesses:

      While the light characteristics are well documented and EDI calculated for all of the photoreceptors, it is not very clear why these irradiances and spectra were chosen. It would be helpful if the authors explained the logic behind the four chosen light conditions tested. Also, the lights chosen have cone-opic EDI values in a high correlation with the melanopic EDI, therefore we can't distinguish if the effects seen here are driven by melanopsin and/or other photoreceptors. In order to provide a more mechanistic insight into the light-driven effects on cognition ideally one would use a silent substitution approach to distinguish between different photoreceptors. This may be something to consider when designing the follow-up studies.

      We thank the reviewer for acknowledging the quality and interest of our work and agree with the weaknesses they pointed out.

      Blue-enriched light illuminances were set according to the technical characteristics of the light source and to keep the overall photon flux similar to prior 3T MRI studies of our team (between ~1012 and 1014 ph/cm²/s) (Vandewalle et al. 2010 PNAS, Vandewalle et al. 2011 Biol. Psy.). The orange light was introduced as a control visual stimulation for potential secondary whole-brain analyses. It’s photopic illuminance should ideally have been set similar to the low illuminance blue-enriched light condition, but it was not the case. For the present region of interest analyses, we discarded colour differences between the light conditions and only considered illuminance as indexed by mel EDI lux. This constitutes indeed a limitation of our study as it does not allow attributing the findings to a particular photoreceptor class.

      The revised version of the manuscript will include a better explanation as to the choice of illuminances and spectra. The discussion will make clear that these choices limit the interpretation about the photoreceptors involved. The discussion will also point out that silent substitution could be used in the future to resolve such question.

      Reviewer #2 (Public Review):

      [...] By shedding light on these complex interactions, this research endeavors to contribute to the foundational knowledge necessary for developing innovative therapeutic strategies aimed at enhancing cognitive function through environmental modulation.

      Strengths:

      (1) Considerable Sample Size and Detailed Analysis: The study leverages a robust sample size and conducts a thorough analysis of hypothalamic dynamics, which enhances the reliability and depth of the findings.

      (2) Use of High-Resolution Imaging: Utilizing 7 Tesla fMRI to analyze brain activity during cognitive tasks offers high-resolution insights into the differential effects of illuminance on hypothalamic activity, showcasing the methodological rigor of the study.

      (3) Novel Insights into Illuminance Effects: The manuscript reveals new understandings of how different regions of the hypothalamus respond to varying illuminance levels, contributing valuable knowledge to the field.

      (4) Exploration of Potential Therapeutic Applications: Discussing the potential therapeutic applications of light modulation based on the findings suggests practical implications and future research directions.

      Weaknesses:

      (1) Foundation for Claims about Orexin and Histamine Systems: The manuscript needs to provide a clearer theoretical or empirical foundation for claims regarding the impact of light on the orexin and histamine systems in the abstract.

      (2) Inclusion of Cortical Correlates: While focused on the hypothalamus, the manuscript may benefit from discussing the role of cortical activation in cognitive performance, suggesting an opportunity to expand the scope of the manuscript.

      (3) Details of Light Exposure Control: More detailed information about how light exposure was controlled and standardized is needed to ensure the replicability and validity of the experimental conditions.

      (4) Rationale Behind Different Exposure Protocols: To clarify methodological choices, the manuscript should include more in-depth reasoning behind using different protocols of light exposure for executive and emotional tasks.

      We thank the reviewer for recognising the interest and strength of our study. We agree that corrections and clarifications to the text were needed. We will address the weaknesses they pointed out as follows:

      (1) As detailed in the discussion, we do believe orexin and histamine are excellent candidates for mediating the results we report. As also pointing out, however, we are in no position to know which neurons, nuclei, neurotransmitter and neuromodulator underlie the results. We will therefore remove the last sentence of the abstract as we agree our final statement in the abstract was too strong. We will carefully reconsider the discussion to avoid such overstatements.

      (2) We are unsure at this stage how to address the comment of the reviewer without considerably lengthening the manuscript with statements which can only be putative. Hypothalamus nuclei are connected to multiple cortical (and subcortical) structures. The relevance of these projections will vary with the cognitive task considered. In addition, we have not yet considered the cortex in our analyses such that truly integrating cortical structures appears premature. We will nevertheless refer to the general statement that subcortical structures (and particularly those receiving direct retinal projections) are likely to receive light illuminance signal first before passing on the light modulation to the cortical regions involved in the ongoing cognitive process.

      (3) Illuminance and spectra could not be directly measured within the MRI scanner due to the ferromagnetic nature of measurement systems. The MR coil and the associated optic fibre stand, together with the entire lighting system were therefore placed outside of the MR room to reproduce the experimental conditions of the in a completely dark room. A sensor was placed 2 cm away from the mirror of the coil (mounted at eye level), i.e. where the eye of the first author of the paper would be positioned, to measure illuminance and spectra. The procedure was repeated 4 times for illuminance and twice for spectra and measurements were averaged. This procedure does not take into account inter-individual variation in head size and orbit shape such that the reported illuminance levels may have varied slightly across subjects. The relative differences between illuminance are very unlikely to vary substantially across participants such that statistics consisting of tests for the impact of relative differences in illuminance were not affected. We will report these methodological details in the supplementary material file associated to the paper.

      (4) The comment is similar to the issue raised by reviewer 1 (and reviewer 3) so we refer to the response provided to reviewer 1 to address the final comment of reviewer 2.

      Reviewer #3 (Public Review):

      [...] The authors find evidence in support of a posterior-to-anterior gradient of increased blood flow in the hypothalamus during task performance that they later relate to performance on two different tasks. The results provide an enticing link between light levels, hypothalamic activity, and cognitive/affective function, however, clarification of some methodological choices will help to improve confidence in the findings.

      Strengths:

      The authors' focus on the hypothalamus and its relationship to light intensity is an important and understudied question in neuroscience.

      Weaknesses:

      I found it challenging to relate the authors' hypotheses, which I found to be quite compelling, to the apparatus used to test the hypotheses - namely, the use of orange light vs. different light intensities; and the specific choice of the executive and emotional tasks, which differed in key features (e.g., block-related vs. event-related designs) that were orthogonal to the psychological constructs being challenged in each task.

      Given the small size of the hypothalamus and the irregular size of the hypothalamic parcels, I wondered whether a more data-driven examination of the hypothalamic time series would have provided a more parsimonious test of their hypothesis.

      We thank the reviewer for acknowledging the originality and interest of our study. We agree that some methodological choices needed more explanations. We will address the weaknesses they pointed out as follows:

      The first comment questions the choices of the light conditions and of the tasks. Regarding light conditions, since reviewer 1 (and reviewer 2) raised a similar issue, we refer to the response provided to reviewer 1. We agree that many different tasks could have been used to test our hypotheses. Prior work of our team showed that the n-back task and emotional task we used were successful probes to demonstrate that light illuminance modulates cognitive activity, including within subcortical structures (though resolution did not allow precise isolation of nuclei or subparts). When taking the step of ultra-high field imaging we therefore opted for these tasks as our goal was to show that illuminance affects subcortical brain activity across cognitive domains in general and we were not interested in tasks that would test specific aspects of these domains. The fact that one task is event-related while the other consists of a block design adds, in our view, to the robustness of our finding that a similar anterior-posterior gradient of activity modulation by illuminance is present in hypothalamus. We will update the discussion to highlight this aspect.

      As mentioned in the text, the protocol also included an auditory attentional task that could have further broadened the potential generalisability of our findings, but it was not part of the analyses as it could only include 2 illuminance levels due to time constrains.

      We agree that a data driven approach could have constituted an alternative means to tests our hypothesis. We opted for an approach that we mastered best while still allowing to conclusively test for regional differences in activity across the hypothalamus. Examination of time series of the very same data we used will mainly confirm the results of our analyses – an anterior-posterior gradient in the impact of illuminance - and may yield slight differences in the limits of the subparts of the hypothalamus undergoing decreased or increased activity with increasing illuminance. While the suggested approach may have been envisaged if we had been facing negative results (i.e. no differences between subparts, potentially because subparts would not correspond functional differences in response to illuminance change), it would now constitute a circular confirmation of our main findings (i.e. using the same data). While we truly appreciate the suggestion, we do not consider that it would constitute a more parsimonious test of our hypothesis now that we successfully applied GLM/parcellation and GLMM approaches.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Bell et al. provide an exhaustive and clear description of the diversity of a new class of predicted type IV restriction systems that the authors denote as CoCoNuTs, for their characteristic presence of coiled-coil segments and nuclease tandems. Along with a comprehensive analysis that includes phylogenetics, protein structure prediction, extensive protein domain annotations, and an in-depth investigation of encoding genomic contexts, they also provide detailed hypotheses about the biological activity and molecular functions of the members of this class of predicted systems. This work is highly relevant, it underscores the wide diversity of defence systems that are used by prokaryotes and demonstrates that there are still many systems to be discovered. The work is sound and backed-up by a clear and reasonable bioinformatics approach. I do not have any major issues with the manuscript, but only some minor comments.

      Strengths:

      The analysis provided by the authors is extensive and covers the three most important aspects that can be covered computationally when analysing a new family/superfamily: phylogenetics, genomic context analysis, and protein-structure-based domain content annotation. With this, one can directly have an idea about the superfamily of the predicted system and infer their biological role. The bioinformatics approach is sound and makes use of the most current advances in the fields of protein evolution and structural bioinformatics.

      Weaknesses:

      It is not clear how coiled-coil segments were assigned if only based on AF2-predicted models or also backed by sequence analysis, as no description is provided in the methods. The structure prediction quality assessment is based solely on the average pLDDT of the obtained models (with a threshold of 80 or better). However, this is not enough, particularly when multimeric models are used. The PAE matrix should be used to evaluate relative orientations, particularly in the case where there is a prediction that parts from 2 proteins are interacting. In the case of multimers, interface quality scores, such as the ipTM or pDockQ, should also be considered and, at minimum, reported.

      A description of the coiled-coil predictions has been added to the Methods. For multimeric models, PAE matrices and ipTM+pTM scores have been included in Supplementary Data File S1.

      Reviewer #2 (Public Review):

      Summary:

      In this work, using in-depth computational analysis, Bell et al. explore the diverse repertoire of type IV McrBC modification-dependent restriction systems. The prototypical two-component McrBC system has been structurally and functionally characterised and is known to act as a defence by restricting phage and foreign DNA containing methylated cytosines. Here, the authors find previously unanticipated complexity and versatility of these systems and focus on detailed analysis and classification of a distinct branch, the so-called CoCoNut, named after its composition of coiled-coil structures and tandem nucleases. These CoCoNut systems are predicted to target RNA as well as DNA and to utilise defence mechanisms with some similarity to type III CRISPR-Cas systems.

      Strengths:

      This work is enriched with a plethora of ideas and a myriad of compelling hypotheses that now await experimental verification. The study comes from the group that was amongst the first to describe, characterize, and classify CRISPR-Cas systems. By analogy, the findings described here can similarly promote ingenious experimental and conceptual research that could further drive technological advances. It could also instigate vigorous scientific debates that will ultimately benefit the community.

      Weaknesses:

      The multi-component systems described here function in the context of large oligomeric complexes. Some of the single chain AF2 predictions shown in this work are not compatible, for example, with homohexameric complex formation due to incompatible orientation of domains. The recent advances in protein structure prediction, in particular AlphaFold2 (AF2) multimer, now allow us to confidently probe potential protein-protein interactions and protein complex formation. This predictive power could be exploited here to produce a better glimpse of these multimeric protein systems. It can also provide a more sound explanation for some of the observed differences amongst different McrBC types.

      Hexameric CnuB complexes with CnuC stimulatory monomers for Type I-A, I-B, I-C, II, and III-A CoCoNuT systems have been modeled with AF2 and included in Supplementary Data File S1, albeit without the domains fused to the GTPase N-terminus (with the exception of Type I-B, which lacks the long coiled-coil domain fused to the GTPase and was modeled with its entire sequence). Attempts to model the other full-length CnuB hexamers did not lead to convincing results.

      Recommendations for the authors:

      Reviewing Editor:

      The detailed recommendations by the two reviewers will help the authors to further strengthen the manuscript, but two points seem particularly worth considering: 1. The methods are barely sketched in the manuscript, but it could be useful to detail them more closely. Particularly regarding the coiled-coil segments, which are currently just statists, useful mainly for the name of the family, more detail on their prediction, structural properties, and purpose would be very helpful. 2. Due to its encyclopedic nature, the wealth of material presented in the paper makes it hard to penetrate in one go. Any effort to make it more accessible would be very welcome. Reviewer 1 in particular has made a number of suggestions regarding the figures, which would make them provide more support for the findings described in the text.

      A description of the techniques used to identify coiled-coil segments has been added to the Methods. Our predictions ranged from near certainty in the coiled-coils detected in CnuB homologs, to shorter helices at the limit of detection in other factors. We chose to report all probable coiled-coils, as the extensive coiled-coils fused to CnuB, which are often the only domain present other than the GTPase, imply involvement in mediating complex formation by interacting with coiled-coils in other factors, particularly the other CoCoNuT factors. The suggestions made by Reviewer 1 were thoughtful and we made an effort to incorporate them.

      Reviewer #1 (Recommendations For The Authors):

      I do not have any major issues with the manuscript. I have however some minor comments, as described below.

      • The last sentence of the abstract at first reads as a fact and not a hypothesis resulting from the work described in the manuscript. After the second read, I noticed the nuances in the sentence. I would suggest a rephrasing to emphasize that the activity described is a theoretical hypothesis not backed-up by experiments.

      This sentence has been rephrased to make explicit the hypothetical nature of the statement.

      • In line 64, the authors rename DUF3578 as ADAM because indeed its function is not unknown. Did the authors consider reaching out to InterPro to add this designation to this DUF? A search in interpro with DUF3578 results in "MrcB-like, N-terminal domain" and if a name is suggested, it may be worthwhile to take it to the IntrePro team.

      We will suggest this nomenclature to InterPro.

      • I find Figure 1E hard to analyse and think it occupies too much space for the information it provides. The color scheme, the large amount of small slices, and the lack of numbers make its information content very small. I would suggest moving this to the supplementary and making it instead a bar plot. If removed from Figure 1, more space is made available for the other panels, particularly the structural superpositions, which in my opinion are much more important.

      We have removed Figure 1E from the paper as it adds little information beyond the abundance and phyletic distribution of sequenced prokaryotes, in which McrBC systems are plentiful.

      • In Figure 2, it is not clear due to the presence of many colorful "operon schemes" that the tree is for a single gene and not for the full operon segment. Highlighting the target gene in the operons or signalling it somehow would make the figure easy to understand even in the absence of the text and legend. The same applies to Supplementary Figure 1.

      The legend has been modified to show more clearly that this is a tree of McrB-like GTPases.

      • In line 146, the authors write "AlphaFold-predicted endonucelase fold" to say that a protein contains a region that AF2 predicts to fold like an endonuclease. This is a weird way of writing it and can be confusing to non-expert readers. I would suggest rephrasing for increased clarity.

      This sentence has been rephrased for greater clarity.

      • In line 167, there is a [47]. I believe this is probably due to a previous reference formatting.

      Indeed, this was a reference formatting error and has been fixed.

      • In most figures, the color palette and the use of very similar color palettes for taxonomy pie charts, genomic context composition schemes, and domain composition diagrams make it really hard to have a good understanding of the image at first. Legends are often close to each other, and it is not obvious at first which belong to what. I would suggest changing the layouts and maybe some color schemes to make it easier to extract the information that these figures want to convey.

      It seemed that Figure 4 was the most glaring example of these issues, and it has been rearranged for easier comprehension.

      • In the paragraph that starts at line 199, the authors mention an Ig-like domain that is often found at the N-terminus of Type I CoCoNuTs. Are they all related to each other? How conserved are these domains?

      These domains are all predicted to adopt a similar beta-sandwich fold and are found at the N-terminus of most CoCoNuT CnuC homologs, suggesting they are part of the same family, but we did not undertake a more detailed sequenced-based analysis of these regions.

      We also find comparable domains in the CnuC/McrC-like partners of the abundant McrB-like NxD motif GTPases that are not part of CoCoNuT systems, and given the similarity of some of their predicted structures to Rho GDP-dissociation inhibitor 1, we suspect that they have coevolved as regulators of the non-canonical NxD motif GTPase type. Our CnuBC multimer models showing consistent proximity between these domains in CnuC and CnuB GTPase domains suggest this could indeed be the case. We plan to explore these findings further in a forthcoming publication.

      • In line 210, the authors write "suggesting a role in overcrowding-induced stress response". Why so? In >all other cases, the authors justify their hypothesis, which I really appreciated, but not here.

      A supplementary note justifying this hypothesis has been added to Supplementary Data File S1.

      • At the end of the paragraph that starts in line 264, the authors mention that they constructed AF2 multimeric models to predict if 2 proteins would interact. However, no quality scores were provided, particularly the PAE matrix. This would allow for a better judgement of this prediction, and I would suggest adding the PAE matrix as another panel in the figure where the 3D model of the complex is displayed.

      The PAE matrix and ipTM+pTM scores for this and other multimer models have been added to Supplementary Data File S1. For this model in particular, the surface charge distribution of the model has been presented to support the role of the domains that have a higher PAE in RNA binding.

      • In line 306, "(supplementary data)" refers to what part of the file?

      This file has been renamed Supplementary Table S3 and referenced as such.

      • In line 464, the authors suggest that ShdA could interact with CoCoNuTs. Why not model the complex as done for other cases? what would co-folding suggest?

      As we were not able to convincingly model full-length CnuB hexamers with N-terminal coiled-coils, we did not attempt modeling of this hypothetical complex with another protein with a long coiled-coil, but it remains an interesting possibility.

      • In line 528, why and how were some genes additionally analyzed with HHPred?

      Justification for this analysis has been added to the Methods, but briefly, these genes were additionally analyzed if there were no BLAST hits or to confirm the hits that were obtained.

      • In the first section of the methods, the first and second (particularly the second) paragraphs are extremely long. I would suggest breaking them to facilitate reading.

      This change has been made.

      • In line 545, what do the authors mean by "the alignment (...) were analyzed with HHPred"?

      A more detailed description of this step has been added to the Methods.

      • The authors provide the models they produced as well as extensive supplementary tables that make their data reusable, but they do not provide the code for the automated steps, as to excise target sequence sections out of multiple sequence alignments, for example.

      The code used for these steps has been in use in our group at the NCBI for many years. It will be difficult to utilize outside of the NCBI software environment, but for full disclosure, we have included a zipped repository with the scripts and custom-code dependencies, although there are external dependencies as well such as FastTree and BLAST. In brief, it involves PSI-BLAST detection of regions with the most significant homology to one of a set of provided alignments (seals-2-master/bin/wrappers/cog_psicognitor). In this case, the reference alignments of McrB-like GTPases and DUF2357 were generated manually using HHpred to analyze alignments of clustered PSI-BLAST results. This step provided an output of coordinates defining domain footprints in each query sequence, which were then combined and/or extended using scripts based on manual analysis of many examples with HHpred (footprint_finders/get_GTPase_frags.py and footprint_finders/get_DUF2357_frags.py), then these coordinates were used to excise such regions from the query amino acid sequence with a final script (seals-2-master/bin/misc/fa2frag).

      Reviewer #2 (Recommendations For The Authors):

      (1) Page 4, line 77 - 'PUA superfamily domains' could be more appropriate to use instead of "EVE superfamily".

      While this statement could perhaps be applied to PUA superfamily domains, our previous work we refer to, which strongly supports the assertion, was restricted to the EVE-like domains and we prefer to retain the original language.

      (2) Page 5. lines 128-130 - AF2 multimer prediction model could provide a more sound explanation for these differences.

      Our AF2 multimer predictions added in this revision indeed show that the NxD motif McrB-like CoCoNuT GTPases interact with their respective McrC-like partners such that an immunoglobulin-like beta-sandwich domain, fused to the N-termini of the McrC homologs and similar to Rho GDP-dissociation inhibitor 1, has the potential to physically interact with the GTPase variants. However, we did not probe this in greater detail, as it is beyond the scope of this already highly complex article, but we plan to study it in the future.

      (3) Page 8, line 252 - The surface charge distribution of CnuH OB fold domain looks very different from SmpB (pdb3iyr). In fact, the regions that are in contact with RNA in SmpB are highly acidic in CoCoNut CnuH. Although it looks likely that this domain is involved in RNA binding, the mode of interaction should be very different.

      We did not detect a strong similarity between the CnuH SmpB-like SPB domain and PDB 3IYR, but when we compare the surface charge distribution of PDB 1WJX and the SPB domain, while there is a significant area that is positively charged in 1WJX that is negatively charged in SPB, there is much that overlaps with the same charge in both domains.

      The similarity between SmpB and the SPB domain is significant, but definitely not exact. An important question for future studies is: If the domains are indeed related due to an ancient fusion of SmpB to an ancestor of CnuH, would this degree of divergence be expected?

      In other words, can we say anything about how the function of a stand-alone tmRNA-binding protein could evolve after being fused to a complex predicted RNA helicase with other predicted RNA binding domains already present? Experimental validation will ultimately be necessary to resolve these kinds of questions, but for now, it may be safe to say that the presence of this domain, especially in conjunction with the neighboring RelE-like RTL domain and UPF1-like helicase domain, signals a likely interaction with the A-site of the ribosome, and perhaps restriction of aberrant/viral mRNA.

    1. Author Response

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

      eLife assessment

      This work provides a valuable contribution and assessment of what it means to replicate a null study finding, and what are the appropriate methods for doing so (apart from a rote p-value assessment). Through a convincing re-analysis of results from the Reproducibility Project: Cancer Biology using frequentist equivalence testing and Bayes factors, the authors demonstrate that even when reducing 'replicability success' to a single criterion, how precisely replication is measured may yield differing results. Less focus is directed to appropriate replication of non-null findings.

      Reviewer #1 (Public Review):

      Summary:

      The goal of Pawel et al. is to provide a more rigorous and quantitative approach for judging whether or not an initial null finding (conventionally with p ≥ 0.05) has been replicated by a second similarly null finding. They discuss important objections to relying on the qualitative significant/non-significant dichotomy to make this judgment. They present two complementary methods (one frequentist and the other Bayesian) which provide a superior quantitative framework for assessing the replicability of null findings.

      Strengths:

      Clear presentation; illuminating examples drawn from the well-known Reproducibility Project: Cancer Biology data set; R-code that implements suggested analyses. Using both methods as suggested provides a superior procedure for judging the replicability of null findings.

      Weaknesses:

      The proposed frequentist and the Bayesian methods both rely on binary assessments of an original finding and its replication. I'm not sure if this is a weakness or is inherent to making binary decisions based on continuous data.

      For the frequentist method, a null finding is considered replicated if the original and replication 90% confidence intervals for the effects both fall within the equivalence range. According to this approach, a null finding would be considered replicated if p-values of both equivalences tests (original and replication) were, say, 0.049, whereas would not be considered replicated if, for example, the equivalence test of the original study had a p-value of 0.051 and the replication had a p-value of 0.001. Intuitively, the evidence for replication would seem to be stronger in the second instance. The recommended Bayesian approach similarly relies on a dichotomy (e.g., Bayes factor > 1).

      Thanks for the suggestions, we now emphasize more strongly in the “Methods for assessing replicability of null results” and “Conclusions” sections that both TOST p-values and Bayes factors are quantitative measures of evidence that do not require dichotomization into “success” or “failure”.

      Reviewer #2 (Public Review):

      Summary:

      The study demonstrates how inconclusive replications of studies initially with p > 0.05 can be and employs equivalence tests and Bayesian factor approaches to illustrate this concept. Interestingly, the study reveals that achieving a success rate of 11 out of 15, or 73%, as was accomplished with the non-significance criterion from the RPCB (Reproducibility Project: Cancer Biology), requires unrealistic margins of Δ > 2 for equivalence testing.

      Strengths:

      The study uses reliable and shareable/open data to demonstrate its findings, sharing as well the code for statistical analysis. The study provides sensitivity analysis for different scenarios of equivalence margin and alfa level, as well as for different scenarios of standard deviations for the prior of Bayes factors and different thresholds to consider. All analysis and code of the work is open and can be replicated. As well, the study demonstrates on a case-by-case basis how the different criteria can diverge, regarding one sample of a field of science: preclinical cancer biology. It also explains clearly what Bayes factors and equivalence tests are.

      Weaknesses:

      It would be interesting to investigate whether using Bayes factors and equivalence tests in addition to p-values results in a clearer scenario when applied to replication data from other fields. As mentioned by the authors, the Reproducibility Project: Experimental Philosophy (RPEP) and the Reproducibility Project: Psychology (RPP) have data attempting to replicate some original studies with null results. While the RPCB analysis yielded a similar picture when using both criteria, it is worth exploring whether this holds true for RPP and RPEP. Considerations for further research in this direction are suggested. Even if the original null results were excluded in the calculation of an overall replicability rate based on significance, sensitivity analyses considering them could have been conducted. The present authors can demonstrate replication success using the significance criteria in these two projects with initially p < 0.05 studies, both positive and non-positive.

      Other comments:

      • Introduction: The study demonstrates how inconclusive replications of studies initially with p > 0.05 can be and employs equivalence tests and Bayesian factor approaches to illustrate this concept. Interestingly, the study reveals that achieving a success rate of 11 out of 15, or 73%, as was accomplished with the non-significance criterion from the RPCB (Reproducibility Project: Cancer Biology), requires unrealistic margins of Δ > 2 for equivalence testing.

      • Overall picture vs. case-by-case scenario: An interesting finding is that the authors observe that in most cases, there is no substantial evidence for either the absence or the presence of an effect, as evidenced by the equivalence tests. Thus, using both suggested criteria results in a picture similar to the one initially raised by the paper itself. The work done by the authors highlights additional criteria that can be used to further analyze replication success on a case-by-case basis, and I believe that this is where the paper's main contributions lie. Despite not changing the overall picture much, I agree that the p-value criterion by itself does not distinguish between (1) a situation where the original study had low statistical power, resulting in a highly inconclusive non-significant result that does not provide evidence for the absence of an effect and (2) a scenario where the original study was adequately powered, and a non-significant result may indeed provide some evidence for the absence of an effect when analyzed with appropriate methods. Equivalence testing and Bayesian factor approaches are valuable tools in both cases.

      Regarding the 0.05 threshold, the choice of the prior distribution for the SMD under the alternative H1 is debatable, and this also applies to the equivalence margin. Sensitivity analyses, as highlighted by the authors, are helpful in these scenarios.

      Thank you for the thorough review and constructive feedback. We have added an additional “Appendix C: Null results from the RPP and EPRP” that shows equivalence testing and Bayes factor analyses for the RPP and EPRP null results.

      Reviewer #3 (Public Review):

      Summary:

      The paper points out that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. Also, it can not be considered a "replication success". The main point of the paper is rather obvious. It may be that both studies are underpowered, in which case their non-significance does not prove anything. The absence of evidence is not evidence of absence! On the other hand, statistical significance is a confusing concept for many, so some extra clarification is always welcome.

      One might wonder if the problem that the paper addresses is really a big issue. The authors point to the "Reproducibility Project: Cancer Biology" (RPCB, Errington et al., 2021). They criticize Errington et al. because they "explicitly defined null results in both the original and the replication study as a criterion for replication success." This is true in a literal sense, but it is also a little bit uncharitable. Errington et al. assessed replication success of "null results" with respect to 5 criteria, just one of which was statistical (non-)significance.

      It is very hard to decide if a replication was "successful" or not. After all, the original significant result could have been a false positive, and the original null-result a false negative. In light of these difficulties, I found the paper of Errington et al. quite balanced and thoughtful. Replication has been called "the cornerstone of science" but it turns out that it's actually very difficult to define "replication success". I find the paper of Pawel, Heyard, Micheloud, and Held to be a useful addition to the discussion.

      Strengths:

      This is a clearly written paper that is a useful addition to the important discussion of what constitutes a successful replication.

      Weaknesses:

      To me, it seems rather obvious that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. I'm not sure how often this mistake is made.

      Thanks for the feedback. We do not have systematic data on how often the mistake of confusing absence of evidence with evidence of absence has been made in the replication context, but we do know that it has been made in at least three prominent large-scale replication projects (the RPP, RPEP, RPCB). We therefore believe that there is a need for our article.

      Moreover, we agree that the RPCB provided a nuanced assessment of replication success using five different criteria for the original null results. We emphasize this now more in the “Introduction” section. However, we do not consider our article as “a little bit uncharitable” to the RPCB, as we discuss all other criteria used in the RPCB and note that our intent is not to diminish the important contributions of the RPCB, but rather to build on their work and provide constructive recommendations for future researchers. Furthermore, in response to comments made by Reviewer #2, we have added an additional “Appendix B: Null results from the RPP and EPRP” that shows equivalence testing and Bayes factor analyses for null results from two other replication projects, where the same issue arises.

      Reviewer #1 (Recommendations For The Authors):

      The authors may wish to address the dichotomy issue I raise above, either in the analysis or in the discussion.

      Thank you, we now emphasize that Bayes factors and TOST p-values do not need to be dichotomized but can be interpreted as quantitative measures of evidence, both in the “Methods for assessing replicability of null results” and the “Conclusions” sections.

      Reviewer #2 (Recommendations For The Authors):

      Given that, here follow additional suggestions that the authors should consider in light of the manuscript's word count limit, to avoid confusing the paper's main idea:

      2) Referencing: Could you reference the three interesting cases among the 15 RPCB null results (specifically, the three effects from the original paper #48) where the Bayes factor differs qualitatively from the equivalence test?

      We now explicitly cite the original and replication study from paper #48.

      3) Equivalence testing: As the authors state, only 4 out of the 15 study pairs are able to establish replication success at the 5% level, in the sense that both the original and the replication 90% confidence intervals fall within the equivalence range. Among these 4, two (Paper #48, Exp #2, Effect #5 and Paper #48, Exp #2, Effect #6) were initially positive with very low p-values, one (Paper #48, Exp #2, Effect #4) had an initial p of 0.06 and was very precisely estimated, and the only one in which equivalence testing provides a clearer picture of replication success is Paper #41, Exp #2, Effect #1, which had an initial p-value of 0.54 and a replication p-value of 0.05. In this latter case (or in all these ones), one might question whether the "liberal" equivalence range of Δ = 0.74 is the most appropriate. As the authors state, "The post-hoc specification of equivalence margins is controversial."

      We agree that the post hoc choice of equivalence ranges is a controversial issue. The margins define an equivalence region where effect sizes are considered practically negligible, and we agree that in many contexts SMD = 0.74 is a large effect size that is not practically negligible. We therefore present sensitivity analyses for a wide range of margins. However, we do not think that the choice of this margin is more controversial for the mentioned studies with low p-values than for other studies with greater p-values, since the question of whether a margin plausibly encodes practically negligible effect sizes is not related to the observed p-value of a study. Nevertheless, for the new analyses of the RPP and EPRP data in Appendix B, we have added additional sensitivity analyses showing how the individual TOST p-values and Bayes factors vary as a function of the margin and the prior standard deviation. We think that these analyses provide readers with an even more transparent picture regarding the implications of the choice of these parameters than the “project-wise” sensitivity analyses in Appendix A.

      4) Bayes factor suggestions: For the Bayes factor approach, it would be interesting to discuss examples where the BF differs slightly. This is likely to occur in scenarios where sample sizes differ significantly between the original study and replication. For example, in Paper #48, Exp #2 and Effect #4, the initial p is 0.06, but the BF is 8.1. In the replication, the BF dramatically drops to < 1/1000, as does the p-value. The initial evidence of 8.1 indicates some evidence for the absence of an effect, but not strong evidence ("strong evidence for H0"), whereas a p-value of 0.06 does not lead to such a conclusion; instead, it favors H1. It would be interesting if the authors discussed other similar cases in the paper. It's worth noting that in Paper #5, Exp #1, Effect #3, the replication p-value is 0.99, while the BF01 is 2.4, almost indicating "moderate" evidence for H0, even though the p-value is inconclusive.

      We agree that some of the examples nicely illustrate conceptual differences between p-values and Bayes factors, e.g., how they take into account sample size and effect size. As methodologists, we find these aspects interesting ourselves, but we think that emphasizing them is beyond the scope of the paper and would distract eLife readers from the main messages.

      Concerning the conceptual differences between Bayes factors and TOST p-values, we already discuss a case where there are qualitative differences in more detail (original paper #48). We added another discussion of this phenomenon in the Appendix C as it also occurs for the replication of Ranganath and Nosek (2008) that was part of the RPP.

      5) p-values, magnitude and precision: It's noteworthy to emphasize, if the authors decide to discuss this, that the p-value is influenced by both the effect's magnitude and its precision, so in Paper #9, Exp #2, Effect #6, BF01 = 4.1 has a higher p-value than a BF01 = 2.3 in its replication. However, there are cases where both p-values and BF agree. For example, in Paper #15, Exp #2, Effect #2, both the original and replication studies have similar sample sizes, and as the p-value decreases from p = 0.95 to p = 0.23, BF01 decreases from 5.1 ("moderate evidence for H0") to 1.3 (region of "Absence of evidence"), moving away from H0 in both cases. This also occurs in Paper #24, Exp #3, Effect #6.

      We appreciate the suggestions but, as explained before, think that the message of our paper is better understood without additional discussion of more general differences between p-values and Bayes factors.

      6) The grey zone: Given the above topic, it is important to highlight that in the "Absence of evidence grey zone" for the null hypothesis, for example, in Paper #5, Exp #1, Effect #3 with a p = 0.99 and a BF01 = 2.4 in the replication, BF and p-values reach similar conclusions. It's interesting to note, as the authors emphasize, that Dawson et al. (2011), Exp #2, Effect #2 is an interesting example, as the p-value decreases, favoring H1, likely due to the effect's magnitude, even with a small sample size (n = 3 in both original and replications). Bayes factors are very close to one due to the small sample sizes, as discussed by the authors.

      We appreciate the constructive comments. We think that the two examples from Dawson et al. (2011) and Goetz et al. (2011) already nicely illustrate absence of evidence and evidence of absence, respectively, and therefore decided not to discuss additional examples in detail, to avoid redundancy.

      7) Using meta-analytical results (?): For papers from RPCB, comparing the initial study with the meta-analytical results using Bayes factor and equivalence testing approaches (thus, increasing the sample size of the analysis, but creating dependency of results since the initial study would affect the meta-analytical one) could change the conclusions. This would be interesting to explore in initial studies that are replicated by much larger ones, such as: Paper #9, Exp #2, Effect #6; Goetz et al. (2011), Exp #1, Effect #1; Paper #28, Exp #3, Effect #3; Paper #41, Exp #2, Effect #1; and Paper #47, Exp #1, Effect #5).

      Thank you for the suggestion. We considered adding meta-analytic TOST p-values and Bayes factors before, but decided that Figure 3 and the results section are already quite technical, so adding more analyses may confuse more than help. Nevertheless, these meta-analytic approaches are discussed in the “Conclusions” section.

      8) Other samples of fields of science: It would be interesting to investigate whether using Bayes factors and equivalence tests in addition to p-values results in a clearer scenario when applied to replication data from other fields. As mentioned by the authors, the Reproducibility Project: Experimental Philosophy (RPEP) and the Reproducibility Project: Psychology (RPP) have data attempting to replicate some original studies with null results. While the RPCB analysis yielded a similar picture when using both criteria, it is worth exploring whether this holds true for RPP and RPEP. Considerations for further research in this direction are suggested. Even if the original null results were excluded in the calculation of an overall replicability rate based on significance, sensitivity analyses considering them could have been conducted. The present authors can demonstrate replication success using the significance criteria in these two projects with initially p < 0.05 studies, both positive and non-positive.

      Thank you for the excellent suggestion. We added an Appendix B where the null results from the RPP and EPRP are analyzed with our proposed approaches. The results are also discussed in the “Results” and “Conclusions” sections.

      9) Other approaches: I am curious about the potential impact of using an approach based on equivalence testing (as described in https://arxiv.org/abs/2308.09112). It would be valuable if the authors could run such analyses or reference the mentioned work.

      Thank you. We were unaware of this preprint. It seems related to the framework proposed by Stahel W. A. (2021) New relevance and significance measures to replace p-values. PLoS ONE 16(6): e0252991. https://doi.org/10.1371/journal.pone.0252991

      We now cite both papers in the discussion.

      10) Additional evidence: There is another study in which replications of initially p > 0.05 studies with p > 0.05 replications were also considered as replication successes. You can find it here: https://www.medrxiv.org/content/10.1101/2022.05.31.22275810v2. Although it involves a small sample of initially p > 0.05 studies with already large sample sizes, the work is currently under consideration for publication in PLOS ONE, and all data and materials can be accessed through OSF (links provided in the work).

      Thank you for sharing this interesting study with us. We feel that it is beyond the scope of the paper to include further analyses as there are already analyses of the RPCB, RPP, and EPRP null results. However, we will keep this study in mind for future analysis, especially since all data are openly available.

      11) Additional evidence 02: Ongoing replication projects, such as the Brazilian Reproducibility Initiative (BRI) and The Sports Replication Centre (https://ssreplicationcentre.com/), continue to generate valuable data. BRI is nearing completion of its results, and it promises interesting data for analyzing replication success using p-values, equivalence regions, and Bayes factor approaches.

      We now cite these two initiatives as examples of ongoing replication projects in the introduction. Similarly as for your last point, we think that it is beyond the scope of the paper to include further analyses as there are already analyses of the RPCB, RPP, and EPRP null results.

      Reviewer #3 (Recommendations For The Authors):

      I have no specific recommendations for the authors.

      Thank you for the constructive review.

      Reviewing Editor (Recommendations For the Authors):

      I recognize that it was suggested to the authors by the previous Reviewing Editor to reduce the amount of statistical material to be made more suitable for a non-statistical audience, and so what I am about to say contradicts advice you were given before. But, with this revised version, I actually found it difficult to understand the particulars of the construction of the Bayes Factors and would have appreciated a few more sentences on the underlying models that fed into the calculations. In my opinion, the provided citations (e.g., Dienes Z. 2014. Using Bayes to get the most out of non-significant results) did not provide sufficient background to warrant a lack of more technical presentation here.

      Thank you for the feedback. We added a new “Appendix C: Technical details on Bayes factors” that provides technical details on the models, priors, and calculations underlying the Bayes factors.

    1. Author response:

      We thank the reviewers for their attention to our study and for their fair and reasonable assessment of the strengths and weaknesses of our work. We believe the reviewers adequately captured both the potential implications of our work as well as its major current limitations. As both reviewers noted, we believe the work presented in this manuscript is an exciting first step in adapting minibinders as antigen sensors for synthetic receptors but many questions remain before these new tools can be widely adopted. We hope that this work will catalyze others to try minibinders as potential antigen sensors when developing novel synthetic receptors, and we hope that future work will more thoroughly test a wide range of linkers to better optimize antigen sensor function across synthetic receptors.

      In our future work, we intend to evaluate a greater diversity of minibinders across different relevant therapeutic targets. We are working to test both existing minibinders as well as generate novel minibinders using deep-learning-based de novo protein design methods. We further hope to explore additional linker modifications, especially focusing on modifications that will allow minibinder coupled-synthetic receptors to escape the glycocalyx of engineered cells. We hope to share findings on these topics in either an update to this manuscript or in future manuscripts, depending on the results of our studies in progress.

      Finally, reviewers noted a mismatch in the data displayed in Figure 5A and 5C, whereby LCB-CAR-expressing cells induced higher lysis in Figure 5C than in Figure 5A. This is due to figure 5C showing only 24 hours of incubation between effector and target cells, as opposed to the 72 hours of incubation that is quantitated in 5A. These mismatched timepoints were selected because linker-dependent differences in lysis were most readily apparent at 24 hours and were negligible at 72 hours. The full-time course of lysis for this experiment can be seen in Supplemental Figure 2D.

    1. Author response:

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

      We thank the reviewers for their thorough review of and overall positive comments on our manuscript. We have revised the manuscript to address most of the concerns raised. Below is a point-by-point response to the reviewers’ comments outlining these changes.

      The novelty of the study is compromised due to the recently published structure of unliganded PRex1 (Chang et al. 2022). The unliganded and IP4-bound structure of P-Rex1 appear virtually identical, however, no clear comparison is presented in the manuscript. In the same paper, a very similar model of P-Rex1 activation upon binding to PIP3 membranes and Gbeta/gamma is presented.

      This comparison has been added as Supplemental Figure 5. Although similar models of activation are presented in our manuscript and in that of Chang et al. 2022, our model is extended to incorporate inhibition by IP4 and other aspects of regulation not previously incorporated, shown in both schematic form (Figure 6B) and including supporting data (Figure 6A). We also point out that in the work by Chang et al. they used domain insertions to stabilize the structure, and here we present the native protein structure. It turns out that they look similar, but our work reduces concerns over possible engineering artifacts. Finally, our model is further informed by HDX-MS measurements of the enzyme bound to PIP3 in liposomes (Figure 6A and Supplemental figure 8), which reveal the regions of the protein subject to higher dynamics and are consistent with a more fully extended conformation.

      The authors demonstrate that IP4 binding to P-Rex1 results in catalytic inhibition and increased protection of autoinhibitory interfaces, as judged by HDX. The relevance of this in a cellular setting is not clear and is not experimentally demonstrated. Further, mechanistically, it is not clear whether the biochemical inhibition by IP4 of PIP3 activated P-Rex1 is due to competition of IP4 with activating PIP3 binding to the PH domain of P-Rex1, or due to stabilizing the autoinhibited conformation, or both.

      We feel that both occur. IP4 and PIP3 bind to the same site of the PH domain, thus they must be competitive at the very least. We also show that IP4 stabilizes the autoinhibited conformation (based on both our cryo-EM and HDX-MS data). Because PIP3 does not activate either DH/PH or DH/PH-DEP1 (nor does IP4 inhibit, see Sup. Fig. 1), it is not possible for us to tell with this suite of experiments how much the inhibition is due to competition versus stabilization of the autoinhibited conformation.

      It is difficult to judge the error in the HDX experiments presented in Sup. data 1 and 2. In the method section, it is stated that the results represent the average from two samples. How is the SD error calculated in Fig.1B-C?

      To clarify, the following passages have been revised:

      Figure 1 legend – “Graphs show the exchange over time for select regions in the P-Rex1 (B) PH domain and (C) a IP4P region that was disordered in the P-Rex1–Gbg structure. Shown is the average of two experiments with error bars representing the mean ± standard deviation.” Methods section – “Each sample was analyzed twice by HDX-MS, and the data shown in graphs represent the average of these experiments. For each peptide, the average of all five time points was calculated and used to plot the difference data onto the coordinates.”

      As mentioned, from the explanations in the manuscript it is difficult to judge the differences between the unliganded and the IP4 bound structure. A superposition, pointing to the main differences, would help. Are there any additional interactions observed that could explain a more stable autoinhibitory conformation?

      Added as Supplemental Figure 5. Although there are global shifts in some of the domains, the overall structures are similar to one another. Due to the moderate resolution of both structures (~4.2 Å), accurate placement of sidechains is difficult, in some places more than others. Because of this, we cannot pinpoint many specific sidechain interactions with certainty. There are no obvious interactions observed in our IP4 bound structure compared to that of 7SYF that would explain a more stable autoinhibited conformation, and thus the evidence comes primarily from the HDX-MS data.

      The cellular significance of IP4 regulation is not clear. Finding a way to manipulate intracellular IP4 levels and showing that this affects P-Rex1 cellular activity would greatly increase the significance of this finding.

      We agree that this would be an informative experiment, but not one that we currently have the means to perform.

      From the presented data it is not clear if inhibition by IP4 is due to competition with PIP3 or due to the proposed stabilization of P-Rex1 autoinhibition. Performing a study as shown in Fig.1D, but with the DH/PH construct could resolve this question.

      First, please see our response to the similar concern from Reviewer 1 above. It is not possible for us to test the DH/PH construct and assess if there is direct competition with PIP3. To emphasize this point (and to correct the error that we never made a call to Sup. Fig. 1C in the original manuscript), we added the following lines to the first paragraph of the Results.

      “Negatively charged liposomes (containing PC/PS), including those that also contain PIP3, unexpectedly inhibit the GEF activity of the DH/PH-DEP1 and DH/PH fragments (Sup. Fig. 1C). Because full-length P-Rex1 is not affected by PC/PS liposomes, it suggests this the observed inhibition represents a non-productive interaction of the DH/PH-DEP1 and DH/PH fragments with negatively charged surfaces in our assay. The lack of activation of DH/PH-DEP1 by PIP3 prevents us from testing whether IP4 can directly inhibit via direct competition with PIP3.”

      If I understand correctly, the data shown in Supplementary Data 1 and 2 are averages of 2 measurements, which makes it difficult to judge real signals from outliers. Perhaps, rather than showing the average, the results from the two experiments could be shown. Also, please explain how the SD error is calculated in Fig.1B-C if the data points indeed are averages of 2 measurements.

      We are sorry for the confusion. The data shown in Sup. Data 1 and 2 are not averages of two experiments. The Methods section has therefore been modified to read: “Each image in Supplemental Data 1 and 2 shows one experiment (rainbow plots) or a difference analysis from those experiments (red to blue plots). Only one of the two sets of experiments performed for each condition (+/- liposomes or +/- IP4) is shown here.” As described above, text has been added to clarify the SD error calculated in Fig. 1B and 1C.

      The authors claim that the data presented in Fig 4B suggests that the salt bridge formed by K207 and E251 is important for autoinhibition. If so, the authors should explain why the K207C mutant is not activated.

      Multiple reviewers had problems with this panel, and we now recognize that we misinterpreted the data, which did not help with this. Because this data is largely just supportive of our structure and SAXS data, Figure 4 was moved to the Supplement and this section of the results now reads:

      “Flexibility of the hinge in the a6-aN helix of the DH/PH module is important for autoinhibition.

      One of our initial goals in this project was to determine a high-resolution structure of the autoinhibited DH/PH-DEP1 core by X-ray crystallography. To this end, we started with the DH/PH-DEP1 A170K variant, which was more inhibited than wild-type but still dynamic, and then introduced S235C/M244C and K207C/E251C double mutants to completely constrain the hinge in the a6-aN helix via disulfide bond formation in a redox sensitive manner. Single cysteine variants K207C and M244C were generated as controls. The S235C/M244C variant performed as expected, decreasing the activity of the A170K variant to nearly background in the oxidized but not the reduced state (Supplemental Fig. 4). However, the M244C single mutant exhibited similar effects, suggesting that it forms disulfide bonds with cysteine(s) other than S235C. Indeed, the side chains of Cys200 and Cys234 are very close to that of M244C. The K207C/E251C mutant was similar to S235C/M244C under oxidized conditions, but ~15-fold more active (similar to WT DH/PH levels, see Fig. 3C) under reducing conditions. The K270C variant, on the other hand, exhibited higher activity than A170K on its own under oxidizing conditions, but similar activity to all the variants except K207C/E251C when reduced. These results suggest that K207C/E251C in a reduced state and K270C in an oxidized state favor a configuration where the DEP1 domain is less able to engage the DH domain and maintain the kinked state. The mechanism for this is not known. Regardless, these data show that perturbation of contacts between the kinked segments of the a6-aN helix can have profound consequences on the activity of the DH/PH-DEP1 core.”

      In the low-resolution cryo-EM study, it is mentioned that only a few classes exhibit the extra density that ultimately corresponds to autoinhibited P-Rex1. If so, is this also the case in the high-resolution study and how many of the most populated classes contribute to the autoinhibited structure? It would be informative for the reader to provide this information.

      Indeed, only a small subset of the particles are in the autoinhibited conformation in the Krios data set, similar to the Glacios. How many classes these particles partition to is dependent on how many classes are asked for during 2D classification and how many “garbage” particles are present at the different stages of particle stack cleaning during 2D classification. Also, because of the preferred orientation problem, many of the particles in this conformation segregate together during 2D classification. Therefore, in addition to the information show in Sup. Fig. 2, we think a more informative metric to answer the reviewer’s question is the number of particles at the start of data processing compared to at the end, which is shown in Table 1.

      Page 10, line 217: "The kink .... is important for autoinhibition". It seems unlikely that there is no kink in the activated state. Perhaps it should say something like "Mobility in the kink is important ..."

      Agreed. In fact, the SAXS data we reported on the DH/PH module in Ravala et al. (2020) is most consistent with a DH/PH that exhibits both extended and condensed conformations in solutions.

      Fig. 4A: It would help to label helices alpha6 and alphaN.

      These helices have now been labeled.

      Page 11, lines 223 and 228 are contradictory: In line 223 it is stated that K207C/E251C exhibit reduced GEF activity, while on line 228 it says this has little effect under non-reducing conditions.

      We thank the reviewer for this catch. We have modified the text to make it self-consistent.

      In Fig.5B, it would help if the authors mention in the legend that a trans-well migration assay was used, in order to know what the increase in stained cells signifies.

      The legend has been modified to include this information.

      The previous work by Chang et al., 2022 (PMID: 35864164) found that the final DH domain α6 formed the hinge helix (the kink in this manuscript), which undergoes a significant conformational change between closed and opened conformations of P-Rex1. Could the authors discuss the state of the kink in the presence of IP4 and in the P-Rex1 variants A170K and L177E?

      We have now included an alignment of our structure in the presence of IP4 with the Chang et al., 2022 structure (Supplemental Figure 5). There is very little difference in the kink region. Because the A170K variant exhibits reduced GEF activity and a smaller Dmax, it could be speculated that the kink might be further stabilized as compared to wild-type. The L177E variant exhibited activity similar to that of DH/PH alone, implying a relief of the kink. This interpretation is supported by our SAXS analysis of A170K and L177E in Fig. 3.

      I am a bit confused about the set of experiments with the intended DH-DEP1 interface disruptive mutation A170K, which later turned out to enhance P-Rex1 activity inhibition. The authors explained that the DH K170 salt bridges with DEP1 Glu411 stabilize the DH-DEP1 interaction. Next, the authors used P-Rex1 A170K mutant as the backbone for the introduction of disulfide bonds to block the closed configuration of the DH-PH hinge region by creating some mutants S235C/M244C and K207C/E251C. The first intended C235-C244 disulfide bond did not show any effect on the GEF activity because C235 is so close to the native C234 for a potential disulfide bond. I would recommend putting the data of S235C/M244C into a supplemental figure. Also, I am wondering if the GEF activity measurements in Fig 4B could be performed in the presence or absence of IP4 to see whether the IP4-induced autoinhibition form is distinct from the natural autoinhibitory once the kink was unblocked by reducing agent DTT.

      The confusion was warranted by our poor analysis of this data, rectified as discussed above.

      With regards to experiments plus/minus IP4, due to the absence of the IP4P domain, IP4 had no inhibitory effect on the activity of DH/PH or DH/PH-DEP1 (Supplemental Figure 1A and 1B) and as such this experiment would not likely be informative (or at best very hard to interpret).

      For the IP4 versus PIP3 activity assays, the authors indicated that P-Rex1 inhibition is dependent on the Inositol 3-phosphate. Have the authors tested and could they test with either Ins (1,3,4)P3 or Ins(1,3,5)P3?

      In these assays (Figure 1D), we show that inhibition does not occur with Ins(1,4,5)P3. Based on previous structures of IP4 bound to the PH domain and supporting biochemical assays (Cash et al., 2016, Structure), the 3- and 4-phosphates are the most highly coordinated and the next most thermostabilizing headgroup other than IP4 was Ins(1,3,4)P3. Therefore, we would anticipate that Ins(1,3,4)P3 might stabilize the autoinhibited state, perhaps at higher concentrations, but we have not directly tested this.

      The authors should provide the electron density maps of the P-REX1-IP4 complex in the supplemental figure and highlight the maps for two key interactions between DEP1 and DH and between PH and IP4P 4-helix bundle subdomain.

      The Coulomb potential map of this complex is shown in Figure 2A. Due to the moderate resolution of the reconstruction, side chain details cannot be unambiguously modeled at these interfaces, which is why we do not highlight any observed, specific interactions between sidechains.

      The manuscript was written very well and there is only one typing error in the legend of Supplemental Figure 1.

      Thank you for this catch.

      Details of EM density at significant domain interfaces and at the IP4 binding site should be provided as supplementary material.

      Beyond our comment about interfaces above, we have now provided the map representing the bound IP4 as Figure 4B.

      Line 123: It is difficult to discern in Figure 2A the "severe bend" in the helix that connects the DH and PH domains. It was not apparent (to me, at least) where this helix is located until eventually encountering Figure 4. It would be helpful to highlight or label (maybe with an asterisk) the bend site in Fig 2A.

      This has been labeled in Figure 2A.

      Line 125-126: likewise, It would be helpful to the reader to highlight the GTPase binding site in the DH domain.

      This has been labeled in Figure 2A.

      Line 159. Consider adding a supplementary figure showing a superposition of the two pREX-1 regulatory interfaces in the present structure and in 7SYF.

      A superposition of the two structures has now been added as Supplemental Figure 5. Because both structures are of moderate resolution, it is difficult to place side chains with a high degree of certainty. Thus, we did not think it wise to draw conclusions from comparisons between the details of these interfaces.

      Is the positioning of IP4 dictated by the EM density, prior knowledge from high-resolution structures, or both? A rendering of the EM density over the stick model as a supplementary figure would be helpful.

      This was modeled based on both. This image has now been added as Figure 4B.

      It should be emphasized that the jackknife model is similar to the hinge model proposed by Chang et al (2022).

      Mention of similarity between our model and the model proposed by Chang et al., 2022 occurs twice in the manuscript.

    1. Author response:

      eLife assessment

      In this valuable study, Kumar et al., provide evidence suggesting that the p130Cas drives the formation of condensates that sprout from focal adhesions to cytoplasm and suppress translation. Pending further substantiation, this study was found to be likely to provide previously unappreciated insights into the mechanisms linking focal adhesions to the regulation of protein synthesis and was thus considered to be of broad general interest. However, the evidence supporting the proposed model was incomplete; additional evidence is warranted to substantiate the relationship between p130Cas condensates and mRNA translation and establish corresponding functional consequences.

      We thank the Elife editorial team for their positive assessment of the broad significance of our manuscript. We fully agree that the functional consequences need to be explored in more detail. We feel that many of the criticisms are valid points that are not easily addressed via available tools, thus, should be considered limitations of present approaches. We hope that readers appreciate that identification of a new class of liquid-liquid phase separations calls for much more work to fully explore their characteristics, regulation and function, which will likely advance many areas of cell biology and perhaps even medicine.

      Reviewer #1 (Public Review):

      Summary:

      The authors demonstrated the phenomenon of p130Cas, a protein primarily localized at focal adhesions, and its formation of condensates. They identified the constituents within the condensates, which include other focal adhesion proteins, paxillin, and RNAs. Furthermore, they proposed a link between p130Cas condensates and translation.

      Strengths:

      Adhesion components undergo rapid exchange with the cytoplasm for some unclear biological functions. Given that p130Cas is recognized as a prominent mechanical focal adhesion component, investigating its role in condensate formation, particularly its impact on the translation process, is intriguing and significant.

      We thank the reviewer for recognizing the functional significance of the work.

      Weaknesses:

      The authors identified the disordered region of p130Cas and investigated the formation of p130Cas condensate. They attempted to demonstrate that p130Cas condensates inhibit translation, but the results did not fully support this assertion. There are several comments below:

      (1) Despite isolating p130Cas-GFP protein using GFP-trap beads, the authors cannot conclusively eliminate the possibility of isolating p130Cas from focal adhesions. While the characterization of the GFP-tagged pulls can reveal the proteins and RNAs associated with p130Cas, they need to clarify their intramolecular mechanism of localization within p130Cas droplets. Whether the protein condensates retain their liquid phase or these GFP-p130Cas pulls represent protein aggregate remains uncertain.

      We agree, the isolation from cell lysates does not distinguish between focal adhesions and cytoplasmic LLPS. We note that p130Cas in focal adhesions also appears to be in LLPS. But there are no methods available to isolate them separately. We acknowledge this is a limitation of the study.

      (2) The authors utilized hexanediol and ammonium acetate to highlight the phenomenon of p130Cas condensates. Although hexanediol is an inhibitor for hydrophobic interactions and ammonium acetate is a salt, a more thorough explanation of the intramolecular mechanisms underlying p130Cas protein-protein interaction is required. Additionally, given that the size of p130Cas condensates can exceed >100um2, classification is needed to differentiate between p130Cas condensates and protein aggregation.

      Ammonium acetate, which works by promoting hydrophobic interactions and weak Van der Waals forces, has been widely used in phase separation studies to change ionic strength without altering intracellular pH. Conversely, hexanediol weakens hydrophobic/ Van der Walls interactions that commonly mediate phase separation of IDRs. In the case of p130Cas, the multiple tyrosines and within the scaffolding domain are obvious targets. If the reviewer is asking us to resolve the detailed hydrophobic interactions within the scaffolding domain, this is far beyond the scope of the current paper.

      Protein aggregates are defined by their characteristics (e.g irreversibility, departure from spherical) not by size. Older, larger droplets remain circular and show slower but still measurable rates of exchange. Moreover, droplets are essentially absent after trypsinizing and replating cells. All these results argue against aggregates.

      (3) The connection between p130Cas condensates and translation inhibition appears tenuous. The data only suggests a correlation between p130Cas expression and translation inhibition. Further evidence is required to bolster this hypothesis.

      The optogenetic experiment shows that triggering LLPS by dimerizing p130Cas results in inhibition of translation. This is a causal not a correlative experiment. The reviewer may be thinking that dimerizing p130Cas could stimulate focal adhesion signaling, activating FAK or a src family kinase or other signals. However, none of these signals has been linked to inhibition of cell growth or migration. Thus, we agree that this is a limitation but consider it a low probability mechanism.

      Reviewer #2 (Public Review):

      Summary:

      In this article, Kumar et al., report on a previously unappreciated mechanism of translational regulation whereby p130Cas induces LLPS condensates that then traffic out from focal adhesion into the cytoplasm to modulate mRNA translation. Specifically, the authors employed EGFP-tagged p130Cas constructs, endogenous p130Cas, and p130Cas knockouts and mutants in cell-based systems. These experiments in conjunction with various imaging techniques revealed that p130Cas drives assembly of LLPS condensates in a manner that is largely independent of tyrosine phosphorylation. This was followed by in vitro EGFP-tagged p130Cas-dependent induction of LLPS condensates and determination of their composition by mass spectrometry, which revealed enrichment of proteins involved in RNA metabolism in the condensates. The authors excluded the plausibility that p130Cas-containing condensates co-localize with stress granules or p-bodies. Next, the authors determined mRNA compendium of p130Cas-containing condensates which revealed that they are enriched in transcripts encoding proteins implicated in cell cycle progression, survival, and cell-cell communication. These findings were followed by the authors demonstrating that p130Cas-containing condensates may be implicated in the suppression of protein synthesis using puromycylation assay. Altogether, it was found that this study significantly advances the knowledge pertinent to the understanding of molecular underpinnings of the role of p130Cas and more broadly focal adhesions on cellular function, and to this end, it is likely that this report will be of interest to a broad range of scientists from a wide spectrum of biomedical disciplines including cell, molecular, developmental and cancer biologists.

      Strengths:

      Altogether, this study was found to be of potentially broad interest inasmuch as it delineates a hitherto unappreciated link between p130Cas, LLPS, and regulation of mRNA translation. More broadly, this report provides unique molecular insights into the previously unappreciated mechanisms of the role of focal adhesions in regulating protein synthesis. Overall, it was thought that the provided data sufficiently supported most of the authors' conclusions. It was also thought that this study incorporates an appropriate balance of imaging, cell and molecular biology, and biochemical techniques, whereby the methodology was found to be largely appropriate.

      We thank reviewer for this positive assessment.

      Weaknesses:

      Two major weaknesses of the study were noted. The first issue is related to the experiments establishing the role of p130Cas-driven condensates in translational suppression, whereby it remained unclear whether these effects are affecting global mRNA translation or are specific to the mRNAs contained in the condensates. Moreover, some of the results in this section (e.g., experiments using cycloheximide) may be open to alternative interpretation. The second issue is the apparent lack of functional studies, and although the authors speculate that the described mechanism is likely to mediate the effects of focal adhesions on e.g., quiescence, experimental testing of this tenet was lacking.

      We appreciate the reviewer’s insights. Assessing translational inhibition for specific genes rather than global measurement of translation is an important direction for future work.

      Regarding the cycloheximide experiments, we are unsure what the reviewer means. We used it as a control for puromycin labeling but this is a very standard approach. It seems more likely that the question concerns Fig 5G, where we used it to sequester mRNAs on ribosomes to deplete from other pools. In this case, p130cas condensates decrease after 2 minutes. The reviewer may be suggesting that this effect could be due to blocked translation per se and loss of short-lived proteins. We acknowledge that this is possible but given the very rapid effect (2 min), we think it unlikely.

      Lastly, we agree with the reviewer that further functional studies in quiescence or senescence are warranted; however, these are extensive, open-ended studies and we will not be able to include them as part of the current paper.

    1. Author response:

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

      eLife assessment

      In this valuable study, the authors investigate the transcriptional landscape of tuberculous meningitis, revealing important molecular differences contributed by HIV co-infection. Whilst some of the evidence presented is compelling, the bioinformatics analysis is limited to a descriptive narrative of gene-level functional annotations, which are somewhat basic and fail to define aspects of biology very precisely. Whilst the work will be of broad interest to the infectious disease community, validation of the data is critical for future utility.

      We appreciate with eLife’s positive assessment, although we challenge the conclusion that we ‘fail to define aspects of biology very precisely’. Our stated objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis and the eLife assessment affirms we have investigated ‘the transcriptional landscape of tuberculous meningitis’. To more precisely define aspects of the biology will require another study with different design and methods.

      Reviewer #1 (Public Review):

      Summary:

      Tuberculous meningitis (TBM) is one of the most severe forms of extrapulmonary TB. TBM is especially prevalent in people who are immunocompromised (e.g. HIV-positive). Delays in diagnosis and treatment could lead to severe disease or mortality. In this study, the authors performed the largest-ever host whole blood transcriptomics analysis on a cohort of 606 Vietnamese participants. The results indicated that TBM mortality is associated with increased neutrophil activation and decreased T and B cell activation pathways. Furthermore, increased angiogenesis was also observed in HIV-positive patients who died from TBM, whereas activated TNF signaling and down-regulated extracellular matrix organisation were seen in the HIV-negative group. Despite similarities in transcriptional profiles between PTB and TBM compared to healthy controls, inflammatory genes were more active in HIV-positive TBM. Finally, 4 hub genes (MCEMP1, NELL2, ZNF354C, and CD4) were identified as strong predictors of death from TBM.

      Strengths:

      This is a really impressive piece of work, both in terms of the size of the cohort which took years of effort to recruit, sample, and analyse, and also the meticulous bioinformatics performed. The biggest advantage of obtaining a whole blood signature is that it allows an easier translational development into a test that can be used in the clinical with a minimally invasive sample. Furthermore, the data from this study has also revealed important insights into the mechanisms associated with mortality and the differences in pathogenesis between HIV-positive and HIV-negative patients, which would have diagnostic and therapeutic implications.

      Weaknesses:

      The data on blood neutrophil count is really intriguing and seems to provide a very powerful yet easy-to-measure method to differentiate survival vs. death in TBM patients. It would be quite useful in this case to perform predictive analysis to see if neutrophil count alone, or in combination with gene signature, can predict (or better predict) mortality, as it would be far easier for clinical implementation than the RNA-based method. Moreover, genes associated with increased neutrophil activation and decreased T cell activation both have significantly higher enrichment scores in TBM (Figure 9) and in morality (Figure 8). While I understand the basis of selecting hub genes in the significant modules, they often do not represent these biological pathways (at least not directly associated in most cases). If genes were selected based on these biologically relevant pathways, would they have better predictive values?

      We conducted a sensitivity analysis including blood neutrophil as a potential predictor in the multivariate Cox elastic-net regression model for important predictor selection (Table S14). In this analysis, all six selected important predictors (genes and clinical risk factors) identified in the original analysis (Table S13) were also selected, together with blood neutrophil number. Additionally, we evaluated the predictive value of blood neutrophil alone, which demonstrated poor performance, with an optimism-corrected AUC of 0.63 for all TBM, 0.67 for HIV-negative TBM, and 0.70 for HIV-positive TBM. Even when combined with identified gene signatures, blood neutrophil did not improve the overall performance of predictive model (optimism-corrected AUC of 0.79 for all TBM, 0.76 for HIV-negative TBM, and 0.80 for HIV-positive). These results indicate that identified hub genes exhibit better predictive values compared to blood neutrophil alone or in combination. These findings have been incorporated into our manuscript results.

      To test whether pathway representative genes have better predictive values than hub genes, we included all these genes in the analysis for important predictor selection. Pathway representative genes comprised ANXA3 and CXCR2 representing neutrophil activation and IL1b representing acute inflammatory response. We observed that all hub genes (MCEMP1, NELL2, ZNF354C, and CD4) consistently emerged as the most important genes with the highest selection in the models, compared to the rest, in both the HIV-negative TBM and HIV-positive TBM cohorts. Additionally, these identified hub genes were still selected when testing together with other hub genes representing relevant biological pathways associated with TBM mortality, such as CYSTM1 involved in neutrophil activation, TRAF5 involved in NF-kappa B signaling pathway, CD28 and TESPA1 involved in T cell receptor signaling. These results show that selected genes based on known biologically relevant pathways did not give better predictive values than the identified hub genes in the significant modules.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript describes the analysis of blood transcriptomic data from patients with TB meningitis, with and without HIV infection, with some comparison to those of patients with pulmonary tuberculosis and healthy volunteers. The objectives were to describe the comparative biological differences represented by the blood transcriptome in TBM associated with HIV co-infection or survival/mortality outcomes and to identify a blood transcriptional signature to predict these outcomes. The authors report an association between mortality and increased levels of acute inflammation and neutrophil activation, but decreased levels of adaptive immunity and T/B cell activation. They propose a 4-gene prognostic signature to predict mortality.

      Strengths:

      Biological evaluations of blood transcriptomes in TB meningitis and their relationship to outcomes have not been extensively reported previously.

      The size of the data set is a major strength and is likely to be used extensively for secondary analyses in this field of research.

      Weaknesses:

      The bioinformatic analysis is limited to a descriptive narrative of gene-level functional annotations curated in GO and KEGG databases. This analysis cannot be used to make causal inferences. In addition, the functional annotations are limited to 'high-level' terms that fail to define biology very precisely. At best, they require independent validation for a given context. As a result, the conclusions are not adequately substantiated. The identification of a prognostic blood transcriptomic signature uses an unusual discovery approach that leverages weighted gene network analysis that underpins the bioinformatic analyses. However, the main problem is that authors seem to use all the data for discovery and do not undertake any true external validation of their gene signature. As a result, the proposed gene signature is likely to be overfitted to these data and not generalisable. Even this does not achieve significantly better prognostic discrimination than the existing clinical scoring.

      As explained in response to the eLife assessment, our objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis. We agree that ‘This analysis cannot be used to make causal inferences’: that would require different study design and approaches. The proposed gene signature has higher AUC values than the existing clinical model alone or in combination with clinical risk factors (Table 4). We agree that independent validation of the gene signature will be a crucial next step for future utility. We have performed qPCR in another sample set, and have added these results in the revision (Table 4 and supplementary figure S8)

      Reviewer #1 (Recommendations For The Authors):

      I have a few additional comments most of which are relatively minor:

      (1) Can the authors please clarify if all the PTB cases are also HIV-negative?

      This has been added to the methods section.

      (2) For Table 1, can the authors please list the total number of patients with microbiologically confirmed TB regardless of the methods used? And for the two TBM groups, was the positive microbiology based on CSF findings?

      The total number of patients with microbiologically confirmed TB was presented in Table 2 in definite TBM group, which was microbiologically confirmed TB diagnosed using microscopy, culture, and Xpert testing in cerebrospinal fluid (CSF) samples. We have updated the note in Table 2 to provide clarity on the definition.

      (3) How was the discovery and validation set selected? Was it based on randomisation?

      We randomly split TBM data into two datasets, a discovery cohort (n=142) and a validation cohort (n=139) with a purpose to ensure reproducibility of data analysis. We described this in the methods section.

      (4) Line 107 can be better clarified by stating that the overall 3-month mortality rate is 21.7% for TBM regardless of HIV status.

      Thank you, we have restated this sentence in the results section.

      (5) The authors stated that samples were collected at enrolment when patients would have received less than 6 days of anti-tubercular treatment. Is there information on the median and IQR on the number of days that the patients would have received Rx, especially between the groups? Did the authors control for this variable when analysing for DEGs?

      One of criteria to enroll participants in LAST-ACT and ACT-HIV trials is that they must receive less than 6 consecutive days of two or more drugs active against M. tuberculosis. However, the information of the days that the patients would have received Rx was not recorded and we could not control this variable when performing differential expression analysis for DEGs. This has been clarified further in the methods section: ‘The samples were taken at enrollment, when patients could not have received more than 6 consecutive days of two or more drugs active against M. tuberculosis.’

      (6) I am a little bit concerned with the reads mapping accuracy (57%) to the human genome, which is fairly low. Did the authors investigate the reasons behind this low accuracy?

      Thank you. It was indeed a typo. We have corrected it in the results section.

      (7) On Tables S2-S4, can the authors please clarify what the last column (labelled as "B") shows?

      Tables S2-S4 now have been changed to S3-S5. We have updated the legend of these tables to provide clarification regarding the meaning of the last column.

      Reviewer #2 (Recommendations For The Authors):

      If the authors wish to revise their manuscript, I suggest the following amendments:

      (1) Provide a consort diagram for the selection of samples included in the present analysis (from parent study cohorts), allocation to test and validation splits for bioinformatics analysis, and outcomes.

      We have provided our consort diagram in supplementary Figure S10.

      (2) Provide details of inclusion criteria for pulmonary TB cohort, and how samples from this cohort were selected for inclusion in the present analysis. Please clarify whether this cohort excluded HIV-positive participants by design or by chance.

      The inclusion criteria for the pulmonary TB cohort were described in the methods section. Due to the very low prevalence of HIV in this prospective observational study, HIV-positive participants were excluded. We have clarified in the amended manuscript that the pulmonary TB cohort only included HIV-negative participants.

      (3) Baseline characteristics of HIV-positive participants (Table 1) should include CD4 count, HIV viral load, and whether anti-retroviral therapy was naïve or experienced.

      We have included pre-treatment CD4 cell count, information on anti-retroviral therapy, and HIV viral load data in Table 1, as well as described these information in the results section.

      (4) I note that the TBM samples were derived from RCTs of adjunctive steroid therapy, but not stratified in the present analysis by treatment arm allocation. Clearly, this may affect the survival/mortality outcomes that are the central focus of this manuscript. Therefore, they should be included in the models for differential gene expression analysis and prognostic signature discovery. To do so, the authors may need to wait until they are able to unblind the trial metadata.

      With permission from the trial investigators, we were able to adjust the analyses for treatment with corticosteroids. The investigators remained blind to the allocation and we have not reported any direct effects of corticosteroids on outcome – such an analysis could only be done once the LAST-ACT trial has been reported (which won’t be until the end of 2024). Treatment outcome and effect were blinded by extracting only the fold change difference between survival and death in the linear regression model, in which gene expression was outcome and survival and treatment were covariates.

      (5) I understood from the methods (lines 460-461) that batch correction of the RNAseq data was necessary. However, it is not clear how the samples were batched. PCA of the transcriptomes before and after batch correction with batch and study group labels should be provided. I would also advocate for a sensitivity analysis to check the robustness of the main findings without batch correction. I assume Fig2A represents batch-corrected data, but this is not clear.

      We have now added information about the RNA sequencing batch and the batch correction approach, analyses and data visualizations utilized batch-corrected data in the methods section. We have also updated results related to batch correction in Fig. 2A and Supplementary Figure S9.

      (6) I would encourage the authors to include a differential gene expression analysis to directly compare the transcriptome of TBM to that of pulmonary TB. I think it would add additional value to their focus on describing the transcriptome in TBM.

      We thank for reviewer’s suggestion. Conducting differential gene expression analysis to compare the transcriptome of TBM with that of PTB is beyond the scope of this manuscript and we will examine this question separately.

      (7) I don't really understand the purpose of splitting their data set into test and validation for the purposes of showing that WGCNA analysis is mostly reproduced in the two halves of the data. I would advocate that they scrap this approach to maximise the statistical power of their analysis in the descriptive work.

      As mentioned in response to reviewer #1 in question #3, the purpose of splitting data is to ensure the reproducibility of the data analysis as suggested by Langfelder et al. (PMID: 21283776). This approach served two purposes: (i) to affirm the existence of functional modules in an independent cohort and (ii) to validate the association of interested modules or their hub genes with survival outcomes.

      (8) The authors should soften the confidence in their interpretation of the GO/KEGG annotations of WGCNA modules. At least, they should include a paragraph that explicitly details the limitations of their analyses, including (i) the accuracy GO/KEGG annotations are not validated in this context (if at all), (ii) that none of the data can be used to make causal inferences and (iii) that peripheral blood assessments that are obviously impacted by changes in cellular composition of peripheral blood do not necessarily reflect immunopathogenesis at the site of disease - in fact if circulating cells are being recruited to the site of disease or other immune compartments, then quite the opposite interpretations may be true.

      We appreciate the reviewer's comment. (i) In our analysis, we initially confirmed the existence of Weighted Gene Co-expression Network Analysis (WGCNA) modules in discovery cohort and validated the association of these modules with mortality outcomes in validation cohort. We then applied GO/KEGG annotations to define the biological functions involved in WGCNA modules. Finally, we performed Qusage analysis to directly test the association of top-hit pathways of each WGCNA module with mortality outcomes (see supplementary S6). This analysis approach helped to identify and validate modules and biological pathways associated with TBM mortality in this context, avoiding potential false positives in GO/KEGG annotations of WGCNA modules. (ii) We agree with the assessment that 'This analysis cannot be used to make causal inferences,' as that would require a different study design and approach. (iii) The focus of this study is to investigate the pathogenesis of TBM in the systemic immune system. We have highlighted this focus in the title and the aim of the manuscript.

      (9) For the prognostic signature discovery and validation, I strongly recommend the authors include more robust validation. For example, to undertake an 80:20 split for sequential discovery (for feature selection and derivation of a prognostic model), followed by validation of a 'locked' model in data that made no contribution to discovery. In two separate sensitivity analyses. I also suggest they split their dataset (i) by treatment allocation in the RCT and (ii) by HIV status. In addition, their method for feature selection has to be clearer- precisely how they select hub genes from their WGCNA analysis as candidate predictors is not explained. Since this is such a prominent output of their manuscript, the results of this analysis should really be included in the main manuscript, and all performance metrics for discrimination should include confidence intervals.

      Employing an 80:20 split for training and testing models is a good approach for an internal validation. However, we addressed the issue of overestimating the performance of a prognostic model by bootstrapping sampling approach proposed by Steyerberg et al. (PMID: 11470385). This approach has been proven to provide stable estimates with low bias. The overall model performance for discrimination, reported in our manuscript, was corrected for “optimism” to ensure internal validity. This adjustment was achieved through a 1000-times bootstrapping approach, which effectively accounted for estimation uncertainty. As such, there is no need to present confidence intervals for these metrics.

      Moreover, in our revision, to confirm prognostic signatures independently, we have evaluated the predictive value of identified gene signatures using qPCR in another set of samples. The results have been added in Table 4, supplementary Figure S8 and the results section.

      For the reasons given above (comment 4), we are unable to split our dataset by treatment allocation in this analysis. But as described, we have adjusted the analysis for corticosteroid treatment. Once the primary results of the LAST ACT trial have been published, we will examine the impact of corticosteroids on TBM pathophysiology and outcomes, seeking to better understand the mechanisms by which steroids have their therapeutic effects.

      Given the difference in pathogenesis and immune response by HIV-coinfection, we stratified our analysis by HIV status. As the reviewer’s suggestion, we have provided additional details in the methods section regarding the selection of hub genes from associated WGCNA modules and the feature selection process for predictive modeling.

    1. Author response:

      The following is the authors’ response to the previous reviews

      We extend our sincere gratitude for the invaluable comments provided by the reviewers and yourself, along with the constructive suggestions to enhance the quality of our manuscript. In response to this invaluable feedback, we have diligently revised and resubmitted our paper as an article, introducing five primary figures, seven supplementary figures, and two supplementary data files. Importantly, this work represents a significant contribution to the field, presenting novel findings for the first time without any prior publication.

      Within the enclosed document, we have provided a comprehensive response to the editor and reviewer comments, addressing each point meticulously and specifically. We extend our heartfelt thanks to the reviewers and yourself for your diligent examination of our manuscript and for offering insightful recommendations.

      In our latest revision, we have taken great care to address every comment, ensuring that we clarify the manuscript and provide robust evidence where required. We have meticulously highlighted the modifications within the manuscript in yellow for your convenience, while also including the modifications made in response to each specific comment. The primary focus of these revisions was to provide additional context regarding the relationship between PARP-1 and mono-methylated histones. Substantial modifications were made to our discussion section to address this point.

      Another concern raised was regarding the discrepancy in the relationship of PR-SET7 and PARP-1 between our study and the recent study by Estève et al. (PMID: 36434141). We have revised the results and discussion sections to discuss this concern.

      Addressing Reviewer 2’s concern about the potential indirect role of PARP1 in the regulation of some metabolic genes despite its direct binding to loci coding for metabolic genes we revised the discussion section to highlight this possibility.

      Enclosed, you will find a detailed, point-by-point response to each of the editor’s and reviewers' comments, showcasing our commitment to addressing their concerns with precision.

      We firmly believe that our revisions successfully resolve all the concerns raised by the editor and the reviewers, and we are confident that this improved version of our manuscript contributes significantly to the scientific discourse. Once again, we thank you for considering our work, and please feel free to contact me if you require any additional information.

      In the revised manuscript, most of the concerns raised by the reviewers have been addressed satisfactorily. However, as suggested by reviewer#2, it would have been more significant, if the PARP1-mediated reading of global mono-methylation of histone could be addressed. At least the mechanisms of selectivity of PARP1 need further convincing discussion.

      We thank the editor for their valuable comments. We have extended our discussion section to discuss in more detail the relationship between PARP1 and mono-methylated histones. In our refined Discussion section, we have endeavored to articulate more clearly how PARP-1 may be selectively recruited to active chromatin domains through its interaction with mono-methylated histone marks. We propose a model wherein PARP-1 actively participates in the turnover process, contributing to the maintenance of an active chromatin environment. This mechanism entails PARP-1 selectively binding to mono-methylated active histone marks associated with highly transcribed genes. Upon activation, PARP-1 undergoes automodification, leading to its release from chromatin and facilitating the reassembly of nucleosomes carrying the mono-methylated marks. Subsequently, the enzymatic action of Poly(ADP)-ribose glycohydrolase (PARG) cleaves pADPr, enabling the restoration of PARP-1's binding affinity to mono-methylated active histone marks. This proposed hypothesis is consistent with existing research across various model organisms and aligns with the known association of PARP-1 with highly expressed genes, as well as its role in mediating nucleosome dynamics and assembly.

      Our modified Discussion section unfolds as follows:

      "Finally, highly transcribed genes have been reported to present a high turnover of mono-methylated modifications, maintaining a state of low methylation (50). Moreover, our previous study revealed that PARP1 preferentially binds to highly active genes (34).  Consequently, our findings suggest an active involvement of PARP-1 in the turnover process to maintain an active chromatin environment. This proposed mechanism unfolds in the following steps: 1) PARP-1 selectively binds to mono-methylated active histone marks associated with highly transcribed genes. 2) Upon activation, PARP-1 undergoes automodification and subsequently disengages from chromatin, facilitating the reassembly of nucleosomes carrying the mono-methylated marks. 3) The enzymatic action of Poly(ADP)-ribose glycohydrolase (PARG) cleaves pADPr, restoring PARP-1's binding affinity to mono-methylated active histone marks. This proposed hypothesis is consistent with existing research conducted across various model organisms, including mice, Drosophila, and Humans (7, 24, 30, 51-53). Notably, previous studies have consistently demonstrated that PARP-1 predominantly associates with highly expressed genes and plays a crucial role in mediating nucleosome dynamics and assembly. Thus, our proposed model provides a molecular framework that may contribute to understanding the relationship between PARP-1 and the epigenetic regulation of gene expression."

      We trust that these revisions effectively address the editor’s comment and enhance the overall strength and clarity of our manuscript.

      Furthermore, recent developments in the area are omitted, as an important publication hasn't been discussed anywhere in the work (PMID: 36434141).

      We appreciate the editor's thorough review of our revised manuscript and the responses to the previous reviewer's comments. To address this important concern, we have carefully investigated the levels of PR-SET7 in parp1 hypomorphic conditions.

      Supplemental Fig. S4 and S5 demonstrate that in the absence of Parp1, there were no significant changes observed in PR-SET7 RNA or protein levels, respectively. This finding supports the conclusion that Parp1 is not directly involved in the regulation of PR-SET7 in Drosophila contrasting with the findings of Estève et al.'s study (PMID: 36434141). This discrepancy may arise from differing relationships between PARP-1 and PR-SET7, which could cooperate in the context of Drosophila development while playing antagonistic roles in specific cell lines or under particular conditions.

      We have updated the Results section to explicitly mention this observation:

      "Interestingly, in the absence of PARP-1, neither PR-SET7 RNA nor protein levels were affected (Supplemental Fig.S4-5), indicating that PARP-1 is not directly implicated in the regulation of pr-set7. This finding contrasts with recent evidence demonstrating PARP1-induced degradation of PR-SET7/SET8 in human cells (16)."

      Furthermore, we have modified the discussion section to address this discrepancy:

      "A recent study demonstrated that in human cells overexpressing PARP-1, PR-SET7/SET8 is degraded, whereas depletion of PARP-1 leads to an increase in PR-SET7/SET8 levels (16). However, in our study involving parp-1 mutant in Drosophila third-instar larvae revealed a nuanced scenario: we detected a minor but not significant reduction in both PR-SET7 RNA and protein levels (Supplemental Fig.S4 and S5). This outcome stands in stark contrast to the previous study's findings. The discrepancy could be due to the distinct experimental approaches used: the previous research focused on mammalian cells and in vitro experiments, whereas our study examined the functions of PARP-1 in whole Drosophila third-instar larvae during development. Consequently, while PARP-1 may cooperate with PR-SET7 in the context of Drosophila development, it could exhibit antagonistic roles against PR-SET7 in specific cell lines and under certain biological or developmental conditions."

      We believe that these modifications effectively address the raised concern and provide a more comprehensive understanding of the relationship between PARP1 and PR-SET7 in our study. We hope these clarifications enhance the overall robustness and clarity of our findings.

      Reviewer #2 (Public Review):

      Summary:

      This study from Bamgbose et al. identifies a new and important interaction between H4K20me and Parp1 that regulates inducible genes during development and heat stress. The authors present convincing experiments that form a mostly complete manuscript that significantly contributes to our understanding of how Parp1 associates with target genes to regulate their expression.

      Strengths:

      The authors present 3 compelling experiments to support the interaction between Parp1 and H4K20me, including:

      (1) PR-Set7 mutants remove all K4K20me and phenocopy Parp mutant developmental arrest and defective heat shock protein induction.

      (2) PR-Set7 mutants have dramatically reduced Parp1 association with chromatin and reduced poly-ADP ribosylation.

      (3) Parp1 directly binds H4K20me in vitro.

      Weaknesses:

      (1) The RNAseq analysis of Parp1/PR-Set7 mutants is reasonable, but there is a caveat to the author's conclusion (Line 251): "our results indicate H4K20me1 may be required for PARP-1 binding to preferentially repress metabolic genes and activate genes involved in neuron development at co-enriched genes." An alternative possibility is that many of the gene expression changes are indirect consequences of altered development induced by Parp1 or PR-Set7 mutants. For example, Parp1 could activate a transcription factor that represses metabolic genes. The authors counter this model by stating that Parp1 directly binds to "repressed" metabolic genes. While this argument supports their model, it does not rule out the competing indirect transcription factor model. Therefore, they should still mention the competing model as a possibility.

      We appreciate Reviewer 2's insightful comments during both rounds of revision, which have significantly enriched the quality of our manuscript. The binding of PARP1 to loci encoding metabolic genes indeed suggests a direct role of PARP1 in their regulation. However, we acknowledge Reviewer 2's point that some of these targets might be regulated indirectly, with PARP1 potentially modulating the expression of intermediary transcription factors.

      To address this possibility, we have revised the discussion section of our manuscript accordingly:

      "Remarkably, our observations indicate a notable affinity of PARP-1 for binding to the gene bodies of these metabolic genes (34), suggesting a direct involvement of PARP1 in their regulation. Nonetheless, it remains plausible that certain genes may be indirectly regulated by PARP1 through intermediary transcription factors."

      We trust that this modification adequately addresses Reviewer 2's concern.

      (2) The section on inducibility of heat shock genes is interesting but missing an important control that might significantly alter the author's conclusions. Hsp23 and Hsp83 (group B genes) are transcribed without heat shock, which likely explains why they have H4K20me without heat shock. The authors made the reasonable hypothesis that this H4K20me would recruit Parp-1 upon heat shock (line 270). However, they observed a decrease of H4K20me upon heat shock, which led them to conclude that "H4K20me may not be necessary for Parp1 binding/activation" (line 275). However, their RNA expression data (Fig4A) argues that both Parp1 and H40K20me are important for activation. An alternative possibility is that group B genes indeed recruit Parp1 (through H4K20me) upon heat shock, but then Parp1 promotes H3/H4 dissociation from group B genes. If Parp1 depletes H4, it will also deplete H4K20me1. To address this possibility, the authors should also do a ChIP for total H4 and plot both the raw signal of H4K20me1 and total H4 as well as the ratio of these signals. The authors could also note that Group A genes may similarly recruit Parp1 and deplete H3/H4 but with different kinetics than Group B genes because their basal state lacks H4K20me/Parp1. To test this possibility, the authors could measure Parp association, H4K20methylation, and H4 depletion at more time points after heat shock at both classes of genes.

      We sincerely appreciate Reviewer 2 for their insightful comment on our manuscript. Your hypothesis regarding the potential induction of H3/H4 dissociation from group B genes by PARP-1, leading to a reduction in H4K20me1, offers a thought-provoking perspective. However, our findings suggest an alternative interpretation.

      Our data indicate that while H4K20me1 is indeed present under normal conditions at group B genes, its reduction following heat shock does not seem to impede PARP-1's role in transcriptional activation (Fig. 4A, C, and E). Instead, we propose that this decrease in H4K20me1 might signify a regulatory shift in chromatin structure, facilitating transcriptional activation during heat shock, with PARP-1 playing an independent facilitating role. Moreover, existing studies have highlighted the dual role of H4K20me1, acting as a promoter of transcription elongation in certain contexts and as a repressor in others.

      The elevated enrichment of H4K20me1 in group B genes under normal conditions may indeed indicate a repressive state that requires alleviation for transcriptional activation. Additionally, we cannot discount the possibility of unique regulatory functions associated with PR-SET7, extending beyond its recognized role as a histone methylase. Non-catalytic activities and potential interactions with non-histone substrates might contribute to the nuanced control exerted by PR-SET7 on group B genes during heat stress.

      Furthermore, our exploration of pr-set720 and ParpC03256 mutants reveals distinct roles for PARP-1 and H4K20me1 in modulating gene expression (Fig 3E). This reinforces the notion that the interplay between PR-SET7 and PARP-1 involves a multifaceted regulatory mechanism.

      To address these points, we have revised the discussion section of our manuscript accordingly:

      "Another plausible explanation could be that the recruitment of PARP-1 to group B genes loci promotes H4 dissociation and then leads to a reduction of H4K20me1. However, our findings suggest an alternative interpretation: the decrease in H4K20me1 at group B genes during heat shock does not seem to impede PARP-1's role in transcriptional activation, (Fig.4A, C and E). Rather than disrupting PARP-1 function, we propose that this reduction in H4K20me1 may signify a regulatory shift in chromatin structure, priming these genes for transcriptional activation during heat shock, with PARP-1 playing an independent facilitating role. Moreover, existing studies have highlighted the dual role of H4K20me1, acting as a promoter of transcription elongation in certain contexts and as a repressor in others (13, 26, 39, 40, 42-46). The elevated enrichment of H4K20me1 in group B genes under normal conditions may indicate a repressive state that requires alleviation for transcriptional activation. Additionally, we cannot discount the possibility of unique regulatory functions associated with PR-SET7, extending beyond its recognized role as a histone methylase. Non-catalytic activities and potential interactions with non-histone substrates might contribute to the nuanced control exerted by PR-SET7 on group B genes during heat stress (47, 48). Furthermore, our exploration of pr-set720 and parp-1C03256 mutants reveals distinct roles for PARP-1 and H4K20me1 in modulating gene expression (Fig 3E). This reinforces the notion that the interplay between PR-SET7 and PARP-1 involves a multifaceted regulatory mechanism. Understanding the intricate relationship between these molecular players is crucial for elucidating the complexities of gene expression modulation under heat stress conditions."

      We believe that this modification enhances the clarity of our conclusions and adequately addresses Reviewer 2's concerns regarding the intricate relationship between PARP-1, H4K20me1, and PR-SET7 in transcriptional regulation under heat stress conditions.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      The endocannabinoid system (ECS) components are dysregulated within the lesion microenvironment and systemic circulation of endometriosis patients. Using endometriosis mouse models and genetic loss of function approaches, Lingegowda et al. report that canonical ECS receptors, CNR1 and CNR2, are required for disease initiation, progression, and T-cell dysfunction.

      Strengths:

      The approach uses genetic approaches to establish in vivo causal relationships between dysregulated ECS and endometriosis pathogenesis. The experimental design incorporates bulk RNAseq approaches, as well as imaging mass spectrometry to characterize the mouse lesions. The identification of immune-related and T-cell-specific changes in the lesion microenvironment of CNR1 and CNR2 knockout (KO) mice represents a significant advance

      Weaknesses:

      Although the mouse phenotypic analyses involve a detailed molecular characterization of the lesion microenvironment using genomic approaches, detailed measurements of lesion size/burden and histopathology would provide a better understanding of how CNR1 or CNR2 loss contributes to endometriosis initiation and progression. The cell or tissue-specific effects of the CNR1 and CNR2 are not incorporated into the experimental design of the studies. Although this aspect of the approach is recognized as a major limitation, global CNR1 and CNR2 KO may affect normal female reproductive tract function, ovarian steroid hormone levels, decidualization response, or lead to preexisting alterations in host or donor tissues, which could affect lesion establishment and development in the surgically induced, syngeneic mouse model of endometriosis.

      We appreciate the reviewer's thoughtful and constructive feedback. We agree that the additional measurements of lesion size/burden and histopathology would provide valuable insights into the specific contributions of CNR1 and CNR2 to endometriosis progression. However, the focus of this study was on assessing the alterations in complex immune microenvironment due to the absence of CNR1 and CNR2, given their close relation in regulating immune cell populations. We will plan to incorporate these measurements in future studies to further strengthen the understanding of the disease pathogenesis. Regarding the potential effects of global knockout, the reviewer raises a valid concern. To address this, we will explore cell and/or tissue-specific knockout models in future experiments to better isolate the direct effects of CNR1 and CNR2 on the disease process, while minimizing potential confounding factors from systemic alterations.

      Reviewer #2 (Public Review):

      Summary:

      The endocannabinoid system (ECS) regulates many critical functions, including reproductive function. Recent evidence indicates that dysregulated ECS contributes to endometriosis pathophysiology and the microenvironment. Therefore, the authors further examined the dysregulated ECS and its mechanisms in endometriosis lesion establishment and progression using two different endometrial sources of mouse models of endometriosis with CNR1 and CNR2 knockout mice. The authors presented differential gene expressions and altered pathways, especially those related to the adaptive immune response in CNR1 and CNR2 ko lesions. Interestingly, the T-cell population was dramatically reduced in the peritoneal cavity lacking CNR2, and the loss of proliferative activity of CD4+ T helper cells. Imaging mass cytometry analysis provided spatial profiling of cell populations and potential relationships among immune cells and other cell types. This study provided fundamental knowledge of the endocannabinoid system in endometriosis pathophysiology.

      Strengths:

      Dysregulated ECS and its mechanisms in endometriosis pathogenesis were assessed using two different endometrial sources of mouse models of endometriosis with CNR1 and CNR2 knockout mice. Not only endometriotic lesions, but also peritoneal exudate (and splenic) cells were analyzed to understand the specific local disease environment under the dysregulated ECS.

      Providing the results of transcriptional profiles and pathways, immune cell profiles, and spatial profiles of cell populations support altered immune cell population and their disrupted functions in endometriosis pathogenesis via dysregulation of ECS.

      In line 386: Role of CNR2 in T cells. The finding that nearly absent CD3+ T cells in the peritoneal cavity of CNR2 ko mice is intriguing.

      The interpretation of the results is well-described in the Discussion.

      Weaknesses:

      The study was terminated and characterized 7 days after EM induction surgery without the details for selecting the time point to perform the experiments.

      The authors also mentioned that altered eutopic endometrium contributes to the establishment and progression of endometriosis. This reviewer agrees with lines 324-325. If so, DEGs are likely identified between eutopic endometrium (with/without endometriosis lesion induction) and ectopic lesions. It would be nice to see the data (even though using publicly available data sets).

      Figure 7 CDEF. The results of the statistical analyses and analyzed sample numbers should be added. Lines 444-450 cannot be reviewed without them.

      This reviewer agrees with lines 498-500. In contrast, retrograded menstrual debris is not decidualized. The section could be modified to avoid misunderstanding.

      We would like to thank the reviewer for insightful comments, suggestions and acknowledging the importance of the work presented in this manuscript.

      Regarding 7-day time point, we have provided rationale in lines 479-481, but agree that it isn’t sufficient and hence we have provided additional details on the selection of the 7-day time point for the experiments in methods section (Mouse model of EM). We have also noted the suggestion on providing comparison of differentially expressed genes in the eutopic endometrium vs ectopic lesions. Since there are publications comparing the eutopic vs ectopic gene expression patterns (PMIDs: 33868805 and 18818281), including a study exploring the ECS genes in the endometrium throughout different menstrual cycles (PMID: 35672435), we believe additional analysis using the same dataset may not yield new information. However, we see the value in reviewer’s comment, and we will look at the gene expression patterns in the uterine vs endometriosis like lesions in our future studies with tissue or cell specific CNR1 and CNR2 knockout models to understand functional relevance of ECS in endometriosis initiation.

      Since the IMC study was exploratory for proof of concept, we did not have enough biological replicates for meaningful statistical validation (n = 2-3). We have clarified this information in the methods, results, and figure legends for appropriately representing the limitations of the current setup.

      Finally, we appreciate the feedback on the section discussing retrograded menstrual debris. Even though the menstrual debris may not be decidualized, some endometriotic lesions have the ability to decidualize based on their response to estrogen and progesterone in a cycling manner (PMID: 26450609), similar to the endometrium in the uterine cavity. We have clarified this in the revised MS.

    1. Author response:

      Public Reviews:

      Reviewer #1:

      Summary:

      Casas-Tinto et al. present convincing data that injury of the adult Drosophila CNS triggers transdifferentiation of glial cells and even the generation of neurons from glial cells. This observation opens up the possibility of getting a handle on the molecular basis of neuronal and glial generation in the vertebrate CNS after traumatic injury caused by Stroke or Crush injury. The authors use an array of sophisticated tools to follow the development of glial cells at the injury site in very young and mature adults. The results in mature adults revealing a remarkable plasticity in the fly CNS and dispels the notion that repair after injury may be only possible in nerve cords which are still developing. The observation of so-called VC cells which do not express the glial marker repo could point to the generation of neurons by former glial cells.

      Conclusion:

      The authors present an interesting story that is technically sound and could form the basis for an in-depth analysis of the molecular mechanism driving repair after brain injury in Drosophila and vertebrates.

      Strengths:

      The evidence for transdifferentiation of glial cells is convincing. In addition, the injury to the adult CNS shows an inherent plasticity of the mature ventral nerve cord which is unexpected.

      Weaknesses:

      Traumatic brain injury in Drosophila has been previously reported to trigger mitosis of glial cells and generation of neural stem cells in the larval CNS and the adult brain hemispheres. Therefore this report adds to but does not significantly change our current understanding. The origin and identity of VC cells is unclear.

      The Reviewer correctly points out that it has been reported that traumatic brain injury trigger generation of neural stem cells. However, according to previous reports, those cells where quiescent Dpn+ neuroblast. We now report that already differentiated adult neuropil glia transdifferentiate into neurons. Which is a new mechanism not previously reported.

      We agree with the reviewer regarding the identity of VC neurons although according to the results of G-TRACE experiments the origin is clear, they originate from neuropil glia (i.e. Astrocyte-like glia and ensheathing glia). We will use a battery of antibodies previously reported to identify specific subtypes of neurons to identify these newly generated neurons.

      Reviewer #2:

      Summary:

      Casas-Tinto et al., provide new insight into glial plasticity using a crush injury paradigm in the ventral nerve cord (VNC) of adult Drosophila. The authors find that both astrocyte-like glia (ALG) and ensheating glia (EG) divide under homeostatic conditions in the adult VNC and identify ALG as the glial population that specifically ramps up proliferation in response to injury, whereas the number of EGs decreases following the insult. Using lineage-tracing tools, the authors interestingly observe the interconversion of glial subtypes, especially of EGs into ALGs, which occurs independent of injury and is dependent on the availability of the transcription factor Prospero in EGs, adding to the plasticity observed in the system. Finally, when tracing the progeny of differentiated glia, Casas-Tinto and colleagues detect cells of neuronal identity and provide evidence that such glia-derived neurogenesis is specifically favored following ventral nerve cord injury, which puts forward a remarkable way in which glia can respond to neuronal damage.

      Numerous experiments have been carried out in 7-day-old flies, showing that the observed plasticity is not due to residual developmental remodeling or a still immature VNC.

      By elegantly combining different genetic tools, the authors show glial divisions with mitotic-dependent tracing and find that the number of generated glia is refined by apoptosis later on.

      The work identifies Prospero in glia as an important coordinator of glial cell fate, from development to the adult context, which draws further attention to the upstream regulatory mechanisms.

      We express our gratitude to the reviewer for their keen appreciation of our efforts and their enthusiasm for the outcomes of this research.

      Weaknesses:

      Although the authors do use a variety of methods to show glial proliferation, the EdU data (Figure 1B) could be more informative (Figure 1B) by displaying images of non-injured animals and providing quantifications or the mention of these numbers based on results previously acquired in the system.

      We appreciate the Reviewer’s comment. We believed that adding images of non-injured animals did not add new information as we already quantified the increase of glial proliferation upon injury in Losada-Perez let al. 2021. Besides, the porpoise of this experiment was to figure out if dividing cells where Astrocyte-like glia rather than the number of dividing cells. Comparing independent experiments could be tricky but if we compare the quantifications of G2-M glia (repo>fly-Fucci) done in Losada-Perez et al 2021 (fig 1C) with the quantifications of G2-M neuropil glia done in this work (fig 1C) we can see that the numbers are comparable.

      The experiments relying on the FUCCI cell cycle reporter suggested considerable baseline proliferation for EGs and ALGs, but when using an independent method (Twin Spot MARCM), mitotic marking was only detected for ALGs. This discrepancy could be addressed by assessing the co-localization of the different glia subsets using the identified driver lines with mitotic markers such as PH3.

      In our understanding this discrepancy could be explained by the magnitude of proliferation. The lower proliferation rate of EG (as indicate the fly-fucci experiments) combining with the incomplete efficiency of MARCM clones induction reduces considerably the chances of finding EG MARCM clones. PH3 is a mitotic marker but it is also found in apoptotic cells (Kim and Park 2012. DOI: 10.1371/journal.pone.0044307), however we can do the suggested experiment and quantify the results.

      The data in Figure 1C would be more convincing in combination with images of the FUCCI Reporter as it can provide further information on the location and proportion of glia that enter the cell cycle versus the fraction that remains quiescent.

      We will add the suggested images.

      The analyses of inter-glia conversion in Figure 3 are complicated by the fact that Prospero RNAi is both used to suppress EG - to ALG conversion and as a marker to establish ALG nature. Clarifications if the GFP+ cells still expressed Pros or were classified as NP-like GFP cells are required here.

      As described in the text, Pros is a marker for ALG and the results suggest that Prospero expression is required for the EG to ALG transition. We will clarify these concepts in the text accordingly. In figure 3 we showed images of NP-like cells originated from EG that are prospero+, and therefore supporting the transdifferentiation from EG to ALG.

      The conclusion that ALG and EG glial cells can give rise to cells of neuronal lineage is based on glial lineage information (GFP+ cells from glial G-trace) and staining for the neuronal marker Elav. The use of other neuronal markers apart from Elav or morphological features would provide a more compelling case that GFP+ cells are mature neurons.

      We completely agree with the reviewer's observation regarding the identity of VC neurons. We will try to identify the identity of these cells using previously described antibodies to identify neuronal populations. We will also appreciate any suggestions regarding the antibodies we can use

      Although the text discusses in which contexts, glial plasticity is observed or increased upon injury, the figures are less clear regarding this aspect. A more systematic comparison of injured VNCs versus homeostatic conditions, combined with clear labelling of the injury area would facilitate the understanding of the panels.

      We appreciate the Reviewer’s observation. We will carefully check all figures in order to increase their clarity

      Context/Discussion

      The study finds that glia in the ventral cord of flies have latent neurogenic potential. Such observations have not been made regarding glia in the fly brain, where injury is reported to drive glial divisions or the proliferation of undifferentiated progenitor cells with neurogenic potential.

      Discussing this different strategy for cell replacement adopted by glia in the VNC and pointing out differences to other modes seems fascinating. Highlighting differences in the reactiveness of glia in the VNC compared to the brain also seems highly relevant as they may point to different properties to repair damage.

      Based on the assays employed, the study points to a significant amount of glial "identity" changes or interconversions, which is surprising under homeostatic conditions. The significance of this "baseline" plasticity remains undiscussed, although glia unarguably show extensive adaptations during nervous system development.

      It would be interesting to know if the "interconversion" of glia is determined by the needs in the tissue or would shift in the context of selective ablation/suppression of a glial type.

      We deeply appreciate the Reviewer’s enthusiasm on this subject, it is indeed fascinating. We made a reduced discussion in order to fit in the eLife Short report requirements but the specific condition that trigger glial interconversion are of great interest for us. To compromise EG or ALG viability and evaluate the behaviour of glial cells is of great interest for developmental biology and regeneration, but the precise scenario to develop these experiments is not well defined. In this report, we aim to reproduce an injury in Drosophila brain and this model should serve to analyze cellular behaviours. The scenario where we deplete on specific subpopulation of glial cells is conceptually attractive, but far away from the scope of this report.

      Reviewer #3:

      In this manuscript, Casas-Tintó et al. explore the role of glial cells in the response to a neurodegenerative injury in the adult brain. They used Drosophila melanogaster as a model organism and found that glial cells are able to generate new neurons through the mechanism of transdifferentiation in response to injury.

      This paper provides a new mechanism in regeneration and gives an understanding of the role of glial cells in the process.

    1. Author response:

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

      Reviewer #1 (Public Review):

      In this manuscript, Huang and colleagues explored the role of iron in bacterial therapy for cancer. Using proteomics, they revealed the upregulation of bacterial genes that uptake iron, and reasoned that such regulation is an adaptation to the iron-deficient tumor microenvironment. Logically, they engineered E. Coli strains with enhanced iron-uptake efficiency, and showed that these strains, together with iron scavengers, suppress tumor growth in a mouse model. Lastly, they reported the tumor suppression by IroA-E. Coli provides immunological memory via CD8+ T cells. In general, I find the findings in the manuscript novel and the evidence convincing.

      (1) Although the genetic and proteomic data are convincing, would it be possible to directly quantify the iron concentration in (1) E. Coli in different growth environments, and (2) tumor microenvironment? This will provide the functional consequences of upregulating genes that import iron into the bacteria.

      We appreciate the reviewer’s comment regarding the precise quantification of iron concentrations. In our study, we attempted various experimental approaches, including Immunohistochemistry utilizing an a Fe3+ probe, iron assay kit (ab83366), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite these attempts, the quantification of oxidized Fe3+ concentrations proved challenging due to the inherently low levels of Fe ions and difficulty to distinguish Fe2+ and Fe3+. We observed measurements below the detection threshold of even the sensitive ICP-MS technique. To circumvent this limitation, we designed an experiment wherein bacteria were cultured in a medium supplemented with Chrome Azurol S (CAS) reagent, which colormetrically detects siderophore activity. We compared WT bacteria and IroA-expressing bacteria at varying levels of Lcn2 proteins. The outcome, as depicted in the updated Fig. 3b, reveals an enhanced iron acquisition capability in IroA-E. coli under the presence of Lcn2 proteins, in comparison to the wild-type E. coli strains. In addition to the Lcn2 study, the proteomic study in Figure 4 highlights the competitive landscape between cancer cells and bacteria. We observed that IroA-E. coli showed reduced stress responses and exerted elevated iron-associated stress to cancer cells, thus further supporting the IroA-E. coli’s iron-scavenging capability against nutritional immunity.

      (2) Related to 1, the experiment to study the synergistic effect of CDG and VLX600 (lines 139-175) is very nice and promising, but one flaw here is a lack of the measurement of iron concentration. Therefore, a possible explanation could be that CDG acts in another manner, unrelated to iron uptake, that synergizes with VLX600's function to deplete iron from cancer cells. Here, a direct measurement of iron concentration will show the effect of CDG on iron uptake, thus complementing the missing link.

      We appreciate the reviewer’s comment and would like to point the reviewer to our results in Figure S3, which shows that the expression of CDG enhances bacteria survival in the presence of LCN2 proteins, which reflects the competitive relationship between CDG and enterobactin for LCN2 proteins as previously shown by Li et al. [Nat Commun 6:8330, 2015]. We regret to inform the reviewer that direct measurement of iron concentration was attempted to no avail due to the limited sensitivity of iron detecting assays. We do acknowledge that CDG may exert different effects in addition to enhancing iron uptake, particularly the potentiation of the STING pathway. We pointed out such effect in Fig 2c that shows enhanced macrophage stimulation by the CDG-expressing bacteria. We would like to accentuate, however, that a primary objective of the experiment is to show that the manipulation of nutritional immunity for promoting anticancer bacterial therapy can be achieved by combining bacteria with iron chelator VLX600. The multifaceted effects of CDG prompted us to focus on IroA-E. coli in subsequent experiments to examine the role of nutritional immunity on bacterial therapy. We have updated the associated text to better convey our experimental design principle.

      Lines 250-268: Although statistically significant, I would recommend the authors characterize the CD8+ T cells a little more, as the mechanism now seems quite elusive. What signals or memories do CD8+ T cells acquire after IroA-E. Coli treatment to confer their long-term immunogenicity?

      We apologize for the overinterpretation of the immune memory response in our previous manuscript and appreciate the reviewer’s recommendation to further characterize CD8+ T cells post-IroA-E. coli treatment. Our findings, which show robust tumor inhibition in rechallenge studies, indicate establishment of anticancer adaptive immune responses. As the scope of the present work is aimed at demonstrating the value of engineered bacteria for overcoming nutritional immunity, expounding on the memory phenotypes of the resulting cellular immunity is beyond the scope of the study. We do acknowledge that our initial writing overextended our claims and have revised the manuscript accordingly. The revised manuscript highlights induction of anticancer adaptive immunity, attributable to CD8+ T cells, following the bacterial therapy.

      (3) Perhaps this goes beyond the scope of the current manuscript, but how broadly applicable is the observed iron-transport phenomenon in other tumor models? I would recommend the authors to either experimentally test it in another model or at least discuss this question.

      We highly appreciate the reviewer’s suggestion regarding the generalizability of the iron-transport phenomenon in diverse tumor models. To address this, we extended our investigations beyond the initial model, employing B16-F10 melanoma and E0771 breast cancer in mouse subcutaneous models. The results, as depicted in Figures 3g to 3j and Figure S5, demonstrate the superiority of IroA-E. coli over WT bacteria in tumor inhibition. These findings support the broad implication of nutritional immunity as well as the potential of iron-scavenging bacteria for different solid tumor treatments.

      Reviewer #2 (Public Review):

      Summary:

      The authors provide strong evidence that bacteria, such as E. coli, compete with tumor cells for iron resources and consequently reduce tumor growth. When sequestration between LCN2 and bacterobactin is blocked by upregulating CDG(DGC-E. coli) or salmochelin(IroA-E.coli), E. coli increase iron uptake from the tumor microenvironment (TME) and restrict iron availability for tumor cells. Long-term remission in IroA-E.coli treated mice is associated with enhanced CD8+ T cell activity. Additionally, systemic delivery of IroA-E.coli shows a synergistic effect with chemotherapy reagent oxaliplatin to reduce tumor growth.

      Strengths:

      It is important to identify the iron-related crosstalk between E. coli and TME. Blocking lcn2-bacterobactin sequestration by different strategies consistently reduces tumor growth.

      Weaknesses:

      As engineered E.coli upregulate their function to uptake iron, they may increase the likelihood of escaping from nutritional immunity (LCN2 becomes insensitive to sequester iron from the bacteria). Would this raise the chance of developing sepsis? Do authors think that it is safe to administrate these engineered bacteria in mice or humans?

      We appreciate the reviewer’s comment on the safety evaluation of the iron-scavenging bacteria. To address the concern, we assessed the potential risk of sepsis development by measuring the bacterial burden and performing whole blood cell analyses following intravenous injection of the engineered bacteria. As illustrated in Figures 3k and 3l, our findings indicate that the administration of these engineered bacteria does not elevate the risk of sepsis. The blood cell analysis suggests that mice treated with the bacteria eventually return to baseline levels comparable to untreated mice, supporting the safety of this approach in our experimental models.

      Reviewer #3 (Public Review):

      Summary:

      Based on their observation that tumor has an iron-deficient microenvironment, and the assumption that nutritional immunity is important in bacteria-mediated tumor modulation, the authors postulate that manipulation of iron homeostasis can affect tumor growth. They show that iron chelation and engineered DGC-E. coli have synergistic effects on tumor growth suppression. Using engineered IroA-E. coli that presumably have more resistance to LCN2, they show improved tumor suppression and survival rate. They also conclude that the IroA-E. coli treated mice develop immunological memory, as they are resistant to repeat tumor injections, and these effects are mediated by CD8+ T cells. Finally, they show synergistic effects of IroA-E. coli and oxaliplatin in tumor suppression, which may have important clinical implications.

      Strengths:

      This paper uses straightforward in vitro and in vivo techniques to examine a specific and important question of nutritional immunity in bacteria-mediated tumor therapy. They are successful in showing that manipulation of iron regulation during nutritional immunity does affect the virulence of the bacteria, and in turn the tumor. These findings open future avenues of investigation, including the use of different bacteria, different delivery systems for therapeutics, and different tumor types.

      Weaknesses:

      • There is no discussion of the cancer type and why this cancer type was chosen. Colon cancer is not one of the more prominently studied cancer types for LCN2 activity. While this is a proof-of-concept paper, there should be some recognition of the potential different effects on different tumor types. For example, this model is dependent on significant LCN production, and different tumors have variable levels of LCN expression. Would the response of the tumor depend on the role of iron in that cancer type? For example, breast cancer aggressiveness has been shown to be influenced by FPN levels and labile iron pools.

      We highly appreciate the reviewer’s insightful comment on the varying LCN2 activities across different tumor types. In light of the reviewer’s suggestion, we extended our investigations beyond the initial colon cancer model, employing B16-F10 melanoma and E0771 breast cancer in mouse subcutaneous models. The results, as depicted in Figures 3g to 3j and Figure S5, demonstrate that IroA-E. coli consistently outperforms WT bacteria in tumor inhibition. We acknowledge the reviewer’s comment regarding LCN2 being more prominently examined in breast cancer and have highlighted this aspect in the revised manuscript. For colon and melanoma cancers, several reports have pointed out the correlation of LCN2 expression and the aggressiveness of these cancers [Int J Cancer. 2021 Oct 1;149(7):1495-1511][Nat Cancer. 2023 Mar;4(3):401-418], albeit to a lesser extent. These findings support the broad implication of nutritional immunity as well as the potential of iron-scavenging bacteria for different solid tumor treatments. The manuscript has been revised to reflect the reviewer’s insightful comment.

      • Are the effects on tumor suppression assumed to be from E. coli virulence, i.e. Does the higher number of bacteria result in increased immune-mediated tumor suppression? Or are the effects partially from iron status in the tumor cells and the TME?

      We appreciate the reviewer’s question regarding the therapeutic mechanism of IroA-E. coli. Bacterial therapy exerts its anticancer action through several different mechanisms, including bacterial virulence, nutrient and ecological competition, and immune stimulation. Decoupling one mechanism from another would be technically challenging and beyond the scope of the present work. With the objective of demonstrating that an iron-scavenging bacteria can elevate anticancer activity by circumventing nutritional immunity, we highlight our data in Fig. S6, which shows that IroA-E. coli administration resulted in higher bacterial colonization within solid tumors compared to WT-E. coli on Day 15. This increased bacterial presence supports our iron-scavenging bacteria design, and we highlight a few anticancer mechanisms mediated by the engineered bacteria. Firstly, as shown in Fig. 4d, IroA-E. coli is shown to induce an elevated iron stress response in tumor cells as the treated tumor cells show increased expression of transferrin receptors. Secondly, our experiments involving CD8+ T cell depletion indicates that the IroA-E. coli establishes a more robust anticancer CD8+ T cell response than WT bacteria. Both immune-mediated responses and alterations in iron status within the tumor microenvironment are demonstrated to contribute to the enhanced anticancer activity of IroA-E. coli in the present study.

      • If the effects are iron-related, could the authors provide some quantification of iron status in tumor cells and/or the TME? Could the proteomic data be queried for this data?

      We appreciate the reviewer’s query regarding the quantification of iron concentrations. In our study, we attempted various experimental approaches, including Immunohistochemistry utilizing an a Fe3+ probe, iron assay kit (ab83366), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite these attempts, the quantification of oxidized Fe3+ concentrations proved challenging due to the inherently low levels of Fe ions and difficulty to distinguish Fe2+ and Fe3+. We observed measurements below the detection threshold of even the sensitive ICP-MS technique. Consequently, to circumvent this limitation, we designed an experiment wherein bacteria were cultured in a medium supplemented with Chrome Azurol S (CAS) reagent, which colormetrically detects siderophore activity. We compared WT bacteria and IroA-expressing bacteria at varying levels of Lcn2 proteins. The outcome, as depicted in the updated Fig. 3b, reveals an enhanced iron acquisition capability in IroA-E. coli under the presence of Lcn2 proteins, in comparison to the wild-type E. coli strains. In addition to the Lcn2 study, the proteomic study in Figure 4 highlights the competitive landscape between cancer cells and bacteria. We observed that IroA-E. coli showed reduced stress responses and exerted elevated iron-associated stress to cancer cells, thus further supporting the IroA-E. coli’s iron-scavenging capability against nutritional immunity.

      Reviewing Editor:

      The authors provide compelling technically sound evidence that bacteria, such as E. coli, can be engineered to sequester iron to potentially compete with tumor cells for iron resources and consequently reduce tumor growth. Long-term remission in IroA-E.coli treated mice is associated with enhanced CD8+ T cell activity and a synergistic effect with chemotherapy reagent oxaliplatin is observed to reduce tumor growth. The following additional assessments are needed to fully evaluate the current work for completeness; please see individual reviews for further details.

      We appreciate the editor’s positive comment.

      (1) The premise is one of translation yet the authors have not demonstrated that manipulating bacteria to sequester iron does not provide a potential for sepsis or other evidence that this does not increase the competitiveness of bacteria relative to the host. Only tumor volume was provided rather than animal survival and cause of death, but bacterial virulence is enhanced including the possibility of septic demise. Alternatively, postulated by the authors, that tumor volume is decreased due to iron sequestration but they do not directly quantify the iron concentration in (1) E. Coli in different growth environments, and (2) tumor microenvironment. These important endpoints will provide the functional consequences of upregulating genes that import iron into the bacteria.

      We appreciate the editor’s comment and have added substantial data to support the translational potential of the iron-scavenging bacteria. In particular, we added evidence that the iron-scavenging bacteria does not increase the risk of sepsis (Fig. 3k, l), evidence of increased bacteria competitiveness and survival in tumor (Fig. S6), and iron-scavenging bacteria’s superior anticancer ability and survival benefit across 3 different tumor models (Fig. 3e-j; Fig. S5). While direct measurement of iron concentration in the tumor environment is technically difficult due to the challenge in differentiating Fe2+ and Fe3+ by available techniques, we added a colormetric CAS assay to demonstrate the iron-scavenging bacteria can more effectively utility Fe than WT bacteria in the presence of LCN2 (Fig. 3b). These results substantiate the translational relevance of the engineered bacteria.

      (2) There is no discussion of the cancer type and why this cancer type was chosen. If the current tumor modulation system is dependent on LCN2 activity, there would need to be some recognition that different tumors have variable levels of LCN expression. Would the response of the tumor depend on the role of iron in that cancer type?

      We appreciate the comment and added relevant text and citations describing clinical relevance of LCN2 expression associated with the tumor types used in the study (breast cancer, melanoma, and colon cancer). Elevated LCN2 has been associated with higher aggressiveness for all three cancer types.

      (3) To demonstrate long-term anti-cancer memory was established through enhancement of CD8+ T cell activity (Fig 5c), the "2nd seeding tumor cells" experiment may need to be done in CD8 antibody-treated IronA mice since CD8+ T cells may play a role in tumor suppression regardless of whether or not iron regulation is being manipulated. It appears that the control group for this experiment is naive mice (and not WT-E. coli treated mice), in which case the immunologic memory could be from having had tumor/E. coli rather than the effect of IroA-E. coli.

      We acknowledge that our prior writing may have overstated our claim on immunological memory. Our intention is to show that upon treatment and tumor eradication by iron-scavenging bacteria, adaptive immunity mediated by CD8 T cells can be elicited. We also did not consider a WT-E. coli control as no WT-E. coli treated group achieved complete tumor regression. We have modified our text to reflect our intended message.

      Reviewer #1 (Recommendations For The Authors):

      All the figures seem to be in low resolution and pixelated. Please upload high-resolution ones.

      We have updated figures to high-resolution ones.

      Reviewer #2 (Recommendations For The Authors):

      Some specific comments towards experiments:

      (1) For Fig 2 f/ Fig 3f/ Fig 5d/Fig6c, the survival rate is based on the tumor volume (the mouse was considered dead when the tumor volume exceeded 1,500 mm3). Did the mice die from the experiment (how many from each group)? If it only reflects the tumor size, do these figures deliver the same information as the tumor growth figure?

      We appreciate the reviewer’s comment. The survival rate is indeed based on tumor volume, and we used a cutoff of 1500 mm3. No death event was observed prior to the tumors reaching 1500 mm3. Although the survival figures cover some of the information conveyed by the tumor volume tracking, the figures offer additional temporal resolution of tumor progression with the survival figures. Having both tumor volume and survival tracking are commonly adopted to depict tumor progression. We have the protocol regarding survival monitoring to the materials and method section.

      (2) Fig 3a, not sure if entE is a good negative control for this experiment. Neg. Ctrl should maintain its CFU/ml at a certain level regardless of Lcn2 conc. However, entE conc. is at 100 CUF/ml throughout the experiment suggesting there is no entE in media or if it is supersensitive to Lcn2 that bacteria die at the dose of 0.1nM?

      We appreciate the reviewer’s comment. The △entE-E. coli was indeed observed to be highly sensitive to LCN2. We included the control to highlight the competitive relationship between entE and LCN2 for iron chelation, which is previously reported in literature [Biometals 32, 453–467 (2019)].

      (3) Fig 4, the authors harvested bacteria from the tumor by centrifuging homogenized samples at different speeds. Internal controls confirming sample purity (positive for bacteria and negative for cells for panels a,b,c; or vice versa for panel d) may be necessary. This comment may also apply to samples from Fig 1.

      We acknowledge the reviewer’s concern and would like to point out that the proteomic analysis was performed using a highly cited protocol that provides reference and normalization standards for E. coli proteins [Mol Cell Proteomics. 2014 Sep; 13(9): 2513–2526]. The reference is cited in the Materials and Method section associated with the proteomic analysis.

      (4) To demonstrate long-term anti-caner memory was established through enhancement of CD8+ T cell activity, the "2nd seeding tumor cells" experiment may need to be done in CD8 antibody-treated IronA mice.

      We have modified our claims to highlight that the tumor eradication by iron scavenging bacteria can establish adaptive anticancer immunity through the elicitation of CD8 T cells. We apologize for overstating our claim in the previous manuscript draft.

      Minor suggestions:

      (1) Please include the tumor re-challenge experiment in the method section.

      The re-challenge experiment has been added to the method section as instructed.

      (2) Please cite others' and your previous work. E.g. line 281, 282, line 306-307.

      We have added the citations as instructed.

      (3) Line 448, BL21 is bacteria, not cells.

      We have made the correction accordingly.

      Reviewer #3 (Recommendations For The Authors):

      • The authors postulate that IroA-E. coli is more potent than DGC-E. coli in resisting LCN2 activity, and that this potency is the cause of the increased tumor suppression of this engineered strain. If so, Fig 3a should include DGC-E. coli for direct comparison.

      We appreciate the reviewer for the comment and would like to clarify that we intended construct IroA-E. coli as a more specific iron-scavenging strategy, which can aide the discussion of nutritional immunity and minimize compounding factors from the immune-stimulatory effect of CDG. We have modified our text to clarify our stance.

      • The data refers to the effects of WT bacteria-mediated tumor suppression, e.g. Figure 3e shows that even WT bacteria have a significant suppressive effect on tumor growth. Could the authors provide background on what is known about the mechanism of this tumor suppression, outside of tumor targeting and engineerability? They only reference "immune system stimulation."

      We appreciate the reviewer’s comment and would like to refer the reviewer to our recently published article [Lim et al., EMBO Molecular Medicine 2024; DOI: 10.1038/s44321-023-00022-w], which shows that in addition to immune system stimulation, WT bacteria can also be perceived as an invading species in the tumor that can exert differential selective pressure against cancer cells. Competition for nutrient is highlighted as a major contribution to contain tumor growth. In fact, the nutrient competition that we observed in the prior article inspired the design of the iron scavenging bacteria towards overcoming nutritional immunity. We have cited this recently published article to the revised manuscript to enrich the background.

      • The authors claim that there is immunologic memory because of tumor resistance in re-challenged mice after IroA-E. coli treatment (Fig 5c). It appears that the control group for this experiment is naive mice (and not WT-E. coli treated mice), in which case the immunologic memory could be from having had tumor/E. coli rather than the effect of IroA-E. coli.

      We have modified our claims to highlight that the tumor eradication by iron scavenging bacteria can establish adaptive anticancer immunity through the elicitation of CD8 T cells. We did not intend to highlight that the adaptive immunity stemmed from IroA-E. coli only, and we intend to build upon current literature that has reported CD8+ T cell elicitation by bacterial therapy. The IroA-E.coli is shown to enhance adaptive immunity. We also did not consider a WT-E. coli control as no WT-E. coli treated group achieved complete tumor regression.

      • The authors claim that CD8+ T cells are mechanistically important in the effects of iron status manipulation in E. coli-mediated tumor suppression (Fig 5). In order to show this, it seems that Fig 5c should include WT-E. coli and WT-E. coli+CD8 ab groups, as it may be that CD8+ T cells play a role in tumor suppression regardless of whether or not iron regulation is being manipulated.

      We apologize for the confusion from our prior writing. We have modified our claims to highlight that the tumor eradication by iron scavenging bacteria can establish adaptive anticancer immunity through the elicitation of CD8 T cells. We did not intend to convey that CD8+ T cells are mechanistically important in the effects of iron status manipulation.

    1. Author response:

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

      We thank the editorial team and reviewers for their continued contributions to improve our work.

      Below we have addressed the final recommendations to the authors

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I asked previously why the suppression depth should vary based on the contrast change speed. I now understand that the authors expect this variation from a working model based on neural adaptation (lines 274-277 and 809-820). I suggest the authors specify this prediction also on lines 473-479, where there is room for improved clarity (the words/phrases 'impact,' 'be sensitive to,' and 'covary' are non-directional).

      We have now specified this prediction to improve clarity:

      Line 475 – 486

      “In the context of the tCFS method, the steady increases and decreases in the target’s actual strength (i.e., its contrast) should, respectively, boost its emergence from suppression (bCFS) and facilitate its reversion to suppression (reCFS) as it competes against the mask. Whether construed as a consequence of neural adaptation or error signal, we surmise that these cycling state transitions defining suppression depth should be sensitive to the rate of contrast change of the monocular target. Specifically, the slower the contrast change, the greater the amount of accrued adaptation, which will contract the range between breakthrough and suppression thresholds according to an adapting reciprocal inhibition model. For fast contrast change, there will be less accrual of adaptation meaning that the range between breakthrough and suppression thresholds will exhibit less contraction. Expressed in operational terms, the depth of suppression should be positively related to the rate of target change. Experiment 3 tested this supposition using three rates of contrast change.”

      Line 108: 'By comparing the thresholds for a target to transition into (reCFS) and out of awareness (bCFS)'-are 'into' and 'out of' reversed?

      They were, thank you, these have now been corrected.

      Lines 696-698 read, 'Figure 3 shows that polar patterns tend to emerge from suppression at slightly lower contrasts than do gratings.' In the same paragraph, lines 716-171 read, 'Figure 3 shows that bCFS and reCFS thresholds are very similar for all image categories.' There is a statistically significant effect of category in these results; meanwhile, the differences among categories are arguably small. Which side do the authors intend to emphasize? Are the readers meant to interpret this as a glass-half-full, half-empty situation?

      We have now revised this paragraph. We emphasise that the small differences do not support ‘preferential processing’ of the magnitude that would be expected from category specific neural CRFs.

      From Line 702

      “Next we turn to another question raised about our conclusion concerning invariant depth of suppression. If a certain image type had overall lower bCFS and reCFS contrast thresholds relative to another image type (despite equivalent suppression depth), would that imply the former image enjoyed “preferential processing” relative to the latter? And, what would determine the differences in bCFS and reCFS thresholds? Figure 3 shows that polar patterns tend to emerge from suppression at slightly lower contrasts than do gratings and that polar patterns, once dominant, tend to maintain dominance to lower contrasts than do gratings and this happens even though the rate of contrast change is identical for both types of stimuli. But while rate of contrast change is identical, the neural responses to those contrast changes may not be the same: neural responses to changing contrast will depend on the neural contrast response functions (CRFs) of the cells responding to each of those two types of stimuli, where the CRF defines the relationship between neural response and stimulus contrast. CRFs rise monotonically with contrast and typically exhibit a steeply rising initial response as stimulus contrast rises from low to moderate values, followed by a reduced growth rate for higher contrasts. CRFs can vary in how steeply they rise and at what contrast they achieve half-max response. CRFs for neurons in mid-level vision areas such as V4 and FFA (which respond well to polar stimuli and faces, respectively) are generally steeper and shifted towards lower contrasts than CRFs for neurons in primary visual cortex (which respond well to gratings). Therefore, the effective strength of the contrast changes in our tCFS procedure will depend on the shape and position of the underlying CRF, an idea we develop in more detail in Supplementary Appendix 1, comparing the case of V1 and V4 CRFs. Interestingly, the comparison of V1 and V4 CRFs shows two interesting points: (i) that V4 CRFs should produce much lower bCFS and reCFS thresholds than V1 CRFs, and (ii) that V4 CRFs should produce much more suppression than V1 CRFs. Our data do not support either prediction: bCFS and reCFS thresholds for the polar shape are not ‘much lower’ than those for gratings (Fig. 3) and neither is there ‘much more’ suppression depth for the polar form. There is no room in these results to support the claim that certain images are special and receive “preferential processing” or processing outside of awareness. Instead, the similar data patterns for all image types is most parsimoniously explained by a single mechanism processing all images (see Appendix 1), although there are many other kinds of images still to be tested in tCFS and exceptions may yet be found. As a first step in exploring this idea, one could use standard psychophysical techniques (e.g., (Ling & Carrasco, 2006)) to derive CRFs for different categories of patterns and then measure suppression depth associated with those patterns using tCFS.”

    1. Author response:

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

      Reviewer 1

      (1) Given the low trial numbers, and the point of sequential vs clustered reactivation mentioned in the public review, it would be reassuring to see an additional sanity check demonstrating that future items that are currently not on-screen can be decoded with confidence, and if so, when in time the peak reactivation occurs. For example, the authors could show separately the decoding accuracy for near and far items in Fig. 5A, instead of plotting only the difference between them.

      We have now added the requested analysis showing the raw decoded probabilities for near and distant items separately in Figure 5A. We have also chosen to replace Figure 5B with the new figure as we think it provides more information than the previous Figure 5B. Instead, we have moved Figure 5B to the supplement. The median peak decoded accuracy for near and distant items is equivalent. We have added the following description to the figure:

      “Decoded raw probabilities for off-screen items, that were up to two steps ahead of the current stimulus cue (‘near’,) vs. distant items that were more than two steps away on the graph, on trials with correct answers. The median peak decoded probability for near and distant items was at the same time point for both probability categories. Note that displayed lines reflect the average probability while, to eliminate influence of outliers, the peak displays the median.”

      (2) The non-sequential reactivation analyses often use a time window of peak decodability, and it was not entirely clear to me what data this time window is determined on, e.g., was it determined based on all future reactivations irrespective of graph distance? This should be clarified in the methods.

      Thank you for raising this. We now clarify this in the relevant section to read: “First, we calculated a time point of interest by computing the peak probability estimate of decoders across all trials, i.e., the average probability for each timepoint of all trials (except previous onscreen items) of all distances, which is equivalent to the peak of the differential reactivation analysis”

      (3) Fig 4 shows evidence for forward and backward sequential reactivation, suggesting that both forward and backward replay peak at a lag of 40-50msec. It would be helpful if this counterintuitive finding could be picked up in the discussion, explaining how plausible it is, physiologically, to find forward and backward replay at the same lag, and whether this could be an artifact of the TDLM method.

      This is an important point and we agree that it appears counterintuitive. However, we would highlight this exact time range has been reported in previous studies, though t never for both forward and backward replay. We now include a discussion of this finding. The section now reads:

      “[… ] Even though we primarily focused on the mean sequenceness scores across time lags, there appears s to be a (non-significant) peak at 40-60 milliseconds. While simultaneous forward and backward replay is theoretically possible, we acknowledge that it is somewhat surprising and, given our paradigm, could relate to other factors such as autocorrelations (Liu, Dolan, et al., 2021).”

      (4) It is reported that participants with below 30% decoding accuracy are excluded from the main analyses. It would be helpful if the manuscript included very specific information about this exclusion, e.g., was the criterion established based on the localizer cross-validated data, the temporal generalisation to the cued item (Fig. 2), or only based on peak decodability of the future sequence items? If the latter, is it applied based on near or far reactivations, or both?

      We now clarify this point to include more specific information, which reads:

      “[…] Therefore, we decided a priori that participants with a peak decoding accuracy of below 30% would be excluded from the analysis (nine participants in all) as obtained from the cross-validation of localizer trials”

      (5) Regarding the low amount of data for the reactivation analysis, the manuscript should be explicit about the number of trials available for each participant. For example, Supplemental Fig. 1 could provide this information directly, rather than the proportion of excluded trials.

      We have adapted the plot in the supplement to show the absolute number of rejected epochs per participant, in addition to the ratio.

      (6) More generally, the supplements could include more detailed information in the legends.

      We agree and have added more extensive explanation of the plots in the supplement legends.

      (7) The choice of comparing the 2 nearest with all other future items in the clustered reactivation analysis should be better motivated, e.g., was this based on the Wimmer et al. (2020) study?

      We have added our motivation for taking the two nearest items and contrasting them with the items further away. The paragraph reads:

      “[…] We chose to combine the following two items for two reasons: First, this doubled the number of included trials; secondly, using this approach the number of trials for each category (“near” and “distant”) was more balanced. […]”

      Reviewer 2

      (1) Focus exclusively on retrieval data (and here just on the current image trials).

      If I understand correctly, you focus all your analyses (behavioural as well as MEG analyses) on retrieval data only and here just on the current image trials. I am surprised by that since I see some shortcomings due to that. These shortcomings can likely be addressed by including the learning data (and predecessor image trials) in your analyses.

      a) Number of trials: During each block, you presented each of the twelve edges once. During retrieval, participants then did one "single testing session block". Does that mean that all your results are based on max. 12 trials? Given that participants remembered, on average, 80% this means even fewer trials, i.e., 9-10 trials?

      This is correct and a limitation of the paper. However, while we used only correct trials for the reactivation analysis, the sequential analysis was conducted using all trials disregarding the response behaviour. To retain comparability with previous studies we mainly focused on data from after a consolidation phase. Nevertheless, despite the trial limitation we consider the results are robust and worth reporting. Additionally, based on the suggestion of the referee, we now include results from learning blocks (see below).

      b) Extend the behavioural and replay/reactivation analysis to predecessor images.

      Why do you restrict your analyses to the current image trials? Especially given that you have such a low trial number for your analyses, I was wondering why you did not include the predecessor trials (except the non-deterministic trials, like the zebra and the foot according to Figure 2B) as well.

      We agree it would be great to increase power by adding the predecessor images to the current image cue analysis, excluding the ambiguous trials, we did not do so as we considered the underlying retrieval processes of these trial types are not the same, i.e. cannot be simply combined. Nevertheless, we have performed the suggested analysis to check if it increases our power. We found, that the reactivation effect is robust and significant at the same time point of 220-230 ms. However, the effect size actually decreased: While before, peak differential reactivation was at 0.13, it is now at 0.07. This in fact makes conceptual sense. We suspect that the two processes that are elicited by showing a single cue and by showing a second, related, cue are distinct insofar as the predecessor image acts as a primer for the current image, potentially changing the time course/speed of retrieval. Given our concerns that the two processes are not actually the same we consider it important to avoid mixing these data.

      We have added a statement to the manuscript discussing this point. The section reads:

      “Note that we only included data from the current image cue, and not from the predecessor image cue, as we assume the retrieval processes differ and should not be concatenated.”

      c) Extend the behavioural and replay/reactivation analysis to learning trials.

      Similar to point 1b, why did you not include learning trials in your analyses?

      The advantage of including (correct and incorrect) learning trials has the advantage that you do not have to exclude 7 participants due to ceiling performance (100%).

      Further, you could actually test the hypothesis that you outline in your discussion: "This implies that there may be a switch from sequential replay to clustered reactivation corresponding to when learned material can be accessed simultaneously without interference." Accordingly, you would expect to see more replay (and less "clustered" reactivation) in the first learning blocks compared to retrieval (after the rest period).

      To track reactivation and replay over the course of learning is a great idea. We have given a lot of thought as to how to integrate these findings but have not found a satisfying solution. Thus, analysis of the learning data turned out to be quite tricky: We decided that each participant should perform as many blocks as necessary to reach at least 80% (with a limit of six and lower bound of two, see Supplement figure 4). Indeed, some participant learned 100% of the sequence after one block (these were mostly medical students, learning things by hard is their daily task). With the benefit of hindsight, we realise our design means that different blocks are not directly comparable between participants. In theory, we would expect that replay emerges in parallel with learning and then gradually changes to clustered reactivation as memory traces become consolidated/stronger. However, it is unclear when replay should emerge and when precisely a switch to clustered reactivation would happen. For this reason, we initially decided not to include the learning trials into the paper.

      Nevertheless, to provide some insight into the learning process, and to see how consolidation impacts differential reactivation and replay, we have split our data into pre and post resting state, aggregating all learning trials of each participant. While this does not allow us to track processes on a block basis, it does offer potential (albeit limited) insight into the hypothesis we outline in the discussion.

      For reactivation, we see emergence of a clear increase, further strengthening the outlined hypothesis, however, for replay the evidence is less clear, as we do not know over how many learning blocks replay is expected.

      We calculated individual trajectories of how reactivation and replay changes from learning to retrieval and related these to performance. Indeed, we see an increase of reactivation is nominally associated with higher learning performance, while an increase in replay strength is associated with lower performance (both non-significant). However, due to the above-mentioned reasons we think it would premature to add this weak evidence to the paper.

      To mitigate problems of experiment design in relation to this question we are currently implementing a follow-study, where we aim to normalize the learning process across participants and index how replay/reactivation changes over the course of learning and after consolidation.

      We have added plots showing clustered reactivation sequential replay measures during learning (Figure 5D and Supplement 8)

      The added section(s) now read:

      “To provide greater detail on how the 8-minute consolidation period affected reactivation we, post-hoc, looked at relevant measures across learning trials in contrast to retrieval trials. For all learning trials, for each participant, we calculated differential reactivation for the same time point we found significant in the previous analysis (220-260 milliseconds). On average, differential reactivation probability increased from pre to post resting state (Figure 5D). […]

      Nevertheless, even though our results show a nominal increase in reactivation from learning to retrieval (see Figure 5D), due to experimental design features our data do not enable us to test for an hypothesized switch for sequential replay (see also “limitations” and Supplement 8).”

      d) Introduction (last paragraph): "We examined the relationship of graph learning to reactivation and replay in a task where participants learned a ..." If all your behavioural analyses are based on retrieval performance, I think that you do not investigate graph learning (since you exclusively focus the analyses on retrieving the graph structure). However, relating the graph learning performance and replay/reactivation activity during learning trials (i.e., during graph learning) to retrieval trials might be interesting but beyond the scope of this paper.

      We agree. We have changed the wording to be more accurate. Indeed, we do not examine graph learning but instead examine retrieval from a graph, after graph learning. The mentioned sentence now read

      “[…] relationship of retrieval from a learned graph structure to reactivation [...]”

      e) It is sometimes difficult to follow what phase of the experiment you refer to since you use the terms retrieval and test synonymously. Not a huge problem at all but maybe you want to stick to one term throughout the whole paper.

      Thank you for pointing this out. We have now adapted the manuscript to exclusively refer to “retrieval” and not to “test”.

      (2) Is your reactivation clustered?

      In Figure 5A, you compare the reactivation strength of the two items following the cue image (i.e., current image trials) with items further away on the graph. I do not completely understand why your results are evidence for clustered reactivation in contrast to replay.

      First, it would be interesting to see the reactivation of near vs. distant items before taking the difference (time course of item probabilities).

      (copied answer from response to Reviewer 1, as the same remark was raised)

      We have added the requested analysis showing the raw decoded probabilities for near and distant items separately in Figure 5A. We have chosen to replace Figure 5B with the new figure as we think that it offers more information than the previous Figure 5B. Instead, we have moved Figure 5B to the supplement. The median peak decoded accuracy for near and distant items is equivalent. We have added the following description to the figure:

      “Decoded raw probabilities for off-screen items, that were up to two steps ahead of the current stimulus cue (‘near’,) vs. distant items that were more than two steps away on the graph, on trials with correct answers. The median peak decoded probability for near and distant items was at the same time point for both probability categories. Note that displayed lines reflect the average probability while, to eliminate influence of outliers, the peak displays the median. .”

      Second, could it still be that the first item is reactivated before the second item? By averaging across both items, it becomes not apparent what the temporal courses of probabilities of both items look like (and whether they follow a sequential pattern). Additionally, the Gaussian smoothing kernel across the time dimension might diminish sequential reactivation and favour clustered reactivation. (In the manuscript, what does a Gaussian smoothing kernel of  = 1 refer to?). Could you please explain in more detail why you assume non-sequential clustered reactivation here and substantiate this with additional analyses?

      We apologise for the unclear description. Note the Gaussian kernel is in fact only used for the reactivation analysis and not the replay analysis, so any small temporal successions would have been picked up by the sequential analysis. We now clarify this in the respective section of the sequential analysis and also explain the parameter of delta= 1 in the reactivation analysis section. The paragraph now reads

      “[…] As input for the sequential analysis, we used the raw probabilities of the ten classifiers corresponding to the stimuli. [...]

      […] Therefore, to address this we applied a Gaussian smoothing kernel (using scipy.ndimage.gaussian_filter with the default parameter of σ=1 which corresponds approximately to taking the surrounding timesteps in both direction with the following weighting: current time step: 40%, ±1 step: 25%, ±2 step: 5%, ±3 step: 0.5%) [...]”

      (3) Replay and/or clustered reactivation?

      The relationship between the sequential forward replay, differential reactivation, and graph reactivation analysis is not really apparent. Wimmer et al. demonstrated that high performers show clustered reactivation rather than sequential reactivation. However, you did not differentiate in your differential reactivation analysis between high vs. low performers. (You point out in the discussion that this is due to a low number of low performers.)

      We agree that a split into high vs low performers would have been preferably for our analysis. However, there is one major obstacle that made us opt for a correlational analysis instead: We employed criteria learning, rendering a categorical grouping conceptually biased. Even though not all participants reached the criteria of 80%, our sample did not naturally split between high and low performers but was biased towards higher performance, leaving the groups uneven. The median performance was 83% (mean ~81%), with six of our subjects (~1/4th of included participant) having this exact performance. This makes a median or mean split difficult, as either binning assignment choice would strongly affect the results. We have added a limitations section in which we extensively discuss this shortcoming and reasoning for not performing a median split as in Wimmer et al (2020). The section now reads:

      “There are some limitations to our study, most of which originate from a suboptimal study design. [...], as we performed criteria learning, a sub-group analysis as in Wimmer et al., (2020) was not feasible, as median performance in our sample would have been 83% (mean 81%), with six participants exactly at that threshold. [...]”

      It might be worth trying to bring the analysis together, for example by comparing sequential forward replay and differential reactivation at the beginning of graph learning (when performance is low) vs. retrieval (when performance is high).

      Thank you for the suggestion to include the learning segments, which we think improves the paper quite substantially. However, analysis of the learning data turned out to be quite tricky> We had decided that each participant should perform as many blocks as necessary to reach at least 80% accuracy (with a limit of six and lower bound of two, see Supplement figure 4). Some participants learned 100% of the sequence after one block (these were mostly medical students, learning things by hard is their daily task). This in hindsight is an unfortunate design feature in relation to learning as it means different blocks are not directly comparable between participants.

      In theory, we would expect that replay emerges in parallel with learning and then gradually change to clustered reactivation, as memory traces get consolidated/stronger. However, it is unclear when replay would emerge and when the switch to reactivation would happen. For this reason, we initially decided not to include the learning trials into the paper at all.

      Nevertheless, to give some insight into the learning process and to see how consolidation effects differential reactivation and replay, we have split our data into pre and post resting state, aggregating all learning trials of each participant. While this does not allow us to track measures of interest on a block basis, it gives some (albeit limited) insight into the hypothesis outlined in our discussion.

      For reactivation, we see a clear increase, further strengthening the outlined hypothesis, However, for replay the evidence is less obvious, potentially due to that fact that we do not know across how many learning blocks replay is to be expected.

      The added section(s) now read:

      “To examine how the 8-minute consolidation period affected reactivation we, post-hoc, looked at relevant measures during learning trials in contrast to retrieval trials. For all learning trial, for each participant, we calculated differential reactivation for the time point we found significant during the previous analysis (220-260 milliseconds). On average, differential reactivation probability increased from pre to post resting state (Figure 5D).

      […]

      Nevertheless, even though our results show a nominal increase in reactivation from learning to retrieval (see Figure 5D), our data does not enable us to show an hypothesized switch for sequential replay (see also “limitations” and Supplement 8).”

      Additionally, the main research question is not that clear to me. Based on the introduction, I thought the focus was on replay vs. clustered reactivation and high vs. low performance (which I think is really interesting). However, the title is more about reactivation strength and graph distance within cognitive maps. Are these two research questions related? And if so, how?

      We agree we need to be clearer on this point. We have added two sentences to the introduction, which should address this point. The section now reads:

      “[…] In particular, the question remains how the brain keeps track of graph distances for successful recall and whether the previously found difference between high and low performers also holds true within a more complex graph learning context.”

      (4) Learning the graph structure.

      I was wondering whether you have any behavioural measures to show that participants actually learn the graph structure (instead of just pairs or triplets of objects). For example, do you see that participants chose the distractor image that was closer to the target more frequently than the distractor image that was further away (close vs. distal target comparison)? It should be random at the beginning of learning but might become more biased towards the close target.

      Thanks, this is an excellent suggestion. Our analysis indeed shows that people take the near lure more often than the far lure in later blocks, while it is random in the first block.

      Nevertheless, we have decided to put these data into the supplement and reference it in the text. This is because analysis of the learning blocks is challenging and biased in general. Each participant had a different number of learning blocks based on their learning rate, and this makes it difficult to compare learning across participants. We have tried our best to accommodate and explain these difficulties in the figure legend. Nevertheless, we thank the referee for guidance here and this analysis indeed provides further evidence that participants learned the actual graph structure.

      The added section reads

      “Additionally, we have included an analysis showing how wrong answers participants provided were random in the first block and biased towards closer graph nodes in later blocks. This is consistent with participants actually learning the underlying graph structure as opposed to independent triplets (see figure and legend of Supplement 6 for details).”

      (5) Minor comments

      a) "Replay analysis relies on a successive detection of stimuli where the chance of detection exponentially decreases with each step (e.g., detecting two successive stimuli with a chance of 30% leaves a 9% chance of detecting the replay event). " Could you explain in more detail why 30% is a good threshold then?

      Thank you. We have further clarified the section. As we are working mainly with probabilities, it is useful to keep in mind that accuracy is a class metric that only provides a rough estimate of classifier ability. Alternatively, something like a Top-3-Accuracy would be preferable, but also slightly silly in the context of 10 classes.

      Nevertheless, subtle changes in probability estimates are present and can be picked up by the methods we employ. Therefore, the 30% is a rough lower bound and decided based on pilot data that showed that clean MEG data from attentive participants can usually reach this threshold. The section now reads:

      “(e.g., detecting two successive stimuli with a chance of 30% leaves a 9% chance of detecting a replay event). However, one needs to bear in mind that accuracy is a “winnertakes-all” metric indicating whether the top choice also has the highest probability, disregarding subtle, relative changes in assigned probability. As the methods used in this analysis are performed on probability estimates and not class labels, one can expect that the 30% are a rough lower bound and that the actual sensitivity within the analysis will be higher. Additionally, based on pilot data, we found that attentive participants were able to reach 30% decodability, allowing us to use decodability as a data quality check. “

      b) Could you make explicit how your decoders were designed? Especially given that you added null data, did you train individual decoders for one class vs. all other classes (n = 9 + null data) or one class vs. null data?

      We added detail to the decoder training. The section now reads

      “Decoders were trained using a one-vs-all approach, which means that for each class, a separate classifier was trained using positive examples (target class) and negative examples (all other classes) plus null examples (data from before stimulus presentation, see below). In detail, null data was.”

      c) Why did you choose a ratio of 1:2 for your null data?

      Our choice for using a higher ratio was based upon previous publications reporting better sensitivity of TDLM using higher ratios, as spatial sensor correlations are decreasing. Nevertheless, this choice was not well investigated beforehand. We have added more information to this to the manuscript

      d) You could think about putting the questionnaire results into the supplement if they are sanity checks.

      We have added the questionnaire results. However, due to the size of the tables, we have decided to add them as excel files into the supplementary files of the code repository. We have mentioned the existence file in the publication.

      e) Figure 2. There is a typo in D: It says "Precessor Image" instead of "Predecessor Image".

      Fixed typo in figure.

      f) You write "Trials for the localizer task were created from -0.1 to 0.5 seconds relative to visual stimulus onset to train the decoders and for the retrieval task, from 0 to 1.5 seconds after onset of the second visual cue image." But the Figure legend 3D starts at -0.1 seconds for the retrieval test.

      We have now clarified this. For the classifier cross-validation and transfer sanity check and clustered analysis we used trials from -0.1 to 0.5s, whereas for the sequenceness analysis of the retrieval, we used trials from 0 to 1.5 seconds

    1. Author response:

      Factual error in the eLife assessment to be corrected:

      In the eLife assessment, "ribosomal protein H59" should be changed to "helix 59 of the 28S ribosomal RNA" to make this factually correct.

      Provisional author response

      We thank the reviewers for their thorough and thoughtful readings of the manuscript. Our responses to the four suggestions made in their public reviews are below.

      Reviewer #1 (Public Review):

      Major points:

      (1) The identification of RAMP4 is a pivotal discovery in this paper. The sophisticated AlphaFold prediction, de novo model building of RAMP4's RBD domain, and sequence analyses provide strong evidence supporting the inclusion of RAMP4 in the ribosome-translocon complex structure.

      However, it is crucial to ensure the presence of RAMP4 in the purified sample. Particularly, a validation step such as western blotting for RAMP4 in the purified samples would strengthen the assertion that the ribosome-translocon complex indeed contains RAMP4. This is especially important given the purification steps involving stringent membrane solubilization and affinity column pull-down.

      As suggested, we will revise the manuscript to include Western blots showing that RAMP4 is retained at secretory translocons (and not multipass translocons) after solubilisation, affinity purification, and recovery of ribosome-translocon complexes.

      (2) Despite the comprehensive analyses conducted by the authors, it is challenging to accept the assertion that the extra density observed in TRAP class 1 corresponds to calnexin. The additional density in TRAP class 1 appears to be less well-resolved, and the evidence for assigning it as calnexin is insufficient. The extra density there can be any proteins that bind to TRAP. It is recommended that the authors examine the density on the ER lumen side. An investigation into whether calnexin's N-globular domain and P-domain are present in the ER lumen in TRAP class 1 would provide a clearer understanding.

      We agree that the Calnexin assignment is less confident than the other assignments in this manuscript, and that further support would be ideal. We have exhaustively searched our maps for any unexplained density connected with the putative Calnexin TMD, and have found none. This is consistent with Calnexin's lumenal domain being flexibly linked to its TMD, and thus would not be resolved in a ribosome-aligned reconstruction.

      Our assignment of this TMD to Calnexin was based on existing biochemical data (referenced in the paper) favouring this as the best working hypothesis by far: Calnexin is TRAP’s only abundant co-purifying factor, and their interaction is sensitive to point mutations in the Calnexin TMD. Recognising that this is not conclusive, we will ensure that the text and figures consistently describe this assignment as provisional or putative.

      (3) In the section titled 'TRAP competes and cooperates with different translocon subunits,' the authors present a compelling explanation for why TRAP delta defects can lead to congenital disorders of glycosylation. To enhance this explanation, it would be valuable if the authors could provide additional analyses based on mutations mentioned in the references. Specifically, examining whether these mutations align with the TRAP delta-OSTA structure models would strengthen the link between TRAP delta defects and the observed congenital disorders of glycosylation.

      We agree that mapping disease-causing point mutants to the TRAP delta structure could be potentially informative. Unfortunately, the referenced TRAP delta disease mutants act by simply impairing TRAP delta expression, and thus admit no such fine-grained analyses. However, sequence conservation is our next best guide to mutant function. We note in the text that the contact site charges on TRAP delta and RPN2 are conserved, and that the closest-juxtaposed interaction pair (K117 on TRAPδ and D386 on RPN2) is also the most conserved.

      Reviewer #2 (Public Review):

      Strengths:

      The manuscript contains numerous novel new structural analyses and their potential functional implications. While all findings are exciting, the highlight is the discovery of RAMP4/SERP1 near the Sec61 lateral gate. Overall, the strength is the thorough and extensive structural analysis of the different high-resolution RTC classes as well as the expert bioinformatic evolutionary analysis.

      Weaknesses:

      A minor downside of the manuscript is the sheer volume of analyses and mechanistic hypotheses, which makes it sometimes difficult to follow. The authors might consider offloading some analyses based on weaker evidence to the supplement to maximize impact.

      We agree that the manuscript is long, and we will seek ways to streamline it in revision while avoiding the undesirable side effect of making important findings undiscoverable via literature searches (an unfortunate consequence of many supplemental data). Indeed, we chose eLife for its flexibility regarding article length and suitability for extended and detailed analyses.

    1. Author response:

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

      Reviewer #1:

      We are grateful for the overall positive feedback from the reviewer.

      We agree with the reviewer that our data showing cellular co-localization between PRC1 and BIN1 requires further investigation in future studies, however, we are confident that in the current form, our manuscript already presents multiple evidences for the role of BIN1 in mitotic processes. We would like to emphasize that PRC1 is not the sole BIN1 partner that connects it to mitotic processes, but it is only one out of more than a dozen that we identified in our study. Furthermore, the mitotic connection with BIN1 is not absolutely novel as BIN1 levels are mildly fluctuating during the cell cycle, similar to other proteins involved in the regulation of the cell cycle (Santos et al., 2015) and because DNM2 is also a well-accepted actor during mitosis (Thompson et al., 2002).

      The less marked co-localization between BIN1 and PRC1 compared to the strong co-localization between BIN1 and DNM2 can be a consequence of their weaker affinity and their partial binding. Yet, this does not necessarily imply that stronger interactions have more biological significance. For example, weaker affinities can be compensated by local concentrations to achieve an even higher degree of cellular complexes than of strongly binding interactions that are separated within the cell. Furthermore, even the degree of complex formation cannot be used intuitively to estimate the biological significance of a complex because complexes can trigger very important biological processes even at very low abundances, e.g. by catalyzing enzymatic reactions. Deciding what is and what is not “biologically significant” among the identified interactions remains to be answered in the future, once we are able to overview complex biological processes in a holistic manner.

      In the revised version, we implemented minor changes to further clarify the raised points.

      Reviewer #2:

      We thank the reviewer for the careful assessment and we are pleased to see the positive enthusiasm regarding our affinity interactomic strategy.

      The reviewer points out that affinities were only measured with a single technique, which is relatively unproven. While it is true that our work uses two techniques building on the same holdup concept, we rather believe that this approach is well-proven. The original holdup method was described almost 20 years ago and since then, it has been used in more than 10 publications for quantitative interactomics. Over the years, at least five distinct generations of the assay were developed, all building on the expertise of the preceding one. In the past, we extensively proved that the resulting affinities show excellent agreement with affinities measured with other methods, such as fluorescence polarization, isothermal titration calorimetry, or surface plasmon resonance (for example in Vincentelli et al. Nat. Meth. 2015; Gogl et al. 2020 Structure; Gogl et al. 2022 Nat.Com.). However, it is true that the most recent variation of this method family, called native holdup, is a fairly new approach published just a bit more than a year ago and this is only the third work that utilizes this method. Yet, in our original work describing the method, we demonstrated good agreement with the results of previous holdup experiments, as well as with orthogonal affinity measurements (Zambo et al. 2022).

      Importantly, the reviewer raises concerns regarding the number of replicates used in our study, as well as the reliability of our methodology. We are glad for such a comment as it allows us to explain our motives behind experimental design which is most often left out from scientific works to save space and keep focus on results. The reason why we use technical replicates instead of the typical biological replicates lies in the nature of the holdup assay. In a typical interactomic assay, such as immunoprecipitation, a lot of variables can perturb the outcome of the measurement, such as bait immobilization, or captured prey leakage during washing steps. The output of such an experiment is a list of statistically significant partners and to minimize these variabilities, biological replicates are used. In the case of a native holdup approach, a panel of an equal amount of resins, all saturated with different baits or controls, is mixed with an equal amount of cell extract, taken from a single tube, and after a brief incubation, the supernatant of this mixture is analyzed. The output of such an experiment is a list of relative concentrations of prey and to maximize its accuracy, we use technical replicates. Using an ideal analytical method, such as fluorescence, it is not necessary to use technical replicates to reach accurate results. For example, the general accuracy of a holdup experiment coupled with a robust analytical approach can be seen clearly in our fragmentomic holdup data shown in Figure 7C where mutant domains that do not have any impact on the interactome show extreme agreement in affinities. Unfortunately, mass spectrometry is less accurate as an analytical method, hence we use technical triplicates to compensate for this. Finally, in the case of BIN1, an independent nHU measurement was also performed using a less capable mass spectrometer. Not counting the 117 detected partners of BIN1 that were only detected in only one of these proteomic measurements, 29 partners were identified as common significant partners in both of these measurements showing nearly identical affinities with a mean standard deviation between measured pKapp values of 0.18, meaning that the obtained dissociation constants are within a <2.5-fold range with >95% probability. There were also 61 BIN1 partners that were detected in both proteomic measurements but were only identified as a significant interaction partner in one of these experiments. Yet many of them show binding in both assays, albeit were found to be not significant in one of these assays. For example, CDC20 shows 66% depletion in one assay (significant binding) while it shows 54% depletion in the other (not significant binding), or CKAP2 shows 58% depletion in one assay (significant binding) while it shows 41% depletion in the other (not significant binding). We hope that these examples show that statistical significance in nHU experiments rather signifies how certain we are in a particular affinity measurement and not the accuracy of the affinity measurement itself. While there are true discrepancies between some of the affinity measurements between these experiments, that would be possible to clarify with more experimental replicates, the raw data presented in our work clearly demonstrate the strength and robustness of a fully quantitative interactomic assay.

      In the revised version, we clarified the number of replicates in the text, in the figure legends, and included some of this discussion in the method section.

      The reviewer had some very useful comments regarding affinity differences between short fragments and full-length proteins. In his comment, he possibly made a typo as we find that fulllength proteins typically interact with higher affinities compared to short PxxP motif fragments in isolation and not weaker. The reviewer also comments that we explain this difference with cooperativity. In a previous preprint version, which the reviewer may have seen, this was indeed the case, but since we realized that we did not have sufficient evidence supporting this model, therefore we did not discuss this in detail in the last version submitted to eLife. To clarify this, we included more discussion about the observed differences in the affinities between fragments and full-length proteins, but since we have limited data to make solid conclusions, we do not go into details about underlying models.

      Instead of cooperativity, the reviewer suggests that the observed differences may originate from additional residues that were not included in our peptides. Indeed, many similar experiments fail because of suboptimal peptide library design. Our peptide library was constructed as 15-mer, xxxxxxPxxPxxxxx motifs and we do not see a strong contribution of residues at the far end of these peptides. Specificity logo reconstructions are expected to identify all key residues that participate in SH3 domain binding, and based on this, all key residues of the identified motifs can be included in shorter 10-mer, xxxPxxPxxx motifs. Therefore, it is unlikely that residues outside our peptide regions will greatly contribute to the site-specific interactions of SH3 domains. It is however possible that other sites, that are sequentially far away from the studied PxxP motifs, are also capable of binding to SH3 through a different surface, but in light of the small size of an isolated SH3 domain, we believe it is very unlikely. It is also possible that BIN1 could also interact with other types of SH3 binding motifs that were not included in our peptide library. We think a more likely explanation is some sort of cooperativity. Cooperativity, or rather synergism between different sites can be easily explained in typical situations, such as in the case of a bimolecular interaction that is mediated by two independent sites. In such an event, once one site is bound, the second binding event will likely also occur because of the high effective local concentration of the binding sites. However, cooperativity can also form in atypical conditions and a molecular explanation for these events is rather elusive. As BIN1 contains a single SH3 domain, its binding to targets containing more binding sites can be challenging to interpret. If these sites are part of a greater Pro-rich region, such as in the case of DNM2, it is possible that the entire region adopts a fuzzy, malleable, yet PPII-like helical conformation. Once the SH3 domain is recruited to this helical region, it can freely trans-locate within this region via lateral diffusion and it will pause on optimal PxxP motifs. As an alternative to this sliding mechanism, a diffusion-limited cooperative binding can also occur. If the two motifs are not part of the same Pro-rich region, but are relatively close in space, such as in the case of ITCH or PRC1, once a BIN1 molecule dissociates from one site, it has a higher chance to rebind to the second site due to higher local concentrations. Such an event can more likely occur if a transient, but relatively stable encounter complex exists between the two molecules, from which complex formation can occur at both sites (A+B↔AB; AB↔ABsite1; AB*↔ABsite2). However, this large effective local concentration in this encounter complex is only temporary because diffusion rapidly diminishes it, although weak electrostatic interactions can increase the lifetime of such encounter complexes. In contrast, the large effective local concentration in conventional multivalent binding is time-independent and only determined by the geometry of the complex. Finally, it may also occur that our empirical bait concentration estimation for immobilized biotinylated proteins is less accurate than the concentration estimation of peptide baits because we approximate this value based on peptide baits. For this technical reason, which was discussed in detail in the original paper describing the nHU approach, we are carefully using apparent affinities for nHU experiments. Nevertheless, even without accurate bait concentrations, our nHU experiment provides precise relative affinities and, thus partner ranking. Either of the mechanisms underlying the interactions we study would be difficult to further explore experimentally, especially at the proteomic level.

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      The data is poorly dealt with, and the figures are shown poorly. For example, Figure 2A is not even shown totally.

      We apologize for any difficulties that the reviewer encountered while attempting to view the figures. We have confirmed that all figures, including all panels of Figure 2, display correctly on the HTML and PDF versions of the article hosted at bioRxiv. The HTML and PDF versions generated by eLife also appears to contain all figures and panels in their entirety.

      Reviewer #2 (Recommendations For The Authors):

      Please refer to the public review for possible revisions.

      We thank Reviewer #2 for the summary and thoughtful comments provided in the Public Review. We note the point of possible revision noted from the Public Review: “It can be informative to directly demonstrate DPYD promoter-enhancer interactions. However, the genetic variants support the integration of regulatory activities.” In Figure 4, we provide evidence for direct promoterenhancer interaction though the use of 3C. We furthermore demonstrate that these interactions are dependent upon genotype at rs4294451 as stated by the reviewer. We have highlighted the promoter-enhancer interaction in the revised manuscript, lines 323-325. The role of genotype in this interaction is also specifically discussed in lines 378-381.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Gap junction channels establish gated intercellular conduits that allow the diffusion of solutes between two cells. Hexameric connexin26 (Cx26) hemichannels are closed under basal conditions and open in response to CO2. In contrast, when forming a dodecameric gapjunction, channels are open under basal conditions and close with increased CO2 levels. Previous experiments have implicated Cx26 residue K125 in the gating mechanism by CO2, which is thought to become carbamylated by CO2. Carbamylation is a labile post-translational modification that confers negative charge to the K125 side chain. How the introduction of a negative charge at K125 causes a change in gating is unclear, but it has been proposed that carbamylated K125 forms a salt bridge with the side chain at R104, causing a conformational change in the channel. It is also unclear how overall gating is controlled by changes in CO2, since there is significant variability between structures of gap-junction channels and the cytoplasmic domain is generally poorly resolved. Structures of WT Cx26 gap-junction channels determined in the presence of various concentrations of CO2 have suggested that the cytoplasmatic N-terminus changes conformation depending on the concentration of the gas, occluding the pore when CO2 levels are high.

      In the present manuscript, Deborah H. Brotherton and collaborators use an intercellular dyetransfer assay to show that Cx26 gap-junction channels containing the K125E mutation, which mimics carbamylation caused by CO2, is constitutively closed even at CO2 concentrations where WT channels are open. Several cryo-EM structures of WT and mutant Cx26 gap junction channels were determined at various conditions and using classification procedures that extracted more than one structural class from some of the datasets. Together, the features on each of the different structures are generally consistent with previously obtained structures at different CO2 concentrations and support the mechanism that is proposed in the manuscript. The most populated class for K125E channels determined at high CO2 shows a pore that is constricted by the N-terminus, and a cytoplasmic region that was better resolved than in WT channels, suggesting increased stability. The K125E structure closely resembles one of the two major classes obtained for WT channels at high CO2. These findings support the hypothesis that the K125E mutation biases channels towards the closed state, while WT channels are in an equilibrium between open and closed states even in the presence of high CO2. Consistently, a structure of K125E obtained in the absence of CO2 appeared to also represent a closed state but at lower resolution, suggesting that CO2 has other effects on the channel beyond carbamylation of K125 that also contribute to stabilizing the closed state. Structures determined for K125R channels, which are constitutively open because arginine cannot be carbamylated, and would be predicted to represent open states, yielded apparently inconclusive results.

      A non-protein density was found to be trapped inside the pore in all structures obtained using both DDM and LMNG detergents, suggesting that the density represents a lipid rather than a detergent molecule. It is thought that the lipid could contribute to the process of gating, but this remains speculative. The cytoplasmic region in the tentatively closed structural class of the WT channel obtained using LMNG was better resolved. An additional portion of the cytoplasmic face could be resolved by focusing classification on a single subunit, which had a conformation that resembled the AlphaFold prediction. However, this single-subunit conformation was incompatible with a C6-symmetric arrangement. Together, the results suggest that the identified states of the channel represent open states and closed states resulting from interaction with CO2. Therefore, the observed conformational changes illuminate a possible structural mechanism for channel gating in response to CO2.

      Some of the discussion involving comparisons with structures of other gap junction channels are relatively hard to follow as currently written, especially for a general readership. Also, no additional functional experiments are carried out to test any of the hypotheses arising from the data. However, structures were determined in multiple conditions, with results that were consistent with the main hypothesis of the manuscript. No discussion is provided, even if speculative, to explain the difference in behavior between hemichannels and gap junction channels. Also, no attempt was made to measure the dimensions of the pore, which is relevant because of the importance of identifying if the structures indeed represent open or closed states of the channel.

      We have considerably revised the manuscript in an attempt to make it more tractable. We respond to the individual comments below.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Brotherton et al. describes a structural study of connexin-26 (Cx26) gap junction channel mutant K125E, which is designed to mimic the CO2-inhibited form of the channel. In the wild-type Cx26, exposure to CO2 is presumed to close the channel through carbamylation of the residue K125. The authors mutated K125 to a negatively charged residue to mimic this effect, and they observed by cryo-EM analysis of the mutated channel that the pore of the channel is constricted. The authors were able to observe conformations of the channel with resolved density for the cytoplasmic loop (in which K125 is located). Based on the observed conformations and on the position of the N-terminal helix, which is involved in channel gating and in controlling the size of the pore, the authors propose the mechanisms of Cx26 regulation.

      Strengths:

      This is a very interesting and timely study, and the observations provide a lot of new information on connexin channel regulation. The authors use the state of the art cryo-EM analysis and 3D classification approaches to tease out the conformations of the channel that can be interpreted as "inhibited", with important implications for our understanding of how the conformations of the connexin channels controlled.

      Weaknesses:

      My fundamental question to the premise of this study is: to what extent can K125 carbamylation by recapitulated by a simple K125E mutation? Lysine has a large side chain, and its carbamylation would make it even slightly larger. While the authors make a compelling case for E125-induced conformational changes focusing primarily on the negative charge, I wonder whether they considered the extent to which their observation with this mutant may translate to the carbamoylated lysine in the wild-type Cx26, considering not only the charge but also the size of the modified side-chain.

      This is an important point. We agree that the difference in size will have a different effect on the structure. For kinases, aspartate or glutamate are often used as mimics of phosphorylated serine or threonine and these will have the same issues. The fact that we cannot resolve the relevant side-chains in the density may be indicative that the mutation doesn’t give the whole story. It may be able to shift the equilibrium towards the closed conformation, but not stably trap the molecule in that conformation. We include a comment to this effect in the revised manuscript.

      Reviewer #3 (Public Review):

      Summary:

      The mechanism underlying the well-documented CO2-regulated activity of connexin 26 (Cx26) remains poorly understood. This is largely due to the labile nature of CO2-mediated carbamylation, making it challenging to visualize the effects of this reversible posttranslational modification. This paper by Brotherton et al. aims to address this gap by providing structural insights through cryo-EM structures of a carbamylation-mimetic mutant of the gap junction protein.

      Strengths:

      The combination of the mutation, elevated PCO2, and the use of LMNG detergent resulted in high-resolution maps that revealed, for the first time, the structure of the cytoplasmic loop between transmembrane helix (TM) 2 and 3.

      Weaknesses:

      The presented maps merely reinforce their previous findings, wherein wildtype Cx26 favored a closed conformation in the presence of high PCO2. While the structure of the TM2-TM3 loop may suggest a mechanism for stabilizing the closed conformation, no experimental data was provided to support this mechanism. Additionally, the cryo-EM maps were not effectively presented, making it difficult for readers to grasp the message.

      We have extensively revised the manuscript so that the novelty of this study is more apparent. There are three major points

      (1) The carbamylation mimetic pushes the conformation towards the closed conformation. Previously we just showed that CO2 pushes the conformation towards this conformation. Though we could show this was not due to pH, and could speculate this was due to carbamylation as suggested by previous mutagenesis studies, our data did not provide any mechanism whereby Lys125 was involved.

      (2) In going from the open to closed conformations, not only is a conformational change in TM2 involved, as we saw previously, but also a conformational change in TM1, the linker to the N-terminus and the cytoplasmic loop. Thus there is a clear connection between Lys125 and the conformation of the pore-closing N-terminus.

      (3) We observe for the first time in any connexin structure, density for the cytoplasmic loop. Since this loop is important in regulation, knowing how it might influence the positions of the transmembrane helices is important information if we are to understand how connexins can be regulated.

      Reviewing Editor:

      The reviewers have agreed on a list of suggested revisions that would improve the eLife assessment if implemented, which are as follows:

      (1) For completeness, Figure 1 could be supplied with an example of how the experiment would look like in the presence of CO2 - for the wild-type and for the K125E mutant. presumably for the wild-type this has been done previously in exactly this assay format, but this control would be an important part of characterization for the mutant. Page 4, lines 105106; "unsurprisingly, Cx26K125E gap junctions remain closed at a PCO2 of 55 mmHg." The data should be presented in the manuscript.

      We have now included the data with a PCO2 of 55mmH. This is now Figure 4 in our revised manuscript.

      (2) Would AlphaFold predictions show any interpretable differences in the E125 mutant, compared to the K125 (the wild-type)?

      We tried this in response to the reviewer’s suggestion. We did not see any interpretable differences. In general AlphaFold is not recognised as giving meaningful information around point mutations.

      (3) The K125R mutant appears to be a more effective control for extracting significant features from the K125E maps. Given that the use of a buffer containing high PCO2 is essential for obtaining high-resolution maps, wildtype Cx26 is unsuitable as an appropriate control. The K125R map, obtained at a high resolution (2.1Å), supports its suitability as a robust control.

      Though we are unsure what the referee is referring to here, we have rewritten this section and compare against the K125R map (figure 5a) as well as that derived from the wild-type protein. The important point is that the K125E mutant, causes a structural change that is consistent with the closure of the gap junctions that we observe in the dye-transfer assays.

      (4) Likewise, the rationale for using wildtype Cx26 maps obtained in DDM is unclear. Wildtype Cx26 seems to yield much better cryo-EM maps in LMNG. We suggest focusing the manuscript on the higher-quality maps, and providing supporting information from the DDM maps to discuss consistency between observations and the likely possibility that the nonprotein density in the pore is lipid and not detergent.

      The rationale for comparing the mutants against the wt Cx26 maps obtained in DDM was because the mutants were also solubilised in DDM. However, taking the lead from the referees’ comments, we have now rewritten the manuscript so that we first focus on the data we obtain from protein solubilised in LMNG. We feel this makes our message much clearer.

      (5) In general, the rationale for utilizing cryo-EM maps with the entire selected particles is unclear. Although the overall resolutions may slightly improve in this approach, the regions of interest, such as the N-terminus and the cytoplasmic loop, appear to be better ordered afer further classifications. The paper would be more comprehensible if it focuses solely on the classes representing the pore-constricting N-terminus (PCN) and the pore-open flexible Nterminus (POFN) conformations. Also, the nomenclatures used in the manuscript, such as "WT90-Class1", "K125E90-1", "LMNG90-class1", "LMNG90-mon-pcn" are confusing.

      LMNG90s are also wildtype; K125E-90-1 is in Class1 for this mutant and is similar to WT90Class2, which represents the PCN conformation. More consistent and intuitive nomenclatures would be helpful.

      We agree with the referees’ comments. This should now be clearer with our rewritten manuscript where we have simplified this considerably. We now call the conformations NConst (N-terminus defined and constricting the pore) and NFlex (N-terminus not visible) and keep this consistent throughout.

      (6) A potential salt bridge between the carbamylated K125 and R104 is proposed to account for the prevalence of Class-1 (i.e., PCN) in the majority of cryo-EM particles. However, the side chain densities are not well-defined, suggesting that such an interaction may not be strong enough to trap Cx26 in a closed conformation. Furthermore, the absence of experimental data to support this mechanism makes it unclear how likely this mechanism may be. Combining simple mutagenesis, such as R104E, with a dye transfer assay could offer support for this mechanism. Are there any published experimental results that could help address this question without the need for additional experimental work? Alternatively, as acknowledged in the discussion, this mechanism may be deemed as an "over-simplification." What is an alternative mechanism?

      R104 has been mutated to alanine in gap junctions and tested in a dye transfer assay as now mentioned in the text (Nijar et al, J Physiol 2021) supporting this role. In hemichannels R104 has been mutated to both alanine and glutamate and tested through dye loading assays Meigh et al, eLife 2013). Also in hemichannels R104 and K125 have been mutated to cysteines allowing them to be cross-linked through a disulphide bond. This mutant responds to a change in redox potential in a similar way to which the wild type protein responds to CO2 (Meigh et al, Open Biol 2015). Therefore, there is no doubt that the residues are important for the mechanism and the salt-bridge interaction seems a plausible mechanism to reconcile the mutagenesis data, however we cannot be sure that there are not other interactions involved that are necessary for closure. This information has now been included in the text.

      (7) The cryo-EM maps presented in the manuscript propose that gap junctions are constitutively open under normal PCO2 as the flexible N-terminus clears the solute permeation pathway in the middle of the channel. However, hemichannels appear to be closed under normal PCO2. It is puzzling how gap junctions can open when hemichannels are closed under normal PCO2 conditions. If this question has been addressed in previous studies, the underlying mechanism should be explicitly described in the introduction. If it remains an open question, differences in the opening mechanisms between hemichannels and gap junctions should be investigated.

      We suspect this is due to the difference in flexibility of gap junctions relative to hemichannels. However, a discussion of this is beyond this paper and would be complete speculation based on hemichannel structures of other connexins, performed in different buffering systems. There are no high resolution structures of Cx26 hemichannels.

      (8) A mystery density likely representing a lipid is abruptly introduced, but the significance of this discovery is unclear. It is hard to place the lipid on Figure S6 in the wider context of everything else that is discussed in the text. It would be helpful for readers if a figure were provided to show where the density is located in relation to all the other regions that are extensively discussed in the text.

      In the revised text this section has been completely rewritten. We have now include a more informative view in a new figure (Figure 1 – figure supplement 3).

      (9) Including and displaying even tentative pore-diameter measurements for the different states - this would be helpful for readers and provide a more direct visual cue as to the difference between open and closed states.

      We have purposely avoided giving precise measurements to the pore-diameter, since this depends on how we model the N-terminus. The first three residues are difficult to model into the density without causing stearic clashes with the neighbouring subunits.

      (10) Given that no additional experiments for channel function were carried out, it would be useful if to provide a more detailed discussion of additional mutagenesis results from the literature that are related to the experimental results presented.

      We have amplified this in the discussion (see answer to point 6).

      The reviewers also agreed that improvements in the presentation of the data would strengthen the manuscript. Here is a summary list of suggestions by reviewers aimed at helping improve how the data is presented:

      (1) Why is the pipette bright green in the top image, but rather weakly green in the bottom image in Figure 1 - is this the case for all images?

      (Now figure 4) This depends on whether the pipette was in the focal plane of view or not. The important point of these images is the difference in intensity of the donor vs the recipient cell. The graphs in figure 4c illustrate clearly the difference between the wild-type and the mutant gap junctions.

      (2) In figures 2-5, labels would help a lot in understanding what is shown - while the legends do provide the information on what is presented, it would help the reader to see the models/maps with labels directly in the panel. For example, Figure 2a/b - just indicating "WT90 Cx26" in pink and "K125E90" in blue directly in the panel would reduce the work for the reader.

      We have extensively modified the labels in the figures to address this issue.

      (3) Figure 4 - magenta and pink are fairly close, and to avoid confusion it might be useful to use a different color selection. This is especially true when structures are overlayed, as in this figure - the presentation becomes rather complicated, so the less confusion the color code can introduce, the better.

      (Now Figure 2) We have now changed pink to blue.

      (4) Figure 5 - a remarkably under-labelled figure.

      Now added labels.

      (5) Figure 6 - it would be interesting to add a comparison to Cx32 here as well for completeness, since the structure has been published in the meantime.

      Cx32 has now been included.

      (6) Figure 7 - please add equivalent labels on both sides of the model, left and right. Add the connecting lines for all of the tubes TM helices - this will help trace the structural elements shown. The legend does not quite explain the colors.

      We have modified the figure as suggested and explained the colours in the legend.

      (8) Fig.1 legend; Unclear what mCherry fluorescence represents. State that Cx26 was expressed as a translational fusion with mCherry.

      Now figure 4. We have now written “Montages each showing bright field DIC image of HeLa cells with mCherry fluorescence corresponding to the Cx26K125E-mCherry fusion superimposed (leftmost image) and the permeation of NBDG from the recorded cell to coupled cells.”

      (9) Fig. 3 b); Show R104 in the figure. Also E129-R98/R99 interaction is hard to acknowledge from the figure. It seems that the side chain density of E129 is not strong enough to support the modeled orientation.

      This is now Figure 1c. While the density in this region is sufficient to be confident of the main chain, we agree that the side chain density for the E129-R98/R99 interaction is not sufficiently clear to draw attention to and have removed the associated comment from the figure legend. The density is focussed on the linker between TM1 and the N-terminus and the KVRIEG motif. We prefer to omit R104, in order to keep the focus on this region. As described in the manuscript, the density for the R104 side chain is poor.

      (10) Fig. 3 c); Label the N-terminus and KVRIEG motif in the figure.

      Now Figure 1b. We have labelled the N-terminus. The KVRIEG motif is not visible in this map.

      (11) Page 9, lines 246-248; Restate, "We note, however, density near to Lys125, between Ser19 in the TM1-N-term linker, Tyr212 of TM4 and Tyr97 on TM3 of the neighbouring subunit, which we have been unable to explain with our modelling."

      We have reworded this.

      (12) Page 14, line 399; Patch clamp recording is not included in the manuscript.

      Patch clamp recordings were used to introduce dye into the donor cell.

      (13) On the same Figure 2, clashes are mentioned but these are hard to appreciate in any of the figures shown. Perhaps would be useful to include an inset showing this.

      We have modified Figure 2b slightly and added an explanation to highlight the clash. It is slightly confusing because the residues involved belong to neighbouring subunits.

      (14) The discussion related to Figure 6 is very hard to follow for readers who are not familiar with the context of abbreviations included on the figure labels. This figure could be improved to allow a general readership to identify more clearly each of the features and structural differences that are discussed in the text.

      We have extensively changed the text and updated the labels on the figure to make it much easier for the reader to follow.

      Below, you can find the individual reviews by each of the three reviewers.

      Reviewer #1 (Recommendations For The Authors):

      (1) In Figure 2d-e, the text discusses differences between K125E 90-1 and WT 90-class2 (7QEW), yet the figure compares K125E with 7QEQ. I suggest including a figure panel with a comparison between the two structures discussed in the manuscript text.

      This has been changed in the revised manuscript.

      Other comments have been addressed above.

    1. Author response:

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

      The reviewers thoughtful comments have helped us make the manuscript both more comprehensive and clearer. Thank you for your time and effort. We know that this is a long and technical paper. In our responses we refer to three documents:

      • Original: the first original submission

      • Revision: the revised document (02 MillardFranklinHerzog2023 v2.pdf)

      • Difference: a document that shows the changes made to text (but not figures or tables) from the original to revision (03 MillardFranklinHerzog2023 diff.pdf).

      Reviewer #1 (Recommendations For The Authors):

      (1) In general, the paper is well written and addresses important questions of muscle mechanics and muscle modeling. In the current version, the model limitations are briefly summarized in the abstract. However, the discussion needs a more complete description of limitations as well as a discussion of types of data (in vivo, ex vivo, single fiber, wholes muscle, MTU, etc.) that can be modeled using this approach.

      Please see the response to comment 23 for more details of the limitations that have been added to the revised document.

      (2) The choice of a model with several tendon parameters for simulating single muscle fiber experiments is not well justified.

      A rigid-tendon model with a slack length of zero was, in fact, used for these simulations for both the VEXAT and Hill models. In case this is still not clear: a rigid-tendon model of zero length is equivalent to no tendon at all. The text that first mentions the tendon model has now been modified to make it clearer that the parameters of the model were set to be consistent with no tendon at all:

      Please see the following text:

      Original:

      • page 17, column 1, line 28 ”... rigid tendon of zero length,”

      • page 17, column 1, line 51 ”... rigid tendon of zero length.”

      Revision:

      • page 19, column 1, line 19 ”... we used a rigid-tendon of zero length (equivalent to ignoring the tendon)”

      • page 19, column 1, line 38 ”... coupled with a rigid-tendon of zero-length.”

      Difference:

      • page 21, column 1, line 19 ”... we used a rigid-tendon... ”

      • page 21, column 1, line 45 ”... rigid-tendon of zero length ...”

      (3) A table that clarifies how all model parameters were estimated needs to be included in the main part of the manuscript.

      Two tables have been added to the manuscript that detail the parameters of the elastic-tendon cat soleus model (in the main body of the text) and the rabbit psoas fibril model (in an appendix). Each table includes:

      • A plain language parameter name

      • The mathematical symbol for the parameter

      • The value and unit of the parameter

      • A coded reference to the data source that indicates both the experimental animal and how the data was used to evaluate the parameter.

      Please see the following text:

      Revision:

      • page 11

      • page 42

      Difference:

      • page 11

      • page 46

      (4) The supplemental information is not properly referenced in the main text. There are a number of smaller issues that also need to be addressed.

      Thank for your attention to detail. The following problems related to Appendix referencing have been fixed:

      • Appendices are now parenthetically referenced at the end of a sentence. However, a few references to figures (that are contained within anAppendix) still appear in the body of the sentence since moving these figure references makes the text difficult to understand.

      • All Appendices are now referenced in the main body of the text.

      (5) Abstract, line 6: While it is commonly assumed that the short range stiffness of muscle is due to cross bridges, Rack & Westbury (1974) noted that it occurs over a distance of 25-35 nm, and that many cross-bridges must be stretched even farther than this distance (their p. 348 middle). It seems unlikely that cross-bridges alone can actually account for the short-range stiffness.

      There are three parts to our response to this comment:

      (a) Rack & Westbury’s definition of short-range-stiffness and unrealistic cross-bridge stretches

      (b) Rack & Westbury’s definition of short-range-stiffness vs. linear-timeinvariant system theory

      (c) Updates to the paper

      a. Rack & Westbury’s definition of short-range-stiffness and unrealistic cross-bridge stretches.

      As you note, on page 348, Rack and Westbury write that ”If the short range stiffness is to be explained in terms of extension of cross-bridges, then many of them must be extended further than the 25-35 nm mentioned above.” Having re-read the paper, its not clear how these three factors are being treated in the 25−35 nm estimate:

      • the elasticity of the tendon and aponeurosis,

      • the elasticity of actin and myosin filaments,

      • and the cycling rate of the cross-bridges.

      Obviously the elasticity of the tendon, aponeurosis, actin, and myosin filaments will reduce the estimated amount of crossbridge strain during Rack and Westbury’s experiments. A potentially larger factor is the cycling rate of each cross-bridge. If each crossbridge cycles faster than 11 Hz (the maximum frequency Rack and Westbury used), then no single crossbridge would stretch by 25-35 nm. So why didn’t Rack and Westbury consider the cycling rate of crossbridges?

      Rack and Westbury’s reasoned that a perfectly elastic work loop would necessarily mean that all crossbridges stayed attached: as soon as a crossbridge cycles it would release its stored elastic energy and the work loop would no longer be elastic. Since Rack and Westbury measured some nearly perfect elastic work loops (the smallest loops in Fig. 2,3, and 4), I guess they assumed crossbridges remained attached during the 25-35 nm crossbridge stretch estimate. However, even Rack and Westbury note that none of the work loops they measured were perfectly elastic and so there is room to entertain the idea that crossbridges are cycling.

      Fortunately, for this discussion, crossbridge cycling rates have been measured.

      In-vitro measurements by Uyeda et al. show that crossbridges are cycling at 30 Hz when moving at 0.5-1.2 length/s. At this rate, there would be enough time for a single crossbridge to cycle nearly 2.72 times for every cycle of the 11 Hz sinusoidal perturbations, reducing its expected strain from 25-35 nm down to 9.2−12.9µm. This effect becomes even more pronounced if crossbridge cycling rate is used to explain the difference in sliding velocity between Uyeda et al.’s in-vitro data (0.5-1.2 length/s) and the maximum contraction velocity of an in-situ cat soleus (4.65 lengths/s, Scott et al.).

      b. Rack & Westbury’s definition of short-range-stiffness vs. linear-time-invariant system theory

      Rack and Westbury defined short-range-stiffness to describe a specific kind of force response of the muscle to cyclical length changes:

      • muscle force is linear with length change,

      • and independent of velocity.

      Rack and Westbury’s definition therefore fails when viscous forces become noticeable, because viscous forces are velocity dependent.

      On line 6 of the abstract the term ‘short-range-stiffness’ is not used because Rack and Westbury’s definition is too narrow for our purposes. Instead we are using the more general approach of approximating muscle as a linear-timeinvariant (LTI) system, where it is assumed that

      • the response of the system is linear

      • and time invariant.

      To unpack that a little, a muscle is considered in the ‘short-range’ in our work if it meets the criteria of a linear time-invariant (LTI) system:

      • the force response of muscle can be accurately described as a linear function of its length and velocity (its state)

      • and its response is not a function of time (which means constant stimulation, and no fatigue).

      In contrast to Rack and Westbury’s definition, the ‘short-range’ in linear systems theory is general enough to accommodate both elastic and viscous forces. In physical terms, small for an LTI approximation of muscle is larger than the short-range defined by Rack and Westbury: an LTI system can include velocity dependence, while short-range-stiffness ends when velocity dependence begins.

      c. Updates to the paper

      To make the differences between Rack and Westbury’s ‘short-range-stiffness’ and LTI system theory clearer: - We have removed all occurrences of ‘short-range’ that were associated with Kirsch et al. and have replaced this phrase with ‘small’.

      • On the first mention of Kirsch’s work we have made the wording more specific

      Revision:

      • page 1, column 1, lines 4,5

      • page 1, column 2, lines 14-21 ”Under constant activation ...”

      Difference: page 1, column 2, line 19-26

      • page 1, column 1, lines 4,5

      • page 1, column 2, lines 20-27 ”Under constant activation ...”

      • A footnote has been added to contrast the definition of ‘small’ in the context of an linear time invariant system to ‘short-range’ in the context of Rack and Westbury’s definition of short-range-stiffness.

      Revision: page 1, column 2, bottom

      Difference: page 1, column 2, bottom

      • In addition, we have added a brief overview of LTI system theory to make the analysis and results more easily understood:

      Revision: Figure 4 paragraph beginning on page 10, column 2, line 15 ”As long as ...”

      Difference: Figure 4 paragraph beginning on page 12, column 1, line 46 ”As long as ...”

      (6) Page 3, lines 6-8: It also seems unlikely that 25% of cross-bridges are attached at one time (Howard, 1997) even for supramaximal isometric stimulation. The number should be less than 20%. What would the ratio of load path stiffness be for low force movements such as changing the direction of a frictionless manipulandum or slow walking? The range of relative stiffnesses is of more interest than the upper limit.

      We have made the following updates to address this comment:

      • A 20% duty cycle now defines the upper bound stiffness of the actinmyosin load path.

      • We have also evaluated the lower bound actin-myosin stiffness when a single crossbridge is attached.

      • The stiffness of titin from Kellermayer et al. has been digitized at a length of 2 µm and 4 µm to more accurately capture the length dependence of titin’s stiffness.

      • We have added a new figure (Figure 14) to make it easier to compare the range of actin-myosin stiffness to titin-actin stiffness.

      • The text in the main body of the paper and the Appendix has been updated.

      • The script ’main ActinMyosinAndTitinStiffness.m’ used to perform the calculations and generate the figure is now a part of the code repository.

      Please see the following text:

      Revision

      • The paragraph beginning at page 2, column 2, line 45 ”The addition of a titin element ...”

      • Appendix A

      • Figure 14 (in Appendix A)

      Difference

      • The paragraph beginning at page 3, column 1, line 6: ”The addition of a titin element ...”

      • Appendix A

      • Figure 14 (in Appendix A)

      (7) Page 5, line 12: A word seems to be missing here, ”...together to further...”.

      Thank you for your attention to detail. The sentence has been corrected.

      Please see the following text:

      • Revision: page 4, column 2, line 40 ”... into a single ...”

      • Difference: page 5, column 1, line 18

      (8) Page 5, line 24-27: These ”theories” are not mutually exclusive, and it is misleading to suggest they are. There is evidence for binding of titin to actin at multiple locations and there is no reason why evidence supporting one binding location must detract from the evidence supporting other binding locations.

      The text has been modified to make it clear to readers that the different titinactin binding locations are not mutually exclusive. Please see the following text:

      • Revision: page 5, column 1, lines 17-19, the sentence beginning ”As previously mentioned, ...”

      • Difference: page 5, column 1, lines 41-44

      (9) Page 5, lines 48-51: Should cite Kellermayer and Granzier (1996) not Kellermayer et al. (1997).

      The reference to ‘Kellermayer et al.’ has been changed to ‘Kellermayer and Granzier’. The comment that the year of the reference should be changed from (1997) to (1996) is confusing: the 1996 paper is being referenced.

      For further details please see:

      • Revision: page 5, column 1, 39-40

      • Difference: page 5, column 2, line 19-22

      (10) Also, Dutta et al. (2018) should be cited as further showing that N2A titin by itself slows actin motility on myosin.

      Thank you for the suggestion. The sentence has been modified to include Dutta et al.:

      For further details please see:

      • Revision: page 5, column 1, 40

      • Difference: page 5, column 2, line 19-22

      (11) Figure 2 legend and elsewhere: it is odd to say that experiments used ”a cat soleus” when more than one cat coleus was used. Change to ”cat coleus”. See also page 15, line 15.

      Thank you for your attention to detail. All occurrences of ‘a cat soleus’ have been changed, with some sentence revision, to ‘cat soleus’.

      (12) Page 6, line 10: It is not clear why an MTU was used to simulate single muscle fiber experiments. What is the justification for choosing this particular model? Also, the choice of model might explain why the version with stiff tendon performs better than the version with an elastic tendon, but this is never mentioned. Why not use a muscle model with no tendon (e.g., Wakeling et al., 2021 J. Biomech.)?

      Please see the response to comment 2.

      (13) Millard et al.’s activation dynamics model also fails to capture the lengthdependence of activation dynamics (Shue and Crago, 1998; Sandercock and Heckman, 1997), which should be noted in the discussion along with other limitations.

      An additional limitations paragraph is in the revised manuscript that addresses this comment specifically. However, we have used Stephenson and Wendt as a reference for the shift in peak isometric force that comes with submaximal activation. In addition, we also reference Chow and Darling for the property that the maximum shortening velocity is reduced with submaximal activations.

      • Revision: page 22, column 1, line 41 ”Finally, the VEXAT model ...”

      • Difference: page 24, column 2, line 12 ”Finally, the VEXAT model ...”

      In addition, please see the response to comment 23.

      (14) Page 6, line 22: ”An underbar...”.

      Thank you for your attention to detail, this correction has been made.

      (14) Page 7, lines 27-32: This and other issues should be described in the Discussion under a heading of model limitations.

      Please see the response to comment 23.

      (15) Page 7, lines 43-44: Numerous papers from the last author’s laboratory contradict the claim that there is no force enhancement on the ascending limb by demonstrating that force enhancement does occur on the ascending limb (see e.g., Leonard & Herzog 2002, Peterson et al., 2004 and several papers from the Rassier laboratory).

      Thank you for your attention to detail. This statement is in error and has been removed. To improve this section of the paper, a paragraph has been added to briefly mention the experimental observations of residual force enhancement before proceeding to explain how this phenomena is represented by the model.

      Please see the following text:

      Revision:

      • the paragraph starting on page 7, column 2, line 43 ”When active muscle is lengthened, ...”

      • and the following paragraph starting on page 8, column 1, line 3 “To develop RFE, ”

      Difference:

      • the paragraph starting on page 8, column 2, line 15

      • and the following paragraph starting on page 9, column 1, line 6

      (17) Figure 3 legend and elsewhere: The authors use Prado et al. (2005) to determine several titin parameters, however the simulations seem to focus on cat soleus, but Prado et al.’s paper is on rabbits. More clarity is needed about which specific results from which species and muscles were used to parameterize the model.

      The new parameter table includes coded entries to indicate the literature source for experimental data, the animal it came from, and how the data was used. For example, the ‘ECM fraction’ has a source of ‘R[57]’ to show that the data came from rabbits from reference 57. For further details, please see the response to comment #3

      Please see the following text:

      • Revision: page 11, column 2, table section H: ‘ECM fraction’.

      • Difference: page 11, column 2, table section H: ‘ECM fraction’.

      To address this comment in a little more detail, we have had to use Prado et al. (2005) to give us estimates for only one parameter: P, the fraction of the passive force-length relation that is due to titin. Prado et al.’s measurements relating to P are unique to our knowledge: these are the only measurements we have to estimate P in any muscle, cat soleus or otherwise. Here we use the average of the values for P across the 5 muscles measured by Prado et al. as a plausible default value for all of our simulations.

      (18) Figure 4 seems unnecessary.

      Figure 4 has been removed.

      (19) Page 10, lines 17-18: provide the abbreviation (VAF) here with the definition (variance accounted for).

      Thank you for your attention to detail. The abbreviation has been added.

      Please see these parts of the manuscripts for details:

      • Revision: page 12, column 2, line 13

      • Difference: page 13, column 2, line 32

      (20) Page 11, lines 2-3: Here and elsewhere, it is clear that some model parameters have been optimized to fit the model. The main paper should include a table that lists all model parameters and how they were chosen or optimized, including but not limited to the information in Table 1 of the supplemental information section.

      See response to comment 3.

      (20) Page 17, lines 45 -49: Again, a substantial number of ad hoc adjustments to the model appear to be required. These should be described in the Discussion under limitations, and accounted for in the parameters table. See also legends to Fig. 12 and 13, page 19, lines 23-26.

      Please see the response to comment #3: a coded entry now appears to indicate the data source, the animal used in the experiment, and the method used to process the data. This includes entries for parameters which were estimated

      ‘E’ so that the model produced acceptable results in the simulations presented. In addition, the new discussion paragraph includes a number of sentences that use the adjustment to the active-titin-damping coefficient as an opening to discuss the limitations of the VEXAT’s titin-actin bond model and the circumstances under which the model’s parameters would need to be adjusted.

      Please see responses to comments 3 and 23 for additional details. In addition, please see the specific discussion text mentioning the change to βoPEVK:

      • Revision: page 22, column 1, line 30 ”In Sec. 3.3 we had ...”

      • Difference: page 24, column 1, line 49

      (22) Page 20, lines 50-11: It should be noted here that Tahir et al.’s (2018) model has both series and parallel elastic elements, provided by superposition of rotation (series) and translation (parallel) of a pulley.

      While it is true that Tahir et al.’s (2018) model has series and parallel elements, as do the other models mentioned, these models do not have the correct structure to yield a gain and phase response that mimics biological muscle. The text that I originally wrote attempted to explain this without going into the details. As you note, this explanation leaves something to be desired. The original text commenting on the models of Forcinito et al, Tahir et al, Haeufle et al., and Gunther et al. has been updated to be more specific.¨ Please see the parts of the following manuscripts for details:

      • Revision: page 22, column 2, line 20, the paragraph beginning ”The models of Forcinito ...”

      • Difference: page 24, column 2, line 44

      (23) Discussion: This section should include a description of model limitations, including the relatively large number of ad hoc modifications and how many parameters must be found by optimization in practice. The authors should discuss what types of data are most compatible for use with the model (ex vivo, in vivo, single fiber, whole muscle, MTU), requirements for applying the model to different types of data, and impediments to using the model on different types of data.

      An additional limitations paragraph has been added to the discussion.

      Please see the following text:

      • Revision: the paragraph beginning on page 22, column 1, line 11 ”Both the viscoelastic ...”

      • Difference: the paragraph beginning on page 24, column 1, line 27.

      Reviewer #2 (Recommendations For The Authors):

      (1) If it is possible to compare the output of this model to other more contemporary models which incorporate titin but are also simple enough to implement in whole-body simulation (such as the winding filament model), this would seem to greatly strengthen the paper.

      That’s an excellent idea, though beyond the scope of this already lengthy paper. Even though the Hill model we evaluated is a bit old it is widely used, and so, many readers will be interested in seeing the benchmark results. As benchmarking work is both difficult to fund and undertake, we do hope that others will evaluate their own models using the code and data we have provided.

      (2) I’m a little unclear on the basis for the transition between short- and midrange length changes, both in reality and in the model. And also about the range of strains that qualify as ”short”. It seems like there is potential for short range stiffness, although I would have thought more in the range of 1-2% strains than >3%, to be due to currently attached crossbridges. There is clear evidence that active titin is responsible for the low stiffness at very large strains that exceed actin-myosin overlap. But I am not clear on how a transitional stiffness on the descending limb of the force-length relationship is implemented in the model, and what aspect of physiology this is replicating. It may be helpful to clarify this further and indicate where in the model this stiffness arises.

      This question has several parts to it which I will paraphrase here:

      A Short-range stiffness acts over smaller strains than 3.8%. How is shortrange defined?

      B Where is the transition made between short-range and mid-range force response, both in reality and in the model. Also how does this change on the descending limb?

      C What components in the model contribute to the stiffness of the CE?

      A. Short-range stiffness acts over smaller strains than 3.8%. How is shortrange defined?

      The response to Reviewer 1’s comment # 5 directly addresses this question.

      B. Where is the transition made between short-range and mid-range forceresponse, both in reality and in the model. Also how does this change on the descending limb? We are going to rephrase the question because of changes in terminology that we have made in response to Reviewer 1’s comment #5.

      (i) What is the basis for the transition between the muscle behaving like an LTI system? Both in reality, and in the model. (ii) What happens outside the LTI range? (iii) Also how does this change on the descending limb?

      We will address this question one part at a time:

      (i) What is the basis for the transition between the muscle behaving like an LTI system? Both in reality, and in the model.

      A system’s response can be approximated as a linear-time-invariant (LTI) system as long as it is time-invariant, and its output can be expressed as a linear function of its input. In the context of Kirsch et al.’s experiment, the ‘system’ is the muscle, the ‘input’ is the time series of length data, and the ‘output’ is the time series of force data. Due to the requirement for timeinvariance, two experimental conditions must be met to approximate muscle as an LTI system:

      • the nominal length of the muscle stays constant over long periods of time,

      • and the nominal activation of the muscle stays constant.

      These conditions were met by default in Kirch et al.’s experiment, and also in our simulations of this experiment. The one remaining condition to assess is whether or not the muscle’s response is linear.

      To evaluate whether the muscle’s force is a linear function of the length change, Kirch et al. evaluated (Cxy)2 the coherence squared between the length and force time-series data. Even though the mathematical underpinnings of (Cxy)2 are complicated, the interpretation of (Cxy)2 is simple: muscle can be accurately approximated as a linear system if (Cxy)2 is close to 1, but the accuracy of this approximation becomes poor as (Cxy)2 approaches 0. Kirsch et al. used (Cxy)2 to identify a bandwidth in which the response of the muscle to the 1−3.8%ℓoM length changes was sufficiently linear for analysis: a lower bound of 4 Hz was identified using (Cxy)2 and the bandwidth of the input signal (15 Hz, 35 Hz, or 90 Hz) set the upper bound. In Fig. 3 of Kirsch et al. the (Cxy)2 at 4 Hz has a value of at least 0.67 for the 15 Hz and 90 Hz signals. To minimize error in our analysis and yet be consistent with Kirsch et al., we analyze the bandwidth common to both (Cxy)2 ≥ 0.67 and Kirsch et al.’s defined range. Though the bandwidth defined by the criteria (Cxy)2 ≥ 0.67 is usually larger than the one defined by Kirsch et al., there are some exceptions where the lower frequency bound of the models is higher than 4 Hz (now reported in Tables 4D and 5D).

      (ii) What happens outside the LTI range?

      When a muscle’s output cannot be considered a LTI it means that either that its length or activation is time-varying, or the relationship between length and force is no longer linear. In short, that the muscle is behaving as one would normally expect: time-varying and non-linearly. The wonderful part of Kirsch et al.’s work is that they found a surprisingly large region in the frequency domain where muscle behaves linearly and can be analyzed using the powerful tools of linear systems and signals.

      (iii) Also how does this change on the descending limb?

      Since nominal length of Kirsch et al.’s experiments is ℓoM it is not clear how the results of the perturbation experiments will change if the nominal length is moved firmly to the descending limb. However, we can see how the stiffness and damping values will change by examining Figure 9C and 9D which shows the calculated stiffness and damping of the VEXAT and Hill models as ℓM is lengthened from ℓoM down the descending limb: the stiffness and damping of the VEXAT model does not change much, while the Hill model’s stiffness changes sign and the damping coefficient changes a lot. What cannot be seen from Figure 9C and 9D is how the bandwidth over which the models are considered linear changes.

      We have made a number of updates to the text to more clearly communicate these details of our response to part (i):

      • Text has been edited so that it is clear that the terms ’short-range stiffness’ and ’small’ from Rack and Westbury’s work is not confused with ’stiffness’ and ’small’ from the LTI system’s analysis. Please see our response to comment # 5 for details.

      • We have added text to the main body of the paper to explain how the coherence squared metric was used to select a bandwidth in which the response of the system is approximately linear:

      • Revision: the paragraph that starts on page 11, column 1, line 3 ”Kirsch et al. used system identification ...”

      – Difference: page 13, column 2, line 1

      – Coherence is defined in Appendix D

      – Coherence is now also included in the example script ‘main SystemIdentificationExample.m’

      • The bandwidth over which model output can be considered linear (coherence squared > 0.67) has been added to Tables 4 and 5

      – Revision: see Table 4D, and Table 5D in Appendix E

      – Difference: see Table 4D, and Table 5D in Appendix E

      • Figures 6 and Figures 16 are annotated now if the plotted signal does not meet the linearity requirement of Cxy > 0.67.

      C. What components in the model contribute to the stiffness of the CE?

      There are three components that contribute to the stiffness of the CE which are pictured in Figure 1, appear in Eqn. 15, and are listed explicitly in Eqn. 76:

      (a) The XE, as represented by the afL(ℓ˜S+L˜M)k˜oX term in Eqn. 15.

      (b) The elasticity of the distal segment of titin, f2(ℓ˜2). Only f2(ℓ˜2) appears in Eqn. 15 because ℓ˜1 is a model state.

      (c) The extracellular matrix, as represented by the fECM(ℓ˜ECM)

      There is also a compressive element fKE, but it plays no role in the simulations presented in this work because it only begins to produce force at extremely short CE lengths (ℓ˜M < 0.1ℓoM).

      We have made the following changes to make these components clearer

      Figure 1A has been updated:

      – The symbols for a spring and a damper are now defined in Figure 1A

      – The ECM now has a spring symbol. Now all springs and dampers have the correct symbol in Figure 1A.

      – The caption now explicitly lists the rigid, viscoelastic, and elastic elements in the model

      The equations for the VEXAT’s CE stiffness and damping are now compared and contrasted to the the Hill model’s stiffness and damping in Sec. 3.1.

      – Revision: starting at page 14, column 2, line 1: Eqn. 28 and Eqn. 29 and surrounding text

      – Difference: page 17, column 1, line 22

      (3) This model appears to be an amalgamation of a phenomenological (forcelength and force-velocity relationships) and a mechanistic (crossbridge and titin stiffness and damping) model. While this may improve predictions, and so potentially be useful, it also seems like it limits the interpretation of physiological underpinnings of any findings. It may be helpful to explore in greater detail the implications of this approach.

      We have added a limitations paragraph to the discussion which addresses this comment and can be found in:

      • Revision: the paragraph beginning on page 22, column 1, line 11 ”Both the viscoelastic ...”

      • Difference: the paragraph beginning on page 24, column 1, line 27

      (4)As a biologist, I found the interpretation of phase and gain a little difficult and it may help the reader to show in greater detail the time series data and model predictions to highlight conditions under which the models do not accurately capture the magnitude and timing of force production.

      It is important that the ideas of phase and gain are understood, especially because little information can be gleaned from the time series data directly. There is some time series data in the paper already that compares each model’s response to its spring-damper of best fit: plots of the force response of each model and its spring damper of best fit can be found in Figures 6A, 6D, 6G, 6J, 16A, 16D, 16G, and 16J in the revised manuscript. While it is clear that models with a higher VAF more closely match the spring-damper of best fit, there is not much more that can be taken from time series data: the systematic differences, particularly in phase, are just not visually apparent in the time-domain but are clear in gain and phase plots in the frequency-domain.

      To make the meaning of phase and gain plots clearer, Figure 4 (Figure 5 in the first submission) has been completely re-made and includes plots that illustrate the entire process of going from two length and force timedomain signals to gain and phase plots in the frequency-domain. Included in this figure is a visual representation of transforming a signal from the time to the frequency domain (Fig. 4B and 4C), and also an illustration of the terms gain and phase (Fig. 4D). In addition, a small example file ’main SystemIdentificationExample.m’ has been added to the matlab code repository in the elife2023 branch to accompany Appendix D, which goes through the mathematics used to transform input and output time domain signals into gain and phase plots of the input-output relation. Small updates have been made to Figure 6 and 16 in the revised paper (Figures 7 and 18 in the first submission) to make the time domain signals from the spring-damper of best fit and the model output clearer. Finally, I have re-calculated the gain and phase profiles using a more advanced numerical method that trades off some resolution in frequency for more accuracy in the magnitude. This has allowed me to make Figures 6 and 16 easier to follow because the gain and phase responses are now lines rather than a scattering of points. We hope that these additions make the interpretation of gain and phase clearer.

      Please see

      Revision:

      – Figure 4 and caption on page 12

      – The opening 2 paragraphs of Sec 3.1 starting on page 10, column 2, line 4 ”In Kirsch et al.’s ...”

      – Figure 6 & 16: spring damper and model annotation added, plotted the gain and phase as lines

      – Appendix D: Updated to include coherence and the more advanced method used to evaluate the system transfer function, gain, and phase.

      Difference:

      – Figure 4 and caption on page 12

      – The opening 2 paragraphs of Sec 3.1 starting on page 12, column 1, line 34 and ending on page 13, column 2, line 29

      – Figure 6 & 16: spring damper and model annotation added

      – Appendix D

      (5) The actin-myosin and actin-titin load pathways are depicted as distinct in the model. However, given titin’s position in the center of myosin and the crossbridge connections between actin and myosin, this would seem to be an oversimplification. It seems worth considering whether the separation of these pathways is justified if it has any effect on the conclusions or interpretation.

      We have reworked one of the discussion paragraphs to focus on how our simulations would be affected by two mechanisms (Nishikawa et al.’s winding filament theory and DuVall et al.’s titin entanglement hypothesis) that make it possible for crossbridges to do mechanical work on titin.

      • Revision: the paragraph beginning on page 21, column 2, line 42 “The active titin model ...”

      • Difference: the paragraph beginning on page 23, column 2, line 48

      References

      Nishikawa KC, Monroy JA, Uyeno TE, Yeo SH, Pai DK, Lindstedt SL. Is titin a ‘winding filament’? A new twist on muscle contraction. Proceedings of the royal society B: Biological sciences. 2012 Mar 7;279(1730):981-90.

      DuVall M, Jinha A, Schappacher-Tilp G, Leonard T, Herzog W. I-Band Titin Interaction with Myosin in the Muscle Sarcomere during Eccentric Contraction: The Titin Entanglement Hypothesis. Biophysical Journal. 2016 Feb 16;110(3):302a.

    1. Author response:

      Reviewer #1 (Public Review):

      In this manuscript, Naseri et al. present a new strategy for identifying human genetic variants with recessive effects on disease risk by the genome-wide association of phenotype with long runs-of-homozygosity (ROH). The key step of this approach is the identification of long ROH segments shared by many individuals (termed "shared ROH diplotype clusters" by the authors), which is computationally intensive for large-scale genomic data. The authors circumvented this challenge by converting the original diploid genotype data to (pseudo-)haplotype data and modifying the existing positional Burrow-Wheeler transformation (PBWT) algorithms to enable an efficient search for haplotype blocks shared by many individuals. With this method, the authors identified over 1.8 million ROH diplotype clusters (each shared by at least 100 individuals) and 61 significant associations with various non-cancer diseases in the UK Biobank dataset.

      Overall, the study is well-motivated, highly innovative, and potentially impactful. Previous biobank-based studies of recessive genetic effects primarily focused on genome-wide aggregated

      ROH content, but this metric is a poor proxy for homozygosity of the recessive alleles at causal loci. Therefore, searching for the association between phenotype and specific variants in the homozygous state is a key next step towards discovering and understanding disease genes/alleles with recessive effects. That said, I have some concerns regarding the power and error rate of the methods, for both identification of ROH diplotype clusters and subsequent association mapping. In addition, some of the newly identified associations need further validation and careful consideration of potential artifacts (such as cryptic relatedness and environment sharing).

      1) Identification of ROH diplotype clusters.

      The practice of randomly assigning heterozygous sites to a homozygous state is expected to introduce errors, leading to both false positives and false negatives. An advantage that the authors claim for this practice is to reduce false negatives due to occasional mismatch (possibly due to genotyping error, or mutation), but it's unclear how much the false positive rate is reduced compared to traditional ROH detection algorithm. The authors also justified the "random allele drawing" practice by arguing that "the rate of false positives should be low" for long ROH segments, which is likely true but is not backed up with quantitative analysis. As a result, it is unclear whether the trade-off between reducing FNs and introducing FPs makes the practice worthwhile (compared to calling ROHs in each individual with a standard approach first followed by scanning for shared diplotypes across individuals using BWT). I would like to see a combination of back-of-envelope calculation, simulation (with genotyping errors), and analysis of empirical data that characterize the performance of the proposed method.

      In particular, I find the high number of ROH clusters in MHC alarming, and I am not convinced that this can be fully explained by a high density of SNPs and low recombination rate in this region. The authors may provide further support for their hypothesis by examining the genome-wide relationship between ROH cluster abundance and local recombination rate (or mutation rate).

      Thanks for this insightful comment. Through additional experiments, we confirmed that the excessive number of ROH clusters in the MHC region is due to the higher density of markers per centimorgan. As discussed above at Essential Revision 2, we took this opportunity to modify our code to search for clusters with the minimum length in terms of cM instead of sites. We have also provided the genetic distance for reported clusters in the MHC region with significant association (genetic length (cM) column in Tables 1 and 2). We include the following in the main text:

      “We searched for ROH clusters using a minimum target length of 0.1 cM (Figure 3–figure supplement 1). As shown in the figure, there is no excessive number of ROH clusters in chromosome 6 as was spotted using a minimum number of variant sites.”

      Methods section, ROH algorithm subsection:

      “We implemented ROH-DICE to allow direct use of genetic distances in addition to variant sites for L. The program can take minimum target length L directly in cM and detect all ROH clusters greater than or equal to the target length in cM. The program holds a genetic mapping table for all the available sites, and cPBWT was modified to work directly with the genetic length instead of the number of sites.”

      2) Power of ROH association. Given that the authors focused on long segments only (which is a limitation of the current method), I am concerned about the power of the association mapping strategy, because only a small fraction of causal alleles are expected to be present in long, homozygous haplotypes shared by many individuals. It would be useful to perform a power analysis to estimate what fraction of true causal variants with a given effect size can be detected with the current method. To demonstrate the general utility of this method, the authors also need to characterize the condition(s) under which this method could pick up association signals missed by standard GWAS with recessive effects considered. I suspect some variants with truly additive effects can also be picked up by the ROH association, which should be discussed in the manuscript to guide the interpretation of results.

      We added a new experiment in the Results section “Evaluation of ROH clusters in simulated data” under Power of ROH-DICE in association studies. We compared the power of the ROH cluster with additive, recessive, and dominant models. Our simulation shows that using ROH clusters outperforms standard GWAS when a phenotype is associated with a set of consecutive homozygous sites. We added the following text:

      “...We calculated the p-values for both ROH clusters and all variant sites. We used a p-value cut-off of 0.05 divided by the number of tests for each phenotype to determine whether the calculated p-value was smaller than the threshold, indicating an association. For GWAS, only one variant site within the ROH cluster, contributing to the phenotype, was required. We tested for all additive, dominant, and recessive effects (Figure 1–figure supplement 3). The figure demonstrates that ROH-DICE outperforms GWAS when a phenotype is associated with a set of consecutive homozygous sites. The maximum effect size of 0.3 resulted in ROH clusters achieving a power of 100%, whereas the additive model only achieved 11%, and the dominant and recessive models achieved 52% and 70%, respectively. The GWAS with recessive effect yields the best results among other GWAS tests, however, its power is still lower than using ROH clusters.”

      3) False positives of ROH association. GWAS is notoriously prone to confounding by population and environmental stratification. Including leading principal components in association testing alleviates this issue but is not sufficient to remove the effects of recent demographic structure and local environment (Zaidi and Mathieson 2020 eLife). Similar confounding likely applies to homozygosity mapping and should be carefully considered. For example, it is possible that individuals who share a lot of ROH diplotypes tend to be remotely related and live near each other, thus sharing similar environments. Such scenarios need to be excluded to further support the association signals.

      We acknowledge that there could be confounding factors that may affect the association's results. To address this, we utilized principal component (PC) values and additional covariates while using PHESANT after our initial Chi-square tests. We also included your comments in our Discussion section:

      "We used age, gender, and genetic principal components as confounding variables in the association analysis. Genetic principal components can reduce the confounding effect brought on by population structure but it may be insufficient to completely eliminate the effects of recent demographic structure and the local environment45. For example, individuals sharing excessive ROH diplotypes may share similar environments since they are closely related and reside close to one another. Since we did not rule out related individuals, some of the reported GWAS signals may not be attributable to ROH.”

      4) Validation of significant associations. It is reassuring that some of the top associations are indirectly corroborated by significant GWAS associations between the same disease and individual SNPs present in the ROH region (Tables 1 and 2). However, more sanity checks should be done to confirm consistency in direction of effect size (e.g., risk alleles at individual SNPs should be commonly present in risk-increasing ROH segment, and vice versa) and the presence of dominance effect.

      The beta values for effect size are now included in all reported tables. All beta values for ROH-DICE are positive indicating carriers of these ROH diplotypes may increase the risk of certain non-cancerous diseases. Moreover, we conducted the suggested sanity check to confirm the consistency of the direction of risk-inducing ROH diplotypes and risk alleles.

      We also computed D’ as a measure of linkage between the reported GWAS results and ROH clusters. We found that most of the GWAS results and ROH clusters are strongly correlated. However, in a few cases, D' is small or close to zero. In such cases, the reported p-value from GWAS was also insignificant, while the ROH cluster indicated a significant association. We included these points in the Results section.

      Reviewer #3 (Public Review):

      A classic method to detect recessive disease variants is homozygosity mapping, where affected individuals in a pedigree are scanned for the presence of runs of homozygosity (ROH) intersecting in a given region. The method could in theory be extended to biobanks with large samples of unrelated individuals; however, no efficient method was available (to the best of my knowledge) for detecting overlapping clusters of ROH in such large samples. In this paper, the authors developed such a method based on the PBWT data structure. They applied the method to the UK biobank, finding a number of associations, some of them not discovered in single SNP associations.

      Major strengths:

      •           The method is innovative and algorithmically elegant and interesting. It achieves its purpose of efficiently and accurately detecting ROH clusters overlapping in a given region. It is therefore a major methodological advance.

      •           The method could be very useful for many other researchers interested in detecting recessive variants associated with any phenotype.

      •           The statistical analysis of the UK biobank data is solid and the results that were highlighted are interesting and supported by the data.

      Major weaknesses:

      •           The positions and IDs of the ROH clusters in the UK biobank are not available for other researchers. This means that other researchers will not be able to follow up on the results of the present paper.

      We included the SNP IDs, positions, and consensus alleles for all reported loci in the main tables. Moreover, additional information including beta and D’ values were added. The current information should allow researchers to follow up on the results. Supplementary File 2 contains beta, D’ values for all reported clusters.

      Supplementary File 3 contains the SNP IDs and consensus alleles for all reported clusters in Tables 1 and 2. The consensus allele denotes the allele with the highest occurrence in the reported clusters.

      •           The vast majority of the discoveries were in regions already known to be associated with their respective phenotypes based on standard GWAS.

      We agree that a majority of the ROH regions are indeed consistent with GWAS. However, some regions were missed by standard GWAS (e.g. chr6:25969631-26108168, hemochromatosis). Our message is that our method is a complementary approach to standard GWAS and will not replace standard GWAS analysis. See our response to Reviewer #2 Point Six.

      •           The running time seems rather long (at least for the UK biobank), and therefore it will be difficult for other researchers to extensively experiment with the method in very large datasets. That being said, the method has a linear running time, so it is already faster than a naïve algorithm.

      Thank you for your input. The algorithm used to locate matching blocks is efficient and the total CPU hours it consumed was the reported run time. Since it consumes very little memory and resources, it can be executed simultaneously for all chromosomes. We also noticed that a significant time was being spent parsing the input file and slightly modified our script to improve the parsing. We also re-ran it for all chromosomes in parallel and reported the elapsed time which was only 18 hours and 54 minutes.

      “This was achieved by running the ROH-DICE program, with a wall clock time of 18 hours and 54 minutes where the program was executed for all chromosomes in parallel (total CPU hours of ~ 242.5 hours). The maximum residence size for each chromosome was approximately 180 MB.”

    1. Author response;

      Reviewer #1 (Public Review):

      Authors investigated the role of OBOX4 in the zygotic genome activation (ZGA) in mice. Obox4 genes form an array of duplicated genes they were identified as a candidate ZGA factor based on expression patterns during early development. The role of OBOX4 was subsequently studied in embryonic stem cells and early embryos. It was found that transcriptional activation mediated by OBOX4 has similar features as that of DUX, which was previously identified as a zygotic transcription factor involved in ZGA and a major activator of the zygotic expression program. It was, however, unexpected that Dux knock-out did not impair embryonic development. The work by Guo et al. provides several lines of evidence that OBOX4-mediated activation of gene expression considerably overlaps with that of DUX and this redundancy might explain the loss of early developmental phenotype in Dux mutants. Consistent with this model, double mutants of Obox4 and Dux show impaired development. Given the difficulties with investigating details of the genetic model in double mutants at the preimplantation embryo stage, authors not only crossed genetic mutants, but also used (1) nuclear transfer of mutated nuclei of ESCs, which could be characterized on their own in separate experiments, and (2) antisense oligonucleotides (ASO) microinjection, which included a rescue control demonstrating that reintroducing OBOX4 is sufficient to rescue the phenotype caused by blocking both, Dux and Obox4.

      This work is important for the field because it reveals functional redundancy and plasticity of the zygotic genome activation in mammals, where the mouse model stands as a remarkable example of genome activation, which massively integrated long terminal repeat (LTR)-derived enhancers from retrotransposons and now two of the key activating zygotic factors appear to be encoded by tandemly duplicated clusters of different phylogenetic age. Identification of OBOX4 as a second factor partially redundant with DUX now allows us to decipher what constitutes the essential part of the ZGA program.

      We are grateful for the reviewer’s appreciation of our work, particularly the technical difficulty of knocking out two multicopy genes and the value of the rescue experiment.

      Reviewer #2 (Public Review):

      In this study, Guo et al., screened a few homeobox transcription factors and identified that Obox4 can induce the 2-cell like state in mouse embryonic stem cells (mESCs) (Fig. 1 and 2). The authors also compared in detail how Obox4 vs. Dux in activating 2C repeats and genes in mESCs (Fig. 3). Compared to Dux, Obox4 activates fewer 2C genes (Fig. 2). In addition, although both Obox4 and Dux bind to MERVL elements, Obox4 additionally binds to ERVK (Fig. 3). The authors then used three different approaches (i.e., SCNT-mediated KO, ASO-mediated KD, and genetic KO) to study how Obox4 and Dux regulates zygotic genome activation in embryos. Although there are some inconsistencies among different approaches, the authors were able to show that loss of both Obox4 and Dux causes more severe consequences than loss of single protein in embryonic development and zygotic genome activation (Fig. 4 and 5).

      Overall, this is a comprehensive study that addresses an important question that puzzles the community. However, some comparisons to the recent work by Ji et al (PMID: 37459895) are highly recommended. Ji et al knocked out the entire Obox cluster (including Obox4) in mice and found that Obox cluster KO causes 2-4 cell arrest without affecting Dux. That said, Obox proteins seem more critical than Dux in regulating ZGA, and Obox cluster KO cannot be compensated by Dux. Ji et al., also reported that maternal (Obox1, 2, 5, 7) and zygotic (Obox3, 4) Obox proteins redundantly regulate embryogenesis because loss of either is compatible to development. Consistent with Ji's work, Obox4 KO embryos generated in this study can develop to adulthood and are fertile. Since these two studies are highly relevant, some comparisons of Obox4 KO and Obox4/Dux DKO with the previous Obox cluster KO will greatly benefit the community.

      We thank the reviewer for appreciating the value of our study. We are aware of the work done to high standard by Ji et al. and have included a comparison between our data and the data by Ji et al. in the revised manuscript. Despite repeated attempts, various crossing strategies failed to produce Obox4KO/DuxKO mating pairs that could be used to produce large number of Obox4KO/DuxKO embryos required for in-depth transcriptome analysis. Based on the quality of the RNA-seq, we decided to perform comparative analysis using our ASO KD data and showed that Obox4 has distinct regulatory targets from those of other Obox family members, which is consistent with the phylogenetic distance within the family.

    1. Author response:

      A general comment was that this study left several key questions unanswered, in particular the causal mechanism for the reported ribosomal distributions. We have been interested in the evolution of asymmetric bacterial growth and aging for many years. However, a motivational difference is that we are more interested in the evolutionary process, and evolution by natural selection works on the phenotype. Thus, we wanted to start with the phenotype closest to fitness, appropriately defined for the conditions, work downwards. We examined first the asymmetry of elongation rates in single cells, then gene products, and now ribosomes. As we have pointed out, our demonstration of ribosomal asymmetry shows that the phenomenon was not peculiar and unique to the gene products we examined. Rather, the asymmetry is acting higher up in the metabolic network and likely affecting all genes. We find such conceptual guidance to be important. In the ideal world, of course we would have liked to have worked out the causal mechanisms in one swoop. In a less than ideal situation, it is a subjective decision as where to stop. We believe that the publication of this manuscript is more than appropriate at this juncture. We work at the interface of evolutionary theory and microbiology. Our results could appeal to both fields. If we attract new researchers, progress could be accelerated. Could the delay caused by publishing only completed stories slow the rate of discovery? These questions are likely as old as science (e.g., https://telliamedrevisited.wordpress.com/2021/01/28/how-not-to-write-a-response-to-reviewers/).

      We present below our response to specific comments by reviewers. We have not added a new discussion of papers suggested by Reviewer #1 because we feel that the speculations would have been too unfocused. We were already criticized for speculation in the Discussion about a link between aggregate size and ribosomal density.

      Respond to Major comments by Reviewer #1.

      (a) Fig. 1 only shows 2 divisions (rather than 3 as per Rev1) to avoid an overly elaborate figure. We have added text to the figure legend that the old and new poles and daughters in the subsequent 3, 4, 5, 6, and 7 generations can be determined by following the same notations and tracking we presented for generations 1 and 2 in Fig. 1. For example, if we know the old and new poles of any of the four daughters after 2 divisions (as in Fig. 1), and allow that daughter to elongate, become a mother, and divide to produce 2 “grand-daughters”, the polarity of the grand-daughters can also be determined.

      (b) Because division times were normalized and analyzed as quartiles, the raw values were never used. Rather than annotating unused values, we have provided the mean division times in the Material and Methods section on normalization to provide representative values.

      (c) We did not quantify in our study the changes over generations for three reasons. First, the sample sizes for the first generations (cohorts of 1, 2, 4, and 8 cells) are statistically small. Second, and most importantly, cells on an agar pad in a microscope slide, despite being inoculated as fresh exponentially growing cells, experience a growth lag, as all cells transferred to a new physiological condition. Thus, to be safe, we do not collect data from cohorts 1, 2, 4, and 8 to ensure that our cells are as much as possible physiologically uniform. Lastly, as we noted in the Material and Methods they also slow down after 7 generations (128 cells). Thus, we have collected ribosome and length measurements primarily from cohorts 16, 32, 64, and 128. Measurable cells from the 128 cohort are actually rare because a colony with that many cells often starts to form double layers, which are not measurable. Most of our measurements came from the 16, 32, and 64 cohorts, in which case a time series would not be meaningful. Some of these details were not included in our manuscript but have been added to the Material and Methods (Microscopy and time-lapse movies). For these reasons we have not added a time series as requested by the reviewer.

      (d) We have added the additional figure as requested, but as a supplement rather than in the main article (Supplemental Materials Fig. S1). This figure showed the normalized density of ribosomes along the normalized length of old and new daughters. The density was continuous rather than quartiles. This figure was included in the original manuscript, but readers recommended that it be removed because the all the analyzed data had been done with quartiles. Readers felt mislead and confused.

    1. Author response:

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

      We greatly appreciate the comments from the editor and the reviewers, based on which we have made the revisions. We have responded to all the questions and summarized the revisions below. The changes are also highlighted in the manuscript.

      Additionally, we’ve noticed a few typos in the manuscript presented on the eLife website, which were not there in our originally submitted file.

      (1) In both the “Full text” presented on the eLife website and the pdf file generated after clicking “Download”: the last FC1000 in the second paragraph of the “Extensive induction curves fitting of TetR mutants” section should be FC1000WT .

      (2) In the pdf file generated after clicking “Download”: the brackets are all incorrectly formatted in the captions of Figure 4 and Figure 3—figure supplement 6.

      eLife assessment

      The fundamental study presents a two-domain thermodynamic model for TetR which accurately predicts in vivo phenotype changes brought about as a result of various mutations. The evidence provided is solid and features the first innovative observations with a computational model that captures the structural behavior, much more than the current single-domain models.

      We appreciate the supportive comments by the editor and reviewers.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors’ earlier deep mutational scanning work observed that allosteric mutations in TetR (the tetracycline repressor) and its homologous transcriptional factors are distributed across the structure instead of along the presumed allosteric pathways as commonly expected. Especially, in addition, the loss of the allosteric communications promoted by those mutations, was rescued by additional distributed mutations. Now the authors develop a two-domain thermodynamic model for TetR that explains these compelling data. The model is consistent with the in vivo phenotypes of the mutants with changes in parameters, which permits quantification. Taken together their work connects intra- and inter-domain allosteric regulation that correlate with structural features. This leads the authors to suggest broader applicability to other multidomain allosteric proteins. Here the authors follow their first innovative observations with a computational model that captures the structural behavior, aiming to make it broadly applicable to multidomain proteins. Altogether, an innovative and potentially useful contribution.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      None that I see, except that I hope that in the future, if possible, the authors would follow with additional proteins to further substantiate the model and show its broad applicability. I realize however the extensive work that this would entail.

      We thank the reviewer for the supportive comments and the suggestion to extend the model to other proteins, which we indeed plan to pursue in future studies.

      Reviewer #2 (Public Review):

      Summary:

      This combined experimental-theoretical paper introduces a novel two-domain statistical thermodynamic model (primarily Equation 1) to study allostery in generic systems but focusing here on the tetracycline repressor (TetR) family of transcription factors. This model, building on a function-centric approach, accurately captures induction data, maps mutants with precision, and reveals insights into epistasis between mutations.

      Strengths:

      The study contributes innovative modeling, successful data fitting, and valuable insights into the interconnectivity of allosteric networks, establishing a flexible and detailed framework for investigating TetR allostery. The manuscript is generally well-structured and communicates key findings effectively.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The only minor weakness I found was that I still don’t have a better sense into (a) intuition and (b) mathematical derivation of Equation 1, which is so central to the work. I would recommend that the authors provide this early on in the main text.

      We thank the reviewer for the suggestion. The full mathematical derivation of Equation 1 is given in the first section of the supplementary file. Given the length of the derivation, we think it’s better to keep it in the supplementary file rather than the main text. In the main text, the first subsection (overview of the two-domain thermodynamic model of allostery) of the Results section and the paragraph right before Equation 1 are meant for providing intuitive understandings of the two-domain model and the derivation of Equation 1, respectively.

      We would also like to point the reviewer to Figure 2-figure supplement 2 and Equations (12) to (18) in the supplementary file for an alternative derivation. They show that the equilibria among all molecular species containing the operator are dictated by the binding free energies, the ligand concentration, and the allosteric parameters. The probability of an unbound operator (proportional to the probability that the promoter is bound by a RNA polymerase, or the gene expression level) can thus be calculated using Equation (12), which then leads to main text Equation 1 following the derivation given there.

      Additionally, we’ve added a paragraph to the main text (line 248-260) to aid an intuitive understanding of Equation 1.

      “The distinctive roles of the three biophysical parameter on the induction curve as stipulated in Equation 1 could be understood in an intuitive manner as well. First, the value of εD controls the intrinsic strength of binding of TetR to the operator, or the intrinsic difficulty for ligand to induce their separation. Therefore, it controls how tightly the downstream gene is regulated by TetR without ligands (reflected in leakiness) and affects the performance limit of ligands (reflected in saturation). Second, the value of εL controls how favorable ligand binding is in free energy. When εL increases, the binding of ligand at low concentrations become unfavorable, where the ligands cannot effectively bind to TetR to induce its separation from the operator. Therefore, the fold-change as a function of ligand concentration only starts to noticeably increase at higher ligand concentrations, resulting in larger EC50. Third, as discussed above, γ controls the level of anti-cooperativity between the ligand and operator binding of TetR, which is the basis of its allosteric regulation. In other words, γ controls how strongly ligand binding is incompatible with operator binding for TetR, hence it controls the performance limit of ligand (reflected in saturation).”

      We hope that the reviewer will find this explanation helpful.

      Reviewer #3 (Public Review):

      Summary:

      Allosteric regulations are complicated in multi-domain proteins and many large-scale mutational data cannot be explained by current theoretical models, especially for those that are neither in the functional/allosteric sites nor on the allosteric pathways. This work provides a statistical thermodynamic model for a two-domain protein, in which one domain contains an effector binding site and the other domain contains a functional site. The authors build the model to explain the mutational experimental data of TetR, a transcriptional repress protein that contains a ligand and a DNA-binding domain. They incorporate three basic parameters, the energy change of the ligand and DNA binding domains before and after binding, and the coupling between the two domains to explain the free energy landscape of TetR’s conformational and binding states. They go further to quantitatively explain the in vivo expression level of the TetR-regulated gene by fitting into the induction curves of TetR mutants. The effects of most of the mutants studied could be well explained by the model. This approach can be extended to understand the allosteric regulation of other two-domain proteins, especially to explain the effects of widespread mutants not on the allosteric pathways. Strengths: The effects of mutations that are neither in the functional or allosteric sites nor in the allosteric pathways are difficult to explain and quantify. This work develops a statistical thermodynamic model to explain these complicated effects. For simple two-domain proteins, the model is quite clean and theoretically solid. For the real TetR protein that forms a dimeric structure containing two chains with each of them composed of two domains, the model can explain many of the experimental observations. The model separates intra and inter-domain influences that provide a novel angle to analyse allosteric effects in multi-domain proteins.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      As mentioned above, the TetR protein is not a simple two-main protein, but forms a dimeric structure in which the DNA binding domain in each chain forms contacts with the ligand-binding domain in the other chain. In addition, the two ligand-binding domains have strong interactions. Without considering these interactions, especially those mutants that are on these interfaces, the model may be oversimplified for TetR.

      We thank the reviewer for this valid concern and acknowledge that TetR is a homodimer. However, we’ve deliberately chosen to simplify this complexity in our model for the following reasons.

      (1) In this work, we aim to build a minimalist model for two-domain allostery withonly the most essential parameters for capturing experimental data. The simplicity of the model helps promote its mechanistic clarity and potential transferability to other allosteric systems.

      (2) Fewer parameters are needed in a simpler model. Our two-domain modelcurrently uses only three biophysical parameters, which are all demonstrated to have distinct influences on the induction curve (see the main text section “System-level ramifications of the two-domain model”). This enables the inference of parameters with high precision for the mutants, and the quantification of the most essential mechanistic effects of their mutations, provided that the model is shown to accurately recapitulate the comprehensive dataset. Thus, we found it was unnecessary to add another parameter for explicitly describing inter-chain coupling, which would likely incur uncertainty in the inference of parameters due to the redundancy of their effects on induction data, and prevent the model from making faithful predictions.

      (3) From a more biological point of view, TetR is an obligate dimer, meaning thatthe two chains must synchronize for function, supporting the two-domain simplification of TetR for binding concerns.

      Additionally, as shown in the subsection “Inclusion of single-ligand-bound state of repressor” of section 1 of the supplementary file, incorporating the dimeric nature of TetR in our model by allowing partial ligand binding does not change the functional form of main text equation 1 in any practical sense. Therefore, considering all the factors stated above, we think that increasing the complexity of the two-domain model will only be necessary if additional data emerge to suggest the limitation of our model.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      This is an excellent work. I have only one suggestion for the authors. Interestingly, the authors also note that the epistatic interactions that they obtain are consistent with the structural features of the protein, which is not surprising. Within this framework, have the authors considered rescue mutations? Please see for example PMID: 18195360 and PMID: 15683227. If I understand right, this might further extend the applicability of their model. If so, the authors may want to add a comment to that effect.

      We thank the reviewer for the supportive comments and for pointing us to the useful references. We have added some comments to the main text regarding this point in line 332-336: “The diverse mechanistic origins of the rescuing mutations revealed here provide a rational basis for the broad distributions of such mutations. Integrating such thermodynamic analysis with structural and dynamic assessment of allosteric proteins for efficient and quantitative rescuing mutation design could present an interesting avenue for future research, particularly in the context of biomedical applications (PMID: 18195360, PMID: 15683227).”

      Reviewer #3 (Recommendations For The Authors):

      The authors should try to build a more realistic dimeric model for TetR to see if it could better explain experimental data. If it were too complicated for a revision, more discussions on the weakness of the current model should be given.

      We thank the reviewer for this valid concern and for the suggestion. The reasons for refraining from increasing the complexity of the model are fully discussed in our response to the reviewer’s public review given above. Primarily, we think that the value of a simple physical model is two-fold (e.g., the paradigm Ising model in statistical physics and the classic MWC model), first, its mechanistic clarity and potential transferability makes it a useful conceptual framework for understanding complex systems and establishing universal rules by comparing seemingly unrelated phenomena; second, it provides useful insights and design principles of specific systems if it can quantitatively capture the corresponding experimental data. Thus, given the current experimental data set, we believe it is justified to keep the two-domain model in its current form, while additional experimental data could necessitate a more complex model for TetR allostery in the future. Relevant discussions are added to the main text (line 443-446) and section 8 of the supplementary file.

      “It’s noted that the homodimeric nature of TetR is ignored in the current two-domain model to minimize the number of parameters, and additional experimental data could necessitate a more complex model for TetR allostery in the future (see supplementary file section 8 for more discussions).”

      Minor issues:

      (1) There is an error in Figure 3A, the 13th and 14th subgraphs are the same and should be corrected.

      We thank the reviewer for capturing this error, which has been corrected in the revised manuscript.

      (2) The criteria for the selection of mutants for analysis should be clearly given. Apart from deleting mutants that are in direct contact with the ligand of DNA, how many mutants are left, and how far are they are from the two sites? In line 257, what are the criteria for selecting these 15 mutants? Similarly, in line 332, what are the criteria for selecting these 8 mutants?

      We thank the reviewer for this comment. The data selection criteria are now added in section 7 of the supplementary file. The distances to the DNA operator and ligand of the 21 residues under mutational study are now added in Table 1 (Figure 3-figure supplement 9). The added materials are referenced in the main text where relevant.

      “7. Mutation selection for two-domain model analysis

      In this work, there are 24 mutants studied in total including the WT, and they contain mutations at 21 WT residues. We did not perform model parameter inference for the mutant G102D because of its flat induction curve (see the second subsection of section 2 and main text Figure 2—figure Supplement 3). Therefore, there are 23 mutants analyzed in main text Figure 5.

      Measuring the induction curve of a mutant involves a significant amount of experimental effort, which therefore is hard to be extended to a large number of mutants. Nonetheless, we aim to compose a set of comprehensive induction data here for validating our two-domain model for TetR allostery. To this end, we picked 15 individual mutants in the first round of induction curve measurements, which contains mutations spanning different regions in the sequence and structure of TetR (main text Figure 3—figure Supplement 1). Such broad distribution of mutations across LBD, DBD and the domain interface could potentially lead to diverse induction curve shapes and mutant phenotypes for validating the two-domain model. Indeed, as discussed in the main text section "Extensive induction curves fitting of TetR mutants", the diverse effects on induction curve from mutations perturbing different allosteric parameters predicted by the model, are successfully observed in these 15 experimental induction curves. Additionally, 5 of the 15 mutants contain a dead-rescue mutation pair, which helps us validate the model prediction that a dead mutation could be rescued by rescuing mutations that perturb the allosteric parameters in various ways.

      Eight mutation combinations were chosen for the second round of induction curve measurement for studying epistasis, where we paired up C203V and Y132A with mutations from different regions of the TetR structure. Such choice is largely based on two considerations. 1. As both C203V and Y132A greatly enhance the allosteric response of TetR, we want to probe why they cannot rescue a range of dead mutations as observed previously (PMID: 32999067). 2. C203V and Y132A are the only two mutants that show enhanced allosteric response in the first round of analysis. Combining detrimental mutations of allostery in a combined mutant could potentially lead to near flat induction curve, which is less useful for inference (see the second subsection of section 2).”

      Since the number of hotspots identified by DMS is not very large, why not analyze them all?

      We thank the reviewer for this comment. There are 41 hotspot residues in TetR (PMID: 36226916), which have 41*19=779 possible single mutations. It’s unfeasible to perform induction curve measurements for all of these 779 mutants in our current experiment. However, we agree that it would be helpful if we can obtain such a dataset in an efficient way.

      In line 257, there are 15 mutants mentioned, while in Figure 5, there are 23 mutants mentioned, in Figure 3-figure supplement 1, there are 21 mutants mentioned, and in line 226 of the supplementary file, there are 24 mutants mentioned, which is very confusing. Therefore, the data selection criteria used in this article should be given.

      We thank the reviewer for this comment. The data selection criteria are now given in section 7 of the supplementary file, which should clarify this confusion.

      (3) In Figure 4 of the Exploring epistasis between mutations section, the 6 weights of the additive models corresponding to each mutation combination are different. On one hand, it seems that there are no universal laws in these experimental data. On the other hand, unique parameters of a single mutation combination were not validated in other mutation combinations, which somewhat weakened the conclusions about the potential physical significance of these additive weights.

      We thank the reviewer for this comment. We admit that a quantitative universal law for tuning the 6 weights of the additive model does not manifest in our data, which indicates the mutation-specific nature of epistatic interactions in TetR as hinted in the different rescuing mutation distributions of different dead mutations (PMCID: PMC7568325). However, clear common trends in the weight tuning of combined mutants that contain common mutations do emerge, which comply with the structural features of the protein and provide explanations as to why C203V and Y132A don’t rescue a range of dead mutations (main text section “Exploring epistasis between mutations”). Additionally, the lack of a quantitative universal rule for tuning the 6 weights in our simple model doesn’t exclude the possibility of the existence of universal law for epistasis in TetR in another functional form, a point that could be explored in the future with more extensive joint experimental and computational investigations.

      In Eq. (27) of the supplementary file, the prior distribution of inter-domain coupling γ is given as a Gaussian distribution centered at 5 kBT. Since the absolute value of γ is important, can the authors explain why the prior distribution of γ is set to this value and what happens if other values are used?

      We thank the reviewer for the question. As explained in the corresponding discussions of Eq. (27) in the supplementary file, the prior of γ is chosen to serve as a soft constraint on its possible values based on the consideration that 1. inter-domain energetics for a TetR-like protein should be on the order of a few kBT; and 2. the prior distribution should reflect the experimental observation in the literature that γ has a small probability of adopting negative values upon mutations. Given our thorough validation of the statistical model and computational algorithm (see section 3 of the supplementary file), and the high precision in the parameter fitting results using experimental data (Figure 3 and Figure 4-figure supplement 2), we conclude that 1. the physical range of parameters encoded in their chosen prior distributions agrees well with the value reflected in the experimental data; 2. the inference results are predominantly informed by the data. Thus, changing the mean of the prior distribution of γ should not affect the inference results significantly given that it remains in the physical range.

      This point is explicitly shown in the added Table 2 (Figure 3-figure supplement 10), where we compare the current Bayesian inference results with those obtained after increasing the standard deviation of the Gaussian prior of γ from 2.5 to 5 kBT. As shown in the table, most inference results stay virtually unchanged at the use of this less informative prior, which confirms that they are predominantly informed by the data. The only exceptions are the slight increase of the inferred γ values for C203V, C203V-Y132A and C203V-G102D-L146A, reflecting the intrinsic difficulty of precise inference of large γ values with our model, as is already discussed in the second subsection of section 3 of the supplementary file. However, such observations comply with the common trend of epistatic interactions involving C203V presented in the main text and don’t compromise the ability of our model to accurately capture the induction curves of mutants. Relevant discussions are now added to the second subsection of section 3 of the supplementary file (line 368-385).

      “In our experimental dataset, such inference difficulty is only observed in the case of C203V, Y132A-C203V and C203V-G102D-L146A due to their large γ and γ + εL values (see main text Figure 3, Figure 3—figure Supplement 10 and Figure 4). As shown in main text Figure 3—figure Supplement 10, the inference results for the other 20 mutants stay highly precise and virtually unchanged after increasing the standard deviation of the Gaussian prior of γ (gstdγ ) from 2.5 to 5 kBT. This demonstrates that the inference results for these mutants are strongly informed by the induction data and there is no difficulty in the precise inference of the parameter values. On the other hand, the inferred γ values (especially the upper bound of the 95% credible region) for C203V, Y132A-C203V and C203V-G102D-L146A increased with gstdγ . This is because the induction curves in these cases are not sensitive to the value of γ given that it’s large enough as discussed above. Hence, when unphysically large γ values are permitted by the prior distribution, they could enter the posterior distribution as well. Such difficulty in the precise inference of γ values for these three mutants however, doesn’t compromise the ability of our model in accurately capturing the comprehensive set of induction data (see part iv below). Additionally, the increase of the inferred γ value of C203V at the use of larger gstdγ complies with the results presented in main text Figure 4, which show that the effect of C203V on γ tends to be compromised when combined with mutations closer to the domain interface."

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      (1) Figures 1B, S4, and S5, Tibia sections would be more informative and promising as the growth plate is flat. Otherwise, histology of the knee would be preferred.

      We have added the tibia section images in Figures 1B, S4, and S5 (New Figure 1B, Figure 2-figure supplement 3A, and Figure 3-figure supplement 1A).

      (2) Figure 1C, The authors performed immunostaining for vimentin, alpha-SMA, Col1a1 and Col1a2. The authors should use adjusted sections for the immunostaining for different antibodies. It would avoid region-specific variations in the size and shape of sections and the data would be more reliable. Please correct and revise.

      We have provided immunostaining results using consecutive sections at the similar locations of the external ear (Figure 1C).

      (3) Figure 2A and throughout the manuscript where authors performed p-smad1/5/9 fluorescent immunostaining, the authors should also show non-phospho levels of p-smad1/5/9. Please correct and revise.

      We have tried different anti-Smad1/5/9 antibodies and the signals have very high background and are not presentable. We instead did a western blot on auricle samples and the results are in Figure 2-figure supplement 1A, suggesting that ablation of Bmpr1a led to loss of activation of Smad1/5/9 without affecting their expression. For different segments of external ear, we also provided WB results in Figure 2-figure supplement 4B. In addition, we added RNA-seq data regarding the Smad1,5,9 mRNA levels, which were not affected by Bmpr1a ablation (Figure 4-figure supplement 1B). Overall, these results suggest that Bmpr1a ablation does not affect the expression of Smad1/5/9.

      (4) Result 2, lines 131-134, the authors mentioned in the text that they observed no ear phenotype of Prrx1CreERT or Bmpr1af/f mice compared with wild-type mice (Figures S2A and S2B). However, the figures did not show histology pictures of wild-type mice. Please correct and revise.

      We have provided histological pictures of wild type mice (Figure 2-figure supplement 2C).

      (5) Result 5, lines 173-174 "We generated....Bmpr1a floxed mice". How did authors generate Col1a2-CreERT; Bmpr1af/f mice by crossing Prrx1Cre-ERT and Bmpr1af/f mice? Please correct and revise.

      It is a typo and has been corrected.

      (6) In the previous study by Soma Biswas et al., (Scientific Reports 2018, PMID 29855498) the authors mentioned in the result section that the mice with deletion of Bmpr1a using Prx1Cre looked morphologically normal. They did not mention the ear phenotype/microtia. Please explain how this study differs from current work and what are the limitations in the discussion.

      We did not observe an obvious ear phenotype in the adult transgenic Prrx1-CreERT; Bmpr1af/f mice. The reason could be that that the transgene label too few auricle chondrocytes as it has been for endosteal bones and periosteal bones in adult mice (Liu et al. Nat Genet 2022; Wilk, K. et al. Stem Cell Rep 2017; Julien A et al. J Bone Miner Res 2022). The difference is likely caused by the fact that the transgenic CreERT line was driven by a 2.3 kilobase promoter of Prrx1 that was inserted to unknow location in the genome. Since we do not carry the transgenic line any more, we cannot directly test the labelling efficiency of the transgenic line in auricle. We have discussed this point in the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      Chondrocytes are present in many parts of the body; some components are replaced by osteoblast cells, but others stay with their morphology. These cells are in different morphological and cellular conditions throughout the body. Is there any human variant study of Prrx1 and their association with auricle chondrocytes is present?

      We searched the literature and found no study on Prrx1 in auricle chondrocytes in human.

      Do auricle chondrocytes have Prrx1+ through their developmental stage, and what's the expression situation of Prrx1+ at articular cartilage and growth plates throughout development? Only a small population is positive throughout the development, or they lose as they develop.

      We traced Prrx1 lineage cells in Prrx1-CreERT; R26tdTomato mice that received TAM at E8.5, E13.5, or p21. We found that auricle chondrocytes were Tomato+ under these conditions even only one dose of TAM (1/10 of the dose for adult mice) was given to the pregnant mice at E8.5 or E13.5 (Figure 1-figure supplement 1). However, while E8.5 mice showed Tomato+ chondrocytes at both articular cartilage and growth plate, E13.5 or p21 mice showed much fewer Tomato+ chondrocytes at articular cartilage and growth plate (Figure 1-figure supplement 1). These results indicate that Prrx1 expression differs in cartilages during development, growth, and maintenance.

      What's your rationale for studying Bmpr1a ablation at the adult stage?

      Organ development and maintenance are different processes, especially for slow-turnover tissues. Organ maintenance is also important since it accounts for 90% of the lifetime of mice. While previous studies have uncovered essential roles for BMP signaling in chondrogenic differentiation during development, it remains unclear whether BMP signaling plays a role in cartilage maintenance in adult mice.

      Line no 128: Chondrocytes are shirked but still have normal proliferation; what's the author's thought about it?

      Sorry that we did not make it clear enough. Actually there were very few cells undergoing proliferation in auricle cartilage and Bmpr1a ablation did not alter that. We have rephrased these sentences.

      Do chondrocytes have protein trafficking defects or ER/Golgi stress?

      We checked the expression of proteins involved in protein trafficking and found that some were up-regulated and some were down-regulated (Figure 4-figure supplement 1D), which may reflect the shift from chondrocytes to osteoblasts and warrants further investigation. However, the expression of ER or Golgi stress-related genes, which play critical roles in chondrocyte differentiation and survival (Wang et al. 2018; Horigome et al. 2020), was not altered by Bmpr1a ablation (Figure 4-figure supplement 1E and 1F).

      How many Prrx paralogs are there in the system? Are all associated with auricle chondrocytes and similar mechanisms?

      There is one Prrx1 paralog, Prrx2. While Prrx1-/- mice lived for up to 24 hours after birth with low-set ears (Martin JF. Eta al. Genes Dev. 1995), Prrx2-/- mice are perfectly normal. Prx1-/-Prx2-/- double mutant mice died within an hour after birth and the pups showed no external ears (ten Berge D. et al. Development. 1998). We have added this information into the revised manuscript.

      Extracellular matrix (ECM) provides cell-to-cell interaction and environment for cell growth. Does Bmpr1a ablation lead to any changes in ECM at the auricle or growth plate chondrocytes?

      Our analysis showed that the expression of many ECM proteins was down-regulated in auricle cartilage of Prrx1-CreERT; Bmpr1af/f mice (Figure 4-figure supplement 1A). This may reflect the shift from chondrocytes to osteoblasts and warrants further investigation. However, immunostaining revealed that the expression of Aggrecan and Col10 in the growth plates was unaltered in adult Prrx1-CreERT; Bmpr1af/f mice compared to control mice (Figure 4-figure supplement 1C), likely due to the lack of marking of chondrocytes in growth plates.

      Microtia usually develops during the first trimester of pregnancy in humans. What's your view about studying at the adult stage compared to intrauterine development?

      Congenital microtia is a problem with the formation of external ear whereas microtia development in adult mice is a problem with the maintenance of the auricle chondrocytes. Organ maintenance is also an important process as it starts from 3 months of age and lasts for 90% of the lifetime of mice.

      In RNA sequencing protocol, Wikipedia pages keep updating, so it is very strange to cite the Wikipedia pages. Cite a research article for it.

      We have replaced this reference.

      Why do the authors have a very low FDR value for this study? How does this value strengthen the study?

      It was a typo that has been corrected.

      It needs further validation to show that Prrx1 marked cells are a good model for auricular chondrocyte-related studies.

      We show that Prrx1 marks auricle chondrocytes but few growth plate or articular chondrocytes in adult mice, suggestive its specificity. However, the use of Prrx1-CreERT line in auricle cartilage studies is complicated by the labelling of dermal cells in the external ear by Prrx1. We have discussed this point in the revised manuscript.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      In this study, the authors address a fundamental unresolved question in cerebellar physiology: do synapses between granule cells (GCs) and Purkinje cells (PCs) made by the ascending part of the axon (AA) have different synaptic properties from those made by parallel fibers? This is an important question, as GCs integrate sensorimotor information from numerous brain areas with a precise and complex topography.

      Summary:

      The authors argue that CGs located close to PCs essentially contact PC dendrites via the ascending part of their axons. They demonstrate that joint high-frequency (100 Hz) stimulation of distant parallel fibers and local CGs potentiates AA-PC synapses, while parallel fiber-PC synapses are depressed. On the basis of paired-pulse ratio analysis, they concluded that evoked plasticity was postsynaptic. When individual pathways were stimulated alone, no LRP was observed. This associative plasticity appears to be sensitive to timing, as stimulation of parallel fibers first results in depression, while stimulation of the AA pathway has no effect. NMDA, mGluR1 and GABAA receptors are involved in this plasticity.

      Strengths:

      Overall, the associative modulation of synaptic transmission is convincing, and the experiments carried out support this conclusion. However, weaknesses limit the scope of the results.

      Weaknesses:

      One of the main weaknesses of this study is the suggestion that high-frequency parallel-fiber stimulation cannot induce long term potentiation unless combined with AA stimulation. Although we acknowledge that the stimulation and recording conditions were different from those of other studies, according to the literature (e.g. Bouvier et al 2016, Piochon et al 2016, Binda et al, 2016, Schonewille et al 2021 and others), high-frequency stimulation of parallel fibers leads to long-term postsynaptic potentiation under many different experimental conditions (blocked or unblocked inhibition, stimulation protocols, internal solution composition). Furthermore, in vivo experiments have confirmed that high-frequency parallel fibers are likely to induce long-term potentiation (Jorntell and Ekerot, 2002; Wang et al, 2009). This article provides further evidence that long-term plasticity (LTP and LTD) at this connection is a complex and subtle mechanism underpinned by many different transduction pathways. It would therefore have been interesting to test different protocols or conditions to explain the discrepancies observed in this dataset.

      Even though this is not the main result of this study, we acknowledge that the control experiments done on PF stimulation add a puzzling result to an already contradictory literature. High frequency parallel fibre stimulation (in isolation) has been shown to induce long term potentiation in vitro, but not always, and most importantly, this has been shown in vivo. This was in fact the reason for choosing that particular stimulation protocol. Examination of in vitro studies, however, show that the results are variable and even contradictory. Most were done in the presence of GABAA receptor antagonists, including the SK channel blocker Bicuculline, whereas in the study by Binda (2016), LTP was blocked by GABAA receptor inhibition. In some studies also, LTP was under the control of NMDAR activation only, whereas in Binda (2016), it was under the control of mGluR activation. Moreover, most experiments were done in mice, whereas our study was done in rats. Our results reveal intricate mechanisms working together to produce plasticity, which are highly sensitive to in vitro conditions. We designed our experiments to be close to physiological conditions, with inhibition preserved and a physiological chloride gradient. It is likely that experimental differences have given rise to the variability of the results and our inability to reproduce PF-LTP, but it was not the aim of this study to dissect the subtleties of the different experimental protocols and models. We will modify the Discussion to describe that point fully including differences in experimental conditions.

      Another important weakness is the lack of evidence that the AAs were stimulated. Indeed, without filling the PC with fluorescent dye or biocytin during the experiment, and without reconstructing the anatomical organization, it is difficult to assess whether the stimulating pipette is positioned in the GC cluster that is potentially in contact with the PC with the AAs. According to EM microscopy, AAs account for 3% of the total number of synapses in a PC, which could represent a significant number of synapses. Although the idea that AAs repeatedly contact the same Purkinje cell has been propagated, to the best of the review author's knowledge, no direct demonstration of this hypothesis has yet been published. In fact, what has been demonstrated (Walter et al 2009; Spaeth et al 2022) is that GCs have a higher probability of being connected to nearby PCs, but are not necessarily associated with AAs.

      We fully agree with the reviewer that we have not identified morphologically ascending axon synapses, and we stress this fact both in the first paragraph of the Results section, and again at the beginning of Discussion. Our point is mainly topographical, given the well documented geometrical organisation of the cerebellar cortex, and strictly speaking, inputs are local (including ascending axon) or distal (parallel fibre). Similarly, the studies by Isope and Barbour (2002) and Walter et al. (2009), just like Sims and Hartell (2005 and 2006), have coined the term ‘ascending axon’ when drawing conclusions about locally stimulated inputs. Moreover, our results do not rely on or assume multiple contacts, stronger connections, or higher probability of connections between ascending axons and Purkinje cells. Our results only demonstrate a different plasticity outcome for the two types of inputs. Therefore, our manuscript could be rephrased with the terms ‘local’ and ‘distal’ granule cell inputs, but this would have no more implication for the results or the computation performed in Purkinje cells. However, in our experience, this is more confusing to the reader, and as we already stress this point in the manuscript, we do not wish to make this modification. However we will modify the abstract of the manuscript to clarify that point.

      Reviewer #2 (Public Review):

      Summary:

      The authors describe a form of synaptic plasticity at synapses from granule cells onto Purkinje cells in the mouse cerebellum, which is specific to synapses proximal to the cell body but not to distal ones. This plasticity is induced by the paired or associative stimulation of the two types of synapses because it is not observed with stimulation of one type of synapse alone. In addition, this form of plasticity is dependent on the order in which the stimuli are presented, and is dependent on NMDA receptors, metabotropic glutamate receptors and to some degree on GABAA receptors. However, under all experimental conditions described, there is a progressive weakening or run-down of synaptic strength. Therefore, plasticity is not relative to a stable baseline, but relative to a process of continuous decline that occurs whether or not there is any plasticity-inducing stimulus.

      As highlighted by the reviewer, we observed a postsynaptic rundown of the EPSC amplitude for both input pathways. Rundown could be mistaken for a depression of synaptic currents, not for a potentiation, and the progressive decrease of the EPSC amplitude during the course of an experiment leads to an underestimate of the absolute potentiation. We have taken the view to provide a strong set of control data rather than selecting experiments based on subjective criteria or applying a cosmetic compensation procedure. We have conducted control experiments with no induction (n = 17), which give a good indication of the speed and amplitude of the rundown. Comparison shows a highly significant potentiation of the ascending axon EPSC. Depression of the parallel fibre EPSC, on the other hand, was not significantly different from rundown, and we have not spoken of parallel fibre long term depression. The data show thus very clearly that ascending axon and parallel fibre synapses behave differently following the costimulation protocol.

      Strengths:

      The focus of the authors on the properties of two different synapse-types on cerebellar Purkinje cells is interesting and relevant, given previous results that ascending and parallel fiber synapses might be functionally different and undergo different forms of plasticity. In addition, the interaction between these two synapse types during plasticity is important for understanding cerebellar function. The demonstration of timing and order-dependent potentiation of only one pathway, and not another, after associative stimulation of both pathways, changes our understanding of potential plasticity mechanisms. In addition, this observation opens up many new questions on underlying intracellular mechanisms as well as on its relevance for cerebellar learning and adaptation.

      Weaknesses and suggested improvements:

      A concern with this study is that all recordings demonstrate "rundown", a progressive decrease in the amplitude of the EPSC, starting during the baseline period and continuing after the plasticity-induction stimulus. In the absence of a stable baseline, it is hard to know what changes in strength actually occur at any set of synapses. Moreover, the issues that are causing rundown are not known and may or may not be related to the cellular processes involved in synaptic plasticity. This concern applies in particular to all the experiments where there is a decrease in synaptic strength.

      We have provided an answer to that point directly below the summary paragraph. Moreover, if the phenomenon causing rundown was involved in plasticity, it should affect plasticity of both inputs, which was not the case, clearly distinguishing the ascending axon and parallel fibre inputs.

      The authors should consider changes in the shape of the EPSC after plasticity induction, as in Fig 1 (orange trace) as this could change the interpretation.

      Figure 1 shows an average response composed of evoked excitatory and inhibitory synaptic currents. The third section of Supplementary material (supplementary figure 3) shows that this complex shape is given by an EPSC followed by a delayed disynaptic IPSC. We would like to point out that while separating EPSC from IPSC might appear difficult from average traces due to the averaged jitter in the onset of the synaptic currents, boundaries are much clearer when analysing individual traces. In the same section we discuss the results of experiments in which transient applications of SR 95531 before and after the induction protocol allowed us to measure the EPSC, while maintaining the experimental conditions during induction. Analysis of the kinetics of the EPSCs during gabazine application at the beginning and end of experiments, showed that there is no change in the time to peak of both AA and PF response. The decay time of AA and PF EPSC are slightly longer at the end of the experiment, even if the difference is not significant for AA inputs (we will add this analysis to the revised version of the paper). Our analysis, that uses as template the EPSCs kinetics measured at the beginning and at the end of the experiments, takes directly into account these changes. The results show clearly that the presence of disynaptic inhibition doesn’t significantly affect the measure of the peak EPSC after the induction protocol nor the estimate of plasticity.

      In addition, the inconsistency with previous results is surprising and is not explained; specifically, that no PF-LTP was induced by PF-alone repeated stimulation.

      In our experimental conditions, PF-LTP was not induced when stimulating PF only, the only condition that reproduces experiments in the literature. As discussed in our response to reviewer 1, a close look at the literature, however, reveals variabilities and contradictions behind seemingly similar results. They reveal intricate mechanisms working together to produce plasticity, which are sensitive to in vitro conditions. We designed our experiments to be close to physiological conditions, with inhibition preserved and a physiological chloride gradient. It is likely that experimental differences have given rise to the variability of the results and our inability to observe PF-LTP. We will modify the discussion section to discuss that point fully in the context of past results.

      The authors test the role of NMDARs, GABAARs and mGluRs in the phenotype they describe. The data suggest that the form of plasticity described here is dependent on any one of the three receptors. However, the location of these receptors varies between the Purkinje cells, granule cells and interneurons. The authors do not describe a convincing hypothetical model in which this dependence can be explained. They suggest that there is crosstalk between AA and PF synapses via endocannabinoids downstream of mGluR or NO downstream of NMDARs. However, it is not clear how this could lead to the long-term potentiation that they describe. Also, there is no long-lasting change in paired-pulse ratio, suggesting an absence of changes in presynaptic release.

      We suggest in the result section that the transient change in paired pulse ratio (PPR) is linked to a transient presynaptic effect only, which has been reported by others. This suggests that the long lasting changes observed are postsynaptic, like other reports with similar trains of stimulation, and we will modify the manuscript to state this clearly.

      Concerning the involvement of multiple molecular pathways, investigators often tested for the involvement of NMDAR or mGluRs in cerebellar plasticity, rarely both. Here we showed that both pathways are involved. The conjunctive requirement for NMDAR and mGluR activation can easily be explained based on the dependence of cerebellar LTP and LTD on the concentrations of both NO and postsynaptic calcium (Coesman et al., 2004; Safo and Regehr, 2005; Bouvier et al., 2016; Piochon et al., 2016). NO production has been linked to the activation of NMDARs in granule cell axons (Casado et al., 2002; Bidoret et al., 2009; Bouvier et al., 2016), occasionally in molecular layer interneurones (Kono et al., 2019). NO diffuses to activate Guanylate Cyclase in the Purkinje cell. Based on the literature also, different mechanisms can feed a calcium increase, including mGluRs activation. Therefore NMDARs and mGluRs can reasonably cooperate to control postsynaptic plasticity. The associative nature of AA-LTP is more complex to explain, i.e. the requirement for co-activation of AA and PF inputs, and indicates a necessary cross talk between synaptic sites. We propose that either one of the receptors is absent from AA synapses, and a signal needs to propagate from PF to AA synapses, or that both receptors are present but a signal is required to activate one of the receptors at AA synapses.

      We also observed an effect of GABAergic inhibition. GABAergic inhibition was elegantly shown by Binda (2016) to regulate calcium entry together with mGluRs, and control plasticity induction. A similar mechanism could contribute to our results, although inhibition might have additional effects. We will modify the discussion of the manuscript and add a diagram to highlight the links between the different molecular pathways and potential cross talk mechanisms, and the location of receptors.

      Is the synapse that undergoes plasticity correctly identified? In this study, since GABAergic inhibition is not blocked for most experiments, PF stimulation can result in both a direct EPSC onto the Purkinje cell and a disynaptic feedforward IPSC. The authors do address this issue with Supplementary Fig 3, where the impact of the IPSC on the EPSC within the EPSC/IPSC sequence is calculated. However, a change in waveform would complicate this analysis. An experiment with pharmacological blockade will make the interpretation more robust. The observed dependence of the plasticity on GABAA receptors is an added point in favor of the suggested additional experiments.

      We did consider that due to long recording times there might be kinetic changes, and that’s the reason why the experiments of Supplementary figure 3 were done with pharmacological blockade of GABAAR with gabazine, both before and again after LTP induction. The estimate of the amplitude of the EPSC is based on the actual kinetics of the response at both times.

      A primary hypothesis of this study is that proximal, or AA, and distal, or PF, synapses are different and that their association is specifically what drives plasticity. The alternative hypothesis is that the two synapse-types are the same. Therefore, a good control for pairing AA with PF would be to pair AA with AA and PF with PF, thereby demonstrating that pairing with each other is different from pairing with self.

      Pairing AA with AA would be difficult because stimulation of AA can only be made from a narrow band below the PC and we would likely end up stimulating overlapping sets of synapses.. However, Figure 5 shows the effect of stimulating PF and PF, while also mimicking the sparse and dense configuration of the usual experiment. It shows that sparse PF do not behave like AA. Sims and Hartell (2006) also made an experiment with sparse PF inputs and observed clear differences between sparse local (AA) and sparse distal (PF) synapses.

      It is hypothesized that the association of a PF input with an AA input is similar to the association of a PF input with a CF input. However, the two are very different in terms of cellular location, with the CF input being in a position to directly interact with PF-driven inputs. Therefore, there are two major issues with this hypothesis: 1) how can sub-threshold activity at one set of synapses affect another located hundreds of micrometers away on the same dendritic tree? 2) There is evidence that the CF encodes teaching/error or reward information, which is functionally meaningful as a driver of plasticity at PF synapses. The AA synapse on one set of Purkinje cells is carrying exactly the same information as the PF synapses on another set of Purkinje cells further up and down the parallel fiber beam. It is suggested that the two inputs carry sensory vs. motor information, which is why this form of plasticity was tested. However, the granule cells that lead to both the AA and PF synapses are receiving the same modalities of mossy fiber information. Therefore, one needs to presuppose different populations of granule cells for sensory and motor inputs or receptive field and contextual information. As a consequence, which granule cells lead to AA synapses and which to PF synapses will change depending on which Purkinje cell you're recording from. And that's inconsistent with there being a timing dependence of AA-PF pairing in only one direction. Overall, it would be helpful to discuss the functional implications of this form of plasticity.

      We do not hypothesise that association of the AA and PF inputs is similar to the association of PF and climbing fibre inputs. We compare them because it is the only other known configuration triggering associative plasticity in Purkinje cells. We conclude that ‘The climbing fibre is not the only key to associative plasticity’, and it is indeed interesting to observe that even if the inputs are very small compared to the powerful climbing fibre input, they can be effective at inducing plasticity. Physiologically, the climbing fibre signal has been clearly linked to error and reward signals, but reward signals are also encoded by granule cell inputs (Wagner et al., 2017). We will modify the discussion to make sure that we do not suggest equivalence with CF induced LTD.

      Moreover, we fully agree that AA and PF synapses made up by a given granule cell carry the same information, and cannot encode sensory and motor information at the same time. Yet, these synapses carry different information. AA synapses from a local granule cell deliver information about the local receptive field, but PF synapses from the same granule cell will deliver contextual information about that receptive field to distant Purkinje cells. In the context of sensorimotor learning, movement is learnt with respect to a global context, not in isolation, therefore learning a particular association must be relevant. The associative plasticity we describe here could help explain this functional association. Difference in timing of the inputs therefore should represent difference in the timing of activation of different granule cells which receive either local information or information from different receptive fields. We will modify the discussion to make sure we do not suggest association between sensory and motor inputs, and clarify our view of local receptive field and context about ongoing activity.

      Reviewer #3 (Public Review):

      Granule cells' axons bifurcate to form parallel fibers (PFs) and ascending axons (AAs). While the significance of PFs on cerebellar plasticity is widely acknowledged, the importance of AAs remains unclear. In the current paper, Conti and Auger conducted electrophysiological experiments in rat cerebellar slices and identified a new form of synaptic plasticity in the AA-Purkinje cell (PC) synapses. Upon simultaneous stimulation of AAs and PFs, AA-PC EPSCs increased, while PFs-EPSCs decreased. This suggests that synaptic responses to AAs and PFs in PCs are jointly regulated, working as an additional mechanism to integrate motor/sensory input. This finding may offer new perspectives in studying and modeling cerebellum-dependent behavior. Overall, the experiments are performed well. However, there are two weaknesses. First, the baseline of electrophysiological recordings is influenced significantly by run-down, making it difficult to interpret the data quantitatively. The amplitude of AA-EPSCs is relatively small and the run-down may mask the change. The authors should carefully reexamine the data with appropriate controls and statistics. Second, while the authors show AA-LTP depends on mGluR, NMDA receptors, and GABA-A receptors, which cell types express these receptors and how they contribute to plasticity is not clarified. The recommended experiments may help to improve the quality of the manuscript.

      As highlighted by the reviewer and developed above in response to reviewer 2, we observed a postsynaptic rundown of the EPSC amplitude. Rundown could be mistaken for a depression of synaptic currents, not for a potentiation. Moreover, we have conducted control experiments with no induction (n = 17), which give a good indication of the speed and amplitude of the rundown, and provide a baseline. Comparison shows a highly significant potentiation of the ascending axon EPSC, relative to baseline and relative to these control experiments. Depression of the parallel fibre EPSC on the other hand was not significantly different from rundown. For that reason we have not spoken of parallel fibre long term depression. The data, however, show that ascending axon and parallel fibre synapses behave very differently following the costimulation protocol.

      We have discussed above in our response to reviewer 2 the potential involvement of mGluRs, NMDARs and GABAARs. We will modify the discussion of the manuscript and add a diagram to highlight the links between the different molecular pathways and potential cross talk mechanisms, and the location of receptors.

    1. Author Response:

      We greatly appreciate the insightful feedback provided by the reviewers and the editor on our manuscript titled "Automated workflow for the cell cycle analysis of non-adherent and adherent cells using a machine learning approach".  We will provide a revised version of the manuscript aiming to address the comments and recommendations provided by the reviewers to enhance the quality and clarity of our work. In detail:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript proposes a series of steps using the FIJI environment, the authors have created a plugin for the initial steps of the process, merging images into an RGB stack, conversion to HSV, and then using brightness for reference and hue to distinguish the phases of the cycle. Then, the well-known Trackmate plugin was used to identify single cells and extract intensities. The data was further post-processed in R, where a series of steps, smoothing, scaling, and addressing missing frames were used to train a random forest. Hard-coded values of hue were used to distinguish G1, S, and G2/M. The process was validated with a score comparing the quality of the tracks and the authors reported the successful measure of the cell cycles.

      Strengths:

      The implementation of the pipeline seems easy, although it requires two separate platforms: Fiji and R. A similar approach could be implemented in a single programming environment like Python or Matlab and there would not be any need to export from one to the other. However, many labs have similar setups and that is not necessarily a problem.

      Weaknesses:

      I found two important weaknesses in the proposal:

      (1) The pipeline relies on a large number of hard-coded conditions: size of Gaussian blur (Gaussian should be written in uppercase), values of contrast, size of filters, levels of intensity, etc. Presumably, the authors followed a heuristic approach and tried values of these and concluded that the ones proposed were optimal. A proper sensitivity analysis should be performed. That is, select a range of values of the variables and measure the effect on the output.

      (2) Linked to the previous comments. Other researchers that want to follow the pipeline would have either to have exactly the same acquisition conditions as the manuscript or start playing with values and try to compensate for any difference in their data (cell diameter, fluorescent intensity, etc.) to see if they can match the results of the manuscript.

      We thank Reviewer #1 for the insightful comments. We acknowledge the importance of ensuring the reproducibility and robustness of our pipeline among different sample types, acquisition conditions and, consequently, image S/N ratio and resolution. To address the concerns regarding the reliance on hard-coded conditions and the impact of varying parameter values on the output, we will complete the Methods section of the manuscript and the “Usage” section of the README file in the Github repository (https://github.com/ieoresearch/cellcycle-image-analysis)  providing a summary of best practices that should be applied in the pre-processing part of the analysis. As an example, the usable image filters types and their settings related to cells with different size, fluorescence intensities and acquisition conditions will be analysed in detail and general guidelines will be provided.

      Moreover, we will provide detailed documentation on the acquisition conditions required for reproducibility in the README file and Methods section.

      For the Tracking Analysis part, we will refer to the well documented TrackMate tutorial to adapt the tracking analysis to different cell types, image resolution and intensities.

      Reviewer #2 (Public Review):

      Summary:

      This paper presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.

      Strengths:

      This is a METHODS paper. The one potentially novel finding is that they can identify cells that are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to the change seen as cells enter G2/M.

      Weaknesses:

      They did not clearly indicate whether the G1/S cells are identified automatically or need to be identified by the person reviewing the data. In Figures 1 and S1, the movie shows cells with no color at a time corresponding to what is about the G1/S transition. Their assigned cell cycle phase is shown in Figure 1 but not in Figure S1. None of these pictures show the G1/S cells that they talk about being able to detect with a different color.

      Thank you for your valuable feedback regarding the identification of G1/S cells in our pipeline. To clarify, the G1/S phase identification process is entirely automated within our pipeline. We apologize for any confusion caused by the lack of explicit indication in our manuscript. We will ensure to update the manuscript to clearly state that the identification of G1/S cells is performed automatically by our algorithm, eliminating the need for manual intervention.

      Regarding the visualization of G1/S cells in Figures 1 and S1, we will revise the figures to include all the available frames referred to the G1/S transition. It's important to note that during this transition, fluorescence intensities for both the green and the red channels, are dimmer in comparison with their intensity levels during the G2/M transitions. This can result in frames that may seem visually darker, despite both colors coexisting at the same time point. In our revised figures, we will ensure to include all available frames relevant to the G1/S transition and provide a clearer representation of this phenomenon.

      In response to Reviewer #2's recommendation, we plan to conduct additional experiments to further validate our observations. We will utilize the EdU technology to highlight the S-phase in FUCCI cells, allowing for better discrimination between the red and green fluorescence of the FUCCI reporter during the initial S-phase.

      Additionally, we acknowledge that the link to the Docker container (https://hub.docker.com/r/emanuelsoda/rf_semi_sup)  was not included in the manuscript. We apologize for this oversight, and it will be included in the revised version of the paper.

    1. Author response:

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

      Reviewer #1 (Public Review):

      A summary of what the authors were trying to achieve.

      The authors cultured pre- and Post-vaccine PBMCs with overlapping peptides encoding S protein in the presence of IL-2, IL-7, and IL-15 for 10 days, and extensively analyzed the T cells expanded during the culture; by including scRNAseq, scTCRseq, and examination of reporter cell lines expressing the dominant TCRs. They were able to identify 78 S epitopes with HLA restrictions (by itself represents a major achievement) together with their subset, based on their transcriptional profiling. By comparing T cell clonotypes between pre- and post-vaccination samples, they showed that a majority of pre-existing S-reactive CD4+ T cell clones did not expand by vaccinations. Thus, the authors concluded that highly-responding S-reactive T cells were established by vaccination from rare clonotypes.

      An account of the major strengths and weaknesses of the methods and results.

      Strengths:

      Selection of 4 "Ab sustainers" and 4 "Ab decliners" from 43 subjects who received two shots of mRNA vaccinations.

      Identification of S epitopes of T cells together with their transcriptional profiling. This allowed the authors to compare the dominant subsets between sustainers and decliners.

      Weaknesses were properly addressed in the revised manuscript, and I do not have any additional concerns.

      We appreciate the reviewer for the constructive comments and recommendations, which were a great help for us to improve our manuscript.

      Reviewer #3 (Public Review):

      Summary:

      The paper aims to investigate the relationship between anti-S protein antibody titers with the phenotypes&clonotypes of S-protein-specific T cells, in people who receive SARS-CoV2 mRNA vaccines. To do this, the paper recruited a cohort of Covid-19 naive individuals that receives the SARS-CoV2 mRNA vaccines and collect sera and PBMCs samples on different timepoints. Then they mainly generate three sets of data: 1). Anti-S protein antibody titers on all timepoints. 2) Single-cell RNAseq/TCRseq dataset for divided T cells after stimulation by Sprotein for 10 days. 3) Corresponding epitopes for each expanded TCR clones. After analyzing these result, the paper reports two major findings&claims: A) Individuals having sustained anti-S protein antibody response also have more so-called Tfh cells in their single-cell dataset. B). S-reactive T cells do exist before the vaccination, but they seems to be unable to response to Covid-19 vaccination properly.

      The paper's strength is it uses a very systemic and thorough strategy trying to dissect the relationship between antibody titers, T cell phenotypes, TCR clonotypes and corresponding epitopes, and indeed it reports several interesting findings about the relationship of Tfh clonotypes/sustained antibody and about the S-reactive clones that exist before the vaccination. The conclusion is solid in general but some claims are overstated. My suggestion is the authors should further limit their claims in abstract, for example,

      ”Even before vaccination, S-reactive CD4+ T cell clonotypes did exist, most of which (MAY) cross-reacted with environmental or symbiotic bacteria" -- The paper don't have experimental evidence to show these TCR clones respond to these epitopes.

      We thank the reviewer for pointing out the insufficient demonstration of experimental evidence. We have added the relevant data to Fig. S5 in the newly revised manuscript.

      "These results suggest that de novo acquisition of memory Tfh-like cells upon vaccination (LIKELY) contributes to the longevity of anti-S antibody titers." --Given the small sample size and the statistical analysis was not significant, this claim was overstated.

      "S-reactive T cell clonotypes detected immediately after 2nd vaccination polarized to follicular helper T (Tfh)-like cells (UNDER IN VITRO CULTURE)". -- the conclusion was based on vitro cultured cells, which had limitation.

      We thank the reviewer for the helpful suggestion. We have corrected some sentences in line with these suggestions in the newly revised manuscript.

      Recommendations for the authors:

      Please note: Though most of the overstatement was removed from the original manuscript, authors still need to modify some of the statements in "Abstract".

      We thank the reviewer for carefully reading our manuscript and giving us detailed suggestions. We have modified these statements in “Abstract” accordingly in the newly revised manuscript.

    1. Author Response

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

      At this stage the referees had only minor comments. Referee #1 asked whether archerfish indeed generalize in egocentric rather than allocentric coordinates. It might be that the current results do not rule out the idea that archerfish are unaware of changes in body position, they continue with previously successful actions, that seems as egocentric generalization. We agree with referee #1 and updated lines 255-260 in the results and added lines 329-336 in the discussion text that mentions this possibility. Referee #2 mentioned that a portion of fish did not make it to the final test which raises the question whether all individuals are able to solve the task. We agree with referee #2 and added paragraph at the discussion section to mention this point (lines 384-388). We also added the salinity of the water in the water tanks (line 98) as per suggestion of the Referee #2. Referee #2 suggested using a different term than “washout” in the behavioral experiments. Since the term “washout” is standard in the field, we keep the term in the text.


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

      eLife assessment

      This useful study explores how archerfish adapt their shooting behavior to environmental changes, particularly airflow perturbations. It will be of interest to experts interested in mechanisms for motor learning. While the evidence for an internal model for adaptation is solid, evidence for adaptation to light refraction, as initially hypothesized, is inconclusive. As such, the evidence supporting an egocentric representation might be caused by alternative mechanisms to airflow perturbations.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors examined whether archerfish have the capacity for motor adaptation in response to airflow perturbations. Through two experiments, they demonstrated that archerfish could adapt. Moreover, when the fish flipped its body position with the perturbation remaining constant, it did not instantaneously counteract the error. Instead, the archerfish initially persisted in correcting for the original perturbation before eventually adapting, consistent with the notion that the archerfish's internal model has been adapted in egocentric coordinates.

      Evaluation:

      The results of both experiments were convincing, given the observable learning curve and the clear aftereffect. The ability of these fish to correct their errors is also remarkable. Nonetheless, certain aspects of the experiment's motivation and conclusions temper my enthusiasm.

      (1) The authors motivated their experiments with two hypotheses, asking whether archerfish can adapt to light refractions using an innate look-up table as opposed to possessing a capacity to adapt. However, the present experiments are not designed to arbitrate between these ideas. That is, the current experiments do not rule out the look-up table hypothesis, which predicts, for example, that motor adaptation may not generalize to de novo situations with arbitrary actionoutcome associations. Such look-up table operations may also show set-size effects, whereas other mechanisms might not. Whether their capacity to adapt is innate or learned was also not directly tested, as noted by the authors in the discussion. Could the authors clarify how they see their results positioned in light of the two hypotheses noted in the Introduction?

      We agree with the referee that look up tables only confuse the issue. The question we tested is whether or not the fish uses adaptation mechanisms to correct its shooting. We have now changed the introduction both to eliminate the entire question of look up tables and also to clarify that both innate mechanisms and learning mechanisms can contribute to fish shooting, and that our research focuses on the question of whether the fish can adapt to a perturbation in its shooting caused by a change in its physical environment.

      (2) The authors claim that archerfish use egocentric coordinates rather than allocentric coordinates. However, the current experiments do not make clear whether the archerfish are "aware" that their position was flipped (as the authors noted, no visual cues were provided). As such, for example, if the fish were "unaware" of the switch, can the authors still assert that generalization occurs in egocentric coordinates? Or simply that, when archerfish are ostensibly unaware of changes in body position, they continue with previously successful actions.

      The fish has access to the body position switch: there are clues in a water tank that can help the fish orient inside the water tank. Additionally, there are no clues to the presence or direction of the air flow above the water tank. Moreover, previous experience has shown that the fish is sensitive to the visual cues and uses them to achieve consistent orientation within the tank when possible. These points have been added to the main text [lines 143-144, 254-257]

      (3) The experiments offer an opportunity to examine whether archerfish demonstrate any savings from one session to another. Savings are often attributed to a faster look-up table operation. As such, if archerfish do not exhibit savings, it might indicate a scenario where they do not possess a refined look-up table and must rely on implicit mechanisms to relearn each time.

      This is an important question. Indeed, we looked for the ‘saving’ effect in the data, but its noisy nature prevented us from drawing a concrete conclusion. We now mention this in lines 247-249.

      We have also eliminated the discussion of look up tables from the article.

      (4) The authors suggest that motor adaptation in response to wind may hint at mechanisms used to adapt to light refraction. However, how strong of a parallel can one draw between adapting to wind versus adapting to light refraction? This seems important given the claims in this paper regarding shared mechanisms between these processes. As a thought experiment, what would the authors predict if they provided a perturbation more akin to light refraction (e.g., a film that distorts light in a new direction, rather than airflow)?

      This is an important point. Indeed, our project started by looking for options to distort the refraction index or distort the light in a new direction. However, given the available ways of distorting the light to a new direction, it is hard to achieve that on the technical level. Initially, we tried using prism goggles, however the archerfish found it hard to shoot with the heavy load on the head. We have also explored oil on the water surface. However, given the available oils and the width of the film above water, it is hard to achieve considerable perturbation.

      Fish response to the perturbation matches the response to what would be expected for a change in light refraction. Light refraction perturbation does not change with the change in fish body position relative to the target. However, in response to (and in agreement with) the referees, we have generalized the context in which we see our results and discuss the results in terms of adaptation of the fish shooting behavior to changes in physical factors including light refraction, wind, fatigue, and others.

      (5) The number of fish excluded was greater than those included. This raises the question as to whether these fish are merely elite specimens or representative of the species in general.

      The filtering of the fish was in the training stage. The requirements were quite strict: the fish had to produce enough shots each day in the experimental setup. Very few fish succeeded. But all fish that got to the stage of perturbation exhibited the adaptation effect. We do not see a reason to think that the motivation to shoot will have a strong interaction with the shooting adaptation mechanisms.

      Reviewer #2 (Public Review):

      Summary:

      The work of Volotsky et al presented here shows that adult archerfish are able to adjust their shooting in response to their own visual feedback, taking consistent alterations of their shot, here by an air flow, into account. The evidence provided points to an internal mechanism of shooting adaptation that is independent of external cues, such as wind. The authors provide evidence for this by forcing the fish to shoot from 2 different orientations to the external alteration of their shots (the airflow). This paper thus provides behavioral evidence of an internal correction mechanism, that underlies adaptive motor control of this behavior. It does not provide direct evidence of refractory index-associated shoot adjustance.

      Strengths:

      The authors have used a high number of trials and strong statistical analysis to analyze their behavioral data.

      Weaknesses:

      While the introduction, the title, and the discussion are associated with the refraction index, the latter was not altered, and neither was the position of the target. The "shot" was altered, this is a simple motor adaptation task and not a question related to the refractory index. The title, abstract, and the introduction are thus misleading. The authors appear to deduce from their data that the wind is not taken into account and thus conclude that the fish perceive a different refractory index. This might be based on the assumption that fish always hit their target, which is not the case. The airflow does not alter the position of the target, thus the airflow does not alter the refractive index. The fish likely does not perceive the airflow, thus alteration of its shooting abilities is likely assumed to be an "internal problem" of shooting. I am sorry but I am not able to understand the conclusion they draw from their data.

      This is an important point. Indeed, our project started by looking for options to distort the refraction index or distort the light in a new direction. However, given the available ways of distorting the light to a new direction, it is hard to achieve that on the technical level. Initially, we tried using prism goggles, however the archerfish found it hard to shoot with the heavy load on the head. We have also explored oil on the water surface. However, given the available oils and the width of the film above water, it is hard to achieve considerable perturbation.

      Fish response to the perturbation matches the response to what would be expected for a change in light refraction. Light refraction perturbation does not change with the change in fish body position relative to the target. However, in response to (and in agreement with) the referees, we have generalized the context in which we see our results and discuss the results in terms of adaptation of the fish shooting behavior to changes in physical factors including light refraction, wind, fatigue, and others.

      Reviewer #2 (Recommendations For The Authors):

      I have had a hard time trying to understand how the authors concluded that the RI is important here as it is not altered. Thus I did not understand the conclusions drawn from this paper. The experiments are well described, but the conclusions are not to me. Maybe schematics would help to clarify. I am from outside the field and represent a naïve reader with an average intellect. The authors need to do a better job of explaining their results if they want others to understand their conclusions.

      See response to the public comments.

      Minor comments:

      Line 9: omit the "an".

      Done.

      Line 11: this sentence would fit way better if it followed the next one.<br /> Done.

      Line 15: and all the rest of the paper: washout is a strange term and for me associated with pharmacological manipulations - might only be me. I suggest using recovery instead throughout the manuscript.

      The term ‘washout’ is often used in the field of motor adaptation to describe the return to original condition. For example:

      Kluzik J, Diedrichsen J, Shadmehr R, Bastian AJ (2008) Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm? J Neurophysiol 100:1455-64. doi: 10.1152/jn.90334.2008

      Donchin O, Rabe K, Diedrichsen J, Lally N, Schoch B, Gizewski ER, Timmann D (2012) Cerebellar regions involved in adaptation to force field and visuomotor perturbation. J Neurophysiol 107:134-47

      Line 19: the fish does not expect the flow, it expects that it shoots too short- no?

      Done.

      Line 35: fix the citation - in your reference manager.

      Done.

      Line 52: provide some examples of the mechanisms you think of or papers of it for naive readers. Otherwise, this sentence is not helpful for the reader.

      Done.

      Line 183: it's unclear which parameter you mean. Rephrase.

      Done.

      Line 197: should read to test "the" - same sentence: you repeat yourself- rephrase the sentence.

      Done.

      Figure 4: it was unclear to me why the figure was differentiating between fishes until I read the legend. Why not include direct information in the figure? A schematic maybe? Legend: you have a double "that" in C.

      We added the title for each column with the information about the direction of air.

      Figures: in all figures, perturbation is wrongly spelled! Change the term washout to recovery.

      Done. We kept the term ‘washout’

    1. Author response:

      We are grateful to reviewer #1 for positive evaluation of our work and for providing valuable comments that will significantly enhance the presentation of our results. We understand reviewer #2's negative assessment because we did not discuss an alternative model of dosage compensation in Drosophila. We will address this omission in the Introduction section of the revised manuscript and remove any controversial statements from other parts of the text. However, it is important to clarify that our study does not focus on the mechanisms of dosage compensation. The main goal of the manuscript was to investigate the assembly of the MSL complex and its specific binding to the Drosophila X chromosome. We utilized male survival data to demonstrate the efficacy of MSL complex binding to the X chromosome, a relationship that has been supported by numerous independent studies. We understand that Reviewer #2 agrees that disruption of the MSL complex binding results in male lethality. As far as we understand, Reviewer #2 suggests that the MSL complex does not activate transcription of X chromosome genes, but instead facilitate the recruitment of MOF protein and potentially other general transcription factors to the X chromosome. This could explain the decrease in autosomal gene expression due to a reduction in activating factors like MOF at autosomal promoters. In the upcoming revision, we aim to strike a balance between the two models that elucidate dosage compensation in Drosophila. We appreciate your feedback and look forward to enhancing the clarity and coherence of our manuscript based on your insightful comments.

      Reviewer #2 (Public Review):

      Summary:

      A deletion analysis of the MSL1 gene to assess how different parts of the protein product interact with the MSL2 protein and roX RNA to affect the association of the MSL complex with the male X chromosome of Drosophila was performed.

      Strengths:

      The deletion analysis of the MSL1 protein and the tests of interaction with MSL2 are adequate.

      We thank the reviewer for the positive assessment of the experimental work done.

      This reviewer does not adhere to the basic premise of the authors that the MSL complex is the primary mediator of dosage compensation of the X chromosome of Drosophila.

      We completely agree with this reviewer's claim. In the Introduction section we’ll attempt to make clear that there are two models for the functional role of specific recruitment of the MSL complex to the X chromosome in males.

      Several lines of evidence from various laboratories indicate that it is involved in sequestering the MOF histone acetyltransferase to the X chromosome but there is a constraint on its action there. When the MSL complex is disrupted, there is no overall loss of compensation but there is an increase in autosomal expression. Sun et al (2013, PNAS 110: E808-817) showed that ectopic expression of MSL2 does not increase expression of the X and indeed inhibits the effect of acetylation of H4Lys16 on gene expression. Aleman et al (2021, Cell Reports 35: 109236) showed that dosage compensation of the X chromosome can be robust in the absence of the MSL complex. Together, these results indicate that the MSL complex is not the primary mediator of X chromosome dosage compensation. The authors use sex-specific lethality as a measure of disruption of dosage compensation, but other modulations of gene expression are the likely cause of these viability effects.

      Sun et al (2013, PNAS 110: E808-817) showed that recruitment of the MSL complex-specific subunit MSL2 or the MOF protein to the UAS promoter resulted in recruitment of the entire MSL complex in males but not transcriptional activation. This important result argues that the MSL complex does not activate transcription. However, it must be taken into account that the GAL4 DNA binding region used to recruit the chimeric MSL2 protein to the UAS promoter was directly fused to the MSL2 RING domain, which is critical for interaction of MSL2 with MSL1 and its ubiquitination activity (this activity could potentially be involved in transcription activation). It also remains poorly understood what happens to the MSL complex after recruitment to the promoters or HAS on the X chromosome. Subcomplex MSL1/MSL3/MOF can acetylate TF and H4K16 during RNA polymerase II elongation, resulting in increasing of transcription. The separate role of MSL2 and MSL1 in the activation of transcription of gene promoters is also shown. Sun et al. showed that in females, recruitment of MOF to the UAS promoter leads to a strong increase in transcription, which is associated with the inclusion of MOF in the non-specific lethal (NSL) complex, which is bound to promoters and is required for strong transcription activation. In males, MOF is preferentially recruited to the UAS promoter in the full MSL complex or perhaps in the MSL1/MSL3/MOF subcomplex, which stimulates transcription during RNA polymerase II elongation much less strongly than NSL complex. The same result was obtained in the Prestel et al. 2010 (Mol Cell 38:815-26). In this study the GAL4 binding sites were inserted upstream of the lacZ and mini-white genes. Activation of transcription after recruitment of GAL4-MOF to the GAL4 sites was studied in males and females. As in Sun et al. 2013, strong activation of the reporter was observed in females. A weak transcriptional activation of the reporter gene in males was shown, and the MOF protein was detected not only on the promoter, but also in the coding and 3’ regions of the reporter.

      We do not understand how the paper by Aleman et al (Cell Reports 35: 109236, 2021) is consistent with the hypothesis that the MSL complex is not involved in the transcriptional activation of X chromosomal genes. The main conclusions of this paper: 1) Inactivation of Mtor leads to selective activation of the male X chromosome. 2) Mtor-driven attenuation of male X occurs in broad domains linked by the MSL complex. 3) Mtor genetically interacts with MSL components and reduces male mortality; 4) Mtor restrains dose-compensated expression at the level of nascent transcription. Thus, the paper shows that the MSL complex has an activator activity that is partially inhibited by Mtor. Accordingly, inactivation of Mtor only partially restored the survival of males in which dosage compensation was not completely inactivated.

      A detailed explanation was provided by Birchler and Veitia (2021, One Hundred Years of Gene Balance: How stoichiometric issues affect gene expression, genome evolution, and quantitative traits. Cytogenetics and Genome Research 161: 529-550).

      We agree that an alternative model of the dosage compensation mechanism is reasonable. We can assume that both mechanisms can function jointly provide effective dosage compensation in Drosophila males. At the suggestion of the reviewer to reconsider the entire context of the article, we will make many small changes throughout the manuscript.

      Reviewer #1 (Recommendations For The Authors):

      Overall, I found the text well written and the figures logically organized (especially Figure 5, which had the potential to confuse). The authors especially excelled in bringing together the decades of literature in the Discussion.

      I offer several suggestions to improve the readability:

      Consider presenting the coiled-coil domain homology in Figure 1A as a contrast for the N-terminal region, which the authors claim is poorly conserved.

      We’ll add the coiled-coil domain homology in Figure 1A in new version of MS.

      It is difficult to visualize the red MSL2 in Figure 2; the green and red panels should be presented separately in the main text, as they are in the Supplemental Figure 2.

      We’ll prepare Figure 2 with separate green and red panels.

      The ChIP-seq experiments for MSL proteins are well presented, but in my opinion, add little to the overall conclusions:

      Figure 6 mostly recapitulates what has already been published and utilized by several groups, most recently the authors themselves (Tikhonova 2019): that MSL expressed in females targets the X/HAS, similar to in males. While these are nice supporting data for the female transgenic system, I do not believe this figure should be prominently featured as if this is a novelty of the current study.

      We fully agree with the reviewer's comment about the limitation of scientific novelty in Figure 6. It has an auxiliary meaning. Therefore, we decided to transfer this figure to Supplementary material.

      The ChIP experiments in Figure 7 agree with the conclusions in Figures 2 and 3 (polytene chromosome immunostaining) when it comes to X/autosome localization. I believe it would help with the flow of the paper if these experiments were combined or at least placed closer together in the narrative, rather than falling at the end.

      We’ll move Figure 7 closer to polytene chromosome immunostaining. We agree with reviewer that this placement of the figure will make it easier to perceive the meaning of the article as a whole.

      I find Figure 8 difficult to understand, especially since the "clusters" are not annotated in the figure, but are described in the text. I struggled to follow the authors' conclusions based on these data. The authors could clarify the figure with annotations, although to be honest I do not currently see the value of this analysis/figure.

      In the new version of the article, we will try to make this figure more understandable: we will add explanations to the figure and a legend to it, and we will also try to place emphasis more clearly in the text of the article.

    1. Author response:

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

      Reviewer #1:

      I have only a few comments that I think will improve the manuscript and help readers better appreciate the context of the reported results.

      We would like to thank the Reviewer for their time in reviewing our manuscript. We appreciate the helpful feedback and assistance in ensuring the highest quality publication possible.

      One paradox, that the authors point out, is that the drastic effects of TALK-1 L114P on plasma membrane potential do not result in a complete loss of insulin secretion. One important consideration is the role of intracellular stores in insulin secretion at physiological levels of hyperglycemia. This needs to be discussed more thoroughly, especially in the light of recent papers like Postic et al 2023 AJP and others. The authors do show an upregulation of IP3-induced Ca release. It is not clear whether they think this is a direct or indirect effect on the ER. Is there more IP3? More IP3R? Are the stores more full?

      The reviewer brings up an important point. Although we see a significant reduction in glucose-stimulated depolarization in most islets from TALK-1 L114P mice, some glucosestimulated calcium influx is still present (especially from female islets); this suggests that a subset of islet β-cells are still capable of depolarization. Because our original membrane potential recordings were done in whole islets without identification of the cell type being recorded, we have now repeated these electrical recordings in confirmed β-cells (see Supplemental figure 6). The new data shows that 33% of TALK-1 L114P β-cells show action potential firing in 11 mM glucose, which would be predicted to stimulate insulin secretion from a third of all TALK-1 L114P β-cells; this could be responsible for the remaining glucosestimulated insulin secretion observed from TALK-1 L114P islets. However, ER calcium store release could also allow for some of the calcium response in the TALK-1 L114P islets. We have now detailed this in the discussion; this now details the Postic et. al. study showing that glucose-stimulated beta-cell calcium increases involve ER calcium release as it occurs in the presence of voltage-dependent calcium channel inhibition. Future studies can assess this using SERCA inhibitors and determining if glucose-stimulated calcium influx in TALK-1 L114P islets is lost. We also find that muscarinic stimulated calcium influx from ER stores is greater in TALK-1 L114P mice. We currently do not have data to support the mechanism for this enhancement of muscarinic-induced islet calcium responses from islets expressing TALK1 L114P. Our hypothesis is that greater TALK-1 current on the ER membrane is enhancing ER calcium release in response to IP3R activation. There is an equivalent IP3R expression in control and TALK-1 L114P islets based on transcriptome analysis, which is now included in the manuscript. However, whether there is greater IP3 production, greater ER calcium storage, and/or greater ER calcium release requires further analysis. Because this finding was not directly related to the metabolic characterization of this TALK-1 L114P MODY mutation, we are planning to examine the ER functions of TALK-1L114P thoroughly in a future manuscript.

      The authors point to the possible roles of TALK-1 in alpha and delta cells. A limitation of the global knock-in approach is that the cell type specificity of the effects can't easily be determined. This should be more explicitly described as a limitation.

      We thank the reviewer for this suggestion and have added this to the discussion. This is now included in a paragraph at the end of the discussion detailing the limitations of this manuscript.

      The official gene name for TALK-1 is KCNK16. This reviewer wonders whether it wouldn't be better for this official name to be used throughout, instead of switching back and forth. The official name is used for Abcc8 for example.

      We thank the reviewer for this suggestion and have revised the manuscript to include Kcnk16 L114P. The instances of TALK-1 L114P that remain in the manuscript are in cases where the text specifically discusses TALK-1 channel function.

      There are several typos and mistakes in editing. For example, on page 5 it looks like "PMID:11263999" has not been inserted. I suggest an additional careful proofreading.

      We have revised this reference, thoroughly proofread the revised manuscript, and corrected typos.

      The difference in lethality between the strains is fascinating. Might be good to mention other examples of ion channel genes where strain alters the severe phenotypes? Additional speculation on the mechanism could be warranted. It also offers the opportunity to search for genetic modifiers. This could be discussed.

      We thank the reviewer for this suggestion and have added details on mutations where strain alters lethality.

      The sex differences are interesting. Of course, estrogen plays a role as mentioned at the bottom of page 16, but there have been more involved analyses of islet sex differences, including a recent paper from the Rideout group. Is there a sex difference in the islet expression of KCNK16 mRNA or protein, in mice or humans?

      We thank the reviewer for the important comments on the TALK-1 L114P sex differences. We have revised the manuscript to include greater discussion about female β cell resilience to stress, which may allow greater insulin secretion in the presence of the TALK-1 L114P channels; this is based on the Brownrigg et. al. study pointed out by the reviewer (PMID: 36690328). Because these sex differences in islet function were examined in mice, we looked at KCNK16 expression in mouse beta-cells. While there is a trend for greater KCNK16 expression in sorted male beta-cells (average RPKM 6296.25 +/-953.84) compared to sorted female beta-cells (5148.25 +/- 1013.22). Similarly, there was a trend toward greater KCNK16 expression in male HFD treated mouse beta-cells (average RPKM 8020.75 +/- 1944.41) compared to female HFD treated mouse beta-cells (average RPKM 7551 +/- 2952.70). We have now added this to the text.

      Page 15-16 "Indeed, it has been well established that insulin signaling is required for neonatal survival; for example, a similar neonatal lethality phenotype was observed in mice without insulin receptors (Insr-/-) where death results from hyperglycemia and diabetic ketoacidosis by P3 (40)." Formally, the authors are not examining insulin signaling. A better comparison is that of the Ins1/Ins2 double knockout model of complete hypoinsulinemia.

      We thank the reviewer for suggesting this as the appropriate comparison model and have now revised the manuscript to detail the 48-hour average life expectancy of Ins1/Ins2 double knockout mice (PMID: 9144203).

      There are probably too many abbreviations in the paper, making it harder to read by nonspecialists. I recommend writing out GOF, GSIS, WT, K2P, etc.

      We thank the reviewer for this suggestion and have revised the manuscript to reduce the use of most abbreviations.

      Reviewer #2:

      We would like to thank the Reviewer for their time in reviewing our manuscript. We appreciate the helpful feedback and assistance in ensuring the highest quality publication possible. We have thoroughly addressed all the reviewer’s comments and revised the manuscript accordingly. These changes have strengthened the manuscript and are summarized below.

      (1) The authors perform an RNA-sequencing showing that the cAMP amplifying pathway is upregulated. Is this also true in humans with this mutation? Other follow-up comments and questions from this observation:

      a) Will this mean that the treatment with incretins will improve glucose-stimulated insulin secretion and Ca2+ signalling and lower blood glucose? The authors should at least present data on glucose-stimulated insulin secretion and/or Ca2+ signalling in the presence of a compound increasing intracellular cAMP.

      b) Will an OGTT give different results than the IPGTT performed due to the fact that the cAMP pathway is upregulated?

      c) Is the increased glucagon area and glucagon secretion a compensatory mechanism that increases cAMP? What happens if glucagon receptors are blocked?

      We thank the reviewer for the suggestions. Although cAMP pathways were upregulated in the TALK-1 L114P islets, the changes in expression were only modest as examined by qRTPCR. Thus, we are not sure if this plays a role in secretion. For humans with this mutation, there have been such a small number of patients and no islets isolated from these patients. Therefore, we are unaware if the cAMP amplifying pathway is upregulated in humans with the MODY associated TALK-1 L114P mutation. We have performed the suggested experiment assessing calcium from TALK-1 L114P islets in response to liraglutide (see Supplemental figure 10); there was no liraglutide response in TALK-1 L114P islets. We have also performed the OGTT experiments as suggested and these have now been added to the manuscript (see Supplemental figure 3). We do not believe that the increased glucagon is a compensatory response, because: 1. TALK-1 deficient islets have less glucagon secretion due to reduced SST secretion (see PMID: 29402588); 2. There is no change in insulin secretion at 7mM glucose, however, glucagon secretion is significantly elevated from islets isolated from TALK-1 L114P mice; 3. TALK-1 is highly expressed in delta-cells, and in these cells TALK-1 L114P would be predicted to cause significant hyperpolarization and significant reductions in calcium entry as well as SST secretion. Thus, reduced SST secretion may be responsible for the elevation of glucagon secretion. We plan to investigate delta-cells within islets from TALK-1 L114P mice in future studies to determine if changes in SST secretion are responsible for the elevated glucagon secretion from TALK-1 L114P islets.

      (2) The performance of measurements in both male and female mice is praiseworthy. However, despite differences in the response, the authors do not investigate the potential reason for this. Are hormonal differences of importance?

      We thank the reviewer for this important point. It is indeed becoming clear that there are many differences between male and female islet function and responses to stress. Thus, we have revised the manuscript to include greater discussion about these differences such as female β cell resilience to stress, which may allow greater insulin secretion in the presence of the TALK-1 L114P channels; this is based on the Brownrigg et. al. study pointed out by reviewer 1 (PMID: 36690328). While the differences in islet function and GTT between male and female L114P mice are clear, they both show diminished islet calcium handling, defective hormone secretion, and development of glucose intolerance. This manuscript was intended to demonstrate how the MODY TALK-1 L114P causing mutation caused glucose dyshomeostasis, which we have determined in both male and female mice. The mechanistic determination for the differences between male and female mice and islets with TALK-1 L114P could be due to multiple potential causes (as detailed in PMID: 36690328), thus, we believe that comprehensive studies are required to thoroughly determine how the TALK-1 L114P mutation differently impacts male and female mice and islets, which we plan to complete in a future manuscript.

      (3) MINOR: Page 5 .." channels would be active at resting Vm PMID:11263999.." The actual reference has not been added using the reference system.

      We thank the reviewer for noticing this mistake, which has now been corrected.

      Reviewer #3:

      The manuscript is overall clearly presented and the experimental data largely support the conclusions. However, there are a number of issues that need to be addressed to improve the clarity of the paper.

      We would like to thank the Reviewer for their time in reviewing our manuscript. We appreciate the helpful feedback and assistance in ensuring the highest quality publication possible. We have thoroughly addressed all the reviewer’s comments and revised the manuscript accordingly. These changes have strengthened and improved the clarity of the manuscript.

      Specific comments:

      (1) Title: The terms "transient neonatal diabetes" and "glucose dyshomeostasis in adults" are used to describe the TALK-1 L114P mutant mice. Transient neonatal diabetes gives the impression that diabetes is resolved during the neonatal period. The authors should clarify the criteria used for transient neonatal diabetes, and the difference between glucose dyshomeostasis and MODY. Longitudinal plasma glucose and insulin data would be very informative and help readers to follow the authors' narrative.

      We appreciate the helpful comment and have added longitudinal plasma glucose from neonatal mice to address this (see Supplemental figure 2). The new data now shows the TALK-1 L114P mutant mice undergo transient hyperglycemia that resolves by p10 and then occurs again at week 15. Insulin secretion from P4 islets is also included that shows that male animals homozygous for the TALK-1 L114P mutation have the largest impairment in glucosestimulated insulin secretion, followed by male heterozygous TALK-1 L114P P4 islets that also have impaired insulin secretion (see Figure 1). The amount of hyperglycemia correlates with the defects in neonatal islet insulin secretion.

      (2) Another concern for the title is the term "α-cell overactivity." This could be taken to mean that individual α-cells are more active and/or that there are more α-cells to secrete glucagon. The study does not provide direct evidence that individual α-cells are more active. This should be clarified.

      We appreciate the helpful comment and have revised the manuscript title accordingly.

      (3) In the Introduction, it is stated that because TALK-1 activity is voltage-dependent, the GOF mutation is less likely to cause neonatal diabetes, yet the study shows the L114P TALK-1 mutation actually causes neonatal diabetes by completely abolishing glucose-stimulated Ca2+ entry. This seems to imply TALK-1 activity (either in the plasma membrane or ER membrane) has more impact on Vm or cytosolic Ca2+ in neonates than initially predicted. Some discussion on this point is warranted.

      These are important points and we have added details to the discussion about this. For example, the discussion now states that, “This suggests a greater impact of TALK-1 L114P in neonatal islets compared to adult islets. Future studies during β-cell maturation are required to determine if TALK-1 activity is greater on the plasma membrane and/or ER membrane compared with adult β-cells.” The introduction has also been revised to clarify the voltagedependence of TALK-1.

      (4) What is the relative contribution of defects in plasma membrane depolarization versus ER Ca2+ handling on defective insulin secretion response?

      We thank the reviewer for bringing up this important point. TALK-1 L114P islets show blunted glucose-stimulated depolarization and glucose-stimulated calcium entry, however, the L114P islets show equivalent Ca2+ entry as control islets in response high KCl (Figure 5GH). As the KCl stimulated Ca2+ influx is similar between control and TALK-1 L11P islets, this indicates that plasma membrane TALK-1 L114P has a hyperpolarizing role that significantly blunts glucose-stimulated depolarization and reduces activation of voltage-dependent calcium channels. We have further tested this by looking at glucose-stimulated β-cell membrane potential depolarization in TALK-1 L11P islets, which is significantly blunted (Figure4 A and B; Supplemental figure 6). However, 33% of TALK-1 L11P β-cells showed glucose-stimulated electrical excitability (Supplemental figure 6), which likely accounts for the modest GSIS from TALK-1 L11P islets. New data has also been included showing that KCl stimulation causes a significant depolarization of β-cells from TALK-1 L11P islets (Supplemental figure 6). Because plasma membrane TALK-1 L114P is largely responsible for the hyperpolarized membrane potential and blunted glucose-stimulated Ca2+ entry, this suggests that TALK-1 L11P on the plasma membrane is primarily responsible for the altered insulin secretion. The discussion has been revised to reflect this.

      (5) The Jacobson group has previously shown that another K2P channel TASK-1 is also involved in ER Ca2+ homeostasis and that TASK inhibitors restored ER Ca2+ in TASK-1 expressing cells. Is TASK-1 expressed in β-cell ER membrane? Can the mishandling of Ca2+ caused by TALK-1 L114P be reversed by TASK-1 inhibitors?

      We thank the reviewer for bringing up this important point in relation to ER calcium handling by K2P channels. We have found that TASK-1 channels expressed in alpha-cells enhance ER calcium release and that inhibitors or TASK-1 channels elevate alpha-cell ER calcium storage. We did not observe any significant changes in the gene (Kcnk3) encoding TASK-1 between islets from control or TALK-1 L11P mice, which has now been added to the manuscript. However, because the TALK-1 L11P-mediated reduction of glucose-stimulated depolarization and inhibition of calcium entry are both prevented in the presence of high KCl (see Figure X); this strongly suggests that TALK-1 L114P K+ flux at the membrane is hyperpolarizing the membrane potential and limiting depolarization and calcium entry. This suggests that TALK-1 L114P control of ER calcium handling is not the primary contributor to the blunted glucose-stimulate calcium handling. Furthermore, acetylcholine stimulation of islets from both control and TALK-1 L114P islets elicited ER calcium release, which indicates that for the most part ER calcium release is still responsive to cues that control release, but they are altered. Taken together this suggests that the TALK-1 L114P impact on ER calcium is not the primary mediator of blunted glucose-stimulated islet calcium entry and insulin secretion.

      (6) The electrical recording experiments were conducted using whole islets. The authors should comment on how the cells were identified as β-cells, especially in mutant islets in which there is an increased number of α-cells.

      The reviewer brings up an important point. As indicated, the original membrane potential recordings were conducted using whole islets. While the recorded cells could mostly be βcells based on mouse islets typically containing >80% β-cells, there is a possibility that some of the cells included in these recordings were α-cells or δ-cells (especially because of the noted α-cell hyperplasia in TALK-1 L114P islets). Thus, we have now included data from bcells that were identified with an adenoviral construct containing a rat insulin promoter driving a fluorescent reporter. This allowed the fluorescent β-cells to be monitored with electrophysiological membrane potential recordings. The new data (see Supplemental figure 6) shows a significant reduction in glucose-stimulated depolarization in 67% of β-cells with the L114P mutation compared to controls.

      Minor:

      (1) Some references need formatting.

      The references have been revised accordingly.

      (2) Please define glucose-stimulated phase 0 Ca2+ response for non-expert readers.

      This has been defined accordingly.

      (3) Page 14 bottom: The sentence "Unlike the only other MODY-associated.........., TALK-1 is not inhibited by sulfonylureas" seems out of place and lacks context.

      We thank the reviewer for this suggestion and have deleted this sentence.

      (4) Figure 6: It would be helpful to provide a protein name for the genes shown in panel D.

      The protein names for the genes have now been included in the discussion of these genes.

    1. Author response:

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

      We appreciate the thoughtful review of our manuscript by the reviewers, along with their valuable suggestions for enhancing our work. In response to these suggestions, we conducted additional experiments and made significant revisions to both the text and figures. In the following sections, we first highlight the major changes made to the manuscript, and thereafter address each reviewer's comments point-by-point. We hope these additional data and revisions have improved the robustness and clarity of the study and manuscript. Please note that as part of a suggested revision we have changed the manuscript title to be: Bacterial vampirism mediated through taxis to serum.

      Major revisions and new data:

      (1) We conducted additional experiments testing taxis to serum using a swine ex vivo enterohemorrhagic lesion model in which we competed wildtype versus chemotaxis deficient strains (Fig. 8). We selected swine for these experiments due to their similarity in gastrointestinal physiology to humans. In these experiments we see that chemotaxis, and the chemoreceptor Tsr, mediate localization to, and migration into, the lesion. We also tested, and confirmed, taxis to serum from swine and serum from horse, that supporting that serum attraction is relevant in other host-pathogen systems.

      (2) We present additional experimental data and quantification of chemotaxis responses to human serum treated with serine-racemase (Fig. S3). This treatment reduces wildtype chemoattraction and the wildtype no longer possesses an advantage over the tsr strain, providing further evidence that L-serine is the specific chemoattractant responsible for Tsr-mediated attraction to serum.

      (3) We present additional data in the form of 17 videos of chemotaxis experiments with norepinephrine and DHMA showing null-responses under various conditions. These data provide additional support to the conclusion that these chemicals are not responsible for bacterial attraction to serum. We have included these raw data as a new supplementary file (Data S1) for those in the field that are interested in these chemicals.

      (4) Based on comments from Reviewer 2 regarding whether the position of the ligand and ligand-binding site residues in the previously-reported EcTsr LBD structure are incorrect, or whether these differences are due to the proteins being from different organisms, we performed paired crystallographic refinements to determine which positions result in model improvement (Fig. 7J). Altering the EcTsr structure to have the ligand and ligandbinding site positions from our new higher resolution and better-resolved structure of Salmonella Typhimurium Tsr results in a demonstrably better model, with both Rwork and Rfree lower by about 1% (Fig. 7J). These data support our conclusion that the correct positions for both structures are as we have modeled them in the S. Typhimurium Tsr structure. We also solved an additional crystal structure of SeTsr LBD captured at neutral pH (7-7.5) that confirms our structure captured with elevated pH (7.5-9.7) has no major changes in structure or ligand-binding interactions (Fig. S6, Table S2).

      (5) Based on comments from Reviewer 2 on the accuracy of the diffusion calculations, we present a new analysis (Fig. S2) comparing the experimentally-determined diffusion of A488 compared to its calculated diffusion. We found that:

      [line 111]: “As a test case of the accuracy of the microgradient modeling, we compared our calculated values for A488 diffusion to the normalized fluorescence intensity at time 120 s. We determined the concentration to be accurate within 5% over the distance range 70270 µm (Fig. S2). At smaller distances (<70 µm) the measured concentration is approximately 10% lower than that predicted by the computation. This could be due to advection effects near the injection site that would tend to enhance the effective local diffusion rate.”

      (6) Both reviewers asked us to better justify why we focused on the chemoreceptor Tsr, and had questions about why we did not investigate Tar. The low concentration of Asp in serum suggests Tar could have some effect, but less so than Trg or Tsr (see Fig. 4A). We have revised the text throughout to better convey that we agree multiple chemoreceptors are involved in the response and clarify our rationale for studying the role of Tsr:

      [line 178]: “We modeled the local concentration profile of these effectors based on their typical concentrations in human serum (Fig. 4B). Of these, by far the two most prevalent chemoattractants in serum are glucose (5 mM) and L-serine (100-300 µM) (Fig. 4B-F). This suggested to us that the chemoreceptors Trg and/or Tsr could play important roles in serum attraction.”

      [line 186]: “Since tsr mutation diminishes serum attraction but does not eliminate it, we conclude that multiple chemoattractant signals and chemoreceptors mediate taxis to serum. To further understand the mechanism of this behavior we chose to focus on Tsr as a representative chemoreceptor involved in the response, presuming that serum taxis involves one, or more, of the chemoattractants recognized by Tsr that is present in serum: L-serine, NE, or DHMA.”

      [line 468] “Serum taxis occurs through the cooperative action of multiple bacterial chemoreceptors that perceive several chemoattractant stimuli within serum, one of these being the chemoreceptor Tsr through recognition of L-serine (Fig. 4).”

      Point-by-point responses to reviewer comments:

      Reviewer #1:

      (1) Presumably in the stomach, any escaping serum will be removed/diluted/washed away quite promptly? This effect is not captured by the CIRA assay but perhaps it might be worth commenting on how this might influence the response in vivo. Perhaps this could explain why, even though the chemotaxis appears rapid and robust, cases of sepsis are thankfully relatively rare.

      To clarify, the Enterobacteriaceae species we have tested here are colonizers of the intestines, not the stomach, and cases of bacteremia from these species are presumably due to bloodstream entry through intestinal lesions. Whether or not intestinal flow acts as a barrier to bloodstream entry is not something we test here, and so we have not commented on this idea in the manuscript. We do demonstrate that attraction to serum occurs within seconds-to-minutes of exposure. We expect that the major protective effects against sepsis are the host antibacterial factors in serum, which are well-described in other work. We have been careful to state throughout the text that we see attraction responses, and growth benefits, to serum that is diluted in an aqueous media, which is different than bacterial growth in 100% serum or in the bloodstream.

      (2) The authors refer to human serum as a chemoattractant numerous times throughout the study (including in the title). As the authors acknowledge, human serum is a complex mixture and different components of it may act as chemoattractants, chemo-repellents (particularly those with bactericidal activities) or may elicit other changes in motility (e.g. chemokinesis). The authors present convincing evidence that cells are attracted to serine within human serum - which is already a well-known bacterial chemoattractant. Indeed, their ability to elucidate specific elements of serum that influence bacterial motility is a real strength of the study. However, human serum itself is not a chemoattractant and this claim should be re-phrased - bacteria migrate towards human serum, driven at least in part by chemotaxis towards serine.

      Throughout the text we have changed these statements, including in the title, to either be ‘taxis to serum’ or ‘serum attraction.’ On the timescales we tested our data support that chemotaxis, not chemokineses or other forms of direction motility, is what drives rapid serum attraction, since a motile but non-chemotactic cheY mutant cannot localize to serum (Fig. 4). We present evidence of one of these chemotactic interactions (L-Ser).

      (3) Linked to the previous point, several bacterial species (including E. coli - one of the bacterial species investigated here) are capable of osmotaxis (moving up or down gradients in osmolality). Whilst chemotaxis to serine is important here, could movement up the osmotic gradient generated by serum injection play a more general role? It could be interesting to measure the osmolality of the injected serum and test whether other solutions with similar osmolality elicit a similar migratory response. Another important control here would be to treat human serum with serine racemase and observe how this impacts bacterial migration.

      As addressed above, we have added additional experiments of serum taxis treated with serine racemase showing competition between WT and cheY, and WT and tsr (Fig. S3). These data support a role for L-serine as a chemoattractant driving attraction to serum. The idea of osmotaxis is interesting, but outside the scope of this work since we focus on chemoattraction to L-serine as one of the mechanisms driving serum attraction, and have multiple lines of evidence to support that.

      (4) The migratory response of E. coli looks striking when quantified (Fig. 6C) but is really unclear from looking at Panel B - it would be more convincing if an explanation was offered for why these images look so much less striking than analogous images for other species (E.g. Fig. 6A).

      We agree that the E. coli taxis to serum response is less obvious. We have brightened those panels to hopefully make it clearer to interpret (more cells in field of view over time). Also, as stated in the y-axes of these plots, this quantification was performed by enumerating the number of cells in the field of view, and the Citrobacter and Escherichia responses are shown on separate y-axes (now Fig. 8C). As indicated, the experiments have different numbers of starting motile cells, which we presume accounts for the difference in attraction magnitude. When investigating diverse bacterial systems we found there to be differences in motility under the culturing and experimental conditions we employed, for multiple reasons, and so for these data we thought it best to report raw cell numbers rather data normalized to the starting number of bacteria, as we do elsewhere. In the specific case of these E. coli responding to serum, please view Supplementary Movie S3, which both clearly shows the attraction response and that the bacteria grew in a longer, semi-filamentous form that seem to impair their swimming speed.

      (5) It is unclear why the fold-change in bacterial distribution shows an approximately Gaussian shape with a peak at a radial distance of between 50 -100 um from the source (see for example Fig. 2H). Initially, I thought that maybe this was due to the presence of the microcapillary needle at the source, but the CheY distribution looks completely flat (Fig. 3I). Is this an artifact of how the fold-change is being calculated? Certainly, it doesn't seem to support the authors' claim that cells increase in density to a point of saturation at the source. Furthermore, it also seems inappropriate to apply a linear fit to these non-linear distributions (as is done in Fig. 2H and in the many analogous figures throughout the manuscript).

      We have revised the text to address this point, and removed the comment about cells increasing in density to a point of saturation: [Line 138] “We noted that in some experiments the population peak is 50-75 µm from the source, possibly due to a compromise between achieving proximity to nutrients in the serum and avoidance of bactericidal serum elements, but this behavior was not consistent across all experiments. Overall, our data show S. enterica serovars that cause disease in humans are exquisitely sensitive to human serum, responding to femtoliter quantities as an attractant, and that distinct reorganization at the population level occurs within minutes of exposure (Fig. 3, Movie 2).”

      We can confirm that this is not an artifact of quantification. Please refer to the videos of these responses, which demonstrates this point (Movies 1-5).

      (6) The authors present several experiments where strains/ serovars competed against each other in these chemotaxis assays. As mentioned, these are a real strength of the study - however, their utility is not always clear. These experiments are useful for studying the effects of competition between bacteria with different abilities to climb gradients.

      However, to meaningfully interpret these effects, it is first necessary to understand how the different bacteria climb gradients in monoculture. As such, it would be instructive to provide monoculture data alongside these co-culture competition experiments.

      Thank you for this suggestion. We agree that the coculture experiments showing strains competing for the same source of effector give a different perspective than monoculture. These experiments allow us to confirm taxis deficiencies or advantages with greater sensitivity, and ensure that the bacteria in competition have experienced the same gradient. This type of competition experiment is often used in in vivo experimentation for the same advantages. We note that in the gut the bacteria are not in monoculture and chemotactic bacteria do have to compete against each other for access to nutrients. Repeating all of the experiments we present to show both the taxis responses in coculture and monoculture would be an extraordinary amount of work that we do not believe would meaningfully change the conclusions of this study.

      (7) Linked to the above point, it would be especially instructive to test a tsr mutant's response in monoculture. Comparing the bottom row of Fig. 3G to Fig. 3I suggests that when in co-culture with a cheY mutant, the tsr mutant shows a higher fold-change in radial distribution than the WT strain. Fig. 4G shows that a tsr mutant can chemotaxis towards aspartate at a similar, but reduced rate to WT. This could imply that (like the trg mutant), a tsr mutant has a more general motility defect (e.g. a speed defect), which could explain why it loses out when in competition with the WT in gradients of human serum, but actually seems to migrate strongly to human serum when in co-culture with a cheY mutant. This should be resolved by studying the response of a tsr mutant in monoculture.

      Addressed above.

      (8) In Fig. 4, the response of the three clinical serovars to serine gradients appears stronger than the lab serovar, whilst in Fig. 1, the response to human serum gradients shows the opposite trend with the lab serovar apparently showing the strongest response. Can the authors offer a possible explanation for these slightly confusing trends?

      We suspect this relates to the fact that pure L-serine is a chemoattractant, whereas treatment with serum exposes the bacteria both to chemoattractants and, likely, chemorepellents. Strains may navigate the landscape of these stimuli different for a variety of reasons that are not simple to tease apart. The final magnitude of change in bacterial localization depends on multiple factors including swimming speed, adaptation, sensitivity of chemoattraction, and cooperative signaling of the chemoreceptor nanoarray. Thus, we cannot state with certainty how and why these strains are different across all experiments, but we can state that they are attracted to both serum and L-serine.

      (9) In Fig. S2, it seems important to present quantification of the effect of serine racemase and the reported lack of response to NE and DHMA - the single time-point images shown here are not easy to interpret.

      As suggested, we present quantification of the serum racemase treated samples (now Fig. S3). To assist in the interpretation of this max projections Fig. S3 now noted the chemotactic response (chemoattraction for L-serine, null-response for NE/DHMA). Further, we revised the text to state: [line 209: “We observed robust chemoattraction responses to L-serine, evident by the accumulation of cells toward the treatment source (Fig. S3E, Movie 4), but no response to NE or DHMA, with the cells remaining randomly distributed even after 5 minutes of exposure (Fig. S3F-I, Movie 5, Movie S1).”

      (10) Importantly, the authors detail how they controlled for the effects of pH and fluid flow (Line 133-136). Did the authors carry out similar controls for the dual-species experiments where fluorescent imaging could have significantly heated the fluid droplet driving stronger flow forces?

      Most of our microfluidics experiments were performed in a temperature-controlled chamber (see Methods). Since the strains in the coculture experiments experienced the same experimental conditions we have no evidence of fluorescence-imaginginduced temperature changes that have impacted whether or not the bacteria are attracted to serum or the effectors we investigated.

      (11) The inference of the authors' genetic analysis combined with the migratory response of E. coli and C. koseri to human serum shown in Fig. 6 is that Tsr drives movement towards human serum across a range of Enterobacteriaceae species. The evidence for the importance of Tsr here is currently correlative - more causal evidence could be presented by either studying the response of tsr mutants in these two species (certainly these should be readily available for E. coli) or by studying the response of these two species to serine gradients.

      We have revised the text to state: [line 402] “Without further genetic analyses in these strain backgrounds, the evidence for Tsr mediating serum taxis for these bacteria remains circumstantial. Nevertheless, taxis to serum appears to be a behavior shared by diverse Enterobacteriaceae species and perhaps also Gammaproteobacteria priority pathogen genera that possess Tsr such as Serratia, Providencia, Morganella, and Proteus (Fig. 8B).”

      We note that other work has thoroughly investigated E. coli serine taxis.

      Figure Suggestions

      (1) Fig. 2 - The inset bar charts in panels H-J and the font size in their axes labels are too small - this suggestion also applies to all analogous figures throughout the manuscript.

      We have increased the size of the text for these inset plots. We have also broken up some of the larger figures.

      (2) Panel 2F - the cartoon bacterial cell and 'number of bacteria' are confusing and seem to contradict the y-axis label. This also applies to several other figures throughout the manuscript where the significance of this cartoon cell is quite hard to interpret.

      As suggested, we have removed this cartoon.

      (3) Panels G-I in Fig. 3 are currently tricky to interpret - it would be easier if the authors were to use three different colours for the three different strains shown across these panels.

      We have broken up Figure 2 (which also had these types of plots) so that hopefully these labels are more clear. For the Figure in question (now Fig. 4), due to the many figures and different types of data and comparisons it was difficult to find a color scheme for these strains that would be consistent across the manuscript. These colors also reflect the fluorescence markers. We note that not only do we use color to indicate the strain but also text labels.

      (4) Panels 3B-F would be best moved to a supplementary figure as this figure is currently very busy. Similarly, I would potentially consider presenting only the bottom row of panels in Panels G-I in the main figure (which would then be consistent with analogous data presented elsewhere).

      We have opted to keep these panels in the main text (now Fig. 4) as they are relevant to understanding (1) our justification for why to pursue certain chemoeffector-chemoreceptor interactions and not others, and (2) how the chemoattraction response can be understood both in terms of bacterial population distribution and relevant cells over time.

      (5) Fig. 4 and possibly elsewhere - perhaps best not to use Ser as an abbreviation for Serine here because it could potentially be confused with an abbreviation for serum.

      It is unfortunate that these two words are so similar. However, Ser is the canonical abbreviation for the amino acid serine. Serum does not have a canonical abbreviation.

      (6) Fig. 4 - I would move panels H - K to a separate supplementary figure - currently, they are too squished together and it is hard to make out the x-axis labels. I would also consider moving panels E-G to supplementary as well so that the microscopy images presented elsewhere in the figure can be presented at an appropriate size.

      Since we are allowed more figures, we could also break some of these figures up into multiple ones.

      (7) Similarly, I would move some panels from Fig. 5 to supplementary as the figure is currently quite busy.

      We have rearranged the figure (now Fig. 7) to move the bioinformatics data to Fig. 8 to allow more space for the panels.

      Other suggestions

      (8) Line 179 - how do the concentrations quote for serine and glucose compare to aspartate? This would be helpful to justify the authors' decision not to investigate Tar as a potential chemoreceptor.

      This is addressed in our comments above and in Fig. 4A and Fig. 4B-F. Human serum L-Asp is much lower concentration (about 20-fold).

      (9) Line 282 - Serine levels in serum are quantified at 241 uM, but this is only discussed in the context of serum growth effects. Could this information be better used to design/ inform the serine gradients that were tested in chemotaxis assays?

      We tested a wide range of serine concentrations and show even much lower sources of serine than is present in serum is sufficient for chemoattraction. Also, the K1/2 for serine is 105 uM (Fig. S4), which is surpassed by the concentration in serum (Fig. S5).

      (10) The word 'potent' in the title might be too vague, especially as the strength of the response varies between strains/species. It may perhaps be more useful to focus on the rapidity/sensitivity of the response. However, presumably the sensitivity of the response will be driven by the sensitivity of the response to serine (which is already known for E. coli at least). Also, as noted in the public review, human serum itself is not a chemoattractant so I would consider re-phasing this in the title and elsewhere.

      As suggested, and discussed above, we have implemented this change.

      (11) Typo line 59 'context of colonizing of a healthy gut'.

      Addressed.

      (12) Typo line 538 - there is an extra full stop here.

      Addressed.

      Reviewer #2:

      (1) This study is well executed and the experiments are clearly presented. These novel chemotaxis assays provide advantages in terms of temporal resolution and the ability to detect responses from small concentrations. That said, it is perhaps not surprising these bacteria respond to serum as it is known to contain high levels of known chemoattractants, serine certainly, but also aspartate. In fact, the bacteria are shown to respond to aspartate and the tsr mutant is still chemotactic. The authors do not adequately support their decision to focus exclusively on the Tsr receptor. Tsr is one of the chemoreceptors responsible for observed attraction to serum, but perhaps, not the receptor. Furthermore, the verification of chemotaxis to serum is a useful finding, but the work does not establish the physiological relevance of the behavior or associate it with any type of disease progression. I would expect that a majority of chemotactic bacteria would be attracted to it under some conditions. Hence the impact of this finding on the chemotaxis or medical fields is uncertain.

      We agree that the data we show are mostly mechanistic and further work is required to learn whether this bacterial behavior is relevant in vivo and during infections. We present new data using an ex vivo intestinal model which supports the feasibility of serum taxis mediating invasion of enterohemorrhagic lesions (Fig. 8).

      (2) The authors also state that "Our inability to substantiate a structure-function relationship for NE/DHMA signaling indicates these neurotransmitters are not ligands of Tsr." Both norepinephrine (NE) and DHMA have been shown previously by other groups to be strong chemoattractants for E. coli (Ec), and this behavior was mediated by Tsr (e.g. single residue changes in the Tsr binding pocket block the response). Given the 82% sequence identity between the Se and Ec Tsr, this finding is unexpected (and potentially quite interesting). To validate this contradictory result the authors should test E. coli chemotaxis to DHMA in their assay. It may be possible that Ec responds to NE and DHMA and Se doesn't. However, currently, the data is not strong enough to rule out Tsr as a receptor to these ligands in all cases. At the very least the supporting data for Tsr being a receptor for NE/DHMA needs to be discussed.

      Addressed above. The focus of this study is serum attraction and the mechanisms thereof. We never saw any evidence to support the idea that NE/DHMA drives attraction to serum, nor are chemoeffectors for Salmonella, and provide these null-results in Data S2.

      (3) The authors also determine a crystal structure of the Se Tsr periplasmic ligand binding domain bound to L-Ser and note that the orientation of the ligand is different than that modeled in a previously determined structure of lower resolution. I agree that the SeTsr ligand binding mode in the new structure is well-defined and unambiguous, but I think it is too strong to imply that the pose of the ligand in the previous structure is wrong. The two conformations are in fact quite similar to one another and the resolution of the older structure, is, in my view, insufficient to distinguish them. It is possible that there are real differences between the two structures. The domains do have different sequences and, moreover, the crystal forms and cryo-cooling conditions are different in each case. It's become increasingly apparent that temperature, as manifested in differential cooling conditions here, can affect ligand binding modes. It's also notable that full-length MCPs show negative cooperativity in binding ligands, which is typically lost in the isolated periplasmic domains. Hence ligand binding is sensitive to the environment of a given domain. In short, the current data is not convincing enough to say that a previous "misconception" is being corrected.

      Thank you for this comment, which spurred us to investigate this idea more rigorously. As described above we performed new refinements of the E. coli structure edited to have the positions of the ligand and ligand-binding site as modeled in our new Tsr structure from Salmonella (Fig. 7J). The best model is obtained with these poses. Along with the poor fit of the E. coli model to the density, the best interpretations for these positions, for both structures, are as we have modeled them in the Salmonella Tsr structures.

      Figure suggestions

      (1) Figure 2 looks busy and unorganized. Fig 2C could be condensed into one image where there are different colored rings coming from the source point that represent different time points.

      Addressed above. Fig. 2 has been broken apart to help improve clarity.

      (2) What is the second (bottom) graph of 2D? I think only the top graph is necessary.

      We have added an explanation to the figure legend that the top graph shows the means and the bottom shows SEM. The plots cannot easily be overlaid.

      (3) Similarly, Fig 2E doesn't need to have so many time points. Perhaps 4 at maximum.

      As the development of the response over time is a key take-home of the study, we do not wish to reduce the timepoints shown.

      (4) The legend for Figure 2F uses the unit 'µM' to mean micrometers but should use 'µm'.

      Corrected.

      (5) In Figures 2H-J, the lime green text is difficult to read. The word "serum" does not need to be at the top of each panel. I recommend shortening the y-axis titles on the graphs so you can make the graphs themselves larger.

      Addressed above.

      (6) In Figures 2H-J, I am confused about what is being shown in the inset graph. The legend says it's the AUC for the data shown. However, in the third panel (S. Typhimurium vs. S. Enteriditus) the data appears to be much more disparate than the inset indicates. I don't think that this inset is necessary either.

      The point of this inset graph is to quantify the response through integration of the curve, i.e., area under the curve, which is a common way to quantify complex curves and compare responses as single values. We are using this method to calculate statistical significant of the response compared to a null response. We have added further clarification to the figure legend regarding these plots: Inset plots show foldchange AUC of strains in the same experiment relative to an expected baseline of 1 (no change). p-values shown are calculated with an unpaired two-sided t-test comparing the means of the two strains, or one-sided t-test to assess statistical significance in terms of change from 1-fold (stars).

      (7) Line 154, change "relevant for" to "observed in".

      Changed.

      (8) Line 171, according to the Mist4 database, Salmonella enterica has seven chemoreceptors. Why are only Tar, Tsr, and Trg mentioned? Why were only Tsr and Trg tested?

      Addressed above.

      (9) Line 192, be clear that you are referring to genes and not proteins, as italics are used.

      Revised to make this distinction clear.

      (10) Line 193, have other studies found a Trg deletion strain to be non-chemotactic? If so, cite this source here.

      We state that the Trg deletion strain had deficiencies in motility, and also have revised the text to include the clarification that this was not noted in earlier work with this strain: [line 173]: We were surprised to find that the trg strain had deficiencies in swimming motility (data not shown). This was not noted in earlier work but could explain the severe infection disadvantage of this mutant 34. Because motility is a prerequisite for chemotaxis, we chose not to study the trg mutant further, and instead focused our investigations on Tsr.

      (11) Why wasn't a Tar deletion mutant also analyzed? The authors say that based on the known composition of serum, serine and glucose are the most abundant. However, the serum does have aspartate at 10s of micromolar concentrations.

      Addressed above.

      (12) “The Tsr deletion strain still exhibits an obvious chemoattraction to serum. There are other protein(s) involved in chemoattraction to serum but the text does not discuss this.”

      Addressed above.

      (13) “In Figure 3B-F, the text is very difficult to read even when zoomed in on.”

      We have increased the font size of these panels.

      (14) “All of the text in Figure 5 is extremely small and difficult to read.”

      Addressed above. We split this figure in two to help improve clarity.

      (15) “I wonder about the accuracy of the concentration modeling. It seems like there are a lot of variables that could affect the diffusion rates, including the accuracy of the delivery system. Could the concentrations be verified by the dye experiments?”

      Addressed above. We provide a new analysis comparing experimental diffusion of A488 dye compared to calculations (Fig. S2).

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      Major comments:

      (1) It is nice that the authors compared their model to the one "without lookahead" in Figure 4, but this comparison requires more evidence in my opinion, as I explain in this comment. The model without lookahead is closely related or possibly equivalent to the standard predictive coding. In predictive coding, one can make the network follow the stimulus rapidly by reducing the time constant tau. However, as the time constant decreases, the network would become unstable both in simulations (due to limited integration time step) and physical implementation (due to noise). Therefore I wonder if the proposed model has an advantage over standard predictive coding with an optimized time constant. Hence I suggest to also add a comparison between the proposed model, and the predictive coding with parameters (such as tau) optimized independently for each model. Of course, we know that the time-constant of biological neurons is fixed, but biological neurons might have had different time constants (by changing leak conductance) and such analysis could shed light on the question of why the neurons are organized the way they are.

      The comparison with a predictive network for which the neuronal time constants shrink towards 0 is in fact helpful. We added two news subsections in the SI that formally compares the NLA with other approaches, Equilibrium propagation and the Latent Equilibrium, with a version of Equilibrium Propagation also covering the standard predictive coding you describe (SI, Sect.C and D). The Subsection C concludes: “In the Equilibrium propagation we cannot simply take the limit t0 since then the dynamics either disappears (when tau remains on the left, t Du  0) or explodes (when t is moved to the right, dt/ t  ∞), leading to either too small or too big jumps.”

      We have also expanded the passage on the predictive coding in the main text, comparing our instantaneous network processing (up to a remaining time constant tin) with experimental data from humans (see page 10 of the revised ms). The new paragraph ends with:

      “Notice that, from a technical perspective, making the time constants of individual cortical neurons arbitrarily short leads to network instabilities and is unlikely the option chosen by the brain (see SI Sect. C, Comparison to the Equilibrium Propagation).”

      A new formal definition of the moving equilibrium in the Methods (Sect. F) helps to understand this notion of being in a balanced equilibrium state during the dynamics. This formal definition directly leads to the contraction analysis in the SI, Sect. D, showing why the Latent Equilibrium is always contractive, while the current form of the NLA may show jumps at the corner of a ReLu (since a second order derivative of the transfer function enters in the error propagation).

      The reviewer perhaps has additional simulations in mind that compare the robustness of the different models. However, as this paper is more about presenting a novel concept with a comprehensive theory (summing up to 45 pages), we prefer to not add more than the simulations necessary to check the statements of the theorems.

      (2) I found this paper difficult to follow, because the Results sections went straight into details, and various elements of the model were introduced without explaining why they are necessary. Furthermore, the neural implementation was introduced after the model simulations. I suggest reorganizing the manuscript, to describe the model following Marr's levels of description and then presenting the results of simulations. In particular, I suggest starting the Results section by explaining what computation the network is trying to achieve (describe the setup, function L, define its integral over time, and explain that the goal is to find a model minimizing this integral). Then, I suggest presenting the algorithm the neurons need to employ to minimize this integral, i.e. their dynamics and plasticity (I wonder if r=rho(u) + tau rho(u)' is a consequence of action minimization or a necessary assumption - please clarify it). Next please explain how the algorithms could be implemented in biological neurons. Afterward please present the results of the simulation.

      We are sorry to realize that we could not convey the main message clearly enough. After rewriting the paper and straightening the narrative, we hope it is simpler to understand now.

      The paper does not suggest a new model to solve a task, and writing down the function to be minimized is not enough. The point of the NLA is that the time integral of our Lagrangian is minimized with respect to the prospective coordinates, i.e. the discounted future voltage. It is about the question how dynamic equations in biology are derived. Of course, we also solve these equations, prove theorems and perform simulations. But the main point that biology seems to deal with time differently than physics deals with time. Biology “thinks” in terms of future quantities, physics “thinks” in terms of current quantities. We tried to explain this better now in the Introduction, the Results (e.g. after Eq. 5) and the Methods.

      (3) Understanding the paper requires background knowledge that most readers of eLife are unlikely to have, even if they are mathematically minded. For example, I am from the field of computational neuroscience, and I have never heard about Least Action principle from physics or the EulerLagrange equation. I felt lost after reading this paper, and to be able to write this review I needed to watch videos on the Euler-Lagrange equation. To help other readers, I have two suggestions: First, I feel that Eq 4-6 could be moved to the methods, because I found the concept of u~ difficult to understand, and it does not appear in the algorithm. Second, I advise to write in the Introduction, what knowledge is required to follow this paper, and point the readers to resources where they can find the required information. The authors may specify what background is required to follow the main text, and what is required to understand the methods.

      We hope that after explaining the rationale better, it becomes clear that we cannot skip the equations for the prospective coordinates. Likewise, the Euler-Lagrange equations need to be presented in the abstract form, since these are the equations that are eventually transformed into the “model”. We tried to give the basic intuition for this in the main text. As we explained above, the equations asked to be skipped represent the essence of the proposal. It is about how to derive a model equations.

      Moreover, we give more explanations in the Methods to understand the derivations, and we refer to the specifically sections in the SI for further details. We are aware that a full understanding of the theory requires some basic knowledge of the calculus of variation.

      We are hesitating to write in the Introduction what type of knowledge is required to understand the paper. An understanding can be on various levels. Moreover, the materials that are considered to be helpful depend on the background. While for some it is a Youtube, for some Wikipedia, and for others it is a textbook where specific ingredients can be extracted. But we do cite two textbooks in the Results and more in the SI, Sect. F, when referring to the principle of least action in physics and the mathematics, including weblinks.

      Minor comments

      Eq.3: The Authors refer to this equation as a Lagrangian. Could you please clarify why? Is the logic to minimize the energy subject to a constraint that Cost = 0?

      Thanks for asking. The cost is not really a constraint, it is globally minimized, in parallel steps. We are explaining this right after Eq. 3. “We `prospectively' minimize L locally across a voltage trajectory, so that, as a consequence, the local synaptic plasticity for W will globally reduce the cost along the trajectory (Theorem 1 below).”

      We were adding two sentence that explain why this function in Eq. 3 is called a Lagrangian: “While in classical energy-based approaches L is called the total energy, we call it the `Lagrangian' because it will be integrated along real and virtual voltage trajectories as done in variational calculus (leading to the Euler-Lagrange equations, see below and SI, Sect. F)”

      p.4, below Eq. 5 - Please explain the rationale behind NLA, i.e. why is it beneficial that "the trajectory u˜(t) keeps the action A stationary with respect to small variations δu˜"? I guess you wish to minimize L integrated over time, but this is not evident from the text.

      Hmm, yes and no. We wish to minimize the cost, and on the way there minimize the action. Since the global minimization of C is technically difficult, one looks for stationary trajectory as defined in the cited sentence, while minimizing L with respect to W, to eventually minimize the cost.

      In the text we now explain after Eq. 5:

      “The motivation to search for a trajectory that keeps the action stationary is borrowed from physics. The motivation to search for a stationary trajectory by varying the near-future voltages ũ instead of u is assigned to the evolutionary pressure in biology to 'think ahead of time'. To not react too late, internal delays involved in the integration of external feedback need to be considered and eventually need to be overcome. In fact, only for the 'prospective coordinates' defined by looking ahead into the future, even when only virtually, will a real-time learning from feedback errors become possible (as expressed by our Theorems below).”

      Bottom of page 8. The authors say that in the case of single equilibrium and strong nudging the model reduced to the Least Control Principle. Does it also reduce to Predictive coding for supervised learning? If so, it would be helpful to state so.

      Yes, in this case the prediction error in the apical dendrite becomes the one of predictive coding. We are stating this now right at the end of the cited sentence:

      “In the case of strong nudging and a single steady-state equilibrium, the NLA principle reduces to the Least-Control Principle (Meulemans et al., 2022) that minimizes the mismatch energy E^M for a constant input and a constant target, with the apical prediction error becoming the prediction error from standard predictive coding (Rao & Ballard, 1999).”

      In the Discussion we also added a further point (iv) to compare the NLA principle with predictive coding. Both “improve” the sensory representation, but the NLA does in favor of an output, and the predictive coding in favor of the sensory prediction itself (see Discussion).

      Whenever you refer to supplementary materials, please specify the section, so it is easier for the reader to find it.

      Done. Sorry to not have done it earlier. We are now also indicate specific sections when referring to the Methods.

      Reviewer #2 (Recommendations For The Authors):

      There are no major issues with this article, but I have several considerations that I think would greatly improve the impact, clarity, and validity of the claims.

      (1) Unifying the narrative. There are many many ideas put forward in what feels like a deluge. While I appreciate the enthusiasm, as a reader I found it hard to understand what it was that the authors thought was the main breakthrough. For instance, the abstract, results, introduction, and discussion all seem to provide different answers to that question. The abstract seems to focus on the motor error idea. The introduction seems to focus on the novel prospective+predictive setup of the energy function. The discussion lists the different perks of the theory (delay compensation, moving equilibrium, microcircuit) without referring to the prospective+predictive setup of the energy function.

      Thanks much for these helpful hints. Yes, the paper became an agglomerate of many ideas, also own to the fact that we wish to show how the NLA principle can be applied to explain various phenomenology in neurosicence. We now simplified the narrative to this one point of providing a novel theoretical framework for neuroscience, and explaining why this is novel and why it “suddenly works” (the prospective minimization of the energy).

      As you can see from the dominating red in the revised pdf, we did fully rewrite Abstract, Introduction and Discussion under the narrative of the NLA and prospective coding.

      (2) Laying out the organization of the notation clearly. There are quite a few subtle distinctions of what is meant by the different weight matrices (omnibus matrix then input vs recurrent then layered architecture), different temporal horizon formalisms (bar, not bar, tilde), different operators (L, curly L, derivative version, integral version). These different levels are introduced on the fly, which makes it harder to grasp. The fact that there are many duplicate notations for the same quantities does not help the reader. For instance u_0 becomes equal to u_N at one point (above Eq 25). Another example is the constant flipping between integrated and 'current input' pictures. So laying out the multiple layers early, making a table or a figure for the notation, or sticking with one level would help convey the idea to a wide readership.

      Thanks for the hints. We included the table you suggested, but put it to the SI as it became a full page itself. We banned the curly L abbreviating the look-ahead operator.

      The “change of notation” you are alluding to is tricky, though. In a recurrent layer, the index of the output neuron is called o. In a forward network with N layer, the index of the output neurons becomes the last layer N. One has to introduce the layer index l anway for the deeper layers l < N, and we found it more consistent to explain that, while switching from the recurrent to the forward network, the voltage of the output layer becomes now u_o = u_N. There are more of these examples, like the weight matrix W splitting into a intrinsic network part W_net across which errors backpropagate, and a part conveying the input, W_in, that has to be excluded when writing the backpropagation formula for general networks. Again, in the case of the feedforward networks, the notation reduces to W_l, with index l coding for the layer. Presenting the general approach and a specific example may appear as we would duplicate notations – we haven’t found a solution here.

      (3) Separate the algorithm from the implementation level. I particularly struggled with separating the ideas that belonged to the algorithm level (cost function, optimization objectives) and the biophysics. The two are interwoven in a way that does not have to be. Particularly, some of the normative elements may be implemented by other types of biophysics than the authors have in mind. It is for this reason that I think that separating more clearly what belongs to the implementation and algorithm levels would help make the ideas more widely understood. On this point, a trigger point for me was the definition of the 'prospective input rates' e_i, which comes in the second paragraph.

      We are very sorry to have made you thinking that the 'prospective input rates' would be e_i. The prospective input rates are r_i. The misunderstanding likely appeared by an unclear formulation from our side that is now corrected (see first and second paragraph of the Results where we introduce r_i and e_i).

      From a biophysical perspective, it is quite arbitrary to define the input to be the difference between the basal input and the somatic (prospective) potential. It sounds like it comes from some unclear normative picture at this point. But the authors seem to have in mind to use the fact that the somatic potential is the sum of apical and basal input, that's the biophysical picture.

      We hope to have disentangled the normative and biophysical view in the 2nd and 3rd paragraph of the Results, respectively. We introduce the prospective error ei as abstract notion in the first paragraph, while explaining that it will be interpreted as somato-dendritic mismatch error in neuron I in the next paragraph. The second paragraph contains the biophysical details with the apical and basal morphology.

      (4) Experts and non-expert would appreciate an explanation of why/how the choice of state variables matters in the NLA. The prospective coding state variables cannot be said to be the naïve guess. Why does the simple u, dot{u} not work as state variables applied on the same energy function, as would be a naïve application of the Lagrangian ideas?

      We are very glad for this hint to present an intuition behind the variation of the action with respect to a prospective state, instead of the state itself. The simple L(u, dot{u}) does not work because one does not obtain the first-order voltage dynamics compatible with the biophysics. We made an effort to explain the intuition to non-experts and experts in an additional paragraph right after presenting the voltage and error dynamics (Eq. 7 on page 4).

      Here is how the paragraph starts (not displaying the formulas here):

      “From the point of view of theoretical physics, where the laws of motion derived from the least-action principle contain an acceleration term (as in Newton's law of motion, like … for a harmonic oscillator), one may wonder why no second-order time derivative appears in the NLA dynamics. As an intuitive example, consider driving into a bend. Looking ahead in time helps us to reduce the lateral acceleration by braking early enough, as opposed to braking only when the lateral acceleration is already present. This intuition is captured by minimizing the neuronal action A with respect to the discounted future voltages ũi instead of the instantaneous voltages ui.

      Keeping up an internal equilibrium in the presence of a changing environment requires to look ahead and compensate early for the predicted perturbations.

      Technically, …”

      More details are given in the Methods after Eq. 20. Moreover, in the last part of the SI, Sect. F, we have made the link to the least-action principle in physics more explicitly. There we show how the voltage dynamics can be derived from the physical least-action principle by including the Rayleigh dissipation (Eq. 92 and 95).

      (5) Specify that the learning rules have not been observed. Though the learning rules are Hebbian, the details of the rules have not to my knowledge been observed. Would be worth mentioning as this is a sticking point of most related theories.

      We agree, and we do now explicitly write in the Discussion that the learning rule still awaits to be experimentally tested.

      6) Some relevant literature. Chalk et al. PNAS (2018) have explored the relationship between temporal predictive coding and Rao & Ballard predictive coding based on the parameters of the cost function. Harkin et al. eLife (2023) have shown that 'prospective coding' also takes place in the serotonergic system, while Kim ... Ma (2021) have put forward similar ideas for dopamine, both may participate in setting the cost function. Instantaneous voltage propagation is also a focus of Greedy et al. (2023). The authors cite Zenke et al. for spiking error propagation, but there are biological references to that end.

      Thanks much for these hints. We do now cite the book of Gerstner & Kistler on spiking neurons, and more specifically the spike-based approach for learning to represent signals (Brendel, .., Machens, Denève, PLoS CB, 2020). Otherwise, we had difficulties to incorporate the other literature that seems to us not directly related to our approach, even when related notions come up (like predictive coding and temporal processing in Chalk et al. (2018), where various temporal coding schemes coding efficiency is studied as a function of the signal-to-noise ratio), or the apical activities in Greedy et al. (2022), where bursting, multiplexing and synaptic facilitation arises). We found it would confuse more than it would help if we would cite these papers too (we do already cite 95 papers).

      (7) In the main text, theorem two is presented as proof without assumptions on the level of nudging, but the actual proof uses strong assumptions in that respect, relying on numerical ad hoc observations for the general case.

      Thanks for pointing this out. We agree it is a better style to state all the critical assumptions in Theorem itself, rather than deferring them to the Methods. We now state: “Then, for suitable top-down nudging, learning rates, and initial conditions, the ….weights …evolve such that…”.

      (8) In the discussion regarding error-backpropagation, it seems to me that it could be clarified that the current algorithm asks for a weight alignment between FF and FB matrices as well as between FB and interneuron circuit matrices. Whether all of these matrices can be learned together remains to be shown; neither Akrout, Kunin nor Max et al. have shown this explicitly. Particularly when there are other inputs to the apical dendrites from other areas.

      Yes, it is difficult to learn to align all in parallel. Nevertheless, our simulations in fact do align the lateral and vertical circuits, at is also claimed in Theorem 2. Yet, as specified in the theorem, “for suitable learning rates” (that were all the same, but were commonly reduced after some training time, as previously explained in the Methods, Details for Fig. 5).

      In the Discussion we now emphasis that, in general, simulating all the circuitries jointly from scratch in a single phase is tricky. We write:

      “A fundamental difficulty arises when the neuronal implementation of the Euler-Lagrange equations requires an additional microcircuit with its own dynamics. This is the case for the suggested microcircuit extracting the local errors. Formally, the representation of the apical feedback errors first needs to be learned before the errors can teach the feedforward synapses on the basal dendrites. We showed that this error learning can itself be formulated as minimizing an apical mismatch energy. What the lateral feedback through interneurons cannot explain away from the top-down feedback remains as apical prediction error.

      Ideally, while the network synapses targetting the basal tree are performing gradient descent on the global cost, the microcircuit synapses involved in the lateral feedback are performing gradient descent on local error functions, both at any moment in time.

      The simulations show that this intertwined system can in fact learn simultaneously with a common learning rate that is properly tuned. The cortical model network of inter- and pyramidal neurons learned to classify handwritten digits on the fly, with 10 digit samples presented per second. Yet, the overall learning is more robust if the error learning in the apical dendrites operates in phases without output teaching but with corresponding sensory activity, as may arise during sleep (see e.g. Deperrois et al., 2022 and 2023).”

      (9) The short-term depression model is assuming a slow type of short-term depression, not the fast types that are the focus of much recent experimental literature (like Campagnola et al. Science 2022).

      This assumption should be specified.

      Thanks for hinting to this literature that we were not aware of. We are now citing the releaseindependent plasticity (Campagnola et al. 2022) in the context of our synaptic depression model.

      (10) There seems to be a small notation issue: Eq 21 combines vectors of the size of the full network (bar{e}) and the size of the readout network (bar{e}star).

      Well, for notational convenience we set the target error to e*=0 for non-output neurons. This way we can write the total error for an arbitrary network neuron as the sum of the backpropagated error plus the putative target error (if the neuron is an output neuron). Otherwise we would always have to distinguish between network neuron that may be output neurons, and those that are not. We did say this in the main text, but are repeating it now again right after Eq. 21. -- Notations are often the result of a tradoff.

    1. Author response:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript presents a compelling model to explain the impact of mosaicism in preimplantation genetic testing for aneuploidies.

      Strengths:

      A new view of mosaicism is presented with a computational model, that brings new insights into an "old" debate in our field. It is a very well-written manuscript.

      Weaknesses:

      Although the manuscript is very well written, this is in a way that assumes that the reader has existing knowledge about specific terms and topics. This was apparent through a lack of definitions and minimal background/context to the aims and conclusions for some of the author's findings.

      There is a need for some examples to connect real evidence and scenarios from clinical reports with the model.

      We thank the reviewer for their assessment. Some background was condensed for space, and we wrote the manuscript to be understood by readers with existing reproductive genetics background. We will add more detail and explain terminology more clearly. There are a number of published case studies that can link real-life clinical data with the model’s findings. We will include a summary of them in the text.

      Reviewer #2 (Public Review):

      Summary:

      Although an oversimplification of the biological complexities, this modeling work does add, in a limited way, to the current knowledge on the theoretical difficulties of detecting mosaicism in human blastocysts from a single trophectoderm biopsy in PGT. However, many of the premises that the modeling was built on are theoretical and based on unproven biological and clinical assumptions that could yet lead to be untrue. Therefore, the work should be considered only as a simplified model that could assist in further understanding of the complexities of preimplantation embryo mosaicism, but assumptions of real-world application are, at this stage, premature and should not be considered as evidence in favour of any clinical strategies.

      Strengths:

      The work has presented an intriguing theoretical model for elaborating on the interpretation of complex and still unclear biological phenomena such as chromosomal mosaicism in preimplantation embryos.

      We thank the reviewer for this detailed review, and that they see the value of theoretical modelling. We agree that this model makes simplifications; we took this simplified approach to focus on the core contradiction between clinical experience and previous modelling. Expanding the model to consider additional aspects of balanced mitotic nondisjunctions and technical accuracy is something we want to address; we are discussing whether this is something that can be practically added to this manuscript, or will involve enough work that should be developed as a further study.

      Weaknesses:

      Lines 134-138: The spatial modeling of mitotic errors in the embryo was oversimplified in this manuscript. There is only limited (and non-comprehensive) evidence that meiotic errors leading to chromosome mosaicism arise from chromosome loss or gain only (e.g. anaphase lag). This work did not take into account the (more recognised) possibility of mitotic nondisjunction where following the event there would be clones of cells with either one more or one less of the same chromosome. Although addressed in the discussion (lines 572-574), not including this in the most basic of modeling is a significant oversight that, based on the simple likelihood, could significantly affect results.

      As above, we certainly plan to address this in future modelling; developing the model to account for this while also incorporating the issue of technical uncertainty in the state of each cell in the biopsy from sequencing.

      General comment: the premise of the manuscript is that an embryologist (embryology laboratory) is aware of and can accurately quantify the number of cells in a blastocyst or TE biopsy. The reality is that it is not possible to accurately do this without the destruction of the sample which is obviously not clinically applicable. Based on many assumptions the findings show that taking small biopsies poorly classifies mosaic embryos, which is not disputed. However, extrapolating this to the clinic and making suggestions to biopsy a certain amount of cells (lines 539-540) is careless and potentially harmful by suggesting the introduction of potential change in clinical practice without validation. Additionally, no embryologist in the field can tell how many cells are present in a clinical TE biopsy, making this suggestion even more impractical.

      We will revise this to make the technical limitations of clinical TE biopsies clearer.

      On a more general clinical consideration, the authors should acknowledge that when reporting findings of unproven clinical utility and unknown predictive values this inevitably results in negative consequences for infertile couples undergoing IVF. It is proven and established that when couples face the decision on how to manage a putative mosaicism finding, the vast majority decide on embryo disposal. It was recently reported in an ESHRE survey that about 75% of practitioners in the field consider discarding or donating to research embryos with reported mosaicism. A prospective clinical trial showed that about 30% live birth rate reduction can be expected if mosaic embryos are not considered (Capalbo et al., AJHG 2021). The real-world experience is that when mosaicism is reported, embryos with almost normal reproductive potential are discarded. The authors should be more careful with the clinical interpretation and translation of these theoretical findings.

      The clinical potential of mosaic embryos is much more nuanced than a simple ‘they should be discarded’ or ‘they should be treated like euploid embryos’. While the study mentioned by the reviewer (Capalbo et al., AJHG 2021) does indeed suggest that embryos with putative low level mosaicism have good potential, it also suggests that embryos with putative high level mosaicism are largely to be considered aneuploid and should therefore be discarded. Therefore, even the mentioned study supports a ‘ranking’ of embryos by their mosaic result. Furthermore, large controlled retrospective studies have indicated that even high level mosaic embryos have reproductive potential (Viotti Fertility & Sterility 2021 and Viotti F&S 2023). Recent case reports have shown that mosaicism can occasionally persist from embryo to late gestation and even birth, at times associating with negative medical findings. Therefore, while the true clinical potential of embryos classified as mosaic is still being defined, here we are merely suggesting that from a modelling standpoint, the features of mosaicism detected with PGT-A can help guide clinical decisions (complementing the observations reported in the clinical studies).

      There is a robust consensus within the field of clinical genetics and genomics regarding the necessity to exclusively report findings that possess well-established clinical validity and utility. This consensus is grounded in the imperative to mitigate misinterpretation and ineffective actions in patient care. However, the clinical framework delineated in this manuscript diverges from the prevailing consensus in clinical genetics. Clinical genetics and genomics prioritize the dissemination of findings that have undergone rigorous validation processes and have demonstrated clear clinical relevance and utility. This emphasis is crucial for ensuring accurate diagnosis, prognosis, and therapeutic decision-making in patient care. By adhering to established standards of evidence and clinical utility, healthcare providers can minimize the potential for misinterpretation and inappropriate interventions. The framework proposed in this manuscript appears to deviate from the established principles guiding clinical genetics practice. It is imperative for clinical frameworks to align closely with the consensus guidelines and recommendations set forth by professional organizations and regulatory bodies in the field. This alignment not only upholds the integrity and reliability of genetic testing and interpretation but also safeguards patient well-being and clinical outcomes.

      References:

      ACMG Board of Directors. (2015). Clinical utility of genetic and genomic services: a position statement of the American College of Medical Genetics and Genomics. Genetics in Medicine, 17(6), 505-507. https://doi.org/10.1038/gim.2014.194.

      Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., ... ACMG Laboratory Quality Assurance Committee. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine, 17(5), 405-424. https://doi.org/10.1038/gim.2015.30

      We will update where necessary to match these references.

      Line 61: "Self correction" - This terminology is unfortunately indiscriminately used in the field for PGT when referring to mosaicism and implies that the embryo can actively correct itself from a state of inherent abnormality. Apart from there being no evidence to suggest that there is an active process by which the embryo itself can correct chromosomal errors, most presumed euploid/aneuploid mosaic embryos will have been euploid zygotes and therefore "self-harm" may be a better explanation. True self-correction in the form of meiotic trisomy/monosomy rescue is of course theoretically possible but not at all clinically significant. The concept being conveyed in this part of the manuscript is not disputed but it is strongly suggested that the term "self correction" is not used in this context, nor in the rest of the manuscript, to prevent the perpetuation of misinformation in the field and instead use a better description.

      This is a good point. We have used ‘self correction’ as a shorthand, but the reality is more nuanced. It will often be a passive process in which aneuploid cell lineages fail to proliferate over time (‘aneuploidy depletion’). The idea of ‘self harm’ is interesting; aneuploidy arising from a healthy euploid embryo. We can also see a further situation where the gametes suffered damage (e.g. DNA fragmentation, unresolved crossovers, persistence of meiotic breaks) leading to mitotic errors. In that case, the embryo would suffer the consequences of harm in the gametes, and ‘aneuploidy rescue’ may be a useful term also. We will discuss this further and reword the terminology along these lines.

      Lines 69-73: The ability to quantify aneuploidy in known admixtures of aneuploid cells is indeed well established. However, the authors claim that the translation of this to embryo biopsy samples is inferred with some confidence and that if a biopsy shows an intermediate chromosome copy number (ICN), that the biopsy and the embryo are mosaic. There are no references provided here and indeed the only evidence in the literature relating to this is to the contrary. Multifocal biopsy studies have shown that an ICN result in a single biopsy is often not seen in other biopsies from the same embryo (Capalbo et al 2021; Kim et al., 2022; Girardi et al., 2023; Marin, Xu, and Treff 2021). Multifocal biopsies showing reciprocal gain and loss which would provide stronger validation for the presence of true mosaicism are also rare. In this work, the entire manuscript is based on the accuracy of ICN in a biopsy being reflective of mosaicism in the embryo. The evidence however points to a large proportion of ICN detected in embryo biopsy potentially being technical artifacts (misdiagnosing both constitutionally normal and abnormal (meiotic aneuploid) embryos as mosaic. Therefore, although results from the modelling provide insight into theoretical results, these can not be used to inform clinical decision-making at all.

      We thank the reviewer for raising this important conceptual point, which needs to be addressed. The fact that mosaicism is often not observed in serial biopsies of the same embryo is precisely an inherent feature of mosaicism and is an invalid argument to discount the original diagnosis as false. The detection of ICN is not trivial and certain PGT-A platforms might not have the capability to discern noise from true ICN, hence the need for proper validation of the technology. The most stringent validation method for mosaicism detection remains the admixture experiment, such that when ICN patterns are detected the most obvious conclusion is that the biopsy contained a mosaic mix of cells. We aim to add wording regarding these points in the manuscript.

      Lines 87-89: The authors make the claim that emerging evidence is suggestive that the majority of embryos are mosaic to some degree. If in fact, mosaicism is the norm, the clinical importance may be limited.

      If the majority of embryos are mosaic to some degree, it is important to understand the impacts that this may have on PGT-A biopsies and how informative such biopsies may be. Returning to the point the reviewer made above about mitotic aneuploidies as an important consideration: a mitotic nondisjunction at the first cleavage would result in a embryo that was entirely aneuploid. A mitotic nondisjunction occurring at the second cleavage would result in an embryo with 50% aneuploid cells, at the third cleavage, 25% aneuploid cells. If these aneuploid cells fail to proliferate, or are removed (either actively or passively), the level of aneuploidy will fall over time. While mosaicism is a binary (an embryo is or is not a mosaic of karyotypes), even if most embryos are mosaic, the clinical importance will depend on the level of aneuploidy.

      Line 102-103: The statement that data shows that the live birth rate per ET is generally lower in mosaic embryos than euploid embryos is from retrospective cohort studies that suffer from significant selection bias. The authors have ignored non-selection study results (Capalbo et al, ajhg 2021) that suggest that putative mosaicism has limited predictive value when assessed prospectively and blinded.

      We will add the referenced multifocal biopsy study, but in contrast to the reviewer we see the data it contains as supporting our position in this paper. Capalbo et al. performed rebiopsies of trophectoderm and a biopsy of inner cell mass and found that high level mosaic or aneuploid trophectoderm tended to correlate with abnormal karyotypes in the inner cell mass while low level mosaics correlated with a normal inner cell mass. This supports our point that measuring levels of aneuploidy in the trophectoderm is relevant, and that this gives useful information for ranking embryos.

      Lines 94-98: The authors have misrepresented the works they have presented as evidence for biopsy result accuracy (Kim et al., 2023; Victor et al 2019; Capalbo et al., 2021; Girardi et al., 2023, and any others). These studies show that a mosaic biopsy is not representative of the whole embryo and can actually be from embryos where the remainder of the embryo shows no evidence of mosaicism. There is also a missing key reference of Capalbo et al, AJHG 2021, and Girardi et al., HR 2023 where multifocal biopsies were taken.

      As above, we will add more information on these multifocal biopsy studies; we believe these studies also support our position: that individual biopsies are not predictive of aneuploidy level in an embryo. If mosaicism is detected in the biopsy, then the embryo is mosaic, but if the remainder of the embryo is euploid then that single biopsy was not an accurate representation of the embryo. This could also apply in reverse - if mosaicism is not detected in the biopsy, it does not mean there is no mosaicism in the embryo, only that mosaicism could not be identified.

      Lines 371-372: "Selecting the embryo with the lowest number of aneuploid cells in the biopsy for transfer is still the most sensible decision". Where is the evidence for this other than the modeling which is affected by oversimplification and unproven assumptions? Although the statement seems logical at face value, there is no concrete evidence that the proportion of aneuploid cells within a biopsy is valuable for clinical outcomes, especially when co-evaluated with other more relevant clinical information.

      We made this statement as part of a thought experiment to explain the difference between the concepts of absolute measurements versus embryo ranking. This section is not a result of the model, or clinical advice; it is a statement that in the specific example embryos given, the embryo with the fewest aneuploid cells in the biopsy would still be the embryo with the fewest aneuploid cells overall, and thus transferring this embryo (in the absence of any other differences of embryo quality) would remain sensible.

      Lines 431-463: In this section, the authors discuss clinical outcome data from the transfer of putative mosaic embryos and make conclusions about the relationship between ICN level in biopsy and successful pregnancy outcomes. The retrospective and selective nature of the data used in forming the results has the potential to lead to incorrect conclusions when applied to prospective unselected data.

      We believe the clinical data is a useful biological reality check, and we are discussing how to integrate it better with the modelling.

      Reviewer #3 (Public Review):

      Unfortunately, this study fails to incorporate the most important variable impacting the ability to predict mosaicism, the accuracy of the test. The fact is that most embryos diagnosed as mosaic are not mosaic. There may be 4 cases out of thousands and thousands of transfers where a confirmation was made. Mosaicism has become a category of diagnosis in which embryos with noisy NGS profiles are placed. With VeriSeq NGS it is not possible to routinely distinguish true mosaicism from noise. An analysis of NGS noise levels (MAPD) versus the rate of mosaics by clinic using the registry will likely demonstrate this is the case. Without accounting for the considerable inaccuracy of the method of testing the proposed modeling is meaningless.

      We disagree with the reviewer that the modelling is meaningless; we disagree that mosaicism is rare (see our other points). However, if we grant that mosaicism is rare, that almost all embryos are euploid or aneuploid, and that technical noise is the primary factor generating intermediate copy number values, then it is still important to understand how to interpret such intermediate values. Low-level mosaics would more likely represent miscalled euploid embryos, and high-level mosaics would more likely represent miscalled aneuploid embryos. We demonstrate that ranking on these intermediate values correlates with implantation rates and live birth rates, supporting their use. We do agree that technical accuracy of the NGS is an important consideration, and we will be incorporating this into our modelling in the future.

      Recent data using more accurate methods of identifying mosaicism indicate that the prevalence of true preimplantation embryonic mosaicism is only 2%, which is also consistent with findings made post-implantation. This model fails to account for the possibility that, because so few embryos are actually mosaic, there is actually no relevance to clinical care whatsoever. In fact, differences in clinical outcomes of embryos designated as mosaic could be entirely attributed to poor embryo quality resulting in noise levels that make NGS results fall into the "mosaic" category.

      As we also wrote in the point above, we disagree; it is possible that a euploid embryo may be misinterpreted as a mosaic. It is also possible that an aneuploid embryo is misinterpreted as a mosaic. Whether the intermediate copy number values arise through biological or technical reasons, they contain information that is useful to decisions on whether to transfer. We also note a recent paper that performed single-cell dissociation of trophectoderm versus inner cell mass which found that mosaicism in human embryos is very common (Chavli et al, 2024, DOI:10.1172/JCI174483).

      Additional comments:

      “Indeed, as more data emerges, it appears that the majority of embryos from both healthy and infertile couples are mosaic to some degree (Coticchio et al., 2021; Griffin et al., 2022).”

      This statement should be softened as all embryos will be considered mosaic when a method with a 10% false positive rate is applied to 10 more parts of the same embryo. The distinction between artifact and true mosaicism cannot be made with nearly all current methods of testing. When virtually no embryos display uniform aneuploidy in a rebiopsy study, there should be great concern over the accuracy of the testing used. The vast majority of aneuploidy is meiotic in origin.

      We note that reviewer 2 wrote that mitotic aneuploidy was the key concern, whereas reviewer 3 states meiotic aneuploidy is more common; we argue that both are relevant; a recent study by McCoy et al, 2023 (DOI:10.1186/s13073-023-01231-1) found that both drive arrest of human IVF embryos.

      “Experimental data provides strong evidence that, for the most part, the biopsy result obtained accurately represents the chromosome constitution of the rest of the embryo (Kim 96 et al., 2022; Navratil et al., 2020; Victor et al., 2019).”

      This statement is incorrect given published systematic review of the literature indicates a 10% false positive rate based on rebiopsy results.

      This shows that accurately classifying a mosaic embryo based on a single biopsy is not robust.

      This is exactly why the practice of designating embryo mosaics with intermediate copy numbers should not exist.

      We agree that accurately classifying a mosaic embryo based on a single biopsy is not robust. That is one of the main messages of this paper. What we show here is that biopsies from a mosaic embryo are indeed likely to disagree with each other - but we find that there is still enough information at a population level for this to be an indicator or embryo outcomes. We have not yet performed modelling to explore the effect of technical error, so we will not speculate on the impact, but we reiterate a point made earlier: the most stringent validation method for mosaicism detection remains the admixture experiment, such that when intermediate copy number patterns are detected the most obvious conclusion is that the biopsy contained a mosaic mix of cells.

    1. Author response:

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

      eLife assessment

      The manuscript by Jingsong Zhou and colleagues tries to uncover the reasons for the resistance of extraocular muscles (EOMs) to degenerative changes induced by amyotrophic lateral sclerosis (ALS). The findings of the study offer valuable information that EOMs are spared in ALS because they produce protective factors for the NMJ and, more specifically, factors secreted by EOM-derived satellite cells. While most of the experimental approaches are convincing, the use of sodium butyrate (NaBu) in this study needs further investigation, as NaBu might have a variety of biological effects. Overall, this work may help develop future therapeutic interventions for patients with ALS.

      We agree with the editor that NaBu have a variety of biological effects that require further investigation. Our team previously have explored the effect of NaBu treatment on intestinal microbiota and intestinal epithelial permeability (DOI: 10.1016/j.clinthera.2016.12.014), on the mitochondrial respiratory function of NSC-34 motor neuron cell line overexpressing hSOD1G93A (DOI: 10.3390/biom12020333) and on the mitochondrial function of skeletal muscle myofibers of G93A mice (DOI: 10.3390/ijms22147412). Other research teams have also explored the role of NaBu (or HDAC inhibition) in neuronal survival and axonal transport (DOIs: 10.1073/pnas.0907935106; 10.1038/s41467-017-00911-y; 10.15252/embj.2020106177; 10.1093/hmg/ddt028).

      Since the theme of this manuscript is the transcriptomic characteristics of EOM SCs, to include data of how NaBu affect cellular/molecular processes of other tissues will somewhat deviate from the theme. It would be more appropriate to develop a separate manuscript focusing on other tissues.

      We appreciate the feedback from the Editors and reviewers. We realized that our previous description on butyrate’s beneficial role might be overstated in the Abstract Section. We have made two changes to avoid potential overstatement of our finding: (1) We modified the Abstract to state that “the NaBu-induced transcriptomic changes resembling the patterns of EOM SCs “may contribute to” (instead of “underlie”) the beneficial effects observed in G93A mice” (Page 1, Line 29); (2) We have edited the corresponding paragraph in the Discussion section to emphasize that the effect of NaBu treatment is multi-faceted (Page 11, Line 459-461).

      Recommendations for the authors:

      Reviewer #3 (Recommendations For The Authors):

      line 388-389. The sentence has been corrected but is still not clear. What do the authors mean by ".....resulting in higher proportion of COX-deficient myofibers than other muscles». What other muscles do they refer to?

      Other muscles refer to muscles whose stem cells remain dormant under physiological conditions (uninjured, innervated), such as EDL. We have edited the sentence accordingly. (Page 10, Line 431-432)

      In reference to the results shown in Fig. 2, 7, 8 and 9. Since the experimenters were not blinded, this should be explicitly stated in the Methods section.

      We have added the disclaimer in the current “Data analysis and statistics” section in Methods as follows: “The experimenters were not blinded to the samples in data collection and analysis.” (Page 15, Line 636)

      Figure 7 C has been amended but now the inserted ANOVA values interfere with the correct visualization of Fig. 7D, can panels D be moved down so that they are better separated from panels in Fig. 7C

      Thanks for the comment and we have edited Figure 7 accordingly.

      Reviewer #4 (Recommendations For The Authors):

      The authors have revised the manuscript per the reviewer's comments in this study. While most of the concerns were addressed, a few concerns remain.

      The molecular basis of how AAV-mediated delivery of Cxcl12 improves the phenotype of satellite cells is still unclear.

      Thanks for the comment. As one of the earliest discovered chemokines, the chemotactic role of Cxcl12-Cxcr4 axis on cells and cellular processes (such as axons) has been comprehensively investigated by different functional assays from overexpression to protein application to inhibitor application to knockdown by shRNAs in different types of tissues. To list a few examples, the establishment of the correct routing trajectories of mammalian motor axons and oculomotor axons during embryonic development (DOIs: 10.1016/j.neuron.2005.08.011; 10.1167/iovs.18-25190). The regeneration of injured motor axon terminals guided by terminal Schwann cells in adult mice (DOI: 10.15252/emmm.201607257). The migration of neural crest cells to sympathetic ganglia in the formation of sympathetic nerve system during embryogenesis (DOI: 10.1523/JNEUROSCI.0892-10.2010). The migration of myoblasts in the process of fusion into myotubes (DOIs: 10.1242/jcs.066241; 10.1111/boc.201200022; 10.1074/jbc.M706730200).

      Because the existence of so many detailed mechanistic studies, our goal for this manuscript is not to identify a novel mechanism of how Cxcl12-mediated chemotaxis is achieved. Rather, we used it as one of the proof-of-concept mechanisms contributing to the resistance of EOMs against ALS and benefits of NaBu treatment. Certainly, it is not the sole mechanism.

      To address the reviewer’s concern, we have expanded discussion about the previous studies regarding the chemotactic effect of Cxcl12 in the discussion section. (Page 10, Line 435-436, Page 11, Line 445-446)

      The NaBu experiments may need additional support from other approaches. NaBu effects may not be directly related to satellite cells or muscle cells. Thus, the animal experiment results need to be carefully interpreted.

      We agree that NaBu have a variety of biological effects that require further investigation. Our team previously have explored the effect of NaBu treatment on intestinal microbiota and intestinal epithelial permeability (DOI: 10.1016/j.clinthera.2016.12.014), on the mitochondrial respiratory function of NSC-34 motor neuron cell line overexpressing hSOD1G93A (DOI: 10.3390/biom12020333) and on the mitochondrial function of skeletal muscle myofibers of G93A mice (DOI: 10.3390/ijms22147412). Other research teams have also explored the role of NaBu (or HDAC inhibition) in neuronal survival and axonal transport (DOIs: 10.1073/pnas.0907935106; 10.1038/s41467-017-00911-y; 10.15252/embj.2020106177; 10.1093/hmg/ddt028).

      Since the theme of this manuscript is the transcriptomic characteristics of EOM SCs, to include data of how NaBu affect cellular/molecular processes of other tissues will somewhat deviate from the theme. It would be more appropriate to develop a separate manuscript specifically addressing the impact of NaBu on other tissues.

      We appreciate the feedback from the reviewers. We realized that our previous description on butyrate’s beneficial role might be overstated in the Abstract Section. In response, we have made two changes to avoid potential overstatement of our finding: (1) We modified the Abstract to state that “the NaBu-induced transcriptomic changes resembling the patterns of EOM SCs “may contribute to” (instead of “underlie”) the beneficial effects observed in G93A mice” (Page 1, Line 29); (2) We edited the corresponding paragraph in the Discussion section to emphasize that the effect of NaBu treatment is multi-faceted (Page 11, Line 459-461).

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Ngo et al. report a peculiar effect where a single base mismatch (CC) can enhance the mechanical stability of a nucleosome. In previous studies, the same group used a similar state-of-the-art fluorescence-force assay to study the unwrapping dynamics of 601-DNA from the nucleosome and observed that force-induced unwrapping happens more slowly for DNA that is more bendable because of changes in sequence or chemical modification. This manuscript appears to be a sequel to this line of projects, where the effect of CC is tested. The authors confirmed that CC is the most flexible mismatch using the FRET-based cyclization assay and found that unwrapping becomes slower when CC is introduced at three different positions in the 601 sequence. The CC mismatch only affects the local unwrapping dynamics of the outer turn of nucleosomal DNA.

      Strengths:

      These results are in good agreement with the previously established correlation between DNA bendability and nucleosome mechanical stability by the same group. This well-executed, technically sound, and well-written experimental study contains novel nucleosome unwrapping data specific to the CC mismatch and 601 sequence, the cyclizability of DNA containing all base pair mismatches, and the unwrapping of 601-DNA from xenophus and yeast histones. Overall, this work will be received with great interest by the biophysics community and is definitely worth attention.

      Weaknesses:

      The scope and impact of this study are somewhat limited due to the lack of sequence variation. Whether the conclusion from this study can be generalized to other sequences and other bendability-enhancing mismatches needs further investigation.

      Major questions:

      (1) As pointed out by the authors, the FRET signal is not sensitive to nucleosome position; therefore, the increasing unwrapping force in the presence of CC can be interpreted as the repositioning of the nucleosome upon perturbation. It is then also possible that CC-containing DNA is not positioned exactly the same as normal DNA from the start upon nucleosome assembly, leading to different unwrapping trajectories. What is the experimental evidence that supports identical positioning of the nucleosomes before the first stretch?

      We added the following and refer to our recent publication1 to address this question.

      “This is consistent with a previous single nucleotide resolution mapping of dyad position from of a library of mismatches in all possible positions along the 601 sequence or a budding yeast native sequence which showed that a single mismatch (A-A or T-T) does not affect the nucleosome position27.”

      (2) The authors chose a constant stretching rate in this study. Can the authors provide a more detailed explanation or rationale for why this rate was chosen? At this rate, the authors found hysteresis, which indicates that stretching is faster than quasi-static. But it must have been slow and weak enough to allow for reversible unwrapping and wrapping of a CC-containing DNA stretch longer than one helical turn. Otherwise, such a strong effect of CC at a single location would not be seen. I am also curious about the biological relevance of the magnitude of the force. Can such force arise during nucleosome assembly in vivo?

      To address the comment about the magnitude of force, we added the following paragraph to Introduction. “RNA polymerase II can initiate transcription at 4 pN of hindering force2 and its elongation activity continues until it stalls at ~ 10 pN of hindering force3,4. Therefore, the transcription machinery can generate picoNewtons of force on chromatin as long as both the machinery and the chromatin segment in contact are tethered to stationary objects in the nucleus. Another class of motor protein, chromatin remodeling enzymes, was also shown to induce processive and directional sliding of single nucleosomes when the DNA is under similar amount of tension (~ 5 pN)5. Therefore, measurements of nucleosomes at a few pN of force will expand our knowledge of the physiology roles of nucleosome structure and dynamics.”

      To address the comment about the stretching rate, we added the following to Results. We note that the physiological loading rate has been challenging to determine for any biomolecular interactions, and the only quantitative measurement we are aware of is that of an integrin that we are citing.

      “The force increases nonlinearly and the loading rate, i.e. the rate at which the force increases, was approximately in the range of 0.2 pN/s to 6 pN/s, similar to the cellular loading rates for a mechanosensitive membrane receptor6.”

      (3) In this study, the CC mismatch is the only change made to the 601 sequence. For readers to truly appreciate its unique effect on unwrapping dynamics as a base pair defect, it would be nice to include the baseline effects of other minor changes to the sequence. For example, how robust is the unwrapping force or dynamics against a single-bp change (e.g., AT to GC) at the three chosen positions?

      Unfortunately, we are unable to perform the suggested unwrapping experiment in a timely manner because the instrument has been disassembled during our recent move. However, we previously performed unwrapping experiments not only as a function of sequence but also as a function of cytosine modification and showed that we can detect even more subtle effects7,8. In addition, please note that we are not claiming that simply changing basepair at the chosen sites changes the mechanical stability of a nucleosome so we do not believe the requested experiment is necessary.

      (4) The last section introduces yeast histones. Based on the theme of the paper, I was expecting to see how the effect of CC is or is not preserved with a different histone source. Instead, the experiment only focuses on differences in the unwrapping dynamics. Although the data presented are important, it is not clear how they fit or support the narrative of the paper without the effect of CC.

      We apologize for giving the reviewer a wrong impression. We included the data because we believe that information on how the histone core can determine the translation of DNA mechanics into nucleosome mechanical stability will be of interest to the readers of this manuscript. We now mention explicitly that the observation was made using intact DNA, i.e. no mismatch, in the abstract and elsewhere.

      (5) It is stated that tRNA was excluded in experiments with yeast-expressed nucleosomes. What is the reason for excluding it for yeast nucleosomes? Did the authors rule out the possibility that tRNA causes the measured difference between the two nucleosome types?

      We normally include tRNA because we found that it reduces sticking of beads to the surface over several hours of experiments. In yeast nucleosomes, we found that tRNA causes the nucleosome to disassemble. Therefore, we did not include tRNA in yeast nucleosome experiments. We now mention this in Methods as reproduced below.

      “tRNA, which we normally include to reduce sticking of beads to the surface over the hours of single molecule experiments in a sealed chamber, was excluded in experiments with yeastexpressed nucleosomes because tRNA induced disassembly of nucleosomes assembled using yeast histones.”

      We cannot not formally rule out the possibility that tRNA causes the measured difference between Xenopus - vs Yeast- nucleosomes. However, we have shown in our previous publication7 that the asymmetric unwrapping in Xenopus nucleosomes was modulated by the DNA sequence. When we swapped the sequence of the inner turn between the two sides, while tRNA was included in all experiments, we observed stochastic unwrapping instead. As part of our response to another reviewer’s comments, we also added the following on the relevant differences between the species in Discussion.

      “The crystal structure of the yeast nucleosome suggests that yeast nucleosome architecture is subtly destabilized in comparison with nucleosomes from higher eukaryotes9. Yeast histone protein sequences are not well conserved relative to vertebrate histones (H2A, 77%; H2B, 73%; H3, 90%; H4, 92% identities), and this divergence likely contributes to differences in nucleosome stability. Substitution of three residues in yeast H3 a3-helix (Q120, K121, K125) very near the nucleosome dyad with corresponding human H3.1/H3.3 residues (QK…K replaced with MP…Q) caused severe growth defects, elevated nuclease sensitivity, reduced nucleosome positioning and nucleosome relocation to preferred locations predicted by DNA sequence alone 10. The yeast histone octamer harboring wild type H3 may be less capable of wrapping DNA over the histone core, leading to reduced resistance to the unwrapping force for the more flexible half of the 601positioning sequence.”

      Reviewer #2 (Public Review):

      Summary:

      Mismatches occur as a result of DNA polymerase errors, chemical modification of nucleotides, during homologous recombination between near-identical partners, as well as during gene editing on chromosomal DNA. Under some circumstances, such mismatches may be incorporated into nucleosomes but their impact on nucleosome structure and stability is not known. The authors use the well-defined 601 nucleosome positioning sequence to assemble nucleosomes with histones on perfectly matched dsDNA as well as on ds DNA with defined mismatches at three nucleosomal positions. They use the R18, R39, and R56 positions situated in the middle of the outer turn, at the junction between the outer turn and inner turn, and in the middle of the inner turn, respectively. Most experiments are carried out with CC mismatches and Xenopus histones. Unwrapping of the outer DNA turn is monitored by singlemolecule FRET in which the Cy3 donor is incorporated on the 68th nucleotide from the 5'-end of the top strand and the Cy5 acceptor is attached to the 7th nucleotide from the 5' end of the bottom strand. Force is applied to the nucleosomal DNA as FRET is monitored to assess nucleosome unwrapping. The results show that a CC mismatch enhances nucleosome mechanical stability. Interestingly, yeast and Xenopus histones show different behaviors in this assay. The authors use FRET to measure the cyclization of the dsDNA substrates to test the hypothesis that mismatches enhance the flexibility of the 601 dsDNA fragment and find that CC, CA, CT, TT, and AA mismatches decrease looping time, whereas GA, GG, and GT mismatches had little to no effect. These effects correlate with the results from DNA buckling assays reported by Euler's group (NAR 41, 2013) using the same mismatches as an orthogonal way to measure DNA kinking. The authors discuss that substitution rates are higher towards the middle of the nucleosome, suggesting that mismatches/DNA damage at this position are less accessible for repair, consistent with the nucleosome stability results.

      Strengths:

      The single-molecule data show clear and consistent effects of mismatches on nucleosome stability and DNA persistence length.

      Weaknesses:

      It is unclear in the looping assay how the cyclization rate relates to the reporting looping time. The biological significance and implications such as the effect on mismatch repair or nucleosome remodelers remain untested. It is unclear whether the mutational pattern reflects the behavior of the different mismatches. Such a correlation could strengthen the argument that the observed effects are relevant for mutagenesis.

      Reviewer #3 (Public Review):

      Summary:

      The mechanical properties of DNA wrapped in nucleosomes affect the stability of nucleosomes and may play a role in the regulation of DNA accessibility in eukaryotes. In this manuscript, Ngo and coworkers study how the stability of a nucleosome is affected by the introduction of a CC mismatched base pair, which has been reported to increase the flexibility of DNA. Previously, the group has used a sophisticated combination of single-molecule FRET and force spectroscopy with an optical trap to show that the more flexible half of a 601 DNA segment provides for more stable wrapping as compared to the other half. Here, it is confirmed with a single-molecule cyclization essay that the introduction of a CC mismatch increases the flexibility of a DNA fragment. Consistent with the previous interpretation, it also increased the unwrapping force for the half of the 601 segment in which the CC mismatch was introduced, as measured with single-molecule FRET and force spectroscopy. Enhanced stability was found up to 56 bp into the nucleosome. The intricate role of mechanical stability of nucleosomes was further investigated by comparing force-induced unwrapping profiles of yeast and Xenopus histones. Intriguingly, asymmetric unwrapping was more pronounced for yeast histones.

      Strengths:

      (1) High-quality single-molecule data.

      (2) Novel mechanism, potentially explaining the increased prominence of mutations near the dyads of nucleosomes.

      (3) A clear mechanistic explanation of how mismatches affect nucleosome stability.

      Weaknesses:

      (1) Disconnect between mismatches in nucleosomes and measurements comparing Xenopus and yeast nucleosome stability.

      (2) Convoluted data in cyclization experiments concerning the phasing of mismatches and biotin site. ---

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific comments:

      In Figure 1 legend, "the black diamonds on the DNA bends represent the mismatch position with R18 and R39 on minor grooves and R56 on a major groove." Minor and major grooves should be phrased as histone-facing minor and major grooves.

      We fixed the problem.

      In Materials and Methods, the sentence that describes the stretching rate cites reference 1, which does not seem to be relevant.

      We fixed the problem.

      Reviewer #2 (Recommendations For The Authors):

      (1) In the introduction, the authors should also discuss the context of mismatches occurring during homologous recombination in meiosis or somatic cells in non-allelic recombination between near identical repeats.

      Introduction now has the following.

      “DNA base-base mismatches are generated by nucleotide misincorporation during DNA synthesis, meiotic recombination, somatic recombination between nearly identical repeats, or chemical modification such as hydrolytic deamination of cytosine.”

      (2) Generally, it seems counter-intuitive in terms of biology that mismatches containing nucleosomes are more stable, as mismatches require repair and/or detection for heteroduplex rejection during recombination. Some discussion of this apparent paradox should be added.

      To address this comment, we added the following to Discussion.

      “The higher frequency of substitutions in the nucleosomal DNA may be attributed to the difficulty of accessing the extra-stable nucleosomes. We also note that even without an enhanced stability, a mismatch within a nucleosome would be more difficult to detect for mismatch repair machineries compared to a mismatch in a non-nucleosomal DNA. Because mismatch repair machineries accompany the replisome, most of nascent mismatches may be detected for repair before nucleosome deposition. Therefore, the decrease in accessibility predicted based on our data here may be important only in rare cases a mismatch is not detected prior to the deposition of a nucleosome on the nascent DNA or in cases where a mismatch is generated via a non-replicative mechanism.”

      (3) The authors discuss that the substitution rate is higher while the indel (insertion and deletion) rate is lower nearer the center of a positioned nucleosome. Are the differences between individual mismatches reported in Figure 6 reflected in the mutagenic profile?

      We cannot currently compare them because the mutagenic profile even when it is available is a complex convolution of mismatch generation, mismatch repair and selection. Mismatch generation occurs through several different processes and how they are affected by nucleosomes and their mismatch type and sequence context is unknown. Mismatch repair process itself depends on mismatch type and sequence context as recently shown by a high throughput in vivo study11. And because the population genetics does not simply reflect de novo mutation profiles due to selection, comparison between mismatch-induced DNA mechanical changes and mutagenic profiles is further complicated. We added the following to the revision.

      “If and how the mismatch type-dependent DNA mechanics affects the sequence-dependent mismatch repair efficiency in vivo, as recently determined in a high through study in E. coli11, remains to be investigated. Comparison of mismatch-type dependent DNA mechanics to population genetics data is challenging because mutation profiles reflect a combined outcome of mismatch-generation, mismatch repair and selection in addition to other mutational processes.”

      (4) The looping assay should be explained better, especially how the cyclization rate is related to the reported looping time.

      We modified Figure 5 to include examples of looping time determination through fitting of the looped fraction vs time, and added the following to the figure caption.

      “To calculate the looping time, the fraction of looped molecules (high FRET) as a function of time is fitted to an exponential function, 𝑒−𝑡⁄(𝑙𝑜𝑜𝑝𝑖𝑛𝑔 𝑡𝑖𝑚𝑒) (right panel for one run of experiments).

      Furthermore, we added the following sentence to Results.

      “The rate of loop formation, which is the inverse of looping time determined from an exponential fitting of loop fraction vs time, was used as a measure of apparent DNA flexibility influenced by a mismatch 12,13.”

      *Reviewer #3 (Recommendations For The Authors):

      I have some concerns that, when addressed upon revision, would improve the manuscript:

      (1) Page 6 and Supplementary Figure S1C: Though the FRET levels are the same for all nucleosomes, the distribution between the two levels is not. The nucleosomes with CC mismatches appear to have a larger fraction in the low-FRET population. This seems to contradict the higher mechanical stability. A comment on this should clarify it, or make this conundrum explicit.

      Thank you for the comment. The low FRET population also includes the nucleosomes that do not have an active acceptor the fraction of which varies between preparations. We now note this in the supplementary figure caption.

      (2) It is intriguing that a more stable nucleosome forms after several pulling cycles and it is argued that this might be due to shifting of the nucleosome. This seems reasonable and has important consequences both for the interpretation of the current experimental data and for the general mechanisms involved in nucleosome maintenance and remodeling. It is puzzling though how this would work mechanistically since it only seems to happen when nucleosomes are half-wrapped and when the unwrapped half contains the mismatch. From the previous work of the group and the current manuscript, it seems that shift does not occur in DNA without mismatches (Correct?). Does shifting happen for the 601-R18 and 601-R56 nucleosomes as well?

      The mismatch-containing half is the half that is mechanically less stable in an intact, mismatch-free 601 nucleosome. So indeed, that is the half that is unwrapped in an intact nucleosome. But because the introduction of mismatch makes that half more mechanically stable, it can stay wrapped until higher forces, and the resulting structural distortion may cause the shift although we acknowledge that this interpretation remains speculative. Shifting occurs for all three constructs with a mismatch but not for the intact nucleosome without a mismatch.

      (3) Could the shifting be related to the differences in sub-population distribution observed in Supplementary Figure S1C?

      /See our response to comment (1) above.

      (4) The paper would have more impact if the mechanism of possible shifting could be clarified. This can be done experimentally with a fluorescent histone, as suggested in the manuscript. But having a FRET pair on positions in the DNA that would shift to closer proximity upon shifting, either at the ED2 or at the ED1 site will also work, is in line with the current experiments and seems feasible.

      We revised the text as follows in order not to exclude labeling configurations with both fluorophores on the DNA while reporting on the shift. We are also happy to add an appropriate reference if the reviewer can help us identify an existing study that measured dyad position shifts through such a labeling configuration.

      “However, since the FRET values in our DNA construct are not sensitive to the nucleosome position, further experiments with fluorophores conjugated to strategic positions that allow discrimination between different dyad positions14 will be required to test this hypothesis.”

      (5) Figures 5 and 6: To appreciate the quality of the data, state the number of molecules that contributed to the cyclization essay, or better, share a figure of the number of looped molecules as a function of time as supplementary data.

      We added the requested figures to Figure 5 and a new supplementary Figure 2, and added the following to Methods.

      “Approximately 2500 – 3500 molecules were quantified at each timestamp during the experiment, and three independent experiments were performed for each sequence (Supplemental Figure S2).”

      (6) Page 8/9: A control is added to confirm that the phasing of the biotin relative to the end affects the observed cyclization rate. However, the mismatch sites were chosen such that they included 5 bp phase shifts. This convolutes the outcomes, as the direction of flexibility due to the phasing of the mismatch relative to the biotin may also influence the rate. Was this checked?

      We would like to clarify that the phasing of the biotin is not so much as with respect to the end, as it is with respect to the full molecule. Static curvature and poloidal angle associated with the DNA molecule (which is something that is ultimately determined by the full chemical composition of the molecule, including its sequence and the mismatch) could make the molecule prefer a looped configuration where the biotin points towards the “inside” of the molecule. Such a configuration would be sterically unfavoured during the single molecule looping reaction where the biotin is attached to a surface via avidin. However, if the biotin is moved by half the helical repeat (or an off multiple of half the helical repeat, essentially 16 nt as done in the manuscript), it would now point to the “outside” of the molecule. Therefore, to make sure that the difference between the looping rates of any two DNA constructs (say the 601-RH and 601-R18-RH) is a better reflection of differences in dynamic flexibility, we ensure that the difference persists even when the biotin is moved by an odd multiple of half the helical repeat. We revised the section as follows.

      “For example, moving the location of the biotin tether by half the helical repeat (~ 5 bp) can lead to a large change in cyclization rate15, likely due to the preferred poloidal angle of a given DNA16 that determines whether the biotin is facing towards the inside of the circularized DNA, thereby hindering cyclization due to steric hindrance caused by surface tethering.”

      (7) Page 9/10: The comparison of yeast vs Xenopus is interesting, albeit a bit disconnected. Since the single-molecule statistics are relatively small, did the nucleosomes show similar bulk FRET distributions, or did they also show a shift in FRET levels?

      We included the data because we believe that information on how the histone core can determine the translation of DNA mechanics into nucleosome mechanical stability will be of interest to the readers of this manuscript. The FRET values were similarly distributed.

      (8) The discussion calls for a more detailed analysis of the structural differences of the histones of the two species to rationalize the observed asymmetry in flexibility dependence: why would yeast nucleosomes be less sensitive to sequence asymmetries?

      We added the following to Discussion to address this comment.

      “The crystal structure of the yeast nucleosome suggests that yeast nucleosome architecture is subtly destabilized in comparison with nucleosomes from higher eukaryotes9. Yeast histone protein sequences are not well conserved relative to vertebrate histones (H2A, 77%; H2B, 73%; H3, 90%; H4, 92% identities), and this divergence likely contributes to differences in nucleosome stability. Substitution of three residues in yeast H3 3-helix (Q120, K121, K125) very near the nucleosome dyad with corresponding human H3.1/H3.3 residues (QK…K replaced with MP…Q) caused severe growth defects, elevated nuclease sensitivity, reduced nucleosome positioning and nucleosome relocation to preferred locations predicted by DNA sequence alone 10. The yeast histone octamer harboring wild type H3 may be less capable of wrapping DNA over the histone core, leading to reduced resistance to the unwrapping force for the more flexible half of the 601positioning sequence.”

      (9) It would also be interesting if the increased stability due to the introduction of mismatches observed on Xenopus nucleosomes holds in yeast. Or does the reduced stability remove this effect? This is relevant to substantiate the broad claims in the context of evolution and cancer that are discussed in the manuscript.

      Unfortunately, we are unable to perform the suggested unwrapping experiment in a timely manner because the instrument has been disassembled during our recent move. However, in terms of cancer relevance, our mismatch dependence experiments were performed using vertebrate nucleosomes (Xenopus) so repeating this for yeast nucleosomes would not provide relevant information.

      Minor comments:

      (1) Supplementary Figure S1 misses the label '(C)' in its caption.

      We fixed it.

      (2) The supplementary data sequences for the fleezer measurements contain entrees 'R39 construct' and miss the positions of the Cy3 and Cy labels; the color code (levels of grey) is not explained.

      We fixed the labeling mistake and added detailed annotations of the highlighted features.

      References

      (1) Park, S., Brandani, G.B., Ha, T. & Bowman, G.D. Bi-directional nucleosome sliding by the Chd1 chromatin remodeler integrates intrinsic sequence-dependent and ATP-dependent nucleosome positioning. Nucleic Acids Res 51, 10326-10343 (2023).

      (2) Fazal, F.M., Meng, C.A., Murakami, K., Kornberg, R.D. & Block, S.M. Real-time observation of the initiation of RNA polymerase II transcription. Nature 525, 274-7 (2015).

      (3) Galburt, E.A., Grill, S.W., Wiedmann, A., Lubkowska, L., Choy, J., Nogales, E., Kashlev, M. & Bustamante, C. Backtracking determines the force sensitivity of RNAP II in a factor-dependent manner. Nature 446, 820-3 (2007).

      (4) Schweikhard, V., Meng, C., Murakami, K., Kaplan, C.D., Kornberg, R.D. & Block, S.M. Transcription factors TFIIF and TFIIS promote transcript elongation by RNA polymerase II by synergistic and independent mechanisms. Proc Natl Acad Sci U S A 111, 6642-7 (2014).

      (5) Kim, J.M., Carcamo, C.C., Jazani, S., Xie, Z., Feng, X.A., Yamadi, M., Poyton, M., Holland, K.L., Grimm, J.B., Lavis, L.D., Ha, T. & Wu, C. Dynamic 1D Search and Processive Nucleosome Translocations by RSC and ISW2 Chromatin Remodelers. bioRxiv (2024). (6) Jo, M.H., Meneses, P., Yang, O., Carcamo, C.C., Pangeni, S. & Ha, T. Determination of singlemolecule loading rate during mechanotransduction in cell adhesion. Science (in press).

      (7) Ngo, T.T., Zhang, Q., Zhou, R., Yodh, J.G. & Ha, T. Asymmetric unwrapping of nucleosomes under tension directed by DNA local flexibility. Cell 160, 1135-44 (2015).

      (8) Ngo, T.T., Yoo, J., Dai, Q., Zhang, Q., He, C., Aksimentiev, A. & Ha, T. Effects of cytosine modifications on DNA flexibility and nucleosome mechanical stability. Nat Commun 7, 10813 (2016).

      (9) White, C.L., Suto, R.K. & Luger, K. Structure of the yeast nucleosome core particle reveals fundamental changes in internucleosome interactions. EMBO J 20, 5207-18 (2001).

      (10) McBurney, K.L., Leung, A., Choi, J.K., Martin, B.J., Irwin, N.A., Bartke, T., Nelson, C.J. & Howe, L.J. Divergent Residues Within Histone H3 Dictate a Unique Chromatin Structure in Saccharomyces cerevisiae. Genetics 202, 341-9 (2016).

      (11) Kayikcioglu, T., Zarb, J.S., Lin, C.-T., Mohapatra, S., London, J.A., Hansen, K.D., Rishel, R. & Ha, T. Massively parallel single molecule tracking of sequence-dependent DNA mismatch repair in vivo. bioRxiv, 2023.01.08.523062 (2023).

      (12) Jeong, J., Le, T.T. & Kim, H.D. Single-molecule fluorescence studies on DNA looping. Methods 105, 34-43 (2016).

      (13) Jeong, J. & Kim, H.D. Base-Pair Mismatch Can Destabilize Small DNA Loops through Cooperative Kinking. Phys Rev Lett 122, 218101 (2019).

      (14) Blosser, T.R., Yang, J.G., Stone, M.D., Narlikar, G.J. & Zhuang, X. Dynamics of nucleosome remodelling by individual ACF complexes. Nature 462, 1022-7 (2009).

      (15) Basu, A., Bobrovnikov, D.G., Qureshi, Z., Kayikcioglu, T., Ngo, T.T.M., Ranjan, A., Eustermann, S., Cieza, B., Morgan, M.T., Hejna, M., Rube, H.T., Hopfner, K.P., Wolberger, C., Song, J.S. & Ha, T. Measuring DNA mechanics on the genome scale. Nature 589, 462-467 (2021).

      (16) Yoo, J., Park, S., Maffeo, C., Ha, T. & Aksimentiev, A. DNA sequence and methylation prescribe the inside-out conformational dynamics and bending energetics of DNA minicircles. Nucleic Acids Res 49, 11459-11475 (2021).

    1. Author response:

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

      Reviewer #1 (Recommendations For The Authors):

      Beyond my general review, some descriptions of the results and methods could be further clarified, which I've outlined below:

      (1) Page 3, Line 118-120: Based on results from Fig 1A, the authors reported 15 nanobodies neutralized both delta and BA.1 out of the 41 tested. However, I only counted 14. Could the authors double check?

      We recounted the nanobodies and confirmed there are 15 as follows:

      (1) RBD-15

      (2) RBD-22

      (3) RBD-24

      (4) RBD-9S1-4

      (5) S1-35

      (6) RBD-6

      (7) RBD-5

      (8) RBD-21

      (9) RBD-16

      (10) S1-46

      (11) S1-49dimer

      (12) S2-10dimer

      (13) S2-3

      (14) S2-62

      (2) Page 5, Lines 134-135: the authors described that the heatmap reflects the neutralizing strength of the representative nanobodies from each group. For groups where multiple nanobodies were selected for visualization, how was the neutralization strength calculated? Was the IC50 averaged first before being converted into the neutralization strength?

      This has been made clear in the legend for Fig. 1 as follows “For groups with multiple nanobodies, the average -log10 (IC50) is first calculated for the nanobodies within that group, then normalized to a neutralization score within the 0–100 range using the min and max average -log10 (IC50) for that group. A higher score indicates more potent neutralization of the variant relative to the wild type.”

      (3) Page 5, Lines 138-139: What was the authors' rationale for selecting certain nanobodies over others for structural modeling and visualizing the neutralization heatmap in Fig 1B? Does it introduce bias to the neutralizing epitope map on the spike protein?

      We only focused on nanobodies for which we had enough epitope mapping data to unambiguously generate docked nanobody-spike models, as explained in our previous study (Mast et. al, eLife 2021). When multiple nanobodies within the same group had sufficient epitope mapping data available, we selected only representative candidates that had better binding affinity and/or neutralization potency. As epitope mapping via escape mutants relied largely on random point mutagenesis of Spike, there should be little introduced bias.

      Overall, groups I-VII cover an exhaustive set of target areas on the RBD (including the lone glycan site in Group-II), while groups VII and IX are representative areas on NTD and S2. Using group-average IC50s and suitable normalization as mentioned in point 3 above further prevent potential biases due to unequal number of Nbs modeled from each group.

      We have modified the text with the following:

      “For computational epitope modeling, we selected nanobody candidates using a series of experimentally obtained structural restraints, as described in Mast, Fridy et al. 2021.”

      (4) Page 5, Lines 161-167: It would be good to include Fig S1 as a main figure as it places the epitope landscape of nanobodies being investigated in this manuscript into the broader context of clinically approved monoclonal antibody therapeutics for COVID-19.

      We have amended the Figures to accommodate the reviewers suggestion. Figure S1 is now Figure 2.

      (5) Page 6, Lines 173-175: The neutralization breadth for S1-46 is quite encouraging. Any speculations on why this particular nanobody is so broadly targeting? Any additional thoughts on why its high binding affinity (nM) did not translate into strong neutralization (as it is in the 0.1-1 uM range)?

      S1-46 binds a region on spike that is conserved across all variants observed to date. Its epitope is difficult to access unless the RBD is in the up conformation, which may explain why monoclonal antibodies rarely bind. We state this in the text as follows:

      “S1-46 binds a region on spike that is conserved across all variants to date, but which may be relatively inaccessible and is not targeted by any of the mAbs that previously received EUA by the FDA (Cox, Peacock et al. 2023).”

      Relating neutralization activity to binding activity requires more insight into the mechanisms of binding and activity. Nonetheless, we are also encouraged by S1-46’s breadth and numerous avenues can be pursued to greatly improve its neutralizing activity (e.g. synergistic combinations).

      (6) Page 6, Lines 173-175: For the remaining two nanobodies S1-31 and S1-RBD-11 in group VII, the target epitopes on the spike proteins of either delta or BA.1 do not seem to bear any mutations, at least based on the mutation maps in Fig 1B. Yet their neutralizing capacities against delta and BA.1 variants were abolished. Do the authors have any idea about what is going on here?

      For group VII, only the epitope of S1-46 was mapped whereas S1-31 and S1-RBD-11 were assigned to group VII based on our lower resolution binning experiments. Thus, without knowing precisely where they bind, we can make only limited conclusions at this time. In the absence of supporting structural information, we speculate that the epitopes of RBD-11 and S1-31 may be in a region that overlaps with or is in close proximity to a mutation that could affect the binding of the nanobody enough to result in loss of neutralizing ability.

      (7) Page 7, Line 195-200: Please provide PRNT50 or logPRNT50 for the five nanobodies selected for BA.4/5 PRNT assay.

      We have added this suggested information. Additionally, a supporting table (Table S1) is now provided.

      (8) Page 8, Lines 223-224: Similar to comment 3, what was the rationale here for choosing certain nanobodies over others for structural modeling and visualizing the binding heatmap in Fig 2B?

      The set of nanobodies chosen for structural modeling and visualization of neutralization data is identical to the set of anti-RBD nanobodies chosen for binding.

      (9) Page 11, Lines 326-328: Can the authors include mutation maps as part of Fig 4C to show the mutation distributions on the XBB/BQ.1/BQ/1.1 spikes?

      We have updated and added a supplemental figure to accompany Fig. 5 (called “supplement for Figure 5”) showing the mutation maps.

      (10) Page 14, Line 409-418: This paragraph is well considered. Given the large number of nanobodies assessed in this manuscript, it would be helpful if the authors could highlight some candidate nanobodies as lead candidates for further optimization.

      While our intention in this manuscript was not to provide targeted recommendations for lead candidates, but rather to reiterate the collective potential of a Nb pool originally targeted towards the 2019 Wuhan variant, the reviewers point is interesting. We speculate that any of the Nbs we have demonstrated to show pan-VoC activity, would be prime candidates for further optimization.

      We have added a statement to this effect as follows: “We propose that any of the Nbs we have demonstrated to show pan-VoC activity, would be prime candidates for further optimization.”

      Reviewer #2 (Recommendations For The Authors):

      Major concerns:

      (1) The main message of the article is the prediction that nanobodies that retain binding to the different SARS-CoV-2 variants including early Omicron strains will retain binding and neutralization against currently circulating strains such XBB and BQ. However, no evidence either via modeling or experimental testing has been provided for that prediction. The study will benefit from mapping amino acid mutations in RBD of XBB and BQ lineages compared to BA.4/5 and demonstrating via computation docking that epitopes of the five nanobodies that retain binding to BA.4/5 RBD are not affected. For example, the crystal structure of XBB.1 RBD PDB:8OIV is available. Binding/neutralization experiment with currently circulating SARS-CoV-2 strains would still be the gold standard test given the fact that only five out of 41 nanobodies retained binding and neutralization to BA.4/5 lineage. Loss of neutralization ability against BA.4/5 without a significant decrease in binding affinity for nanobodies S1-46 and S1-RBD-22 further indicates that neutralization of XBB and BQ lineage should be performed.

      The docking protocol used to predict the spike epitopes uses a C-alpha resolution to represent protein residues, and is data-driven, i.e. it assumes that binding happens in the first place, and then utilizes experimentally obtained structural restraints. So, concluding possible binding from such a docking protocol alone would be noisy. In our revised manuscript we have a new Figure 3B, which shows epitopes of 4 out of the 5 pan-VoC nanobodies, i.e. S1-RBD-{9, 22, 40) and S1-46 mapped to the RBD structures of XBB.1 (8IOU) and BQ.1.1 (8FXC), and we have updated Figure 4 with a supplemental showing the mutation maps.

      (2) Described nanobodies are positioned as very potent neutralizers of SARS-CoV-2. However, they are much less potent in neutralization of ancestral strain as well as early VOCs compared to the mAbs that were approved for COVID-19 treatment. For example, IC50 for casirivimab and imdevimab are 37.4 pM and 42.1 pM, respectively. That is about 27-fold more than IC50 for the most potent nanobody reported in the article, S1-RDB-15.

      This comparison is fraught for several reasons. 1. Experimental differences in pseudovirus assay systems usually result in significant differences in reported IC50s, as IC50 is not an absolute measure, or ultimately comparable to clinical IC50 values. For this reason, in our original publication (Mast et al., 2021) we tested other nanobodies in our experimental set-up as benchmarks (Mast et al., 2021). 2. A typical monoclonal has two binding sites with a large structural Fc linker that is combined ~10 times the size of a nanobody. In a therapeutic setting where monoclonal therapy is provided in g per kg of patient body weight, there is a 5-fold excess of Nb binding to antibody binding capacity. 3. We have previously shown that dimerizing our nanobodies (to produce two antigen binding sites) can dramatically increase potency over 100 fold (Mast et al., 2021).

      In order to make this even clearer in the manuscript, we have added the following: “We note that IC50s are not directly comparable across different experimental set-ups because measured values are highly dependent on the experimental conditions. For this reason, we included other published nanobodies as benchmarks in our original publication and have subsequently maintained standard experimental conditions (Mast, Fridy et al. 2021)”.

      (3) Figure 1A. If each dot represents an independent measurement of the same nanobody, IC50 variation seems too high. For some nanobodies it ranges for almost a log of magnitude, e.g S1-RDB-24, S1-RBD-46, S2-3. Why is that?

      We have deliberately explored the full range of effects that could contribute to experimental variability in our pseudovirus assay, using different batches of nanobody and pseudovirus in each replicate to provide as impartial and comprehensive analysis as possible. While the activity of some nanobodies is remarkably stable from batch to batch, others show the variation noticed by the Reviewer, hence why we performed multiple replicates to define the average IC50 value for our nanobodies.

      (4) The drop in IC50 for BA.1 neutralization is about one log for the majority of tested nanobodies. This should be outlined in the text. For example, for the most potent neutralizer, S1-RDB-15, the drop in IC50 for BA.1 is about 100-fold compared to IC50 for the Delta and Wuhan strains. It is important to note that out of 9 nanobodies for that drop in neutralizing capacity against BA.1 and Delta variants less than one log of magnitude 2 have epitopes in the S2 domain of SRS-CoV-2 spike. Resistance of mAbs targeting the S2 part of the spike has been extensively described in the literature as being due to the highly conserved structure of this region that facilitates membrane fusion. Presented data demonstrate that >80% of the nanobody repertoire is affected by mutations on spike protein. Additionally, it can be helpful for readers if the fold-change in IC50 between Wuhan, Delta, and BA.1 is presented in the text or added to Figure 1 or a table.

      We agree with the Reviewer and to make this more explicit we have made the following change: “In comparison, groups I, I/II, I/IV, V, VII, VIII and the anti-S2 nanobodies contained the majority of omicron BA.1 neutralizers, though here the neutralization potency of many nanobodies was generally decreased tenfold compared to wild-type (emphasis added).”

      (5) The authors should either present the results of the formal correlation analysis or avoid using misleading verbiage such as: "the decrease in neutralization potency largely correlates with the accumulation of omicron BA.1 specific mutations throughout the RBD" or "significant decrease in binding affinity correlated to decreases neutralization potency".

      We thank the Reviewer for this constructive feedback. To address this question, we have performed a correlation analysis using Pearson and Spearman's methods to quantitatively assess the relationship between nanobody neutralization potency (IC50) and binding affinity (KD) across SARS-CoV-2 variants, including the wildtype, delta, and omicron BA.1 variants. Our results indicate a statistically significant correlation for the delta variant (Pearson's PCC: 0.71, p-value: 0.01; Spearman's rho: 0.63, p-value: 0.07), supporting our statement regarding the correlation between decreased neutralization potency and reduced binding affinity for this variant. However, for the wildtype and omicron BA.1 variants, the correlations were not statistically significant (wildtype Pearson's: 0.10, p-value: 0.70; omicron BA.1 Pearson's: 0.27, p-value: 0.31), which we acknowledge does not fully align with the verbiage used in the manuscript. Therefore, we have revised the manuscript to present the correlation analysis data accurately and ensure the discussion is reflective of the statistical evidence as follows:

      “SPR binding assessments to the spike S1 domain or RBD of delta revealed a pattern: nanobodies maintaining binding affinity generally also neutralized the virus with a statistically significant correlation between binding affinity and neutralization efficacy (Pearson's Correlation Coefficient: 0.71, p-value: 0.01; Spearman's rho: 0.63, p-value: 0.07). However, this correlation was not statistically significant for omicron BA.1 (Pearson's Correlation Coefficient: 0.27, p-value: 0.31) (Fig. 3A, Table 1). Notably, while some nanobodies bound to the variants, they did not consistently neutralize them, suggesting additional factors influence neutralization beyond mere binding.”

      (6) Figure 3 shows approximated curves for live virus neutralization assay with quite a broad 90% CI. It will be helpful to present, at least, in supplementary, primary data for live-virus neutralization that were used to perform non-linear regression.

      We have added the reviewer’s suggestion.

      (7) It is not clear what are the "variant-specific nanobody groups" exactly? A definition/description of the term is not provided. If the nanobody library was generated with the Wuhan strain, how did strain-specific nanobodies that bind/neutralize only Delta, BA.1 or BA.4/5 appear in the repertoire and were isolated? This statement also contradicts data in Table 4 where all nanobodies listed bind and neutralize Wuhan strain.

      We agree with the reviewer. All nanobodies tested bind/neutralize the Wuhan strain as they were selected from our original repertoire of 116 nanobodies (Mast, et al., 2021). To clarify, variant-specific nanobodies are nanobodies that bind only one variant that arose from the original Wuhan strain. They were categorized into variant-specific groups based on whether they were able to bind each variant (other than Wuhan).

      We have thus added to the manuscript, “we define variant-specific nanobodies as nanobodies that bind a single additional variant alongside the original Wuhan strain...”

      (8) Describing the categorization of nanobody epitope groups presented in Figure 4, the authors state that binding to Wuhan, Delta, BA/1, and BA.4/5 predicts that these nanobodies will be "effective binders against current circulating strains of the virus including XBB and BQ lineages"? How exactly is this conclusion corollary to the data shown?

      The epitopes of XBB and BQ.1 are not divergent enough within the regions we propose the nanobodies to bind, to suggest that nanobodies that bind in those regions will lose binding ability. We hypothesize that the region at which these nanobodies bind represents regions on spike that are vulnerable to our specified nanobodies in Fig. 4. We have generated a new Fig. 3B and added a supporting figure for Fig. 4 to address this.

      (9) Figures 4C and 6 describe how the nanobodies will retain binding to currently circulating strains of XBB lineage. However, epitopes are mapped on the same Wuhan, Delta, BA.1, and BA.4/5 virus strains. The predicted binding of nanobodies to XBB lineage RBD is not actually shown in Figure 6. It is clear from the figure that the nanobody binding footprint (red area) decreases with antigenic distance in every spike projection from Wuhan through the BA.4/5 strain. It is unclear how this indicates that nanobodies will remain active against even more distant XBB, BQ, EU, and CH strains accumulating more mutations in spike protein.

      We have added the following to the manuscript to clarify: “Strikingly, we have in our cohort 8 nanobodies able to bind delta, and the omicron lineages BA.1/BA.4/BA.5/XBB/BQ.1.1 (Fig. 5B). We further predict these 8 nanobodies will be effective binders against current circulating strains of the virus including omicron EG.5 and HV.1 as the epitope regions (or predicted epitopes) of these nanobodies do not vary significantly from omicron lineages XBB and BQ.1.1 (Fig. 5C and Supplement to Fig. 5).”

      (10) Despite major advances in the development of nanobodies as therapeutic molecules there are only a few nanobody-based drugs that have so far been approved for clinical use and all of them are nanobody fusions to immunoglobulin Fc fragment. It is dictated by the small size of the nanobody itself, 15 kDa molecule, that leads to rapid kidney clearance within hours post-injection, and also by the necessity of having antibody effector functions allowing for example killing of malignant cells. It is hard to predict how each individual nanobody will tolerate multimerization and if it will still retain binding ability as its size dramatically increases. It should be noted that IC50 for BA.4/5 is in the submicromolar range for the 5 nanobodies retaining neutralization of this strain. From a therapeutic perspective, this is quite a high IC50 that dictates a high dosage to achieve a therapeutic effect. Furthermore, it can be expected that additional mutations in the SARS-CoV-2 spike will further affect binding affinity and therefore reduce the neutralization ability of these nanobodies resulting in even higher doses required to achieve therapeutic effect. Therefore, authors should discuss the limitations of the nanobody approach as a therapeutic intervention more granularly.

      While Fc fusions are not strictly required for clinical use (for instance Caplacizumab is not an Fc fusion, being a multimer containing an albumin-binding nanobody), we agree that reformulation would indeed be required to optimize pharmacokinetics for eventual clinical use. Increased valency through multimerizeration is in fact one of several strategies, which also includes synergistic combinations, for significantly enhancing effective IC50. Preclinical nanobody engineering is not within the scope of this paper, but we acknowledge this challenge.

      Minor points:

      (1) Table S1 is missing.

      This is an .xlsx file uploaded as Supplementary File 3. Labeled now as “Figure 6–Source data 2. Neutralization data from synergy experiment”.

      (2) Because Table 1 summarizes all neutralization and binding data, it will be helpful to refer to it while describing data presented in Figure 1.

      This has been added to the revised manuscript.

      (3) Live SARS-CoV-2 PRNT is not described in Materials and Methods.

      This has been added to the revised manuscript.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The aim of the present work is to evaluate the role of BMP9 and BMP10 in liver by depleting Bmp9 and Bmp10 from the main liver cell types (endothelial cells (EC), hepatic stellate cells (HSC), Kupffer cells (KC) and hepatocytes (H)) using cell-specific cre recombinases. They show that HSCs are the main source of BMP9 and BMP10 in the liver. Using transgenic ALK1 reporter mice, they show that ALK1, the high affinity type 1 receptor for BMP9 and BMP10, is expressed on KC and EC. They have also performed bulk RNAseq analyses on whole liver, and cell-sorted EC and KC, and showed that loss of Bmp9 and Bmp10 decreased KC signature and that KC are replaced by monocyte-derived macrophages. EC derived from these Bmp9fl/flBmp10fl/flLratCre mice also lost their identity and transdifferentiated into continuous ECs. Liver iron metabolism and metabolic zonation were also affected in these mice. In conclusion, this work supports that BMP9 and BMP10 produced by HSC play a central role in mediating liver cell-cell crosstalk and liver homeostasis.

      We appreciate the comprehensive summary of reviewer 1.

      Strengths:

      This work further supports the role of BMP9 and BMP10 in liver homeostasis. Using a specific HSC-Cre recombinase, the authors show for the first time that it is the BMP9 and BMP10 produced by HSC that play a central role in mediating liver cell-cell crosstalk to maintain a healthy liver. Although the overall message of the key role of BMP9 in liver homeostasis has been described by several groups, the role of hepatic BMP10 has not been studied before. Thus, one of the novelties of this work is to have used liver cell specific Cre recombinase to delete hepatic Bmp9 and Bmp10. The second novelty is the demonstration of the role of BMP9 and BMP10 in KC Differentiation/homeostasis which has already been slightly addressed by this group by knocking out ALK1, the high affinity receptor of BMP9 and BMP10 (Zhao et al. JCI, 2022).

      We appreciate the positive comment of reviewer 1.

      Weaknesses:

      This work remains rather descriptive and the molecular mechanisms are barely touched upon and could have been more explored. Some references should be added; In particular, a work that has already demonstrated, using a different approach (in situ hybridization RNAscope), that in the liver BMP9 and BMP10 are expressed by HSC (Tillet et al., J Biol Chem 2018). Another publication (Bouvard et al., Cardiovasc Res, 2021) has previously showed that deletion of Bmp9 and Bmp10 leads to liver fibrosis and could have thus been cited. There is also a reference that is not correctly cited. Ref 26 (Herrera et al., 2014) does not say that "BMP10 is mostly expressed in the heart, followed by the liver" or that "BMP9 and BMP10 also bind to ALK2" as cited in the manuscript.

      We agree with the comment of reviewer 1 that the molecular mechanisms were barely investigated in our work. Indeed, it has been reported that BMP9/10 induce the expression of ID1/3 in KCs and GATA4 and Maf in liver ECs in vitro culture system. These master regulators play an important role in the differentiation of the two cell types. Thus, we think that the reduced expression of these master regulators can explain the phenotype in KCs and ECs observed in Bmp9fl/flBmp10fl/flLratCre mice. In addition, according to the reviewer’s suggestion, these references will be added or corrected in our revised manuscript.

      The gating strategies for cell sorting which is used for bulk RNAseq and FACS analyses should be better described in order to better follow the manuscript. This point is particularly important for KC gating as the authors show that Tim4 is very strongly decreased in Bmp9fl/flBmp10fl/flLratCre (Fig 2c), yet, it seems that this marker is used for gating macrophages (Suppl fig4). Same question with F4/80 which is strongly decreased in Bmp9fl/flBmp10fl/flLratCre (Fig 2d) and also used for gating. It is important to show the gating strategy for both Control and Bmp9fl/flBmp10fl/flLratCre mice.

      The authors should explain how they selected the genes shown on each heatmaps and add references that can justify the choice of the genes.

      Thank you for your suggestion. In our study, we used CD45+ Ly6C- F4/80+ CD64+ cells to define liver macrophages. We will delete Tim4 FACS plot from Suppl fig4 to avoid the misunderstanding. Although F4/80 positive cells were reduced in the livers of Bmp9fl/flBmp10fl/flLratCre mice, double staining by anti-F4/80 and anti-CD64 fluorescence antibodies can still clearly distinguish liver macrophages based on above gating strategy. Gating strategy for both control and Bmp9fl/flBmp10fl/flLratCre mice will be presented in our revised manuscript.

      Quantifications of Immunostaining and FACS data should be added as well as statistical analyses.

      Quantitative data will be added in our revised manuscript.

      Reviewer #2 (Public Review):

      Summary:

      The authors characterized the contribution of BMP9/BMP10 expression/secretion from all different hepatic cell types and analysed their impact on the other cell types. They are able to show that HSC derived BMP9/BMP10 controls Kupffer cell and EC differentiation and functions.

      We appreciate the comprehensive summary of reviewer 2.

      Strengths:

      This is the first study to my knowledge to comprehensively analyze the contribution of BMP9/BMP10 expression in such systematic fashion in vivo. This study therefore is a significant contribution to the field and further supports previous studies that have already implied BMP9 and BMP10 in Kupffer cell and EC functions but did not unravel the intercellular cross talk in such detailed fashion.

      We appreciate the positive comment of reviewer 2.

      Weaknesses:

      Several findings such as the impact of BMP9/10 on Kupffer cells and EC were already known. So these findings are not innovative, however I still believe that the elucidation of the cellular crosstalk makes this publication highly interesting to a broad scientific community.

      Overall the authors achieved their aims and the results are well supporting the conclusions and discussion.

      We appreciate the positive comment of reviewer 2. We agree with the comment of reviewer 2 that although some findings in our paper are somehow expected, the detailed investigation of the crosstalk between different liver cell types is still needed and beneficial to this field.

    1. Author response:

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

      Responses to Reviewer 1:

      It wouldn't be very surprising to identify the association between PhenoAgeAccel and cancer risk, since the PhenoAgeAccel was constructed as a predictor for mortality which attributed a lot to cancer. Although cancer is an essential mediator for the association, sensitivity analyses using cancer-free mortality may provide an additional angle.

      As suggested, we retrained the PhenoAge in cancer-free participants based on mortality and recalculated PhenoAgeAccel in the UK Biobank. As expected, the re-calculated PhenoAgeAccel was still significantly associated with an increased risk of overall cancer in both men and women. The relevant results have been added to Appendix 1-table6.

      It would be interesting to see, to what extent, PhenoAgeAccel could be reversed by environmental or lifestyle factors. G by E for PhenoAgeAccel might be worth a try.

      As suggested, we performed interaction analysis between genetic and lifestyle factors on PhenoAgeAccel, and added the methods and results in the revision as follows:

      “55 independent PhenoAgeAccel-associated SNPs (P < 5 × 10-8) and corresponding effect sizes were derived from a large-scale PhenoAgeAccel GWAS including 107,460 individuals of European ancestry (Kuo, Pilling, Liu, Atkins, & Levine, 2021). A PhenoAgeAccel PRS was created using an additive model as previously described (Dai et al., 2019). In short, the genotype dosage of each risk allele for each individual was summed after multiplying by its respective effect size of PhenoAgeAccel.” (Page 6)

      “We performed additive interaction analysis between genetic risk (defined by CPRS) and PhenoAgeAccel on overall cancer risk, as well as genetic risk (defined by PhenoAgeAccel PRS) and lifestyle on PhenoAgeAccel using two indexes: the relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP).” (Page 9)

      “However, we did not observe any interaction between genetic risk and lifestyle on PhenoAgeAccel in both men and women (Appendix 1-table 11).” (Page 13)

      Responses to Reviewer 2:

      Since the UK biobank has a large sample size, it should have enough power to split the dataset into discovery and validation sets. Why did the authors use 10-fold cross-validation instead of splitting the dataset?

      There may have been some misunderstandings in the interpretation of methods that 10-fold cross-validation was applied to select biomarkers when calculating PhenoAge in the previous manuscript (Levine et al., 2018). In this study, we analyzed the association between PhenoAgeAccel and incident cancer risk by dividing participants into ten groups based on the deciles of PhenoAgeAccel and assessed the associations of each group compared to the lowest decile. To avoid any confusion, we have removed the description of 10-fold cross-validation from the Methods section (Page 5).

      Recommendations for the authors:

      In addition, there is extant literature on the role of Phenotypic Age Acceleration in cancer risk and mortality that should be reviewed. Please also address possible overlap with previous work that used the UK Biobank cohort study (PMCID: PMC9958377).

      As suggested, we have reviewed the association of Phenotypic Age Acceleration with cancer risk, and added it into the Discussion section as follows:

      “Recently, several studies have confirmed the associations between PhenoAgeAccel and cancer risk. Mak et al. explored three measures of biological age, including PhenoAge, and assessed their associations with the incidence of overall cancer and five common cancers (breast, prostate, lung, colorectal, and melanoma) (Mak et al., 2023). In our previous study, we investigated the association between PhenoAgeAccel and lung cancer risk and analyzed the joint and interactive effects of PhenoAgeAccel and genetic factors on the risk of lung cancer (Ma et al., 2023). In comparison to these studies, our analysis expanded the range of cancers to 20 types and further explored the associations in different genetic and lifestyle contexts. Moreover, we also evaluated the potential implications of PhenoAge in population-level cancer screening.” (Page 15).

      Other minor comments:

      Line 216, "-4.35 to -1.25" or "-4.35, -1.25" may be better.

      As suggested, we have adjusted text accordingly.

      Line 260, please clarify the PRS used for G by E interaction testing. It could be site-specific PRS or CPRS.

      We used CPRS for G by E interaction testing, and we have changed the description of our methods as follows:

      “We performed additive interaction analysis between genetic risk (defined by CPRS) and PhenoAgeAccel on overall cancer risk, as well as genetic risk (defined by PhenoAgeAccel PRS) and lifestyle on PhenoAgeAccel using two indexes: the relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP).” (Page 9)

      Line 223, The discussion/interpretation for "while negatively associated with risk of prostate cancer" is lacking.

      As suggested, we have discussed this as follows:

      “In addition, we observed a negative association between PhenoAgeAccel and prostate cancer risk. The unexpected association may have been confounded by diabetes and altered glucose metabolism, both of which are closely linked to aging. When we removed HbA1c and serum glucose from the biological age algorithms, the association became non-statistically significant. Similar findings were also reported by Mak et al. (Mak et al., 2023) and Dugue et al. (Dugue et al., 2021).” (Page 15).

      It is not clear how to define "biologically older" and "biologically younger". Whether the individuals fall in the "middle area" will impact the results.

      We defined "biologically older" and "biologically younger" based on Phenotypic Age Acceleration (PhenoAgeAccel), which was defined as the residual obtained from a linear model when regressing Phenotypic Age on chronological age. We categorized individuals with PhenoAgeAccel > 0 as biologically older and those with PhenoAgeAccel < 0 as biologically younger.

      Compared with individuals at low accelerated aging (the bottom quintile of PhenoAgeAccel), we found those in the "middle area" (quintiles 2 to 4) and high accelerated aging (the top quintile) had a significantly higher risk of overall cancer (Table 2). Individuals fall in the "middle area" also had a moderate risk of overall cancer, when reclassified accelerated aging levels according to quartiles or tertiles of the PhenoAgeAccel (Appendix 1-table 2).

      Do men and women have distinct biological ages, so they were analyzed separately?

      We found that men (median PhenoAgeAccel: 0.34, IQR: -2.42 to 3.53) have higher biological ages than women (median PhenoAgeAccel: -1.38, IQR: -4.26 to 1.96) (P < 0.0001). In addition, men and women have different cancer incidence patterns (Rubin, 2022). Therefore, we conducted separate analyses to investigate the associations of PhenoAgeAccel with cancer risk in men and women.

      Dai, J., Lv, J., Zhu, M., Wang, Y., Qin, N., Ma, H., . . . Shen, H. (2019). Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations. Lancet Respir Med, 7(10), 881-891. doi: 10.1016/S2213-2600(19)30144-4

      Dugue, P. A., Bassett, J. K., Wong, E. M., Joo, J. E., Li, S., Yu, C., . . . Milne, R. L. (2021). Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study. JNCI Cancer Spectr, 5(1). doi: 10.1093/jncics/pkaa109

      Kuo, C. L., Pilling, L. C., Liu, Z., Atkins, J. L., & Levine, M. E. (2021). Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell, 20(6), e13376. doi: 10.1111/acel.13376

      Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., . . . Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10(4), 573-591. doi: 10.18632/aging.101414

      Ma, Z., Zhu, C., Wang, H., Ji, M., Huang, Y., Wei, X., . . . Shen, H. (2023). Association between biological aging and lung cancer risk: Cohort study and Mendelian randomization analysis. iScience, 26(3), 106018. doi: 10.1016/j.isci.2023.106018

      Mak, J. K. L., McMurran, C. E., Kuja-Halkola, R., Hall, P., Czene, K., Jylhava, J., & Hagg, S. (2023). Clinical biomarker-based biological aging and risk of cancer in the UK Biobank. Br J Cancer, 129(1), 94-103. doi: 10.1038/s41416-023-02288-w

      Rubin, J. B. (2022). The spectrum of sex differences in cancer. Trends Cancer, 8(4), 303-315. doi: 10.1016/j.trecan.2022.01.013

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In the manuscript titled "Disease modeling and pharmacological rescue of autosomal dominant Retinitis Pigmentosa associated with RHO copy number variation" the authors describe the use of patient iPSC-derived retinal organoids to evaluate the pathobiology of a RHO-CNV in a family with dominant retinitis pigmentosa (RP). They find significantly increased expression of rhodopsin, especially within the photoreceptor cell body, and defects in photoreceptor cell outer segment formation/maturation. In addition, they demonstrate how an inhibitor of NR2E3 (a rod transcription factor required for inducing rhodopsin expression), can be used to rescue the disease phenotype.

      Strengths:

      The manuscript is very well written, the illustrations and data presented are compelling, and the authors' interpretation/discussion of their findings is logical.

      Weaknesses:

      A weakness, which the authors have addressed in the discussion section, is the lack of an isogenic control, which would allow for direct analysis of the RHO-CNV in the absence of the other genetic sequence contained within the duplicated region. As the authors suggest, CRISPR correction of a large CNV in the absence of inducing unwanted on-target editing events in patient iPSCs is often very challenging. Given that they have used a no-disease iPSC line obtained from a family member, controlled for organoid differentiation kinetics/maturation state, and that no other complete disease-causing gene is contained within the duplicated region, it is unlikely that the addition of an isogenic control would yield significantly different results.

      Aims and conclusions:

      This reviewer is of the opinion that the authors have achieved their aims and that their results support their conclusions.

      Discussion:

      The authors have provided adequate discussion on the utility of the methods and data as well as the impact of their work on the field.

      We thank the reviewer for their insightful, and encouraging review of our work that has taken several years to get to current stage.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Kandoi et al. describes a new 3D retinal organoid model of a mono-allelic copy number variant of the rhodopsin gene that was previously shown to induce autosomal dominant retinitis pigmentosa via a dominant negative mechanism in patients. With advancements in the low-cost genomics application to detect copy number variations, this is a timely article that highlights a potential disease mechanism that goes beyond the retina field. The evidence is relatively strong that the rod photoreceptor phenotype observed in an adult patient with RP in vivo is similar to that phenotype observed in human stem cell-derived retinal organoids. Increases in RHO expression detected by qPCR, RNA-seq, and IHC support this phenotype. Importantly, the amelioration of photoreceptor rhodopsin mislocalization and related defects using the small molecule drug photoregulin demonstrates an important potential clinical application.

      Overall, the authors succeeded in providing solid evidence that copy number variation via a genomic RHO duplication leads to abnormalities in rod photoreceptors that can be partially blocked by photoregulin. However, there are several points that should be addressed that will enhance this paper.

      Strengths:

      • The use of patient-derived organoids from patients that have visual defects is a major strength of this work and adds relevance to the disease phenotype.

      • The rod phenotype assessed by qPCR, RNA-seq, and IHC supports a phenotype that shares similarities with the patient.

      • The use of a small molecule drug that selectively targets rod photoreceptors, as opposed to cones, is a noteworthy strength.

      We thank the reviewers for highlighting the key strengths of the paper.

      Weaknesses:

      (1) The chromosomal segment that was duplicated had 3 copies of RHO in addition to three copies of each of the flanking genes (IFT122, HIF100, PLXND1). Discussion of the involvement of these genes would be helpful. Would duplication of any of these genes alone cause or contribute to adRP? As an example, a missense mutation in IFT122 was previously implicated in photoreceptor loss (PMID: 33606121 PMCID: PMC8519925).

      Thank you for your comment. It is an interesting question on the contribution of the other duplicated genes. Of these, IFT122 is particularly interesting as pointed out. We did a thorough survey through literature and our genetic testing partner’s database, BluePrint Genetics. We did not find any human retinal degeneration cases with variants in IFT122. IFT122 has been shown to cause recessive phenotype in dogs and in complete knockout zebrafish model but dominant or overexpression has not been shown to have a phenotype. Interestingly, recessive biallelic IFT122 mutation can cause Cranioectodermal Dysplasia (Sensenbrenner syndrome, PMID: 24689072) and none of these patient exhibited retinal dystrophy. HIF100 is an epigenetic modifier gene while PLXND1 is expressed in endothelial cells. We will include a discussion on this in the revised manuscript.

      (2) Related to #1, have the authors considered inserting extra copies of RHO (and/or the flanking genes) of these at a genomic safe harbor site? Although not required, this would allow one to study cells with isogenic-matched genetic backgrounds and would partially address the technical challenge of repairing a 188kb duplication, which as the authors note would be difficult to do. Demonstrating that excess copy numbers in different genetic backgrounds would be a huge contribution to the field. At a minimum, a discussion of the role of the nearby genes should be included. 


      Thank you for your suggestion. We plan to test the relative role of 1-3 extra copies of RHO driven off a NRL promoter in order to drive it only in rods in our future mechanistic analysis studies. We will include a discussion on the potential role of the other genes in the revised manuscript.

      (3) In the patient, the central foveal region was spared suggesting that cones were normal. Was there a similar assessment that cones are unaffected in retinal organoids? 


      We will include this data in our revised manuscript but overall did not see a cone defect in RHO CNV organoids. Additionally, although it is true that the central foveal region was relatively spared in this patient, the cones are definitely not normal. The macular cones that remain have been damaged by chronic edema, and photoreceptor and RPE atrophy has progressed into the macula, sparing only the foveal cones.

      (4) Pathway analysis indicated that glycosylation was perturbed and this was proposed as an explanation as to why rhodopsin was mislocalized. Have the authors verified that there is an actual decrease in glycosylation? 


      These studies are ongoing. We are currently looking into the details of cellular pathophysiology focusing on RHO trafficking in RHO-CNV including role of glycosylation and other post-translational modifications defects.

      (5) Line 182: by what criteria are the authors able to state that " there were no clear visible anatomical changes in apical-basal retinal cell type distribution during the early differentiation timeframe (data not shown)." Was this based on histological staining with antibodies, nuclear counter-staining, or some other evaluation?


      This was based on both IHC for various cell type markers and nuclear (DAPI) staining.

      (6) Figure 2C - the appearance of the inner segments in RC and RM looks very different from one another. Have the authors ruled out the possibility that the RC organoid cell isn't a cone? In addition, the RM structure has what appears to be a well-defined OLM which would suggest well-formed Muller glia. Do these structures also exist in RC organoids? Typically the OLM does form in older organoids. In addition, was this representative in numerous EM preparations?


      For clarification on EM data, we will include additional images in the revision as supplementary data. We have not carefully compared OLM between the patient and control organoids but do observe them in both conditions in the older organoids. The EM preparations were made from multiple organoids from two different batches with consistent results.

      (7) What criteria were used to assess cell loss? Has any TUNEL labeling been performed to confirm cell loss? From the existing data, it seems that rod outer segments appear to be affected in organoids. However, it's not clear if the photoreceptors themselves actually die in this model.

      TUNEL was used to assess cell loss and it was not significantly different between the control and patient organoids at the timepoints examined. We did not expect a change as the disease in the patient developed over decades.

      (8) Figure 5B. The RHO staining in the vehicle-treated sample is perturbed relative to the PR3 treatments as indicated in the text. In the vehicle-treated sample, the number of DAPI-positive cells that are completely negative proximal to the inner segments suggests that there might be non-rod cells there. Have the authors confirmed whether these are cones? Labels would be helpful in the left vehicle panel as the morphology looks very different than the treated samples.


      Thank you very much for the various suggestions and these will be included in the revised manuscript version. A number of the cells in the negative regions are OTX2+/NRL- and likely to be cones (Figure 4 A and B). Unfortunately, we do not have a very good cone nuclear marker as RXRγ does not consistently stain mature cones.

      (9) It is interesting that in addition to increases in RHO, and photo-transduction, there are also increases in PTPRT which is related to synaptic adhesion. Is there evidence of ectopic neurites that result from PTPRT over-expression?

      You are absolutely correct that PTPRT data is very interesting. PTPRT requires similar PTMs like RHO in photoreceptors for its synaptic localization. We did not specifically look at ectopic neurites and test that in the revision. It will interesting to follow-up on its expression pattern to see if it gets processed or localized normally if we can find a working antibody. It is also possible that the gene-expression increase due to feedback upregulation secondary to improper protein processing.

      Reviewer #3 (Public Review):

      This manuscript reports a novel pedigree with four intact copies of RHO on a single chromosome which appears to lead to overexpression of rhodopsin and a corresponding autosomal dominant form of RP. The authors generate retinal organoids from patient- and control-derived cells, characterize the phenotypes of the organoids, and then attempt to 'treat' aberrant rhodopsin expression/mislocalization in the patient organoids using a small molecule called photoregulin 3 (PR3). While this novel genetic mechanism for adRP is interesting, the organoid work is not compelling. There are multiple problems related to the technical approaches, the presentation of the results, and the interpretations of the data. I will present my concerns roughly in the order in which they appear in the manuscript.

      Major concerns:

      (1) Individual human retinal organoids in culture can show a wide range of differentiation phenotypes with respect to the expression of specific markers, percentages of given cell types, etc. For this reason, it can be very difficult to make rigorous, quantitative comparisons between 'wild-type' and 'mutant' organoids. Despite this difficulty, the author of the present manuscript frequently presents results in an impressionistic manner without quantitation. Furthermore, there is no indication that the investigator who performed the phenotypic analyses was blind with respect to the genotype. In my opinion, such blinding is essential for the analysis of phenotypes in retinal organoids. To give an example, in lines 193-194 the authors write "we observed that while the patient organoids developing connecting cilium and the inner segments similar to control organoids, they failed to extend outer segments". Outer segments almost never form normally in human retinal organoids, even when derived from 'wild-type' cells. Thus, I consider it wholly inadequate to simply state that outer segment formation 'failed' without a rigorous, quantitative, and blinded comparison of patient and control organoids.

      We agree it is challenging to generate outer segments in retinal organoids but we are not the first to show this. This has been demonstrated by multiple independent labs (Mayerl et al (PMID: 36206764), Wahlin et al (PMID: 28396597), West at al (PMID: 35334217) including ours (Chirco et al (PMID: 34653402). To clarify, we did not observe any OS like tissue in the patient organoids across multiple EM preps of a number of organoids from two independent 300+ day experiments which matched the phase microscopy data presented in Fig2B.

      (2) The presentation of qPCR results in Figure 3A is very confusing. First, the authors normalize expression to that of CRX, but they don't really explain why. In lines 210-211, they write "CRX, a ubiquitously expressing photoreceptor gene maintained from development to adulthood." Several parts of this sentence are misleading or incomplete. First, CRX is not 'ubiquitously expressed' (which usually means 'in all cell types') nor is it photoreceptor-specific: CRX is expressed in rods, cones, and bipolar cells. Furthermore, CRX expression levels are not constant in photoreceptors throughout development/adulthood. So, for these reasons alone, CRX is a poor choice for the normalization of photoreceptor gene expression.

      As you are aware, all housekeeping genes have shortcomings when used for normalizing PCR data. We went with CRX as within the timepoints chosen, it is not expected to change much and thus represent a good equalizer for relative photoreceptor numbers between the organoids and conditions. While we agree that CRX is weakly expressed in bipolar cells (Yamamoto et al 2020), it is not expected to bias the data too much as we have not seen nor have other reported a huge relative difference in bipolar cell number in organoids. We also confirm this by showing equivalent expression of OTX2, RCVRN and NRL between all conditions.

      Second, the authors' interpretation of the qPCR results (lines 216-218) is very confusing. The authors appear to be saying that there is a statistically significant increase in RHO levels between D120 and D300. However, the same change is observed in both control and patient organoids and is not unexpected, since the organoids are more mature at D300. The key comparison is between control and patient organoids at D300. At this time point, there appears to be no difference between control and patient. The authors don't even point this out in the main text.

      Thank you for the comment and we apologize if this confused you. However, as can been seen in the graph in Figure 3A, we do compare expression of genes including RHO between control and patient organoids at two different time points. There are four conditions: D120-RC, D120-RM, D300-RC and D300-RM with individual data points and error bars for each condition. There is a statistically significant increase at both time points upon comparing the control and patient organoids for RHO. We compared RHO expression between patient organoids at the two time points and it was not statistically different.

      Third, the variability in the number of photoreceptor cells in individual organoids makes a whole-organoid comparison by qPCR fraught with difficulty. It seems to me that what is needed here is a comparison of RHO transcript levels in isolated rod photoreceptors.

      We agree that this makes it challenging. This was the exact reasoning for using CRX for normalization since it is predominantly present in photoreceptors. This was validated by the data showing no difference in expression of photoreceptor markers OTX2, RCVRN or NRL between the organoids.

      (3) I cannot understand what the authors are comparing in the bulk RNA-seq analysis presented in the paragraph starting with line 222 and in the paragraph starting with line 306. They write "we performed bulk-RNA sequencing on 300-days-old retinal organoids (n=3 independent biological replicates). Patient retinal organoids demonstrated upregulated transcriptomic levels of RHO... comparable to the qRT-PCR data." From the wording, it suggests that they are comparing bulk RNA-seq of patients and control organoids at D300. However, this is not stated anywhere in the main text, the figure legend, or the Methods. Yet, the subsequent line "comparable to the qRT-PCR data" makes no sense, because the qPCR comparison was between patient samples at two different time points, D120 and D300, not between patient and control. Thus, the reader is left with no clear idea of what is even being compared by RNA-seq analysis.

      We apologize if the conditions were not obvious and will clarify this in the revised version. The conditions compared are control and patient organoids at D300. Regarding comparison to RT-PCR, as stated above, the comparison shown is between patient and control organoids at two different timepoints.

      Remarkably, the exact same lack of clarity as to what is being compared is found in the second RNA-seq analysis presented in the paragraph starting with line 306. Here the authors write "We further carried out bulk RNA-sequencing analysis to comprehensively characterize three different groups of organoids, 0.25 μM PR3-treated and vehicle-treated patient organoids and control (RC) organoids from three independent differentiation experiments. Consistent with the qRT-PCR gene expression analysis, the results showed a significant downregulation in RHO and other rod phototransduction genes." Here, the authors make it clear that they have performed RNA-seq on three types of samples: PR3-treated patient organoids, vehicle-treated patient organoids, and control organoids (presumably not treated). Yet, in the next sentence, they state "the results showed a significant downregulation in RHO", but they don't state what two of the three conditions are being compared! Although I can assume that the comparison presented in Fig. 6A is between patient vehicle-treated and PR3-treated organoids, this is nowhere explicitly stated in the manuscript.

      Thank you for the comment and we will explicitly state various comparisons in the revised version.

      (4) There are multiple flaws in the analysis and interpretation of the PR3 treatment results. The authors wrote (lines 289-2945) "We treated long-term cultured 300-days-old, RHO-CNV patient retinal organoids with varying concentrations of PR3 (0.1, 0.25 and 0.5 μM) for one week and assessed the effects on RHO mRNA expression and protein localization. Immunofluorescence staining of PR3-treated organoids displayed a partial rescue of RHO localization with optimal trafficking observed in the 0.25 μM PR3-treated organoids (Figure 5B). None of the organoids showed any evidence of toxicity post-treatment."

      There are multiple problems here. First, the results are impressionistic and not quantitative. Second, it's not clear that the investigator was blinded with respect to the treatment condition. Third, in the sections presented, the organoids look much more disorganized in the PR3-treated conditions than in the control. In particular, the ONL looks much more poorly formed. Overall, I'd say the organoids looked considerably worse in the 0.25 and 0.5 microM conditions than in the control, but I don't know whether or not the images are representative. Without rigorously quantitative and blinded analysis, it is impossible to draw solid conclusions here. Lastly, the authors state that "none of the organoids showed any evidence of toxicity post-treatment," but do not explain what criteria were used to determine that there was no toxicity.

      Thank you for your critical insight. The RHO localization data is qualitative as it is very difficult to accurately quantify rhodopsin trafficking within the cell in the organoid. Thus, for quantitative comparison, we have provided expression level changes. Regarding toxicity, we analyzed the organoids by morphology and TUNEL and did not observe significant difference between the conditions. This closely mimics mouse data on PR3 which suppressed rod function in mice following IP injection without any obvious toxicity.

      (5) qPCR-based quantitation of rod gene expression changes in response to PR3 treatment is not well-designed. In lines 294-297 the authors wrote "PR3 drove a significant downregulation of RHO in a dose-dependent manner. Following qRT-PCR analysis, we observed a 2-to-5 log2FC decrease in RHO expression, along with smaller decreases in other rod-specific genes including NR2E3, GNAT1 and PDE6B." I assume these analyses were performed on cDNA derived from whole organoids. There are two problems with this analysis/interpretation. First, a decrease in rod gene expression can be caused by a decrease in the number of rods in the treated organoids (e.g., by cell death) or by a decrease in the expression of rod genes within individual rods. The authors do not distinguish between these two possibilities. Second, as stated above, the percentage of cells that are rods in a given organoid can vary from organoid to organoid. So, to determine whether there is downregulation of rod gene expression, one should ideally perform the qPCR analysis on purified rods.

      The reviewer is correct in pointing the potential reasons for reduction in RHO levels following PR3 treatment. Thus, we have provided NRL expression levels in the graph to show that this key rod-specific gene does not change suggesting equivalent number of rod photoreceptor cells. The suggestion of using purified rods is not practical here, as we do not have any way to sort human rods due to the lack of a rod-specific cell surface marker.

      (6) In Figure 4B 'RM' panels, the authors show RHO staining around the somata of 'rods' but the inset images suggest that several of these cells lack both NRL and OTX2 staining in their nuclei. All rods should be positive for NRL. Conversely, the same image shows a layer of cells scleral to the cells with putative RHO somal staining which do not show somal staining, and yet they do appear to be positive for NRL and OTX2. What is going on here? The authors need to provide interpretations for these findings.

      Since RHO is a cytoplasmic marker and photoreceptor are tightly packed, it is difficult to make a 1:1 comparison to NRL/OTX2 nuclear marker to RHO. Additionally, as the RHO+ cytoplasm moves towards scleral surface, it is expected to pass adjacent to other nuclei. Few of the rods do still have normal Rhodopsin trafficking and it is likely these will not have somal RHO similar to control conditions. We do rarely observe these cells as highlighted by the occasional RHO in IS/OS of RM organoids in the figure. We do agree that the NRL staining in the figure 4B (>D250) is not extremely crisp and we will include an updated figure in the revised version.

    1. Author response:

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

      General comments

      All three experts have raised excellent ideas and made important suggestions to extend the scope of our study and provide additional information. While we fully acknowledge that these points are valid and would provide exciting new knowledge, we also should not lose track of the fact that a single study cannot cover all bases. Sulfated steroids, for example, are clearly essential components of mouse urine. Unfortunately, however, all chemical analysis approaches are limited and the one we opted for is not suitable for analysis of such signaling molecules. Future studies should certainly focus on these aspects. The same holds true for the fact that we do not know which of the identified compounds are actually VSN ligands. These are inherent limitations of the approach, and we are not claiming otherwise.

      Reviewer #1 (Public Review):

      (1) In this manuscript, Nagel et al. sought to comprehensively characterize the composition of urinary compounds, some of which are putative chemosignals. They used urines from adult males and females in three different strains, including one wild-derived strain. By performing mass spectrometry of two classes of compounds: volatile organic compounds and proteins, they found that urines from inbred strains are qualitatively similar to those of a wild strain. This finding is significant because there is a high degree of genetic diversity in wild mice, with chemosensory receptor genes harboring many polymorphisms.

      We agree and thank the Reviewer for his / her positive assessment.

      (2) In the second part of this work, the authors used calcium imaging to monitor the pattern of vomeronasal neuron responses to these urines. By performing pairwise comparisons, the authors found a large degree of strain-specific response and a relatively minor response to sex-specific urinary stimuli. This is a finding generally in agreement with previous calcium imaging work by Ron Yu and colleagues in 2008. The authors extend the previous work by using urines from wild mice. They further report that the concentration diversity of urinary compounds in different urine batches is largely uncorrelated with the activity profiles of these urines. In addition, the authors found that the patterns of vomeronasal neuron response to urinary cues are not identical when measured using different recipient strains. This fascinating finding, however, requires an additional control to exclude the possibility that this is not due to sampling error.

      We thank Reviewer 1 for pointing this out. We agree that this is truly a “fascinating finding.” Reviewer 1 emphasizes that we need to add an “additional control to exclude […] that this is not due to sampling error”, and he / she elaborates on the required control in his / her Recommendations For The Authors (see below). Reviewer 1 states that “for Fig. 5, in order to conclude that the same urine activates a different population of VSNs in two different strains, a critical control is needed to demonstrate that this is not due to the sampling variability - as compositions of V1Rs and V2Rs could vary between different slices, one preferred control is to use VNO slices from the same strain and compare the selectivity used here across the A-P axis.” Importantly, we believe that this is already controlled for. In fact, for each experiment, we routinely prepare VNO slices along the organ’s entire anterior-to-posterior axis (not including the most anterior tip, where the VNO lumen tapers into the vomeronasal duct, and the most posterior part, the lumen ‘‘twists’’ toward the ventral aspect and its volume decreases (see Figs. 7 & S7 in Hamacher et al., 2024, Current Biology)). This usually yields ~7 slices per individual experiment / session. Therefore, we routinely sample and average across the entire VNO anterior-to-posterior axis for each experiment. In Fig. 5, in which we analyzed whether the “same urine activates a different population of VSNs in two different strains”, individual independent experiments from each strain (C57BL/6 versus BALB/c) amounted to (a) n = 6 versus n = 8; (b) n = 10 versus n = 10; (c) n = 7 versus n = 9; (d) n = 9 versus n = 10; (e) n = 10 versus n = 9; and (f) n = 12 versus n = 10. Together, we conclude that it is very unlikely that the considerably different response profiles measured in different recipient strains result from a “sampling error.”

      To clarify this point in the revised manuscript, we now explain our sampling routine in more detail in the Materials and Methods. Moreover, we now also refer to this point in the Results.

      (3) There are several weaknesses in this manuscript, including the lack of analysis of the compositions of sulfated steroids and other steroids, which have been proposed to be the major constituents of vomeronasal ligands in urines and the indirect (correlational) nature of their mass spectrometry data and activity data.

      Reviewer 1 is correct to point out that our chemical profiling approach omits (sulfated) steroids. We are aware of this weakness. We deliberately decided to omit steroids as well as other nonvolatile small organic molecules for three main reasons: (i) as the reviewer points out, (sulfated) steroid composition has been the focus of analysis in several previous studies and there is ample published information available on their role as VSN stimuli; (ii) the analytical tools available to us do not allow comprehensive profiling of non-volatile small organic molecules; employing two-dimensional head-space GC-MS as well as LC-MS/MS is not suitable for steroid detection; and (iii) the relatively small sample volumes forced us to prioritize and focus on specific chemical classes (in our case, VOCs and proteins). We made an effort to use of the exact same stimuli as previously employed to investigate sensory representations in the accessory olfactory bulb (AOB) (Bansal et al., 2021), a feature that we consider a strength of the current study. However, this entailed that we had to effectively split our samples, further reducing the available sample volume.

      We acknowledge that we did not sufficiently describe our rationale for focusing on VOCs and proteins on the previous version of the manuscript (nor did we discuss the known role of (sulfated) steroids in VSN signaling in adequate detail). We have now made an effort to address these shortcomings in the revised manuscript. Specifically, we have added new text to the Introduction (“Prominent molecularly identified VSN stimuli include various sulfated steroids (Celsi et al., 2012; Fu et al., 2015; Haga-Yamanaka et al., 2015, 2014; Isogai et al., 2011; Nodari et al., 2008; Turaga and Holy, 2012), which could reflect the dynamic endocrine state of an individual.”) and the Discussion (“Notably, our chemical profiling approach omits (sulfated) steroids other non-volatile small organic molecules, which have previously been identified in mouse urine as VSN stimuli (Nodari et al., 2008). Caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone.” & “In line with the notion of highly selective vomeronasal sampling is our observation that the concentration differences between compounds shared among strains, which are often substantial, are not reflected by similarly pronounced differences in response strength among generalist VSNs. There are several, not necessarily mutually exclusive explanations for this finding: First, concentration could simply not be a read-out parameter for VSNs, which would support previous ideas of concentration-invariant VSN activity (Leinders-Zufall et al., 2000). Second, the concentrations in freshly released urine could just exceed the dynamic tuning range of VSNs since, particularly for VOCs, natural signals (e.g., in scent marks) must be accessible to a recipient for a prolonged amount of time (sometimes days). A similar rationale could explain the increased protein concentrations in male urine, since male mice use scent marking to establish and maintain their territories and urinary lipocalins serve as long-lasting reservoirs of VOCs (Hurst et al., 1998). Third, generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations. In fact, in the most extreme scenario, several compounds that do display substantial strain- and/or sex-specific differences in concentration might not act as chemosignals at all. Forth, to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.”).

      (4) Overall, the major contribution of this work is the identification of specific molecules in mouse urines. This work is likely to be of significant interest to researchers in chemosensory signaling in mammals and provides a systematic avenue to exhaustively identify vomeronasal ligands in the future.

      We thank the Reviewer for his / her generally positive assessment.

      Reviewer #2 (Public Review):

      (1) This manuscript by Nagel et al provides a comprehensive examination of the chemical composition of mouse urine (an important source of semiochemicals) across strain and sex, and correlates these differences with functional responses of vomeronasal sensory neurons (an important sensory population for detecting chemical social cues). The strength of the work lies in the careful and comprehensive imaging and chemical analyses, the rigor of quantification of functional responses, and the insight into the relevance of olfactory work on lab-derived vs wild-derived mice.

      We thank the Reviewer for his / her generally positive assessment.

      (2) With regards to the chemical analysis, the reader should keep in mind that a difference in the concentration of a chemical across strain or sex does not necessarily mean that that chemical is used for chemical communication. In the most extreme case, the animals may be completely insensitive to the chemical. Thus, the fact that the repertoire of proteins and volatiles could potentially allow sex and/or strain discrimination, it is unclear to what degree both are used in different situations.

      Reviewer 2 is correct to point out that sex- and/or strain-dependent differences in urine molecular composition do not automatically attribute a signaling function to those molecules. We concur and, in fact, stress this point many times throughout the manuscript. In the Results, for example, we point out (i) that “in female urine, BALB/c-specific proteins are substantially underrepresented, a fact not reflected by VSN response profiles”, (ii) that “as observed in C57BL/6 neurons, the skewed distributions of protein concentration indices were not reflected by BALB/c generalist VSN profiles”, and (iii) that “VSN population response profiles do not reflect the global molecular content of urine, suggesting that the VNO functions as a rather selective molecular detector.” Moreover, in the Discussion, we state (i) that “caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone”; (ii) that, for several sex- and/or strain-specific molecules, none “has previously been attributed a chemosensory function. Challenging the mouse VNO with purified recombinant protein(s) will help elucidate whether such functions exist”; (iii) that “generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations”; and (iv) that “to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.”

      In the revised manuscript, we now aim to even more strongly emphasize the point made by Reviewer 2. In the Discussion, we have deleted a sentence that read: “Sex- and strain-specific chemical profiles give rise to unique VSN activity patterns.” Moreover, we have added the following statement: “In fact, in the most extreme scenario, several compounds that do display substantial strain- and/or sex-specific differences in concentration might not act as chemosignals at all.”

      Reviewer #3 (Public Review):

      (1) One of the primary objectives in this study is to ascertain the extent to which the response profiles of VSNs are specific to sex and strain. The design of these Ca2+ imaging experiments uses a simple stimulus design, using two interleaved bouts of stimulation with pairs of urine (e.g. male versus female C57BL/6, male C57BL/6 versus male BALB/c) at a single dilution factor (1:100). This introduces two significant limitations: (1) the "generalist" versus "specialist" descriptors pertain only to the specific pairwise comparisons made and (2) there is no information about the sensitivity/concentration-dependence of the responses.

      Reviewer 3 points to two limitations of our VSN activity assay. He / she is correct to mention that characterizing a VSN as generalist or specialist based on a “pairwise comparison” should not be the basis of attributing such a “generalist” or “specialist” label in general (i.e., regarding the global stimulus space). We acknowledge this point, but we do not regard this as a limitation of our study since we are not investigating rather broad (i.e., multidimensional) questions of selectivity. All we are asking in the context of this study is whether VSNs - when being challenged with pairs of sex- or strain-specific urine samples - act as rather selective semiochemical detectors. Of course, one can always think of a study design that provides more information. However, we here opted for an assay that - in our hands - is robust, “low noise” (i.e., displays low intrinsic signal variability as evident form reliability index calculations), ensures recovery from VSN adaptation (Wong et al., 2018), and, importantly, answers the specific question we are asking.

      Regarding the second point (“there is no information about the sensitivity/concentrationdependence of the responses”), we would like to emphasize that this was not a focus of our study either. In fact, concentration-dependence of VSN activity has been a major focus of several previous studies referenced in our manuscript (e.g., Leinders-Zufall et al., 2000; He et al., 2008), albeit with contradictory results. In our study, we ask whether a pair of stimuli that we have shown to display, in part, strikingly different chemical composition (both absolute and relative) preferentially activates the same or different VSNs. With this question in mind, we believe that our assay (and its results) are highly informative.

      (2) The functional measurements of VSN tuning to various pairs of urine stimuli are consistently presented alongside mass spectrometry-based comparisons. Although it is clear from the manuscript text that the mass spectrometry-based analysis was separated from the VSN tuning experiments/analysis, the juxtaposition of VSN tuning measurements with independent molecular diversity measurements gives the appearance to readers that these experiments were integrated (i.e., that the diversity of ligands was underlying the diversity of physiological responses). This is a hypothesis raised by the parallel studies, not a supported conclusion of the work. This data presentation style risks confusing readers.

      As Reviewer 3 points out correctly “it is clear from the manuscript text that the mass spectrometry-based analysis was separated from the VSN tuning experiments/analysis.” In the figures, we try make the distinction between VSN response statistics and chemical profiling more obvious by gray shadows that link the plots depicting VSN response characteristics to the general pie charts.

      We now also made an extra effort to avoid “confusing readers” by stating in the Discussion (i) that “caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone”; (ii) that, for several sex- and/or strain-specific molecules, none “has previously been attributed a chemosensory function. Challenging the mouse VNO with purified recombinant protein(s) will help elucidate whether such functions exist”; (iii) that “generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations”; and (iv) that “to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.” Moreover, we have deleted a sentence that read: “sex- and strain-specific chemical profiles give rise to unique VSN activity patterns”, and we have added the following statement: “In fact, in the most extreme scenario, several compounds that do display substantial strain- and/or sex-specific differences in concentration might not act as chemosignals at all.”

      However, we believe that there is value in presenting “VSN tuning measurements” next to “independent molecular diversity measurements.” While these are independent measurements, their similarity or, quite frequently, lack thereof are informative. We are sure that by taking the above “precautions” we have now mitigated the risk of “confusing readers.”

      (3) The impact of mass spectrometry findings is limited by the fact that none of these molecules (in bulk, fractions, or monomolecular candidate ligands) were tested on VSNs. It is possible that only a very small number of these ligands activate the VNO. The list of variably expressed proteins - especially several proteins that are preferentially found in female urine - is compelling, but, again, there is no evidence presented that indicates whether or not these candidate ligands drive VSN activity. It is noteworthy that the largest class of known natural ligands for VSNs are small nonvolatiles that are found at high levels in mouse urine. These molecules were almost certainly involved in driving VSN activity in the physiology assays (both "generalist" and "specialist"), but they are absent from the molecular analysis.

      Reviewer 3 is right, of course, that at this point we have not tested the identified molecules on VSNs. This is clearly beyond the scope of the present study. We believe that the data we present will be the basis of (several full-length) future studies that aim to identify specific ligands and - best case scenario - receptor-ligand pairs. We find it hard to concur that our study, which provides the necessary basis for those future endeavors, is regarded as “incomplete”. By design, all studies are somewhat incomplete, i.e., there are always remaining questions and we are not contesting that.

      It is true, of course, that a class of “known natural ligands for VSNs are small nonvolatiles.” As we replied above, our chemical profiling approach omits (sulfated) steroids. We are aware of this weakness. We deliberately decided to omit steroids as well as other non-volatile small organic molecules for three main reasons: (i) steroid composition has been the focus of analysis in several previous studies and there is ample published information available on their role as VSN stimuli; (ii) the analytical tools available to us do not allow comprehensive profiling of non-volatile small organic molecules; employing two-dimensional head-space GC-MS as well as LC-MS/MS is not suitable for steroid detection; and (iii) the relatively small sample volumes forced us to prioritize and focus on specific chemical classes (in our case, VOCs and proteins). We made an effort to use of the exact same stimuli as previously employed to investigate sensory representations in the accessory olfactory bulb (AOB) (Bansal et al., 2021), a fact that we consider a key strength of our current study. However, this entailed that we had to effectively split our samples, further reducing the available sample volume.

      We acknowledge that we did not sufficiently describe our rationale for focusing on VOCs and proteins on the previous version of the manuscript (nor did we discuss the known role of (sulfated) steroids in VSN signaling in adequate detail). We have now made an effort to address these shortcomings in the revised manuscript. Specifically, we have added new text to the Introduction (“Prominent molecularly identified VSN stimuli include various sulfated steroids (Celsi et al., 2012; Fu et al., 2015; Haga-Yamanaka et al., 2015, 2014; Isogai et al., 2011; Nodari et al., 2008; Turaga and Holy, 2012), which could reflect the dynamic endocrine state of an individual.”) and the Discussion (“Notably, our chemical profiling approach omits (sulfated) steroids other non-volatile small organic molecules, which have previously been identified in mouse urine as VSN stimuli (Nodari et al., 2008). Caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone.” & “In line with the notion of highly selective vomeronasal sampling is our observation that the concentration differences between compounds shared among strains, which are often substantial, are not reflected by similarly pronounced differences in response strength among generalist VSNs. There are several, not necessarily mutually exclusive explanations for this finding: First, concentration could simply not be a read-out parameter for VSNs, which would support previous ideas of concentration-invariant VSN activity (Leinders-Zufall et al., 2000). Second, the concentrations in freshly released urine could just exceed the dynamic tuning range of VSNs since, particularly for VOCs, natural signals (e.g., in scent marks) must be accessible to a recipient for a prolonged amount of time (sometimes days). A similar rationale could explain the increased protein concentrations in male urine, since male mice use scent marking to establish and maintain their territories and urinary lipocalins serve as long-lasting reservoirs of VOCs (Hurst et al., 1998). Third, generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations. In fact, in the most extreme scenario, several compounds that do display substantial strain- and/or sex-specific differences in concentration might not act as chemosignals at all. Forth, to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.”).

      Reviewer #1 (Recommendations For The Authors):

      (1) I find that the study is highly valuable for researchers in this field. With the finding that wild mouse urines do not elicit significantly more variable responses from urines from inbred strains, researchers can now be reassured to use inbred strains to gain general insights on pheromone signaling.

      A major omission of this study is non-volatile small organic molecules such as steroids. These compounds are the only molecular class in urine that have been identified to stimulate specific vomeronasal receptors to date. It is unclear to me that the specificity of VOC and proteins can alone fully explain the response specificity of the VSNs that have been monitored in this study. The discussion of this topic is highly beneficial for the readers.

      Reviewer 1 is correct to point out that our chemical profiling approach omits (sulfated) steroids. We are aware of this weakness. We deliberately decided to omit steroids as well as other nonvolatile small organic molecules for three main reasons: (i) as the reviewer points out, (sulfated) steroid composition has been the focus of analysis in several previous studies and there is ample published information available on their role as VSN stimuli; (ii) the analytical tools available to us do not allow comprehensive profiling of non-volatile small organic molecules; employing two-dimensional head-space GC-MS as well as LC-MS/MS is not suitable for steroid detection; and (iii) the relatively small sample volumes forced us to prioritize and focus on specific chemical classes (in our case, VOCs and proteins). We made an effort to use of the exact same stimuli as previously employed to investigate sensory representations in the accessory olfactory bulb (AOB) (Bansal et al., 2021), a fact that we consider a key strength of our current study. However, this entailed that we had to effectively split our samples, further reducing the available sample volume.

      We acknowledge that we did not sufficiently describe our rationale for focusing on VOCs and proteins on the previous version of the manuscript (nor did we discuss the known role of (sulfated) steroids in VSN signaling in adequate detail). We have now made an effort to address these shortcomings in the revised manuscript. Specifically, we have added new text to the Introduction (“Prominent molecularly identified VSN stimuli include various sulfated steroids (Celsi et al., 2012; Fu et al., 2015; Haga-Yamanaka et al., 2015, 2014; Isogai et al., 2011; Nodari et al., 2008; Turaga and Holy, 2012), which could reflect the dynamic endocrine state of an individual.”) and the Discussion (“Notably, our chemical profiling approach omits (sulfated) steroids other non-volatile small organic molecules, which have previously been identified in mouse urine as VSN stimuli (Nodari et al., 2008). Caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone.” & “In line with the notion of highly selective vomeronasal sampling is our observation that the concentration differences between compounds shared among strains, which are often substantial, are not reflected by similarly pronounced differences in response strength among generalist VSNs. There are several, not necessarily mutually exclusive explanations for this finding: First, concentration could simply not be a read-out parameter for VSNs, which would support previous ideas of concentration-invariant VSN activity (Leinders-Zufall et al., 2000). Second, the concentrations in freshly released urine could just exceed the dynamic tuning range of VSNs since, particularly for VOCs, natural signals (e.g., in scent marks) must be accessible to a recipient for a prolonged amount of time (sometimes days). A similar rationale could explain the increased protein concentrations in male urine, since male mice use scent marking to establish and maintain their territories and urinary lipocalins serve as long-lasting reservoirs of VOCs (Hurst et al., 1998). Third, generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations. Forth, to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.”).

      (2) How many different wild mouse urines were tested in this study? Is this sufficient to capture the diversity of wild M. musculus in local (Prague) habitats?

      We thank the reviewer for pointing this out. For the present study, 20 male (M) and 27 female (F) wild mice were caught at six different sites in the broader Prague area (i.e., Bohnice (50.13415N, 14.41421E; 2M+4F), Dolni Brezany (49.96321N, 14.4585E; 3M+4F), Hodkovice (49.97227N, 14.48039E; 5M+6F), Písnice (49.98988N, 14.46625E; 3M+6F), Lhota (49.95369N, 14.43087E; 1M+2F), and Zalepy (49.9532N, 14.40829E; 6M+5F). 18 of the 27 wild females were caught pregnant. The remaining 9 females were mated with males caught at the same site and produced offspring within a month. When selecting 10 male and 10 female individuals from first-generation offspring for urine collection, we ensured that all six capture sites were represented and that age-matched animals displayed similar weight (~17g). We believe that this capture / breeding strategy sufficiently represents “the diversity of wild M. musculus in local (Prague) habitats.” In the revised manuscript, we have now included these details in the Materials and Methods.

      (3) I found Figure 1e and figures in a similar format confusing - one panel describes the response statistics of VSNs, and other panels show the number of compounds found in different MS profiling, which is not immediately obvious from the figures. Is the y-axis legend correct (%)?

      We now try make the distinction between VSN “response statistics” and chemical profiling more obvious by gray shadows that link the plots depicting VSN response characteristics to the general pie charts. Moreover, we thank the Reviewer for pointing out the mislabeling of the y-axis. Accordingly, we have deleted “%” in all corresponding figures.

      (4) For Figure 5, in order to conclude that the same urine activates a different population of VSNs in two different strains, a critical control is needed to demonstrate that this is not due to the sampling variability - as compositions of V1Rs and V2Rs could vary between different slices, one preferred control is to use VNO slices from the same strain and compare the selectivity used here across the A-P axis.

      We thank Reviewer 1 for pointing this out. Importantly, we believe that this is already controlled for (see our response to the Public Review). In fact, for each experiment, we routinely prepare VNO slices along the entire anterior-to-posterior axis (not including the most anterior tip, where the VNO lumen tapers into the vomeronasal duct, and the most posterior part, the lumen ‘‘twists’’ toward the ventral aspect and its volume decreases (see Figs. 7 & S7 in Hamacher et al., 2024, Current Biology)). This usually yields ~7 slices per individual experiment / session. Therefore, we routinely sample and average across the entire VNO anterior-to-posterior axis for each experiment. In Fig. 5, individual independent experiments from each strain (C57BL/6 versus BALB/c) amounted to (a) n = 6 versus n = 8; (b) n = 10 versus n = 10; (c) n = 7 versus n = 9; (d) n = 9 versus n = 10; (e) n = 10 versus n = 9; and (f) n = 12 versus n = 10. Together, we can thus exclude that the considerably different response profiles that we measured using different recipient strains result from a “sampling error.”

      To clarify this point in the revised manuscript, we now explain our sampling routine in more detail in the Materials and Methods. Moreover, we now also mention this point in the Results.

      Reviewer #2 (Recommendations For The Authors):

      (1) Pg 5 Lines 3-16: This summary paragraph contains too much detail given that the reader has not read the paper yet, which makes it bewildering. This should be condensed.

      We agree and have substantially condensed this paragraph.

      (2) Pg 6 Line 5-8: This summary of the experimental design is obtuse and should be edited for clarity.

      We have edited the relevant passage for clarity.

      (3) Pg 6 Line 11: "VSNs were categorized..." Specialist vs generalist is defined as responding to one or both stimuli. This definition is placed right after saying that the cells were also tested with KCl. The reader might think that specialist vs generalist was defined in relation to KCl.

      We have edited this sentence, which now reads: “Dependent on their individual urine response profiles, VSNs were categorized as either specialists (selective response to one stimulus) or generalists (responsive to both stimuli).”

      (4) Pg 6 Line 13: "we recorded urine-dependent Ca2+ signals from a total of 16,715 VSNs". Is a "signal" a response? Did all 16,715 VSNs respond to urine? What was the total of KCl responsive cells recorded?

      We edited the corresponding passage for clarification. The text now reads: “Overall, we recorded >43,000 K+-sensitive neurons, of which a total of 16,715 VSNs (38.4%) responded to urine stimulation. Of these urine-sensitive neurons, 61.4% displayed generalist profiles, whereas 38.6% were categorized as specialists (Figure 1c,d).”

      (5) Pg 7 Line 6: The repeated use of the word "pooled" is confusing as it suggests a variation in the experiment. The authors should establish once in the Methods and maybe in the Results that stimuli were pooled across animals. Then they should just refer to the stimulus as male or female or BALB/c rather than "pooled" male etc.

      We acknowledge the reviewer’s argument. Accordingly, we now introduce the experimental use of pooled urine once in the Methods and in the introductory paragraph of the Results. All other references to “pooled” urine in the Results and Captions have been deleted.

      (6) Pg 7 Line 10: "...detected in >=3 out of 10 male..." For the chemical analysis, were these samples not pooled?

      Correct. We deliberately did not pool samples for chemical analysis, but instead analyzed all individual samples separately (i.e., 60 samples were subjected to both proteomic and metabolomic analyses). Thus, the criterion that a VOC or protein must be detected in at least 3 of the 10 individual samples from a given sex/strain combination for a ‘present’ call (and in at least 6 of the 10 samples to be called ‘enriched’) ensures that the molecular signatures we identify are not “contaminated” by unusual aberrations within single samples.<br /> For clarification, we now explicitly outline this procedure in the Methods (Experimental Design and Statistical Analysis – Proteomics and metabolomics).

      (7) Pg 7 Line 23: In line 7, the specialist rate was defined as 5% in reference to the total KCl responsive cells. Here the specialist rate is defined from responsive cells. This is confusing.

      We apologize for the confusion. In both cases, the numbers (%) refer to all K+-sensitive neurons. We have added this information to both relevant sentences (l. 7 as well as ll. 23-24). Note that the rate in ll. 23-24 refers to generalists.

      (8) Pg 7 Line 25: Concentration index should be defined before its use here.

      We have revised the corresponding sentence, which now reads: “By contrast, analogously calculated concentration indices (see Materials and Methods) that can reflect potential disparities are distributed more broadly and non-normally (Figure 1h).”

      (9) Pg 7 Line 29: change "trivially" to "simply".

      Done

      (10) Pg 7 Line 30: What is meant by a "generalist" ligand? The neurons are generalists. Probably should read "common ligands"

      We have changed the text accordingly.

      (11) Pg 7 Line 31: What is meant by "global observed concentration disparities" ?

      We have changed the text to “…represented by the observed general concentration disparities.”

      (12) Pg 8 Lines 7-11: This section needs to be edited for clarity as it is very difficult to follow. For example, the definition of "enriched" is buried in a parenthetical. Also, it is very difficult to figure out what a "sample" is in this paper. Is it a pooled stimulus, or is it urine from an individual animal?

      We apologize for the confusion. Throughout the paper a “sample” is a pooled stimulus (from all 10 individuals of a given sex/strain combination) for all physiological experiments. For chemical analysis a “sample” refers to urine from an individual animal.

      (13)Pg 8 Line 11: "abundant proteins" Does this mean absolute concentration or enriched in one sample vs another?

      We changed the term “abundant” to “enriched” as this descriptor has been defined (present in ≥6 of 10 individual samples) in the previous sentence.

      (14) Pg 8 Line 18: "While 32.9% of all..." Please edit for clarity. What is the point?

      The main point here is that, for VOCs, the vast majority of compounds (91.3%) are either generic mouse urinary molecules or are sex/strain-specific.

      (15) Pg 10 Line 18: "Increased VSN selectivity..." This title is misleading as it suggests a change in sensitivity with animal exposure. I think the authors are trying to say "VSNs are more selective for strain than for sex". The authors should avoid the term "exposure to" when they mean "stimulation with" as the former suggests chronic exposure prior to testing.

      We thank the reviewer for the advice and have changed the title accordingly. We also edited the text to avoid the term "exposure to" throughout the manuscript.

      (16) Pg 12 Line 10: "we recorded hardly any..." Hardly any in comparison to what? BALB/c?

      We apologize for the confusion. We have edited the text for clarity, which now reads: “In fact, (i) compared to an average specialist rate of 11.2% ± 6.6% (mean ± SD) calculated over all 13 binary stimulus pairs (n = 26 specialist types), we observed only few specialist responses upon stimulation with urine from wild females (2% and 3%, respectively), and…”

      Reviewer #3 (Recommendations For The Authors):

      (1) Related to the pairwise stimulus-response experimental design and analysis: there is precedent in the field for studies that explore the same topic (sex- and strain-selectivity), but measure VSN sensitivity across many urine stimuli, not just two at a time. This has been done both in the VNO (He et al, Science, 2008; Fu, et al, Cell, 2015) and in the AOB (Tolokh, et al, Journal of Neuroscience, 2013). The current manuscript does not cite these studies.

      Reviewer 3 is correct and we apologize for this oversight. We now cite the two VSN-related studies by He et al. and Fu et al. in the Introduction.

      (2) The findings of the mass spectrometry-based profiling of mouse urine - especially for volatiles - is only accessible through repositories, making it difficult to for readers to understand which molecules were found to be highly divergent between sexes/strains. There is value in the list of ligands to further investigate, but this information should be made more accessible to readers without having to comb through the repositories.

      We agree that there “is value in the list of ligands to further investigate” and, accordingly, we now provide a table (Table 1) that lists the top-5 VOCs that – according to sPLS-DA – display the most discriminative power to classify samples by sex (related to Figure 2c) or strain (related to Figure 2d). For ease of identification, all entries list internal mass spectrometry identifiers, identifiers extracted from MS analysis database, the sex or strain that drives separation, which two-dimensional component / x-variate represents the most discriminative variable, PubChem chemical formula, PubChem common or alternative names, Chemical Entities of Biological Interest or PubChem Compound Identification, and the VOC’s putative origin.

      (3) There is a long precedent for integrating molecular assessments and physiological recordings to identify specific ligands for the vomeronasal system: - nonvolatiles (e.g., Leinders-Zufall, et al., Nature, 2000)

      • peptides (e.g., Kimoto et al., Nature, 2005; Leinders-Zufall et al. Science, 2004; Riviere et al., Nature, 2009; Liberles, et al., PNAS, 2009)
      • proteins (e.g., Chamero et al., Nature, 2007; Roberts et al., BMC Biology, 2010)

      • excreted steroids and bile acids (Nodari et al., Journal of Neuroscience, 2008; Fu et al., Cell, 2015; Doyle, et al., Nature Communications, 2016)

      The Leinders-Zufall (2000), Roberts, and Nodari papers are referenced, but the broader efforts by the community to find specific drivers of vomeronasal activity are not fully represented in the manuscript. The focus of this paper is fully related to this broader effort, and it would be appropriate for this work to be placed in this context in the introduction and discussion.

      We now refer to all of the studies mentioned in the Introduction (except the article published by Liberles et al. in 2009, since the authors of that study do not identify vomeronasal ligands).

      (4) Throughout the manuscript (starting in Fig. 1h) the figure panels and captions use the term "response index" whereas the methods define a "preference index." It seems to be the case that these two terms are synonymous. If so, a single term should be consistently used. If not, this needs to be clarified.

      We now consistently use the term “response index” throughout the manuscript.

      (5) It would be useful to provide a table associated with Figure 2 - figure supplement 1 that lists the common names and/or chemical formulas for the volatiles that were found to be of high importance.

      We agree and, accordingly, we now provide a table (Table 2) that lists VOC, which – according to Random Forest classification and resulting Gini importance scores – display the most discriminative power to classify samples by sex (related to Figure 2 - figure supplement 1a) or strain (related to Figure 2 - figure supplement 1b). Notably, it is generally reassuring that several VOCs are listed in both Table 1 and Table 2, emphasizing that two different supervised machine learning algorithms (i.e., sPLS-DA (Table 1) and Random Forest (Table 2)) yield largely congruent results.

      (5) The use of the term "comprehensive" for the molecular analysis is a little bit misleading, as volatiles and proteins are just two of the many categories of molecules present in mouse urine.

      We have now deleted most mentions of the term "comprehensive" when referring to the molecular analysis.

      (7) Page 11, lines 24-27: The sentences starting "We conclude..." and ending in "semiochemical concentrations." These two sentences do not make sense. It is not known how many of the identified proteins are actual VSN ligands. Moreover, there is abundant evidence from other studies that individual VSN activity provides information about distinct semiochemical concentrations.

      We have substantially edited and rephrased this paragraph to better reflect that different scenarios / interpretations are possible. The relevant text now reads: “We conclude that VSN population response strength might not be so strongly affected by strain-dependent concentration differences among common urinary proteins. In that case, it would appear somewhat unlikely that individual VSN activity provides fine-tuned information about distinct semiochemical concentrations. Alternatively, as some (or even many) of the identified proteins could not serve as vomeronasal ligands at all, generalist VSNs might sample information from only a subset of compounds which, in fact, are secreted at roughly similar concentrations.”

      (8) The explanation of stimulus timing is mentioned several times but not defined clearly in methods. Page 19, lines 14-19 have information about the stimulus delivery device, but it would be helpful to have stimulus timing explicitly stated.

      In addition to the relevant captions, we now explicitly state stimulus timing (i.e., 10 s stimulations at 180 s inter-stimulus intervals) in the Results.

      (9) Typos: Page 10, line 7: "male biased" → "male-biased" for clarity

      Wilcoxon "signed-rank" test is often misspelled "Wilcoxon singed ranked test" or "Wilcoxon signed ranked test"

      In the Fig. 3 legend, the asterisk meaning is unspecified.

      "(im)balances" → imbalances (page 27, line 24; page 37, line 16; page 38, line 16)

      Figure 2 - figure supplement 1 and in Figure 2 - figure supplement 2, in the box-andwhisker plots the units are not specified in the graph or legend.”

      We have made all required corrections.

    1. Author response:

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

      eLife assessment

      This important study utilizes a virus-mediated short hairpin RNA (shRNA) approach to investigate in a novel way the role of the wild-type PHOX2B transcription factor in critical chemosensory neurons in the brainstem retrotrapezoid nucleus (RTN) region for maintaining normal CO2 chemoreflex control of breathing in adult rats. The solid results presented show blunted ventilation during elevated inhaled CO2 (hypercapnia) with knockdown of PHOX2B, accompanied by a reduction in expression of Gpr4 and Task2 mRNA for the proposed RTN neuron proton sensor proteins GPR4 and TASK2. These results suggest that maintained expression of wild-type PHOX2B affects respiratory control in adult animals, which complements previous studies showing that PHOX2B-expressing RTN neurons may be critical for chemosensory control throughout the lifespan and with implications for neurological disorders involving the RTN. When some methodological, data interpretation, and prior literature reference issues further highlighting novelty are adequately addressed, this study will be of interest to neuroscientists studying respiratory neurobiology as well as the neurodevelopmental control of motor behavior.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This important study investigated the role of the PHOX2B transcription factor in neurons in the key brainstem chemosensory structure, the retrotrapezoid nucleus (RTN), for maintaining proper CO2 chemoreflex responses of breathing in the adult rat in vivo. PHOX2B has an important transcriptional role in neuronal survival and/or function, and mutations of PHOX2B severely impair the development and function of the autonomic nervous system and RTN, resulting in the developmental genetic disease congenital central hypoventilation syndrome (CCHS) in neonates, where the RTN may not form and is functionally impaired. The function of the wild-type PHOX2B protein in adult RTN neurons that continue to express PHOX2B is not fully understood. By utilizing a viral PHOX2B-shRNA approach for knockdown of PHOX2B specifically in RTN neurons, the authors' solid results show impaired ventilatory responses to elevated inspired CO2, measured by whole-body plethysmography in freely behaving adult rats, that develop progressively over a four-week period in vivo, indicating effects on RTN neuron transcriptional activity and associated blunting of the CO2 ventilatory response. The RTN neuronal mRNA expression data presented suggests the impaired hypercapnic ventilatory response is possibly due to the decreased expression of key proton sensors in the RTN. This study will be of interest to neuroscientists studying respiratory neurobiology as well as the neurodevelopmental control of motor behavior.

      Strengths:

      (1) The authors used a shRNA viral approach to progressively knock down the PHOX2B protein, specifically in RTN neurons to determine whether PHOX2B is necessary for the survival and/or chemosensory function of adult RTN neurons in vivo.

      (2) To determine the extent of PHOX2B knockdown in RTN neurons, the authors combined RNAScope® and immunohistochemistry assays to quantify the subpopulation of RTN neurons expressing PHOX2B and neuromedin B (Nmb), which has been proposed to be key chemosensory neurons in the RTN.

      (3) The authors demonstrate that knockdown efficiency is time-dependent, with a progressive decrease in the number of Nmb-expressing RTN neurons that co-express PHOX2B over a four-week period.

      (4) Their results convincingly show hypoventilation particularly in 7.2% CO2 only for PHOX2B-shRNA RTN-injected rats after four weeks as compared to naïve and non-PHOX2B-shRNA targeted (NT-shRNA) RTN injected rats, suggesting a specific impairment of chemosensitive properties in RTN neurons with PHOX2B knockdown.

      (5) Analysis of the association between PHOX2B knockdown in RTN neurons and the attenuation of the hypercapnic ventilatory response (HCVR), by evaluating the correlation between the number of Nmb+/PHOX2B+ or Nmb+/PHOX2B- cells in the RTN and the resulting HCVR, showed a significant correlation between HCVR and number of Nmb+/PHOX2B+ and Nmb+/PHOX2B- cells, suggesting that the number of PHOX2B-expressing cells in the RTN is a predictor of the chemoreflex response and the reduction of PHOX2B protein impairs the CO2-chemoreflex.

      (6) The data presented indicate that PHOX2B knockdown not only causes a reduction in the HCVR but also a reduction in the expression of Gpr4 and Task2 mRNAs, suggesting that PHOX2B knockdown affects RTN neurons transcriptional activity and decreases the CO2 response, possibly by reducing the expression of key proton sensors in the RTN.

      (7) Results of this study show that independent of the role of PHOX2B during development, PHOX2B is still required to maintain proper CO2 chemoreflex responses in the adult brain, and its reduction in CCHS may contribute to the respiratory impairment in this disorder.

      Weaknesses:

      (1) The authors found a significant decrease in the total number of Nmb+ RTN neurons (i.e., Nmb+/PHOX2B+ plus Nmb+/ PHOX2B-) in NT-shRNA rats at two weeks post viral injection, and also at the four-week period where the impairment of the chemosensory function of the RTN became significant, suggesting some inherent cell death possibly due to off-target toxic effects associated with shRNA procedures that may affect the experimental results.

      (2) The tissue sampling procedures for quantifying numbers of cells expressing proteins/mRNAs throughout the extended RTN region bilaterally have not been completely validated to accurately represent the full expression patterns in the RTN under experimental conditions.

      (3) The inferences about RTN neuronal expression of NMB, GPR4, or TASK2 are based on changes in mRNA levels, so it remains speculation that the observed reduction in Gpr4 and Task2 mRNA translates to a reduction in the protein levels and associated reduction of RTN neuronal chemosensitive properties.

      Thank you for sharing the excitement for our study showing novel findings on the contribution of PHOX2B to the chemoreflex response and activity of adult RTN neurons. We believe that reporting the results on cell death following shRNA viral injections, potentially due to some off-target effects, are important to share with the scientific community to help plan experiments of similar kind in various fields of neuroscience.

      Thanks for pointing out your concerns about cell quantification, we have edited the methods and results section to add clarity about our analytical procedure.

      As we discussed in the manuscript, we were only able to assess mRNA levels of Nmb, Gpr4, Task2 as current available antibodies for the 3 targets are still unreliable. Future studies will benefit from the analysis of changes at protein levels and possibly electrophysiological recordings to verify that chemosensitive properties of RTN neurons are impaired due to reduction of PHOX2B expression. We discuss these limitations in the discussion.

      Reviewer #2 (Public Review):

      Summary:

      The authors used a short hairpin RNA technique strategy to elucidate the functional activity of neurons in the retrotrapezoid nucleus (RTN), a critical brainstem region for central chemoreception. Dysfunction in this area is associated with the neuropathology of congenital central hypoventilation syndrome (CCHS). The subsequent examination of these rats aimed to shed light on the intricate aspects of RTN and its implications for central chemoreception and disorders like CCHS in adults. They found that using the short hairpin RNA (shRNA) targeting Phox2b mRNA, a reduction of Phox2b expression was observed in Nmb neurons. In addition, Phox2b knockdown did not affect breathing in room air or under hypoxia, but the hypercapnia ventilatory response was significantly impaired. They concluded that Phox2b in the adult brain has an important role in CO2 chemoreception. They thought that their findings provided new evidence for mechanisms related to CCHS neuropathology. The conclusions of this paper are well supported by data, but careful discussion seems to be required for comparison with the results of various previous studies performed by different genetic strategies for the RTN neurons.

      Strengths:

      The most exciting aspect of this work is the modelling of the Phox2b knockdown in one element of the central neuronal circuit mediating respiratory reflexes, that is in the RTN. To date, mutations in the PHOX2B gene are commonly associated with most patients diagnosed with CCHS, a disease characterized by hypoventilation and absence of chemoreflexes, in the neonatal period, which in severe cases can lead to respiratory arrest during sleep. In the present study, the authors demonstrated that the role of Phox2b extends beyond the developmental period, and its reduction in CCHS may contribute to the respiratory impairment observed in this disorder.

      Weaknesses:

      Whereas the most exciting part of this work is the knockdown of the Phox2b in the RTN in adult rodents, the weakness of this study is the lack of a clear physiological, developmental, and anatomical distinction between this approach and similar studies already reported elsewhere (Ruffault et al., 2015, DOI: 10.7554/eLife.07051; Ramanantsoa et al., 2011, DOI: 10.1523/JNEUROSCI.1721-11.2011; Huang et al., 2017, DOI: 10.1016/j.neuron.2012.06.027; Hernandez-Miranda et al., 2018, DOI: 10.1073/pnas.1813520115; Ferreira et al., 2022 DOI: 10.7554/eLife.73130; Takakura et al., 2008 DOI: 10.1113/jphysiol.2008.153163; Basting et al., 2015 DOI: 10.1523/JNEUROSCI.2923-14.2015; Marina et al., 2010 DOI: 10.1523/JNEUROSCI.3141-10.2010). In addition, several conclusions presented in this work are not directly supported by the provided data.

      Thanks for the feedback on or manuscript. We have further highlighted in our discussion the previous developmental work aimed at determining the role of PHOX2B in embryonic development. Our study was triggered by the fascinating observations that despite its important role in development of the central and peripheral nervous system, PHOX2B is still present in the adult brain and its function in adult neurons is unknown, thus we aimed to investigate its role in the adult RTN by knocking down its expression with a shRNA approach. Therefore, in our model knockdown of PHOX2B does not affect development of the RTN. Previous studies (mentioned by the reviewer, as well as cited in the manuscript) have focused on investigating 1) the role of PHOX2B in the developmental period, 2) the physiological changes associated with the transgenic expression of mutant forms of PHOX2B in relation to CCHS, 3) the killing or the acute silencing/excitation of neuronal activity of PHOX2B+ RTN neurons. Our study had a different aim: to test whether the transcription factor PHOX2B had a physiologically relevant role in adult RTN neurons. In this experimental approach PHOX2B is not altered throughout embryonic or postnatal development. By knocking down PHOX2B in the Nmb+ cells of the RTN our results show a reduction in chemoreflex response and mRNA expression of protein sensors. Hence, we conclude that PHOX2B alters the function of Nmb+ RTN neurons, possibly through transcriptional changes including the reduction in Gpr4 and Task2 mRNA expression.

      Reviewer #3 (Public Review):

      A brain region called the retrotrapezoid nucleus (RTN) regulates breathing in response to changes in CO2/H+, a process termed central chemoreception. A transcription factor called PHOX2B is important for RTN development and mutations in the PHOX2B gene result in a severe type of sleep apnea called Congenital Central Hypoventilation Syndrome. PHOX2B is also expressed throughout life, but its postmitotic functions remain unknown. This study shows that knockdown of PHOX2B in the RTN region in adult rats decreased expression of Task2 and Gpr4 in Nmb-expressing RTN chemoreceptors and this corresponded with a diminished ventilatory response to CO2 but did not impact baseline breathing or the hypoxic ventilatory response. These results provide novel insight regarding the postmitotic functions of PHOX2B in RTN neurons.

      Main issues:

      (1) The experimental approach was not targeted to Nmb+ neurons and since other cells in the area also express Phox2b, conclusions should be tempered to focus on Phox2b expressing parafacial neurons NOT specifically RTN neurons.

      (2) It is not clear whether PHOX2B is important for the transcription of pH sensing machinery, cell health, or both. If knockdown of PHOX2B knockdown results in loss of RTN neurons this is also expected to decrease Task2 and Gpr4 levels, albeit by a transcription-independent mechanism.

      Although we did not specifically target Nmb+ neurons, we performed viral injections within the area where neurons expressing PHOX2B and Nmb are localized (i.e., the RTN region). We carefully quantified the impact of PHOX2B knockdown on Nmb expressing neurons, as well as the effects on the adjacent TH expressing C1 population and FN neurons (figure 5). As reported in the results section, significant changes in the numbers of PHOX2B expressing neurons was only observed at the site of injection in PHOX2B+/Nmb+ neurons. We did not observe changes in the total number of C1 cells (TH+/PHOX2B+), in the number of TH cells coexpressing PHOX2B, or in the hypoxic ventilatory response (which is dependent on the health status of C1 neuron). We have updated figure 5 to show representative expression of PHOX2B in TH+ neurons in the ventral medulla to complement our cell count analysis. To address potential effects on other cell populations we have edited our discussion as follows:

      “PHOX2B knockdown was also restricted to RTN neurons, as adjacent C1 TH+ neurons did not show any change in number of TH+/PHOX2B+ expressing cells, although we cannot exclude that some C1 cells may have been infected and their relative PHOX2B expression levels were reduced. To support the lack of significant alterations associated with the possible loss of C1 function was the absence of significant changes in the hypoxic response that has been shown to be dependent on C1 neurons (Malheiros-Lima et al., 2017).”

      Where appropriate, we have substituted “RTN” with “Nmb expressing neurons of the RTN” throughout the manuscript.

      We have clarified in the methods and results section how we quantified Task2 and Gpr4 mRNA expression. The quantification was performed on a pool of single cells (200-250/rat) expressing Nmb. Hence, the overall reduction is not a result of general fluorescence loss in the RTN region, but specifically assessed in single cells expressing Nmb. This is therefore independent of the reduction of the total number of Nmb cells.

      We propose that cell death is not a direct effect of PHOX2B knockdown, but rather it is associated with the injection of the viral constructs that have been already reported to promote some off-target effects (as reported in the manuscript). While modest cell death is observed only in the first two weeks post-infection, it does not increase further between 2 and 4 weeks post infection, when the reduction in PHOX2B (not associated with a further reduction in Nmb+ cells, hence no further cell death in RTN) is evident and the respiratory chemoreflex is impaired. These results suggest that 1) reduction of PHOX2B is not responsible for cell death; 2) it is the reduction of PHOX2B levels that promotes chemoreflex impairment. Given the observation that Nmb cells with no detectable PHOX2B protein show reduced expression of Task2 and Gpr4 mRNA, we propose that one of the possible mechanisms of chemoreflex impairment in PHOX2B shRNA rats is the reduction of Task2 and Gpr4. In the discussion we also suggest possible additional mechanisms that can be investigated in further studies.

      Recommendations for the authors:.

      In revising this manuscript, the authors should carefully address the issues raised by the reviewers to substantially improve the manuscript and solidify the reviewers' general assessment of the potential importance of this work.

      Reviewer #1 (Recommendations For The Authors):

      Major concerns:

      (1) The cell counts for Nmb+/PHOX2B+ and Nmb+/PHOX2B- RTN neurons are a critical component of the study, and it is unclear how the tissue sampling procedures (eight sections per animal) for quantifying numbers of cells expressing proteins/mRNAs throughout the extended RTN region bilaterally has been validated to accurately represent the full expression patterns in the RTN under the experimental conditions. It is possible that the sampling/quantification procedures used may be adequate, but validation is important. Also, quantification of the CTCF signal for Nmb, Gpr4, and Task2 mRNA is an important component of this study, but only four sections/rats were used.

      Thank you for pointing out your concern on our quantification method. We have clarified in the methods section the procedure for cell counting and quantification of the CTCF signal. We have sampled the area of the RTN in order to identify Nmb cells of RTN.

      We have edited the methods section as follows:

      “To quantify Nmb+/PHOX2B- and Nmb+/PHOX2B+ neurons within the RTN region, we analysed one in every seven sections (210 µm interval; 8 sections/rat in total) along the rostrocaudal distribution of the RTN on the ventral surface of the brainstem and compared total bilateral cell counts of PHOX2B-shRNA rats with non-target control (NT-shRNA) and naïve rats. Cells that expressed Nmb and Phox2b mRNAs but did not show co-localization with PHOX2B protein were considered Nmb+/PHOX2B-.

      The Corrected Total Cell Fluorescence (CTCF) signal for Nmb, Gpr4 and Task2 mRNAs was quantified as previously described (Cardani et al., 2022; McCloy et al., 2014). Briefly, a Leica TCS SP5 (B-120G) Laser Scanning Confocal microscope was used to acquire images of the tissue. Exposure time and acquisition parameters were set for the naïve group and kept unchanged for the entire dataset acquisition. The collected images were then analysed by selecting a single cell at a time and measuring the area, integrated density and mean grey value (McCloy et al., 2014). For each image, three background areas were used to normalize against autofluorescence. We used 4 sections/rat (210 µm interval) to count Nmb, Gpr4 and Task2 mRNA CTCF in the core of the RTN area where several Nmb cells could be identified. For each section two images were acquired with a 20× objective, so that at least fifty cells per tissue sample were obtained for the mRNA quantification analysis. To evaluate changes in Nmb mRNA expression levels following PHOX2B knockdown at the level of the RTN, we compared, the fluorescence intensity of each RTN Nmb+ cell (223.2 ± 37.1 cells/animal) with the average fluorescent signal of Nmb+ cells located dorsally in the NTS (4.3 ± 1.2 cells/animal) (Nmb CTCF ratio RTN/NTS) as we reasoned that the latter would not be affected by the shRNA infection and knockdown.

      To quantify Gpr4 and Task2 mRNA expression in Nmb cells of the RTN, we first quantified single cell CTCF for either Gpr4 (200.7 ± 13.2 cells/animal) or Task2 (169.6 ± 10.3 cells/animal) mRNA in Nmb+ RTN neurons in the 3 experimental groups (naïve, NT shRNA and PHOX2B shRNA) independent of their PHOX2B expression. We then compared CTCF values of Gpr4 and Task2 mRNA between Nmb+/PHOX2B+ and Nmb+/PHOX2B- RTN neurons in PHOX2B-shRNA rats to address changes in their mRNA expression induced by PHOX2B knockdown.

      (2) Furthermore, to evaluate changes in Nmb mRNA expression following PHOX2B knockdown at the level of the RTN, it is stated in Materials and Methods "we compared, on the same tissue section, the fluorescence intensity of RTN Nmb+ cells with the signal of Nmb+ cells in the NTS (Nmb CTCF ratio RTN/NTS)". How this was accomplished is unclear, considering the non-overlapping locations of the RTN and rostral NTS. Providing images would be helpful.

      The first sections containing Nmb cells in the ventral medulla also express few Nmb cells in the dorsal medulla. We used those cells as reference for fluorescence levels since they would not be affected by the viral infection. Similar cells are also present in the brains of mice and reported in the Allen Brain atlas (https://mouse.brain-map.org/experiment/show/71836874). We have clarified our procedure in the methods section (see above) and included a sample image of Nmb in both ventral and dorsal regions in Figure 5.

      (3) The staining for tyrosine hydroxylase (TH) to identify and quantify C1 cells (TH+/PHOX2B+) following shRNA injection provides important information, and it would be useful to show images of histological examples to accompany Fig. 5A.

      We included in figure 5A a sample image of C1 neurons used for our TH quantification.

      Minor:

      (1) Provide animal ns in the text of the Results section for the four weeks of PHOX2B knockdown.

      They have been included.

      (2) Please state in the legends for Figures 2 & 3, which images are superimposition images.

      We have in the figure information about merged images.

      Reviewer #2 (Recommendations For The Authors):

      This manuscript by Cardani and colleagues attempts to address whether a reduction in Phox2b expression in chemosensitive neuromedin-B (NMB)-expressing neurons in the RTN alters respiratory function. The authors used a short hairpin RNA technique to silence RTN chemosensor neurons. The present study is very interesting, but there are several major concerns that need to be addressed, including the main hypothesis.

      Major

      (1) Page 6, lines 119-121: I did not grasp the mechanistic property described by the authors in this passage, nor did I understand the experiments they conducted to establish a mechanistic link between Phox2b and the chemosensitive property. Could the authors provide further clarification on these points?

      We believe the reviewer refers to this paragraph: “In order to have a better understanding of the role of PHOX2B in the CO2 homeostatic processes we used a non-replicating lentivirus vector of two short-hairpin RNA (shRNA) clones targeting selectively Phox2b mRNA to knockdown the expression of PHOX2B in the RTN of adult rats and tested ventilation and chemoreflex responses. In parallel, we also determined whether knockdown of PHOX2B in adult RTN neurons negatively affected cell survival. Finally, we sought to provide a mechanistic link between PHOX2B expression and the chemosensitive properties of RTN neurons, which have been attributed to two proton sensors, the proton-activated G protein-coupled receptor (GPR4) and the proton-modulated potassium channel (TASK-2).”

      The rationale for running these experiments is based on the fact that it is well known in the literature that PHOX2B is an important transcription factor for the development of several neuronal populations. PHOX2B Knockout mice die before birth and heterozygous mice have some anatomical defects, but respiration is only impaired in the early post-natal period. While many developmental transcription factors are generally downregulated in the post-natal period, PHOX2B is still expressed in some neurons into adulthood. What is the function of PHOX2B in these fully developed neurons? We do not know as we do not yet know the entire set of target genes that PHOX2B regulates in the adult brain. Hence we decided to test what would happen if we knocked down the PHOX2B protein in the Nmb neurons of the RTN, an area that is critical for central chemoreception and involved in the presentation of CCHS. Our results show that reduction of PHOX2B blunts the CO2 chemoreflex response and reduces mRNA expression of Task2 and Gpr4, two pH sensors that have been shown to be key for RTN chemosensitive properties. We also show that the Nmb mRNA and cell survival are not affected by PHOX2B knockdown and we propose that the reduced CO2 chemoreflex may be attributed to a reduction of chemosensory function of Nmb neurons of the RTN due to partial loss of Gpr4 and Task2.

      (2) It is imperative for the authors to enhance the description of their hypothesis, as, from my perspective, the contribution of the data to the field is not clearly articulated. Numerous more selectively designed experiments were conducted to investigate the role of Phoxb-expressing neurons at the RTN level and their involvement in respiratory activity. In summary, the current study appears to lack novelty.

      We respectfully disagree with this statement. We believe we have adequately summarized previous work, although we realize we can’t reference every single publication on this topic. As described above, the developmental role of PHOX2B has been elegantly investigated in mouse embryonic studies (extensively cited in the manuscript). Furthermore, very interesting studies have shown that when the CCHS defining mutant PHOX2B protein (+7Ala PHOX2B) and other mutations linked to CCHS have been transgenically expressed in mice through development, severe anatomical defects are observed and respiratory function is impaired (extensively cited in the manuscript). We have also cited papers relevant to this study that describe the role of PHOX2B/Nmb RTN neurons and the pH protein sensors in the CO2 chemoreflex. If we missed some papers that the reviewer deems essential in the context of this study we will be happy to include them.

      We are not aware of other studies that have investigated the specific role of the PHOX2B protein in the adult RTN in the absence of confounding developmental pathogenesis (i.e. in an otherwise ‘healthy’ animal), and of no other studies that looked at the effects on the RTN proton sensors and Nmb expression following PHOX2B knockdown. Hence we believe that our results are novel and, in our opinion, very interesting.

      (3) On pages 13 and 14 (Results section), I am seeking clarity on the novelty of the findings. Doug Bayliss's prior work has already demonstrated the role of Gpr4 and Task2 on Phox2b neurons in regulating ventilation in conscious rodents.

      Bayliss’ group has elegantly demonstrated that Gpr4 and Task2 are the two proton sensors in the PHOX2B/Nmb neurons of the RTN that have a key role in chemoreception (cited in the manuscript). The novelty of our findings is that we show that a reduction in PHOX2B protein is associated with a reduction of mRNA levels of Gpr4 and Task2. This is a novel finding. Currently, we do not know what transcriptional activity PHOX2B has in adult RTN neurons (i.e., what gene targets PHOX2B has in this cell population and many others) and here we propose that Nmb is not a gene target of PHOX2B while Gpr4 and Task2 are.

      (4) The authors assert that the transcription factor Phox2b remains not fully understood. While I concur, the present study falls short of fully investigating the actual contribution of Phox2b to breathing regulation. In other words, the knockdown of Phox2b neurons did not add much to the knowledge of the field.

      We respectfully disagree with the reviewer. With the exception of very few target genes, the transcriptional role of PHOX2B beyond the embryonic development is poorly understood. No mechanistic connection has been made before between the transcriptional activity of PHOX2B with the expression of proton sensors in the RTN. Other groups have investigated the role of stimulating or depressing the neuronal activity of PHOX2B/NMB neurons in the RTN showing a key role of RTN on respiratory control, but these prior studies did not test whether changing the expression of the PHOX2B protein in these neurons had a role on respiratory control and the central chemoreflex. No other study has investigated the role of the PHOX2B protein within the RTN cells, with the exception of PHOX2B knockout mice or transgenic expression of the mutated PHOX2B that are relevant for CCHS. Again, these previous studies were done on a background of developmental impairment and to the best of our knowledge did not seek to show any association between PHOX2B expression and expression of Gpr4 or Task2.

      (5) I recommend removing the entire section entitled "The role of Phox2b in development and in the adult brain." The authors merely describe Phox2b expression without contextualizing it within the obtained data.

      Because reviewers raised the issue about not including important information about the role of PHOX2B in development and respiratory control we prefer to keep the section.

      (6) Are the authors aware of whether the shRNA in Phox2b/Nmb neurons truly induced cell death or solely depleted the expression of the transcription factor protein? Do the chemosensitive neurons persist?

      This is an excellent question that we tried to address with our study. As we report in figures 2 and 3, we propose that some cell death is occurring as an off-target effect within the first 2 weeks post-infection, likely due to off-target action of the shRNA approach and not dependent on the reduction of PHOX2B expression (discussed in the manuscript). This is further evidenced by our Fig.S1 data in which higher concentrations of shRNA led to more cell death, indicative of off-target effects. We do not believe it is a consequence of our surgical procedure as we do not see similar cell loss when injecting vehicle or other control solutions (unpublished work; Janes et al., 2024).

      During the first 2 weeks post-surgery the proportion of Nmb+/PHOX2B- cells does not change compared to control rats or non-target shRNA (knockdown is not yet visible at protein level). Four weeks post-injection, there is no further cell death (assessed by the total number of NMB cells), whereas the fraction of NMB cells that express PHOX2B is reduced (and the fraction of NMB not expressing PHOX2B is increased), suggesting that the reduction of PHOX2B protein in Nmb cells is not correlated with cell loss/survival whereas the impairment that we observe in terms of central chemoreception is possibly due to the progressive decrease of PHOX2B expression in these neurons.

      (7) In Figures 2 and 3, it is noteworthy that the authors observe peak expression at a very caudal level. In rats, the RTN initiates at the caudal end of the facial, approximately 11.6 mm, and should exhibit a rostral direction of about 2 mm.

      In our experience the Nmb cells on the ventral surface of the medulla peak in number around the caudal tip of the facial nucleus in adult SD rats (Janes et al., 2024). To add clarity to the figure we reported cell count distribution data in relation to the distance from caudal tip of the facial.

      Minor

      (1) I would like to suggest that the authors correct the recurring statement throughout the manuscript that Phox2b is essential only for the development of the autonomic nervous system. In my view, it also plays a crucial role in certain sensory and respiratory systems.

      We have addressed this in the manuscript.

      (2) Page 4, lines 59-60: Out of curiosity, do the data include information from different countries?

      This data refers to information from France and Japan. Currently it is estimated that there are 1000-2000 CCHS patients worldwide.

      (3) Page 7, lines 129-131: In my understanding, the sentence is quite clear; if we knock down the PHOX2B gene, we are expected to reduce or even eliminate the expression of Gpr4 or Task2. Am I right?

      This is what we propose from the results of this study. We would like to point out that the transcriptional activity of PHOX2B (i.e., what genes PHOX2B regulate) in adult neurons has not yet been fully investigated. With the exception of few target genes (e.g., TH, DBH) the transcriptional activity of PHOX2B in neurons is not yet known. Here we report novel findings that suggest that Gpr4 and Task2 are potential target genes of PHOX2B in RTN neurons.

      (4) The authors mentioned that NT-shRNA also impacts CO2 chemosensitivity. Could this effect be attributed to mechanical damage of the tissue resulting from the injection?

      Just to clarify, we observe some impairment in chemosensitivity when NT-shRNA was injected in “larger” (2x 200ul/side) volume. No impairment was observed in NT-shRNA when we injected smaller volumes (2x 100ul/side). Physical damage could be a possibility although in our experience (unpublished work; Janes et al, 2024, Acta Physiologica) injections of similar volume of solution performed by the same investigator in the same brain area and experimental settings did not produce a physical lesion associated with respiratory impairment. Hence we attribute the unexpected results with larger volumes to toxic effects associated with the shRNA viral constructs.

      (5) In the reference section, the authors should review and correct some entries. For instance, Janes, T. A., Cardani, S., Saini, J. K., & Pagliardini, S. (2024). Title: "Etonogestrel Promotes Respiratory Recovery in an In Vivo Rat Model of Central Chemoreflex Impairment." Running title: "Chemoreflex Recovery by Etonogestrel." Some references contain the journal, pages, and volume, while others lack this information entirely.

      We have updated references. Janes et al., 2024 has now been published in Acta Physiologica.

      (6) Why does the baseline have distribution points, whereas the other boxplots do not?

      We have clarified in the figure legend that, to be fair to the presentation of our results, the data points shown in some of the boxplot graphs do not refer to entire baseline data but only the ones that are outliers.

      In our Box-and Whisker-Plots, whiskers represent the 10th and 90th percentiles, showing the range of values for the middle 80% of the data. Individual data values that fall outside the 10th/90th percentile range are represented as single point (outliers).

      Reviewer #3 (Recommendations For The Authors):

      • What is the rationale behind dedicating the first paragraph of results to discussing an artifact?

      We think that it is important to report off target effects of shRNA viral constructs as concentration and volumes of viruses injected in various studies vary considerably and other investigators may attempt to use larger volumes of viruses to obtain more considerable or faster knockdown but would obtain erroneous conclusions if appropriate tests are not performed.

      Furthermore, because some readers could question whether we injected enough virus to knockdown the expression of PHOX2B, and may wonder if with a larger amount of virus we would increase knockdown efficiency, we wanted to show that, in our opinion, we used the maximum amount of virus to knockdown PHOX2B without causing toxic effects or physiological changes that are not dependent on PHOX2B knockdown.

      • All individual data points should be visible in floating bar graphs in Figures 1 and 4. For example, I don't see any dots for naïve animals in any of the panels in Figure 1.

      We have clarified in the figure legend that, to be fair to the presentation of our results, the data points shown in some of the boxplot graphs do not refer to entire baseline data but only the ones that are outliers.

      In our Box-and Whisker-Plots, whiskers represent the 10th and 90th percentiles, showing the range of values for the middle 80% of the data. Individual data values that fall outside the 10th/90th percentile range are represented as single point (outliers).

      • Please include specific F and T values along with DF.

      We have included a table with all the specific values in the supplementary section as Table 1.

      • The C1 and facial partly overlap with the RTN at this level of the medulla and these cells should appear as Phox2b+/Nmb- cells so it is not clear to me why these cells are not evident in the control tissue in Figures 2B and 3B. Also, some of the bregma levels shown in Figure 5A overlap with Figures 2-3 so again it is not clear to me how this non-cell type specific viral approach was targeted to Nmb cells but not nearby TH+ cells. Please clarify.

      In our experience, C1 TH cells are located slightly medial to the Nmb cells and they spread much more caudally than Nmb cells of the RTN. We focused our small volume injection in the core of the RTN to target Nmb cells but we also assessed PHOX2B knockdown in TH C1 cells by counting the PHOX2B/TH cells across treatment groups. Although we can’t exclude subtle changes in the C1 population, we did not observe changes in the total number of C1 cells (TH+/PHOX2B+), in the number of TH cells expressing PHOX2B, or in the hypoxic ventilatory response (which is dependent on the health status of C1 neuron). We have updated figure 5 to show representative expression of PHOX2B in TH+ neurons in the ventral medulla to complement our cell count analysis. To address potential effects on other cell populations we have edited our discussion as follows:

      “PHOX2B knockdown was also restricted to RTN neurons, as adjacent C1 TH+ neurons did not show any change in number of TH+/PHOX2B+ expressing cells, although we cannot exclude that some C1 cells may have been infected and their relative PHOX2B expression levels were reduced. To support the lack of significant alterations associated with the possible loss of C1 function was the absence of significant changes in the hypoxic response that has been shown to be dependent on C1 neurons (Malheiros-Lima et al., 2017).”

      • To confirm, Nmb is not expressed in the NTS, and this region was chosen as a background, right?

      In order to systematically analyze Nmb mRNA expression we decided to use measurement of fluorescence relative to Nmb neurons present in the dorsal brainstem. Here cells are sparse but we used them as reference fluorescence since they would not be affected by the ventral shRNA injection. Similar cells are also present in the brains of mice and reported by the Allen Brain atlas (https://mouse.brain-map.org/experiment/show/71836874). We have clarified our procedure in the methods section (see above) and included a sample image of Nmb in both ventral and dorsal in Figure 5.

      • How do you get a loss of Nmb+ neurons (Figs 2-3) with no change in Nmb fluorescence (Fig. 5B)? In the absence of representative images these results are not compelling and should be substantiated by more readily quantifiable approaches like qPCR.

      We have clarified in the methods and results section our analytical procedure to assess PHOX2B and Nmb expression. Figure 2 and 3 display the results of counting numbers of Nmb+ cells in the RTN. Figure 5B reports the average of total cell fluorescence measured inside Nmb+ cells, not an average fluorescence measurement of the area of the ventral medulla. Basically, our results show that we have less Nmb cells that express PHOX2B but the overall Nmb mRNA fluorescence (expression) in Nmb cells relative to Nmb fluorescence in cells of the dorsal brainstem is the same.

      We have edited the methods as follows:

      “The Corrected Total Cell Fluorescence (CTCF) signal for Nmb, Gpr4 and Task2 mRNAs was quantified as previously described (Cardani et al., 2022; McCloy et al., 2014). Briefly, a Leica TCS SP5 (B-120G) Laser Scanning Confocal microscope was used to acquire images of the tissue. Exposure time and acquisition parameters were set for the naïve group and kept unchanged for the entire dataset acquisition. The collected images were then analysed by selecting a single cell at a time and measuring the area, integrated density and mean grey value (McCloy et al., 2014). For each image, three background areas were used to normalize against autofluorescence. We used 4 sections/rat (210 µm interval) to count Nmb, Gpr4 and Task2 mRNA CTCF in the core of the RTN area where several Nmb cells could be identified. For each section two images were acquired with a 20× objective, so that at least fifty cells per tissue sample were obtained for the mRNA quantification analysis. To evaluate changes in Nmb mRNA expression levels following PHOX2B knockdown at the level of the RTN, we compared the fluorescence intensity of each RTN Nmb+ cell (223.2 ± 37.1 cells/animal) with the average fluorescent signal of Nmb+ cells located dorsally in the NTS ( 4.3 ± 1.2 cells/animal) (Nmb CTCF ratio RTN/NTS) as we reasoned that the latter would not be affected by the shRNA infection and knockdown. “

      A single cell qPCR analysis would be definitely ideal but a qPCR from dissected tissue would not help us determine whether within a cell there was a reduction in Nmb mRNA levels.

      • The boxed RTN region in these examples is all over the place. It the RTN should be consistently placed along the ventral surface under the facial and pprox.. equal distance from the trigeminal and pyramids.

      We have update the figures to consistently present the areas of interest where Nmb cells are located and images are taken.

      • Fluorescent in situ typically appears as discrete puncta so it is not clear to me why that is not the case here.

      Our images are taken at low magnification (20X) where it is difficult to distinguish the single mRNA molecules. However, is it possible to appreciate the differences between the grainy fluorescent signal in the in situ hybridization assay (RNAScope) and the smoother signal of protein detection in the immunofluorescence assay.

      • Can TUNEL staining be done to confirm loss of Nmb neurons is due to death and not re-localization?

      Does the reviewer mean “cell migration” with relocalization? We do not expect that this would occur in our experiments. Although TUNEL in the first week post-infection could be useful to determine cell death in our tissue, we do not expect a cell migration of neurons within the brain as our viral shRNA injections are performed in adult rats when developmental processes are already concluded.

    1. Author response:

      We sincerely thank the editors and reviewers for the rigorous evaluation of our work and the precious time invested. The positive comments resonate with our endeavor to explore the intrinsic role of astrocyte aquaporin in brain water homeostasis. Meanwhile, we very appreciate the constructive suggestions of the reviewers to consolidate this study. Here is the provisional response, which briefly outlines our acknowledgement of the reviewers’ suggestions:

      To Reviewer #1:

      • Imaging data will be examined and collected to determine whether AQP4 inhibition has differential effects on astrocyte calcium signals in terms of cellular locations.

      • New analysis will be performed for CSD swelling data to provide additional kinetic information.

      • The mentioned original papers are important, and will be included in the revision.

      To Reviewer #2:

      We agree, a careful revision will improve and better position the study.

      • Echoing Reviewer #1, the introduction and discussion will be strengthened with current scientific contexts, while paying attention to the important advances in glymphatic system. The limits of the study mentioned in the reviews will be stated.

      • The use of TGN-020 was based on its validation by wide range of ex vivo and in vivo studies. AER-270(271) was nicely introduced by Farr et al., 2019 (PMID: 30738082). Its validation in vivo in AQP4 KO mice, and the comparison to TGN-020, is reported in a very recent study (Giannetto et al., 2024 - PMID: 38363040) that provides valuable insights.

      • The description of specific methodologies, including the DW-MRI, will be reinforced. The presentation of experiments and statistical analysis will be refined.

      To Reviewer #3:

      • Solenov et al., 2004 (PMID: 14576087) used the calcein quenching assay and KO mice convincingly showing AQP4 is a functional water channel in cultured astroctyes. AQP4 deletion reduced both astrocyte water permeability and the absolute amplitude of swelling over comparable time, and also slowed down cell shrinking, which overall parallels our results from acute AQP4 blocking. Yet in Solenovr’s study, the time to swelling plateau was prolonged in AQP4 KO astrocytes, differing from our data of acute blocking. This difference may be due to compensatory mechanisms in chronic AQP4 KO, or reflect the different volume responses in cultured astrocytes from brain slices/in vivo results as noted previously (e.g., Risher et al., 2009 - PMID: 18720409). As suggested, methods for volume recordings will be examined.

      • It is an important point that TGN-020 partially blocks AQP4, implying the actual functional impact of AQP4 per se might be stronger than what we observed. TGN provides a means to acutely probe AQP4 function in situ, still we agree, its limitation needs be acknowledged.

      • As also pointed by Reviewer #2, the description and interpretation of DW-MRI data will be improved.

    1. Author response:

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

      eLife assessment

      This is a valuable computational study that applies the machine learning method of bilinear modeling to the problem of relating gene expression to connectivity. Specifically, the author attempts to use transcriptomic data from mouse retinal neurons to predict their known connectivity. The results are promising, although the reviewers felt that demonstration of the general applicability of the approach required testing it against a second data set. Hence the present results were felt to provide borderline incomplete support for a key premise of the paper.

      We thank the reviewers for their insightful and constructive feedback. In response to the reviews, we have undertaken a comprehensive revision of our manuscript, incorporating changes and improvements as outlined below:

      (1) New results have been included showcasing the application of our bilinear model to a seconddataset focusing on C. elegans gap junction connectivity. This extension validates our model with a biological context other than mouse retina and facilitates a direct comparison with the spatial connectome model (SCM).

      (2) A new section titled "Previous Approaches" has been added to background, situating our studywithin the broader landscape of existing modeling methodologies.

      (3) The discussion sections have been expanded to fully incorporate the suggestions and insightsoffered by the reviewers. This includes a deeper exploration of the implications of our findings, potential applications of our model, and a more thorough consideration of its limitations and future directions.

      (4) To streamline the main text and ensure that the core narrative remains focused and accessible, select figures and tables have been relocated to the "Supplementary Materials" section.

      Reviewer 1 (Public Review):

      Summary of what the author was trying to achieve: In this study, the author aimed to develop a method for estimating neuronal-type connectivity from transcriptomic gene expression data, specifically from mouse retinal neurons. They sought to develop an interpretable model that could be used to characterize the underlying genetic mechanisms of circuit assembly and connectivity.

      Strengths:

      The proposed bilinear model draws inspiration from commonly implemented recommendation systems in the field of machine learning. The author presents the model clearly and addresses critical statistical limitations that may weaken the validity of the model such as multicollinearity and outliers. The author presents two formulations of the model for separate scenarios in which varying levels of data resolution are available. The author effectively references key work in the field when establishing assumptions that affect the underlying model and subsequent results. For example, correspondence between gene expression cell types and connectivity cell types from different references are clearly outlined in Tables 1-3. The model training and validation are sufficient and yield a relatively high correlation with the ground truth connectivity matrix. Seemingly valid biological assumptions are made throughout, however, some assumptions may reduce resolution (such as averaging over cell types), thus missing potentially important single-cell gene expression interactions.

      Thank you for recognizing the strengths of our work, particularly the clarity of the model presentation and its foundation in recommendation systems. In the revised manuscript we have also extended the model’s capabilities to analyze gene interactions for neural connectivity at single-cell resolution, when gene expression and connectivity of each cell are known simultaneously.

      Weaknesses:

      The main results of the study could benefit from replication in another dataset beyond mouse retinal neurons, to validate the proposed method. Dimensionality reduction significantly reduces the resolution of the model and the PCA methodology employed is largely non-deterministic. This may reduce the resolution and reproducibility of the model. It may be worth exploring how the PCA methodology of the model may affect results when replicating. Figure 5, ’Gene signatures associated with the two latent dimensions’, lacks some readability and related results could be outlined more clearly in the results section. There should be more discussion on weaknesses of the results e.g. quantification of what connectivity motifs were not captured and what gene signatures might have been missed.

      We acknowledge the significance of validating our method across different datasets. In line with this, our revised manuscript now includes an expanded analysis utilizing a C. elegans gap junction connectivity dataset, which not only broadens the method’s demonstrated applicability but also underscores its versatility across varied neuronal systems.

      To address the concern of resolution and reproducibility associated with PCA preprocessing, we have conducted a comparative analysis from five replicates of the bilinear model, presenting the results in the revised manuscript (Figure S3). This analysis confirms the consistency of the solutions, as evidenced by the similarity metrics. Furthermore, we discussed alternative methodologies, such as L1 or L2 regularization, to tackle multicollinearity, offering flexibility in preprocessing choices.

      In response to feedback on the original Figure 5’s clarity, we have replaced the original Figure 5e-h with Table S4, which summarizes the gene ontology (GO) enrichment results and quantifies the number of genes associated with aspects of neural development and synaptic organization. This revision aims to improve the interpretability and accessibility of the results, ensuring a clearer presentation of the model’s insights.

      Finally, we have expanded our discussion to address the study’s limitations more comprehensively. This includes exploration of potentially missed connections and gene signatures, such as transcription factors, which might not be captured by a linear model due to its inherent preference for predictors with strong correlations to the target variable.

      The main weakness is the lack of comparison against other similar methods, e.g. methods presented in Barabási, Dániel L., and Albert-László Barabási. "A genetic model of the connectome." Neuron 105.3 (2020): 435-445. Kovács, István A., Dániel L. Barabási, and Albert-László Barabási. "Uncovering the genetic blueprint of the C. elegans nervous system." Proceedings of the National Academy of Sciences 117.52 (2020): 33570-33577. Taylor, Seth R., et al. "Molecular topography of an entire nervous system." Cell 184.16 (2021): 4329-4347.

      We value your suggestion to compare our model with established methods. The revised manuscript now includes a comparative analysis with the spatial connectome model (SCM) using the same C. elegans dataset. In addition, a section reviewing previous approaches has been included in the background part, and the discussion part has been extended for the comparison.

      Appraisal of whether the author achieved their aims, and whether results support their conclusions: The author achieved their aims by recapitulating key connectivity motifs from single-cell gene expression data in the mouse retina. Furthermore, the model setup allowed for insight into gene signatures and interactions, however could have benefited from a deeper evaluation of the accuracy of these signatures. The author claims the method sets a new benchmark for single-cell transcriptomic analysis of synaptic connections. This should be more rigorously proven. (I’m not sure I can speak on the novelty of the method)

      In the revised manuscript. we emphasized the bilinear model’s innovative application in the context of neuronal connectivity analysis, inspired by collaborative filtering in recommendation systems. We present quantitative performance metrics, such as the ROC-AUC score and Pearson correlation coefficient, as well as its comparison with the SCM, to benchmark our model’s efficacy in reconstructing connectivity matrices. We also quantified the overlap of the genetic interactions revealed by the bilinear model and the SCM (using the C. elegans dataset), and reported the percentage of the top genes associated with neural development and synaptic organization (using the mouse retina dataset). These numbers set a precedent for future methodological comparisons.

      Discussion of the likely impact of the work on the field, and the utility of methods and data to the community : This study provides an understandable bilinear model for decoding the genetic programming of neuronal type connectivity. The proposed model leaves the door open for further testing and comparison with alternative linear and/or non-linear models, such as neural networkbased models. In addition to more complex models, this model can be built on to include higher resolution data such as more gene expression dimensions, different types of connectivity measures, and additional omics data.

      We are grateful for your recognition of the study’s potential impact. The bilinear model indeed offers a foundation for future explorations, allowing for integration with more complex models, higher-resolution data, and diverse connectivity measures.

      Reviewer 1 (Recommendations For The Authors):

      The inclusion of predicted connectivity (Figure 6) of unknown BC neurons is useful as it shows that this is a strong hypothesis generation tool. This utility should potentially be showcased more as it is also brought up in the abstract, "genetic manipulation of circuit wiring", with an explanation of how the model could be leveraged as such. The discussion may benefit from a summarizing sentence regarding which key gene signatures were identified and are in line with the literature, which key gene signatures/connectivity motifs may have been missed, and which gene signatures are novel.

      Thank you for the insightful recommendation on emphasizing the model’s utility in generating hypotheses, particularly regarding predicting connectivity. In the revised manuscript, we have expanded the discussion on how our model can be leveraged to guide genetic manipulations at altering circuit wiring and highlighted its potential impact in the field.

      We have discussed key gene signatures identified from our model that are in line with existing literature, such as plexins and cadherins, which have been previously recognized for their involvement in synaptic connection formation and maintenance. We have also introduced potential new candidates, such as delta-protocadherins. In the revised manuscript, we summarized potentially missed gene signatures or synaptic connections, to provide a comprehensive view of our findings.

      Reviewer 2 (Public Review):

      Summary:

      In this study, Mu Qiao employs a bilinear modeling approach, commonly utilized in recommendation systems, to explore the intricate neural connections between different pre- and post-synaptic neuronal types. This approach involves projecting single-cell transcriptomic datasets of pre- and post-synaptic neuronal types into a latent space through transformation matrices. Subsequently, the cross-correlation between these projected latent spaces is employed to estimate neuronal connectivity. To facilitate the model training, connectomic data is used to estimate the ground-truth connectivity map. This work introduces a promising model for the exploration of neuronal connectivity and its associated molecular determinants. However, it is important to note that the current model has only been tested with Bipolar Cell and Retinal Ganglion Cell data, and its applicability in more general neuronal connectivity scenarios remains to be demonstrated.

      Strengths:

      This study introduces a succinct yet promising computational model for investigating connections between neuronal types. The model, while straightforward, effectively integrates singlecell transcriptomic and connectomic data to produce a reasonably accurate connectivity map, particularly within the context of retinal connectivity. Furthermore, it successfully recapitulates connectivity patterns and helps uncover the genetic factors that underlie these connections.

      Thank you for your positive assessment of the paper.

      Weaknesses:

      (1) The study lacks experimental validation of the model’s prediction results.

      We recognize the importance of experimental validation in substantiating the predictions made by computational models. While the primary focus of this study remains computational, we have dedicated a section in the revised manuscript, titled "Experimental Validation of Candidate Genes", to outline proposed methodologies for the empirical verification of our model’s predictions. This section specifically discusses the experimental exploration of novel candidate genes, such as deltaprotocadherins, within the mouse retina using AAV-mediated CRISPR/Cas9 genetic manipulation. We plan to collaborate with experimental laboratories to facilitate the validation. Given the extensive nature of experimental work, both in terms of time and resources, it is more pragmatic to present a comprehensive experimental investigation in a follow-up study.

      (2) The model’s applicability in other neuronal connectivity settings has not been thoroughly explored.

      The question of the model’s broader applicability is well-taken. In response, we have expanded our analysis to include additional neuronal data and connectivity settings. Specifically, the revised manuscript includes results where we apply the model to a dataset of C. elegans gap junction connectivity, demonstrating its potential in different neuronal systems. This extension serves to illustrate the model’s adaptability and potential applicability to a broader range of neuronal connectivity studies.

      (3) The proposed method relies on the availability of neuronal connectomic data for model training,which may be limited or absent in certain brain connectivity settings.

      We acknowledge the limitations posed by the model’s dependency on comprehensive connectomic data, which may not be readily available across all research contexts. To address this, we have discussed in the revised manuscript several alternative strategies to adapt our model to the available data. This includes exploring the potential of applying the model to available data such as projectome, and integrating other data modalities such as electrophysiological measurements. These initiatives aim to enhance the model’s applicability and ensure its utility in a broader spectrum of brain connectivity studies, especially in scenarios where detailed connectomic data are not available.

      Reviewer 2 (Recommendations For The Authors):

      Q1. In this work, the author has mainly been studying the retina neuronal type connectivity, it will be interesting to see whether the model works for other brain regions or other neuronal type connectivity as well.

      We value your interest in the model’s applicability to other brain regions and neuronal types. To address this, we have extended our analysis in the revised manuscript to include a study on gap junction connectivity between C. elegans neurons. This extension demonstrates the model’s versatility and its potential applicability across various nervous systems and connectivity types.

      Q2. Whether the authors can use the same transformation matrices trained from the retina data to predict neuronal connectivity in other brain regions? Or an easier case, the connectivity between RGC types to the neuronal types in SC, dLGN, or other post-RGC-synaptic brain regions. As the neuronal connection mechanisms are conserved and widely shared between different neuronal types, one would expect the same transformation matrices may work in predicting other neuronal type connectivity as well (at least to some extent).

      The idea to use the same transformation matrices for predicting connectivity in other brain regions is intriguing. While direct application of these matrices to different regions remains challenging, we discussed the potential scalability of our model to other brain areas. By applying the model to combined datasets from various regions, we could uncover conserved neuronal connection mechanisms. This approach is theoretically feasible and is supported by the demonstrated scalability of the bilinear model and its deep learning variants in industrial applications.

      Q3. Section 5.2 Connectivity metric generation: in this work, the author uses the stratification profiles of the neurons to estimate the connectivity metric, how reliable this method is? There will be a scenario where though two neuronal types project to a similar inner plexiform layer, they may not have any connection. Have the authors considered combining other experimental data (like electrophysiology data or neuron tracing data)?

      We discussed the reliability of using stratification profiles for estimating connectivity metrics, acknowledging potential limitations. In the revised manuscript, we added discussion on how the integration of additional experimental data, such as electrophysiological and neuron tracing data, could enhance the accuracy of the connectivity metrics.

      Q4. Section 6 Model training and validation: does the author have a potential hypothesis as to why 2 dimensions are the best latent feature spaces dimensionality? One would imagine with more dimensionality, the model will give better results. Could it be that the connectivity data that is used to train the model is only considering the two-dimensional space of the neuronal stratification?

      The selection of two dimensions for the latent feature space was informed by 5-fold cross-validation, aimed at optimizing model generalization to unseen data. Here while increasing dimensionality improves performance on the training set, it does not necessarily enhance generalization to the validation set. Thus, the choice of two dimensions ensures good performance without overfitting to the training data.

      Q5. Could the author provide the source code for the analysis? Or could the author make it a python/R package so that non-computational biologists can easily apply the method to their own data?

      We have included a "Data and Code Availability" section in the revised manuscript. This section provides a link to the source code with pointers to datasets used in our study, facilitating the application of our methods by researchers from various backgrounds.

      Q6. I know it may be difficult for the author to do, but is it possible to design and perform some experiments to validate the model prediction results, either connectivity partners of transcriptomicallydefined RGC types or the function of the key genetic molecules (which hasn’t been discovered before)? The author may consider collaborating with some experimental labs. The author may even consider predicting the connectivity between RGC with some of its post-synaptic neurons in the brain regions, like SC or dLGN, as recently there are a lot of single-cell sequencing data as well as connectivity data.

      We appreciate your suggestion regarding experimental validation. As a future direction, we have discussed potential experimental approaches to validate the model’s predictions in the "Experimental Validation of Candidate Genes" section. Specifically, we propose an experimental design involving the manipulation of delta-protocadherins using AAV-mediated CRISPR/Cas9 and subsequent examination of connectivity phenotypes. We are also open to collaborating with experimental labs to further explore the model’s predictions, particularly in predicting connectivity between RGCs and their post-synaptic neurons in other brain regions.

    1. Author response:

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

      Public Reviews:

      Here we address the major points raised by the reviewers.

      Reviewer #1 (Public Review):

      Weaknesses:

      • The signaling pathway upstream of Maf1 remains unknown. In eukaryotes, Maf1 is a negative regulator of RNA pol III and is regulated by external signals via the TORC pathway. Since TORC components are absent in the apicomplexan lineage, one central question that remains open is how Maf1 is regulated in P. falciparum. Magnesium is probably not the sole stimulus involved, as suggested by the observation that Ile deprivation also down-regulates RNA pol III activity.

      We agree that there is still much to uncover relating to the PfMaf1 signaling pathway. While we still do not know each component, we have been able to link external factors (of course not limited to only magnesium) to the increased nuclear occupancy of PfMaf1. Other protein interactors that potentially regulate PfMaf1, while not confirmed, have been identified in plasma sample as candidates for future experiments to validate their potential involvement of RNA Pol III inhibition.

      • The study does not address why MgCl2 levels vary depending on the clinical state. It is unclear whether plasma magnesium is increased during asymptomatic malaria or decreased during symptomatic infection, as the study does not include control groups with non-infected individuals. Along the same line, MgCl2 supplementation in parasite cultures was done at 3mM, which is higher than the highest concentrations observed in clinical samples.

      This reviewer raised a valid point. The plasma magnesium levels for the wet symptomatic samples (averaging [0.79mM]) were within the normal range of a healthy individual (between [0.75-0.95mM]) while the dry asymptomatic levels were above the normal range (averaging [1.13mM]). Ideally, we would have liked to have control uninfected plasma samples from individuals from The Gambia. Unfortunately, field studies and human volunteer studies do not always have all the ideal controls that in vitro studies have. We recognize that [3mM] is higher than the normal range for magnesium levels, which is why we included a revised Supplementary Figure 3A. This figure shows that magnesium concentrations as low as [1mM] (similar to the levels found in dry asymptomatic samples) reduced the expression of RNA Pol III-transcribed genes.

      • Although the study provides biochemical evidence of Maf1 accumulation in the parasite nuclear fraction upon magnesium addition, this is not fully supported by the immunofluorescence experiments.

      We agree that the resolution of IFA images does not allow to support the WB data. We believe that the importance of the IFA Supplementary Figure is to show that PfMaf1 clusters together in foci, which has not been previously reported.

      Reviewer #2 (Public Review):

      Weaknesses:

      However, most analyses are rather preliminary as only very few (3-5) candidate genes are analyzed by qPCR instead of carrying out comprehensive analyses with a large qPCR panel or RNA-seq experiments with GO term analyses. Data presentation lacks clarity, the number of biological replicates is rather low and the statistical analyses need to be largely revised. Although the in vivo data from wet (mildly symptomatic) and dry (asymptomatic) season parasites with different expression levels of Pol III-regulated genes, var genes, and MgCl2 are interesting, the link between the in vitro data and the in vivo virulence of P. falciparum, which is made in many sections of the manuscript, should be toned down. Especially since (i) the only endothelial receptor studied is CD36, which is associated with parasite binding during mild malaria, and (ii) several studies provide contradictory data on MgCl2 levels during malaria and in different disease states, which is not further discussed, but the authors mainly focused on this external stimulus in their experiments.

      We agree that, ideally, we would have liked to do full RNA-seq on The Gambia samples. However, that was out of the scope of this project. The RNA samples were limited which is why we did not use more primers. We believe that an appropriate number of replicates was done for the experiments. The wet symptomatic samples from this study were from mildly symptomatic individuals, as stated in the manuscript. Therefore, CD36 was a relevant receptor to use for our studies.

      We agree that the published studies about magnesium levels in infected individuals are not always consistent. What these studies do not consider is the time of year, whether the infection occurred during the dry or wet season. These studies were also done in different regions of the world using different technologies. For this reason, we only highlight the observed difference observed in our field study data from The Gambia.

      Reviewer #3 (Public Review):

      Weaknesses:

      (1) The signals upstream of Maf1 remain rather a black box. 4 are tested - heat shock and low-glucose, which seem to suppress ALL transcription; low-Isoleucine and high magnesium, which suppress Pol3. Therefore the authors use Mg supplementation throughout as a 'starvation type' stimulus. They do not discuss why they didn't use amino acid limitation, which could be more easily rationalised physiologically. It may be for experimental simplicity (no need for dropout media) but this should be discussed, and ideally, sample experiments with low-IsoLeu should be done too, to see if the responses (e.g. cytoadhesion) are all the same.

      We agree that deprivation of isoleucine would have been another experimental assay for our study, but it also would not have been as novel as magnesium. While understanding the exact mechanism or involvement of magnesium as a stress condition was not the scope of this manuscript, we believe that our data will be valuable into demonstrating that external stimuli act on P. falciparum virulence gene expression via RNA Pol III inhibition. Since we also had plasma level data for magnesium, and not isoleucine, we believed it made for a better external factor to use for our in vitro studies.

      (2) The proteomics, conducted to seek partners of Maf1, is probably the weakest part. From Figure S3: the proteins highlighted in the text are clearly highly selected (as ones that might be relevant, e.g. phosphatases), but many others are more enriched. It would be good to see the whole list, and which GO terms actually came top in enrichment.

      We apologize if the reviewer did not see the attached supplementary Co-IP MS data. The file includes all proteins found in each sample as well as GO term analysis. For the purpose of this work, we highlight proteins potentially involved in the canonical role of Maf1 that have been shown in model organisms to reversibly inhibit RNA Pol III (phosphatases, RNA Pol III subunits).

      (3) Figure 3 shows the Maf1-low line has very poor growth after only 5 days but it is stated that no dead parasites are seen even after 8 cycles and the merozoites number is down only ~18 to 15... is this too small to account for such poor growth (~5-fold reduced in a single cycle, day 3-5)? It would additionally be interesting to see a cell-cycle length assessment and invasion assay, to see if Maf1-low parasites have further defects in growth.

      We agree with the reviewer that the observed reduced merozoite numbers may not the only cause of the reduced growth rate. Other factors in the PfMaf1 knock-down line may contribute to the observed poor growth.

    1. Author response:

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

      Public Reviews

      Reviewer #1 (Public Review):

      Summary:

      The findings in this manuscript are important in the gene editing in human-derived hematopoietic stem and progenitor cells. By optimizing the delivery tool, adding DNA-PK inhibitor and including spacer-breaking silent mutations, the editing efficiency is significantly increased, and the heterozygosity could be tuned. The editing is even across the hematopoietic hierarchy.

      Strengths:

      The precise gene editing is important in gene therapy in vitro and in vivo. The manuscript provides solid evidence showing the efficacy and uniqueness of their gene editing approach.

      Weaknesses:

      There are several extended and unique points shown in this paper but in a specific cell population.

      The findings are indeed in a specific cell lineage, though it should be noted the editing crossed multiple cell types within that lineage. More importantly though, HSPC have substantial relevance to understanding adult stem cell biology, blood formation, and leukemia. Critically, they are also the target cells for a plethora of gene therapies for anemias, immunodeficiencies, metabolic disorders, and are also being explored for use with CAR technologies. Indeed, CRISPR-based gene therapy was recently approved for clinical use. As such, the findings here are of substantial relevance for multiple areas of research including hematology, stem cell biology, cancer, immunology and more.

      Reviewer #2 (Public Review):

      Summary:

      This work by Cloarec-Ung et al. sets out to uncover strategies that would allow for the efficient and precision editing of primitive human hematopoietic stem and progenitor cells (HSPCs). Such effective editing of HSPCs via homology directed repair has implications for the development of tractable gene therapy approaches for monogenic hematopoietic disorders as well as precise engineering of these cells for clinical regenerative and/or cell therapy strategies. In the setting of experimental hematology, precision introduction of disease relevant mutations would also open the door to more robust disease modeling approaches. It has been recognized that to encourage HDR, NHEJ as the dominant mode of repair in quiescent HSPCs must be inhibited. Testing editing of human cord blood HSPCs the authors first incorporate a prestimulation phase then identify optimal RNP amounts and donor types/amounts using standard editing culture conditions identifying optimal concentrations of AAV and short single-stranded oligonucleode donors (ssODNs) that yield minimal impacts to cell viability while still enabling heightened integration efficiency. They then demonstrate the superiority of AZD7648, an inhibitor of NHEJ-promoting DNA-PK, in allowing for much increased HDR with toxicities imparted by this compound reduced substantially by siRNAs against p53 (mean targeting efficiencies at 57 and 80% for two different loci). Although AAV offered the highest HDR frequencies, differing from ssODN by a factor by ~2-fold, the authors show that spacer breaking sequence mutations introduced into the ssODN to better mimic the disruption of the spacer sequence provided by the synthetic intron in the AAV backbone yielded ssODN HDR frequencies equal to that attained by AAV. By examining editing efficiency across specific immunophenotypically identified subpopulations they further suggest that editing efficiency with their improved strategy is consistent across stem and early progenitors and use colony assays to quantify an approximate 4-fold drop in total colony numbers but no skewing in the potentiality of progenitors in the edited HSPC pool. Finally, the authors provide a strategy using mutation-introducing AAV mixed with different ratios of silent ssODN repair templates to enable tuning of zygosity in edited CD34+ cells.

      Strengths:

      The methods are clearly described and the experiments for the most part also appropriately powered. In addition to using state of the art approaches the authors also provided useful insights into optimizing the practicalities of the experimental procedures that will aid bench scientists in effectively carrying out these editing approaches, for example avoiding longer handling times inherent when scaling up to editing over multiple conditions.

      The sum of the adjustments to the editing procedure have yielded important advances towards minimizing editing toxicity while maximizing editing efficiency in HSPCs. In particular, the significant increase in HDR facilitated by the authors' described application of AZD7648 and the preservation of a pool of targeted progenitors is encouraging that functionally valuable cell types can be effectively edited.

      The discovery of the effectiveness of spacer breaking changes in ssODNs allowing for substantially increased targeting efficiency is a promising advance towards democratizing these editing strategies given the ease of designing and synthesizing ssODNs relative to the production of viral donors.

      The ability to zygosity tune was convincingly presented and provides a valuable strategy to modify this HDR procedure towards more accurate disease modelling.

      Weaknesses:

      Despite providing convincing evidence that functional progenitors can be successfully edited by their procedure, as the authors acknowledge it remains to be verified to what degree the self-renewal capacity and in vivo regenerative potential of the more primitive fractions is maintained with their strategy.

      As other the 53BP1-based editing strategy that also disrupt DNA-PK have demonstrated maintained allele frequencies over engraftment time (De Ravin et al. Blood 2021), this suggests that a transient disruption of DNA-PK shouldn’t compromise regenerative potential. Of course, we strongly agree that maintained regenerative potential is important in any editing strategy. As such, for the version of record we have added clonal LT-CIC assessment using conditions that we’ve previously demonstrated predict long-term repopulating potential (Knapp et al. Nat Cell Bio 2018). This data, which has been added to Figure 3, shows no significant reduction in the frequency of the most potent LT-CIC in edited cells compared to unedited controls.

      Assessments of the potential for off-target effects via the authors' approach was somewhat cursory and would have benefited from a more thorough evaluation.

      Once again in the 53BP1 strategy, the authors of that study already performed CHANGE-seq, long-range PCR, NGS, and SKY with inhibition of this same pathway without obvious increases in off-target editing (as long as HDR donor was present, though they did interestingly observe increased large deletions when HDR donors were absent, De Ravin et al. Blood 2021). Our tests here were designed to confirm that our molecule was similarly not affecting off-target editing rather than to launch a large-scale investigation. We agree, however, that off-targets and particularly structural re-arrangements that could be missed by other approaches remain a concern. We have added in nanopore sequencing of the predicted off-target sites and thus verified more deeply that there was no change (indeed no observable off-target activity) at any of these sites. This data has been added to Figure 2 and to a new supplementary Figure S5. Additionally, while it’s beyond the scope of the current manuscript, a focused follow-up dedicated to structural rearrangements downstream of both single and multiple edits is currently in progress and will be submitted separately later this year.

      Viability was assessed by live cell counting however given the short-term nature of the editing assay, more sensitive readouts of potentially compromised cell health could have provided a more stringent assessment of how the editing methodology impacted cell fitness.

      Of course, we agree that viable cell counting does not fully predict whether the cell is viable in terms of retained proliferative potential or other functional potentials. This point was addressed for myeloid progenitors at least by the CFC assays already in the manuscript, as to form a colony these cells were definitionally viable at input. Indeed, in these tests, we did see a reduction beyond that of the viable counts as already discussed in the text. Similarly, we already inadvertently answered this in the general CD34+CD45RA- population in Figure 4C where we measured clonal growth following editing with different mutant to silent donor ratios. In this instance we observed 30-40% clonogenic frequencies (Figure 4C), though in this case without a specific non-edited control (as this was not the intended question). None-the-less, this would indicate that any general viability loss was no more than observed in the CFC tests (even if we assume 100% cloning efficiency if the cells had been unedited). Finally, the clonal LTC-IC show that while there is perhaps some loss in more committed progenitors, those with the highest self-renewal potential are not compromised in the edited condition compared to control (Figure 3I).

      Recommendations for the authors

      Reviewer #2 (Recommendations For The Authors):

      It will be important to include the author-provided new paragraph in the discussion to contextualize this work in the existing HSPC editing landscape and your unique findings.

      A new paragraph detailing how our manuscript fits with other recently published works is now included in the discussion.

      The legend for Figure 3 needs correction. Panel E is incorrectly labeled as panel D and panel F is incorrectly labeled as panel E.

      Thank you for catching this typo. It has been fixed.

      In Figure 4 axis headings in panel C and D require clarity beyond simply titles of "Mean Frequency".

      These axis labels have been clarified.

    1. Author response:

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

      Public Reviews

      Reviewer #1 (Public Review):

      Summary:

      Dormancy/diapause/hibernation (depending on how the terms are defined) is a key life history strategy that allows the temporal escape from unfavorable conditions. Although environmental conditions do play a major role in inducing and terminating dormancy (authors call this energy limitation hypothesis), the authors test a mutually non-exclusive hypothesis (life-history hypothesis) that sex-specific selection pressures, at least to some extent, would further shape the timing of these life-history events. Authors use a metanalytic approach to collect data (mainly on rodents) on various life-history traits to test trade-offs among these traits between sexes and how they affect entry and termination of dormancy.

      Strengths:

      I found the theoretical background in the Introduction quite interesting, to the point and the arguments were well-placed. How sex-specific selection pressures would drive entry and termination of diapause in insects (e.g. protandry), especially in temperate butterflies, is very well investigated. Authors attempt to extend these ideas to endotherms and trying to find general patterns across ectotherms and endotherms is particularly exciting. This work and similar evidence could make a great contribution to the life-history theory, specifically understanding factors that drive the regulation of life cycle timing.

      Weaknesses:

      (1) I felt that including 'ectotherms' in the title is a bit misleading as there is hardly (in fact any?) any data presented on ectotherms. Also, most of the focus of the discussion is heavily mammal (rodent) focussed. I believe saying endotherms in the title as well is a bit misleading as the data is mammalfocused.

      We change the title to : "Evolutionnary trade-offs in dormancy phenology". This is a hybrid article comprising both a meta-analysis and a literature review. Each of these parts brings new elements to the hypotheses presented. The statistical analyses only concern mammals and especially rodent species. But the literature review highlighted links between the evolution of dormancy in ectotherms and endotherms that have not been linked in previous studies. We feel it is important for readers to know that much of the discussion will focus on the comparison of these two groups. But we understand that placing the term ectotherms in the title might suggest a meta-analysis including these two groups.

      In addition, we indicated more specifically in the abstract and at the end of the introduction that the article includes two approaches associated with different groups of animals.

      We also specified in the section « review criteria » that:

      Only one bird species is considered to be a hibernator, and no information is available on sex differences in hibernation phenology (Woods and Brigham 2004, Woods et al. 2019).

      We have also added a "study limitations" section, which explains that although the meta-analysis is limited by the data available in the literature, the information available for the species groups not studied seems to support our results.

      (2) I think more information needs to be provided early on to make readers aware of the diversity of animals included in the study and their geographic distribution. Are they mostly temperate or tropical? What is the span of the latitude as day length can have a major influence on dormancy timings? I think it is important to point out that data is more rodent-centric. Along the line of this point, is there a reason why the extensively studied species like the Red Deer or Soay Sheep and other well-studied temperate mammals did not make it into the list?

      We specified in the abstract and at the end of the introduction that the species studied in the metaanalysis are mainly Holarctic species. We have also added a map showing all the study sites used in the meta-analysis. Finally, we've noted in the methods and added a "study limitation" section at the end of the discussion an explanation for those species that were not studied in the meta-analysis and the consequences for the interpretation of results

      The hypotheses developed in this article are based on the survival benefits of seasonal dormancy thanks to a period of complete inactivity lasting several months. The Red Deer or Soay Sheep remain active above ground throughout the year.

      The effect of photoperiod on phenology is one of the mechanisms that has evolved to match an activity with the favorable condition. In this study, we are not interested in the mechanisms but in the evolutionary pressures that explain the observed phenology. Interspecific variation in the effect of photoperiod results from different evolutionary pressures, which we are trying to highlight. It is therefore not necessary to review mechanisms and effects of photoperiod, themselves requiring a lengthy review.

      We also tested the “physiological constraint hypothesis” on several variables. Temperature and precipitation are factors correlated with sex differences in phenology of hibernation. These factors allow consideration of the geographical differences that influence hibernation phenology.

      (3) Isn't the term 'energy limitation hypothesis' which is used throughout the manuscript a bit endotherm-centric? Especially if the goal is to draw generalities across ectotherms and endotherms. Moreover, climate (e.g. interaction of photoperiod and temperature in temperatures) most often induces or terminates diapause/dormancy in ectotherms so I am not sure if saying 'energy limitation hypothesis' is general enough.

      We renamed this hypothesis the "physiological constraint hypothesis" and we have made appropriate changes in the text so as not to focus physiological constraints solely on energy aspects.

      (4) Since for some species, the data is averaged across studies to get species-level trait estimates, is there a scope to examine within population differences (e.g. across latitudes)? This may further strengthen the evidence and rule out the possibility of the environment, especially the length of the breeding season, affecting the timing of emergence and immergence.

      For a given species, data on hibernation phenology are averaged for different populations, but also for the same population when measurements are taken over several years. To test these hypotheses on a population scale, precise data on reproductive effort would be needed for each population tested, but this concerns very few species (less than 5).

      Testing the effects of temperature and precipitation allows us to take into account the effects of climate on phenology.

      (5) Although the authors are looking at the broader patterns, I felt like the overall ecology of the species (habitat, tropical or temperate, number of broods, etc.) is overlooked and could act as confounding factors.

      Yes, that's why we also tested the physiological constraints hypothesis, including the effect of temperature and precipitation. For the life-history hypothesis, we also tested reproductive effort, which takes into account the number of offspring per year.

      (6) I strongly think the data analysis part needs more clarity. As of now, it is difficult for me to visualize all the fitted models (despite Table 1), and the large number of life-history traits adds to this complexity. I would recommend explicitly writing down all the models in the text. Also, the Table doesn't make it clear whether interaction was allowed between the predictors or not. More information on how PGLS were fitted needs to be provided in the main text which is in the supplementary right now. I kept wondering if the authors have fit multiple models, for example, with different correlation structures or by choosing different values of lambda parameter. And, in addition to PGLS, authors are also fitting linear regressions. Can you explain clearly in the text why was this done?

      To simplify the results, we reduced the number of models to just three: one for emergence and two for immergence. In place of Table 1, we have written the structure of the models used. We have added a sentence to the statistics section: “each PGLS model produces a λ parameter representing the effect of phylogeny ranging between 0 (no phylogeny effect) and 1 (covariance entirely explained by co-ancestry)”. We have tested only three PGLS models and the estimated lambda value for these models is 0.

      (7) Figure 2 is unclear, and I do not understand how these three regression lines were computed. Please provide more details.

      We tested new models and modified existing figures.

      Reviewer #2 (Public Review):

      Summary:

      An article with lots of interesting ideas and questions regarding the evolution of timing of dormancy, emphasizing mammalian hibernation but also including ectotherms. The authors compare selective forces of constraints due to energy availability versus predator avoidance and requirements and consequences of reproduction in a review of between and within species (sex) differences in the seasonal timing of entry and exit from dormancy.

      Strengths:

      The multispecies approach including endotherms and ectotherms is ambitious. This review is rich with ideas if not in convincing conclusions.

      Weaknesses:

      The differences between physiological requirements for gameatogenesis between sexes that affect the timing of heterothermy and the need for euthermy during mammalian hibernator are significant issues that underlie but are under-discussed, in this contrast of selective pressures that determine seasonal timing of dormancy. Some additional discussion of the effects of rapid climate change on between and within species phenologies of dormancy would have been interesting.

      Reviewer #2 (Recommendations For The Authors):

      This review provides a very interesting and ambitious among and within-species comparison of the seasonal timing of entry and exit from dormancy, emphasizing literature from hibernating mammals (sans bats and bears) and with attention to ectotherms. The authors test hypotheses related to the timing of food availability (energy) versus life history considerations (requirements for reproduction, avoiding predation) while acknowledging that these are not mutually exclusive. I offer advice for clarifications and description of the limitations of the data (accuracy of emergence and immergence times), but mainly seek more emphasis for small mammalian hibernators on the contrast for requirements for significant periods of euthermy prior to the emergence in males versus females, a contrast that has energetic and timing consequences in both the active and hibernation seasons.

      A consideration alluded to but not fully explained or discussed is the differences in mammals between species and sexes in the timing of what can be called ecological hibernation, which is the seasonal duration that an animal remains sequestered in its burrow or den, and heterothermic hibernation, between the beginning and end of the use of torpor. The two are not synonymous. When "emergence" is the first appearance above ground, there is a significant missing observation key to the energetic contrasts discussed in this review, that of this costly pre-emergence behavior.

      To explain the difference between heterothermic hibernation and ecological hibernation, we've added a section in review Criteria from materials and methods :

      “In this study, we addressed what can be called ecological hibernation, i.e. the seasonal duration that an animal remains sequestered in its burrow or den, which is assumed to be directly linked to the reduced risk of predation. In contrast, we did not consider heterothermic hibernation, which corresponds to the time between the beginning and end of the use of torpor. So when we mention hibernation, emergence or immergence, the specific reference is to ecological hibernation.”

      In arctic and other ground squirrel species, males remain at high body temperatures after immerging and remaining in their burrows in the fall for several days to a week, and more consistently and importantly, males that will attempt to breed in the spring end torpor but remain constantly in their burrows for as much as one month at great expense whilst undergoing testicular growth, spermatogenesis, spemiation, and sperm capacitation, processes that require continuous euthermy. Female arctic ground squirrels and non-breeding males do not and typically enter their first torpor bout 1-2 days after immergence and first appear above ground 1-3 days after their last arousal in spring.

      The weeks spent euthermic in a cold burrow in spring by males while undergoing reproductive maturation require a significant energetic investment (can equate to the cost of the previous heterothermic period) that contrasts profoundly with the pre-mating energetic investment by females.

      Males cache food in their hibernacula and extend their active season in late summer/fall in order to do so and feed from these caches in spring after resuming euthermy, often emerging at body weights similar to that at immergence. Similar between-sex differences in the timing of hibernation and heterothermy occur in golden-mantled and Columbian ground squirrels and likely most other Urocitellus spp., though less well described in other species. These differences are related to life histories and requirements for male vs. female gameatogenesis and, at the same time, energetic considerations in the costs to males for remaining euthermic while undergoing spermatogenesis and the cost related to whether males undergo gonadal development being dependent on individual body mass and cache size. These issues should be better discussed in this review.

      It is the time required to complete spermatogenesis, spermiation, and maturation of sperm not the time for growth of different sizes of testes that drives the preparation time for males. This is relatively constant among rodents. I challenge the assumption that larger testes take longer to grow than smaller ones.

      We took this comment into account. As we found little evidence of an increase in testicular maturation time with relative testicular size (apart from table 4 in Kenagy and Trombulak, 1986), we no longer tested the effect of relative testicular size on protandry.

      We examined whether the ability to store food before hibernation might reduce protandry. Although food storage in the burrow may be favored for overcoming harsh environments or predation, model selection did not retain the food-storing factor. Thus, the ability to accumulate food in the burrow was not by itself likely to keep males of some species from emerging earlier (e.g. Cricetus cricetus, protandry : 20 day, Siutz et al., 2016). Early emerging males may benefit from consuming higher quality food or in competition with other males (e.g., dominance assertion or territory establishment, Manno and Dobson 2008).

      We developed these aspects in the discussion

      While it is admirable to include ectotherms in such a broad review and modelling, I can't tell what data from how many ectothermic species contributed to the models and summary data included in the figures.

      Too few data on ectotherms were available to include ectotherms in the meta-analysis

      Some consideration should be made to the limitations of the data extracted from the literature of the accuracy of emergence and immergence dates when derived from only observations or trapping data. The most accurate results come from the use of telemetry for location and data logging reporting below vs. above ground positioning and body temperature.

      We added a "study limits" section to the discussion to address all the limits in this commentary.

      L64 "favor reproduction", better to say "allow reproduction", since there is strong evolutionary pressure to initiate reproduction early, often anticipating favorable conditions for reproduction, to maximize the time available for young to grow and prepare for overwintering themselves.

      Also, generally, it is not how "harsh" an environment is but rather how short the growing season is.

      We took this comment into account.

      L80 More simply, individuals that have amassed sufficient energy reserves as fat and caches to survive through winter may opt to initiate dormancy. This may decrease but not obviate predation, since hibernating animals are dug from their burrows and eaten by predators such as bears and ermine.

      In this sentence, we indicated a gap between dormancy phenology and the growing season, which suggests survival benefits of dormancy other than from a physiological point of view. We've changed the sentence to make it clearer : “However, some animals immerge in dormancy while environnemental conditions would allow them (from a physiological point of view) to continue their activity, suggesting other survival benefits than coping with a short growing season”

      L88 other physiological or ecological factors.... (gameatogenesis).

      In this study, we examine possible evolutionary pressures and therefore the environmental factors that may influence hibernation phenology. We focus on reproductive effort because, assuming predation pressure, we would expect a trade-off between survival and reproduction.

      L113 beginning early to afford long active seasons to offspring while not compromising the survival of parents.

      We added to the sentence:

      “For females, emergence phenology may promote breeding and/or care of offspring during the most favorable annual period (e.g., a match of the peak in lactational energy demand and maximum food availability, Fig. 1) or beginning early to afford long active seasons to offspring while not compromising the survival of parents.”

      L117 based on adequate preparation for overwintering and enter dormancy....

      We modified the sentence as follows :

      recovering from reproduction, and after acquiring adequate energy stores for overwintering”

      L123 given that males outwardly invest the least time in reproduction yet generally have shorter hibernation seasons would seem to reject this hypothesis. This changes if you overtly include the time and energy that males expend while remaining euthermic preparing for hibernation, a cost that can be similar to energy expended during heterothermy.

      Males invest a lot of time in reproduction before females emerge (whether for competition or physiological maturation) and some males seem to be subject to long-term negative effects linked to reproductive stress (see Millesi, E., Huber, S., Dittami, J., Hoffmann, I., & Daan, S. (1998). Parameters of mating effort and success in male European ground squirrels, Spermophilus citellus. Ethology, 104(4), 298-313). Both processes may contribute to reducing the duration of male hibernation.

      L125 again, costs to support euthermy in males undergoing reproductive development is an investment in reproduction.

      You're right, but it's difficult to quantify. We tested a model that takes into account the reproductive effort during reproduction and prior to reproduction. We also considered the hypothesis that species living in a cold climate might have a low protandry while having a high reproductive effort due to their ability to feed in the burrow (interaction effect between reproductive effort and temperature). We think these changes answer your comment.

      L134 It isn't growing large testes that takes time, but instead completing spermatogenesis and maturation of sperm in the epdidymides.

      We removed this part.

      L140 Later immergence in male ground squirrels is related to accumulation and defense of cached food, activities that are related to reproduction the next spring. An experimental analysis that would be revealing is to compare immergence times in females that completed lactation to the independence of their litters vs. females that did not breed or lost their litters. Who immerges first?

      Body mass variation from emergence to the end of mating in males seems to explain the delayed immergence of males in species that don't hide food in their burrows for hibernation. For example, in spermophilus citellus, males immege on average more than 3 weeks after females, yet they do not hide food in their burrows for the winter.

      Such a study already exists and shows that non-breeding females immerge earlier than breeding females. We refer to it

      L386: “In mammals, males and females that invest little or not at all in reproduction exhibit advances in energy reserve accumulation and earlier immergence for up to several weeks, while reproductive congeners continue activity (Neuhaus 2000, Millesi et al. 2008a).”

      L164 So you examined literature from 152 species but included data from only 29 species? Did you include data from social hibernators (marmots) that mate before emergence?

      With current models, we have 28 different species. We have few species because very few have data on both sex difference data and information on reproductive effort data (especially for males).

      Data on sex differences in hibernation were not available for social hibernating species.

      L169 Were these data from trapping or observation results? How reliable are these versus the use of information from implanted data loggers or collars that definitively document when euthermy is resumed and/or when immergence and first emergence occurs (through light loggers)?

      We did not focus heterothermic hibernation, but in ecological hibernation. We have no idea of the margin of error for these types of data, but we have discussed these limitations in the "Study limitations" section.

      L180, again, it is the time required to complete spermatogenesis and spermiation not the time for the growth of different sizes of testes that drives the preparation time for males. This is relatively constant among rodents. I challenge the assumption that larger testes take longer to grow than smaller ones.

      We removed this part.

      L200 Males that accumulate caches in fall and then feed from those during the spring pre-emergence euthermic interval and after will often be at their seasonal maximum in body mass. Declining from that peak may not be stressful.

      It has been suggested that reproductive effort in Spermophilus citellus might induce long-term negative effects that delay male immergence.

      Millesi, E., Huber, S., Dittami, J., Hoffmann, I., & Daan, S. (1998). Parameters of mating effort and success in male European ground squirrels, Spermophilus citellus. Ethology, 104(4), 298-313.

      L210 How about altitude, which affects the length of the growing season at similar latitudes?

      We extracted the location of each study site to determine the temperature and precipitation at that precise location (based on interpolated climate surface). We therefore take into account differences in growing season (based on temperature) in altitude between sites.

      L267 How did whether males cache food or not figure into these comparisons? Refeeding before mating occurs during the pre-emergence euthermic interval.

      We removed this part.

      L332, 344 not a "proxy" but functionally related to advantages in mating systems with multiple mating males.

      We removed this part.

      L353 The need for a pre-emergence euthermic interval in male ground squirrels requires costs in the previous active season in accumulating and defending a cache and the proximal costs in spring while remaining at high body temperatures prior to emergence with resulting loss in body mass or devouring of the cache.

      You're right, but in this section, we quickly explain the benefits of food catching compared with other species that don't do so.

      L385 This review should discuss why females are not known to cache and contrast as "income breeders" from "capital breeder" males. What advantages of caches are females indifferent to (no need for a prolonged pre-emergence period) and what costs of accumulating caches do they avoid (prolonged activity period and defense of caches).

      We clarified the case of female emergence.

      L321 : “Thus, an early emergence of males may have evolved in response to sexual selection to accumulate energy reserve in anticipation of reproductive effort. Females, on the contrary, are not subject to intraspecific competition for reproduction and may have sufficient time before (generally one week after emergence) and during the breeding period to improve their body condition.”

      L388 I don't understand the logic of the conclusion that "did not ...adequately explain the late male immergence" in this section. The greater mass loss in males over the mating period is afforded by the presence of a cache that requires later immergence.

      We removed this part.

      L412 Not just congeners that invest less in reproduction, but within species individuals that do not attempt to breed in one or more years and thus have no reproductive costs should be an interesting comparison for differences in phenology from individuals that do breed. Non-breeders are often yearlings but can be a significant overall proportion of males that fail to fatten or cache enough to afford a pre-emergence euthermic period.

      L385: “In mammals, males and females that invest little or not at all in reproduction exhibit advances in energy reserve accumulation and earlier immergence for up to several weeks, while reproductive congeners continue activity (Neuhaus 2000, Millesi et al. 2008a).”

      The sentence refers to individuals who reproduce little or not at all.

      L445 Males that gain weight between emergence and mating may do so by feeding from a cache regardless of how "harsh" an environment is.

      We observe this phenomenon even in species that are not known to hoard food

      “Gains in body mass observed for some individuals, even in species not known to hoard food, may indicate that the environment allows a positive energy balance for other individuals with comparable energy demands.”

      L492 Some insects retreat to refugia in mid-summer to avoid parasitism (Gynaephora).

      Escape from parasites is also a benefit of dormancy.

      Fig 1 - It is difficult to see the differences in black and green colors, esp if color blind.<br /> Maternal effort is front-loaded within the active season (line for "optimal period" shown in midseason).

      Add "energy" underneath c) Prediction (H1) and "reproduction" underneath d) "Prediction (H2). Explain the orange vs black, green colors of triangles.

      We made the necessary changes

      Fig 2 - I don't buy the regression lines as significant in this figure. The red line, cannot have a regression with two sample points and without the left-hand most dot, nothing is significant.

      We deleted this graph.

      Fig 3 - females only?

      We deleted this graph.

    1. Author response:

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

      We are grateful to the Editors for overseeing the review of our manuscript, and to the two reviewers for their thoughtful comments and suggestions for how it can be improved.

      I submit at this time a revision, as well as a detailed response (below) to each of the points raised in the first round of review.

      We feel the manuscript has been significantly improved by taking the reviewers' comments to heart. In a nutshell, we added new key pieces of data (impact of WIN site inhibition on global translation, rRNA production, as well as the requested cell biology analyses showing nucleolar stress), new analyses of the proteomics to counter potential concerns with normalization, and expanded/revised verbiage in key areas to clarify parts of the text that were confusing or problematic. The main figures have not changed; all new material is included in supplements to figures 2 and 3.

      Public Reviews

      Reviewer #1 (Public Review):

      Building on previous work from the Tansey lab, here Howard et al. characterize transcriptional and translational changes upon WIN site inhibition of WDR5 in MLL-rearranged cancer cells. They first analyze whether C16, a newer generation compound, has the same cellular effects as C6, an early generation compound. Both compounds reduce the expression of WDR5-bound RPGs in addition to the unbound RPG RPL22L1. They then investigate differential translation by ribo-seq and observe that WIN site inhibition reduces the translational RPGs and other proteins related to biomass accumulation (spliceosome, proteasome, mitochondrial ribosome). Interestingly, this reduction adds to the transcriptional changes and is not limited to RPGs whose promoters are bound by WDR5. Quantitative proteomics at two-time points confirmed the downregulation of RPGs. Interestingly, the overall effects are modest, but RPL22LA is strongly affected. Unexpectedly, most differentially abundant proteins seem to be upregulated 24 h after C6 (see below). A genetic screen showed that loss of p53 rescues the effect of C6 and C16 and helped the authors to identify pathways that can be targeted by compounds together with WIN site inhibitors in a synergistic way. Finally, the authors elucidated the underlying mechanisms and analyzed the functional relevance of the RPL22, RPL22L1, p53, and MDM4 axis.

      While this work is not conceptually new, it is an important extension of the observations of Aho et al. The results are clearly described and, in my view, very meaningful overall.

      Major points:

      (1) The authors make statements about the globality/selectivity of the responses in RNA-seq, ribo-seq, and quantitative proteomics. However, as far as I can see, none of these analyses have spike-in controls. I recommend either repeating the experiments with a spike-in control or carefully measuring transcription and translation rates upon WIN site inhibition and normalizing the omics experiments with this factor.

      The reviewer is correct that we did not include spike-in controls in our omics experiments. We would like to emphasize that none of the omics data in this manuscript have been processed in unorthodox ways, and that the major conclusions each have independent corroborating data.

      The selectivity in RPG suppression observed in RNA-Seq, for example, is supported by results from our target engagement (QuantiGene) assays; suppression of RPL22L1 mRNA levels is supported by quantitative and semi-quantitative RT-PCR, by western blotting, and by the results of our proteomic profiling; alternative splicing (and expression) of MDM4—and its dependency on RPL22—is also backed up by similar RT-PCR and western blotting data. The same applies for alternative splicing of RPL22L1.

      That said, we do appreciate the point the reviewer is making here, and have done our best to respond. We do not think it is a prudent investment in resources to repeat the numerous omics assays in the manuscript. We also considered normalizing for bulk transcription and translation rates as suggested, but it is not clear in practice how this would be done, and it could introduce additional variables and uncertainties that may skew the interpretation of results. Instead, to respond to this comment, we made the following changes to the manuscript:

      (1) We now explicitly state, for all omics assays, that spike-in controls were not included. These statements will prompt the reader to make their own assessment of the robustness of each of our findings and interpretations.

      (2) We have added new data to the manuscript (Figure 2—figure supplement 1A–B) measuring the impact of C6 and C16 on bulk translation using the OPP labeling method. These new data demonstrate that WIN site inhibitors induce a progressive yet modest decline in protein synthesis capacity. At 24 hours, there is no significant effect of either agent on protein synthesis levels. By 48 hours, a small but significant effect is observed, and by 96 hours translation levels are ~60% of what they are in vehicle-treated control cells. These new data are important because they support the idea that normalization has not blunted the responses we observe—the magnitude of the effects are consistent between the different assays and tend to cap out at two-fold in terms of RPG suppression, translation efficiency, ribosomal protein levels, and protein synthesis capacity.

      (3) We have included additional analysis regarding the LFQMS, as described below, that specifically addresses the issue of normalization in our proteomics experiments.

      (2) Why are the majority of proteins upregulated in the proteomics experiment after 24 h in C6 (if really true after normalization with general protein amount per cell)? This is surprising and needs further explanation.

      The reviewer is correct in noting that (by LFQMS) ~700 proteins are induced after 24 hours of treatment of MV4:11 cells with C16 (not C6, as stated). The reviewer would like us to examine whether this apparent increase in proteins is a normalization artifact. In response to this comment, we have made the following changes to the manuscript:

      (1) Our new OPP labeling experiments (Figure 2—figure supplement 1A–B) show that there is no significant reduction in overall protein synthesis following 24 hours of C16 treatment. In light of this finding, it is unlikely that normalization artifacts, resulting from diminution of the pool of highly abundant proteins, create the appearance of these 700 proteins being induced. We now explicitly make this point in the text.

      (2) We now clarify in the methods how we seeded identical numbers of cells for DMSO and C16-treated cultures in these experiments, and—consistent with our finding that WIN site inhibitors have little if any effect on protein synthesis or proliferation at the 24 hour timepoint— extracted comparable amounts of proteins from these two treatment conditions (DMSO: 344.75 ± 21.7 µg; C16: 366.50 ± 15.8 µg; [Mean ± SEM]).

      (3) We now include in Figure 3—figure supplement 1A a plot showing the distribution of peptide intensities for each protein detected in each run of LFQMS before and after equal median normalization. This new analysis reveals that the distribution of intensities is not appreciably changed via normalization. Specifically, there is not a reduction in peptide intensities in the unnormalized data from 24 hours of C16 treatment that is reversed or tempered by normalization. This analysis provides further support for the notion that the increase we observe is not a normalization artifact.

      (4) We now include in Figure 3—figure supplement 1B–D a set of new analyses examining the relationship between the initial intensity of proteins in DMSO control samples (a crude proxy for abundance) versus the fold change in response to WIN site inhibitor. This analysis shows that we have as many "highly abundant" (10th decile) proteins increasing as we do decreasing in response to WINi. Thus, it appears as though the wholesale clearance of highly abundant proteins from the cell is not occurring at this early treatment timepoint. In addition, this analysis also shows that ribosomal proteins (RP) are generally the most abundant, most suppressed, proteins and that their fold-change at the protein level at 24 hours is less than two-fold, consistent again with the magnitude of transcriptional effects of C16, as measured by RNA-Seq and QuantiGene. The fact that the drop in RP levels is consistent with expectations based on other analyses provides further empirical support for the notion that protein levels inferred from LFQMS are authentic and not skewed by global changes in the proteome.

      The increase in proteins at this time point, we argue, is thus most likely genuine. It is not surprising that—at a timepoint at which protein synthesis is unaffected—several hundred proteins are induced by a factor of two. How this occurs, we do not know. It may be a transient compensatory mechanism, or it may be an early part of the active response to WIN site inhibitors. Lest the reader be confused by this finding, we have now added text to this section of the manuscript discussing and explaining the phenomenon in more detail.

      (3) The description of the two CRISPR screens (GECKO and targeted) is a bit confusing. Do I understand correctly that in the GECKO screen, the treated cells are not compared with nontreated cells of the same time point, but with a time point 0? If so, this screen is not very meaningful and perhaps should be omitted. Also, it is unclear to me what the advantages of the targeted screen are since the targets were not covered with more sgRNAs (data contradictory: 4 or 10 sgRNAs per target?) than in Gecko. Also, genome-wide screens are feasible in culture for multiple conditions. Overall, I find the presentation of the screening results not favorable.

      In essence, this is a single screen performed in two tiers. In Tier 1, we screened a complete GECKO library (six sgRNA/gene) with the earliest generation (less potent) inhibitor C6, and compared sgRNA representation against the time zero population. This screen would reveal sgRNAs that are specifically associated with response to C6, as well as those that are associated with general cell fitness and viability. We then identified genes connected to these sgRNAs, removed those that are pan essential, and built a custom library for the second tier using sgRNAs from the Brunello library (four sgRNA/gene). We then screened this custom library with both C6 and the more potent inhibitor C16, this time against DMSO-treated cells from the same timepoint.

      We acknowledge that this is not the most streamlined setup for a screen. But our intention was to compare two inhibitors (C6 and C16) and identify high confidence 'hits' that are disconnected from general cell viability, rather than generate an exhaustive list of all genes that, when disrupted, skew the response to WIN site inhibitor. The final result of this screen (Figure 4E) is a gene list that has been validated with two chemically distinct WIN site inhibitors and up to 10 unique sgRNAs per gene. We may not have captured every gene that can modulate response to WIN site inhibitor, but those appearing in Figure 4E are highly validated.

      To answer the reviewer's specific questions: (i) we cannot omit the Tier 1 screen because then there would be no rationale for what was screened in the second Tier; and (ii) the advantage of the custom Tier 2 library is that it allowed us to screen hits from the Tier 1 screen with four completely independent sgRNAs. Although there are not more sgRNAs for each gene in the Tier 2 versus the Tier 1 library, these sgRNAs are different and thus, for C6 at least, hits surviving both screens were validated with up to 10 unique sgRNAs.

      We apologize that the description of the CRISPR screens was not clearer, and have reworked this section of the manuscript to make our intent and our actions clearer.

      (4) Can Re-expression of RPL22 rescue the growth arrest of C6?.

      We have not attempted to complement the RPL22 knock out. But we do note that evidence supporting the idea that loss of RPL22 confers resistance to WIN site inhibitor is strong—six (out of six) sgRNAs against RPL22 were significantly enriched in the Tier 1 screen, and independent knock out of RPL22 with the Synthego multi-guide system in MV4;11 and MOLM13 cells increases the GI50 for C16.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Howard et al reports the development of high-affinity WDR5-interaction site inhibitors (WINi) that engage the protein to block the arginine-dependent engagement with its partners. Treatment of MLL-rearranged leukemia cells with high-affinity WINi (C16) decreases the expression of genes encoding most ribosomal proteins and other proteins required for translation. Notably, although these targets are enriched for WDR5-ChIP-seq peaks, such peaks are not universally present in the target genes. High concordance was found between the alterations in gene expression due to C16 treatment and the changes resulting from treatment with an earlier, lower affinity WINi (C6). Besides protein synthesis, genes involved in DNA replication or MYC responses are downregulated, while p53 targets and apoptosis genes are upregulated. Ribosome profiling reveals a global decrease in translational efficiency due to WINi with overall ribosome occupancies of mRNAs ~50% of control samples. The magnitude of the decrements of translation for most individual mRNAs exceeds the respective changes in mRNA levels genome-wide. From these results and other considerations, the authors hypothesize that WINi results in ribosome depletion. Quantitative mass spec documents the decrement in ribosomal proteins following WINi treatment along with increases in p53 targets and proteins involved in apoptosis occurring over 3 days. Notably, RPL22L1 is essentially completely lost upon WINi treatment. The investigators next conduct a CRISPR screen to find moderators and cooperators with WINi. They identify components of p53 and DNA repair pathways as mediators of WINi-inflicted cell death (so gRNAs against these genes permit cell survival). Next, WINi are tested in combination with a variety of other agents to explore synergistic killing to improve their expected therapeutic efficacy. The authors document the loss of the p53 antagonist MDM4 (in combination with splicing alterations of RPL22L1), an observation that supports the notion that WINi killing is p53-mediated.

      Strengths:

      This is a scientifically very strong and well-written manuscript that applies a variety of state-ofthe art molecular approaches to interrogate the role of the WDR5 interaction site and WINi. They reveal that the effects of WINi seem to be focused on the overall synthesis of protein components of the translation apparatus, especially ribosomal proteins-even those that do not bind WDR5 by ChIP (a question left unanswered is how much the WDR5-less genes are nevertheless WINi targeted). They convincingly show that disruption of the synthesis of these proteins is accompanied by DNA damage inferred by H2AX-activation, activation of the p53pathway, and apoptosis. Pathways of possible WINi resistance and synergies with other antineoplastic approaches are explored. These experiments are all well-executed and strongly invite more extensive pre-clinical and translational studies of WINi in animal studies. The studies also may anticipate the use of WINi as probes of nucleolar function and ribosome synthesis though this was not really explored in the current manuscript.

      Weaknesses:

      A mild deficiency in the current manuscript is the absence of cell biological methods to complement the molecular biological and biochemical approaches so ably employed. Some microscopic observations and confirmation of nucleolar dysfunction and DNA damage would be reassuring.

      We thank the reviewer for their comments. We agree that an absence of cell biological methods was a deficiency in the original manuscript. In response to this comment, we have now added immunofluorescence (IF) analyses, examining the impact of C16 on nucleolar integrity and nucleophosmin (NPM1) distribution (Figure 3—figure supplement 4). These new data clearly show that C16 induces nucleolar stress at 72 hours—as measured by the redistribution of NPM1 from the nucleolus to the nucleoplasm. These new data fill an important gap in the story, and we are grateful to the reviewer for prompting us to perform these experiments.

      As part of the above study, we also probed for gamma-H2AX, expecting that we may see some signs of accumulation in the nucleoli (see comment #4 from Reviewer #2, below). We did not observe this response. Importantly, however, we did see that gamma-H2AX staining occurs only in what are overtly apoptotic cells. This is an important finding, because we had previously speculated that the induction of gamma-H2AX observed by Western blotting reflected part of a bona-fide response to DNA damage elicited by WIN site inhibitors. Instead, the IF data now leads us to conclude that this signal simply reflects the established fact that WIN site inhibitors induce apoptosis in this cell line (Aho et al., 2019). In response to this new finding, we have added additional discussion to the text and have removed or de-emphasized the potential contribution of DNA damage to the mechanism of action of WDR5 WIN site inhibitors. Again, we are grateful for this comment as it has prevented us from continuing to report/pursue erroneous observations.

      Recommendations for the authors

      Reviewer #1 (Recommendations For The Authors):

      There is a typo in "but are are linked to mRNA instability when translation is inhibited".

      Thank you for catching this typo. It has now been corrected.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors report that WINi initially (at 24 hrs) increases the expression of most proteins while decreasing ribosomal proteins, but at 72 hours all proteins are depressed. The transient bump-up of non-translation-related proteins seems odd. A simple resolution to this somewhat strange observation is that there is no real increase in the other proteins, but because of the loss of a large fraction of the most abundant cellular proteins (the ribosomal proteins), the relative fraction of all other proteins is increased; that is, the increase of non-ribosomal proteins may be an artifact of normalization to a lower total protein content. Can this be explored?

      We are grateful to the reviewer for this comment. We have tried our best to respond, as detailed above in response to Reviewer #1 Public Comment #2.

      (2) It would be really nice to assess nucleolar status microscopically. Do nucleoli get bigger? Smaller? Do they have abnormal morphology? Is there nucleolar stress? What happens to rRNA synthesis and processing?

      We agree and thank the reviewer for raising this point. As noted in our response to Reviewer #2, above, we have included new IF that shows: (i) no obvious effect on nucleolar integrity, (ii) redistribution of NPM1 to the nucleoplasm (indicative of nucleolar stress), and (iii) induction of gamma-H2AX staining in apoptotic cells (indicative of apoptosis).

      Additionally, in response to this comment, we also looked at the impact of WIN site inhibitors on rRNA synthesis, using AzCyd labeling. These new data appear in Figure 3—figure supplement 3. Interestingly, these new data show that there is a progressive decline in rRNA synthesis, and that by 96 hours of treatment levels of both 18S and 28S rRNAs are reduced— again by about a factor of two. Our interpretation of this finding is that in response to the progressive decline in RPG transcription there is a secondary decrease in rRNA synthesis. This result is perhaps not surprising, but it does again add an important missing piece to our characterization of WIN site inhibitors and is further support for the concept that inhibition of ribosome production is a dominant part of the response to these agents.

      (3) The WINi elicited DNA damage is incompletely characterized, rather it is inferred from H2AX activation. Comet assays would help to confirm such damage.

      As noted in our response to Reviewer #2, our original inference of DNA damage, prompted by gamma-H2AX activation, is erroneous, and due instead to the ability of WIN site inhibitors to induce apoptosis. We thus did not pursue comet assays, etc., and removed discussion of potential DNA damage from the manuscript.

      (4) Staining and microscopic observation of H2AX would be very useful. Is the WINi provoked DNA damage nucleolar-localized? Does the deficiency of ribosomal proteins lead to localized genotoxic nucleolar stress - or alternatively does the paucity of ribosomes and decreased translation lead to imbalances in other cellular pathways, perhaps including some involved in overall genome maintenance which would provoke more global DNA damage and H2AX staining, not limited to the nucleolus.

      Again, please see our response to the Public Comment from Reviewer #2.

      (5) It would be important to assess the influence and effects of WINi on some p53 mutant, p53-/- and p53 wild-type cell lines. Given their prevalence, p53 status may be expected to alter WINi efficacy.

      The issue of how p53 status impacts the response to WINi is interesting and important, but we feel this is beyond the scope of the current manuscript. It is likely that many factors contribute to the response of cancer cells to these agents, and thus simply surveying some cancer lines for their response and linking this to their p53 status is unlikely to be very informative. Making definitive statements about the contribution of p53, and the differences between wild-type, lossof-function mutants, gain of function mutants, and null mutants will require more extensive analyses and is fertile territory for future studies, in our opinion.